CN108428459A - A kind of VoLTE speech quality assessment methods and system - Google Patents
A kind of VoLTE speech quality assessment methods and system Download PDFInfo
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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 present embodiments relate to fields of communication technology, and in particular to a kind of VoLTE speech quality assessment methods and system.
Background technology
In 2/3/4G converged network, usually there is voice quality distortion phenomenon.VoLTE network elements and node multiplication,
Structure is more complicated, commercial initial stage, but will there are problems that voice distortion when VoLTE user is with 2/3G user's intercommunication, seriously affect
Actual user perceive, there is an urgent need for it is a set of effectively be directed to VoLTE network voice quality appraisal procedures, can to speech service quality into
Row assessment, supervision and inquiry, provide to the user high quality, satisfaction service.
The monitoring method of LTE network voice service is directed to CSFB scenes in existing method, to 4G user in LTE network and 2/
Speech quality evaluation analysis is carried out in 3G network communication process.Analysis to LTE network, need acquire S1-U, S1-MME, S6a,
The EPC domain interface data such as SGs, S10, S11 fill user information and position letter by specific user-association rule for S1-U
Breath；Analysis to 2/3G networks needs the data for acquiring the interfaces such as Nb, Mc and AoIP, by specific user-association rule, is
Nb fills user information and location information；To associated CSFB user speech record, Cause and Effect matrix, flow chart and dimension rank are utilized
Section statistical method carries out voice quality analysis, obtains the voice qualities index such as corresponding single-pass, noise, echo, interrupted.Or it adopts
With based on the voice quality objective evaluation method for improving E-Model models, existing E-Model models first according to network delay, make an uproar
The parameters such as sound, audio coder ＆ decoder (codec) calculate transmission performance equivalent coefficient R, then convert R to equivalent MOS score values, but improve
E-Model models are also gradually increasing, there are quality of evaluation when packet loss gradually increases with the mean square error of PESQ scorings
Problem of dtmf distortion DTMF；Improve E-Model model applied statistics analysis and least square fitting technology, with the actual loss time of voice come
Instead of packet loss and burst ratio so that the mean square error of E-Model models does not change with packet loss and is changed substantially after improvement,
The accuracy of speech quality evaluation is provided.
During realizing the embodiment of the present invention, inventor has found the monitoring method of existing LTE network voice service
It is only capable of belonging to the excellent of LTE initial stages in the voice quality analysis of LTE network and 2/3G networks for 4G user under CSFB scenes
Change method, into after the VoLTE commercial phases, be more VoLTE user and VoLTE user intercommunication scene and VoLTE user and
The scene of 2/3G user's intercommunication, this method can not provide the speech quality detection method to VoLTE networks；And only for 2/3G nets
Network wireless access part carries out voice quality analysis, fails to carry out analysis assessment to the voice quality of 4G and VoLTE networks；It is based on
The voice quality objective evaluation method for improving E-Model models biases toward the description to algorithm itself, does not specify how to utilize and be somebody's turn to do
Method carries out speech quality evaluation in existing net, in addition, E-Model model algorithms pass through fitting parameter (time delay, shake, packet loss)
Obtain MOS values, model algorithm is simpler, and assessment accuracy is difficult to control.
Invention content
The problem of can not ensureing due to the assessment accuracy of existing method, the embodiment of the present invention proposes a kind of VoLTE voices
Method for evaluating quality and system.
In a first aspect, the embodiment of the present invention proposes a kind of VoLTE speech quality assessment methods, including：
Acquire voice and signaling data of each interface after light splitting and convergence；
The voice and signaling data are parsed, and mean subjective opinion point is carried out to the Media Stream that parsing obtains
MOS values are assessed, and speech quality evaluation data are obtained；
User information and location information are added in the speech quality evaluation data according to control face data, and according to adding
The speech quality evaluation data after user information and location information are added to generate speech quality evaluation file；
The speech quality evaluation file is stored to database server.
Optionally, voice and signaling data of each interface of acquisition after light splitting and convergence, specifically includes：
The Mb, the Nb interface, S11 interfaces, Gm, the Media Stream at Mc interface are acquired, to the media
Stream carries out 2/8 light splitting, and is converged to the Media Stream of 20% after light splitting, obtains the voice and signaling data.
Optionally, described that the voice and signaling data are parsed, and the Media Stream obtained to parsing is averaged
Subjective opinion divides MOS values to assess, and obtains speech quality evaluation data, specifically includes：
The voice and signaling data are parsed, according to default flow and preset field to the voice after parsing
It is screened with signaling data, obtains the Media Stream of Mb or Nb interface, and using the P.563 algorithm of ITU specifications to described
Media Stream carries out MOS value assessments, obtains speech quality evaluation data.
Optionally, described that user information and position letter are added in the speech quality evaluation data according to control face data
Breath, and speech quality evaluation file is generated according to the speech quality evaluation data after addition user information and location information,
It specifically includes：
User's letter is added in the speech quality evaluation data according to the session_setup tables in control face data
Breath, according to the sipcall tables in control face data in the speech quality evaluation data point of addition information, and according to addition
The speech quality evaluation data after user information and location information generate speech quality evaluation file.
Optionally, described to store the speech quality evaluation file to database server, further include：
Real-time query is carried out to each speech quality evaluation file stored in the database server according to preset condition,
The convergence table of preset time period is generated, and the convergence table is stored to the database server.
