CN117479051A

CN117479051A - Quality difference identification method, quality difference identification device, optical line terminal, optical line system and storage medium

Info

Publication number: CN117479051A
Application number: CN202210871666.9A
Authority: CN
Inventors: 李浩琳; 张德智; 翁颐; 蒋铭
Original assignee: China Telecom Corp Ltd
Current assignee: China Telecom Corp Ltd
Priority date: 2022-07-22
Filing date: 2022-07-22
Publication date: 2024-01-30

Abstract

The application provides a quality difference identification method, a quality difference identification device, an optical line terminal, an optical line system and a storage medium. The quality difference identification method comprises the following steps: collecting service flow messages flowing through an optical line terminal; carrying out deep packet detection on the service flow message on the optical line terminal to obtain suspicious bad quality service; identifying a detection object corresponding to the suspicious quality error service; and carrying out quality difference identification of fine period granularity on the detection object to obtain a quality difference identification result. The quality difference recognition method and device can improve accuracy of quality difference recognition to a certain extent.

Description

Quality difference identification method, quality difference identification device, optical line terminal, optical line system and storage medium

Technical Field

The present disclosure relates to the field of optical communications technologies, and in particular, to a quality difference identifying method, a quality difference identifying device, an optical line terminal, a system, and a storage medium.

Background

As optical communication technologies develop, passive optical networks (Passive Optical Network, PON) have been deployed on a large scale. In order to enhance the user experience, quality difference recognition needs to be performed on the service to determine the service affecting the user experience. Currently, quality difference identification is mainly performed by a Core Router (CR) deployed at the metropolitan area network exit and near a broadband remote access server (Broadband Remote Access Server, BRAS) at the convergence layer. The method has the advantages that the homologous and homologous problems are missing to report the message information, the hierarchy position is high, and the accuracy of quality difference identification is poor.

Disclosure of Invention

An object of the present application is to provide a quality difference identification method, a quality difference identification device, an optical line terminal, a quality difference identification system and a storage medium, which at least improve accuracy of quality difference identification to a certain extent.

According to an aspect of embodiments of the present application, there is provided a quality difference identifying method, including:

collecting service flow messages flowing through an optical line terminal;

carrying out deep packet detection on the service flow message on the optical line terminal to obtain suspicious bad quality service;

identifying a detection object corresponding to the suspicious quality error service;

and carrying out quality difference identification of fine period granularity on the detection object to obtain a quality difference identification result.

According to an aspect of the embodiments of the present application, there is provided a quality difference identifying apparatus, including:

the acquisition module is used for acquiring service flow messages flowing through the optical line terminal;

the deep packet detection module is used for carrying out deep packet detection on the service flow message on the optical line terminal to obtain suspicious bad quality service;

the first identification module is used for identifying a detection object corresponding to the suspicious quality difference service;

and the second recognition module is used for carrying out quality difference recognition of fine cycle granularity on the detection object to obtain a quality difference recognition result.

In some embodiments of the present application, based on the above technical solutions, the quality difference identifying device is configured to:

extracting the communication port attribute and the service attribute of the service flow message on the optical line terminal based on a preset deep packet inspection rule;

determining a communication index mapped by the communication port attribute based on a mapping function corresponding to the service attribute; the communication index is an index for indicating communication quality, and the mapping function is a function for describing a mapping relationship between the communication port attribute and the communication index;

and if the communication index mapped by the communication port attribute is detected to be in the range of the reference suspicious quality difference service index, determining the suspicious quality difference service according to the service attribute.

receiving an updated suspicious quality difference service index range sent by rule updating; the updated suspicious quality difference business index range is obtained based on the obstacle removal result of the quality difference identification result; the obstacle removing result is data which is used for indicating the accuracy of the quality difference recognition result and is obtained by performing fault removal on the communication link based on the quality difference recognition result;

And updating the reference suspicious quality difference service index range according to the updated suspicious quality difference service index range.

periodically collecting network quality parameters corresponding to the detection objects according to the fine period granularity;

and sending the network quality parameters corresponding to the detection objects to a quality difference identification party so that the quality difference identification party can identify the network quality parameters corresponding to the detection objects to obtain the quality difference identification result.

and identifying at least one of a communication port, a virtual local area network and a service queue where the suspicious bad service is located as a detection object corresponding to the suspicious bad service.

receiving the service category sent by the quality difference identification party;

and collecting service flow messages corresponding to the service categories according to the service categories.

