Network data analysis method and communication device

Abstract

Provided are a network data analysis method and a communication device, the method being applied to a recommendation service function, the method comprising: receiving first information from a first network element, the first information comprising a first request and indication information; the first request is used for requesting to recommend data, the indication information is used for indicating to collect first data, and the first data is network data which is determined by a first network element and is associated with the recommend data; obtaining a prediction analysis result, wherein the prediction analysis result is obtained based on the first data; determining the recommendation data based on the prediction analysis result; and sending the recommendation data to the first network element. According to the data recommendation method and device, the globally optimized data recommendation service can be provided without understanding the business logic of each network element, and the adaptability and flexibility of the data recommendation service can be improved.

Description

Technical Field

[0001] This application relates to the field of communication technologies, and in particular, to a method for network data analysis and a communication device. Background Art

[0002] Network data analysis is a service that collects relevant network data from network function network elements, application function network elements, or network management systems, uses machine learning techniques to perform correlation analysis on various dimensions of the collected data, trains and fits a model, and uses the model to provide data output. Among them, the network element providing network data analysis can be a network data analytics function (NWDAF) network element, a management data analytics function (MDAF) network element, etc. Taking NWDAF as an example, other network function (NF) network elements in the network can consume the data analysis service provided by NWDAF and take actions to adjust network operation according to the output of NWDAF.

[0003] As Figure 1 shown, the central network automation function network element can obtain the target application experience quality level (i.e., the target value) expected to be achieved in a specified location area and the network operation prediction result output by NWDAF (i.e., the prediction output, such as the experience analysis prediction result, the user plane congestion prediction result, the user plane function (UPF) load prediction result, etc.), so as to determine the recommended actions taken by each network element in the specified location area (such as the recommended UPF network element to be selected, the control policy recommended to be set for the terminal, the service message sending parameters recommended for the application, etc.). The factors affecting the service experience quality are controlled by multiple network elements such as the policy control function (PCF), the session management function (SMF), and the application function (AF). These network elements will adjust the network according to the recommended actions determined by the central network automation function network element, so as to guide multiple network elements to cooperate with each other to achieve the network automation goal.

[0004] However, in the process of determining the recommended actions of each network element, the central network automation function network element needs to rely on the knowledge graph of each network element or deeply analyze the processing logic relationship between each network element within the network in order to output reasonable recommended actions for each network element. Therefore, it is inevitably necessary to understand the business logic of each network element, and different recommended service functions also need to be developed according to different business logics for different business scenarios. This approach not only increases the workload but also reduces the adaptability and flexibility of the recommended service. Summary of the Invention

[0005] Embodiments of this application provide a method for network data analysis and a communication device. Based on the method described in this application, it is possible to provide a globally optimized data recommendation service without the need to understand the business logic of each network element, which is beneficial to improving the adaptability and flexibility of the data recommendation service.

[0006] In a first aspect, this application provides a method for network data analysis. This method is applied to a recommended service function and includes: receiving first information from a first network element, where the first information includes a first request and indication information; the first request is used to request recommended data, and the indication information is used to indicate the collection of first data, which is network data determined by the first network element and associated with the recommended data; obtaining a predictive analysis result, which is obtained based on the first data; determining the recommended data based on the predictive analysis result; and sending the recommended data to the first network element.

[0007] Based on the method described in the first aspect, since the specified first data to be collected is determined by the first network element (for example, the first network element can determine the first data to be collected according to the business processing logic), the predictive analysis result determined based on the first data can reflect the impact of network adjustment actions on the network; and the association relationship between the first data and the recommended data can also be determined by the first network element according to the business processing logic. Therefore, the working process executed by the recommended service function after receiving the first information is data-driven, and it is possible to provide a globally optimized data recommendation service without the need to understand the business logic of each network element, reducing the workload and being beneficial to improving the adaptability and flexibility of the data recommendation service.

[0008] In a possible implementation, the indication information is further used to indicate the data source of the first data. Based on this method, the source of the first data can be clarified. The data source of the first data is used to indicate collecting the first data from one or more specified network elements. The first network element can use one or more network elements that can best reflect the impact of network adjustment actions on the network determined according to the service processing logic as the source of the first data. Without the need for the recommendation service function to understand the service logics of each network element, the data collection efficiency is improved, the workload is further reduced, and the adaptability and flexibility of the data recommendation service are enhanced.

[0009] In a possible implementation, the first information further includes an analysis type identifier; alternatively, the method further includes: determining the analysis type identifier based on the target parameter and the first request. Based on this method, it is beneficial to improve the flexibility of determining the analysis type identifier.

[0010] In a possible implementation, the analysis type identifier indicates any one of the following types: service experience analysis, user plane congestion analysis, network element load analysis.

[0011] In a possible implementation, the target parameter includes any one of the following parameters: quality of experience, user plane congestion level, load levels of multiple UPFs.

[0012] In a possible implementation, obtaining the predictive analysis result includes: collecting the second data corresponding to the first data and the analysis type identifier; where the first data belongs to the second data, or the first data does not belong to the second data; determining the predictive analysis result based on the first data and the second data. Based on this method, it is beneficial to improve the accuracy of the predictive analysis result, thereby improving the efficiency of the recommendation service.

[0013] In a possible implementation, determining the prediction analysis result based on the first data and the second data includes: increasing the weight of the first data relative to the other data in the second data except the first data according to the indication information; or increasing the weight of the first data relative to the second data according to the indication information; determining the prediction analysis result based on the weight of the first data, the weight of the second data, the first data, and the second data. It can be understood that when the first data belongs to the second data, during the process of obtaining the prediction analysis result, the recommendation service function can increase the weight of the first data relative to the other data in the second data except the first data according to the indication for collecting the first data, so that the prediction analysis result can more accurately reflect the impact of network adjustment behavior on the network; when the first data does not belong to the second data, in addition to based on the second data, it is also necessary to obtain the prediction analysis result based on the first data at the same time, and the weight of the first data relative to the second data can be increased according to the indication for collecting the first data, so that the prediction analysis result can reflect the impact of network adjustment behavior on the network. Based on this method, it is beneficial to improve the accuracy of the prediction analysis result, thereby further improving the efficiency of the recommendation service.

[0014] In a possible implementation, obtaining the prediction analysis result includes: sending a second request to the prediction analysis function; the second request is used to request the prediction analysis result, and the second request includes an analysis type identifier and the indication information; receiving the prediction analysis result from the prediction analysis function. Among them, the indication information is used to indicate the collection of the first data, and optionally, the indication information is also used to indicate the data source of the first data. When the first data belongs to the second data corresponding to the analysis type identifier, the prediction analysis function can increase the weight of the first data relative to the other data in the second data except the first data according to the indication for collecting the first data, so that the prediction analysis result can more accurately reflect the impact of network adjustment behavior on the network. When the first data does not belong to the second data, in addition to collecting the second data, it is also necessary to obtain the prediction analysis result based on the first data at the same time, and the weight of the first data relative to the second data can be increased according to the indication for collecting the first data, so that the prediction analysis result can reflect the impact of network adjustment behavior on the network. When the indication information also includes the data source of the first data, the prediction analysis function collects the first data from one or more network elements specified by the data source. Based on this method, the prediction analysis result can reflect the impact of network adjustment behavior on the network, which is beneficial to improve the accuracy of the prediction analysis result, thereby further improving the efficiency of the recommendation service.

[0015] In a possible implementation, the method further includes: determining analysis filtering information based on the first request; the analysis filtering information is used to indicate the network scope corresponding to the predictive analysis result. Specifically, the analysis filtering information is determined according to the indication information for collecting the first data in the first request; or, the analysis filtering information is determined according to the data source of the first data in the first request. The second request further includes the analysis filtering information. Based on this method, the efficiency of predictive analysis can be improved.

[0016] In a possible implementation, the analysis type identifier indicates service experience analysis; the first network element includes an Application Function (AF), a Policy Control Function (PCF), and a Session Management Function (SMF); the first data includes the peak rate and average rate of the first application at the current moment and / or historical moments. Based on this method, the Quality of Experience (QoE) of the application can be improved.

[0017] In a possible implementation, when the first network element is AF, the recommended data includes the recommended server instance of the first application.

[0018] In a possible implementation, when the first network element is PCF, the recommended data includes the recommended peak rate and recommended average rate of the first application.

[0019] In a possible implementation, when the first network element is SMF, the recommended data includes the recommended data network access identifier of the first application.

[0020] In a possible implementation, the analysis type identifier indicates user plane congestion analysis; the first request includes the identifier of the second application; the first network element includes PCF; the first data includes the user plane congestion level of the second application at the current moment and / or historical moments; the recommended data includes the recommended peak throughput and / or recommended average throughput of the second application. Based on this method, QoS parameters can be adjusted to avoid user plane congestion.

[0021] In a possible implementation, the analysis type identifier indicates network element load analysis; the first network element includes at least one SMF; the first data includes the number of N4 interface sessions established by at least one SMF on multiple User Plane Functions (UPFs) at the current moment and / or historical moments; the recommended data includes the number of N4 interface sessions recommended to be established by at least one SMF on multiple UPFs. Based on this method, the selection and distribution ratio of each UPF can be confirmed to prevent uneven UPF load or overload.

[0022] In a possible implementation, the first information further includes the expected value of the target parameter.

[0023] In a possible implementation, determining the recommended data based on the prediction analysis result includes: determining a deviation value based on the prediction analysis result and the expected value of the target parameter; adjusting the model parameters of the first data optimizer in the direction of reducing the deviation value to obtain a second data optimizer; and using the second data optimizer to determine the recommended data. Based on this method, the accuracy of the recommended data can be improved.

[0024] In a second aspect, the present application provides a method for network data analysis, which is applied to the prediction analysis function. The method includes: receiving a second request from the recommendation service function; the second request is used to request the prediction analysis result, and the second request includes an analysis type identifier and indication information, and the indication information is used to indicate collecting first data, where the first data is network data determined by a first network element and associated with the recommended data output by the recommendation service function; collecting the second data corresponding to the first data and the analysis type identifier; where the first data belongs to the second data, or the first data does not belong to the second data; determining a prediction analysis result based on the first data and the second data; and sending the prediction analysis result to the recommendation service function.

[0025] For the beneficial effects of the possible implementation manners of the second aspect, reference may be made to the beneficial effects of the possible implementation manners of the first aspect, which will not be elaborated here.

[0026] In a possible implementation, the indication information is further used to indicate the data source of the first data; collecting the first data includes: collecting the first data from the data source. The data source may be one or more network elements.

[0027] In a possible implementation, determining the prediction analysis result based on the first data and the second data includes: increasing the weight of the first data relative to other data in the second data except the first data according to the indication information; or increasing the weight of the first data relative to the second data according to the indication information; and determining the prediction analysis result based on the weight of the first data, the weight of the second data, the first data, and the second data. It can be understood that when the first data belongs to the second data, in the process of obtaining the prediction analysis result, the prediction analysis function can increase the weight of the first data relative to other data in the second data except the first data according to the indication for collecting the first data, so that the prediction analysis result can more accurately reflect the impact of network adjustment behavior on the network; when the first data does not belong to the second data, in addition to based on the second data, it is also necessary to obtain the prediction analysis result based on the first data at the same time, and increase the weight of the first data relative to the second data according to the indication for collecting the first data, so that the prediction analysis result can reflect the impact of network adjustment behavior on the network.

[0028] In a possible implementation, the analysis type identifier indicates service experience analysis; the first network element includes an AF, a PCF, and an SMF; the first data includes the peak rate and average rate of a first application at the current moment and / or historical moments; the first data is collected by a UPF; the second data includes second information collected from the AF, and third information collected from the UPF and / or the SMF; the second information includes the quality of experience of the first application, the server instance of the first application, and the identifier of the first application at the current moment and / or historical moments; the third information includes the data network access identifier of the first application, the user data rate, the user packet delay, and the quality of service traffic flow identifier corresponding to the application packet at the current moment and / or historical moments.

[0029] In a possible implementation, the second request further includes analysis filtering information, and the analysis filtering information is used to indicate the network scope corresponding to the analysis prediction result.

[0030] In a possible implementation, the analysis type identifier indicates user plane congestion analysis; the first network element includes a PCF; the first data includes the user plane congestion level of a second application at the current moment and / or historical moments; the first data is collected by a UPF; the second data includes fourth information collected from the UPF and / or the SMF; the fourth information includes the peak throughput, average throughput, identifier of the second application, and location information of the terminal device of the second application at the current moment and / or historical moments.

[0031] In a possible implementation, the analysis type identifier is network element load analysis; the first network element includes at least one SMF; the first data includes the number of N4 interface sessions established by the at least one SMF on multiple UPFs at the current moment and / or historical moments; the first data is collected by the multiple UPFs; the second data includes fifth information collected from the multiple UPFs; the fifth information includes the load level data of the multiple UPFs at the current moment and / or historical moments.

[0032] In a third aspect, the present application provides a method for network data analysis. The method is applied to a first network element and includes: sending first information to a recommendation service function, where the first information includes a first request and indication information; the first request is used to request recommendation data, and the indication information is used to indicate collecting first data, where the first data is network data determined by the first network element and associated with the recommendation data; receiving recommendation data from the recommendation service function; and determining a first parameter based on the recommendation data.

[0033] For the beneficial effects of the possible implementation manners of the third aspect, reference may be made to the beneficial effects of the possible implementation manners of the first aspect, which will not be elaborated here.

[0034] In a possible implementation, the method further includes: determining that the first data is associated with the recommended data based on the service processing logic of the first network element.

[0035] In a possible implementation, the first data is a network operation metric affected by a first parameter.

[0036] In a possible implementation, the first information further includes an analysis type identifier. Optionally, the analysis type identifier indicates any one of the following types: service experience analysis, user plane congestion analysis, network element load analysis.

[0037] In a possible implementation, the method further includes: determining the analysis type identifier based on the service processing logic of the first network element; determining the first request and the indication information based on the analysis type identifier.

[0038] In a possible implementation, the analysis type identifier indicates service experience analysis; the first network element includes an AF, a PCF, and an SMF; the first data includes the peak rate and average rate of a first application at the current moment and / or historical moments.

[0039] In a possible implementation, when the first network element is an AF, the recommended data is the recommended server instance of the first application, and the first parameter is the server instance selected by the first application.

