WO2020253683A1 - 数据处理方法及装置 - Google Patents

数据处理方法及装置 Download PDF

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Publication number
WO2020253683A1
WO2020253683A1 PCT/CN2020/096391 CN2020096391W WO2020253683A1 WO 2020253683 A1 WO2020253683 A1 WO 2020253683A1 CN 2020096391 W CN2020096391 W CN 2020096391W WO 2020253683 A1 WO2020253683 A1 WO 2020253683A1
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WIPO (PCT)
Prior art keywords
load
network element
data
service
information
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PCT/CN2020/096391
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English (en)
French (fr)
Inventor
李卓明
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华为技术有限公司
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Application filed by 华为技术有限公司 filed Critical 华为技术有限公司
Priority to EP20825654.5A priority Critical patent/EP3975479A4/en
Publication of WO2020253683A1 publication Critical patent/WO2020253683A1/zh
Priority to US17/549,239 priority patent/US20220103435A1/en

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/50—Network service management, e.g. ensuring proper service fulfilment according to agreements
    • H04L41/5003—Managing SLA; Interaction between SLA and QoS
    • H04L41/5009—Determining service level performance parameters or violations of service level contracts, e.g. violations of agreed response time or mean time between failures [MTBF]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/06—Generation of reports
    • H04L43/065—Generation of reports related to network devices
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/40—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using virtualisation of network functions or resources, e.g. SDN or NFV entities
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/04—Processing captured monitoring data, e.g. for logfile generation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/06—Generation of reports
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
    • H04L43/0876—Network utilisation, e.g. volume of load or congestion level
    • H04L43/0882—Utilisation of link capacity
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/20—Arrangements for monitoring or testing data switching networks the monitoring system or the monitored elements being virtualised, abstracted or software-defined entities, e.g. SDN or NFV
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/50—Testing arrangements
    • H04L43/55—Testing of service level quality, e.g. simulating service usage
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/04—Arrangements for maintaining operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/0284—Traffic management, e.g. flow control or congestion control detecting congestion or overload during communication
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/08—Testing, supervising or monitoring using real traffic

Definitions

  • This application relates to the field of communication technology, and in particular to a data processing method and device.
  • the fifth generation (5th generation, 5G) mobile communication technology has a complex topology.
  • 5G mobile communication technology introduces a network data analysis function (NWDAF). NWDAF receives the requester's analysis request, and then collects resource data of the target network function (NF) instance. NWDAF analyzes the collected data and obtains the analysis result.
  • NWDAF network data analysis function
  • the embodiments of the present application provide a data processing method and device, which can improve the accuracy of the analysis result of the target network element.
  • this application provides a data processing method, which can be executed by a data analysis network element.
  • the method includes: the data analysis network element receives the analysis request from the requester, after receiving the analysis request, the data analysis network element obtains the load data of the target network element according to the analysis request, and then generates the target network according to the obtained load data. Meta analysis results, and send the analysis results to the requester.
  • the analysis request includes the information of the target network element
  • the load data includes service load information.
  • the data analysis network element receives the analysis request from the requester, and obtains the load data of the target network element according to the analysis request. After the load data is obtained, the analysis result of the target network element is generated based on the load data. To send the analysis results to the requester.
  • the analysis request includes the information of the target network element
  • the load data includes service load information.
  • the data analysis network element can only obtain the resource data of the target network element and provide the requester with the resource analysis result of the target network element. The analysis result cannot accurately show the load status of the target network element, and the analysis result is accurate Poor sex.
  • the data processing method of the embodiment of the present application can obtain load data of the target network element, and the load data includes service load information. Compared with the resource data of the target network element, the load data can more accurately characterize the load status of the target network element. The analysis results obtained based on the load data can accurately present the load status of the target network element with high accuracy.
  • the service load information includes at least one of the service load, the upper limit of the service load, and the ratio of the service load.
  • the service load may be the key performance indicator KPI of the target network element.
  • the KPIs corresponding to different network elements are different.
  • the service load of the target network element is the KPI of the entire target network element.
  • the KPI of the SMF can be the average number of sessions in the connection state.
  • the load data may also be the service load related to the target network element and the network slice, that is, the target network element belongs to the service load of the network slice.
  • the KPI of the target network element belonging to the network slice may be: the statistical result of the average number of registered users divided according to the network slice.
  • the analysis result includes at least one of the average value of the service load information of the target network element and the peak value of the service load information.
  • the data processing method provided in this application further includes:
  • the data analysis network element obtains the overload information of the target network element according to the analysis request
  • the data analysis network element generates the overload analysis result of the target network element according to the overload information
  • the data analysis network element sends the overload analysis result to the requester.
  • the data analysis network element can also analyze the overload status of the target network element to provide the requester with the overload analysis result, making the analysis result more accurate and comprehensive.
  • the overload analysis result includes the probability of the target network element being overloaded.
  • the load data further includes resource load information
  • the analysis result also includes at least one of the average value of the resource load information of the target network element and the peak value of the resource load information.
  • the resource load information includes at least one of resource load and resource load upper limit.
  • the resource load represents the resources actually occupied by the target network element, such as the running status of the central processing unit CPU and the occupied status of memory.
  • the upper limit of resource load indicates the maximum allowable resources of the target network element, such as the maximum memory that can be occupied.
  • the analysis request also includes network slice information; the load data is load data corresponding to the network slice.
  • the target network element belongs to multiple network slices, and the target network element includes a common module, and the common module is used to process services of multiple network slices.
  • the data processing method provided in this application also includes:
  • the data analysis network element obtains the resource load information of the network slice corresponding to the network slice information of the common module according to the service load information corresponding to the multiple slices and the resource load information of the common module.
  • the data analysis network element can also determine the target network element based on the service load information corresponding to the multiple slices and the resource load information of the common module
  • the public module in the middle belongs to the resource load information of the network slice corresponding to the network slice information, and provides the requester with the analysis result of the resource of the target network element belonging to the network slice.
  • the target network element also includes multiple proprietary modules, and the multiple proprietary modules are used to process services of multiple network slices respectively.
  • the resource load information includes resource load information of a dedicated module of a network slice corresponding to the network slice information and resource load information of a common module of multiple slices.
  • the data processing method provided in this application also includes:
  • the data analysis network element obtains the resource load information of the network slice corresponding to the network slice information according to the resource load information of the proprietary module and the resource load information of the network slice corresponding to the network slice information of the public module.
  • both the proprietary and public modules in the target network element can process the network slicing services corresponding to the network slicing information, and the data analysis network element can also be based on the resource load of the proprietary module
  • the information and common modules belong to the resource load information of the network slice corresponding to the network slice information, to determine the resource load information of the network slice corresponding to the network slice information of the target network element, and to provide the requester with the analysis result of the resource of the target network element belonging to the network slice .
  • the service load information includes load data related to the quality of service flow.
  • the load data related to the service quality flow includes one or more of the following information: service quality flow data, service quality flow performance measurement data, and the target network element and service quality flow related service load account The ratio of the maximum business load of the quality of service flow.
  • the service load information includes load data related to the service quality flow of the service type of the target network element;
  • the analysis result includes the average value and load data of the load data related to the service quality flow of the service type of the target network element At least one of the peaks.
  • the service load information includes load data related to the quality of service flow of multiple service types of the target network element; the analysis result includes: the target network element is under conditions of multiple service types and different service load ratios. At least one of the average value of the traffic load information and the peak value of the traffic load information.
  • the data analysis network element obtains the load data of the target network element according to the analysis request, including:
  • the data analysis network element obtains historical load data from the operation management and maintenance OAM according to the analysis request, where the historical load data includes one or more of business load, business load upper limit, and service quality flow performance measurement data.
  • the data analysis network element can obtain historical load data of the target network element from the OAM, such as service load, service load upper limit, and service quality flow performance measurement data, etc., to analyze based on the obtained historical load data to obtain the analysis result .
  • the data analysis network element obtains the load data of the target network element according to the analysis request, including:
  • the data analysis network element obtains current load data from the network warehouse function NRF according to the analysis request.
  • the current load data includes at least the ratio of the service load and the load related to the target network element and the service quality flow to the maximum service load of the service quality flow One.
  • the data analysis network element can obtain the current load data of the target network element from the NRF, such as: the proportion of service load, the ratio of the load related to the target network element and the quality of service flow to the maximum service load of the quality of service flow, etc., based on the obtained Analyze the current load data to obtain the analysis result.
  • this application provides a communication method, which can be executed by the network element corresponding to the requester.
  • the method includes: after a requester sends an analysis request to a data analysis network element, receiving an analysis result from the data analysis network element, and performing processing operations based on the analysis result.
  • the analysis request includes the information of the target network element.
  • the requester receives the analysis result from the data analysis network element. Since the analysis result can accurately present the load status of the target network element, the accuracy is high.
  • the requester can perform processing operations, such as network function selection, network path selection, network resource adjustment, etc., based on the more accurate analysis results, which greatly reduces the probability of network element overload.
  • the processing operation is performed according to the analysis result, including: selecting the target network element according to the analysis result. For example, selecting a target network element whose load meets the requirements and/or selecting a target network element whose resource conditions meet the requirements.
  • the target network element may be a user plane function network element, and the requester may be a session management function network element.
  • this application provides a data processing device, which may be the data analysis network element in the first aspect described above.
  • the device includes a processing unit, a receiving unit and a sending unit.
  • the receiving unit is configured to receive an analysis request from the requester, and the analysis request includes information of the target network element.
  • the receiving unit is also used to obtain load data of the target network element according to the analysis request, and the load data includes service load information.
  • the processing unit is used to generate the analysis result of the target network element according to the load data.
  • the sending unit is used to send the analysis result to the requester.
  • the service load information includes at least one of the service load, the upper limit of the service load, and the ratio of the service load.
  • the analysis result includes at least one of the average value of the service load information of the target network element and the peak value of the service load information.
  • the receiving unit is also used to obtain the overload information of the target network element according to the analysis request;
  • the processing unit is also used to generate an overload analysis result of the target network element according to the overload information
  • the sending unit is also used to send the overload analysis result to the requester.
  • the overload analysis result includes the probability of the target network element being overloaded.
  • the load data further includes resource load information
  • the analysis result also includes at least one of the average value of the resource load information of the target network element and the peak value of the resource load information.
  • the analysis request also includes network slice information; the load data is load data corresponding to the network slice.
  • the target network element belongs to multiple network slices, the target network element includes a common module, and the common module is used to process services of multiple network slices;
  • the processing unit is further configured to obtain the resource load information of the network slice corresponding to the network slice information that the common module belongs to according to the service load information corresponding to the multiple slices and the resource load information of the common module.
  • the target network element also includes multiple proprietary modules, and the multiple proprietary modules are used to process services of multiple network slices respectively;
  • the resource load information includes the resource load information of the dedicated module of the network slice corresponding to the network slice information and the resource load information of the public modules of multiple slices;
  • the processing unit is further configured to obtain resource load information belonging to the network slice corresponding to the network slice information according to the resource load information of the proprietary module and the resource load information of the network slice corresponding to the network slice information of the public module.
  • the service load information includes load data related to the quality of service flow.
  • the load data related to the service quality flow includes one or more of the following information: service quality flow data, service quality flow performance measurement data, and the target network element and service quality flow related service load account The ratio of the maximum business load of the quality of service flow.
  • the service load information includes load data related to the service quality flow of the service type of the target network element;
  • the analysis result includes the average value and load data of the load data related to the service quality flow of the service type of the target network element At least one of the peaks.
  • the service load information includes load data related to the quality of service flow of multiple service types of the target network element; the analysis result includes: the target network element is under conditions of multiple service types and different service load ratios. At least one of the average value of the traffic load information and the peak value of the traffic load information.
  • the receiving unit is used to obtain load data of the target network element according to the analysis request, specifically: obtaining historical load data from the operation management and maintenance OAM according to the analysis request, where the historical load data includes business One or more of load, upper limit of business load, and service quality flow performance measurement data.
  • the receiving unit is used to obtain the load data of the target network element according to the analysis request, specifically: according to the analysis request, obtain the current load data from the network warehouse function NRF, where the current load data includes business load At least one of the ratio and the ratio of the load related to the quality of service flow of the target network element to the maximum service load of the quality of service flow.
  • the present application provides a communication device, which may be a network element corresponding to the requester in the second aspect.
  • the device includes a processing unit, a receiving unit and a sending unit.
  • the sending unit is used to send an analysis request to the data analysis network element, and the analysis request includes the information of the target network element;
  • the receiving unit is used to receive the analysis result from the data analysis network element;
  • the processing unit is used to, according to the analysis result, Perform processing operations.
  • the processing unit is used to perform processing operations based on the analysis result, specifically: selecting the target network element according to the analysis result. For example, selecting a target network element whose load meets the requirements and/or selecting a target network element whose resource conditions meet the requirements.
  • the target network element may be a user plane function network element, and the requester may be a session management function network element.
  • the present application provides a data processing device, which is used to implement the function of the data analysis network element in the first aspect described above, or to implement the function of the requestor in the second aspect described above.
  • the present application provides a data processing device, which has the function of implementing the data processing method of any one of the foregoing aspects.
  • This function can be realized by hardware, or by hardware executing corresponding software.
  • the hardware or software includes one or more modules corresponding to the above-mentioned functions.
  • the present application provides a data processing device including: a processor and a memory; the memory is used to store computer execution instructions, and when the data processing device is running, the processor executes the computer execution instructions stored in the memory, So that the data processing device executes the data processing method of any one of the above aspects.
  • the present application provides a data processing device, including: a processor; the processor is configured to couple with a memory, and after reading an instruction in the memory, execute the data processing method according to any one of the above aspects according to the instruction .
  • the present application provides a computer-readable storage medium that stores instructions in the computer-readable storage medium, and when it runs on a computer, the computer can execute the data processing method of any one of the above aspects .
  • this application provides a computer program product containing instructions, which when running on a computer, enables the computer to execute the data processing method of any one of the above aspects.
  • the present application provides a circuit system, the circuit system includes a processing circuit, and the processing circuit is configured to execute the data processing method of any one of the foregoing aspects.
  • the present application provides a chip, the chip includes a processor, the processor is coupled with a memory, the memory stores program instructions, and when the program instructions stored in the memory are executed by the processor, the data of any one of the above aspects is realized Approach.
  • the present application provides a communication system.
  • the communication system includes the data analysis network element in any of the above aspects, the network element corresponding to the requester in any aspect, and the target network element.
  • Figure 1 is a network architecture diagram provided by an embodiment of the application
  • Figure 2 is a system architecture diagram provided by an embodiment of the application.
  • FIG. 3 is a flowchart of a data processing method provided by an embodiment of the application.
  • FIG. 4 is a flowchart of a method for generating analysis results on resources provided by an embodiment of the application
  • FIG. 5 is a flowchart of another method for generating analysis results on resources provided by an embodiment of the application.
  • FIG. 6 is a flowchart of a data processing method provided by an embodiment of the application.
