Model identifier determination method and device, communication equipment and storage medium

Abstract

The invention relates to the technical field of communication, in particular to a model identifier determination method and device, communication equipment and a storage medium, and the model identifier determination method comprises the steps that a first type model identifier allocated to a first AI model is determined, and the first type model identifier is used for distinguishing a plurality of AI models in the same AI function. According to the method and the device, under the condition that one AI function and a plurality of AI models are mapped, definition and management of the first type of model identification can be realized, and for the same AI model, the number of bits occupied by the first type of model identification allocated to the same AI model is smaller than the number of bits occupied by the second type of model identification allocated to the same AI model. Therefore, when the first type model identifier is used for subsequent model management, the signaling overhead in the model management process can be saved.

Description

Method, device, communication equipment and storage medium for determining model identification Technical Field

[0001] The present disclosure relates to the field of communication technology, and in particular to a method, apparatus, communication device, and storage medium for determining a model identifier. Background Art

[0002] The widespread application of 5G technology has brought about tremendous changes in all aspects of people's lives. According to the vision of the International Telecommunication Union (ITU), 5G will penetrate into all areas of future society and build a comprehensive information ecosystem centered on users. Among them, 5G user experience rates can reach 100Mbit / s to 1Gbit / s, which can support ultimate business experiences such as mobile virtual reality; 5G peak rates can reach 10Gbit / s to 20Gbit / s, and traffic density can reach 10Mbit / s / m 2 , capable of supporting more than a thousand-fold growth in mobile business traffic in the future; the 5G connection density can reach 1 million / m 2 , effectively supporting massive numbers of IoT devices; 5G transmission latency can reach milliseconds, meeting the stringent requirements of the Internet of Vehicles and industrial control; and 5G can support mobile speeds of 500 km / h, ensuring a good user experience in high-speed rail environments. Thus, 5G, as a representative of new infrastructure, will reshape the future information society.

[0003] In recent years, artificial intelligence (AI) technology has achieved continuous breakthroughs in numerous fields. The continued development of fields such as intelligent voice and computer vision has not only brought a rich variety of applications to smart terminals, but has also found widespread application in education, transportation, home living, healthcare, retail, security, and other fields. This has brought convenience to people's lives while also promoting industrial upgrading across various industries. AI technology is also rapidly interpenetrating with other disciplines, integrating knowledge from different disciplines while also providing new directions and methods for their development.

[0004] In 3GPP Release 18, a research project on artificial intelligence technology in wireless air interfaces was established within RAN1. This project aims to study how to introduce artificial intelligence technology into wireless air interfaces and explore how artificial intelligence technology can assist in improving wireless air interface transmission technologies.

[0005] For example, application cases of artificial intelligence may include but are not limited to: AI-based channel state information (CSI) enhancement, AI-based beam management, AI-based positioning, etc.

[0006] Summary of the Invention

[0007] The embodiments of the present disclosure provide a method, apparatus, communication device, and storage medium for determining a model identifier to solve technical problems in related technologies.

[0008] According to a first aspect of an embodiment of the present disclosure, a method for determining a model identifier is provided, which is executed by a terminal. The method includes:

[0009] Determine a first type of model identifier assigned to a first AI model, wherein the first type of model identifier is used to distinguish multiple AI models within the same AI function.

[0010] According to a second aspect of an embodiment of the present disclosure, a method for determining a model identifier is provided, which is executed by a network device. The method includes:

[0011] Determine a first type of model identifier assigned to a first AI model, wherein the first type of model identifier is used to distinguish multiple AI models within the same AI function.

[0012] According to a third aspect of an embodiment of the present disclosure, a device for determining a model identifier is provided, the device comprising:

[0013] A first processing module is configured to determine a first type of model identifier assigned to a first AI model, wherein the first type of model identifier is used to distinguish multiple AI models within the same AI function.

[0014] According to a fourth aspect of an embodiment of the present disclosure, a device for determining a model identifier is provided, the device comprising:

[0015] The second processing module is used to determine a first type of model identifier assigned to the first AI model, wherein the first type of model identifier is used to distinguish multiple AI models within the same AI function.

[0016] According to a fifth aspect of an embodiment of the present disclosure, a terminal is proposed, comprising: one or more processors; wherein the terminal is used to execute the method for determining the model identification of the first aspect above.

[0017] According to a sixth aspect of an embodiment of the present disclosure, a network device is proposed, comprising: one or more processors; wherein the network device is used to execute the method for determining the model identification of the second aspect above.

[0018] According to the seventh aspect of an embodiment of the present disclosure, a communication device is proposed, comprising: one or more processors; wherein the processor is used to call instructions so that the communication device executes the method for determining the model identification of the above-mentioned first aspect, and / or the method for determining the model identification of the above-mentioned second aspect.

[0019] According to the sixth aspect of an embodiment of the present disclosure, a communication system is proposed, comprising a terminal and a network device, wherein the terminal is configured to implement the method for determining the model identifier of the above-mentioned first aspect, and the network device is configured to implement the method for determining the model identifier of the above-mentioned second aspect.

[0020] According to the seventh aspect of an embodiment of the present disclosure, a storage medium is proposed, which stores instructions. When the instructions are executed on a communication device, the communication device executes the method for determining the model identification of the first aspect above and / or the method for determining the model identification of the second aspect above.

[0021] According to an embodiment of the present disclosure, a terminal may determine a first type model identifier assigned to a first AI model, wherein the first type model identifier is used to distinguish multiple AI models within the same AI function. Accordingly, when one AI function is mapped to multiple AI models, the definition and management of the first type model identifier may be implemented. Furthermore, since the number of bits occupied by the first type model identifier assigned to the same AI model is less than the number of bits occupied by the second type model identifier assigned to the same AI model, the use of the first type model identifier for subsequent model management is beneficial in saving signaling overhead during the model management process. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0023] FIG1 is a schematic diagram showing the architecture of a communication system according to an embodiment of the present disclosure.

[0024] FIG2 is an interactive schematic diagram illustrating a method for determining a model identifier according to an embodiment of the present disclosure.

[0025] FIG3 is a schematic flowchart illustrating a method for determining a model identifier according to an embodiment of the present disclosure.

[0026] FIG4 is a schematic flowchart showing another method for determining a model identifier according to an embodiment of the present disclosure.

[0027] FIG5 is a schematic block diagram showing a device for determining a model identifier according to an embodiment of the present disclosure.

[0028] FIG6 is a schematic block diagram showing another apparatus for determining a model identifier according to an embodiment of the present disclosure.

[0029] FIG7 is a schematic structural diagram of a communication device proposed in an embodiment of the present disclosure.

[0030] FIG8 is a schematic diagram of the structure of a chip proposed in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0031] The embodiments of the present disclosure provide a method, apparatus, communication device, and storage medium for determining a model identifier.

[0032] In a first aspect, an embodiment of the present disclosure proposes a method for determining a model identifier, which is executed by a terminal. The method includes: determining a first type of model identifier assigned to a first AI model, wherein the first type of model identifier is used to distinguish multiple AI models within the same AI function.

[0033] In the above embodiment, the terminal can determine a first-type model identifier assigned to the first AI model, where the first-type model identifier is used to distinguish multiple AI models within the same AI function. Accordingly, when one AI function is mapped to multiple AI models, the definition and management of the first-type model identifier can be implemented. Moreover, since the number of bits occupied by the first-type model identifier assigned to the same AI model is less than the number of bits occupied by the second-type model identifier assigned to the same AI model, the use of the first-type model identifier for subsequent model management is beneficial in saving signaling overhead during the model management process.

[0034] In combination with some embodiments of the first aspect, in some embodiments, the method further includes: determining whether to assign a first type model identifier to the first AI model.

[0035] In conjunction with some embodiments of the first aspect, in some embodiments, the first AI model includes an AI model that has undergone model certification within the first AI function.

[0036] In combination with some embodiments of the first aspect, in some embodiments, the first AI model includes an AI model within the first AI function that has been assigned a second type model identifier.

[0037] In conjunction with some embodiments of the first aspect, in some embodiments, the first AI model is an available AI model or a candidate AI model.

[0038] In combination with some embodiments of the first aspect, in some embodiments, the first type of model identifier is used to distinguish multiple AI models within the same AI feature, wherein one AI feature includes at least one AI function.

[0039] In conjunction with some embodiments of the first aspect, in some embodiments, the first AI model includes an AI model that has been model-certified within the first AI feature.

[0040] In combination with some embodiments of the first aspect, in some embodiments, the first AI model includes an AI model that has been assigned a second type model identifier within the first AI feature.

[0041] In conjunction with some embodiments of the first aspect, in some embodiments, the first AI function mapped to the first AI model is an available AI function.

[0042] In conjunction with some embodiments of the first aspect, in some embodiments, the first AI model is an available AI model or a candidate AI model.

[0043] In combination with some embodiments of the first aspect, in some embodiments, the first type of model identifier is used to distinguish multiple AI models in the same terminal, where a terminal supports at least one AI feature.

[0044] In conjunction with some embodiments of the first aspect, in some embodiments, the first AI model includes an AI model that has been model-certified in the first terminal.

[0045] In conjunction with some embodiments of the first aspect, in some embodiments, the first AI model includes an AI model in the first terminal that has been assigned a second type model identifier.

[0046] In combination with some embodiments of the first aspect, in some embodiments, the first AI feature is an available AI feature, wherein the first AI feature includes a first AI function mapped to the first AI model.

[0047] In conjunction with some embodiments of the first aspect, in some embodiments, the first AI function is an available AI function.

[0048] In conjunction with some embodiments of the first aspect, in some embodiments, the first AI model is an available AI model or a candidate AI model.

[0049] In combination with some embodiments of the first aspect, in some embodiments, determining the first type of model identifier assigned to the first AI model includes: determining, based on first information sent by the network device, the first type of model identifier assigned to the first AI model by the network device.