[0040] In a possible implementation, when the first network element is a PCF, the recommended data includes the recommended peak rate of the first application and the recommended average rate of the first application; the first parameter includes the maximum flow bit rate or the aggregated maximum rate, and the first parameter further includes the guaranteed bit rate; determining the first parameter based on the recommended data includes: determining the maximum flow bit rate or the aggregated maximum rate based on the recommended peak rate of the first application, and determining the guaranteed bit rate based on the recommended average rate of the first application.

[0041] In a possible implementation, when the first network element is an SMF, the recommended data is the recommended data network access identifier of the first application, and the first parameter is the user plane path selected by the first application.

[0042] In a possible implementation, the analysis type identifier indicates user plane congestion analysis; the first request includes the identifier of a second application; the first network element includes a PCF; the first parameter is a session service quality parameter; the first data includes the user plane congestion level of the second application at the current moment and / or historical moments; the recommended data includes the recommended peak throughput and / or recommended average throughput of the second application.

[0043] In a possible implementation, the analysis type identifier indicates network element load analysis; the first network element includes at least one SMF; the first parameter includes the selection ratio of the at least one SMF for multiple UPFs; the first data includes the number of N4 interface sessions established by the at least one SMF on the multiple UPFs at the current moment and / or historical moments; the recommended data includes the number of N4 interface sessions recommended to be established by the at least one SMF on the multiple UPFs.

[0044] In a possible implementation, the indication information is further used to indicate the data source of the first data.

[0045] In a possible implementation, the first information further includes an expected value of a target parameter. Optionally, the target parameter includes any one of the following parameters: quality of experience, user plane congestion level, load levels of multiple UPFs.

[0046] In a fourth aspect, the present application provides a communication device. The communication device includes a processor, and when the processor calls a computer program in a memory, the methods described in the first aspect to the third aspect are executed.

[0047] In a fifth aspect, the present application provides a communication device. The communication device includes a processor and a memory, and the processor is coupled to the memory; the processor is used to implement the methods described in the first aspect to the third aspect.

[0048] In a sixth aspect, the present application provides a communication device. The communication device includes a processor, a memory and a transceiver, and the processor is coupled to the memory; the transceiver is used to transmit and receive data, and the processor is used to implement the methods described in the first aspect to the third aspect.

[0049] In a seventh aspect, the present application provides a chip. The chip includes a processor and an interface, and the processor is coupled to the interface; the interface is used to receive or output signals, and the processor is used to execute code instructions to enable the methods described in the first aspect to the third aspect to be executed.

[0050] In an eighth aspect, the present application provides a computer-readable storage medium. The storage medium stores a computer program or instruction, and when the computer program or instruction is executed by a communication device, the methods described in the first aspect to the third aspect are implemented.

[0051] In a ninth aspect, the present application provides a communication system. The communication system includes a recommendation service function and a first network element. The recommendation service function is used to execute the method described in the first aspect, and the first network element is used to execute the method described in the third aspect.

[0052] Tenth aspect, the present application provides a communication system, which includes a recommendation service function, a predictive analysis function, and a first network element. The recommendation service function is used to execute the method described in the first aspect, the predictive analysis function is used to execute the method described in the second aspect, and the first network element is used to execute the method described in the third aspect.

[0053] Eleventh aspect, the present application provides a computer program product including instructions. When a computer reads and executes the computer program product, it causes the computer to execute the methods described in the first aspect to the third aspect. Description of the Drawings

[0054] Figure 1 is a schematic diagram of a basic architecture of a recommendation service provided by an embodiment of the present application;

[0055] Figure 2 is a schematic diagram of a network system architecture provided by an embodiment of the present application;

[0056] Figure 3 is a schematic diagram of a CU-DU architecture provided by an embodiment of the present application;

[0057] Figure 4 is a schematic diagram of a 5G network architecture provided by an embodiment of the present application;

[0058] Figure 5 is a schematic diagram of a basic architecture of a new recommendation service provided by an embodiment of the present application;

[0059] Figure 6 is a schematic flowchart of a method for network data analysis provided by an embodiment of the present application;

[0060] Figure 7A is a schematic flowchart of a process for a second network element to obtain a predictive analysis result provided by an embodiment of the present application;

[0061] Figure 7B is a schematic flowchart of another process for a second network element to obtain a predictive analysis result provided by an embodiment of the present application;

[0062] Figure 7C is a schematic flowchart of a process for a second network element to determine recommendation data based on the predictive analysis result provided by an embodiment of the present application;

[0063] Figure 8 is a schematic flowchart of another method for network data analysis provided by an embodiment of the present application;

[0064] Figure 9A is a schematic flowchart of another process for a second network element to obtain a predictive analysis result provided by an embodiment of the present application;

[0065] Figure 9BIt is a schematic flowchart of another process for a second network element to obtain a prediction and analysis result provided by an embodiment of the present application;

[0066] Figure 10 It is a schematic flowchart of another method for network data analysis provided by an embodiment of the present application;

[0067] Figure 11 It is a schematic flowchart of another method for network data analysis provided by an embodiment of the present application;

[0068] Figure 12 It is a schematic structural diagram of a communication device provided by an embodiment of the present application;

[0069] Figure 13 It is a schematic structural diagram of another communication device provided by an embodiment of the present application;

[0070] Figure 14 It is a schematic structural diagram of a chip provided by an embodiment of the present application. Detailed implementation manners

[0071] Terms such as "first" and "second" in the specification, claims and drawings of the present application are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or devices.

[0072] Referring to "embodiment" in this context means that a specific feature, structure or characteristic described in connection with the embodiment can be included in at least one embodiment of the present application. The appearance of this phrase in various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0073] In this application, "at least one (item)" means one or more, "a plurality" means two or more, "at least two (items)" means two, three or more, and "and / or" is used to describe the corresponding relationship of associated objects, indicating that there can be three relationships. For example, "A and / or B" can mean: only A exists, only B exists, and both A and B exist at the same time. Here, A and B can be singular or plural. The character " / " generally indicates that the associated objects before and after are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single item (item) or plural items (items). For example, at least one (item) of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a, b and c", where a, b, c can be single or multiple.

[0074] To better understand the embodiments of this application, the system architecture involved in the embodiments of this application will be introduced first as follows:

[0075] The technical solutions of the embodiments of this application can be applied to various communication systems, such as: satellite communication systems, traditional mobile communication systems. Among them, satellite communication systems can be integrated with traditional mobile communication systems (i.e., terrestrial communication systems). Mobile communication systems include, for example: wireless local area network (WLAN) communication systems, wireless fidelity (Wi-Fi) systems, long term evolution (LTE) systems, LTE frequency division duplex (FDD) systems, LTE time division duplex (TDD), fifth generation (5G) systems or new radio (NR), and other future communication systems, such as sixth generation (6G) systems, etc., and also support communication systems that integrate multiple wireless technologies. For example, it can also be applied to systems that integrate non-terrestrial networks (NTN) such as drones, satellite communication systems, and high altitude platform station (HAPS) communication with terrestrial mobile communication networks. It can be understood that the system architecture described in the embodiments of this application is to more clearly illustrate the technical solutions of the embodiments of this application and does not constitute a limitation on the technical solutions provided by the embodiments of this application.

[0076] Please refer to Figure 2 , Figure 2It is a schematic diagram of a network system architecture provided by an embodiment of the present application. As Figure 2 shown, the next-generation mobile communication network architecture defined by the 3rd generation partnership project (3GPP) standard is called the 5G network architecture. A terminal device can be connected to a wireless network to obtain services from an external network (such as a data network (DN)) through the wireless network, or communicate with other devices through the wireless network, such as communicating with other terminal devices. The wireless network includes a (radio) access network ((R)AN) and a core network (CN). Among them, the (R)AN (described as RAN later) is used to connect the terminal device to the wireless network, and the CN is used to manage the terminal device and provide a gateway for communicating with the DN. The terminal device, RAN, CN, and DN involved in the network architecture will be described in detail below. Figure 2 The terminal device, RAN, CN, and DN involved in the network architecture will be described in detail below.

[0077] I. Terminal Device

[0078] A terminal device includes a device that provides voice and / or data connectivity to a user. For example, a terminal device is a device with wireless transceiver capabilities that can be deployed on land, including indoor or outdoor, handheld, wearable, or vehicle-mounted; it can also be deployed on water (such as a ship); or it can be deployed in the air (such as an airplane, balloon, satellite, etc.). Specifically, a terminal device can refer to a user equipment (UE), an access terminal, a subscriber unit, a user station, a mobile station, a remote station, a remote terminal, a mobile device, a user terminal, a wireless communication device, a user agent, or a user device. A terminal device can also be a satellite phone, a cellular phone, a smartphone, a wireless data card, a wireless modem, a machine type communication device, a cordless phone, a session initiation protocol (SIP) phone, a wireless local loop (WLL) station, a PDA, a handheld device with wireless communication capabilities, a computing device, or other processing devices connected to a wireless modem, a vehicle-mounted device, a communication device carried on a high-altitude airplane, a wearable device, a drone, a robot, a terminal in device-to-device (D2D) communication, a terminal in vehicle to everything (V2X), a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal in industrial control, a wireless terminal in self-driving, a wireless terminal in remote medical, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in smart city, a wireless terminal in smart home, or a terminal device in a future communication network, etc., and this application makes no restrictions. A terminal can also be fixed or mobile. Additionally, in this application, when not specifically stated, "terminal device" can refer to either the terminal device itself or a component within the terminal device, such as a chip system, SoC, and this component can be installed in the terminal device. It can be understood that all or part of the functions of the terminal in this application can also be implemented through software functions running on hardware or through virtualization functions instantiated on a platform (such as a cloud platform). The terminal device in this application can be a terminal for 5G or a terminal for 6G, and this application does not limit this.

[0079] II. RAN

[0080] The RAN may include one or more RAN devices (or access network devices). The interface between the access network device and the terminal device may be the Uu interface (or also referred to as the air interface). Of course, in the communication evolved after 5G, the names of these interfaces may remain unchanged, or other names may be used instead, and this application does not limit this.

[0081] The access network device is a node or device that connects the terminal device to the wireless network. The access network device includes, for example, but is not limited to: the next generation node B (gNB) in the 5G communication system, the evolved node B (eNB), the next generation evolved node B (ng-eNB), the wireless backhaul device, the radio network controller (RNC), the node B (NB), the home evolved node B (HeNB) or (home node B, HNB), the baseband unit (BBU), the transmitting and receiving point (TRP), the transmitting point (TP), the mobile switching center, the device-to-device (D2D), the vehicle-to-everything (V2X), the device that undertakes the base station function in the machine-to-machine (M2M) communication, etc. It may also include the centralized unit (CU) and the distributed unit (DU) in the cloud radio access network (C-RAN) system, the network device in the non-terrestrial network (NTN) communication system, that is, it can be deployed on the high-altitude platform or satellite, etc. The embodiments of this application do not make specific limitations on this. The RAN in this application may be the RAN for 5G or the RAN for 6G, and this application does not limit this.

[0082] Taking the RAN including the gNB as an example, the RAN may be connected to the core network (for example, it may be the core network of LTE or the core network of 5G, etc.). As Figure 3As shown, the CU and DU can be understood as a logical-functional division of a base station (such as a gNB). Physically, the CU and DU can be separated or deployed together. Multiple DUs can share a single CU. Of course, a single DU can also be connected to multiple CUs ( Figure 2 not shown in Figure 2 ). The CU and DU can be connected via an interface, such as the F1 interface. The CU and DU can be divided according to the protocol layers of the radio network. For example, one possible division method is as follows: the CU is used to perform the functions of the radio resource control (RRC) layer, the service data adaptation protocol (SDAP) layer, and the packet data convergence protocol (PDCP) layer, while the DU is used to perform the functions of the radio link control (RLC) layer, the media access control (MAC) layer, the physical layer, etc. It can be understood that the division of the processing functions of the CU and DU according to these protocol layers is only an example, and other division methods can also be used. For example, the CU or DU can be divided into functions with more protocol layers. For example, the CU or DU can also be divided into partial processing functions of the protocol layers. In one design, some functions of the RLC layer and the protocol layers above the RLC layer are set in the CU, and the remaining functions of the RLC layer and the protocol layers below the RLC layer are set in the DU. In another design, the functions of the CU or DU can also be divided according to the service type or other system requirements. For example, in terms of latency division, the functions that need to meet the latency requirements in terms of processing time are set in the DU, and the functions that do not need to meet this latency requirement are set in the CU. In another design, the CU can also have one or more functions of the core network. One or more CUs can be centrally set or separated. For example, the CU can be set on the network side for convenient centralized management. The DU can have multiple radio frequency functions, or the radio frequency functions can be remotely set.

[0083] III. CN

[0084] The CN can include one or more CN devices (which can be understood as network element devices or network functions (NFs)). Hereinafter, the CN devices are collectively referred to as core network elements.

[0085] Please refer to Figure 4 , Figure 4 which is a schematic diagram of a 5G network architecture provided by this application. In Figure 4The CN shown includes multiple CN devices: Network Slice Selection Function (NSSF), Network Exposure Function (NEF), Network Function Repository Function (NRF), Policy Control Function (PCF), Unified Data Management (UDM), Unified Data Repository (UDR), Application Function (AF), Network Slice Specific Authentication and Authorization Function (NSSAAF), Authentication Server Function (AUSF), Access and Mobility Management Function (AMF), Session Management Function (SMF), User Plane Function (UPF), Service Communication Proxy (SCP), Network Slice Admission Control Function (NSACF), Data Analysis Functions (such as Network Data Analytics Function (NWDAF), Management Data Analytics Function (MDAF)), etc. Additionally, in Figure 4 it also includes a terminal device (UE), an access network device (RAN), a data network (DN), and an Operation Administration and Maintenance (OAM) system, and the access network device can communicate with the OAM system. Among them:

[0086] The AF mainly interacts with the 5G core network to provide services, such as supporting the following functions: application impact on service routing, exposure of access network capabilities, and interaction with the policy framework for policy control. According to the operator's deployment strategy, the trusted AF can directly access the internal network elements of the 5G core network to improve service processing efficiency. Of course, it can also adopt a general external network element function access framework to interact with the corresponding internal network element functions through the NEF.

[0087] The AMF is a control plane function provided by the operator's network, responsible for access control and mobility management of the terminal device accessing the operator's network. For example, it includes functions such as mobile status management, allocation of user temporary identity identifiers, authentication and authorization of users, etc.

