CN111884832B - A method for acquiring passive network topology information and related equipment - Google Patents

Abstract

The embodiment of the application provides a method and a related device for acquiring passive network topology information, wherein a passive network comprises a plurality of node devices, the plurality of node devices comprise at least one intermediate node device and at least one end node device, and the method for acquiring the passive network topology information comprises the following steps: acquiring characteristic data of one or more terminal node devices in the passive network at a target moment; performing cluster analysis on the one or more terminal node devices according to the characteristic data of the one or more terminal node devices to obtain a cluster list corresponding to the target moment; and fusing cluster lists corresponding to a plurality of target moments respectively to obtain the topology information of the passive network. By implementing the embodiment of the application, the topological connection relation of the passive network can be more accurately determined.

Description

A method for acquiring passive network topology information and related equipment

technical field

The present application relates to the field of communication technologies, and in particular, to a method for acquiring passive network topology information and related devices.

Background technique

The existing passive network is composed of some or all passive devices, and because some or all of the passive network devices are passive devices, the topology connection relationship of the maintenance devices cannot be automatically updated through the network. Connection information generally mainly depends on maintenance and updates by professionals. However, when passive network devices are added or removed from the passive network, professionals may not enter and update topology information in time, resulting in missing or incorrect topology information, and the absence and error of passive network device topology information will lead to related operations. Maintenance work is difficult to carry out. For example: accurate fault location of home broadband access network, if the topology information of the optical path distribution network ODN is missing, it will be difficult to accurately locate the fault location, and it can only rely on the installation and maintenance personnel to check whether the equipment at all levels of the ODN is faulty. The efficiency is low, and it is easy to cause repeated or invalid boarding, which greatly wastes manpower and material resources.

At present, in order to determine the passive network topology information, professionals may be required to actively interrupt the working state of the main branch passive network equipment; then collect the alarm information and performance index data generated by the end node equipment under the main branch passive network equipment. When the node is set up and generates an alarm or the performance index data is interrupted, it belongs to the same passive network device. However, in the process of active data identification, the implementation of this solution also needs to interrupt the use of the passive network by the user, which affects the user's business.

Therefore, how to accurately determine the topological connection relationship of a passive network without affecting user services is an urgent problem to be solved.

SUMMARY OF THE INVENTION

Embodiments of the present application provide a method and related equipment for acquiring topology information of a passive network, so as to accurately determine the topology connection relationship of a passive network.

In a first aspect, an embodiment of the present application provides a method for acquiring topology information of a passive network, where the passive network includes multiple node devices, and the multiple node devices include at least one intermediate node device and at least one end node device ; may include:

Acquire characteristic data of one or more end node devices in the passive network at a target time, where the target time is when the performance index information of the one or more end node devices changes beyond a preset fluctuation range and/or an alarm occurs information, the feature data includes the performance indicator information and/or the alarm information, and the one or more end node devices are one or more of the at least one end node device; according to the one feature data of one or more end node devices, perform cluster analysis on the one or more end node devices, and obtain a cluster list corresponding to the target time, the cluster list including at least one first cluster cluster, wherein each first cluster in the at least one first cluster includes an end node device under the same intermediate node device among the one or more end node devices; A list of corresponding clusters, obtain topology information of the passive network, the topology information includes at least one second cluster, and each second cluster in the at least one second cluster includes the Some or all of the end node devices under the same intermediate node device in a passive network.

By implementing the embodiment of the present application, the cluster list of each target time among the multiple target times is obtained, and the cluster lists corresponding to the multiple target times are fused to obtain the topology information of the passive network. Wherein, the target time is the time when there are one or more end node devices in the passive network when the change of performance index information exceeds the preset fluctuation range and/or the alarm information occurs; at this time, it can be obtained that the change of performance index information exceeds the preset value Fluctuation range and/or feature data of one or more end node devices with alarm information; according to the obtained feature data, a cluster list corresponding to the target moment is obtained through cluster analysis; and in the same way, multiple A cluster list corresponding to the target time is obtained, and then a plurality of cluster lists corresponding to the target time are merged to obtain the topology information of the passive network. The characteristic data includes performance index information and/or the alarm information, and the end node devices where the change of the performance index information exceeds the preset fluctuation range and/or the alarm information occurs at each of the multiple target times may be different. Therefore, at a target moment, among the end node devices that generate fluctuations or alarm information from one or more performance data, the end node devices under the same intermediate node device are clustered and analyzed, and the topology information corresponding to the passive network is obtained. The implementation method can greatly reduce the workload of operation and maintenance personnel, and does not require relevant personnel to actively obtain a large amount of data information, but only needs to monitor the occurrence of characteristic data or the change of characteristic data exceeds the preset range. Secondly, the cluster lists corresponding to multiple target moments are fused to finally obtain the topology information of the passive network, which can avoid the problem of inaccurate restoration of topology information when there is only one cluster list at the target moment, and improve the passive network topology information. accuracy. By restoring the topology information of the passive network, the topology information of the passive network can be restored, so as to further perform automatic maintenance and update of the passive network, assist in the accurate demarcation of faults, reduce repeated access and invalid access, and reduce Operational labor costs.

In a possible implementation manner, the cluster analysis is performed on the one or more end node devices according to the feature data of the one or more end node devices to obtain the cluster clusters corresponding to the target moment. The list includes: calculating the similarity between the one or more end node devices at the target moment according to the feature data of the one or more end node devices, and obtaining a similarity matrix; based on the similarity matrix , performing cluster analysis on the one or more end node devices to obtain a cluster list corresponding to the target moment. Implementing the embodiments of the present application, when the passive network changes in the end node device, it is reflected on the monitored end node device, that is, the change in the characteristic data of the end node device, that is, the change in the performance index information exceeds the preset fluctuation range or occurs. In addition, the data fluctuations of end node devices under the same intermediate node device are similar, and the alarm types with alarm information are also similar. The greater the similarity, the tighter the connection. Clustering analysis of end node devices with fluctuating data can distinguish intermediate node devices to which multiple end node devices belong, which is beneficial to improve the accuracy of topology information.

In a possible implementation manner, the passive network is an optical path distribution network, the end node device is an optical network unit, and the intermediate node device is an optical splitter. To implement the embodiment of the present application, first obtain the unit identifiers and characteristic data of one or more optical network units at the moment when the optical path changes occur, and then perform cluster analysis according to the characteristic data of the one or more optical network units to obtain the multiple optical network units. The cluster lists corresponding to the time of each change of the light path respectively, and finally, the topology information of the light path distribution network can be obtained by merging the lists of the clusters corresponding to the time of the change of the light path. Among them, the moment when the optical path change occurs in the optical path distribution network, that is, the moment when the optical network unit generates abnormal performance data or alarm information, at this time, it can be clustered from one or more optical network units that generate abnormal performance data or alarm information By analyzing the optical network units under the same optical splitter, this realization method of obtaining the topology information corresponding to the optical path distribution network without interrupting the user network can greatly reduce the workload of operation and maintenance personnel, and ensure that users network requirements. Secondly, the obtained multiple cluster lists in the preset time period are combined to finally obtain the topology information of the optical path distribution network, which can avoid the problem of inaccurate restoration of the topology information when there is only one optical path fluctuation, and improve the accuracy of the topology information of the optical path distribution network. sex.

其中，k为所述多个目标时刻中第k个目标时刻，T为所述当前时间点，

为所述第k个目标时刻在所述多个目标时刻中的权重核函数，g_k(i,j)为所述第k个目标时刻对应的目标子图中所述节点i和所述节点j之间的边权重；基于图的社群检测算法或embedding算法分割所述拓扑图，获得所述拓扑信息。实施本申请实施例，针对拓扑序列采用基于图的融合方式，在融合了多个时刻的聚类列表的同时，可以有效解决不同时刻识别结果的冲突问题，提高无源网络的拓扑信息确定的准确率。In a possible implementation manner, a target subgraph corresponding to the target moment is constructed according to the cluster list corresponding to the target moment, and the target subgraph includes a fully connected graph or a minimum spanning tree construction graph, wherein, The edge weight between the node i and the node j in the target subgraph is the similarity measure function between the end node device i and the end node device j in the one or more end node devices corresponding to the target moment and/or inherent attributes; the method of fusing the cluster lists corresponding to the multiple target moments, and obtaining the topology information of the passive network, includes: fusing the target subgraphs corresponding to the multiple target moments based on the graph to obtain a topology map , where the edge weights between nodes in the topology graph are calculated as follows:

Wherein, k is the k-th target moment in the multiple target moments, T is the current time point,

is the weight kernel function of the kth target moment in the multiple target moments, and g _k (i,j) is the node i and the node in the target subgraph corresponding to the kth target moment The edge weight between j; the graph-based community detection algorithm or the embedding algorithm divides the topology graph to obtain the topology information. In the implementation of the embodiments of the present application, a graph-based fusion method is adopted for the topology sequence, which can effectively solve the conflict of identification results at different times while merging the clustering lists at multiple times, and improve the accuracy of the determination of the topology information of the passive network. Rate.

