Model reasoning method and apparatus in communication system, and device and medium

Abstract

The present application belongs to the field of communications. Disclosed are a model reasoning method and apparatus in a communication system, and a device and a medium. The method in the embodiments of the present application comprises: a first device receiving first information sent by a second device, wherein at least one of knowledge information and calling interface information of the knowledge information is stored in the first device, and the knowledge information comprises at least one of the following: a knowledge graph and a knowledge vector library; and the first device sending second information to a third device, wherein a first model is deployed on the third device, the second information is determined on the basis of the first information and the knowledge information stored in or called by the first device, and the first information comprises at least one of the following: assistance information for the first model to perform reasoning, requirement information for the first model to perform reasoning, input information of the first model, the calling interface information of the knowledge information, a receiving device indication of the second information, and a sample indication corresponding to the input information of the first model.

Description

Model reasoning method, device, equipment and medium in communication system

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to the Chinese patent application filed with the China Patent Office on November 20, 2023, with application number 202311551035.X and invention name “Model reasoning method, device, equipment and medium in communication system”, the entire contents of which are incorporated by reference into this application. Technical Field

[0003] The present application belongs to the field of communication technology, and specifically relates to a model reasoning method, apparatus, device, and medium in a communication system. Background Art

[0004] In mobile communication systems, artificial intelligence (AI) is increasingly being incorporated into use cases. For example, at the physical layer, AI-based CSI (channel state information) prediction and feedback compression, AI-based beam management, and AI-based positioning are all examples. To ensure universal applicability across diverse communication scenarios, the application of large models to communication networks has been proposed to address system issues. While large models typically perform better in solving general problems, communication systems present a wide range of diverse challenges, such as beamforming, resource allocation, and channel prediction. Therefore, ensuring the performance of large models in solving specific downstream problems is a pressing issue. Summary of the Invention

[0005] The embodiments of the present application provide a model reasoning method, apparatus, device, and medium in a communication system, which can improve the performance of the model.

[0006] In a first aspect, a model reasoning method in a communication system is provided, the method comprising:

[0007] A first device receives first information sent by a second device, wherein at least one of knowledge information and calling interface information of the knowledge information is stored on the first device, wherein the knowledge information includes at least one of the following: a knowledge graph and a knowledge vector library;

[0008] The first device sends second information to a third device, the first model is deployed on the third device, and the second information is determined based on the first information and knowledge information stored or called on the first device;

[0009] The first information includes at least one of the following:

[0010] Auxiliary information used for reasoning with the first model;

[0011] Requirement information for reasoning with the first model;

[0012] input information of the first model;

[0013] Calling interface information of knowledge information;

[0014] an indication of a receiving device of the second information;

[0015] An indication of a sample corresponding to the input information of the first model.

[0016] In a second aspect, a model reasoning method in a communication system is provided, the method comprising:

[0017] The second device sends first information to the first device, wherein the first device stores at least one of knowledge information and calling interface information of the knowledge information, wherein the knowledge information includes at least one of the following: a knowledge graph and a knowledge vector library;

[0018] The first information includes at least one of the following:

[0019] Auxiliary information used for reasoning by the first model;

[0020] Requirement information for the first model to perform reasoning;

[0021] Input information of the first model;

[0022] Calling interface information of knowledge information;

[0023] an indication of a receiving device of the second information;

[0024] An indication of a sample corresponding to the input information of the first model.

[0025] In a third aspect, a model reasoning method in a communication system is provided, including:

[0026] The third device obtains second information from the first device or the target device, wherein the first model is deployed on the third device, and at least one of knowledge information and calling interface information of the knowledge information is stored on the first device or the target device, wherein the target device is a device accessed using the calling interface information, and the knowledge information includes at least one of the following: a knowledge graph and a knowledge vector library;

[0027] Wherein, the second information is determined based on the first information and knowledge information stored or called on the first device;

[0028] The first information includes at least one of the following:

[0029] Auxiliary information used for reasoning with the first model;

[0030] Requirement information for reasoning with the first model;

[0031] input information of the first model;

[0032] Calling interface information of knowledge information;

[0033] an indication of a receiving device of the second information;

[0034] An indication of a sample corresponding to the input information of the first model.

[0035] In a fourth aspect, a communication device is provided, including:

[0036] a communication unit, configured to receive first information sent by a second device, wherein the communication device stores at least one of knowledge information and information on a calling interface of the knowledge information, wherein the knowledge information includes at least one of the following: a knowledge graph and a knowledge vector library; and

[0037] Sending second information to a third device, deploying the first model on the third device, where the second information is determined based on the first information and knowledge information stored or called on the first device;

[0038] The first information includes at least one of the following:

[0039] Auxiliary information used for reasoning with the first model;

[0040] Requirement information for reasoning with the first model;

[0041] input information of the first model;

[0042] Calling interface information of knowledge information;

[0043] an indication of a receiving device of the second information;

[0044] An indication of a sample corresponding to the input information of the first model.

[0045] In a fifth aspect, a communication device is provided, including:

[0046] A communication unit, configured to send first information to a first device, wherein the first device stores at least one of knowledge information and information on a calling interface for the knowledge information, wherein the knowledge information includes at least one of the following: a knowledge graph and a knowledge vector library;

[0047] The first information includes at least one of the following:

[0048] Auxiliary information used for reasoning by the first model;

[0049] Requirement information for the first model to perform reasoning;

[0050] Input information of the first model;

[0051] Calling interface information of knowledge information;

[0052] an indication of a receiving device of the second information;

[0053] An indication of a sample corresponding to the input information of the first model.

[0054] In a sixth aspect, a communication device is provided, including:

[0055] a communication unit, configured to obtain second information from a first device or a target device, wherein the first model is deployed on the third device, and at least one of knowledge information and calling interface information of the knowledge information is stored on the first device or the target device, wherein the target device is a device accessed using the calling interface information, and the knowledge information includes at least one of the following: a knowledge graph and a knowledge vector library;

[0056] Wherein, the second information is determined based on the first information and knowledge information stored or called on the first device;

[0057] The first information includes at least one of the following:

[0058] Auxiliary information used for reasoning with the first model;

[0059] Requirement information for reasoning with the first model;

[0060] input information of the first model;

[0061] Calling interface information of knowledge information;

[0062] an indication of a receiving device of the second information;

[0063] An indication of a sample corresponding to the input information of the first model.

[0064] In the seventh aspect, a communication device is provided, which network side device includes a processor and a memory, the memory storing a program or instruction that can be run on the processor, and the program or instruction, when executed by the processor, implements the steps of the method described in any one of the first to third aspects.

[0065] In an eighth aspect, a readable storage medium is provided, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the method described in any one of the first to third aspects are implemented.

[0066] In the ninth aspect, a wireless communication system is provided, comprising: a first device, a second device and a third device, wherein the first device can be used to execute the steps of the method described in the first aspect, the second device can be used to execute the steps of the method described in the second aspect, and the third device can be used to execute the steps of the method described in the third aspect.

[0067] In the tenth aspect, a chip is provided, comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the method described in any one of the first to third aspects.

[0068] In the eleventh aspect, a computer program / program product is provided, which is stored in a storage medium and is executed by at least one processor to implement the method as described in any one of the first to third aspects.

[0069] In an embodiment of the present application, the second device can assist the third device in obtaining the second information by sending the first information to the first device. Furthermore, the third device can use the second information to assist in model reasoning, which is beneficial to improving the reasoning performance of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] FIG1 is a schematic diagram of a communication system provided in an embodiment of the present application.

[0071] FIG2 is a schematic diagram of a model reasoning method in a communication system provided in an embodiment of the present application.

[0072] FIG3 is a schematic diagram of a hidden variable provided in an embodiment of the present application.

[0073] Figures 4 to 14 are schematic interaction diagrams of the model reasoning method provided in the embodiments of the present application.

[0074] FIG15 is a schematic diagram of a communication device provided in an embodiment of the present application.

[0075] FIG16 is a schematic diagram of another communication device provided in an embodiment of the present application.

[0076] FIG17 is a schematic diagram of another communication device provided in an embodiment of the present application.

[0077] Figure 18 is a schematic diagram of a communication device provided in an embodiment of the present application.

[0078] FIG19 is a hardware structure diagram of a terminal provided in an embodiment of the present application.

[0079] Figure 20 is a hardware structure diagram of a network-side device provided in an embodiment of the present application.

[0080] Figure 21 is a hardware structure diagram of another network-side device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0081] The following will be combined with the accompanying drawings in the embodiments of this application to clearly describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of this application.

[0082] The terms "first", "second", etc. in this application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way are interchangeable where appropriate, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "or" in this application represents at least one of the connected objects. For example, "A or B" covers three options, namely, Option 1: including A but not including B; Option 2: including B but not including A; Option 3: including both A and B. The character " / " generally indicates that the objects associated before and after are in an "or" relationship.

[0083] The term "indication" in this application can be either a direct indication (or explicit indication) or an indirect indication (or implicit indication). A direct indication can be understood as the sender explicitly informing the receiver of specific information, the operation to be performed, or the requested result, etc. in the instruction sent; an indirect indication can be understood as the receiver determining the corresponding information based on the instruction sent by the sender, or making a judgment and determining the operation to be performed or the requested result, etc. based on the judgment result.

[0084] It is worth noting that the technology described in the embodiments of the present application is not limited to the Long Term Evolution (LTE) / LTE-Advanced (LTE-A) system, but can also be used in other wireless communication systems, such as Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access (OFDMA), Single-carrier Frequency-Division Multiple Access (SC-FDMA) or other systems. The terms "system" and "network" in the embodiments of the present application are often used interchangeably, and the technology described can be used for the systems and radio technologies mentioned above, as well as for other systems and radio technologies. The following description describes a New Radio (NR) system for illustrative purposes, and NR terminology is used in most of the following description, but these technologies can also be applied to systems other than NR systems, such as 6th generation (6G) systems. th Generation, 6G) communication system.

