WO2017035946A1 - 一种确定光分离器位置的方法及装置 - Google Patents

一种确定光分离器位置的方法及装置 Download PDF

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WO2017035946A1
WO2017035946A1 PCT/CN2015/093573 CN2015093573W WO2017035946A1 WO 2017035946 A1 WO2017035946 A1 WO 2017035946A1 CN 2015093573 W CN2015093573 W CN 2015093573W WO 2017035946 A1 WO2017035946 A1 WO 2017035946A1
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unit
class
target
optical network
optical
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French (fr)
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沈纲祥
陈浩
李泳成
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Suzhou University
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Suzhou University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B10/00—Transmission systems employing electromagnetic waves other than radio-waves, e.g. infrared, visible or ultraviolet light, or employing corpuscular radiation, e.g. quantum communication
    • H04B10/27—Arrangements for networking
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B10/00—Transmission systems employing electromagnetic waves other than radio-waves, e.g. infrared, visible or ultraviolet light, or employing corpuscular radiation, e.g. quantum communication
    • H04B10/27—Arrangements for networking
    • H04B10/272—Star-type networks or tree-type networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B10/00—Transmission systems employing electromagnetic waves other than radio-waves, e.g. infrared, visible or ultraviolet light, or employing corpuscular radiation, e.g. quantum communication
    • H04B10/07—Arrangements for monitoring or testing transmission systems; Arrangements for fault measurement of transmission systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B10/00—Transmission systems employing electromagnetic waves other than radio-waves, e.g. infrared, visible or ultraviolet light, or employing corpuscular radiation, e.g. quantum communication
    • H04B10/25—Arrangements specific to fibre transmission
    • H04B10/2589—Bidirectional transmission
    • H04B10/25891—Transmission components
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04Q—SELECTING
    • H04Q11/00—Selecting arrangements for multiplex systems
    • H04Q11/0001—Selecting arrangements for multiplex systems using optical switching
    • H04Q11/0062—Network aspects
    • H04Q11/0067—Provisions for optical access or distribution networks, e.g. Gigabit Ethernet Passive Optical Network (GE-PON), ATM-based Passive Optical Network (A-PON), PON-Ring
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B10/00—Transmission systems employing electromagnetic waves other than radio-waves, e.g. infrared, visible or ultraviolet light, or employing corpuscular radiation, e.g. quantum communication

Definitions

  • the present application relates to the field of optical fiber network technology, and more particularly to a method and apparatus for determining the position of an optical splitter.
  • the fiber subscriber access network FTTX includes an active fiber network AON and a passive fiber network PON.
  • the passive optical network PON has lower cost, so the application range is wider.
  • FIG. 1 is a PON network illustrated in the present application.
  • the PON network comprises a central unit 1, a plurality of optical splitters 2 connected to the central processing unit via optical fibers, and a plurality of optical network units 3 connected to each optical splitter 2 optical fiber.
  • For each PON network there is typically a light separation ratio that is used to control the number of optical network units that are connected to the same optical splitter.
  • the location of the central unit 1 in a PON network is fixed and the optical network unit is typically located in the user's home, the location is also fixed, only the position of the optical splitter is variable.
  • the operating cost of the entire PON network is mainly concentrated on the cost of the fiber and the cost of deep trenching for laying the fiber. Therefore, how to properly set the number of optical splitters and the position of each optical splitter makes the total in the PON network The lowest fiber length has become an urgent problem to be solved.
  • the present application provides a method and apparatus for determining the position of an optical splitter for providing a solution for determining an optical splitter placement position in a PON network, so that the total optical fiber length in the entire PON network is the lowest, and the cost is reduced. .
  • a method of determining the position of an optical splitter comprising:
  • the optical network unit is divided into K classes by using a K-means clustering algorithm
  • the central unit is separately added to each category to obtain K new classes
  • the method when determining that the number of optical network units in a certain class exceeds a threshold, the method further includes:
  • the K-means clustering algorithm is used to classify each optical network unit to obtain several sub-classes;
  • the step of determining the class in which the number of optical network units exceeds the threshold is determined as the target class.
  • the determining the location of the optical splitter corresponding to the new class comprises:
  • the Weiszfeld algorithm is used to determine the position of the optical splitter corresponding to the new class.
  • the method for determining the K value includes:
  • the cluster metric value is the K-class cluster index value obtained by the K-means clustering algorithm. average value
  • the method further comprises:
  • a line from the location of the central unit to the location of the target point is determined as a target fiber channel, and the plurality of fibers from the central unit to each of the optical splitters in the target set share the target fiber channel.
  • a device for determining the position of an optical splitter comprising:
  • a data receiving unit configured to receive a location of a central unit of the input PON network and a location of each optical network unit
  • a clustering unit for referring to a location of the optical network unit, and dividing the optical network unit into K classes by using a K-means clustering algorithm
  • a new class determining unit configured to add the central unit to each category to obtain K new classes when determining that the number of optical network units in each category does not exceed a threshold
  • An optical splitter position calculating unit configured to determine a position of the optical splitter corresponding to the new class for each new class, wherein the position of the optical splitter and the optical network unit in the new class are guaranteed The sum of the distances of the position and the position of the central unit is the smallest.
  • the method further comprises:
  • a target class determining unit configured to determine, as a target class, a class in which the number of optical network units exceeds a threshold when determining that the number of optical network units in a certain class exceeds a threshold
  • a target class dividing unit is configured to refer to locations of optical network units in the target class, and classify each optical network unit by using a K-means clustering algorithm to obtain a plurality of sub-classes;
  • the subclass determining unit is configured to detect whether there is still a subclass whose number of optical network units exceeds a threshold, and if yes, return to execute the target class determining unit.
