CN110636531A - Method and device for identifying abnormal subscription user - Google Patents
Method and device for identifying abnormal subscription user Download PDFInfo
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- CN110636531A CN110636531A CN201810539333.XA CN201810539333A CN110636531A CN 110636531 A CN110636531 A CN 110636531A CN 201810539333 A CN201810539333 A CN 201810539333A CN 110636531 A CN110636531 A CN 110636531A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/04—Arrangements for maintaining operational condition
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/20—Services signaling; Auxiliary data signalling, i.e. transmitting data via a non-traffic channel
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W8/00—Network data management
- H04W8/18—Processing of user or subscriber data, e.g. subscribed services, user preferences or user profiles; Transfer of user or subscriber data
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Abstract
The embodiment of the invention provides a method and a device for identifying signing abnormal users, wherein the method comprises the following steps: screening out candidate signing abnormal users according to a preset abnormal scene signaling characteristic model and long term evolution voice over long term evolution (VoLTE) service signaling data of each user; and performing signing inquiry on each signing associated network element corresponding to the candidate signing abnormal user, and identifying the signing abnormal user according to a signing inquiry result. The scheme provided by the embodiment of the invention can actively identify users with abnormal endorsements, avoid the check after the users perceive the abnormal users and complain, improve the timeliness of finding problems, and facilitate the timely repair of the abnormal users, thereby improving the service experience of the users.
Description
Technical Field
The embodiment of the invention relates to the technical field of communication, in particular to a method and a device for identifying a signing abnormal user.
Background
With the maturity and deep coverage of an LTE (Long Term Evolution) network, in order to further improve the user Voice service perception, a Voice over LTE (LTE Voice)/esvcc (Enhanced single radio Voice Call Continuity) service is widely popularized.
The network architecture of the VoLTE service is very complex, and relates to various network elements such AS VoLTE AS (Application Server), I \ S \ P-CSCF (interworking \ Serving \ Proxy Call Session Control Function), SBC (Session Border Control), ENS (Enhanced Name Server), mgcf (media Gateway Control Function), HSS (Home Subscriber Server), MRFC (Multimedia Resource Controller), MRFP (Multimedia Resource Processor), and the like, and the subscription data of the VoLTE user also relates to various aspects. When a user opens a VoLTE service, a BOSS (Business operation support System) needs to send a plurality of service opening instructions to three types of network elements, namely an HSS, an ENS and a VoLTE AS, and excellent experience of the user when the user uses the VoLTE service can be guaranteed only if user subscription data of the three types of network elements are normal and effective.
In the prior art, the network side guarantees the accuracy of the subscription data of the VoLTE user mainly by the order of the BOSS side optimized service opening instruction, and first issues the VoLTE TAG (TAG) of the user, the IMS (Internet Protocol Multimedia Subsystem ) APN (Access Point Name, Access Point Name), STNSR (Session Transfer Number) data to the HSS, then issues the Multimedia user Information of the user to the VoLTE AS, then issues the NAPTR (Name authority) data of the user to the ENS, and finally supplements the VoLTE subscription authority (Terminating-camelness subscription Information) data of the user to the HSS.
However, when the user performs operations such as card replacement, service inquiry/synchronization, new service activation, existing service cancellation, etc., the subscription data of the VoLTE user may be changed. When the service experience of the VoLTE user is influenced by abnormal data, the problem can be discovered and solved only through complaints initiated by the user to a network and a support department, the network and the support side are difficult to actively discover the subscription problem related to the VoLTE user, and the timeliness of problem discovery is low.
Disclosure of Invention
Aiming at the defects in the prior art, the method and the device for identifying the users with abnormal signing provided by the embodiment of the invention can actively find the users with abnormal signing, improve the timeliness of finding problems, and facilitate the timely repair of the abnormity, thereby improving the service experience of the users.
In one aspect, an embodiment of the present invention provides a method for identifying a signed abnormal user, including:
screening out candidate signing abnormal users according to a preset abnormal scene signaling characteristic model and long term evolution voice over long term evolution (VoLTE) service signaling data of each user;
and performing signing inquiry on each signing associated network element corresponding to the candidate signing abnormal user, and identifying the signing abnormal user according to a signing inquiry result.
In another aspect, an embodiment of the present invention provides a device for identifying a subscription abnormal user, including:
the candidate abnormal screening unit is used for screening out candidate signing abnormal users according to a preset abnormal scene signaling feature model and long term evolution voice VoLTE service signaling data of each user;
and the abnormal user identification unit is used for carrying out subscription inquiry on each subscription associated network element corresponding to the candidate subscription abnormal user and identifying the subscription abnormal user according to a subscription inquiry result.
In another aspect, an embodiment of the present invention provides an electronic device, including a processor, a memory, and a bus, where:
the processor and the memory complete mutual communication through a bus;
the processor may invoke a computer program in memory to perform: screening out candidate signing abnormal users according to a preset abnormal scene signaling characteristic model and long term evolution voice over long term evolution (VoLTE) service signaling data of each user; and performing signing inquiry on each signing associated network element corresponding to the candidate signing abnormal user, and identifying the signing abnormal user according to a signing inquiry result.
In another aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and the program, when executed by a processor, implements: screening out candidate signing abnormal users according to a preset abnormal scene signaling characteristic model and long term evolution voice over long term evolution (VoLTE) service signaling data of each user; and performing signing inquiry on each signing associated network element corresponding to the candidate signing abnormal user, and identifying the signing abnormal user according to a signing inquiry result.