[0088] The SMF is a control plane function provided by the operator's network, responsible for managing the protocol data unit (PDU) session of the terminal device. The PDU session is a channel for transmitting PDUs, and the terminal device needs to transmit PDUs to and from the DN through the PDU session. The establishment, maintenance, and deletion of the PDU session are the responsibilities of the SMF. The SMF includes session management (such as session establishment, modification, and release, including tunnel maintenance between the UPF and the RAN), selection and control of the UPF, service and session continuity (SSC) mode selection, roaming, and other session-related functions.

[0089] The PCF is a control plane function provided by the operator, including user subscription data management functions, policy control functions, charging policy control functions, quality of service (QoS) control, etc., mainly used to provide policies for the PDU session to the SMF. Among them, the policies can include charging-related policies, QoS-related policies, authorization-related policies, etc.

[0090] The UPF is a gateway provided by the operator and is the gateway for communication between the operator's network and the DN. The UPF includes user plane-related functions such as packet routing and transmission, packet detection, QoS processing, uplink packet detection, and downlink packet storage.

[0091] The UDM is mainly used to manage the user's subscription data and authentication data, as well as perform authentication credit processing, user identification processing, access authorization, registration / mobility management, subscription management, and short message management, etc. In some embodiments, the UDM may also include a unified data repository (UDR). Or, in some other embodiments, the 3GPPSBA of the 5G system may also include the UDR. Among them, the UDR is used for storage and retrieval for PCF policies, storage and retrieval of open structured data, and storage of user information for application function requests, etc.

[0092] The UDR is mainly used to store and retrieve subscription data, policy data, common architecture data, etc., and provide relevant data for the UDM, PCF, and NEF. The UDR should be able to have different data access authentication mechanisms for different types of data, such as subscription data and policy data, to ensure the security of data access. For illegal service-oriented operations or data access requests, the UDR should be able to return a failure response with an appropriate cause value.

[0093] The OAM system usually divides the network management work into three categories according to the actual needs of operator network operation: operation, administration, and maintenance. Operation mainly completes the analysis, prediction, planning, and configuration work for daily networks and services; maintenance mainly conducts daily operation activities such as testing and fault management of the network and its services.

[0094] The data analysis function may include a network function that provides predictive analysis services (i.e., the predictive analysis function), which can collect data from various NFs (such as AF, AMF, SMF, PCF, NEF, etc.) or from the OAM system and perform analysis and prediction. In a 5G communication system, the data analysis function may specifically be NWDAF, MDAF, etc. In the embodiments of this application, the data analysis function is taken as an example of NWDAF for illustration, but no specific form and implementation form are limited.

[0095] It should be noted that in the embodiments of this application, a new data-driven network function, that is, the recommendation service function, can be added, which can be used to execute the recommendation service. The specific content of the recommendation service function will be described later and will not be described here for the time being. Among them, the recommendation service function and the predictive analysis function can be concentrated in the same network element (i.e., the recommendation service function and the predictive analysis function are the same data analysis function). For example, the recommendation service function can be a unit within the NWDAF. In this case, the NWDAF network element is called the NWDAF network element that supports the recommendation service; the recommendation service function can also be an independent network element, which is not limited here.

[0096] In addition, the above CN device can also be called a network element or a functional network element. In a 5G communication system, each functional network element can be Figure 4 the names of the respective functional network elements shown in Figure 4 In a communication system evolved after 5G (such as a 6G communication system), each functional network element can still be the names of the respective functional network elements shown in or may have other names. For example, in a 5G communication system, the user plane function can be UPF. In a communication system evolved after 5G (such as a 6G communication system), the user plane function can still be UPF, or may have other names, which are not limited in this application.

[0097] It should also be noted that in a 5G communication system, the functions implemented by each functional network element can be as Figure 4 shown to be independent. In a communication system evolved after 5G (such as a 6G communication system), each functional network element can still be in an independent state as Figure 4 shown, or the functions of multiple functional network elements in Figure 4 it can be implemented by an integrated functional network element. For example, in a 5G communication system, the functions related to the user plane are implemented by the UPF, and the functions related to access and mobility management are implemented by the AMF. In a communication system evolved after 5G (such as a 6G communication system), the functions related to the user plane can still be implemented by the UPF, and the functions related to access and mobility management can still be implemented by the AMF, or the functions related to the user plane and the functions related to access and mobility management can also be implemented simultaneously by an integrated functional network element. The present application does not limit this.

[0098] Figure 4 In

[0099] IV. DN

[0100] DN can also be referred to as a packet data network (PDN), which is a network outside the operator's network. The operator's network can access multiple DNs, and application servers corresponding to various services can be deployed in the DNs to provide various possible services for terminal devices.

[0101] To facilitate understanding of the solution provided by the embodiments of the present application, the recommended services involved in the embodiments of the present application are introduced below:

[0102] Taking NWDAF as an example, other NF network elements in the network can consume the prediction analysis services provided by NWDAF and take actions to adjust the network operation according to the prediction output of NWDAF.

[0103] For example, the SMF may select or reselect a UPF for an established or establishing protocol data unit (PDU) session based on the UPF load prediction analysis result output by the NWDAF, achieving load balancing among multiple UPF network elements and preventing a certain UPF from being overloaded. The SMF network element may also select a user plane path (UP path) with a better service experience for a session transmitting application service messages based on the service experience analysis prediction of a certain application output by the NWDAF, where the UP path includes a UPF, and / or a data network access identifier (DNAI), and / or an application server instance; the DNAI is used to identify a mobile edge computing (MEC) node.

[0104] For another example, the PCF may determine a traffic splitting control policy based on the service experience analysis prediction of a certain application output by the NWDAF, and have the service messages of the application processed at a nearby MEC node.

[0105] For another example, the AF may deploy an application server at an MEC with a better service experience based on the service experience analysis prediction of this application output by the NWDAF, and select a suitable application server instance for the user through application load balancing.

[0106] For another example, the PCF network element may adjust the quality of service (QoS) parameters of several applications with the largest traffic based on the user plane congestion analysis prediction output by the NWDAF, limit the maximum data rate of these applications, and avoid congestion in the user plane.

[0107] However, when multiple different NF network elements use the prediction output of the NWDAF to make decisions and take actions respectively, there are scenarios where there is a lack of mutual cooperation. For example, for the actions taken by the SMF and the AF based on the NWDAF service experience prediction at the same time, the UPF and DNAI selected by the SMF may not match the application server instance selected by the AF (i.e., they are not located on the same MEC node), and thus the best service experience cannot be achieved. Another example is that when multiple SMFs select UPFs based on the UPF load prediction at the same time, they may simultaneously select to allocate a large number of newly established PDU sessions to the UPF with the lowest load, and the load of this UPF will suddenly increase, resulting in load fluctuations of the UPF.

[0108] Therefore, a global recommendation service output is needed to guide multiple NF network elements to cooperate with each other to take better actions for the overall network. As Figure 1As shown in the figure, a basic architecture of a recommendation service has been proposed currently. A central network automation functional network element is added. The central network automation functional network element is used to obtain the target application experience quality level (i.e., the target value) expected to be achieved in a specified location area and the network operation prediction results output by the NWDAF (i.e., the prediction output, such as experience analysis prediction results, user plane congestion prediction results, UPF load prediction results, etc.), so as to determine the recommendation actions to be taken by each network element in the specified location area (such as the recommended UPF network element to be selected, the control policy recommended to be set for the terminal, the service message sending parameters recommended for the application, etc.). The above factors affecting the service experience quality are controlled by multiple network elements such as the PCF, SMF, and AF. These network elements will adjust the network according to the recommendation actions determined by the central network automation functional network element, so as to guide the cooperation of multiple network elements to achieve the network automation goal.

[0109] However, in the process of determining the recommendation actions of each network element, the central network automation functional network element needs to output reasonable recommendation actions for each network element according to the knowledge graph of each network element or deeply analyze the processing logic relationship between each network element inside the network. Therefore, it is inevitably necessary to understand the service logic of each network element, and different recommendation service functions also need to be developed according to different service logics for different service scenarios. Such a method will not only increase the workload but also reduce the adaptability and flexibility of the recommendation service.

[0110] Therefore, in order to provide a globally optimized data recommendation service without the need to understand the service logic of each network element, which is beneficial to improving the adaptability and flexibility of the data recommendation service, the embodiments of the present application provide a new basic architecture of a recommendation service. As Figure 5 shown, the basic architecture of the recommendation service includes a first network element (i.e., the recommendation service consumer), a recommendation service function, and a prediction analysis function.

[0111] Among them, the first network element refers to the network element for consumption recommendation services. For example, it can be functional network elements such as SMF, PCF, AF that make up the network, or the OAM system of the network. The recommendation service function is a newly added data-driven network function, which is used to receive recommendation service requests or recommendation subscription requests sent by other NF network elements, obtain the network data of the network operation at a certain future moment output by the predictive analysis function, and determine the network data output by the recommendation service by combining the predicted network data and the network operation target. The recommendation service function can specifically include a data optimizer unit and a data deviation monitoring unit. Among them, the data deviation monitoring unit can determine the deviation between its output and the optimized target data according to the output of the prediction model; the data optimizer determines the recommended data output that can be close to the optimized target according to the deviation of the target data. The predictive analysis function can be the NWDAF mentioned above. It should be noted that the recommendation service function and the predictive analysis function can be concentrated in the same network element (that is, the recommendation service function and the predictive analysis function are the same data analysis function). For example, the recommendation service function can be a unit within the NWDAF. At this time, the NWDAF network element is called the NWDAF network element that supports the recommendation service. That is to say, the NWDAF network element that supports the recommendation service includes both the predictive analysis function and the recommendation service function. Of course, the recommendation service function can also be an independent network element.

[0112] Specifically, the first network element can send a recommendation service request or subscription to the recommendation service function, specifying the data that needs to be collected in the recommendation service request or subscription; the recommendation service function sends the specified data to be collected to the predictive analysis function through a predictive analysis subscription; the predictive analysis function can collect the specified data and network data from each network element (NFs), analyze and predict the collected data using a prediction model, obtain the predictive analysis result, and send the predictive analysis function to the recommendation service function; the recommendation service function processes it using the data deviation monitoring unit and the data optimizer to obtain the recommended data, and sends the recommended data to the first network element, so that the first network element can adjust the network according to the recommended data, and multiple network elements cooperate with each other to achieve the optimization goal. Among them, the recommendation service function and the predictive analysis function can be concentrated in the same network element or can be independent network elements respectively, which is not limited here.

[0113] Since the data specified to be collected is determined by the first network element according to the service processing logic, the prediction output of the prediction analysis function can reflect the impact of network adjustment actions on the network. At the same time, there is an association between the recommended data and the data specified to be collected by the first network element. This association is determined by the first network element based on the service processing logic and does not need to be mastered by the recommendation service function. Therefore, the entire working process of the recommendation service function is data-driven. Without the need to understand the service logics of each network element, it can also provide a globally optimized data recommendation service, reducing the workload and facilitating improving the adaptability and flexibility of the data recommendation service.

[0114] The method and communication device for network data analysis provided by the embodiments of the present application will be further described in detail below.

[0115] Figure 6 It is a schematic flowchart of a method for network data analysis provided by an embodiment of the present application. As Figure 6 shown, the method for network data analysis includes the following steps S601 to S605. Figure 6 The execution subject of the method shown can be the first network element and the second network element. Alternatively, Figure 6 the execution subject of the method shown can be the chip in the first network element and the chip in the second network element, which is not limited in the embodiments of the present application. Figure 6 Taking the first network element and the second network element as the execution subjects of the method as an example for illustration. Among them, the second network element includes a recommendation service function. For example, the second network element can be a recommendation service function (it can be considered that the recommendation service function is an independent network element); the second network element can also include both a recommendation service function and a prediction analysis function (it can be considered that the recommendation service function and the prediction analysis function are concentrated in the same network element, that is, the recommendation service function and the prediction analysis function are the same data analysis function, such as the NWDAF network element that supports the recommendation service).

[0116] S601. The first network element sends a first message to the second network element. The first message includes a first request and indication information. The first request is used to request recommended data, and the indication information is used to indicate collecting first data. The first data is network data determined by the first network element and associated with the recommended data. Correspondingly, the second network element receives the first message from the first network element.

[0117] In an embodiment of the present application, the first request here can be regarded as a recommendation service subscription request for requesting recommendation data. The indication information can specify the first data to be collected. Optionally, the indication information can also indicate the data source of the first data, and the data source of the first data is used to indicate collecting the first data from one or more specified network elements or a specified network scope. Optionally, after receiving the recommendation service subscription request from the first network element, the second network element can send a subscription success response message to the first network element. Among them, the indication information can also be carried in the first request, which is not limited herein.

[0118] In a possible implementation manner, the method further includes: the first network element determines that the first data is associated with the recommendation data based on the service processing logic of the first network element. Among them, the so-called service processing logic refers to the rules and processes that a network element has when providing services to other network elements or consuming services provided by other network elements.

[0119] It can be understood that the first data specified for collection is determined by the first network element according to the service processing logic, and the subsequent prediction analysis result determined based on the first data can naturally also reflect the impact of the network adjustment behavior on the network; and the association relationship between the first data and the recommendation data is also determined by the first network element based on the service processing logic. Therefore, the working process executed by the second network element after receiving the first information is data-driven. Without the need to understand the service logics of each network element, a global optimized data recommendation service can be provided, reducing the workload and being beneficial to improving the adaptability and flexibility of the data recommendation service.

[0120] In a possible implementation manner, the first information further includes an expected value of a target parameter. For example, the target parameter can be quality of experience, user plane congestion level, or the load level of multiple UPFs. Of course, the target parameter can also be other parameters, which are not limited herein. The expected value of the target parameter here can be regarded as an optimization target. It should be noted that if the expected value of the target parameter is not carried in the first information sent by the first network element, the second network element can also obtain the expected value of the target parameter (i.e., the optimization target) through pre-configuration or other means.

[0121] In a possible implementation manner, the second network element also needs to determine an analysis type identifier, which is used to indicate the prediction analysis type of the collected first data, and the analysis direction of the first data can be determined according to the analysis type identifier subsequently. For example, the analysis type identifier can indicate any one of the following types: service experience analysis, user plane congestion analysis, network element load analysis. Of course, the analysis type identifier can also indicate other analysis types, which are not limited herein.

