CN108428459A - A kind of VoLTE speech quality assessment methods and system - Google Patents
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Abstract
The embodiment of the invention discloses a kind of VoLTE speech quality assessment methods and system, method to include：Acquire voice and signaling data of each interface after light splitting and convergence；Voice and signaling data are parsed, carrying out mean subjective opinion to the Media Stream that parsing obtains divides MOS values to assess, and obtains speech quality evaluation data；User information and location information are added in speech quality evaluation data according to control face data, generate speech quality evaluation file；Speech quality evaluation file is stored to database server.By generating speech quality evaluation file after adding user information and location information in speech quality evaluation data, facilitate inquiry, each period suitable for VoLTE, it is particularly suitable for VoLTE and dials VoLTE user, VoLTE dials the scene of traditional 2/3G user, and by carrying out MOS value assessments so that assessment result is reliable, accurate.
Description
Technical Field
The embodiment of the invention relates to the technical field of communication, in particular to a VoLTE voice quality evaluation method and system.
Background
In 2/3/4G converged networks, distortion of speech quality often occurs. Network elements and nodes of the VoLTE network are multiplied, the structure is more complex, voice distortion problems exist when the VoLTE user and 2/3G users are communicated at the initial stage of business, the perception of actual users is seriously influenced, and an effective method for evaluating the voice quality of the VoLTE network is urgently needed, so that the voice service quality can be evaluated, supervised and inquired, and high-quality and satisfactory services are provided for the users.
In the existing method, a monitoring method of an LTE network voice service performs speech quality assessment analysis on a 4G user in a communication process between an LTE network and an 2/3G network for a CSFB scenario. For the analysis of the LTE network, EPC domain interface data such as S1-U, S1-MME, S6a, SGs, S10, S11 and the like need to be collected, and user information and position information are filled for S1-U through a specific user association rule; for the analysis of the 2/3G network, data of interfaces such as Nb, Mc and AoIP need to be collected, and user information and position information are filled for Nb through a specific user association rule; and carrying out voice quality analysis on the related CSFB user voice record by using a fishbone diagram, a flow chart and a dimension stage statistical method to obtain corresponding voice quality indexes such as single pass, noise, echo, interruption and the like. Or adopting a voice quality objective evaluation method based on an improved E-Model, firstly calculating a transmission performance grade coefficient R by the existing E-Model according to parameters such as network delay, noise, voice coder-decoder and the like, and then converting the R into an equivalent MOS (metal oxide semiconductor) score, but when the packet loss rate of the improved E-Model is gradually increased, the mean square error of the improved E-Model and the PESQ score is also gradually increased, and the problem of quality evaluation distortion exists; the improved E-Model applies statistical analysis and least square fitting technology, and replaces the packet loss rate and the burst rate with the actual loss time of the voice, so that the mean square error of the improved E-Model is basically not changed along with the change of the packet loss rate, and the accuracy of voice quality evaluation is provided.
In the process of implementing the embodiment of the invention, the inventor finds that the existing monitoring method of the voice service of the LTE network can only analyze the voice quality of the 4G user in the LTE network and the 2/3G network under the CSFB scene, belongs to an optimization method at the initial stage of the LTE, and after entering the VoLTE business period, more scenes of intercommunication between the VoLTE user and scenes of intercommunication between the VoLTE user and the 2/3G user exist, and the method can not provide a voice quality detection method for the VoLTE network; voice quality analysis is only carried out on the 2/3G network wireless access part, and the voice quality of the 4G and VoLTE networks cannot be analyzed and evaluated; the objective speech quality evaluation method based on the improved E-Model focuses on the description of the algorithm, and how to evaluate the speech quality in the current network by using the method is not described.
Disclosure of Invention
Due to the problem that the evaluation accuracy of the conventional method cannot be guaranteed, the embodiment of the invention provides a VoLTE voice quality evaluation method and system.
In a first aspect, an embodiment of the present invention provides a VoLTE voice quality assessment method, including:
collecting voice and signaling data after light splitting and convergence at each interface;
analyzing the voice and signaling data, and performing mean subjective opinion score (MOS) value evaluation on the media stream obtained by analysis to obtain voice quality evaluation data;
adding user information and position information into the voice quality evaluation data according to control surface data, and generating a voice quality evaluation file according to the voice quality evaluation data added with the user information and the position information;
and storing the voice quality evaluation file to a database server.
Optionally, the acquiring voice and signaling data at each interface after light splitting and convergence specifically includes:
collecting media streams at the Mb interface, the Nb interface, the S11 interface, the Gm interface and the Mc interface, splitting the media streams into 2/8 light, and converging 20% of the split media streams to obtain the voice and signaling data.
Optionally, the analyzing the voice and signaling data, and performing mean subjective opinion score MOS value evaluation on the media stream obtained by the analyzing to obtain voice quality evaluation data specifically includes:
and analyzing the voice and signaling data, screening the analyzed voice and signaling data according to a preset flow and a preset field to obtain a media stream of an Mb interface or an Nb interface, and evaluating an MOS value of the media stream by adopting a P.563 algorithm of ITU specification to obtain voice quality evaluation data.
Optionally, the adding, according to the control plane data, user information and location information to the voice quality assessment data, and generating, according to the voice quality assessment data to which the user information and the location information are added, a voice quality assessment file, specifically includes:
adding user information in the voice quality evaluation data according to a session _ setup table in the control surface data, adding position information in the voice quality evaluation data according to an sipcall table in the control surface data, and generating a voice quality evaluation file according to the voice quality evaluation data added with the user information and the position information.
Optionally, after storing the voice quality assessment file in a database server, the method further includes:
and inquiring each voice quality evaluation file stored in the database server in real time according to preset conditions to generate a convergence table of a preset time period, and storing the convergence table to the database server.