Second aspect, the embodiment of the present invention also propose a kind of VoLTE speech quality assessment systems, including：Acquisition server,
Processing server, data backfill loader servers, distributed treatment hadoop cluster system and database server；
The acquisition server acquires voice and signaling data of each interface after light splitting and convergence, and by institute's predicate
Sound and signaling data are sent to processing server；
The processing server parses the voice and signaling data, and the Media Stream obtained to parsing is put down
Subjectivity opinion divides MOS values to assess, and obtains speech quality evaluation data, and the speech quality evaluation data is sent to described
Loader servers；
The loader servers according to control face data add in the speech quality evaluation data user information with
Location information, and generate speech quality evaluation according to the speech quality evaluation data after addition user information and location information
File, and the speech quality evaluation file is sent to hadoop cluster system；
The hadoop cluster system stores the speech quality evaluation file to database server；
The database server is for storing the speech quality evaluation file.
Optionally, the acquisition server connects specifically for acquiring the Mb, the Nb interface, S11 interfaces, Gm
Media Stream at mouth, Mc interface carries out 2/8 light splitting to the Media Stream, and is carried out to the Media Stream of 20% after light splitting
Convergence, obtains the voice and signaling data.
Optionally, the processing server is specifically used for parsing the voice and signaling data, according to default stream
Journey and preset field to after parsing the voice and signaling data screen, obtain the Media Stream of Mb or Nb interface,
And MOS value assessments are carried out to the Media Stream using the P.563 algorithm of ITU specifications, obtain speech quality evaluation data.
Optionally, the loader servers are specifically used for according to the session_setup tables in control face data in institute
User information is added in Voice Quality assessment data, is commented in institute's Voice Quality according to the sipcall tables in control face data
Estimate point of addition information in data, and is generated according to the speech quality evaluation data after addition user information and location information
Speech quality evaluation file.
Optionally, the hadoop cluster system is additionally operable to according to preset condition to storing in the database server
Each speech quality evaluation file carries out real-time query, generates the convergence table of preset time period, and the convergence table is stored to institute
State database server.
As shown from the above technical solution, the embodiment of the present invention by speech quality evaluation data add user information and
Speech quality evaluation file is generated after location information, facilitates inquiry, is suitable for each period of VoLTE, is particularly suitable for VoLTE
VoLTE user is dialed, VoLTE dials the scene of traditional 2/3G user, and by carrying out MOS value assessments so that assessment result
Reliably, accurately.
Description of the drawings
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technology description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with
Other attached drawings are obtained according to these figures.
Fig. 1 is a kind of flow diagram for VoLTE speech quality assessment methods that one embodiment of the invention provides；
Fig. 2 is the Mb schematic diagram that one embodiment of the invention provides；
Fig. 3 is the Nb interface schematic diagram that one embodiment of the invention provides；
Fig. 4 is the structural schematic diagram for the VoLTE speech quality assessment systems that one embodiment of the invention provides；
Fig. 5 is the principle schematic for the VoLTE speech quality assessment systems that one embodiment of the invention provides；
Fig. 6 is the acquisition interface schematic diagram for the VoLTE speech quality assessment systems that one embodiment of the invention provides；
Fig. 7 is the light-dividing principle schematic diagram for the VoLTE speech quality assessment methods that one embodiment of the invention provides；
Fig. 8 is the flow diagram of the user data backfill for the Mb that one embodiment of the invention provides；
Fig. 9 is the flow diagram of the user data backfill for the Nb interface that one embodiment of the invention provides；
Figure 10 is the hadoop cluster configuration diagram that one embodiment of the invention provides；
Figure 11 is a kind of structural schematic diagram for VoLTE speech quality assessment systems that another embodiment of the present invention provides；
Figure 12 is the logic diagram of electronic equipment in one embodiment of the invention.
Specific implementation mode
Below in conjunction with the accompanying drawings, the specific implementation mode of the present invention is further described.Following embodiment is only used for more
Technical scheme of the present invention is clearly demonstrated, and not intended to limit the protection scope of the present invention.
Fig. 1 shows a kind of flow diagram of VoLTE speech quality assessment methods provided in this embodiment, including：
The voice and signaling data of S101, each interface of acquisition after light splitting and convergence.
Wherein, the interface include two Media Streams interface (Mb, Nb) and three control planes interface (S11, Gm,
Mc).Specifically, RTP voice packets can be obtained by acquiring two interfaces of Mb, Nb；It can be by acquiring S11, Gm and Mc tri-
Interface obtains corresponding control face data.
Above 5 interfaces are to carry out Data Convergence by way of 2/8 light splitting in the sides CE, after light splitting, 80% smooth work(
Rate returns to former operator's primary link all the way, and 20% luminous power accesses to the TAP shunting devices of this system all the way, carries out data remittance
Poly- convergence.
S102, the voice and signaling data are parsed, and mean subjective meaning is carried out to the Media Stream that parsing obtains
See a point MOS values assessment, obtains speech quality evaluation data.
Wherein, the P.563 algorithm of the ITU specifications is that the MOS quality evaluations based on no reference model that ITU is released are calculated
Method belongs to disclosed canonical algorithm in the world.
Specifically, the voice and signaling data are parsed, obtains the Media Stream of above-mentioned 5 interfaces, by screening,
The Media Stream of Mb or Nb interface is screened, and using P.563 algorithm, carries out MOS values for all RTP packets and comment
Divide, it can be achieved that the data of big data quantity efficiently and rapidly inquire and handle.