[0122] Among them, the second network element can determine the analysis type identifier in one of the following two ways, which will be specifically described below. Based on this method, it is beneficial to improve the flexibility of determining the analysis type identifier.

[0123] Method 1: The first information further includes the analysis type identifier.

[0124] In a specific implementation, after the first network element determines the analysis type identifier, it can carry the analysis type identifier in the first information and send it to the second network element.

[0125] Optionally, the method further includes: The first network element determines the analysis type identifier based on the service processing logic of the first network element.

[0126] For example, assume that the first network element includes at least one SMF, and at least one SMF expects the second network element to recommend the selection and distribution ratio of each UPF to prevent uneven or overloaded UPF loads. At this time, the SMF can determine according to the service processing logic that it is necessary to analyze the running load levels of each UPF, so the analysis type identifier is determined to indicate network element load analysis.

[0127] Another example, assume that the first network element includes an AF, a PCF, and an SMF. The AF, PCF, and SMF expect the second network element to guide the improvement of the quality of experience (QoE) of the application. The AF, PCF, and SMF can determine according to the service processing logic that it is necessary to analyze the QoE of the application, so the analysis type identifier is determined to indicate service experience analysis.

[0128] Another example, assume that the first network element includes a PCF, and the PCF expects the second network element to adjust QoS parameters to avoid user plane congestion. The PCF can determine according to the service processing logic that it is necessary to analyze the user plane congestion, so the analysis type identifier is determined to indicate user plane congestion analysis.

[0129] Optionally, the method further includes: The first network element determines the first request and the indication information based on the analysis type identifier.

[0130] For example, assume that the analysis type identifier indicates network element load analysis. Then the first request of at least one SMF can be used to request the number of N4 interface sessions recommended to be established on multiple UPFs; the indication information can indicate the number of N4 interface sessions established by at least one SMF on multiple UPFs at the current moment and / or historical moments.

[0131] For another example, assume that the analysis type identifier indicates service experience analysis. Then, the first request of the AF can be used to request a recommendation server instance of the first application; the first request of the PCF can be used to request the recommended peak rate and the recommended average rate of the first application; and the first request of the SMF can be used to request the recommended DNAI of the first application. The indication information can indicate the collection of the peak rate and the average rate of the first application at the current moment and / or historical moments.

[0132] For another example, assume that the analysis type identifier indicates user plane congestion analysis. Then, the first request of the PCF can be used to request the recommended peak throughput and / or the recommended average throughput of the second application; and the indication information can indicate the collection of the user plane congestion level of the second application at the current moment and / or historical moments.

[0133] Method 2: The second network element determines the analysis type identifier based on the target parameter and the first request.

[0134] In a specific implementation, the second network element can determine the analysis type identifier by itself according to the target parameter and the first request.

[0135] For example, assume that the target parameter is the load levels of multiple UPFs, and the first request is used to request the number of N4 interface sessions recommended to be established on the multiple UPFs. Then, it can be determined that the analysis type identifier indicates network element load analysis.

[0136] For another example, assume that the target parameter is quality of experience, the first request for the AF is used to request a recommendation server instance of the first application, the first request for the PCF is used to request the recommended peak rate and the recommended average rate of the first application, and the first request for the SMF is used to request the recommended DNAI of the first application. Then, it can be determined that the analysis type identifier indicates service experience analysis.

[0137] For another example, assume that the target parameter is the user plane congestion level, and the first request for the PCF is used to request the recommended peak throughput and / or the recommended average throughput of the second application. Then, it can be determined that the analysis type identifier indicates user plane congestion analysis.

[0138] S602. The second network element obtains a prediction analysis result, which is obtained based on the first data.

[0139] In an embodiment of the present application, after the second network element receives the first information, it further obtains a predictive analysis result based on the first data. Here, the second network element may be a recommendation service function (it can be considered that the recommendation service function is an independent network element); the second network element may also include both a recommendation service function and a predictive analysis function (it can be considered that the recommendation service function and the predictive analysis function are concentrated in the same network element, that is, the recommendation service function and the predictive analysis function are the same data analysis function). The following specifically describes the specific implementation methods for the second network element to obtain the predictive analysis result in these two cases.

[0140] Case 1: The second network element is a recommendation service function.

[0141] The specific implementation method for the second network element to obtain the predictive analysis result may include the following steps s11 to s14, as Figure 7A shown. Based on this method, the predictive analysis result can reflect the impact of network adjustment actions on the network, which is beneficial to improving the accuracy of the predictive analysis result, thereby further improving the efficiency of the recommendation service.

[0142] s11. The recommendation service function sends a second request to the predictive analysis function; this second request is used to request the predictive analysis result, and this second request includes an analysis type identifier and indication information. Correspondingly, the predictive analysis function receives the second request from the recommendation service function.

[0143] In specific implementation, this second request can be considered a predictive analysis subscription request. When the second network element is a recommendation service function, the recommendation service function can request the predictive analysis result from the predictive analysis function.

[0144] Optionally, the method further includes: the recommendation service function determines analysis filtering information based on this first request; this analysis filtering information is used to indicate the network scope corresponding to the predictive analysis result.

[0145] Specifically, this first request includes indication information, and the analysis filtering information can be determined according to the indication information for collecting the first data in the first request. For example, according to the indication information in the first request for obtaining the recommended DNAI of the first application, the peak rate and average rate of the first application, the analysis filtering information can be determined as the first application; or, the analysis filtering information can be determined according to the indication information of the data source of the first data in the first request. For example, if the data source of the first data in the first request specifies one or more UPFs, the analysis filtering information can be determined as a list of network element instances including the specified one or more UPFs. Among them, this second request also includes this analysis filtering information. Based on this method, the efficiency of predictive analysis can be improved.

[0146] It should be noted that the application identifier (App ID) can be used as the analysis and filtering information. In this case, it is necessary to analyze and predict the business experience of the application corresponding to this application identifier. Alternatively, a certain network area or a certain type of network element function can be used as the analysis and filtering information. In this case, it is necessary to analyze and predict the business experience of this network area or this type of network element function. Of course, other forms can also be used to represent the analysis and filtering information, which is not limited here.

[0147] S12. The prediction and analysis function collects the first data and the second data corresponding to the analysis type identifier; wherein, the first data belongs to the second data, or the first data does not belong to the second data.

[0148] In specific implementation, in addition to collecting the specified first data, the prediction and analysis function also needs to collect the second data corresponding to the analysis type identifier. Here, the second data can be regarded as the network data that needs to be collected for the analysis type identifier. Among them, the specified first data collected can belong to the network data that needs to be collected for the analysis type identifier, or can also not belong to the network data that needs to be collected for the analysis type identifier, which is not limited here. For example, when the analysis type identifier is network element load analysis, the first data indicating the number of N4 interface sessions established on the UPF does not belong to the second data corresponding to the network element load analysis type identifier; for another example, when the analysis type identifier is business experience analysis, the first data indicating the peak rate and average rate of the first application belongs to the second data corresponding to the network element load analysis type identifier.

[0149] Further optionally, the indication information is also used to indicate the data source of the first data; the specific implementation manner for the prediction and analysis function to collect the first data can be: the prediction and analysis function collects the first data from the data source. It can be understood that the prediction and analysis function can collect the first data from the specified data source (that is, one or more specified network elements or a specified network range as the source of network data). Based on this method, the first network element can use one or more network elements that can best reflect the impact of network adjustment behavior on the network determined according to the service processing logic as the source of the first data, improving the efficiency of data collection without the recommendation service function needing to understand the service logic of each network element, further reducing the workload, and improving the adaptability and flexibility of the data recommendation service.

[0150] S13. The prediction and analysis function determines the prediction and analysis result based on the first data and the second data.

[0151] In a specific implementation, the prediction analysis function can use the first data and the second data for offline model training to obtain a prediction analysis model; it can also obtain the prediction analysis model from network elements such as the model training logic function (MTLF), which is not limited here. Then, the prediction analysis model is used to process the data collected in the first data at the current moment to obtain a prediction analysis result.

[0152] In a possible implementation manner, the specific implementation manner for the prediction analysis function to determine the prediction analysis result based on the first data and the second data can be: increasing the weight of the first data relative to other data in the second data except the first data according to the indication information; or, increasing the weight of the first data relative to the second data according to the indication information; determining the prediction analysis result based on the weight of the first data, the weight of the second data, the first data, and the second data.

[0153] It can be understood that when the first data belongs to the second data, in the process of obtaining the prediction analysis result using the prediction analysis model, the prediction analysis function can increase the weight of the first data relative to other data in the second data except the first data according to the indication for collecting the first data (it can be understood that the weight of the first data is higher than that of other data in the second data except the first data), so that the prediction analysis result can more accurately reflect the impact of network adjustment behavior on the network; when the first data does not belong to the second data, in addition to being based on the second data, it is also necessary to obtain the prediction analysis result based on the first data at the same time, and increase the weight of the first data relative to the second data according to the indication for collecting the first data (it can be understood that the weight of the first data is higher than that of the second data), so that the prediction analysis result can reflect the impact of network adjustment behavior on the network. Based on this method, it is beneficial to improve the accuracy of the prediction analysis result, thereby further improving the efficiency of the recommendation service.

[0154] S14. The prediction analysis function sends the prediction analysis result to the recommendation service function. Correspondingly, the recommendation service function receives the prediction analysis result from the prediction analysis function.

[0155] Case 2: The second network element includes both the recommendation service function and the prediction analysis function.

[0156] The specific implementation manner for the second network element to obtain the prediction analysis result can include the following steps S21 and S22, as Figure 7B shown. Based on this method, it is beneficial to improve the accuracy of the prediction analysis result and improve the efficiency.

[0157] S21. The second network element collects first data and second data corresponding to the analysis type identifier; wherein, the first data belongs to the second data, or the first data does not belong to the second data.

[0158] In a specific implementation, when the second network element includes a recommendation service function and a predictive analysis function, the second network element can collect the specified first data and the second data corresponding to the analysis type identifier by itself. The second data here can be regarded as the network data that needs to be collected for the analysis type identifier. Among them, the specified first data collected can belong to the network data that needs to be collected for the analysis type identifier, or can not belong to the network data that needs to be collected for the analysis type identifier, and this is not limited here.

[0159] S22. The second network element determines a predictive analysis result based on the first data and the second data.

[0160] In a specific implementation, the second network element can perform offline model training using the first data and the second data by itself to obtain a predictive analysis model; it can also obtain a predictive analysis model from network elements such as MTLF, and this is not limited here. Then, the predictive analysis model is used to process the data collected for the current moment in the first data to obtain a predictive analysis result.

[0161] In a possible implementation manner, determining a predictive analysis result based on the first data and the second data includes: increasing the weight of the first data relative to other data in the second data except the first data according to the indication information; or increasing the weight of the first data relative to the second data according to the indication information; determining a predictive analysis result based on the weight of the first data, the weight of the second data, the first data, and the second data.

[0162] It can be understood that when the first data belongs to the second data, in the process of obtaining a predictive analysis result using the predictive analysis model, the recommendation service function can increase the weight of the first data relative to other data in the second data except the first data according to the indication for collecting the first data (it can be understood that the weight of the first data is higher than the weight of other data in the second data except the first data), so that the predictive analysis result can more accurately reflect the impact of network adjustment behavior on the network; when the first data does not belong to the second data, in addition to being based on the second data, it is also necessary to obtain a predictive analysis result based on the first data at the same time, and the weight of the first data relative to the second data can be increased according to the indication for collecting the first data (it can be understood that the weight of the first data is higher than the weight of the second data), so that the predictive analysis result can reflect the impact of network adjustment behavior on the network. Based on this method, it is beneficial to improve the accuracy of the predictive analysis result, and thus further improve the efficiency of the recommendation service.

[0163] S603. The second network element determines recommended data based on the prediction analysis result.

[0164] In a possible implementation manner, the specific implementation manner for the second network element to determine recommended data based on the prediction analysis result may include the following steps s31 to s33, as Figure 7C shown. Based on this manner, the accuracy of the recommended data can be improved.

[0165] s31. The second network element determines a deviation value based on the prediction analysis result and the expected value of the target parameter.

[0166] In specific implementation, the data deviation monitoring unit of the recommendation service function is used to calculate the deviation value between the prediction analysis result and the expected value of the target parameter.

[0167] s32. The second network element adjusts the model parameters of the first data optimizer in the direction of reducing the deviation value to obtain a second data optimizer.

[0168] In specific implementation, the second network element includes a recommendation service function. The first data optimizer in the recommendation service function can also be a machine learning model with initially configured model parameters. The initially configured model parameters can be obtained from theoretical calculations, or from online learning during the operation of an experimental network or other networks, which is not limited here. The first data optimizer continuously adjusts the machine learning model parameters in the direction of reducing the deviation value according to the deviation value, and converges to the operation target faster to obtain the final second data optimizer. It should be noted that the second network element can adjust the model parameters of the first data optimizer every unit time (such as 1 second or 0.5 minutes, etc.) to obtain the latest second data optimizer. During the network operation, the model parameters are adjusted through online learning, and the first data optimizer becomes more and more accurate.

[0169] s33. The second network element uses the second data optimizer to determine the recommended data.

[0170] In specific implementation, the second network element further obtains third data at the current moment, such as information such as bit rate, DNAI, application identifier, user permanent identifier, allocated network slice instance identifier, expiration date, spatial effect, etc., which is not limited here. The second network element uses the second data optimizer to process the third data and outputs the recommended data.

[0171] S604. The second network element sends the recommended data to the first network element. Correspondingly, the first network element receives the recommended data from the second network element.

[0172] S605. The first network element determines a first parameter based on the recommended data.

[0173] In a possible implementation, the first data is a network operation metric affected by the first parameter.

[0174] It can be understood that after the first network element obtains the recommended data, it can execute actions according to the recommended data, that is, determine the first parameter to achieve the network operation goal.

[0175] For example, assume that the first network element includes at least one SMF, and the recommended data for the at least one SMF is the number of N4 interface sessions recommended to be established on multiple UPFs. Then, the at least one SMF can determine the selection and distribution ratio (i.e., the first parameter) for the multiple UPFs based on the recommended data.

