CN116248637B - IP autonomous learning method and system for AMF network element interface - Google Patents

IP autonomous learning method and system for AMF network element interface Download PDF

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CN116248637B
CN116248637B CN202211729354.0A CN202211729354A CN116248637B CN 116248637 B CN116248637 B CN 116248637B CN 202211729354 A CN202211729354 A CN 202211729354A CN 116248637 B CN116248637 B CN 116248637B
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amf
imsi
guti
network element
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CN116248637A (en
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许存鹏
沈飞
张新波
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Wuhan Boyixun Information Technology Co ltd
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Wuhan Boyixun Information Technology Co ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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  • Computer Networks & Wireless Communication (AREA)
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Abstract

The application discloses an IP autonomous learning method and system for an AMF network element interface, and relates to the field of network element IP configuration. The method comprises the following steps: acquiring an AMF end IP and index information of an N1N2 interface data stream; acquiring AMF end IP of all other interfaces to be learned which belong to the same AMF network element as the current N1N2 interface according to the index information; and determining the IP being used by each interface in all the acquired AMF end IPs of each interface to be learned. The IP of each interface to be learned is dynamically acquired and determined according to the actual situation, so that the IP of each interface of the AMF network element can be ensured to be accurately acquired, and the integrity and the accuracy of subsequent signaling analysis of the AMF network element are ensured.

Description

IP autonomous learning method and system for AMF network element interface
Technical Field
The application relates to the field of network element IP configuration, in particular to an IP autonomous learning method and system for an AMF network element interface.
Background
The 5G (fifth generation mobile communication technology) is a new generation broadband mobile communication technology with the characteristics of high speed, low time delay and large connection, and can meet the application requirements of multiple industries, such as virtual reality, augmented reality, ultra-high definition video, internet of vehicles, telemedicine and the like, and multiple service fields in the future. With the increasing number of mobile devices in the 5G era, the data traffic increases rapidly, and the demand for signaling analysis of the 5G core network is more and more obvious (for example, extracting position information in the 5G data to generate a travel code, etc.).
When analyzing the signaling, all data streams of different interfaces of the same AMF network element (Access and Mobility Management Function, network element with access and mobility management functions) are required to be equally distributed to the same signaling analysis equipment due to decryption and association of the signaling. At present, people generally acquire the IP of each interface of the AMF network element through a pre-stored IP industrial parameter table, however, when the industrial parameter table information is wrong or the interface IP of the AMF network element is reconfigured, the signaling analysis device cannot fully receive the data streams of all interfaces of the AMF network element, so that the integrity and accuracy of the signaling streams of the subsequent AMF network element are reduced.
Disclosure of Invention
Aiming at the defects in the prior art, the application solves the technical problems as follows: how to accurately acquire the IP of each interface of the AMF network element, thereby improving the integrity and accuracy when the AMF network element is subjected to signaling analysis in the follow-up process.
In order to achieve the above objective, the present application provides an IP autonomous learning method for an AMF network element interface, where the interface to be learned of the AMF network element includes an N1N2 interface, an N8 interface, an N11 interface, an N12 interface, an N14 interface, an N15 interface, and an N26 interface; the method comprises the following steps: acquiring an AMF end IP and index information of an N1N2 interface data stream; acquiring AMF end IP of all other interfaces to be learned which belong to the same AMF network element as the current N1N2 interface according to the index information; and determining the IP being used by each interface in all the acquired AMF end IPs of each interface to be learned.
On the basis of the technical scheme, the process for acquiring the AMF end IP and index information of the N1N2 interface data stream comprises the following steps: acquiring an AMF end IP, an IMSI, a 5G-GUTI and extraction time of an N1N2 interface data stream of each AMF network element, and generating an AMF end IP table of the AMF network element corresponding to the N1N2 interface according to the AMF end IP of a single N1N2 interface; generating IMSI index tables of all AMF network elements according to IMSI of all N1N2 interfaces, and associating each IMSI in the IMSI index tables with a corresponding AMF end IP table; generating 5G-GUTI index tables of all AMF network elements according to the 5G-GUTI of all the N1N2 interfaces, and associating each 5G-GUTI in the 5G-GUTI index tables with a corresponding AMF end IP table.
