CN113365164A - Active identification two-stage light splitting method and device based on big data analysis - Google Patents
Active identification two-stage light splitting method and device based on big data analysis Download PDFInfo
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- CN113365164A CN113365164A CN202110576767.9A CN202110576767A CN113365164A CN 113365164 A CN113365164 A CN 113365164A CN 202110576767 A CN202110576767 A CN 202110576767A CN 113365164 A CN113365164 A CN 113365164A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04Q—SELECTING
- H04Q11/00—Selecting arrangements for multiplex systems
- H04Q11/0001—Selecting arrangements for multiplex systems using optical switching
- H04Q11/0062—Network aspects
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04Q—SELECTING
- H04Q11/00—Selecting arrangements for multiplex systems
- H04Q11/0001—Selecting arrangements for multiplex systems using optical switching
- H04Q11/0062—Network aspects
- H04Q2011/0079—Operation or maintenance aspects
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04Q—SELECTING
- H04Q11/00—Selecting arrangements for multiplex systems
- H04Q11/0001—Selecting arrangements for multiplex systems using optical switching
- H04Q11/0062—Network aspects
- H04Q2011/0079—Operation or maintenance aspects
- H04Q2011/0081—Fault tolerance; Redundancy; Recovery; Reconfigurability
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Abstract
The invention discloses a method and a device for actively identifying secondary light splitting based on big data analysis, wherein the method comprises the following steps: analyzing online and offline information of a user on AAA in real time through a big data platform by utilizing the relation between the user and a secondary optical splitter and OLT equipment and a PON port to which the user belongs, and judging secondary optical splitting disconnection if more than three ONUs are offline in the same time period and are not recovered under the same secondary optical splitter and no PON port light receiving alarm exists at the PON port; if the secondary light splitting is judged, a secondary light splitting alarm is generated and is sent to the electronic operation and maintenance system, and the electronic operation and maintenance system sends a list to maintenance personnel for inspection. The method and the device realize active discovery of the secondary light splitting interruption by utilizing big data.
Description
Technical Field
The invention relates to the field of passive ODN networks, in particular to a method and a device for actively identifying secondary light splitting and breaking based on big data analysis.
Background
For a passive ODN network (the ODN network is an FTTH optical cable network based on PON devices), at present, a primary optical splitter can be judged by a PON port optical alarm, but for a secondary optical splitter or a secondary optical splitter failure, a PON network manager cannot actively identify the failure, and needs to wait for a user failure report, and a maintenance person can find the failure by on-site inspection, so that the failure cannot be found by a client before the client finds the failure and actively provides notification service, and a failure list cannot be timely dispatched, which causes long failure repair time and affects client perception.
Disclosure of Invention
In order to solve the problem that the PON network manager can not actively identify the second-level optical splitting, the invention provides a method and a device for actively identifying the second-level optical splitting based on big data analysis, which realize the active discovery of the second-level optical splitting by using big data.
In order to achieve the purpose, the invention adopts the following technical scheme:
in an embodiment of the present invention, a method for actively identifying a secondary optical splitting based on big data analysis is provided, where the method includes:
analyzing online and offline information of a user on AAA in real time through a big data platform by utilizing the relation between the user and a secondary optical splitter and OLT equipment and a PON port to which the user belongs, and judging secondary optical splitting disconnection if more than three ONUs are offline in the same time period and are not recovered under the same secondary optical splitter and no PON port light receiving alarm exists at the PON port;
if the secondary light splitting is judged, a secondary light splitting alarm is generated and is sent to the electronic operation and maintenance system, and the electronic operation and maintenance system sends a list to maintenance personnel for inspection.
Further, the method further comprises:
and the big data platform monitors user online information on the AAA in real time, and if at least one ONU is online under the secondary optical splitter, the secondary optical splitter is considered to be in alarm recovery.
Further, the second-stage light splitting judgment comprises the following steps:
and the OLT equipment acquires the online states of all the ONUs under the current secondary optical splitter, if the ONUs are not online and the last offline time is the latest three minutes, the offline reason codes of the ONUs are identified, and if the ONUs are all broken fibers, the secondary optical splitter is judged to be broken.
Furthermore, the information of the "second-level optical splitting interruption" alarm is associated with and provides a second-level optical splitter name, user account information, OLT equipment information and PON port information to which the OLT belongs.