[0176] For another example, assume that the first network element includes an AF, a PCF, and an SMF. The recommended data for the AF is the recommended server instance of the first application. The AF can select a server instance (i.e., the first parameter) based on the recommended data. The recommended data for the PCF is the recommended peak rate and the recommended average rate of the first application. The PCF can determine the QoS parameters (i.e., the first parameter) of the application service packets based on the recommended data. For example, determine the maximum flow bit rate (MFBR) or the aggregate maximum bit rate (AMBR) based on the recommended peak rate of the first application, and determine the guaranteed flow bitrate (GFBR) based on the recommended average rate of the first application. The recommended data for the SMF is the recommended DNAI of the first application. The SMF can perform user plane path selection (i.e., the first parameter) based on the recommended data.

[0177] For another example, assume that the first network element includes a PCF, and the recommended data for the PCF is the recommended peak throughput and / or the recommended average throughput of the second application. The PCF can determine the QoS parameters (i.e., the first parameter) of several main applications based on the recommended data. For example, set the MFBR according to the recommended peak throughput of the second application, and set the GFBR according to the recommended average throughput of the second application. The PCF can also further determine the 5G QoS Identifier (5QI) used by the application data based on the recommended data.

[0178] It should be noted that steps S602 to S605 are repeatedly executed every preset time period (for example, every 30 seconds) until the first network element sends a recommended service unsubscribe message to the second network element.

[0179] It can be seen that based on Figure 6The described method can provide a globally optimized data recommendation service without the need to understand the service logics of individual network elements, reducing the workload and facilitating the improvement of the adaptability and flexibility of the data recommendation service.

[0180] The method and communication device for network data analysis provided in the embodiments of the present application will be further described in detail below for different scenarios.

[0181] 1. To improve the QoE of an application to the expected value, the PCF, SMF, and AF request recommended data from the recommendation service function.

[0182] Figure 8 is a schematic flowchart of another method for network data analysis provided in the embodiments of the present application. As Figure 8 shown, the method for network data analysis includes the following steps S801 to S811. Figure 8 The execution subject of the method shown can be the first network element and the second network element. Alternatively, Figure 8 the execution subject of the method shown can be the chips in the first network element and the chips in the second network element, which is not limited in the embodiments of the present application. Figure 8 Taking the first network element and the second network element as the execution subjects of the method as an example for illustration. Among them, the first network element includes the PCF, SMF, and AF; the second network element includes the recommendation service function. For example, the second network element can be the recommendation service function (it can be considered that the recommendation service function is an independent network element), and the second network element can also include both the recommendation service function and the prediction analysis function (it can be considered that the recommendation service function and the prediction analysis function are concentrated in the same network element, that is, the recommendation service function and the prediction analysis function are the same data analysis function. For example, the NWDAF network element that supports the recommendation service).

[0183] S801. The AF sends the first information corresponding to the AF to the second network element. The first information corresponding to the AF includes a first request and indication information; the first request is used to request the recommended data corresponding to the AF, and the indication information is used to indicate collecting the first data corresponding to the AF. The first data is the network data determined by the AF and associated with the recommended data. Correspondingly, the second network element receives the first information from the AF.

[0184] In the embodiments of the present application, when the AF sends the first information corresponding to the AF to the second network element, the recommended data corresponding to the AF requested by the first request is the recommended server instance of the first application, and the first data corresponding to the AF indicated to be collected by the indication information is the peak rate and average rate of the first application at the current moment and / or historical moments. The indication information can also be carried in the first request, which is not limited here.

[0185] In a possible implementation, the first information corresponding to the AF further includes the expected value of the target parameter. Here, the target parameter is the quality of experience. It should be noted that if the expected value of the target parameter is not carried in the first information sent by the AF, the second network element can also obtain the expected value of the target parameter (i.e., the optimization target) through pre-configuration or other means.

[0186] In a possible implementation, the method further includes: the AF determines that the first data is associated with the recommended data based on the service processing logic of the AF.

[0187] In a possible implementation, the second network element also needs to determine the analysis type identifier corresponding to the AF. Here, the analysis type identifier corresponding to the AF indicates service experience analysis. The second network element can determine the analysis type identifier corresponding to the AF in one of the following two ways, which will be specifically described below.

[0188] Method 1: The first information corresponding to the AF further includes the analysis type identifier corresponding to the AF.

[0189] Method 2: The second network element determines the analysis type identifier corresponding to the AF based on the target parameter and the first request corresponding to the AF.

[0190] Among them, the specific implementation of step S801 can refer to the specific implementation of step S601 above. The main difference is that the first network element is the AF, which will not be elaborated here.

[0191] S802. The PCF sends the first information corresponding to the PCF to the second network element. The first information corresponding to the PCF includes a first request and an indication information. The first request is used to request the recommended data corresponding to the PCF, and the indication information is used to indicate collecting the first data corresponding to the PCF. The first data is the network data determined by the PCF to be associated with the recommended data. Correspondingly, the second network element receives the first information from the PCF.

[0192] In the embodiments of the present application, when the PCF sends the first information corresponding to the PCF to the second network element, the recommended data corresponding to the PCF requested by the first request is the recommended peak rate and / or recommended average rate of the first application, and the first data corresponding to the PCF indicated to be collected by the indication information is the peak rate and average rate of the first application at the current moment and / or historical moment.

[0193] In a possible implementation, the first information corresponding to the PCF further includes the expected value of the target parameter. Here, the target parameter is the quality of experience. It should be noted that if the expected value of the target parameter is not carried in the first information sent by the PCF, the second network element can also obtain the expected value of the target parameter (i.e., the optimization target) through pre-configuration or other means.

[0194] In a possible implementation, the method further includes: the PCF determines that the first data is associated with the recommended data based on the service processing logic of the PCF.

[0195] In a possible implementation, the second network element also needs to determine the analysis type identifier corresponding to the PCF, where the analysis type identifier corresponding to the PCF indicates service experience analysis. The second network element can determine the analysis type identifier corresponding to the PCF in one of the following two ways, which will be specifically described below.

[0196] Method 1: The first information corresponding to the PCF further includes the analysis type identifier corresponding to the PCF.

[0197] Method 2: The second network element determines the analysis type identifier corresponding to the PCF based on the target parameter and the first request corresponding to the PCF.

[0198] Among them, the specific implementation of step S802 can refer to the specific implementation of the above step S601. The main difference is that the first network element is the PCF, which will not be elaborated here.

[0199] S803. The SMF sends the first information corresponding to the SMF to the second network element. The first information corresponding to the SMF includes a first request and an indication information; the first request is used to request the recommended data corresponding to the SMF, and the indication information is used to indicate collecting the first data corresponding to the SMF, and the first data is the network data determined by the SMF to be associated with the recommended data. Correspondingly, the second network element receives the first information from the SMF.

[0200] In the embodiments of the present application, when the SMF sends the first information corresponding to the SMF to the second network element, the recommended data corresponding to the SMF requested by the first request is the recommended DNAI of the first application, and the first data corresponding to the SMF indicated to be collected by the indication information is the peak rate and average rate of the first application at the current moment and / or historical moments.

[0201] In a possible implementation, the first information corresponding to the SMF further includes the expected value of the target parameter. Among them, the target parameter is the quality of experience. It should be noted that if the expected value of the target parameter is not carried in the first information sent by the SMF, the second network element can also obtain the expected value of the target parameter (i.e., the optimization target) through pre-configuration or other channels.

[0202] In a possible implementation, the method further includes: the SMF determines that the first data is associated with the recommended data based on the service processing logic of the SMF.

[0203] In a possible implementation, the second network element also needs to determine the analysis type identifier corresponding to the SMF, where the analysis type identifier corresponding to the SMF indicates service experience analysis. The second network element can determine the analysis type identifier corresponding to the SMF in one of the following two ways, which will be specifically described below.

[0204] Method 1: The first information corresponding to the SMF further includes the analysis type identifier corresponding to the SMF.

[0205] Method 2: The second network element determines the analysis type identifier corresponding to the SMF based on the target parameter and the first request corresponding to the SMF.

[0206] Among them, the specific implementation of step S803 can refer to the specific implementation of step S601 above. The main difference is that the first network element is the SMF, which will not be elaborated here.

[0207] It should be noted that the execution order of the above steps S801, S802, and S803 is not limited.

[0208] S804. The second network element obtains the predicted analysis result corresponding to the AF, the predicted analysis result corresponding to the PCF, and the predicted analysis result corresponding to the SMF.

[0209] In the embodiments of the present application, the predicted analysis result corresponding to the AF, the predicted analysis result corresponding to the PCF, and the predicted analysis result corresponding to the SMF are obtained based on the first data. Among them, the predicted analysis result corresponding to the AF, the predicted analysis result corresponding to the PCF, and the predicted analysis result corresponding to the SMF are all the QoE of the first application at the next moment.

[0210] The second network element can be a recommendation service function (it can be considered that the recommendation service function is an independent network element); the second network element can also include both the recommendation service function and the prediction analysis function (it can be considered that the recommendation service function and the prediction analysis function are concentrated in the same network element). The following specifically describes the specific implementation of the second network element obtaining the predicted analysis result corresponding to the AF, the predicted analysis result corresponding to the PCF, and the predicted analysis result corresponding to the SMF for these two cases.

[0211] Case 1: The second network element is a recommendation service function.

[0212] The specific implementation of the second network element obtaining the predicted analysis result corresponding to the AF, the predicted analysis result corresponding to the PCF, and the predicted analysis result corresponding to the SMF can include the following steps s41 to s44, as Figure 9A shown.

[0213] S41. The recommendation service function sends a second request to the prediction analysis function; the second request is used to request the prediction analysis results corresponding to the AF, the prediction analysis results corresponding to the PCF, and the prediction analysis results corresponding to the SMF, and the second request includes an analysis type identifier and indication information. Correspondingly, the prediction analysis function receives the second request from the recommendation service function.

[0214] Optionally, the method further includes: the recommendation service function determines analysis filtering information based on the first request; the analysis filtering information is used to indicate the network scope corresponding to the prediction analysis result; the second request further includes the analysis filtering information. Among them, the indication information may be carried in the first request. Specifically, the analysis filtering information may be determined according to the indication information of collecting the first data in the first request; or, the analysis filtering information may be determined according to the data source of the first data in the first request.

[0215] Of course, the recommendation service function may also send multiple second requests to the prediction analysis function to respectively request the prediction analysis results corresponding to the AF, the prediction analysis results corresponding to the PCF, and the prediction analysis results corresponding to the SMF, which is not limited herein.

[0216] S42. The prediction analysis function collects the first data and the second data corresponding to the analysis type identifier; where the first data belongs to the second data, or the first data does not belong to the second data. Among them, the first data collected by the prediction analysis function is the peak rate and average rate of the first application at the current moment and / or historical moments, and the first data is collected through the UPF. The second data collected by the prediction analysis function may include the second information collected from the AF, and the third information collected from the UPF and / or SMF; the second information includes the experience quality of the first application, the server instance of the first application, and the identifier of the first application at the current moment and / or historical moments; the third information includes the DNAI of the first application, the user data rate (BitRate), the user packet delay (PktDelay), and the service quality traffic flow identifier (QoSflow identifier, QFI) corresponding to the application packet at the current moment and / or historical moments.

[0217] S43. The prediction analysis function determines the prediction analysis results corresponding to the AF, the prediction analysis results corresponding to the PCF, and the prediction analysis results corresponding to the SMF based on the first data and the second data.

[0218] In a possible implementation manner, the specific implementation manner for the prediction analysis function to determine the prediction analysis result corresponding to the AF, the prediction analysis result corresponding to the PCF, and the prediction analysis result corresponding to the SMF based on the first data and the second data may be: increasing the weight of the first data relative to other data in the second data except the first data according to the indication information; or, increasing the weight of the first data relative to the second data according to the indication information; determining the prediction analysis result corresponding to the AF, the prediction analysis result corresponding to the PCF, and the prediction analysis result corresponding to the SMF based on the weight of the first data, the weight of the second data, the first data, and the second data.

[0219] S44. The prediction analysis function sends the prediction analysis result corresponding to the AF, the prediction analysis result corresponding to the PCF, and the prediction analysis result corresponding to the SMF to the recommendation service function. Correspondingly, the recommendation service function receives the prediction analysis result corresponding to the AF, the prediction analysis result corresponding to the PCF, and the prediction analysis result corresponding to the SMF from the prediction analysis function.

[0220] Case 2: The second network element includes both the recommendation service function and the prediction analysis function.

[0221] The specific implementation manner for the second network element to obtain the prediction analysis result corresponding to the AF, the prediction analysis result corresponding to the PCF, and the prediction analysis result corresponding to the SMF may include the following steps S51 and S52, as Figure 9B shown.

[0222] S51. The second network element collects the first data and the second data corresponding to the analysis type identifier; wherein, the first data belongs to the second data, or the first data does not belong to the second data.

[0223] Among them, the first data collected by the second network element is the peak rate and average rate of the first application at the current moment and / or historical moment, and the first data is collected by the UPF. The second data collected by the second network element may include the second information collected from the AF, and the third information collected from the UPF and / or SMF; the second information includes the experience quality of the first application, the server instance of the first application, and the identifier of the first application at the current moment and / or historical moment; the third information includes the DNAI of the first application, the user data rate, the user packet delay, and the QFI corresponding to the application packet at the current moment and / or historical moment.

[0224] S52. The second network element determines the prediction analysis result corresponding to the AF, the prediction analysis result corresponding to the PCF, and the prediction analysis result corresponding to the SMF based on the first data and the second data.

[0225] In a possible implementation, the specific implementation of determining the prediction analysis results corresponding to the AF, the prediction analysis results corresponding to the PCF, and the prediction analysis results corresponding to the SMF based on the first data and the second data may be as follows: increasing the weight of the first data relative to other data in the second data except the first data according to the indication information; or increasing the weight of the first data relative to the second data according to the indication information; determining the prediction analysis results corresponding to the AF, the prediction analysis results corresponding to the PCF, and the prediction analysis results corresponding to the SMF based on the weight of the first data, the weight of the second data, the first data, and the second data.

[0226] It should be noted that the specific implementation of the second network element obtaining the prediction analysis results corresponding to the AF, the prediction analysis results corresponding to the PCF, and the prediction analysis results corresponding to the SMF may refer to the specific implementation of step S602 above. The main difference is that the first network element includes the AF, the PCF, and the SMF, which will not be elaborated here.