On the basis of the above technical solution, the process of obtaining the AMF end IP of all other interfaces to be learned belonging to the same AMF network element as the current N1N2 interface according to the index information includes: for an N8 interface, an N11 interface, an N12 interface, an N15 interface and an N26 interface, acquiring IMSI and IP in the corresponding interfaces in effective time, determining an AMF end IP table corresponding to the current IMSI in an IMSI index table, and filling the acquired current IP into the corresponding interface IP in the AMF end IP table;
for an N14 interface, acquiring 5G-GUTI and IP in the interface in effective time, determining an AMF end IP table corresponding to the current 5G-GUTI in a 5G-GUTI index table, and filling the acquired current IP into the corresponding interface IP in the AMF end IP table.
On the basis of the technical scheme, after filling the IP in the AMF end IP table, the method further comprises the following steps: counting the filling times; the process of determining the IP being used by each interface in all the acquired AMF end IPs of each interface to be learned includes: after the appointed duration, filling the IP with the largest frequency into each interface to be learned as the IP which is being used by the interface to be learned.
Based on the technical scheme, the effective time is 3-5 s after the extraction time, and the specified duration is at least 3600s after the extraction time.
The application provides an IP autonomous learning system for an AMF network element interface, wherein interfaces to be learned of the AMF network element comprise an N1N2 interface, an N8 interface, an N11 interface, an N12 interface, an N14 interface, an N15 interface and an N26 interface; the system comprises an index information acquisition module, an IP association module and an IP learning module;
the index information acquisition module is used for: acquiring an AMF end IP and index information of an N1N2 interface data stream;
the IP association module is used for: acquiring AMF end IP of all other interfaces to be learned which belong to the same AMF network element as the N1N2 interface according to the index information;
the IP learning module is used for: and determining the IP being used by each interface in all the acquired AMF end IPs of each interface to be learned.
On the basis of the technical scheme, the workflow of the index information acquisition module comprises the following steps: acquiring an AMF end IP, an IMSI, a 5G-GUTI and extraction time of an N1N2 interface data stream of each AMF network element, and generating an AMF end IP table of the AMF network element corresponding to the N1N2 interface according to the AMF end IP of a single N1N2 interface; generating IMSI index tables of all AMF network elements according to IMSI of all N1N2 interfaces, and associating each IMSI in the IMSI index tables with a corresponding AMF end IP table; generating 5G-GUTI index tables of all AMF network elements according to the 5G-GUTI of all the N1N2 interfaces, and associating each 5G-GUTI in the 5G-GUTI index tables with a corresponding AMF end IP table.
On the basis of the technical scheme, the workflow of the IP association module comprises the following steps: for an N8 interface, an N11 interface, an N12 interface, an N15 interface and an N26 interface, acquiring IMSI and IP in the corresponding interfaces in effective time, determining an AMF end IP table corresponding to the current IMSI in an IMSI index table, and filling the acquired current IP into the corresponding interface IP in the AMF end IP table;
for an N14 interface, acquiring 5G-GUTI and IP in the interface in effective time, determining an AMF end IP table corresponding to the current 5G-GUTI in a 5G-GUTI index table, and filling the acquired current IP into the corresponding interface IP in the AMF end IP table.
On the basis of the technical scheme, the IP association module is further used for: after filling IP in the IP table of the AMF end, counting filling times; the work flow of the IP learning module comprises the following steps: after the appointed duration, filling the IP with the largest frequency into each interface to be learned as the IP which is being used by the interface to be learned.
Based on the technical scheme, the effective time is 3-5 s after the extraction time, and the specified duration is at least 3600s after the extraction time.
Compared with the prior art, the application has the advantages that:
the application associates the AMF end IP of all interfaces to be learned of the same AMF network element by acquiring the index information associated with the N1N2 interface data stream and other interfaces. On the basis, the application can determine the IP of the AMF end actually used by each interface to be learned by counting the IP collection condition of each interface to be learned of the same AMF network element in the appointed time. Therefore, compared with the prior art that the IP of each interface to be learned is obtained and determined dynamically according to actual conditions, the application not only can not cause the condition of error of the static obtaining IP in the prior art, but also can obtain and adjust the IP in time even if the IP is reconfigured, thereby ensuring that the IP of each interface of the AMF network element is accurately obtained, and consequently ensuring the integrity and accuracy of the subsequent signaling analysis of the AMF network element.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of an IP autonomous learning method for an AMF network element interface in an embodiment of the application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The flow diagrams depicted in the figures are merely illustrative and not necessarily all of the elements and operations/steps are included or performed in the order described. For example, some operations/steps may be further divided, combined, or partially combined, so that the order of actual execution may be changed according to actual situations.