[0085] FIG1 is a block diagram of a wireless communication system applicable to an embodiment of the present application. The wireless communication system includes a terminal 11 and a network-side device 12. The terminal 11 may be a mobile phone, a tablet computer (Tablet Personal Computer), a laptop computer (Laptop Computer), a notebook computer, a personal digital assistant (PDA), a handheld computer, a netbook, an ultra-mobile personal computer (UMPC), a mobile internet device (MID), an augmented reality (AR), a virtual reality (VR) device, a robot, a wearable device (Wearable Device), an aircraft (Flight Vehicle), a vehicle-mounted device (VUE), a ship-mounted device, a pedestrian user equipment (PUE), a smart home (home appliances with wireless communication capabilities, such as refrigerators, televisions, washing machines, or furniture), a game console, a personal computer (PC), an ATM, or a self-service machine, or other terminal-side devices. Wearable devices include: smart watches, smart bracelets, smart headphones, smart glasses, smart jewelry (smart bracelets, smart bracelets, smart rings, smart necklaces, smart anklets, smart anklets, etc.), smart wristbands, smart clothing, etc. Among them, the vehicle-mounted device can also be called a vehicle-mounted terminal, vehicle-mounted controller, vehicle-mounted module, vehicle-mounted component, vehicle-mounted chip or vehicle-mounted unit, etc. It should be noted that the specific type of the terminal 11 is not limited in the embodiment of the present application.

[0086] A terminal may also be referred to as user equipment (UE), terminal equipment, access terminal, user unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, terminal, wireless communication device, user agent or user device, etc.

[0087] The network-side device 12 may include an access network device or a core network device, wherein the access network device may also be referred to as a radio access network (RAN) device, a radio access network function, or a radio access network unit. The access network device may include a base station, a wireless local area network (WLAN) access point (AS), or a wireless fidelity (WiFi) node. Among them, the base station can be referred to as Node B (NB), Evolved Node B (eNB), the next generation Node B (gNB), New Radio Node B (NR Node B), access point, Relay Base Station (RBS), Serving Base Station (SBS), Base Transceiver Station (BTS), radio base station, radio transceiver, Basic Service Set (BSS), Extended Service Set (ESS), Home Node B (HNB), Home evolved Node B (home evolved Node B), Transmission Reception Point (TRP) or other appropriate terms in the relevant field. As long as the same technical effect is achieved, the base station is not limited to specific technical vocabulary. It should be noted that in the embodiment of the present application, only the base station in the NR system is used as an example for introduction, and the specific type of the base station is not limited.

[0088] The core network device may include but is not limited to at least one of the following: core network node, core network function, mobility management entity (MME), access mobility management function (AMF), session management function (SMF), user plane function (UPF), policy control function (PCF), policy and charging rules function unit (PCRF), edge application server discovery function (EASDF), unified data management (UDM), unified data storage (UDR), home user server (HSS), centralized network configuration (CNC), network storage function (NRF), network exposure function (NEF), local NEF (L-NEF), binding support function (BNSF), network access function (UE ... Function, BSF), application function (Application Function, AF), etc. It should be noted that in the embodiment of the present application, only the core network device in the NR system is introduced as an example, and the specific type of the core network device is not limited.But not limited to at least one of the following: core network node, core network function, Mobility Management Entity (MME), Access and Mobility Management Function (AMF), Session Management Function (SMF), User Plane Function (UPF), Policy Control Function (PCF), Policy and Charging Rules Function (PCRF), Edge Application Server Discovery Function (EASDF), Unified Data Management (UDM), Unified Data Repository (UDR), Home Subscriber Server (HSS), Centralized Network Configuration (CNC), Network Repository Function (NRF), Network Exposure Function (NEF), Local NEF (L-NEF), Binding Support Function (BSF), Application Function (AF), etc. It should be noted that in the embodiments of this application, only the core network equipment in the NR system is introduced as an example, and the specific type of the core network equipment is not limited.

[0089] To facilitate understanding of the embodiments of the present application, the technologies related to the present application are explained.

[0090] 1. Knowledge Graph

[0091] A knowledge graph is a knowledge base that uses graph structures or topologies to represent and integrate data. It can store descriptions of relationships between entities (objects, events, situations, or abstract concepts) and also encode semantic relationships between entities. Knowledge graphs have broad application prospects. They can not only improve the effectiveness of data services such as information retrieval, search engines, and recommendation systems, but also support intelligent interactions such as natural language question-answering, dialogue, and reasoning. They possess strong representational capabilities, high flexibility, strong computability, and strong cross-domain capabilities.

[0092] Knowledge graphs store a large amount of knowledge in an explicit and structured manner, which can be used to enhance the knowledge awareness of large models. Incorporating knowledge graphs into large models during the reasoning phase and retrieving knowledge from the knowledge graph can significantly improve the performance of large models in accessing domain-specific knowledge.

[0093] In the field of communications, network data knowledge graphs are mainly used for knowledge representation, association analysis and deep mining, providing effective knowledge rules and knowledge computing support for the intelligentization of communication systems.

[0094] The network structure, terminal type, terminal behavior, data service requirements, and system resources of the communication system are all highly dynamic, time-sensitive, and mutually coupled. Mobile communication data faces many challenges, such as the difficulty in obtaining scattered data, the wide variety and complex structure, and the difficulty in mining complex associations.

[0095] The use of knowledge graphs can effectively clarify the various relationships between data fields and communication network indicators, and further in-depth mining can be carried out based on the established relationships, such as quantifying the degree of correlation between relationships, characterizing the characteristic attributes of data fields and indicators, etc.

[0096] In some implementations, the knowledge graph can be represented by the correlation between entities. One representation can be:

[0097] (Entity 1, Correlation coefficient between entity 1 and entity 2, entity 2).

[0098] For example, entity 1 is the cell throughput, entity 2 is the L1-RSRP of the strongest beam, and the correlation coefficient is 0.5.

[0099] In other implementations, the knowledge graph can be represented by a subject attribute object (Subject, Predicate, Object, SPO) triple, for example, an SPO triple of (cell id, shaped codebook, codebook indication).

[0100] 2. Knowledge Vector Library

[0101] A knowledge vector library is a database used to store, retrieve, and analyze vectors. It is called a database because it has the following characteristics:

[0102] a) Provide a standard access interface to lower the user's usage threshold;

[0103] b) Provide efficient data organization, retrieval, and analysis capabilities. While storing and retrieving vectors, users generally need to manage structured data, such as supporting the structured data management capabilities of traditional databases.

[0104] Therefore, the knowledge vector library can be simply understood as a database for storing model input feature vectors.

[0105] For example, when using images to search for images, or using voice to search for voice, what is stored and compared in the knowledge vector library is not the images and voice clips, but the "features" extracted by algorithms such as deep learning, such as 256 or 512 floating-point number arrays, which can be represented by vectors in mathematics.

[0106] In some cases, the knowledge vector library can be a model whose input is an image or text. For example, in the field of communications, the input of the model can be the measurement results of wireless signals, and the output is a feature vector that represents the measurement results.

[0107] The following methods can be used locally to use the knowledge graph or knowledge vector library:

[0108] 1) Directly used as input to the model;

[0109] 2) After processing the model input using the knowledge graph, the processed data is input into the model together with the original model input.

[0110] 3) After processing the model input using the knowledge vector library, the processed data is input into the model together with the original model input, or the processed data is directly input into the model.

[0111] 3. Prompt Engineering

[0112] Prompt engineering is a technique in natural language processing (NLP) that creates text snippets, prompts, or templates to guide pre-trained large language models to produce high-quality output for specific tasks or applications. It is widely used in areas such as question answering, summarization, translation, sentiment analysis, and text generation. Prompt engineering can leverage the powerful capabilities of pre-trained large language models to implement a variety of complex natural language processing tasks, reducing reliance on labeled data and model fine-tuning, lowering development costs and time, and improving the interpretability and controllability of pre-trained large language models, thereby increasing user trust and satisfaction. Common prompts include zero-shot prompting, few-shot prompting, and chain of thought prompting.

[0113] In prompt engineering, a description of the task is embedded into the input, guiding the generative AI solution to produce the desired output.

[0114] In mobile communication systems, artificial intelligence (AI) is increasingly being integrated into use cases. For example, at the physical layer, AI-based channel state information (CSI) prediction and feedback compression, AI-based beam management, and AI-based positioning are all examples. In some scenarios, AI-based energy conservation and load balancing are also being considered. In the future, even more AI-integrated use cases will emerge in mobile communication systems.

[0115] In recent years, large language models have gained widespread attention in fields such as chat and image generation. Therefore, large models are being considered for use in communication networks as a tool to optimize their performance. However, large communication models present two challenges.

[0116] Question 1: Large models are very large. How can we deploy them to devices with limited storage resources, such as base stations or terminals, for inference? A typical approach is to process the large models using compression methods such as quantization and pruning before deploying them to communication devices.

[0117] Problem 2: Large models often solve general problems, but communications encompass many diverse challenges, such as beamforming, resource allocation, and channel prediction. Currently, there is no mature solution in communications systems for matching large models to specific downstream problems.

[0118] Below, in combination with the accompanying drawings, the reasoning method based on the artificial intelligence AI model in the communication system provided by the embodiment of the present application is described in detail through some embodiments and their application scenarios.

[0119] FIG2 shows a schematic diagram of a model reasoning method in a communication system according to an embodiment of the present application. As shown in FIG2 , the method 200 includes:

[0120] S201: A first device receives first information sent by a second device, wherein at least one of knowledge information and information on a calling interface of the knowledge information is stored on the first device;

[0121] S202: The first device sends second information to a third device, on which a first model is deployed, wherein the second information is determined based on the first information and knowledge information.

[0122] In some embodiments, the first model is a compressed AI model. For example, the first model is an AI model that is compressed from a large model. The compression here may include but is not limited to quantization, pruning (for example, deleting some layers in the model, or deleting some nodes in a layer), etc.

[0123] It should be noted that in the embodiments of the present application, the AI ​​model may also be referred to as an AI unit, an ML (machine learning) model, an ML unit, an AI structure, an AI function, an AI feature, a machine learning model, a neural network, a neural network function, a neural network function, etc., or an AI model may also refer to a processing unit that can implement specific algorithms, formulas, processing procedures, capabilities, etc. related to AI, or an AI model may be a processing method, algorithm, function, module or unit for a specific data set, or an AI model may be a processing method, algorithm, function, module or unit running on AI / ML related hardware such as a graphics processing unit (GPU), a neural network processing unit (NPU), a tensor processing unit (TPU), an application specific integrated circuit (ASIC), etc. This application does not make any specific limitations on this.