  • FIG. 7 is a flowchart of yet another data processing method provided by an embodiment of the application.
  • FIG. 8 is a flowchart of yet another data processing method provided by an embodiment of this application.
  • FIG. 9 is a flowchart of a method for obtaining service quality flow data provided by an embodiment of this application.
  • FIG. 10 is a flowchart of a data processing method provided by an embodiment of this application.
  • FIG. 11 is a flowchart of yet another data processing method provided by an embodiment of this application.
  • FIG. 12 is a flowchart of a method for generating an overload analysis result according to an embodiment of the application.
  • FIG. 13 is a schematic structural diagram of a data processing device provided by an embodiment of this application.
  • FIG. 14 is a schematic structural diagram of another data processing device provided by an embodiment of this application.
  • NS is a logical network customized according to different service requirements on a physical or virtual network infrastructure.
  • a network slice can be a complete end-to-end network including terminals, access networks, transmission networks, core networks, and application servers. It can provide complete communication services and have certain network capabilities.
  • the network slice can also be any combination of the aforementioned terminals, access networks, transmission networks, core networks, and application servers.
  • NS provides customers with network slice as a service (network slice as a service, NSaaS).
  • network slice as a service NSaaS
  • an operator creates a network slicing instance to provide services to third parties, where the third parties may include enterprises, Internet service providers, operators, and so on.
  • the NS uses a single network slice selection assistance information (S-NSSAI) to identify it.
  • S-NSSAI single network slice selection assistance information
  • NSI Network slice instance
  • NSI is an instantiation of NS, that is, a real operating logical network that can meet certain network characteristics or service requirements.
  • An NSI can provide one or more services.
  • NSI can be created by a network slice management function device.
  • One network slice management function device can create multiple NSIs and manage them at the same time, including but not limited to performance monitoring and fault management during the NSI operation.
  • NSI can be created from a network slicing template or not from a network slicing template.
  • the NSI uses a network slice instance identity (NSI-ID) to identify it.
  • NSI-ID network slice instance identity
  • NF is a processing function in the network, which defines functional behavior and interfaces.
  • NF can be realized by dedicated hardware, by running software on dedicated hardware, or in the form of virtual functions on a general hardware platform. Therefore, from the perspective of implementation, NF can be divided into physical network functions and virtual network functions. From the perspective of usage, NF can be divided into dedicated network functions and shared network functions. Specifically, for multiple network slice instances/network slice subnet instances, different network functions can be used independently. This network function is called a dedicated network function, or it can share the same network function. This network function is called It is a shared network function.
  • NWDAF network data analysis function
  • NWDAF can collect data from various network functions (NF), application functions (AF), operation administration and maintenance (OAM), and perform network function analysis and prediction.
  • NF network functions
  • AF application functions
  • OAM operation administration and maintenance
  • TS third generation partnership project
  • NWDAF uses event subscription to collect data from NF, AF and OAM systems, retrieve information from the data warehouse, based on NF, AF or OAM needs to provide corresponding network function analysis and prediction results.
  • NF can include access and mobility management function (AMF), session management function (session management function, SMF), policy control function (PCF), user data management (unified data) management, UDM), network exposure function (NEF), user plane function (UPF).
  • AMF access and mobility management function
  • SMF session management function
  • PCF policy control function
  • UDM user data management
  • NEF network exposure function
  • UPF user plane function
  • OAM may include a network management system (network management system, NMS).
  • NMS network management system
  • the data warehouse may include a user-related information warehouse (unified data repository, UDR), a network function information warehouse (network repository function, NRF), and a network slice selection function (network slice selection function, NSSF).
  • UDR user-related information warehouse
  • NRF network repository function
  • NSSF network slice selection function
  • NWDAF can be used as a data analysis network element. See Figure 1, which shows the location of NWDAF in the network architecture. NWDAF can obtain data from NRF, OAM, NEF and SMF to generate analysis results. Among them, NEF can manage AF. Exemplarily, referring to Figure 1, one NEF can manage one AF. The dashed box represents the network slice. A network slice can use some network functions independently, for example, each network slice has its own SMF and UPF. Exemplarily, referring to FIG. 1, there is one SMF and one UPF managed by the SMF in the first network slice, and there is one SMF and two UPFs managed by the SMF in the second network slice. Multiple network slices can share some network functions, such as AMF and PCF.
  • NWDAF can only collect resource data, and then analyze the resource data to obtain the analysis result about the resource.
  • the resource data of the NF represents the overall resource status of the NF, and is not related to the service type and network slice.
  • the analysis result is obtained based on the resource data of the NF. In this way, the analysis result cannot correctly show the true load state of the NF instance, which reduces the accuracy of the analysis result.
  • the embodiment of the present application provides a data processing method, and the data processing method provided in the embodiment of the present application can be applied to the data processing system shown in FIG. 2.
  • the system includes requestor, data analysis network element, NRF and OAM.
  • the requester can exchange information with the data analysis network element, for example, the requester sends an analysis request to the data analysis network element, where the analysis request includes the information of the target network element.
  • the requester can receive the analysis result from the data analysis network element, where the analysis result is the analysis result about the target network element.
  • the data analysis network element can collect data from NRF and OAM.
  • the data analysis network element can also collect data from the target network element or network elements related to the service processing of the target network element.
  • the data analysis network element may be NWDAF.
  • the target network element may be a UPF
  • the network element related to the service processing of the target network element may be a network element that manages the target network element, such as an SMF that manages the UPF.
  • the target network element and the network elements related to the service processing of the target network element are not shown in FIG. 2).
  • the embodiment of the application provides a data processing method, which is applied in the process of network element load analysis.
  • the data processing method of the embodiment of the present application may include S301 to S305:
  • the requester sends an analysis request to the data analysis network element.
  • the data analysis network element receives the analysis request from the requester.
  • the requester can be NF, the requester can also be AF, and the requester can also be OAM.
  • the data analysis network element can collect data based on the analysis request and provide the requester with corresponding analysis results.
  • the data analysis network element may be NWDAF.
  • the analysis request may include the information of the target network element to request analysis of the status of the target network element.
  • the information of the target network element may be the identification of the target network element.
  • the identifier of the target network element may be a target UPF instance identifier to request analysis of the status of the network element corresponding to the target UPF instance identifier.
  • the number of target network elements can be one or multiple.
  • the information of the target network element may also be the type of the target network element.
  • the type of the target network element may be UPF to request analysis of the status of all UPF network elements.
  • the analysis request may also include network slice information to request analysis of the status of the target network element belonging to a certain network slice.
  • the network slice information may be a network slice identifier, such as S-NSSAI or NSI-ID.
  • the analysis request includes network slice information, when the data analysis network element executes S302, the acquired load data are all load data corresponding to the network slice.
  • the analysis request can be a query analysis request or a subscription analysis request. If the analysis request is a query request, the data analysis network element provides the requester with a one-time analysis result. If the analysis request is a subscription request, after the data analysis network element provides the requester with the analysis result, when the load data changes, it will analyze the changed load data to obtain new analysis results, or periodically according to the requester’s request Analyze the load data and obtain new analysis results to send to the requester until the subscription analysis request is cancelled.
  • both the query-type analysis request and the subscription-type analysis request can refer to the prior art, which will not be repeated here.
  • the data analysis network element obtains load data of the target network element according to the analysis request.
  • the load data may include service load information, so that the data analysis network element generates an analysis result about the load.
  • the load data may also include resource load information, so that the data analysis network element generates analysis results about resources.
  • the load data includes service load information.
  • the business load information includes at least one of a business load, a business load upper limit, and a business load ratio.
  • the business load information may include any of the business load, the upper limit of the business load, and the proportion of the business load, or any two of the three, the upper limit of the business load, and the proportion of the business load, and it may also include the business load. , Business load upper limit and business load ratio.
  • the service load may be the key performance indicators (KPI) of the target network element.
  • KPI key performance indicators
  • the KPIs corresponding to different network elements are different.
  • the service load of the target network element is the KPI of the entire target network element.
  • the KPI of SMF can be the average number of sessions in the connection state.
  • the load data may also be the service load related to the target network element and the network slice, that is, the target network element belongs to the service load of the network slice.
  • the KPI of the target network element belonging to the network slice may be: the statistical result of the average number of registered users divided according to the network slice.
  • the upper limit of the business load is the maximum value of the business load.
  • the upper limit of the service load of the target network element is the smaller of the following two values: First, the maximum service load supported by the resources that the target network element can use ; Second, the maximum value of the service load that the target network slice to which the target network element belongs can be configured, for example, the maximum number of registered users supported by the target network slice in the service area of the target network element.
  • the target network slice is the slice corresponding to the network slice information in the analysis request.
  • the service load ratio may be the ratio of the service load of the target network element to the maximum service load.
  • the service load ratio may be the ratio of the service load of the target network element to the maximum service load of the target network element.
  • the service load ratio may be the ratio of the service load of the target network element belonging to a certain network slice to the maximum service load of the network slice.
  • the service load of which network slice the target network element belongs to can be determined by analyzing the network slice information in the request.
  • the data analysis network element can analyze the load of the target network element based on the service load information to generate an analysis result about the load, so that the requestor can select the load based on the load analysis result.
  • Smaller network element For example, when the access and mobility management function AMF serves as the requester, the AMF selects the session management function SMF with a small current load based on the analysis result to avoid SMF overload.
  • Service load information also includes load data related to quality of service flow (QoS Flow).
  • QoS Flow quality of service flow
  • the load data related to QoS flow includes one or more of the following information: QoS flow data, QoS flow performance measurement data, target network element and QoS flow related business load accounted for the maximum business load of QoS flow Ratio.
  • the quality of service flow data may include quality of service (QoS) configuration, QoS parameters, QoS features, and so on.
  • QoS quality of service
  • the service quality flow data can be divided into service quality flow data of different business types. For example, service quality flow data can be divided into guaranteed bit rate quality of service flow (GBR QoS Flow), bandwidth-critical GBR QoS Flow, and delay-sensitive quality of service flow (latency-critical GBR QoS Flow), Service flow data of three service types that do not guarantee bandwidth quality (non-GBR QoS Flow).
  • the quality of service flow data can also use different quality of service indicators (QoS classify indicator, QCI) or fifth-generation communication quality of service indicators (5G QoS indicator, 5QI) to facilitate the classification of service types at a finer granularity.
  • QCI quality of service indicator
  • 5G QoS indicator, 5QI fifth-generation communication quality of service indicators
  • the service quality flow performance measurement data may include one or more of the following information: the number of establishment of the service quality flow, the number of release of the service quality flow, and information about the sustainability of the service quality flow.
  • the maximum service load of the quality of service flow can be:
  • the maximum forwarding speed of this service is 1000Mbps.
  • a certain UPF supports this service, and 600 Mbps is currently allocated for this type of quality of service flow.
  • the ratio is 60%.
  • the service load information may include load data related to the quality of service flow of a certain type of business.
  • the service load information may be load data related to the quality of service flow of a certain type of business.
  • the data analysis network element analyzes the load data related to the service quality flow of the service type to generate the analysis result of the target network element.
  • the analysis result is a load analysis result of a business type, which can specifically include the average value of the business data information related to the service quality flow of the business type, or the peak value of the business data information related to the service quality flow of the business type. It may also include the average value and peak value of the service data information related to the service quality flow of the service type.
  • the service load information may also include load data related to quality of service flows of multiple service types.
  • the data analysis network element analyzes the load data related to the quality of service flow of multiple service types to generate the analysis result of the target network element.
  • the analysis result is the load analysis result of multiple service types under different service load ratios. For example, the average value and peak value of the service load information of the two service types when the service load ratio is 2:8.
  • the load analysis results of multiple service types under different service load ratios may include the average value of service load information, may include the peak value of service load information, and may also include the average value and peak value of service load information.
  • the service load information can include load data related to the quality of service flow of one type of business, and it can also include load data related to the quality of service flow of multiple types of business to meet the analysis needs of the requester in different scenarios. Provide a variety of analysis results.
  • the load data also includes resource load information, so that the data analysis network element can analyze the resource status of the target network element.
  • the resource load information includes at least one of resource load and resource load upper limit.
  • the resource load represents the resources actually occupied by the target network element, such as the running status of the central processing unit/processor (CPU) and the occupied status of the memory.
  • the upper limit of resource load indicates the maximum allowable resources of the target network element, such as the maximum memory that can be occupied.
  • the resource load of the target network element belonging to this network slice is the same as the resource load of the target network element itself as a whole.
  • the upper limit of the resource load of the target network element belonging to this network slice is the same as the upper limit of the resource load of the target network element itself as a whole.
  • the target network element may include a dedicated module and a common module, and the common module is used to process services of the multiple network slices to which the target network element belongs.
  • the public module may be a network transceiver module or a service distribution module.
  • a proprietary module is only used to process services of a certain network slice among multiple network slices.
  • the dedicated module may be a signaling processing module, and the number of dedicated modules may be one or more.
  • the target network element may include a dedicated module to process services of a certain network slice among the multiple network slices to which the target network element belongs.
  • the target network element may also include two proprietary modules to respectively process services of a certain network slice among the multiple network slices to which the target network element belongs.
  • the target network element may also include multiple proprietary modules, that is, each network slice of the multiple network slices corresponds to a dedicated module.
  • the data processing method of the embodiment of the present application can also analyze the resource status of the target network element belonging to the network slice. If the network slice service corresponding to the network slice information is only used by the public module Processing, after the data analysis network element executes S301, it can also execute S401, and then execute S303:
  • the data analysis network element obtains resource load information of the network slice corresponding to the network slice information that the common module belongs to according to the service load information corresponding to the multiple slices and the resource load information of the common module.
  • the service load information corresponding to each slice may be the number of registered users accessed by each network slice.
  • the resource load information of the common module may be the amount of resources occupied by the common module.
  • the resource load information of the network slice corresponding to the common module belonging to the network slice information satisfies the following formula:
  • a 1 represents the resource load information of the network slice corresponding to the public module
  • B 1 represents the resource load information of the public module
  • n represents the number of network slices to which the target network element belongs
  • k represents the network slice information corresponding to the Network slice
  • x 1 represents the service load information of the target network element in the n network slices belonging to the first network slice
  • x 2 represents the service load information of the target network element in the n network slices belonging to the second network slice
  • x n represents the service load information of the target network element belonging to the nth network slice in n network slices
  • xk represents the service load information of the target network element belonging to the kth network slice in the n network slices.
  • the data analysis network element can also determine the target network element based on the service load information corresponding to the multiple slices and the resource load information of the common module
  • the public module in the middle belongs to the resource load information of the network slice corresponding to the network slice information, and provides the requester with the analysis result of the resource of the target network element belonging to the network slice.
  • the network slicing service corresponding to the network slicing information is processed by the proprietary module and the public module, as shown in Fig. 5, after the data analysis network element executes S301, it can also execute S401 and S402, and then execute S303:
  • the data analysis network element obtains resource load information of the network slice corresponding to the network slice information that the common module belongs to according to the service load information corresponding to the multiple slices and the resource load information of the common module.
