CN113887144A - Electric power communication optical cable comprehensive availability analysis method considering service characteristics - Google Patents

Electric power communication optical cable comprehensive availability analysis method considering service characteristics Download PDF

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CN113887144A
CN113887144A CN202111229873.6A CN202111229873A CN113887144A CN 113887144 A CN113887144 A CN 113887144A CN 202111229873 A CN202111229873 A CN 202111229873A CN 113887144 A CN113887144 A CN 113887144A
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张丽霞
屈蓓蓓
王慧芳
霍美如
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Information and Telecommunication Branch of State Grid Shanxi Electric Power Co Ltd
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Abstract

The invention discloses a comprehensive availability analysis method of an electric power communication optical cable considering service characteristics, which comprises the following steps: extracting the characteristics and values of the service carried by the optical cable, and calculating the service volume; extracting the characteristics and values of the optical cable, and calculating the availability of the optical cable; and (5) counting the residual proportion of the fiber core resources. Cleaning a data object at a pole end by using the service volume, the availability of the optical cable and the residual proportion of fiber core resources, and constructing an analysis data set; establishing a difference degree matrix; estimating the number of clusters by using a clustering trend visual evaluation algorithm; circularly executing a fuzzy clustering algorithm to obtain an optimized clustering result; and analyzing the resource availability of the power communication optical cable of each cluster through a data visualization technology. The invention combines the service and the optical cable characteristics, comprehensively solves the problem of reasonable utilization of the power communication optical cable resources, is beneficial to accurately identifying the service bearing condition of the optical cable and the availability of the optical cable resources, reasonably plans and deploys the power communication service, and reduces the optical cable load risk.

Description

Electric power communication optical cable comprehensive availability analysis method considering service characteristics
Technical Field
The invention relates to the technical field of power communication, in particular to a power communication optical cable comprehensive availability analysis method considering service characteristics.
Background
The power communication optical cable is an important carrying medium for power communication services. The effective utilization of the optical cable resources is a key means for reducing the network operation cost and improving the network service quality. The availability of the power communication optical cable resource not only depends on the use condition of the physical fiber core resource, but also is closely related to the availability of the optical cable and the characteristics of the service carried by the optical cable. In order to reduce the risk of large-scale service interruption caused by optical cable failure events, the availability of power communication optical cable resources must comprehensively consider the service characteristics carried by the optical cable, the reliability of the optical cable and the residual resources of a fiber core. If the fiber core margin of the optical cable is used as the only service deployment basis, the service carried by the optical cable is likely to be in a high-risk operation state. The method for analyzing the availability of the power communication optical cable resource considering the service characteristics comprises the steps of firstly, calculating and counting the service volume, the availability of the optical cable and the residual proportion of fiber core resources according to the current service characteristics borne by the optical cable and the characteristics of the optical cable; then, by utilizing the calculation statistical result, the pole data objects are cleaned, an analysis data set is constructed, and the matrix is standardized. Constructing a difference matrix among the data objects on the basis, and estimating the optimal clustering number by utilizing a clustering trend visual evaluation algorithm; determining a fuzzy clustering fuzzy set overlapping coefficient through a multi-cycle execution fuzzy clustering algorithm to obtain an optimized clustering result; and finally, analyzing the resource availability of the power communication optical cable by using a data visualization tool on the basis of optimizing the clustering result.
At present, in the aspect of the utilization of the power communication optical cable resources, a method for analyzing the utilization effectiveness of the power communication optical cable resources by considering optical cable service characteristics and utilizing a fuzzy clustering technology does not exist.
Disclosure of Invention
The invention aims to provide a comprehensive availability analysis method of an electric power communication optical cable considering service characteristics, which is beneficial to accurately identifying the service bearing condition of the electric power communication optical cable and the availability of optical cable resources, reasonably planning and deploying communication services, reducing the heavy load risk of the optical cable and improving the service reliability so as to solve the problems in the background art.
In order to achieve the purpose, the invention provides the following technical scheme:
a comprehensive availability analysis method of an electric power communication optical cable considering service characteristics comprises the following steps:
s1: extracting the characteristics and values of the service carried by the power communication optical cable, and calculating the service volume; extracting the characteristics and values of the power communication optical cable, and calculating the availability of the optical cable; counting the utilization condition of the fiber core of the optical cable, and calculating the residual proportion of fiber core resources;
s2: cleaning a data object at a pole end by using the service volume, the availability of the optical cable and the residual proportion of fiber core resources, and constructing an analysis data set;
s3: establishing a difference matrix, and estimating the number of clusters by using a clustering trend visual evaluation algorithm; circularly executing a fuzzy clustering algorithm to obtain an optimized clustering result;
s4: and processing the clustering result, and analyzing the resource availability of the power communication optical cable of each cluster through a data visualization technology.
Further, the calculating of the traffic volume in S1 includes characteristics of the service carried by the power communication optical cable, which specifically includes: service level, service type, bearing mode, channel mode, service voltage level and service quantity; wherein, the values of the service level are respectively as follows: headquarters, branches, and provincial companies; the values of the service types are respectively as follows: relay protection, safety and stability control, data network scheduling and program control service scheduling; the values of the bearing modes are respectively as follows: dedicated optical fibers and multiplexing; the channel mode values are respectively: primary use and standby use; the service voltage class values are respectively as follows: AC 1000kV, DC + -800 kV, DC + -660 kV, AC 500kV and AC 220 kV;
the traffic S of the power communication optical cable is represented as:
Figure BDA0003315593890000021
wherein, S (i) represents the traffic of the ith optical cable; n (i) is the current service quantity borne by the ith optical cable; fnNumber of traffic characteristics, j 1,2, F, for participating in traffic calculationsnAnd xi (j) is the weight of the jth feature, and meets the condition:
Figure BDA0003315593890000031
ω (j, k) is the jth feature, the kth value.