According to the method and the device for identifying the abnormal subscription users, the candidate abnormal subscription users are screened out according to the preset abnormal scene signaling feature model and the long-term evolution voice VoLTE service signaling data of each user; and performing signing inquiry on each signing associated network element corresponding to the candidate signing abnormal user, and identifying the signing abnormal user according to a signing inquiry result. Therefore, users with abnormal endorsements are actively identified through VoLTE service signaling data, the checking after the users perceive the abnormal users and complain is avoided, the timeliness of finding problems is improved, the abnormal users can be conveniently repaired in time, and the service experience of the users is improved.
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, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 illustrates an exemplary flowchart of a subscription anomaly user identification method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a subscription anomaly subscriber identification device according to an embodiment of the present invention;
fig. 3 shows a physical structure diagram of an electronic device according to an embodiment of the invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As used in this application, the terms "module," "device," and the like are intended to encompass a computer-related entity, such as but not limited to hardware, firmware, a combination of hardware and software, or software in execution. For example, a module may be, but is not limited to: a process running on a processor, an object, an executable, a thread of execution, a program, and/or a computer. For example, an application running on a computing device and the computing device may both be a module. One or more modules may reside within a process and/or thread of execution and a module may be localized on one computer and/or distributed between two or more computers.
The technical scheme of the invention is explained in detail in the following with the accompanying drawings.
Referring to fig. 1, an exemplary flowchart of a subscription anomaly user identification method according to an embodiment of the present invention is shown.
As shown in fig. 1, the method for identifying a subscription abnormal user according to an embodiment of the present invention may include the following steps:
s110: and screening out candidate signing abnormal users according to a preset abnormal scene signaling characteristic model and long term evolution voice over long term evolution (VoLTE) service signaling data of each user.
The signing abnormal user identification method provided by the embodiment of the invention can be suitable for a signing abnormal user identification device used for identifying the VoLTE signing abnormal user in a network domain. The VoLTE service may include, but is not limited to: VoLTE registration services and VoLTE call services.
In practical application, network signaling generated when a user uses a VoLTE service every day can be analyzed in advance, and various scenes of call connection failure caused by abnormal VoLTE subscription of the user can be checked and summarized. In this embodiment of the present invention, the signing data exception scenario may include: a VoLTE AS subscription data exception scenario, a scenario in which subscription data on ENS is not deleted after HSS subscriber release, a scenario in which anchor TCSI data is not deleted when HSS subscriber release, and a scenario in which IMSI (International Mobile subscriber identity Number) in HSS is inconsistent.
And analyzing the signaling flow of each signing data abnormal scene, and extracting the signaling characteristics of the signing data abnormal scene so as to obtain an abnormal scene signaling characteristic model.
In the embodiment of the present invention, signaling data of all VoLTE services in the network (which refers to a basic flow record of a control plane and a user plane generated for a signaling monitoring platform and a signaling application after processing based on full signaling data) can be pulled from the signaling platform in real time or periodically.
In this way, the generalized abnormal scene signaling feature model can be used as a condition to filter out data of the signing data abnormal scene in the signaling data, so as to screen out candidate signing abnormal users suspected of VoLTE signing abnormality.
S120: and performing signing inquiry on each signing associated network element corresponding to the candidate signing abnormal user, and identifying the signing abnormal user according to a signing inquiry result.
In the embodiment of the present invention, after the candidate abnormal subscription user suspected of having the abnormal subscription due to the VoLTE is screened out in step S110, the candidate abnormal subscription user may further query the subscription information from the subscription-related network elements such AS HSS, ENS, VoLTE AS, and the like, to determine whether the candidate abnormal subscription user is a true subscription abnormality, and if so, identify the candidate abnormal subscription user AS the abnormal subscription user.
According to the signing abnormal user identification method provided by the embodiment of the invention, candidate signing abnormal users are screened out according to a preset abnormal scene signaling feature model and long term evolution voice VoLTE service signaling data of each user; and performing signing inquiry on each signing associated network element corresponding to the candidate signing abnormal user, and identifying the signing abnormal user according to a signing inquiry result. Therefore, users with abnormal endorsements are actively identified through VoLTE service signaling data, the checking after the users perceive the abnormal users and complain is avoided, the timeliness of finding problems is improved, the abnormal users can be conveniently repaired in time, and the service experience of the users is improved.
Further, on the basis of the foregoing embodiment, in a method for identifying a signed abnormal user according to another embodiment of the present invention, the abnormal scenario signaling feature model includes:
the system comprises a first abnormal scene signaling characteristic model, a second abnormal scene signaling characteristic model and a third abnormal scene signaling characteristic model.
The first abnormal scene signaling feature model comprises first signaling features which accord with an abnormal scene of an AS (application server) signed data of a VoLTE (voice over long term evolution) application server.
The second abnormal scene signaling characteristic model comprises a second signaling characteristic which accords with a scene that the subscription data on the enhanced name server ENS is not deleted after the home subscriber server HSS is registered or a scene that the subscription information TCSI data of the intelligent network is not deleted when the HSS is registered.
The third abnormal scenario signaling feature model includes a third signaling feature that conforms to a scenario in which an IMSI (International Mobile Subscriber identity) in the HSS is inconsistent.