[0050] In combination with some embodiments of the first aspect, in some embodiments, the method further includes: sending second information to a network device, wherein the second information includes a first type model identifier assigned to the first AI model and first management information, the first management information being used to indicate at least one of the following: whether the first AI model is an available model; whether the first AI model is a candidate model; whether a first AI function mapped to the first AI model is an available function; and whether a first AI feature belonging to the first AI model is an available feature.

[0051] In a second aspect, an embodiment of the present disclosure proposes a method for determining a model identifier, which is executed by a network device. The method includes: determining a first type of model identifier assigned to a first AI model, wherein the first type of model identifier is used to distinguish multiple AI models within the same AI function.

[0052] In combination with some embodiments of the second aspect, in some embodiments, the method further includes: determining whether to assign a first type model identifier to the first AI model.

[0053] In conjunction with some embodiments of the second aspect, in some embodiments, the first AI model includes an AI model that has undergone model certification within the first AI function.

[0054] In conjunction with some embodiments of the second aspect, in some embodiments, the first AI model includes an AI model within the first AI function that has been assigned a second type model identifier.

[0055] In conjunction with some embodiments of the second aspect, in some embodiments, the first AI model is an available AI model or a candidate AI model.

[0056] In combination with some embodiments of the second aspect, in some embodiments, the first type of model identifier is used to distinguish multiple AI models within the same AI feature, wherein one AI feature includes at least one AI function.

[0057] In conjunction with some embodiments of the second aspect, in some embodiments, the first AI model includes an AI model that has been model-certified within the first AI feature.

[0058] In conjunction with some embodiments of the second aspect, in some embodiments, the first AI model includes an AI model that has been assigned a second type model identifier within the first AI feature.

[0059] In conjunction with some embodiments of the second aspect, in some embodiments, the first AI function mapped to the first AI model is an available AI function.

[0060] In conjunction with some embodiments of the second aspect, in some embodiments, the first AI model is an available AI model or a candidate AI model.

[0061] In conjunction with some embodiments of the second aspect, in some embodiments, the first type of model identifier is used to distinguish multiple AI models within the same terminal, where a terminal supports at least one AI feature.

[0062] In conjunction with some embodiments of the second aspect, in some embodiments, the first AI model includes an AI model that has been model-certified in the first terminal.

[0063] In conjunction with some embodiments of the second aspect, in some embodiments, the first AI model includes an AI model in the first terminal that has been assigned a second type model identifier.

[0064] In conjunction with some embodiments of the second aspect, in some embodiments, the first AI feature is an available AI feature, wherein the first AI feature includes a first AI function mapped to the first AI model.

[0065] In conjunction with some embodiments of the second aspect, in some embodiments, the first AI function is an available AI function.

[0066] In conjunction with some embodiments of the second aspect, in some embodiments, the first AI model is an available AI model or a candidate AI model.

[0067] In combination with some embodiments of the second aspect, in some embodiments, the method further includes: sending first information to the terminal, wherein the first information is used to indicate a first type model identifier assigned by the network device to the first AI model.

[0068] In combination with some embodiments of the second aspect. In some embodiments, the method further includes: sending third information to the terminal, wherein the third information includes a first type model identifier assigned to the first AI model and second management information, and the second management information is used to indicate at least one of the following: the first AI model is an AI model selected by the network device; the first AI model is an AI model determined to be activated by the network device; and the first AI model is an AI model determined to be switched by the network device.

[0069] In a third aspect, an embodiment of the present disclosure provides a device for determining a model identifier, the device comprising:

[0070] A first processing module is configured to determine a first type of model identifier assigned to a first AI model, wherein the first type of model identifier is used to distinguish multiple AI models within the same AI function.

[0071] In a fourth aspect, an embodiment of the present disclosure provides a device for determining a model identifier, the device comprising:

[0072] The second processing module is used to determine a first type of model identifier assigned to the first AI model, wherein the first type of model identifier is used to distinguish multiple AI models within the same AI function.

[0073] In a fifth aspect, an embodiment of the present disclosure proposes a terminal, comprising: one or more processors; wherein the terminal is used to execute the method for determining the model identifier described in the first aspect and the optional embodiment of the first aspect.

[0074] In a sixth aspect, an embodiment of the present disclosure proposes a network device, comprising: one or more processors; wherein the network device is used to execute the method for determining the model identifier described in the second aspect and the optional embodiment of the second aspect.

[0075] In the seventh aspect, an embodiment of the present disclosure proposes a communication device, which includes: one or more processors; one or more memories for storing instructions; wherein the processor is used to call the instructions so that the communication device executes the method described in the first and second aspects, and the optional embodiments of the first and second aspects.

[0076] In the eighth aspect, an embodiment of the present disclosure proposes a communication system, which includes: a terminal and a network device; wherein the terminal is configured to execute the method described in the first aspect and the optional embodiment of the first aspect, and the network device is configured to execute the method described in the second aspect and the optional embodiment of the second aspect.

[0077] In the ninth aspect, an embodiment of the present disclosure proposes a storage medium, which stores instructions. When the instructions are executed on a communication device, the communication device executes the method described in the first and second aspects, and the optional embodiments of the first and second aspects.

[0078] In a tenth aspect, an embodiment of the present disclosure proposes a program product. When the program product is executed by a communication device, the communication device executes the method described in the first and second aspects, and the optional embodiments of the first and second aspects.

[0079] In an eleventh aspect, an embodiment of the present disclosure proposes a computer program, which, when executed on a computer, enables the computer to execute the methods described in the first and second aspects, and the optional embodiments of the first and second aspects.

[0080] It is understandable that the above-mentioned devices, terminals, network devices, communication devices, communication systems, storage media, program products, and computer programs are all used to execute the methods proposed in the embodiments of the present disclosure. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects of the corresponding methods and will not be repeated here.

[0081] The present disclosure provides a method, apparatus, communication device, and storage medium for determining a model identifier. In some embodiments, the terms "method for determining a model identifier" and "information processing method" and "communication method" are interchangeable; the terms "apparatus for determining a model identifier" and "information processing device" and "communication device" are interchangeable; and the terms "system for determining a model identifier" and "communication system" are interchangeable.

[0082] The embodiments of the present disclosure are not exhaustive and are merely illustrative of some embodiments, and are not intended to be a specific limitation on the scope of protection of the present disclosure. In the absence of contradiction, each step in a certain 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 certain embodiment can also be implemented as an independent embodiment, and the order of the steps in a certain embodiment can be arbitrarily exchanged. In addition, the optional embodiments in a certain embodiment can be arbitrarily combined; in addition, the embodiments can be arbitrarily combined. For example, some or all steps of different embodiments can be arbitrarily combined, and a certain embodiment can be arbitrarily combined with the optional embodiments of other embodiments.

[0083] In each embodiment of the present disclosure, unless otherwise specified or provided for by logic, the terms and / or descriptions between the embodiments are consistent and can be referenced by each other. The technical features in different embodiments can be combined to form a new embodiment based on their inherent logical relationships.

[0084] The terms used in the embodiments of the present disclosure are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure.

[0085] In the embodiments of the present disclosure, unless otherwise specified, elements expressed in the singular form, such as "a", "an", "the", "above", "said", "aforementioned", "this", etc., may mean "one and only one", or "one or more", "at least one", etc.

[0086] For example, when using articles such as “a”, “an”, and “the” in English in translation, the noun following the article can be understood as a singular expression or a plural expression.

[0087] In the embodiments of the present disclosure, “plurality” refers to two or more.

[0088] In some embodiments, the terms "at least one," "one or more," "a plurality of," "multiple," etc. may be used interchangeably.

[0089] In some embodiments, descriptions such as "at least one of A and B," "A and / or B," "A in one case, B in another case," or "in response to one case A, in response to another case B" may include the following technical solutions depending on the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed); and in some embodiments, A and B (both A and B are executed). The above is also applicable when there are more branches such as A, B, and C.

[0090] In some embodiments, "A or B" and other descriptions may include the following technical solutions depending on the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed). The above is also applicable when there are more branches such as A, B, C, etc.

[0091] The prefixes such as "first" and "second" in the embodiments of the present disclosure are only used to distinguish different description objects and do not constitute any restrictions on the position, order, priority, quantity or content of the description objects. For the statement of the description objects, please refer to the description in the context of the claims or embodiments, and no unnecessary restrictions should be constituted due to the use of prefixes.

[0092] For example, if the description object is "field," the ordinal number preceding "field" in "first field" and "second field" does not restrict the position or order of the "fields." "First" and "second" do not restrict whether the modified "fields" are in the same message, nor do they restrict the order of the "first field" and "second field." For another example, if the description object is "level," the ordinal number preceding "level" in "first level" and "second level" does not restrict the priority of the "levels." For another example, the number of description objects is not restricted by the ordinal number and can be one or more. For example, in the case of "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 description object is "device," the "first device" and "second device" can be the same or different devices, and their types can be the same or different. For another example, if the description object is "information," the "first information" and "second information" can be the same or different information, and their content can be the same or different.

[0093] In some embodiments, “including A,” “comprising A,” “used to indicate A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0094] In some embodiments, terms such as "in response to...", "in response to determining...", "in the case of...", "at the time of...", "when...", "if...", "if...", etc. can be used interchangeably.

[0095] In some embodiments, terms such as "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 less than", and "above" can be replaced with each other, and terms such as "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" can be replaced with each other.

[0096] In some embodiments, devices and the like can be interpreted as physical or virtual, and their names are not limited to those in the embodiments.

[0097] The recorded names, "device", "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", "subject" and other terms can be used interchangeably.

[0098] In some embodiments, "network" can be interpreted as devices included in the network (eg, access network equipment, core network equipment, etc.).

[0099] 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", "bandwidth part (BWP)" and the like may be used interchangeably.

[0100] 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, client, etc. can be used interchangeably.