The to-be-learned IP interface (to-be-learned interface hereinafter) related to the AMF network element in the embodiment of the present application includes AN interface N1 interface between the UE and the AMF, (R) AN interface N2 interface between the AN and the AMF (since the N1 interface and the N2 interface have the same roles and resolved message types in the present application, the N1 interface and the N2 interface are regarded as 1 interface and are called as N1N2 interface in the present application), AN N8 interface between the AMF and the UDM, AN N11 interface between the AMF and the SMF, AN N12 interface between the AMF and the AUF, AN N14 interface between the AMF and the AMF, AN N15 interface between the AMF and the PCF, and AN N26 interface between the AMF and the MME, and the data flows of the other interfaces are not used in the present application, i.e. the IP of the other interfaces need not be learned autonomously.
The IP autonomous learning method for the AMF network element interface in the embodiment of the application comprises the following steps: acquiring an AMF end IP and index information of an N1N2 interface data stream; acquiring AMF end IP of all other interfaces to be learned which belong to the same AMF network element as the current N1N2 interface according to the index information; and determining the IP which is being used by each interface in all the acquired AMF end IPs of each interface to be learned, wherein the IP can be sent to the splitter to serve as a follow-up splitting basis.
Therefore, the application can be used for associating the AMF end IP of all interfaces to be learned of the same AMF network element by acquiring the index information of the N1N2 interface data stream associated with other interfaces. On the basis, the application can determine the IP of the AMF end actually used by each interface to be learned by counting the IP collection condition of each interface to be learned of the same AMF network element in the appointed time. Therefore, compared with the prior art that the IP of each interface to be learned is obtained and determined dynamically according to actual conditions, the application not only can not cause the condition of error of the static obtaining IP in the prior art, but also can obtain and adjust the IP in time even if the IP is reconfigured, thereby ensuring that the IP of each interface of the AMF network element is accurately obtained, and consequently ensuring the integrity and accuracy of the subsequent signaling analysis of the AMF network element.
The following is a description of the principles of operation of the present application.
According to analysis of the messages of the interfaces to be learned, index information of the interfaces to be learned, which are associated with the N1N2 interfaces, is IMSI (International Mobile Subscriber Identity ) and 5G-GUTI (5G-Global unique temporary identifier), and other interfaces are IMSI except that the N14 interface is 5G-GUTI; thus, the index information in this embodiment uses IMSI and 5G-GUTI. At the same time, through extensive experimentation and research, the applicant has determined that the manner of dynamically creating a dynamic IP table and dynamically correlating and counting with the IP of other interfaces to be learned by retrieving information is relatively efficient and accurate.
On the basis, the flow of acquiring the AMF end IP and index information of the N1N2 interface data flow in the method comprises the following steps: acquiring an AMF end IP, an IMSI, a 5G-GUTI and extraction time of an N1N2 interface data stream of each AMF network element, and generating an AMF end IP table of the AMF network element corresponding to the N1N2 interface according to the AMF end IP of a single N1N2 interface; generating IMSI index tables of all AMF network elements according to IMSI of all N1N2 interfaces, and associating each IMSI in the IMSI index tables with a corresponding AMF end IP table; generating 5G-GUTI index tables of all AMF network elements according to the 5G-GUTI of all the N1N2 interfaces, and associating each 5G-GUTI in the 5G-GUTI index tables with a corresponding AMF end IP table.
Correspondingly, the AMF end IP of the other interfaces to be learned needs to be acquired according to the information and correlated with the corresponding AMF end IP table, but in order to ensure that the IP is correctly acquired, statistics is required to be performed on data of the other interfaces to be learned in a short time after the N1N2 interface data stream is extracted (the data stream with long distance time may belong to other AMF network elements). Therefore, the process of acquiring the AMF end IP of all other interfaces to be learned which belong to the same AMF network element as the current N1N2 interface according to the index information in the method includes: and in the effective time, acquiring IMSI or 5G-GUTI of other data flows of the interfaces to be learned, wherein the IMSI or 5G-GUTI is specifically:
and for the N8 interface, the N11 interface, the N12 interface, the N15 interface and the N26 interface, acquiring the IMSI and the IP in the corresponding interfaces, determining an AMF end IP table corresponding to the current IMSI in the IMSI index table, and filling the acquired current IP into the corresponding interface IP in the AMF end IP table.