In practical application, for a scenario one, a scenario where the VoLTE AS subscription data is abnormal, when the user has subscribed the VoLTE service data in the HSS, but the VoLTE AS subscription data is abnormal, the third party registration of the user may fail, and the VoLTE call connection may be affected. During calling, the call drops from the VoLTE to a CS (Circuit Switched) domain to continue to be initiated, the connection time is long, VoLTE experience is affected, the called party can be connected, but no VoLTE AS call ticket is generated, and charging is affected.
In a scene, when a user calls, the SCSCF where the user is located sends an invite message to the VoLTE AS according to the IFC signed by the user HSS, and the VoLTE AS returns a 500internal server error response (warming contains no user data or query dba fail or query ssdb fail) due to the abnormal subscription of the user to the VoLTE AS.
Therefore, in this embodiment of the present invention, the first signaling feature includes: the first disconnect network element is VoLTE AS, the disconnect message is 500 messages, and the warning code in the 500 messages includes any one of the following: no userdata, query dbafiil and query ssdb fail.
In practical application, for a scenario two, where the subscription data on the ENS is not deleted after HSS sales, when a user cancels the VoLTE service, if the VoLTE subscription data in the HSS is deleted but the data on the ENS is left, the user may hear the blank number prompt when calling the user in the VoLTE domain by other users, which seriously affects user experience.
Under the second scenario, when other users call the user in the VoLTE domain, because the ENS data of the user is not deleted, the calling SCSCF queries the ENS for the ICSCF address of the user, and generates invite message to the ICSCF. The ICSCF sends a LIR (Location Info Request) message to a called home HSS to inquire the SCSCF address registered by the called party, and the HSS returns a LIA (Location Info Answer) message to the ICSCF because other VoLTE subscription data of the called party are deleted, and the Location Info Answer message carries 5001 errors. After receiving the message, the called ICSCF returns a 404 Not found or 604Does Not Exist Anywhere message to the calling side, the call connection fails, and the calling side listens to the prompt tone that the called is blank number.
For the scenario three and the scenario where the anchoring TCSI data is not deleted when the HSS sells, if the user cancels the VoLTE service and the rest VoLTE subscription data is cleared, but the anchoring TCSI data is not deleted, the null number prompt is heard when the user is called in the CS/PSTN domain by other users, which seriously affects the user experience.
In a third scenario, when another user calls the user in a CS/PSTN (Public Switched Telephone Network), because the user signs a VoLTE anchor TCSI, the call is sent to an ICSCF in a VoLTE domain via an MGCF (media gateway Control Function), the ICSCF sends a LIR message to a HSS to which the called user belongs to query an SCSCF address registered by the called user, and because other VoLTE subscription data of the called user is deleted, the HSS returns a LIA message to the ICSCF, carrying a 5001diameter-error-user-unknown error. After receiving the message, the called ICSCF returns 404 message to MGCF side, the calling connection fails, and the calling listens the calling tone of the called number being null.
Therefore, in this embodiment of the present invention, the second signaling feature includes: the first disconnect network element is HSS, the disconnect message is LIA (Location-Info-Answer), and the cause value is: 5001.
for the scenario four and the scenario where the IMSI in the HSS is inconsistent, after the user opens the VoLTE service, there may be actions such as card supplementing, VoLTE service logout, and VoLTE service reopening, and in the process, due to reasons such as supporting side data, the IMSI of the user in the SEA-HSS may be inconsistent with the IMSI of the user in the IMS-HSS, thereby affecting VoLTE registration of the user, and causing the service to be unavailable.
In the fourth scenario, when the user initially registers, the ICSCF sends a UAR message to the HSS to which the user belongs to obtain the SCSCF capability set, and the HSS replies a UAA message after checking the user data is abnormal, which carries 5001 errors.
Therefore, in this embodiment of the present invention, the third signaling feature includes: the first disconnecting network element is HSS, the disconnecting message is UAA (User-Authorization-Answer), and the cause value is: 5001.
other steps of the embodiment of the present invention are similar to those of the previous embodiment, and are not described again in the embodiment of the present invention.
According to the signing abnormal user identification method provided by the embodiment of the invention, an abnormal scene signaling characteristic model formed by signaling characteristics under the signing data abnormal scene is obtained by checking and inducing a typical scene of call connection failure caused by the VoLTE signing abnormality of a user; therefore, users suspected of being abnormal in VoLTE subscription can be accurately and quickly screened out through the abnormal scene signaling feature model, and the efficiency and accuracy of identifying users with abnormal subscription are improved.
Further, on the basis of the foregoing embodiment, in a method for identifying a subscription abnormal user provided in another embodiment of the present invention, the screening out a candidate subscription abnormal user according to a preset abnormal scenario signaling feature model and long term evolution voice over lte service signaling data of each user includes:
for each VoLTE service, if the signaling data of the VoLTE service has data matched with the first signaling feature, identifying a calling number or a called number of the VoLTE service as a candidate signed abnormal user matched with a first abnormal scene signaling feature model;
if the signaling data of the VoLTE starting service has data matched with the second signaling characteristic, identifying the called number of the VoLTE starting service as a candidate signed abnormal user matched with a second abnormal scene signaling characteristic model;
and if the signaling data of the starting VoLTE service contains the data matched with the third signaling characteristic, identifying the registered user in the starting VoLTE service as a candidate signed abnormal user matched with a third abnormal scene signaling characteristic model.