In a possible implementation manner, the fusion of the cluster lists corresponding to the multiple target moments to obtain the topology information of the passive network includes: respectively merging the cluster lists corresponding to the multiple target moments in the cluster lists. For the first cluster corresponding to the same intermediate node device, obtain the corresponding second cluster; and obtain the topology information according to a plurality of the second clusters. By implementing this embodiment of the present application, the first clusters under the same intermediate node device corresponding to different target moments can be merged into second clusters, and the merged cluster list obtained is the topology information of the passive network. Therefore, the inaccurate problem of determining the topology information when there is only one optical path fluctuation can be avoided, thereby improving the accuracy of the passive network topology information.

In a second aspect, an embodiment of the present application provides an apparatus for acquiring topology information of a passive network, where the passive network includes multiple node devices, and the multiple node devices include at least one intermediate node device and at least one end node device ; the device includes:

an acquisition unit, configured to acquire characteristic data of one or more end node devices in the passive network at a target time, where the target time is when the performance index information of the one or more end node devices changes beyond a preset fluctuation range and/or the moment when alarm information occurs, the feature data includes the performance indicator information and/or the alarm information, and the one or more end node devices are one or more of the at least one end node device ;

The clustering unit is configured to perform cluster analysis on the one or more end node devices according to the characteristic data of the one or more end node devices, and obtain a cluster list corresponding to the target time, and the cluster The cluster list includes at least one first cluster, wherein each first cluster in the at least one first cluster includes the one or more end node devices under the same intermediate node device. end node equipment;

A fusion unit, configured to fuse the cluster lists corresponding to multiple target moments respectively, and obtain topology information of the passive network, where the topology information includes at least one second cluster, the at least one second cluster Each of the second clusters in the passive network includes some or all of the end node devices under the same intermediate node device in the passive network.

In a possible implementation manner, the clustering unit is specifically configured to: according to the characteristic data of the one or more end node devices, calculate the distance between the one or more end node devices at the target time The similarity is obtained, and a similarity matrix is obtained; based on the similarity matrix, cluster analysis is performed on the one or more end node devices to obtain a cluster list corresponding to the target time.

In a possible implementation manner, the passive network is an optical path distribution network, the end node device is an optical network unit, and the intermediate node device is an optical splitter.

其中，k为所述多个目标时刻中第k个目标时刻，T为所述当前时间点，

为所述第k个目标时刻在所述多个目标时刻中的权重核函数，g_k(i,j)为所述第k个目标时刻对应的目标子图中所述节点i和所述节点j之间的边权重；基于图的社群检测算法或embedding算法分割所述拓扑图，获得所述拓扑信息。In a possible implementation manner, the apparatus further includes: a subgraph unit, configured to construct a target subgraph corresponding to the target moment according to a cluster list corresponding to the target moment, and the target subgraph includes A fully connected graph or a minimum spanning tree construction graph, wherein the edge weight between node i and node j in the target subgraph is the end node device i in the one or more end node devices corresponding to the target moment The similarity measurement function and/or the inherent attribute between the terminal node device j; the fusion unit is specifically used to: fuse the target subgraphs corresponding to multiple target moments based on the graph to obtain a topology map, wherein the topology The edge weights between nodes in the graph are calculated as:

Wherein, k is the k-th target moment in the multiple target moments, T is the current time point,

is the weight kernel function of the kth target moment in the multiple target moments, and g _k (i,j) is the node i and the node in the target subgraph corresponding to the kth target moment The edge weight between j; the graph-based community detection algorithm or the embedding algorithm divides the topology graph to obtain the topology information.

In a possible implementation manner, the fusion unit is specifically configured to: respectively fuse the first clusters corresponding to the same intermediate node device in the cluster list corresponding to the multiple target moments, and obtain the corresponding the second cluster; obtaining the topology information according to a plurality of the second clusters.

In a third aspect, an embodiment of the present application provides a service device, the service device includes a processor, and the processor is configured to support the service device to implement corresponding functions in the method for acquiring passive network topology information provided in the first aspect. The service device may also include memory, coupled to the processor, which holds program instructions and data necessary for the service device. The service device may also include a communication interface for the service device to communicate with other devices or a communication network.

In a fourth aspect, an embodiment of the present application provides a computer storage medium for storing computer software instructions used for the device for acquiring passive network topology information provided in the second aspect, which includes a computer software instruction for executing the design in the above aspect program of.

In a fifth aspect, an embodiment of the present application provides a computer program, the computer program includes instructions, when the computer program is executed by a computer, the computer can execute the device for acquiring passive network topology information in the second aspect above. process.

In a sixth aspect, the present application provides a chip system, the chip system includes a processor for supporting a terminal device to implement the functions involved in the above first aspect, for example, generating or processing the above method for acquiring passive network topology information the information involved. In a possible design, the chip system further includes a memory for storing necessary program instructions and data of the data sending device. The chip system may be composed of chips, or may include chips and other discrete devices.

Description of drawings

In order to more clearly describe the technical solutions in the embodiments of the present application or the background technology, the accompanying drawings required in the embodiments or the background technology of the present application will be described below.

FIG. 1A is a schematic diagram of a system architecture for acquiring passive network topology information provided by an embodiment of the present application.

FIG. 1B is a schematic diagram of a system architecture for acquiring ODN topology information of an optical path distribution network provided by an embodiment of the present application.

FIG. 2 is a schematic structural diagram of a topology information acquisition device provided by an embodiment of the present application.

FIG. 3 is a schematic flowchart of a method for acquiring passive network topology information provided by an embodiment of the present application.

FIG. 4 is a schematic diagram of a variation of received optical power of an optical network unit according to an embodiment of the present application.

FIG. 5 and FIG. 6 are minimum spanning tree construction diagrams composed of a group of end node devices corresponding to a target time provided by an embodiment of the present application.

FIG. 7 is a full connection diagram composed of end node devices corresponding to a target time according to an embodiment of the present application.

FIG. 8 is a topology diagram provided by an embodiment of the present application.

FIG. 9 is a schematic flowchart of fusing multiple target subgraphs according to an embodiment of the present application.

FIG. 10 is a kind of multiple target subgraphs and a topology diagram after merging the multiple target subgraphs provided by an embodiment of the present application.

FIG. 11 is a schematic structural diagram of an apparatus for obtaining topology information provided by an embodiment of the present application.

FIG. 12 is a schematic structural diagram of another apparatus for obtaining topology information provided by an embodiment of the present application.

Detailed ways

The embodiments of the present application will be described below with reference to the accompanying drawings in the embodiments of the present application.

The terms "first" and "second" in the description and claims of the present application and the drawings are used to distinguish different objects, rather than to describe a specific order. Furthermore, the terms "comprising" and "having", and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but optionally also includes unlisted steps or units, or optionally also includes For other steps or units inherent to these processes, methods, products or devices.

Reference herein to an "embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor a separate or alternative embodiment that is mutually exclusive of other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

The terms "component", "module", "system" and the like are used in this specification to refer to a computer-related entity, hardware, firmware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a computing device and the computing device may be components. One or more components may reside within a process and/or thread of execution, and a component may be localized on one computer and/or distributed between 2 or more computers. In addition, these components can execute from various computer readable media having various data structures stored thereon. A component may, for example, be based on a signal having one or more data packets (eg, data from two components interacting with another component between a local system, a distributed system, and/or a network, such as the Internet interacting with other systems via signals) Communicate through local and/or remote processes.

First, some terms in this application are explained for the convenience of those skilled in the art to understand.

(1) Optical Distribution Network (ODN), the ODN network is an FTTH optical cable network based on PON equipment. The main function of the ODN is to provide an optical transmission channel between the OLT and the ONU to complete the distribution of optical signal power. ODN is a purely passive optical distribution network composed of passive optical devices (such as optical fibers, optical connectors, optical attenuators, optical couplers and optical wavelength division multiplexers, etc.).

(2) Passive devices, which are used to work when there is a signal in the entire passive network without the need for external power supply, mainly including resistors, capacitors, inductors, converters, faders, optical splitters, matching Networks, resonators, filters, mixers, switches, and passive optical devices, etc.