For an N14 interface, acquiring 5G-GUTI and IP in the interface, determining an AMF end IP table corresponding to the current 5G-GUTI in a 5G-GUTI index table, and filling the acquired current IP into the corresponding interface IP in the AMF end IP table.
Preferably, after filling the IP in the AMF end IP table, the method further comprises the following steps: counting the filling times; in the method, in all the acquired AMF end IPs of each interface to be learned, the process of determining the IP being used by each interface comprises the following steps: after the specified duration, each interface to be learned is filled with the IP with the largest number of times, which is used as the IP being used by the interface to be learned, because the accuracy of the IP with the largest number of times is definitely the highest compared with other IPs.
Preferably, the effective time in the method is 3-5 s after the extraction time, and the appointed duration is at least 3600s after the extraction time.
The method of the present application is described below by way of a specific example.
Referring to fig. 1, the IP autonomous learning method for an AMF network element interface in this embodiment includes the following steps:
s1: when the data flow of the N1N2 interface is monitored, the data flow of the N1N2 interface is extracted to obtain the extraction time, the AMF end IP, the IMSI and the 5G-GUTI of the N1N2 interface, wherein the extraction mode is as follows: the protocols related to the N1N2 interface are NAS protocol and NGAP protocol, and the N1 message is required to be packaged in the N2 message for transmission; therefore, during extraction, the NGAP protocol is analyzed first, then the NAS protocol is analyzed, and the IP, the IMSI and the 5G-GUTI of the AMF end of the N1N2 interface can be obtained. Specifically: the IMSI can be obtained by analyzing REGISTRATION REQUEST information, and the 5G-GUTI distributed to the UE by the core network can be obtained by analyzing REGISTRATION ACCEPT information, wherein the destination IP of REGISTRATION REQUEST and the source IP of REGISTRATION ACCEPT information are the AMF end IP of the N1N2 interface.
Generating TABLE_N1N2N 2 (namely an AMF end IP TABLE) by taking the AMF_N1N2_IP as a key, wherein the TABLE_N1N2 (namely the AMF end IP TABLE) is used for storing IP information of each interface to be learned of the AMF; generating a hash TABLE imsi_hash (i.e. an IMSI index TABLE) by taking the IMSI as a key, and inquiring a TABLE_N1N2 node; and generating a hash table guti_hash (namely a 5G-GUTI index table) by taking the 5G-GUTI as a key, and establishing a corresponding relation between the 5G-GUTI and the IMSI.
S2: and within 5 seconds after the time is extracted, acquiring the IP of other interfaces to be learned and accumulating the count, wherein the method specifically comprises the following steps:
for the N8 interface: obtaining IMSI and AMF end IP of N8 interface by analyzing Nudm_UECM_Regulation message, inquiring imsi_hash TABLE according to IMSI to obtain TABLE_N1N2 node, filling N8 interface AMF end IP into TABLE_N1N2 node, and accumulating count;
for the N11 interface: analyzing the NSmf_PDUSion_UpdateSMContext message to obtain the IMSI and the AMF end IP of the N11 interface, inquiring the imsi_hash TABLE according to the IMSI to obtain a TABLE_N1N2 node, filling the AMF end IP of the N11 interface into the TABLE_N1N2 node, and accumulating the counts;
for the N12 interface: analyzing the Nausf_UEauthentication_ Authenticate Request message to obtain an IMSI and an AMF end IP of an N12 interface, inquiring an imsi_hash TABLE according to the IMSI to obtain a TABLE_N1N2 node, filling the AMF end IP of the N12 interface into the TABLE_N1N2 node, and accumulating the counts;
for the N14 interface:
analyzing the Namf_communication_UEContexttransfer message to obtain a 5G-GUTI and an AMF end IP of an N14 interface, inquiring a guti_hash TABLE according to the 5G-GUTI to obtain a TABLE_N1N2 node, filling the AMF end IP of the N14 interface into the TABLE_N1N2 node, and accumulating the counts;
for the N15 interface: analyzing the Npcf_UEPolicyControl_Create message to obtain an IMSI and an AMF end IP of an N15 interface, inquiring an imsi_hash TABLE according to the IMSI to obtain a TABLE_N1N2 node, filling the AMF end IP of the N15 interface into the TABLE_N1N2 node, and accumulating the counts;
for the N26 interface: and analyzing Forward Relocation Request information to obtain the IMSI and the AMF end IP of the N26 interface, inquiring the imsi_hash TABLE according to the IMSI to obtain a TABLE_N1N2 node, filling the AMF end IP of the N26 interface into the TABLE_N1N2 node, and accumulating the counts.