[0124] In the embodiments of the present application, the first device can be considered as a device that stores knowledge-related information, or a knowledge storage device. For example, the first device stores knowledge information, information about the calling interface of the knowledge information, etc. The second device can be considered as a demand initiating device, such as a device that initiates an inference demand. The third device can be considered as an inference device, or an AI model deployment device. The second device and the third device can be the same device, or they can be different devices.

[0125] In some embodiments, the knowledge information includes but is not limited to at least one of a knowledge graph and a knowledge vector library.

[0126] In some embodiments, the third device may utilize the second information to expand the input of the first model, thereby assisting the first model in performing accurate reasoning.

[0127] Therefore, in an embodiment of the present application, by obtaining the second information from a knowledge storage device and further using the model for reasoning, the second information and the input information of the first model can be used, so that the model can obtain more input information and improve the reasoning performance of the model.

[0128] In some embodiments, the first device is an access network device (e.g., a base station) or a core network function, such as a core network function with a data storage function, such as a database, a data function, or a network repository function (NRF) or UDM, or an AI model library, or an AI model management function or a third-party server of a terminal, etc., the second device is a terminal, and the third device is a terminal.

[0129] In other embodiments, the first device is a core network function, such as a core network function with a data storage function, such as a database, a data function or a network repository function (NRF) or UDM, or an AI model library, or an AI model management function or a third-party server of a terminal, etc., the second device is a terminal or an access network device (such as a base station), and the third device is an access network device (such as a base station).

[0130] In some further embodiments, the first device is a core network function, such as a core network function with a data storage function, such as a database, a data function or a network repository function (NRF) or UDM, or an AI model library, or an AI model management function or a third-party server of a terminal, etc.; the second device is a terminal or an access network device (such as a base station), a third-party server, a core network function (such as an AI control function, a task control function, a collaborative control function or other core network functions, etc.); and the third device is the inference function of the core network.

[0131] In an embodiment of the present application, the AI ​​control node can be used to control AI-related functions, and the task control node can be used to control service-related functions, or task-related control functions. For example, the task may include but is not limited to new network capabilities involving the coordination and deployment of connections, computing, data and algorithm resources in multi-node scenarios to jointly achieve a specific goal. The collaborative control node can be used for collaborative management between multiple functions (such as communication functions, data functions, computing power functions, algorithm functions, and model functions).

[0132] In an embodiment of the present application, information exchange between a terminal and an access network device may be performed through at least one of the following signaling: layer 1 signaling, layer 2 signaling, layer 3 signaling, data plane signaling, and AI layer signaling. Information exchange between a terminal and a core network device may be performed through at least one of the following signaling: NAS signaling, data plane signaling, and AI layer signaling. Information exchange may be performed directly between the terminal and the core network device, or information may be forwarded through other devices, such as through a communication control function (such as an access mobility management function AMF), an AI control function, a task control function, a collaborative control function, a network open function, and the like.

[0133] It should be understood that access network devices and core network devices, as well as core network devices, can directly interact with each other, or information can be forwarded through other devices, such as through communication control functions (such as access mobility management function AMF), AI control functions, task control functions, collaborative control functions, network open functions, etc.

[0134] In some embodiments, if the first device is an access network device and the second device is a terminal, the first information is carried in at least one of the following signaling: layer 1 signaling, layer 2 signaling, layer 3 signaling, data plane signaling, and AI layer signaling. Exemplarily, the layer 1 signaling may include, but is not limited to, a PDCCH. The layer 2 signaling may, for example, include, but is not limited to, a downlink MAC CE, and the layer 3 signaling may, for example, include, but is not limited to, RRC signaling.

[0135] In other embodiments, if the first device is a core network function and the second device is a terminal, the first information is carried in at least one of the following signaling: NAS signaling, data plane signaling, and AI layer signaling.

[0136] In some embodiments, if the first device is an access network device and the third device is a terminal, the second information is carried in at least one of the following signaling: layer 1 signaling, layer 2 signaling, layer 3 signaling, data plane signaling, and AI layer signaling. Exemplarily, the layer 1 signaling may include, but is not limited to, a PDCCH. The layer 2 signaling may, for example, include, but is not limited to, a downlink MAC CE, and the layer 3 signaling may, for example, include, but is not limited to, RRC signaling.

[0137] In other embodiments, if the first device is a core network function, the third device is a terminal, and the second information is carried in at least one of the following signaling: NAS signaling, data plane signaling, and AI layer signaling.

[0138] In some embodiments of the present application, the first device may also send the first information to the third device to assist the third device in performing model reasoning. For example, the first information may be carried in the second information, such as the second information includes the content of the first information.

[0139] In some embodiments, the first information includes at least one of the following:

[0140] Auxiliary information used for reasoning with the first model;

[0141] Requirement information for reasoning with the first model;

[0142] input information of the first model;

[0143] Calling interface information of knowledge information;

[0144] an indication of a receiving device of the second information;

[0145] An indication of a sample corresponding to the input information of the first model.

[0146] Therefore, the demand initiating device indicates the relevant information (such as the first information) used for model reasoning to the knowledge storage device, and the knowledge storage device can determine the second information based on the first information and the knowledge information. For example, the knowledge information of the professional field is obtained according to the first information, thereby obtaining the second information, and further sending the second information to the reasoning device. In this way, the reasoning device uses the second information to assist in model reasoning, which can achieve accurate reasoning of professional field tasks, reduce the illusion problem in model reasoning, and improve the reasoning performance of the model.

[0147] In some embodiments, the receiving device indication may be identification information of the receiving device of the second device, which is used by the first device to determine to which device to send the second information.

[0148] In some embodiments, the sample indication may be an indication of which sample the input information of the first model in the first information corresponds to, so as to prevent the inference device from performing model inference using the first information and the second information corresponding to different samples, thereby affecting the inference result. For example, the sample indication may be a sample identifier.

[0149] In some embodiments, the auxiliary information used for reasoning with the first model includes at least one of the following:

[0150] AI model identification information, AI model function indication, AI model feature indication, target knowledge graph indication, target knowledge vector library indication, cell identification, and bandwidth part (Band Width Part, BWP) identification.

[0151] In some embodiments, the identification information of the AI ​​model can be, for example, the model identification (model ID) of the AI ​​model, which can be used for AI structure identification, AI algorithm identification, or identification of a specific data set associated with the AI ​​model, or identification of specific scenarios, environments, channel characteristics, and devices related to AI / ML, or identification of functions, features, capabilities, or modules related to AI / ML. This application does not make specific limitations on this.

[0152] In some embodiments, the characteristic indication of the AI ​​model may explicitly or implicitly indicate the characteristics supported by the AI ​​model, such as support for CSI prediction and compressed feedback, beam prediction, positioning, load balancing, resource allocation, etc. By indicating the characteristics of the AI ​​model to the first device, the first device can be assisted in obtaining knowledge information related to the characteristics, and then, based on the knowledge information, corresponding second information can be obtained to assist the third device in performing model reasoning.

[0153] In the embodiments of the present application, the characteristics of the AI ​​model under a specific configuration can be considered as the functions of the AI ​​model.

[0154] For example, the characteristic of the AI ​​model is beam prediction, and the function of the AI ​​model is time domain beam prediction when the base station is configured with 32 transmit beams.

[0155] In some embodiments, the AI ​​model's functional indication may explicitly or implicitly indicate the functions supported by the AI ​​model, such as support for CSI prediction and compressed feedback under a specific configuration, beam prediction, positioning, load balancing, resource allocation, etc. By indicating the AI ​​model's function to the first device, the first device can be assisted in obtaining knowledge information related to the function, and then, based on the knowledge information, corresponding second information can be obtained to assist the third device in performing model reasoning.

[0156] In some embodiments, the target knowledge graph indication can be used to indicate the knowledge graph that the second device expects to use. By indicating the desired target knowledge graph to the first device, the first device can be assisted in using the target knowledge graph to obtain corresponding second information for assisting the third device in performing model reasoning.

[0157] In some implementations, the target knowledge graph indication may explicitly indicate the target knowledge graph.

[0158] Exemplarily, the target knowledge graph indicates at least one of the following:

[0159] The relationships between entities in the target knowledge graph;

[0160] Attribute information of entities in the target knowledge graph;

[0161] The data structure of the target knowledge graph;

[0162] The correlation between entities in the target knowledge graph;

[0163] The subject attribute object (Subject, Predicate, Object, SPO) triple of the sentence in the target knowledge graph;

[0164] Target knowledge graph file.

[0165] In some embodiments, the relationship between entities in the target knowledge graph can be expressed in the following format:

[0166] (Entity 1, Relationship, Entity 2).

[0167] Optionally, the relationship between entities may include but is not limited to hierarchical relationship, inheritance relationship, etc.

[0168] In some embodiments, the attribute information of an entity in the target knowledge graph can be represented in the following format:

[0169] (entity, attribute name, attribute value).

[0170] In some embodiments, the relationship between nodes can be represented by a triple, for example, a relationship can be (node ​​1, edge, node 2).

[0171] In some embodiments, the correlation between entities in the target knowledge graph can be expressed in the following format:

[0172] (Entity 1, Correlation coefficient between entity 1 and entity 2, entity 2).

[0173] For example, entity 1 is the cell throughput, entity 2 is the L1-RSRP of the strongest beam, and the correlation coefficient is 0.8.

[0174] In some embodiments, the SPO triples of a sentence in the target knowledge graph are used to describe the subject, predicate, and object of the sentence.

[0175] In other implementations, the target knowledge graph indication may implicitly indicate the target knowledge graph.

[0176] Exemplarily, the target knowledge graph indication includes but is not limited to at least one of the following:

[0177] Knowledge graph identification;

[0178] Knowledge graph file address.

[0179] In some embodiments, multiple knowledge graph identifiers can be predefined or preconfigured (for example, pre-indicated by a network-side device), and each knowledge graph identifier corresponds to a knowledge graph. For example, the second device can select one or more knowledge graph identifiers from the multiple knowledge graph identifiers and indicate them to the first device. Furthermore, the first device can determine the corresponding knowledge graph based on the indicated knowledge graph identifier, and further determine the second information based on the knowledge graph.