In a second aspect, an embodiment of the present invention further provides a VoLTE voice quality evaluation system, including: the system comprises an acquisition server, a processing server, a data backfill loader server, a distributed processing hadoop cluster system and a database server;
the acquisition server acquires voice and signaling data subjected to light splitting and convergence at each interface and sends the voice and signaling data to the processing server;
the processing server analyzes the voice and signaling data, performs mean subjective opinion score (MOS) value evaluation on the media stream obtained by analysis to obtain voice quality evaluation data, and sends the voice quality evaluation data to the loader server;
the loader server adds user information and position information into the voice quality evaluation data according to control surface data, generates a voice quality evaluation file according to the voice quality evaluation data added with the user information and the position information, and sends the voice quality evaluation file to a hadoop cluster system;
the hadoop cluster system stores the voice quality evaluation file to a database server;
the database server is used for storing the voice quality evaluation file.
Optionally, the collection server is specifically configured to collect media streams at the Mb interface, the Nb interface, the S11 interface, the Gm interface, and the Mc interface, split the media streams by 2/8, and aggregate 20% of the split media streams to obtain the voice and signaling data.
Optionally, the processing server is specifically configured to analyze the voice and signaling data, screen the analyzed voice and signaling data according to a preset flow and a preset field to obtain a media stream of an Mb interface or an Nb interface, and perform MOS value evaluation on the media stream by using a p.563 algorithm of the ITU specification to obtain voice quality evaluation data.
Optionally, the loader server is specifically configured to add user information to the voice quality assessment data according to a session _ setup table in the control plane data, add location information to the voice quality assessment data according to an sipcall table in the control plane data, and generate a voice quality assessment file according to the voice quality assessment data to which the user information and the location information are added.
Optionally, the hadoop cluster system is further configured to perform real-time query on each voice quality assessment file stored in the database server according to a preset condition, generate a convergence table of a preset time period, and store the convergence table to the database server.
According to the technical scheme, the voice quality evaluation file is generated by adding the user information and the position information into the voice quality evaluation data, so that the voice quality evaluation file is convenient to query, is suitable for various periods of VoLTE, and is particularly suitable for VoLTE dialing VoLTE users and VoLTE dialing traditional 2/3G users, and the evaluation result is reliable and accurate by evaluating the MOS value.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic flowchart of a VoLTE voice quality evaluation method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an Mb interface according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an Nb interface according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a VoLTE voice quality evaluation system according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a VoLTE voice quality evaluation system according to an embodiment of the present invention;
fig. 6 is a schematic diagram of an acquisition interface of a VoLTE voice quality evaluation system according to an embodiment of the present invention;
fig. 7 is a schematic diagram of a light splitting principle of the VoLTE voice quality assessment method according to an embodiment of the present invention;
FIG. 8 is a schematic flow chart of user data backfilling of the Mb interface provided in an embodiment of the present invention;
fig. 9 is a schematic flowchart of user data backfill of the Nb interface according to an embodiment of the present invention;
fig. 10 is a schematic diagram of a hadoop cluster architecture according to an embodiment of the present invention;
fig. 11 is a schematic structural diagram of a VoLTE voice quality evaluation system according to another embodiment of the present invention;
FIG. 12 is a logical block diagram of an electronic device in one embodiment of the invention.
Detailed Description
The following further describes embodiments of the present invention with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
Fig. 1 shows a schematic flow diagram of a VoLTE voice quality assessment method provided in this embodiment, including:
s101, voice and signaling data subjected to light splitting and convergence at each interface are collected.
Wherein the interfaces comprise interfaces of two media streams (Mb, Nb) and interfaces of three control planes (S11, Gm, Mc). Specifically, an RTP voice packet can be obtained by collecting an Mb interface and an Nb interface; corresponding control surface data can be acquired by collecting three interfaces S11, Gm and Mc.
The above 5 interfaces all perform data convergence on the CE side by means of 2/8 light splitting, after light splitting, one path of 80% optical power returns to the original operator main link, and one path of 20% optical power is accessed to the TAP splitter of the system to perform data convergence.
S102, analyzing the voice and signaling data, and performing mean subjective opinion score (MOS) value evaluation on the media stream obtained through analysis to obtain voice quality evaluation data.
The P.563 algorithm of the ITU specification is a Metal Oxide Semiconductor (MOS) quality evaluation algorithm based on a non-reference model and proposed by the ITU, and belongs to a specification algorithm disclosed internationally.
Specifically, the voice and signaling data are analyzed to obtain media streams of the 5 interfaces, the media streams of the Mb interface or the Nb interface are screened out through screening, and a p.563 algorithm is adopted to perform MOS value scoring on all RTP packets, so that high-efficiency and rapid query and processing of data with large data volume can be realized.
S103, adding user information and position information in the voice quality evaluation data according to the control surface data, and generating a voice quality evaluation file according to the voice quality evaluation data added with the user information and the position information.
The control plane data is data received from three interfaces of S11, Gm and Mc, and comprises user information and location information.
And S104, storing the voice quality evaluation file to a database server.
Specifically, the present embodiment is mainly based on the following two scenarios of speech quality assessment problem:
scene one: the interworking of VoLTE users with VoLTE users requires the assessment of the voice quality of the Mb interface media stream. As shown in fig. 2, the scenario includes an EPC domain and an IMS domain. The EPC domain comprises logic network elements such as MME, SGW and PGW, and the IMS domain comprises logic network elements such as P-CSCF, I/S-CSCF, BGCF, MGCF, VoLTE-SBC, IM-MGW and CS-MGW. The Mb interface is an interface between the PGW and the VoLTE-SBC, and carries media stream data of the EPC domain and the IMS domain, and the media streams of all VoLTE networks can perform data transmission through the interface. And analyzing the RTP packet of the interface to obtain the voice quality index of the VoLTE network. The S11 and Gm interface carry data of the control plane, and user information and location information may be padded for the media stream data of the Mb interface.