According to an aspect of the embodiments of the present application, there is provided an optical line terminal, including:

one or more of the processors;

and a storage device for storing one or more programs, which when executed by the one or more processors, cause the optical line terminal to implement the method described above.

According to an aspect of embodiments of the present application, there is provided a quality difference recognition system, including:

the optical line terminal is used for requesting the quality difference identification party to carry out quality difference identification of fine cycle granularity on the detection object;

the quality difference identification party is used for responding to the request of the optical line terminal, carrying out quality difference identification of fine period granularity on the detection object and obtaining a quality difference identification result; sending the quality difference identification result to a rule updating party;

the rule updating is used for receiving the quality difference identification result sent by the quality difference identification party and obtaining an obstacle removing result of the quality difference identification result; obtaining an updated suspicious quality difference service index range according to the obstacle removal result; the updated suspicious quality difference service index range is sent to the optical line terminal, so that the optical line terminal updates the reference suspicious quality difference service index range according to the updated suspicious quality difference service index range; the obstacle removing result is data which is used for indicating the accuracy of the quality difference identification result and is obtained by performing fault removal on the communication link based on the quality difference identification result.

According to an aspect of embodiments of the present application, a computer program medium having computer readable instructions stored thereon, which when executed by a processor of a computer, cause the computer to perform the methods provided in the various alternative implementations described above is disclosed.

According to an aspect of embodiments of the present application, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions are read from the computer-readable storage medium by a processor of a computer device, and executed by the processor, cause the computer device to perform the methods provided in the various alternative implementations described above.

In the technical scheme provided by the embodiment of the application, the optical line terminal is used for carrying out deep packet detection on the collected service flow message, and after suspicious quality difference service is found, quality difference identification with fine cycle granularity is carried out on the detection object corresponding to the suspicious quality difference service, so that a quality difference identification result is obtained. The deep packet inspection is deployed at an optical line terminal, and compared with a core router deployed at an outlet of a metropolitan area network and a broadband remote access server of a convergence layer, the position of the optical line terminal in a communication network is favorable for more accurate quality difference identification. Meanwhile, quality difference identification with fine period granularity is combined, and accuracy of quality difference identification results is improved.

Other features and advantages of the present application will be apparent from the following detailed description, or may be learned in part by the practice of the application.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.

Drawings

The above and other objects, features and advantages of the present application will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

Fig. 1 shows a flow chart of a quality difference recognition method according to a first embodiment of the present application.

Fig. 2 is a flow chart illustrating a quality difference recognition method according to a second embodiment of the present application.

Fig. 3 shows a schematic structural diagram of a quality difference recognition system according to a third embodiment of the present application.

Fig. 4 shows a schematic structural diagram of a further embodiment of a quality difference recognition system according to a third embodiment of the present application.

Fig. 5 shows a schematic structural diagram of a quality difference recognition device according to an embodiment of the present application.

Fig. 6 shows a schematic structural diagram of an optical line terminal according to an embodiment of the present application.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The drawings are merely schematic illustrations of the present application and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted.

Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more example embodiments. In the following description, numerous specific details are provided to give a thorough understanding of example embodiments of the present application. However, those skilled in the art will recognize that the aspects of the present application may be practiced with one or more of the specific details omitted, or with other methods, components, steps, etc. In other instances, well-known structures, methods, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the application.

Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor devices and/or microcontroller devices.

Fig. 1 is a schematic flow chart of a quality difference identifying method according to an embodiment of the present application, where the quality difference identifying method includes:

step S101: collecting service flow messages flowing through an optical line terminal;

The quality difference recognition method may be performed by the optical line terminal.

As an optional implementation manner, collecting a service flow message flowing through an optical line terminal includes: receiving a service class sent by a quality difference identification party; and collecting service flow messages corresponding to the service categories according to the service categories.

Traffic categories such as categories of traffic for games, video, etc. By adopting the mode, the service flow messages of the specific service type can be collected according to the actual quality difference identification requirement of the quality difference identification party, so that the quality difference identification of the service flow messages of the specific service type can be conveniently carried out subsequently. Quality difference identification can be performed pertinently according to service types.

As an optional implementation manner, collecting a service flow message flowing through an optical line terminal includes: and the mirror image collects the service flow message flowing through the optical line terminal.

The service flow message can be acquired by presetting a port mirror image and mirror (copying) one service flow message of an uplink port or a passive optical network port. Rules may be preset to specify whether an upstream port or a passive optical network port is to be used. For an optical line terminal that does not have the capability of mirroring the service flow messages of all ports at the same time, only the service flow messages of the uplink ports can be mirrored through preset rules. The port mirror image is arranged at the optical line terminal to collect the service flow message, so that the bandwidth of a service board card data uploading channel is not influenced, and the network transmission pressure is reduced.