(3) Passive Optical Network (PON), Passive Optical Network (PON) is a pure medium network, which avoids the electromagnetic interference and lightning effects of external equipment, and reduces the failure of lines and external equipment. It improves system reliability and saves maintenance costs. It is a technology long-awaited by telecom maintenance departments. Light sourceless network is a point-to-multipoint optical fiber transmission and access technology. Downlink adopts broadcast mode and uplink adopts time division multiple access mode. It can flexibly form tree, star, bus and other topological structures. It only needs to install a simple optical splitter, so it has the advantages of saving optical cable resources, sharing bandwidth resources, saving equipment room investment, fast network construction, and low cost of comprehensive network construction. Passive optical networks include ATM-PON and Ethernet-PON.

In order to facilitate understanding of the embodiments of the present application, one of the system architectures on which the embodiments of the present application are based is first described below. Please refer to FIG. 1A . FIG. 1A is a schematic diagram of a system architecture for acquiring passive network topology information provided by an embodiment of the present application. As shown in FIG. 1A , the system architecture is mainly composed of a service device, at least one intermediate node device, at least one end node device and other devices. in,

The service equipment is used to supervise the terminal equipment of the passive network. For example, it may include one or more servers (multiple servers may form a server cluster), and may include but not limited to background servers, cloud servers, data processing servers, and the like. Wherein, part or all of the passive network may be composed of passive devices. As shown in FIG. 1A , the service device may acquire characteristic data of one or more end node devices in the passive network at a target time, where the target time is when the performance index information of the one or more end node devices changes by more than The preset fluctuation range and/or the time when the alarm information occurs, the characteristic data includes the performance index information and/or the alarm information, and the one or more end node devices are the at least one end node device. One or more; according to the feature data of the one or more end node devices, perform cluster analysis on the one or more end node devices to obtain a list of clusters corresponding to the target time, the cluster The cluster list includes at least one first cluster, wherein each first cluster in the at least one first cluster includes an end of the one or more end node devices under the same intermediate node device node device; fuse the cluster lists corresponding to multiple target moments respectively, and obtain topology information of the passive network, where the topology information includes at least one second cluster, each of which is in the at least one second cluster. The second cluster includes some or all of the end node devices in the passive network under the same intermediate node device.

Intermediate node equipment, which can be passive network equipment, can be used to work when there is a signal without adding power in the entire passive network, which can be resistors, capacitors, inductors, converters, faders, optical splitters, matching networks, Resonators, filters, mixers and switches, etc.

The end node device is the passive network device of the end node in the passive network. There are different types of end node devices in different passive networks, such as the optical network unit in the passive optical network, the resistance of the passive two-terminal network terminal and many more.

It should be noted that the performance indicator information of the end node device mentioned in this application can be understood as the key performance indicator/key performance indicator (Key Performance Indicator, KPI) information of the end node device, which is used to indicate the performance of the end node device. data, functional information or working status, etc. Among them, the performance index information matches the type of the passive network, for example: when the passive network is an optical path distribution network, the performance index information can be received optical power, optical link loss, etc.; when the passive network is passive two When the terminal network is connected, the performance index information can be voltage, current, resistance and so on.

It should also be noted that the passive network includes multiple node devices, and the multiple node devices include at least one intermediate node device and at least one end node device; when the intermediate node device is connected to the two end node devices, there are two data streams, It can be sent to two end node devices respectively, and at this time, the two end node devices can be regarded as node devices under the intermediate node device. As shown in FIG. 1A , the three-gray end node device is the node device under the intermediate node device connected to it.

It should also be noted that the system architecture for acquiring passive network topology information in FIG. 1A is only some exemplary implementations in the embodiments of the present application, and the system architecture for acquiring passive network topology information in the embodiments of the present application includes but is not limited to. The above system architecture for acquiring passive network topology information.

Based on the system architecture provided in FIG. 1A , please refer to FIG. 1B . FIG. 1B is a schematic diagram of a system architecture for acquiring ODN topology information of an optical path distribution network provided by an embodiment of the present application. As shown in Figure 1B, the system architecture is mainly composed of passive devices such as asset management system, optical cable transfer box, primary and secondary optical splitters, optical network units/terminals (ONU/ONT), and can also include network management equipment , Optical Line Terminal. Among them, the asset management system is equivalent to the service equipment in the above Figure 1A; the optical cable transfer box, the first-level optical splitter, the second-level optical splitter and the network management equipment, and the optical line terminal are equivalent to the above-mentioned intermediate node equipment in Figure 1A; the optical network unit/ The terminal (ONU/ONT) corresponds to the end node device in FIG. 1A described above. in,

Optical cable transfer box is a transfer equipment that provides optical cable termination and jumper connection for backbone layer optical cable and distribution layer optical cable. After the optical cable is introduced into the optical cable junction box, after fixing, termination, and fiber distribution, the trunk layer optical cable and the distribution layer optical cable are connected by using jumper fibers.

Optical splitters are passive devices, also known as optical splitters, which do not require external energy, as long as there is input light. The beamsplitter consists of entrance and exit slits, mirrors and dispersive elements, whose function is to separate out the desired resonant absorption lines. The key component of the beam splitter is the dispersive element, and now commercial instruments use gratings. The first-level optical splitter is generally used in places where users are concentrated, and is suitable for one-time access. The optical splitter is generally relatively large. The secondary optical splitter is generally used in places where users are scattered, such as towns and villages, and the ratio is generally small.

Optical network units/terminals (Optical Network Unit/Terminals, ONU/ONT) are divided into active optical network units and passive optical network units. The optical network units/terminals in this application generally refer to passive optical network units/terminals , refers to the equipment or functional block in the optical access network that provides the user-side interface (direct or remote) and is connected to the optical path distribution network ODN. ONU is a terminal device for optical fiber access. It should be used in conjunction with the optical line terminal OLT. The optical line terminal OLT is generally stored in the central computer room of the ISP.

The optical line terminal, on the one hand, aggregates the signals carrying various services at the central office, and sends them to the access network according to a certain signal format for transmission to the end users. in various business networks. The realized functions are: 1. Connect to the front-end (convergence layer) switch with a network cable, convert it into an optical signal, and use a single fiber to interconnect with the optical splitter at the user end. 2. Realize the functions of control, management and ranging of the ONU of the client equipment. 3. Like ONU equipment, OLT equipment is also an optoelectronic integrated equipment. For example, in this embodiment of the present application, a frame/slot type passive optical network interface may be provided.

Network management equipment, also known as network connector and protocol converter, is a computer system or device that provides data conversion services between multiple networks.

The asset management system can use the scheme of this application to determine the ODN topology information of the optical path distribution network, and can restore the topology connection relationship of passive devices (such as: optical network units/terminals) in the ODN, and realize the automatic completion of topology data in the asset management data. and proofreading.

In case 1, the asset management system can be a server in the cloud. This server and local passive devices form a system. As shown in Figure 1B, the system architecture can include one or more servers (multiple servers can form a server cluster) , which may include but are not limited to background servers, cloud servers, data processing servers, etc. When the above asset management system is a server, the server may run a corresponding server-side program to provide a corresponding optical path distribution network topology information restoration service. For example, acquiring characteristic data of one or more end node devices in the passive network at a target time, where the target time is the time when the optical path changes in the optical path distribution network, and the characteristic data includes the received light of the optical network unit. at least one of power, optical link loss, and alarm information; according to the characteristic data of the one or more optical network units, perform cluster analysis on the one or more optical network units, and obtain the corresponding A cluster list, the cluster list includes at least one first cluster, wherein each first cluster in the at least one first cluster includes the one or more optical network units The optical network unit under the same optical splitter; fuse the cluster lists corresponding to multiple target moments respectively, and obtain the topology information of the passive network, where the topology information includes at least one second cluster, the at least one second cluster. Each second cluster in a second cluster includes part or all of the optical network units in the passive network under the same optical splitter.

In case 2, the asset management system may be a device, which may be a local terminal, and the system of the terminal device includes but is not limited to different platforms such as windows, iOS, and Android. For example, the terminal may acquire characteristic data of one or more end node devices in the passive network at the target time; Perform cluster analysis to obtain a list of clusters corresponding to the target time; and obtain topology information of the passive network by fusing the lists of clusters corresponding to multiple target times respectively. The terminal in this embodiment of the solution may include, but is not limited to, any electronic product capable of running a browser, which can perform human-computer interaction with the user through input devices such as a keyboard, a virtual keyboard, a touchpad, a touchscreen, and a voice control device, such as Smartphones, Tablets, PCs, etc. The smart operating system includes, but is not limited to, any operating system that enriches device functions by providing various mobile applications to the mobile device, such as: Android (Android ^TM ), iOS ^TM , Windows Phone ^TM and the like.