  • the optical splitter position calculation unit comprises:
  • the first optical splitter position calculating subunit is configured to determine a position of the optical splitter corresponding to the new class by using a Weiszfeld algorithm according to the position of each optical network unit in the new class and the position of the central unit.
  • the clustering unit comprises:
  • a line graph creation unit for taking the K value as the X-axis independent variable and the cluster metric value as the Y
  • the axis dependent variable establishes a line graph, wherein the cluster metric value is an average value of the cluster index values of the K classes obtained by the K-means clustering algorithm;
  • the K value selection unit is configured to select the K value corresponding to the point where the rate of change of the cluster metric value is the largest on the line graph, as the target K value.
  • the method further comprises:
  • a target set establishing unit configured to form an optical splitter corresponding to each subclass of the central unit and the target class into a target set
  • a target point location determining unit configured to determine, by using a Weiszfeld algorithm, a target point location corresponding to the target set, where a distance and a value of each target location in the target set are the smallest;
  • a target fiber channel determining unit configured to determine a line between the location of the central unit and the target point location as a target fiber channel, and the plurality of fibers shared by the central unit to each optical splitter in the target set The target fiber channel.
  • the method in the embodiment of the present application refers to the location of each optical network unit in the PON network, and uses the K-means clustering algorithm to divide the optical network unit into K classes, and multiple lights in the same class.
  • the distance between the network elements is relatively close.
  • the central unit is added to each category to obtain K new classes, and then determined for each new class.
  • the position of the optical splitter needs to be guaranteed to be the smallest of the sum of the distances from the individual element points in the new class.
  • the solution of the present application clusters the optical network units in the PON network, so that the optical network units that are close to each other are grouped into one class, and the position of the optical splitter is further determined from various types, so that the position of the optical splitter is separated from the points of each element in the class. And minimum, reducing the total length of the fiber, reducing the cost of the entire PON network.
  • FIG. 1 is a schematic diagram of a PON network illustrated in the present application.
  • FIG. 2 is a flow chart of a method for determining a position of an optical splitter according to an embodiment of the present application
  • FIG. 3 is a flowchart of another method for determining a position of an optical splitter according to an embodiment of the present disclosure
  • FIG. 4 is a flow chart of another method for determining the position of an optical splitter according to an embodiment of the present application.
  • FIG. 5 is a schematic diagram of a shared optical fiber channel according to an example of an embodiment of the present application.
  • FIG. 6 is a schematic structural diagram of an apparatus for determining a position of an optical splitter according to an embodiment of the present disclosure
  • FIG. 7 is a schematic structural diagram of another apparatus for determining a position of an optical splitter according to an embodiment of the present disclosure.
  • FIG. 8 is a schematic structural diagram of a position calculation unit of an optical splitter according to an embodiment of the present application.
  • FIG. 9 is a schematic structural diagram of a clustering unit according to an embodiment of the present disclosure.
  • FIG. 10 is a schematic structural diagram of another apparatus for determining a position of an optical splitter according to an embodiment of the present application.
  • FIG. 2 is a flowchart of a method for determining a position of an optical splitter according to an embodiment of the present application.
  • the method includes:
  • Step S200 receiving a location of a central unit of the input PON network and a location of each optical network unit;
  • the position of the central unit in the PON network and the position of each optical network unit are fixed, and thus their position coordinates can be acquired.
  • Step S210 Referring to the location of the optical network unit, the optical network unit is divided into K classes by using a K-means clustering algorithm;
  • each optical network unit can be regarded as a point, and all points are clustered.
  • K-means clustering algorithm can be used for clustering.
  • the K-means clustering algorithm is an existing algorithm that clusters points so that the sum of the squares of the distances between each point and the cluster center in each class is the smallest.
  • Step S220 When it is determined that the number of optical network units in each category does not exceed the threshold, the central unit is separately added to each category, and K new classes are obtained;
  • the present application can determine the value as a threshold, and the K obtained by clustering in the previous step. For each type, it is detected whether the number of optical network units in each class exceeds a threshold. When the number of optical network units of each class does not exceed the threshold, the central unit is added to each class to obtain K new classes. .
  • Step S230 Determine, for each new class, the location of the optical splitter corresponding to the new class.
  • an optical splitter needs to be provided.
  • the optical splitter needs to be connected to each optical network unit in the new class and also to the central unit. Therefore, in order to minimize the total fiber length, the position of the determined optical splitter needs to meet the following conditions:
  • the sum of the positions of the optical splitters and the distances of the positions of the optical network units in the new class and the positions of the central unit is the smallest.
  • the method of the embodiment of the present application refers to the location of each optical network unit in the PON network, and uses the K-means clustering algorithm to divide the optical network unit into K classes, and the distances of multiple optical network units in the same class are relatively close, further When it is determined that the number of various types of medium optical network units does not exceed the threshold, the central unit is added to each category to obtain K new classes, and then the position of the corresponding optical splitter is determined for each new class. The position of the separator needs to be guaranteed to have the smallest sum of distances from the points of the individual elements in the new class.
  • the solution of the present application clusters the optical network units in the PON network, so that the optical network units that are close to each other are grouped into one class, and the position of the optical splitter is further determined from various types, so that the position of the optical splitter is separated from the points of each element in the class. And minimum, reducing the total length of the fiber, reducing the cost of the entire PON network.
  • step S230 when determining the location of the optical splitter corresponding to the new class in step S230, determining, according to the location of each optical network unit in the new class and the location of the central unit, using the Weiszfeld algorithm The position of the light separator corresponding to the new class.
  • this embodiment introduces the process of dividing the optical network unit by the K-means clustering algorithm.
  • the K-means clustering algorithm divides n points into K classes (K ⁇ n), and the sum of the squares of the distance between each point and the cluster center in each class is the smallest.
  • the cluster metric value is the K-class cluster index value obtained by the K-means clustering algorithm. average value
  • the cluster indicator value may be the maximum distance from all points in the same class to the cluster center.