[0180] In some embodiments, multiple knowledge graph file addresses can be predefined or preconfigured (for example, pre-indicated by a network side device), and each knowledge graph file address corresponds to a knowledge graph file. For example, the second device can select one or more knowledge graph file addresses from the multiple knowledge graph file addresses and indicate them to the first device. Furthermore, the first device can obtain the corresponding knowledge graph file according to the indicated knowledge graph file address, and further determine the second information based on the knowledge graph file.

[0181] In some embodiments, taking the AI ​​model's task of beam prediction as an example, the knowledge graph can be used in the following ways:

[0182] Based on the cell identification and target task, the knowledge graph can be used to retrieve the cell transmission beam configuration, such as the direction of the transmission beam or the shaped codebook.

[0183] In some implementations, the target knowledge vector library indication may implicitly indicate the target knowledge vector library that the second device desires to use. By indicating the desired target knowledge vector library to the first device, the first device may be assisted in using the target knowledge vector library to obtain corresponding second information for assisting the third device in performing model reasoning.

[0184] Exemplarily, the target knowledge vector library indicates at least one of the following:

[0185] Knowledge vector library identifier;

[0186] Knowledge vector library file address.

[0187] In some embodiments, multiple knowledge vector library identifiers can be predefined or preconfigured (for example, pre-indicated by a network-side device), and each knowledge vector library identifier corresponds to a knowledge vector library. For example, the second device can select one or more knowledge vector library identifiers from the multiple knowledge vector library identifiers and indicate them to the first device. Furthermore, the first device can determine the corresponding knowledge vector library based on the indicated knowledge vector library identifier, and further determine the second information based on the knowledge vector library.

[0188] In some embodiments, multiple knowledge vector library file addresses can be predefined or preconfigured (for example, pre-indicated by a network side device), and each knowledge vector library file address corresponds to a knowledge vector library file. For example, the second device can select one or more knowledge vector library file addresses from the multiple knowledge vector library file addresses and indicate them to the first device. Furthermore, the first device can obtain the corresponding knowledge vector library file according to the indicated knowledge vector library file address, and further determine the second information based on the knowledge vector library file.

[0189] In some embodiments, taking the AI ​​model's task of beam prediction as an example, the knowledge vector library may be used as follows:

[0190] According to the cell identification and target task, the knowledge vector library is used to obtain the cell transmission beam configuration, such as the direction of the transmission beam or the feature vector related to the shaped codebook.

[0191] In some embodiments, the cell identifier may be used by the first device to obtain knowledge information of the cell corresponding to the cell identifier, such as cell configuration, etc. Further, the second information may be determined based on the knowledge information.

[0192] In some embodiments, the BWP identifier may be used by the first device to obtain knowledge information of the BWP corresponding to the BWP identifier, such as BWP configuration, etc. Further, the second information may be determined based on the knowledge information.

[0193] In some embodiments, the requirement information used for reasoning with the first model includes at least one of the following:

[0194] Indication of the target tasks to which the AI ​​model needs to be adapted;

[0195] Quality of Experience (QoE) requirements for AI models;

[0196] Business processing latency of AI models;

[0197] The business processing accuracy of the AI ​​model;

[0198] The business computing capacity of AI models;

[0199] The data processing scale of AI models.

[0200] For example, the first device may select knowledge information related to the target task according to the target task indication, and then determine the second information according to the knowledge information.

[0201] For another example, the second device may select knowledge information that meets QoE requirements, or processing delay, processing accuracy, or processing scale, and then determine the second information based on the knowledge information.

[0202] In some embodiments, the input information of the first model is related to the target task to which the first model needs to adapt. Taking the target task as beam prediction as an example, the input information may include a cell identifier and a measurement quality of a cell transmitted beam.

[0203] In some embodiments, the calling interface information of the knowledge information includes but is not limited to at least one of the following:

[0204] The name of the calling interface, the input data format of the calling interface, the output data format of the calling interface, and the input data of the calling interface. The input data format, output data format, and input data are related to the domain, task, scenario, etc.

[0205] In some embodiments, the calling interface may include but is not limited to an application programming interface (API) or other interfaces for accessing external devices.

[0206] Communication systems are constantly iterating and updating, and so is the knowledge within them. After network deployment, updated system knowledge, such as the latest knowledge from organizations like 3GPP, can be stored in external databases. In this case, services can be provided to the communication system by calling APIs, improving the applicability of the technical solution of this application.

[0207] In some embodiments, the second device and the third device are both terminals, and the first information may be sent under specific circumstances, for example:

[0208] When at least one of the following events occurs, the second device sends the first information to the first device:

[0209] Event 1: receiving a first instruction, where the first instruction is used to instruct the terminal to activate or switch a model;

[0210] Event 2: the terminal determines to activate or switch the model;

[0211] Event 3: the terminal determines that the currently used model does not meet the requirements;

[0212] Event 4: A second indication is received, where the second indication is used to indicate that the model currently used by the terminal does not meet the requirements.

[0213] In some embodiments, the event that triggers the second device to send the first information to the first device can be predefined, or configured by the network side device. When the event that triggers the first information is met, the second device can send the identification information of the event to the network side device, for example, carried in the first information and sent to the network side device, so that the network side device can be aware of the event occurring on the second device.

[0214] In some embodiments, the first indication may be sent by a network side device. For example, the network side device may control the activation or switching of the AI ​​model. For example, the network side device may send a first indication to the terminal when the currently used model does not meet the requirements, so that the second device may send the first information according to the first indication.

[0215] In some embodiments, the terminal may autonomously determine to activate or switch a model when the performance of the currently used model does not meet the requirements.

[0216] In some embodiments, the second indication may be sent by a network side device. For example, when the model currently used does not meet the requirements, the network side device may send a second indication to the terminal to indicate that the model currently used by the terminal does not meet the requirements. Furthermore, the terminal may independently determine whether to activate or switch the model based on the second indication.

[0217] In some embodiments, the model not meeting the requirements may refer to the model not meeting the requirement information, for example, it may include but is not limited to at least one of the following: the model's reasoning does not meet the service quality of experience (Quality of Experience, QoE) does not meet the QoE requirements, the model's service processing delay does not meet the delay requirements, and the model's service processing accuracy does not meet the accuracy requirements.

[0218] In some embodiments of the present application, the second information includes at least one of the following:

[0219] Hidden variables, where the hidden variables are feature vectors obtained by mapping the input information of the first model;

[0220] Prompt information;

[0221] Configuration information of network-side devices;

[0222] Scene type information;

[0223] A sample indication corresponding to the input information of the first model, or a sample indication in the first information used to determine the second information.

[0224] In some embodiments, the sample indication can be used to indicate the sample from which the second information is determined. The sample indication can be a sample identifier. Thus, when the inference device learns the first and second information, it can use the sample indication to determine the sample to which the first and second information correspond. Furthermore, when performing model inference, the first and second information corresponding to the same sample can be used, avoiding the use of first and second information corresponding to different samples for model inference, which could affect the inference results.

[0225] In some embodiments, the hidden variable may refer to a low-dimensional feature obtained by mapping input information with physical meaning through an encoder, or a feature vector of the original input after redundancy is removed.

[0226] For example, as shown in Figure 3, an autoencoder includes an encoder and a decoder. The encoder maps the input image to low-dimensional features (hidden variables in Figure 3), and the decoder restores the input image based on the hidden variables, where the hidden variables contain the feature information of the input image.

[0227] In some embodiments, the prompt information can be used to prompt or guide the first model to infer an output that meets expectations, thereby improving the reasoning performance of the model.

[0228] Exemplarily, the prompt information includes at least one of the following:

[0229] A chain of thought related to the target task adapted by the first model;

[0230] Prompt text related to the target task;

[0231] Prompt files related to the target task;

[0232] a prompt code related to the target task;

[0233] A thinking chain identifier related to the target task, used to identify a thinking chain;

[0234] A prompt text identifier related to the target task, used to identify a prompt text;

[0235] A prompt file identifier associated with the target task, used to identify a prompt file;

[0236] A prompt code identifier related to the target task, used to identify a prompt code;

[0237] The address of the prompt file related to the target task.

[0238] In some embodiments, the configuration information of the network-side device includes but is not limited to at least one of the following:

[0239] The number of transmit antennas on the network-side device;

[0240] The number of transmit beams of the network-side device;

[0241] Number of antenna ports on network-side equipment;

[0242] The transmit power of the network-side device;

[0243] Antenna gain of network-side equipment;

[0244] The beam 3dB bandwidth of the network-side equipment;

[0245] The distance between network-side devices;

[0246] Frequency of network-side equipment;

[0247] System bandwidth;

[0248] Terminal distribution characteristics under the coverage of network-side equipment.

[0249] In some embodiments, the scene type information includes at least one of the following:

[0250] Line of sight (LOS), non line of sight (NLOS), outdoor, indoor, urban microcell (UMi), urban macrocell (UMa), rural macrocell (RMa), rural microcell (RMi), high speed, low speed.

[0251] In some embodiments, the second information may be determined by the first device, for example, based on the first information and knowledge information stored on the first device, or based on the first information and called knowledge information, for example, the first device may use the calling interface information to obtain knowledge information from the target device, and further determine the second information based on the knowledge information, or the first information and the knowledge information.

[0252] In other embodiments, the second information may also be determined by the target device to be accessed using the calling interface information. Optionally, after determining the second information, the target device may directly send it to the third device, or may send it to the third device via another device, such as forwarding it via a network development function, or sending it to the third device via the first device.

[0253] The following describes the specific implementation of the third device acquiring the second information in conjunction with specific embodiments.

[0254] Embodiment 1: The first device stores knowledge information for assisting the first model in reasoning.

[0255] In this case, after the first device receives the first information from the second device, it can determine the second information based on the first information combined with the knowledge stored on the first device. Furthermore, the first device can send the second information to a third device, which can then use the second information as auxiliary information when performing inference using the first model, thereby improving the model's inference performance.

[0256] Example 2: The first device does not store knowledge information for assisting the first model in reasoning, and the first information includes calling interface information of the knowledge information. In this case, as shown in FIG4 , this can be achieved by the following steps:

[0257] S211: The second device sends first information to the first device. The first information includes calling interface information of knowledge information, which may be calling interface information of knowledge information that the second device expects to use. At this time, the calling interface information of the knowledge information includes a calling interface name and input data of the calling interface.

[0258] S212, the first device sends first information to a target device according to the calling interface information of the knowledge information, where the target device is a device to be accessed using the calling interface information;

[0259] S213, the target device determines second information based on the first information;

[0260] S214, the target device sends second information to the first device;

[0261] S215: The first device sends second information to the third device.

