CN111818404B - Method for judging discretization link state of passive optical network user side reflected signal - Google Patents
Method for judging discretization link state of passive optical network user side reflected signal Download PDFInfo
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Abstract
The invention discloses a method for judging the discretization link state of a reflection signal of a passive optical network user end, which comprises the following steps of firstly, carrying out sectional integration on the signal according to a starting point and an end point of a user signal to obtain user link state information; then, determining the optimal number of classification groups according to the user link state information; and finally, calculating corresponding clustering centers according to the optimal classification group number, clustering the link state information of each user, and returning a discretization link state result. The invention reduces the difficulty of identifying the link state and reduces the misjudgment rate of the system.
Description
Technical Field
The invention belongs to the technical field of optical fiber communication, and particularly relates to a method for judging the discretization link state of a reflection signal of a passive optical network user side.
Background
The Passive Optical Network (PON) technology has been developed and evolved to the next generation passive optical network stage 2(NG-PON2), and with many advantages of its passive characteristics, large capacity, high rate, low loss, large splitting ratio, etc., it has been developed into a bearer pipeline for multiple services and services, such as traditional broadband access, mobile fronthaul/backhaul, and data center, etc., and is an optimal solution to meet the increasing traffic bandwidth requirements of fixed broadband users and 5G mobile communications. Due to higher requirements of the NG-PON2 system on network security and protection, real-time monitoring of the optical fiber link is one of important guarantees of the NG-PON2 system reliability.
In order to solve the problem of real-time monitoring of multi-branch optical fiber link faults in the NG-PON2 system, various optical fiber link monitoring technologies based on user-side reflected signals are proposed. Such techniques utilize a probe signal light source deployed at the central office, and a reflector deployed at the subscriber premises to generate a reflected signal having unique characteristics for each subscriber. It should be noted that, since the link status signals of the users are affected by many factors, the link status signal strengths of different users are different, especially for users at equal distances, the link status determination difficulty is increased by the difference, and if a method of presetting a threshold is adopted, the link status of each user needs to be set independently, so that the link monitoring system cannot be deployed in an actual network quickly.
Disclosure of Invention
In order to solve the technical problems mentioned in the background art, the invention provides a method for judging the discretization link state of a reflection signal of a passive optical network user end.
In order to achieve the technical purpose, the technical scheme of the invention is as follows:
a method for judging the discretization link state of a reflection signal of a passive optical network user side comprises the following steps:
step A: performing segmented integration on the signals according to the starting point and the ending point of the user signals to obtain user link state information;
and B: determining the optimal number of classification groups according to the user link state information;
and C: and calculating corresponding clustering centers according to the optimal classification group number, clustering the link state information of each user, and returning a discretization link state result.
Further, in step a, the method for dividing the starting point and the ending point of the user signal is as follows:
if the reflection intensity of the sampling signal is greater than the upper limit of the noise intensity, the system judges that the signal is a user signal starting point, the signal is integrated, and when the intensity of the sampling signal is less than or equal to the upper limit of the noise intensity, the user signal is stopped, and the integration is finished.
Further, in step B, the number of classification groups depends on the contour coefficient of the segmentation integration result, the value of the contour coefficient is in the range of [ -1,1], and the larger the value of the contour coefficient in the range, the better the classification is.
Further, in step B, the method for determining the number of classification groups is as follows:
(a) calculating the average distance a from the segmented integral value i to the integral values of other segments in the cluster Ci，aiThe smaller the segmentation integration value, the more the segmentation integration value should be classified into the cluster C, and aiIntra-cluster dissimilarity referred to as a segmented integral value i;
(b) calculating the segmented integral value i to other clusters CjAverage distance b of all samplesij，bijReferred to as segmented integral value i and other clusters CjDegree of dissimilarity of, bijThe larger the value is, the less the segmentation integral value i belongs to other clusters;
inter-cluster dissimilarity b defining a piecewise integral value ii：
bi＝min{bi1,bi2...,bik}
The subscript k is the number of other clusters corresponding to the segmented integral value i; biThe larger the segmentation integral value i is, the less the segmentation integral value i belongs to the existing classification cluster;
(c) intra-cluster dissimilarity a based on segmented integral value iiDegree of dissimilarity with clusters biProfile coefficients s (i) defining the segmented integral value i:
if S (i) is close to 1, the segmentation integral value i is reasonably clustered; if S (i) is close to-1, the segmentation integral value i is more classified into other clusters; the mean value of S (i) of all samples is called the contour coefficient of the clustering result and is used for measuring the quality of the whole clustering result.
