Communication method and device

Abstract

The invention provides a communication method and device, and relates to the technical field of communication, a first network function receives a request message from a third network function, and the request message is used for requesting an access address of a second network function associated with target artificial intelligence (AI) information; according to the request message, querying an association relationship to obtain an access address of a second network function associated with the target AI information, the association relationship indicating a relationship between at least one piece of AI information supported by at least one second network function and the access address of the second network function associated with each piece of AI information, and the target AI information being one of the at least one piece of AI information; and sending a request response message to the third network function, wherein the request response message is used for indicating an access address of the second network function associated with the target AI information. In this way, intelligent services can be provided for the equipment.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of communication technology, and in particular, to a communication method and device. Background Art

[0002] In terms of standardization, the Third Generation Partnership Project (3GPP) has begun to expand the systematic intelligence and management architecture of the 5th generation (5G), and has standardized the definition of intelligent services, such as artificial intelligence (AI) services. However, there is no solution for how to provide services to devices through intelligent services. Summary of the invention

[0003] The present application provides a communication method and a communication device to provide intelligent services for devices.

[0004] In a first aspect, the present application provides a communication method, which can be applied to a first network function, wherein the first network function registers AI information of a network element, and the first network function can be an artificial intelligence storage service (artificial intelligence repository function, AIRF), which is specifically used to implement the registration of AI information, and can also be a functional entity set in a network storage function (network repository function, NRF) network element, which is used to implement the registration of AI information. The present application is not specifically limited here. When the first network function executes the method, it includes:

[0005] Receive a request message from a third network function, where the request message is used to request the access address of the second network function associated with the target AI information; obtain the access address of the second network function associated with the target AI information by querying the association relationship according to the request message, where the association relationship indicates a relationship between at least one AI information supported by at least one second network function and the access address of the second network function associated with each AI information, respectively, and the target AI information is one of the at least one AI information; send a request response message to the third network function, where the request response message is used to indicate the access address of the second network function associated with the target AI information.

[0006] In the present application, the first network function receives a request message for an access address associated with the target AI information, queries the corresponding access address, and feeds back the access address to the third network function, so that the third network function can obtain intelligent services through the access address. In this way, AI services can be provided to the third network function.

[0007] In an optional manner, when the request message is a subscription request message, sending a request response message to the third network function includes: sending a notification message of successful subscription to the third network function, and sending an access address of the second network function associated with at least one AI information to the third network function.

[0008] In actual application, the notification of successful subscription and the access address of the second network function associated with at least one AI information are not sent at the same time, for example, a notification message of successful subscription is sent at the first time, and the access address of the second network function associated with at least one AI information is sent at the second time. In addition, the above-mentioned notification of successful subscription and the access address of the second network function associated with at least one AI information can be sent using the same signaling, or different signaling, which is not specifically limited here. By adopting a subscription request message as a request message, the present application can reuse the message format of the subscription request message. This method does not require the message format to be reconstructed, which can reduce the complexity of data processing.

[0009] In an optional manner, when the request message is a query request message, sending a request response message to a third network function includes: sending an access address of a second network function associated with target AI information to the third network function.

[0010] The present application uses a query request message as a request message, and can reuse the message format of the query request message. This method does not require reconstructing the message format, and can reduce the complexity of data processing.

[0011] In an optional manner, the first network function also receives a registration request message from at least one second network function, where the registration request message is used to indicate at least one AI information supported by the second network function and an access address of the second network function associated with each AI information respectively; and determines the association relationship based on the registration request message.

[0012] In the present application, after the first network function receives the AI ​​information registered by the second network function, it can establish an association relationship between the AI ​​information and the access address of the second network function, so that when the third network function requests the access address of the second network function associated with the AI ​​information from the first network function, the first network function can query the access address of the second network function that can provide AI services based on the above association relationship and return it to the third network function.

[0013] In an optional manner, the first network function further sends a monitoring message to the second network function, where the monitoring message is used to monitor the working status of the second network function; and receives a monitoring response message from the second network function.

[0014] In the present application, the working status of the second network function can be understood as the change of AI information supported by the second network function, or the actual working status of the second network function, which is not specifically limited in the present application.

[0015] In one case, after the second network function registers certain AI information with the first network function, the AI ​​information supported by the second network function may change. For example, the second network function is NWDAF1, and NWDAF1 has registered its AI information supporting image processing with the first network function at the first time. At the second time (i.e., the time after the first time), the AI ​​information supported by NWDAF1 changes, such as the addition of AI information for audio processing. If the first network function sends a monitoring message to NWDAF1, the change message of the AI ​​information supported by the second network function can be monitored in real time, so as to timely adjust the registration information stored by the first network function.

[0016] In another case, after the second network function registers certain AI information with the first network function, the actual working status of the second network function changes. For example, the second network function is NWDAF1, and NWDAF1 has registered its AI information supporting image processing with the first network function at the first time. At the second time, the NWDAF1 device crashes. If the first network function sends a monitoring message to NWDAF1, NWDAF1 can be monitored in real time. If it is determined that NWDAF1 is down, it is determined that NWDAF1 cannot provide AI services for image processing, so that the registration information stored by the first network function can be adjusted in time.

[0017] In an optional manner, the third network function is a terminal; receiving a request message from the third network function includes: the first network function receives a request message sent by the third network function through a fourth network function, the fourth network function being used to convert the format of messages exchanged between the terminal and the first network function, and / or to perform identity authentication on the terminal and the first network function.

[0018] In the present application, the fourth network function is used as a bridge for information interaction between the first network function and the terminal, so that the terminal can request the access address associated with the AI ​​information from the first network function through the fourth network function. In this way, the terminal can obtain intelligent services. In addition, the fourth network function can convert the format of the messages exchanged between the terminal and the first network function, which can improve data processing efficiency. In addition, identity authentication of the terminal and the first network function can ensure the security of information interaction between the terminal and the first network function.

[0019] In an optional manner, the AI ​​information includes: AI services, and / or AI computing power.

[0020] In this application, AI information may be AI services, such as image processing, audio processing, neural network model building, etc., which are only illustrative and not specifically limited. AI information can also be AI computing power, such as the central processing unit (CPU) of a network element can support the calculation of the random forest algorithm, etc., which are only illustrative and not specifically limited.

[0021] In an optional manner, the AI ​​information is an AI service; the registration request message includes: an identifier of the AI ​​service and an access address of a second network function that provides the AI ​​service.

[0022] In the present application, when the AI ​​information is an AI service, the registration request message includes the identifier of the AI ​​service and the access address of the second network function that provides the AI ​​service, so that the first network function can know the specific information of the AI ​​service and the access address of the second network function that provides the AI ​​service. For example, the AI ​​service identifier is T1 (predefined T1 corresponds to image recognition processing), and it can be known that the second network function registered with the first network function supports image recognition processing function.

[0023] In an optional manner, the registration request information further includes one or more of the following:

[0024] The categories of AI services, the categories of models supported by AI services, the service objects of AI services, the data formats supported by AI services, and the data computing scale of AI services; among them, the service objects of AI services include terminal device types, APP names, terminal users belonging to preset number segments, geographic location information, slice information, and access point identifiers.

[0025] Specifically, the categories of AI services (e.g., cloud photography, image processing, face recognition, translation, chatbot model (chat generative pre-trained transformer, GPTChat) dialogue, etc.), the categories of models supported by AI services (e.g., convolutional neural network (CNN), recurrent neural network (RNN), etc.), network,RNN), random forest, etc.), service objects of AI services (i.e., target objects of AI services), data formats supported by AI services (e.g., AI services for image recognition, data formats for RGB (RGB image is a digital image format, the name of which refers to both the color space (red, green, blue) used by the image format and the image itself, which contains pixel values ​​of the red, green, and blue channels) data or YUV (YUV image is a digital image format that separates image brightness (Y) and color (U, V), commonly used in the fields of video encoding and digital image processing. The following introduces common YUV sampling and arrangement methods) data), data calculation scale of AI services (e.g., AI services for image recognition, data calculation scale for the number and size of image recognition, etc.); among them, service objects of AI services include terminal device types (e.g., IoT devices, mobile phones, etc.), APP names (e.g., instant messaging software, video software, etc.), terminal users belonging to preset number segments (e.g., international mobile subscriber identity (IMSI) / mobile station International ISDN number (mobile station International ISDN number, MSISD) (i.e., mobile phone number) number segment of the terminal user), geographic location information (e.g., located in a specific location), slice information (e.g., receiving services through a certain slice) and access point identifier (e.g., receiving services through transmission reception point (TRP) 1). For example, the identifier of the AI ​​service is AI1, and the service object of AI1 is the Internet of Vehicles equipment located in Shanghai, then AI1 only provides AI services for the Internet of Vehicles equipment in Shanghai. This is only an example explanation, and the service object of the AI ​​service is not specifically limited.

[0026] In an optional manner, the AI ​​information is AI computing power; the registration request information includes: an identifier of the AI ​​computing power and an access address of a second network function that provides the AI ​​computing power.

[0027] In the present application, when the AI ​​information is AI computing power, the registration request message includes the identifier of the AI ​​computing power and the access address of the second network function that provides the AI ​​computing power, so that the first network function can know the specific information of the AI ​​computing power and the access address of the second network function that provides the AI ​​computing power. For example, the AI ​​computing power identifier is S1 (pre-defined S1 corresponds to random forest calculation), and it can be known that the second network function registered with the first network function supports the random forest calculation function.

[0028] In an optional manner, the registration request information further includes one or more of the following:

[0029] The computing power type of AI computing power, the computing power specification of AI computing power, the AI ​​framework type indicated by AI computing power, the AI ​​model category indicated by AI computing power, and the operator information of AI computing power.

[0030] Specifically, the computing power type of AI computing power (such as X86 / ARM CPU, graphics processing unit (GPU) and embedded neural network processing unit (NPU) and other computing power resources that can run AI tasks), the computing power specifications of AI computing power (such as computing power evaluation indicators such as transactions per second (TPS) and queries per second (QPS)), the AI ​​framework type indicated by AI computing power (such as TensorFlow, Mindspore, SKlearn and other AI framework types), the AI ​​model category indicated by AI computing power (such as CNN, RNN, random forest, etc.) and the operator information of AI computing power (such as vector calculation, matrix calculation, etc.).

[0031] In a second aspect, the present application provides a communication method, which can be applied to a fourth network function, where the fourth network function is a proxy network element, which can be used to build a connection bridge between the third network function and the first network function, and can also be used to authenticate the identities of the first network function and the third network function. The present application is not specifically limited here. When the fourth network function executes the method, it includes:

[0032] Receive a request message from a third network function, the request message is used to request the access address of the second network function associated with the target artificial intelligence AI information; send a request message to the first network function; receive a request response message from the first network function, the request response message is used to indicate the access address of the second network function associated with the target AI information; send a request response message to the third network function.

[0033] In an optional manner, when the request message is a subscription request message, receiving a request response message from the first network function includes: receiving a notification message of successful subscription, and receiving an access address of a second network function associated with at least one AI information from the first network function.

[0034] In an optional manner, when the request message is a query request message, receiving a request response message from the first network function includes: receiving an access address of a second network function associated with target AI information from the first network function.

[0035] In an optional manner, the third network function is a terminal; the fourth network function further performs format conversion on messages exchanged between the third network function and the first network function; and / or performs identity authentication on the third network function and the first network function.

[0036] In an optional manner, the fourth network function also receives data to be processed from the terminal; converts the data to be processed into a data format corresponding to the AI ​​information required by the terminal and sends it to the access address of the second network function associated with the AI ​​information required by the terminal; receives the data processing result from the second network function; and sends the data processing result to the terminal.

[0037] In a third aspect, the present application provides a communication method, which can be applied to a third network function, where the third network function is other network elements in a terminal or a core network. The present application is not specifically limited herein. When the third network function executes the method, it includes:

[0038] Send a request message to the fourth network function, the request message is used to request the access address of the second network function associated with the target artificial intelligence AI information; receive a request response message from the fourth network function, the request response message is used to indicate the access address of the second network function associated with the target AI information; send the data to be processed to the access address of the second network function associated with the target AI information through the fourth network function; receive the data processing result from the second network function through the fourth network function.

[0039] In an optional manner, the third network function is a terminal.

[0040] In a fourth aspect, the present application provides a communication method, which can be applied to a second network function, the second network function has the ability to perform AI processing, can perform AI services, or perform AI calculations, which is not specifically limited in the present application. When the second network function executes the method, it includes:

[0041] Send a registration request message to the first network function, where the registration request message is used to indicate at least one AI information supported by the second network function and an access address of the second network function associated with each AI information; and receive a response message of successful registration from the second network function.

[0042] In an optional manner, the second network function also receives a monitoring message from the first network function, where the monitoring message is used to monitor the working status of the second network function; and sends a monitoring response message to the first network function, where the monitoring response message includes the working status of the second network function.