S103, user information and location information are added in the speech quality evaluation data according to control face data, and
Speech quality evaluation file is generated according to the speech quality evaluation data after addition user information and location information.
Wherein, the control face data is the data received from tri- interfaces of S11, Gm, Mc, including user information and position
Confidence ceases.
S104, the speech quality evaluation file is stored to database server.
Specifically, the present embodiment is based primarily upon the speech quality evaluation problem of following two scenes：
Scene one：The intercommunication of VoLTE user and VoLTE user need the voice quality for assessing Mb Media Stream.Such as figure
Shown in 2, which includes the domains EPC and IMS domain.The domains EPC include the logical nes such as MME, SGW and PGW, IMS domain include P-CSCF,
The logical nes such as I/S-CSCF, BGCF, MGCF, VoLTE-SBC, IM-MGW and CS-MGW.Mb is PGW and VoLTE-SBC
Between interface, the interface bearing media stream data in the domains EPC and IMS domain, the Media Stream of all VoLTE networks can pass through
This interface carries out data transfer.The RTP packets for analyzing the interface can obtain the voice quality index of VoLTE networks.S11 and Gm
The interface bearing data of control plane can be that the media stream data of Mb fills user information and location information.
Scene two：The intercommunication of VoLTE user and 2/3G user need the voice quality for assessing Nb interface Media Stream.Such as Fig. 3
Shown, Nb interface is the interface between IM-MGW and CS-MGW, main to complete IMS domain and the domains CS user plane wide and narrow strip bearing interconnection
It is converted with Codec encoding and decoding.Under the scene of VoLTE user and 2/3G user's intercommunication, the RTP Media Streams of VoLTE user pass through
When the interface, code encoding/decoding mode can convert, and user plane becomes narrowband from broadband.This transformation may cause VoLTE to use
The voice quality at family is deteriorated, and needs to carry out speech quality evaluation analysis to the RTP Media Streams of the interface.The control plane number of Mc interface
According to the media stream data filling user information and location information that can be Nb interface.
Specifically, as shown in figure 4, the corresponding system of the present embodiment by acquisition server, processing server (MOS assessments),
Loader servers (data backfill), hadoop cluster (distributed treatment), database server and application server are totally 6 big
Part forms, these servers are all made of distributed structure/architecture, data interchange is carried out with three-tier switch.
System is acquired decoded mode and is obtained data source using local, in each interface (including Mb, Nb, S11, Gm and Mc totally 5
A interface) corresponding sides CE carry out 2/8 light splitting, the data after light splitting are converged using TAP current dividers, then according to based on
IP filter methods and load balancing principle are sent to every acquisition server.Acquisition server acquires the source code of above-mentioned 5 interfaces
Stream, then source code flow is handled by processing server, only retain needs flow and field (such as S11 interfaces are only protected
Stay session_setup flows).In processing procedure, by matchmaker of the P.563 algorithm to two interfaces of Mb and Nb for being embedded in platform
Body stream carries out MOS value assessments, then distinguishes equal relation mechanism (detailed pass according to port and IP by lorder servers
Online system can see below the content of third portion) data of related media stream and control plane are gone, it is that the data filling of Media Stream is important
User information (such as IMSI, MSISDN, IMEI) and location information (such as SGW, TAC, ENB, CI).After having filled, system
These data can be converted to the text file of TXT formats, and these files are sent to hadoop cluster system, by hadoop
Cluster carries out storage and query processing to these files, and query result is imported into ORACLE as required, and final data
Using be based on ORACLE data.
This system needs to acquire two interfaces of Mb, Nb, obtains RTP voice packets, while needing to acquire S11, Gm and Mc tri-
Interface obtains corresponding control face data.Referring to Fig. 5, first, system collects the RTP of Mb, Nb interface by acquisition server
After speech code stream, processing can be decoded to these source code flows, the P.563 algorithm then embedded by system is to these voices
Packet carries out MOS value assessments.RTP speech code streams itself are no user information and location information, are needed through relevant control
Face data is associated backfill.The data of the XDR Yu S11, Gm, Mc of Mb, Nb interface these control planes after MOS is assessed according to
Certain relation mechanism is associated, and backfills the user data informations such as corresponding IMSI, cell.By association backfill after Mb,
The XDR of Nb interface, has had user information and location information of cell, can meet various function application and specific analysis need
It asks.
The present embodiment after adding user information and location information in speech quality evaluation data by generating voice quality
File is assessed, inquiry is facilitated, is suitable for each period of VoLTE, VoLTE is particularly suitable for and dials VoLTE user, VoLTE is dialled
The scene of traditional 2/3G user is beaten, and by carrying out MOS value assessments so that assessment result is reliable, accurate.
Further, on the basis of above method embodiment, S101 is specifically included：
The Mb, the Nb interface, S11 interfaces, Gm, the Media Stream at Mc interface are acquired, to the media
Stream carries out 2/8 light splitting, and is converged to the Media Stream of 20% after light splitting, obtains the voice and signaling data.
Specifically, system need acquire two Media Streams interface (Mb, Nb) and three control planes interface (S11, Gm,
Mc), Fig. 6 be to need the interface diagram that acquires.