  • the data analysis network element obtains the resource load information of the network slice corresponding to the network slice information belonging to the target network element according to the resource load information of the proprietary module and the resource load information of the network slice corresponding to the network slice information of the public module.
  • the resource load information of the network slice corresponding to the target network element belonging to the network slice information satisfies the following formula:
  • A indicates that the target network element belongs to the resource load information of the network slice corresponding to the network slice information
  • a 1 indicates that the public module belongs to the resource load information of the network slice corresponding to the network slice information
  • a 2 indicates that the dedicated module belongs to the network slice information.
  • the resource load information of the network slice B 1 represents the resource load information of the common module, n represents the number of network slices to which the target network element belongs, k represents the network slice corresponding to the network slice information, and x 1 represents the target network in n network slices X 2 represents the service load information of the target network element belonging to the second network slice in n network slices, x n represents the service load information of the target network element belonging to the first network slice in n network slices Service load information of n network slices, x k represents the service load information of the target network element in the n network slices belonging to the kth network slice.
  • both the proprietary and public modules in the target network element can process the network slicing services corresponding to the network slicing information, and the data analysis network element can also be based on the resource load of the proprietary module
  • the information and common modules belong to the resource load information of the network slice corresponding to the network slice information, to determine the resource load information of the network slice corresponding to the network slice information of the target network element, and to provide the requester with the analysis result of the resource of the target network element belonging to the network slice .
  • the resource load represents the resources actually occupied by the proprietary module or the public module in the target network element in the service processing process.
  • the upper limit of the resource load indicates the maximum resources allowed to be occupied by a proprietary module or a public module in the target network element during service processing.
  • both A 1 and B 1 in formula (1) represent resource loads, based on formula (1), it can be determined that the common module belongs to the resource load of the network slice corresponding to the network slice information.
  • both A 1 and B 1 in formula (1) represent the upper limit of resource load, based on formula (1), it can be determined that the public module belongs to the upper limit of resource load of the network slice corresponding to the network slice information.
  • A, A 1 , A 2 and B 1 in formula (2) all represent resource loads, based on formula (2), it can be determined that the target network element belongs to the resource load of the network slice corresponding to the network slice information.
  • A, A 1 , A 2 and B 1 in formula (2) all represent the upper limit of resource load, based on formula (2), it can be determined that the target network element belongs to the upper limit of resource load of the network slice corresponding to the network slice information.
  • the load data may include at least one of historical load data and current load data.
  • the steps performed by the data analysis network element are different.
  • the load data is historical load data
  • the historical load data is one or more of business load, business load upper limit, and service quality flow performance measurement data
  • S302 can be specifically implemented as S3021:
  • the data analysis network element obtains historical load data from the operation management and maintenance OAM according to the analysis request.
  • OAM is a unified name for various network entities that can perform network management.
  • the main functions of OAM include: complete daily network and business analysis, prediction, planning, and configuration; daily network and business testing and fault management Operational activities.
  • OAM can store the historical load data of the target network element.
  • the historical load data includes one or more of business load, business load upper limit, and service quality flow performance measurement data.
  • the data analysis network element sends a historical load data request to the OAM.
  • the historical load data request includes the information of the target network element, and may also include network slice information.
  • OAM After receiving the historical load data request, OAM sends historical load data about the target network element to the data analysis network element. If the analysis request is a query request, the historical load data request is a query request, and OAM sends historical load data about the target network element to the data analysis network element. Referring to Figure 7, if the analysis request is a subscription request, the historical load data request is a subscription request.
  • OAM After OAM sends the historical load data of the target network element to the data analysis network element once, each time the historical load data of the target network element When there is a change, or according to the time interval required by the requester, after a certain time interval, the historical load data of the target network element will be sent to the data analysis network element once until the analysis request is cancelled.
  • both the query-type historical load data request and the subscription-type historical load data request can refer to the prior art, which will not be repeated here.
  • the data analysis network element can obtain historical load data of the target network element from the OAM, such as service load, service load upper limit, and service quality flow performance measurement data, etc., to analyze based on the obtained historical load data to obtain the analysis result .
  • S302 can be specifically implemented as S3022:
  • the data analysis network element obtains service quality flow data from the network element of the management target network element according to the analysis request.
  • SMF1 serves as the requester.
  • the information of the target network element is the target UPF instance identifier.
  • the data analysis network element collects service quality flow data from each SMF of the management target UPF.
  • the network elements that manage the target UPF are SMF1 and SMF2, and the data analysis network element can collect service quality flow data about the target UPF from SMF1 and SMF2.
  • S302 can be specifically implemented as S3023:
  • the data analysis network element obtains service quality flow data from the target network element according to the analysis request.
  • the target network element is the target UPF.
  • the data analysis network element obtains service quality flow data from the target UPF.
  • the data analysis network element collects service quality flow data through the service interface of the target UPF.
  • S302 can be specifically implemented as S3024:
  • the data analysis network element obtains current load data from the network warehouse function NRF according to the analysis request.
  • NRF can provide current load information of the target network element.
  • the current load data includes at least one of the service load ratio and the ratio of the load related to the target network element and the service quality flow to the maximum service load of the service quality flow.
  • the data analysis network element sends a current load data request to the NRF.
  • the current load data request includes information of the target network element, and may also include network slice information.
  • the NRF After receiving the current load data request, the NRF sends the current load data about the target network element to the data analysis network element. If the analysis request is an inquiry request, the current load data request is an inquiry request, and the NRF sends the current load data of the target network element to the data analysis network element once. Referring to Figure 7, if the analysis request is a subscription request, the current load data request is a subscription request.
  • NRF After NRF sends the current load data of the target network element to the data analysis network element once, every time the current load data of the target network element When there is a change, or according to the time interval required by the requester, after a certain interval, the current load data of the target network element will be sent to the data analysis network element once until the analysis request is cancelled.
  • both the query-type current load data request and the subscription-type current load data request can be referred to the prior art, which will not be repeated here.
  • the data analysis network element can obtain the current load data of the target network element from the NRF, such as: the proportion of service load, the ratio of the load related to the target network element and the quality of service flow to the maximum service load of the quality of service flow, etc., based on the obtained Analyze the current load data to obtain the analysis result.
  • the data analysis network element generates an analysis result of the target network element according to the load data.
  • the analysis result corresponds to the type of load data. For example, if the load data includes business load information, the analysis result includes the load analysis result. If the load data includes resource load information, the analysis result includes the resource analysis result.
  • the analysis result is the analysis result about the load.
  • the analysis result may include at least one of the average value of the service load information of the target network element and the peak value of the service load information.
  • the analysis result may include the average value of the service load information of the target network element, may also include the peak value of the service load information of the target network element, and may also include the average value and peak value of the service load information of the target network element.
  • the average value of the service load information may be the average value of the service load.
  • the data analysis network element can obtain the service load of the target network element at different times.
  • the data analysis network element determines the time period to be analyzed. If the time period is a historical time period, the data analysis network element calculates the average value of the service load of the target network element in the historical time period. If the time period is a future time period, the data analysis network element determines the change trend of the service load of the target network element based on the acquired service load data to predict the average value of the target network element's service load in the future time period .
  • the data analysis network element may also use a regression algorithm to train a regression model.
  • the regression model can be a regression model of the target network element under different service types and different service load ratios.
  • the independent variable of the regression model can be service quality flow data, service quality flow performance measurement data, or service quality flow data and service quality flow performance measurement data.
  • the dependent variable of the regression model is business load. Commonly used regression algorithms include: linear regression algorithms, logistic regression algorithms, polynomial regression algorithms, etc. The embodiments of the present application do not limit the regression algorithms.
  • the data analysis network element inputs service quality flow data or service quality flow performance measurement data into the regression model, and both can obtain the business load.
  • the average value of the service load information may also be the average value of the service load ratio.
  • the data analysis network element can obtain the service load of the target network element at different times, and divide the service load at each time by the upper limit of the service load at that time, so as to obtain the service load ratio at that time.
  • the data analysis network element determines the time period to be analyzed. If the time period is a historical time period, the data analysis network element calculates the average value of the business load ratio of the target network element in the historical time period based on the acquired data. If the time period is a future time period, the data analysis network element determines the changing trend of the business load proportion of the target network element based on the acquired data to predict the average value of the business load proportion of the target network element in the future time period.
  • the peak value of the service load information may be the peak value of the service load.
  • the data analysis network element determines the peak value of the service load in the historical time period, the maximum value of the service load in the time period may be taken as the peak value of the service load. If the data analysis network element predicts the peak value of the service load in the future time period, the data analysis network element determines the change trend of the target network element's service load based on the acquired service load data to predict the target network element in the future time period Peak of the business load.
  • the peak value of the service load information may be the peak value of the service load ratio.
  • the data analysis network element determines the peak value of the service load ratio in the historical time period, the maximum value of the service load ratio in the time period may be used as the peak value of the service load ratio. If the data analysis network element predicts the peak value of the business load proportion in the future time period, the data analysis network element determines the change trend of the business load proportion of the target network element based on the acquired data of the business load proportion to predict the target network element’s The peak value of the business load ratio in the future time period.
  • the data analysis network element can provide the requester with load analysis results such as the average value or peak value of the service load information, so that the requester can select a network element with a smaller load based on the load analysis result.
  • load analysis results such as the average value or peak value of the service load information
  • the accuracy of the analysis result is higher.
  • the analysis result is the analysis result of the target network element under different service types and different service load ratios, since the analysis result fully considers the impact of different service types on the target network element, the accuracy of the analysis result is higher. Based on the more accurate analysis result, the requester selects the network element in the slice corresponding to the network slice information, which greatly reduces the risk of network element overload and avoids network shock.
  • the analysis result further includes at least one of the average value of the resource load information of the target network element and the peak value of the resource load information.
  • the analysis result may include the average value of resource load information of the target network element, may also include the peak value of the resource load information of the target network element, and may also include the average value and peak value of the resource load information of the target network element.
  • the average value of resource load information may be the average value of resource load.
  • the data analysis network element can obtain the resource load of the target network element at different times and the service load of different service types. The data analysis network element determines the time period to be analyzed. If the time period is a historical time period, the data analysis network element calculates the average value of the resource load of the target network element in the historical time period based on the acquired resource load data.
  • the data analysis network element calculates the ratio of the service load of different service types, trains to obtain a regression model between the service load and the average resource load at different service load ratios, and then based on the obtained
  • the service load data, resource load data and resource load upper limit of different service types are determined to determine the change trend of the resource load of the target network element to predict the average resource load of the target network element in the future time period.
  • the peak value of resource load information may be the peak value of resource load.
  • the data analysis network element determines the peak value of the resource load in the historical time period, the maximum value of the resource load in the time period may be taken as the peak value of the resource load. If the data analysis network element predicts the peak resource load in the future time period, the data analysis network element first calculates the ratio of the service load of different service types, and trains the regression model between the service load and the peak resource load at different service load ratios , And then the data analysis network element determines the change trend of the peak resource load of the target network element based on the acquired service load data of different service types, resource load data and resource load upper limit, so as to predict the target network element in the future time The peak resource load of the segment.
  • the data analysis network element can provide the requester with resource analysis results such as the average value or peak value of the resource load information.
  • resource analysis results such as the average value or peak value of the resource load information.
  • the data analysis network element will update the analysis results after a certain time interval based on the changed data or at the time interval required by the requester until the analysis request got canceled.
  • the data analysis network element sends the analysis result to the requester.
  • the requester receives the analysis result from the data analysis network element.
  • the data analysis network element continuously sends updated analysis results to the requester.
  • the analysis result is carried by the subscription notification message, and the data analysis network element sends the subscription notification message.
  • Method send analysis results to the requester until the analysis request is cancelled.
  • the data analysis network element receives the analysis request from the requester, and obtains the load data of the target network element according to the analysis request. After the load data is obtained, the target network element is generated according to the load data. Analyze the result and send the analysis result to the requester.
  • the analysis request includes the information of the target network element
  • the load data includes service load information.
  • the data analysis network element can only obtain the resource data of the target network element and provide the requester with the resource analysis result of the target network element. The analysis result cannot accurately show the load status of the target network element, and the analysis result is accurate Poor sex.
  • the data processing method of the embodiment of the present application can obtain load data of the target network element, and the load data includes service load information. Compared with the resource data of the target network element, the load data can more accurately characterize the load status of the target network element. The analysis results obtained based on the load data can accurately present the load status of the target network element with high accuracy.
  • the requester performs processing operations according to the analysis result.
  • the requester may select the network function based on the analysis result.
  • the AMF selects an SMF with a smaller current load based on the analysis result to avoid SMF overload.
  • the requester can select the network path based on the analysis result.
  • the SMF selects the UPF and path based on the analysis result to establish a data connection for the terminal.
  • the requester can adjust network resources based on the analysis result.
  • OAM can perform resource expansion in advance when resources are tight to ensure business experience.
  • the requester receives the analysis result from the data analysis network element. Since the analysis result can accurately present the load status of the target network element, the accuracy is high.
  • the requester can perform processing operations, such as network function selection, network path selection, network resource adjustment, etc., based on the more accurate analysis results, which greatly reduces the probability of network element overload.
  • the data processing method provided by the embodiment of the present application can also analyze the overload condition of the target network element.
  • the data processing method of the embodiment of the present application further includes S306 to S308:
  • the data analysis network element obtains the overload information of the target network element according to the analysis request.
  • the overload information may include historical overload information, such as overload event information sent by the target network element, overload warning information, and so on.
  • the historical overload information may be information obtained by the data analysis network element from the OAM.
  • the overload information may include current overload information, such as status information about whether the target network element is currently overloaded.
  • the current overload information is the information obtained by the data analysis network element from the NRF.
  • the data analysis network element generates an overload analysis result of the target network element according to the overload information.
  • the overload analysis result may be the probability that the target network element is overloaded.
  • the data analysis network element determines the length of time that the overload occurs in the time period to be analyzed according to the time of each overload in the historical overload information, and the data analysis network element determines the ratio of the time length of the overload to the time period to be analyzed according to the time period of the overload. As the probability of overload occurrence, it is stored in the overload analysis result. Alternatively, the data analysis network element determines the change trend of the probability of occurrence of overload based on historical overload information, so as to predict the probability of occurrence of overload in the future time period.
  • the current overload information can record whether the target network element is overloaded.
  • the data analysis network element determines the total number of current overload information collected from the NRF and the number of overload occurrences, determines the ratio of the number of overload occurrences to the total number of current overload information collections, and takes it as the target network element.
  • the probability of overload is stored in the overload analysis result.
  • the data analysis network element determines the change trend of the probability of occurrence of overload based on the number of overloads, so as to predict the probability of occurrence of overload in the future time period.
  • the data analysis network element sends the overload analysis result to the requester.
  • the requester receives the overload analysis result from the data analysis network element.