[0227] S805. The second network element determines the recommended data corresponding to the AF based on the prediction analysis result corresponding to the AF, determines the recommended data corresponding to the PCF based on the prediction analysis result corresponding to the PCF, and determines the recommended data corresponding to the SMF based on the prediction analysis result corresponding to the SMF.

[0228] Among them, the specific implementation of the second network element determining the recommended data corresponding to the AF based on the prediction analysis result corresponding to the AF may refer to the specific implementation of step S603 above. The main difference is that the first network element is the AF, which will not be elaborated here. The specific implementation of the second network element determining the recommended data corresponding to the PCF based on the prediction analysis result corresponding to the PCF may also refer to the specific implementation of step S603 above. The main difference is that the first network element is the PCF, which will not be elaborated here. The specific implementation of the second network element determining the recommended data corresponding to the SMF based on the prediction analysis result corresponding to the SMF may also refer to the specific implementation of step S603 above. The main difference is that the first network element is the SMF, which will not be elaborated here.

[0229] S806. The second network element sends the recommended data corresponding to the AF to the AF. Correspondingly, the AF receives the recommended data corresponding to the AF from the second network element.

[0230] S807. The AF determines the first parameter corresponding to the AF based on the recommended data corresponding to the AF.

[0231] In a possible implementation, the first data corresponding to the AF is the network operation index affected by the first parameter corresponding to the AF.

[0232] In the embodiments of the present application, the recommended data corresponding to AF is the recommended server instance of the first application, and AF can select a server instance (i.e., the first parameter) based on the recommended data corresponding to AF.

[0233] S808. The second network element sends the recommended data corresponding to the PCF to the PCF. Correspondingly, the PCF receives the recommended data corresponding to the PCF from the second network element.

[0234] S809. The PCF determines the first parameter corresponding to the PCF based on the recommended data corresponding to the PCF.

[0235] In a possible implementation manner, the first data corresponding to the PCF is the network operation index affected by the first parameter corresponding to the PCF.

[0236] In the embodiments of the present application, the recommended data corresponding to the PCF is the recommended peak rate of the first application and the recommended average rate of the first application. The PCF can determine the QoS parameter (i.e., the first parameter) of the application service packet based on the recommended data corresponding to the PCF. For example, the MFBR or AMBR is determined based on the recommended peak rate of the first application, and the GFBR is determined based on the recommended average rate of the first application.

[0237] S810. The second network element sends the recommended data corresponding to the SMF to the SMF. Correspondingly, the SMF receives the recommended data corresponding to the SMF from the second network element.

[0238] It should be noted that the execution order of steps S806, S808, and S810 is not limited.

[0239] S811. The SMF determines the first parameter corresponding to the SMF based on the recommended data corresponding to the SMF.

[0240] In a possible implementation manner, the first data corresponding to the SMF is the network operation index affected by the first parameter corresponding to the SMF.

[0241] In the embodiments of the present application, the recommended data corresponding to the SMF is the recommended DNAI of the first application, and the SMF can perform user plane path selection (i.e., the first parameter) based on the recommended data corresponding to the SMF.

[0242] It should be noted that the execution order of steps S806 and S807, S808 and S809, S810 and S811 is not limited. Moreover, steps S804 to S811 are repeatedly executed every preset time period (for example, every 30 seconds) until the AF, PCF, and SMF send a recommended service unsubscribe message to the second network element.

[0243] It can be seen that based on Figure 8The described method can provide a globally optimized data recommendation service without the need to understand the service logics of individual network elements, reducing the workload and facilitating the improvement of the adaptability and flexibility of the data recommendation service.

[0244] Second, for the use case of adjusting QoS parameters to avoid user plane congestion, the PCF requests recommended data from the recommendation service function.

[0245] Figure 10 It is a schematic flowchart of another method for network data analysis provided by an embodiment of the present application. As Figure 10 shown, the method for network data analysis includes the following steps S1001 to S1005. Figure 10 The execution subject of the method shown can be the first network element and the second network element. Or, Figure 10 the execution subject of the method shown can be the chips in the first network element and the chips in the second network element, which is not limited in the embodiments of the present application. Figure 10 Taking the first network element and the second network element as the execution subjects of the method as an example for illustration. Among them, the first network element includes a PCF; the second network element can be a recommendation service function (it can be considered that the recommendation service function is an independent network element), and the second network element can also include both a recommendation service function and a prediction analysis function (it can be considered that the recommendation service function and the prediction analysis function are concentrated in the same network element, that is, the recommendation service function and the prediction analysis function are the same data analysis function, such as the NWDAF network element that supports the recommendation service).

[0246] S1001. The PCF sends first information to the second network element, and the first information includes a first request and indication information; the first request is used to request recommended data, and the indication information is used to indicate the collection of first data, which is network data determined by the PCF and associated with the recommended data. Accordingly, the second network element receives the first information from the PCF.

[0247] In the embodiments of the present application, the recommended data requested by the first request is the recommended peak throughput and / or recommended average throughput of the second application. The first data indicated by the indication information to be collected is the user plane congestion level of the second application at the current moment and / or historical moments. Among them, the indication information can also be carried in the first request, which is not limited here. Optionally, the first request includes the identifier of the second application.

[0248] In a possible implementation manner, the first information further includes an expected value of a target parameter. Among them, the target parameter is the user plane congestion level. It should be noted that if the expected value of the target parameter is not carried in the first information sent by the PCF, the second network element can also obtain the expected value of the target parameter (i.e., the optimization target) through pre-configuration or other means.

[0249] In a possible implementation, the method further includes: The PCF determines, based on the service processing logic of the PCF, that the first data is associated with the recommended data.

[0250] In a possible implementation, the second network element also needs to determine an analysis type identifier, where the analysis type identifier indicates user plane congestion analysis. The second network element can determine the analysis type identifier in one of the following two ways, which are specifically described below.

[0251] Way 1: The first information further includes an analysis type identifier.

[0252] Way 2: The second network element determines the analysis type identifier based on the target parameter and the first request.

[0253] Among them, the specific implementation of step S1001 can refer to the specific implementation of step S601 above. The main difference is that the first network element is the PCF, which will not be elaborated here.

[0254] S1002. The second network element obtains a prediction analysis result, which is obtained based on the first data.

[0255] Among them, the prediction analysis result includes the user plane congestion level at the next moment, the peak throughput of the second application at the next moment, and the average throughput of the second application at the next moment.

[0256] The second network element can be a recommendation service function (it can be considered that the recommendation service function is an independent network element); the second network element can also include both a recommendation service function and a prediction analysis function (it can be considered that the recommendation service function and the prediction analysis function are concentrated in the same network element). The following respectively describes the specific implementation of the second network element obtaining the prediction analysis result for these two cases.

[0257] Case 1: The second network element is a recommendation service function.

[0258] The specific implementation of the second network element obtaining the prediction analysis result can include the following steps s11 to s14, as Figure 7A shown.

[0259] s11. The recommendation service function sends a second request to the prediction analysis function; the second request is used to request the prediction analysis result, and the second request includes an analysis type identifier and indication information. Correspondingly, the prediction analysis function receives the second request from the recommendation service function.

[0260] Optionally, the method further includes: the recommendation service function determines analysis and filtering information based on the first request; the analysis and filtering information is used to indicate the network scope corresponding to the prediction analysis result; the second request further includes the analysis and filtering information. Among them, the indication information can be carried in the first request. Specifically, the analysis and filtering information can be determined according to the indication information for collecting the first data in the first request; or, the analysis and filtering information can be determined according to the data source of the first data in the first request.

[0261] s12. The prediction analysis function collects the first data and the second data corresponding to the analysis type identifier; among them, the first data belongs to the second data, or the first data does not belong to the second data. Among them, the first data collected by the prediction analysis function is the user plane congestion level of the second application at the current moment and / or historical moments, and the first data is collected through the UPF. The second data collected by the prediction analysis function includes the fourth information collected from the UPF and / or the SMF, and the fourth information includes the peak throughput of the second application, the average throughput of the second application, the identifier of the second application, and the location information of the terminal device at the current moment and / or historical moments.

[0262] s13. The prediction analysis function determines the prediction analysis result based on the first data and the second data.

[0263] In a possible implementation manner, the specific implementation manner for the prediction analysis function to determine the prediction analysis result based on the first data and the second data may be: increasing the weight of the first data relative to the other data in the second data except the first data according to the indication information; or, increasing the weight of the first data relative to the second data according to the indication information; determining the prediction analysis result based on the weight of the first data, the weight of the second data, the first data, and the second data.

[0264] s14. The prediction analysis function sends the prediction analysis result to the recommendation service function. Correspondingly, the recommendation service function receives the prediction analysis result from the prediction analysis function.

[0265] Case 2: The second network element includes both the recommendation service function and the prediction analysis function.

[0266] The specific implementation manner for the second network element to obtain the prediction analysis result may include the following steps s21 and s22, as Figure 7B shown.

[0267] s21. The second network element collects the first data and the second data corresponding to the analysis type identifier; among them, the first data belongs to the second data, or the first data does not belong to the second data.

[0268] Among them, the first data collected by the prediction analysis function is the user plane congestion level of the second application at the current moment and / or historical moments, and this first data is collected by the UPF. The second data collected by the prediction analysis function includes the fourth information collected from the UPF and / or SMF, and this fourth information includes the peak throughput of the second application, the average throughput of the second application, the identifier of the second application, and the location information of the terminal device at the current moment and / or historical moments.

[0269] s22. The second network element determines a prediction analysis result based on this first data and this second data.

[0270] In a possible implementation manner, determining a prediction analysis result based on this first data and this second data includes: increasing the weight of the first data relative to other data in the second data except the first data according to this indication information; or increasing the weight of the first data relative to the second data according to this indication information; determining a prediction analysis result based on the weight of the first data, the weight of the second data, this first data, and this second data.

[0271] It should be noted that the specific implementation manner of step S1002 may refer to the specific implementation manner of the above step S602. The main difference is that the first network element is the PCF, which will not be elaborated here.

[0272] S1003. The second network element determines recommended data based on this prediction analysis result.

[0273] Among them, the specific implementation manner of step S1003 may refer to the specific implementation manner of the above step S603. The main difference is that the first network element is the PCF, which will not be elaborated here.

[0274] S1004. The second network element sends this recommended data to the PCF. Correspondingly, the PCF receives the recommended data from the second network element.

[0275] S1005. The PCF determines a first parameter based on this recommended data.

[0276] In a possible implementation manner, this first data is a network operation metric affected by this first parameter.

[0277] In the embodiments of the present application, the recommended data corresponding to the PCF is the recommended peak throughput and / or recommended average throughput of the second application. The PCF may determine QoS parameters of several main applications based on this recommended data. For example, set the MFBR according to the recommended peak throughput of the second application, and set the GFBR according to the recommended average throughput of the second application. The PCF may further determine the 5QI used by the application data according to the recommended data.

[0278] It should be noted that steps S1002 to S1005 are repeatedly executed at intervals of a preset time period (for example, every 30 seconds) until the PCF sends a recommended service unsubscribe message to the second network element.

[0279] It can be seen that based on Figure 10 the method described above, without the need to understand the service logics of each network element, it is possible to provide a globally optimized data recommendation service, reduce the workload, and is beneficial to improving the adaptability and flexibility of the data recommendation service.

[0280] Third, in order to confirm the selection and distribution ratios of each UPF to prevent uneven UPF load or overload, at least one SMF requests recommended data from the recommendation service function.

[0281] Figure 11 is a schematic flowchart of another method for network data analysis provided by an embodiment of the present application. As Figure 11 shown, this method for network data analysis includes the following steps S1101 to S1105. Figure 11 The execution subject of the method shown can be the first network element and the second network element. Or, Figure 11 the execution subject of the method shown can be the chips in the first network element and the chips in the second network element, which is not limited in the embodiments of the present application. Figure 11 Taking the first network element and the second network element as the execution subjects of the method as an example for illustration. Among them, the first network element includes at least one SMF; the second network element includes a recommendation service function. For example, the second network element can be a recommendation service function (it can be considered that the recommendation service function is an independent network element), and the second network element can also include both a recommendation service function and a predictive analysis function (it can be considered that the recommendation service function and the predictive analysis function are concentrated in the same network element, that is, the recommendation service function and the predictive analysis function are the same data analysis function. For example, the NWDAF network element that supports the recommendation service).

[0282] S1101. At least one SMF sends first information to the second network element. The first information includes a first request and indication information; the first request is used to request recommended data, and the indication information is used to indicate the collection of first data, which is network data determined by at least one SMF and associated with the recommended data. Correspondingly, the second network element receives the first information from at least one SMF.

[0283] In the embodiments of the present application, the recommended data requested by the first request is the number of N4 interface sessions recommended and established by at least one SMF on the multiple UPFs. The first data indicated by the indication information to be collected is the number of N4 interface sessions established by the at least one SMF on the multiple UPFs at the current moment and / or historical moments. Among them, the indication information can also be carried in the first request, which is not limited herein.

[0284] In a possible implementation, the first information further includes the expected value of the target parameter. Wherein, the target parameter is the load levels of multiple UPFs. It should be noted that if the expected value of the target parameter is not carried in the first information sent by the SMF, the second network element can also obtain the expected value of the target parameter (i.e., the optimization target) through pre-configuration or other means.

[0285] In a possible implementation, the method further includes: at least one SMF determines that the first data is associated with the recommended data based on the service processing logic of the at least one SMF.

[0286] In a possible implementation, the second network element also needs to determine an analysis type identifier, where the analysis type identifier indicates network element load analysis. The second network element can determine the analysis type identifier in one of the following two ways, which will be specifically described below.

[0287] Way 1: The first information further includes the analysis type identifier.

[0288] Way 2: The second network element determines the analysis type identifier based on the target parameter and the first request.

[0289] Among them, the specific implementation manner of step S1101 can refer to the specific implementation manner of the above step S601. The main difference is that the first network element is at least one SMF, which will not be elaborated here.

[0290] S1102. The second network element obtains the prediction analysis result, and the prediction analysis result is obtained based on the first data.

[0291] Among them, the prediction analysis result includes the load levels of multiple UPFs at the next moment.

[0292] The second network element can be a recommendation service function (it can be considered that the recommendation service function is an independent network element); the second network element can also include both the recommendation service function and the prediction analysis function (it can be considered that the recommendation service function and the prediction analysis function are concentrated in the same network element). The following specifically describes the specific implementation manner for the second network element to obtain the prediction analysis result in these two cases.