  • the cluster cluster indicator value can also choose its form, such as the distance between two points in the same class with the largest distance.
  • the K values are experimentally small from small to large until it is found that when the K value is further increased, the current K value is selected when there is no significant change in the cluster metric value.
  • clustering is performed according to the determined K value.
  • FIG. 3 is a flowchart of another method for determining the position of an optical splitter according to an embodiment of the present application.
  • the method includes:
  • Step S300 receiving a location of a central unit of the input PON network and a location of each optical network unit;
  • the position of the central unit in the PON network and the position of each optical network unit are fixed, and thus their position coordinates can be acquired.
  • Step S310 referring to the location of the optical network unit, dividing the optical network unit into K classes by using a K-means clustering algorithm;
  • each optical network unit can be regarded as a point, and all points are clustered.
  • K-means clustering algorithm can be used for clustering.
  • the K-means clustering algorithm is an existing algorithm that clusters points so that the sum of the squares of the distances between each point and the cluster center in each class is the smallest.
  • Step S320 determining whether there is a class of the number of optical network units exceeding the threshold, and if so, executing step S330, and if not, executing step S340;
  • Step S330 further classifying the number of the optical network unit exceeding the threshold to ensure that each of the divided classes meets the threshold requirement, and further performing step S340;
  • the class that does not meet the threshold requirement is further divided into multiple classes, and each class satisfies the number of optical network units not exceeding a threshold.
  • Step S340 adding the central unit to each category to obtain K new classes
  • Step S350 Determine, for each new class, the location of the optical splitter corresponding to the new class.
  • step C Detect whether there is still a subclass whose number of optical network units exceeds the threshold, and if yes, return to step A.
  • the class that does not satisfy the threshold requirement is divided according to the K-means clustering algorithm until all classes satisfy the requirements.
  • the class is directly divided into a target number, and the number of targets is the number of optical network units in the class.
  • the ratio to the threshold is determined to be the smallest integer greater than the ratio.
  • FIG. 4 is a flowchart of still another method for determining the position of an optical splitter according to an embodiment of the present application.
  • the method includes:
  • Step S400 receiving a location of a central unit of the input PON network and a location of each optical network unit;
  • the position of the central unit in the PON network and the position of each optical network unit are fixed, and thus their position coordinates can be acquired.
  • Step S410 Referring to the location of the optical network unit, the optical network unit is divided into K classes by using a K-means clustering algorithm;
  • each optical network unit can be regarded as a point, and all points are clustered.
  • K-means clustering algorithm can be used for clustering.
  • the K-means clustering algorithm is an existing algorithm that clusters points so that the sum of the squares of the distances between each point and the cluster center in each class is the smallest.
  • Step S420 determining whether there is a class of the number of optical network units exceeding the threshold, and if so, executing step S430, and if not, executing step S440;
  • Step S430 further dividing the target class of the number of optical network units exceeding the threshold, obtaining a plurality of sub-classes, ensuring that each sub-class meets the threshold requirement, and further performing step S440;
  • the class that does not meet the threshold requirement is further divided into multiple classes, and each class satisfies the number of optical network units not exceeding a threshold.
  • Step S440 adding the central unit to each category to obtain K new classes
  • Step S450 determining, for each new class, a location of the optical splitter corresponding to the new class
  • Step S460 forming an optical splitter corresponding to each subclass of the central unit and the target class into a target set
  • Step S470 Determine a target point position corresponding to the target set by using a Weiszfeld algorithm, where a distance and a value of each target position in the target set are the smallest;
  • Step S480 determining a line between the location of the central unit and the location of the target point as a target fiber channel.
  • the plurality of optical fibers from the central unit to the optical splitters in the target set share the target optical fiber channel.
  • the Weiszfeld algorithm is used to determine the target point for the optical splitter and the central unit of each subclass divided by the target class.
  • the target point position is a distance from the central unit, the optical splitter of each sub-class, and a minimum position, and further determining a line from the central unit to the target point position as a target fiber channel, the channel being A common fiber channel of a plurality of optical fibers from the central unit to the sub-class optical splitters.
  • FIG. 5 is a schematic diagram of a shared optical fiber channel according to an example of an embodiment of the present application.
  • the central unit 1 is connected to the optical splitter 21, the optical splitter 22, and the optical splitter 23 via three optical fibers.
  • the target point position is determined by using the Weiszfeld algorithm for the set of the central unit 1, the optical splitter 21, the optical splitter 22, and the optical splitter 23, and the line between the central unit 1 and the target point position is determined as the target optical fiber channel 4.
  • the three optical fibers from the central unit 1 to the optical splitter 21, the optical splitter 22, and the optical splitter 23 share the target optical fiber channel 4.
  • the length of the target fiber channel 4 the distance from the distal end of the target fiber channel 4 to each optical splitter, and the minimum, the cost of deep trenching and laying the fiber is the lowest.
  • the apparatus for determining the position of the optical splitter provided by the embodiment of the present application is described below.
  • the apparatus for determining the position of the optical splitter described below and the apparatus for determining the position of the optical splitter described above may be referred to each other.
  • FIG. 6 is a schematic structural diagram of an apparatus for determining a position of an optical splitter according to an embodiment of the present application.
  • the device includes:
  • a data receiving unit 61 configured to receive a location of a central unit of the input PON network and a location of each optical network unit;
  • the clustering unit 62 is configured to refer to the location of the optical network unit, and divide the optical network unit into K classes by using a K-means clustering algorithm;
  • a new class determining unit 63 configured to add the central unit to each category to obtain K new classes when determining that the number of optical network units in each category does not exceed a threshold;
  • the optical splitter position calculating unit 64 is configured to determine a position of the optical splitter corresponding to the new class for each new class, wherein the position of the optical splitter and each optical network unit in the new class are guaranteed The sum of the positions of the positions of the central unit and the position of the central unit is the smallest.