Step S102: carrying out deep packet detection on the service flow message on the optical line terminal to obtain suspicious bad quality service;

suspicious bad traffic is traffic where there is a bad likelihood.

As an optional implementation mode, a resource board card is arranged in the optical line terminal, a deep packet detection module is arranged in the resource board card, and the deep packet detection module carries out deep packet detection on the service flow message to obtain suspicious bad quality service. The deep packet inspection is carried out through the resource board card, so that the processing performance of the main control and user service board card is not occupied, and the processing pressure of equipment can be reduced.

Compared with deployment depth packet detection near a CR (computed tomography) position of a metropolitan area network outlet and a BRAS (broadband remote control) of a convergence layer, deployment depth packet detection at an optical line terminal can avoid missing message information due to the homologous and homologous problem existing in deployment of deployment depth packet detection near the CR position of the metropolitan area network outlet and the BRAS of the convergence layer, and avoid the problem that a large number of faults are concentrated below an access network according to the statistics of the existing network, and the problem that fault positioning is difficult to be performed on the deployment depth packet detection at an upper layer, so that quality difference identification accuracy is higher.

Compared with the way of side deep packet inspection in the user terminal, the method and the device for detecting the deep packet in the optical line terminal can be used for solving the problem that the cost of purchasing ONU is high due to the fact that the performance requirement on a processor of ONU equipment is too high, and solving the problem that the network link resources are occupied by the whole amount of user terminal probes and the problem that the quality difference recognition accuracy of the ONU on the upper network link layer is low is solved, and the accuracy of the quality difference recognition result is improved on the whole.

In an embodiment, performing deep packet inspection on a service flow packet on an optical line terminal to obtain a suspicious bad service, including: and extracting the user port attribute of the service flow message through deep packet detection, calculating and analyzing the communication index of the transmission application layer, and identifying suspicious bad quality service according to a specific bad quality service judging rule. The communication indexes such as transmission control protocol (Transmission Control Protocol, TCP) link handshake delay, round-Trip Time (RTT) of the uplink and downlink network, packet loss retransmission rate, response success rate, occurrence Time period, and the like are parameters for describing communication quality or communication status.

Bad quality service judgment rules include: and if the sum of the uplink network segment average link establishment time delay and the downlink network segment average link establishment time delay and the first request time delay is larger than a first preset value, the corresponding service is suspicious poor quality service. Alternatively, the quality difference service judgment rule further includes: the sum of the average round trip delay of the uplink network segment and the average round trip delay of the downlink network segment is larger than a second preset value. Based on the communication index calculation analysis and the summarization index of the TCP/UDP transmission application layer, the preset threshold rule of the classified service is compared to judge the suspicious quality difference, and the service perception matching performance is better.

Step S103: identifying a detection object corresponding to the suspicious quality error service;

in one embodiment, identifying a detection object corresponding to suspicious bad quality traffic includes: and identifying at least one of a communication port, a virtual local area network and a service queue where the suspicious quality difference service is located as a detection object corresponding to the suspicious quality difference service.

Communication ports such as network ports, user ports, and traffic ports. By identifying the detection object corresponding to the suspicious quality difference service, further quality difference identification can be performed on the detection object, so that the obtained quality difference identification result reflects the state of the detection object, and the detection object can be effectively delimited and positioned to determine whether the quality difference occurs or not, and quality difference segmentation and possible root cause can be defined.

Step S104: and carrying out quality difference identification of fine period granularity on the detection object to obtain a quality difference identification result.

In an embodiment, performing quality difference recognition of fine cycle granularity on a detection object to obtain a quality difference recognition result, including: according to the fine period granularity, periodically collecting network quality parameters corresponding to the detection objects, and performing quality difference recognition on the network quality parameters corresponding to the detection objects to obtain quality difference recognition results.

The fine cycle granularity is granularity information indicating a cycle size for performing quality difference recognition. Compared with the coarse period granularity, the fine period granularity has smaller period, can perform data acquisition and quality difference identification at high frequency, and reflects the details of service flow so as to be convenient for fault diagnosis. The fine cycle granularity may be set to n seconds, for example, i.e., the quality difference recognition is performed every n seconds. It can be understood that the range interval of the fine period granularity can be set according to actual needs, and in order to accurately reflect the details of the traffic flow, the fine period granularity can be set below 1 minute. Because the suspicious quality error service is accurately detected through deep packet detection targeting, the data acquisition quantity is relatively greatly reduced when fine-cycle granularity data acquisition and quality error identification are carried out on a detection object corresponding to the suspicious quality error service. By setting the fine cycle granularity to 1 minute or less, quality difference recognition can be performed more finely, and both the processing capability and the storage capability of hardware can be achieved.