Scene two: the interworking of VoLTE users with 2/3G users requires an assessment of the voice quality of the Nb interface media stream. As shown in FIG. 3, the Nb interface is an interface between the IM-MGW and the CS-MGW, and mainly completes the interworking of the user plane wide and narrow band bearers between the IMS domain and the CS domain and the Codec conversion. Under the scene of intercommunication between the VoLTE user and the 2/3G user, when the RTP media stream of the VoLTE user passes through the interface, the coding and decoding modes of the RTP media stream can be changed, and the user plane is changed from a broadband to a narrowband. This transformation, which may lead to a degradation of the voice quality of VoLTE users, requires a voice quality assessment analysis of the RTP media stream of the interface. The control plane data of the Mc interface may pad the media stream data of the Nb interface with user information and location information.
Specifically, as shown in fig. 4, the system corresponding to this embodiment is composed of most 6 of an acquisition server, a processing server (MOS evaluation), a loader server (data backfill), a hadoop cluster (distributed processing), a database server, and an application server, and these servers all adopt a distributed architecture and use a three-tier switch for data intercommunication.
The system acquires a data source by adopting a local acquisition decoding mode, performs 2/8 light splitting on a CE side corresponding to each interface (comprising 5 interfaces of Mb, Nb, S11, Gm and Mc), converges the split data by utilizing a TAP (TAP access port) splitter, and then transmits the converged data to each acquisition server according to an IP (Internet protocol) filtering method and a load balancing principle. The acquisition server acquires the original code streams of the 5 interfaces, and then processes the original code streams through the processing server, and only the required flow and field are reserved (for example, the S11 interface only reserves the session _ setup flow). In the processing procedure, the MOS value of the media stream of the Mb interface and the Nb interface is evaluated by the p.563 algorithm embedded in the platform, and then the media stream and the data of the control plane are disassociated by the lorder server according to the association mechanism (the detailed association mechanism can be seen in the content of the following part 3) that the port and the IP are respectively equal, and the data of the media stream is filled with important user information (such as IMSI, MSISDN, IMEI, etc.) and location information (such as SGW, TAC, ENB, CI, etc.). After the data are filled, the system converts the data into text files in a TXT format and sends the text files to the hadoop cluster system, the hadoop cluster stores and queries the files, the query result is imported to the ORACLE according to the requirement, and the final data application is based on the ORACLE data.
The system needs to acquire two interfaces Mb and Nb to acquire an RTP voice packet, and needs to acquire three interfaces S11, Gm and Mc to acquire corresponding control plane data. Referring to fig. 5, firstly, after the system collects the RTP voice code streams of the Mb and Nb interfaces through the collection server, the system decodes the original code streams, and then performs MOS value evaluation on the voice packets through a p.563 algorithm embedded in the system. The RTP voice code stream itself has no user information and location information, and needs to be backfilled by correlation through related control plane data. And correlating the XDR of the Mb and Nb interfaces after the MOS evaluation with the data of the control planes S11, Gm and Mc according to a certain correlation mechanism, and backfilling corresponding user data information such as IMSI, cells and the like. The XDR of the Mb interface and the Nb interface after the relevant backfill already has user information and cell position information, and can meet the requirements of various functional applications and special analysis.
In the embodiment, the voice quality evaluation file is generated by adding the user information and the position information into the voice quality evaluation data, so that the voice quality evaluation file is convenient to query, is suitable for each period of VoLTE, is particularly suitable for a VoLTE user dialing VoLTE, and a scene of a traditional 2/3G user dialing VoLTE, and enables the evaluation result to be reliable and accurate by carrying out MOS value evaluation.
Further, on the basis of the above method embodiment, S101 specifically includes:
collecting media streams at the Mb interface, the Nb interface, the S11 interface, the Gm interface and the Mc interface, splitting the media streams into 2/8 light, and converging 20% of the split media streams to obtain the voice and signaling data.
Specifically, the system needs to collect interfaces of two media streams (Mb, Nb) and three control planes (S11, Gm, Mc), and fig. 6 is a schematic diagram of the interfaces that need to be collected.
The above 5 interfaces all perform data convergence on the CE side by means of 2/8 light splitting, after light splitting, one path of 80% optical power returns to the original operator main link, and one path of 20% optical power is accessed to the TAP splitter of the system to perform data convergence. Fig. 7 is a spectroscopic diagram: after the data source is converged by the TAP equipment, the data is distributed to each acquisition server by a certain rule. The acquisition server adopts a distributed architecture and utilizes three layers of switches for intercommunication. The time precision synchronization is carried out among a plurality of servers by using protocols such as NTP and the like, and the requirement of 1-50 millisecond time precision is met. The key function of the acquisition server is to access the original code stream to the system, convert the original code stream into a file format recognizable by the cost system after primary processing, and send the file format to the processing server for more targeted processing.
In this embodiment, convergence of data sources is performed from the CE side of each interface by adopting an 2/8 light splitting manner, after the convergence, the TAP distribution device distributes an original code stream to each acquisition server, and the acquisition servers adopt a distributed architecture and use a three-layer switch for data intercommunication.
Further, on the basis of the above method embodiment, S102 specifically includes:
and analyzing the voice and signaling data, screening the analyzed voice and signaling data according to a preset flow and a preset field to obtain a media stream of an Mb interface or an Nb interface, and evaluating an MOS value of the media stream by adopting a P.563 algorithm of ITU specification to obtain voice quality evaluation data.
Specifically, the original code stream of each interface has multiple flows, and each flow has several fields. This embodiment may not decode all flows and all fields of the interface, which may greatly increase the load of the system and affect the processing performance of the system. The processing server can carry out flow and field screening on the data sent by the acquisition server through flow self-defining (for example, an S11 interface only defines a session _ setup flow, and other flows are not output) and field checking, remove unnecessary flows and reserve required flows and fields. Meanwhile, through a P.563 algorithm embedded in the system, instead of needing a separate algorithm server like other schemes, MOS value scoring is carried out on RTP packets of the Mb port and the Nb port, and MOS values are obtained correspondingly.