Further, the specific process of step C is as follows:
(1) selecting k samples as clustering centers according to the optimal classification group number k obtained in the step B;
(2) calculating the distance between each segmented integral value and each clustering center, dividing the sample into the nearest clustering center, calculating the mean value of all integral sample characteristics divided into each category, and taking the mean value as the central value of the corresponding group of the next round of clustering; repeating the step until all the segmented integral values are grouped;
(3) and outputting the final clustering center and the category to which each segmented integral value belongs, calculating the average value of each group, comparing with a standard value, judging the user link state corresponding to each group, and giving a discretization link state result.
Adopt the beneficial effect that above-mentioned technical scheme brought:
1. the invention adopts a method of clustering a large amount of user link state information, balances the problem of link state signal intensity difference among users, automatically judges the user link state information through a clustering algorithm, and greatly reduces the misjudgment rate of a system;
2. the invention adopts a method of segmentation integral re-clustering, the method is simple and reliable, the integral operation can be realized by system hardware, and the occupation of system software resources is reduced.
Drawings
FIG. 1 is an overall process flow diagram of the present invention;
FIG. 2 is a diagram showing an actual waveform of a sampling signal in the embodiment;
fig. 3 is a diagram of the classification result of the integrated intensity of the user reflection signal in the embodiment.
Detailed Description
The technical scheme of the invention is explained in detail in the following with the accompanying drawings.
The invention designs a method for judging the discretization link state of a passive optical network user side reflected signal, which comprises the following steps as shown in figure 1:
step A: performing segmented integration on the signals according to the starting point and the ending point of the user signals to obtain user link state information;
and B: determining the optimal number of classification groups according to the user link state information;
and C: and calculating corresponding clustering centers according to the optimal classification group number, clustering the link state information of each user, and returning a discretization link state result.
In this embodiment, preferably, in step a, the method for dividing the starting point and the ending point of the user signal comprises: if the reflection intensity of the sampling signal is greater than the upper limit of the noise intensity, the system judges that the signal is a user signal starting point, the signal is integrated, and when the intensity of the sampling signal is less than or equal to the upper limit of the noise intensity, the user signal is stopped, and the integration is finished.
In this embodiment, preferably, in step B, for m sampling result signals, the sampling result signals are divided into 2 to n classes, which need to study the contour coefficients of the result of the signal segment integration. The contour coefficient is an important coefficient for measuring the similarity between the signal strength and the class to which it belongs. The value is in the range of [ -1,1 ]. The larger the value of the contour coefficient, the more closely the relation between the segment signal and the target set is. If the value of the generated contour coefficient is relatively high in a data cluster in the data set, the grouping is appropriate and acceptable.
The method for determining the number of classification groups is as follows:
(a) calculating the average distance a from the segmented integral value i to the integral values of other segments in the cluster Ci，aiThe smaller the segmentation integration value, the more the segmentation integration value should be classified into the cluster C, and aiIntra-cluster dissimilarity referred to as a segmented integral value i;
(b) calculating the segmented integral value i to other clusters CjAverage distance b of all samplesij，bijReferred to as segmented integral value i and other clusters CjDegree of dissimilarity of, bijThe larger the value is, the less the segmentation integral value i belongs to other clusters;
inter-cluster dissimilarity b defining a piecewise integral value ii：
bi＝min{bi1,bi2...,bik}
The subscript k is the number of other clusters corresponding to the segmented integral value i; biThe larger the segmentation integral value i is, the less the segmentation integral value i belongs to the existing classification cluster;
(c) intra-cluster dissimilarity a based on segmented integral value iiHezhou clusterDegree of dissimilarity between them biProfile coefficients s (i) defining the segmented integral value i:
if S (i) is close to 1, the segmentation integral value i is reasonably clustered; if S (i) is close to-1, the segmentation integral value i is more classified into other clusters; the mean value of S (i) of all samples is called the contour coefficient of the clustering result and is used for measuring the quality of the whole clustering result.
In this embodiment, preferably, step C adopts the following steps:
(1) selecting k samples as clustering centers according to the optimal classification group number k obtained in the step B;
(2) calculating each segmented integral value i and each cluster center
The distance of (2) to the nearest cluster center
And calculating and dividing into cluster centers
Each of the categories
And taking the mean value of all the integral sample characteristics as the central value of the corresponding group of the next round of clustering
t is iteration times, and the step is repeated until all the segmentation integral values are completely grouped;
(3) and outputting the final clustering center and the category to which each segmented integral value belongs, calculating the average value of each group, comparing with a standard value, judging the user link state corresponding to each group, and giving a discretization link state result.