If the quality difference recognition is directly carried out on all the data according to the fine period granularity, the data acquisition frequency is higher in the quality difference recognition process, and a large amount of non-quality difference key data possibly occupy effective uplink bandwidth resources, so that the cost of the local side analysis system is increased. In order to avoid the problem, in this embodiment, fine-cycle granularity quality difference identification is performed on the detection object of the suspicious quality difference service, so that the problem that communication resources are too much consumed in fine-cycle granularity quality difference identification is performed on the detection object of the non-suspicious quality difference service is avoided, and the cost of quality difference identification is reduced.

In a part of scenes, the optical line terminal periodically collects network quality parameters corresponding to the detection objects according to fine period granularity, and performs quality difference recognition on the network quality parameters corresponding to the detection objects to obtain quality difference recognition results.

The network quality parameters include parameters describing network quality, such as transmitted and received message flow, packet loss number, CRC error packet number, optical module transmitting/receiving optical power, time delay and the like. The quality difference recognition of the network quality parameters can be obtained by recognition based on a final quality difference recognition rule. The final quality difference recognition rule may be that the network quality parameters all exceed the threshold value in a preset statistical period; or the number of times that the network quality parameter exceeds the threshold value is larger than a first preset threshold value in a preset statistical period. In addition, the service class can be combined, and in a preset statistical period, the number of times that the network quality parameter of the specific service class exceeds the threshold value is greater than a second preset threshold value, so that the quality is judged to be poor.

Because the quality difference recognition is carried out on the detection object, after the quality difference is judged, the detection object, such as a specific user port, a specific VLAN or a specific service queue, is determined to have the quality difference, so that the boundary positioning is carried out, and the object with the quality difference can be more accurately determined.

In an embodiment, performing quality difference recognition of fine cycle granularity on a detection object to obtain a quality difference recognition result, including: periodically collecting network quality parameters corresponding to the detection objects according to the fine period granularity; and sending the network quality parameters corresponding to the detection objects to the quality difference recognition party so that the quality difference recognition party recognizes the network quality parameters corresponding to the detection objects to obtain quality difference recognition results.

In order to uniformly perform quality difference identification of fine period granularity, centralized processing is performed to save the cost of deploying the quality difference identification method, improve the convenience and efficiency of deploying the method in actual service application, and avoid the situation that deployment convenience is poor due to the fact that the quality difference identification method is independently and completely deployed at each optical line terminal, so that actual service application is not facilitated.

Under the condition that a plurality of optical line terminals exist, the quality difference identification party can identify network quality parameters sent by the plurality of optical line terminals, so that quality difference identification results are uniformly obtained, and the quality difference identification results obtained by identifying the network quality parameters of the plurality of optical line terminals are more comprehensive on the whole.

As an alternative implementation mode, the method can be combined with an artificial intelligent analysis mode to learn the characteristics of the network quality parameters of the quality difference service, train a machine learning model, and predict the network quality parameters based on the trained machine learning model to obtain the quality difference recognition result.

As an alternative embodiment, the quality difference recognition result includes a quality difference segment and a possible root cause.

As an optional implementation manner, the quality difference recognition result includes judgment information about whether the service corresponding to the detection object is a quality difference service.

As an optional implementation manner, the data sent by the optical line terminal to the quality difference identifier includes information such as service attribute, suspicious quality difference parameter, user terminal equipment identifier, physical address, port, VLAN, etc. Service attributes such as service type and service code. The service code is used to identify the service. The suspicious quality parameters comprise network quality parameters of the detection objects corresponding to suspicious quality services.

By adopting the mode, the service flow message characteristics of the port are monitored by deploying and starting the deep packet inspection function at the optical line terminal, the bandwidth type state of the application layer is calculated, analyzed and transmitted, after the suspicious bad quality service is found, the network quality parameters with fine period granularity are pertinently started according to the inspection objects such as the network port, the user port, the service VLAN or the service queue, and the like, and are acquired and analyzed in real time, so that the positioning network, the service and the user quality difference can be delimited more rapidly and accurately, and the active operation and maintenance capability is improved.