[0043] In an optional manner, the AI ​​information includes: AI services, and / or AI computing power.

[0044] In an optional manner, the AI ​​service includes one or more of the following: an identifier of the AI ​​service, an access address of a second network function providing the AI ​​service, a category of the AI ​​service, a category of models supported by the AI ​​service, a service object of the AI ​​service, a data format supported by the AI ​​service, and a data calculation scale of the AI ​​service; wherein the service objects of the AI ​​service include terminal device type, APP name, terminal users belonging to a preset number segment, geographic location information, slice information, and access point identifier.

[0045] In an optional manner, AI computing power includes one or more of the following:

[0046] The identification of AI computing, the computing power type of AI computing power, the computing power specification of AI computing power, the AI ​​framework type indicated by AI computing power, the AI ​​model category indicated by AI computing power, and the operator information of AI computing power.

[0047] In a fifth aspect, an embodiment of the present application provides a communication device, which may be the above-mentioned first network function or a chip disposed inside the first network function, or the above-mentioned second network function or a chip disposed inside the second network function, or the above-mentioned third network function or a chip disposed inside the third network function, or the above-mentioned fourth network function or a chip disposed inside the fourth network function. The communication device has the function of implementing any aspect of the above-mentioned first to fourth aspects, for example, the communication device includes a module or unit or means corresponding to the steps involved in any aspect of the above-mentioned first to fourth aspects, and the function or unit or means may be implemented by software, or by hardware, or may be implemented by hardware executing the corresponding software.

[0048] In one possible design, the communication device includes a processing unit and a transceiver unit, wherein the transceiver unit can be used to send and receive signals to achieve communication between the communication device and other devices; the processing unit can be used to perform some internal operations of the communication device. The transceiver unit can be called an input-output unit, a communication unit, etc., and the transceiver unit can be a transceiver; the processing unit can be a processor. When the communication device is a module (such as a chip) in a communication device, the transceiver unit can be an input-output interface, an input-output circuit or an input-output pin, etc., and can also be called an interface, a communication interface or an interface circuit, etc.; the processing unit can be a processor, a processing circuit or a logic circuit, etc.

[0049] In another possible design, the communication device includes a processor and may also include a transceiver, the transceiver is used to send and receive signals, and the processor executes program instructions to complete the method in any possible design or implementation of the first to fourth aspects above. The communication device may also include one or more memories, the memory is used to couple with the processor, and the memory can store the necessary computer programs or instructions for implementing the functions involved in any of the first to fourth aspects above. The processor can execute the computer program or instructions stored in the memory, and when the computer program or instructions are executed, the communication device implements the method in any possible design or implementation of the first to fourth aspects above.

[0050] In another possible design, the communication device includes a processor, which can be used to couple with a memory. The memory can store necessary computer programs or instructions for implementing the functions involved in any one of the first to fourth aspects. The processor can execute the computer program or instructions stored in the memory, and when the computer program or instructions are executed, the communication device implements the method in any possible design or implementation of the first to fourth aspects.

[0051] In another possible design, the communication device includes a processor and an interface circuit, wherein the processor is used to communicate with other devices through the interface circuit and execute the method in any possible design or implementation of the first to fourth aspects above.

[0052] It can be understood that in the fifth aspect above, the processor can be implemented by hardware or by software. When implemented by hardware, the processor can be a logic circuit, an integrated circuit, etc.; when implemented by software, the processor can be a general-purpose processor, which is implemented by reading the software code stored in the memory. In addition, the above processors can be one or more, and the memories can be one or more. The memory can be integrated with the processor, or the memory can be separately set from the processor. In the specific implementation process, the memory can be integrated with the processor on the same chip, or can be set on different chips respectively. The embodiment of the present application does not limit the type of memory and the setting method of the memory and the processor.

[0053] In a sixth aspect, an embodiment of the present application provides a communication system, which includes the first network function, the second network function, the third network function and the third network function in the above-mentioned first to fourth aspects.

[0054] In a seventh aspect, the present application provides a chip system, which includes a processor and may also include a memory, for implementing the method described in any possible design of the first to fourth aspects above. The chip system may be composed of a chip, or may include a chip and other discrete devices.

[0055] In an eighth aspect, the present application also provides a computer-readable storage medium, in which computer-readable instructions are stored. When the computer-readable instructions are executed on a computer, the computer executes a method in any possible design of the first to fourth aspects.

[0056] In a ninth aspect, the present application provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute the methods of the embodiments of the first to fourth aspects described above.

[0057] For the technical effects that can be achieved in the above-mentioned second to ninth aspects, please refer to the description of the technical effects that can be achieved by the corresponding possible design schemes in the above-mentioned first aspect, and this application will not repeat them here. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1A A schematic diagram of a communication system is shown;

[0059] Figure 1B A schematic diagram of another communication system is shown;

[0060] Figure 2 A schematic diagram of yet another communication system is shown;

[0061] Figure 3 A schematic flow chart of a communication method is shown;

[0062] Figure 4 A schematic flow chart of a communication method is shown;

[0063] Figure 5 A schematic flow chart of a communication method is shown;

[0064] Figure 6 A schematic flow chart of a communication method is shown;

[0065] Figure 7 A schematic flow chart of a communication method is shown;

[0066] Figure 8 A schematic flow chart of a communication method is shown;

[0067] Fig. 9 A schematic flow chart of a communication method is shown;

[0068] Fig.10 A schematic flow chart of a communication method is shown;

[0069] Fig.11 A schematic diagram of the structure of a communication device provided in an embodiment of the present application is shown;

[0070] Fig.12 A schematic diagram showing the structure of another communication device provided in an embodiment of the present application is shown;

[0071] Fig.13 A schematic diagram of the structure of another communication device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0072] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings. The specific operating method in the method embodiment can also be applied to the device embodiment or system embodiment. Among them, in the description of the present application, unless otherwise specified, the meaning of "multiple" is two or more. Therefore, the implementation of the device and the method can refer to each other, and the repetitions will not be repeated.

[0073] In order to meet the challenges of wireless broadband technology and maintain the leading edge of the third generation partnership project (3GPP) network, the 3GPP standards group has developed the next generation mobile communication network system (Next Generation System) architecture, called the 5G network architecture. This architecture not only supports the wireless access technology defined by the 3GPP standards group (such as long term evolution (LTE) access technology, RAN access technology, etc.) to access the 5G core network (CN), but also supports the use of non-3GPP (non-3GPP) access technology through the non-3GPP interworking function (N3IWF) or the next generation packet data gateway (ngPDG) to access the core network.

[0074] See also Figure 1A , which is a schematic diagram of the network architecture for non-roaming scenarios. In this network architecture, network slice selection function (NSSF), network exposure function (NEF), NRF, policy control function (PCF), unified data management (UDM), application function (AF), authentication server function (AUSF), access and mobility management function (AMF) and session management function (SMF) can communicate with each other based on service methods. Of course, for two network elements to communicate, one network element needs to open the corresponding service method to the other network element. Figure 1AIn the figure, Nnssf can be regarded as the service interface of NSSF. Similarly, Nnef is the service interface of NEF, Npcf is the service interface of PCF, Nudm is the service interface of UDM, Naf is the service interface of AF, Nausf is the service interface of AUSF, Namf is the service interface of AMF, and Nsmf is the service interface of SMF. In addition, AMF can communicate with terminal equipment through N1 interface, AMF can communicate with (R)AN through N2 interface, SMF can communicate with user plane function (UPF) through N4 interface, terminal equipment can communicate with (R)AN through air interface, (R)AN and UPF can communicate through N3 interface, and UPF can communicate with data network (DN) through N6 interface.

[0075] The mobility management function network element is, for example, MME in the 4G system and AMF in the 5G system. The embodiments of the present application are not limited thereto and may also be implemented through other network elements in other communication systems. Taking the mobility management function network element as AMF as an example, AMF is mainly responsible for wireless docking, terminating the radio access network (RAN) control plane (CP) interface, that is, the N2 interface, terminating the non-access-stratum (NAS) and NAS encryption and integrity protection, registration management, connection management, reachability management, mobility management, transmitting session management (SM) messages between user equipment (UE) and SMF, or UE mobility event notification and other functions.

[0076] The session management function network element, in the 5G system, is, for example, SMF, and can also be implemented through other network elements in other communication systems. Taking the session management function network element as SMF as an example, SMF can provide session management functions such as session establishment, modification and release, including tunnel maintenance functions between UPF and access network (AN) nodes, UE Internet protocol (IP) address allocation and management, dynamic host control protocol (DHCP), selection and control of user plane (UP) functions, configuration of UPF diversion functions, termination of policy control function interfaces, billing, roaming functions, or policy control related functions.

[0077] The user plane function network element, in the 5G system, is, for example, UPF, and can also be implemented by other network elements in other communication systems. Taking the user plane function network element as UPF as an example, UPF is the entity for user plane data forwarding. As the external PDU session point for data network interconnection, it has functions such as message routing and forwarding, message detection, user plane partial policy execution, legal monitoring, traffic usage report, or quality of service (QoS) processing.

[0078] The network function repository functional network element, in the 5G system, is, for example, NRF, and can also be implemented through other network elements in other communication systems. Taking the network function repository functional network element as NRF as an example, NRF can support service discovery functions, maintain NF configuration of available network function (NF) instances and other functions. In the roaming scenario, it is divided into visit NRF (visit NRF, vNRF) and home NRF (home NRF, hNRF). Based on the network implementation, it is divided into public land mobile network (public land mobile network, PLMN) level NRF, shared slice NRF, or specific slice NRF. Among them, the PLMN-level NRF can be understood as different PLMNs can use different NRFs; the shared slice NRF can be understood as different network slices can share one NRF; the specific slice NRF can be understood as different network slices use different NRFs.

[0079] Figure 1A Among them, Nnef, Npcf, Nudm, Naf, Namf, Nsmf, N1, N2, N3, N4, and N6 are interface serial numbers. The meanings of these interface serial numbers can be found in the meanings defined in the 3GPP standard protocol and are not limited here.

[0080] like Figure 1B The following is a schematic diagram of the 5G network architecture based on a point-to-point interface. The functions of the network elements can be found in Figure 1A The introduction of the functions of the corresponding network elements will not be repeated here. Figure 1B and Figure 1A The main differences are: Figure 1B The interfaces between the various network elements in a network are point-to-point interfaces, rather than service-oriented interfaces.

[0081] exist Figure 1BIn the architecture shown, the interface between the UE and the AMF network element is called the N1 interface, the interface between the AMF network element and the RAN device is called the N2 interface, the interface between the RAN device and the UPF network element can be called the N3 interface, the interface between the SMF network element and the UPF network element is called the N4 interface, the interface between the PCF network element and the AF network element is called the N5 interface, the interface between the UPF network element and the DN is called the N6 interface, the interface between the SMF network element and the PCF network element is called the N7 interface, the interface between the AMF network element and the UDM network element is called the N8 interface, the interface between different UPF network elements is called the N9 interface, the interface between the UDM network element and the SMF network element is called the N10 interface, the interface between the AMF network element and the SMF network element is called the N11 interface, the interface between different AMF network elements is called the N14 interface, and the interface between the AMF network element and the PCF network element is called the N15 interface.

[0082] It is understandable that the above network element or function can be a network element in a hardware device, a software function running on dedicated hardware, or a virtualized function instantiated on a platform (e.g., a cloud platform). Optionally, the above network element or function can be implemented by one device, or by multiple devices, or a functional module in one device, which is not specifically limited in the embodiments of the present application.

[0083] The mobility management network element, session management network element, policy control network element, application function network element, access network device, and network storage network element in this application can be Figure 1A or Figure 1B The AMF, SMF, PCF, AF, RAN, and NRF in the network may also be network elements with the functions of the above-mentioned AMF, SMF, PCF, AF, RAN, and NRF in future communications such as the sixth generation (6G) network, and this application is not limited to this. For the convenience of explanation, this application takes the mobility management network element, session management network element, policy control network element, application function network element, access network equipment, and network storage network element as the above-mentioned AMF, SMF, PCF, AF, RAN, and NRF as examples for explanation.

[0084] For ease of explanation, in the embodiments of the present application, a base station (such as the 4th generation (4G) eNB, 5G gNB or base stations in future communications) is used as an example of an access network device for explanation, and the subsequent "base station" can be replaced by "access network device". In the embodiments of the present application, a UE is used as an example of a terminal for explanation, and the subsequent "UE" can be replaced by "terminal".

[0085] It is understandable that the core network may also include other network functional entities, which is not limited in this application.

[0086] The technical solution provided in the embodiment of the present application can be applied to various communication systems. For example, it can be applied to 5G systems, and it can also be applied to other new systems facing the future, such as 6G systems, etc. The embodiment of the present application does not specifically limit this. In addition, the term "system" can be interchangeable with "network".