As shown in fig. 2, there are three integration intervals in the diagram, two user link state signals are superimposed together in both integration intervals 1 and 2, only one user link state signal is in integration interval 3, and three regions are integrated, and since the optical pulse intensity has the characteristic of linear superposition, it is not difficult to find that the approximate value of the relative size of the integration values in the three sections is 2: 2: 1.
fig. 3 shows the classification result of the integrated intensity of the user reflection signal. The integral value of the reflection intensity of a single user is 105The method for clustering the data comprises the steps of firstly judging the number of clusters to be clustered, wherein the number of the clusters can be seen to be 4, namely the number of reflection points of a user can be 0,1,2 and 3, and after comparing the grouping number of 2-6 clusters, the value of the contour coefficient is higher when the number of the clusters is 4. So the clustering starts with a number of clustering targets of 4 in this example.
And 4 scattered clustering center points are randomly selected during cluster initialization. And then taking out the next data point from the data set, calculating the nearest neighbor central point of the next data point, calculating the arithmetic mean value of the nearest neighbor central point and the nearest neighbor cluster central point, taking the arithmetic mean value as the central point of the next iteration, and obtaining all classification results by analogy. And after clustering is finished, calculating the average value in each cluster, normalizing the average value to obtain discrete user number information, and finally obtaining the link state information of all users.
The embodiments are only for illustrating the technical idea of the present invention, and the technical idea of the present invention is not limited thereto, and any modifications made on the basis of the technical scheme according to the technical idea of the present invention fall within the scope of the present invention.
Claims (2)
1. A method for judging the discretization link state of a reflection signal of a passive optical network user side is characterized by comprising the following steps:
step A: performing segmented integration on the signals according to the starting point and the ending point of the user signals to obtain user link state information; the method for dividing the starting point and the ending point of the user signal is as follows:
if the reflection intensity of the sampling signal is greater than the upper limit of the noise intensity, the system judges that the signal is a user signal starting point, the signal is integrated, and when the intensity of the sampling signal is less than or equal to the upper limit of the noise intensity, the user signal is stopped and the integration is finished;
and B: determining the optimal number of classification groups according to the user link state information; the number of classification groups depends on the profile coefficient of the segmentation integration result, the value of the profile coefficient is in the range of [ -1,1], and in the range, the greater the value of the profile coefficient is, the better the classification is;
the method for determining the number of classification groups is as follows:
(a) calculating the average distance a from the segmented integral value i to the integral values of other segments in the cluster Ci，aiThe smaller the segmentation integration value, the more the segmentation integration value should be classified into the cluster C, and aiIntra-cluster dissimilarity referred to as a segmented integral value i;
(b) calculating the segmented integral value i to other clusters CjAverage distance b of all samplesij，bijReferred to as segmented integral value i and other clusters CjDegree of dissimilarity of, bijThe larger the value is, the less the segmentation integral value i belongs to other clusters;
inter-cluster dissimilarity b defining a piecewise integral value ii：
bi＝min{bi1,bi2...,bik}
The subscript k is the number of other clusters corresponding to the segmented integral value i; biThe larger the segmentation integral value i is, the less the segmentation integral value i belongs to the existing classification cluster;
(c) intra-cluster dissimilarity a based on segmented integral value iiDegree of dissimilarity with clusters biProfile coefficients s (i) defining the segmented integral value i:
if S (i) is close to 1, the segmentation integral value i is reasonably clustered; if S (i) is close to-1, the segmentation integral value i is more classified into other clusters; the mean value of S (i) of all samples is called the contour coefficient of the clustering result and is used for measuring the quality of the whole clustering result;
and C: and calculating corresponding clustering centers according to the optimal classification group number, clustering the link state information of each user, and returning a discretization link state result.
2. The method for distinguishing the discretization link state of the reflected signal at the user end of the passive optical network according to claim 1, wherein the specific process of the step C is as follows:
(1) selecting k samples as clustering centers according to the optimal classification group number k obtained in the step B;
(2) calculating the distance between each segmented integral value and each clustering center, dividing the sample into the nearest clustering center, calculating the mean value of all integral sample characteristics divided into each category, and taking the mean value as the central value of the corresponding group of the next round of clustering; repeating the step until all the segmented integral values are grouped;
(3) and outputting the final clustering center and the category to which each segmented integral value belongs, calculating the average value of each group, comparing with a standard value, judging the user link state corresponding to each group, and giving a discretization link state result.
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