Information processing method, node, communication device, communication system, and storage medium

Abstract

Embodiments of the present disclosure provide an information processing method, a node, a communication device, a communication system, and a storage medium. The method is performed by a first node. The method comprises: receiving first information sent by a second node, wherein the first information is used for requesting an artificial intelligence (AI) service; responding to the AI service requested by the first information; and sending second information to the second node, wherein the second information comprises a response result of the AI service. In the technical solution provided by embodiments of the present disclosure, the first node centrally executes the AI service and provides a result of the AI service to the external second node, so that the second node does not need to execute the AI service by itself, and the second node is able to directly use network resources of the first node to obtain the AI service.

Description

Information processing methods, nodes, communication equipment, communication systems, and storage media Technical Field

[0001] This disclosure relates to the field of communication technology, and in particular to information processing methods, nodes, communication devices, communication systems and storage media. Background Technology

[0002] Artificial intelligence (AI) will become one of the core technologies of future communications, particularly in 6G mobile communications (6G). th In Generation 6 (G) networks, AI will become an indispensable part, playing a crucial role.

[0003] Summary of the Invention

[0004] This disclosure provides an information processing method, a node, a communication device, and a storage medium.

[0005] According to a first aspect of the present disclosure, an information processing method is provided, wherein the method is executed by a first node, the method comprising: receiving first information sent by a second node, the first information being used to request an artificial intelligence (AI) service; responding to the AI ​​service requested by the first information; and sending second information to the second node, the second information including the response result of the AI ​​service.

[0006] According to a second aspect of the present disclosure, an information processing method is provided, wherein the method is executed by a second node, the method comprising: sending first information to a first node, the first information being used to request an AI service; and receiving second information sent by the first node, the second information including a response result of the AI ​​service.

[0007] According to a third aspect of the present disclosure, an information processing method is provided, wherein the method is executed by a third node, the method comprising: receiving third information sent by a first node based on first information; the first information being used to request an AI service; and sending a response result of the AI ​​service to the first node.

[0008] According to a fourth aspect of the present disclosure, an information processing method is provided, wherein the method is executed by a communication system, the method comprising: a second node sending first information to a first node, the first information being used to request an artificial intelligence (AI) service; the first node responding to the AI ​​service requested by the first information; and sending second information to the second node, the second information including the response result of the AI ​​service.

[0009] According to a fifth aspect of the present disclosure, a first node is provided, wherein the first node includes: a first receiving module configured to receive first information sent by a second node, the first information being used to request an artificial intelligence (AI) service; a processing module configured to respond to the AI ​​service requested by the first information; and a first sending module configured to send second information to the second node, the second information including the response result of the AI ​​service.

[0010] According to a sixth aspect of the present disclosure, a second node is provided, wherein the second node includes: a second sending module configured to send first information to a first node, the first information being used to request an AI service; and a second receiving module configured to receive second information sent by the first node, the second information including a response result of the AI ​​service.

[0011] According to a seventh aspect of the present disclosure, a third node is provided, wherein the third node includes: a third receiving module configured to receive third information sent by a first node based on first information; the first information is used to request an AI service; and a third sending module configured to send a response result of the AI ​​service to the first node.

[0012] According to an eighth aspect of the present disclosure, a communication system is provided, wherein the communication system includes a first node, a second node, and a third node, the first node being configured to implement the information processing method provided in the first aspect, the second node being configured to implement the information processing method provided in the second aspect, and the third node being configured to implement the information processing method provided in the third aspect.

[0013] According to a ninth aspect of the present disclosure, a communication device is provided, wherein the communication device includes:

[0014] One or more processors;

[0015] The processor is used to invoke instructions to cause the communication device to execute the information processing method provided by the first aspect, the second aspect, or the third aspect.

[0016] According to a tenth aspect of the present disclosure, a storage medium is provided, wherein the storage medium stores instructions that, when executed on a communication device, cause the communication device to perform the information processing method provided in the first aspect, the second aspect, or the third aspect.

[0017] According to an eleventh aspect of the present disclosure, a program product is provided, wherein when the program product is executed by a communication device, the communication device performs the information processing method provided by the first aspect, the second aspect, or the third aspect.

[0018] According to a twelfth aspect of the present disclosure, a computer program is provided that, when run on a computer, causes the computer to perform the information processing method provided in the first, second, or third aspect.

[0019] The technical solution provided in this disclosure embodiment involves a first node centrally executing AI services and opening the results of the AI ​​services to an external second node; this eliminates the need for the second node to execute AI services itself; and enables the second node to directly utilize the network resources of the first node to obtain AI services.

[0020] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the embodiments of this disclosure. Attached Figure Description

[0021] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the embodiments of the invention.

[0022] Figure 1A is a schematic diagram of the architecture of a communication system according to an exemplary embodiment;

[0023] Figure 1B is a schematic diagram illustrating a usage scenario of a novel mobile communication system according to an exemplary embodiment;

[0024] Figure 1C is a schematic diagram illustrating the network capabilities of a novel mobile communication system according to an exemplary embodiment;

[0025] Figure 1D is a schematic diagram illustrating an ML model training process according to an exemplary embodiment;

[0026] Figure 2 is an interactive schematic diagram of an information processing method according to an exemplary embodiment;

[0027] Figure 3A is a flowchart illustrating an information processing method according to an exemplary embodiment;

[0028] Figure 3B is a schematic flowchart of an information processing method according to an exemplary embodiment;

[0029] Figure 4 is a flowchart illustrating an information processing method according to an exemplary embodiment;

[0030] Figure 5 is a flowchart illustrating an information processing method according to an exemplary embodiment;

[0031] Figure 6 is an interactive schematic diagram of an information processing method according to an exemplary embodiment;

[0032] Figure 7A is a schematic diagram of a service-based AI-as-a-service architecture in a 6G system according to an exemplary embodiment;

[0033] Figure 7B is an interactive schematic diagram of an information processing method according to an exemplary embodiment;

[0034] Figure 8A is a schematic diagram of the structure of a first node according to an exemplary embodiment;

[0035] Figure 8B is a schematic diagram of the structure of a second node according to an exemplary embodiment;

[0036] Figure 8C is a schematic diagram of the structure of a third node according to an exemplary embodiment;

[0037] Figure 9A is a schematic diagram of the structure of a communication device according to an exemplary embodiment;

[0038] Figure 9B is a schematic diagram of the structure of a chip according to an exemplary embodiment. Detailed Implementation

[0039] This disclosure provides an information processing method and apparatus, a communication device, a communication system, and a storage medium.

[0040] In a first aspect, embodiments of this disclosure provide an information processing method, wherein the method is executed by a first node, and the method includes: receiving first information sent by a second node, the first information being used to request an artificial intelligence (AI) service; responding to the AI ​​service requested by the first information; and sending second information to the second node, the second information including the response result of the AI ​​service.

[0041] In the above embodiment, upon receiving the first information sent by the second node, the first node responds to the AI ​​service requested by the first information and sends the response result of the AI ​​service to the second node through the second information; thus, the first node centrally executes the AI ​​service and opens the result of the AI ​​service to the external second node; so that the second node does not need to execute the AI ​​service itself; and the second node can directly use the network resources of the first node to obtain the AI ​​service.

[0042] In conjunction with some embodiments of the first aspect, in some embodiments, the AI ​​service responding to the first information request includes: selecting a third node to respond to the AI ​​service based on at least one of the request information for the AI ​​service and the status information of candidate nodes; the third node being used to provide a response result of the AI ​​service; sending third information to the third node based on the first information; and receiving a response result sent by the third node based on the third information.

[0043] In the above embodiments, the first node selects a suitable third node to respond to the AI ​​service requested by the second node based on the AI ​​service request information from the second node and / or the status information of alternative nodes. In this way, the first node can rationally allocate AI services and effectively utilize the network resources of the third node, enabling the AI ​​service to be responded to more efficiently.

[0044] In some embodiments, in conjunction with the first aspect, sending the second information to the second node includes: sending the second information to the second node based on the response result provided by the third node.

[0045] In the above embodiments, the first node centrally manages the allocation and delivery of AI services, and the third node responds to the allocated AI services, thereby providing AI services to the second node more effectively.

[0046] In conjunction with some embodiments of the first aspect, in some embodiments, the service type of the AI ​​service includes at least one of the following: AI model training service; AI model inference service; AI model storage service.

[0047] In the above embodiments, the first node can provide training services, inference services and / or storage services for the AI ​​model to the external second node; in this way, all services related to the AI ​​model are centrally provided by the first node, which makes it easier for the first node to better manage the data related to the AI ​​model (such as model parameters); while the second node, as a consumer of network services, can directly obtain any of the above-mentioned AI model-related services through the first node.

[0048] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes: determining the demand information requesting the AI ​​service based on the first information, the demand information including at least one of the following: first type information, used to indicate the service type of the requested AI service; fourth information, used to indicate the service parameters for providing the AI ​​service, the service parameters including at least one of the following: the type of AI model, the training method of the AI ​​model, and the inference method of the AI ​​model.

[0049] In the above embodiment, the first node determines the service type of the AI ​​service requested by the second node and the service parameters for providing the AI ​​service based on the first information; thus, in the subsequent selection process of the third node, the first node can select an appropriate third node based on the service type and service parameters of the AI ​​service to ensure that the selected third node has the corresponding capabilities to respond to the AI ​​service.

[0050] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes: receiving the status information sent by the third node, the status information including at least one of the following: second type information, used to indicate the service type of the AI ​​service supported by the third node; capability information, used to indicate the processing capability of the third node; load information, used to indicate the load intensity of the third node.

[0051] In the above embodiments, the first node receives status information sent by the third node to learn about the service type of the AI ​​service supported by the third node, the processing capacity and load intensity of the third node, so as to assist the first node in selecting a suitable third node to respond to the AI ​​service requested by the second node, and ensure that the AI ​​service can be responded to more efficiently.

[0052] In conjunction with some embodiments of the first aspect, in some embodiments, the operation of the training service includes at least one of the following: training operation of the AI ​​model; collection operation of training data; and delivery operation of training results.

[0053] In the above embodiments, when the second node requests training services for the AI ​​model, the first node provides a response result of the training service to the second node by performing at least one of the following operations: training operation of the AI ​​model, collection operation of training data, and delivery operation of training results.

[0054] In conjunction with some embodiments of the first aspect, in some embodiments, the first information includes at least one of the following: an AI model to be trained; training data of the AI ​​model to be trained; and metadata of the AI ​​model to be trained, the metadata being used to indicate model attributes of the AI ​​model to be trained.

[0055] In the above embodiments, when the second node requests training services for the AI ​​model, the first information sent by the second node to the first node includes the AI ​​model to be trained (model parameters), training data, and metadata, so that the first node can train the AI ​​model based on the training data to obtain a trained AI model and complete the training service requested by the second node.

[0056] In conjunction with some embodiments of the first aspect, in some embodiments, the metadata includes at least one of the following: the model version of the AI ​​model; the model identifier of the AI ​​model; the model size of the AI ​​model; and the vendor identifier of the AI ​​model.