[0087] In order to better adapt to the needs of artificial intelligence, the service-oriented architecture provided in this application introduces artificial intelligence storage service (artificial intelligence repository function, AIRF) and artificial intelligence service agent (AIService Agent) such as Figure 2As shown, the AIRF can be set up together with the above-mentioned NRF network element, or it can be set up independently. This application is not specifically limited here. The AIRF is mainly used to receive AI information registration and provide AI information query services to other network elements. AIRF can receive information registration from the model training logical function (MTLF) and data analysis function (ANLF) in the network data analytics function (NWDAF), and can also receive information registration from other network elements. This application is not specifically limited here. In actual application, AIRF can directly provide AI information query services to AI consumers (ie, AI Compute) in SMF, and can also provide AI information query services to AI consumers in UPF. This is not specifically limited here. In addition, UE is usually isolated from the core network, and UE can receive AI information query services from AIRF through AI Service Agent. In addition, some network elements that do not support direct interaction with AIRF can also establish communication connections with AIRF through AI Service Agent to obtain AI information query services. The service-oriented architecture provided here is only a schematic illustration, and does not specifically limit how AIRF and AI Service Agent are set up. Among them, the interface between NSSF and AMF network element is called N22 interface, the interface between AMF network element and RAN equipment is called N12 interface, the interface between AIRF and AI Service Agent is called Nairf interface, the interface between PDU session anchor UPF (packet dataunit session anchor UPF, PSA-UPF) and NWDAF is called Nnf interface, the interface between SMF and NWDAF is called Nnwdaf interface, the interface between PCF and NWDAF is called Nnwdaf interface, and the interface between intermediate UPF (intermediateUPF, I-UPF) and PSA-UPF is N9 interface.

[0088] In terms of standardization, 3GPP SA2 and SA5 have already started and expanded on the systematic intelligence and management architecture of 5G, and standardized the definition of NWDAF and management data analytics function (MDAF) and related mechanism processes. There is no clear definition of how the next-generation 5.5G or 6G intelligent plane defines the architecture, business processes, and interactive interfaces of the Network for AI to provide AI service capabilities or AI computing power built by the core network to terminal users, APPs, or AFs. In other words, the AI ​​service capabilities or AI computing power built by the core network do not have appropriate architectural definitions, functional decomposition, and message interaction processes to provide corresponding AI service capabilities or AI computing power to terminal users UE or application servers AF and other NFs. Based on this, the present application provides a communication method to provide intelligent services for devices.

[0089] participate Figure 3 A flow chart of a communication method provided by the present application, which method can be implemented through the interaction of a first network function and other network functions, which is not specifically limited in the present application, wherein the first network function is registered with the AI ​​information of the network element, and the first network function can be AIRF, which is specifically used to implement the registration of AI information, and can also be a functional entity set in an NRF network element, which is used to implement the registration of AI information, and the second network function has the ability to perform AI processing, can execute AI services, or perform AI calculations, which is not specifically limited in the present application, and the second network function can be NWDAF, MDAF, NF, etc., which is not specifically limited in the present application, and the third network function is other network elements in the terminal or core network, which is not specifically limited in the present application, and the fourth network function is an agent network element such as AI Service Agent, which can be used to build a connection bridge between the third network function and the first network function, and can also be used to authenticate the identities of the first network function and the third network function, and convert the format of the interactive messages, which is not specifically limited in the present application, Figure 3 Take the third network function as terminal, NF1, NF2, the fourth network function as AI Service Agent, the second network function as NWDAF1, and the first network function as AIRF as an example to illustrate, and execute as follows:

[0090] Step 301: NWDAF1 sends a registration request message to AIRF, where the registration request message is used to indicate at least one AI information supported by the second network function and an access address of the second network function respectively associated with each AI information.

[0091] Step 302 : AIRF determines the association relationship according to the registration request message in step 301 .

[0092] The association relationship indicates the relationship between at least one AI information supported by at least one second network function and the access address of the second network function associated with each AI information. For example, according to the AI ​​information 1 and the access address 1 carried in the NWDAF1 registration request message, the AIRF can determine that the AI ​​information 1 and the access address 1 have an association relationship. If there is a network element that subscribes to or queries the AI ​​information 1, the AIRF can return the access address 1 of the NWDAF1 to the network element.

[0093] In actual application, AIRF can not only receive the registration request message of the above AIRF, but also receive the registration request message of other second network functions, which is not specifically limited in this application, that is, AIRF can receive the registration request message from at least one second network function. In addition, the second network function can support one or more AI information, and the second network function can register all the AI ​​information it supports with AIRF, or only register part of the AI ​​information, which is not specifically limited here, and different second network functions can support the same AI information, which is not specifically limited here.

[0094] The AI ​​information includes: AI services (such as image classification, image recognition, audio synthesis, etc., which are only exemplified here and are not specifically limited), and / or, AI computing power (such as: the feature vector of the neural network model used for AI calculation calculated by the CPU, etc.). If the AI ​​information is an AI service; the above registration request message includes: the identifier (or name) of the AI ​​service and the access address of the second network function that provides the AI ​​service. Among them, the identifier of the AI ​​service can be the ID of the AI ​​service, such as the ID of the image classification task, the ID of the audio synthesis task, etc., which are only exemplified here. The access address of the second network function can be the physical address information of the second network function, or the full qualified domain name (FQDN). Since the second network function may include multiple processing units, different processing units are isolated from each other, and the access address of the second network function can be multiple address information that meets the conditions. When other network elements access the second network element, they can access it based on a specific physical address, or they can access it based on the FQDN query to the specific physical address. In addition, the registration request information also includes one or more of the following: the category of AI services (for example: cloud photography, image processing, face recognition, translation, GPT Chat dialogue, etc.), the category of models supported by AI services (for example, CNN, RNN, random forest, etc.), the service object of AI services (that is, the target object of AI services), the data format supported by AI services (for example, AI services are image recognition, and the data format is RGB data or YUV data), and the data calculation scale of AI services (for example, AI services are image recognition, and the data calculation scale is the number and size of image recognition, etc.); among which, the service objects of AI services include terminal device types (for example, IoT devices, mobile phones, etc.), APP names (for example, instant messaging software, video software, etc.), terminal users belonging to preset number segments (for example, terminal users of IMSI / MSISD number segments), geographic location information (for example, located in a specific location), slice information (for example, receiving services through a certain slice), and access point identifiers (for example, receiving services through TRP1). For example, if the identifier of the AI ​​service is AI1, and the service object of AI1 is the Internet of Vehicles devices located in Shanghai, then AI1 only provides AI services for Internet of Vehicles devices in Shanghai. This is only an example explanation and does not specifically limit the service objects of AI services.

[0095] If the AI ​​information is AI computing power; the registration request information includes: the identifier of the AI ​​computing power and the access address of the second network function that provides the AI ​​computing power. In addition, the registration request information also includes one or more of the following: the computing power type of the AI ​​computing power (such as X86 / ARM CPU, NPU, GPU and other computing power resources that can run AI tasks), the computing power specifications of the AI ​​computing power (such as computing power evaluation indicators TPS, QPS), the AI ​​framework type indicated by the AI ​​computing power (such as TensorFlow, Mindspore, SKlearn and other AI framework types), the AI ​​model category indicated by the AI ​​computing power (such as CNN, RNN, random forest, etc.) and the operator information of the AI ​​computing power (such as vector calculation, matrix calculation, etc.).

[0096] In the present application, after the first network function receives the AI ​​information registered by the second network function, it can establish an association relationship between the AI ​​information and the access address of the second network function, so that when the third network function requests the access address of the second network function associated with the AI ​​information from the first network function, the first network function can query the access address of the second network function that can provide AI services based on the above association relationship and return it to the third network function.

[0097] In addition, the AI ​​information supported by the second network function may be updated regularly. In order to ensure better adaptation to the user's application needs, AIRF can send a monitoring message to the second network function. The monitoring message is used to monitor the working status of the second network function. After that, AIRF can receive the detection response message of the second network function. Of course, AIRF can send detection and monitoring messages to the second network function regularly, such as sending monitoring messages once a day. Of course, the second network function can also report change information to AIRF regularly, such as reporting the AI ​​information of the second network function that changes when it registers with AIRF once every 4 hours. The second network function can also report directly to AIRF after the supported AI information changes. This application is not specifically limited here.

[0098] In the present application, the working status of the second network function can be understood as the change of AI information supported by the second network function, or the actual working status of the second network function, which is not specifically limited in the present application.

[0099] In one case, after the second network function registers certain AI information with the first network function, the AI ​​information supported by the second network function may change. For example, the second network function is NWDAF1, and NWDAF1 has registered its AI information supporting image processing with the first network function at the first time. At the second time (i.e., the time after the first time), the AI ​​information supported by NWDAF1 changes, such as the addition of AI information for audio processing. If the first network function sends a monitoring message to NWDAF1, the change message of the AI ​​information supported by the second network function can be monitored in real time, so as to timely adjust the registration information stored by the first network function.

[0100] In another case, after the second network function registers certain AI information with the first network function, the actual working status of the second network function changes. For example, the second network function is NWDAF1, and NWDAF1 has registered its AI information supporting image processing with the first network function at the first time. At the second time, the NWDAF1 device crashes. If the first network function sends a monitoring message to NWDAF1, NWDAF1 can be monitored in real time. If it is determined that NWDAF1 is down, it is determined that NWDAF1 cannot provide AI services for image processing, so that the registration information stored by the first network function can be adjusted in time.

[0101] Among them, the terminal can refer to steps 303a to 307a to obtain the access address for executing the second network function associated with the target AI information (the target AI information is one of the at least one AI information, which is the AI ​​information required by the terminal), NF1 can refer to steps 303b to 307b to obtain the access address for executing the second network function associated with the target AI information, and NF2 can refer to steps 303c to 305c to obtain the access address for executing the second network function associated with the target AI information. The specific implementation is as follows:

[0102] Step 303a: The terminal sends a request message to the AI ​​Service Agent, where the request message is used to request an access address of a second network function associated with the target AI information.

[0103] Step 304a, the AI ​​Service Agent sends a request message to the AIRF.

[0104] Step 305a: AIRF queries the association relationship according to the request message to obtain the access address of the second network function associated with the target AI information.

[0105] Step 306a, AIRF sends a request response message to the AI ​​Service Agent, where the request response message is used to indicate the access address of the second network function associated with the target AI information.

[0106] Step 307a, the AI ​​Service Agent sends a request response message to the terminal.

[0107] In addition, the terminal can also send the data to be processed to the AI ​​Service Agent, and the AI ​​Service Agent can also convert the data to be processed into a data format corresponding to the AI ​​information required by the terminal and send it to the access address of the second network function associated with the AI ​​information required by the terminal; and receive the data processing results from the second network function; and then send the data processing results to the terminal.

[0108] Step 303b, NF1 sends a request message to the AI ​​Service Agent, where the request message is used to request the access address of the second network function associated with the target AI information.

[0109] Step 304b, AI Service Agent sends a request message to AIRF.

[0110] Step 305b: AIRF queries the association relationship according to the request message to obtain the access address of the second network function associated with the target AI information.

[0111] Step 306b, AIRF sends a request response message to the AI ​​Service Agent, where the request response message is used to indicate the access address of the second network function associated with the target AI information.

[0112] Step 307b, AI Service Agent sends a request response message to NF1.

[0113] Step 303c, NF2 sends a request message to AIRF.

[0114] Step 304c: AIRF queries the association relationship according to the request message to obtain the access address of the second network function associated with the target AI information.

[0115] Step 305c: AIRF sends a request response message to NF2, where the request response message is used to indicate the access address of the second network function associated with the target AI information.

[0116] The request message in the above steps 303a to 305c may be a subscription request message or a query request message, which is not specifically limited in this application. If the request message is a subscription request message, before sending the request response message, a notification message of successful subscription may be sent to the terminal, NF1, and NF2, and then the access address of the second network function associated with at least one AI information may be sent. Of course, in actual application, the third network function may not only subscribe to the access address of the second network function associated with the required AI information, but also subscribe to the access address of the second network function associated with other AI information. The subscription information is not specifically limited here. In actual application, The third network function may carry reference information so that AIRF can query the access address of the second network function associated with the AI ​​information that is more suitable for the subscription requirements of the third network function. For example, if the reference information carried by the third network function is an AI service identifier, AIRF can query the address of the second network function associated with the AI ​​service identifier and feed it back to the third network function, or if the third network function carries the AI ​​service identifier and the APP name APP1, AIRF can query which of the AI ​​service identifiers supports APP1, and select the address of the second network function associated with the AI ​​service that supports APP1 and feed it back to the third network function. This is only an exemplary explanation and is not specifically limited. In actual application, the notification of successful subscription and the access address of the second network function associated with at least one AI information are not sent at the same time, such as sending a notification message of successful subscription at the first time, and sending the access address of the second network function associated with at least one AI information at the second time, where the first time is before the second time. In addition, the above-mentioned notification of successful subscription and the access address of the second network function associated with at least one AI information can be sent using the same signaling, or different signaling, which is not specifically limited here. By adopting a subscription request message as a request message, the present application can reuse the message format of the subscription request message. In this way, there is no need to reconstruct the message format, which can reduce the complexity of data processing. In addition, by adopting a query request message as a request message, the present application can reuse the message format of the query request message. In this way, there is no need to reconstruct the message format, which can reduce the complexity of data processing.