[0262] It should be understood that the interaction of the first information and the second information between the first device and the target device can be forwarded through other nodes (such as network open functions), or the information can be directly interacted.

[0263] Embodiment 3: The first device does not store knowledge information for assisting the first model in reasoning, and the first information does not include calling interface information of the knowledge information.

[0264] In this case, as an embodiment, as shown in FIG5 , it can be implemented by the following steps:

[0265] S221: The second device sends first information to the first device, where the first information does not include calling interface information of the knowledge information.

[0266] S222: The first device determines calling interface information of the knowledge information according to the first information.

[0267] For example, the first device can determine the target calling interface among multiple calling interfaces based on the requirement information for the first model reasoning in the first information, thereby obtaining information such as the calling interface name of the target calling interface, the input data format of the calling interface, the output data format of the calling interface, and the input data of the calling interface.

[0268] S223: The first device sends third information to the target device, where the third information includes the calling interface information of the knowledge information. The target device is the device that needs to be accessed using the calling interface information. The calling interface information of the knowledge information includes a calling interface name and input data for the calling interface. For example, the calling interface name is determined based on the requirement information, auxiliary information, or calling interface information in the first information, and the input data for the calling interface is determined based on the input information of the first model in the first information.

[0269] S224, the target device determines the second information based on the third information;

[0270] S225, the target device sends second information to the first device;

[0271] S226: The first device sends second information to the third device.

[0272] For example, if the second device does not indicate the calling interface information, the first device can independently determine the calling interface information of the knowledge information and further send the calling interface information, or the calling interface information and the first information, to the target device, so that the target device can obtain the corresponding knowledge information based on the calling interface information, thereby obtaining the second information. The target device can then send the determined second information to the first device, which will then send the second information to the third device.

[0273] It should be understood that the interaction of the third information and the second information between the first device and the target device can be forwarded through other nodes (such as network open functions), or the information interaction can be performed directly.

[0274] In this case, as another embodiment, as shown in FIG6 , it can be implemented by the following steps:

[0275] S231: The second device sends first information to the first device, where the first information does not include calling interface information of the knowledge information.

[0276] S232: The first device determines calling interface information of the knowledge information according to the first information.

[0277] For example, the first device can determine the target calling interface among multiple calling interfaces based on the requirement information for the first model reasoning in the first information, thereby obtaining information such as the calling interface name of the target calling interface, the input data format of the calling interface, the output data format of the calling interface, and the input data of the calling interface.

[0278] S233: The first device sends fourth information to the third device, where the fourth information includes the calling interface information of the knowledge information, or the fourth information includes the first information and the calling interface information of the knowledge information. The calling interface information of the knowledge information includes the calling interface name of the target calling interface, or the calling interface name of the target calling interface and the input data format of the calling interface, or the calling interface name of the target calling interface and the input data format of the calling interface and the output data format of the calling interface.

[0279] S234, the third device sends the seventh information to the target device based on the fourth information, wherein the seventh information includes the calling interface information of the knowledge information; wherein the calling interface information of the knowledge information includes the calling interface name and the input data of the calling interface. For example, the calling interface name is included in the calling interface information of the knowledge information in the fourth information, and the input data of the calling interface is determined based on the input data format of the calling interface of the knowledge information in the fourth information and the input information of the first model that can be provided by the third device. Alternatively, the calling interface name is determined based on the requirement information or auxiliary information or calling interface information in the first information (contained in the fourth information), and the input data of the calling interface is determined based on the input information of the first model in the first information (contained in the fourth information).

[0280] S235: The target device determines second information according to the seventh information, and sends the second information to the third device.

[0281] For example, when the second device does not indicate the calling interface information, the first device can autonomously determine the calling interface information of the knowledge information, and further send the calling interface information, or the calling interface information and the first information to the third device, and the third device autonomously requests the second information from the target device, and then the target device can send the determined second information to the third device.

[0282] It should be understood that the interaction between the seventh information and the second information between the third device and the target device can be forwarded through other nodes (such as network open functions), or the information interaction can be performed directly.

[0283] In some embodiments of the present application, the method 200 further includes at least one of the following steps:

[0284] The first device receives the fifth information sent by the target device;

[0285] The first device registers at least one calling interface information according to the fifth information;

[0286] The first device sends sixth information to the target device.

[0287] In some embodiments, the fifth information includes but is not limited to at least one of the following:

[0288] The calling interface name, calling interface parameter list, calling interface description, calling interface usage examples, and calling interface instruction set. The calling interface parameter list includes the calling interface's input parameters and output parameters, and each parameter includes at least one of the following: parameter name, parameter description, data type, and default value. The calling interface description is used to describe the calling interface's function.

[0289] In some embodiments, the sixth information includes but is not limited to at least one of the following:

[0290] Whether the calling interface is successfully registered, whether the calling interface is rejected, a list of registered calling interfaces, and a list of failed registration calling interfaces.

[0291] Furthermore, when the subsequent first information does not include the calling interface information, the first device may determine the target calling interface information in the registered calling interface information according to the first information, and obtain the corresponding knowledge information using the target calling interface information.

[0292] In some embodiments of the present application, if the third device is a terminal, the method 200 further includes:

[0293] The third device sends at least one of the eighth information and the first capability information to the network side device;

[0294] Among them, the first model is deployed on the third device, the eighth information is used to indicate the auxiliary information required for the third device to perform reasoning based on the first model, and the first capability information is used to indicate the capability information of the third device to perform reasoning based on the first model.

[0295] In some embodiments, the eighth information or the first capability information can be used by the network side device to determine the second information to indicate to the terminal.

[0296] In some embodiments, the eighth information includes at least one of the following:

[0297] An indication of a network element where knowledge information required for reasoning by the third device based on the first model is located;

[0298] An indication of knowledge information required for the third device to perform reasoning based on the first model;

[0299] An instruction for calling an interface for the knowledge information required for the third device to perform reasoning based on the first model;

[0300] an indication of a preprocessing method of the second information;

[0301] A format indication of the second information.

[0302] Optionally, the indication of the network element where the knowledge information required for the third device to perform reasoning based on the first model is located may be a network element identifier. For example, if there are multiple network elements for storing knowledge information, and the knowledge required for the third device to perform model reasoning is stored on the first network element, the third device may indicate the network element identifier of the first network element to the network-side device.

[0303] In some embodiments, the indication of the knowledge information required for the third device to perform reasoning based on the first model may include indication information of the knowledge graph and / or indication information of the knowledge vector library required for the third device to perform reasoning based on the first model. For example, the indication information of the knowledge graph is one or more knowledge graph identifiers, or a knowledge graph group identifier, a knowledge graph file address, etc., or explicitly indicates one or more knowledge graphs. For example, the indication information of the knowledge vector library is used to indicate one or more knowledge vector library identifiers, or explicitly indicates one or more knowledge vector libraries.

[0304] Exemplarily, the indication information of the knowledge graph includes at least one of the following:

[0305] Knowledge graph identification;

[0306] Knowledge graph file address.

[0307] In some embodiments, the calling interface indication of the knowledge information required for reasoning by the third device based on the first model may explicitly indicate one or more calling interface information, or may implicitly indicate one or more calling interface identifiers, which correspond to a set of calling interface information.

[0308] In some embodiments, the format indication of the second information may be used to indicate the model input format of the second information. For example, the model input format may include the content included in the model input (e.g., including prompt information, including prompt information and calling interface information, including prompt information and configuration information of network-side devices, etc.), as well as the dimension, quantization accuracy, size limit, etc. of the second information.

[0309] In some embodiments, the preprocessing method indication of the second information can be used to indicate the method for preprocessing the second information, such as biasing the numerical value, scaling the numerical value, performing a polynomial function transformation on the numerical value, performing multidimensional mapping on a single numerical value, etc.

[0310] In some embodiments, the first capability information includes at least one of the following:

[0311] An indication of knowledge information supported by reasoning performed by a third device based on the first model;

[0312] An indication of a calling interface for knowledge information supported by reasoning performed by a third device based on the first model;

[0313] supported preprocessing methods for the second information;

[0314] An indication of a supported format of the second information.

[0315] In some embodiments, the indication of the knowledge information supported by the third device for reasoning based on the first model may include indication information of the knowledge graph supported by the third device for reasoning based on the first model and / or indication information of the knowledge vector library. For example, the indication information of the knowledge graph is one or more knowledge graph identifiers, or a knowledge graph group identifier, a knowledge graph file address, etc., or explicitly indicates one or more knowledge graphs. For example, the indication information of the knowledge vector library is used to indicate one or more knowledge vector library identifiers, or explicitly indicates one or more knowledge vector libraries.

[0316] In some embodiments, the calling interface indication of the knowledge information supported by the third device for reasoning based on the first model may explicitly indicate one or more calling interface information, or may implicitly indicate one or more calling interface identifiers corresponding to a set of calling interface information.

[0317] In some embodiments, the format indication of the second information supported by the third device can be used to indicate the model input format of the second information supported by the third device. For example, the model input format may include what the model input includes (for example, prompt information, prompt information and calling interface information, prompt information and configuration information of the network side device, etc.), as well as the dimensions, quantization accuracy, size limit, etc. of this information.

[0318] In some embodiments, the preprocessing method of the second information supported by the third device can be used to indicate the processing methods supported by the third device. These processing methods are ways in which the network-side device preprocesses the second information, such as biasing a value, scaling a value, performing a polynomial function transformation on a value, performing multidimensional mapping on a single value, etc.

[0319] In some embodiments, the third device sends the eighth information and / or the first capability information to the network side device during a model registration (or model identification) process or a capability reporting process.

[0320] It should be understood that the eighth information and the first capability information may be reported to the network side device via one signaling, or may be reported to the network side device via different signaling, and this application does not limit this.

[0321] The following describes the model reasoning method in the communication system provided by the present application in combination with the specific embodiments shown in Figures 7 to 14.

[0322] Example 1: Inference device is a terminal

[0323] As shown in Figure 7, the first device is an access network device or a core network function, the second device and the third device are the same device and are terminals, and the first model is deployed on the terminal. The inference method may include the following steps:

[0324] Step 1: The terminal sends first information to the access network device or the core network device to request second information.