In the embodiment of the invention, aiming at each VoLTE service, the signaling data of the VoLTE service can be compared with the signaling characteristics in the signaling characteristic models of different abnormal scenes.
If the signaling data of the starting VoLTE service has the data matched with the first signaling characteristic, the starting VoLTE service is indicated to conform to the abnormal situation of the VoLTE AS signed data, and the calling number or the called number in the starting VoLTE service can be identified AS a candidate signed abnormal user matched with the first abnormal situation signaling characteristic model. In practical application, the calling number or the called number in the VoLTE service may also be identified AS a candidate signing abnormal user matching the first signaling feature or the VoLTE AS signing data abnormal scene.
In practical application, if the signaling data of the VoLTE service has data matched with the first signaling feature, judging whether the first disconnecting network element is a VoLTE AS of a calling side; if yes, identifying the calling number of the VoLTE service as a candidate signing abnormal user; if not, identifying the called number of the VoLTE service as a candidate signing abnormal user.
If the signaling data of the VoLTE starting service has the data matched with the second signaling characteristic, the VoLTE starting service is indicated to be in accordance with a scene that the subscription data on the ENS is not deleted after HSS is released or a scene that the TCSI data is not deleted when the HSS is released, and the called number of the VoLTE starting service can be identified as a candidate subscription abnormal user matched with a second abnormal scene signaling characteristic model. In practical application, the called number of the start VoLTE service may also be identified as a candidate signed-up abnormal user matching the second signaling feature. Alternatively, the called number of the start VoLTE service may be identified AS a candidate subscriber anomaly user matching the VoLTE AS subscription data anomaly scenario or the scenario in which the anchor TCSI data is not deleted when the HSS is terminated.
If the signaling data of the starting VoLTE service contains the data matched with the third signaling characteristic, the starting VoLTE service is indicated to be in accordance with the scene of inconsistent IMSI in the HSS, and the registered user in the starting VoLTE service can be identified as the candidate signed abnormal user matched with the third abnormal scene signaling characteristic model. In practical application, the registered user in the VoLTE service may also be identified as a candidate subscriber-subscribed abnormal user matching with a scenario where the third signaling feature or the IMSI in the HSS is inconsistent.
Other steps of the embodiment of the present invention are similar to those of the previous embodiment, and are not described again in the embodiment of the present invention.
According to the signing abnormal user identification method provided by the embodiment of the invention, the VoLTE service with suspected signing data abnormality is screened out through comparing the signaling data of the VoLTE service with the signaling characteristics in the abnormal scene signaling characteristic models corresponding to different signing data abnormal scenes, so that the candidate signing abnormal users with suspected signing data abnormality are identified, and meanwhile, the identification rate and the accuracy rate can be improved.
Further, on the basis of the foregoing embodiment, in a method for identifying a signing abnormal user according to another embodiment of the present invention, the performing a signing query on each signing associated network element corresponding to the candidate signing abnormal user, and identifying the signing abnormal user according to a signing query result includes:
for candidate signed abnormal users matched with the first abnormal scene signaling feature model, after the candidate signed abnormal users are determined to be VoLTE users, if the user signed data in HSS and the user signed data in ENS are normal and no user signed data exists in VoLTE AS, the candidate signed abnormal users are identified AS signed abnormal users;
for candidate signed abnormal users matched with the second abnormal scene signaling feature model, determining that the candidate signed abnormal users are non-VoLTE users and after IMS user signed data does not exist in HSS, if user signed data exists in ENS or TCSI in HSS is anchoring AS, identifying the candidate signed abnormal users AS signed abnormal users;
and for the candidate signed abnormal user matched with the third abnormal scene signaling feature model, determining that the candidate signed abnormal user is a VoLTE user, and after IMS user signed data exists in the HSS, if the IMSI in the SEA-HSS is not consistent with the IMSI in the IMS-HSS, identifying the candidate signed abnormal user as a signed abnormal user.
In the embodiment of the invention, in order to accurately identify the abnormal signing user, the HSS, the ENS and the HSS can be further inquired about the signing information of the candidate abnormal signing user with suspected abnormal signing data so as to determine whether the signing data is abnormal or not.
Specifically, for the candidate signed abnormal user matching the first abnormal scenario signaling feature model, it may be determined whether the VoLTE TAG in the HSS is true, and if so, it is determined that the candidate signed abnormal user is a VoLTE user; after the candidate signing abnormal user is determined to be a VoLTE user, whether user signing data in ENS is normal or not is further judged, if the user signing data in HSS and the user signing data in ENS are normal, whether user signing data exist in VoLTE AS or not is further judged, if no user signing data exist in VoLTE AS, the candidate signing abnormal user is indicated to have the problem of AS signing abnormity, and the candidate signing abnormal user can be identified to be a signing abnormal user.
For the candidate signed abnormal user matched with the second abnormal scene signaling feature model, whether the VOLTE TAG in the HSS is false or not can be judged firstly, if yes, the candidate signed abnormal user is a non-VoLTE user; after determining that the candidate signed abnormal user is a non-VoLTE user and the HSS does not have the IMS user signed data, whether the ENS has the user signed data or not can be further judged, and whether the TCSI in the HSS anchors the AS or not can be judged.
If the ENS has the user signing data, the situation that the candidate signing abnormal user has the ENS signing abnormal problem is indicated, and the candidate signing abnormal user can be identified as the signing abnormal user.