[0101] In some embodiments, the access network device, the core network device, or the network device can be replaced by a terminal. For example, the various embodiments of the present disclosure can also be applied to a structure in which the communication between the access network device, the core network device, or the network device and the terminal is replaced by communication between multiple terminals (for example, device-to-device (D2D), vehicle-to-everything (V2X), etc.). In this case, it is also possible to set the structure in which the terminal has all or part of the functions of the access network device. In addition, terms such as "uplink" and "downlink" can also be replaced by terms corresponding to communication between terminals (for example, "side"). For example, uplink channels, downlink channels, etc. can be replaced by side channels, and uplinks, downlinks, etc. can be replaced by side links.

[0102] 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, the core network device, or the network device may have a structure that has all or part of the functions of the terminal.

[0103] In some embodiments, obtaining data, information, etc. may comply with the laws and regulations of the country where the data is obtained.

[0104] In some embodiments, data, information, etc. may be obtained with the user's consent.

[0105] In addition, each element, each row, or each column in the table of the embodiment of the present disclosure can be implemented as an independent embodiment, and the combination of any elements, any rows, and any columns can also be implemented as an independent embodiment.

[0106] FIG1 is a schematic diagram showing the architecture of a communication system according to an embodiment of the present disclosure.

[0107] As shown in FIG1 , a communication system 100 includes a terminal 101 and a network device 102 , wherein the network device includes at least one of the following: an access network device and a core network device.

[0108] In some embodiments, the terminal 101 includes, for example, a mobile phone, a wearable device, an Internet of Things device, a car with communication function, a smart car, a tablet computer, a computer with wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device in industrial control, a wireless terminal device in self-driving, a wireless terminal device in remote medical surgery, a wireless terminal device in a smart grid, a wireless terminal device in transportation safety, a wireless terminal device in a smart city, and at least one of a wireless terminal device in a smart home, but is not limited thereto.

[0109] In some embodiments, the access network device is, for example, a node or device that accesses a terminal to a wireless network. The access network device may include an evolved NodeB (eNB), a next generation evolved NodeB (ng-eNB), a next generation NodeB (gNB), a node B (NB), a home node B (HNB), a home evolved nodeB (HeNB), a wireless backhaul device, a radio network controller (RNC), a base station controller (BSC), a base transceiver station (BTS), a base band unit (BBU), a mobile switching center, a base station in a 6G communication system, an open base station (Open RAN), a cloud base station (Cloud RAN), a base station in other communication systems, and at least one of an access node in a Wi-Fi system, but is not limited thereto.

[0110] In some embodiments, a core network device may be a device including one or more network elements, or may be multiple devices or device groups, each including all or part of the one or more network elements. The network element 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), and a Next Generation Core (NGC).

[0111] In some embodiments, the technical solution of the present disclosure can be applied to the Open RAN architecture. In this case, the interfaces between or within the access network devices involved in the embodiments of the present disclosure can be transformed into internal interfaces of the Open RAN, and the processes and information interactions between these internal interfaces can be implemented through software or programs.

[0112] In some embodiments, the access network device can be composed of a centralized unit (CU) and a distributed unit (DU), where the CU can also be called a control unit. The CU-DU structure can be used to split the protocol layer of the access network device, with the functions of some protocol layers centrally controlled by the CU, and the functions of the remaining part or all of the protocol layers distributed in the DU, which is centrally controlled by the CU, but is not limited to this.

[0113] It can be understood that the communication system described in the embodiment of the present disclosure is for the purpose of more clearly illustrating the technical solution of the embodiment of the present disclosure, and does not constitute a limitation on the technical solution proposed in the embodiment of the present disclosure. Ordinary technicians in this field can know that with the evolution of the system architecture and the emergence of new business scenarios, the technical solution proposed in the embodiment of the present disclosure is also applicable to similar technical problems.

[0114] The following embodiments of the present disclosure may be applied to the communication system 100 shown in FIG1 , or a portion thereof, but are not limited thereto. The entities shown in FIG1 are illustrative only. The communication system may include all or part of the entities shown in FIG1 , or may include other entities outside of FIG1 . The number and form of the entities are arbitrary, and the entities may be physical or virtual. The connection relationships between the entities are illustrative only. The entities may be connected or disconnected, and the connection may be in any manner, including direct or indirect, wired or wireless.

[0115] The embodiments of the present disclosure 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 (registered trademark)), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, Ultra-WideBand (UWB), Bluetooth (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 utilizing other communication methods, and next-generation systems based on and extending these methods. Furthermore, multiple systems may be combined (for example, a combination of LTE or LTE-A with 5G).

[0116] FIG2 is an interactive schematic diagram illustrating a method for determining a model identifier according to an embodiment of the present disclosure.

[0117] As shown in FIG2 , the method for determining the model identification includes:

[0118] In step S201, a network device determines a first type model identifier assigned to a first AI model.

[0119] In some embodiments, the first AI model may be any AI model deployed on the terminal.

[0120] In some embodiments, the first type of model identifier is used to distinguish multiple AI models within the same AI function.

[0121] In some embodiments, the first type of model identifier may be a local model identifier, for example, a local model ID.

[0122] In some embodiments, the second type model identifier may be a global model identifier, such as a global model ID. For the same AI model, the second type model identifier assigned thereto may be a global model identifier, such as a global model ID. For the same AI model, the number of bits occupied by the second type model identifier assigned thereto may be:

[0123] In some embodiments, the method further includes: the network device determining whether to assign a first type model identifier to the first AI model.

[0124] In some embodiments, the first AI model comprises a model-certified AI model within a first AI function.

[0125] In some embodiments, the first AI model comprises an AI model within a first AI function that has been assigned a second type model identifier.

[0126] In some embodiments, the first AI model is an available AI model or a candidate AI model.

[0127] In some embodiments, the first type of model identifier is used to distinguish multiple AI models within the same AI feature, where one AI feature includes at least one AI function.

[0128] In some embodiments, the first AI model comprises a model-certified AI model within a first AI feature.

[0129] In some embodiments, the first AI model includes an AI model within a first AI characteristic that has been assigned a second type model identifier.

[0130] In some embodiments, the first AI function mapped to the first AI model is an available AI function.

[0131] In some embodiments, the first AI model is an available AI model or a candidate AI model.

[0132] In some embodiments, the first type of model identifier is used to distinguish multiple AI models within the same terminal, where a terminal supports at least one AI feature.

[0133] In some embodiments, the first AI model includes an AI model that has been model-certified in the first terminal.

[0134] In some embodiments, the first AI model includes an AI model in the first terminal that has been assigned a second type model identifier.

[0135] In some embodiments, the first AI feature is an available AI feature, wherein the first AI feature includes a first AI function mapped to the first AI model.

[0136] In some embodiments, the first AI function is an available AI function.

[0137] In some embodiments, the first AI model is an available AI model or a candidate AI model.

[0138] Step S202: The network device sends first information to the terminal.

[0139] In some embodiments, the first information is used to indicate a first type of model identifier assigned by the network device to the first AI model.

[0140] In some embodiments, the terminal receives first information sent by the network device.

[0141] In some embodiments, the terminal determines, based on the first information, a first type model identifier assigned by the network device to the first AI model.

[0142] Step S203: The terminal sends second information to the network device.

[0143] In some embodiments, the second information includes a first type model identifier assigned to the first AI model and first management information. The first management information is used to indicate at least one of the following: whether the first AI model is an available AI model; whether the first AI model is a candidate AI model; whether a first AI function mapped to the first AI model is an available AI function; or whether a first AI feature belonging to the first AI model is an available AI feature.

[0144] In some embodiments, the network device receives second information sent by the terminal.

[0145] Step S204: The network device sends third information to the terminal.

[0146] In some embodiments, the third information includes a first type model identifier assigned to the first AI model and second management information. The second management information indicates at least one of the following: the first AI model is an AI model selected by the network device; the first AI model is an AI model determined to be activated by the network device; or the first AI model is an AI model determined to be switched by the network device.

[0147] In some embodiments, the terminal receives third information sent by the network device.

[0148] The communication method involved in the embodiments of the present disclosure may include at least one of steps S201 to 204. For example, steps S201, S202, S203, and S204 may be implemented as independent embodiments, and steps S201+S202 and steps S203+S204 may be implemented as independent embodiments, but are not limited thereto.

[0149] In some embodiments, steps S203 and S204 may be executed in an interchanged order or simultaneously.

[0150] In some embodiments, steps S201 , S202 , S203 , and S204 are optional, and one or more of these steps may be omitted or replaced in different embodiments.

[0151] In some embodiments, reference may be made to other optional embodiments described before or after the description corresponding to FIG. 2 .

[0152] In 3GPP, AI functionality is further defined within the AI ​​feature framework. An AI feature may include multiple AI functions. AI functions are implemented based on AI models and correspond to a series of parameters.

[0153] For example, for the AI ​​feature of AI-based beam management, two AI functions are defined. One AI function corresponds to an AI model input with a beam number of 4, 8, or 16, while the other AI function corresponds to an AI model input with a beam number of 32 or 64.

[0154] In some embodiments, one AI function may include multiple AI models, that is, one AI function may be mapped to multiple AI models.

[0155] In actual deployments, the network can control AI operations on the terminal side based on the granularity of AI functions or the granularity of AI models. However, the management of AI models must be based on the management of AI functions.

[0156] In some embodiments, the terminal side can perform model authentication (model identification) to the network side. During the model authentication process, the terminal side can report relevant information of the AI ​​model to the network side, including the AI ​​function corresponding to the AI ​​model, and the network side can assign a model identifier to the AI ​​model that has passed the model authentication, such as a globally unique model identifier (global model ID). It can be seen from the above embodiment that since the global model ID occupies a large number of bits, using the global model ID for subsequent model management may increase the signaling overhead in the model management process.