Based on the above system architecture, the embodiments of the present application provide a service device that can be applied to the system architecture of FIG. 1A and the asset management system of the system architecture of FIG. 1B . Please refer to FIG. 2 , which is an embodiment of the present application. A schematic structural diagram of a topology information acquisition device, as shown in FIG. 2 , the topology information acquisition device may include a data acquisition module 001 , a topology restoration algorithm module 002 , a restoration result module 003 and a correction topology data module 004 .

The topology information acquisition device firstly collects the data of the end node devices on the passive network, including performance index data, alarm data, etc., and then inputs the data into the topology restoration algorithm module, performs topology restoration to obtain restoration results, and then combines the pre-stored topology Data, correct and confirm the topology data, and finally feed back the corrected topology data to the original topology data to form a closed loop of topology data correction. The passive network includes a plurality of node devices including at least one intermediate node device and at least one end node device. in,

A data collection module 001 is configured to collect characteristic data of the end node device, the characteristic data includes the performance index information or the alarm information of the end node device, wherein the performance index information and the alarm information are related to the type of the passive network. Matching, for example: when the passive network is an optical path distribution network, the performance index information can be received optical power, optical link loss, etc.; when the passive network is a passive two-terminal network, the performance index information can be voltage, current , resistors, etc. The data collection module 001 includes a data collection module and an alarm data collection module. The data collection module is used to collect performance index information, which may be key performance indicator KPI data; the alarm data collection module is used to collect alarm data, and the alarm data may include: alarm type, device identification of the alarm end node device , at least one of the alarm start time and the alarm end time.

The topology restoration algorithm module 002 is configured to restore the topology information of the passive network according to the characteristic data collected by the data collection module 001 and the device identifier of the end node device corresponding to the characteristic data. For example, the topology restoration algorithm module 002 may acquire characteristic data of one or more end node devices in the passive network at a target time, where the target time is when the performance index information of the one or more end node devices changes more than a predetermined time. Suppose the fluctuation range and/or the time when the alarm information occurs, the characteristic data includes the performance index information and/or the alarm information, and the one or more end node devices are one of the at least one end node device or more; according to the feature data of the one or more end node devices, perform cluster analysis on the one or more end node devices to obtain a list of clusters corresponding to the target time, and the cluster clusters The list includes at least one first cluster, wherein each first cluster in the at least one first cluster includes an end node under the same intermediate node device in the one or more end node devices equipment; fuse the cluster lists corresponding to multiple target moments respectively, and obtain the topology information of the passive network, where the topology information includes at least one second cluster, each of the at least one second cluster The second cluster includes some or all end node devices under the same intermediate node device in the passive network. The device identifier of the end node device is used to identify the end node device.

The restoration result module 003 is configured to obtain the topology information of the passive network restored by the above topology restoration algorithm module 002 and the originally stored topology information of the passive network.

The correction topology data module 004, in combination with the asset management data and the restored topology information, corrects the topology data in the asset management data, and finally feeds back and updates the corrected topology information to the topology data.

It can be understood that the architecture of the topology information acquisition device in FIG. 2 is only an exemplary implementation in the embodiment of the present application, and the topology information acquisition device in the embodiment of the present application includes but is not limited to the above topology information acquisition device.

Based on the system architecture provided in FIG. 1A and FIG. 1B and the structure of the topology information acquisition device provided in FIG. 2, combined with the method for acquiring passive network topology information provided in this application, the technical problems proposed in this application are specifically analyzed and solved. .

Please refer to FIG. 3. FIG. 3 is a schematic flowchart of a method for obtaining passive network topology information provided by an embodiment of the present application. The method can be applied to the system architecture described in FIG. 1A and FIG. 1B above, wherein the topology information The acquisition device can be used to support and execute the method flow steps S301 to S304 shown in FIG. 3 . The description will be given below from the topology information acquiring device side with reference to FIG. 3 . The method may include the following steps S301, S302, and S304, and optionally, may further include step S303.

Step S301: Acquire characteristic data of one or more end node devices in the passive network at the target time.

Specifically, the topology information acquisition device acquires characteristic data of one or more end node devices in the passive network at a target time, where the target time is when the performance index information of the one or more end node devices changes by more than a preset value The fluctuation range and/or the moment when the alarm information occurs, the characteristic data includes the performance index information and/or the alarm information, and the one or more end node devices are one or more of the at least one end node device. multiple. Wherein, the device identifier of the end node device can also be obtained, which is used to identify the end node device. Both the performance index information and the alarm information match the type of the passive network. For example, when the passive network is an optical path distribution network, the performance index information can be received optical power, optical link loss, etc.; When it is a passive two-terminal network, the performance index information can be voltage, current, resistance and so on. The alarm information includes one or more of an alarm type, an alarm identifier, an alarm start time, and an alarm end time. For example, when the performance index information of an end node device changes beyond the preset fluctuation range or alarm information occurs in the passive network, this time can be understood as the target time, and the characteristics of the change of the end node device can be obtained at this time. data. For example, when the change of performance index information exceeds the preset fluctuation range, obtain performance index data; when alarm information occurs, obtain alarm information. In addition, it is not excluded that when the alarm information occurs, the performance indicator data is acquired, which is not specifically limited in this embodiment of the present application. Another example: when the performance index information of one end node device changes beyond the preset fluctuation range in the passive network, and another end node device generates alarm information, this time can also be understood as the target time. In addition, at different target moments, there may be different end-node devices and/or different numbers of end-node devices that show performance index information changes exceeding the preset fluctuation range and/or alarm information.

Optionally, the multiple target moments are multiple within a preset time period, and the preset time period is a time period between the preset time point and the current time point. It should be noted that, within the preset time period, there may be multiple times when the performance index information changes beyond the preset fluctuation range or when alarm information occurs, that is, multiple target times. Because at each target time, the end node devices where the change of performance index information exceeds the preset fluctuation range or the alarm information may be different may be different. Therefore, the topology information acquisition device can periodically determine the feature data at multiple times and restore the passive network. It integrates the identification results of multiple topology information, and improves the accuracy of topology information restoration in passive networks. It should be noted that when there is no optical path change in the passive network, or the degree of change in the performance index information is not obvious and does not exceed the preset change range, the topology information obtaining device cannot obtain the device identification and characteristic data of the end node device.

In a possible implementation manner, when the passive network is an optical path distribution network, the passive network is an optical path distribution network, the end node device is an optical network unit, and the intermediate node device is an optical splitter. When an optical path change occurs in the optical path distribution network, characteristic data of the changed optical network unit can be obtained, which is used to analyze whether the optical network unit is under the same optical splitter. Among them, if some or all of the optical network units in the optical path distribution network have changes in the optical path, it can be considered that the ONU of the optical network unit has alarm information or the performance index data of the ONU of the optical network unit has changed (for example, the received optical power has changed), or the performance index has changed. The change in the data is outside a certain threshold range. Wherein, please refer to the following Table 1. Table 1 is an alarm information table of alarm information of an ONU of an optical network unit provided by an embodiment of the present application.

Table 1: Alarm Information Table

ONU unit identification Alert Type Alarm ID Alarm start time Alarm end time 005 A Aaaaa1 2020-05-26 22:29.39 2020-05-26 22:32.30 … … … … … 198 B Bbbbb2 2020-05-2912:29.39 2020-05-2912:32.52 201 B Bbbbb2 2020-05-2912:29.39 2020-05-2912:33.09

The above alarm information table includes: ONU unit identification, alarm type, alarm identification, alarm start time, alarm end time, etc., which are used to describe the alarm type and time when the optical network unit generates an alarm, so that the topology information acquisition device can respond to the alarm. The information of the optical network units is clustered. Wherein, when the time and alarm type of the two optical network units when the alarm information occurs are very similar, it can be considered that the two optical network units are under the same optical splitter. As shown in Table 1, when the two optical network units of ONU unit identifier 198 and ONU unit identifier 201, when the time and alarm type of the alarm information are very similar, it can be considered that the two optical network units are under the same optical splitter .