[0117] In the present application, the first network function receives a request message for an access address associated with the target AI information, queries the corresponding access address, and feeds back the access address to the third network function, so that the third network function can obtain intelligent services through the access address. In this way, AI services can be provided to the third network function.

[0118] In order to better illustrate the solution of this application, Figure 4The following is an introduction to the registration process of AI information. The registration process can be performed through data interaction between NWDAF, AIRF, and NFs that can provide AI computing power (such as UPF, SMF, and other NF-native AI computing power resources or independently deployed AI computing power), including ANLF, MTLF, and other AI service instances or computing power. The execution is as follows:

[0119] The following describes the registration process for AI services through steps 401A to 403A, and the following describes the registration process for AI computing power through steps 401B to 403B.

[0120] Step 401A: NWDAF sends a registration request message to AIRF.

[0121] The registration request message can be sent through Nairf_AIManage_AIServiceRegister_Request (artificial intelligence storage service request, artificial intelligence management request, artificial intelligence service registration request), which can carry the identification of the AI ​​service, the instance providing the AI ​​service (that is, the access address of NWDAF), the category of the AI ​​service, the model category supported by the AI ​​service, the service object of the AI ​​service, the data format supported by the AI ​​service, and the data calculation scale of the AI ​​service. You can refer to the above to understand it and will not repeat it here. The instances in this application and the access address of the second network function can be replaced with each other unless otherwise specified.

[0122] Step 402A, AIRF stores the registration request message.

[0123] Step 403A, AIRF replies a registration success response message to NWDAF.

[0124] The registration success response message can be sent through Nairf_AIManage_AIServiceRegister_Response (artificial intelligence storage service request response, artificial intelligence management request response, artificial intelligence service registration response). The registered AI service can provide the AI ​​service consumer with the query and subscription capabilities of the AI ​​service instance. The query key information carried can be the AI ​​service ID or name, etc. Other NFs that can provide AI service capabilities (such as UPF, SMF and other NF-native AI services or independent AI services) can also perform the AI ​​service registration process through Nairf_AIManage_AIServiceRegister_Request, which will not be explained in detail here.

[0125] Step 401B: NF sends a registration request message to AIRF.

[0126] The registration request message can be sent via Nairf_AIManage_AIServiceRegister_Request, and can carry the identification of the AI ​​computing power, the instance providing the AI ​​computing power (i.e., the logical address of the NF), the computing power type of the AI ​​computing power, the computing power specification of the AI ​​computing power, the AI ​​framework type indicated by the AI ​​computing power, the AI ​​model category indicated by the AI ​​computing power, and the operator information of the AI ​​computing power. Please refer to the above for understanding, and do not repeat it here.

[0127] Step 402B: AIRF stores the registration request message.

[0128] Step 403B, AIRF replies a registration success response message to NF.

[0129] The registration success response message can be sent via Nairf_AIManage_AIServiceRegister_Response. The registered AI computing power can provide AI computing power consumers with the ability to query and subscribe to the AI ​​computing power instance. The query key information carried can be the AI ​​computing resource type, AI framework type, AI model category, operator capability, etc.

[0130] AIRF supports sensing the status of NF (or NWDAF) by actively initiating heartbeat messages (i.e. the above-mentioned monitoring messages) to NF (or NWDAF) that has registered AI services or AI computing power. At the same time, NF that has registered AI services or AI computing power can also refresh the status of NF (or NWDAF) by updating registration information. AIRF supports providing Nairf_AIManage_AIServiceDiscovery Request / Response messages to consumers to provide the discovery capability of AI service instances. It also supports Nairf_AIManage_AIComputeResouceDiscovery Request / Response messages to consumers to provide the discovery capability of AI computing power instances.

[0131] In addition to supporting the discovery capabilities of AI service instances and AI computing power instances, AIRF also supports providing Nairf_AIManage_AIServiceStatusSubscribe / Nairf_AIManage_AIServiceStatusNotify (artificial intelligence storage service notification, artificial intelligence management notification, artificial intelligence service status subscription notification, artificial intelligence storage service notification, artificial intelligence management notification, artificial intelligence service status notification), Nairf_AIManage_AIComputeStatusSubscribe / Nairf_AIManage_AIComputeStatusNotify (artificial intelligence storage service notification, artificial intelligence management notification, artificial intelligence computing power status subscription notification, artificial intelligence storage service notification, artificial intelligence management notification, artificial intelligence computing power status notification) to consumers of AI services or AI computing power.

[0132] The AIRF provides a registration and discovery framework for AI services or computing power, and a basis for UE, AF or other NFs to discover and use the AI ​​services or computing power of the core network as consumers.

[0133] Next, through Figure 5-6 This article introduces how UE can use AI services and AI computing power. For details about how UE can use AI services, please refer to Figure 5 To execute, UE uses AI computing power for reference Figure 6 It can be executed through data interaction between UE, RAN, AMF, SMF, AI Service Agent, UPF, AIRF and PCF. UE interacts with AI Service Agent through the user plane to query and use network AI service capabilities.

[0134] Case 1: UE uses AI services

[0135] See also Figure 5 Execute as follows:

[0136] Step 501: After being activated, the UE initiates a session establishment request to the SMF through the RAN and AMF.

[0137] Step 502: SMF sends a session policy control request message to PCF.

[0138] The session policy control request message can be sent via Npcf_SMPolicyControl_Create Request to obtain contract information with the subscribed user, such as control policies and billing rules. In addition, the contract information can also include the address information of the AIService Agent service.

[0139] Step 503: PCF sends a response message of the session policy control request message to SMF.

[0140] The response message can be returned to SMF via the Npcf_SMPolicyControl_Create Response message. If the contract information includes the address information of the AI ​​Service Agent service, AMF can record the address information of the AI ​​Service Agent service, and can also configure the address information of the AIServiceAgent service locally, such as: AMF configures the AI ​​Service Agent address information on SMF or AMF through configuration commands or files, and carries it in the corresponding message, without the need to pass it from PCF to UE in sequence along the path of PCF-》SMF-》AMF-》RAN-》UE, which is not specifically limited in this application.

[0141] Step 504: SMF selects UPF to establish a session for the UE.

[0142] Step 505, UPF establishes a session for the UE based on the session establishment request message of SMF, and installs the PDR rules (message detection rules) issued by SMF, executes corresponding perception, statistics and control strategies for the user's service flow, and returns N4 session establishment success response to SMF.

[0143] Step 506, SMF returns the address information of the AI ​​Service Agent service issued by the PCF or configured locally to the UE in the session context establishment response Nsmf_PDUSession_CreateSMContextRequest.

[0144] After the UE receives the address information of the AI ​​Service Agent service sent by the SMF during the session establishment process, it can interact with the AI ​​Service Agent through the data plane channel of the session to query and use the AI ​​services provided by the core network. Here, the UE can also configure the address information of the AI ​​Service Agent service, such as directly configuring the address information of the AIServiceAgent through the setting interface on the terminal side.

[0145] Step 507: The UE establishes a connection with the AI ​​Service Agent through the data plane channel, and sends an AIServiceAgent_AIServiceDiscovery_Request message through the data plane, which is forwarded to the AI ​​Service Agent through the UPF to query available AI services.

[0146] The UE in the above step 507 can call the channel and data interface provided by the UE through an APP. The AIServiceAgent_AIServiceDiscovery_Request message is a query request message or a subscription message (the query request message is used as an example below), which can carry some conditional parameters for the AI ​​services required by the terminal (for example: globally defined AI service ID or name, AI service type, APP name and other parameters), so that the core network can filter out the AI ​​services that the terminal is concerned about and provide them to the UE for use. In addition, in actual applications, the AI ​​Service Agent can also be co-located with the UPF, which is not specifically limited here.

[0147] Step 508: The AI ​​Service Agent receives the AI ​​service query request message initiated by the UE, converts it into AI service query request Nairf_AIManage_AIServiceDiscovery_Request of AIRF, and initiates the AI ​​service query process to AIRF.

[0148] Step 509: AIRF receives the AI ​​service query request message initiated by the AI ​​Service Agent, and determines the AI ​​service list and the instance information corresponding to the AI ​​service.

[0149] Specifically, AIRF can match the conditional parameters of the AI ​​service carried in the query request message, the attribute parameters of the consumer terminal (ie, UE) itself (for example, IMSI, MSISDN, terminal type, APP name or ID, etc.) and other information and the registration information of each AI service, and feedback the list of available AI services and their instance information that meet the conditions of the AI ​​service provider and consumer to AIServiceAgent.

[0150] Step 510: AIRF returns the AI ​​service query request response message to the AI ​​Service Agent.

[0151] The query request response message can be sent via Nairf_AIManage_AIServiceDiscovery_Response, and the parameters that can be carried include: AI service ID or name, AI service type, AI service function description, AI model type, instance information (the address or fully qualified domain name providing the AI ​​service), and the data format corresponding to the AI ​​model.

[0152] Step 511, the AI ​​Service Agent records the AI ​​service ID or name and the data format corresponding to the AI ​​model locally based on the AI ​​service instance information and results carried in the AI ​​service query request response message returned by AIRF, and returns the AI ​​service information that can be provided to the UE through AIServiceAgent_AIServiceDiscovery_Response, carrying the AI ​​service ID or name, AI service type, AI service function description, AI model type, instance information, and the data format corresponding to the AI ​​model.

[0153] The data format corresponding to a specific AI model may be an example of a json file definition as shown below. The hierarchical domain name and domain value relationship pairs are used to describe the data items that the model needs to use, as well as the relationship between each data item and the original data, including what feature engineering (including summation, mean, variance, weighting, matrix operations, etc.) calculations and processing need to be performed based on the original data.

[0154]

[0155]

[0156] The UE receives the available AI services returned by the AIServiceAgent, and based on the parameters of each AI service, when the AI ​​service needs to be used, carries the AI ​​service ID or name, original data or normalized data samples, AI training or reasoning operation tasks, initiates the corresponding AI service training or reasoning application process through the AIServiceAgent, and obtains the AI ​​service.

[0157] Specifically, if the UE wants to train an AI model that meets the requirements of accuracy, robustness and other indicators based on an AI service provided by the core network and local data, it selects an available AI service based on the AI ​​service information returned by the core network (ie, AIRF), and initiates an AI service training request message to the AIServiceAgent through the AIServiceAgent_AIServiceTrain_Request message, which carries information including: AI service ID or name, AI training task ID, original data or data samples that have been normalized, AI training operation code, and training task specification parameters (for example: the minimum number of samples for training startup, training startup time, training duration, number of training iterations, precision, accuracy, and recall index requirements for successful training, etc.). The AIServiceAgent receives the AI ​​service training request message initiated by the UE, finds the address information of the corresponding AIService according to the AI ​​service ID or name carried in the training request message, and then performs feature engineering on the original data carried in the UE message according to the data format of the AI ​​service or verifies the data samples after the feature engineering processing is completed. After completing the data sample processing, Nnwdaf_AIManage_AIServiceTrain_Request is initiated to the corresponding AIService instance (NWDAF can also be replaced by AI services provided by other NFs), carrying the AI ​​service ID or name, AI training task ID, normalized data samples, and training task specifications. NWDAF or other AIService instances provided by NFs receive the AI ​​training request initiated by AIServiceAgent, allocate corresponding training resources based on the AI ​​service ID or name it carries, verify the data samples (if AIServiceAgent and AIService are set together, data normalization and verification may be combined), and then start the training task at an appropriate time based on the training task specifications. After the training task is completed and meets the specification parameter requirements, the trained AI model (the trained model can also be saved in the AIService instance, and an AI model ID is assigned to return to the UE, and the UE can subsequently start training or reasoning tasks through this ID) is returned to the AIServiceAgent through Nnwdaf_AIManage_AIServiceTrain_Response. (If the training task cannot meet the specification requirements after multiple training tasks are started, the training failure result can also be returned to the AIServiceAgent).