  • the data analysis network element can also analyze the overload status of the target network element to provide the requester with the overload analysis result, making the analysis result more accurate and comprehensive.
  • the data analysis network element and the network element corresponding to the requester include hardware structures and/or software modules corresponding to each function.
  • the embodiments of the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed by hardware or computer software-driven hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods for each specific application to implement the described functions, but such implementation should not be considered as going beyond the scope of the technical solutions of the embodiments of the present application.
  • the embodiment of the present application may divide the data processing apparatus into functional units according to the foregoing method examples.
  • each functional unit may be divided corresponding to each function, or two or more functions may be integrated into one processing unit.
  • the above-mentioned integrated unit can be implemented in the form of hardware or software functional unit. It should be noted that the division of units in the embodiments of the present application is illustrative, and is only a logical function division, and there may be other division methods in actual implementation.
  • FIG. 13 shows a schematic block diagram of a data processing device provided in an embodiment of the present application.
  • the data processing apparatus 1300 may exist in the form of software, and may also be a device, or a component (such as a chip system) in the device.
  • the data processing device 1300 includes a storage unit 1301, a processing unit 1302, and a communication unit 1303.
  • the communication unit 1303 can also be divided into a sending unit (not shown in FIG. 13) and a receiving unit (not shown in FIG. 13).
  • the sending unit is used to support the data processing device 1300 to send information to other network elements.
  • the receiving unit is configured to support the data processing apparatus 1300 to receive information from other network elements.
  • the storage unit is used to store the program code and data of the data processing device 1300, and the data may include but not limited to raw data or intermediate data.
  • the receiving unit is used to receive an analysis request from the requester.
  • the analysis request includes the information of the target network element and is also used to obtain load data of the target network element according to the analysis request. Including service load information; processing unit, used to generate analysis results of the target network element based on load data; sending unit, used to send analysis results to the requester; storage unit, used to store load data and analysis results.
  • the service load information includes at least one of the service load, the upper limit of the service load, and the ratio of the service load.
  • the analysis result includes at least one of the average value of the service load information of the target network element and the peak value of the service load information.
  • the receiving unit is also used to obtain the overload information of the target network element according to the analysis request;
  • the processing unit is also used to generate an overload analysis result of the target network element according to the overload information
  • the sending unit is also used to send the overload analysis result to the requester.
  • the overload analysis result includes the probability of the target network element being overloaded.
  • the load data further includes resource load information
  • the analysis result also includes at least one of the average value of the resource load information of the target network element and the peak value of the resource load information.
  • the analysis request also includes network slice information; the load data is load data corresponding to the network slice.
  • the target network element belongs to multiple network slices, the target network element includes a common module, and the common module is used to process services of multiple network slices;
  • the processing unit is further configured to obtain the resource load information of the network slice corresponding to the network slice information that the common module belongs to according to the service load information corresponding to the multiple slices and the resource load information of the common module.
  • the target network element also includes multiple proprietary modules, and the multiple proprietary modules are used to process services of multiple network slices respectively;
  • the resource load information includes the resource load information of the dedicated module of the network slice corresponding to the network slice information and the resource load information of the public modules of multiple slices;
  • the processing unit is further configured to obtain resource load information belonging to the network slice corresponding to the network slice information according to the resource load information of the proprietary module and the resource load information of the network slice corresponding to the network slice information of the public module.
  • the service load information includes load data related to the quality of service flow.
  • the load data related to the service quality flow includes one or more of the following information: service quality flow data, service quality flow performance measurement data, and the target network element and service quality flow related service load account The ratio of the maximum business load of the quality of service flow.
  • the service load information includes load data related to the service quality flow of the service type of the target network element;
  • the analysis result includes the average value and load data of the load data related to the service quality flow of the service type of the target network element At least one of the peaks.
  • the service load information includes load data related to the quality of service flow of multiple service types of the target network element; the analysis result includes: the target network element is under conditions of multiple service types and different service load ratios. At least one of the average value of the traffic load information and the peak value of the traffic load information.
  • the receiving unit is used to obtain load data of the target network element according to the analysis request, specifically: obtaining historical load data from the operation management and maintenance OAM according to the analysis request, where the historical load data includes business One or more of load, upper limit of business load, and service quality flow performance measurement data.
  • the receiving unit is used to obtain the load data of the target network element according to the analysis request, specifically: according to the analysis request, obtain the current load data from the network warehouse function NRF, where the current load data includes business load At least one of the ratio and the ratio of the load related to the quality of service flow of the target network element to the maximum service load of the quality of service flow.
  • the sending unit is used to send an analysis request to the data analysis network element, the analysis request includes information of the target network element; the receiving unit is used to receive the analysis result from the data analysis network element.
  • the processing unit is used to perform processing operations based on the analysis results.
  • the storage unit is used to store the analysis results.
  • the processing unit may be a processor or a controller, for example, a CPU, a general-purpose processor, DSP, ASIC, FPGA, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logical blocks, modules and circuits described in conjunction with the disclosure of this application.