  • the device in the embodiment of the present application refers to the location of each optical network unit in the PON network, and uses the K-means clustering algorithm to divide the optical network unit into K classes, and the distances of multiple optical network units in the same class are relatively close, further When it is determined that the number of all types of medium optical network units does not exceed the threshold, the central unit is added to each category to obtain K new classes, and then the corresponding light is determined for each new class.
  • the position of the separator, the position of the light separator needs to be guaranteed to have the smallest sum of distances from the points of the individual elements in the new class.
  • the solution of the present application clusters the optical network units in the PON network, so that the optical network units that are close to each other are grouped into one class, and the position of the optical splitter is further determined from various types, so that the position of the optical splitter is separated from the points of each element in the class. And minimum, reducing the total length of the fiber, reducing the cost of the entire PON network.
  • FIG. 7 illustrates another optional structure of the device for determining the position of the optical splitter.
  • the device may further include:
  • a target class determining unit 64 configured to determine, as a target class, a class in which the number of optical network units exceeds a threshold when determining that the number of optical network units in a certain class exceeds a threshold;
  • the target class dividing unit 65 is configured to refer to the location of each optical network unit in the target class, and classify each optical network unit by using a K-means clustering algorithm to obtain a plurality of sub-classes;
  • the subclass determining unit 66 is configured to detect whether there is still a subclass whose number of optical network units exceeds a threshold, and if yes, return to execute the target class determining unit 64.
  • FIG. 8 illustrates an optional structure of the optical splitter position calculating unit 64, and the optical splitter position calculating unit 64 may include:
  • the first optical splitter position calculating sub-unit 641 is configured to determine the position of the optical splitter corresponding to the new class by using the Weiszfeld algorithm according to the position of each optical network unit in the new class and the position of the central unit.
  • FIG. 9 illustrates an optional structure of the foregoing clustering unit 62, and the clustering unit 62 may include:
  • a line graph establishing unit 621 is configured to use a K value as an X-axis independent variable and a cluster metric value as a Y-axis dependent variable to establish a line graph, wherein the cluster metric value is a K divided by a K-means clustering algorithm.