Above 5 interfaces are to carry out Data Convergence by way of 2/8 light splitting in the sides CE, after light splitting, 80% smooth work(
Rate returns to former operator's primary link all the way, and 20% luminous power accesses to the TAP shunting devices of this system all the way, carries out data remittance
Poly- convergence.Fig. 7 is light-dividing principle figure：Data source takes data distribution to every acquisition after the convergence of TAP equipment, through certain rule
Business device.Acquisition server uses distributed structure/architecture, and carries out intercommunication using three-tier switch.NTP is used between multiple servers
Etc. agreements carry out time precision synchronization, accomplish 1-50 Millisecond time precision requirements.The key effect of acquisition server is will be former
Beginning code stream is linked into this system, and after preliminary treatment, the identifiable file format of conversion cost system is retransmited to processing and serviced
Device makees more targeted processing.
The present embodiment carries out the convergence of data source by using 2/8 spectroscopic modes from the sides CE of each interface, TAP points after convergence
Source code flow can be distributed to each acquisition server by flow device, and acquisition server uses distributed structure/architecture, uses three-tier switch
Carry out data interchange.
Further, on the basis of above method embodiment, S102 is specifically included：
The voice and signaling data are parsed, according to default flow and preset field to the voice after parsing
It is screened with signaling data, obtains the Media Stream of Mb or Nb interface, and using the P.563 algorithm of ITU specifications to described
Media Stream carries out MOS value assessments, obtains speech quality evaluation data.
Specifically, the source code flow of each interface has multiple flows, and each flow has several fields.This implementation
Example all can not possibly decode all flows, all fields of interface to come, and can greatly increase the load of system in this way, influence system
Process performance.Processing server can (such as S11 interfaces only define session_setup flows, remaining flow by user-defined flow
Do not export) and field choose, the data that acquisition server is brought are carried out with the screening of flow and field, removes and does not need
, retain the flow and field of needs.Meanwhile the P.563 algorithm by being embedded in system, and without needing list as other schemes
Only arithmetic server carries out MOS value scorings to Mb mouthfuls and Nb mouthfuls of RTP packets, accordingly obtains MOS values.
After processing server processing and MOS scorings, the present embodiment can generate as shown in the table various in each interface
XDR files and corresponding critical field：
Further, on the basis of above method embodiment, S103 is specifically included：
User's letter is added in the speech quality evaluation data according to the session_setup tables in control face data
Breath, according to the sipcall tables in control face data in the speech quality evaluation data point of addition information, and according to addition
The speech quality evaluation data after user information and location information generate speech quality evaluation file.
Specifically, for the assessment of voice quality, the voicecall_infost of Mb mouthfuls and Nb mouthfuls of analysis is needed, but should
XDR only has the relevant data of voice quality, there is no any user information and location information, need the data by control plane come
It is filled.
(1) Mb mouthfuls of user data backfills mechanism
S11 mouthfuls directly decode generation session_setup tables, and there are the information such as IMSI, MSISDN in the inside；Gm mouthfuls of generations
Sipcall tables include the information such as LAC, CI；Mb mouthfuls of generation voicecall_infost tables, have MOS values (by front
P.563 algorithm is assessed) etc. voice qualities achievement data, but can not directly obtain about user information and subdistrict position letter
The data of breath, these data are required to be filled by backfill mechanism, wherein location information of cell needs pass through association
Sipcall tables obtain, and the user informations such as IMSI needs are obtained indirectly by session_setup tables.
The backfill flow of user data is as shown in figure 8, including step in detail below：
A1, the relation table for building MSISDN and IMSI.The session_setup flows of S11 interfaces carry MSISDN and
IMSI can obtain the relation table of MSISDN and IMSI, and can carry out real-time update automatically.
A2, IMSI is filled for sipcall flows.The sipcall flows of Gm can directly decode out the position of TAC, CI
Confidence ceases, and the MSISDN with calling and called, but lacks IMSI, needs to be backfilled by S11 interfaces, detailed process is such as
Under：
A21, MSISDN and the IMSI relation tables built using the first step carry out the filling of IMSI.When type of call is
When exhalation (Call_Type=1), the association that IMSI is carried out using sipcall.sip_from=MSISDN is filled；Work as calling
When type is incoming call (Call_Type=0), the association that IMSI is carried out using sipcall.sip_to=MSISDN is filled.Note：
It needs that the number format of MSISDN, sip_from, sip_to is normalized, otherwise number format can influence to match.
A22, still there is fraction to fail to be filled into IMSI after a, for the certain customers, utilize IP address phase
Deng principle backfilled.When the field User_IP_Value in Gm sipcall flows is equal to S11 interfaces Session_
When field User_IP_Value_v6 in Setup flows, filling out for IMSI is carried out after finding related Session_Setup records
It fills.
A3, Mb mouthfuls of user data backfill.Voice_ can be obtained by being directly decoded by the RTP Media Streams to Mb
The XDR of info goes association to match according to the four-tuple of the XDR principle equal with the four-tuple in Gm sipcall flows
The user data information (including IMSI, MSISDN, TAC, CI) of sipcall flows.Four-tuple is equal to refer to：Voice_info's
Srcipv6, destipv6, srcport, destport respectively with the CallingVoice_ in Gm sipcall flows
Addr, CalledVoice_Addr, CallingVoice_Port, CalledVoice_Port are strictly equal.