When the performance index data of the ONU of the optical network unit changes, taking the change of the received optical power as an example, please refer to FIG. Wherein: the characteristics of the change of the received optical power of the ONU of the optical network unit may include: the degree of jitter, the number of jitters, the degree of cliff, the degree of trend deterioration, etc. of the received optical power, and the degree of jitter may be the received optical power of the ONU per unit time. The degree of change of the data, the number of times of jitter is the cumulative number of times that the degree of jitter of the ONU is greater than the preset threshold, and the degree of cliff is used to indicate the attenuation change of the received optical power of the ONU from a stable value to another stable value within a unit time. size, the trend deterioration degree is represented by the trend coefficient of linear fitting after exponentially weighted moving average of the received optical power within a certain period of time. Wherein, when the received optical powers of multiple ONUs have similar characteristics or are within a certain threshold range, it can be considered that the multiple ONUs are under the same optical splitter. As shown in Figure 4, a schematic diagram of the change of optical power received by the optical network unit, the left side is the unit identifier of the optical network unit, and the right side is the size of the received optical power corresponding to the optical network unit over time. As shown in Figure 4, the ONU units are identified as 015, 016 and 017 for the three optical network units, the received optical power jitter degree, jitter times, cliff degree, trend deterioration degree, etc. are relatively similar, it can be considered that the three optical network units The optical network units are under the same optical splitter. The ONU units identified as 005 and 053 optical network units have different performance index fluctuations from 015, 016 and 017 optical network units. It can be considered that the optical network units 005 and 053 are not under the same optical splitter as the above three optical network units.

It should be noted that, in the embodiment of the present application, the time when the optical path starts to change is used as the target time, and the time when the change rate of the optical path is the largest during the change of the optical path may also be used as the target time, which is not specifically limited in the embodiment of the present application.

Step S302: Perform cluster analysis on one or more end node devices according to the characteristic data of one or more end node devices, and obtain a cluster list corresponding to the target time.

Specifically, the topology information acquisition device performs cluster analysis on the one or more end node devices according to the characteristic data of the one or more end node devices, and obtains a cluster list corresponding to the target time, the The cluster list includes at least one first cluster, wherein each first cluster in the at least one first cluster includes one or more end node devices under the same intermediate node device. end node device. It should be noted that cluster analysis refers to the analysis process of grouping a collection of physical or abstract objects into multiple classes composed of similar objects. It should also be noted that when cluster analysis is performed on one or more end node devices, the types of the characteristic data are the same, for example, all are performance indicators or all are alarm information. Through cluster analysis, the end node devices belonging to the same intermediate node device can be classified into one category to restore the topology information of the passive network, wherein the topology information includes the end node devices under different intermediate node devices in the passive network . For example, in this embodiment of the present application, a density space-based clustering algorithm (Density-Based Spatial Clustering of Applications with Noise, DBSCAN) may be used, which defines a cluster as the largest set of density-connected points, and can classify a cluster with a sufficiently high density. Regions are divided into clusters, and clusters of arbitrary shape can be found in a spatial database of noise. Therefore, there is no need to specify the number of clusters, no need to calculate the centroid of the clusters, and noise points can be identified. After cluster analysis, the topology information acquisition device can output the list of clusters at the t-th target time, where the clusters A list can be represented as: {c ₁ ,c ₂ ,…,c _n } _t . Wherein, cn is the device identifier of the _nth end node device corresponding to the tth target time, representing the nth end node device in the cluster list. By analyzing the data of multiple end node devices corresponding to the moment when each optical path changes, the topology information at this moment can be obtained, which is simple and fast, and the obtained topology information is relatively accurate, and no manual input is required to maintain the passive network device. topology information.

Optionally, according to the feature data of the one or more end node devices, perform cluster analysis on the one or more end node devices to obtain a cluster list corresponding to the target time, including: according to the Feature data of one or more end node devices, calculate the similarity between the one or more end node devices at the target moment, and obtain a similarity matrix; Each end node device performs cluster analysis to obtain a cluster list corresponding to the target moment. Because the passive network is reflected in the monitored end node device when the end node device changes, that is, the change of the characteristic data of the end node device, that is, the change of the performance index information exceeds the preset fluctuation range or the alarm information occurs; and , the data fluctuations of the end node devices under the same intermediate node device are similar, and the alarm types of the alarm information are also similar. The greater the similarity, the tighter the connection. Clustering analysis of node devices can distinguish intermediate node devices to which multiple end node devices belong, which is beneficial to improve the accuracy of topology information.

其中，t为m个目标时刻中第t个目标时刻，Norm(i)_t为所述第t个目标时刻对应光网络单元i接收光功率的归一化函数，Norm(j)_t为所述第t个目标时刻对应光网络单元j接收光功率的归一化函数，L₁(Norm(i)_t,Norm(j)_t)为所述光网络单元i和所述光网络单元j之间基于接收光功率的距离度量函数，count_nan(i∪j)为所述光网络单元i和所述光网络单元j合并后空值计数函数，α为预设空值惩罚系数，t＝1、2……m，m为大于1的正整数，i、j分别为光网络单元的标识。拓扑信息获取设备可以根据光网络单元的接收光功率，计算在每个目标时刻出现光路变化的一个或多个光网络单元之间的相似度，当两个光网络单元的相似度在一定的阈值范围内时，可以认为该两个光网络单元处于同一个分光器下。进一步的，拓扑信息获取设备在计算光网络单元之间的相似度时，可以首先将一个或多个光网络单元接收光功率进行归一化处理，然后，基于归一化处理后的接收光功率，通过距离度量函数计算一个或多个光网络单元之间的相似度，有利于提高聚类分析的准确度。In a possible implementation manner, the passive network is an optical path distribution network, the end node device is an optical network unit, the intermediate node device is an optical splitter, and the characteristic data is the reception of the end node device optical power; according to the characteristic data of the one or more end node devices, calculating the similarity between the one or more end node devices at the target moment to obtain a similarity matrix, including: according to the received light power and the first calculation formula, calculate the similarity between one or more optical network units corresponding to each target moment, and obtain the similarity matrix S, wherein, the first calculation formula is

Among them, t is the t-th target time among the m target times, Norm(i) _t is the normalized function of the received optical power of the optical network unit i corresponding to the t-th target time, and Norm(j) _t is the The t-th target time corresponds to the normalized function of the optical power received by the optical network unit j, and L ₁ (Norm(i) _t , Norm(j) _t ) is the distance between the optical network unit i and the optical network unit j The distance metric function based on the received optical power, count_nan(i∪j) is the null count function after the optical network unit i and the optical network unit j are combined, α is the preset null penalty coefficient, t=1, 2 ...m, m are positive integers greater than 1, i, j are the identifiers of the optical network units, respectively. The topology information acquisition device can calculate the similarity between one or more optical network units with optical path changes at each target moment according to the received optical power of the optical network unit. When the similarity between the two optical network units is within a certain threshold When within the range, it can be considered that the two optical network units are under the same optical splitter. Further, when calculating the similarity between the optical network units, the topology information acquisition device may first normalize the received optical power of one or more optical network units, and then, based on the normalized received optical power. , the similarity between one or more optical network units is calculated by the distance metric function, which is beneficial to improve the accuracy of cluster analysis.

其中，t为m个目标时刻中第t个目标时刻，As(i)为第t个目标时刻对应光网络单元i的告警开始时间，A_e(i)为所述第t个目标时刻对应光网络单元i的告警结束时间，A_s(j)为所述第t个目标时刻对应光网络单元j的告警开始时间，A_e(j)为所述第t个目标时刻对应光网络单元j的告警结束时间，L₁(A_s(i),A_s(j))所述光网络单元i和所述光网络单元j之间基于所述告警开始时间的距离度量函数，L₁(A_e(i),A_e(j))为所述光网络单元i和所述光网络单元j之间基于所述告警结束时间的距离度量函数，t＝1、2……m，m为大于1的正整数，i、j分别为光网络单元的标识。拓扑信息获取设备可以根据告警开始时间和告警结束时间，计算在每个目标时刻出现告警信息的一个或多个光网络单元之间的相似度，当两个光网络单元的相似度在一定的阈值范围内时，可以认为该两个光网络单元处于同一个分光器下。进一步的，在计算光网络单元之间的相似度时，可以首先确定一个或多个光网络单元出现告警信息到的开始时间和结束时间，然后，基于告警开始时间和告警结束时间，通过距离度量函数计算一个或多个光网络单元之间的相似度，有利于提高聚类分析的准确度。In a possible implementation manner, the passive network is an optical path distribution network, the end node device is an optical network unit, the intermediate node device is an optical splitter, and the characteristic data is an alarm of the end node device information, the alarm information includes an alarm start time and an alarm end time; calculating the similarity between the one or more end node devices at the target time according to the characteristic data of the one or more end node devices obtaining a similarity matrix, including: calculating the similarity between one or more optical network units corresponding to the target moment according to the alarm information and the second calculation formula, and obtaining a similarity matrix S, wherein the The second calculation formula is