[0057] In the above embodiments, the metadata of the AI ​​model may include at least one of the following: the model version of the AI ​​model, the model identifier of the AI ​​model, the model size of the AI ​​model, and the vendor identifier of the AI ​​model, so that the first node can more accurately determine the AI ​​model requested for training by the second node based on the metadata of the AI ​​model to be trained.

[0058] In conjunction with some embodiments of the first aspect, in some embodiments, the second information includes at least one of the following: model parameters of the trained AI model; metadata of the trained AI model.

[0059] In the above embodiment, when the second node requests AI model training services, the first node responds to the AI ​​model training services and sends the model parameters and / or metadata of the trained AI model to the second node via second information. In this way, the second node can utilize the network resources of the first node to perform AI model training services.

[0060] In conjunction with some embodiments of the first aspect, in some embodiments, the third node responding to the training service includes at least one of the following: a first network function, wherein the first network function is a core network function having a first capability and a second capability; a second network function, wherein the second network function is a core network function having the first capability; the first capability is the training capability of an AI model; and the second capability is different from the first capability.

[0061] In the above embodiments, two network functions with different capability ranges are used to respond to the training service of the AI ​​model, so that the first node can select the network function with a more suitable capability range to respond to the processing parameters required by the AI ​​service requested by the second node.

[0062] In conjunction with some embodiments of the first aspect, in some embodiments, the operation of the inference service includes at least one of the following: inference operation of the AI ​​model; delivery operation of the inference result.

[0063] In the above embodiments, when the second node requests the inference service of the AI ​​model, the first node provides the inference result of the inference service to the second node by performing the inference operation and / or delivery operation of the AI ​​model.

[0064] In conjunction with some embodiments of the first aspect, in some embodiments, the first information includes at least one of the following: model parameters of the AI ​​model; model identification information for indicating the AI ​​model; and input information, including input data required to perform inference operations of the AI ​​model.

[0065] In the above embodiments, when the second node requests the inference service of the AI ​​model, the first information sent by the second node to the first node includes at least one of the model parameters, model identification information and input information of the AI ​​model; enabling the first node to determine the AI ​​model to be inferred and the input data of the AI ​​model to be inferred based on the first information, thereby inputting the input data into the AI ​​model to obtain the inference result of the AI ​​model and completing the inference service requested by the second node.

[0066] In conjunction with some embodiments of the first aspect, in some embodiments, the second information includes: the inference results of the AI ​​model.

[0067] In the above embodiment, when the second node requests the inference service of the AI ​​model, the first node responds with the inference service of the AI ​​model and sends the obtained inference result of the AI ​​model to the second node via second information. In this way, the second node can utilize the network resources of the first node to execute the inference service of the AI ​​model.

[0068] In conjunction with some embodiments of the first aspect, in some embodiments, the third node responding to the inference service includes at least one of the following: a third network function, which is a core network function having a third capability and a fourth capability; a fourth network function, which is a core network function having the third capability; the third capability is the inference capability of an AI model, which is different from the fourth capability.

[0069] In the above embodiments, two network functions with different capability ranges are used to respond to the inference service of the AI ​​model, so that the first node can select the network function with a more suitable capability range to respond to the processing parameters required by the AI ​​service requested by the second node.

[0070] In conjunction with some embodiments of the first aspect, in some embodiments, the operation of the storage service includes at least one of the following: uploading the AI ​​model; downloading the AI ​​model; deleting the AI ​​model.

[0071] In the above embodiments, the first node can respond to the service requests of the second node and better manage the stored AI models by uploading, downloading and / or deleting AI models.

[0072] In conjunction with some embodiments of the first aspect, in some embodiments, the AI ​​service responding to the first information request includes:

[0073] Based on the first information, determine whether the second node is authorized to obtain the AI ​​service; wherein, the first information includes: fifth information, used to instruct the second node; if it is determined that the second node is authorized to obtain the AI ​​service, respond to the AI ​​service requested by the first information.

[0074] In the above embodiment, the first node performs an authorization check on the second node based on the fifth information in the first information. If it is determined that the second node is authorized to obtain AI services, the first node responds to the AI ​​services requested by the second node, so that the first node provides AI services to the second node with the authority to obtain AI services, thereby ensuring security.

[0075] Secondly, embodiments of this disclosure provide an information processing method, wherein the method is executed by a second node, the method comprising: sending first information to a first node, the first information being used to request an AI service; and receiving second information sent by the first node, the second information including a response result of the AI ​​service.

[0076] In the above embodiment, the second node requests AI services from the first node by sending first information to the first node; and obtains the response result of the AI ​​services by receiving second information. In this way, the first node centrally executes the AI ​​services and opens the results of the AI ​​services to the external second node, so that the second node does not need to execute the AI ​​services itself, reducing the occupation of the second node's network resources.

[0077] Thirdly, this disclosure provides an information processing method, wherein the method is executed by a third node, and the method includes: receiving third information sent by a first node based on first information; the first information being used to request an AI service; and sending a response result of the AI ​​service to the first node.

[0078] In the above embodiment, the third node receives the third information sent by the first node and responds to the AI ​​service allocated by the first node, providing the response result of the AI ​​service to the first node; thus, the first node centrally manages the allocation and delivery of AI services, and the third node responds to the allocated AI services; thereby providing AI services to the second node in a better manner.

[0079] Fourthly, embodiments of this disclosure provide an information processing method, which is executed by a communication system. The method includes: a second node sending first information to a first node, the first information being used to request an artificial intelligence (AI) service; the first node responding to the AI ​​service requested by the first information; and sending second information to the second node, the second information including the response result of the AI ​​service.

[0080] Fifthly, embodiments of this disclosure provide a first node, wherein the first node includes: a first receiving module configured to receive first information sent by a second node, the first information being used to request an artificial intelligence (AI) service; a processing module configured to respond to the AI ​​service requested by the first information; and a first sending module configured to send second information to the second node, the second information including the response result of the AI ​​service.

[0081] In a sixth aspect, embodiments of this disclosure provide a second node, wherein the second node includes: a second sending module configured to send first information to a first node, the first information being used to request an AI service; and a second receiving module configured to receive second information sent by the first node, the second information including a response result of the AI ​​service.

[0082] In a seventh aspect, embodiments of this disclosure provide a third node, wherein the third node includes: a third receiving module configured to receive third information sent by a first node based on first information; the first information is used to request an AI service; and a third sending module configured to send a response result of the AI ​​service to the first node.

[0083] Eighthly, embodiments of this disclosure provide a communication system, wherein the communication system includes a first node, a second node, and a third node, the first node being configured to implement the information processing method described in the optional implementation of the first aspect, the second node being configured to implement the information processing method described in the optional implementation of the second aspect, and the third node being configured to implement the information processing method described in the optional implementation of the third aspect.

[0084] Ninthly, embodiments of this disclosure provide a communication device, the communication device comprising:

[0085] One or more processors;

[0086] The processor is used to invoke instructions to cause the communication device to execute the information processing method described in the optional implementation of the first, second, or third aspect.

[0087] In a tenth aspect, embodiments of this disclosure provide a storage medium storing instructions that, when executed on a communication device, cause the communication device to perform the information processing method described in an optional implementation of the first, second, or third aspect.

[0088] In one aspect, embodiments of this disclosure provide a program product that, when executed by a communication device, causes the communication device to perform the information processing method described in the optional implementations of the first, second, or third aspects.

[0089] In a twelfth aspect, embodiments of this disclosure provide a computer program that, when run on a computer, causes the computer to perform the information processing method described in an optional implementation of the first, second, or third aspect.

[0090] It is understood that the aforementioned first information indicating device, second information indicating device, communication equipment, communication system, storage medium, program product, and computer program are all used to execute the methods provided in the embodiments of this disclosure. Therefore, the beneficial effects that can be achieved can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0091] This disclosure provides an information processing method, a node, a communication device, a communication system, and a storage medium. In some embodiments, the terms "information processing method" and "information transmission method" can be used interchangeably, as can the terms "communication system" and "information processing system".

[0092] This disclosure is not exhaustive, but merely illustrative of some embodiments, and is not intended to limit the scope of protection of this disclosure. Unless otherwise specified, each step in a particular embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a particular embodiment can also be implemented as an independent embodiment, and the order of the steps in a particular embodiment can be arbitrarily interchanged. Furthermore, the optional implementation methods in a particular embodiment can be arbitrarily combined; moreover, the embodiments can be arbitrarily combined, for example, some or all steps of different embodiments can be arbitrarily combined, and a particular embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.

[0093] In each of the disclosed embodiments, unless otherwise specified or in case of logical conflict, the terminology and / or descriptions of the embodiments are consistent and can be referenced by each other. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0094] The terminology used in the embodiments of this disclosure is for the purpose of describing particular embodiments only and is not intended to limit the scope of this disclosure.

[0095] In this embodiment of the disclosure, unless otherwise stated, elements expressed in the singular form, such as "a," "an," "the," "the," "the," "the," "the," "the," "this," etc., can mean "one and only one," or "one or more," "at least one," etc. For example, when using articles such as "a," "an," "the," etc. in translation, the noun following the article can be understood as either a singular expression or a plural expression.

[0096] In the embodiments disclosed herein, "multiple" refers to two or more.

[0097] In some embodiments, the terms “at least one of”, “one or more”, “a plurality of”, “multiple”, etc., may be used interchangeably.

[0098] In some embodiments, the notation "at least one of A and B", "A and / or B", "A in one case, B in another", "A in one case, B in another", etc., may include the following technical solutions depending on the situation: in some embodiments, A (A is executed regardless of B); in some embodiments, B (B is executed regardless of A); in some embodiments, execution is selected from A and B (A and B are selectively executed); in some embodiments, A and B (both A and B are executed). The same applies when there are more branches such as A, B, C, etc.

[0099] In some embodiments, the notation "A or B" may include the following technical solutions, depending on the situation: in some embodiments, A (execution of A regardless of B); in some embodiments, B (execution of B regardless of A); in some embodiments, execution is selected from A and B (A and B are selectively executed). The same applies when there are more branches such as A, B, C, etc.

[0100] The prefixes "first," "second," etc., used in the embodiments of this disclosure are merely for distinguishing different descriptive objects and do not impose restrictions on the position, order, priority, quantity, or content of the descriptive objects. The description of the descriptive objects is found in the claims or the context of the embodiments, and the use of prefixes should not constitute unnecessary restrictions. For example, if the descriptive object is a "field," the ordinal numbers preceding "field" in "first field" and "second field" do not restrict the position or order of the "fields." "First" and "second" do not restrict whether the "fields" they modify are in the same message, nor do they restrict the order of "first field" and "second field." Similarly, if the descriptive object is a "level," the ordinal numbers preceding "level" in "first level" and "second level" do not restrict the priority between "levels." Furthermore, the number of descriptive objects is not limited by ordinal numbers and can be one or more. For example, in "first device," the number of "devices" can be one or more. Furthermore, the objects modified by different prefixes can be the same or different. For example, if the object being described is "device", then "first device" and "second device" can be the same device or different devices, and their types can be the same or different. Similarly, if the object being described is "information", then "first information" and "second information" can be the same information or different information, and their content can be the same or different.