And moreover, suspicious bad user service is identified through DPI calculation analysis, and then fine cycle granularity acquisition and analysis of network links below two layers of service boards are selected and started in a targeted manner, so that compared with the conventional pure two-layer link index acquisition or high-layer network DPI analysis, the analysis and judgment are more comprehensive, bad users can be more accurately delimited and positioned, and meanwhile, the PON equipment and platform service board resource pressure and link pressure caused by large data acquisition and reporting are greatly reduced.

Fig. 2 is a schematic flow chart of a quality difference identifying method according to a second embodiment of the present application, where the quality difference identifying method includes:

step S201: collecting service flow messages flowing through an optical line terminal;

Step S202: on an optical line terminal, extracting the communication port attribute and the service attribute of the service flow message based on a preset deep packet detection rule;

as an alternative embodiment, the service attribute includes a service type, and the communication port attribute is mainly an attribute that the data has when flowing through the communication port. Such as time attributes, response attributes, etc. of data flowing through the communication port. The service type such as a game service type, a video service type, etc.

As an alternative embodiment, the service attributes include a service type and a service code.

As an optional implementation manner, on a resource board card of an optical line terminal, a deep packet inspection module deployed on the resource board card extracts a user port attribute and a service type of a service flow message based on a deep packet inspection rule.

Step S203: determining a communication index mapped by the communication port attribute based on a mapping function corresponding to the service attribute; the communication index is an index for indicating communication quality, and the mapping function is a function describing a mapping relationship between the communication port attribute and the communication index;

the mapping function is used for calculating a communication index corresponding to the communication port attribute. For example, the time delay information is calculated according to the time attribute of the data flowing through the communication port, or the corresponding success rate is calculated according to the response attribute, or the packet loss retransmission rate is calculated according to the packet loss attribute, etc.

Step S204: if the communication index mapped by the communication port attribute is detected to be in the range of the reference suspicious quality difference service index, determining suspicious quality difference service according to the service attribute;

the reference suspicious quality error traffic index range is information indicating a range in which a communication index of suspicious quality error traffic is located. If the communication index is in the index range, the corresponding service is determined to belong to the suspicious quality difference service.

The reference suspicious quality error service index range may be a range in which a certain communication index of the suspicious quality error service is located, or may be a range in which a sum of a plurality of communication indexes of the suspicious quality error service is located. Such as: the reference suspicious bad quality service index range is the range of the average packet loss retransmission rate of suspicious bad quality service; or the reference suspicious quality error service index range is the range of the sum of the uplink network segment average link establishment time delay, the downlink network segment average link establishment time delay and the first request time delay of the suspicious quality error service. Or the reference suspicious bad quality service index range is the range where the sum of the average round trip delay of the uplink network segment and the average round trip delay of the downlink network segment of the suspicious bad quality service is located. If one or more communication indexes are in the corresponding reference suspicious quality error service index range, the corresponding service can be determined to be suspicious quality error service.

Step S205: identifying a detection object corresponding to the suspicious quality error service;

step S206: and carrying out quality difference identification of fine period granularity on the detection object to obtain a quality difference identification result.

In an embodiment, the quality difference recognition of the fine cycle granularity is performed on the detection object, and after the quality difference recognition result is obtained, the method further includes: receiving an updated suspicious quality difference service index range sent by rule updating; the updated suspicious quality difference business index range is obtained based on the obstacle removal result of the quality difference identification result; the obstacle removing result is data which is used for indicating the accuracy of the quality difference recognition result and is obtained by performing fault removal on the communication link based on the quality difference recognition result; and updating the reference suspicious quality difference service index range according to the updated suspicious quality difference service index range.

By adopting the mode, the service index range of the reference suspicious quality difference is updated, so that quality difference identification can be iterated continuously, and accuracy of quality difference identification is improved.

The above technical solution is described below in connection with a specific scenario. In the scene, the quality difference recognition party is a PON service perception analysis system, and the rule updating is an OSS operation and maintenance/dispatch system.

The PON service perception analysis system presets service analysis categories including video categories and game categories according to actual service conditions. And the service perception analysis module is started to preset DPI mirror image source, target parameters and analysis algorithm suspicious quality difference discrimination rule parameters, and the parameters are issued to the OLT resource calculation board card DPI analysis module.

The OLT starts the analysis function of the DPI module built in the resource board, configures the mirror image to collect and monitor the uplink port service flow message, and can also monitor and collect all PON ports simultaneously.