S3: and taking a node in TABLE_N1N2 within one hour from the extraction time, respectively sequencing the counts of N8, N11, N12, N14, N15 and N26 interfaces IP in the node, taking the IP with the first ranking of each interface as the IP which is being used by the interface, combining the IP with AMF_N1N2_IP into a mapping relation, and sending the mapping relation to a shunt as a shunt basis.
The IP autonomous learning system for the AMF network element interface in the embodiment of the application comprises an N1N2 interface, an N8 interface, an N11 interface, an N12 interface, an N14 interface, an N15 interface and an N26 interface, wherein the interfaces to be learned of the AMF network element are related to the system; the system comprises an index information acquisition module, an IP association module and an IP learning module;
the index information acquisition module is used for: acquiring an AMF end IP and index information of an N1N2 interface data stream;
the IP association module is used for: acquiring AMF end IP of all other interfaces to be learned which belong to the same AMF network element as the N1N2 interface according to the index information;
the IP learning module is used for: and determining the IP being used by each interface in all the acquired AMF end IPs of each interface to be learned.
On the basis of the technical scheme, the workflow of the index information acquisition module comprises the following steps: acquiring an AMF end IP, an IMSI, a 5G-GUTI and extraction time of an N1N2 interface data stream of each AMF network element, and generating an AMF end IP table of the AMF network element corresponding to the N1N2 interface according to the AMF end IP of a single N1N2 interface; generating IMSI index tables of all AMF network elements according to IMSI of all N1N2 interfaces, and associating each IMSI in the IMSI index tables with a corresponding AMF end IP table; generating 5G-GUTI index tables of all AMF network elements according to the 5G-GUTI of all the N1N2 interfaces, and associating each 5G-GUTI in the 5G-GUTI index tables with a corresponding AMF end IP table.
On the basis of the technical scheme, the workflow of the IP association module comprises the following steps: for an N8 interface, an N11 interface, an N12 interface, an N15 interface and an N26 interface, acquiring IMSI and IP in the corresponding interfaces in effective time, determining an AMF end IP table corresponding to the current IMSI in an IMSI index table, and filling the acquired current IP into the corresponding interface IP in the AMF end IP table;
for an N14 interface, acquiring 5G-GUTI and IP in the interface in effective time, determining an AMF end IP table corresponding to the current 5G-GUTI in a 5G-GUTI index table, and filling the acquired current IP into the corresponding interface IP in the AMF end IP table.
On the basis of the technical scheme, the IP association module is further used for: after filling IP in the IP table of the AMF end, counting filling times; the work flow of the IP learning module comprises the following steps: after the appointed duration, filling the IP with the largest frequency into each interface to be learned as the IP which is being used by the interface to be learned.
Based on the technical scheme, the effective time is 3-5 s after the extraction time, and the specified duration is at least 3600s after the extraction time.
Those of ordinary skill in the art will appreciate that all or some of the steps, systems, functional modules/units in the apparatus, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between the functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed cooperatively by several physical components. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer-readable storage media, which may include computer-readable storage media (or non-transitory media) and communication media (or transitory media).
The foregoing is merely a specific implementation of the embodiment of the present application, but the protection scope of the embodiment of the present application is not limited thereto, and any person skilled in the art may easily think of various equivalent modifications or substitutions within the technical scope of the embodiment of the present application, and these modifications or substitutions should be covered in the protection scope of the embodiment of the present application. Therefore, the protection scope of the embodiments of the present application shall be subject to the protection scope of the claims.