[0157] In a first aspect, embodiments of the present disclosure provide a method for determining a model identifier. Figure 3 is a schematic flow chart illustrating a method for determining a model identifier according to an embodiment of the present disclosure. The method for determining a model identifier illustrated in this embodiment can be executed by a terminal.

[0158] As shown in FIG3 , the method for determining the model identifier may include the following steps:

[0159] In step S301 , a first type model identifier assigned to a first AI model is determined, wherein the first type model identifier is used to distinguish multiple AI models within the same AI function.

[0160] It should be noted that the embodiment shown in FIG. 3 can be implemented independently or in combination with at least one other embodiment in the present disclosure. The specific selection can be made as needed and the present disclosure does not limit it.

[0161] In some embodiments, a terminal may support one or more AI features. An AI feature may include one or more AI functions. An AI function may be mapped to multiple AI models.

[0162] In some embodiments, the first AI model may be any AI model deployed on the terminal.

[0163] In some embodiments, the first type of model identifier may be a local model identifier, for example, a local model ID.

[0164] In some embodiments, the second type model identifier may be a global model identifier, such as a global model ID. For the same AI model, the number of bits occupied by the first type model identifier allocated thereto is smaller than the number of bits occupied by the second type model identifier allocated thereto.

[0165] In some embodiments, the network side may assign a first type model identifier to each AI model and inform the terminal side of the assignment result.

[0166] In some possible implementations, determining the first type of model identifier assigned to the first AI model includes determining, based on first information sent by the network device, the first type of model identifier assigned by the network device to the first AI model. Optionally, the network device may send first information to the terminal, where the first information indicates the first type of model identifier assigned by the network device to the first AI model. Optionally, the terminal may receive the first information sent by the network device.

[0167] For example, during the model authentication process, the terminal can report relevant information of its deployed first AI model (Model#1) to the network device, and the network device can assign a local model ID to the authenticated Model#1 based on the relevant information of Model#1. Furthermore, the network device can send a first message to the terminal to indicate the local model ID assigned to Model#1. Furthermore, the terminal can determine the local model ID assigned by the network device to Model#1 based on the first information sent by the network device.

[0168] In some embodiments, the network side and the terminal side can independently determine the first type model identifier assigned to each AI model based on the same rules.

[0169] In some possible implementations, in step S301, the terminal may determine a first type model identifier assigned to the first AI model based on a first rule. Alternatively, the network device may determine a first type model identifier assigned to the first AI model based on the first rule.

[0170] The first rule may be preset, sent by the network device to the terminal, or sent by the terminal to the network device. This disclosure does not limit the specific content of the first rule.

[0171] In some embodiments, the first type of model identifier is used to distinguish multiple AI models within the same AI function. In this case, if the first type of model identifier is defined for AI models within the same AI function, the specific value of the first type of model identifier can be determined based on the number of AI models within the AI ​​function that require the first type of model identifier (e.g., the number of AI models within the AI ​​function that have the second type of model identifier).

[0172] For example, an AI function (Functionality#1) is mapped to four AI models (Model#1, Model#2, Model#3, and Model#4). The global model IDs of these four AI models are 1024, 2025, 3146, and 4958, respectively. The local model IDs assigned to these four AI models can be 1, 2, 3, and 4, respectively.

[0173] For another example, the first rule includes a mapping relationship between the value of a preset second-type model identifier and the value of a first-type model identifier; based on the value of the global model ID assigned to Model#1, the value of the local model ID assigned to Model#1 can be determined.

[0174] It should be noted that, in the above embodiment, if the first type model identifier is defined for the AI ​​model within the same AI function, the first type model identifier assigned to the AI ​​models in different AI functions may be repeated, but it can still be guaranteed that the subsequent model management process can proceed normally. For example, another AI function (Functionality#2) is mapped to two AI models (Model#5 and Model#6), and the global model IDs of these two AI models are 1025 and 1026 respectively. The local model IDs assigned to these two AI models can be 1 and 2 respectively. In the subsequent model management process, the AI ​​function and the local model ID can be combined for distinction. For example, "Functionality#1+local model ID=1" is used to identify the AI ​​model Model#1, and "Functionality#2+local model ID=1" is used to identify the AI ​​model Model#5.

[0175] In some embodiments, the first type of model identifier is used to distinguish multiple AI models within the same AI feature, where one AI feature includes at least one AI function.

[0176] In some embodiments, if first-type model identifiers are defined for AI models within the same AI feature, duplicate first-type model identifiers assigned to AI models within different AI features may exist, while duplicate first-type model identifiers assigned to AI models within different AI functions within the same AI feature must exist. This ensures that subsequent model management processes proceed normally. The specific implementation is similar to the above-mentioned embodiment of defining first-type model identifiers for AI models within the same AI function, and this disclosure will not elaborate further on this.

[0177] In some embodiments, the first type model identifier is used to distinguish multiple AI models in the same terminal, wherein a terminal supports at least one AI feature,

[0178] In some embodiments, if a first-type model identifier is defined for an AI model within the same terminal, the first-type model identifiers assigned to AI models within different terminals may be duplicated, while the first-type model identifiers assigned to AI models within different AI features supported by the same terminal must not be duplicated to ensure that the subsequent model management process proceeds normally. The specific implementation method is similar to the above-mentioned embodiment of defining the first-type model identifier for AI models within the same AI function, and this disclosure will not elaborate on this.

[0179] According to an embodiment of the present disclosure, a terminal may determine a first type model identifier assigned to a first AI model, wherein the first type model identifier is used to distinguish multiple AI models within the same AI function. Accordingly, when one AI function is mapped to multiple AI models, the definition and management of the first type model identifier may be implemented. Furthermore, since the number of bits occupied by the first type model identifier assigned to the same AI model is less than the number of bits occupied by the second type model identifier assigned to the same AI model, the use of the first type model identifier for subsequent model management is beneficial in saving signaling overhead during the model management process.

[0180] In some embodiments, the method further includes: determining whether to assign a first type model identifier to the first AI model.

[0181] For example, if only one local model ID is allocated to each AI model that has passed model certification, and the first AI model has not passed model certification, the terminal may determine not to allocate a local model ID for the first AI model.

[0182] In one possible implementation, the terminal may determine, based on an instruction from the network device, whether the network device has assigned a first-type model identifier to the first AI model. For example, the first information may indicate whether the network device has assigned a first-type model identifier to the first AI model and / or the first-type model identifier assigned to the first AI model by the network device.

[0183] In another possible implementation, the terminal may determine whether to assign a first type model identifier to the first AI model based on a first rule.

[0184] In some embodiments, the first type of model identifier is used to distinguish multiple AI models within the same AI function.

[0185] In one possible implementation, the first AI model includes an AI model that has undergone model certification within the first AI function.

[0186] In a possible implementation, the first AI model includes an AI model within the first AI function that has been assigned a second type model identifier.

[0187] In a possible implementation, the first AI model is an available AI model or a candidate AI model.

[0188] The available AI model refers to an AI model reported by the terminal as an applicable model. The candidate AI model refers to an AI model reported by the terminal as a candidate model.

[0189] In some embodiments, the first type of model identifier is used to distinguish multiple AI models within the same AI feature, where one AI feature includes at least one AI function.

[0190] In one possible implementation, the first AI model includes an AI model that has been model-certified within a first AI feature.

[0191] In a possible implementation, the first AI model includes an AI model that has been assigned a second type model identifier within the first AI characteristic.

[0192] In a possible implementation, the first AI function mapped to the first AI model is an available AI function.

[0193] The available AI function refers to the AI ​​function reported by the terminal as applicable functionality.

[0194] In a possible implementation, the first AI model is an available AI model or a candidate AI model.

[0195] In some embodiments, the first type of model identifier is used to distinguish multiple AI models within the same terminal, where a terminal supports at least one AI feature.

[0196] In one possible implementation, the first AI model includes an AI model that has been model-certified in the first terminal.

[0197] In a possible implementation, the first AI model includes an AI model in the first terminal that has been assigned a second type model identifier.

[0198] In a possible implementation, the first AI feature is an available AI feature, wherein the first AI feature includes a first AI function mapped to the first AI model.

[0199] The available AI feature refers to an AI feature reported by the terminal as an applicable feature.

[0200] In a possible implementation, the first AI function is an available AI function.

[0201] In a possible implementation, the first AI model is an available AI model or a candidate AI model.

[0202] The following will introduce in detail three ways of defining the first type of model identifier in conjunction with specific embodiments.

[0203] Method 1: Define a first type of model identifier for AI models within the same AI function.

[0204] In some embodiments, defining a first type of model identifier for AI models within the same AI function includes at least one of the following: (1-1) assigning a first type of model identifier to all model-certified AI models within the same AI function; (1-2) assigning a first type of model identifier to some model-certified AI models within the same AI function.

[0205] Method (1-1)

[0206] In one possible implementation, allocating the first type of model identifier to all AI models that have passed model certification within the same AI function includes allocating the first type of model identifier to the AI ​​models that have been allocated the second type of model identifier within the same AI function.

[0207] For example, an AI function (Functionality#1) is mapped to four AI models (Model#1, Model#2, Model#3, Model#4). These four AI models have been assigned a global model ID. It can be determined that a local model ID is assigned to each of these four AI models.

[0208] Method (1-2)

[0209] In one possible implementation, assigning a first type of model identifier to all model-certified AI models within the same AI function includes assigning a first type of model identifier to an available AI model or a candidate AI model within the same AI function that has been assigned a second type of model identifier.

[0210] For example, an AI function (Functionality#1) is mapped to four AI models (Model#1, Model#2, Model#3, and Model#4). These four AI models have all been assigned global model IDs, and Model#1 and Model#2 are reported by the terminal as available models. In this case, for Functionality#1, only local model IDs can be assigned to the two available AI models, Model#1 and Model#2. It should be noted that before assigning local model IDs to AI models, the terminal can report available models based on the global model ID.