  • the processor may also be a combination of computing functions, for example, a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and so on.
  • the communication unit may be a communication interface, a transceiver or a transceiver circuit, etc., where the communication interface is a general term.
  • the communication interface may include multiple interfaces, for example, may include: the terminal and the interface between the terminal and/or Other interfaces.
  • the storage unit may be a memory.
  • the processing unit is a processor
  • the communication unit is a communication interface
  • the storage unit is a memory
  • the data processing apparatus 1400 involved in the embodiment of the present application may be as shown in FIG. 14.
  • the data processing device 1400 includes: a processor 1402, a transceiver 1403, and a memory 1401.
  • the transceiver 1403 may be an independently set transmitter, which may be used to send information to other devices, and the transceiver may also be an independently set receiver, which is used to receive information from other devices.
  • the transceiver may also be a component that integrates the functions of sending and receiving information. The embodiment of the present application does not limit the specific implementation of the transceiver.
  • the data processing apparatus 1400 may further include a bus 1404.
  • the transceiver 1403, the processor 1402, and the memory 1401 may be connected to each other through a bus 1404; the bus 1404 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) Bus etc.
  • the bus 1404 can be divided into an address bus, a data bus, a control bus, and so on. For ease of representation, only one thick line is used in FIG. 14, but it does not mean that there is only one bus or one type of bus.
  • the computer program product includes one or more computer instructions.
  • the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.
  • the computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium.
  • the computer instructions may be transmitted from a website, computer, server, or data center. Transmission to another website, computer, server or data center via wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.).
  • the computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or a data center integrated with one or more available media.
  • the usable medium may be a magnetic medium, (for example, a floppy disk, a hard disk, and a magnetic tape), an optical medium (for example, a digital video disc (digital video disc, DVD)), or a semiconductor medium (for example, a solid state disk (SSD)) )Wait.
  • a magnetic medium for example, a floppy disk, a hard disk, and a magnetic tape
  • an optical medium for example, a digital video disc (digital video disc, DVD)
  • a semiconductor medium for example, a solid state disk (SSD)
  • the disclosed system, device, and method may be implemented in other ways.
  • the device embodiments described above are only illustrative.
  • the division of the units is only a logical function division, and there may be other divisions in actual implementation, for example, multiple units or components can be combined or It can be integrated into another system, or some features can be ignored or not implemented.
  • the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical or other forms.
  • the units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed to multiple network devices (for example, Terminal). Some or all of the units may be selected according to actual needs to achieve the objectives of the solutions of the embodiments.
  • the functional units in the various embodiments of the present application may be integrated into one processing unit, or each functional unit may exist independently, or two or more units may be integrated into one unit.
  • the above-mentioned integrated unit may be implemented in the form of hardware, or may be implemented in the form of hardware plus software functional units.

Abstract

本申请提供一种数据处理方法及装置，涉及通信技术领域，能够提高目标网元的分析结果的准确性。该方法包括：数据分析网元接收来自请求者的分析请求，在收到分析请求之后，数据分析网元根据分析请求，获取目标网元的负荷数据，再根据获取到的负荷数据，生成目标网元的分析结果，向请求者发送分析结果。其中，分析请求包括目标网元的信息，负荷数据包括业务负荷信息。该方法应用在网元负荷分析过程中。

Description

数据处理方法及装置
本申请要求于2019年06月17日提交国家知识产权局、申请号为201910522986.1、发明名称为“数据处理方法及装置”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及通信技术领域，尤其涉及一种数据处理方法及装置。
背景技术
第五代(5th generation，5G)移动通信技术的拓扑结构复杂，为了保障网络性能和业务体验，5G移动通信技术引入网络数据分析功能(network data analytics function，NWDAF)。NWDAF接收请求者的分析请求，再收集目标网络功能(network function，NF)实例的资源数据。NWDAF对收集的数据进行分析，得到分析结果。
现有的针对NF实例的资源数据进行分析的方法不准确。
发明内容
本申请实施例提供一种数据处理方法及装置，能够提高目标网元的分析结果的准确性。
为达到上述目的，本申请实施例采用如下技术方案：
第一方面，本申请提供一种数据处理方法，该方法可以由数据分析网元执行。该方法包括：数据分析网元接收来自请求者的分析请求，在收到分析请求之后，数据分析网元根据分析请求，获取目标网元的负荷数据，再根据获取到的负荷数据，生成目标网元的分析结果，向请求者发送分析结果。其中，分析请求包括目标网元的信息，负荷数据包括业务负荷信息。
本申请提供的数据处理方法，数据分析网元接收来自请求者的分析请求，根据分析请求，获取目标网元的负荷数据，获取到负荷数据之后，再根据负荷数据，生成目标网元的分析结果，向请求者发送分析结果。其中，分析请求包括目标网元的信息，负荷数据包括业务负荷信息。相对于现有技术中，数据分析网元仅能够获取目标网元的资源数据，为请求者提供目标网元的资源分析结果，分析结果无法准确示出目标网元的负荷状态，分析结果的准确性差。本申请实施例数据处理方法能够获取目标网元的负荷数据，负荷数据包括业务负荷信息。与目标网元的资源数据相比，负荷数据更能够准确的表征目标网元的负荷状态。基于负荷数据所得到的分析结果，能够精确的呈现目标网元的负荷状态，准确性高。
在一种可能的设计中，业务负荷信息至少包括业务负荷、业务负荷上限和业务负荷比例中的至少一个。其中，业务负荷可以是目标网元的关键性能指标KPI。不同网元所对应的KPI不同。在目标网元属于一个网络切片时，目标网元的业务负荷就是整个目标网元的KPI。以会话管理功能SMF为例，SMF的KPI可以是连接状态的会话平均数。在目标网元属于多个网络切片时，负荷数据也可以是目标网元与网络切片相关的业务负荷，即目标网元属于该网络切片的业务负荷。以接入与移动性管理功能AMF为例，目标网元属于网络切片的KPI可以为：注册用户平均数按照网络切片划分后的统计结果。
在一种可能的设计中，分析结果包括目标网元的业务负荷信息的平均值和业务负荷信 息的峰值中的至少一个。
在一种可能的设计中，本申请提供的数据处理方法还包括：
数据分析网元根据分析请求，获取目标网元的过载信息；
数据分析网元根据过载信息，生成目标网元的过载分析结果；
数据分析网元向请求者发送过载分析结果。
如此，数据分析网元基于分析请求，还能够对目标网元的过载状况进行分析，以为请求者提供过载分析结果，使得分析结果更准确、更全面。
在一种可能的设计中，过载分析结果包括目标网元发生过载的概率。
在一种可能的设计中，负荷数据还包括资源负荷信息，分析结果还包括目标网元的资源负荷信息的平均值和资源负荷信息的峰值中的至少一个。其中，资源负荷信息包括资源负荷和资源负荷上限中的至少一个。资源负荷表示目标网元实际占用的资源，如中央处理器CPU的运行状态、内存的占用状态。资源负荷上限表示目标网元最大允许占用的资源，如最大可占用的内存。
在一种可能的设计中，分析请求还包括网络切片信息；负荷数据为网络切片对应的负荷数据。
在一种可能的设计中，目标网元属于多个网络切片，目标网元包括公共模块，公共模块用于处理多个网络切片的业务。本申请提供的数据处理方法还包括：
数据分析网元根据多个切片对应的业务负荷信息和公共模块的资源负荷信息，获取公共模块属于网络切片信息对应的网络切片的资源负荷信息。
如此，在分析请求中包括网络切片信息时，即使目标网元属于多个网络切片，数据分析网元也能够基于多个切片对应的业务负荷信息和公共模块的资源负荷信息，来确定目标网元中公共模块属于网络切片信息对应的网络切片的资源负荷信息，以为请求者提供目标网元属于网络切片的资源的分析结果。
在一种可能的设计中，目标网元还包括多个专有模块，多个专有模块用于分别处理多个网络切片的业务。资源负荷信息包括网络切片信息对应的网络切片的专有模块的资源负荷信息和多个切片的公共模块的资源负荷信息。本申请提供的数据处理方法还包括：
数据分析网元根据专有模块的资源负荷信息和公共模块属于网络切片信息对应的网络切片的资源负荷信息，获取属于网络切片信息对应的网络切片的资源负荷信息。
如此，在分析请求中包括网络切片信息时，目标网元中的专有模块和公共模块均能够处理网络切片信息所对应的网络切片的业务，数据分析网元也能够基于专有模块的资源负荷信息和公共模块属于网络切片信息对应的网络切片的资源负荷信息，来确定目标网元属于网络切片信息对应的网络切片的资源负荷信息，以为请求者提供目标网元属于网络切片的资源的分析结果。
在一种可能的设计中，业务负荷信息包括服务质量流相关的负荷数据。
在一种可能的设计中，服务质量流相关的负荷数据包括以下信息中的一种或多种：服务质量流数据、服务质量流性能测量数据、目标网元与服务质量流相关的业务负荷占服务质量流最大业务负荷的比值。
在一种可能的设计中，业务负荷信息包括目标网元的业务类型的服务质量流相关的负荷数据；分析结果包括目标网元的业务类型的服务质量流相关的负荷数据的平均值、负荷 数据的峰值中的至少一个。
在一种可能的设计中，业务负荷信息包括目标网元的多种业务类型的服务质量流相关的负荷数据；分析结果包括：目标网元在多种业务类型、不同业务负荷比例的条件下，业务负荷信息的平均值、业务负荷信息的峰值中的至少一个。
在一种可能的设计中，数据分析网元根据分析请求，获取目标网元的负荷数据，包括：
数据分析网元根据分析请求，从操作管理和维护OAM获取历史负荷数据，其中，历史负荷数据包括业务负荷、业务负荷上限和服务质量流性能测量数据中的一种或多种。
如此，数据分析网元可以从OAM中获取目标网元的历史负荷数据，例如：业务负荷、业务负荷上限和服务质量流性能测量数据等，以基于获取到的历史负荷数据进行分析，得到分析结果。
在一种可能的设计中，数据分析网元根据分析请求，获取目标网元的负荷数据，包括：
数据分析网元根据分析请求，从网络仓库功能NRF获取当前负荷数据，其中，当前负荷数据包括业务负荷比例和目标网元与服务质量流相关的负荷占服务质量流最大业务负荷的比值中的至少一个。
如此，数据分析网元可以从NRF中获取目标网元的当前负荷数据，例如：业务负荷比例、目标网元与服务质量流相关的负荷占服务质量流最大业务负荷的比值等，以基于获取到的当前负荷数据进行分析，得到分析结果。
第二方面，本申请提供一种通信方法，该方法可以由请求者所对应的网元执行。该方法包括：请求者向数据分析网元发送分析请求之后，接收来自数据分析网元的分析结果，根据分析结果，进行处理操作。其中，分析请求包括目标网元的信息。
如此，请求者接收来自数据分析网元的分析结果，由于分析结果能够精确的呈现目标网元的负荷状态，准确性高。请求者即可基于准确性更高的分析结果，进行处理操作，如网络功能选择、网络路径选择、网络资源调整等，大大降低网元过载的概率。
在一种可能的设计中，根据分析结果，进行处理操作，包括：根据分析结果选择目标网元。例如，选择负荷满足要求的目标网元和/或选择资源状况满足要求的目标网元。
在一种可能的设计中，目标网元可以是用户面功能网元，请求者可以是会话管理功能网元。
第三方面，本申请提供一种数据处理装置，该装置可以为上述第一方面中的数据分析网元。该装置包括处理单元、接收单元和发送单元。具体的，接收单元，用于接收来自请求者的分析请求，分析请求包括目标网元的信息。接收单元，还用于根据分析请求，获取目标网元的负荷数据，负荷数据包括业务负荷信息。处理单元，用于根据负荷数据，生成目标网元的分析结果。发送单元，用于向请求者发送分析结果。
在一种可能的设计中，业务负荷信息至少包括业务负荷、业务负荷上限和业务负荷比例中的至少一个。
在一种可能的设计中，分析结果包括目标网元的业务负荷信息的平均值和业务负荷信息的峰值中的至少一个。
在一种可能的设计中，接收单元，还用于根据分析请求，获取目标网元的过载信息；
处理单元，还用于根据过载信息，生成目标网元的过载分析结果；
发送单元，还用于向请求者发送过载分析结果。
在一种可能的设计中，过载分析结果包括目标网元发生过载的概率。
在一种可能的设计中，负荷数据还包括资源负荷信息，分析结果还包括目标网元的资源负荷信息的平均值和资源负荷信息的峰值中的至少一个。