(2) Nb mouthfuls of user data backfills mechanism
Mc mouthfuls directly decode eventually 3 XDR of generation, are H248, voicecall_moc and voicecall_mtc3 respectively
Open original table.H248 is to be directed to Nb mouthfuls of chain of command agreement；Voicecall_moc and voicecall_mtc is Mc mouthfuls of main quilts respectively
The information such as detailed IMSI, MSISDN, LAC, CI are contained in the control face data cried, the inside.
Nb mouthfuls directly decode generation VOICE_INFOST tables, include the voice qualities achievement data such as MOS values, but without appointing
What user information and location information.The user data of VoLTE networks is needed through the control plane information of Gm mouthfuls and S11 mouthfuls back and forth
It fills out, and the user data of 2/3G networks then needs the control plane information by Mc mouthfuls to backfill.
The backfill flow of user data is as shown in figure 9, specifically include following steps：
The user data of B1, VoLTE network backfills.Here backfill mechanism backfills mechanism one with above-mentioned Mb mouthfuls of user
Sample is not repeated to illustrate.
The user data of B2,2/3G network backfills.In two kinds of situation：The first is the user for the non-IPization of 2G networks,
Second is the user for being directed to 2/3G network IPization.
B21, it is directed to the first：When the triple ternary with voicecall_moc and voicecall_mtc respectively of H248
When group is stringent equal, the information such as IMSI, MSISDN, LAC, CI are filled for H248.Triple refers to herein：Pcm, timeslot and
opc。
B22, it is directed to second：When binary of the binary composition not with voicecall_moc and voicecall_mtc of H248
When group is stringent equal, the information such as IMSI, MSISDN, LAC, CI are filled for H248.Two tuples refer to herein：Locconnaddr (this
Ground link address) and loctransport (locality connection port).
After B21 and B22, user information backfill related to location information is completed for H248 tables.Hereafter, when
When VOICE_INFOST tables and the strictly equal four-tuple of H248, IMSI, MSISDN, LAC, CI are filled for VOICE_INFOST tables
Etc. information.Four-tuple refers to herein：Srcip, srcport, destip and destport of VOICE_INFOST tables,
Locconnaddr, loctransport, remconnaddr of voicecall_moc and voicecall_mtc and
remtransport。
The data correlation that VoLTE network multi-interfaces are carried out by the equal principle of four-tuple, is finally embodied as Mb mouthfuls of matchmaker
The backfill of body flow data crucial user information (IMSI/MSISDN) and location information (SGW/TAC/ENB/CI).
Further, on the basis of above method embodiment, after S104, further include：
S105, each speech quality evaluation file stored in the database server is carried out in real time according to preset condition
Inquiry, generates the convergence table of preset time period, and the convergence table is stored to the database server.
Wherein, the preset condition is preset according to specific requirement.
Specifically, source code flow, will be with TXT text formattings after acquisition decoding, MOS assessments and user data backfill
It is pushed to hadoop cluster by socket modes.Hadoop is received using storm and converting system pushes the TXT texts to come
Then file is issued HDFS by certain mechanism after overcompression and is stored.Hive and Spark is mainly the text stored to HDFS
Part is inquired and is handled, the former focuses on non-real-time data query processing, and the latter focuses on real-time data query process.
Result after query processing can import ORACLE database purchases, and foreground application is developed based on ORACLE.System
The hadoop ecosphere Organization Charts of system are as shown in Figure 10：TXT files that are that Storm is received and being stored in HDFS are each interface solutions
Code out original XDR (session_setup, sipcall, voicecall_infost, voicecall_mtc,
Voicecall_moc and H248) and XDR (mb_voicecall and nb_voicecall) after data backfill.hadoop
Cluster can carry out corresponding Data Integration to the above XDR, generate various self-defined XDR, and be conducted into ORACLE data
Library facilitates application server to call.In the present system, hadoop cluster mainly completes the data query function of following aspect：
(1) non real-time nature data query.
The XDR text files being stored in HDFS are purposefully inquired using Hive functions, and query result is direct
Display is exported or is imported into ORACLE with formats such as TXT/CSV.
Application scenarios one：Voice quality achievement data that is provisional, randomly inquiring certain user or cell is needed, is needed
The data volume of inquiry is larger, and query time is longer, not high to requirement of real-time.Such scene belongs to immediate inquiring, it is provisional and
Randomness feature is apparent.
Application scenarios two：Based on two kinds of XDR text files of voicecall_mb and voicecall_nb, can it is self-defined,
All kinds of convergence Table X DR are automatically generated, and result is imported into ORACLE.For example, can be based on voicecall_mb generates user
Or the day granularity convergence table (mb_imsi_day or mb_ci_day) of cell dimension.
(2) real-time data is inquired.
Using Spark functions efficiently, rapidly inquire XDR text files, generate various small time granularities (such as 5 minutes or
15 minutes granularities) convergence Table X DR, and imported into ORACLE.For example, can be based on voicecall_nb generates user or cell
15 minutes granularities convergence table (nb_imsi_15min or nb_ci_15min) of dimension imports ORACLE, and application program passes through calling
These convergence tables of ORACLE, are presented the voice quality index situation of TOP N users or TOP N cells in real time.
The present embodiment can solve VoLTE user and two kinds of VoLTE user's intercommunication, VoLTE user and 2/3G user's intercommunication
Speech quality evaluation analysis under scene will be divided to understand using the various applications that this method is done below around both scenes
Analysis.
Scene one：VoLTE user and VoLTE user's intercommunication.