If the TCSI in the HSS is the anchor AS, it indicates that the candidate signed abnormal user has the problem of the HSSTCSI signed abnormal, and may identify the candidate signed abnormal user AS the signed abnormal user.
For the candidate signed abnormal user matched with the third abnormal scene signaling feature model, whether a VoLTE TAG in the HSS is true or not can be judged, and if yes, the candidate signed abnormal user is a VoLTE user; after the candidate signed abnormal user is determined to be the VoLTE user and the IMS user signed data exists in the HSS, the IMSI in the SEA-HSS to which the user belongs and the IMSI in the IMS-HSS can be inquired by using the MSISDN respectively, and the comparison is carried out, if the IMSI in the SEA-HSS is not consistent with the IMSI in the IMS-HSS, the candidate signed abnormal user is the signed abnormal user.
Other steps of the embodiment of the present invention are similar to those of the previous embodiment, and are not described again in the embodiment of the present invention.
The method for identifying the abnormal signing user provided by the embodiment of the invention further queries the signing information from the HSS, the ENS and the HSS for the candidate abnormal signing user with suspected abnormal signing data so as to check the abnormal signing user with the abnormal signing problem, thereby ensuring the identification accuracy.
Further, on the basis of the foregoing embodiment, in a method for identifying a subscriber who signs up abnormally according to another embodiment of the present invention, the method further includes:
determining the signing abnormal type of the signing abnormal user according to the abnormal scene signaling feature model matched with the signing abnormal user;
the subscription abnormal category of the subscription abnormal user is specifically any one of the following categories: AS subscription anomaly, ENS subscription anomaly, HSS TCSI subscription anomaly, and IMS user subscription anomaly in IMS-HSS.
In the embodiment of the invention, for the signed abnormal user matched with the first abnormal scene signaling feature model, the signed abnormal type corresponding to the signed abnormal user is determined to be AS signed abnormal.
And for the signed abnormal user matched with the second abnormal scene signaling feature model, if user signed data exists in the ENS, determining that the signed abnormal type corresponding to the signed abnormal user is ENS signed abnormal.
And for the signed abnormal user matched with the second abnormal scene signaling feature model, if the TCSI in the HSS is the anchoring AS, determining that the signed abnormal type corresponding to the signed abnormal user is HSS TCSI signed abnormal.
And for the signed abnormal user matched with the third abnormal scene signaling feature model, determining that the signed abnormal type corresponding to the signed abnormal user is the IMS user signed abnormal in an IMS-HSS.
Other steps of the embodiment of the present invention are similar to those of the previous embodiment, and are not described again in the embodiment of the present invention.
According to the signing abnormal user identification method provided by the embodiment of the invention, the signing abnormal type of the signing abnormal user is determined according to the abnormal scene signaling characteristic model matched with the signing abnormal user for the user identified as the signing abnormal user, so that the abnormal signing network element, namely the network element with the signing abnormality, can be positioned, and the subsequent user signing repair can be facilitated.
Further, on the basis of the foregoing embodiment, in a method for identifying a subscriber who signs up abnormally according to another embodiment of the present invention, the method further includes:
and according to the signing abnormal category of the signing abnormal user, positioning the abnormal signing network element, and repairing the signing data corresponding to the signing abnormal user on the abnormal signing network element.
In the embodiment of the invention, if the signing abnormal type of the signing abnormal user is AS signing abnormal, the abnormal signing network element can be positioned AS the AS corresponding to the signing abnormal user.
If the subscription abnormal type of the subscription abnormal user is ENS subscription abnormal, the network element which is abnormally subscribed can be positioned to be ENS.
If the subscription abnormal type of the subscription abnormal user is HSS TCSI subscription abnormal, the abnormal subscription network element can be positioned to be HSS.
If the subscription abnormal type of the subscription abnormal user is the subscription abnormal of the IMS user in the IMS-HSS, the network element which is abnormally subscribed can be positioned to be the HSS.
In practical application, a repair instruction script can be automatically generated aiming at the network element with the abnormal subscription and the subscription data of the abnormal subscription user, and the data such AS VoLTE AS, ENS, TCSI, IMSI and the like associated with the abnormal subscription user can be repaired.
Other steps of the embodiment of the present invention are similar to those of the previous embodiment, and are not described again in the embodiment of the present invention.
The method for identifying the abnormal subscription user provided by the embodiment of the invention can improve the service experience of the user by repairing the subscription data.
On the basis of the foregoing embodiments, another embodiment of the present invention provides a subscriber identification apparatus with abnormal subscription.
Referring to fig. 2, a schematic structural diagram of a subscription anomaly user identification device according to an embodiment of the present invention is shown.
As shown in fig. 2, the abnormal subscriber identity device 200 according to an embodiment of the present invention may include: a candidate anomaly screening unit 201 and an anomaly user identification unit 202.
The candidate abnormal screening unit 201 is configured to screen out candidate signing abnormal users according to a preset abnormal scene signaling feature model and long term evolution voice over lte (voice over lte) service signaling data of each user.
The abnormal user identification unit 202 is configured to perform subscription inquiry on each subscription associated network element corresponding to the candidate subscription abnormal user, and identify the subscription abnormal user according to a subscription inquiry result.