  • the K value selection unit 622 is configured to select the K value corresponding to the point where the rate of change of the cluster metric value on the line graph is the largest, as the target K value.
  • FIG. 10 illustrates another optional structure of the device for determining the position of the optical splitter.
  • the device may further include:
  • a target set establishing unit 67 configured to form, by the central unit, the optical splitters corresponding to the subclasses obtained by dividing the target class into a target set;
  • a target point location determining unit 68 configured to determine, by using a Weiszfeld algorithm, a target point location corresponding to the target set, where a distance and a value of each target location in the target set are the smallest;
  • a target fiber channel determining unit 69 configured to determine a line between the location of the central unit and the target point location as a target fiber channel, and the plurality of fibers from the central unit to each optical splitter in the target set Sharing the target fiber channel.

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Abstract

本申请公开了一种确定光分离器位置的方法及装置，其中方法参考了PON网络中各光网络单元的位置，采用K均值聚类算法将光网络单元划分为K个类，同一类中的多个光网络单元的距离较近，进一步在确定各类中光网络单元的个数均未超过阈值时，将中央单元分别添加到各类中，得到K个新类，然后针对各个新类，确定与之对应的光分离器的位置，光分离器的位置需要保证其与新类中各个元素点的距离的和值最小。本申请方案通过对PON网络中的光网络单元进行聚类，使得位置靠近的光网络单元聚为一类，进一步从各类确定光分离器位置，使得光分离器位置与类内各元素点距离和最小，降低了光纤总长度，降低了整个PON网络的成本开销。

Description

一种确定光分离器位置的方法及装置
本申请要求于2015年09月01日提交中国专利局、申请号为201510551398.2、发明名称为“一种确定光分离器位置的方法及装置”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及光纤网络技术领域，更具体地说，涉及一种确定光分离器位置的方法及装置。
背景技术
由于光纤用户接入网FTTx固有的优势，如超高带宽、低成本、抗干扰性强等，其广泛应用于社会生活的方方面面。光纤用户接入网FTTX包括有源光纤网络AON和无源光纤网络PON。其中，无源光纤网络PON成本更低，因此应用范围更广。
参见图1，图1为本申请示意的一个PON网络。PON网络包括一个中央单元1，与中央处理单元通过光纤连接的若干个光分离器2，与每个光分离器2光纤连接的多个光网络单元3。对于每个PON网络，通常都有个光分离比，光分离比用于控制最大连接到同一光分离器的光网络单元的个数。
考虑到一个PON网络中的中央单元1的位置是固定的，而光网络单元一般设置在用户家庭中，因此位置也是固定的，只有光分离器的位置是可变的。而整个PON网络的运营成本主要集中在光纤成本以及为了铺设光纤而深挖沟渠所带来的成本消耗，因此如何合理设置光分离器的个数及各个光分离器的位置，使得PON网络中总光纤长度最低，成为亟待解决的问题。
发明内容
有鉴于此，本申请提供了一种确定光分离器位置的方法及装置，用于提供一种确定PON网络中光分离器布置位置的方案，使得整个PON网络中总光纤长度最低，降低成本开销。
为了实现上述目的，现提出的方案如下：
一种确定光分离器位置的方法，包括：
接收输入的PON网的中央单元的位置以及各光网络单元的位置；
参考所述光网络单元的位置，采用K均值聚类算法将所述光网络单元划分为K个类；
在确定各类中的光网络单元的个数均未超过阈值时，将所述中央单元分别添加到各类中，得到K个新类；
分别针对各个新类，确定与所述新类对应的光分离器的位置，其中，保证所述光分离器的位置与所述新类中各光网络单元的位置、所述中央单元的位置的距离的和值最小。
优选地，在确定某个类中的光网络单元的个数超过阈值时，该方法还包括：
将光网络单元个数超过阈值的类确定为目标类；
参考所述目标类中各光网络单元的位置，采用K均值聚类算法对各光网络单元进行类划分，得到若干子类；
检测是否还存在光网络单元个数超过阈值的子类，若是，返回所述将光网络单元个数超过阈值的类确定为目标类的步骤。
优选地，所述确定与所述新类对应的光分离器的位置，包括：
依据所述新类中各光网络单元的位置以及中央单元的位置，采用Weiszfeld算法确定与所述新类对应的光分离器的位置。
优选地，在采用K均值聚类算法将所述光网络单元划分为K个类时，K值的确定方法包括：
以K值作为X轴自变量，类簇度量标准值作为Y轴因变量，建立折线图，其中类簇度量标准值为，采用K均值聚类算法划分所得的K个类的类簇指标值的平均值；
选取折线图上类簇度量标准值变化率最大的点所对应的K值，作为目标K值。
优选地，还包括：
将所述中央单元、所述目标类划分所得的各子类对应的光分离器组成一 个目标集合；
采用Weiszfeld算法确定所述目标集合对应的目标点位置，所述目标点位置与所述目标集合中各个元素位置的距离和值最小；
将由所述中央单元的位置至所述目标点位置间的线路确定为目标光纤渠道，由所述中央单元至所述目标集合中各光分离器的多条光纤共用所述目标光纤渠道。
一种确定光分离器位置的装置，包括：