[0101] In some embodiments, “including A,” “containing A,” “for indicating A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0102] In some embodiments, terms such as “…”, “determine…”, “in the case of…”, “when…”, “when…”, “if…”, etc. can be used interchangeably.

[0103] In some embodiments, the terms “greater than,” “greater than or equal to,” “not less than,” “more than,” “more than or equal to,” “not less than,” “higher than,” “higher than or equal to,” “not lower than,” and “above” can be used interchangeably, as can the terms “less than,” “less than or equal to,” “not greater than,” “less than,” “less than or equal to,” “not more than,” “lower than,” “lower than or equal to,” “not higher than,” and “below”.

[0104] In some embodiments, devices, etc., can be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. Terms such as “device”, “equipment”, “circuit”, “network element”, “node”, “function”, “unit”, “section”, “system”, “network”, “chip”, “chip system”, “entity”, and “subject” can be used interchangeably.

[0105] In some embodiments, "network" can be interpreted as devices included in a network (e.g., access network devices, core network devices, etc.).

[0106] In some embodiments, the terms "access network device (AN device)," "radio access network device (RAN device)," "base station (BS)," "radio base station," "fixed station," "node," "access point," "transmission point (TP)," "reception point (RP)," "transmission / reception point (TRP)," "panel," "antenna panel," "antenna array," "cell," "macro cell," "small cell," "femto cell," "pico cell," "sector," "cell group," "serving cell," "carrier," "component carrier," and "bandwidth part (BWP)" can be used interchangeably.

[0107] In some embodiments, the terms "terminal", "terminal device", "user equipment (UE)", "user terminal", "mobile station (MS)", "mobile terminal (MT)", "subscriber station", "mobile unit", "subscriber unit", "wireless unit", "remote unit", "mobile device", "wireless device", "wireless communication device", "remote device", "mobile subscriber station", "access terminal", "mobile terminal", "wireless terminal", "remote terminal", "handset", "user agent", "mobile client", and "client" can be used interchangeably.

[0108] In some embodiments, access network devices, core network devices, or network devices can be replaced by terminals. For example, embodiments of this disclosure can also be applied to structures where communication between access network devices, core network devices, or network devices and terminals is replaced by communication between multiple terminals (e.g., device-to-device (D2D), vehicle-to-everything (V2X), etc.). In this case, the structure can also be configured such that the terminal has all or part of the functions of the access network device. Furthermore, terms such as "uplink" and "downlink" can be replaced with terms corresponding to communication between terminals (e.g., "sidelink"). For example, uplink channel, downlink channel, etc., can be replaced with sidelink channel, and uplink link, downlink, etc., can be replaced with sidelink link.

[0109] In some embodiments, the terminal may be replaced by an access network device, a core network device, or a network device. In this case, the access network device, core network device, or network device may also be configured to have all or some of the functions of the terminal.

[0110] In some embodiments, the acquisition of data, information, etc., may comply with the laws and regulations of the country where the location is situated.

[0111] In some embodiments, data, information, etc., may be obtained with the user's consent.

[0112] Furthermore, each element, each row, or each column in the table of this disclosure can be implemented as an independent embodiment, and any combination of any element, any row, or any column can also be implemented as an independent embodiment.

[0113] Figure 1A is a schematic diagram of the architecture of a communication system according to an exemplary embodiment.

[0114] As shown in Figure 1A, the communication system 100 includes: a first node 101, a second node 102, and a third node 103.

[0115] In some embodiments, the first node may be a core network device.

[0116] In some embodiments, the second node may be a terminal or a core network device.

[0117] In some embodiments, the third node may be a core network device.

[0118] In some embodiments, the terminal includes, but is not limited to, at least one of the following: mobile phone, wearable device, Internet of Things device, car with communication function, smart car, tablet computer, computer with wireless transceiver function, virtual reality (VR) terminal device, augmented reality (AR) terminal device, wireless terminal device in industrial control, wireless terminal device in self-driving, wireless terminal device in remote medical surgery, wireless terminal device in smart grid, wireless terminal device in transportation safety, wireless terminal device in smart city, and wireless terminal device in smart home.

[0119] In some embodiments, a core network device may be a single device including one or more network functions, or it may be multiple devices or a group of devices, each including network functions. Network functions may be virtual or physical. The core network may include, for example, at least one of an Evolved Packet Core (EPC), a 5G Core Network (5GCN), or a Next Generation Core (NGC).

[0120] It is worth noting that the first node, the second node, and the third node can be different network functions.

[0121] It is understood that the communication system described in this disclosure is for the purpose of more clearly illustrating the technical solutions of this disclosure, and does not constitute a limitation on the technical solutions provided in this disclosure. As those skilled in the art will know, with the evolution of system architecture and the emergence of new business scenarios, the technical solutions provided in this disclosure are also applicable to similar technical problems.

[0122] The following embodiments of this disclosure can be applied to the communication system 100 shown in FIG1A, or to some of the main bodies, but are not limited thereto. The main bodies shown in FIG1A are illustrative. The communication system may include all or some of the main bodies in FIG1A, or it may include other main bodies outside of FIG1A. The number and form of each main body are arbitrary. The connection relationship between the main bodies is illustrative. The main bodies may not be connected or may be connected. The connection can be in any way, it can be a direct connection or an indirect connection, it can be a wired connection or a wireless connection.

[0123] The embodiments disclosed herein can be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 5G new radio (NR), Future Radio Access (FRA), New-Radio Access Technology (RAT), New Radio (NR), New radio access (NX), Future generation radio access (FX), Global System for Mobile communications (GSM), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), and IEEE 802.20, Ultra-Wideband (UWB), Bluetooth (a registered trademark), Public Land Mobile Network (PLMN) networks, Device-to-Device (D2D) systems, Machine-to-Machine (M2M) systems, Internet of Things (IoT) systems, Vehicle-to-Everything (V2X) systems, systems utilizing other information processing methods, and next-generation systems built upon them, etc. Furthermore, multiple systems can be combined (e.g., a combination of LTE or LTE-A with 5G).

[0124] As shown in Figures 1B and 1C, Figure 1B is a schematic diagram illustrating a usage scenario of a novel mobile communication system according to an exemplary embodiment. Figure 1C is a schematic diagram illustrating the network capabilities of a novel mobile communication system according to an exemplary embodiment.

[0125] Compared to the three existing use cases of mobile communication systems—Enhance Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and Massive Machine Type Communication (mMTC)—the aforementioned new mobile communication systems are expected to support expanded and new use cases.

[0126] In some embodiments, the novel mobile communication system described above supports use cases including: immersive communication, AI and communication, ultra-reliable low-latency communication, ubiquitous connectivity, massive communication, and integrated sensing and communication.

[0127] Specifically, for AI and communication scenarios, distributed computing and artificial intelligence applications will be supported. Typical use cases include: autonomous driving assistance; autonomous collaboration between devices to achieve medical assistance applications; decentralizing computationally intensive operations across devices and networks; and creating digital twins for event prediction.

[0128] This use case will require support for high regional traffic capacity and user experience rates, as well as low latency and high reliability, depending on the specific use case. Beyond communications, this use case is expected to include a range of new capabilities related to the integration of artificial intelligence and computing functions into IMT-2030, including data acquisition, preparation, and processing from diverse sources; distributed AI model training, model sharing, and distributed inference across systems; and computing resource coordination and interlocking.

[0129] At the same time, these new mobile communication systems offer enhanced and new capabilities. Furthermore, each capability may have different relevance and adaptability in different use cases.

[0130] For example, AI-related capabilities and interoperability capabilities. AI-related capabilities refer to the ability to provide certain functions in new mobile communication systems to support AI applications. These functions include distributed data processing, distributed learning, AI computation, AI model execution, and AI model inference. Interoperability capabilities refer to radio interfaces based on member inclusion and transparency to enable functionality between different entities within the system.

[0131] In some embodiments, the Network Data Analytics Function (NWDAF) provides the Analytics Data Repository Function (ADRF) service;

[0132] ADRF service operations can be used to store data or analytics in ADRF, retrieve data or analytics from ADRF, or delete data or analytics from ADRF.

[0133] ADRF operations can also be used to store machine learning (ML) models in an ADRF, retrieve ML models from an ADRF, or delete ML models from an ADRF.

[0134] As shown in Table 1, Table 1 lists the network function services provided by ADRF.

[0135] Table 1

[0136] NWDAF provides analytics for other network functions (NFs) and operation administration and maintenance (OAM) systems in the 5G core network (5GC). NWDAF may include the following logical functions:

[0137] (1) Analytics Logical Function (AnLF): A logical function in NWDAF that performs inference, derives analytics information (i.e., derives statistical data and / or forecasts based on analytics consumer requests) and exposes analytics services, namely Nnwdaf_AnalyticsSubscription or Nnwdaf_AnalyticsInfo.

[0138] (2) Model Training Logical Function (MTLF): A logical function in NWDAF that is used to train ML models and expose new training services (e.g., providing a fully trained ML model).

[0139] Note: NWDAF may include MTLF and / or AnLF.

[0140] As shown in Figure 1D, which is a schematic diagram of an ML model training process according to an exemplary embodiment, a consumer of an NWDAF service (i.e., an NWDAF containing MTLF) uses the process shown in Figure 1D to subscribe to another NWDAF (i.e., an NWDAF containing MTLF) to obtain an ML model trained based on ML model files or ML model information (such as that provided by the NWDAF service consumer). The NWDAF containing MTLF can use this service to enable or update the ML model. The NWDAF service consumer also uses this service to request the NWDAF containing MTLF to prepare for ML model training or modify the training of an existing ML model.

[0141] In NWDAF, model training, model inference, or storage are performed only within NWDAF. It does not provide training, inference, or storage services to AF, or support 6G NF with AI capabilities.

[0142] Currently, NWDAF including MTLF has been extended to train AI or ML models for AI localization only for the Location Management Function (LMF), but NWDAF does not support providing AI training or inference services for Application Function (AF).

[0143] It has become a consensus to reuse the capabilities of 5G to expose potential 6G network services. However, AI as a Service is not yet well defined in 5G or 6G systems. For example, how to reuse the Network Exposure Function (NEF) interface to expose 6G AI as a Service.

[0144] Figure 2 is an interactive schematic diagram of an information processing method according to an exemplary embodiment. As shown in Figure 2, this disclosure relates to an information processing method for a communication system 100, the method comprising:

[0145] Step S2101: The second node sends the first information to the first node.

[0146] In some embodiments, the first node receives first information sent by the second node.

[0147] In some embodiments, the first information is used to request AI services.

[0148] In some embodiments, the first information may be an AI service request.

[0149] In some embodiments, the service type of the AI ​​service may include at least one of the following: AI model training service; AI model inference service; AI model storage service.

[0150] In some embodiments, the first information may include service information indicating the type of the requested AI service.

[0151] In some embodiments, the first node may be an AI-related network function.