After the processing and MOS scoring by the processing server, the present embodiment generates various XDR files and corresponding key fields as shown in the following table at each interface:
further, on the basis of the above method embodiment, S103 specifically includes:
adding user information in the voice quality evaluation data according to a session _ setup table in the control surface data, adding position information in the voice quality evaluation data according to an sipcall table in the control surface data, and generating a voice quality evaluation file according to the voice quality evaluation data added with the user information and the position information.
Specifically, for the evaluation of voice quality, voicecall _ info of the Mb port and the Nb port needs to be analyzed, but the XDR only has voice quality related data, does not have any user information and location information, and needs to be filled with data of the control plane.
(1) Mb-port user data backfill mechanism
S11 port decodes directly to generate session _ setup table, which contains IMSI, MSISDN and other information; the Gm port generates a sipcall table which contains LAC, CI and other information; the Mb port generates a voicecall _ info table, which has voice quality index data such as MOS value (evaluated by the foregoing p.563 algorithm), but cannot directly obtain data related to user information and cell location information, and these data all need to be filled by a backfill mechanism, where the cell location information needs to be obtained by associating with a sipcall table, and the user information such as IMSI needs to be obtained indirectly by a session _ setup table.
The backfill process of the user data is shown in fig. 8, and comprises the following specific steps:
a1, building a relation table of MSISDN and IMSI. The session _ setup procedure of the S11 interface carries the MSISDN and IMSI, and can obtain a relationship table between the MSISDN and the IMSI, and can automatically perform real-time update.
And A2, filling IMSI for the sipcall flow. The sipcall process of the Gm interface can directly decode the location information of the TAC and CI, and has MSISDN of the calling and called parties, but lacks IMSI, and requires backfilling through an S11 interface, and the specific process is as follows:
and A21, filling the IMSI by using the MSISDN and IMSI relation table constructed in the first step. When the Call Type is an outgoing Call (i.e. Call _ Type ═ 1), performing association padding of the IMSI using sipcall. When the Call Type is incoming Call (i.e. Call _ Type ═ 0), association population of IMSI is performed using sipcall. Note: normalization processing needs to be performed on the number formats of the MSISDN, sip _ from and sip _ to, otherwise, the number formats affect matching.
And A22, after a, a small part of the users still can not be filled with IMSI, and the part of users are backfilled by utilizing the principle that the IP addresses of the users are equal. And when the field User _ IP _ Value in the Gm interface sipcall flow is equal to the field User _ IP _ Value _ v6 in the S11 interface Session _ Setup flow, filling the IMSI after finding the relevant Session _ Setup record.
User data backfill at a3, Mb port. The XDR of voice _ info can be obtained by directly decoding the RTP media stream of the Mb interface, and the user data information (including IMSI, MSISDN, TAC and CI) of the matching sipcall flow is correlated according to the principle that the quadruplet of the XDR is equal to the quadruplet in the Gm interface sipcall flow. Quadruple equality means: srcipv6, destipv6, srcport and destport of voice _ info are respectively strictly equal to CallingVoice _ Addr, CalledVoice _ Addr, CallingVoice _ Port and CalledVoice _ Port in the Gm interface sipcall flow.
(2) User data backfill mechanism of Nb port
The direct decoding by interface Mc will eventually generate 3 XDRs, which are H248, voicecall _ moc and voicecall _ mtc3 original tables, respectively. H248 is for Nb interface control plane protocol; voicecall _ moc and voicecall _ mtc are control plane data of the calling and called parties in the Mc interface, and each control plane data contains detailed information such as IMSI, MSISDN, LAC, and CI.
The Nb port directly decodes to generate a VOICE _ INFOST table which contains VOICE quality index data such as MOS value, but does not contain any user information and position information. User data of VoLTE network needs to be backfilled through control plane information of Gm port and S11 port, and user data of 2/3G network needs to be backfilled through control plane information of Mc port.
The backfill process of the user data is shown in fig. 9, and specifically includes the following steps:
b1, backfilling user data of the VoLTE network. The backfilling mechanism is the same as the user backfilling mechanism of the Mb port, and the explanation is not repeated.
User data backfilling for B2, 2/3G networks. There are two cases: the first is for non-IP users of the 2G network and the second is for IP users of the 2/3G network.
B21, for the first: when the triplets of H248 are exactly equal to the triplets of voicecall _ moc and voicecall _ mtc, respectively, H248 is populated with information such as IMSI, MSISDN, LAC, CI, etc. Here, the triplet refers to: pcm, timeshot and opc.
B22, for the second: when the duplet of H248 is strictly equal to the duplets of voicecall _ moc and voicecall _ mtc, H248 is filled with information such as IMSI, MSISDN, LAC, CI, etc. The binary group here means: locconaddr (local connection address) and locctransport (local connection port).
After B21 and B22, the relevant backfilling of user information and location information has been completed for the H248 table. Thereafter, when the VOICE _ INFOST table is strictly equal to the quadruplet of H248, the VOICE _ INFOST table is filled with the information of IMSI, MSISDN, LAC, CI, etc. Here, the quadruple means: srip, srcport, destip, and destport of VOICE _ INFOST table, locconaddr, loctrasport, remconaddr, and remtranport of VOicecall _ moc and VOicecall _ mtc.
And carrying out data association of multiple interfaces of the VoLTE network by a four-tuple equal principle, and finally realizing the backfilling of key user information (IMSI/MSISDN) and position information (SGW/TAC/ENB/CI) of the media stream data of the Mb interface.