[0293] Case 1: The second network element is a recommendation service function.

[0294] The specific implementation manner for the second network element to obtain the prediction analysis result can include the following steps s11 - s14, as Figure 7A shown.

[0295] s11. The recommendation service function sends a second request to the prediction analysis function; the second request is used to request the prediction analysis result, and the second request includes an analysis type identifier and indication information. Accordingly, the prediction analysis function receives the second request from the recommendation service function.

[0296] Optionally, the method further includes: the recommendation service function determines analysis filtering information based on the first request; the analysis filtering information is used to indicate the network scope corresponding to the prediction analysis result; the second request further includes the analysis filtering information. Among them, the indication information may be carried in the first request. Specifically, the analysis filtering information may be determined according to the indication information for collecting the first data in the first request; or, the analysis filtering information may be determined according to the data source of the first data in the first request.

[0297] s12. The prediction analysis function collects the first data and the second data corresponding to the analysis type identifier; where the first data belongs to the second data, or the first data does not belong to the second data.

[0298] Among them, the first data collected by the prediction analysis function is the number of N4 interface sessions established by the at least one SMF on multiple UPFs at the current moment and / or historical moments; the first data is collected through the multiple UPFs. The second data collected by the prediction analysis function includes the fifth information collected from the multiple UPFs, and the fifth information includes the load level data of the multiple UPFs at the current moment and / or historical moments.

[0299] s13. The prediction analysis function determines the prediction analysis result based on the first data and the second data.

[0300] In a possible implementation manner, the specific implementation manner for the prediction analysis function to determine the prediction analysis result based on the first data and the second data may be: increasing the weight of the first data relative to other data in the second data except the first data according to the indication information; or increasing the weight of the first data relative to the second data according to the indication information; determining the prediction analysis result based on the weight of the first data, the weight of the second data, the first data, and the second data.

[0301] s14. The prediction analysis function sends the prediction analysis result to the recommendation service function. Accordingly, the recommendation service function receives the prediction analysis result from the prediction analysis function.

[0302] Case 2: The second network element includes both the recommendation service function and the prediction analysis function.

[0303] The specific implementation manner for the second network element to obtain the prediction analysis result may include the following steps s21 and s22, as Figure 7B shown.

[0304] The second network element collects first data and second data corresponding to the analysis type identifier; wherein, the first data belongs to the second data, or the first data does not belong to the second data.

[0305] Among them, the first data collected by the predictive analysis function is the number of N4 interface sessions established by the at least one SMF on multiple UPFs at the current moment and / or historical moments; the first data is collected through the multiple UPFs. The second data collected by the predictive analysis function includes fifth information collected from the multiple UPFs, and the fifth information includes the load level data of the multiple UPFs at the current moment and / or historical moments.

[0306] S22. The second network element determines a predictive analysis result based on the first data and the second data.

[0307] In a possible implementation manner, determining a predictive analysis result based on the first data and the second data includes: increasing the weight of the first data relative to other data in the second data except the first data according to the indication information; or increasing the weight of the first data relative to the second data according to the indication information; determining a predictive analysis result based on the weight of the first data, the weight of the second data, the first data, and the second data.

[0308] It should be noted that the specific implementation manner of step S1102 may refer to the specific implementation manner of step S602 above. The main difference is that the first network element is at least one SMF, which will not be elaborated here.

[0309] S1103. The second network element determines recommended data based on the predictive analysis result.

[0310] Among them, the specific implementation manner of step S1103 may refer to the specific implementation manner of step S603 above. The main difference is that the first network element is at least one SMF, which will not be elaborated here.

[0311] S1104. The second network element sends the recommended data to at least one SMF. Correspondingly, the PCF receives the recommended data from the second network element.

[0312] S1105. At least one SMF determines a first parameter based on the recommended data.

[0313] In a possible implementation manner, the first data is a network operation metric affected by the first parameter.

[0314] In the embodiments of the present application, the recommended data corresponding to at least one SMF is the number of N4 interface sessions recommended to be established on multiple UPFs. At least one SMF may determine a selection and distribution ratio (i.e., the first parameter) for the multiple UPFs based on the recommended data.

[0315] It should be noted that steps S1102 to S1105 are repeatedly executed at intervals of a preset time period (for example, every 30 seconds) until the SMF sends a recommended service unsubscribe message to the second network element.

[0316] It can be seen that based on Figure 11 the described method, without the need to understand the service logics of each network element, it is possible to provide a globally optimized data recommendation service, reduce the workload, and is conducive to improving the adaptability and flexibility of the data recommendation service.

[0317] Please refer to Figure 12 , Figure 12 which shows a schematic structural diagram of a communication device 1200 according to an embodiment of the present application. Figure 12 The described communication device may be a recommendation service function, a prediction analysis function, or a first network element, or a device in the recommendation service function, a prediction analysis function, or a first network element, or a device that can be used in combination with the recommendation service function, a prediction analysis function, or a first network element. Specifically, as Figure 12 shown, the communication device 1200 may include a communication unit 1201 and a processing unit 1202. Among them, the processing unit 1202 is used for data processing. The communication unit 1201 is used for communication. Optionally, the communication unit 1201 integrates a receiving unit and a sending unit. The communication unit 1201 may also be referred to as a transceiver unit. Alternatively, the communication unit 1201 may be split into a receiving unit and a sending unit.

[0318] In one implementation, when the communication device 1200 may be a recommendation service function, or a device in the recommendation service function, or a device that can be used in combination with the recommendation service function, where:

[0319] The communication unit 1201 is configured to receive first information from the first network element, where the first information includes a first request and indication information; the first request is used to request recommended data, and the indication information is used to indicate the collection of first data, where the first data is network data determined by the first network element and associated with the recommended data;

[0320] The processing unit 1202 is configured to obtain a prediction analysis result, which is obtained based on the first data;

[0321] The processing unit 1202 is configured to determine the recommended data based on the prediction analysis result;

[0322] The communication unit 1201 is further configured to send the recommended data to the first network element.

[0323] In a possible implementation, the indication information is further used to indicate the data source of the first data. Optionally, the data source of the first data is used to indicate collecting the first data from one or more specified network elements.

[0324] In a possible implementation, the first information further includes an analysis type identifier; alternatively, the method further includes: determining an analysis type identifier based on the target parameter and the first request.

[0325] In a possible implementation, the analysis type identifier indicates any one of the following types: service experience analysis, user plane congestion analysis, network element load analysis.

[0326] In a possible implementation, the target parameter includes any one of the following parameters: quality of experience, user plane congestion level, load levels of multiple UPFs.

[0327] In a possible implementation, when obtaining the prediction analysis result, the processing unit 1202 is specifically configured to: collect the first data and the second data corresponding to the analysis type identifier; where the first data belongs to the second data, or the first data does not belong to the second data; determine the prediction analysis result based on the first data and the second data.

[0328] In a possible implementation, when determining the prediction analysis result based on the first data and the second data, the processing unit 1202 is specifically configured to: increase the weight of the first data relative to the other data in the second data except the first data according to the indication information; or increase the weight of the first data relative to the second data according to the indication information; determine the prediction analysis result based on the weight of the first data, the weight of the second data, the first data, and the second data.

[0329] In a possible implementation, when obtaining the prediction analysis result, the processing unit 1202, and the communication unit 1201 is configured to send a second request to the prediction analysis function; the second request is used to request the prediction analysis result, and the second request includes the analysis type identifier and the indication information; the communication unit 1201 is configured to receive the prediction analysis result from the prediction analysis function.

[0330] In a possible implementation, the processing unit 1202 is further configured to: determine analysis filtering information based on the first request and the analysis type identifier; the analysis filtering information is used to indicate the network scope corresponding to the prediction analysis result; the second request further includes the analysis filtering information. Specifically, the processing unit 1202 determines the analysis filtering information according to the indication information for collecting the first data in the first request; or the processing unit 1202 determines the analysis filtering information according to the data source of the first data in the first request.

[0331] In a possible implementation, the analysis type identifier indicates service experience analysis; the first network element includes an AF, a PCF, and an SMF; the first data includes the peak rate and average rate of a first application at the current moment and / or historical moments.

[0332] In a possible implementation, when the first network element is an AF, the recommended data includes a recommended server instance of the first application.

[0333] In a possible implementation, when the first network element is a PCF, the recommended data includes a recommended peak rate and a recommended average rate of the first application.

[0334] In a possible implementation, when the first network element is an SMF, the recommended data includes a recommended data network access identifier of the first application.

[0335] In a possible implementation, the analysis type identifier indicates user plane congestion analysis; the first request includes an identifier of a second application; the first network element includes a PCF; the first data includes the user plane congestion level of the second application at the current moment and / or historical moments; the recommended data includes a recommended peak throughput and / or a recommended average throughput of the second application.

[0336] In a possible implementation, the analysis type identifier indicates network element load analysis; the first network element includes at least one SMF; the first data includes the number of N4 interface sessions established by at least one SMF on multiple UPFs at the current moment and / or historical moments; the recommended data includes the number of N4 interface sessions recommended to be established by at least one SMF on multiple UPFs.

[0337] In a possible implementation, the first information further includes an expected value of a target parameter.

[0338] In a possible implementation, when determining the recommended data based on the prediction analysis result, the processing unit 1202 is specifically configured to: determine a deviation value based on the prediction analysis result and the expected value of the target parameter; adjust the model parameters of the first data optimizer in the direction of reducing the deviation value to obtain a second data optimizer; and use the second data optimizer to determine the recommended data.

[0339] In an implementation, when the communication device 1200 can be a prediction analysis function, or a device in the prediction analysis function, or a device that can be used in matching with the prediction analysis function, where:

[0340] A communication unit 1201, configured to receive a second request from a recommendation service function; the second request is used to request a predictive analysis result, the second request includes an analysis type identifier and indication information, and the indication information is used to indicate the collection of first data, where the first data is network data determined by a first network element and associated with recommended data output by the recommendation service function;

[0341] A processing unit 1202, configured to collect second data corresponding to the first data and the analysis type identifier; where the first data belongs to the second data, or the first data does not belong to the second data;

[0342] The processing unit 1202 is further configured to determine a predictive analysis result based on the first data and the second data;

[0343] The communication unit 1201 is further configured to send the predictive analysis result to the recommendation service function.

[0344] In a possible implementation manner, the indication information is further used to indicate a data source of the first data; collecting the first data includes: collecting the first data from the data source. Optionally, the data source of the first data is used to indicate collecting the first data from one or more specified network elements.

[0345] In a possible implementation manner, determining a predictive analysis result based on the first data and the second data includes: increasing the weight of the first data relative to other data in the second data except the first data according to the indication information; or increasing the weight of the first data relative to the second data according to the indication information; determining a predictive analysis result based on the weight of the first data, the weight of the second data, the first data, and the second data.

[0346] In a possible implementation manner, the analysis type identifier indicates service experience analysis; the first network element includes an AF, a PCF, and an SMF; the first data includes the peak rate and average rate of a first application at the current moment and / or historical moments; the first data is collected by a UPF; the second data includes second information collected from an AF, and third information collected from a UPF and / or an SMF; the second information includes the experience quality of the first application, the server instance of the first application, and the identifier of the first application at the current moment and / or historical moments; the third information includes the data network access identifier, user data rate, user packet delay, and service quality traffic flow identifier corresponding to the application packet of the first application at the current moment and / or historical moments.

[0347] In a possible implementation manner, the second request further includes analysis filtering information, and the analysis filtering information is used to indicate a network scope corresponding to the analysis prediction result.

[0348] In a possible implementation, the analysis type identifier indicates user plane congestion analysis; the first network element includes a PCF; the first data includes the user plane congestion level of the second application at the current moment and / or historical moments; the first data is collected by a UPF; the second data includes fourth information collected from a UPF and / or an SMF; the fourth information includes the peak throughput, average throughput, identifier of the second application, and location information of the terminal device of the second application at the current moment and / or historical moments.

[0349] In a possible implementation, the analysis type identifier is network element load analysis; the first network element includes at least one SMF; the first data includes the number of N4 interface sessions established by the at least one SMF on multiple UPFs at the current moment and / or historical moments; the first data is collected by the multiple UPFs; the second data includes fifth information collected from the multiple UPFs; the fifth information includes the load level data of the multiple UPFs at the current moment and / or historical moments.

[0350] In an implementation, when the communication device 1200 can be the first network element, or a device in the first network element, or a device that can be used in matching with the first network element, where:

[0351] A communication unit 1201 is configured to send first information to a recommendation service function, where the first information includes a first request and indication information; the first request is used to request recommendation data, and the indication information is used to indicate collection of first data, where the first data is network data determined by the first network element and associated with the recommendation data;

[0352] The communication unit 1201 is further configured to receive recommendation data from the recommendation service function;

[0353] A processing unit 1202 is configured to determine a first parameter based on the recommendation data.

[0354] In a possible implementation, the processing unit 1202 is further configured to: determine that the first data is associated with the recommendation data based on the service processing logic of the first network element.

[0355] In a possible implementation, the first data is a network operation metric affected by the first parameter.

[0356] In a possible implementation, the first information further includes an analysis type identifier. Optionally, the analysis type identifier indicates any one of the following types: service experience analysis, user plane congestion analysis, network element load analysis.

[0357] In a possible implementation, the processing unit 1202 is further configured to: determine an analysis type identifier based on the service processing logic of the first network element; and determine the first request and the indication information based on the analysis type identifier.

[0358] In a possible implementation, the analysis type identifier indicates service experience analysis; the first network element includes an AF, a PCF, and an SMF; and the first data includes the peak rate and average rate of the first application at the current moment and / or historical moments.

[0359] In a possible implementation, when the first network element is an AF, the recommended data is the recommended server instance of the first application, and the first parameter is the server instance selected by the first application.

[0360] In a possible implementation, when the first network element is a PCF, the recommended data includes the recommended peak rate of the first application and the recommended average rate of the first application; the first parameter includes the maximum flow bit rate or the aggregated maximum rate, and the first parameter further includes the guaranteed bit rate; determining the first parameter based on the recommended data includes: determining the maximum flow bit rate or the aggregated maximum rate based on the recommended peak rate of the first application, and determining the guaranteed bit rate based on the recommended average rate of the first application.

[0361] In a possible implementation, when the first network element is an SMF, the recommended data is the recommended data network access identifier of the first application, and the first parameter is the user plane path selected by the first application.