在一种可能的设计中，分析请求还包括网络切片信息；负荷数据为网络切片对应的负荷数据。
在一种可能的设计中，目标网元属于多个网络切片，目标网元包括公共模块，公共模块用于处理多个网络切片的业务；
处理单元，还用于根据多个切片对应的业务负荷信息和公共模块的资源负荷信息，获取公共模块属于网络切片信息对应的网络切片的资源负荷信息。
在一种可能的设计中，目标网元还包括多个专有模块，多个专有模块用于分别处理多个网络切片的业务；
资源负荷信息包括网络切片信息对应的网络切片的专有模块的资源负荷信息和多个切片的公共模块的资源负荷信息；
处理单元，还用于根据专有模块的资源负荷信息和公共模块属于网络切片信息对应的网络切片的资源负荷信息，获取属于网络切片信息对应的网络切片的资源负荷信息。
在一种可能的设计中，业务负荷信息包括服务质量流相关的负荷数据。
在一种可能的设计中，服务质量流相关的负荷数据包括以下信息中的一种或多种：服务质量流数据、服务质量流性能测量数据、目标网元与服务质量流相关的业务负荷占服务质量流最大业务负荷的比值。
在一种可能的设计中，业务负荷信息包括目标网元的业务类型的服务质量流相关的负荷数据；分析结果包括目标网元的业务类型的服务质量流相关的负荷数据的平均值、负荷数据的峰值中的至少一个。
在一种可能的设计中，业务负荷信息包括目标网元的多种业务类型的服务质量流相关的负荷数据；分析结果包括：目标网元在多种业务类型、不同业务负荷比例的条件下，业务负荷信息的平均值、业务负荷信息的峰值中的至少一个。
在一种可能的设计中，接收单元，用于根据分析请求，获取目标网元的负荷数据，具体为：根据分析请求，从操作管理和维护OAM获取历史负荷数据，其中，历史负荷数据包括业务负荷、业务负荷上限和服务质量流性能测量数据中的一种或多种。
在一种可能的设计中，接收单元，用于根据分析请求，获取目标网元的负荷数据，具体为：根据分析请求，从网络仓库功能NRF获取当前负荷数据，其中，当前负荷数据包括业务负荷比例和目标网元与服务质量流相关的负荷占服务质量流最大业务负荷的比值中的至少一个。
第四方面，本申请提供一种通信装置，该装置可以为上述第二方面中的请求者所对应的网元。该装置包括处理单元、接收单元和发送单元。具体的，发送单元，用于向数据分析网元发送分析请求，分析请求包括目标网元的信息；接收单元，用于接收来自数据分析网元的分析结果；处理单元，用于根据分析结果，进行处理操作。
在一种可能的设计中，处理单元，用于根据分析结果，进行处理操作，具体为：根据分析结果选择目标网元。例如，选择负荷满足要求的目标网元和/或选择资源状况满足要求的目标网元。
在一种可能的设计中，目标网元可以是用户面功能网元，请求者可以是会话管理功能网元。
第五方面，本申请提供一种数据处理装置，用于实现上述第一方面中数据分析网元的功能，或用于实现上述第二方面中请求者的功能。
第六方面，本申请提供一种数据处理装置，该装置具有实现上述任一方面中任一项的数据处理方法的功能。该功能可以通过硬件实现，也可以通过硬件执行相应的软件实现。该硬件或软件包括一个或多个与上述功能相对应的模块。
第七方面，本申请提供一种数据处理装置，包括：处理器和存储器；该存储器用于存储计算机执行指令，当该数据处理装置运行时，该处理器执行该存储器存储的该计算机执行指令，以使该数据处理装置执行如上述任一方面中任一项的数据处理方法。
第八方面，本申请提供一种数据处理装置，包括：处理器；处理器用于与存储器耦合，并读取存储器中的指令之后，根据指令执行如上述任一方面中任一项的数据处理方法。
第九方面，本申请提供一种计算机可读存储介质，该计算机可读存储介质中存储有指令，当其在计算机上运行时，使得计算机可以执行上述任一方面中任一项的数据处理方法。
第十方面，本申请提供一种包含指令的计算机程序产品，当其在计算机上运行时，使得计算机可以执行上述任一方面中任一项的数据处理方法。
第十一方面，本申请提供一种电路系统，电路系统包括处理电路，处理电路被配置为执行如上述任一方面中任一项的数据处理方法。
第十二方面，本申请提供一种芯片，芯片包括处理器，处理器和存储器耦合，存储器存储有程序指令，当存储器存储的程序指令被处理器执行时实现上述任一方面任意一项的数据处理方法。
第十三方面，本申请提供一种通信系统，通信系统包括上述各个方面中任一方面中的数据分析网元和任一方面中的请求者所对应的网元，以及目标网元。
其中，第二方面至第十三方面中任一种设计方式所带来的技术效果可参见第一方面中不同设计方式所带来的技术效果，此处不再赘述。
附图说明
图1为本申请实施例提供的一种网络架构图；
图2为本申请实施例提供的一种系统架构图；
图3为本申请实施例提供的一种数据处理方法流程图；
图4为本申请实施例提供的一种生成关于资源的分析结果的方法流程图；
图5为本申请实施例提供的又一种生成关于资源的分析结果的方法流程图；
图6为本申请实施例提供的一种数据处理方法流程图；
图7为本申请实施例提供的又一种数据处理方法流程图；
图8为本申请实施例提供的再一种数据处理方法流程图；
图9为本申请实施例提供的一种获取服务质量流数据的方法流程图；
图10为本申请实施例提供的一种数据处理方法流程图；
图11为本申请实施例提供的又一种数据处理方法流程图；
图12为本申请实施例提供的一种生成过载分析结果的方法流程图；
图13为本申请实施例提供的一种数据处理装置的结构示意图；
图14为本申请实施例提供的又一种数据处理装置的结构示意图。
具体实施方式
本申请的说明书以及附图中的术语“第一”和“第二”等是用于区别不同的对象，或者用于区别对同一对象的不同处理，而不是用于描述对象的特定顺序。此外，本申请的描述中所提到的术语“包括”和“具有”以及它们的任何变形，意图在于覆盖不排他的包含。例如包含了一系列步骤或单元的过程、方法、系统、产品或设备没有限定于已列出的步骤或单元，而是可选地还包括其他没有列出的步骤或单元，或可选地还包括对于这些过程、方法、产品或设备固有的其它步骤或单元。需要说明的是，本申请实施例中，“示例性的”或者“例如”等词用于表示作例子、例证或说明。本申请实施例中被描述为“示例性的”或者“例如”的任何实施例或设计方案不应被解释为比其它实施例或设计方案更优选或更具优势。确切而言，使用“示例性的”或者“例如”等词旨在以具体方式呈现相关概念。
为了使得本申请实施例更加的清楚，首先对本申请实施例中涉及到的相关技术作简单介绍。
网络切片(network slice，NS)：
由于不同的通信业务对网络性能的需求存在显著的区别，第三代合作伙伴项目(3rd generation partnership project，3GPP)提出的第五代(5th generation，5G)移动通信技术引入了NS，以满足不同通信业务对网络性能的差异化需求。NS是在物理或者虚拟的网络基础设施上，根据不同的服务需求定制化的逻辑网络。网络切片可以是一个包括了终端、接入网、传输网、核心网和应用服务器的完整的端到端网络，能够提供完整的通信服务，具有一定网络能力。网络切片也可以是上述终端、接入网、传输网、核心网和应用服务器的任意组合。
通常，NS以网络切片作为服务(network slice as a service，NSaaS)方式提供给客户。例如，运营商创建一个网络切片实例，以向第三方提供服务，其中，第三方可以包括企业、互联网服务提供商、运营商等。NS使用单个网络切片选择辅助信息(single network slice selection assistance information，S-NSSAI)来标识。
网络切片实例(network slice instance，NSI)：
NSI是对NS的实例化，即是一个真实运行的逻辑网络，能满足一定网络特性或服务需求。一个NSI可以提供一种或多种服务。NSI可以由网络切片管理功能设备创建，一个网络切片管理功能设备可以创建多个NSI，同时对它们进行管理，包括但不限于在NSI运行过程中的性能监视和故障管理等。当多个NSI共存时，NSI之间可以共享部分网络资源和网络功能。NSI可以从网络切片模板创建，也可以不从网络切片模板创建。通常，NSI采用网络切片实例标识(network slice instance identity，NSI-ID)来标识。
网络功能(network function，NF)：
NF是网络中的一种处理功能，定义了功能性的行为和接口。NF可以通过专用硬件实现，也可以通过在专用硬件上运行软件实现，也可以在通用的硬件平台上以虚拟功能的形式实现。因此，从实现的角度，可以将NF分为物理网络功能和虚拟网络功能。从使用的角度，NF可以分为专属网络功能和共享网络功能。具体地，对于多个网络切片实例/网络切片子网实例而言，可以独立地使用不同的网络功能，这种网络功能称为专属网络功能，也可以共享同一个网络功能，这种网络功能称为共享网络功能。
网络数据分析功能(network data analytics function，NWDAF)：
为了保障网络性能和业务体验，5G移动通信技术引入NWDAF。NWDAF可以从各个网络功能(network function，NF)、应用功能(application function，AF)、运行管理和维护(operation administration and maintenance，OAM)收集数据，进行网络功能分析和预测。在第三代合作伙伴计划(3rd generation partnership project，3GPP)技术规范(technical specification，TS)中，NWDAF使用事件订阅的方式从NF、AF和OAM系统采集数据，从数据仓库检索信息，基于NF、AF或OAM的需求，提供相应的网络功能分析和预测结果。
其中，NF可以包括接入与移动性管理功能(access and mobility management function，AMF)、会话管理功能(session management function，SMF)、策略控制功能(policy control function，PCF)、用户数据管理(unified data management，UDM)、网络能力开放功能(network exposure function，NEF)、用户面功能(user plane function，UPF)。
其中，OAM可以包括网络管理系统(network management system，NMS)。
其中，数据仓库可以包括用户相关信息仓库(unified data repository，UDR)、网络功能信息仓库(network repository function，NRF)、网络切片选择功能(network slice selection function，NSSF)。
NWDAF可以作为一种数据分析网元。参见图1，图1示出了NWDAF在网络架构中的位置。NWDAF可以从NRF、OAM、NEF和SMF获取数据，以生成分析结果。其中，NEF能够管理AF。示例性的，参见图1，一个NEF可以管理一个AF。虚线框表示网络切片。一个网络切片可以独自使用一些网络功能，如每个网络切片中均存在各自的SMF和UPF。示例性的，参见图1，在第一网络切片中存在一个SMF和该SMF所管理的一个UPF，在第二个网络切片中存在一个SMF和该SMF所管理的两个UPF。多个网络切片可以共享使用一些网络功能，如AMF和PCF。
NWDAF作为数据分析网元，仅能够收集资源数据，再对资源数据进行分析，以得到关于资源的分析结果。以NF的资源数据为例，NF的资源数据是表征该NF整体的资源状态，与业务类型和网络切片均不相关。分析结果是基于NF的资源数据得到的，如此，分析结果也就无法正确的示出该NF实例的真实的负荷状态，降低分析结果的准确性。
有鉴于此，本申请实施例提供了一种数据处理方法，本申请实施例提供的数据处理方法可以适用于如图2所示的数据处理系统。参见图2，该系统包括请求者、数据分析网元、NRF和OAM。其中，请求者能够与数据分析网元进行信息交互，如请求者向数据分析网元发送分析请求，其中，分析请求中包括目标网元的信息。请求者能够接收来自数据分析网元的分析结果，其中，分析结果为关于目标网元的分析结果。数据分析网元能够从NRF、OAM收集数据。数据分析网元还能够从目标网元或与目标网元的业务处理相关的网元收集数据。其中，数据分析网元可以为NWDAF。目标网元可以是UPF，与目标网元的业务处理相关的网元可以是管理目标网元的网元，如管理UPF的SMF。(目标网元、与目标网元的业务处理相关的网元在图2中均未示出)。
本申请实施例提供一种数据处理方法，该方法应用在网元负荷分析过程中。
参见图3，本申请实施例的数据处理方法可以包括S301至S305：
S301、请求者向数据分析网元发送分析请求。
相应的，数据分析网元接收来自请求者的分析请求。
其中，请求者可以是NF，请求者也可以是AF，请求者还可以是OAM。
其中，数据分析网元能够基于分析请求，收集数据，为请求者提供相应的分析结果。示例性的，数据分析网元可以是NWDAF。
其中，分析请求可以包括目标网元的信息，以请求分析目标网元的状况。目标网元的信息可以是目标网元的标识。示例性的，目标网元的标识可以是目标UPF实例标识，以请求分析该目标UPF实例标识所对应的网元的状况。目标网元的数量可以是一个，也可以是多个。目标网元的信息还可以是目标网元的类型。示例性的，目标网元的类型可以为UPF，以请求分析所有UPF网元的状况。
分析请求还可以包括网络切片信息，以请求分析目标网元属于某一网络切片的状况。其中，网络切片信息可以是网络切片标识，如S-NSSAI或NSI-ID。在分析请求中包括网络切片信息时，数据分析网元在执行S302时，所获取到的负荷数据均是关于网络切片对应的负荷数据。
需要说明的是，分析请求可以是查询式的分析请求，也可以是订阅式的分析请求。若分析请求为查询式请求，则数据分析网元为请求者提供一次性分析结果。若分析请求为订阅式请求，则数据分析网元为请求者提供分析结果之后，在负荷数据发生变化时，对变化后的负荷数据进行分析，得到新的分析结果，或者按照请求者的要求定期对负荷数据进行分析，得到新的分析结果，以发送给请求者，直至订阅式的分析请求被取消。其中，关于查询式的分析请求和订阅式的分析请求均可参见现有技术，这里不再赘述。
S302、数据分析网元根据分析请求，获取目标网元的负荷数据。
其中，负荷数据可以包括业务负荷信息，以使数据分析网元生成关于负荷的分析结果。负荷数据还可以包括资源负荷信息，以使数据分析网元生成关于资源的分析结果。
作为第一种可能的实现方式，负荷数据包括业务负荷信息。业务负荷信息至少包括业务负荷、业务负荷上限和业务负荷比例中的至少一个。例如，业务负荷信息可以包括业务负荷、业务负荷上限和业务负荷比例三者中的任一个，也可以包括业务负荷、业务负荷上限和业务负荷比例三者中的任两个，还可以包括业务负荷、业务负荷上限和业务负荷比例。
其中，业务负荷可以是目标网元的关键性能指标(key performance indicators，KPI)。不同网元所对应的KPI不同。在目标网元属于一个网络切片时，目标网元的业务负荷就是整个目标网元的KPI。以SMF为例，SMF的KPI可以是连接状态的会话平均数。在目标网元属于多个网络切片时，负荷数据也可以是目标网元与网络切片相关的业务负荷，即目标网元属于该网络切片的业务负荷。以AMF为例，目标网元属于网络切片的KPI可以为：注册用户平均数按照网络切片划分后的统计结果。
其中，业务负荷上限是业务负荷的最大值。无论目标网元属于一个网络切片，还是属于多个网络切片，目标网元的业务负荷上限是以下两者的较小值：第一、目标网元能够使用的资源所支持的业务负荷的最大值；第二、目标网元所属目标网络切片所能够配置的业务负荷的最大值，例如，目标网络切片在目标网元的服务区域内支持的注册用户最大数。其中，目标网络切片是分析请求中网络切片信息所对应的切片。
其中，业务负荷比例可以是目标网元的业务负荷占最大业务负荷的比值。当目标网元专属于某一网络切片时，业务负荷比例可以是目标网元的业务负荷占该目标网元的最大业 务负荷的比值。当目标网元属于多个网络切片时，业务负荷比例可以是目标网元属于某一网络切片的业务负荷占该网络切片最大业务负荷的比值。这里，目标网元属于哪一网络切片的业务负荷，可以由分析请求中的网络切片信息确定。
如此，在负荷数据包括业务负荷信息时，数据分析网元可以基于业务负荷信息，对目标网元的负荷进行分析，以生成关于负荷的分析结果，以使请求者基于负荷的分析结果，选择负荷较小的网元。例如，在接入和移动管理功能AMF作为请求者时，AMF基于分析结果选择当前负荷较小的会话管理功能SMF，以避免SMF过载。
业务负荷信息还包括服务质量流(quality of service flow，QoS Flow)相关的负荷数据。其中，服务质量流相关的负荷数据包括以下信息中的一种或多种：服务质量流数据、服务质量流性能测量数据、目标网元与服务质量流相关的业务负荷占服务质量流最大业务负荷的比值。
其中，服务质量流数据可以包括服务质量(quality of service，QoS)配置、QoS参数、QoS特征等。其中，服务质量流数据可以分为不同业务类型的服务质量流数据。例如，服务质量流数据可以分为保证带宽质量的服务质量流(guaranteed bit rate quality of service flow，GBR QoS Flow)、保证带宽和时延敏感质量的服务质量流(latency-critical GBR QoS Flow)、不保证带宽质量的业务流(non-GBR QoS Flow)三种业务类型的服务质量流数据。另外，服务质量流数据还可以使用不同的服务质量类标识(QoS classify indicator，QCI)或第五代通信服务质量标识(5G QoS indicator，5QI)，以便于在更细的粒度上划分业务类型，如不同QCI的服务质量流数据、不同5QI的服务质量流数据。
其中，服务质量流性能测量数据可以包括以下信息中的一种或多种：服务质量流的建立数量、服务质量流的释放数量、关于服务质量流的可持续性的信息。
其中，关于“目标网元与服务质量流相关的业务负荷占服务质量流最大业务负荷的比值”的说明，这里以保证带宽和时延类型的业务为例，服务质量流最大业务负荷可以是：该业务最大转发速度为1000Mbps。某个UPF支持该业务，目前已为此种类型的服务质量流分配了600Mbps。此时，比值即为60％。
需要说明的是，业务负荷信息可以包括某一种业务类型的服务质量流相关的负荷数据，例如，业务负荷信息可以是某一种业务类型的服务质量流相关的负荷数据。此时，数据分析网元对该种业务类型的服务质量流相关的负荷数据进行分析，生成目标网元的分析结果。分析结果则为一种业务类型的负荷分析结果，具体可以包括该业务类型的服务质量流相关的业务数据信息的平均值，也可以包括该业务类型的服务质量流相关的业务数据信息的峰值，还可以包括该业务类型的服务质量流相关的业务数据信息的平均值和峰值。