Among them, t is the t-th target time among the m target times, As(i) is the alarm start time of the optical network unit i corresponding to the t-th target time, and A _e (i) is the optical network unit i corresponding to the t-th target time. The alarm end time of the network unit i, A _s (j) is the alarm start time of the optical network unit j corresponding to the t-th target time, and A _e (j) is the t-th target time corresponding to the optical network unit j. Alarm end time, L ₁ (A _s (i), A _s (j)) The distance metric function between the optical network unit i and the optical network unit j based on the alarm start time, L ₁ (A _e (i), A _e (j)) is the distance metric function between the optical network unit i and the optical network unit j based on the alarm end time, t=1, 2...m, m is greater than 1 A positive integer of , i and j are the IDs of the optical network units respectively. The topology information acquisition device can calculate the similarity between one or more optical network units that have alarm information at each target time according to the alarm start time and the alarm end time. When the similarity between the two optical network units is within a certain threshold When within the range, it can be considered that the two optical network units are under the same optical splitter. Further, when calculating the similarity between optical network units, it is possible to first determine the start time and end time when the alarm information occurs in one or more optical network units, and then, based on the alarm start time and the alarm end time, measure the distance through distance measurement. The function calculates the similarity between one or more optical network units, which is beneficial to improve the accuracy of cluster analysis.

Step S303: Construct a target subgraph corresponding to the target moment according to the cluster list corresponding to the target moment.

Specifically, the topology information acquisition device constructs a target subgraph corresponding to the target moment according to the cluster list corresponding to the target moment, and the target subgraph includes a fully connected graph or a minimum spanning tree construction graph, wherein the The edge weight between the node i and the node j in the target subgraph is the similarity measure function between the end node device i and the end node device j in the one or more end node devices corresponding to the target moment and/ or inherent properties. Among them, i and j are the device identifiers of the corresponding end node devices.

For example, please refer to FIG. 5 and FIG. 6 . FIG. 5 and FIG. 6 are minimum spanning tree construction diagrams composed of a group of end node devices corresponding to a target moment provided by an embodiment of the present application. When the number of one or more end node devices corresponding to the target moment is less than or equal to the preset threshold (for example, the default threshold is 3 by default, which can be adjusted according to specific scenarios), a fully connected graph can be constructed, and when the target moment corresponds to When the number of one or more end node devices is greater than a preset threshold, the graph can be constructed using a minimum spanning tree. As shown in Figure 5, the four nodes whose device IDs are 000,037,041,019 respectively represent the end node devices under the same intermediate node device, wherein the numbers between the two nodes in the four nodes of 000,037,041,019 represent the edges between the nodes Weights. As shown in Figure 6, the target subgraph is a minimum spanning tree construction graph of twelve nodes including 009, 027, 016, 033, 034, 044, 043, 023, 001, 024, 042, 018, wherein the six end node devices with the device identifier (009, 027, 016, 024, 042, 018) in the figure are under the same intermediate node device. Therefore, it can be seen from FIG. 6 that the graph construction method of the minimum spanning tree makes the nodes corresponding to the six end node devices tend to converge. Please refer to FIG. 7 . FIG. 7 is a full connection diagram composed of end node devices corresponding to a target time according to an embodiment of the present application. As shown in Figure 7, the three nodes with the device ID of 043,031,005 represent the end node devices under the same intermediate node device, wherein the numbers between the two nodes in the three nodes of 043,031,005 represent the edges between the nodes Weights. Since the number of end node devices is less than or equal to the preset threshold, 043,031,005 three end node devices form a fully connected graph.

Step S304: Integrate the cluster lists corresponding to the multiple target moments respectively to obtain the topology information of the passive network.

Specifically, the topology information acquisition device fuses the cluster lists corresponding to multiple target times to acquire topology information of the passive network, where the topology information includes at least one second cluster, the at least one second cluster Each second cluster in the cluster includes part or all of the end node devices in the passive network under the same intermediate node device. Combining the obtained multiple cluster lists in the preset time period to finally obtain the topology information of the passive network can avoid the problem of inaccurate restoration of the topology information when there is only one optical path fluctuation, and improve the accuracy of the topology information of the passive network. Wherein, after fusing the cluster lists corresponding to the multiple target moments to obtain the topology information of the passive network, the topology information can also be divided into at least one cluster, and each of the at least one cluster can be divided into at least one cluster. The cluster includes the device identifiers of some or all end node devices under an intermediate node device. For example, please refer to the following Table 2. Table 2 is a segmented terminal node device topology information table provided by an embodiment of the present application.

Table 2: Topology Information Table

Group No split cluster 1 {'009','027','016'} 2 {'034','004','021'} 3 {'002','042','001','018'} 4 {'011','008'} 5 {'041','037','019','000'} 6 {'033','029','044','022'} 7 {'040','100'} 8 {'030','038'} 9 {'031','043','023','005','024'} 10 {'039','013','012'} … …

As shown in Table 2, each group of clusters after division includes a device identifier of an end node device under an intermediate node device. The complete topology information is divided into one or more clusters, so that the user can intuitively know the topology information of the end node devices under the same intermediate node device in the passive network.

Optionally, respectively fuse the first cluster clusters corresponding to the same intermediate node device in the cluster cluster lists corresponding to the multiple target moments to obtain the corresponding second cluster cluster; Cluster clusters to obtain the topology information. That is, the topology information obtaining device can merge the first clusters under the same intermediate node device corresponding to different target moments into the second cluster, and the obtained merged cluster list is the topology information of the passive network , so the obtained cluster lists corresponding to multiple target moments in the preset time period are combined, and finally the topology information of the passive network is obtained, which can avoid the problem of inaccurate restoration of topology information when there is only one optical path fluctuation, and improve the passive network. Accuracy of topology information. Secondly, it can also realize automatic maintenance and update of topology information in asset management, assist in accurate fault demarcation, reduce repeated and invalid server uploads, and reduce operating labor costs.

其中，k为所述多个目标时刻中第k个目标时刻，T为所述当前时间点，

为所述第k个目标时刻在所述多个目标时刻中的权重核函数，g_k(i,j)为所述第k个目标时刻对应的目标子图中所述节点i和所述节点j之间的边权重；基于图的社群检测算法或embedding算法分割所述拓扑图，获得所述拓扑信息。其中，该权重核函数代表多个目标时刻中每一个目标时刻在部分或全部目标时刻中所占的权重大小。例如：可以设置

即，每个目标时刻的权重大小与当前时间点有关；另外还可以设置

为高斯核函数，以考虑拓扑序列的时间属性，即，与当前时刻时间越接近的目标时刻在部分或全部目标时刻的权重越大；也可以设置

即，每个目标时刻的权重大小相同，其中m为目标时刻的数量。需要说明的是，拓扑图所述节点i和所述节点j之间的边权重仅仅有同时包括节点i和节点j的一个或多个目标子图确定。例如：请参考附图8，图8是本申请实施例提供的一种拓扑图。基于上述表2所示的末端节点设备的信息，该示例的分割结果可视化的拓扑图，如图8所示，该拓扑图是将多个目标子图合并后获得的，将该拓扑图基于图的社群检测算法或embedding算法分割后，可以获得上述表2中的拓扑信息，从表2的图分割结果，结合各子图可分析得知，该拓扑图中组1为合并子图后形成的全连接子图，包括'009','027','016'三个末端节点设备；组2为原目标子图中存在的全连接子图，组5为孤立的子图，包括{'041','037','019','000'}四个末端节点设备，组10为合并(039，012)，(039，013)两个子图的结果等等。Optionally, based on the graph, target subgraphs corresponding to multiple target moments are merged to obtain a topology graph, wherein the calculation method of edge weights between nodes in the topology graph is:

Wherein, k is the k-th target moment in the multiple target moments, T is the current time point,

is the weight kernel function of the kth target moment in the multiple target moments, and g _k (i,j) is the node i and the node in the target subgraph corresponding to the kth target moment The edge weight between j; the graph-based community detection algorithm or the embedding algorithm divides the topology graph to obtain the topology information. The weight kernel function represents the weight of each of the multiple target moments in some or all of the target moments. For example: you can set

That is, the weight of each target moment is related to the current time point; in addition, you can set

is a Gaussian kernel function to take into account the time attribute of the topological sequence, that is, the target moment that is closer to the current moment has a greater weight in some or all target moments; it can also be set

That is, each target moment has the same weight, where m is the number of target moments. It should be noted that, the edge weight between the node i and the node j in the topology graph is only determined by one or more target subgraphs including both the node i and the node j. For example, please refer to FIG. 8 , which is a topology diagram provided by an embodiment of the present application. Based on the information of the end node devices shown in Table 2 above, the topological graph of the visualization of the segmentation result in this example is shown in Figure 8. The topological graph is obtained by merging multiple target subgraphs. The topological graph is based on the graph After the community detection algorithm or embedding algorithm is divided, the topology information in the above table 2 can be obtained. From the graph segmentation results in table 2, it can be analyzed in combination with each subgraph that group 1 in the topology graph is formed by merging the subgraphs. The fully connected subgraph of , including '009', '027', '016' three end node devices; group 2 is the fully connected subgraph existing in the original target subgraph, and group 5 is the isolated subgraph, including {'041','037','019','000'} four end node devices, group 10 is the result of merging (039, 012), (039, 013) two subgraphs and so on.