[0152] In some embodiments, the first node can be used to provide AI model-related services.

[0153] It should be noted that the first node is the provider of AI services; the first node can be a network function capable of providing at least one of the following: training services, inference services, and storage services for AI models. The second node is the consumer of AI services, and the first node provides the second node with at least one of the following: training services, inference services, and storage services for AI models.

[0154] In some embodiments, the first node includes at least one of the following: network functions participating in the training service of the AI ​​model; network functions participating in the inference service of the AI ​​model; and network functions participating in the storage service of the AI ​​model.

[0155] It should be noted that, if the first node is a network function participating in AI model training, it can also provide AI model training services to the second node. Similarly, if the first node is a network function participating in AI model inference, it can provide AI model inference services to the second node. Finally, if the first node is a network function participating in AI model storage services, it can provide AI model storage services to the second node.

[0156] In some embodiments, the operation of the training service may include at least one of the following: training the AI ​​model; collecting training data; and delivering training results.

[0157] In some embodiments, the operations of the inference service may include at least one of the following: inference operations of the AI ​​model; and delivery operations of the inference results.

[0158] In some embodiments, the operation of the storage service includes at least one of the following: uploading an AI model; downloading an AI model; deleting an AI model.

[0159] It should be noted that after determining the service type of the AI ​​service requested by the first information, the first node can provide the requested AI service to the second node by performing the operation corresponding to the service type of the AI ​​service.

[0160] In some embodiments, the second node may include at least one of the following: NF; AF; terminal; application service (AS).

[0161] It should be noted that in this embodiment, the first node can be an AI model-related service provided by NF, AF, terminal, and / or AS. It is worth noting that the second node is a consumer of AI services, but it can also be a provider of other business services.

[0162] In some embodiments, the second node and the first node can be deployed in different core network networks. It is understood that the second node needs to request AI services from the first node via NEF.

[0163] In some embodiments, the second node and the first node can be deployed within the same core network. It is understood that the second node can directly request AI services from the first node without going through NEF.

[0164] In some embodiments, the first information includes at least one of the following: the AI ​​model to be trained; the training data of the AI ​​model to be trained; and the metadata of the AI ​​model to be trained.

[0165] It should be noted that, in the case of requesting training services for an AI model, the first information may include at least one of the following: the AI ​​model to be trained, the training data of the AI ​​model to be trained, and the metadata of the AI ​​model to be trained.

[0166] Here, the first information includes the AI ​​model to be trained, which may be the model parameters of the AI ​​model to be trained carried in the first information, so that the first node can determine the AI ​​model to be trained based on the model parameters in the first information.

[0167] Model parameters include at least one of the following: model features, model structure, and weights. Model features refer to the data attributes or dimensions input into the AI ​​model. Model structure indicates the optimization algorithm, loss function, and network structure used by the AI ​​model. Weights are parameters indicating the connections of each neuron or layer in the model.

[0168] It's important to note that AI model parameters can be divided into two categories: trainable parameters (e.g., weights) and non-trainable parameters (e.g., model structure). Trainable parameters are automatically learned through optimization algorithms during model training. Non-trainable parameters are fixed and do not update during model training.

[0169] In some embodiments, the model parameters of the AI ​​model to be trained may include at least one of the following: non-training parameters; initial parameter values ​​for trainable parameters.

[0170] In some embodiments, training data is used for training, validating, and testing the AI ​​model to be trained.

[0171] It should be noted that training data is one of the most important parts of training an AI model; training an AI model refers to using the training data to make the AI ​​model fit the patterns in the training data through certain methods, and determining the model parameters (i.e., training parameters) of the AI ​​model.

[0172] In some embodiments, when the first information requests training services for the AI ​​model and the first information does not include training data of the AI ​​model to be trained, the first node collects the training data of the AI ​​model to be trained.

[0173] In some embodiments, the metadata of the AI ​​model to be trained is used to indicate the model attributes of the AI ​​model to be trained.

[0174] It's important to note that metadata is data that describes various attributes and information about an AI model. Metadata helps in understanding and managing AI models.

[0175] In some embodiments, metadata may include at least one of the following: the model version of the AI ​​model; the model identifier of the AI ​​model; the model size of the AI ​​model; the vendor identifier of the AI ​​model; the licensing information of the AI ​​model; and the description information of the AI ​​model.

[0176] In some embodiments, the first information includes at least one of the following: model parameters of the AI ​​model; model identification information; and input information.

[0177] It should be noted that AI model inference is the process of using a trained AI model to calculate input data and obtain predictive inference results.

[0178] In the case of requesting inference services from an AI model using the first information, the first information may include at least one of the AI ​​model's model parameters, model identification information, and input data.

[0179] Here, the first piece of information includes the model parameters of the AI ​​model, which are the parameters of the trained AI model. It is understandable that when different model parameters are used in an AI model, the inference results obtained by computing the same input data using AI models with different parameters will be different.

[0180] Model identification information is used to indicate the AI ​​model. It can be understood that the first node can determine the AI ​​model and / or model parameters that match the model identification information from the locally stored AI models based on the model identification information.

[0181] Input information includes the input data required to perform the inference operations of the AI ​​model. It is understood that the first node obtains the inference result output by the AI ​​model by inputting the data indicated by the input information into the AI ​​model.

[0182] In some embodiments, when the first information requests the storage service of the AI ​​model, the first information is also used to instruct the operation of the requested storage service.

[0183] In some embodiments, the first information may include operation information for indicating the operation of the requested storage service.

[0184] Here, the operation information can be one or more indicator bits, and different bit values ​​of the indicator bits can be used to indicate different operations of the storage service.

[0185] In some embodiments, the first information may further include at least one of the following: model parameters of the AI ​​model; model identification information; and metadata of the AI ​​model.

[0186] In some embodiments, the first information further includes: fifth information, used to indicate the second node.

[0187] Here, the fifth piece of information can be identification information indicating the second node, or it can be other information that can be used to indicate the identity of the second node.

[0188] In some embodiments, the fifth information is also used by the first node to determine whether the second node is authorized to obtain AI services.

[0189] It should be noted that after receiving the first message from the second node, the first node needs to authenticate the second node based on the fifth message within the first message to determine whether the second node has the authority to access AI services.

[0190] Step S2102: The first node responds to the AI ​​service of the first information request.

[0191] In some embodiments, the AI ​​service that the first node responds to in response to the first information request includes at least one of the following:

[0192] In the case where the first node includes network functions that participate in the training service of the AI ​​model, the first node responds to the training service of the AI ​​model in response to the first information request;

[0193] In the case where the first node includes network functions that participate in the inference service of the AI ​​model, the first node responds to the inference service of the AI ​​model that is the first information request.

[0194] In the case where the first node includes network functions that participate in the storage service of the AI ​​model, the first node responds to the storage service of the AI ​​model in response to the first information request.

[0195] In some embodiments, the method further includes:

[0196] Based on the initial information, determine the required information for requesting AI services.

[0197] In some embodiments, demand information is used to indicate the second node's demand for the response results of the requested AI service.

[0198] For example, if the AI ​​service requested by the first information is an inference service for an AI model, the requirement information determined based on the first information may include inference accuracy requirements and / or inference duration requirements, etc. Alternatively, if the AI ​​service requested by the first information is a training service for an AI model, the requirement information determined based on the first information may include training accuracy requirements, training step requirements, and / or training stopping condition requirements, etc.

[0199] In some embodiments, demand information can be used by the first node in response to the first information request's AI service.

[0200] In some embodiments, the demand information includes at least one of the following: a first type of information indicating the service type of the requested AI service; and a fourth type of information indicating the service parameters for providing the AI ​​service.

[0201] The first node can determine the first type of information based on the service information in the first information. It is understandable that the operations the first node needs to perform to provide the AI ​​service will differ depending on the type of AI service requested in the first information.

[0202] The fourth information indicates that the service parameters for providing AI services can be execution parameters related to the AI ​​services.

[0203] In some embodiments, the service parameters include at least one of the following: the type of AI model; the training method of the AI ​​model; and the inference method of the AI ​​model.

[0204] It's important to note that AI models can encompass several different types, each suitable for different tasks and problems. The network structures of different types of AI models may differ. For example, AI models can include feedforward neural networks (FNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). FNNs consist of input layers, hidden layers, and output layers, and are suitable for classification and regression problems. RNNs are neural networks with recurrent connections, suitable for sequential data, such as natural language processing. GANs consist of a generator and a discriminator, used to generate realistic data samples.

[0205] In some embodiments, the type of AI model may be determined by the business scenario to which the AI ​​model is applicable.

[0206] For example, in extended reality (XR) business scenarios, the second node needs to process the audio and video data streams in the XR business using an AI model; the second node can send a first message to the first node to request training of an AI model for the audio and video data streams.

[0207] For example, in a location-based service scenario, a second node (e.g., LMF) can send the first message to the first node to request the training of an AI model for location services.

[0208] It should be noted that the network structure of the AI ​​model may differ depending on the business scenario.

[0209] The type of AI model is used in the first node to determine how the AI ​​model is trained and / or inferred.

[0210] AI model training methods can include supervised learning, unsupervised learning, and federated learning. It should be noted that different training methods are suitable for different types of AI models, different problems, and / or different application scenarios.

[0211] AI models can employ inference methods such as distributed inference and sequential inference. It's important to note that different inference methods result in varying inference accuracy, inference time, and / or inference complexity. Different inference methods are suitable for different types of AI models.

[0212] In some embodiments, the AI ​​service that the first node responds to the first information request includes:

[0213] Based on the demand information, execute the requested AI service.

[0214] It should be noted that the first node can determine the operations related to the AI ​​service based on the demand information; and execute the operations related to the AI ​​service to obtain the response results of the AI ​​service.

[0215] For example, in the case of a first information request for storage services of an AI model, the operation related to the AI ​​service determined by the first node based on the request information is the uploading operation of the AI ​​model. The first node can store the model parameters, model identification information and metadata of the AI ​​model carried in the first information into the AI ​​model's repository.

[0216] For example, in the case of a first information request for AI model training services, the first node determines the training method of the AI ​​model based on the request information, and performs the training operation of the AI ​​model according to the training method to obtain a trained AI model; and sends the trained AI model to the second node.

[0217] For example, in the case of a first information request for the inference service of an AI model, the first node determines the inference method of the AI ​​model according to the request information, performs the inference operation of the AI ​​model according to the inference method of the AI ​​model, obtains the inference result, and sends the inference result to the second node.

[0218] It is worth noting that in this embodiment of the disclosure, the first node itself responds to the AI ​​service of the first information request and provides the response result of the AI ​​service.

[0219] In some embodiments, the AI ​​service that the first node responds to the first information request includes:

[0220] Based on the first piece of information, determine whether the second node is authorized to access AI services;

[0221] Once it is determined that the second node is authorized to obtain AI services, the AI ​​service responds to the first information request.

[0222] Here, the first information may include a fifth information used to instruct the second node; the first node may determine whether the second node is authorized to obtain AI services based on the fifth information within the first information.