Further, on the basis of the above embodiment of the method, after S104, the method further includes:
and S105, inquiring each voice quality evaluation file stored in the database server in real time according to preset conditions, generating a convergence table of a preset time period, and storing the convergence table to the database server.
Wherein the preset condition is preset according to specific requirements.
Specifically, after the original code stream is subjected to acquisition and decoding, MOS evaluation and user data backfill, the original code stream is pushed to a hadoop cluster in a TXT text format in a socket mode. The Hadoop receives and converts TXT text files pushed by the system by using the storm, and then the compressed TXT text files are sent to the HDFS for storage according to a certain mechanism. Hive and Spark are mainly used for inquiring and processing files stored in the HDFS, the former focuses on non-real-time data inquiry processing, and the latter focuses on real-time data inquiry processing. The results after query processing can be imported into ORACLE database storage, while foreground applications are developed more based on ORACLE. The hadoop ecosphere architecture diagram of the system is shown in fig. 10: the TXT file received by Storm and stored in HDFS is the original XDR (session _ setup, sipcall, voicecall _ info, voicecall _ mtc, voicecall _ moc and H248) decoded by each interface and the XDR (mb _ voicecall and nb _ voicecall) after data backfill. The hadoop cluster can perform corresponding data integration on the XDRs to generate various custom XDRs, and the custom XDRs are guided into an ORACLE database, so that the calling of an application server is facilitated. In the system, the hadoop cluster mainly completes the data query function in the following aspects:
(1) non-real-time data queries.
The method utilizes the Hive function to purposefully inquire the XDR text files stored in the HDFS, and directly display the inquiry result, export the inquiry result in a format of TXT/CSV or the like or import the inquiry result into ORACLE.
The application scene one: the voice quality index data of some users or cells need to be inquired temporarily and randomly, the data quantity needing to be inquired is large, the inquiry time is long, and the requirement on instantaneity is not high. The scene belongs to instant query and has obvious characteristics of temporality and randomness.
Application scenario two: based on two XDR text files, namely, voicecall _ mb and voicecall _ nb, various aggregation table XDRs can be generated in a user-defined and automatic mode, and the result is led into ORACLE. For example, a day-granularity convergence table (mb _ imsi _ day or mb _ ci _ day) for a user or cell dimension may be generated based on voicecall _ mb.
(2) And (5) inquiring real-time data.
The XDR text file is efficiently and quickly inquired by utilizing a Spark function, the XDR of the aggregation table with various small time granularities (such as 5 minutes or 15 minutes granularity) is generated, and the XDR is imported into ORACLE. For example, ORACLE may be imported based on voicecall nb to generate a 15 minute granularity convergence table (nb _ imsi _15min or nb _ ci _15min) for user or cell dimensions, and the application program may present the voice quality indicator of TOP N users or TOP N cells in real time by calling these convergence tables of ORACLE.
The embodiment can solve the problem of voice quality evaluation analysis under two scenes of intercommunication between the VoLTE user and intercommunication between the VoLTE user and the 2/3G user, and various application analyses made by the method can be known around the two scenes.
Scene one: the VoLTE user and the VoLTE user are intercommunicated.
The scenario is based on a media stream with an Mb port, i.e., application analysis can be performed based on XDR data of voicecall _ Mb that has completed data backfilling. The speech quality assessment analysis is mainly performed on the scene by the following several applications.
The application one is as follows: TOP N cell real-time monitoring
By using the Hive function, a 15-minute granularity convergence table mb _ ci _15min of the cell dimension is generated in real time based on voicecall _ mb, and is imported into an ORACLE database. The application server executes a corresponding program through a custom task to call mb _ ci _15min, TOP N cells with the worst MOS value are generated every 15 minutes, the TOP N cells can be rendered and presented in a map form, and meanwhile, a report form can be generated or the result can be imported into an ORACLE database.
Based on mb _ ci _15min, aggregate tables mb _ ci _ hour and mb _ ci _ day of hour and day granularities can be easily obtained.
The application II comprises the following steps: network element level real-time monitoring
And generating a convergence table mb _ ne _15min of network element dimension and 15-minute granularity in real time based on the voicecall _ mb table. The network elements of the aggregation table may include a TAC, an SGW, and a ballast area. Based on the convergence table, the voice quality index conditions of the TAC, the SGW and each town area, such as MOS value, jitter, time delay, packet loss rate and the like, can be monitored in real time.
The application is as follows: TOP N user analysis
And generating a user dimension hour granularity or day granularity convergence table mb _ imsi _ hour or mb _ imsi _ day in real time based on the voicecall _ mb. By utilizing the convergence table, a TOP N user list with the worst MOS value can be automatically generated, the cell information of the TOP N user is obtained, and whether the TOP N user list is caused by the wireless side problem of the cell or not is analyzed.
And application four: customer complaint analysis
Aiming at temporary and random batch user complaint numbers, voicecall _ mb is inquired by Hive, cell information of complaint users 2 hours before complaint time (the time can be defined by self) and corresponding MOS value indexes are obtained, and whether complaint reasons are related to cell wireless environment or not is analyzed.
Scene two: VoLTE users interwork with 2/3G users.
The scene is based on a media stream of an Nb port (interface between an IM-MGW and a CS-MGW), namely, application analysis can be carried out based on XDR data of voicecall _ Nb which is backfilled by data. The speech quality assessment analysis is mainly performed on the scene by the following several applications.
The application one is as follows: VoLTE network user voice quality analysis based on Nb port
In XDR of voicecall nb, the rat type field characterizes the network type, and takes values of 2, 3, and 4, representing 2G, 3G, and 4G networks, respectively. When the rat _ type is taken to be 4, the overall voice quality of the VoLTE user at the Nb port in the scenario of the VoLTE user interworking with 2/3G user can be analyzed.
The application II comprises the following steps: mb port and Nb port based speech quality difference analysis
The voice quality of the Mb port and the voice quality of the Nb port are respectively evaluated for the users, and the difference is analyzed.