The DPI analysis module recognizes and extracts user port attributes and service types according to service flow message characteristics, calculates and analyzes communication indexes of a transmission application layer, and comprises states such as TCP link establishment handshake time delay, RTT time delay of an uplink network and a downlink network, packet loss retransmission rate, response success rate, occurrence time period and the like. The average communication index at the transmission application level within a specific period may be summarized. For example, the average RTT delay of the uplink segmented network within 5 minutes, the maximum RTT delay of the uplink segmented network, and the average packet loss retransmission rate.

And the DPI analysis module identifies suspicious bad quality service according to a preset DPI bad quality service judgment rule and reports the suspicious bad quality service to the PON service bad quality analysis module. The reported content may include attribute information such as bad quality service type, service code, suspicious bad quality parameters, user terminal equipment ID, MAC, port, VLAN, etc. The specific detail degree can be set according to the requirement, so that analysis is required and reporting channel pressure is reduced as much as possible. If the analysis calculation power and the storage capacity of the OLT are enough, the related functions can be moved downwards, and the data are not reported to directly collect and report the quality parameters of the OLT network.

The suspicious quality error judgment rule can be preset according to the actual service requirement. For example, in the case of being within a certain period of time, and/or a certain traffic type and a certain traffic code: and if the average uplink network segment link establishment time delay, the average downlink network segment link establishment time delay and the first request time delay are greater than X ms, judging that the corresponding service is suspicious poor quality service. Alternatively, in the case of being within a certain period of time, and/or a certain traffic type and a certain traffic code: and if the uplink network segment average RTT time delay and the downlink network segment average RTT time delay are more than X ms, judging that the corresponding service is the suspicious poor quality service. If the success rate of the link establishment is lower than the preset success rate, the corresponding service is judged to be the suspicious poor quality service. And if the packet loss times exceeds the preset packet loss times, judging that the corresponding service is the suspicious poor quality service. And if the retransmission rate exceeds the preset retransmission rate, judging that the corresponding service is the suspicious poor quality service. If the number of the user side terminals is in the interval corresponding to the reference suspicious service, the corresponding service is judged to be the suspicious poor quality service. However, the method is not limited thereto, and other suspicious quality error service judgment rules may be set according to actual situations.

The PON service quality difference analysis module informs the PON network quality parameter acquisition module according to the reporting condition and combining the user grade, the service importance and the like, and issues an acquisition object, an acquisition time period, a fine granularity period and the like. The acquisition and reporting suspicious adopts a telemethod, has the second-level reporting period capability, and can more precisely discover and determine the quality difference corresponding relation.

And the OLT selectively enables the real-time acquisition of network quality parameters with fine period granularity according to the network port, the user port, the service port, the VLAN or the service queue where the suspicious poor quality service is located, and reports the network quality parameters to the PON network quality parameter acquisition module for analysis. Specific parameters may include: the packet flow, packet loss number, CRC packet error number, optical module transmitting/receiving optical power and the like.

And the PON network quality parameter acquisition module analyzes according to the acquired data reported by the OLT to obtain a data analysis result.

And the PON service perception analysis system synthesizes the suspicious quality difference result of the DPI and the network quality acquisition parameter calculation result of the fine period granularity, finally analyzes and defines the quality difference segmentation position and possible root cause according to the quality difference judgment rule, and reports the OSS operation and maintenance/dispatch system. Wherein: the final quality difference judgment rule may include: the service class, whether 1 or more suspicious quality difference judging parameters in a specific statistical period exceed a threshold value and the exceeding times, etc., the period is, for example, 1 hour, 1 day, 1 week, etc., and the suspicious quality difference judging parameters are, for example, the average time delay, the times, etc.

The OSS operation maintenance/order delivery system arranges the obstacle removal according to the quality difference reporting condition, delivers order delivery service to solve according to the requirement, and feeds back the processing result.

And the PON service perception analysis system updates and upgrades DPI quality difference judgment rules or index parameters according to the accuracy of the closed loop processing result fed back by the OSS operation and maintenance/dispatching system, so that the accuracy is further improved.

By adopting the mode, the method has the following technical effects:

firstly, the OLT equipment can realize DPI analysis function by newly adding or utilizing the deployed general calculation resource board, and fully exert the equipment edge calculation capability. The functions of the user service perception analysis system module can be newly built as required or a corresponding service analysis module can be deployed or upgraded on demand on the existing PON network management analysis platform and the like, the ONU terminal does not need to be changed, and the overall implementation cost is low.

In addition, users with poor quality and service sources of PON network service can be identified automatically in time, quality difference accurate delimitation, positioning and obstacle removal are realized by combining a closed-loop targeted operation and maintenance obstacle removal process, actual perception of user service is improved, the entry cost of maintenance personnel is reduced, and active operation and maintenance is realized. Meanwhile, the load influence of a large amount of collection on the PON master control service board card and the network flow can be effectively reduced, and the stable operation of the service equipment is ensured.