The scene is based on Mb mouthfuls of Media Stream, you can the XDR numbers based on the voicecall_mb that data backfill is completed
According to progress applied analysis.Speech quality evaluation analysis is mainly carried out to the scene by following application.
Using one：TOP N cells monitor in real time
Using Hive functions, 15 minutes granularity convergence table mb_ci_ of cell dimension are generated in real time based on voicecall_mb
15min, and import ORACLE databases.Application server executes corresponding program by custom task and calls mb_ci_15min,
The worst TOP N cells of MOS values were generated every 15 minutes, these TOP N cells can be rendered by map view and be presented,
Report can also be generated simultaneously or result is imported into ORACLE databases.
Based on mb_ci_15min, it is readily apparent convergence the table mb_ci_hour and mb_ of hour granularity and day granularity
ci_day。
Using two：Network element level monitors in real time
Generate the convergence table mb_ne_15min of network element dimension, 15 minutes granularities in real time based on voicecall_mb tables.The remittance
The network element of poly- table may include TAC, SGW and township.TAC, SGW and each township can be monitored in real time based on the convergence table
Voice quality index situation, such as MOS values, shake, delay, packet loss.
Using three：TOP N customer analysis
Generated in real time based on voicecall_mb user's dimension hour granularity or day granularity convergence table mb_imsi_hour or
mb_imsi_day.Using the convergence table, the worst TOP N user lists of MOS values can be automatically generated, and gets TOP N and uses
Cell information where family, analyses whether to cause for the wireless side problem of cell.
Using four：Customer complaint is analyzed
For interim, random batch customer complaint number, voicecall_mb is inquired using Hive, obtains to complain and use
Reason is complained in family cell information and corresponding MOS values index where (time can customize), analysis 2 hours before complaining the time
It is whether related with cell wireless environment.
Scene two：VoLTE user and 2/3G user's intercommunication.
The scene is the Media Stream based on Nb mouthfuls (interface between IM-MGW and CS-MGW), you can based on data are completed
The XDR data of the voicecall_nb of backfill carry out applied analysis.Language is mainly carried out to the scene by following application
Sound quality analysis and assessment.
Using one：Based on Nb mouthfuls of VoLTE network user's voice quality analysis
In the XDR of voicecall_nb, rat_type fields characterize network type, which is 2,3,4, respectively
Represent 2G, 3G and 4G network.When taking rat_type=4, VoLTE user and VoLTE under 2/3G user's intercommunication scene can be directed to
User analyzes in Nb mouthfuls of whole voice quality.
Using two：Difference analysis based on Mb mouthfuls with Nb spoken language sound qualities
There are IMSI the and MSISDN words backfilled in voicecall_mb and Nb mouthfuls of Mb mouthfuls of voicecall_nb
Section, by the field, can find identical VoLTE user in two interfaces, Mb mouthfuls and Nb are assessed respectively for these users
The voice quality of mouth, analyzes otherness therein.
Data of the present embodiment based on Mb carry out comprehensive for the scene of VoLTE user and VoLTE user's intercommunication
Speech quality evaluation.Existing method is realized not yet to the acquiring of the interface, rational server combination and algorithm, mass data
Analyzing processing technology, the present embodiment can carry out speech quality evaluation from dimensions such as user, terminal, cell, TAC, and analysis is each
Kind dimension influences the factor of voice quality, and the method for finding optimization；Data based on Nb interface are directed to VoLTE user and 2/3G
The scene of user's intercommunication.This 90% or more scene accounting within several years ago, innovatively propose acquisition based on IMGW, storage and point
Analysis carries out the whole speech quality evaluation of Nb mouthfuls of VoLTE users；Mb mouthfuls and Nb mouthfuls are assessed for identical VoLTE user simultaneously
Voice quality, analyze otherness therein.
The present embodiment is associated analysis for the data of multiplex roles (including Mb, Nb, S11, Gm and Mc interface)；Using point
Cloth framework using acquisition server, processing server, loader servers, hadoop cluster, database server and is answered
It, can real-time (granularity is presented within 15 minutes), magnanimity (the whole network 2W user), efficiently (inquiry of minute rank finishes) with the combination of server
Ground handles the data of above-mentioned 5 interfaces；For the speech quality evaluation of RTP Media Streams, using ITU specifications P.563 algorithm into
Row assessment, assessment result are reliable, accurate.Compared with traditional drive test result, the MOS result degrees of fitting based on P.563 algorithm reach
0.82, error mean is less than 0.3, and the MOS results based on E-Model model algorithms and the error mean of traditional drive test result are super
Cross 0.45；Data cleansing, inquiry and processing are carried out using hadoop cluster.Various reports and convergence table self-defined can be generated, are converged
Poly- table can import ORACLE databases.Customized convergence table can mitigate the load of ORACLE, relative to directly store original table,
This mode can be brought for the I/O performances of ORACLE and be obviously improved；Application of function is extensive, can be directed to VoLTE user and VoLTE
User's intercommunication (Mb Media Stream), VoLTE user and 2/3G user's intercommunication (Nb interface Media Stream) both scenes carry out real
When (15 minutes granularities), network-wide basis ground voice quality analysis.