业务负荷信息也可以包括多种业务类型的服务质量流相关的负荷数据。此时，数据分析网元对多种业务类型的服务质量流相关的负荷数据进行分析，生成目标网元的分析结果。分析结果则为多种业务类型在不同业务负荷比例下的负荷分析结果。例如，两种业务类型在业务负荷比例为2:8时的业务负荷信息的平均值和峰值。类似的，多种业务类型在不同业务负荷比例下的负荷分析结果可以包括业务负荷信息的平均值，也可以包括业务负荷信息的峰值，还可以包括业务负荷信息的平均值和峰值。如此，业务负荷信息可以包括一种业务类型的服务质量流相关的负荷数据，也可以包括多种业务类型的服务质量流相关的负荷数据，以满足请求者在不同场景下的分析需求，为请求者提供多种分析结果。
作为第二种可能的实现方式，负荷数据还包括资源负荷信息，以使数据分析网元对目标网元的资源状况进行分析。其中，资源负荷信息包括资源负荷和资源负荷上限中的至少一个。资源负荷表示目标网元实际占用的资源，如中央处理器(central processing unit/processor，CPU)的运行状态、内存的占用状态。资源负荷上限表示目标网元最大允许占用的资源，如最大可占用的内存。
在目标网元属于一个网络切片时，目标网元的属于这个网络切片的资源负荷与目标网元自身作为一个整体的资源负荷相同。目标网元的属于这个网络切片的资源负荷上限与目标网元自身作为一个整体的资源负荷上限相同。
在目标网元属于多个网络切片时，目标网元可以包括专属模块和公共模块，公共模块用于处理目标网元所属的多个网络切片的业务。示例性的，公共模块可以是网络收发模块或业务分发模块。一个专有模块只用于处理多个网络切片中某一网络切片的业务。示例性的，专有模块可以是信令处理模块，专有模块的数量可以是一个，也可以是多个。目标网元可以包括一个专有模块，以处理目标网元所属的多个网络切片中某一网络切片的业务。目标网元也可以包括两个专有模块，以分别处理目标网元所属的多个网络切片中某一个网络切片的业务。目标网元也可以包括多个专有模块，即多个网络切片中的每一网络切片均对应一个专有模块。
参见图4，在分析请求包括网络切片信息时，本申请实施例数据处理方法还能够对目标网元属于网络切片的资源状况进行分析，若网络切片信息所对应的网络切片的业务仅由公共模块处理，数据分析网元在执行S301之后，还可以执行S401，再执行S303：
S401、数据分析网元根据多个切片对应的业务负荷信息和公共模块的资源负荷信息，获取公共模块属于网络切片信息对应的网络切片的资源负荷信息。
示例性的，每个切片对应的业务负荷信息可以是每个网络切片所接入的注册用户数。公共模块的资源负荷信息可以是该公共模块所占用的资源量。公共模块属于网络切片信息对应的网络切片的资源负荷信息满足如下公式：
Figure PCTCN2020096391-appb-000001
其中，A 1表示公共模块属于网络切片信息对应的网络切片的资源负荷信息，B 1表示公共模块的资源负荷信息，n表示目标网元所属的网络切片的数量，k表示网络切片信息所对应的网络切片，x 1表示n个网络切片中目标网元的属于第一个网络切片的业务负荷信息，x 2表示n个网络切片中目标网元的属于第二个网络切片的业务负荷信息，x n表示n个网络切片中目标网元的属于第n个网络切片的业务负荷信息，x k表示n个网络切片中目标网元的属于第k个网络切片的业务负荷信息。
如此，在分析请求中包括网络切片信息时，即使目标网元属于多个网络切片，数据分析网元也能够基于多个切片对应的业务负荷信息和公共模块的资源负荷信息，来确定目标网元中公共模块属于网络切片信息对应的网络切片的资源负荷信息，以为请求者提供目标网元属于网络切片的资源的分析结果。
若网络切片信息所对应的网络切片的业务由专有模块和公共模块处理，参见图5，数据分析网元在执行S301之后，还可以执行S401和S402，再执行S303：
S401、数据分析网元根据多个切片对应的业务负荷信息和公共模块的资源负荷信息，获取公共模块属于网络切片信息对应的网络切片的资源负荷信息。
S402、数据分析网元根据专有模块的资源负荷信息和公共模块属于网络切片信息对应的网络切片的资源负荷信息，获取目标网元属于网络切片信息对应的网络切片的资源负荷信息。
示例性的，目标网元属于网络切片信息对应的网络切片的资源负荷信息满足如下公式：
Figure PCTCN2020096391-appb-000002
其中，A表示目标网元属于网络切片信息对应的网络切片的资源负荷信息，A 1表示公共模块属于网络切片信息对应的网络切片的资源负荷信息，A 2表示专有模块属于网络切片信息对应的网络切片的资源负荷信息，B 1表示公共模块的资源负荷信息，n表示目标网元所属的网络切片的数量，k表示网络切片信息所对应的网络切片，x 1表示n个网络切片中目标网元的属于第一个网络切片的业务负荷信息，x 2表示n个网络切片中目标网元的属于第二个网络切片的业务负荷信息，x n表示n个网络切片中目标网元的属于第n个网络切片的业务负荷信息，x k表示n个网络切片中目标网元的属于第k个网络切片的业务负荷信息。
如此，在分析请求中包括网络切片信息时，目标网元中的专有模块和公共模块均能够处理网络切片信息所对应的网络切片的业务，数据分析网元也能够基于专有模块的资源负荷信息和公共模块属于网络切片信息对应的网络切片的资源负荷信息，来确定目标网元属于网络切片信息对应的网络切片的资源负荷信息，以为请求者提供目标网元属于网络切片的资源的分析结果。
需要说明的是，在目标网元属于多个网络切片时，资源负荷表示目标网元中的专有模块或公共模块在业务处理过程中实际占用的资源。资源负荷上限表示目标网元中的专有模块或公共模块在业务处理过程中允许占用的最大资源。在公式(1)中的A 1和B 1均表示资源负荷时，基于公式(1)能够确定公共模块属于网络切片信息对应的网络切片的资源负荷。在公式(1)中的A 1和B 1均表示资源负荷上限时，基于公式(1)能够确定公共模块属于网络切片信息对应的网络切片的资源负荷上限。类似的，在公式(2)中的A、A 1、A 2和B 1均表示资源负荷时，基于公式(2)能够确定目标网元属于网络切片信息对应的网络切片的资源负荷。在公式(2)中的A、A 1、A 2和B 1均表示资源负荷上限时，基于公式(2)能够确定目标网元属于网络切片信息对应的网络切片的资源负荷上限。
需要说明的是，负荷数据可以包括历史负荷数据和当前负荷数据中的至少一个。负荷数据不同，数据分析网元所执行的步骤不同。在负荷数据为历史负荷数据，且历史负荷数据为业务负荷、业务负荷上限和服务质量流性能测量数据中的一种或多种时，参见图6，S302具体可以实现为S3021：
S3021、数据分析网元根据分析请求，从操作管理和维护OAM获取历史负荷数据。
其中，OAM是能够进行网络管理的各种网络实体的统一称呼，OAM的主要功能包括：完成日常网络和业务的分析、预测、规划和配置工作；对网络及其业务的测试和故障管理的日常操作活动。OAM能够存储目标网元的历史负荷数据。
其中，历史负荷数据包括业务负荷、业务负荷上限和服务质量流性能测量数据中的一种或多种。
示例性的，数据分析网元向OAM发送历史负荷数据请求。其中，历史负荷数据请求包括目标网元的信息，也可以包括网络切片信息。OAM接收到历史负荷数据请求之后，向数据分析网元发送关于目标网元的历史负荷数据。若分析请求为查询式请求，则历史负荷数 据请求为查询式请求，OAM向数据分析网元发送一次关于目标网元的历史负荷数据。参见图7，若分析请求为订阅式请求，则历史负荷数据请求为订阅式请求，OAM向数据分析网元发送一次关于目标网元的历史负荷数据之后，在每次目标网元的历史负荷数据发生变化时，或按照请求者所要求的时间间隔，在每间隔一定时间后，均会向数据分析网元发送一次关于目标网元的历史负荷数据，直至分析请求被取消。其中，关于查询式的历史负荷数据请求和订阅式的历史负荷数据请求均可参见现有技术，这里不再赘述。
如此，数据分析网元可以从OAM中获取目标网元的历史负荷数据，例如：业务负荷、业务负荷上限和服务质量流性能测量数据等，以基于获取到的历史负荷数据进行分析，得到分析结果。
在负荷数据为历史负荷数据，且历史负荷数据为服务质量流数据时，参见图8，S302具体可以实现为S3022：
S3022、数据分析网元根据分析请求，从管理目标网元的网元获取服务质量流数据。
示例性的，参见图9，SMF1作为请求者。在SMF1所发送的分析请求中，目标网元的信息为目标UPF实例标识。数据分析网元基于分析请求，向管理目标UPF的各个SMF收集服务质量流数据。例如，管理目标UPF的网元是SMF1和SMF2，数据分析网元可以从SMF1和SMF2收集关于目标UPF的服务质量流数据。
在负荷数据为历史负荷数据，且历史负荷数据为服务质量流数据时，参见图10，S302具体可以实现为S3023：
S3023、数据分析网元根据分析请求，从目标网元获取服务质量流数据。
示例性的，目标网元为目标UPF。参见图9，数据分析网元从目标UPF获取服务质量流数据。例如，数据分析网元通过目标UPF的服务化接口，收集服务质量流数据。
在负荷数据为当前负荷数据时，参见图11，S302具体可以实现为S3024：
S3024、数据分析网元根据分析请求，从网络仓库功能NRF获取当前负荷数据。
其中，NRF能够提供目标网元的当前负荷信息。
其中，当前负荷数据包括业务负荷比例和目标网元与服务质量流相关的负荷占服务质量流最大业务负荷的比值中的至少一个。
示例性的，数据分析网元向NRF发送当前负荷数据请求。其中，当前负荷数据请求包括目标网元的信息，也可以包括网络切片信息。NRF接收到当前负荷数据请求之后，向数据分析网元发送关于目标网元的当前负荷数据。若分析请求为查询式请求，则当前负荷数据请求为查询式请求，NRF向数据分析网元发送一次关于目标网元的当前负荷数据。参见图7，若分析请求为订阅式请求，则当前负荷数据请求为订阅式请求，NRF向数据分析网元发送一次关于目标网元的当前负荷数据之后，在每次目标网元的当前负荷数据发生变化时，或按照请求者所要求的时间间隔，在每间隔一定时间后，均会向数据分析网元发送一次关于目标网元的当前负荷数据，直至分析请求被取消。其中，关于查询式的当前负荷数据请求和订阅式的当前负荷数据请求均可参见现有技术，这里不再赘述。
如此，数据分析网元可以从NRF中获取目标网元的当前负荷数据，例如：业务负荷比例、目标网元与服务质量流相关的负荷占服务质量流最大业务负荷的比值等，以基于获取到的当前负荷数据进行分析，得到分析结果。
S303、数据分析网元根据负荷数据，生成目标网元的分析结果。
其中，分析结果与负荷数据的类型相对应。例如，若负荷数据包括业务负荷信息，则分析结果包括负荷分析结果。若负荷数据包括资源负荷信息，则分析结果包括资源分析结果。
作为第一种可能的实现方式，负荷数据包括业务负荷信息时，分析结果是关于负荷的分析结果。分析结果可以包括目标网元的业务负荷信息的平均值和业务负荷信息的峰值中的至少一个。例如，分析结果可以包括目标网元的业务负荷信息的平均值，也可以包括目标网元的业务负荷信息的峰值，还可以包括目标网元的业务负荷信息的平均值和峰值。
其中，业务负荷信息的平均值可以是业务负荷的平均值。
示例性的，数据分析网元能够获取目标网元在不同时刻的业务负荷。数据分析网元确定待分析的时间段。若该时间段为历史时间段，则数据分析网元计算目标网元在历史时间段的业务负荷的平均值。若该时间段为未来时间段，则数据分析网元基于所获取到的业务负荷的数据，确定目标网元的业务负荷的变化趋势，以预测目标网元在未来时间段的业务负荷的平均值。
示例性的，数据分析网元还可以采用回归算法，训练得到回归模型。回归模型可以是目标网元在不同业务类型、不同业务负荷比例下的回归模型。回归模型的自变量可以是服务质量流数据，也可以是服务质量流性能测量数据，还可以是服务质量流数据和服务质量流性能测量数据，回归模型的应变量为业务负荷。常用的回归算法包括：线性回归(linear regression)算法、逻辑回归(logistic regression)算法、多项式回归(polynomial regression)算法等，本申请实施例对回归算法不作限定。数据分析网元向回归模型中输入服务质量流数据或服务质量流性能测量数据，均能够得到业务负荷。
业务负荷信息的平均值也可以是业务负荷比例的平均值。示例性的，数据分析网元能够获取目标网元在不同时刻的业务负荷，将每一时刻的业务负荷除以该时刻的业务负荷上限，从而该得到该时刻的业务负荷比例。数据分析网元确定待分析的时间段。若该时间段为历史时间段，则数据分析网元基于获取到的数据，计算目标网元在历史时间段的业务负荷比例的平均值。若该时间段为未来时间段，则数据分析网元基于所获取到的数据，确定目标网元的业务负荷比例的变化趋势，以预测目标网元在未来时间段的业务负荷比例的平均值。
其中，业务负荷信息的峰值可以是业务负荷的峰值。示例性的，若数据分析网元确定历史时间段的业务负荷的峰值，则可以将该时间段内的业务负荷的最大值作为业务负荷的峰值。若数据分析网元预测未来时间段的业务负荷的峰值，则数据分析网元基于所获取到的业务负荷的数据，确定目标网元的业务负荷的变化趋势，以预测目标网元在未来时间段的业务负荷的峰值。
其中，业务负荷信息的峰值可以是业务负荷比例的峰值。示例性的，若数据分析网元确定历史时间段的业务负荷比例的峰值，则可以将该时间段内的业务负荷比例的最大值作为业务负荷比例的峰值。若数据分析网元预测未来时间段的业务负荷比例的峰值，则数据分析网元基于所获取到的业务负荷比例的数据，确定目标网元的业务负荷比例的变化趋势，以预测目标网元在未来时间段的业务负荷比例的峰值。
如此，数据分析网元能够为请求者提供关于业务负荷信息的平均值或峰值等负荷分析结果，以便于请求者基于负荷分析结果，选择负荷较小的网元。分析结果为目标网元在网 络切片信息所对应切片内的分析结果时，由于分析结果考虑了不同网络切片对目标网元的影响，使得分析结果的准确性更高。分析结果为目标网元在不同业务类型、不同业务负荷比例下的分析结果时，由于分析结果充分考虑了不同业务类型对目标网元的影响，使得分析结果的准确性更高。请求者基于准确性更高的分析结果，在网络切片信息所对应切片内选择网元，大大降低了网元过载风险，避免网络震荡。
作为第二种可能的实现方式，分析结果还包括目标网元的资源负荷信息的平均值和资源负荷信息的峰值中的至少一个。例如，分析结果可以包括目标网元的资源负荷信息的平均值，也可以包括目标网元的资源负荷信息的峰值，还可以包括目标网元的资源负荷信息的平均值和峰值。
其中，资源负荷信息的平均值可以是资源负荷的平均值。示例性的，数据分析网元能够获取目标网元在不同时刻的资源负荷和不同业务类型的业务负荷。数据分析网元确定待分析的时间段。若该时间段为历史时间段，则数据分析网元基于所获取到的资源负荷的数据，计算目标网元在历史时间段的资源负荷的平均值。若该时间段为未来时间段，则数据分析网元计算不同业务类型的业务负荷的比例，训练获得在不同业务负荷比例时业务负荷和资源负荷平均值之间的回归模型，然后基于所获取到的不同业务类型的业务负荷的数据、资源负荷的数据和资源负荷上限，确定目标网元的资源负荷的变化趋势，以预测目标网元在未来时间段的资源负荷的平均值。
其中，资源负荷信息的峰值可以是资源负荷的峰值。示例性的，若数据分析网元确定历史时间段的资源负荷的峰值，则可以将该时间段内的资源负荷的最大值作为资源负荷的峰值。若数据分析网元预测未来时间段的资源负荷的峰值，则数据分析网元首先计算不同业务类型的业务负荷的比例，训练获得在不同业务负荷比例时业务负荷和资源负荷峰值之间的回归模型，然后数据分析网元基于所获取到的不同业务类型的业务负荷的数据、资源负荷的数据和资源负荷上限，确定目标网元的资源负荷的峰值的变化趋势，以预测目标网元在未来时间段的资源负荷的峰值。
如此，数据分析网元能够为请求者提供关于资源负荷信息的平均值或峰值等资源分析结果。在资源紧张时，以便于请求者提前进行资源扩容，保障业务体验。
需要说明的是，若分析请求为订阅式请求，则数据分析网元基于变化后的数据，或按照请求者所要求的时间间隔，在每间隔一定时间后，均会更新分析结果，直至分析请求被取消。
S304、数据分析网元向请求者发送分析结果。
相应的，请求者接收来自数据分析网元的分析结果。
需要说明的是，若分析请求为订阅式请求，则数据分析网元不断地向请求者发送更新后的分析结果，例如，通过订阅通知消息承载分析结果，数据分析网元以发送订阅通知消息的方式，向请求者发送分析结果，直至分析请求被取消。
本申请实施例提供的数据处理方法，数据分析网元接收来自请求者的分析请求，根据分析请求，获取目标网元的负荷数据，获取到负荷数据之后，再根据负荷数据，生成目标网元的分析结果，向请求者发送分析结果。其中，分析请求包括目标网元的信息，负荷数据包括业务负荷信息。相对于现有技术中，数据分析网元仅能够获取目标网元的资源数据，为请求者提供目标网元的资源分析结果，分析结果无法准确示出目标网元的负荷状态，分 析结果的准确性差。本申请实施例数据处理方法能够获取目标网元的负荷数据，负荷数据包括业务负荷信息。与目标网元的资源数据相比，负荷数据更能够准确的表征目标网元的负荷状态。基于负荷数据所得到的分析结果，能够精确的呈现目标网元的负荷状态，准确性高。
S305、请求者根据分析结果，进行处理操作。
示例性的，请求者可以基于分析结果进行网络功能选择，在AMF作为请求者时，AMF基于分析结果选择当前负荷较小的SMF，以避免SMF过载。请求者可以基于分析结果进行网络路径选择，在SMF作为请求者时，SMF基于分析结果选择UPF和路径，以为终端建立数据连接。请求者可以基于分析结果进行网络资源调整，在OAM作为请求者时，OAM基于分析结果，可在资源紧张时提前进行资源扩容，以保障业务体验。
如此，请求者接收来自数据分析网元的分析结果，由于分析结果能够精确的呈现目标网元的负荷状态，准确性高。请求者即可基于准确性更高的分析结果，进行处理操作，如网络功能选择、网络路径选择、网络资源调整等，大大降低网元过载的概率。
另外，参见图12，本申请实施例提供的数据处理方法还能够对目标网元的过载状况进行分析，在步骤S301之后，本申请实施例数据处理方法还包括S306至S308：
S306、数据分析网元根据分析请求，获取目标网元的过载信息。
其中，过载信息可以包括历史过载信息，如目标网元发送过载事件信息、过载警告信息等。历史过载信息可以是数据分析网元从OAM获取的信息。
其中，过载信息可以包括当前过载信息，如目标网元当前是否发生过载的状态信息。当前过载信息是数据分析网元从NRF中获取的信息。
S307、数据分析网元根据过载信息，生成目标网元的过载分析结果。
其中，过载分析结果可以是目标网元发生过载的概率。
示例性的，数据分析网元依据历史过载信息中每次过载的时间，来确定待分析时间段内发生过载的时间长度，数据分析网元根据发生过载的时间长度占待分析时间段的比值，作为过载发生的概率，存储在过载分析结果中。或者，数据分析网元基于历史过载信息，确定过载发生概率的变化趋势，以预测未来时间段的过载发生的概率。