[0223] It should be noted that the first node may store sixth information, which includes information related to nodes authorized to obtain AI services; the first node can determine whether the second node is authorized to obtain AI services by determining whether the sixth information contains the second node indicated by the fifth information.

[0224] If it is determined that the second node is authorized to obtain the AI ​​service, the first node responds to the AI ​​service requested by the first information request. If it is determined that the second node is not authorized to obtain the AI ​​service, the first node refuses to respond to the AI ​​service requested by the first information request.

[0225] In some embodiments, the method further includes:

[0226] If the first node refuses to respond to the AI ​​service requesting the first information, the first node sends a seventh information to the second node, which indicates the reason for the refusal to respond.

[0227] Step S2103: The first node selects the third node.

[0228] In some embodiments, the third node is used to provide the response results of the AI ​​service.

[0229] It should be noted that the third node can be the executor of AI services.

[0230] In some embodiments, the first node selects the third node including at least one of the following:

[0231] In the case of the first information requesting the training service of the AI ​​model, the first node selects the third node that responds to the training service;

[0232] In the case of the first information requesting the inference service of the AI ​​model, the first node selects the third node to respond to the inference service;

[0233] In the case of the first information request for the storage service of the AI ​​model, the first node selects a third node that responds to the storage service.

[0234] It should be noted that, in this embodiment of the disclosure, the communication system may include multiple third nodes, wherein different third nodes may be able to respond to different types of AI services. Therefore, when selecting a third node, the first node needs to select a third node that can respond to the AI ​​service type requested by the first information.

[0235] It is worth noting that in this embodiment of the disclosure, the response result of the AI ​​service is provided by a third node, and the first node selects a suitable third node based on the first information.

[0236] In some embodiments, the third node of the response training service includes at least one of the following:

[0237] The first network function is a core network function with first and second capabilities.

[0238] The second network function is a core network function with the first capability.

[0239] In some embodiments, the first capability is the training capability of the AI ​​model, and the second capability is different from the first capability.

[0240] In some embodiments, the second capability may be a capability required to process any service of the core network. For example, the second capability may be a core network capability such as session management capability, access management capability, or mobility management capability.

[0241] It should be noted that the first network function can be an existing network function in the communication system. This embodiment of the disclosure provides AI model training services by reusing the existing first network function of the communication system. It is understood that the first network function, in addition to performing its original function, can also be used to train the AI ​​model. For example, the first network function can be an LMF with training capabilities. Another example is a Network Repository Function (NRF) with training capabilities.

[0242] The second network function can be a newly defined network function in a communication system, specifically designed for handling the training services of AI models within the core network. It is understood that in a communication system, the second network function can only be used to handle the training services of AI models and cannot perform other functions of the core network.

[0243] It is worth noting that in the embodiments of this disclosure, both the first network function and the second network function can respond to the training service of the AI ​​model; however, the processing capabilities of the first network function and the second network function are different.

[0244] In some embodiments, the third node of the response reasoning service includes at least one of the following:

[0245] The third network function is a core network function with third and fourth capabilities.

[0246] The fourth network function is a core network function with third capabilities.

[0247] In some embodiments, the third capability is the reasoning capability of the AI ​​model, and the third capability is different from the fourth capability.

[0248] In some embodiments, the fourth capability may be a capability required to process any service of the core network. For example, the fourth capability may be a core network capability such as location management capability, access management capability, or mobility management capability.

[0249] It should be noted that the third network function can be an existing network function in the communication system. This embodiment of the disclosure provides AI model inference services by reusing existing third network functions of the communication system. It is understood that, in addition to performing its original functions, the third network function can also be used to perform AI model inference. For example, the third network function can be an LMF with inference capabilities. Another example is an AnLF with inference capabilities.

[0250] The fourth network function can be a newly defined network function in a communication system, specifically designed for handling inference services for AI models within the core network. It is understood that in a communication system, the fourth network function can only be used for handling inference services for AI models and cannot perform other core network functions.

[0251] It is worth noting that in the embodiments of this disclosure, both the third network function and the fourth network function can respond to the inference service of the AI ​​model; however, the processing capabilities of the third network function and the fourth network function are different.

[0252] In some embodiments, the third node of the response storage service includes at least one of the following:

[0253] Unified Data Repository (UDR) functionality;

[0254] The Analytics Data Repository Function (ADRF).

[0255] It should be noted that the embodiments of this disclosure reuse existing network functions in the communication system, namely UDR or ADRF, to perform operations related to the storage services of AI models.

[0256] In some embodiments, the method further includes: the first node receiving status information sent by the third node, the status information including at least one of the following:

[0257] The second type of information is used to indicate the service type of the AI ​​services supported by the third node;

[0258] Capability information, used to indicate the processing capabilities of the third node;

[0259] Load information is used to indicate the load intensity of the third node.

[0260] It should be noted that, in this embodiment of the disclosure, the third node in the communication system can periodically send status information to the first node to assist the first node in selecting a third node to respond to the AI ​​service. Alternatively, upon receiving the first information, the first node can publish task information related to the AI ​​service; after receiving the task information, the third node in the communication system can send status information to the first node.

[0261] In some embodiments, the first node selecting the third node includes: selecting a third node that responds to the AI ​​service based on at least one of the request information for the AI ​​service and the status information of the candidate nodes.

[0262] It should be noted that the first node can select a third node to respond to the AI ​​service based on the request information. For example, the first node selects a third node to respond to the AI ​​service based on the training method of the AI ​​model indicated by the fourth information. If the fourth information indicates that the AI ​​model is trained using federated learning, the first node can select the first network function and the second network function as the third node to respond to the AI ​​service.

[0263] Alternatively, the first node can select a third node to respond to the AI ​​service based on the status information of the candidate nodes. For example, the first node can select a third node whose capability information matches the requested AI service, based on the capability information of the candidate nodes.

[0264] Alternatively, the first node can select a third node to respond to the AI ​​service based on demand information and the status information of candidate nodes. For example, the first node can select a third node to respond to the AI ​​service based on the inference method of the AI ​​model indicated by the fourth information and the load information of the candidate nodes.

[0265] Step S2104: The first node sends third information to the third node.

[0266] In some embodiments, the third node receives third information sent by the first node.

[0267] In some embodiments, the third information is used to indicate the response result of the third node providing the AI ​​service.

[0268] In some embodiments, the first node sends third information to the third node based on the first information.

[0269] It should be noted that after the third node responding to the AI ​​service is identified, the first node can determine the third information based on the first information and send the third information to the third node.

[0270] In some embodiments, the first node sends third information to the third node based on the first information, including at least one of the following:

[0271] In the case of the first information requesting the training service of the AI ​​model, the first node sends the third information to the third node, which includes the model parameters and training data of the AI ​​model to be trained.

[0272] In the case of the first information requesting the inference service of the AI ​​model, the first node sends the third information to the third node, which includes the model parameters and input information of the AI ​​model;

[0273] In the case of the first information requesting the storage service of the AI ​​model, the first node sends third information to the third node. The third information includes operation information and model information related to the operation information.

[0274] It should be noted that in the case of the first information request for AI model training services, the first node sends the model parameters and training data of the AI ​​model to be trained to the third node, so that the third node can use the training data to train the model parameters of the AI ​​model to be trained, so as to obtain the trained AI model.

[0275] Here, the training data in the third information can be the training data carried in the first information, or the training data collected by the first node.

[0276] In the case of a first request for an AI model inference service, the first node sends the model parameters and input information of the AI ​​model to the third node, so that the third node can determine the AI ​​model to be inferred based on the model parameters of the AI ​​model; and inputs the data indicated by the input information into the AI ​​model to be inferred to obtain the inference result output by the AI ​​model.

[0277] Operation information is used to instruct the third node on the operation of the storage service. This operation includes uploading, downloading, and deleting AI models. The first node instructs the third node to perform the specific operation by including operation information in the third information.

[0278] In some embodiments, the model information related to the operation information may include at least one of the following: model parameters of the AI ​​model; model identification information; and metadata of the AI ​​model. It is understood that the model information related to the operation information is used to determine the AI ​​model on which the storage service operation is performed. The first node instructs the third node to perform the storage service operation on which AI model by carrying the model information related to the operation information in the third information.

[0279] In some embodiments, the method further includes:

[0280] The third node executes AI service operations based on third-party information.

[0281] In some embodiments, the third node performs AI service operations based on third-party information, including:

[0282] In the case of the first information requesting the training service of the AI ​​model, the third node performs the training operation of the AI ​​model based on the third information;

[0283] In the case of the first information requesting the training service of the AI ​​model, the third node performs the inference operation of the AI ​​model based on the third information;

[0284] In the event that the first information requests storage services for the AI ​​model, the third node performs at least one of the following operations based on the third information: uploading the AI ​​model, downloading the AI ​​model, and deleting the AI ​​model.

[0285] Step S2105: The third node sends the response result of the AI ​​service to the first node.

[0286] In some embodiments, the first node receives the response result of the AI ​​service sent by the third node.

[0287] In some embodiments, where the AI ​​service is a training service for an AI model, the third node sends the training results to the first node, and the training results include the model parameters of the AI ​​model that has completed training.

[0288] It should be noted that after receiving the third information, the third node trains the AI ​​model based on the third information to obtain the model parameters of the trained AI model; and sends the model parameters of the trained AI model as the training result to the first node so that the first node can deliver the training result to the second node.

[0289] In some embodiments, the training results may also include metadata of the trained AI model.

[0290] Understandably, after the third node completes the training of the AI ​​model, it can send the metadata of the trained AI model to the first node, so that the first node can manage the trained AI model based on the metadata of the trained AI model.

[0291] In some embodiments, where the AI ​​service is an inference service for an AI model, the third node sends the inference result to the first node so that the first node can deliver the inference result relative to the second node.

[0292] It should be noted that after receiving the third information, the third node performs reasoning on the AI ​​model based on the third information to obtain the reasoning result, and then sends the reasoning result to the first node.

[0293] In some embodiments, where the AI ​​service is a storage service for an AI model, the third node sends a storage result to the first node, the storage result indicating that the third node has successfully executed the storage service operation.

[0294] It should be noted that when the AI ​​service is a storage service for the AI ​​model, the third node performs the storage service operation based on the third information, and after completing the storage service operation, sends the storage result to the first node to inform the first node.

[0295] For example, when a third node performs a deletion operation on an AI model based on third information, the third node can send an instruction to the first node indicating that the third node has successfully deleted the stored result of the AI ​​model after completing the deletion operation.

[0296] In some embodiments, where the AI ​​service is a storage service for the AI ​​model and the operation information indicates an upload operation for the AI ​​model, the third node sends the storage result to the first node, and the storage result includes the storage address of the AI ​​model.

[0297] It should be noted that when the third node performs the AI ​​model upload operation based on the third information, after the third node completes the storage of the AI ​​model's model information, it can send the storage address as the storage result to the first node.

[0298] In some embodiments, where the AI ​​service is a storage service for an AI model and the operation information indicates a download operation for the AI ​​model, the third node sends the storage result to the first node; the storage result includes at least one of the following: model parameters of the AI ​​model; metadata of the AI ​​model.