数据接收单元，用于接收输入的PON网的中央单元的位置以及各光网络单元的位置；
聚类划分单元，用于参考所述光网络单元的位置，采用K均值聚类算法将所述光网络单元划分为K个类；
新类确定单元，用于在确定各类中的光网络单元的个数均未超过阈值时，将所述中央单元分别添加到各类中，得到K个新类；
光分离器位置计算单元，用于分别针对各个新类，确定与所述新类对应的光分离器的位置，其中，保证所述光分离器的位置与所述新类中各光网络单元的位置、所述中央单元的位置的距离的和值最小。
优选地，还包括：
目标类确定单元，用于在确定某个类中的光网络单元的个数超过阈值时，将光网络单元个数超过阈值的类确定为目标类；
目标类划分单元，用于参考所述目标类中各光网络单元的位置，采用K均值聚类算法对各光网络单元进行类划分，得到若干子类；
子类判断单元，用于检测是否还存在光网络单元个数超过阈值的子类，若是，返回执行所述目标类确定单元。
优选地，所述光分离器位置计算单元包括：
第一光分离器位置计算子单元，用于依据所述新类中各光网络单元的位置以及中央单元的位置，采用Weiszfeld算法确定与所述新类对应的光分离器的位置。
优选地，所述聚类划分单元包括：
折线图建立单元，用于以K值作为X轴自变量，类簇度量标准值作为Y 轴因变量，建立折线图，其中类簇度量标准值为，采用K均值聚类算法划分所得的K个类的类簇指标值的平均值；
K值选取单元，用于选取折线图上类簇度量标准值变化率最大的点所对应的K值，作为目标K值。
优选地，还包括：
目标集合建立单元，用于将所述中央单元、所述目标类划分所得的各子类对应的光分离器组成一个目标集合；
目标点位置确定单元，用于采用Weiszfeld算法确定所述目标集合对应的目标点位置，所述目标点位置与所述目标集合中各个元素位置的距离和值最小；
目标光纤渠道确定单元，用于将由所述中央单元的位置至所述目标点位置间的线路确定为目标光纤渠道，由所述中央单元至所述目标集合中各光分离器的多条光纤共用所述目标光纤渠道。
从上述的技术方案可以看出，本申请实施例的方法参考了PON网络中各光网络单元的位置，采用K均值聚类算法将光网络单元划分为K个类，同一类中的多个光网络单元的距离较近，进一步在确定各类中光网络单元的个数均未超过阈值时，将中央单元分别添加到各类中，得到K个新类，然后针对各个新类，确定与之对应的光分离器的位置，光分离器的位置需要保证其与新类中各个元素点的距离的和值最小。本申请方案通过对PON网络中的光网络单元进行聚类，使得位置靠近的光网络单元聚为一类，进一步从各类确定光分离器位置，使得光分离器位置与类内各元素点距离和最小，降低了光纤总长度，降低了整个PON网络的成本开销。
附图说明
为了更清楚地说明本申请实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据提供的附图获得其他的附图。
图1为本申请示意的一个PON网络示意图；
图2为本申请实施例公开的一种确定光分离器位置的方法流程图；
图3为本申请实施例公开的另一种确定光分离器位置的方法流程图；
图4为本申请实施例公开的又一种确定光分离器位置的方法流程图；
图5为本申请实施例示例的一种共用光纤渠道示意图；
图6为本申请实施例公开的一种确定光分离器位置的装置结构示意图；
图7为本申请实施例公开的另一种确定光分离器位置的装置结构示意图；
图8为本申请实施例公开的一种光分离器位置计算单元结构示意图；
图9为本申请实施例公开的一种聚类划分单元结构示意图；
图10为本申请实施例公开的又一种确定光分离器位置的装置结构示意图。
具体实施方式
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
参见图2，图2为本申请实施例公开的一种确定光分离器位置的方法流程图。
如图2所示，该方法包括：
步骤S200、接收输入的PON网的中央单元的位置以及各光网络单元的位置；
具体地，PON网中的中央单元的位置和各个光网络单元的位置是固定的，因此可以获取它们的位置坐标。
步骤S210、参考所述光网络单元的位置，采用K均值聚类算法将所述光网络单元划分为K个类；
具体地，可以将每个光网络单元看作一个点，并对所有的点进行聚类划分。聚类时可以采用K均值聚类算法。K均值聚类算法是一个现有的算法，其通过对点进行聚类，使得每个类中，每个点与聚类中心间的距离平方和是最小的。
步骤S220、在确定各类中的光网络单元的个数均未超过阈值时，将所述中央单元分别添加到各类中，得到K个新类；
由于每个PON网络中均规定了最大光分离比，也即与一个光分离器连接的光网络单元的最大个数，本申请可以将该数值确定为阈值，对于上一步骤聚类所得的K各类，检测每个类中的光网络单元的个数是否超过阈值，在各个类的光网络单元的个数均未超过阈值时，将中央单元分别添加到各个类中，得到K个新类。
步骤S230、分别针对各个新类，确定与所述新类对应的光分离器的位置。
具体地，针对每一个新类，均需要设置一个光分离器。光分离器需要与新类中的各个光网络单元相连，同时还要与中央单元相连。因此，为了使总光纤长度最低，确定的光分离器的位置需要满足如下条件：
所述光分离器的位置与所述新类中各光网络单元的位置、所述中央单元的位置的距离的和值最小。
本申请实施例的方法参考了PON网络中各光网络单元的位置，采用K均值聚类算法将光网络单元划分为K个类，同一类中的多个光网络单元的距离较近，进一步在确定各类中光网络单元的个数均未超过阈值时，将中央单元分别添加到各类中，得到K个新类，然后针对各个新类，确定与之对应的光分离器的位置，光分离器的位置需要保证其与新类中各个元素点的距离的和值最小。本申请方案通过对PON网络中的光网络单元进行聚类，使得位置靠近的光网络单元聚为一类，进一步从各类确定光分离器位置，使得光分离器位置与类内各元素点距离和最小，降低了光纤总长度，降低了整个PON网络的成本开销。
可选的，在上述步骤S230、确定与所述新类对应的光分离器的位置时，可以依据所述新类中各光网络单元的位置以及中央单元的位置，采用Weiszfeld算法确定与所述新类对应的光分离器的位置。
其中，Weiszfeld算法为现有的算法，此处不进行详细介绍。
接下来，本实施例介绍一下采用K均值聚类算法对光网络单元进行划分的过程。
K均值聚类算法是将n个点划分为K个类(K<n)，每个类中，每个点与聚类中心间的距离平方和是最小的。
在使用K均值聚类算法时，首先要确定最佳的K值。K值的确定方法可以有多种，本实施例介绍一种可选的方式：
以K值作为X轴自变量，类簇度量标准值作为Y轴因变量，建立折线图，其中类簇度量标准值为，采用K均值聚类算法划分所得的K个类的类簇指标值的平均值；
具体地，类簇指标值可以是同一类中所有点到聚类中心的距离最大值。当然，类簇指标值还可以选取它形式，如同一类中距离最大的两个点间的距离等等。
选取折线图上类簇度量标准值变化率最大的点所对应的K值，作为目标K值。
具体的来讲，从小到大实验不同的K值，直至发现当进一步增加K值时，类簇度量标准值不会有明显的变化时，则选择当前的K值。