[0211] For another example, an AI function (Functionality#1) is mapped to four AI models (Model#1, Model#2, Model#3, Model#4). These four AI models have all been assigned global model IDs, and these four AI models have all been reported by the terminal as candidate models. In this case, for Functionality#1, local model IDs can be assigned to these four AI models respectively.

[0212] Method 2: Define the first type of model identifier for AI models within the same AI feature.

[0213] In some embodiments, the definition of the first type of model identifier for the AI ​​models within the same AI feature includes at least one of the following: (2-1) assigning a first type of model identifier to all model-certified AI models within the same AI feature; (2-2) assigning a first type of model identifier to all model-certified AI models within some AI functions included in the same AI feature; (2-3) assigning a first type of model identifier to some model-certified AI models within some AI functions included in the same AI feature.

[0214] Method (2-1):

[0215] In a possible implementation, assigning a first type model identifier to all AI models that have passed model certification within the same AI characteristic includes assigning a first type model identifier to an AI model that has been assigned a second type model identifier within the same AI characteristic.

[0216] For example, an AI feature (Feature#1) includes two AI functions (Functionality#1 and Functionality#2). One AI function (Functionality#1) is mapped to four AI models (Model#1, Model#2, Model#3, and Model#4). Another AI function (Functionality#2) is mapped to two AI models (Model#5 and Model#6). These six AI models have all been assigned global model IDs. It can be determined that a local model ID is assigned to each of the six AI models.

[0217] Method (2-2):

[0218] In one possible implementation, all AI models that have passed model certification within some AI functions included in the same AI feature are assigned a first type model identifier, including: within the available AI functions included in the same AI feature, an AI model that has been assigned a second type model identifier is assigned a first type model identifier.

[0219] For example, an AI feature (Feature#1) includes two AI functions (Functionality#1 and Functionality#2). One AI function (Functionality#1) is mapped to four AI models (Model#1, Model#2, Model#3, and Model#4), and another AI function (Functionality#2) is mapped to two AI models (Model#5 and Model#6). These six AI models have all been assigned global model IDs, among which Functionality#1 is reported by the terminal as an available function. In this case, for Feature#1, local model IDs can be assigned only to the four AI models mapped to the available AI function Functionality#1.

[0220] Method (2-3):

[0221] In one possible implementation, the assigning of a first type of model identifier to some AI models that have undergone model certification within some AI functions included in the same AI feature includes assigning a first type of model identifier to available AI models or candidate AI models that have been assigned a second type of model identifier within the available AI functions included in the same AI feature.

[0222] For example, an AI feature (Feature#1) includes two AI functions (Functionality#1 and Functionality#2). One AI function (Functionality#1) is mapped to four AI models (Model#1, Model#2, Model#3, and Model#4), and another AI function (Functionality#2) is mapped to two AI models (Model#5 and Model#6). These six AI models have all been assigned global model IDs. Among them, Functionality#1 is reported by the terminal as an available function, and Model#1 and Model#2 are reported by the terminal as available models. In this case, for Feature#1, local model IDs can be assigned only to the two available AI models, Model#1 and Model#2, which are mapped to the available AI function Functionality#1.

[0223] Method 3: Define the first type of model identifier for the AI ​​model in the same terminal.

[0224] In some embodiments, the definition of a first type of model identifier for AI models within the same terminal includes at least one of the following: (3-1) assigning a first type of model identifier to all model-certified AI models within the same terminal; (3-2) assigning a first type of model identifier to all model-certified AI models within some AI features supported by the same terminal; (3-3) assigning a first type of model identifier to all model-certified AI models within some AI functions included in some AI features supported by the same terminal; (3-4) assigning a first type of model identifier to some model-certified AI models within some AI functions included in some AI features supported by the same terminal.

[0225] Method (3-1):

[0226] In some embodiments, allocating the first type of model identifier to all AI models that have passed model certification in the same terminal includes allocating the first type of model identifier to the AI ​​model that has been allocated the second type of model identifier in the same terminal.

[0227] For example, a terminal (UE#1) supports three AI features (Feature#1, Feature#2, Feature#3), where Feature#1 includes two AI functions, Feature#2 includes three AI functions, and Feature#3 includes one AI function. Each AI model mapped to these six AI functions has been assigned a global model ID. In this case, for UE#1, it can be determined that a local model ID is assigned to each AI model mapped to these six AI functions.

[0228] Method (3-2):

[0229] In some embodiments, allocating a first type model identifier to all AI models that have passed model certification within some AI features supported by the same terminal includes allocating a first type model identifier to an AI model that has been allocated a second type model identifier within the available AI features supported by the same terminal.

[0230] For example, a terminal (UE#1) supports three AI features (Feature#1, Feature#2, Feature#3), among which Feature#1 includes two AI functions (Functionality#1, Functionality#2), Feature#2 includes three AI functions (Functionality#3, Functionality#4, Functionality#5), and Feature#3 includes one AI function (Functionality#6). Each AI model mapped to these six AI functions has been assigned a global model ID, among which Feature#1 is reported by the terminal as an available feature; in this case, for UE#1, only a local model ID can be assigned to each AI model mapped to Functionality#1 and Functionality#2.

[0231] Method (3-3):

[0232] In some embodiments, all AI models that have passed model certification within some AI functions included in some AI features supported by the same terminal are assigned a first type model identifier, including: within the available AI functions included in the available AI features supported by the same terminal, the AI ​​model that has been assigned a second type model identifier is assigned a first type model identifier.

[0233] For example, a terminal (UE#1) supports three AI features (Feature#1, Feature#2, Feature#3), among which Feature#1 includes two AI functions (Functionality#1, Functionality#2), Feature#2 includes three AI functions (Functionality#3, Functionality#4, Functionality#5), and Feature#3 includes one AI function (Functionality#6). Each AI model mapped to these six AI functions has been assigned a global model ID, among which Feature#1 is reported by the terminal as an available feature, and Functionality#1 is reported by the terminal as an available function; in this case, for UE#1, a local model ID can be assigned only to each AI model mapped to Functionality#1.

[0234] Method (3-4):

[0235] In some embodiments, the allocating of a first type of model identifier to some of the AI ​​models that have been model-certified within some of the AI ​​functions included in some of the AI ​​features supported by the same terminal includes: allocating a first type of model identifier to an available AI model or candidate AI model that has been assigned a second type of model identifier within the available AI functions included in the available AI features supported by the same terminal.

[0236] For example, a terminal (UE#1) supports three AI features (Feature#1, Feature#2, Feature#3), of which Feature#1 includes two AI functions (Functionality#1, Functionality#2), Feature#2 includes three AI functions (Functionality#3, Functionality#4, Functionality#5), and Feature#3 includes one AI function (Functionality#6). Each AI model mapped to these six AI functions has been assigned a global model. ID; within Feature#1, one AI function (Functionality#1) is mapped to four AI models (Model#1, Model#2, Model#3, Model#4), and another AI function (Functionality#2) is mapped to two AI models (Model#5, Model#6); within Feature#3, AI function (Functionality#6) is mapped to one AI model (Model#7); among them, Feature#1 and Feature#3 are reported by the terminal as available features, Functionality#1 and Functionality#6 are reported by the terminal as available functions, and Model#1, Model#2, and Model#7 are reported by the terminal as available models; in this case, for UE#1, local model IDs can be assigned only to the three available AI models, Model#1, Model#2, and Model#7.

[0237] In one or more of the above embodiments, by assigning first-type model identifiers to all AI models, the first-type model identifiers can be used for subsequent model management, which helps save signaling overhead during model management. Furthermore, by assigning first-type model identifiers to some AI models, the number of AI models that need to be assigned first-type model identifiers can be reduced, which helps further reduce the number of bits occupied by first-type model identifiers and further saves signaling overhead during model management.

[0238] In one or more of the above embodiments, by defining a first type of model identifier for an AI model within the same AI function, compared with the implementation method of defining a first type of model identifier for an AI model within the same AI feature or defining a first type of model identifier for an AI model within the same terminal, it is beneficial to further reduce the number of bits occupied by the first type of model identifier, which is beneficial to further save signaling overhead in the model management process.

[0239] In some embodiments, the method further includes: sending second information to the network device, wherein the second information includes a first type model identifier assigned to the first AI model and first management information. The first management information is used to indicate at least one of the following: whether the first AI model is an available model; whether the first AI model is a candidate model; whether a first AI function mapped to the first AI model is an available function; and whether a first AI feature to which the first AI model belongs is an available feature.

[0240] For example, after assigning local model IDs to all Models for the terminal (UE#1), the terminal can report Model#1 as an available AI model to the network device based on the local model ID assigned to Model#1, Functionality#1 mapped to Model#1, and / or Feature#1 corresponding to Model#1.

[0241] Optionally, the network device may receive second information sent by the terminal.

[0242] In the above embodiment, before assigning a first type of model identifier to the AI ​​model, the terminal may first report relevant information of the AI ​​model based on the second type of model identifier, and after assigning a first type of model identifier to the AI ​​model, the terminal may report relevant information of the AI ​​model based on the first type of model identifier, thereby saving signaling overhead in the model management process.

[0243] In some embodiments, the method further includes receiving third information sent by the network device, wherein the third information includes a first type model identifier assigned to the first AI model and second management information. The second management information is used to indicate at least one of the following: the first AI model is an AI model selected by the network device; the first AI model is an AI model determined to be activated by the network device; and the first AI model is an AI model determined to be switched by the network device.

[0244] For example, after assigning local model IDs to all Models for the terminal (UE#1), the network device can instruct UE#1 to select to perform relevant operations on Model#1 based on the local model ID assigned to Model#1, Functionality#1 mapped to Model#1, and / or Feature#1 corresponding to Model#1.

[0245] Optionally, the network device may send third information to the terminal.