In an embodiment of the present invention, an active identification two-stage optical splitting device based on big data analysis is further provided, and the device includes:
the big data platform is used for analyzing the online and offline information of the user on the AAA in real time, and performing secondary light splitting disconnection judgment if more than three ONUs are identified to be offline and not recovered in the same time period under the same secondary light splitter and no PON port receiving no light alarm exists at the PON port; if the secondary light splitting is judged, generating a secondary light splitting alarm and sending the alarm to the electronic operation and maintenance system;
the electronic operation and maintenance system is used for sending the secondary light splitting and breaking alarm to maintenance personnel for inspection;
the PON network management is used for receiving and sending the optical alarm through the PON port;
and the OLT equipment is used for automatically acquiring the online states, offline reasons, the last offline time and the last online time of all the ONUs under the current second-level optical splitter.
Further, the apparatus further comprises:
and the big data platform is used for monitoring user online information on the AAA in real time, and if at least one ONU is online under the secondary optical splitter, the secondary optical splitter is considered to be in alarm recovery.
Further, the second-stage light splitting judgment comprises the following steps:
and the OLT equipment acquires the online states of all the ONUs under the current secondary optical splitter, if the ONUs are not online and the last offline time is the latest three minutes, the offline reason codes of the ONUs are identified, and if the ONUs are all broken fibers, the secondary optical splitter is judged to be broken.
Furthermore, the information of the "second-level optical splitting interruption" alarm is associated with and provides a second-level optical splitter name, user account information, OLT equipment information and PON port information to which the OLT belongs.
In an embodiment of the present invention, a computer device is further provided, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, the processor implements the aforementioned active identification two-stage optical splitting method based on big data analysis.
In an embodiment of the present invention, a computer-readable storage medium is further provided, where a computer program for executing the active identification two-stage optical splitting method based on big data analysis is stored in the computer-readable storage medium.
Has the advantages that:
according to the invention, a large data platform constructed by PON (passive optical network) network management is utilized, in the application of analyzing the customer perception of network quality, a part of constructed calculation analysis modules (for example, user online and offline information on AAA (authentication, authorization and accounting)) are reused, secondary light splitting is actively discovered at low cost, a fault is discovered before a customer discovers a fault, a fault list is dispatched in time (active notification of user fault service can be provided for a customer service system subsequently), the fault duration is reduced, and the customer perception improvement is assisted.
Drawings
FIG. 1 is a block diagram of an active identification two-level optical splitting method based on big data analysis according to an embodiment of the present invention;
FIG. 2 is a flow chart of an active identification two-stage optical splitting method based on big data analysis according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of an active identification two-stage optical splitting device based on big data analysis according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
The principles and spirit of the present invention will be described below with reference to several exemplary embodiments, which should be understood to be presented only to enable those skilled in the art to better understand and implement the present invention, and not to limit the scope of the present invention in any way. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
As will be appreciated by one skilled in the art, embodiments of the present invention may be embodied as a system, apparatus, device, method, or computer program product. Accordingly, the present disclosure may be embodied in the form of: entirely hardware, entirely software (including firmware, resident software, micro-code, etc.), or a combination of hardware and software.
According to the embodiment of the invention, a method and a device for actively identifying the secondary optical splitting break based on big data analysis are provided, the secondary optical splitting cable break, namely the secondary optical splitting break, can not directly generate alarm output on OLT equipment, the AAA (AAA is a system for accessing a user to network control, namely authenticating, authorizing and accounting the user) of a user under a secondary optical splitter is analyzed in real time by using a big data platform, the offline code of an ONU is analyzed at the same time, and whether the occurrence of a secondary optical splitting cable break event occurs is deduced after the primary optical splitting break is eliminated by combining with the judgment of whether a PON port receives no light alarm.
The principles and spirit of the present invention are explained in detail below with reference to several representative embodiments of the invention.