[0158] AIServiceAgent receives the training completion response message returned by the AIService instance provided by NWDAF or other NF, which carries the AI ​​service ID or name, AI training task ID, and training result code. If successful, it can synchronously carry the trained AI model file (of course, the trained AI model can also be delivered to the terminal side through AIServiceAgent through an independent message) or the assigned AI model ID. The UE determines to continue training or reasoning based on the AI ​​model according to the AI ​​service ID or name, AI training task ID, training result code, AI model or AI model ID carried in the received training completion response message (it can be local reasoning or also use the AIService instance to perform reasoning tasks).

[0159] Specifically, if the UE wants to infer a result based on an AI service provided by the core network and local data, it selects an available AI service based on the AI ​​service information returned by the core network (AI service ID or name (mandatory), AI service type, function description, AI model type, and data sample normalization format definition description corresponding to the AI ​​model), and initiates an AI service inference request message to the AIServiceAgent through the AIServiceAgent_AIServiceInference_Request message, which carries information including: AI service ID or name, AI inference task ID, original data or data samples that have been normalized, AI inference operation code, and specification parameters of the inference task (for example: number of batch samples for inference startup, inference startup time, and confidence assessment result requirements for inference results). The AIServiceAgent receives the AI ​​service inference request message initiated by the UE, finds the address information of the corresponding AIService according to the AI ​​service ID or name carried in the inference request message, and then performs feature engineering on the original data carried in the UE message according to the data format of the AI ​​service or verifies the data sample after the feature engineering processing is completed. After completing the data sample processing, Nnwdaf_AIManage_AIServiceInference_Request is initiated to the corresponding AIService instance (NWDAF can also be replaced by AI services provided by other NFs), carrying the AI ​​service ID or name, AI reasoning task ID, normalized data samples and their IDs (here an ID is assigned to associate reasoning results), and the specification parameters of the reasoning task. NWDAF or other AIService instances provided by NFs receive the AI ​​reasoning request initiated by AIServiceAgent, allocate corresponding reasoning resources based on the AI ​​service ID or name it carries, verify the data samples (if AIServiceAgent and AIService are set together, data normalization and verification may be combined), and then start the reasoning task at the appropriate time based on the specification parameters of the reasoning task. After the reasoning task is completed and meets the specification parameter requirements, the AI ​​reasoning task ID and the reasoning results corresponding to each sample ID will be returned to AIServiceAgent through Nnwdaf_AIManage_AIServiceInference_Response. (If the specification requirements cannot be met after launching the inference task multiple times, the inference failure result of a certain sample ID can also be returned to AIServiceAgent. Each message can carry multiple sample IDs and their inference results).

[0160] AIServiceAgent receives the inference completion response message returned by the AIService instance provided by NWDAF or other NF, carrying the AI ​​service ID or name, AI inference task ID, and the inference result corresponding to each sample ID and returns it to the UE. The UE performs subsequent processing based on the AI ​​service ID or name, AI inference task ID, and the inference result corresponding to each sample ID carried in the received inference completion response message, and completes the interactive process of the AIService inference task.

[0161] In this way, UE can use AIServiceAgent to realize functions such as discovery of AI services deployed in the core network, execution of AI training and reasoning tasks, raw data and its normalized processing capabilities, acquisition of AI service training results and models, and reasoning results of each data, thereby achieving the goal of local intelligent data processing using AI services deployed in the core network.

[0162] Case 2: UE uses AI computing power

[0163] Step 601: After being activated, the UE initiates a session establishment request to the SMF through the RAN and AMF.

[0164] Step 602: SMF sends a session policy control request message to PCF.

[0165] Step 603: PCF sends a response message of the session policy control request message to SMF.

[0166] Step 604: SMF selects UPF to establish a session for the UE.

[0167] Step 605, UPF establishes a session for the UE based on the session establishment request message of SMF, installs the PDR rules (message detection rules) issued by SMF, performs corresponding perception, statistics and control strategies on the user's service flow, and returns N4 session establishment success response to SMF.

[0168] Among them, steps 601 to 605 can refer to the above Figure 5 The steps 501 to 505 in the figure can be understood and will not be described in detail here.

[0169] Step 606, SMF returns the address information of the AI ​​Service Agent service issued by the PCF or locally configured to the UE in the session context establishment response Nsmf_PDUSession_CreateSMContextRequest.

[0170] After the UE receives the address information of the AI ​​Service Agent service sent by the SMF during the session establishment process, it can interact with the AI ​​Service Agent service through the data plane channel of the session to query and use the AI ​​computing power provided by the core network. Here, the UE can also configure the address information of the AI ​​Service Agent service.

[0171] Step 607: The UE establishes a connection with the AI ​​Service Agent through the data plane channel, and sends an AIServiceAgent_AIServiceDiscovery_Request message through the data plane, which is forwarded to the AI ​​Service Agent through the UPF to query the available AI computing power.

[0172] The AIServiceAgent_AIServiceDiscovery_Request message is a query request message or a subscription message (the query request message is used as an example below), which can carry some conditional parameters for the AI ​​computing power required by the terminal (for example: AI computing resource type (X86 / ARM CPU, NPU, GPU, etc. computing resources that can run AI tasks), AI computing power specifications, AI framework types (such as TensorFlow, Mindspore, SKlearn, etc. AI framework types), AI model categories (such as CNN, RNN, random forest, etc.), operator capabilities (such as vector calculation, matrix calculation, etc.) and other parameters), so that the core network can filter out the AI ​​computing power required by the terminal and provide it to the UE for use.

[0173] Step 608: The AI ​​Service Agent receives the AI ​​computing power query request message initiated by the UE, converts it into AIRF's AI computing power query request Nairf_AIManage_AIServiceDiscovery_Request, and initiates the AI ​​computing power query process to AIRF.

[0174] Step 609: AIRF receives the AI ​​service query request message initiated by the AI ​​Service Agent and determines the instance information corresponding to the AI ​​computing power provider and the AI ​​computing power.

[0175] Specifically, AIRF matches the AI ​​computing power condition parameters, AI computing power specifications, AI framework type, AI model category, operator capabilities and other parameters carried in the query request message with the registration information of each AI computing power instance, and provides the AI ​​computing power and its instance information that are available and meet the conditions of AI computing power providers and consumers to AIServiceAgent.

[0176] Step 610: AIRF returns the AI ​​computing power query request response message to the AI ​​Service Agent.

[0177] The query request response message can be sent through Nairf_AIManage_AIServiceDiscovery_Response, and the parameters that can be carried include: AI computing power ID or name, AI computing resource type, AI computing power specification, AI framework type, AI model category, operator capabilities (such as vector calculation, matrix calculation, etc. parameters, and instance information.

[0178] Step 611, AIServiceAgent returns the AI ​​computing power ID or name, instance information, and the list of available AI computing power to UE through AIServiceAgent_AIComputeDiscovery_Response based on the AI ​​computing power instance information and results carried in the AI ​​computing power query response message returned by AIRF, carrying parameters such as AI computing power instance ID or name, AI computing resource type, AI computing power specification, AI framework type, AI model category, and operator capability.

[0179] The UE receives the list of available AI computing power returned by AIServiceAgent, and selects a suitable AI computing power instance based on the parameters of each AI service, including AI computing power ID or name, AI computing resource type, AI computing power specification, AI framework type, AI model category, operator capability and other parameters. Then, when the AI ​​computing power instance needs to be used, it carries the AI ​​computing instance ID or name, AI model or AI model ID (at this time, the AI ​​model has been saved in the previous AI computing power instance) original data (at this time, the normalized format definition description of the data sample corresponding to the AI ​​model is required) or the data sample that has been normalized, etc., to initiate the corresponding AI training or reasoning application process to the corresponding AI computing power instance through AIServiceAgent, and obtain the AI ​​processing results.

[0180] Specifically, if the UE wants to train an AI model that meets the requirements of accuracy, robustness and other indicators based on an AI computing power instance, an initial model and local data provided by the core network, it selects an available AI computing power instance based on the AI ​​computing power instance information returned by the core network (AI computing power ID or name, AI computing resource type, AI computing power specification, AI framework type, AI model category, operator capability and other parameters), and initiates an AI computing power training request message to the AIServiceAgent through the AIServiceAgent_AIComputeTrain_Request message. The information carried includes: AI computing power instance ID or name, AI training task ID, AI model or AI model ID, original data or data samples that have been normalized, AI training operation code, and specification parameters of the training task (for example: the minimum number of samples for training startup, training startup time, training duration, number of training iterations, precision, accuracy, recall rate indicator requirements for successful training, etc.).

[0181] AIServiceAgent receives the AI ​​service training request message initiated by the UE, finds the address information of the corresponding AIService according to the AI ​​service ID or name carried in the training request message, and then performs feature engineering processing on the original data carried in the UE message according to the normalized format definition description of the data sample of the AI ​​service, or verifies the data sample after the feature engineering processing. After completing the data sample processing, AIManage_AIServiceTrain_Request is initiated to the corresponding AIService instance (NWDAF can also be replaced with AI services provided by other NFs), AI computing instance ID or name, AI model or AI model ID (at this time, the AI ​​model has been saved in the previous AI computing instance), AI training task ID, original data (at this time, the data sample normalization format definition description corresponding to the AI ​​model must be carried) or data samples that have been normalized, specification parameters of the training task, and other information to initiate a training task to the corresponding AI computing instance.

[0182] The AICompute instance provided by NWDAF or other NF receives the AI ​​training request initiated by AIServiceAgent, allocates the corresponding training resources based on the AI ​​computing power ID or name it carries, verifies the data samples (if AIServiceAgent and AIService are set up together, data normalization and verification may be combined), and then starts the training task at the appropriate time based on the AI ​​model or AI model ID, AI training task ID, and training task specification parameters specified by the training task. After the training task is completed and meets the specification parameter requirements, the trained AI model (the trained model can also be saved in the AICompute instance, and an AI model ID is assigned to return to the UE, and the UE can subsequently start training or reasoning tasks through this ID) will be returned to the AIServiceAgent through AIManage_AIServiceTrain_Response. (If the training task cannot meet the specification requirements after multiple starts, the result of training failure can also be returned to the AIServiceAgent). AIServiceAgent receives the training completion response message returned by the AICompute instance provided by NWDAF or other NF, which carries the AI ​​service ID or name, AI training task ID, and training result code. If successful, it can synchronously carry the trained AI model file (of course, it can also pass the trained AI model to the terminal side through AIServiceAgent in an independent message) or the assigned AI model ID. The UE decides to continue training or reasoning based on the AI ​​model based on the AI ​​service ID or name, AI model ID or training task ID, training result code, AI model or AI model ID carried in the received training completion response message (it can be local reasoning or also use the AIService instance to perform reasoning tasks).

[0183] Specifically, if the UE wants to infer a result based on an AI computing power instance provided by the core network and local data, it selects an available AI computing power instance based on the AI ​​computing power instance information returned by the core network (AI computing power ID or name, AI computing resource type, AI computing power specification, AI framework type, AI model category, operator capability and other parameters), and initiates an AI service inference request message to the AIServiceAgent through the AIServiceAgent_AIComputeInference_Request message. The information carried includes: AI computing power instance ID or name, AI model or AI model ID, AI inference task ID, original data (if it is the first time to send, the data sample normalization format definition description information must be carried) or data samples that have been normalized, AI inference operation codes, and specification parameters of the inference task (for example: the number of batch samples started for inference, the inference start time, the confidence assessment result requirements of the inference result, etc.).

[0184] AIServiceAgent receives the AI ​​computing power inference request message initiated by the UE, finds the address information of the corresponding AIService according to the AI ​​computing power instance ID or name carried in the inference request message, and then performs feature engineering processing on the original data carried in the UE message or verifies the data samples after feature engineering processing according to the data sample normalization format definition description of the AI ​​model corresponding to the AI ​​inference task. After completing the data sample processing, AIManage_AIComputeInference_Request is initiated to the corresponding AICompute instance (NWDAF can also be replaced by AI services provided by other NFs), carrying the AI ​​computing power instance ID or name, AI inference task ID, normalized data samples and their IDs (here an ID is assigned to associate inference results), and specification parameters of the inference task (for example: the number of batch samples to start inference, inference start time, confidence assessment result requirements for inference results, etc.). The AICompute instance provided by NWDAF or other NF receives the AI ​​inference request initiated by AIServiceAgent, allocates the corresponding inference resources based on the AI ​​computing instance ID or name it carries, verifies the data samples (if AIServiceAgent and AIService are set up together, data normalization and verification may be combined), and then starts the inference task at the appropriate time based on the AI ​​model of the inference task and the inference specification parameters (for example: the number of batch samples started for inference, the inference start time, the confidence assessment result requirements for the inference results, etc.). After the inference task is completed and meets the specification parameter requirements, the AI ​​inference task ID and the inference results corresponding to each sample ID will be returned to AIServiceAgent through AIManage_AIServiceInference_Response. (If the specification requirements cannot be met by starting the inference task multiple times, the inference failure result of a certain sample ID can also be returned to AIServiceAgent. Each message can carry multiple sample IDs and their inference results)

[0185] AIServiceAgent receives the inference completion response message returned by the AICompute instance provided by NWDAF or other NFs, and returns it to the UE with the AI ​​computing instance ID or name, AI model ID, AI inference task ID, and the inference result corresponding to each sample ID. The UE performs subsequent processing based on the AI ​​computing instance ID or name, AI inference task ID, AI model ID or inference task ID, and the inference result corresponding to each sample ID carried in the received inference completion response message, and completes the interactive process of the AIService inference task.