[0325] Step 2: The access network device or the core network device sends the second information to the terminal according to the first information.

[0326] Thus, the inference device can obtain the second information. Further, the inference device can use the second information to assist in the reasoning of the model, thereby improving the reasoning performance of the model.

[0327] Embodiment 2: The inference device is an access network device, such as a base station.

[0328] For example, in the example of FIG8 , the first device is a core network function, the second device and the third device are the same device and are access network devices, and the first model is deployed on the access network device.

[0329] As shown in FIG8 , the reasoning method may include the following steps:

[0330] Step 1: The access network device sends first information to the core network device to request second information.

[0331] Step 2: The core network device sends the second information to the access network device according to the first information.

[0332] Thus, the inference device can obtain the second information. Further, the inference device can use the second information to assist in the reasoning of the model, thereby improving the reasoning performance of the model.

[0333] For another example, in the example of Figure 9, the first device is a core network function, the second device is a terminal, and the third device is an access network device. The first model is deployed on the access network device, such as the inference device is an access network device, such as a base station.

[0334] As shown in FIG9 , the reasoning method may include the following steps:

[0335] Step 1: The terminal sends first information to the core network function to request second information.

[0336] Optionally, in step 1, the terminal may also send the first information to the access network device.

[0337] Optionally, the first information sent by the terminal to the access network device may be different from the first information sent to the core network function.

[0338] For example, the first information sent by the terminal to the core network may include auxiliary information used for inference by the first model and input information for the first model; whereas the first information sent by the terminal to the access network device may only include the input information for the first model. Furthermore, to inform the core network device to whom the second information is sent, the first information may also include a receiving device indicator, such as a third device, to indicate the receiving device of the second information.

[0339] Step 2: The core network device sends the second information to the access network device according to the first information.

[0340] In this case, the access network device's input for the first model consists of two parts: the first information sent by the terminal and the second information sent by the core network. Therefore, the access network device needs to synchronize these two parts to generate complete input information for the first model. This prevents the merging of the first information of sample 1 and the second information of sample 2, which could result in an incorrect sample. Therefore, both the first and second information need to include a sample indicator, such as a sample identifier.

[0341] Optionally, when the core network device sends the second information to the access network device, it may also send the first information to the access network device. Thus, the inference device can obtain the first information and the second information. Furthermore, the inference device can use the first information and the second information to assist in model inference, thereby improving the model's inference performance.

[0342] For another example, in the example of Figure 10, the first device is a core network function, the second device is a terminal, and the third device is an access network device. The first model is deployed on the access network device, such as the inference device is an access network device, such as a base station.

[0343] As shown in FIG10 , the reasoning method may include the following steps:

[0344] Step 1: The terminal sends first information to the core network device to request second information.

[0345] Step 2: The core network device sends second information to the terminal according to the first information.

[0346] Step 3: The terminal sends the second information to the access network device.

[0347] Optionally, the terminal may also send the first information to the access network device.

[0348] For example, in this example, the second information is not sent directly from the knowledge storage device to the reasoning device, but is forwarded to the reasoning device by the demand initiating device.

[0349] Thus, the inference device can obtain the first information and the second information. Further, the inference device can use the first information and the second information to assist in the inference of the model, thereby improving the inference performance of the model.

[0350] For another example, in the example of Figure 11, the first device is a core network function, the second device is a terminal, and the third device is an access network device. The first model is deployed on the access network device, such as the inference device is an access network device, such as a base station.

[0351] As shown in FIG11 , the reasoning method may include the following steps:

[0352] Step 1: The terminal sends first information to the core network device to request second information.

[0353] The first information includes at least one of the following:

[0354] The receiving device indication is used to indicate the receiving device of the second information.

[0355] Auxiliary information used for reasoning with the first model;

[0356] Requirement information for reasoning with the first model;

[0357] Step 2: The core network device sends the first information and the second information to the access network device according to the first information.

[0358] Thus, the inference device can obtain the first information and the second information. Further, the inference device can use the first information and the second information to assist in the inference of the model, thereby improving the inference performance of the model.

[0359] Example 3: The inference device is a core network function.

[0360] For example, in the example of FIG12 , the first device is a core network function, the second device is a terminal or an access network device or a core network device, and the third device is a core network function.

[0361] As shown in FIG12 , the reasoning method may include the following steps:

[0362] Step 1: The second device sends first information to the first device to request second information.

[0363] The first information includes at least one of the following:

[0364] a receiving device indication, used to indicate a receiving device of the second information;

[0365] Auxiliary information used for reasoning with the first model;

[0366] Requirement information for reasoning with the first model;

[0367] The input information of the first model.

[0368] Optionally, in step 1, the second device may also send the first information to a third device to assist the third device in model inference. In this case, the third device's input to the first model consists of two parts: one part from the first information sent by the second device, and one part from the second information sent by the first device. Therefore, the third device needs to synchronize the two parts of information to generate complete input information for the first model. This prevents the first information of sample 1 and the second information of sample 2 from being combined to generate an erroneous sample. Therefore, the first and second information need to include a sample indication, such as a sample identifier.

[0369] Step 2: The first device sends second information to the third device according to the first information.

[0370] Thus, the inference device can obtain the first information and the second information. Further, the inference device can use the first information and the second information to assist in the inference of the model, thereby improving the inference performance of the model.

[0371] For another example, in the example of FIG13 , the first device is a core network function, the second device is a terminal or an access network device or a core network device, and the third device is a core network function.

[0372] As shown in FIG13 , the reasoning method may include the following steps:

[0373] Step 1: The second device sends first information to the first device to request second information.

[0374] Step 2: The first device sends second information to the second device according to the first information.

[0375] Step 3: The second device sends the second information to the third device. Optionally, the first information may also be sent to the third device.

[0376] Thus, the inference device can obtain the second information, or the first information and the second information. Further, the inference device can use the second information, or the first information and the second information to assist in the reasoning of the model, which can improve the reasoning performance of the model.

[0377] For another example, in the example of FIG14 , the first device is a core network function, the second device is a terminal or an access network device or a core network device, and the third device is a core network function.

[0378] As shown in FIG14 , the reasoning method may include the following steps:

[0379] Step 1: The second device sends first information to the first device to request second information.

[0380] The first information includes at least one of the following:

[0381] a receiving device indication, used to indicate a receiving device of the second information;

[0382] Auxiliary information used for reasoning with the first model;

[0383] Requirement information for reasoning with the first model;

[0384] The input information of the first model.

[0385] Step 2: The first device sends the first information and the second information to the third device according to the first information.

[0386] Thus, the inference device can obtain the second information, or the first information and the second information. Further, the inference device can use the second information, or the first information and the second information to assist in the reasoning of the model, which can improve the reasoning performance of the model.

[0387] It should be understood that in an embodiment of the present application, when the inference device obtains the first information and the second information, the first information and the second information include a sample indication, which is used to avoid the inference device using the first information and the second information of different samples for inference, affecting the inference result.

[0388] In summary, in the embodiments of the present application, by obtaining the second information from a device storing knowledge information and further using the model for reasoning, more information can be obtained as input to the model, thereby improving the reasoning performance of the model.

[0389] The above text, in combination with Figures 2 to 14, describes in detail the method embodiment of the present application. The following text, in combination with Figures 15 to 21, describes in detail the device embodiment of the present application. It should be understood that the device embodiment and the method embodiment correspond to each other, and similar descriptions can refer to the method embodiment.

[0390] The model reasoning method in the communication system provided in the embodiment of the present application can be executed by a communication device. In the embodiment of the present application, the communication device provided in the embodiment of the present application is described by taking the communication device executing the model reasoning method in the communication system as an example.

[0391] FIG15 shows a schematic block diagram of a communication device 500 according to an embodiment of the present application. As shown in FIG15 , the device 500 includes:

[0392] The communication unit 510 is configured to receive first information sent by a second device, wherein the communication device 500 stores at least one of knowledge information and information on a calling interface for the knowledge information; wherein the knowledge information includes at least one of the following: a knowledge graph, a knowledge vector library; and

[0393] Sending second information to a third device, on which the first model is deployed, wherein the second information is determined based on the first information and knowledge information stored or called on the communication apparatus 500;

[0394] The first information includes at least one of the following:

[0395] Auxiliary information used for reasoning with the first model;

[0396] Requirement information for reasoning with the first model;

[0397] input information of the first model;

[0398] Calling interface information of knowledge information;

[0399] an indication of a receiving device of the second information;

[0400] An indication of a sample corresponding to the input information of the first model.

[0401] In some embodiments, the auxiliary information includes at least one of the following:

[0402] Artificial intelligence AI model identification information, AI model function indication, AI model feature indication, target knowledge graph indication, cell identification, and bandwidth part BWP identification.

[0403] In some embodiments, the requirement information includes at least one of the following:

[0404] Indication of the target tasks to which the AI ​​model needs to be adapted;

[0405] Service Quality of Experience (QoE) requirements for AI models;

[0406] Business processing latency of AI models;

[0407] The business processing accuracy of the AI ​​model;

[0408] The business computing capacity of AI models;

[0409] The data processing scale of AI models.

[0410] In some embodiments, the second information includes at least one of the following:

[0411] Hidden variables, where the hidden variables are feature vectors obtained by mapping the input information of the first model;

[0412] Prompt information;

[0413] Configuration information of network-side devices;

[0414] Scene type information;

[0415] An indication of a sample corresponding to the input information of the first model.

[0416] In some embodiments, the prompt information includes at least one of the following:

[0417] A chain of thought related to the target task adapted by the first model;

[0418] Prompt text related to the target task;

[0419] Prompt files related to the target task;

[0420] a prompt code related to the target task;

[0421] Identification of thought chains related to the target task;

[0422] Prompt text labels related to the target task;

[0423] The prompt file identifier related to the target task;

[0424] A prompt code identifier related to the target task;

[0425] The address of the prompt file related to the target task.

[0426] In some embodiments, the configuration information of the network-side device includes at least one of the following:

[0427] The number of transmit antennas on the network-side device;

[0428] The number of transmit beams of the network-side device;

[0429] Number of antenna ports on network-side equipment;

[0430] The transmit power of the network-side device;

[0431] Antenna gain of network-side equipment;

[0432] The beam 3dB bandwidth of the network-side equipment;

[0433] The distance between network-side devices;

[0434] Frequency of network-side equipment;

[0435] System bandwidth;

[0436] Terminal distribution characteristics under the coverage of network-side equipment.