Further, the calculating of the availability of the optical cable in S1 includes characteristics of the power communication optical cable, which specifically include: cable voltage class, cable type, cable length and operational life; wherein, the optical cable voltage grade values are respectively: AC 1000kV, DC + -800 kV, DC + -660 kV, AC 500kV, AC 220kV, AC 110kV and AC 35 kV; the values of the optical cable types are respectively as follows: optical fiber Composite Overhead Ground Wire (OPGW) and All-Dielectric Self-Supporting fiber optic cable (ADSS); the optical cable length refers to the actual physical length of the optical cable, and is in km; the operation year limit refers to the number of years from the input operation to the extraction of the characteristic data, and the number of years is taken as a unit;
the cable availability is called reliability and availability a is expressed as:
A(i)＝1-τ(i)·λ(i)·L(i)
wherein A (i) indicates the availability of the ith cable; τ (i) is the average repair time for a cable break, depending on the cable type; l (i) is the cable length; λ (i) is the failure rate per km of cable, related to the cable voltage class, cable type and operational age.
Further, the failure rate per km of cable includes:
the failure rate per km of cable is expressed as:
Figure BDA0003315593890000032
wherein, KVIs the optical cable voltage grade coefficient;
Figure BDA0003315593890000033
the failure rate of the optical cable in the stable operation stage, namely the interruption times of the optical cable per hour, is related to the type of the optical cable; t is the operating life of the optical cable; t is t0The time length of the stable operation stage of the optical cable is expressed in years; kλThe aging coefficient of the optical cable is related to the type of the optical cable.
Further, the calculating of the core resource remaining proportion in S1 includes that the power communication cable core resource remaining proportion is expressed as:
Figure BDA0003315593890000041
wherein eta (i) represents the residual proportion of the fiber core resource of the ith power communication optical cable; n isz(i) The total fiber core number of the ith power communication optical cable is counted; n iss(i) The number of the residual fiber cores of the ith power communication optical cable belongs to available optical cable resources.
Further, the specific method in S2 is:
obtaining a traffic S, an optical cable availability A and a fiber core residual resource occupation ratio eta 3 vectors through S1; analyzing the element values of the 3 vectors by using a mathematical statistical method, taking data outside a confidence interval of 99% as extreme data objects, and cleaning;
let n be the number of cables used for analysis after data cleaningrUsing the cleaned 3 vectors, a matrix B' is constructed, i.e.
Figure BDA0003315593890000042
Standardizing B' to obtain a matrix
Figure BDA0003315593890000043
The normalized expression is:
Figure BDA0003315593890000044
matrix array
Figure BDA0003315593890000045
I.e. the constructed analytical data set.
Further, the specific method for estimating the number of clusters by using the clustering trend visualization evaluation algorithm, which is to establish the difference matrix in S3, is as follows:
aiming at the matrix B, a difference degree matrix is established
Figure BDA0003315593890000046
Wherein the content of the first and second substances,
Figure BDA0003315593890000047
performing an improved Visual Assessment of clustering Tendency (iVAT) algorithm on the difference matrix D to obtain a matrix
Figure BDA0003315593890000048
Will matrix D*Carrying out 256-level gray scale transformation to obtain a difference degree gray scale matrix
Figure BDA0003315593890000049
The expression of the gray scale conversion is
Figure BDA0003315593890000051
By observing the degree of difference gray matrix
Figure BDA0003315593890000052
The number of clusters C is estimated.
Further, in S3, the fuzzy clustering algorithm is cyclically executed, and the obtained optimized clustering result specifically includes:
the 4 parameters of the fuzzy clustering algorithm are the clustering number C, the fuzzy set overlapping coefficient m and the maximum iteration number l of the algorithmmaxAnd minimum step distance epsilon of target functionThe value range of the overlapping coefficient m of the fuzzy set is [1.1,5.0 ]]The interval is divided into N at equal intervalsmSub-interval and setting the cycle number of the fuzzy clustering algorithm as Nm；
Giving the clustering number C, the fuzzy set overlapping coefficient m and the maximum iteration number l of the algorithmmaxAnd the minimum step distance epsilon parameter of the objective function, for analyzing the data set matrix
Figure BDA0003315593890000053
Executing a fuzzy clustering algorithm; an objective function of
Figure BDA0003315593890000054
Wherein, bi＝(bi,1,bi,2,bi,3) Is the ith vector (row vector), v, of matrix BkFor the k-th cluster ckThe center vector (row vector) of (c),
Figure BDA0003315593890000055
Figure BDA0003315593890000056
is under the condition of miBelong to cluster ckThe degree of membership of (a) is,
Figure BDA0003315593890000057
using mui,kForm a membership matrix UpI.e. Up＝{μi,k}; using a cluster centre vector vkForm a central matrix, i.e.
Figure BDA0003315593890000058
Through successive iterations, the objective function f (n)rC, m) are gradually reduced, and a membership matrix U is formedpTends to be stable; when | | | UP+1-UpIf | < epsilon or the number of iterations reaches the maximum value lmaxThen stopping iteration, and considering the objective function value to be minimum, at this moment, the membership degree matrix
Figure BDA0003315593890000061
And a central matrix
Figure BDA0003315593890000062
Obtaining fuzzy clustering results;
to obtain an optimized clustering result, the algorithm needs N for different values of mmA secondary loop, which tries the optimal clustering result under the condition of different m values;
selecting PBMF (Pakhira, Bandy & Maulik-Index for Fuzzy C-means Clustering) as a Fuzzy Clustering algorithm Validity Index (CVI), wherein the expression is as follows:
Figure BDA0003315593890000063
wherein C is the number of clusters and is determined by an iVAT algorithm; m is a fuzzy set overlapping coefficient; n isrIs the number of rows of matrix B; v is the vector of the mean of the matrix B, i.e.,
Figure BDA0003315593890000064
when the CVI takes the maximum value, the fuzzy clustering result is the best, namely the m corresponding to the best clustering result satisfies the following formula:
m＝arg max CVI(C,m)
at this time, the membership matrix U and the Center matrix Center obtained by the fuzzy clustering algorithm are the optimized clustering result.