[0246] In the above embodiment, before assigning a first type of model identifier to the AI ​​model, the network device may first indicate the object of the relevant operation to the terminal based on the second type of model identifier, and after assigning a first type of model identifier to the AI ​​model, the network device may indicate the object of the relevant operation to the terminal based on the first type of model identifier, thereby saving signaling overhead in the model management process.

[0247] In a second aspect, embodiments of the present disclosure provide a method for determining a model identifier. Figure 4 is a schematic flow chart illustrating another method for determining a model identifier according to an embodiment of the present disclosure. The method for determining a model identifier shown in this embodiment can be executed by a network device.

[0248] As shown in FIG4 , the method for determining the model identifier may include the following steps:

[0249] In step S401 , a first type model identifier assigned to a first AI model is determined, wherein the first type model identifier is used to distinguish multiple AI models within the same AI function.

[0250] It should be noted that the embodiment shown in FIG. 4 can be implemented independently or in combination with at least one other embodiment in the present disclosure. The specific selection can be made as needed and the present disclosure does not limit it.

[0251] In some embodiments, a terminal may support one or more AI features. An AI feature may include one or more AI functions. An AI function may be mapped to multiple AI models. The first AI model may be any AI model deployed on the terminal.

[0252] In some embodiments, the first type of model identifier may be a local model identifier, for example, a local model ID.

[0253] In some embodiments, the second type model identifier may be a global model identifier, such as a global model ID. For the same AI model, the number of bits occupied by the first type model identifier allocated thereto is smaller than the number of bits occupied by the second type model identifier allocated thereto.

[0254] In some embodiments, the network side may assign a first type model identifier to each AI model and inform the terminal side of the assignment result.

[0255] In some possible implementations, the method further includes: sending first information to a terminal, wherein the first information is used to indicate a first type model identifier assigned by the network device to the first AI model. Optionally, the terminal may receive the first information sent by the network device and may also determine the first type model identifier assigned by the network device to the first AI model based on the first information.

[0256] For example, the network device may determine the local model ID assigned to the first AI model (Model#1); further, the network device may send first information to the terminal to indicate the local model ID assigned to Model#1. Further, the terminal may determine the local model ID assigned to Model#1 by the network device based on the first information sent by the network device.

[0257] In some embodiments, the network side and the terminal side can independently determine the first type model identifier assigned to each AI model based on the same rules.

[0258] In some possible implementations, the network device may determine, based on the first rule, a first type model identifier assigned to the first AI model. Alternatively, the terminal may determine, based on the first rule, a first type model identifier assigned to the first AI model.

[0259] The first rule may be preset, sent by the network device to the terminal, or sent by the terminal to the network device. This disclosure does not limit the specific content of the first rule.

[0260] In some embodiments, the first type of model identifier is used to distinguish multiple AI models within the same AI function. In this case, if the first type of model identifier is defined for AI models within the same AI function, the specific value of the first type of model identifier can be determined based on the number of AI models within the AI ​​function that require the first type of model identifier (e.g., the number of AI models within the AI ​​function that have the second type of model identifier).

[0261] For example, an AI function (Functionality#1) is mapped to four AI models (Model#1, Model#2, Model#3, and Model#4). The global model IDs of these four AI models are 1024, 2025, 3146, and 4958, respectively. The local model IDs assigned to these four AI models can be 1, 2, 3, and 4, respectively.

[0262] For another example, the first rule includes a mapping relationship between the value of a preset second-type model identifier and the value of a first-type model identifier; based on the value of the global model ID assigned to Model#1, the value of the local model ID assigned to Model#1 can be determined.

[0263] It should be noted that, in the above embodiment, if the first type model identifier is defined for the AI ​​model within the same AI function, the first type model identifier assigned to the AI ​​models in different AI functions may be repeated, but it can still be guaranteed that the subsequent model management process can proceed normally. For example, another AI function (Functionality#2) is mapped to two AI models (Model#5 and Model#6), and the global model IDs of these two AI models are 1025 and 1026 respectively. The local model IDs assigned to these two AI models can be 1 and 2 respectively. In the subsequent model management process, the AI ​​function and the local model ID can be combined for distinction. For example, "Functionality#1+local model ID=1" is used to identify the AI ​​model Model#1, and "Functionality#2+local model ID=1" is used to identify the AI ​​model Model#5.

[0264] In some embodiments, the first type of model identifier is used to distinguish multiple AI models within the same AI feature, where one AI feature includes at least one AI function.

[0265] In some embodiments, if a first type model identifier is defined for an AI model within the same AI feature, the first type model identifiers assigned to AI models within different AI features may be repeated, while the first type model identifiers assigned to AI models within different AI functions included in the same AI feature cannot be repeated, so as to ensure that the subsequent model management process proceeds normally.

[0266] In some embodiments, the first type model identifier is used to distinguish multiple AI models in the same terminal, wherein a terminal supports at least one AI feature,

[0267] In some embodiments, if a first type model identifier is defined for an AI model within the same terminal, then the first type model identifiers assigned to AI models within different terminals may be repeated, while the first type model identifiers assigned to AI models within different AI features supported by the same terminal cannot be repeated, so as to ensure that the subsequent model management process proceeds normally.

[0268] According to an embodiment of the present disclosure, a network device may determine a first type model identifier assigned to a first AI model, wherein the first type model identifier is used to distinguish multiple AI models within the same AI function. Accordingly, when one AI function is mapped to multiple AI models, the definition and management of the first type model identifier may be implemented. Furthermore, since the number of bits occupied by the first type model identifier assigned to the same AI model is less than the number of bits occupied by the second type model identifier assigned to the same AI model, the use of the first type model identifier for subsequent model management is beneficial in saving signaling overhead during the model management process.

[0269] In some embodiments, the method further includes: determining whether to assign a first type model identifier to the first AI model.

[0270] For example, if only one local model ID is assigned to each AI model that has undergone model certification, and the first AI model has not undergone model certification, the network device may determine not to assign a local model ID to the first AI model.

[0271] In one possible implementation, the network device may indicate to the terminal whether to assign a first-type model identifier to the first AI model. For example, the first information may be used to indicate: whether the network device assigns a first-type model identifier to the first AI model, and / or the first-type model identifier assigned to the first AI model by the network device.

[0272] In another possible implementation, the network device may determine whether to assign a first type model identifier to the first AI model based on a first rule.

[0273] In some embodiments, the first type of model identifier is used to distinguish multiple AI models within the same AI function.

[0274] In one possible implementation, the first AI model includes an AI model that has undergone model certification within the first AI function.

[0275] In a possible implementation, the first AI model includes an AI model within the first AI function that has been assigned a second type model identifier.

[0276] In a possible implementation, the first AI model is an available AI model or a candidate AI model.

[0277] The available AI model refers to an AI model reported by the terminal as an applicable model. The candidate AI model refers to an AI model reported by the terminal as a candidate model.

[0278] In some embodiments, the first type of model identifier is used to distinguish multiple AI models within the same AI feature, where one AI feature includes at least one AI function.

[0279] In one possible implementation, the first AI model includes an AI model that has been model-certified within a first AI feature.

[0280] In a possible implementation, the first AI model includes an AI model that has been assigned a second type model identifier within the first AI characteristic.

[0281] In a possible implementation, the first AI function mapped to the first AI model is an available AI function.

[0282] The available AI function refers to the AI ​​function reported by the terminal as applicable functionality.

[0283] In a possible implementation, the first AI model is an available AI model or a candidate AI model.

[0284] In some embodiments, the first type of model identifier is used to distinguish multiple AI models within the same terminal, where a terminal supports at least one AI feature.

[0285] In one possible implementation, the first AI model includes an AI model that has been model-certified in the first terminal.

[0286] In a possible implementation, the first AI model includes an AI model in the first terminal that has been assigned a second type model identifier.

[0287] In a possible implementation, the first AI feature is an available AI feature, wherein the first AI feature includes a first AI function mapped to the first AI model.

[0288] The available AI feature refers to an AI feature reported by the terminal as an applicable feature.

[0289] In a possible implementation, the first AI function is an available AI function.

[0290] In a possible implementation, the first AI model is an available AI model or a candidate AI model.

[0291] In some embodiments, the method further includes: receiving second information sent by a terminal, wherein the second information includes a first type model identifier and first management information assigned to the first AI model, and the first management information is used to indicate at least one of the following: whether the first AI model is an available AI model; whether the first AI model is a candidate AI model; whether the first AI function mapped to the first AI model is an available AI function; and whether the first AI feature belonging to the first AI model is an available AI feature.

[0292] Optionally, the terminal may send the second information via a network device.

[0293] In some embodiments, the method further includes: sending third information to the terminal, wherein the third information includes a first type model identifier assigned to the first AI model and second management information, and the second management information is used to indicate at least one of the following: the first AI model is an AI model selected by the network device; the first AI model is an AI model determined to be activated by the network device; the first AI model is an AI model determined to be switched by the network device.

[0294] Optionally, the terminal may receive third information sent by the network device.

[0295] In some embodiments, the names of information, etc. are not limited to the names described in the embodiments, and terms such as "information", "message", "signal", "signaling", "report", "configuration", "indication", "instruction", "command", "channel", "parameter", "domain", "field", "symbol", "symbol", "codeword", "codebook", "codeword", "codepoint", "bit", "data", "program", and "chip" can be used interchangeably.

[0296] In some embodiments, terms such as "moment", "time point", "time", and "time position" can be replaced with each other, and terms such as "duration", "period", "time window", "window", and "time" can be replaced with each other.

[0297] In some embodiments, the terms "component carrier (CC)", "cell", "frequency carrier", "carrier frequency" and the like can be used interchangeably.

[0298] In some embodiments, "obtain", "get", "get", "receive", "transmit", "bidirectional transmission", "send and / or receive" can be interchangeable, and can be interpreted as receiving from other entities, obtaining from protocols, obtaining from higher layers, obtaining by self-processing, autonomous implementation, etc.