The embodiment performs comprehensive voice quality assessment on the scene where the VoLTE user and the VoLTE user are intercommunicated based on the data of the Mb interface. The existing method does not realize the acquisition of the interface, reasonable server combination and algorithm, and analysis and processing technology of mass data, and the embodiment can perform voice quality evaluation from the dimensions of users, terminals, cells, TACs and the like, analyze factors of various dimensions influencing voice quality, and find an optimization method; the Nb interface based data is for the scenario where VoLTE users interwork with 2/3G users. In the current years, the scene accounts for more than 90%, and acquisition, storage and analysis based on IMGW are innovatively proposed to carry out overall voice quality evaluation of Nb-port VoLTE users; and simultaneously evaluating the voice quality of the Mb port and the Nb port aiming at the same VoLTE user, and analyzing the difference.
The embodiment performs correlation analysis on data of multiple interfaces (including Mb, Nb, S11, Gm and Mc interfaces); by adopting a distributed architecture and utilizing the combination of the acquisition server, the processing server, the loader server, the hadoop cluster, the database server and the application server, the data of the 5 interfaces can be processed in real time (granularity presentation for 15 minutes), in a large amount (users in the whole network and 2W), and efficiently (minute-level query is finished); and for the voice quality evaluation of the RTP media stream, the evaluation is carried out by adopting a P.563 algorithm of ITU specification, and the evaluation result is reliable and accurate. Compared with the traditional drive test result, the fitting degree of the MOS result based on the P.563 algorithm reaches 0.82, the error mean value is less than 0.3, and the error mean value of the MOS result based on the E-Model algorithm and the traditional drive test result exceeds 0.45; and (4) carrying out data cleaning, query and processing by using the hadoop cluster. Various reports and convergence tables can be generated by self-defining, and the convergence tables can be imported into an ORACLE database. The user-defined convergence table can reduce the load of the ORACLE, and compared with a directly stored original table, the mode can bring obvious improvement to the I/O performance of the ORACLE; the function is widely applied, and real-time (15-minute granularity) and full-network-range voice quality analysis can be carried out on two scenes, namely intercommunication between a VoLTE user and a VoLTE user (Mb interface media stream) and intercommunication between the VoLTE user and an 2/3G user (Nb interface media stream).
Fig. 11 shows a schematic structural diagram of a VoLTE voice quality assessment system provided in this embodiment, where the system includes: the system comprises an acquisition server 1101, a processing server 1102, a data backfill loader server 1103, a distributed processing hadoop cluster system 1104 and a database server 1105;
the collection server 1101 collects voice and signaling data subjected to light splitting and convergence at each interface, and sends the voice and signaling data to the processing server 1102;
the processing server 1102 analyzes the voice and signaling data, performs mean subjective opinion score MOS value evaluation on the media stream obtained by analysis to obtain voice quality evaluation data, and sends the voice quality evaluation data to the loader server 1103;
the loader server 1103 adds user information and location information to the voice quality assessment data according to the control surface data, generates a voice quality assessment file according to the voice quality assessment data to which the user information and the location information are added, and sends the voice quality assessment file to the hadoop cluster system 1104;
the hadoop cluster system 1104 stores the voice quality evaluation file to a database server;
the database server 1105 is used to store the voice quality assessment files.
In the embodiment, the voice quality evaluation file is generated by adding the user information and the position information into the voice quality evaluation data, so that the voice quality evaluation file is convenient to query, is suitable for each period of VoLTE, is particularly suitable for a VoLTE user dialing VoLTE, and a scene of a traditional 2/3G user dialing VoLTE, and enables the evaluation result to be reliable and accurate by carrying out MOS value evaluation.
Further, on the basis of the above apparatus embodiment, the collection server 1101 is specifically configured to collect media streams at the Mb interface, the Nb interface, the S11 interface, the Gm interface, and the Mc interface, split the media streams by 2/8, and aggregate 20% of the split media streams to obtain the voice and signaling data.
Further, on the basis of the above apparatus embodiment, the processing server 1102 is specifically configured to analyze the voice and signaling data, screen the analyzed voice and signaling data according to a preset flow and a preset field to obtain a media stream of an Mb interface or an Nb interface, and perform MOS value evaluation on the media stream by using a p.563 algorithm of the ITU specification to obtain voice quality evaluation data.
Further, on the basis of the foregoing device embodiment, the loader server 1103 is specifically configured to add user information to the voice quality assessment data according to a session _ setup table in the control plane data, add location information to the voice quality assessment data according to an sipcall table in the control plane data, and generate a voice quality assessment file according to the voice quality assessment data to which the user information and the location information are added.
Further, on the basis of the above apparatus embodiment, the hadoop cluster system 1104 is further configured to perform real-time query on each voice quality assessment file stored in the database server according to a preset condition, generate a convergence table of a preset time period, and store the convergence table to the database server.
The speech quality assessment storage system described in this embodiment may be used to implement the above method embodiments, and the principle and technical effect are similar, which are not described herein again.
Referring to fig. 12, the electronic device includes: a processor (processor)1201, a memory (memory)1202, and a bus 1203;
wherein,
the processor 1201 and the memory 1202 communicate with each other via the bus 1203;
the processor 1201 is configured to call program instructions in the memory 1202 to perform the methods provided by the above-mentioned method embodiments, including:
collecting voice and signaling data after light splitting and convergence at each interface;
analyzing the voice and signaling data, and performing mean subjective opinion score (MOS) value evaluation on the media stream obtained by analysis to obtain voice quality evaluation data;
adding user information and position information into the voice quality evaluation data according to control surface data, and generating a voice quality evaluation file according to the voice quality evaluation data added with the user information and the position information;
and storing the voice quality evaluation file to a database server.