Further, the specific method in S4 is as follows:
according to the optimized fuzzy clustering result, the analysis data set is divided into C clusters by utilizing the maximum value of each row of elements of the membership matrix U, the data point Center of each cluster is determined by the Center matrix Center, the power communication optical cable resource availability analysis can be regarded as a characteristic explanation process of the optical cable clusters, the optical cable resource availability can be comprehensively analyzed and visually displayed by means of a data visualization technology, and the optical cable resource availability can be regarded as high in optical cable resource availability for optical cables with low service volume, high availability and high fiber core resource residual occupation ratio; and for the optical cable with high traffic, low availability and low residual fiber core resource occupation, the availability of the optical cable resource can be regarded as low.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a comprehensive availability analysis method of an electric power communication optical cable considering service characteristics, which respectively calculates the service volume, the availability of the optical cable and the residual proportion of fiber core resources by extracting the characteristics and the values of the service carried by the electric power communication optical cable and extracting the characteristics and the values of the optical cable; aiming at an analysis data set constructed by the traffic, the availability of the optical cable and the residual proportion of fiber core resources, the invention circularly executes a fuzzy clustering algorithm and obtains an optimized clustering result; by means of a data visualization technology, the usability of the optical cable resources can be visually and comprehensively displayed; by using the method and the system, the electric power communication operation personnel can accurately identify the service bearing condition of the electric power communication optical cable and the availability of optical cable resources, can more reasonably plan and deploy communication services, reduce the heavy load risk of the optical cable and improve the service reliability.
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FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a flow chart of the fuzzy clustering algorithm of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, an embodiment of the present invention provides a method for analyzing comprehensive availability of an electric power communication optical cable in consideration of service characteristics, including the following steps:
the first step is as follows: extracting the characteristics and values of the service carried by the power communication optical cable, and calculating the service volume; extracting the characteristics and values of the power communication optical cable, and calculating the availability of the optical cable; counting the utilization condition of the fiber core of the optical cable, and calculating the residual proportion of fiber core resources;
the second step is that: cleaning a data object at a pole end by using the service volume, the availability of the optical cable and the residual proportion of fiber core resources, and constructing an analysis data set;
the third step: establishing a difference matrix, and estimating the number of clusters by using a clustering trend visual evaluation algorithm; circularly executing a fuzzy clustering algorithm to obtain an optimized clustering result;
the fourth step: and processing the clustering result, and analyzing the resource availability of the power communication optical cable of each cluster through a data visualization technology.
Calculating the service volume in the first step, the embodiment of the invention selects the contents of service level, service type, service bearing mode, service channel mode, service voltage level, service quantity and the like as the characteristics of the electric power communication optical cable service; the value conditions of different service characteristics are as follows: the service level values are respectively as follows: headquarters, branches, and provincial companies; the values of the service types are respectively as follows: relay protection, safety and stability control, data network scheduling, program control service scheduling, other services and the like; the values of the service bearing modes are respectively as follows: dedicated optical fibers and multiplexing; the values of the service channel modes are respectively as follows: primary use and standby use; the service voltage class values are respectively as follows: AC 1000kV, DC + -800 kV, DC + -660 kV, AC 500kV and AC 220 kV;
according to the service characteristics and the values thereof, the embodiment of the invention calculates the service volume, wherein the service volume S of the power communication optical cable is expressed as:
Figure BDA0003315593890000081
wherein, S (i) represents the traffic of the ith optical cable; n (i) is the current service quantity borne by the ith optical cable; fnNumber of traffic characteristics, j 1,2, F, for participating in traffic calculationsnThe invention is provided with Fn(ii) 5; xi (j) is the weight of the jth feature, and satisfies the condition:
Figure BDA0003315593890000082
ω (j, k) is the jth feature, the kth value; for example, the value headquarters, branches and provinces of the feature "business level" correspond to ω (j, k) as follows: 0.9, 0.7 and 0.5, assignment of other traffic characteristics, and so on.
Calculating the availability of the optical cable in the first step, the embodiment of the invention selects the contents of the voltage level of the optical cable, the type of the optical cable, the length of the optical cable, the operating life and the like as the characteristics of the electric power communication optical cable; wherein, the value conditions of different electric power communication optical cable characteristics are as follows:
the values of the voltage class of the optical cable are respectively as follows: AC 1000kV, DC + -800 kV, DC + -660 kV, AC 500kV, AC 220kV, AC 110kV and AC 35 kV; the values of the optical cable types are respectively as follows: optical fiber Composite Overhead Ground Wire (OPGW) and All-Dielectric Self-Supporting fiber optic cable (ADSS); the optical cable length refers to the actual physical length of the optical cable, and is in km; the operation year refers to the years of the optical cable from the time of putting into operation to the time of extracting the characteristic data, and the year is taken as a unit;
according to the characteristics and values of the electric power communication optical cable, the availability of the optical cable is calculated, the availability of the optical cable is called as reliability, and the availability A is expressed as:
A(i)＝1-τ(i)·λ(i)·L(i)
wherein A (i) indicates the availability of the ith cable; tau (i) is the average repair time of the optical cable interruption, is related to the type of the optical cable, and has the value range of [8,12] hours; λ (i) is the failure rate per km of cable, related to the cable voltage level, cable type and operational age, in units of "1/hour"; l (i) is the cable length in km.