[0362] In a possible implementation, the analysis type identifier indicates user plane congestion analysis; the first request includes the identifier of the second application; the first network element includes a PCF; the first parameter is the session quality of service parameter; the first data includes the user plane congestion level of the second application at the current moment and / or historical moments; and the recommended data includes the recommended peak throughput and / or recommended average throughput of the second application.

[0363] In a possible implementation, the analysis type identifier indicates network element load analysis; the first network element includes at least one SMF; the first parameter includes the selection ratio of the at least one SMF for multiple UPFs; the first data includes the number of N4 interface sessions established by the at least one SMF on the multiple UPFs at the current moment and / or historical moments; and the recommended data includes the number of N4 interface sessions recommended to be established by the at least one SMF on the multiple UPFs.

[0364] In a possible implementation, the indication information is further configured to indicate the data source of the first data. Optionally, the data source of the first data is used to indicate collecting the first data from one or more specified network elements.

[0365] In a possible implementation, the first information further includes the expected value of the target parameter. Optionally, the target parameter includes any one of the following parameters: quality of experience, user plane congestion level, load levels of multiple UPFs.

[0366] Figure 13 The structural schematic diagram of another communication device is given. The communication device 1300 may be the recommendation service function, prediction analysis function, or the first network element in the above method embodiments, or may also be a chip, chip system, or processor that supports the recommendation service function, prediction analysis function, or the first network element to implement the above method. This communication device can be used to implement the method described in the above method embodiments. For details, reference can be made to the description in the above method embodiments.

[0367] The communication device 1300 may include one or more processors 1301. The processor 1301 may be a general-purpose processor or a dedicated processor, etc. For example, it may be a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, and the central processing unit can be used to control the communication device (such as a base station, baseband chip, terminal, terminal chip, DU, or CU, etc.), execute software programs, and process the data of software programs.

[0368] Optionally, the communication device 1300 may include one or more memories 1302, on which there may be instructions 1304 that can be run on the processor 1301, enabling the communication device 1300 to execute the method described in the above method embodiments. Optionally, the memory 1302 may also store data. The processor 1301 and the memory 1302 may be provided separately or integrated together.

[0369] Optionally, the communication device 1300 may further include a transceiver 1305 and an antenna 1306. The transceiver 1305 may be referred to as a transceiver unit, transceiver, or transceiver circuit, etc., for implementing the transceiver function. The transceiver 1305 may include a receiver and a transmitter. The receiver may be referred to as a receiver or a receiving circuit, etc., for implementing the receiving function; the transmitter may be referred to as a transmitter or a transmitting circuit, etc., for implementing the transmitting function. Among them, Figure 12 The shown processing unit 1202 may be the processor 1301. The communication unit 1201 may be the transceiver 1305.

[0370] In another possible design, the processor 1301 may include a transceiver for implementing the receiving and sending functions. For example, the transceiver may be a transceiver circuit, or an interface, or an interface circuit. The transceiver circuit, interface, or interface circuit for implementing the receiving and sending functions may be separate or integrated. The above-mentioned transceiver circuit, interface, or interface circuit may be used for reading and writing code / data, or the above-mentioned transceiver circuit, interface, or interface circuit may be used for transmitting or delivering signals.

[0371] In another possible design, optionally, the processor 1301 may store an instruction 1303, and the instruction 1303 runs on the processor 1301, so that the communication device 1300 can perform the method described in the above method embodiment. The instruction 1303 may be solidified in the processor 1301, in which case the processor 1301 may be implemented by hardware.

[0372] In another possible design, the communication device 1300 may include a circuit that can implement the functions of sending or receiving or communicating in the aforementioned method embodiments. The processor and transceiver described in the embodiments of the present application can be implemented in an integrated circuit (IC), an analog IC, a radio frequency integrated circuit RFIC, a mixed signal IC, an application specific integrated circuit (ASIC), a printed circuit board (PCB), an electronic device, etc. The processor and transceiver can also be manufactured using various IC process technologies, such as complementary metal oxide semiconductor (CMOS), N-type metal oxide semiconductor (nMetal-oxide-semiconductor, NMOS), P-type metal oxide semiconductor (positive channelmetal oxide semiconductor, PMOS), bipolar junction transistor (Bipolar Junction Transistor, BJT), bipolar CMOS (BiCMOS), silicon germanium (SiGe), gallium arsenide (GaAs), etc.

[0373] The communication device described in the above embodiments may be a recommendation service function, a prediction analysis function or a first network element, but the scope of the communication device described in the embodiments of the present application is not limited thereto, and the structure of the communication device may not be limited thereto. Figure 13 The communication device may be an independent device or may be part of a larger device. For example, the communication device may be:

[0374] (1) An independent integrated circuit IC, or chip, or chip system or subsystem;

[0375] (2) A set having one or more ICs, optionally, the IC set may also include a storage component for storing data and instructions;

[0376] (3) ASIC, such as a modem (MSM);

[0377] (4) A module that can be embedded in other devices;

[0378] (5) A receiver, terminal, smart terminal, cellular phone, wireless device, handset, mobile unit, vehicle-mounted device, network device, cloud device, artificial intelligence device, etc.;

[0379] (6) Others, etc.

[0380] For the case where the communication device can be a chip or a chip system, reference can be made to Figure 14 The structural schematic diagram of the chip shown. Figure 14 The chip 1400 shown includes a processor 1401 and an interface 1402. Optionally, a memory 1403 may also be included. Among them, the number of processors 1401 can be one or more, and the number of interfaces 1402 can be multiple.

[0381] For the case where the chip is used to implement the recommended service function, predictive analysis function or the first network element in the embodiments of the present application:

[0382] The interface 1402 is used to receive or output signals;

[0383] The processor 1401 is used to execute the data processing operations of the recommended service function, predictive analysis function or the first network element.

[0384] It can be understood that some optional features in the embodiments of the present application can, in some scenarios, be independently implemented without relying on other features, such as the current scheme it is based on, to solve the corresponding technical problems and achieve the corresponding effects. In some scenarios, they can also be combined with other features according to requirements. Correspondingly, the communication devices given in the embodiments of the present application can also correspondingly implement these features or functions, which will not be elaborated here.

[0385] It should be understood that the processor in the embodiments of the present application may be an integrated circuit chip with signal processing capabilities. In the implementation process, the steps of the above method embodiments may be completed by the integrated logic circuit in the hardware of the processor or instructions in the form of software. The above processor may be a general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0386] It can be understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchlink dynamic random access memory (SLDRAM), and direct rambus random access memory (DR RAM). It should be noted that the memory of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0387] The present application also provides a computer-readable medium, in which a computer program or instruction is stored. When the computer program or instruction is executed by a communication device, the functions of any of the above method embodiments are implemented.

[0388] The present application also provides a computer program product including instructions. When a computer reads and executes the computer program product, the computer is enabled to implement the functions of any one of the above method embodiments.

[0389] The present application provides a communication system, which includes a recommendation service function and a first network element. Among them, the recommendation service function is used to execute the method executed by the recommendation service function in the above embodiments, and the first network element is used to execute the method executed by the first network element in the above embodiments.

[0390] The present application provides a communication system, which includes a recommendation service function, a predictive analysis function and a first network element. Among them, the recommendation service function is used to execute the method executed by the recommendation service function in the above embodiments, the predictive analysis function is used to execute the method executed by the predictive analysis function in the above embodiments, and the first network element is used to execute the method executed by the first network element in the above embodiments.

[0391] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that includes one or more integrated available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a high-density digital video disc (DVD)), or a semiconductor medium (such as a solid state disk (SSD)), etc.

[0392] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims described above.

Claims

1. A method for network data analysis, characterized in that, The method is applied to the recommendation service function, and the method includes: Receiving first information from a first network element, where the first information includes a first request and indication information; the first request is used to request recommendation data, and the indication information is used to indicate collecting first data, where the first data is network data determined by the first network element and associated with the recommendation data; Obtaining a prediction analysis result, where the prediction analysis result is obtained based on the first data; Determining the recommendation data based on the prediction analysis result; Sending the recommendation data to the first network element.

2. The method according to claim 1, wherein The indication information is further used to indicate the data source of the first data.

3. The method according to claim 1 or 2, characterized in that, The first information further includes an analysis type identifier; or, The method further includes: Determining the analysis type identifier based on a target parameter and the first request.

4. The method according to any one of claims 1 to 3, characterized in that The obtaining the prediction analysis result includes: Collecting second data corresponding to the first data and the analysis type identifier; where the first data belongs to the second data, or the first data does not belong to the second data; Determining a prediction analysis result based on the first data and the second data.

5. The method according to any one of claims 1 to 3, characterized in that, The obtaining the prediction analysis result includes: Sending a second request to a prediction analysis function; the second request is used to request a prediction analysis result, and the second request includes an analysis type identifier and the indication information; Receiving the prediction analysis result from the prediction analysis function.

6. The method according to claim 5, wherein The method further includes: Determining analysis filtering information based on the first request; the analysis filtering information is used to indicate the network range corresponding to the prediction analysis result; The second request further includes the analysis filtering information.

7. The method according to any one of claims 3 to 6, characterized in that The analysis type identifier indicates service experience analysis; the first network element includes an Application Function (AF), a Policy Control Function (PCF), and a Session Management Function (SMF); the first data includes the peak rate and average rate of a first application at the current moment and / or historical moments.

8. The method according to claim 7, characterized in that When the first network element is the AF, the recommendation data includes a recommended server instance of the first application.

9. The method according to claim 7, characterized in that When the first network element is the PCF, the recommendation data includes a recommended peak rate and a recommended average rate of the first application.

10. The method according to claim 7, wherein When the first network element is the SMF, the recommendation data includes a recommended data network access identifier of the first application.

11. The method according to any one of claims 3 to 6, characterized in that, The analysis type identifier indicates user plane congestion analysis; the first request includes an identifier of a second application; The first network element includes a PCF; the first data includes the user plane congestion level of the second application at the current moment and / or historical moments; the recommendation data includes a recommended peak throughput and / or a recommended average throughput of the second application.

12. The method according to any one of claims 3 to 6, characterized in that, The analysis type identifier indicates network element load analysis; the first network element includes at least one SMF; The first data includes the number of N4 interface sessions established by the at least one SMF on multiple User Plane Functions (UPFs) at the current moment and / or historical moments; the recommendation data includes the number of N4 interface sessions recommended to be established by the at least one SMF on the multiple UPFs.

13. The method according to any one of claims 1 to 12, characterized in that, The first information further includes an expected value of the target parameter.

14. The method according to claim 13, wherein Determining the recommended data based on the prediction analysis result includes: Determining a deviation value based on the prediction analysis result and the expected value of the target parameter; Adjusting the model parameters of the first data optimizer in the direction of reducing the deviation value to obtain a second data optimizer; Using the second data optimizer to determine the recommended data.

15. A method for network data analysis, characterized in that, The method is applied to a prediction analysis function, and the method includes: Receiving a second request from a recommendation service function; the second request is used to request a prediction analysis result, and the second request includes an analysis type identifier and indication information, and the indication information is used to indicate collecting first data, where the first data is network data determined by a first network element and associated with the recommended data output by the recommendation service function; Collecting the first data and second data corresponding to the analysis type identifier; wherein, the first data belongs to the second data, or the first data does not belong to the second data; Determining a prediction analysis result based on the first data and the second data; Sending the prediction analysis result to the recommendation service function.

16. The method according to claim 15, wherein The indication information is further used to indicate the data source of the first data; The collecting the first data includes: Collecting the first data from the data source.

17. The method according to claim 15 or 16, characterized in that, The determining a prediction analysis result based on the first data and the second data includes: Enhancing the weight of the first data relative to other data in the second data except the first data according to the indication information; or enhancing the weight of the first data relative to the second data according to the indication information; Determining a prediction analysis result based on the weight of the first data, the weight of the second data, the first data, and the second data.

18. A method for network data analysis, characterized in that, The method is applied to a first network element, and the method includes: Sending first information to a recommendation service function, where the first information includes a first request and indication information; the first request is used to request recommended data, and the indication information is used to indicate collecting first data, where the first data is network data determined by the first network element and associated with the recommended data; Receiving the recommended data from the recommendation service function; Determining a first parameter based on the recommended data.

19. The method according to claim 18, characterized in that The method further includes: Determining that the first data is associated with the recommended data based on the service processing logic of the first network element.

20. The method according to claim 18 or 19, characterized in that The first data is a network operation index affected by the first parameter.

21. The method according to any one of claims 18 to 20, characterized in that, The first information further includes an analysis type identifier.

22. The method according to claim 21, wherein The method further includes: Determining the analysis type identifier based on the service processing logic of the first network element; Determining the first request and the indication information based on the analysis type identifier.

23. The method according to any one of claims 18 to 22, characterized in that, The indication information is further used to indicate the data source of the first data.

24. A communication system, characterized in that, Including a recommendation service function and a first network element; wherein, the recommendation service function is used to execute the method according to any one of claims 1 to 14, and the first network element is used to execute the method according to any one of claims 18 to 23.

25. A communication system, characterized in that, It includes a recommendation service function, a predictive analysis function, and a first network element; wherein, the recommendation service function is used to execute the method described in any one of claims 1 to 14, the predictive analysis function is used to execute the method described in any one of claims 15 to 17, and the first network element is used to execute the method described in any one of claims 18 to 23.

26. A communication device, characterized in that, It includes a unit for executing the method described in any one of claims 1 to 14, or includes a unit for executing the method described in any one of claims 15 to 17, and includes a unit for executing the method described in any one of claims 18 to 23.

27. A communication device, characterized in that, It includes a processor and a memory, the processor is coupled to the memory, and the processor is used to implement the method described in any one of claims 1 to 14, or the processor is used to implement the method described in any one of claims 15 to 17, or the processor is used to implement the method described in any one of claims 18 to 23.

28. A chip, characterized in that, It includes a processor and an interface, the processor is coupled to the interface; the interface is used to receive or output signals, and the processor is used to execute code instructions to cause the method described in any one of claims 1 to 14 to be executed, or to cause the method described in any one of claims 15 to 17 to be executed, or the processor is used to implement the method described in any one of claims 18 to 23.

29. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when called by the computer, cause the computer to execute the method described in any one of claims 1 to 14 above, or cause the computer to execute the method described in any one of claims 15 to 17 above, or cause the computer to execute the method described in any one of claims 18 to 23 above.

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