[0186] In this way, UE can use AIServiceAgent to realize functions such as discovery of AI computing power deployed in the core network, execution of AI training and reasoning tasks, raw data and its normalized processing capabilities, acquisition of AI service training results and models, and reasoning results of each data, thereby achieving the goal of local intelligent data processing using AI services deployed in the core network.

[0187] Next, through Figure 7 to Figure 10 This article introduces how AF / NEF / NF uses AI services and AI computing power, which can be performed through data interaction between NF, AIService Agent, UPF, and AIRF. In actual application, it can be divided into two different implementation methods, one is to use AI services and AI computing power through AI Service Agent, and the other is to use AI services and AI computing power directly. The details are as follows:

[0188] Method 1: AF / NEF / NF uses AI services and AI computing power through AI Service Agent

[0189] Case 1: AF / NEF / NF using AI services

[0190] Here we take NF as an example, see Figure 7 Execute as follows:

[0191] Step 701: NF establishes a connection with AI Service Agent, initiates the process of querying or subscribing to AI services provided by the core network, and sends an AIServiceAgent_AIServiceDiscovery_Request message to AIService Agent through the data plane to query available AI services.

[0192] The query message may also carry some conditional parameters for the AI ​​services required by the NF (for example, globally defined AI service ID or name, AI service type, APP name or ID, etc.), so that the core network can filter out the AI ​​services that the terminal is concerned about and provide them to AF / NEF or other NFs for use.

[0193] Step 702: AIServiceAgent receives the AI ​​service discovery request message initiated by NF, converts it into AIRF's AI service discovery request Nairf_AIManage_AIServiceDiscovery_Request, and initiates the service discovery process to AIRF.

[0194] The service discovery request message carries the condition parameters of the AI ​​service required by the AF / NEF or other NFs, and the attribute parameters of the NF itself (for example, APP name or ID, etc.).

[0195] Step 703: AIRF receives the AI ​​service discovery request message initiated by AIServiceAgent, and matches it based on the AI ​​service condition parameters, NF attribute parameters and other information carried by it and the information registered by each AI service, and determines to give the AIServiceAgent a list of available AI services that meet the conditions of AI service providers and consumers and their instance information.

[0196] Step 704, the parameters carried by the AI ​​service discovery response message Nairf_AIManage_AIServiceDiscovery_Response returned by AIRF include: AI service ID or name, AI service type, function description, AI model type, instance information (the address or fully qualified domain name providing AI service), and the normalized format definition description of the data sample corresponding to the AI ​​model.

[0197] Step 705, AIServiceAgent records the AI ​​service ID or name, and the normalized format definition description of the data samples corresponding to the AI ​​model locally based on the AI ​​service instance information and results carried in the AI ​​service discovery response message returned by AIRF, and returns the AI ​​service that can be provided to AF / NEF or other NF through AIServiceAgent_AIServiceDiscovery_Response, which carries the AI ​​service ID or name, AI service type, function description, AI model type, instance information, and the normalized format definition description of the data samples corresponding to the AI ​​model.

[0198] NF receives the available AI services returned by AIServiceAgent and selects the appropriate AI service based on the parameters of each AI service. Then, when the AI ​​service is needed, it carries the AI ​​service ID or name, original data or normalized data samples, and AI training or reasoning operation tasks to execute the corresponding AI service training or reasoning process through AIServiceAgent and obtain the AI ​​service.

[0199] Specifically, NF can refer to the above Figure 5 The process in which UE uses AI services to obtain AI training models and processing results will not be repeated here.

[0200] In this way, AF / NEF or other NFs can use AIServiceAgent to realize the discovery of AI services deployed in the core network, the execution of AI training and reasoning tasks, the processing capabilities of raw data and its normalization, the acquisition of AI service training results and models, and the reasoning results of each data, thereby achieving the goal of local intelligent data processing using AI services deployed in the core network.

[0201] Case 2: AF / NEF / NF uses AI computing power

[0202] Here we take NF as an example, see Figure 8 Execute as follows:

[0203] Step 801: NF establishes a connection with AI Service Agent, initiates the process of querying or subscribing to the AI ​​computing power provided by the core network, and sends an AIServiceAgent_AIComputeDiscovery_Request message to AIService Agent through the data plane to query the available AI computing power.

[0204] The query message may also carry some conditional parameters for the AI ​​computing power required by AF / NEF or other NFs (for example, AI computing resource types (computing resources such as X86 / ARM CPU, NPU, GPU, etc. that can run AI tasks), AI computing power specifications, AI framework types (such as TensorFlow, Mindspore, SKlearn, etc.), AI model categories (such as CNN, RNN, random forest, etc.), operator capabilities (such as vector calculation, matrix calculation, etc.) and instance information (the address or fully qualified domain name providing AI services), so that the core network can filter out the AI ​​computing power required by AF / NEF or other NFs and provide it to AF / NEF or other NFs for use.

[0205] Step 802: AIServiceAgent receives the AI ​​computing power discovery request message initiated by NF, converts it into AIRF's AI computing power discovery request Nairf_AIManage_AIComputeDiscovery_Request, and initiates the service discovery process to AIRF.

[0206] The computing power discovery request message carries the conditional parameters of the AI ​​computing power required by the terminal.

[0207] Step 803: AIRF receives the AI ​​computing power discovery request message initiated by AIServiceAgent, matches the AI ​​computing power condition parameters carried by it with the registration information of each AI computing power instance, and determines to provide the AI ​​computing power and its instance information that meet the conditions of AI computing power providers and consumers to AIServiceAgent.

[0208] Step 804: The AI ​​computing power discovery response message Nairf_AIManage_AIComputeDiscovery_Response returned by AIRF carries the conditional parameters of the AI ​​computing power.

[0209] Step 805: Based on the AI ​​service instance information and results carried in the AI ​​service discovery response message returned by AIRF, AIServiceAgent returns the AI ​​service ID or name, instance information, and the list of available AI computing power to NF through AIServiceAgent_AIComputeDiscovery_Response, which carries parameters such as AI computing power instance ID or name, AI computing resource type, AI computing power specification, AI framework type, AI model category, and operator capability.

[0210] NF receives the list of available AI computing power returned by AIServiceAgent, and selects a suitable AI computing power instance based on the condition parameters of each AI computing power. Then, when the AI ​​computing power instance needs to be used, it carries the AI ​​computing power instance ID or name, AI model or AI model ID (at this time, the AI ​​model has been saved in the previous AI computing power instance), original data (at this time, the normalized format definition description of the data sample corresponding to the AI ​​model is required) or data samples that have been normalized, etc., to initiate the corresponding AI training or reasoning application process to the corresponding AI computing power instance through AIServiceAgent, and obtain the AI ​​processing results.

[0211] Specifically, if NF can refer to the above Figure 6 The process in which UE uses AI computing power to obtain AI training models and processing results will not be repeated here.

[0212] In this way, NF can use AIServiceAgent to realize the discovery of AI computing power deployed in the core network, the execution of AI training and reasoning tasks, the processing capabilities of raw data and its normalization, the acquisition of AI service training results and models, and the reasoning results of each data, thereby achieving the goal of realizing local intelligent data processing using AI services deployed in the core network.

[0213] Method 1: AF / NEF / NF directly uses AI services and AI computing power

[0214] Case 1: AF / NEF / NF using AI services

[0215] Here we take NF as an example, see Fig. 9 Execute as follows:

[0216] Step 901, NF establishes a connection with AIRF, and AI service discovery request Nairf_AIManage_AIServiceDiscovery_Request initiates a service discovery process to AIRF.

[0217] The service discovery request message carries the conditional parameters of the AI ​​service required by the NF (for example, globally defined AI service ID or name, AI service type and other parameters) and the attribute parameters of the NF itself (for example, APP name or ID, etc.), so that the core network can filter out the AI ​​services that the terminal is concerned about and provide them to the NF for use.

[0218] Step 902: AIRF receives the AI ​​service discovery request message initiated by NF, and matches it based on the AI ​​service condition parameters, NF's own attribute parameters and other information carried by it and the information registered by each AI service, and determines to give the list of available AI services and their instance information that meet the conditions of AI service providers and consumers to NF.

[0219] Step 903: AIRF returns an AI service discovery response message.

[0220] The service discovery response message is sent through Nairf_AIManage_AIServiceDiscovery_Response, and the parameters that can be carried include: AI service ID or name, AI service type, function description, AI model type, instance information (the address or fully qualified domain name providing the AI ​​service), and the normalized format definition description of the data sample corresponding to the AI ​​model.

[0221] Based on the AI ​​service instance information and results carried in the AI ​​service discovery response message returned by AIRF, NF records the AI ​​service ID or name, and the normalized format definition description of the data sample corresponding to the AI ​​model locally, and selects the appropriate AI service based on the parameters of each AI service. Then, when the AI ​​service is needed, it carries the AI ​​service ID or name, original data or normalized data samples, and AI training or reasoning operation tasks to initiate the corresponding AI service training or reasoning application process and obtain the AI ​​service.

[0222] Specifically, if NF wants to train an AI model that meets the requirements of accuracy, robustness and other indicators based on an AI service provided by the core network and local data, it selects an available AI service based on the AI ​​service information returned by the core network (AI service ID or name, AI service type, function description, AI model type, and the normalized format definition of the data sample corresponding to the AI ​​model), and performs feature engineering on the original data according to the normalized format definition of the data sample of the AI ​​service. After completing the data sample processing, Nnwdaf_AIManage_AIServiceTrain_Request is initiated to the corresponding AIService instance (NWDAF can also be replaced by AI services provided by other NFs), carrying the AI ​​service ID or name, AI training task ID, normalized data samples, and training task specifications (for example: the minimum number of samples for training startup, training startup time, training duration, number of training iterations, precision, accuracy, and recall index requirements for successful training, etc.).

[0223] The AIService instance provided by NWDAF or other NF receives the AI ​​training request initiated by AF / NEF or other NF, allocates the corresponding training resources based on the AI ​​service ID or name it carries, verifies the data samples, and then starts the training task at the appropriate time based on the specification parameters of the training task (for example: the minimum number of samples for training startup, training startup time, training duration, number of training iterations, accuracy, precision, recall rate index requirements for successful training, etc.). After the training task is completed and meets the specification parameter requirements, the trained AI model (the trained model can also be saved in the AIService instance, and an AI model ID is assigned to return to AF / NEF or other NF. Subsequently, AF / NEF or other NF can use this ID to start training or reasoning tasks) will be returned to AF / NEF or other NF through Nnwdaf_AIManage_AIServiceTrain_Response. (If the specification requirements cannot be met after starting the training task multiple times, the result of training failure can also be returned to AF / NEF or other NF)

[0224] AF / NEF or other NFs decide to continue training or reasoning based on the AI ​​model according to the AI ​​service ID or name, AI training task ID, training result code, AI model or AI model ID carried in the received training completion response message (local reasoning or reasoning tasks can be performed using the AIService instance).

[0225] Specifically, if AF / NEF or other NF wants to infer a result based on an AI service provided by the core network and local data, it selects an available AI service based on the AI ​​service information returned by the core network (AI service ID or name, AI service type, function description, AI model type, and normalized format definition description of the data sample corresponding to the AI ​​model). After completing the data sample processing, it initiates Nnwdaf_AIManage_AIServiceInference_Request to the corresponding AIService instance (NWDAF can also be replaced with AI services provided by other NFs), carrying the AI ​​service ID or name, AI reasoning task ID, normalized data samples and their IDs (an ID is assigned here to associate reasoning results), and specification parameters of the reasoning task (for example: the number of batch samples to start reasoning, the reasoning start time, the confidence assessment result requirements of the reasoning result, etc.).

[0226] The AIService instance provided by NWDAF or other NF receives the AI ​​inference request initiated by AF / NEF or other NF, allocates the corresponding inference resources based on the AI ​​service ID or name it carries, verifies the data samples (if AIServiceAgent and AIService are set up together, data normalization and verification may be combined), and then starts the inference task at the appropriate time based on the specification parameters of the inference task (for example: the number of batch samples to start inference, the inference start time, the confidence assessment result requirements of the inference result, etc.). After the inference task is completed and meets the specification parameter requirements, the AI ​​inference task ID and the inference result corresponding to each sample ID will be returned to AF / NEF or other NF through Nnwdaf_AIManage_AIServiceInference_Response. (If the specification requirements cannot be met by starting the inference task multiple times, the inference failure result of a certain sample ID can also be returned to AIServiceAgent. Each message can carry multiple sample IDs and their inference results). AF / NEF or other NFs perform subsequent processing based on the AI ​​service ID or name, AI reasoning task ID, and reasoning results corresponding to each sample ID carried in the received reasoning completion response message to complete the interaction process of the AIService reasoning task.