Optionally, the abnormal scenario signaling feature model includes:
a first abnormal scene signaling characteristic model, a second abnormal scene signaling characteristic model and a third abnormal scene signaling characteristic model;
the first abnormal scene signaling feature model comprises first signaling features which accord with an abnormal scene of an AS (application server) signed data of a VoLTE (voice over long term evolution) application server;
the second abnormal scene signaling characteristic model comprises a second signaling characteristic which accords with a scene that the subscription data on the enhanced name server ENS is not deleted after the home subscriber server HSS is registered or a scene that the subscription information TCSI data of the terminating intelligent network is not deleted when the HSS is registered;
the third abnormal scene signaling feature model comprises a third signaling feature which accords with the scene that the IMSI of the International Mobile Subscriber Identity (IMSI) in the HSS is inconsistent.
Optionally, the candidate exception screening unit 201 is specifically configured to, for each start of VoLTE service, identify, if data matching the first signaling feature exists in signaling data of the start of VoLTE service, that a calling number or a called number of the start of VoLTE service is a candidate signed exception user matching the first exception scenario signaling feature model; if the signaling data of the VoLTE starting service has data matched with the second signaling characteristic, identifying the called number of the VoLTE starting service as a candidate signed abnormal user matched with a second abnormal scene signaling characteristic model; and if the signaling data of the starting VoLTE service contains the data matched with the third signaling characteristic, identifying the registered user in the starting VoLTE service as a candidate signed abnormal user matched with a third abnormal scene signaling characteristic model.
Optionally, the candidate exception screening unit 201 is specifically configured to determine whether the first disconnect network element is a calling-side VoLTE AS if there is data matching the first signaling feature in the signaling data of the VoLTE service; if yes, identifying the calling number of the VoLTE service as a candidate signing abnormal user; if not, identifying the called number of the VoLTE service as a candidate signing abnormal user.
Optionally, the abnormal user identification unit 202 is specifically configured to, after determining that the candidate signed abnormal user is a VoLTE user with respect to the candidate signed abnormal user matching the first abnormal scenario signaling feature model, identify the candidate signed abnormal user AS a signed abnormal user if the user subscription data in the HSS and the user subscription data in the ENS are normal and there is no user subscription data in the VoLTE AS; for candidate signed abnormal users matched with the second abnormal scene signaling feature model, determining that the candidate signed abnormal users are non-VoLTE users and after IMS user signed data does not exist in HSS, if user signed data exists in ENS or TCSI in HSS is anchoring AS, identifying the candidate signed abnormal users AS signed abnormal users; and for the candidate signed abnormal user matched with the third abnormal scene signaling feature model, determining that the candidate signed abnormal user is a VoLTE user, and after IMS user signed data exists in the HSS, if the IMSI in the SEA-HSS is not consistent with the IMSI in the IMS-HSS, identifying the candidate signed abnormal user as a signed abnormal user.
Optionally, the abnormal user identification unit 202 is further configured to determine a subscription abnormal category of the subscription abnormal user according to the abnormal scenario signaling feature model matched with the subscription abnormal user.
The subscription abnormal category of the subscription abnormal user is specifically any one of the following categories: AS subscription anomaly, ENS subscription anomaly, HSS TCSI subscription anomaly, and IMS user subscription anomaly in IMS-HSS.
Optionally, the abnormal user identification unit 202 is further configured to locate an abnormally signed network element according to the signing abnormal category of the signing abnormal user, and repair the signing data corresponding to the signing abnormal user on the abnormally signed network element.
According to the signing abnormal user identification device provided by the embodiment of the invention, candidate signing abnormal users are screened out according to a preset abnormal scene signaling feature model and long term evolution voice VoLTE service signaling data of each user; and performing signing inquiry on each signing associated network element corresponding to the candidate signing abnormal user, and identifying the signing abnormal user according to a signing inquiry result. Therefore, users with abnormal endorsements are actively identified through VoLTE service signaling data, the checking after the users perceive the abnormal users and complain is avoided, the timeliness of finding problems is improved, the abnormal users can be conveniently repaired in time, and the service experience of the users is improved.
The embodiment of the subscriber identity module according to the present invention may be specifically configured to execute the processing procedure of the method embodiment, and the functions of the embodiment are not described herein again, and refer to the detailed description of the method embodiment.
Referring to fig. 3, a physical structure diagram of an electronic device according to an embodiment of the invention is shown. As shown in fig. 3, the electronic device 300 may include: a processor (processor)301, a memory (memory)302 and a bus 303, wherein the processor 301 and the memory 302 communicate with each other via the bus 303.
Processor 301 may invoke a computer program in memory 302 to perform the method provided by the method embodiment of fig. 1 described above, including, for example:
screening out candidate signing abnormal users according to a preset abnormal scene signaling characteristic model and long term evolution voice over long term evolution (VoLTE) service signaling data of each user;
and performing signing inquiry on each signing associated network element corresponding to the candidate signing abnormal user, and identifying the signing abnormal user according to a signing inquiry result.
In another embodiment, the abnormal situation signaling feature model includes:
a first abnormal scene signaling characteristic model, a second abnormal scene signaling characteristic model and a third abnormal scene signaling characteristic model;
the first abnormal scene signaling feature model comprises first signaling features which accord with an abnormal scene of an AS (application server) signed data of a VoLTE (voice over long term evolution) application server;
the second abnormal scene signaling characteristic model comprises a second signaling characteristic which accords with a scene that the subscription data on the enhanced name server ENS is not deleted after the home subscriber server HSS is registered or a scene that the subscription information TCSI data of the terminating intelligent network is not deleted when the HSS is registered;
the third abnormal scene signaling feature model comprises a third signaling feature which accords with the scene that the IMSI of the International Mobile Subscriber Identity (IMSI) in the HSS is inconsistent.