[0299] In some embodiments, terms such as "send", "transmit", "report", "download", "transmit", "bidirectional transmission", "send and / or receive" can be used interchangeably.

[0300] Corresponding to the aforementioned embodiment of the method for determining a model identification, the present disclosure also provides an embodiment of an apparatus for determining a model identification.

[0301] FIG5 is a schematic block diagram of a device for determining a model identifier according to an embodiment of the present disclosure. As shown in FIG5 , the device for determining a model identifier 500 includes a first processing module 501 .

[0302] In some embodiments, the first processing module is used to determine a first type of model identifier assigned to a first AI model, wherein the first type of model identifier is used to distinguish multiple AI models within the same AI function.

[0303] In some embodiments, the first processing module is further used to determine whether to assign a first type model identifier to the first AI model.

[0304] In some embodiments, the first AI model comprises a model-certified AI model within a first AI function.

[0305] In some embodiments, the first AI model comprises an AI model within a first AI function that has been assigned a second type model identifier.

[0306] In some embodiments, the first AI model is an available AI model or a candidate AI model.

[0307] In some embodiments, the first type of model identifier is used to distinguish multiple AI models within the same AI feature, where one AI feature includes at least one AI function.

[0308] In some embodiments, the first AI model comprises a model-certified AI model within a first AI feature.

[0309] In some embodiments, the first AI model includes an AI model within a first AI characteristic that has been assigned a second type model identifier.

[0310] In some embodiments, the first AI function mapped to the first AI model is an available AI function.

[0311] In some embodiments, the first AI model is an available AI model or a candidate AI model.

[0312] In some embodiments, the first type of model identifier is used to distinguish multiple AI models within the same terminal, where a terminal supports at least one AI feature.

[0313] In some embodiments, the first AI model includes an AI model that has been model-certified in the first terminal.

[0314] In some embodiments, the first AI model includes an AI model in the first terminal that has been assigned a second type model identifier.

[0315] In some embodiments, the first AI feature is an available AI feature, wherein the first AI feature includes a first AI function mapped to the first AI model.

[0316] In some embodiments, the first AI function is an available AI function.

[0317] In some embodiments, the first AI model is an available AI model or a candidate AI model.

[0318] In some embodiments, the first processing module is used to determine a first type of model identifier assigned by the network device to the first AI model based on first information sent by the network device.

[0319] In some embodiments, the apparatus 500 further includes:

[0320] A first transceiver module is configured to send second information to a network device, wherein the second information includes a first type model identifier assigned to the first AI model and first management information, where the first management information is used to indicate at least one of the following:

[0321] Whether the first AI model is an available model;

[0322] whether the first AI model is a candidate model;

[0323] whether the first AI function mapped to the first AI model is an available function;

[0324] Whether the first AI feature to which the first AI model belongs is an available feature.

[0325] FIG6 is a schematic block diagram of another apparatus for determining a model identifier according to an embodiment of the present disclosure. As shown in FIG6 , the apparatus for determining a model identifier 600 includes a second processing module 601 .

[0326] In some embodiments, the second processing module is used to determine a first type of model identifier assigned to the first AI model, wherein the first type of model identifier is used to distinguish multiple AI models within the same AI function.

[0327] In some embodiments, the second processing module is further used to determine whether to assign a first type model identifier to the first AI model.

[0328] In some embodiments, the first AI model comprises a model-certified AI model within a first AI function.

[0329] In some embodiments, the first AI model comprises an AI model within a first AI function that has been assigned a second type model identifier.

[0330] In some embodiments, the first AI model is an available AI model or a candidate AI model.

[0331] In some embodiments, the first type of model identifier is used to distinguish multiple AI models within the same AI feature, where one AI feature includes at least one AI function.

[0332] In some embodiments, the first AI model comprises a model-certified AI model within a first AI feature.

[0333] In some embodiments, the first AI model includes an AI model within a first AI characteristic that has been assigned a second type model identifier.

[0334] In some embodiments, the first AI function mapped to the first AI model is an available AI function.

[0335] In some embodiments, the first AI model is an available AI model or a candidate AI model.

[0336] In some embodiments, the first type of model identifier is used to distinguish multiple AI models within the same terminal, where a terminal supports at least one AI feature.

[0337] In some embodiments, the first AI model includes an AI model that has been model-certified in the first terminal.

[0338] In some embodiments, the first AI model includes an AI model in the first terminal that has been assigned a second type model identifier.

[0339] In some embodiments, the first AI feature is an available AI feature, wherein the first AI feature includes a first AI function mapped to the first AI model.

[0340] In some embodiments, the first AI function is an available AI function.

[0341] In some embodiments, the first AI model is an available AI model or a candidate AI model.

[0342] In some embodiments, the apparatus 600 further includes:

[0343] The second transceiver module is used to send first information to the terminal, wherein the first information is used to indicate a first type model identifier assigned by the network device to the first AI model.

[0344] In some embodiments, the second transceiver module is further configured to send third information to the terminal, wherein the third information includes a first type model identifier assigned to the first AI model and second management information, and the second management information is configured to indicate at least one of the following:

[0345] The first AI model is an AI model selected by the network device;

[0346] The first AI model is an AI model determined to be activated by the network device;

[0347] The first AI model is an AI model used by the network device to determine switching.

[0348] It should be noted that the modules included in the model identification determination device 500 and / or the model identification determination device 600 are not limited to the modules described in the above embodiments, and may also include other modules, such as a transceiver module, a storage module, a display module, etc.

[0349] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can be referred to the partial description of the method embodiment. The device embodiment described above is merely illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art can understand and implement it without paying any creative work.

[0350] An embodiment of the present disclosure further proposes a terminal, comprising: one or more processors; wherein the processor is used to call instructions to enable the terminal to execute the method for determining the model identifier described in the first aspect and the optional embodiment of the first aspect.

[0351] An embodiment of the present disclosure further proposes a network device, comprising: one or more processors; wherein the processor is used to call instructions to enable the network device to execute the method for determining the model identifier described in the second aspect and the optional embodiment of the second aspect.

[0352] An embodiment of the present disclosure also proposes a communication device, comprising: one or more processors; wherein the processor is used to call instructions to enable the communication device to execute the method for determining the model identification described in the first aspect and the second aspect, and the optional embodiments of the first aspect and the second aspect.

[0353] An embodiment of the present disclosure also proposes a communication system, including a terminal and a network device, wherein the terminal is configured to implement the method for determining the model identifier described in the first aspect and the optional embodiment of the first aspect, and the network device is configured to implement the method for determining the model identifier described in the second aspect and the optional embodiment of the second aspect.

[0354] An embodiment of the present disclosure further proposes a storage medium storing instructions, which, when executed on a communication device, enables the communication device to execute the method for determining a model identifier described in the first and second aspects, and the optional embodiments of the first and second aspects.

[0355] The embodiments of the present disclosure further provide an apparatus for implementing any of the above methods. For example, an apparatus is provided, comprising units or modules for implementing each step performed by a terminal in any of the above methods. For another example, another apparatus is provided, comprising units or modules for implementing each step performed by a network device (e.g., an access network device, a core network function node, a core network device, etc.) in any of the above methods.

[0356] It should be understood that the division of the various units or modules in the above device is merely a division of logical functions. In actual implementation, they may be fully or partially integrated into a physical entity, or they may be physically separated. In addition, the units or modules in the device may be implemented in the form of a processor calling software: for example, the device includes a processor, the processor is connected to a memory, and the memory stores instructions. The processor calls the instructions stored in the memory to implement any of the above methods or implement the functions of the various units or modules of the above device, wherein the processor is, for example, a general-purpose processor, such as a central processing unit (CPU) or a microprocessor, and the memory is a memory within the device or a memory outside the device. Alternatively, the units or modules in the device can be implemented in the form of hardware circuits, and the functions of some or all of the units or modules can be realized by designing the hardware circuits. The above-mentioned hardware circuits can be understood as one or more processors; for example, in one implementation, the above-mentioned hardware circuit is an application-specific integrated circuit (ASIC), which realizes the functions of some or all of the above units or modules by designing the logical relationship of the components in the circuit; for example, in another implementation, the above-mentioned hardware circuit can be realized by a programmable logic device (PLD). Taking a field programmable gate array (FPGA) as an example, it can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured by configuring the configuration file, thereby realizing the functions of some or all of the above units or modules. All units or modules of the above devices can be realized in the form of software called by the processor, or in the form of hardware circuits, or in part by the form of software called by the processor, and the rest by hardware circuits.

[0357] In the embodiments of the present disclosure, the processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction reading and execution capabilities, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a microprocessor), or a digital signal processor (DSP). In another implementation, the processor can implement certain functions through the logical relationship of the hardware circuit. The logical relationship of the above-mentioned hardware circuit is fixed or reconfigurable. For example, the processor is a hardware circuit implemented by 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 implementing the hardware circuit configuration 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.

[0358] Figure 7 is a schematic diagram of the structure of a communication device 7100 proposed in an embodiment of the present disclosure. Communication device 7100 can be a network device (e.g., an access network device, a core network device, etc.), a terminal (e.g., a user equipment, etc.), a chip, a chip system, or a processor that supports a network device to implement any of the above methods, or a chip, a chip system, or a processor that supports a terminal to implement any of the above methods. Communication device 7100 can be used to implement the methods described in the above method embodiments. For details, please refer to the description of the above method embodiments.

[0359] As shown in Figure 7, the communication device 7100 includes one or more processors 7101. The processor 7101 can be a general-purpose processor or a dedicated processor, for example, a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, and the central processing unit can be used to control the communication device (such as a base station, a baseband chip, a terminal device, a terminal device chip, a DU or a CU, etc.), execute programs, and process program data. The processor 7101 is used to call instructions to enable the communication device 7100 to perform any of the above methods.