[0227] Case 2: AF / NEF / NF uses AI computing power

[0228] Here we take NF as an example, see Fig.10 Execute as follows:

[0229] Step 1001, NF establishes a connection with AIRF and initiates a service discovery process to AIRF through the AI ​​computing power discovery request Nairf_AIManage_AIComputeDiscovery_Request.

[0230] The service discovery request message carries the conditional parameters of the AI ​​computing power required by the terminal (for example, AI computing resource type (X86 / ARM CPU, NPU, GPU, etc. computing power resources that can run AI tasks), AI computing power specifications, AI framework types (such as TensorFlow, Mindspore, SKlearn, etc. AI framework types), AI model categories (such as CNN, RNN, random forest, etc.), operator capabilities (such as vector calculation, matrix calculation, etc.), and other parameters).

[0231] Step 1002: AIRF receives the AI ​​computing power discovery request message initiated by NF, and matches it with the information of each AI computing power instance registration based on the AI ​​service condition parameters, AI computing power specifications, AI framework type, AI model category, operator capabilities and other parameters carried by the message, and determines to provide the AI ​​computing power and its instance information that meet the conditions of AI computing power providers and consumers to AIServiceAgent.

[0232] Step 1003, AIRF returns the AI ​​computing power discovery response message.

[0233] The AI ​​computing power discovery response message is sent through Nairf_AIManage_AIComputeDiscovery_Response, and the parameters that can be carried include: AI computing power ID or name, AI computing resource type (X86 / ARM CPU, NPU, GPU and other computing power resources that can run AI tasks), AI computing power specifications, AI framework types (such as TensorFlow, Mindspore, SKlearn and other AI framework types), AI model categories (such as CNN, RNN, random forest, etc.), operator capabilities (such as vector calculation, matrix calculation, etc.), instance information (the address or fully qualified domain name providing AI services).

[0234] NF receives the list of available AI computing power returned by AIRF, and selects the appropriate AI computing power instance based on the parameters of each AI service, including AI computing power ID or name, AI computing resource type (X86 / ARM CPU, NPU, GPU and other computing power resources that can run AI tasks), AI computing power specifications, AI framework type (such as TensorFlow, Mindspore, SKlearn and other AI framework types), AI model category (such as CNN, RNN, random forest, etc.), operator capabilities (such as vector calculation, matrix calculation, etc.), etc., and then when the AI ​​computing power instance needs to be used, it carries the AI ​​computing instance ID or name, AI model or AI model ID (at this time, the AI ​​model has been saved in the previous AI computing power instance) original data (at this time, the normalized format definition description of the data sample corresponding to the AI ​​model is required) or the data sample that has been normalized, etc. to initiate the corresponding AI training or reasoning application process to the corresponding AI computing power instance and obtain the AI ​​processing result.

[0235] Specifically, if AF / NEF or other NFs want to train an AI model that meets the requirements of accuracy, robustness, etc. based on an AI computing power instance, initial model, and local data provided by the core network, based on the AI ​​computing power instance information returned by the core network (AI computing power ID or name, AI computing resource type (X86 / ARM The NF-based AI training task is initiated by using the following parameters: (1) the NF-based AI training task is initiated by using the following parameters: (1) the NF-based AI training task is initiated by using the following parameters: (a) the NF-based AI training task is initiated by using the following parameters: (a) the NF-based AI training task is initiated by using the following parameters: (b) the NF-based AI training task is initiated by using the following parameters: (c) the NF-based AI training task is initiated by using the following parameters: (d) the NF-based AI training task is initiated by using the following parameters: (c) the NF-based AI training task is initiated by using the following parameters: (c) the NF-based AI training task is initiated by using the following parameters:

[0236] The AICompute instance provided by NWDAF or other NF receives the AI ​​training request initiated by AF / NEF or other NF, allocates the corresponding training resources based on the AI ​​computing power ID or name it carries, verifies the data samples, and then starts the training task at the appropriate time based on the AI ​​model or AI model ID, AI training task ID, and training task specification parameters specified by the training task (for example: the minimum number of samples for training startup, training startup time, training duration, number of training iterations, accuracy, precision, recall rate index requirements for successful training, etc.). After the training task is completed and meets the specification parameter requirements, the trained AI model (the trained model can also be saved in the AICompute instance, and an AI model ID is assigned to return to the UE, and the UE can subsequently start training or reasoning tasks through this ID) will be returned to AF / NEF or other NFs through AIManage_AIServiceTrain_Response. (If the training task cannot meet the specification requirements after multiple starts, the result of training failure can also be returned to AF / NEF or other NFs)

[0237] AF / NEF or other NFs decide whether to continue training or reasoning based on the AI ​​model according to the AI ​​service ID or name, AI model ID or training task ID, training result code, AI model or AI model ID carried in the received training completion response message (local reasoning or reasoning tasks can be performed using AI computing power instances).

[0238] Specifically, if AF / NEF or other NF wants to infer a result based on an AI computing power instance provided by the core network and local data, it selects an available AI computing power instance based on the AI ​​computing power instance information returned by the core network (AI computing power ID or name, AI computing resource type (X86 / ARM CPU, NPU, GPU, etc. that can run AI tasks), AI computing power specifications, AI framework types (such as TensorFlow, Mindspore, SKlearn, etc. AI framework types), AI model categories (such as CNN, RNN, random forest, etc.), operator capabilities (such as vector calculation, matrix calculation, etc.) and other parameters. After completing the data sample processing, it initiates AIManage_AIComputeInference_Request to the corresponding AICompute instance (NWDAF can also be replaced by AI services provided by other NFs), carrying the AI ​​computing power instance ID or name, AI reasoning task ID, normalized data samples and their IDs (here an ID is assigned for associating reasoning results), and reasoning task specification parameters (for example: the number of batch samples started for reasoning, the reasoning start time, the confidence assessment result requirements for the reasoning result, etc.).

[0239] The AICompute instance provided by NWDAF or other NF receives the AI ​​inference request initiated by AF / NEF or other NF, allocates the corresponding inference resources based on the AI ​​computing instance ID or name it carries, verifies the data samples (if AIServiceAgent and AIService are set up together, data normalization and verification may be combined), and then starts the inference task at the appropriate time based on the AI ​​model of the inference task and the inference specification parameters (for example: the number of batch samples started for inference, the inference start time, the confidence assessment result requirements of the inference result, etc.). After the inference task is completed and meets the specification parameter requirements, the AI ​​inference task ID and the inference result corresponding to each sample ID will be returned to AF / NEF or other NF through AIManage_AIServiceInference_Response. (If the specification requirements cannot be met by starting the inference task multiple times, the inference failure result of a certain sample ID can also be returned to AF / NEF or other NF. Each message can carry multiple sample IDs and their inference results)

[0240] AF / NEF or other NFs perform subsequent processing on the inference results corresponding to each sample ID based on the AI ​​computing instance ID or name, AI inference task ID, AI model ID or inference task ID carried in the received inference completion response message, to complete the interaction process for the AIService inference task.

[0241] The above mainly introduces the solution provided by the embodiment of the present application from the perspective of device interaction. It is understandable that, in order to implement the above functions, each device may include a hardware structure and / or software module corresponding to each function. It should be easily appreciated by those skilled in the art that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the embodiments of the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0242] The embodiment of the present application can divide the functional units of the device according to the above method example, for example, each functional unit can be divided according to each function, or two or more functions can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units.

[0243] In the case of an integrated unit, Fig.11 FIG. 1 shows a possible exemplary block diagram of a communication device involved in an embodiment of the present application. Fig.11 As shown, the communication device 1100 may include: a processing unit 1101 and a transceiver unit 1102. The processing unit 1101 is used to control and manage the actions of the communication device 1100. The transceiver unit 1102 is used to support the communication between the communication device 1100 and other devices. Optionally, the transceiver unit 1102 may include a receiving unit and / or a sending unit, which are respectively used to perform receiving and sending operations. Optionally, the communication device 1100 may also include a storage unit for storing program code and / or data of the communication device 1100. The transceiver unit may be called an input-output unit, a communication unit, etc., and the transceiver unit may be a transceiver; the processing unit may be a processor. When the communication device is a module (such as a chip) in a communication device, the transceiver unit may be an input-output interface, an input-output circuit or an input-output pin, etc., and may also be called an interface, a communication interface or an interface circuit, etc.; the processing unit may be a processor, a processing circuit or a logic circuit, etc. Specifically, the device may be the above-mentioned first network function, the second network function, the third network function, and the fourth network function, etc.

[0244] In one embodiment, the communication device 1100 is a first network function, and the transceiver unit 1102 is used to receive a request message from a third network function, where the request message is used to request the access address of the second network function associated with the target artificial intelligence AI information; the processing unit 1101 is used to obtain the access address of the second network function associated with the target AI information by querying the association relationship according to the request message, where the association relationship indicates the relationship between at least one AI information supported by at least one second network function and the access address of the second network function associated with each AI information, and the target AI information is one of the at least one AI information; the transceiver unit 1102 is also used to send a request response message to the third network function, where the request response message is used to indicate the access address of the second network function associated with the target AI information.

[0245] In an optional manner, the request message is a subscription request message; the transceiver unit 1102 is used to send a notification message of successful subscription to the third network function, and send an access address of the second network function associated with at least one AI information to the third network function.

[0246] In an optional manner, the request message is a query request message; the transceiver unit 1102 is used to send the access address of the second network function associated with the target AI information to the third network function.

[0247] In an optional manner, the transceiver unit 1102 is used to receive a registration request message from at least one second network function, where the registration request message is used to indicate at least one AI information supported by the second network function and an access address of the second network function associated with each AI information respectively; the processing unit 1101 is used to determine an association relationship based on the registration request message.

[0248] In an optional manner, the transceiver unit 1102 is further used to send a monitoring message to the second network function, where the monitoring message is used to monitor the working status of the second network function; the transceiver unit 1102 is further used to receive a monitoring response message from the second network function.

[0249] In an optional manner, the third network function is a terminal; the transceiver unit 1102 is also used to receive a request message sent by the third network function through a fourth network function, and the fourth network function is used to convert the format of messages exchanged between the terminal and the first network function, and / or to perform identity authentication on the terminal and the first network function.

[0250] In an optional manner, the AI ​​information includes: AI services, and / or AI computing power.

[0251] In an optional manner, the AI ​​information is an AI service; the registration request message includes: an identifier of the AI ​​service and an access address of a second network function that provides the AI ​​service.

[0252] In an optional manner, the registration request information further includes one or more of the following:

[0253] Categories of AI services, categories of models supported by AI services, service objects of AI services, data formats supported by AI services, and data computing scale of AI services;

[0254] Among them, the service objects of AI services include terminal device type, APP name, terminal users belonging to the preset number segment, geographic location information, slice information and access point identification.

[0255] In an optional manner, the AI ​​information is AI computing power; the registration request information includes: an identifier of the AI ​​computing power and an access address of a second network function that provides the AI ​​computing power.

[0256] In an optional manner, the registration request information further includes one or more of the following:

[0257] The computing power type of AI computing power, the computing power specification of AI computing power, the AI ​​framework type indicated by AI computing power, the AI ​​model category indicated by AI computing power, and the operator information of AI computing power.

[0258] In another embodiment, the communication device 1100 is a fourth network function, and the transceiver unit 1102 is used to receive a request message from a third network function, where the request message is used to request an access address of a second network function associated with the target artificial intelligence AI information; the transceiver unit 1102 is also used to send a request message to the first network function; receive a request response message from the first network function, where the request response message is used to indicate an access address of the second network function associated with the target AI information; and send a request response message to the third network function.

[0259] In an optional manner, the request message is a subscription request message; the transceiver unit 1102 is used to receive a notification message of successful subscription and receive an access address of a second network function associated with at least one AI information from the first network function.

[0260] In an optional manner, the request message is a query request message; the transceiver unit 1102 is used to receive an access address of a second network function associated with target AI information from a first network function.

[0261] In an optional manner, the third network function is a terminal; the processing unit 1101 is used to convert the format of messages exchanged between the third network function and the first network function; and / or to perform identity authentication on the third network function and the first network function.

[0262] In an optional manner, the transceiver unit 1102 is used to receive data to be processed from the terminal; convert the data to be processed into a data format corresponding to the AI ​​information required by the terminal and then send it to the access address of the second network function associated with the AI ​​information required by the terminal; receive the data processing result from the second network function; and send the data processing result to the terminal.