The invention mainly utilizes big data to realize active discovery of secondary light splitting interruption, and the main principle is as follows: on the premise that no PON port receives no alarm, if all ONUs under a certain or some secondary optical splitters are off-line in batch and the reason for off-line is optical fiber breakage (keyword los or los), the secondary optical splitter is judged to be broken. As shown in fig. 1 and 2, the method includes:
by utilizing the relationship between a user and a secondary optical splitter and the OLT equipment and the PON port which the user belongs to, the online and offline information of the user on the AAA is analyzed in real time through a big data platform, if the fact that more than three ONUs are offline in the same time period and are not recovered under the same secondary optical splitter is identified, and when the PON port does not have 'no light received by the PON port' alarm, secondary optical splitting disconnection judgment is carried out:
the method comprises the steps that OLT equipment collects the online states of all ONUs under a current secondary optical splitter, if the ONUs are not online and the last offline time is the last three minutes, offline reason codes of the ONUs are identified, if the ONUs are all broken fibers (keywords los or los), the ONUs are judged to be the secondary optical splitter, a secondary optical splitter alarm is generated, the names of the secondary optical splitters, user account information, OLT equipment information and PON port information of the ONUs are provided in alarm information in an associated mode, the OLT equipment information and the PON port information are dispatched to an electronic operation and maintenance system, and the electronic operation and maintenance system dispatches orders to maintenance personnel for inspection;
meanwhile, the big data platform monitors user online information on AAA in real time, and if at least one ONU is online under the secondary optical splitter, the secondary optical splitter is considered to be in alarm recovery.
It should be noted that although the operations of the method of the present invention have been described in the above embodiments and the accompanying drawings in a particular order, this does not require or imply that these operations must be performed in this particular order, or that all of the operations shown must be performed, to achieve the desired results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions.
For a clearer explanation of the above active identification two-stage optical splitting method based on big data analysis, a specific embodiment is described below, however, it should be noted that the embodiment is only for better illustration of the present invention and should not be construed as an undue limitation of the present invention.
The relationship between the user, the secondary optical splitter, and the affiliated OLT device and PON port is illustrated in table 1 below:
TABLE 1
And (3) generating an alarm format:
SUMMARY-ITE-DevMono SOBDOPtical LossCheckAlarm, < oltip > 10.110.193.6 < oltponport > NA-0-4-3 < sobdid > 000107410000000006518100 < sobdname > Nobdname > Nonbc-Narduo P208-5-1 pole/GF 021/GFH014 < AffectUserNum > itv:5, pppo: 5 < AffectOnIdNum > 1,3,16,23,32 < remak > sob-Optic loss < AAprocTime > 2021-01-2207:25:54
And (3) equipment verification, namely offline at the same time point, and Losi:
AAA offline record: (Start is the on-line, stop is the off-line)
start；；07780277849；；1599802652；；222.217.181.75；；；；05400830ppp04e074e19a82969123dc；；100.84.48.140；；171.109.242.179；；8192；；12287；；；；；；；；0
start；；07713110855；；1599802652；；222.217.167.67；；；；05372178ppp258cc4b8b5f2ba4653ab；；100.64.102.118；；116.8.39.229；；61440；；65535；；；；；；；；0
start；；07730783355；；1599802652；；222.217.174.8；；；；05372165ppp15400cc6cc63bc72a263；；171.107.75.39；；；；；；；；；；；；；；0
start；；iptv7721733955@iptv.gx；；1599802652；；222.217.170.35；；；；GX-LZ-R0221027500004532831a191274；；10.253.74.159；；；；；；；；；；；；；；0
start；；07710168686；；1599802652；；222.217.167.220；；；；05372228ppp1ab7a091c8446e79d514；；100.64.55.45；；171.110.187.229；；53248；；57343；；；；；；；；0
start；；07711922927；；1599802652；；222.217.167.84；；；；05372220ppp18e75ca1765b1acbc937；；100.64.22.134；；113.17.66.219；；36864；；40959；；；；；；；；0
start；；07710480958；；1599802653；；222.217.167.65；；；；05372232ppp082e0023c984141661bf；；100.106.82.218；；180.139.200.159；；16384；；20479；；；；；；；；0
start；；07720117615；；1599802653；；222.217.170.38；；；；05372244ppp252dc83a356b7960ca43；；100.72.128.14；；171.107.47.29；；53248；；57343；；；；；；；；0
start；；07760067689；；1599802653；；222.217.179.42；；；；05372259ppp0da3a8154d1cb2cb8711；；100.90.28.41；；180.137.141.119；；45056；；49151；；；；；；；；0
stop；；07700251985；；1599800663；；222.217.185.18；；；；GX-FCG-0924128370023129b1a7119128；；116.11.210.229；；；；；；；；；；；；；；0
stop；；07751835960；；1599800664；；222.217.176.79；；；；00462809ppp0bfd18aaca7b6e7ccd76；；100.95.60.71；；116.11.101.199；；45056；；49151；；；；；；；；0
stop；；07750222012；；1599800664；；222.217.176.71；；；；05021647ppp11358069332b8286011f；；100.78.46.23；；113.16.28.239；；40960；；45055；；；；；；；；0
stop；；07751443238；；1599800664；；222.217.176.73；；；；05022528ppp10cd80693322bf6a9dbe；；100.87.25.52；；116.11.97.79；；45056；；49151；；；；；；；；0