确定了最佳K值之后，按照确定的K值，进行聚类划分。
参见图3，图3为本申请实施例公开的另一种确定光分离器位置的方法流程图。
如图3所示，该方法包括：
步骤S300、接收输入的PON网的中央单元的位置以及各光网络单元的位置；
具体地，PON网中的中央单元的位置和各个光网络单元的位置是固定的，因此可以获取它们的位置坐标。
步骤S310、参考所述光网络单元的位置，采用K均值聚类算法将所述光网络单元划分为K个类；
具体地，可以将每个光网络单元看作一个点，并对所有的点进行聚类划分。聚类时可以采用K均值聚类算法。K均值聚类算法是一个现有的算法，其通过对点进行聚类，使得每个类中，每个点与聚类中心间的距离平方和是最小的。
步骤S320、判断是否存在光网络单元的个数超过阈值的类，若是，执行步骤S330，若否，执行步骤S340；
步骤S330、对光网络单元个数超过阈值的类进一步划分，保证划分后的各个类满足阈值要求，并进一步执行步骤S340；
具体地，通过对不满足阈值要求的类进一步进行划分，将其划分为多个类，每个类均满足光网络单元的个数不超过阈值。
具体划分方式可以参照下文介绍。
步骤S340、将所述中央单元分别添加到各类中，得到K个新类；
步骤S350、分别针对各个新类，确定与所述新类对应的光分离器的位置。
可选的，对于上述步骤S330的具体实施方式，可以参照下述方案实现：
A、将光网络单元个数超过阈值的类确定为目标类；
B、参考所述目标类中各光网络单元的位置，采用K均值聚类算法对各光网络单元进行类划分，得到若干子类；
C、检测是否还存在光网络单元个数超过阈值的子类，若是，返回执行步骤A。
上述划分过程中，通过对不满足阈值要求的类按照K均值聚类算法进行划分，直至所有类均满足要求为止。
当然，除了上述实施方式之外，还可以采用其它方式，例如对于光网络单元个数超过阈值的类，直接将其划分为目标个数的类，目标个数为该类中光网络单元个数与阈值的比值。当然，如果比值为非整数，则将目标个数确定为大于所述比值的最小整数。
参见图4，图4为本申请实施例公开的又一种确定光分离器位置的方法流程图。
如图4所示，该方法包括：
步骤S400、接收输入的PON网的中央单元的位置以及各光网络单元的位置；
具体地，PON网中的中央单元的位置和各个光网络单元的位置是固定的，因此可以获取它们的位置坐标。
步骤S410、参考所述光网络单元的位置，采用K均值聚类算法将所述光网络单元划分为K个类；
具体地，可以将每个光网络单元看作一个点，并对所有的点进行聚类划分。聚类时可以采用K均值聚类算法。K均值聚类算法是一个现有的算法，其通过对点进行聚类，使得每个类中，每个点与聚类中心间的距离平方和是最小的。
步骤S420、判断是否存在光网络单元的个数超过阈值的类，若是，执行步骤S430，若否，执行步骤S440；
步骤S430、对光网络单元个数超过阈值的目标类进一步划分，得到若干个子类，保证各子类满足阈值要求，并进一步执行步骤S440；
具体地，通过对不满足阈值要求的类进一步进行划分，将其划分为多个类，每个类均满足光网络单元的个数不超过阈值。
具体划分方式可以参照下文介绍。
步骤S440、将所述中央单元分别添加到各类中，得到K个新类；
步骤S450、分别针对各个新类，确定与所述新类对应的光分离器的位置；
步骤S460、将所述中央单元、所述目标类划分所得的各子类对应的光分离器组成一个目标集合；
步骤S470、采用Weiszfeld算法确定所述目标集合对应的目标点位置，所述目标点位置与所述目标集合中各个元素位置的距离和值最小；
步骤S480、将由所述中央单元的位置至所述目标点位置间的线路确定为目标光纤渠道。
具体地，由所述中央单元至所述目标集合中各光分离器的多条光纤共用所述目标光纤渠道。
相比于上一实施例，本实施例中在确定了各个类的光分离器的位置之后，进一步对于由目标类划分所得的各个子类的光分离器以及中央单元，采用Weiszfeld算法确定目标点位置，该目标点位置为与中央单元、各子类的光分离器的距离和最小的位置，进一步将从中央单元至所述目标点位置的线路确定为目标光纤渠道，该渠道作为由所述中央单元至各子类的光分离器的多条光纤的共用光纤渠道。
参见图5，图5为本申请实施例示例的一种共用光纤渠道示意图。
如图5所示，中央单元1与光分离器21、光分离器22和光分离器23之间通过三条光纤连接。通过对中央单元1、光分离器21、光分离器22、光分离器23组成的集合使用Weiszfeld算法确定目标点位置，进而将中央单元1至目标点位置间的线路确定为目标光纤渠道4。由中央单元1至光分离器21、光分离器22和光分离器23的三条光纤共用目标光纤渠道4。
通过确定上述目标光纤渠道4，使得目标光纤渠道4的长度、目标光纤渠道4的远端至各光分离器的距离和最小，从而使得深挖沟渠和铺设光纤的花费成本最低。
下面对本申请实施例提供的确定光分离器位置的装置进行描述，下文描述的确定光分离器位置的装置与上文描述的确定光分离器位置的装置可相互对应参照。
参见图6，图6为本申请实施例公开的一种确定光分离器位置的装置结构示意图。
如图6所示，该装置包括：
数据接收单元61，用于接收输入的PON网的中央单元的位置以及各光网络单元的位置；
聚类划分单元62，用于参考所述光网络单元的位置，采用K均值聚类算法将所述光网络单元划分为K个类；
新类确定单元63，用于在确定各类中的光网络单元的个数均未超过阈值时，将所述中央单元分别添加到各类中，得到K个新类；
光分离器位置计算单元64，用于分别针对各个新类，确定与所述新类对应的光分离器的位置，其中，保证所述光分离器的位置与所述新类中各光网络单元的位置、所述中央单元的位置的距离的和值最小。
本申请实施例的装置参考了PON网络中各光网络单元的位置，采用K均值聚类算法将光网络单元划分为K个类，同一类中的多个光网络单元的距离较近，进一步在确定各类中光网络单元的个数均未超过阈值时，将中央单元分别添加到各类中，得到K个新类，然后针对各个新类，确定与之对应的光 分离器的位置，光分离器的位置需要保证其与新类中各个元素点的距离的和值最小。本申请方案通过对PON网络中的光网络单元进行聚类，使得位置靠近的光网络单元聚为一类，进一步从各类确定光分离器位置，使得光分离器位置与类内各元素点距离和最小，降低了光纤总长度，降低了整个PON网络的成本开销。
可选的，图7示例了上述确定光分离器位置的装置的另一种可选结构，结合图6和图7可知，该装置还可以包括：
目标类确定单元64，用于在确定某个类中的光网络单元的个数超过阈值时，将光网络单元个数超过阈值的类确定为目标类；
目标类划分单元65，用于参考所述目标类中各光网络单元的位置，采用K均值聚类算法对各光网络单元进行类划分，得到若干子类；
子类判断单元66，用于检测是否还存在光网络单元个数超过阈值的子类，若是，返回执行所述目标类确定单元64。
可选的，图8示例了上述光分离器位置计算单元64的一种可选结构，光分离器位置计算单元64可以包括：
第一光分离器位置计算子单元641，用于依据所述新类中各光网络单元的位置以及中央单元的位置，采用Weiszfeld算法确定与所述新类对应的光分离器的位置。