[0263] In another embodiment, the communication device 1100 is a third network function, and the transceiver unit 1102 is used to send a request message to a fourth network function, where the request message is used to request the access address of the second network function associated with the target artificial intelligence AI information; the transceiver unit 1102 is also used to receive a request response message from the fourth network function, where the request response message is used to indicate the access address of the second network function associated with the target AI information; send the data to be processed to the access address of the second network function associated with the target AI information through the fourth network function; and receive the data processing result from the second network function through the fourth network function.

[0264] In an optional manner, the communication device 1100 is a terminal.

[0265] In another embodiment, the communication device 1100 is a second network function, and the transceiver unit 1102 is used to send a registration request message to the first network function, where the registration request message is used to indicate at least one AI information supported by the second network function and an access address of the second network function associated with each AI information respectively; the transceiver unit 1102 is also used to receive a response message of successful registration from the second network function.

[0266] In an optional manner, the transceiver unit 1102 is also used to receive a monitoring message from the first network function, the monitoring message is used to monitor the working status of the second network function; and send a monitoring response message to the first network function, the monitoring response message includes the working status of the second network function.

[0267] In an optional manner, the AI ​​information includes: AI services, and / or AI computing power.

[0268] In an optional manner, the AI ​​service includes one or more of the following: an identifier of the AI ​​service, an access address of a second network function providing the AI ​​service, a category of the AI ​​service, a category of models supported by the AI ​​service, a service object of the AI ​​service, a data format supported by the AI ​​service, and a data calculation scale of the AI ​​service; wherein the service objects of the AI ​​service include terminal device type, APP name, terminal users belonging to a preset number segment, geographic location information, slice information, and access point identifier.

[0269] In an optional manner, AI computing power includes one or more of the following:

[0270] The identification of AI computing, the computing power type of AI computing power, the computing power specification of AI computing power, the AI ​​framework type indicated by AI computing power, the AI ​​model category indicated by AI computing power, and the operator information of AI computing power.

[0271] like Fig.12 As shown, a communication device 1200 is also provided in the present application. The communication device 1200 may be a chip or a chip system. The communication device may be located in a device involved in any of the above method embodiments, such as an access network device, or a first core network device, etc., to perform actions corresponding to the device.

[0272] Optionally, the chip system may consist of the chip, or may include the chip and other discrete devices.

[0273] The communication device 1200 includes a processor 1210 .

[0274] The processor 1210 is used to execute the computer program stored in the memory 1220 to implement the actions of each device in any of the above method embodiments.

[0275] The communication device 1200 may further include a memory 1220 for storing computer programs.

[0276] Optionally, the memory 1220 is coupled to the processor 1210. Coupling is an indirect coupling or communication connection between devices, units or modules, which can be electrical, mechanical or other forms, and is used for information exchange between devices, units or modules. Optionally, the memory 1220 is integrated with the processor 1210.

[0277] The processor 1210 and the memory 1220 may be one or more and are not limited.

[0278] Optionally, in practical applications, the communication device 1200 may include a transceiver 1230 or may not include the transceiver 1230, which is illustrated by a dotted box in the figure, and the communication device 1200 may exchange information with other devices through the transceiver 1230. The transceiver 1230 may be a circuit, a bus, a transceiver, or any other device that can be used for information exchange.

[0279] In a possible implementation, the communication device 1200 may be an access network device or a first core network device in the implementation of the above methods.

[0280] The specific connection medium between the transceiver 1230, the processor 1210 and the memory 1220 is not limited in the embodiment of the present application. Fig.12 The memory 1220, the processor 1210 and the transceiver 1230 are connected via a bus. Fig.10 The connections between the other components are shown in bold lines, which are only for illustration and are not intended to be limiting. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig.12 Only one thick line is used to represent it, but it does not mean that there is only one bus or one type of bus. In the embodiment of the present application, the processor can be a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, and can implement or execute the methods, steps and logic block diagrams disclosed in the embodiment of the present application. The general-purpose processor can be a microprocessor or any conventional processor, etc. The steps of the method disclosed in the embodiment of the present application can be directly embodied as a hardware processor for execution, or can be executed by a combination of hardware and software modules in the processor.

[0281] In the embodiment of the present application, the memory may be a non-volatile memory, such as a hard disk drive (HDD) or a solid-state drive (SSD), etc., or a volatile memory (volatile memory), such as a random-access memory (RAM). The memory may also be any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory in the embodiment of the present application may also be a circuit or any other device that can implement a storage function, for storing computer programs, program instructions and / or data.

[0282] Based on the above embodiments, see Fig.13 The embodiment of the present application also provides another communication device 1300, including: an interface circuit 1310 and a logic circuit 1320; the interface circuit 1310 can be understood as an input and output interface, which can be used to execute the sending and receiving steps of each device in any of the above method embodiments, and the logic circuit 1320 can be used to run codes or instructions to execute the method executed by each device in any of the above embodiments, which will not be repeated.

[0283] Based on the above embodiments, the embodiments of the present application further provide a computer-readable storage medium, which stores instructions. When the instructions are executed, the method executed by each device in any of the above method embodiments is implemented. The computer-readable storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk, and other media that can store program codes.

[0284] Based on the above embodiments, an embodiment of the present application provides a communication system, which includes the first network function, the second network function, the third network function and the fourth network function mentioned in any of the above method embodiments, and can be used to execute the method executed by each device in any of the above method embodiments.

[0285] In addition, the above-mentioned communication system may also include a terminal (such as UE), which can execute the relevant method in any of the above-mentioned method embodiments by interacting with the access network device and the core network device.

[0286] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, compact disc read-only memory (CD-ROM), optical storage, etc.) containing computer-usable program codes.

[0287] The present application is described with reference to the flowchart and / or block diagram of the method, device (system), and computer program product according to the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the process and / or box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for implementing the functions specified in one process or multiple processes in the flowchart and / or one box or multiple boxes in the block diagram.

[0288] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0289] These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

Claims

1. A communication method, applied to a first network function, characterized in that: include: Receiving a request message from a third network function, the request message being used to request an access address of a second network function associated with target artificial intelligence AI information; querying the association relationship according to the request message to obtain an access address of the second network function associated with the target AI information, wherein the association relationship indicates a relationship between at least one AI information supported by at least one second network function and an access address of the second network function respectively associated with each of the AI ​​information, and the target AI information is one of the at least one AI information; Send a request response message to the third network function, where the request response message is used to indicate an access address of the second network function associated with the target AI information.

2. The method according to claim 1, characterized in that The request message is a subscription request message; The sending the request response message to the third network function includes: A notification message of successful subscription is sent to the third network function, and an access address of the second network function associated with the at least one AI information is sent to the third network function.

3. The method according to claim 1, characterized in that The request message is a query request message; The sending the request response message to the third network function includes: The access address of the second network function associated with the target AI information is sent to the third network function.

4. The method according to any one of claims 1 to 3, characterized in that: The method further comprises: receiving a registration request message from at least one of the second network functions, where the registration request message is used to indicate the at least one AI information supported by the second network function and an access address of the second network function respectively associated with each of the AI ​​information; The association relationship is determined according to the registration request message.

5. The method according to claim 4, characterized in that The method further comprises: Sending a monitoring message to the second network function, where the monitoring message is used to monitor the working status of the second network function; A monitoring response message is received from the second network function.

6. The method according to any one of claims 1 to 5, characterized in that: The third network function is a terminal; The receiving a request message from a third network function includes: and receiving a request message sent by the third network function through a fourth network function, where the fourth network function is used to perform format conversion on messages exchanged between the terminal and the first network function, and / or to perform identity authentication on the terminal and the first network function.

7. The method according to any one of claims 1 to 6, characterized in that: The AI ​​information includes: AI services, and / or AI computing power.

8. The method according to claim 4, characterized in that The AI ​​information is an AI service; the registration request message includes: an identifier of the AI ​​service and an access address of a second network function that provides the AI ​​service.

9. The method according to claim 8, characterized in that The registration request information may also include one or more of the following: The category of the AI ​​service, the model category supported by the AI ​​service, the service object of the AI ​​service, the data format supported by the AI ​​service, and the data calculation scale of the AI ​​service; Among them, the service objects of the AI ​​service include terminal device type, APP name, terminal users belonging to a preset number segment, geographic location information, slice information and access point identification.

10. The method according to claim 4, characterized in that The AI ​​information is AI computing power; the registration request information includes: an identifier of the AI ​​computing power and an access address of a second network function that provides the AI ​​computing power.

11. The method according to claim 10, characterized in that The registration request information may also include one or more of the following: The computing power type of the AI ​​computing power, the computing power specification of the AI ​​computing power, the AI ​​framework type indicated by the AI ​​computing power, the AI ​​model category indicated by the AI ​​computing power, and the operator information of the AI ​​computing power.

12. A communication method, applied to a fourth network function, characterized in that: include: Receiving a request message from a third network function, the request message being used to request an access address of a second network function associated with target artificial intelligence AI information; Sending the request message to the first network function; receiving a request response message from the first network function, where the request response message is used to indicate an access address of the second network function associated with the target AI information; The request response message is sent to the third network function.

13. The method according to claim 12, characterized in that The request message is a subscription request message; The receiving a request response message from the first network function includes: A notification message of successful subscription is received, and an access address of a second network function associated with at least one AI information from the first network function is received.

14. The method according to claim 12, characterized in that The request message is a query request message; The receiving a request response message from the first network function includes: An access address of a second network function associated with the target AI information is received from the first network function.

15. The method according to any one of claims 12 to 14, characterized in that: The third network function is a terminal; The method further comprises: performing format conversion on messages exchanged between the third network function and the first network function; and / or, Perform identity authentication on the third network function and the first network function.

16. The method according to claim 15, characterized in that Also includes: receiving data to be processed from the terminal; Converting the data to be processed into a data format corresponding to the AI ​​information required by the terminal and then sending the data to an access address of a second network function associated with the AI ​​information required by the terminal; receiving a data processing result from the second network function; The data processing result is sent to the terminal.

17. A communication method, applied to a third network function, characterized in that: include: Sending a request message to the fourth network function, wherein the request message is used to request an access address of the second network function associated with the target artificial intelligence AI information; receiving a request response message from the fourth network function, where the request response message is used to indicate an access address of the second network function associated with the target AI information; Sending the data to be processed to the access address of the second network function associated with the target AI information through the fourth network function; The data processing result from the second network function is received through the fourth network function.

18. The method according to claim 17, characterized in that The third network function is a terminal.

19. A communication method, applied to a second network function, characterized in that: include: Sending a registration request message to the first network function, where the registration request message is used to indicate at least one AI information supported by the second network function and an access address of the second network function associated with each AI information; A registration success response message is received from the second network function.

20. The method according to claim 19, characterized in that The method further comprises: receiving a monitoring message from the first network function, where the monitoring message is used to monitor a working status of the second network function; A monitoring response message is sent to the first network function, where the monitoring response message includes the working status of the second network function.

21. The method according to claim 19 or 20, characterized in that The AI ​​information includes: AI services, and / or AI computing power.

22. The method according to claim 21, characterized in that The AI ​​service includes one or more of the following: an identifier of the AI ​​service, an access address of a second network function providing the AI ​​service, a category of the AI ​​service, a model category supported by the AI ​​service, a service object of the AI ​​service, a data format supported by the AI ​​service, and a data calculation scale of the AI ​​service; Among them, the service objects of the AI ​​service include terminal device type, APP name, terminal users belonging to a preset number segment, geographic location information, slice information and access point identification.

23. The method according to claim 21, characterized in that The AI ​​computing power includes one or more of the following: The identifier of the AI ​​computing power, the computing power type of the AI ​​computing power, the computing power specification of the AI ​​computing power, the AI ​​framework type indicated by the AI ​​computing power, the AI ​​model category indicated by the AI ​​computing power, and the operator information of the AI ​​computing power.

24. A communication device, characterized in that: include: A functional module for implementing the method according to any one of claims 1 to 23.

25. A communication device, characterized in that: include: at least one processor and memory; The memory is used to store computer programs or instructions; The at least one processor is configured to execute the computer program or instructions so that the method according to any one of claims 1 to 23 is performed.

26. A chip system, characterized in that: The chip system comprises: a processing circuit; the processing circuit is coupled to a storage medium; The processing circuit is used to execute part or all of the computer programs or instructions in the storage medium, and when the part or all of the computer programs or instructions are executed, it is used to implement the method according to any one of claims 1 to 23.

27. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed by a computer, the method according to any one of claims 1 to 23 is executed.

28. A computer program product comprising a computer program or instructions, characterized in that When it is run on a computer, the method described in any one of claims 1 to 23 is executed.

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