Please refer to FIG. 9 , which is a schematic flowchart of a fusion of multiple target subgraphs provided by an embodiment of the present application. As shown in FIG. 9 , within the preset time period from the preset time point to the current time point, at the moment when the end node device generates abnormal performance index information or alarm information, obtain the data of one or more end node devices at multiple target times. Device identification and feature data, and then perform cluster analysis according to the feature data of the one or more end node devices to obtain the cluster lists corresponding to the multiple road change times respectively, and construct the cluster list corresponding to each target time based on the The target sub-graph; finally, the target sub-graphs corresponding to the multiple optical path change times are fused and divided to obtain the topology information of the passive network. Please refer to FIG. 10 . FIG. 10 is a schematic diagram of a flow chart of merging multiple target subgraphs based on the above-mentioned FIG. 9 . , as shown in Figure 10, the topology information acquisition device synthesizes a plurality of target subgraphs into a topology map, which includes the topological connection relationship of the end node devices under the passive network, and can also generate the above-mentioned as shown in Table 2 after segmentation. the topology information table.

In the embodiment of the present application, the cluster list of each target time among the multiple target times is obtained, and the cluster lists corresponding to the multiple target times are fused to obtain the topology information of the passive network. Wherein, the target time is the time when there are one or more end node devices in the passive network when the change of performance index information exceeds the preset fluctuation range and/or the alarm information occurs; at this time, it can be obtained that the change of performance index information exceeds the preset value The fluctuation range and/or the characteristic data of one or more end node devices that have alarm information; according to the acquired characteristic data, the cluster list corresponding to the target moment can be obtained through cluster analysis; and in the same way, it can be obtained The clustering cluster lists corresponding to the multiple target moments respectively, so as to fuse the clustering cluster lists corresponding to the multiple target moments respectively, to obtain the topology information of the passive network. The feature data includes performance index information and/or the alarm information, and, at each target time among the multiple target times, one or more end nodes where the change in performance index information exceeds a preset fluctuation range and/or alarm information occurs Equipment may vary. Therefore, from one or more end node devices whose performance data generates fluctuations or alarm information, the end node devices under the same intermediate node device are clustered and analyzed. This realization method of obtaining topology information corresponding to the passive network, It can greatly reduce the workload of operation and maintenance personnel, and does not require relevant personnel to actively obtain a large amount of data information, but only needs to monitor the occurrence of characteristic data or the change of characteristic data exceeds the preset range. Secondly, the obtained multiple cluster lists within the preset time period are combined to finally obtain the topology information of the passive network, which can avoid the problem of inaccurate restoration of topology information when only one feature data appears or the feature data changes beyond the preset range. , to improve the accuracy of passive network topology information. By restoring the topology information of the passive network, the automatic maintenance and update of the topology information in the asset management can be realized, and the auxiliary fault can be accurately delimited.

The methods of the embodiments of the present application are described in detail above, and the related apparatuses of the embodiments of the present application are provided below.

Please refer to FIG. 11 . FIG. 11 is a schematic structural diagram of an apparatus for obtaining topology information provided by an embodiment of the present application. The apparatus for obtaining topology information 10 may include an obtaining unit 101 , a clustering unit 102 , and a fusion unit 103 , and may also include a subgraph unit 104, for determining the topological connection relationship of the intermediate node device. The detailed description of each unit is as follows.

The obtaining unit 101 is configured to obtain characteristic data of one or more end node devices in the passive network at a target time, where the target time is when the performance index information of the one or more end node devices changes by more than a preset fluctuation The range and/or the time when the alarm information occurs, the feature data includes the performance index information and/or the alarm information, and the one or more end node devices are one or more of the at least one end node device. indivual;

The clustering unit 102 is configured to perform cluster analysis on the one or more end node devices according to the characteristic data of the one or more end node devices, and obtain a cluster list corresponding to the target moment, the The cluster list includes at least one first cluster, wherein each first cluster in the at least one first cluster includes one or more end node devices under the same intermediate node device. the end node device;

The fusion unit 103 is configured to fuse the cluster lists corresponding to multiple target moments respectively, and obtain topology information of the passive network, where the topology information includes at least one second cluster, the at least one second cluster Each second cluster in the cluster includes part or all of the end node devices in the passive network under the same intermediate node device.

In a possible implementation manner, the clustering unit 102 is specifically configured to: according to the feature data of the one or more end node devices, calculate the one or more end node devices at the target moment. The similarity between the two is obtained, and a similarity matrix is obtained; based on the similarity matrix, cluster analysis is performed on the one or more end node devices to obtain a cluster list corresponding to the target time.

In a possible implementation manner, the passive network is an optical path distribution network, the end node device is an optical network unit, and the intermediate node device is an optical splitter.

其中，k为所述多个目标时刻中第k个目标时刻，T为所述当前时间点，

为所述第k个目标时刻在所述多个目标时刻中的权重核函数，g_k(i,j)为所述第k个目标时刻对应的目标子图中所述节点i和所述节点j之间的边权重；基于图的社群检测算法或embedding算法分割所述拓扑图，获得所述拓扑信息。In a possible implementation manner, the apparatus further includes: a subgraph unit 104, configured to construct a target subgraph corresponding to the target moment according to the cluster list corresponding to the target moment, the target subgraph It includes a fully connected graph or a minimum spanning tree construction graph, wherein the edge weight between node i and node j in the target subgraph is the end node device in the one or more end node devices corresponding to the target moment. The similarity measurement function and/or inherent attribute between i and the end node device j; the fusion unit 103 is specifically configured to: fuse target subgraphs corresponding to a plurality of target moments respectively based on the graph to obtain a topology graph, wherein all the The calculation method of edge weights between nodes in the above topology graph is:

Wherein, k is the k-th target moment in the multiple target moments, T is the current time point,

is the weight kernel function of the kth target moment in the multiple target moments, and g _k (i,j) is the node i and the node in the target subgraph corresponding to the kth target moment The edge weight between j; the graph-based community detection algorithm or the embedding algorithm divides the topology graph to obtain the topology information.

In a possible implementation manner, the fusion unit 103 is specifically configured to: respectively fuse the first clusters corresponding to the same intermediate node device in the cluster list corresponding to the multiple target moments, and obtain corresponding the second cluster; obtain the topology information according to a plurality of the second clusters.

It should be noted that, for the functions of each functional unit in the apparatus 10 for determining ODN topology information of a passive network described in this embodiment of the present application, reference may be made to the relevant descriptions of steps S301 to S304 in the method embodiment described in FIG. 3 above. , and will not be repeated here.

As shown in FIG. 12 , FIG. 12 is a schematic structural diagram of another apparatus for obtaining topology information provided by an embodiment of the present application. The apparatus 20 includes at least one processor 201 , at least one memory 202 , and at least one communication interface 203 . In addition, the device may also include general components such as an antenna, which will not be described in detail here.

The processor 201 may be a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of programs in the above solutions.

The communication interface 203 is used to communicate with other devices or communication networks, such as Ethernet, Radio Access Network (RAN), Core Network, Wireless Local Area Networks (Wireless Local Area Networks, WLAN) and the like.

The memory 202 may be read-only memory (ROM) or other type of static storage device that can store static information and instructions, random access memory (RAM), or other type of static storage device that can store information and instructions The dynamic storage device can also be an Electrically Erasable Programmable Read-Only Memory (EEPROM), a Compact Disc Read-Only Memory (CD-ROM) or other optical disk storage, optical disk storage ( including compact discs, laser discs, compact discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or capable of carrying or storing desired program code in the form of instructions or data structures and capable of being stored by a computer any other medium taken, but not limited to this. The memory can exist independently and be connected to the processor through a bus. The memory can also be integrated with the processor.