Figure 11 shows a kind of structural schematic diagram of VoLTE speech quality assessment systems provided in this embodiment, the system
System includes：Acquisition server 1101, processing server 1102, data backfill loader servers 1103, distributed treatment
Hadoop cluster system 1104 and database server 1105；
The acquisition server 1101 acquires voice and signaling data of each interface after light splitting and convergence, and by institute
Predicate sound and signaling data are sent to processing server 1102；
The processing server 1102 parses the voice and signaling data, and the media obtained to parsing flow into
Row mean subjective opinion divides MOS values to assess, and obtains speech quality evaluation data, and the speech quality evaluation data are sent to
Loader servers 1103；
The loader servers 1103 add user's letter according to control face data in the speech quality evaluation data
Breath and location information, and generate voice quality according to the speech quality evaluation data after addition user information and location information
File is assessed, and the speech quality evaluation file is sent to hadoop cluster system 1104；
The hadoop cluster system 1104 stores the speech quality evaluation file to database server；
The database server 1105 is for storing the speech quality evaluation file.
The present embodiment after adding user information and location information in speech quality evaluation data by generating voice quality
File is assessed, inquiry is facilitated, is suitable for each period of VoLTE, VoLTE is particularly suitable for and dials VoLTE user, VoLTE is dialled
The scene of traditional 2/3G user is beaten, and by carrying out MOS value assessments so that assessment result is reliable, accurate.
Further, on the basis of above-mentioned apparatus embodiment, the acquisition server 1101 is specifically used for described in acquisition
Mb, the Nb interface, S11 interfaces, Gm, the Media Stream at Mc interface carry out 2/8 light splitting to the Media Stream, and
The Media Stream of 20% after light splitting is converged, the voice and signaling data are obtained.
Further, on the basis of above-mentioned apparatus embodiment, the processing server 1102 is specifically used for institute's predicate
Sound and signaling data are parsed, according to default flow and preset field to after parsing the voice and signaling data sieve
Choosing obtains the Media Stream of Mb or Nb interface, and carries out MOS values to the Media Stream using the P.563 algorithm of ITU specifications and comment
Estimate, obtains speech quality evaluation data.
Further, on the basis of above-mentioned apparatus embodiment, the loader servers 1103 are specifically used for according to control
Session_setup tables in face data processed add user information in the speech quality evaluation data, according to control plane number
Sipcall tables in the point of addition information in the speech quality evaluation data, and according to addition user information and position
The speech quality evaluation data after information generate speech quality evaluation file.
Further, on the basis of above-mentioned apparatus embodiment, the hadoop cluster system 1104 is additionally operable to according to pre-
If condition carries out real-time query to each speech quality evaluation file stored in the database server, preset time period is generated
Convergence table, and the convergence table is stored to the database server.
Speech quality evaluation storage system described in the present embodiment can be used for execute above method embodiment, principle and
Technique effect is similar, and details are not described herein again.
Referring to Fig.1 2, the electronic equipment, including：Processor (processor) 1201,1202 He of memory (memory)
Bus 1203；
Wherein,
The processor 1201 and memory 1202 complete mutual communication by the bus 1203；
The processor 1201 is used to call the program instruction in the memory 1202, is implemented with executing above-mentioned each method
The method that example is provided, such as including：
Acquire voice and signaling data of each interface after light splitting and convergence；
The voice and signaling data are parsed, and mean subjective opinion point is carried out to the Media Stream that parsing obtains
MOS values are assessed, and speech quality evaluation data are obtained；
User information and location information are added in the speech quality evaluation data according to control face data, and according to adding
The speech quality evaluation data after user information and location information are added to generate speech quality evaluation file；
The speech quality evaluation file is stored to database server.
The present embodiment discloses a kind of computer program product, and the computer program product includes being stored in non-transient calculating
Computer program on machine readable storage medium storing program for executing, the computer program include program instruction, when described program instruction is calculated
When machine executes, computer is able to carry out the method that above-mentioned each method embodiment is provided, such as including：
Acquire voice and signaling data of each interface after light splitting and convergence；
The voice and signaling data are parsed, and mean subjective opinion point is carried out to the Media Stream that parsing obtains
MOS values are assessed, and speech quality evaluation data are obtained；
User information and location information are added in the speech quality evaluation data according to control face data, and according to adding
The speech quality evaluation data after user information and location information are added to generate speech quality evaluation file；
The speech quality evaluation file is stored to database server.
The present embodiment provides a kind of non-transient computer readable storage medium, the non-transient computer readable storage medium
Computer instruction is stored, the computer instruction makes the computer execute the method that above-mentioned each method embodiment is provided, example
Such as include：
Acquire voice and signaling data of each interface after light splitting and convergence；
The voice and signaling data are parsed, and mean subjective opinion point is carried out to the Media Stream that parsing obtains
MOS values are assessed, and speech quality evaluation data are obtained；
User information and location information are added in the speech quality evaluation data according to control face data, and according to adding
The speech quality evaluation data after user information and location information are added to generate speech quality evaluation file；
The speech quality evaluation file is stored to database server.
One of ordinary skill in the art will appreciate that：Realize that all or part of step of above method embodiment can pass through
The relevant hardware of program instruction is completed, and program above-mentioned can be stored in a computer read/write memory medium, the program
When being executed, step including the steps of the foregoing method embodiments is executed；And storage medium above-mentioned includes：ROM, RAM, magnetic disc or light
The various media that can store program code such as disk.
The apparatus embodiments described above are merely exemplary, wherein the unit illustrated as separating component can
It is physically separated with being or may not be, the component shown as unit may or may not be physics list
Member, you can be located at a place, or may be distributed over multiple network units.It can be selected according to the actual needs
In some or all of module achieve the purpose of the solution of this embodiment.Those of ordinary skill in the art are not paying creativeness
Labour in the case of, you can to understand and implement.