In the above embodiment, the failure rate per km of the optical cable includes:
the failure rate per km of cable is expressed as:
Figure BDA0003315593890000091
wherein, KVIs a coefficient of the voltage class of the optical cable, depending on the voltage class of the optical cable, the higher the class, KVThe smaller;
Figure BDA0003315593890000092
the failure rate of the optical cable in the stable operation stage, namely the interruption frequency of the optical cable per hour, is related to the type of the optical cable; the OPGW is less inefficient than the ADSS; t is the operating life of the optical cable; t is t0The time length of the optical cable in the stable operation stage is expressed in years; t of OPGW over ADSS0The length is required to be long; kλFor the annual cable aging factor, depending on the cable type, OPGW is compared with the K of ADSSλIs small.
Calculating the remaining fiber core resource occupation ratio in the first step comprises the following steps:
Figure BDA0003315593890000101
wherein eta (i) represents the residual proportion of the fiber core resource of the ith power communication optical cable; n isz(i) The total fiber core number of the ith power communication optical cable is counted; n iss(i) The number of the residual fiber cores of the ith power communication optical cable is nz(i) The number of available fiber core resources left by subtracting the occupied fiber core number belongs to the available physical optical cable resources.
The specific method of the second step comprises the following steps:
calculating to obtain 3 vectors such as the traffic S, the availability A of the optical cable and the residual fiber resource proportion eta through the first step; analyzing the element values of the 3 vectors by using a mathematical statistical method, taking data outside a confidence interval of 99% as extreme data objects, and cleaning;
let the number of data objects for analysis after data cleaning be nrForming a matrix B' by using 3 vectors such as the cleaned traffic S, the availability A of the optical cable and the residual resource proportion eta of the fiber core, namely
Figure BDA0003315593890000102
Standardizing B' to obtain a matrix
Figure BDA0003315593890000103
The normalized expression is:
Figure BDA0003315593890000104
matrix array
Figure BDA0003315593890000105
I.e. the constructed analytical data set.
Establishing a difference matrix in the third step, and estimating the clustering number by using a clustering trend visual evaluation algorithm, wherein the specific method comprises the following steps:
aiming at the matrix B, a difference degree matrix is established
Figure BDA0003315593890000106
Wherein the content of the first and second substances,
Figure BDA0003315593890000107
adopting an improved visual evaluation iVAT algorithm of clustering tendency to reorder the difference matrix D to obtain a reordered matrix
Figure BDA0003315593890000108
For matrix D*Carrying out 256-level gray scale transformation to obtain a difference degree gray scale matrix
Figure BDA0003315593890000109
The expression of the gray scale conversion is
Figure BDA0003315593890000111
By observing the degree of difference gray matrix
Figure BDA0003315593890000112
Estimate the convergence ofThe number of classes C.
The fuzzy clustering algorithm is circularly executed in the third step, and the obtained optimized clustering result specifically comprises the following steps:
the 4 parameters of the fuzzy clustering algorithm are respectively as follows: clustering number C, fuzzy set overlapping coefficient m and maximum iteration number l of algorithmmaxAnd the minimum step distance epsilon of the target function; wherein, the clustering number C is obtained by the estimation of an iVAT algorithm; maximum number of iterations l of the algorithmmaxAnd the minimum step distance epsilon of the target function is determined by the scale of the practical problem; the value range of the overlapping coefficient m of the fuzzy set is generally [1.1,5.0 ]](ii) a In order to obtain the best clustering result, the cycle number of the fuzzy clustering algorithm is set as Nm(ii) a KthmM is then
Figure BDA0003315593890000113
km＝1,2,...,Nm；
Giving the clustering number C, the fuzzy set overlapping coefficient m and the maximum iteration number l of the algorithmmaxAnd the minimum step distance epsilon parameter of the objective function, for analyzing the data set matrix
Figure BDA0003315593890000114
Executing a fuzzy clustering algorithm; an objective function of
Figure BDA0003315593890000115
Wherein, bi＝(bi,1,bi,2,bi,3) Is the ith vector (row vector), v, of matrix BkFor the k-th cluster ckThe center vector (row vector) of (c),
Figure BDA0003315593890000116
Figure BDA0003315593890000117
is under the condition of miBelong to cluster ckThe degree of membership of (a) is,
Figure BDA0003315593890000118
using mui,kForm a membership matrix UpI.e. Up＝{μi,k}; using a cluster centre vector vkForm a central matrix, i.e.
Figure BDA0003315593890000119
Through successive iterations, the objective function f (n)rC, m) are gradually reduced, and a membership matrix U is formedpTends to be stable; when | | | UP+1-UpIf | < epsilon or the number of iterations reaches the maximum value lmaxThen stopping iteration, and considering the objective function value to be minimum, at this moment, the membership degree matrix
Figure BDA0003315593890000121
And a central matrix
Figure BDA0003315593890000122
Obtaining fuzzy clustering results;
to obtain an optimized clustering result, the algorithm needs N for different values of mmA secondary loop, which tries the optimal clustering result under the condition of different m values;
selecting PBMF (Pakhira, Bandy & Maulik-Index for Fuzzy C-means Clustering) as a Fuzzy Clustering algorithm Validity Index (CVI), wherein the expression is as follows:
Figure BDA0003315593890000123
wherein C is the number of clusters and is determined by an iVAT algorithm; m is a fuzzy set overlapping coefficient; n isrIs the number of rows of matrix B; v is the vector of the mean of the matrix B, i.e.,
Figure BDA0003315593890000124
in NmIn the sub-cycle fuzzy clustering, when the CVI takes the maximum value, the fuzzy clustering result is the best, namely the optimal clustering result corresponding to m should satisfy the following formula:
m＝arg max CVI(C,m)
at this time, the membership matrix U and the Center matrix Center obtained by the fuzzy clustering algorithm are the optimized clustering result.