The present embodiment discloses a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, enable the computer to perform the method provided by the above-mentioned method embodiments, for example, comprising:
collecting voice and signaling data after light splitting and convergence at each interface;
analyzing the voice and signaling data, and performing mean subjective opinion score (MOS) value evaluation on the media stream obtained by analysis to obtain voice quality evaluation data;
adding user information and position information into the voice quality evaluation data according to control surface data, and generating a voice quality evaluation file according to the voice quality evaluation data added with the user information and the position information;
and storing the voice quality evaluation file to a database server.
The present embodiments provide a non-transitory computer-readable storage medium storing computer instructions that cause the computer to perform the methods provided by the above method embodiments, for example, including:
collecting voice and signaling data after light splitting and convergence at each interface;
analyzing the voice and signaling data, and performing mean subjective opinion score (MOS) value evaluation on the media stream obtained by analysis to obtain voice quality evaluation data;
adding user information and position information into the voice quality evaluation data according to control surface data, and generating a voice quality evaluation file according to the voice quality evaluation data added with the user information and the position information;
and storing the voice quality evaluation file to a database server.
Those of ordinary skill in the art will understand that: all or part of the steps for implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer readable storage medium, and when executed, the program performs the steps including the method embodiments; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
It should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A VoLTE voice quality assessment method is characterized by comprising the following steps:
collecting voice and signaling data after light splitting and convergence at each interface;
analyzing the voice and signaling data, and performing mean subjective opinion score (MOS) value evaluation on the media stream obtained by analysis to obtain voice quality evaluation data;
adding user information and position information into the voice quality evaluation data according to control surface data, and generating a voice quality evaluation file according to the voice quality evaluation data added with the user information and the position information;
and storing the voice quality evaluation file to a database server.
2. The method according to claim 1, wherein the collecting voice and signaling data split and converged at each interface specifically comprises:
collecting media streams at the Mb interface, the Nb interface, the S11 interface, the Gm interface and the Mc interface, splitting the media streams into 2/8 light, and converging 20% of the split media streams to obtain the voice and signaling data.
3. The method according to claim 1, wherein the parsing the voice and signaling data and performing mean subjective opinion score (MOS) value evaluation on the media stream obtained by parsing to obtain voice quality evaluation data specifically comprises:
and analyzing the voice and signaling data, screening the analyzed voice and signaling data according to a preset flow and a preset field to obtain a media stream of an Mb interface or an Nb interface, and evaluating an MOS value of the media stream by adopting a P.563 algorithm of ITU specification to obtain voice quality evaluation data.
4. The method according to claim 1, wherein the adding user information and location information to the voice quality assessment data according to control plane data, and generating a voice quality assessment file according to the voice quality assessment data to which the user information and the location information are added specifically includes:
adding user information in the voice quality evaluation data according to a session _ setup table in the control surface data, adding position information in the voice quality evaluation data according to an sipcall table in the control surface data, and generating a voice quality evaluation file according to the voice quality evaluation data added with the user information and the position information.
5. The method of claim 1, wherein after storing the speech quality assessment file to a database server, further comprising:
and inquiring each voice quality evaluation file stored in the database server in real time according to preset conditions to generate a convergence table of a preset time period, and storing the convergence table to the database server.
6. A VoLTE voice quality assessment system, comprising: the system comprises an acquisition server, a processing server, a data backfill loader server, a distributed processing hadoop cluster system and a database server;
the acquisition server acquires voice and signaling data subjected to light splitting and convergence at each interface and sends the voice and signaling data to the processing server;
the processing server analyzes the voice and signaling data, performs mean subjective opinion score (MOS) value evaluation on the media stream obtained by analysis to obtain voice quality evaluation data, and sends the voice quality evaluation data to the loader server;
the loader server adds user information and position information into the voice quality evaluation data according to control surface data, generates a voice quality evaluation file according to the voice quality evaluation data added with the user information and the position information, and sends the voice quality evaluation file to a hadoop cluster system;
the hadoop cluster system stores the voice quality evaluation file to a database server;
the database server is used for storing the voice quality evaluation file.
7. The system according to claim 6, wherein the collection server is specifically configured to collect media streams at the Mb interface, the Nb interface, the S11 interface, the Gm interface, and the Mc interface, split the media streams into 2/8 parts, and aggregate 20% of the split media streams to obtain the voice and signaling data.
8. The system of claim 6, wherein the processing server is specifically configured to parse the voice and signaling data, filter the parsed voice and signaling data according to a preset flow and a preset field, obtain a media stream of an Mb interface or an Nb interface, and perform MOS value evaluation on the media stream by using a p.563 algorithm of the ITU specification, so as to obtain voice quality evaluation data.
9. The system according to claim 6, wherein the loader server is specifically configured to add user information to the voice quality assessment data according to a session _ setup table in control plane data, add location information to the voice quality assessment data according to an sipcall table in control plane data, and generate a voice quality assessment file according to the voice quality assessment data to which the user information and the location information are added.
10. The system according to claim 6, wherein the hadoop cluster system is further configured to perform real-time query on each voice quality assessment file stored in the database server according to a preset condition, generate a convergence table of a preset time period, and store the convergence table to the database server.