Through the above description of the embodiments, those skilled in the art can be understood that each embodiment can
It is realized by the mode of software plus required general hardware platform, naturally it is also possible to pass through hardware.Based on this understanding, on
Stating technical solution, substantially the part that contributes to existing technology can be expressed in the form of software products in other words, should
Computer software product can store in a computer-readable storage medium, such as ROM/RAM, magnetic disc, CD, including several fingers
It enables and using so that a computer equipment (can be personal computer, server or the network equipment etc.) executes each implementation
Method described in certain parts of example or embodiment.
It should be noted that：The above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although reference
Invention is explained in detail for previous embodiment, it will be understood by those of ordinary skill in the art that：It still can be right
Technical solution recorded in foregoing embodiments is modified or equivalent replacement of some of the technical features；And this
A little modification or replacements, the spirit and model of various embodiments of the present invention technical solution that it does not separate the essence of the corresponding technical solution
It encloses.
Claims (10)
1. a kind of VoLTE speech quality assessment methods, which is characterized in that including：
Acquire voice and signaling data of each interface after light splitting and convergence；
The voice and signaling data are parsed, and mean subjective opinion is carried out to the Media Stream that parsing obtains and divides MOS values
Assessment, obtains speech quality evaluation data；
User information and location information are added in the speech quality evaluation data according to control face data, and are used according to addition
The speech quality evaluation data after family information and location information generate speech quality evaluation file；
The speech quality evaluation file is stored to database server.
2. according to the method described in claim 1, it is characterized in that, language of each interface of acquisition after light splitting and convergence
Sound and signaling data, specifically include：
The Mb, the Nb interface, S11 interfaces, Gm, the Media Stream at Mc interface are acquired, the media are flowed into
Row 2/8 is divided, and is converged to the Media Stream of 20% after light splitting, and the voice and signaling data are obtained.
3. according to the method described in claim 1, it is characterized in that, described parse the voice and signaling data, and
Carrying out mean subjective opinion to the Media Stream that parsing obtains divides MOS values to assess, and obtains speech quality evaluation data, specifically includes：
The voice and signaling data are parsed, according to default flow and preset field to the voice and letter after parsing
It enables data be screened, obtains the Media Stream of Mb or Nb interface, and using the P.563 algorithm of ITU specifications to the media
Stream carries out MOS value assessments, obtains speech quality evaluation data.
4. according to the method described in claim 1, it is characterized in that, it is described according to control face data in the speech quality evaluation
User information and location information are added in data, and according to the speech quality evaluation after addition user information and location information
Data generate speech quality evaluation file, specifically include：
According to the session_setup tables in control face data user information, root are added in the speech quality evaluation data
According to control face data in sipcall tables in the speech quality evaluation data point of addition information, and according to addition user
The speech quality evaluation data after information and location information generate speech quality evaluation file.
5. according to the method described in claim 1, it is characterized in that, described store the speech quality evaluation file to data
After the server of library, further include：
Real-time query is carried out to each speech quality evaluation file stored in the database server according to preset condition, is generated
The convergence table of preset time period, and the convergence table is stored to the database server.
6. a kind of VoLTE speech quality assessment systems, which is characterized in that including：Acquisition server, processing server, data are returned
Fill out loader servers, distributed treatment hadoop cluster system and database server；
The acquisition server acquires voice and signaling data of each interface after light splitting and convergence, and by the voice and
Signaling data is sent to processing server；
The processing server parses the voice and signaling data, and the Media Stream obtained to parsing carries out average master
Seeing opinion divides MOS values to assess, and obtains speech quality evaluation data, and the speech quality evaluation data is sent to described
Loader servers；
The loader servers add user information and position according to control face data in the speech quality evaluation data
Information, and generate speech quality evaluation text according to the speech quality evaluation data after addition user information and location information
Part, and the speech quality evaluation file is sent to hadoop cluster system；
The hadoop cluster system stores the speech quality evaluation file to database server；
The database server is for storing the speech quality evaluation file.
7. system according to claim 6, which is characterized in that the acquisition server is specifically used for the acquisition Mb and connects
Media Stream at mouth, the Nb interface, S11 interfaces, Gm, Mc interface carries out 2/8 light splitting to the Media Stream, and to dividing
20% Media Stream after light is converged, and the voice and signaling data are obtained.
8. system according to claim 6, which is characterized in that the processing server is specifically used for the voice and letter
Enable data be parsed, according to default flow and preset field to after parsing the voice and signaling data screen, obtain
MOS value assessments are carried out to the Media Stream to Mb or the Media Stream of Nb interface, and using the P.563 algorithm of ITU specifications, are obtained
To speech quality evaluation data.
9. system according to claim 6, which is characterized in that the loader servers are specifically used for according to control plane number
Session_setup tables in add user information in the speech quality evaluation data, according in control face data
Sipcall tables point of addition information in the speech quality evaluation data, and according to addition user information and location information after
The speech quality evaluation data generate speech quality evaluation file.
10. system according to claim 6, which is characterized in that the hadoop cluster system is additionally operable to according to default item
Part carries out real-time query to each speech quality evaluation file stored in the database server, generates the remittance of preset time period
Poly- table, and the convergence table is stored to the database server.
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Application publication date: 20180821 |