In the fourth step, the optimized fuzzy clustering result is obtained through the fuzzy clustering process, the power communication optical cable resource availability is divided into C clusters according to the maximum value of each row of elements of the membership matrix U in the clustering result by the algorithm, the Center matrix Center is used as the data point Center of each cluster, the power communication optical cable resource availability analysis is a characteristic explanation process of the data object clusters, the availability of the optical cable resources can be visually and comprehensively displayed by means of a data visualization technology, the optical cable clusters with low traffic, high availability and high residual fiber core resources are considered as high optical cable resource availability; the optical cable cluster with higher traffic, lower availability and lower residual fiber core resource occupation can be regarded as lower availability of the optical cable resource. Therefore, the classified evaluation of the resource availability of the power communication optical cable is realized.
The design principle is as follows: the invention provides a comprehensive availability analysis method of an electric power communication optical cable considering service characteristics, which has the basic idea that the problem of reasonable utilization of electric power communication optical cable resources is comprehensively solved by combining service and optical cable characteristics; the content comprises the following steps: extracting the characteristics and values of the service carried by the optical cable, and calculating the service volume; extracting the characteristics and values of the optical cable, and calculating the availability of the optical cable; counting the residual proportion of fiber core resources; cleaning a data object at a pole end by using the service volume, the availability of the optical cable and the residual proportion of fiber core resources, and constructing an analysis data set; establishing a difference degree matrix; estimating the number of clusters by using a clustering trend visual evaluation algorithm; circularly executing a fuzzy clustering algorithm to obtain an optimized clustering result; by means of a data visualization technology, the availability of the power communication optical cable resources of each cluster is analyzed, the service bearing condition of the optical cable and the availability of the optical cable resources are accurately identified, the power communication service is reasonably planned and deployed, and the risk of optical cable load is reduced.
To further better illustrate the present invention, the following specific example analytical calculation procedures are also provided:
the embodiment of the invention selects the contents of service level, service type, service bearing mode, service channel mode, service voltage grade, service quantity and the like as the characteristics of the electric power communication optical cable service; the service characteristic weight distribution is respectively as follows: service class xi (1) ═ 0.3, service type xi (2) ═ 0.3, service bearing mode xi (3) ═ 0.05, service channel mode xi (4) ═ 0.05 and service voltage class xi (5) ═ 0.3.
The values of different service characteristics are as follows:
(1) service level: total ω (1, k) is 0.9, division ω (1, k) is 0.7, and provincial ω (1, k) is 0.5.
(2) And (4) service type: the relay protection ω (2, k) is 0.9, the safety and stability control ω (2, k) is 0.8, the scheduling data ω (2, k) is 0.7, the scheduling program control ω (2, k) is 0.7, and other services ω (2, k) are 0.5.
(3) Service bearing mode: the dedicated optical fiber ω (3, k) is 0.9, and the multiplexing ω (3, k) is 0.9.
(4) Service channel mode: primary ω (4, k) is 0.5, and backup ω (4, k) is 0.5.
(5) Service voltage class: ac 1000kV ω (5, k) is 0.9, dc ± 800kV ω (5, k) is 0.9, dc ± 660kV ω (5, k) is 0.9, ac 500kV ω (5, k) is 0.8, and ac 220kV ω (5, k) is 0.7.
The invention calculates the service volume according to the service characteristics and the values thereof, and the service volume S of the power communication optical cable is expressed as
Figure BDA0003315593890000141
Wherein, n (i) is the number of services currently carried by the ith optical cable.
The invention selects the contents of optical cable voltage grade, optical cable type, optical cable length, operation age and the like as the characteristics of the electric power communication optical cable; the value conditions of different power communication optical cable characteristics are as follows:
(1) cable voltage class: AC 1000kV, DC + -800 kV, DC + -660 kV, AC 500kV, AC 220kV, AC 110kV and AC 35 kV.
(2) The type of the optical cable: OPGW and ADSS.
(3) The length of the optical cable is as follows: the actual length of the cable laid in the geographical location is in km.
(4) The service life is as follows: the years of the optical cable from the start of the operation to the time of extracting the characteristic data are taken as the unit of year.
The invention calculates the availability of the optical cable according to the characteristics and the values of the electric power communication optical cable, and the availability A of the optical cable is expressed as
A(i)＝1-τ(i)·λ(i)·L(i)
Wherein tau (i) is the average repair time of the optical cable interruption, is related to the type of the optical cable, and has a value range of [8,12] hours.
For an OPGW cable, τ (i) is 12 hours; for an ADSS cable, τ (i) is 8 hours.
λ (i) is the failure rate per km of cable, related to the cable voltage level, cable type and operational age, in units of "1/hour"; l (i) is the cable length in km.
The calculation method of lambda (i) is as follows:
Figure BDA0003315593890000151
wherein, KVIs a cable voltage class coefficient, and depends on the voltage class of the cable.
If the voltage class is AC 1000kV, or DC + -800 kV, or DC + -660 kV, then K isV＝0.8；
If the voltage class is AC 500kV, then KV＝1.0；
If the voltage class is AC 220kV or AC 110kV, KV＝1.2；
If the voltage class is AC 35kV, KV＝1.5。
Figure BDA0003315593890000152
The failure rate of the optical cable in the stable operation stage is related to the type of the optical cable.
With respect to the optical fiber for the OPGW cable,
Figure BDA0003315593890000153
with respect to the ADSS optical cable,
Figure BDA0003315593890000154
t0the unit of the time length of the optical cable in the stable operation stage is year.
For OPGW optical cables, t012 years old; for ADSS cable, t0For 8 years.
KλThe aging factor of the cable is related to the type of the cable.