[0299] It is understandable that when a third node performs an AI model download operation based on third information, the third node can obtain the model parameters and / or metadata of the AI ​​model, and send the model parameters and / or metadata of the AI ​​model as storage results to the first node.

[0300] Step S2106: The first node sends the second information to the second node.

[0301] In some embodiments, the second node receives second information sent by the first node.

[0302] In some embodiments, the second information includes the response results of the AI ​​service.

[0303] In some embodiments, the second information may be an AI service response.

[0304] In some embodiments, the first node sending second information to the second node includes: the first node sending second information to the second node based on the response result provided by the third node.

[0305] In some embodiments, when the first information requests training services for the AI ​​model, the first node sends second information to the second node, the second information including at least one of the following: model parameters of the AI ​​model that has completed training; metadata of the AI ​​model that has completed training.

[0306] It should be noted that, in the case of the first information requesting the training service of the AI ​​model, the first node sends the response result of the training service, that is, the model parameters and / or metadata of the AI ​​model that has completed training, to the second node through the second information.

[0307] In some embodiments, the second information further includes: training data collected by the first node.

[0308] It should be noted that since the training data used to train the AI ​​model may be different, the model parameters of the trained AI model may be different. Therefore, if the first information requests the training service of the AI ​​model and the first information does not include the training data, the first node can also send the collected training data to the second node through the second information.

[0309] In some embodiments, when the first information requests the inference service of the AI ​​model, the first node sends second information to the second node, the second information including the inference result of the AI ​​model.

[0310] It should be noted that when the first information requests the inference service of the AI ​​model, the first node sends the inference result of the AI ​​model to the second node through the second information.

[0311] In some embodiments, when the first information requests the storage service of the AI ​​model, the first node sends a second information to the second node, the second information including: the storage result of the AI ​​model.

[0312] In some embodiments, the storage result is used to indicate that the third node has successfully performed the storage service operation.

[0313] In some embodiments, the storage result may further include at least one of the following: model parameters of the AI ​​model; metadata of the AI ​​model; storage address of the AI ​​model.

[0314] It should be noted that when the first information requests the storage service of the AI ​​model, the first node sends the storage result of the AI ​​model to the second node through the second information.

[0315] In some embodiments, the term "information" may be used interchangeably with terms such as "message," "signal," "signaling," "report," "configuration," "indication," "instruction," "command," "channel," "parameter," "field," and "data."

[0316] In some embodiments, the term "send" may be used interchangeably with terms such as "transmit," "report," or "transmit."

[0317] The information processing method involved in the embodiments of this disclosure may include at least one of steps S2101 to S2106. For example, step S2101 may be implemented as a standalone embodiment, and steps S2101, S2102 and S2106 may be implemented as standalone embodiments, but are not limited thereto.

[0318] In some embodiments, steps S2102, S2103, S2104, S2105, and S2106 are optional, and one or more of these steps may be omitted or substituted in different embodiments. It is understood that if the first node determines that the second node does not have permission to obtain AI services, the first node may refuse to respond to the AI ​​service requested by the first information request.

[0319] In some embodiments, steps S2103, S2104, and S2105 are optional, and one or more of these steps may be omitted or substituted in different embodiments. It is understood that in the case of a first information request for the AI ​​model's storage service, the first node can directly respond to the AI ​​model's storage service without needing to select a third node to respond.

[0320] Figure 3A is a flowchart illustrating an information processing method according to an exemplary embodiment. As shown in Figure 3A, this embodiment relates to an information processing method executed by a first node, the method comprising:

[0321] Step S3101: Receive the first information.

[0322] In some embodiments, optional implementations of step S3101 can be found in optional implementations of step S2101 in FIG2 and other related parts in the embodiments involved in FIG2, which will not be repeated here.

[0323] Step S3102: Respond to the AI ​​service in response to the first information request.

[0324] In some embodiments, optional implementations of step S3102 can be found in optional implementations of step S2102 in FIG2 and other related parts in the embodiments involved in FIG2, which will not be repeated here.

[0325] Step S3103: Select the third node.

[0326] In some embodiments, optional implementations of step S3103 can be found in optional implementations of step S2103 in FIG2 and other related parts in the embodiments involved in FIG2, which will not be repeated here.

[0327] Step S3104: Send the third message.

[0328] In some embodiments, optional implementations of step S3104 can be found in optional implementations of step S2104 in FIG2 and other related parts in the embodiments involved in FIG2, which will not be repeated here.

[0329] Step S3105: Receive the response result from the AI ​​service.

[0330] In some embodiments, optional implementations of step S3105 can be found in optional implementations of step S2105 in FIG2 and other related parts in the embodiments involved in FIG2, which will not be repeated here.

[0331] Step S3106: Send the second message.

[0332] In some embodiments, optional implementations of step S3106 can be found in optional implementations of step S2106 in FIG2 and other related parts in the embodiments involved in FIG2, which will not be repeated here.

[0333] The information processing method involved in the embodiments of this disclosure may include at least one of steps S3101 to S3106. For example, step S3101 may be implemented as a standalone embodiment, and steps S3101, S3102 and S3106 may be implemented as standalone embodiments, but are not limited thereto.

[0334] In some embodiments, steps S3102, S3103, S3104, S3105, and S3106 are optional, and one or more of these steps may be omitted or substituted in different embodiments. It is understood that if the first node determines that the second node does not have permission to obtain AI services, the first node may refuse to respond to the AI ​​service requested by the first information request.

[0335] In some embodiments, steps S3103, S3104, and S3105 are optional, and one or more of these steps may be omitted or substituted in different embodiments. It is understood that the first node itself can respond to the AI ​​model's storage service, without needing to select a third node to respond to the AI ​​model's storage service.

[0336] Figure 3B is a schematic flowchart of an information processing method according to an exemplary embodiment. As shown in Figure 3B, this embodiment relates to an information processing method, executed by a first node, the method including:

[0337] Step S3201: Receive the first information.

[0338] In some embodiments, the first information is used to request AI services.

[0339] The optional implementation of step S3201 can be found in the optional implementation of step S2101 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0340] Step S3302: Respond to the AI ​​service in response to the first information request.

[0341] The optional implementation of step S3202 can be found in the optional implementation of step S2102 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0342] Step S3203: Send the second message.

[0343] In some embodiments, the second information includes the response results of the AI ​​service.

[0344] The optional implementation of step S3203 can be found in the optional implementation of step S2106 in Figure 2 and other related parts in the embodiments involved in Figure 2, which will not be repeated here.

[0345] Figure 4 is a flowchart illustrating an information processing method according to an exemplary embodiment. As shown in Figure 4, this embodiment relates to an information processing method executed by a second node, the method comprising:

[0346] Step S4101: Send the first message.

[0347] In some embodiments, optional implementations of step S4101 can be found in optional implementations of step S2101 in FIG2 and other related parts in the embodiments involved in FIG2, which will not be repeated here.

[0348] Step S4102: Receive the second information.

[0349] In some embodiments, optional implementations of step S4102 can refer to optional implementations of step S2106 in FIG2, and other related parts in the embodiments involved in FIG2, which will not be repeated here.

[0350] The information processing method involved in the embodiments of this disclosure may include at least one of steps S4101 to S4102. For example, step S4101 may be implemented as a standalone embodiment, but is not limited thereto.

[0351] In some embodiments, step S4102 is optional, and one or more of these steps may be omitted or substituted in different embodiments. It is understood that if the first node determines that the second node does not have permission to access the AI ​​service, the first node may refuse to respond to the AI ​​service requested by the first information request.

[0352] Figure 5 is a flowchart illustrating an information processing method according to an exemplary embodiment. As shown in Figure 5, this embodiment relates to an information processing method executed by a third node, the method comprising:

[0353] Step S5101: Receive third information.

[0354] In some embodiments, optional implementations of step S5101 can refer to optional implementations of step S2104 in FIG2, and other related parts in the embodiments involved in FIG2, which will not be repeated here.

[0355] Step S5102: Send the response result of the AI ​​service.

[0356] In some embodiments, optional implementations of step S5102 can refer to optional implementations of step S2105 in FIG2, and other related parts in the embodiments involved in FIG2, which will not be repeated here.

[0357] Figure 6 is a schematic diagram of an information processing method according to an exemplary embodiment. As shown in Figure 6, this disclosure relates to an information processing method for a communication system 100, the method including one of the following steps:

[0358] Step S6101: The second node sends the first information to the first node.

[0359] Step S6102: The first node responds to the AI ​​service of the first information request.

[0360] Step S6103: The first node sends the second information to the second node.

[0361] In some embodiments, the above methods may include the methods of the above-described communication system side, first node side, second node side, third node side, etc., which will not be described again here.

[0362] This disclosure proposes a unified framework for providing AI as a service in a 6G system, including three AI-related network functions.

[0363] (1) AI Model Storage Service Function (AiSSF) provides AI model storage services, including AI model management, such as uploading, downloading or deleting AI models.

[0364] (2) AI model inference service function (AiISF) provides AI model inference services, including AI inference node selection and AI model selection.

[0365] (3) AI Model Training Service Function (AiTSF) provides AI model training services, including training method selection and training node selection.

[0366] This architecture also defines a new type of 6G NF.

[0367] (1) 6G NF with reasoning capability supports AI model reasoning, such as LMF with reasoning capability, for calculating the location of the terminal.

[0368] (2) 6G NF with training capability, supporting AI model training, such as LMF with training capability, used to train AI models.

[0369] As shown in Figure 7A, Figure 7A is a schematic diagram of a service-based AI-as-a-Service architecture in a 6G system according to an exemplary embodiment. AiSSF is an AI-related network function responsible for storing AI models. Here, AiSSF corresponds to the network function participating in the storage service of AI models in this disclosure.

[0370] AiSSF provides the following services:

[0371] (1) Uploading AI Models; The training NF of the AI ​​model can upload the trained AI model to AiSSF. Optionally, the AF or UE can upload the AI ​​model to AiSSF after passing the necessary authentication or authorization.

[0372] (2) AI model download operation; the AI ​​model's inference NF (e.g., AnLF) or 6G NF with inference capability can download the AI ​​model for inference. Optionally, the UE or AI can download the AI ​​model from AiSSF after passing the necessary authentication or authorization.

[0373] (3) Deletion of AI model; Authorized AF, UE or 6G NF may request AiSSF to perform the deletion of AI model.

[0374] AiISF is an AI-related network function responsible for inference services for AI models. Here, AiISF corresponds to the network function involved in the inference service of AI models in this disclosure.

[0375] AiISF can provide the following services:

[0376] (1) Inference operations of AI models; AiISF performs inference on AI models through input data and provides inference results. Inference operations include: selecting inference methods and inference nodes, delivering inference results, etc.

[0377] (2) Delivery of inference results; performed by AiISF, delivering inference results to one or a group of consumers based on requests, service requirements or policies.

[0378] AiTSF is an AI-related network function responsible for training AI models. Here, AiTSF corresponds to the network function in this disclosure that participates in the training service of AI models.