In another embodiment, the processor 301, when executing the computer program, implements the following method: the method for screening out the candidate signing abnormal users according to the preset abnormal scene signaling feature model and the long-term evolution voice VoLTE service signaling data of each user comprises the following steps:
for each VoLTE service, if the signaling data of the VoLTE service has data matched with the first signaling feature, identifying a calling number or a called number of the VoLTE service as a candidate signed abnormal user matched with a first abnormal scene signaling feature model;
if the signaling data of the VoLTE starting service has data matched with the second signaling characteristic, identifying the called number of the VoLTE starting service as a candidate signed abnormal user matched with a second abnormal scene signaling characteristic model;
and if the signaling data of the starting VoLTE service contains the data matched with the third signaling characteristic, identifying the registered user in the starting VoLTE service as a candidate signed abnormal user matched with a third abnormal scene signaling characteristic model.
In another embodiment, the processor 301, when executing the computer program, implements the following method: the method for screening out the candidate signing abnormal users according to the preset abnormal scene signaling feature model and the long-term evolution voice VoLTE service signaling data of each user comprises the following steps:
for each VoLTE service, if the signaling data of the VoLTE service has data matched with the first signaling feature, identifying a calling number or a called number of the VoLTE service as a candidate signed abnormal user matched with a first abnormal scene signaling feature model;
if the signaling data of the VoLTE starting service has data matched with the second signaling characteristic, identifying the called number of the VoLTE starting service as a candidate signed abnormal user matched with a second abnormal scene signaling characteristic model;
and if the signaling data of the starting VoLTE service contains the data matched with the third signaling characteristic, identifying the registered user in the starting VoLTE service as a candidate signed abnormal user matched with a third abnormal scene signaling characteristic model.
In another embodiment, the processor 301, when executing the computer program, implements the following method: the signing inquiry of each signing associated network element corresponding to the candidate signing abnormal user is carried out, and the identification of the signing abnormal user is carried out according to the signing inquiry result, which comprises the following steps:
for candidate signed abnormal users matched with the first abnormal scene signaling feature model, after the candidate signed abnormal users are determined to be VoLTE users, if the user signed data in HSS and the user signed data in ENS are normal and no user signed data exists in VoLTE AS, the candidate signed abnormal users are identified AS signed abnormal users;
for candidate signed abnormal users matched with the second abnormal scene signaling feature model, determining that the candidate signed abnormal users are non-VoLTE users and after IMS user signed data does not exist in HSS, if user signed data exists in ENS or TCSI in HSS is anchoring AS, identifying the candidate signed abnormal users AS signed abnormal users;
and for the candidate signed abnormal user matched with the third abnormal scene signaling feature model, determining that the candidate signed abnormal user is a VoLTE user, and after IMS user signed data exists in the HSS, if the IMSI in the SEA-HSS is not consistent with the IMSI in the IMS-HSS, identifying the candidate signed abnormal user as a signed abnormal user.
In another embodiment, the processor 301, when executing the computer program, implements the following method: the method further comprises the following steps:
determining the signing abnormal type of the signing abnormal user according to the abnormal scene signaling feature model matched with the signing abnormal user;
the subscription abnormal category of the subscription abnormal user is specifically any one of the following categories: AS subscription anomaly, ENS subscription anomaly, HSS TCSI subscription anomaly, and IMS user subscription anomaly in IMS-HSS.
In another embodiment, the processor 301, when executing the computer program, implements the following method: the method further comprises the following steps:
and according to the signing abnormal category of the signing abnormal user, positioning the abnormal signing network element, and repairing the signing data corresponding to the signing abnormal user on the abnormal signing network element.
The electronic device 300 provided by the embodiment of the invention at least has the following technical effects: screening out candidate signing abnormal users according to a preset abnormal scene signaling feature model and long-term evolution voice VoLTE service signaling data of each user; and performing signing inquiry on each signing associated network element corresponding to the candidate signing abnormal user, and identifying the signing abnormal user according to a signing inquiry result. Therefore, users with abnormal endorsements are actively identified through VoLTE service signaling data, the checking after the users perceive the abnormal users and complain is avoided, the timeliness of finding problems is improved, the abnormal users can be conveniently repaired in time, and the service experience of the users is improved.
An embodiment of the present invention discloses a computer program product, which includes a computer program stored on a non-transitory computer readable storage medium, the computer program including program instructions, when the program instructions are executed by a computer, the computer can execute the methods provided by the above method embodiments, for example, the method includes:
screening out candidate signing abnormal users according to a preset abnormal scene signaling characteristic model and long term evolution voice over long term evolution (VoLTE) service signaling data of each user; and performing signing inquiry on each signing associated network element corresponding to the candidate signing abnormal user, and identifying the signing abnormal user according to a signing inquiry result.