For OPGW optical cables, Kλ0.1; for ADSS cables, Kλ＝0.5。
The residual occupation ratio eta (i) of the fiber core resource of the power communication optical cable is expressed as
Figure BDA0003315593890000155
Wherein n isz(i) The total number of the optical cable cores; n iss(i) The number of the residual fiber cores of the optical cable is left.
Respectively carrying out statistical analysis on 3 vectors such as the traffic S, the optical cable availability A and the fiber core residual resource proportion eta, taking data outside a confidence interval of 99% as an extreme data object, and cleaning; if the data object for analysis after data cleaning is nrThen 3 vectors form the matrix B', i.e.
Figure BDA0003315593890000161
Standardizing B' to obtain matrix
Figure BDA0003315593890000162
The matrix B is the constructed analysis data set.
The invention first establishes a difference matrix of an analysis data set B
Figure BDA0003315593890000163
Then, estimating the optimal clustering number C by using an improved clustering trend visual evaluation iVAT algorithm; then according to the mouldValue range [1.1,5.0 ] of overlapping coefficient m of paste set]Determining the number of times of the cyclic execution of the fuzzy clustering algorithm Nm. And obtaining an optimized clustering result membership matrix U and a Center matrix Center by utilizing the maximum value of the PBMF fuzzy clustering algorithm effectiveness index CVI.
And processing the clustering result, and analyzing the availability of the power communication optical cable resources of different clusters through a data visualization technology.
Referring to fig. 2, a flowchart of the fuzzy clustering algorithm in the embodiment of the present invention is shown in fig. 2, and includes the following steps:
the method comprises the following steps: inputting an analysis data set B, and establishing a difference degree matrix of B
Figure BDA0003315593890000164
Wherein the content of the first and second substances,
Figure BDA0003315593890000165
then, D is reordered by utilizing an iVAT algorithm to obtain a sorted difference matrix
Figure BDA0003315593890000166
In pair matrix D*Carrying out 256-level gray scale transformation to obtain a difference degree gray scale matrix
Figure BDA0003315593890000167
Finally, displaying and observing the difference degree gray matrix
Figure BDA0003315593890000168
Estimating the optimal clustering number C of the images;
step two: setting the cycle execution times of the fuzzy clustering algorithm as NmIn the interval of overlapping coefficient m of fuzzy set [1.1,5.0]Selecting N at equal intervalsmSample points as m values for each cycle; kthmA value of m is
Figure BDA0003315593890000169
km＝1,2,...,Nm(ii) a For example, let Nm40, then m sample point vectors Vm＝{1.11.2.., 5.0 }; under the condition that the given clustering number C, the fuzzy set overlapping coefficient m, the maximum iteration number and the iteration step length are initial parameters, executing a fuzzy clustering algorithm, and performing a common cycle NmSecondly; finally obtaining NmGrouping membership degree matrix U and clustering Center matrix Center under different m values;
step three: the algorithm selects PBMF as a clustering effectiveness index CVI according to NmAnd respectively calculating the CVI of the fuzzy clustering result of the analysis data set B by using the group membership matrix U and the clustering Center matrix Center. And taking the clustering result of the maximum value corresponding to the CVI as an optimization result. And the optimized membership matrix U and the clustering Center matrix Center are used as analysis bases, and the algorithm is used for carrying out optical cable resource availability analysis through data visualization processing.
Setting 732 analyzed electric power communication optical cables, calculating the service volume by considering the characteristics of optical cable service level, service type, service bearing mode, service channel mode, service voltage level, service quantity and the like to obtain an optical cable service volume vector S, and calculating the availability (reliability) of the optical cable by considering the characteristics of optical cable voltage level, optical cable type, optical cable length, operation age and the like to obtain an optical cable availability vector A; counting the residual proportion of fiber core resources of each optical cable to obtain a residual proportion vector eta of the fiber core resources; cleaning the 3 vectors according to a confidence interval of 99%; data outside the confidence interval are regarded as extreme data objects and are cleaned; 4 data objects in 732 optical cables are cleaned; number of data objects n for analysis after data cleansingr728; carrying out standardization processing on a matrix B' formed by 3 eigenvectors to obtain an analysis data set matrix
Figure BDA0003315593890000171
Establishing a disparity matrix of B
Figure BDA0003315593890000172
Reordering D by using iVAT algorithm, and reordering the reordered difference matrix D*Carrying out 256-level gray scale transformation to obtain a difference degree gray scale matrix
Figure BDA0003315593890000173
And displaying and observing the image of the difference degree gray matrix G, and estimating the optimal clustering number C to be 5.
Setting the cycle number N of the fuzzy clustering algorithmmThe fuzzy set overlap factor m is in the interval [1.1,5.0 ═ 40]Taking 40 points at equal intervals to form m sample point vectors Vm1.1, 1.2.., 5.0 }. Setting the clustering number C to 5, and setting the fuzzy set overlapping coefficient m to be VmMaximum number of iterations itmax100, the minimum step distance epsilon of the objective function is 10-5. The algorithm circularly executes fuzzy clustering for 40 times to obtain 40 groups of membership degree matrixes U and clustering Center matrixes Center. For the 40 groups of results, the PBMF clustering validity index CVI is calculated respectively by combining the analysis data set matrix B. Where CVI has a maximum value of 0.073, corresponding to C-5 and m-1.2. And a membership degree matrix U and a clustering Center matrix Center corresponding to C-5 and m-1.2 are optimized fuzzy clustering results.