[0379] AiTSF can provide the following services:

[0380] (1) Training operations for AI models; executed by AiTSF, providing training operations for specific AI models, including selecting training methods and training nodes.

[0381] (2) Training data collection operation; performed by AiTSF, collecting data from other data source nodes (e.g., AF, NF or UE); the collection operation may include the selection of data source nodes.

[0382] (3) Management and operation of trained AI models; AiTSF processes trained AI models, including uploading them to AiSSF and delivering them to consumers who need them.

[0383] The 6G NF1 with inference capability is a 6G network function with AI model inference capability, such as an LMF with the ability to calculate the terminal location using an inference AI positioning model. Here, the 6G NF1 with inference capability corresponds to the third network function in this disclosure.

[0384] The 6G NF2 with training capabilities is a 6G network function capable of training AI models. For example, in federated learning training, this NF can serve as a training node for training AI models. Here, the 6G NF2 with training capabilities corresponds to the first network function in this disclosure.

[0385] 6G AI Training NF is a dedicated network function for training AI models, offering high performance. Here, 6G AI Training NF corresponds to the second network function in this disclosure.

[0386] The 6G Exposure Function is a network function that exposes the services provided by the 6G system.

[0387] In some embodiments, AiSST is connected to the communication bus via a first interface Naix1; AiISF is connected to the communication bus via a second interface Naix2; AiTSF is connected to the communication bus via a third interface Naix3; the 6G AI training NF is connected to the communication bus via a fourth interface Naix4; the 6G NF2 with training capability is connected to the communication bus via a fifth interface Naix5; and the 6G NF1 with inference capability is connected to the communication bus via a sixth interface Naix6.

[0388] In some embodiments, the 6G system further includes: 6G UDR, or 6G ADRF. It should be noted that 6G UDR or 6G ADRF are 6G network functions with AI model storage capabilities.

[0389] The 6G UDR or 6G ADRF connects to the communication bus via the seventh interface, Naix7.

[0390] In some embodiments, the 6G system further includes a 6G open function, which is connected to the communication bus via an eighth interface Nxx.

[0391] It should be noted that for AI-related network functions or 6G NFs, AI-related services are provided or consumed through newly defined interfaces such as Naix1, Naix2, Naix3, Naix4, Naix5, Naix6, and Nxx. For example, the Naix1 interface is used to transmit control plane signaling or user plane data.

[0392] As shown in Figure 7B, Figure 7B is an interactive schematic diagram of an information processing method according to an exemplary embodiment. This disclosure provides a method for providing AI as a Service in a 6G system, the method including:

[0393] Step S7101: The first node receives the AI ​​service request sent by the second node.

[0394] Here, the AI ​​service request is the first piece of information disclosed.

[0395] In some embodiments, the second node may be an AF or a 6G NF.

[0396] In some embodiments, the AI ​​service requested by the AI ​​service request may include at least one of the following:

[0397] AI model training services;

[0398] AI model storage service;

[0399] Inference services for AI models.

[0400] In some embodiments, when the AI ​​service request is a request for training services for an AI model, the AI ​​service request may include: the AI ​​model, training data, and model metadata.

[0401] In some embodiments, model metadata includes model identifier, vendor identifier, size, version, etc.

[0402] In some embodiments, the first node may also collect training data.

[0403] In some embodiments, where the AI ​​service request is a request for inference services of an AI model, the AI ​​service request may include: a model identifier of the AI ​​model or AI pattern, and input data.

[0404] Step S7102: The first node selects a third node that provides AI services.

[0405] In some embodiments, authentication or authorization may be performed upon receiving an AI service request from a second node.

[0406] Understandably, when the first node receives an AI service request from the second node, it can verify or authorize the second node to determine if the second node has the authority to access the AI ​​service. If the second node is determined to have the authority, a third node is selected to provide the AI ​​service.

[0407] Step S7103: The first node sends an AI service response to the second node.

[0408] Here, the AI ​​service response is the second piece of information disclosed herein.

[0409] It should be noted that the first node sends an AI service response to the second node in order to provide the second node with the results of the AI ​​service.

[0410] In some embodiments, where the AI ​​service is a training service for an AI model, the result of the AI ​​service provided by the first node includes the trained AI model and model metadata.

[0411] In some embodiments, where the AI ​​service is an inference service for an AI model, the result of the AI ​​service provided by the first node includes the inference result.

[0412] This disclosure also provides apparatus for implementing any of the above methods. For example, an apparatus is provided that includes units or modules for implementing the steps performed by the terminal in any of the above methods. Alternatively, another apparatus is provided that includes units or modules for implementing the steps performed by a network device (e.g., an access network device, or a core network device) in any of the above methods.

[0413] It should be understood that the division of units or modules in the above device is only a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, the units or modules in the device can be implemented by a processor calling software: for example, the device includes a processor connected to a memory containing instructions. The processor calls the instructions stored in the memory to implement any of the above methods or to implement the functions of the units or modules in the above device. The processor can be, for example, a general-purpose processor, such as a Central Processing Unit (CPU) or a microprocessor, and the memory can be internal or external to the device. Alternatively, the units or modules in the device can be implemented in the form of hardware circuits. The functionality of some or all of the units or modules can be achieved through the design of these hardware circuits, which can be understood as one or more processors. For example, in one implementation, the hardware circuit is an application-specific integrated circuit (ASIC). The functionality of some or all of the units or modules is achieved through the design of the logical relationships between the components within the circuit. In another implementation, the hardware circuit can be implemented using a programmable logic device (PLD). Taking a field-programmable gate array (FPGA) as an example, it can include a large number of logic gates. The connection relationships between the logic gates are configured through configuration files, thereby achieving the functionality of some or all of the units or modules. All units or modules of the above device can be implemented entirely through processor-called software, entirely through hardware circuits, or partially through processor-called software with the remaining parts implemented through hardware circuits.

[0414] In this embodiment, the processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction read and execute capabilities, such as a Central Processing Unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a type of microprocessor), or a digital signal processor (DSP). In another implementation, the processor can implement certain functions through the logical relationships of hardware circuits. The logical relationships of the aforementioned hardware circuits are fixed or reconfigurable. For example, the processor is a hardware circuit implemented using an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document and configuring the hardware circuit can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units or modules. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a Neural Network Processing Unit (NPU), a Tensor Processing Unit (TPU), a Deep Learning Processing Unit (DPU), etc.

[0415] Figure 8A is a schematic diagram of the structure of a first node according to an exemplary embodiment. As shown in Figure 8A, the first node 101 includes: a first receiving module 1011 configured to receive first information sent by a second node, the first information being used to request artificial intelligence (AI) services; a processing module 1012 configured to respond to the AI ​​services requested by the first information; and a first sending module 1013 configured to send second information to the second node, the second information including the response result of the AI ​​services.

[0416] Optionally, the first receiving module 1011 is used to execute the steps related to information receiving performed by the first node in any of the above information processing methods, which will not be described in detail here. Optionally, the processing module 1012 executes the steps related to information processing performed by the first node in any of the above information processing methods, which will not be described in detail here. Optionally, the first sending module 1013 is used to execute the steps related to sending performed by the first node in any of the above methods, which will not be described in detail here.

[0417] Figure 8B is a schematic diagram of the structure of a second node according to an exemplary embodiment. As shown in Figure 8B, the second node 102 includes: a second sending module 1021 configured to send first information to a first node, the first information being used to request AI services; and a second receiving module 1022 configured to receive second information sent by the first node, the second information including the response result of the AI ​​service. Optionally, the second sending module 1021 is used to perform the steps related to information sending performed by the second node in any of the above information processing methods, which will not be described in detail here. Optionally, the second receiving module 1022 is used to perform the steps related to information receiving performed by the second node in any of the above information processing methods. The second node also includes a processing module, the above processing being used to perform the steps related to information processing performed by the second node in any of the above methods, which will not be described in detail here.

[0418] Figure 8C is a schematic diagram illustrating the structure of a third node according to an exemplary embodiment. As shown in Figure 8C, the third node 103 includes: a third receiving module 1031, configured to receive third information sent by a first node based on first information; the first information is used to request AI services; and a third sending module 1032, configured to send a response result of the AI ​​service to the first node. Optionally, the third receiving module 1031 is used to perform the steps related to information receiving performed by the third node in any of the above information processing methods. Optionally, the third sending module 1032 is used to perform the steps related to information sending performed by the third node in any of the above information processing methods, which will not be described in detail here. The third node also includes a processing module, which is used to perform the steps related to information processing performed by the third node in any of the above methods, which will not be described in detail here.

[0419] Figure 9A is a schematic diagram illustrating the structure of a communication device according to an exemplary embodiment. The communication device 9100 can be a first node, a second node, or a third node; it can also be a chip, chip system, or processor that supports network devices in implementing any of the above methods; or it can be a chip, chip system, or processor that supports terminals in implementing any of the above information processing methods. The communication device 9100 can be used to implement the information processing methods described in the above method embodiments, as detailed in the descriptions within the above method embodiments.

[0420] As shown in Figure 9A, the communication device 9100 includes one or more processors 9101. The processor 9101 can be a general-purpose processor or a dedicated processor, such as a baseband processor or a central processing unit (CPU). The baseband processor can be used to process communication protocols and communication data, while the CPU can be used to control communication devices (e.g., base stations, baseband chips, terminal devices, terminal device chips, DUs or CUs, etc.), execute programs, and process program data. The processor 9101 is used to invoke instructions to cause the communication device 9100 to execute any of the above information processing methods.

[0421] In some embodiments, the communication device 9100 further includes one or more memories 9102 for storing instructions. Optionally, all or part of the memories 9102 may also be located outside the communication device 9100.

[0422] In some embodiments, the communication device 9100 further includes one or more transceivers 9103. When the communication device 9100 includes one or more transceivers 9103, the communication steps such as sending and receiving in the above method are performed by the transceivers 9103, and other steps are performed by the processor 9101.

[0423] In some embodiments, a transceiver may include a receiver and a transmitter, which may be separate or integrated. Optionally, the terms transceiver, transceiver unit, transceiver, transceiver circuit, etc., may be used interchangeably; the terms transmitter, transmitting unit, transmitter, transmitting circuit, etc., may be used interchangeably; and the terms receiver, receiving unit, receiver, receiving circuit, etc., may be used interchangeably.

[0424] Optionally, the communication device 9100 further includes one or more interface circuits 9104, which are connected to the memory 9102. The interface circuits 9104 can be used to receive signals from the memory 9102 or other devices, and can be used to send signals to the memory 9102 or other devices. For example, the interface circuits 9104 can read instructions stored in the memory 9102 and send the instructions to the processor 9101.

[0425] The communication device 9100 described in the above embodiments may be a network device or a terminal, but the scope of the communication device 9100 described in this disclosure is not limited thereto, and the structure of the communication device 9100 may not be limited by FIG. 9A. The communication device may be a standalone device or a part of a larger device. For example, the communication device may be: (1) a standalone integrated circuit IC, or chip, or chip system or subsystem; (2) a collection of one or more ICs, optionally, the IC collection may also include storage components for storing data and programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, terminal device, smart terminal device, cellular phone, wireless device, handheld device, mobile unit, vehicle device, network device, cloud device, artificial intelligence device, etc.; (6) others, etc.