Moreover, the method can be suitable for the identification and quality difference analysis of various existing services, and for new services in the future, the identification characteristics and the quality difference judgment threshold are only updated on the resource computing board card, and the corresponding fine granularity acquisition parameters are selected, so that the method has application universality and upgradeability.

Fig. 3 shows a schematic structural diagram of a quality difference recognition system according to a third embodiment of the present application. The quality difference recognition system comprises:

an optical line terminal 301, configured to request a quality difference recognition party to perform quality difference recognition with fine cycle granularity on a detection object;

the quality difference recognition party 302 is configured to perform quality difference recognition of fine cycle granularity on a detection object in response to a request of an optical line terminal, so as to obtain a quality difference recognition result; sending a quality difference identification result to a rule updating party;

the rule updating 303 is configured to receive a quality difference recognition result sent by the quality difference recognition party, and obtain a fault removal result of the quality difference recognition result; obtaining an updated suspicious quality difference service index range according to the obstacle removal result; the updated suspicious quality difference service index range is sent to the optical line terminal, so that the optical line terminal updates the reference suspicious quality difference service index range according to the updated suspicious quality difference service index range; the obstacle removing result is data which is used for indicating the accuracy of the quality difference recognition result and is obtained by performing fault removal on the communication link based on the quality difference recognition result.

Referring to fig. 4, fig. 4 is a schematic diagram of an embodiment of a quality difference recognition system. The quality difference identification party is a PON service perception analysis system, and the rule updating is an OSS operation and maintenance/dispatch system. The quality difference identification system comprises an OSS operation and maintenance/dispatch system, a PON service perception analysis system, an optical line terminal, an ONU and a service terminal which are connected in sequence.

OSS operation and maintenance/order delivery system: and the operation support system of the operator realizes the operation maintenance, fault processing, dispatching processing and the like of the network equipment.

The PON service perception analysis system comprises PON service perception analysis, PON network quality analysis and other modules, can complete network and service performance acquisition analysis of PON network equipment, and outputs a final quality difference result to an OSS operation and maintenance/dispatch system for operation and maintenance troubleshooting or further analysis. And meanwhile, the quality difference processing feedback of the operation and maintenance/dispatch system can be received, the quality difference rule parameters can be adjusted, and the like.

The service perception analysis module is used for generating and transmitting rules and parameters for service perception analysis of suspicious quality differences to the OLT, receiving suspicious quality difference results and belonging position information reported by the OLT, notifying the network quality analysis module to further collect and analyze, and summarizing the results and outputting final quality difference results. And the network quality analysis module is used for selectively issuing an OLT network quality acquisition instruction and a granularity period according to the suspicious bad user service and the belonging position information output by the service perception analysis module, storing and analyzing the data result reported by the OLT, and returning the data result to the PON service perception analysis module.

And the optical line terminal, the PON network local side equipment and the resource computing board card with built-in DPI function are deployed to perform DPI mirror image acquisition analysis, and suspicious quality difference results are analyzed and output according to preset or received issued discriminant rule parameters.

The resource computing board card, namely the resource board card, is deployed on the OLT, and can collect service data of the uplink port or the PON port in a port mirror image mode and the like, complete packet detection and analysis of DPI and the like, and generate and report the original network/service KPI data. And the network quality parameter acquisition module is used for acquiring network quality parameters and sending the network quality parameters to the PON service perception analysis system.

ONU, PON network user equipment.

Service terminal, service corresponding terminal equipment.

By adopting the quality difference identification system, the quality differences of the positioning network, the service and the user can be rapidly and accurately delimited, and the active operation and maintenance capability is improved. According to the accuracy of the OSS operation and maintenance obstacle removal result, the DPI quality difference judgment rule or index parameter is dynamically updated and upgraded, and the accuracy is further improved.

Fig. 5 shows a quality difference recognition device according to a fourth embodiment of the present application, the quality difference recognition device including:

the acquisition module 401 is configured to acquire a service flow packet flowing through the optical line terminal;

the deep packet detection module 402 is configured to perform deep packet detection on the service flow packet on the optical line terminal, so as to obtain a suspicious bad quality service;

A first identifying module 403, configured to identify a detection object corresponding to the suspicious quality error service;

and the second recognition module 404 is configured to perform quality difference recognition with fine cycle granularity on the detection object, so as to obtain a quality difference recognition result.