An embodiment of the present invention provides a non-transitory computer-readable storage medium, where the non-transitory computer-readable storage medium stores a computer program, where the computer program causes the computer to execute the method provided by the foregoing method embodiments, for example, the method includes:
screening out candidate signing abnormal users according to a preset abnormal scene signaling characteristic model and long term evolution voice over long term evolution (VoLTE) service signaling data of each user; and performing signing inquiry on each signing associated network element corresponding to the candidate signing abnormal user, and identifying the signing abnormal user according to a signing inquiry result.
In addition, the logic instructions in the memory may be implemented in the form of software functional units and may be stored in a computer readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
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 according to the various embodiments or some parts of the embodiments.
Finally, 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 method for identifying a signed abnormal user is characterized by comprising the following steps:
screening out candidate signing abnormal users according to a preset abnormal scene signaling characteristic model and long term evolution voice over long term evolution (VoLTE) service signaling data of each user;
and performing signing inquiry on each signing associated network element corresponding to the candidate signing abnormal user, and identifying the signing abnormal user according to a signing inquiry result.
2. The method of claim 1, wherein the abnormal situation signaling feature model comprises:
a first abnormal scene signaling characteristic model, a second abnormal scene signaling characteristic model and a third abnormal scene signaling characteristic model;
the first abnormal scene signaling feature model comprises first signaling features which accord with an abnormal scene of an AS (application server) signed data of a VoLTE (voice over long term evolution) application server;
the second abnormal scene signaling characteristic model comprises a second signaling characteristic which accords with a scene that the subscription data on the enhanced name server ENS is not deleted after the home subscriber server HSS is registered or a scene that the subscription information TCSI data of the terminating intelligent network is not deleted when the HSS is registered;
the third abnormal scene signaling feature model comprises a third signaling feature which accords with the scene that the IMSI of the International Mobile Subscriber Identity (IMSI) in the HSS is inconsistent.
3. The method of claim 2, wherein the screening out candidate subscribed abnormal users according to a preset abnormal scenario signaling feature model and long term evolution voice over lte (VoLTE) service signaling data of each user comprises:
for each VoLTE service, if the signaling data of the VoLTE service has data matched with the first signaling feature, identifying a calling number or a called number of the VoLTE service as a candidate signed abnormal user matched with a first abnormal scene signaling feature model;
if the signaling data of the VoLTE starting service has data matched with the second signaling characteristic, identifying the called number of the VoLTE starting service as a candidate signed abnormal user matched with a second abnormal scene signaling characteristic model;
and if the signaling data of the starting VoLTE service contains the data matched with the third signaling characteristic, identifying the registered user in the starting VoLTE service as a candidate signed abnormal user matched with a third abnormal scene signaling characteristic model.
4. The method according to claim 3, wherein if there is data matching the first signaling feature in the signaling data of the start-of-VoLTE service, identifying the calling number or the called number of the start-of-VoLTE service as a candidate subscribed abnormal user matching the first abnormal scenario signaling feature model includes:
if the signaling data of the VoLTE service has the data matched with the first signaling characteristic, judging whether the first disconnecting network element is a calling side VoLTE AS;
if yes, identifying the calling number of the VoLTE service as a candidate signing abnormal user; if not, identifying the called number of the VoLTE service as a candidate signing abnormal user.
5. The method according to claim 3 or 4, wherein the performing subscription inquiry on each subscription associated network element corresponding to the candidate subscription abnormal user, and performing identification on the subscription abnormal user according to a subscription inquiry result, comprises:
for candidate signed abnormal users matched with the first abnormal scene signaling feature model, after the candidate signed abnormal users are determined to be VoLTE users, if the user signed data in HSS and the user signed data in ENS are normal and no user signed data exists in VoLTEAS, the candidate signed abnormal users are identified as signed abnormal users;
for candidate signed abnormal users matched with the second abnormal scene signaling feature model, determining that the candidate signed abnormal users are non-VoLTE users and after IMS user signed data does not exist in HSS, if user signed data exists in ENS or TCSI in HSS is anchoring AS, identifying the candidate signed abnormal users AS signed abnormal users;
and for the candidate signed abnormal user matched with the third abnormal scene signaling feature model, determining that the candidate signed abnormal user is a VoLTE user, and after IMS user signed data exists in the HSS, if the IMSI in the SEA-HSS is not consistent with the IMSI in the IMS-HSS, identifying the candidate signed abnormal user as a signed abnormal user.
6. The method of claim 5, further comprising:
determining the signing abnormal type of the signing abnormal user according to the abnormal scene signaling feature model matched with the signing abnormal user;
the subscription abnormal category of the subscription abnormal user is specifically any one of the following categories: AS subscription anomaly, ENS subscription anomaly, HSS TCSI subscription anomaly, and IMS user subscription anomaly in IMS-HSS.
7. The method of claim 6, further comprising:
and according to the signing abnormal category of the signing abnormal user, positioning the abnormal signing network element, and repairing the signing data corresponding to the signing abnormal user on the abnormal signing network element.
8. A subscription anomaly user identification device, comprising:
the candidate abnormal screening unit is used for screening out candidate signing abnormal users according to a preset abnormal scene signaling feature model and long term evolution voice VoLTE service signaling data of each user;
and the abnormal user identification unit is used for carrying out subscription inquiry on each subscription associated network element corresponding to the candidate subscription abnormal user and identifying the subscription abnormal user according to a subscription inquiry result.
9. An electronic device comprising a processor, a memory, and a bus, wherein:
the processor and the memory complete mutual communication through a bus;
the processor calls a computer program in a memory to perform the steps of the method according to any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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