[0426] Figure 9B is a schematic diagram of a chip structure according to an exemplary embodiment. For cases where the communication device 9100 can be a chip or a chip system, please refer to the schematic diagram of the chip 9200 shown in Figure 9B, but it is not limited thereto.

[0427] Chip 9200 includes one or more processors 9201, which are used to invoke instructions to cause chip 9200 to execute any of the above information processing methods.

[0428] In some embodiments, chip 9200 further includes one or more interface circuits 9202 connected to memory 9203. Interface circuits 9202 can be used to receive signals from memory 9203 or other devices, and can also be used to send signals to memory 9203 or other devices. For example, interface circuit 9202 can read instructions stored in memory 9203 and send those instructions to processor 9201. Optionally, terms such as interface circuit, interface, transceiver pin, and transceiver can be used interchangeably.

[0429] In some embodiments, chip 9200 further includes one or more memories 9203 for storing instructions. Optionally, all or part of the memories 9203 may be located outside of chip 9200.

[0430] This disclosure also provides a storage medium storing instructions that, when executed on a communication device 9100, cause the communication device 9100 to perform any of the methods described above. Optionally, the storage medium is an electronic storage medium. Optionally, the storage medium is a computer-readable storage medium, but it can also be a storage medium readable by other devices. Optionally, the storage medium can be a non-transitory storage medium, but it can also be a temporary storage medium.

[0431] This disclosure also provides a program product, which, when executed by a communication device 9100, causes the communication device 9100 to perform any of the above information processing methods. Optionally, the program product is a computer program product.

[0432] This disclosure also provides a computer program that, when run on a computer, causes the computer to perform any of the above information processing methods.

[0433] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.

[0434] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. An information processing method, wherein, Executed by the first node, the method includes: Receive the first information sent by the second node, the first information being used to request artificial intelligence (AI) services; The AI ​​service that responds to the first information request; Send a second message to the second node, the second message including the response result of the AI ​​service.

2. The method according to claim 1, wherein, The AI ​​service responding to the first information request includes: Based on at least one of the request information for the AI ​​service and the status information of the candidate nodes, a third node is selected to respond to the AI ​​service; the third node is used to provide the response result of the AI ​​service. Based on the first information, the third information is sent to the third node; Receive the response result sent by the third node based on the third information.

3. The method according to claim 2, wherein, Sending the second information to the second node includes: Based on the response provided by the third node, the second information is sent to the second node.

4. The method according to claim 2 or 3, wherein, The AI ​​service types include at least one of the following: AI model training services; Inference services for AI models; Storage services for AI models.

5. The method according to claim 4, wherein, The method further includes: Based on the first information, the demand information for requesting the AI ​​service is determined, and the demand information includes at least one of the following: The first type of information is used to indicate the type of service for the requested AI service; The fourth piece of information is used to indicate the service parameters for providing the AI ​​service, and the service parameters include at least one of the following: the type of AI model, the training method of the AI ​​model, and the inference method of the AI ​​model.

6. The method according to claim 4 or 5, wherein, The method further includes: Receive the status information sent by the third node, the status information including at least one of the following: The second type of information is used to indicate the service type of the AI ​​services supported by the third node; Capability information, used to indicate the processing capabilities of the third node; Load information, used to indicate the load intensity of the third node.

7. The method according to any one of claims 4 to 6, wherein, The operation of the training service includes at least one of the following: The training operation of the AI ​​model; The process of collecting training data; Delivery of training results.

8. The method according to claim 7, wherein, The first information includes at least one of the following: The AI ​​model to be trained; The training data of the AI ​​model to be trained; The metadata of the AI ​​model to be trained, which is used to indicate the model attributes of the AI ​​model to be trained.

9. The method according to claim 8, wherein, The metadata includes at least one of the following: The model version of the AI ​​model; The model identifier of the AI ​​model; The size of the AI ​​model; The supplier identifier of the AI ​​model.

10. The method according to any one of claims 7 to 9, wherein, The second information includes at least one of the following: The model parameters of the trained AI model; The metadata of the AI ​​model that has completed training.

11. The method according to any one of claims 7 to 10, wherein, The third node responding to the training service includes at least one of the following: The first network function is a core network function with first and second capabilities. The second network function is a core network function that has the first capability. The first capability is the training capability of the AI ​​model; the second capability is different from the first capability.

12. The method according to any one of claims 4 to 6, wherein, The operations of the inference service include at least one of the following: The inference operation of the AI ​​model; The operation of delivering the inference results.

13. The method according to claim 12, wherein, The first information includes at least one of the following: The model parameters of the AI ​​model; Model identification information, used to indicate the AI ​​model; Input information includes the input data required to perform the inference operations of the AI ​​model.

14. The method according to claim 12 or 13, wherein, The second information includes: the reasoning results of the AI ​​model.

15. The method according to any one of claims 12 to 14, wherein, The third node responding to the inference service includes at least one of the following: The third network function is a core network function with third and fourth capabilities; The fourth network function is a core network function that has the third capability; The third capability is the reasoning capability of the AI ​​model, which is different from the fourth capability.

16. The method according to any one of claims 4 to 6, wherein, The operation of the storage service includes at least one of the following: The upload operation of the AI ​​model; The download operation of the AI ​​model; The deletion operation of the AI ​​model.

17. The method according to any one of claims 4 to 16, wherein, The AI ​​service responding to the first information request includes: Based on the first information, it is determined whether the second node is authorized to obtain the AI ​​service; wherein, the first information includes: fifth information, used to instruct the second node; If it is determined that the second node is authorized to obtain the AI ​​service, the AI ​​service is responded to in response to the first information request.

18. An information processing method, wherein, Executed by the second node, the method includes: Send the first message to the first node, the first message being used to request AI services; Receive the second information sent by the first node, the second information including the response result of the AI ​​service.

19. The method according to claim 18, wherein, The AI ​​service types include at least one of the following: AI model training services; Inference services for AI models; Storage services for AI models.

20. The method according to claim 19, wherein, The operation of the training service includes at least one of the following: The training operation of the AI ​​model; The process of collecting training data; Delivery of training results.

21. The method according to claim 19 or 20, wherein, The first information includes at least one of the following: The AI ​​model to be trained; The training data of the AI ​​model to be trained; The metadata of the AI ​​model to be trained, which is used to indicate the model attributes of the AI ​​model to be trained.

22. The method according to claim 21, wherein, The metadata includes at least one of the following: The model version of the AI ​​model; The model identifier of the AI ​​model; The size of the AI ​​model; The supplier identifier of the AI ​​model.

23. The method according to claim 21 or 22, wherein, The second information includes at least one of the following: The model parameters of the trained AI model; The metadata of the AI ​​model that has completed training.

24. The method according to claim 19, wherein, The operations of the inference service include at least one of the following: The inference operation of the AI ​​model; The operation of delivering the inference results.

25. The method according to claim 24, wherein, The first information includes at least one of the following: The model parameters of the AI ​​model; Model identification information, used to indicate the AI ​​model; Input information includes the input data required to perform the inference operations of the AI ​​model.

26. The method according to claim 24 or 25, wherein, The second information includes: the reasoning results of the AI ​​model.

27. The method according to claim 19, wherein, The storage service includes at least one of the following: The upload operation of the AI ​​model; The download operation of the AI ​​model; The deletion operation of the AI ​​model.

28. An information processing method, wherein, Executed by a third node, the method includes: Receive third information sent by the first node based on the first information; the first information is used to request AI services; Send the response result of the AI ​​service to the first node.

29. The method according to claim 28, wherein, The AI ​​service types include at least one of the following: AI model training services; Inference services for AI models; Storage services for AI models.

30. The method according to claim 29, wherein, The method further includes: Send status information to the first node, the status information including at least one of the following: The second type of information is used to indicate the service type of the AI ​​services supported by the third node; Capability information, used to indicate the processing capabilities of the third node; Load information, used to indicate the load intensity of the third node.

31. The method according to claim 29 or 30, wherein, The operation of the training service includes at least one of the following: The training operation of the AI ​​model; The process of collecting training data; Delivery of training results.

32. The method according to claim 31, wherein, The response results of the training service include at least one of the following: The model parameters of the trained AI model; The metadata of the AI ​​model that has completed training.

33. The method according to claim 31 or 32, wherein, The third node includes at least one of the following: The first network function is a core network function with first and second capabilities. The second network function is a core network function that has the first capability. The first capability is the training capability of the AI ​​model; the second capability is different from the first capability.

34. The method according to claim 29 or 30, wherein, The operations of the inference service include at least one of the following: The inference operation of the AI ​​model; The operation of delivering the inference results.

35. The method according to claim 34, wherein, The response results of the inference service include: the inference results of the AI ​​model.

36. The method according to claim 34 or 35, wherein, The third node includes at least one of the following: The third network function is a core network function with third and fourth capabilities; The fourth network function is a core network function that has the third capability; The third capability is the reasoning capability of the AI ​​model, which is different from the fourth capability.

37. The method according to claim 29 or 30, wherein, The storage service includes at least one of the following: The upload operation of the AI ​​model; The download operation of the AI ​​model; The deletion operation of the AI ​​model.

38. An information processing method, wherein, Performed by a communication system, the method includes: The second node sends the first message to the first node, and the first message is used to request artificial intelligence (AI) services. The first node responds to the AI ​​service requested by the first information request; Send a second message to the second node, the second message including the response result of the AI ​​service.

39. A first node, wherein, The first node includes: The first receiving module is configured to receive first information sent by the second node, the first information being used to request artificial intelligence (AI) services. The processing module is configured to provide an AI service in response to the first information request; The first sending module is configured to send second information to the second node, the second information including the response result of the AI ​​service.

40. A second node, wherein, The second node includes: The second sending module is configured to send first information to the first node, the first information being used to request AI services; The second receiving module is configured to receive second information sent by the first node, the second information including the AI ​​service. Response results.

41. A third node, wherein, The third node includes: The third receiving module is configured to receive third information sent by the first node based on the first information; the first information is used to request AI services. The third sending module is configured to send the response result of the AI ​​service to the first node.

42. A communication system, wherein, The communication system includes a first node, a second node, and a third node; the first node is configured to implement the information processing method of any one of claims 1 to 17, the second node is configured to implement the information processing method of any one of claims 18 to 27, and the third node is configured to implement the information processing method of any one of claims 28 to 37.

43. A communication device, wherein, The communication device includes: One or more processors; The processor is configured to invoke instructions to enable the communication device to perform the information processing method according to any one of claims 1 to 17, 18 to 27, and 28 to 37.

44. A storage medium, wherein, The storage medium stores instructions that, when executed on a communication device, cause the communication device to perform the information processing method according to any one of claims 1 to 17, 18 to 27, and 28 to 37.

45. A program product, wherein, When the program product is executed by a communication device, the communication device performs the information processing method according to any one of claims 1 to 17, 18 to 27, and 28 to 37.

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