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109068335A (en) * | 2018-09-10 | 2018-12-21 | 中国联合网络通信集团有限公司 | A kind of method and apparatus of network coverage parameter setting |
| CN109147822A (en) * | 2018-10-26 | 2019-01-04 | 上海创远仪器技术股份有限公司 | System and method for VOLTE voice recording and speech quality evaluation |
| CN109600789A (en) * | 2019-01-28 | 2019-04-09 | 西安海润通信技术有限公司 | A kind of VoLTE voice quality MOS appraisal procedure based on commerce terminal |
| CN109785860A (en) * | 2019-01-08 | 2019-05-21 | 北京中创信测科技股份有限公司 | A kind of method that four domain six-dimension intelligent of 2G3G4G voice quality failure is delimited |
| CN111372209A (en) * | 2018-12-06 | 2020-07-03 | 中国移动通信集团辽宁有限公司 | Signaling data processing method, device, equipment and medium |
| CN111614623A (en) * | 2020-04-21 | 2020-09-01 | 北京红山信息科技研究院有限公司 | VoLTE user perception information processing method, system and medium |
| CN111935769A (en) * | 2019-05-13 | 2020-11-13 | 中国移动通信集团湖北有限公司 | Poor cell identification method, device and equipment |
| TWI717096B (en) * | 2019-04-17 | 2021-01-21 | 麥奇數位股份有限公司 | Interactive quality analysis method of online interactive system and its server |
| CN112260856A (en) * | 2020-09-27 | 2021-01-22 | 北京拓明科技有限公司 | Data quality evaluation method and system based on 5G XDR |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102438266A (en) * | 2011-01-12 | 2012-05-02 | 北京炎强通信技术有限公司 | Method and device for optimizing voice quality of mobile communication network |
| US20130031575A1 (en) * | 2010-10-28 | 2013-01-31 | Avvasi | System for monitoring a video network and methods for use therewith |
| US20140379935A1 (en) * | 2013-06-25 | 2014-12-25 | Cellco Partnership D/B/A Verizon Wireless | Mobile device perceptive audio and video quality analysis using onboard test signals |
| US20160057607A1 (en) * | 2013-03-29 | 2016-02-25 | Mobileum Inc. | ENABLING VOICE OVER LONG TERM EVOLUTION (VoLTE) SERVICES FOR NON-VoLTE INBOUND ROAMERS |
| CN105578491A (en) * | 2014-10-17 | 2016-05-11 | 任子行网络技术股份有限公司 | Method and device for associating 4G user information with application data |
-
2017
- 2017-02-13 CN CN201710076626.4A patent/CN108428459A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130031575A1 (en) * | 2010-10-28 | 2013-01-31 | Avvasi | System for monitoring a video network and methods for use therewith |
| CN102438266A (en) * | 2011-01-12 | 2012-05-02 | 北京炎强通信技术有限公司 | Method and device for optimizing voice quality of mobile communication network |
| US20160057607A1 (en) * | 2013-03-29 | 2016-02-25 | Mobileum Inc. | ENABLING VOICE OVER LONG TERM EVOLUTION (VoLTE) SERVICES FOR NON-VoLTE INBOUND ROAMERS |
| US20140379935A1 (en) * | 2013-06-25 | 2014-12-25 | Cellco Partnership D/B/A Verizon Wireless | Mobile device perceptive audio and video quality analysis using onboard test signals |
| CN105578491A (en) * | 2014-10-17 | 2016-05-11 | 任子行网络技术股份有限公司 | Method and device for associating 4G user information with application data |
Non-Patent Citations (1)
| Title |
|---|
| 高艳等: "VoLTE信令采集及性能测试分析", 《电信科学》 * |
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109068335B (en) * | 2018-09-10 | 2021-09-14 | 中国联合网络通信集团有限公司 | Method and device for setting network coverage parameters |
| CN109068335A (en) * | 2018-09-10 | 2018-12-21 | 中国联合网络通信集团有限公司 | A kind of method and apparatus of network coverage parameter setting |
| CN109147822A (en) * | 2018-10-26 | 2019-01-04 | 上海创远仪器技术股份有限公司 | System and method for VOLTE voice recording and speech quality evaluation |
| CN109147822B (en) * | 2018-10-26 | 2023-05-09 | 上海创远仪器技术股份有限公司 | System and method for VOLTE voice recording and voice quality evaluation |
| CN111372209B (en) * | 2018-12-06 | 2022-04-08 | 中国移动通信集团辽宁有限公司 | Signaling data processing method, device, equipment and medium |
| CN111372209A (en) * | 2018-12-06 | 2020-07-03 | 中国移动通信集团辽宁有限公司 | Signaling data processing method, device, equipment and medium |
| EP3680902A1 (en) * | 2019-01-08 | 2020-07-15 | Beijing Zhongchuang Telecom Test Co., Ltd | 2g3g4g speech quality fault four-domain six-dimension intelligent delimitation method |
| CN109785860A (en) * | 2019-01-08 | 2019-05-21 | 北京中创信测科技股份有限公司 | A kind of method that four domain six-dimension intelligent of 2G3G4G voice quality failure is delimited |
| CN109600789B (en) * | 2019-01-28 | 2021-11-23 | 西安海润通信技术有限公司 | VoLTE voice quality MOS (Metal oxide semiconductor) evaluation method based on business terminal |
| CN109600789A (en) * | 2019-01-28 | 2019-04-09 | 西安海润通信技术有限公司 | A kind of VoLTE voice quality MOS appraisal procedure based on commerce terminal |
| TWI717096B (en) * | 2019-04-17 | 2021-01-21 | 麥奇數位股份有限公司 | Interactive quality analysis method of online interactive system and its server |
| CN111935769A (en) * | 2019-05-13 | 2020-11-13 | 中国移动通信集团湖北有限公司 | Poor cell identification method, device and equipment |
| CN111935769B (en) * | 2019-05-13 | 2022-11-22 | 中国移动通信集团湖北有限公司 | Poor cell identification method, device and equipment |
| CN111614623A (en) * | 2020-04-21 | 2020-09-01 | 北京红山信息科技研究院有限公司 | VoLTE user perception information processing method, system and medium |
| CN111614623B (en) * | 2020-04-21 | 2022-04-08 | 北京红山信息科技研究院有限公司 | VoLTE user perception information processing method, system and medium |
| CN112260856A (en) * | 2020-09-27 | 2021-01-22 | 北京拓明科技有限公司 | Data quality evaluation method and system based on 5G XDR |
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