示例性的，当前过载信息能够记载目标网元是否发生过载。在待分析时间段内，数据分析网元确定从NRF中收集当前过载信息的总次数，以及过载发生的次数，确定过载发生的次数与收集当前过载信息的总次数的比值，作为目标网元发生过载的概率，存储在过载分析结果中。或者，数据分析网元基于过载的次数，确定过载发生概率的变化趋势，以预测未来时间段的过载发生的概率。
S308、数据分析网元向请求者发送过载分析结果。
相应的，请求者接收来自数据分析网元的过载分析结果。
如此，数据分析网元基于分析请求，还能够对目标网元的过载状况进行分析，以为请求者提供过载分析结果，使得分析结果更准确、更全面。
上述主要从不同网元之间交互的角度对本申请实施例提供的方案进行了介绍。可以理解的是，数据分析网元、请求者对应的网元为了实现上述功能，其包含了执行各个功能相应的硬件结构和/或软件模块。结合本申请中所公开的实施例描述的各示例的单元及算法步骤，本申请实施例能够以硬件或硬件和计算机软件的结合形式来实现。某个功能究竟以 硬件还是计算机软件驱动硬件的方式来执行，取决于技术方案的特定应用和设计约束条件。本领域技术人员可以对每个特定的应用来使用不同的方法来实现所描述的功能，但是这种实现不应认为超出本申请实施例的技术方案的范围。
本申请实施例可以根据上述方法示例对数据处理装置进行功能单元的划分，例如，可以对应各个功能划分各个功能单元，也可以将两个或两个以上的功能集成在一个处理单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。需要说明的是，本申请实施例中对单元的划分是示意性的，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式。
图13示出了本申请实施例中提供的数据处理装置的一种示意性框图。该数据处理装置1300可以以软件的形式存在，也可以为设备，或者设备中的组件(比如芯片系统)。该数据处理装置1300包括：存储单元1301、处理单元1302和通信单元1303。
通信单元1303还可以划分为发送单元(并未在图13中示出)和接收单元(并未在图13中示出)。其中，发送单元，用于支持数据处理装置1300向其他网元发送信息。接收单元，用于支持数据处理装置1300从其他网元接收信息。
存储单元，用于存储数据处理装置1300的程序代码和数据，数据可以包括不限于原始数据或者中间数据等。
当数据处理装置作为数据处理网元时，接收单元，用于接收来自请求者的分析请求，分析请求包括目标网元的信息，还用于根据分析请求，获取目标网元的负荷数据，负荷数据包括业务负荷信息；处理单元，用于根据负荷数据，生成目标网元的分析结果；发送单元，用于向请求者发送分析结果；存储单元，用于存储负荷数据、分析结果。
在一种可能的设计中，业务负荷信息至少包括业务负荷、业务负荷上限和业务负荷比例中的至少一个。
在一种可能的设计中，分析结果包括目标网元的业务负荷信息的平均值和业务负荷信息的峰值中的至少一个。
在一种可能的设计中，接收单元，还用于根据分析请求，获取目标网元的过载信息；
处理单元，还用于根据过载信息，生成目标网元的过载分析结果；
发送单元，还用于向请求者发送过载分析结果。
在一种可能的设计中，过载分析结果包括目标网元发生过载的概率。
在一种可能的设计中，负荷数据还包括资源负荷信息，分析结果还包括目标网元的资源负荷信息的平均值和资源负荷信息的峰值中的至少一个。
在一种可能的设计中，分析请求还包括网络切片信息；负荷数据为网络切片对应的负荷数据。
在一种可能的设计中，目标网元属于多个网络切片，目标网元包括公共模块，公共模块用于处理多个网络切片的业务；
处理单元，还用于根据多个切片对应的业务负荷信息和公共模块的资源负荷信息，获取公共模块属于网络切片信息对应的网络切片的资源负荷信息。
在一种可能的设计中，目标网元还包括多个专有模块，多个专有模块用于分别处理多个网络切片的业务；
资源负荷信息包括网络切片信息对应的网络切片的专有模块的资源负荷信息和多个 切片的公共模块的资源负荷信息；
处理单元，还用于根据专有模块的资源负荷信息和公共模块属于网络切片信息对应的网络切片的资源负荷信息，获取属于网络切片信息对应的网络切片的资源负荷信息。
在一种可能的设计中，业务负荷信息包括服务质量流相关的负荷数据。
在一种可能的设计中，服务质量流相关的负荷数据包括以下信息中的一种或多种：服务质量流数据、服务质量流性能测量数据、目标网元与服务质量流相关的业务负荷占服务质量流最大业务负荷的比值。
在一种可能的设计中，业务负荷信息包括目标网元的业务类型的服务质量流相关的负荷数据；分析结果包括目标网元的业务类型的服务质量流相关的负荷数据的平均值、负荷数据的峰值中的至少一个。
在一种可能的设计中，业务负荷信息包括目标网元的多种业务类型的服务质量流相关的负荷数据；分析结果包括：目标网元在多种业务类型、不同业务负荷比例的条件下，业务负荷信息的平均值、业务负荷信息的峰值中的至少一个。
在一种可能的设计中，接收单元，用于根据分析请求，获取目标网元的负荷数据，具体为：根据分析请求，从操作管理和维护OAM获取历史负荷数据，其中，历史负荷数据包括业务负荷、业务负荷上限和服务质量流性能测量数据中的一种或多种。
在一种可能的设计中，接收单元，用于根据分析请求，获取目标网元的负荷数据，具体为：根据分析请求，从网络仓库功能NRF获取当前负荷数据，其中，当前负荷数据包括业务负荷比例和目标网元与服务质量流相关的负荷占服务质量流最大业务负荷的比值中的至少一个。
当数据处理装置作为请求者时，发送单元，用于向数据分析网元发送分析请求，分析请求包括目标网元的信息；接收单元，用于接收来自数据分析网元的分析结果。处理单元，用于根据分析结果，进行处理操作。存储单元，用于存储分析结果。
其中，处理单元可以是处理器或控制器，例如可以是CPU，通用处理器，DSP，ASIC，FPGA或者其他可编程逻辑器件、晶体管逻辑器件、硬件部件或者其任意组合。其可以实现或执行结合本申请公开内容所描述的各种示例性的逻辑方框，模块和电路。处理器也可以是实现计算功能的组合，例如包含一个或多个微处理器组合，DSP和微处理器的组合等等。
通信单元可以是通信接口、收发器或收发电路等，其中，该通信接口是统称，在具体实现中，该通信接口可以包括多个接口，例如可以包括：终端和终端之间的接口和/或其他接口。
存储单元可以是存储器。
当处理单元为处理器，通信单元为通信接口，存储单元为存储器时，本申请实施例所涉及的数据处理装置1400可以为图14所示。
参阅图14所示，该数据处理装置1400包括：处理器1402、收发器1403、存储器1401。
其中，收发器1403可以为独立设置的发送器，该发送器可用于向其他设备发送信息，该收发器也可以为独立设置的接收器，用于从其他设备接收信息。该收发器也可以是将发送、接收信息功能集成在一起的部件，本申请实施例对收发器的具体实现不做限制。
可选的，数据处理装置1400还可以包括总线1404。其中，收发器1403、处理器1402以及存储器1401可以通过总线1404相互连接；总线1404可以是外设部件互连标准 (peripheral component interconnect，PCI)总线或扩展工业标准结构(extended industry standard architecture，EISA)总线等。所述总线1404可以分为地址总线、数据总线、控制总线等。为便于表示，图14中仅用一条粗线表示，但并不表示仅有一根总线或一种类型的总线。
本领域普通技术人员可以理解：在上述实施例中，可以全部或部分地通过软件、硬件、固件或者其任意组合来实现。当使用软件实现时，可以全部或部分地以计算机程序产品的形式实现。所述计算机程序产品包括一个或多个计算机指令。在计算机上加载和执行所述计算机程序指令时，全部或部分地产生按照本申请实施例所述的流程或功能。所述计算机可以是通用计算机、专用计算机、计算机网络、或者其他可编程装置。所述计算机指令可以存储在计算机可读存储介质中，或者从一个计算机可读存储介质向另一个计算机可读存储介质传输，例如，所述计算机指令可以从一个网站站点、计算机、服务器或数据中心通过有线(例如同轴电缆、光纤、数字用户线(digital subscriber line，DSL))或无线(例如红外、无线、微波等)方式向另一个网站站点、计算机、服务器或数据中心进行传输。所述计算机可读存储介质可以是计算机能够存取的任何可用介质或者是包括一个或多个可用介质集成的服务器、数据中心等数据存储设备。所述可用介质可以是磁性介质，(例如，软盘、硬盘、磁带)、光介质(例如，数字视频光盘(digital video disc，DVD))、或者半导体介质(例如固态硬盘(solid state disk，SSD))等。
在本申请所提供的几个实施例中，应该理解到，所揭露的系统，装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络设备(例如终端)上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个功能单元独立存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用硬件加软件功能单元的形式实现。
通过以上的实施方式的描述，所属领域的技术人员可以清楚地了解到本申请可借助软件加必需的通用硬件的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品存储在可读取的存储介质中，如计算机的软盘，硬盘或光盘等，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本申请各个实施例所述的方法。

Claims (33)

  1. 一种数据处理方法，其特征在于，包括：
    数据分析网元接收来自请求者的分析请求，所述分析请求包括目标网元的信息；
    所述数据分析网元根据所述分析请求，获取所述目标网元的负荷数据，所述负荷数据包括业务负荷信息；
    所述数据分析网元根据所述负荷数据，生成所述目标网元的分析结果；
    所述数据分析网元向所述请求者发送所述分析结果。
  2. 根据权利要求1所述的数据处理方法，其特征在于，所述业务负荷信息至少包括业务负荷、业务负荷上限和业务负荷比例中的至少一个。
  3. 根据权利要求1或2所述的数据处理方法，其特征在于，所述分析结果包括所述目标网元的业务负荷信息的平均值和业务负荷信息的峰值中的至少一个。
  4. 根据权利要求1至3任一项所述的数据处理方法，其特征在于，所述方法还包括：
    所述数据分析网元根据所述分析请求，获取所述目标网元的过载信息；
    所述数据分析网元根据所述过载信息，生成所述目标网元的过载分析结果；
    所述数据分析网元向所述请求者发送所述过载分析结果。
  5. 根据权利要求4所述的数据处理方法，其特征在于，所述过载分析结果包括所述目标网元发生过载的概率。
  6. 根据权利要求1至5中任一项所述的数据处理方法，其特征在于，所述负荷数据还包括资源负荷信息，所述分析结果还包括所述目标网元的资源负荷信息的平均值和资源负荷信息的峰值中的至少一个。
  7. 根据权利要求6所述的数据处理方法，其特征在于，所述分析请求还包括网络切片信息；
    所述负荷数据为所述网络切片对应的负荷数据。
  8. 根据权利要求7所述的数据处理方法，其特征在于，所述目标网元属于多个网络切片，所述目标网元包括公共模块，所述公共模块用于处理所述多个网络切片的业务；
    所述方法还包括：
    所述数据分析网元根据所述多个切片对应的业务负荷信息和所述公共模块的资源负荷信息，获取所述公共模块属于所述网络切片信息对应的网络切片的资源负荷信息。
  9. 根据权利要求8所述的数据处理方法，其特征在于，所述目标网元还包括多个专有模块，所述多个专有模块用于分别处理所述多个网络切片的业务；
    所述资源负荷信息包括所述网络切片信息对应的网络切片的专有模块的资源负荷信息和所述多个切片的公共模块的资源负荷信息；
    所述方法还包括：
    所述数据分析网元根据所述专有模块的资源负荷信息和所述公共模块属于所述网络切片信息对应的网络切片的资源负荷信息，获取属于所述网络切片信息对应的网络切片的资源负荷信息。
  10. 根据权利要求1至9任一项所述的数据处理方法，其特征在于，所述业务负荷信息包括服务质量流相关的负荷数据。
  11. 根据权利要求10所述的数据处理方法，其特征在于，所述服务质量流相关的负荷数据包括以下信息中的一种或多种：
    服务质量流数据、服务质量流性能测量数据、所述目标网元与所述服务质量流相关的业务负荷占所述服务质量流最大业务负荷的比值。
  12. 根据权利要求10或11所述的数据处理方法，其特征在于，所述业务负荷信息包括所述目标网元的业务类型的服务质量流相关的负荷数据；所述分析结果包括所述目标网元的业务类型的服务质量流相关的负荷数据的平均值、负荷数据的峰值中的至少一个。
  13. 根据权利要求10或11所述的数据处理方法，其特征在于，所述业务负荷信息包括所述目标网元的多种业务类型的服务质量流相关的负荷数据；所述分析结果包括：所述目标网元在多种业务类型、不同业务负荷比例的条件下，业务负荷信息的平均值、业务负荷信息的峰值中的至少一个。
  14. 根据权利要求1至13任一项所述的数据处理方法，其特征在于，所述数据分析网元根据所述分析请求，获取所述目标网元的负荷数据，包括：
    所述数据分析网元根据所述分析请求，从操作管理和维护OAM获取历史负荷数据，其中，所述历史负荷数据包括业务负荷、业务负荷上限和服务质量流性能测量数据中的一种或多种。
  15. 根据权利要求1至13任一项所述的数据处理方法，其特征在于，所述数据分析网元根据所述分析请求，获取所述目标网元的负荷数据，包括：
    所述数据分析网元根据所述分析请求，从网络仓库功能NRF获取当前负荷数据，其中，所述当前负荷数据包括业务负荷比例和所述目标网元与服务质量流相关的负荷占所述服务质量流最大业务负荷的比值中的至少一个。
  16. 一种数据处理装置，其特征在于，包括：
    接收单元，用于接收来自请求者的分析请求，所述分析请求包括目标网元的信息；
    所述接收单元，还用于根据所述分析请求，获取所述目标网元的负荷数据，所述负荷数据包括业务负荷信息；
    处理单元，用于根据所述负荷数据，生成所述目标网元的分析结果；
    发送单元，用于向所述请求者发送所述分析结果。
  17. 根据权利要求16所述的数据处理装置，其特征在于，所述业务负荷信息至少包括业务负荷、业务负荷上限和业务负荷比例中的至少一个。
  18. 根据权利要求16或17所述的数据处理装置，其特征在于，所述分析结果包括所述目标网元的业务负荷信息的平均值和业务负荷信息的峰值中的至少一个。
  19. 根据权利要求16至18任一项所述的数据处理装置，其特征在于，
    所述接收单元，还用于根据所述分析请求，获取所述目标网元的过载信息；
    所述处理单元，还用于根据所述过载信息，生成所述目标网元的过载分析结果；
    所述发送单元，还用于向所述请求者发送所述过载分析结果。
  20. 根据权利要求19所述的数据处理装置，其特征在于，所述过载分析结果包括所述目标网元发生过载的概率。
  21. 根据权利要求16至20中任一项所述的数据处理装置，其特征在于，所述负荷数据还包括资源负荷信息，所述分析结果还包括所述目标网元的资源负荷信息的平均值和资源 负荷信息的峰值中的至少一个。
  22. 根据权利要求21所述的数据处理装置，其特征在于，所述分析请求还包括网络切片信息；
    所述负荷数据为所述网络切片对应的负荷数据。
  23. 根据权利要求22所述的数据处理装置，其特征在于，所述目标网元属于多个网络切片，所述目标网元包括公共模块，所述公共模块用于处理所述多个网络切片的业务；
    所述处理单元，还用于根据所述多个切片对应的业务负荷信息和所述公共模块的资源负荷信息，获取所述公共模块属于所述网络切片信息对应的网络切片的资源负荷信息。
  24. 根据权利要求23所述的数据处理装置，其特征在于，所述目标网元还包括多个专有模块，所述多个专有模块用于分别处理所述多个网络切片的业务；
    所述资源负荷信息包括所述网络切片信息对应的网络切片的专有模块的资源负荷信息和所述多个切片的公共模块的资源负荷信息；
    所述处理单元，还用于根据所述专有模块的资源负荷信息和所述公共模块属于所述网络切片信息对应的网络切片的资源负荷信息，获取属于所述网络切片信息对应的网络切片的资源负荷信息。
  25. 根据权利要求16至24任一项所述的数据处理装置，其特征在于，所述业务负荷信息包括服务质量流相关的负荷数据。
  26. 根据权利要求25所述的数据处理装置，其特征在于，所述服务质量流相关的负荷数据包括以下信息中的一种或多种：
    服务质量流数据、服务质量流性能测量数据、所述目标网元与所述服务质量流相关的业务负荷占所述服务质量流最大业务负荷的比值。
  27. 根据权利要求25或26所述的数据处理装置，其特征在于，所述业务负荷信息包括所述目标网元的业务类型的服务质量流相关的负荷数据；所述分析结果包括所述目标网元的业务类型的服务质量流相关的负荷数据的平均值、负荷数据的峰值中的至少一个。
  28. 根据权利要求25或26所述的数据处理装置，其特征在于，所述业务负荷信息包括所述目标网元的多种业务类型的服务质量流相关的负荷数据；所述分析结果包括：所述目标网元在多种业务类型、不同业务负荷比例的条件下，业务负荷信息的平均值、业务负荷信息的峰值中的至少一个。
  29. 根据权利要求16至28任一项所述的数据处理装置，其特征在于，所述接收单元，用于根据所述分析请求，获取所述目标网元的负荷数据，具体为：根据所述分析请求，从操作管理和维护OAM获取历史负荷数据，其中，所述历史负荷数据包括业务负荷、业务负荷上限和服务质量流性能测量数据中的一种或多种。
  30. 根据权利要求16至28任一项所述的数据处理装置，其特征在于，所述接收单元，用于根据所述分析请求，获取所述目标网元的负荷数据，具体为：根据所述分析请求，从网络仓库功能NRF获取当前负荷数据，其中，所述当前负荷数据包括业务负荷比例和所述目标网元与服务质量流相关的负荷占所述服务质量流最大业务负荷的比值中的至少一个。
  31. 一种数据处理装置，其特征在于，包括：处理器和存储器，所述处理器和所述存储器耦合，所述存储器存储有程序指令，当所述存储器存储的程序指令被所述处理器执行时，如权利要求1至15中任一项所述的数据处理方法被实现。
  32. 一种通信系统，其特征在于，包括：权利要求16至30任一项所述的数据处理装置以及目标网元。
  33. 一种可读存储介质，其特征在于，包括程序或指令，当所述程序或指令被执行时，如权利要求1至15中任一项所述的数据处理方法被实现。
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