Dividing 728 data objects into 5 clusters based on the optimized clustering result; by means of a data visualization technology, in combination with an optical cable and a service bearing condition thereof, the result of the resource availability analysis of the power communication optical cable considering service characteristics is as follows:
cluster 1(C1) contained 188 cables in a ratio of 25.8%. The optical fiber cable is characterized by low traffic, high availability (reliability) of the optical cable and high residual ratio of fiber core resources; the optical cable service risk is low, and the resource availability is high;
cluster 2(C2) contained 64 cables at a ratio of 8.8%. The optical fiber cable is characterized by high traffic, low availability (reliability) of optical cables, and medium occupation ratio of fiber core resources. The optical cable has high service risk and low resource availability;
cluster 3(C3) contained 77 cables, at a ratio of 10.6%. It is characterized by "medium traffic," medium cable availability (reliability), "low residual occupancy of core resources". The risk of the optical cable service is moderate, the spare resources are less, and the availability of the optical cable resources is moderate and low;
cluster 4(C4) contained 223 cables, 30.6% by weight. The optical fiber cable is characterized by low traffic, high availability (reliability) of optical cables, and medium occupation ratio of fiber core resources. The optical cable service risk is low, the spare resources are moderate, and the availability of the optical cable resources is high;
cluster 5(C5) contained 176 cables, at a rate of 24.2%. The method is characterized by being under the conditions of medium traffic, high availability (reliability) of optical cables and medium residual proportion of fiber core resources. The optical cable service risk is medium, the spare resources are low, and the availability of the optical cable resources is low.
The analysis conclusion is helpful for the electric power communication operation maintenance personnel to accurately identify the service bearing condition of the optical cable, grasp the availability of the optical cable resource, plan and deploy the electric power communication service more reasonably, and further reduce the risk of optical cable load.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be able to cover the technical solutions and the inventive concepts of the present invention within the technical scope of the present invention.

Claims (9)

1. A method for analyzing the comprehensive availability of an electric power communication optical cable in consideration of service characteristics is characterized by comprising the following steps:
s1: extracting the characteristics and values of the service carried by the power communication optical cable, and calculating the service volume; extracting the characteristics and values of the power communication optical cable, and calculating the availability of the optical cable; counting the utilization condition of the fiber core of the optical cable, and calculating the residual proportion of fiber core resources;
s2: cleaning a data object at a pole end by using the service volume, the availability of the optical cable and the residual proportion of fiber core resources, and constructing an analysis data set;
s3: establishing a difference matrix, and estimating the number of clusters by using a clustering trend visual evaluation algorithm; circularly executing a fuzzy clustering algorithm to obtain an optimized clustering result;
s4: and processing the clustering result, and analyzing the resource availability of the power communication optical cable of each cluster through a data visualization technology.
2. The method for analyzing the comprehensive availability of the power communication optical cable considering the service characteristics as claimed in claim 1, wherein: the calculating of the traffic volume in S1 includes characteristics of the service carried by the power communication optical cable, and specifically includes: service level, service type, bearing mode, channel mode, service voltage level and service quantity; wherein, the values of the service level are respectively as follows: headquarters, branches, and provincial companies; the values of the service types are respectively as follows: relay protection, safety and stability control, data network scheduling and program control service scheduling; the values of the bearing modes are respectively as follows: dedicated optical fibers and multiplexing; the channel mode values are respectively: primary use and standby use; the service voltage class values are respectively as follows: AC 1000kV, DC + -800 kV, DC + -660 kV, AC 500kV and AC 220 kV;
the traffic S of the power communication optical cable is represented as:
Figure FDA0003315593880000011
wherein, S (i) represents the traffic of the ith optical cable; n (i) is the current service quantity borne by the ith optical cable; fnNumber of traffic characteristics, j 1,2, F, for participating in traffic calculationsnAnd xi (j) is the weight of the jth feature, and meets the condition:
Figure FDA0003315593880000021
the j-th characteristic and the k-th value are taken.
3. The method for analyzing the comprehensive availability of the power communication optical cable considering the service characteristics as claimed in claim 1, wherein: calculating the availability of the optical cable in the S1 includes the characteristics of the power communication optical cable, which specifically include: cable voltage class, cable type, cable length and operational life; wherein, the optical cable voltage grade values are respectively: AC 1000kV, DC + -800 kV, DC + -660 kV, AC 500kV, AC 220kV, AC 110kV and AC 35 kV; the values of the optical cable types are respectively as follows: the optical fiber composite overhead ground wire and the all-dielectric self-supporting optical cable; the optical cable length refers to the actual physical length of the optical cable, and is in km; the operation year limit refers to the number of years from the input operation to the extraction of the characteristic data, and the number of years is taken as a unit;
the cable availability is called reliability and availability a is expressed as:
A(i)＝1-τ(i)·λ(i)·L(i)
wherein A (i) indicates the availability of the ith cable; τ (i) is the average repair time for a cable break, depending on the cable type; l (i) is the cable length; λ (i) is the failure rate per km of cable, related to the cable voltage class, cable type and operational age.
4. A method for analyzing the comprehensive availability of an optical power communication cable in consideration of service features as claimed in claim 3, wherein: the failure rate per km of cable includes:
the failure rate per km of cable is expressed as:
Figure FDA0003315593880000022
wherein, KVIs the optical cable voltage grade coefficient;
Figure FDA0003315593880000023
the failure rate of the optical cable in the stable operation stage, namely the interruption times of the optical cable per hour, is related to the type of the optical cable; t is the operating life of the optical cable; t is t0The time length of the stable operation stage of the optical cable is expressed in years; kλThe aging coefficient of the optical cable is related to the type of the optical cable.
5. The method for analyzing integrated availability of optical power communication cables considering service features as claimed in claim 1, wherein the calculating of the remaining fiber core resource occupation ratio in S1 includes expressing the remaining fiber core resource occupation ratio of the optical power communication cables as:
Figure FDA0003315593880000031
wherein eta (i) represents the residual proportion of the fiber core resource of the ith power communication optical cable; n isz(i) The total fiber core number of the ith power communication optical cable is counted; n iss(i) The number of the residual fiber cores of the ith power communication optical cable belongs to available optical cable resources.