Claims (4)

1. An IP autonomous learning method for AMF network element interfaces, wherein interfaces to be learned of the AMF network elements comprise an N1N2 interface, an N8 interface, an N11 interface, an N12 interface, an N14 interface, an N15 interface and an N26 interface; characterized in that the method comprises the following steps: acquiring an AMF end IP and index information of an N1N2 interface data stream; acquiring AMF end IP of all other interfaces to be learned which belong to the same AMF network element as the current N1N2 interface according to the index information; determining the IP being used by each interface in all the acquired AMF end IPs of each interface to be learned;
the process for acquiring the AMF end IP and index information of the N1N2 interface data stream comprises the following steps: acquiring an AMF end IP, an IMSI, a 5G-GUTI and extraction time of an N1N2 interface data stream of each AMF network element, and generating an AMF end IP table of the AMF network element corresponding to the N1N2 interface according to the AMF end IP of a single N1N2 interface; generating IMSI index tables of all AMF network elements according to IMSI of all N1N2 interfaces, and associating each IMSI in the IMSI index tables with a corresponding AMF end IP table; generating 5G-GUTI index tables of all AMF network elements according to the 5G-GUTI of all N1N2 interfaces, and associating each 5G-GUTI in the 5G-GUTI index table with a corresponding AMF end IP table;
the process of acquiring the AMF end IP of all other interfaces to be learned which belong to the same AMF network element as the current N1N2 interface according to the index information comprises the following steps: for an N8 interface, an N11 interface, an N12 interface, an N15 interface and an N26 interface, acquiring IMSI and IP in the corresponding interfaces in effective time, determining an AMF end IP table corresponding to the current IMSI in an IMSI index table, and filling the acquired current IP into the corresponding interface IP in the AMF end IP table;
for an N14 interface, acquiring 5G-GUTI and IP in the interface in effective time, determining an AMF end IP table corresponding to the current 5G-GUTI in a 5G-GUTI index table, and filling the acquired current IP into the corresponding interface IP in the AMF end IP table;
after filling the IP into the IP table of the AMF end, the method further comprises the following steps: counting the filling times; the process of determining the IP being used by each interface in all the acquired AMF end IPs of each interface to be learned includes: after the appointed duration, filling the IP with the largest frequency into each interface to be learned as the IP which is being used by the interface to be learned.
2. The IP autonomous learning method for an AMF network element interface of claim 1, wherein: the effective time is 3-5 s after the extraction time, and the specified duration is at least 3600s after the extraction time.
3. An IP autonomous learning system for AMF network element interfaces, wherein interfaces to be learned of the AMF network elements in the system comprise an N1N2 interface, an N8 interface, an N11 interface, an N12 interface, an N14 interface, an N15 interface and an N26 interface; the method is characterized in that: the system comprises an index information acquisition module, an IP association module and an IP learning module;
the index information acquisition module is used for: acquiring an AMF end IP and index information of an N1N2 interface data stream;
the IP association module is used for: acquiring AMF end IP of all other interfaces to be learned which belong to the same AMF network element as the N1N2 interface according to the index information;
the IP learning module is used for: determining the IP being used by each interface in all the acquired AMF end IPs of each interface to be learned;
the workflow of the index information acquisition module comprises the following steps: acquiring an AMF end IP, an IMSI, a 5G-GUTI and extraction time of an N1N2 interface data stream of each AMF network element, and generating an AMF end IP table of the AMF network element corresponding to the N1N2 interface according to the AMF end IP of a single N1N2 interface; generating IMSI index tables of all AMF network elements according to IMSI of all N1N2 interfaces, and associating each IMSI in the IMSI index tables with a corresponding AMF end IP table; generating 5G-GUTI index tables of all AMF network elements according to the 5G-GUTI of all N1N2 interfaces, and associating each 5G-GUTI in the 5G-GUTI index table with a corresponding AMF end IP table;
the workflow of the IP association module comprises: for an N8 interface, an N11 interface, an N12 interface, an N15 interface and an N26 interface, acquiring IMSI and IP in the corresponding interfaces in effective time, determining an AMF end IP table corresponding to the current IMSI in an IMSI index table, and filling the acquired current IP into the corresponding interface IP in the AMF end IP table;
for an N14 interface, acquiring 5G-GUTI and IP in the interface in effective time, determining an AMF end IP table corresponding to the current 5G-GUTI in a 5G-GUTI index table, and filling the acquired current IP into the corresponding interface IP in the AMF end IP table;
the IP association module is further configured to: after filling IP in the IP table of the AMF end, counting filling times; the work flow of the IP learning module comprises the following steps: after the appointed duration, filling the IP with the largest frequency into each interface to be learned as the IP which is being used by the interface to be learned.
4. The IP autonomous learning system for an AMF network element interface as recited in claim 3, wherein: the effective time is 3-5 s after the extraction time, and the specified duration is at least 3600s after the extraction time.
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