[0437] In some embodiments, the scene type information includes at least one of the following:

[0438] Line-of-sight (LOS), non-line-of-sight (NLOS), indoor, outdoor, urban microcell, urban macrocell, suburban microcell, high speed, low speed.

[0439] In some embodiments, the communication device 500 further includes:

[0440] A processing unit is configured to determine the second information based on the first information and the knowledge information stored on the communication device 500 .

[0441] In some embodiments, the communication unit 510 is further configured to:

[0442] The second information is received from a target device, where the target device is a device accessed using the calling interface information.

[0443] In some embodiments, the first information includes calling interface information of knowledge information, and the communication device 500 further includes: a processing unit for determining the second information based on the first information and the knowledge information stored on the communication device 500.

[0444] In some embodiments, the communication unit 510 is further used to: send the first information to a target device according to the calling interface information of the knowledge information, wherein the target device is a device accessed using the calling interface information; and receive the second information from the target device.

[0445] In some embodiments, the first information does not include calling interface information of the knowledge information, and the communication device 500 further includes: a processing unit, configured to determine the calling interface information of the knowledge information based on the first information;

[0446] The communication device 500 is further configured to: send third information to a target device, wherein the target device is a device accessed using the calling interface information, and the third information includes the calling interface information of the knowledge information; and receive the second information from the target device.

[0447] In some embodiments, the first information does not include calling interface information of the knowledge information, and the communication device 500 further includes: a processing unit, configured to determine the calling interface information of the knowledge information based on the first information;

[0448] The communication unit 510 is also used to: send fourth information to the third device, the fourth information includes the calling interface information of the knowledge information, or the fourth information includes the first information and the calling interface information of the knowledge information, and the fourth information is used by the third device to request the target device to obtain the second information, and the target device is the device accessed using the calling interface information.

[0449] In some embodiments, the communication unit 510 is further configured to:

[0450] receiving fifth information from the target device;

[0451] The communication device 500 further includes a processing unit, configured to register at least one calling interface information according to the fifth information;

[0452] The communication unit 510 is further configured to: send sixth information to the target device;

[0453] The fifth information includes at least one of the following: a calling interface name, a calling interface parameter list, a calling interface description, a calling interface usage example, and a calling interface instruction set;

[0454] The sixth information includes at least one of the following:

[0455] Whether the calling interface is successfully registered, whether the calling interface is rejected, a list of registered calling interfaces, and a list of failed registration calling interfaces.

[0456] In some embodiments, the communication unit 510 is further configured to:

[0457] The first information is sent to the third device.

[0458] In some embodiments, the communication device 500 is an access network device or a core network function, and the second device and the third device are terminals; or

[0459] The communication device 500 is a core network function, the second device is a terminal or an access network device, and the third device is an access network device; or

[0460] The communication device 500 is a core network function, the second device is a terminal or an access network device or a core network function, and the third device is a core network function.

[0461] In some embodiments, the communication device 500 is an access network device, the second device is a terminal, and the first information is carried in at least one of the following signaling: layer 1 signaling, layer 2 signaling, layer 3 signaling, AI layer signaling, and data plane signaling; or

[0462] The communication device 500 is a core network function, the third device is a terminal, and the first information is carried in at least one of the following signaling: non-access layer NAS signaling, AI layer signaling, and data plane signaling.

[0463] In some embodiments, the calling interface information includes at least one of the following:

[0464] Calling interface name, calling interface input data format, calling interface output data format, calling interface input data.

[0465] Optionally, in some embodiments, the communication unit may be a communication interface or a transceiver, or an input / output interface of a communication chip or a system on chip.

[0466] It should be understood that the communication device 500 according to the embodiment of the present application may correspond to the first device in the method embodiment of the present application, and the above-mentioned and other operations and / or functions of each unit in the communication device 500 are respectively for realizing the corresponding processes of the first device in the method embodiment shown in Figures 2 to 14 and achieving the same technical effects. To avoid repetition, they will not be repeated here.

[0467] FIG16 shows a schematic block diagram of a communication device 600 according to an embodiment of the present application. As shown in FIG16 , the device 600 includes:

[0468] The communication unit 610 is configured to send first information to a first device, wherein the first device stores at least one of knowledge information and information on a calling interface for the knowledge information, wherein the knowledge information includes at least one of the following: a knowledge graph and a knowledge vector library;

[0469] The first information includes at least one of the following:

[0470] Auxiliary information used for reasoning by the first model;

[0471] Requirement information for the first model to perform reasoning;

[0472] Input information of the first model;

[0473] Calling interface information of knowledge information;

[0474] an indication of a receiving device of the second information;

[0475] An indication of a sample corresponding to the input information of the first model.

[0476] In some embodiments, the auxiliary information includes at least one of the following:

[0477] Artificial intelligence AI model identification information, AI model function indication, AI model feature indication, target knowledge graph indication, cell identification, and bandwidth part BWP identification.

[0478] In some embodiments, the requirement information includes at least one of the following:

[0479] Indication of the target tasks to which the AI ​​model needs to be adapted;

[0480] Service Quality of Experience (QoE) requirements for AI models;

[0481] Business processing latency of AI models;

[0482] The business processing accuracy of the AI ​​model;

[0483] The business computing capacity of AI models;

[0484] The data processing scale of AI models.

[0485] In some embodiments, the second information includes at least one of the following:

[0486] Hidden variables, where the hidden variables are feature vectors obtained by mapping the input information of the first model;

[0487] Prompt information;

[0488] Configuration information of network-side devices;

[0489] Scene type information;

[0490] An indication of a sample corresponding to the input information of the first model.

[0491] In some embodiments, the prompt information includes at least one of the following:

[0492] A chain of thought related to the target task adapted by the first model;

[0493] Prompt text related to the target task;

[0494] Prompt files related to the target task;

[0495] a prompt code related to the target task;

[0496] Identification of thought chains related to the target task;

[0497] Prompt text labels related to the target task;

[0498] The prompt file identifier related to the target task;

[0499] A prompt code identifier related to the target task;

[0500] The address of the prompt file related to the target task.

[0501] In some embodiments, the configuration information of the network-side device includes at least one of the following:

[0502] The number of transmit antennas on the network-side device;

[0503] The number of transmit beams of the network-side device;

[0504] Number of antenna ports on network-side equipment;

[0505] The transmit power of the network-side device;

[0506] Antenna gain of network-side equipment;

[0507] The beam 3dB bandwidth of the network-side equipment;

[0508] The distance between network-side devices;

[0509] Frequency of network-side equipment;

[0510] System bandwidth;

[0511] Terminal distribution characteristics under the coverage of network-side equipment.

[0512] In some embodiments, the scene type information includes at least one of the following:

[0513] Line-of-sight (LOS), non-line-of-sight (NLOS), indoor, outdoor, urban microcell, urban macrocell, suburban microcell, high speed, low speed.

[0514] In some embodiments, the calling interface information includes at least one of the following:

[0515] Calling interface name, calling interface input data format, calling interface output data format, calling interface input data.

[0516] In some embodiments, the communication device 600 is a terminal, and the communication unit 610 is specifically configured to:

[0517] When at least one of the following events occurs, the first information is sent to the first device:

[0518] receiving a first instruction, wherein the first instruction is used to instruct activation or switching to the first model;

[0519] The communication device 600 determines to activate or switch to the first model;

[0520] The communication device 600 determines that the first model does not meet the requirements;

[0521] A second indication is received, where the second indication is used to indicate that the first model does not meet the requirements.

[0522] In some embodiments, the communication unit 610 is further configured to:

[0523] The first information is sent to a third device, and the first model is deployed on the third device.

[0524] Optionally, in some embodiments, the communication unit may be a communication interface or a transceiver, or an input / output interface of a communication chip or a system on chip.

[0525] It should be understood that the communication device 600 according to the embodiment of the present application may correspond to the second device in the method embodiment of the present application, and the above-mentioned and other operations and / or functions of each unit in the communication device 600 are respectively for realizing the corresponding processes of the second device in the method embodiment shown in Figures 2 to 14 and achieving the same technical effects. To avoid repetition, they will not be repeated here.

[0526] FIG17 shows a schematic block diagram of a communication device 700 according to an embodiment of the present application. As shown in FIG17 , the device 700 includes:

[0527] The communication unit 710 is configured to obtain second information from a first device or a target device, wherein a first model is deployed on the communication apparatus 700, and at least one of knowledge information and calling interface information of the knowledge information is stored on the first device or the target device, wherein the target device is a device accessed using the calling interface information, and the knowledge information includes at least one of the following: a knowledge graph and a knowledge vector library;

[0528] Wherein, the second information is determined based on the first information and knowledge information stored or called on the first device;

[0529] The first information includes at least one of the following:

[0530] Auxiliary information used for reasoning with the first model;

[0531] Requirement information for reasoning with the first model;

[0532] input information of the first model;

[0533] Calling interface information of knowledge information;

[0534] an indication of a receiving device of the second information;

[0535] An indication of a sample corresponding to the input information of the first model.

[0536] In some embodiments, the auxiliary information includes at least one of the following:

[0537] Artificial intelligence AI model identification information, AI model function indication, AI model feature indication, target knowledge graph indication, cell identification, and bandwidth part BWP identification.

[0538] In some embodiments, the requirement information includes at least one of the following:

[0539] Indication of the target tasks to which the AI ​​model needs to be adapted;

[0540] Service Quality of Experience (QoE) requirements for AI models;

[0541] Business processing latency of AI models;

[0542] The business processing accuracy of the AI ​​model;

[0543] The business computing capacity of AI models;

[0544] The data processing scale of AI models.

[0545] In some embodiments, the second information includes at least one of the following:

[0546] Hidden variables, where the hidden variables are feature vectors obtained by mapping the input information of the first model;

[0547] Prompt information;

[0548] Configuration information of network-side devices;

[0549] Scene type information;

[0550] An indication of a sample corresponding to the input information of the first model.

[0551] In some embodiments, the prompt information includes at least one of the following:

[0552] A chain of thought related to the target task adapted by the first model;

[0553] Prompt text related to the target task;

[0554] Prompt files related to the target task;

[0555] a prompt code related to the target task;

[0556] Identification of thought chains related to the target task;

[0557] Prompt text labels related to the target task;

[0558] The prompt file identifier related to the target task;

[0559] A prompt code identifier related to the target task;

[0560] The address of the prompt file related to the target task.