6. The method for analyzing the comprehensive availability of an optical power communication cable considering the service characteristics as claimed in claim 1, wherein the specific method in S2 is as follows:
obtaining a traffic S, an optical cable availability A and a fiber core residual resource occupation ratio eta 3 vectors through S1; analyzing the element values of the 3 vectors by using a mathematical statistical method, taking data outside a confidence interval of 99% as extreme data objects, and cleaning;
let n be the number of cables used for analysis after data cleaningrUsing the cleaned 3 vectors, a matrix B' is constructed, i.e.
Figure FDA0003315593880000032
Standardizing B' to obtain a matrix
Figure FDA0003315593880000033
The normalized expression is:
Figure FDA0003315593880000034
matrix array
Figure FDA0003315593880000035
I.e. the constructed analytical data set.
7. The method for analyzing the comprehensive availability of the power communication optical cable considering the service characteristics as claimed in claim 1, wherein the specific method for establishing the difference degree matrix and estimating the clustering number by using the clustering trend visualization evaluation algorithm in the step S3 comprises the following steps:
aiming at the matrix B, a difference degree matrix is established
Figure FDA0003315593880000036
Wherein the content of the first and second substances,
Figure FDA0003315593880000037
carrying out improved clustering trend visual evaluation algorithm aiming at the difference matrix D to obtain a matrix
Figure FDA0003315593880000041
Will matrix D*Carrying out 256-level gray scale transformation to obtain a difference degree gray scale matrix
Figure FDA0003315593880000042
The expression of the gray scale conversion is
Figure FDA0003315593880000043
By observing the degree of difference gray matrix
Figure FDA0003315593880000044
The number of clusters C is estimated.
8. The method for analyzing the comprehensive availability of an electric power communication optical cable considering the service characteristics as claimed in claim 1, wherein the fuzzy clustering algorithm is cyclically executed in S3, and the obtained optimized clustering result specifically comprises:
the 4 parameters of the fuzzy clustering algorithm are the clustering number C, the fuzzy set overlapping coefficient m and the maximum iteration number l of the algorithmmaxAnd the minimum step distance epsilon of the target function, and setting the value range of the overlapping coefficient m of the fuzzy set as [1.1,5.0 ]]The interval is divided into N at equal intervalsmSub-interval and setting the cycle number of the fuzzy clustering algorithm as Nm；
Giving the clustering number C, the fuzzy set overlapping coefficient m and the maximum iteration number l of the algorithmmaxAnd the minimum step distance epsilon parameter of the objective function, for analyzing the data set matrix
Figure FDA0003315593880000045
Executing a fuzzy clustering algorithm; an objective function of
Figure FDA0003315593880000046
Wherein, bi＝(bi,1,bi,2,bi,3) Is the i-th vector of the matrix B, vkFor the k-th cluster ckThe center vector (row vector) of (c),
Figure FDA0003315593880000047
Figure FDA0003315593880000048
is under the condition of miBelong to cluster ckThe degree of membership of (a) is,
Figure FDA0003315593880000049
using mui,kForm a membership matrix UpI.e. Up＝{μi,k}; using a cluster centre vector vkForm a central matrix, i.e.
Figure FDA0003315593880000051
Through successive iterations, the objective function f (n)rC, m) are gradually reduced, and a membership matrix U is formedpTends to be stable; when in use
Figure FDA0003315593880000052
Or the number of iterations reaches a maximum value lmaxThen stopping iteration, and considering the objective function value to be minimum, at this moment, the membership degree matrix
Figure FDA0003315593880000053
And a central matrix
Figure FDA0003315593880000054
Obtaining fuzzy clustering results;
to obtain an optimized clustering result, the algorithm needs N for different values of mmA secondary loop, which tries the optimal clustering result under the condition of different m values;
selecting PBMF as the validity index of the fuzzy clustering algorithm, wherein the expression is as follows:
Figure FDA0003315593880000055
wherein C is the number of clusters and is determined by an iVAT algorithm; m is a fuzzy set overlapping coefficient; n isrIs the number of rows of matrix B; v is the vector of the mean of the matrix B, i.e.,
Figure FDA0003315593880000056
when the CVI takes the maximum value, the fuzzy clustering result is the best, namely the m corresponding to the best clustering result satisfies the following formula:
m＝argmaxCVI(C,m)
at this time, the membership matrix U and the Center matrix Center obtained by the fuzzy clustering algorithm are the optimized clustering result.
9. The method for analyzing the comprehensive availability of an optical power communication cable considering the service characteristics as claimed in claim 1, wherein the specific method in S4 is as follows:
according to the optimized fuzzy clustering result, the analysis data set is divided into C clusters by utilizing the maximum value of each row of elements of the membership matrix U, the data point Center of each cluster is determined by the Center matrix Center, the power communication optical cable resource availability analysis can be regarded as a characteristic explanation process of the optical cable clusters, the optical cable resource availability can be comprehensively analyzed and visually displayed by means of a data visualization technology, and the optical cable resource availability can be regarded as high in optical cable resource availability for optical cables with low service volume, high availability and high fiber core resource residual occupation ratio; and for the optical cable with high traffic, low availability and low residual fiber core resource occupation, the availability of the optical cable resource can be regarded as low.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116150257A (en) * 2023-04-18 2023-05-23 国网湖北省电力有限公司信息通信公司 Visual analysis method, system and storage medium for electric power communication optical cable resources

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116150257A (en) * 2023-04-18 2023-05-23 国网湖北省电力有限公司信息通信公司 Visual analysis method, system and storage medium for electric power communication optical cable resources

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