stop；；iptv7705948168@iptv.gx；；1599800664；；222.217.185.18；；；；GX-FCG-0223223040004551a987260959；；10.242.160.69；；；；；；；；；；；；；；0
Based on the same invention concept, the invention also provides an active identification two-stage light splitting device based on big data analysis. The implementation of the device can be referred to the implementation of the method, and repeated details are not repeated. The term "module," as used below, may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 3 is a schematic structural diagram of an active identification two-stage optical splitting device based on big data analysis according to an embodiment of the present invention. As shown in fig. 3, the apparatus includes:
the big data platform 101 is used for analyzing the online and offline information of a user on the AAA in real time, and if the fact that more than three ONUs are offline in the same time period and are not recovered under the same secondary optical splitter is identified, and a PON port does not have a PON port receiving no light alarm, secondary optical splitting disconnection judgment is carried out: the OLT equipment collects the online states of all ONUs under the current secondary optical splitter, if the ONUs are not online and the last offline time is the latest three minutes, the offline reason codes of the ONUs are identified, and if the ONUs are all broken fibers, the secondary optical splitter is judged to be broken; if the secondary light splitting is judged to be the secondary light splitting, generating a secondary light splitting alarm, providing the name of the secondary light splitter, the user account information, the OLT equipment information and the PON port information in the alarm information in an associated manner, and dispatching the information to the electronic operation and maintenance system; meanwhile, monitoring user online information on AAA in real time, and if at least one ONU is online under the secondary optical splitter, determining that the secondary optical splitter is interrupted and the alarm is recovered;
the electronic operation and maintenance system 102 is used for sending the secondary light splitting and breaking alarm to maintenance personnel for inspection;
a PON network manager 103, configured to receive and transmit an optical alarm through a PON port;
the OLT device 104 is configured to automatically acquire the online states, offline reasons, the last offline time, and the last online time of all ONUs in the current second-stage optical splitter.
It should be noted that although several modules of the active recognition two-stage light splitting device based on big data analysis are mentioned in the above detailed description, such division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more of the modules described above may be embodied in one module according to embodiments of the invention. Conversely, the features and functions of one module described above may be further divided into embodiments by a plurality of modules.
Based on the aforementioned inventive concept, as shown in fig. 4, the present invention further provides a computer device 200, which includes a memory 210, a processor 220, and a computer program 230 stored on the memory 210 and executable on the processor 220, wherein the processor 220 implements the aforementioned active identification two-stage optical splitting method based on big data analysis when executing the computer program 230.
Based on the above inventive concept, the present invention further provides a computer-readable storage medium storing a computer program for executing the above active identification two-stage optical splitting method based on big data analysis.
The method and the device for actively identifying the secondary light splitting failure based on the big data analysis provided by the invention utilize a big data platform constructed by the network management of the access network, and reuse part of constructed calculation analysis modules (for example, AAA (authentication, authorization and accounting) online and offline information) in the application of customer perception of network quality analysis, so that the secondary light splitting failure is actively discovered at low cost, the failure is discovered before the customer, the failure list is dispatched in time (the follow-up customer service system can provide active notification user failure service), the failure time is shortened, and the customer perception is promoted.
While the spirit and principles of the invention have been described with reference to several particular embodiments, it is to be understood that the invention is not limited to the disclosed embodiments, nor is the division of aspects, which is for convenience only as the features in such aspects may not be combined to benefit. The invention is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
The limitation of the protection scope of the present invention is understood by those skilled in the art, and various modifications or changes which can be made by those skilled in the art without inventive efforts based on the technical solution of the present invention are still within the protection scope of the present invention.