[0360] In some embodiments, the communication device 7100 further includes one or more memories 7102 for storing instructions. Optionally, all or part of the memories 7102 may be located outside the communication device 7100.

[0361] In some embodiments, the communication device 7100 further includes one or more transceivers 7103. When the communication device 7100 includes one or more transceivers 7103, the communication steps such as sending and receiving in the above method are performed by the transceiver 7103, and the other steps are performed by the processor 7101.

[0362] 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, and transceiver circuit may be used interchangeably; the terms transmitter, transmitting unit, transmitter, and transmitting circuit may be used interchangeably; and the terms receiver, receiving unit, receiver, and receiving circuit may be used interchangeably.

[0363] Optionally, the communication device 7100 further includes one or more interface circuits 7104, which are connected to the memory 7102. The interface circuits 7104 may be configured to receive signals from the memory 7102 or other devices, and may be configured to send signals to the memory 7102 or other devices. For example, the interface circuits 7104 may read instructions stored in the memory 7102 and send the instructions to the processor 7101.

[0364] The communication device 7100 described in the above embodiment may be a network device or a terminal, but the scope of the communication device 7100 described in the present disclosure is not limited thereto, and the structure of the communication device 7100 may not be limited by FIG. 7 . The communication device may be an independent device or may be part of a larger device. For example, the communication device may be: 1) an independent integrated circuit IC, or a chip, or a chip system or subsystem; (2) a collection of one or more ICs, optionally, the above IC collection may also include a storage component for storing data or programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, a terminal device, an intelligent terminal device, a cellular phone, a wireless device, a handheld device, a mobile unit, an in-vehicle device, a network device, a cloud device, an artificial intelligence device, etc.; (6) others, etc.

[0365] FIG8 is a schematic diagram of the structure of a chip 8200 according to an embodiment of the present disclosure. If the communication device 7100 can be a chip or a chip system, reference can be made to the schematic diagram of the structure of the chip 8200 shown in FIG8 , but the present disclosure is not limited thereto.

[0366] The chip 8200 includes one or more processors 8201, and the processor 8201 is used to call instructions so that the chip 8200 executes any of the above methods.

[0367] In some embodiments, the chip 8200 further includes one or more interface circuits 8202, which are connected to the memory 8203. The interface circuit 8202 can be used to receive signals from the memory 8203 or other devices, and can be used to send signals to the memory.

[0368] 8203 or other devices to send signals. For example, the interface circuit 8202 can read the instructions stored in the memory 8203 and send the instructions to the processor 8201. Optionally, the terms interface circuit, interface, transceiver pin, transceiver, etc. can be used interchangeably.

[0369] In some embodiments, the chip 8200 further includes one or more memories 8203 for storing instructions. Alternatively, all or part of the memories 8203 may be outside the chip 8200.

[0370] The present disclosure also proposes a storage medium having instructions stored thereon. When the instructions are executed on the communication device 7100, the communication device 7100 executes any of the above methods. Optionally, the storage medium is an electronic storage medium. Optionally, the storage medium is a computer-readable storage medium, but is not limited thereto and may also be a storage medium readable by other devices. Optionally, the storage medium may be a non-transitory storage medium, but is not limited thereto and may also be a temporary storage medium.

[0371] The present disclosure also provides a program product, which, when executed by the communication device 7100, enables the communication device 7100 to perform any of the above methods. Optionally, the program product is a computer program product.

[0372] The present disclosure also proposes a computer program, which, when executed on a computer, causes the computer to perform any one of the above methods.

Claims

1. A method for determining a model identifier, characterized in that: Executed by a terminal, the method includes: Determine a first type of model identifier assigned to a first AI model, wherein the first type of model identifier is used to distinguish multiple AI models within the same AI function.

2. The method according to claim 1, characterized in that: The method further comprises: Determine whether to assign a first type model identifier to the first AI model.

3. The method according to claim 1 or 2, characterized in that: The first AI model includes an AI model that has been model-certified within the first AI function.

4. The method according to claim 3, characterized in that The first AI model includes an AI model within the first AI function that has been assigned a second type model identifier.

5. The method according to claim 4, characterized in that The first AI model is an available AI model or a candidate AI model.

6. The method according to claim 1 or 2, characterized in that: The first type of model identifier is used to distinguish multiple AI models within the same AI feature, where one AI feature includes at least one AI function.

7. The method according to claim 6, characterized in that The first AI model includes an AI model that has been model-certified within a first AI feature.

8. The method according to claim 7, characterized in that The first AI model includes an AI model that has been assigned a second type model identifier within the first AI characteristic.

9. The method according to claim 7, characterized in that: The first AI function mapped to the first AI model is an available AI function.

10. The method according to claim 8 or 9, characterized in that: The first AI model is an available AI model or a candidate AI model.

11. The method according to claim 1 or 2, characterized in that: The first type model identifier is used to distinguish multiple AI models in the same terminal, where a terminal supports at least one AI feature.

12. The method according to claim 11, characterized in that The first AI model includes an AI model that has been model-certified in the first terminal.

13. The method according to claim 12, characterized in that The first AI model includes an AI model in the first terminal to which a second type model identifier has been assigned.

14. The method according to claim 13, characterized in that The first AI characteristic is an available AI characteristic, wherein the first AI characteristic includes a first AI function mapped to the first AI model.

15. The method according to any one of claims 13 to 14, characterized in that The first AI function is an available AI function.

16. The method according to any one of claims 13 to 15, characterized in that The first AI model is an available AI model or a candidate AI model.

17. The method according to any one of claims 1 to 16, characterized in that The determining of the first type model identifier assigned to the first AI model includes: Determine, according to first information sent by a network device, a first type model identifier assigned by the network device to the first AI model.

18. The method according to any one of claims 1 to 17, characterized in that The method further comprises: Sending second information to the network device, wherein the second information includes a first type model identifier and first management information assigned to the first AI model, and the first management information is used to indicate at least one of the following: Whether the first AI model is an available AI model; whether the first AI model is a candidate AI model; whether the first AI function mapped to the first AI model is an available AI function; Whether the first AI characteristic to which the first AI model belongs is an available AI characteristic.

19. A method for determining a model identifier, characterized in that: Executed by a network device, the method includes: Determine a first type of model identifier assigned to a first AI model, wherein the first type of model identifier is used to distinguish multiple AI models within the same AI function.

20. The method according to claim 19, characterized in that The method further comprises: Determine whether to assign a first type model identifier to the first AI model.

21. The method according to claim 19 or 20, characterized in that The first AI model includes an AI model that has been model-certified within the first AI function.

22. The method according to claim 21, characterized in that The first AI model includes an AI model within the first AI function that has been assigned a second type model identifier.

23. The method according to claim 22, characterized in that The first AI model is an available AI model or a candidate AI model.

24. The method according to claim 19 or 20, characterized in that The first type of model identifier is used to distinguish multiple AI models within the same AI feature, where one AI feature includes at least one AI function.

25. The method according to claim 24, characterized in that The first AI model includes an AI model that has been model-certified within a first AI feature.

26. The method according to claim 25, characterized in that The first AI model includes an AI model that has been assigned a second type model identifier within the first AI characteristic.

27. The method according to claim 25, characterized in that The first AI function mapped to the first AI model is an available AI function.

28. The method according to claim 26 or 27, characterized in that The first AI model is an available AI model or a candidate AI model.

29. The method according to claim 19 or 20, characterized in that The first type model identifier is used to distinguish multiple AI models in the same terminal, where a terminal supports at least one AI feature.

30. The method according to claim 29, characterized in that The first AI model includes an AI model that has been model-certified in the first terminal.

31. The method according to claim 30, characterized in that The first AI model includes an AI model in the first terminal to which a second type model identifier has been assigned.

32. The method according to claim 31, characterized in that The first AI characteristic is an available AI characteristic, wherein the first AI characteristic includes a first AI function mapped to the first AI model.

33. The method according to any one of claims 31 to 32, characterized in that The first AI function is an available AI function.

34. The method according to any one of claims 31 to 33, characterized in that The first AI model is an available AI model or a candidate AI model.

35. The method according to any one of claims 19 to 34, characterized in that The method further comprises: Send first information to the terminal, wherein the first information is used to indicate a first type of model identifier assigned by the network device to the first AI model.

36. The method according to any one of claims 19 to 35, characterized in that The method further comprises: Sending third information to the terminal, wherein the third information includes a first type model identifier assigned to the first AI model and second management information, where the second management information is used to indicate at least one of the following: The first AI model is an AI model selected by the network device; The first AI model is an AI model determined to be activated by the network device; The first AI model is an AI model used by the network device to determine switching.

37. A device for determining a model identifier, characterized in that: The device comprises: The first processing module is used to determine a first type of model identifier assigned to a first AI model, wherein the first type of model identifier is used to distinguish multiple AI models within the same AI function.

38. A device for determining a model identifier, characterized in that: The device comprises: The second processing module is used to determine a first type of model identifier assigned to the first AI model, wherein the first type of model identifier is used to distinguish multiple AI models within the same AI function.

39. A terminal, characterized in that: include: one or more processors; Wherein, the terminal is used to execute the method for determining the model identification according to any one of claims 1-18.

40. A network device, characterized in that: include: one or more processors; Wherein, the network device is used to execute the method for determining the model identification described in any one of claims 19-36.

41. A communication device, characterized in that: include: one or more processors; The processor is used to call instructions to enable the communication device to execute the method for determining the model identification described in any one of claims 1-36.

42. A communication system, characterized in that: It includes a terminal and a network device, wherein the terminal is configured to implement the method for determining the model identifier described in any one of claims 1-18, and the network device is configured to implement the method for determining the model identifier described in any one of claims 19-36.

43. A storage medium storing instructions, characterized in that: When the instruction is executed on a communication device, the communication device executes the method for determining a model identifier according to any one of claims 1 to 36.

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