可选的，图9示例了上述聚类划分单元62的一种可选结构，聚类划分单元62可以包括：
折线图建立单元621，用于以K值作为X轴自变量，类簇度量标准值作为Y轴因变量，建立折线图，其中类簇度量标准值为，采用K均值聚类算法划分所得的K个类的类簇指标值的平均值；
K值选取单元622，用于选取折线图上类簇度量标准值变化率最大的点所对应的K值，作为目标K值。
可选的，图10示例了上述确定光分离器位置的装置的另一种可选结构，结合图6和图10可知，该装置还可以包括：
目标集合建立单元67，用于将所述中央单元、所述目标类划分所得的各子类对应的光分离器组成一个目标集合；
目标点位置确定单元68，用于采用Weiszfeld算法确定所述目标集合对应的目标点位置，所述目标点位置与所述目标集合中各个元素位置的距离和值最小；
目标光纤渠道确定单元69，用于将由所述中央单元的位置至所述目标点位置间的线路确定为目标光纤渠道，由所述中央单元至所述目标集合中各光分离器的多条光纤共用所述目标光纤渠道。
最后，还需要说明的是，在本文中，诸如第一和第二等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来，而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。而且，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者设备所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括所述要素的过程、方法、物品或者设备中还存在另外的相同要素。
本说明书中各个实施例采用递进的方式描述，每个实施例重点说明的都是与其他实施例的不同之处，各个实施例之间相同相似部分互相参见即可。
对所公开的实施例的上述说明，使本领域专业技术人员能够实现或使用本申请。对这些实施例的多种修改对本领域的专业技术人员来说将是显而易见的，本文中所定义的一般原理可以在不脱离本申请的精神或范围的情况下，在其它实施例中实现。因此，本申请将不会被限制于本文所示的这些实施例，而是要符合与本文所公开的原理和新颖特点相一致的最宽的范围。

Claims (10)

  1. 一种确定光分离器位置的方法，其特征在于，包括：
    接收输入的PON网的中央单元的位置以及各光网络单元的位置；
    参考所述光网络单元的位置，采用K均值聚类算法将所述光网络单元划分为K个类；
    在确定各类中的光网络单元的个数均未超过阈值时，将所述中央单元分别添加到各类中，得到K个新类；
    分别针对各个新类，确定与所述新类对应的光分离器的位置，其中，保证所述光分离器的位置与所述新类中各光网络单元的位置、所述中央单元的位置的距离的和值最小。
  2. 根据权利要求1所述的方法，其特征在于，在确定某个类中的光网络单元的个数超过阈值时，该方法还包括：
    将光网络单元个数超过阈值的类确定为目标类；
    参考所述目标类中各光网络单元的位置，采用K均值聚类算法对各光网络单元进行类划分，得到若干子类；
    检测是否还存在光网络单元个数超过阈值的子类，若是，返回所述将光网络单元个数超过阈值的类确定为目标类的步骤。
  3. 根据权利要求1所述的方法，其特征在于，所述确定与所述新类对应的光分离器的位置，包括：
    依据所述新类中各光网络单元的位置以及中央单元的位置，采用Weiszfeld算法确定与所述新类对应的光分离器的位置。
  4. 根据权利要求1所述的方法，其特征在于，在采用K均值聚类算法将所述光网络单元划分为K个类时，K值的确定方法包括：
    以K值作为X轴自变量，类簇度量标准值作为Y轴因变量，建立折线图，其中类簇度量标准值为，采用K均值聚类算法划分所得的K个类的类簇指标值的平均值；
    选取折线图上类簇度量标准值变化率最大的点所对应的K值，作为目标K值。
  5. 根据权利要求2所述的方法，其特征在于，还包括：
    将所述中央单元、所述目标类划分所得的各子类对应的光分离器组成一个目标集合；
    采用Weiszfeld算法确定所述目标集合对应的目标点位置，所述目标点位置与所述目标集合中各个元素位置的距离和值最小；
    将由所述中央单元的位置至所述目标点位置间的线路确定为目标光纤渠道，由所述中央单元至所述目标集合中各光分离器的多条光纤共用所述目标光纤渠道。
  6. 一种确定光分离器位置的装置，其特征在于，包括：
    数据接收单元，用于接收输入的PON网的中央单元的位置以及各光网络单元的位置；
    聚类划分单元，用于参考所述光网络单元的位置，采用K均值聚类算法将所述光网络单元划分为K个类；
    新类确定单元，用于在确定各类中的光网络单元的个数均未超过阈值时，将所述中央单元分别添加到各类中，得到K个新类；
    光分离器位置计算单元，用于分别针对各个新类，确定与所述新类对应的光分离器的位置，其中，保证所述光分离器的位置与所述新类中各光网络单元的位置、所述中央单元的位置的距离的和值最小。
  7. 根据权利要求6所述的装置，其特征在于，还包括：
    目标类确定单元，用于在确定某个类中的光网络单元的个数超过阈值时，将光网络单元个数超过阈值的类确定为目标类；
    目标类划分单元，用于参考所述目标类中各光网络单元的位置，采用K均值聚类算法对各光网络单元进行类划分，得到若干子类；
    子类判断单元，用于检测是否还存在光网络单元个数超过阈值的子类，若是，返回执行所述目标类确定单元。
  8. 根据权利要求6所述的装置，其特征在于，所述光分离器位置计算单元包括：
    第一光分离器位置计算子单元，用于依据所述新类中各光网络单元的位置以及中央单元的位置，采用Weiszfeld算法确定与所述新类对应的光分离器的位置。
  9. 根据权利要求6所述的装置，其特征在于，所述聚类划分单元包括：
    折线图建立单元，用于以K值作为X轴自变量，类簇度量标准值作为Y轴因变量，建立折线图，其中类簇度量标准值为，采用K均值聚类算法划分所得的K个类的类簇指标值的平均值；
    K值选取单元，用于选取折线图上类簇度量标准值变化率最大的点所对应的K值，作为目标K值。
  10. 根据权利要求7所述的装置，其特征在于，还包括：
    目标集合建立单元，用于将所述中央单元、所述目标类划分所得的各子类对应的光分离器组成一个目标集合；
    目标点位置确定单元，用于采用Weiszfeld算法确定所述目标集合对应的目标点位置，所述目标点位置与所述目标集合中各个元素位置的距离和值最小；
    目标光纤渠道确定单元，用于将由所述中央单元的位置至所述目标点位置间的线路确定为目标光纤渠道，由所述中央单元至所述目标集合中各光分离器的多条光纤共用所述目标光纤渠道。
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