Claims (10)
1. An active identification two-stage light splitting method based on big data analysis is characterized by comprising the following steps:
analyzing online and offline information of a user on AAA in real time through a big data platform by utilizing the relation between the user and a secondary optical splitter and OLT equipment and a PON port to which the user belongs, and judging secondary optical splitting disconnection if more than three ONUs are offline in the same time period and are not recovered under the same secondary optical splitter and no PON port light receiving alarm exists at the PON port;
if the secondary light splitting is judged, a secondary light splitting alarm is generated and is sent to the electronic operation and maintenance system, and the electronic operation and maintenance system sends a list to maintenance personnel for inspection.
2. The active identification two-stage optical splitting method based on big data analysis according to claim 1, characterized in that the method further comprises:
and the big data platform monitors user online information on the AAA in real time, and if at least one ONU is online under the secondary optical splitter, the secondary optical splitter is considered to be in alarm recovery.
3. The active identification two-stage optical splitting method based on big data analysis according to claim 1, wherein the two-stage optical splitting judgment comprises:
and the OLT equipment acquires the online states of all the ONUs under the current secondary optical splitter, if the ONUs are not online and the last offline time is the latest three minutes, the offline reason codes of the ONUs are identified, and if the ONUs are all broken fibers, the secondary optical splitter is judged to be broken.
4. The active identification two-stage optical splitting method based on big data analysis according to claim 1, wherein the information of the "two-stage optical splitting" alarm is associated with a name of a two-stage optical splitter, user account information, OLT device information, and PON port information to which the two-stage optical splitter belongs.
5. An active identification two-stage light splitting device based on big data analysis, which is characterized by comprising:
the big data platform is used for analyzing the online and offline information of the user on the AAA in real time, and performing secondary light splitting disconnection judgment if more than three ONUs are identified to be offline and not recovered in the same time period under the same secondary light splitter and no PON port receiving no light alarm exists at the PON port; if the secondary light splitting is judged, generating a secondary light splitting alarm and sending the alarm to the electronic operation and maintenance system;
the electronic operation and maintenance system is used for sending the secondary light splitting and breaking alarm to maintenance personnel for inspection;
the PON network management is used for receiving and sending the optical alarm through the PON port;
and the OLT equipment is used for automatically acquiring the online states, offline reasons, the last offline time and the last online time of all the ONUs under the current second-level optical splitter.
6. The active identification two-stage optical splitting device based on big data analysis according to claim 5, characterized in that the device further comprises:
and the big data platform is used for monitoring user online information on the AAA in real time, and if at least one ONU is online under the secondary optical splitter, the secondary optical splitter is considered to be in alarm recovery.
7. The active identification two-stage optical splitting device based on big data analysis according to claim 5, wherein the two-stage optical splitting judgment comprises:
and the OLT equipment acquires the online states of all the ONUs under the current secondary optical splitter, if the ONUs are not online and the last offline time is the latest three minutes, the offline reason codes of the ONUs are identified, and if the ONUs are all broken fibers, the secondary optical splitter is judged to be broken.
8. The active identification two-stage optical splitting device based on big data analysis according to claim 5, wherein the information of the "two-stage optical splitting" alarm is associated with a name of a two-stage optical splitter, user account information, OLT equipment information, and information of a PON port to which the two-stage optical splitting device belongs.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1-4 when executing the computer program.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1-4.
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| CN112188326A (en) * | 2020-09-09 | 2021-01-05 | 中盈优创资讯科技有限公司 | Method and device for checking and remedying rogue ONU |
| CN112491598A (en) * | 2020-11-13 | 2021-03-12 | 中盈优创资讯科技有限公司 | OLT power-down off-line early warning analysis method and device |
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| CN116208873A (en) * | 2023-02-28 | 2023-06-02 | 江苏省广电有线信息网络股份有限公司无锡分公司 | ONU operation and maintenance management method, device and system |
| CN116208873B (en) * | 2023-02-28 | 2024-03-22 | 江苏省广电有线信息网络股份有限公司无锡分公司 | ONU operation and maintenance management method, device and system |
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