In an exemplary embodiment of the present application, the quality difference recognition device is configured to:

on an optical line terminal, extracting the communication port attribute and the service attribute of the service flow message based on a preset deep packet detection rule;

determining a communication index mapped by the communication port attribute based on a mapping function corresponding to the service attribute; the communication index is an index for indicating communication quality, and the mapping function is a function describing a mapping relationship between the communication port attribute and the communication index;

if the communication index mapped by the communication port attribute is detected to be in the range of the reference suspicious quality difference service index, the suspicious quality difference service is determined according to the service attribute.

and sending the network quality parameters corresponding to the detection objects to the quality difference recognition party so that the quality difference recognition party recognizes the network quality parameters corresponding to the detection objects to obtain quality difference recognition results.

and identifying at least one of a communication port, a virtual local area network and a service queue where the suspicious quality difference service is located as a detection object corresponding to the suspicious quality difference service.

receiving a service class sent by a quality difference identification party;

An optical line terminal 50 according to an embodiment of the present application is described below with reference to fig. 6. The optical line terminal 50 shown in fig. 6 is only an example, and should not impose any limitation on the functions and scope of use of the embodiments of the present application.

As shown in fig. 6, the optical line terminal 50 is embodied in the form of a general purpose computing device. The components of the optical line terminal 50 may include, but are not limited to: the at least one processing unit 510, the at least one memory unit 520, and a bus 530 connecting the various system components, including the memory unit 520 and the processing unit 510.

Wherein the storage unit stores program code that is executable by the processing unit 510 such that the processing unit 510 performs steps according to various exemplary embodiments of the present application described in the description section of the exemplary method described above in the present specification. For example, the processing unit 510 may perform the various steps as shown in fig. 1.

The storage unit 520 may include readable media in the form of volatile storage units, such as Random Access Memory (RAM) 5201 and/or cache memory unit 5202, and may further include Read Only Memory (ROM) 5203.

The storage unit 520 may also include a program/utility 5204 having a set (at least one) of program modules 5205, such program modules 5205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.

Bus 530 may be one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

The optical line terminal 50 may also communicate with other devices (e.g., ONUs and bad identifiers, etc.). Such communication may occur through an input/output (I/O) interface 550. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with the optical line terminal 50, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.

From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present application may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a usb disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause an optical line terminal to perform the method according to the embodiments of the present application.

In an exemplary embodiment of the present application, there is also provided a computer-readable storage medium having stored thereon computer-readable instructions, which, when executed by a processor of a computer, cause the computer to perform the method described in the method embodiment section above.

According to an embodiment of the present application, there is also provided a program product for implementing the method in the above method embodiment, which may employ a portable compact disc read only memory (CD-ROM) and comprise program code, and may be run on an optical line terminal. However, the program product of the present application is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The computer readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).

It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functions of two or more modules or units described above may be embodied in one module or unit, in accordance with embodiments of the present application. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.

Furthermore, although the various steps of the methods herein are depicted in the accompanying drawings in a particular order, this is not required to either suggest that the steps must be performed in that particular order, or that all of the illustrated steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform, etc.

Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.

Claims

1. A method of quality difference identification, the method comprising:

collecting service flow messages flowing through an optical line terminal;

2. The method of claim 1, wherein performing deep packet inspection on the service flow packet at the optical line terminal to obtain suspicious bad quality service comprises:

3. The method according to claim 2, wherein after performing quality difference recognition of fine cycle granularity on the detection object to obtain quality difference recognition result, the method further comprises:

4. The method according to claim 1, wherein performing quality difference recognition of fine cycle granularity on the detection object to obtain quality difference recognition results comprises:

5. The method of claim 1, wherein identifying a detection object corresponding to the suspected bad traffic comprises:

6. The method of claim 1, wherein collecting traffic messages flowing through the optical line terminal comprises:

receiving a service class sent by a quality difference identification party;

7. A quality difference recognition device, characterized in that the quality difference recognition device comprises:

8. An optical line terminal, characterized in that the optical line terminal comprises:

one or more of the processors;

storage means for storing one or more programs which, when executed by the one or more processors, cause the optical line terminal to implement the method of any of claims 1 to 6.

9. A quality difference identification system, the quality difference identification system comprising:

the optical line terminal of claim 8, configured to request a quality difference recognition party to perform quality difference recognition with fine cycle granularity on a detection object;

10. A computer readable storage medium having stored thereon computer readable instructions which, when executed by a processor of a computer, cause the computer to perform the method of any of claims 1 to 6.