Wherein, the memory 202 is used for storing the application code for executing the above solution, and the execution is controlled by the processor 201 . The processor 201 is configured to execute the application code stored in the memory 202 .

The code stored in the memory 202 can execute the method for obtaining passive network topology information provided in FIG. 3 above, such as: obtaining characteristic data of one or more end node devices in the passive network at a target time, where the target time is all The characteristic data includes the performance index information and/or the alarm information, the one or more The end node devices are one or more of the at least one end node device; according to the characteristic data of the one or more end node devices, perform cluster analysis on the one or more end node devices, and obtain the The cluster list corresponding to the target moment, the cluster list includes at least one first cluster, wherein each first cluster in the at least one first cluster includes the one or more The end node devices under the same intermediate node device among the end node devices; fuse the cluster lists corresponding to multiple target times respectively, and obtain the topology information of the passive network, where the topology information includes at least one second cluster Clusters, each of the at least one second cluster includes part or all of the end node devices in the passive network under the same intermediate node device.

It should be noted that, for the functions of each functional unit in the apparatus 20 for acquiring passive network topology information described in the embodiment of the present application, reference may be made to the relevant descriptions of steps S301 to S304 in the method embodiment described in FIG. 3 above. It will not be repeated here.

In the above-mentioned embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

It should be noted that, for the sake of simple description, the foregoing method embodiments are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the described action sequence. Because in accordance with the present application, certain steps may be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

In the several embodiments provided in this application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the device embodiments described above are only illustrative. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated. to another system, or some features can be ignored, or not implemented. On the other hand, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical or other forms.

The units described above as separate components may or may not be physically separated, and components shown as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware, or may be implemented in the form of software functional units.

If the above-mentioned integrated units are implemented in the form of software functional units and sold or used as independent products, they may be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the present application can be embodied in the form of software products in essence, or the parts that make contributions to the prior art, or all or part of the technical solutions, which are stored in a storage medium. , including several instructions to enable a computer device (which may be a personal computer, a server, or a network device, etc., specifically a processor in the computer device) to execute all or part of the steps of the above methods in various embodiments of the present application. Wherein, the aforementioned storage medium may include: U disk, mobile hard disk, magnetic disk, optical disk, read-only memory (Read-Only Memory, abbreviation: ROM) or random access memory (Random Access Memory, abbreviation: RAM) and so on. A medium that can store program code.

As mentioned above, the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand: The technical solutions described in the embodiments are modified, or some technical features thereof are equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions in the embodiments of the present application.

Claims (12)

1. A method for acquiring passive network topology information, wherein the passive network includes multiple node devices, and the multiple node devices include at least one intermediate node device and at least one end node device; the method include: Acquire characteristic data of one or more end node devices in the passive network at a target time, where the target time is when the performance index information of the one or more end node devices changes beyond a preset fluctuation range and/or an alarm occurs Information time, the feature data includes the performance indicator information and/or the alarm information, and the one or more end node devices are one or more of the at least one end node device; According to the feature data of the one or more end node devices, perform cluster analysis on the one or more end node devices to obtain a cluster list corresponding to the target time, where the cluster list includes at least one A first cluster, wherein each first cluster in the at least one first cluster includes an end node device under the same intermediate node device among the one or more end node devices; Fusing the cluster lists corresponding to multiple target moments respectively, to obtain topology information of the passive network, the topology information includes at least one second cluster, and each second cluster in the at least one second cluster is The cluster includes some or all end node devices under the same intermediate node device in the passive network. 2 . The method according to claim 1 , wherein, according to the characteristic data of the one or more end node devices, cluster analysis is performed on the one or more end node devices to obtain the target time. 3 . A list of corresponding clusters, including: According to the feature data of the one or more end node devices, calculate the similarity between the one or more end node devices at the target moment to obtain a similarity matrix; Based on the similarity matrix, cluster analysis is performed on the one or more end node devices to obtain a cluster list corresponding to the target moment. 3 . The method according to claim 1 , wherein the passive network is an optical path distribution network, the end node device is an optical network unit, and the intermediate node device is an optical splitter. 4 . 4. The method according to any one of claims 1-3, wherein the method further comprises: According to the cluster list corresponding to the target moment, construct a target subgraph corresponding to the target moment, the target subgraph includes a fully connected graph or a minimum spanning tree construction graph, wherein the node i in the target subgraph and the edge weight between the node j is the similarity measure function and/or the inherent attribute between the end node device i and the end node device j in the one or more end node devices corresponding to the target moment; The clustering cluster list corresponding to each of the multiple target moments is merged, and the topology information of the passive network is obtained, including: Based on the graph fusion of target subgraphs corresponding to multiple target moments respectively, a topology graph is obtained, wherein the calculation method of edge weights between nodes in the topology graph is as follows:

Wherein, k is the k-th target moment in the multiple target moments, T is the current time point,

is the weight kernel function of the kth target moment in the multiple target moments, and g _k (i,j) is the node i and the node in the target subgraph corresponding to the kth target moment edge weights between j; A graph-based community detection algorithm or an embedding algorithm divides the topology graph to obtain the topology information. 5. The method according to any one of claims 1-3, wherein the merging cluster lists corresponding to multiple target moments respectively to obtain topology information of the passive network, comprising: Respectively fuse the first clusters corresponding to the same intermediate node device in the cluster list corresponding to the multiple target moments to obtain the corresponding second clusters; Obtain the topology information according to a plurality of the second clusters. 6. An apparatus for acquiring passive network topology information, wherein the passive network comprises multiple node devices, and the multiple node devices include at least one intermediate node device and at least one end node device; the device include: an acquisition unit, configured to acquire characteristic data of one or more end node devices in the passive network at a target time, where the target time is when the performance index information of the one or more end node devices changes beyond a preset fluctuation range and/or the moment when alarm information occurs, the feature data includes the performance indicator information and/or the alarm information, and the one or more end node devices are one or more of the at least one end node device ; The clustering unit is configured to perform cluster analysis on the one or more end node devices according to the characteristic data of the one or more end node devices, and obtain a cluster list corresponding to the target time, and the cluster The cluster list includes at least one first cluster, wherein each first cluster in the at least one first cluster includes the one or more end node devices under the same intermediate node device. end node equipment; A fusion unit, configured to fuse the cluster lists corresponding to multiple target moments respectively, and obtain topology information of the passive network, where the topology information includes at least one second cluster, the at least one second cluster Each of the second clusters in the passive network includes some or all of the end node devices under the same intermediate node device in the passive network. 7. The device according to claim 6, wherein the clustering unit is specifically used for: According to the feature data of the one or more end node devices, calculate the similarity between the one or more end node devices at the target moment to obtain a similarity matrix; Based on the similarity matrix, cluster analysis is performed on the one or more end node devices to obtain a cluster list corresponding to the target moment. 8 . The apparatus according to claim 6 , wherein the passive network is an optical path distribution network, the end node device is an optical network unit, and the intermediate node device is an optical splitter. 9 . 9. The device according to any one of claims 6-8, wherein the device further comprises: A subgraph unit, configured to construct a target subgraph corresponding to the target moment according to the cluster list corresponding to the target moment, where the target subgraph includes a fully connected graph or a minimum spanning tree construction graph, wherein the target The edge weight between node i and node j in the subgraph is the similarity measure function and/or between the end node device i and the end node device j in the one or more end node devices corresponding to the target moment inherent properties; The fusion unit is specifically used for: Based on the graph fusion of target subgraphs corresponding to multiple target moments respectively, a topology graph is obtained, wherein the calculation method of edge weights between nodes in the topology graph is as follows:

Wherein, k is the k-th target moment in the multiple target moments, T is the current time point,

is the weight kernel function of the kth target moment in the multiple target moments, and g _k (i,j) is the node i and the node in the target subgraph corresponding to the kth target moment edge weights between j; A graph-based community detection algorithm or an embedding algorithm divides the topology graph to obtain the topology information. 10. The device according to any one of claims 6-8, wherein the fusion unit is specifically used for: Respectively fuse the first clusters corresponding to the same intermediate node device in the cluster list corresponding to the multiple target moments to obtain the corresponding second clusters; Obtain the topology information according to a plurality of the second clusters. 11. A chip system, characterized in that, the chip system comprises at least one processor, a memory and an interface circuit, wherein the memory, the interface circuit and the at least one processor are interconnected by wires, and the at least one memory Instructions are stored in the ; when the instructions are executed by the processor, the method of any one of claims 1-5 is implemented. 12. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, implements the method according to any one of the preceding claims 1-5.

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