[0561] In some embodiments, the configuration information of the network-side device includes at least one of the following:

[0562] The number of transmit antennas on the network-side device;

[0563] The number of transmit beams of the network-side device;

[0564] Number of antenna ports on network-side equipment;

[0565] The transmit power of the network-side device;

[0566] Antenna gain of network-side equipment;

[0567] The beam 3dB bandwidth of the network-side equipment;

[0568] The distance between network-side devices;

[0569] Frequency of network-side equipment;

[0570] System bandwidth;

[0571] Terminal distribution characteristics under the coverage of network-side equipment.

[0572] In some embodiments, the scene type information includes at least one of the following:

[0573] Line-of-sight (LOS), non-line-of-sight (NLOS), indoor, outdoor, urban microcell, urban macrocell, suburban microcell, high speed, low speed.

[0574] In some embodiments, the communication unit 710 is further configured to:

[0575] receiving fourth information from the first device, where the fourth information includes calling interface information of the knowledge information, or the fourth information includes the first information and calling interface information of the knowledge information;

[0576] Sending seventh information to the target device, wherein the seventh information includes calling interface information of the knowledge information;

[0577] The second information is received from the target device.

[0578] In some embodiments, the communication device 700 is a terminal, and the communication unit 710 is further configured to:

[0579] Send at least one of the eighth information and the first capability information to the network side device, wherein the first model is deployed on the communication device 700, the eighth information is used to indicate the auxiliary information required for the communication device 700 to perform reasoning based on the first model, and the first capability information is used to indicate the capability information of the communication device 700 to perform reasoning based on the first model.

[0580] In some embodiments, the eighth information includes at least one of the following:

[0581] An indication of a network element where knowledge information required for the communication device 700 to perform reasoning based on the first model is located;

[0582] an indication of the knowledge information required by the communication device 700 to perform reasoning based on the first model;

[0583] An instruction for calling an interface for the communication device 700 to perform reasoning based on the first model and the knowledge information required for the reasoning;

[0584] A format indication of the second information.

[0585] In some embodiments, the first capability information includes at least one of the following:

[0586] an indication of knowledge information supported by the communication device 700 in reasoning based on the first model;

[0587] An indication of a calling interface for knowledge information supported by reasoning performed by the communication device 700 based on the first model;

[0588] An indication of a supported format of the second information.

[0589] In some embodiments, the communication unit 710 is further configured to:

[0590] During the model registration process or the capability reporting process, at least one of the eighth information and the first capability information is sent to the network side device.

[0591] In some embodiments, the communication unit 710 is further configured to:

[0592] The first information is received from the second device, and the first model is deployed on the communication apparatus 700 .

[0593] In some embodiments, the communication device 700 further includes:

[0594] A processing unit is configured to use the first model to perform inference based on the second information and input information of the first model.

[0595] In some embodiments, the calling interface information includes at least one of the following:

[0596] Calling interface name, calling interface input data format, calling interface output data format, calling interface input data.

[0597] Optionally, in some embodiments, the communication unit may be a communication interface or a transceiver, or an input / output interface of a communication chip or a system on chip.

[0598] It should be understood that the communication device 700 according to the embodiment of the present application may correspond to the third device in the method embodiment of the present application, and the above-mentioned and other operations and / or functions of each unit in the communication device 700 are respectively for realizing the corresponding processes of the third device in the method embodiment shown in Figures 2 to 14 and achieving the same technical effects. To avoid repetition, they will not be repeated here.

[0599] In some embodiments, the apparatus 500, apparatus 600, and apparatus 700 in the embodiments of the present application may be an electronic device, such as an electronic device having an operating system, or a component in an electronic device, such as an integrated circuit or chip. The electronic device may be a terminal, or may be another device other than a terminal. For example, the terminal may include but is not limited to the types of terminal 11 listed above, and the other device may be a server, a network attached storage (NAS), etc., which is not specifically limited in the embodiments of the present application.

[0600] As shown in Figure 18, an embodiment of the present application also provides a communication device 1000, including a processor 1001 and a memory 1002, and the memory 1002 stores a program or instruction that can be run on the processor 1001. For example, when the communication device 1000 is a first device, the program or instruction is executed by the processor 1001 to implement the steps performed by the first device in the above-mentioned reasoning method embodiment, and can achieve the same technical effect. For example, when the communication device 1000 is a second device, the program or instruction is executed by the processor 1001 to implement the steps performed by the second device in the above-mentioned reasoning method embodiment, and can achieve the same technical effect. For example, when the communication device 1000 is a third device, the program or instruction is executed by the processor 1001 to implement the steps performed by the third device in the above-mentioned reasoning method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0601] The present application also provides a terminal including a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is configured to execute a program or instruction to implement the steps in the method embodiments shown in Figures 2 to 14. This terminal embodiment corresponds to the aforementioned terminal-side method embodiment, and each implementation process and implementation method of the aforementioned method embodiment can be applied to this terminal embodiment and achieve the same technical effects. Specifically, Figure 19 is a schematic diagram of the hardware structure of a terminal implementing an embodiment of the present application.

[0602] The terminal 1100 includes but is not limited to: a radio frequency unit 1101, a network module 1102, an audio output unit 1103, an input unit 1104, a sensor 1105, a display unit 1106, a user input unit 1107, an interface unit 1108, a memory 1109 and at least some of the components of the processor 1110.

[0603] Those skilled in the art will appreciate that the terminal 1100 may also include a power supply (such as a battery) to power various components. The power supply may be logically connected to the processor 1110 via a power management system, thereby implementing functions such as charging, discharging, and power consumption management through the power management system. The terminal structure shown in FIG19 does not constitute a limitation of the terminal. The terminal may include more or fewer components than shown, or combine certain components, or have different component arrangements, which will not be described in detail here.

[0604] It should be understood that in an embodiment of the present application, the input unit 1104 may include a graphics processing unit (GPU) 11041 and a microphone 11042, and the graphics processor 11041 processes the image data of a static picture or video obtained by an image capture device (such as a camera) in a video capture mode or an image capture mode. The display unit 1106 may include a display panel 11061, and the display panel 11061 may be configured in the form of a liquid crystal display, an organic light emitting diode, etc. The user input unit 1107 includes a touch panel 11071 and at least one of other input devices 11072. The touch panel 11071 is also called a touch screen. The touch panel 11071 may include two parts: a touch detection device and a touch controller. Other input devices 11072 may include, but are not limited to, a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which will not be repeated here.

[0605] In the embodiment of the present application, after receiving downlink data from a network-side device, the RF unit 1101 may transmit the data to the processor 1110 for processing. Furthermore, the RF unit 1101 may send uplink data to the network-side device. Typically, the RF unit 1101 includes, but is not limited to, an antenna, an amplifier, a transceiver, a coupler, a low-noise amplifier, a duplexer, and the like.

[0606] The memory 1109 can be used to store software programs or instructions and various data. The memory 1109 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, applications or instructions required for at least one function (such as a sound playback function, an image playback function, etc.). In addition, the memory 1109 may include a volatile memory or a non-volatile memory. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct RAM bus random access memory (DRRAM). The memory 1109 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.

[0607] Processor 1110 may include one or more processing units. Optionally, processor 1110 integrates an application processor and a modem processor. The application processor primarily handles operations related to the operating system, user interface, and application programs, while the modem processor primarily processes wireless communication signals, such as a baseband processor. It is understood that the modem processor may not be integrated into processor 1110.

[0608] It can be understood that the implementation process of each implementation method mentioned in this embodiment can refer to the relevant description of the method embodiments shown in Figures 2 to 14, and achieve the same or corresponding technical effects. To avoid repetition, it will not be repeated here.

[0609] The present application also provides a network-side device, including a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is configured to execute a program or instruction to implement the steps of the method embodiments shown in Figures 2 to 14. This network-side device embodiment corresponds to the above-mentioned access network device-side or core network function-side method embodiments, and each implementation process and implementation method of the above-mentioned method embodiments are applicable to this network-side device embodiment and can achieve the same technical effects.

[0610] Specifically, embodiments of the present application also provide a network-side device. As shown in Figure 20, the network-side device 1200 includes: an antenna 1201, a radio frequency device 1202, a baseband device 1203, a processor 1204, and a memory 1205. Antenna 1201 is connected to radio frequency device 1202. In the uplink direction, radio frequency device 1202 receives information via antenna 1201 and sends the received information to baseband device 1203 for processing. In the downlink direction, baseband device 1203 processes the information to be transmitted and sends it to radio frequency device 1202. Radio frequency device 1202 processes the received information and then sends it through antenna 1201.

[0611] The method executed by the network-side device in the above embodiment may be implemented in the baseband device 1203 , which includes a baseband processor.

[0612] The baseband device 1203 may include, for example, at least one baseband board, on which multiple chips are arranged, as shown in Figure 20, one of the chips is, for example, a baseband processor, which is connected to the memory 1205 through a bus interface to call the program in the memory 1205 and execute the network device operations shown in the above method embodiment.

[0613] The network side device may further include a network interface 1206 , which is, for example, a Common Public Radio Interface (CPRI).

[0614] Specifically, the network side device 1200 of the embodiment of the present application also includes: instructions or programs stored in the memory 1205 and executable on the processor 1204. The processor 1204 calls the instructions or programs in the memory 1205 to execute the methods executed by the modules shown in Figures 15 to 17 and achieve the same technical effects. To avoid repetition, they will not be elaborated here.

[0615] Specifically, the embodiment of the present application further provides a network-side device. As shown in FIG21 , the network-side device 1300 includes: a processor 1301, a network interface 1302, and a memory 1303. The network interface 1302 is, for example, a common public radio interface (CPRI).

[0616] Specifically, the network side device 1300 of the embodiment of the present application also includes: instructions or programs stored in the memory 1303 and executable on the processor 1301. The processor 1301 calls the instructions or programs in the memory 1303 to execute the methods executed by the modules shown in Figures 15 to 17 and achieve the same technical effect. To avoid repetition, they will not be elaborated here.

[0617] An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the various processes of the model reasoning method embodiment in the above-mentioned communication system are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0618] The processor is a processor in the communication device, communication equipment, terminal, or network-side device described in the above embodiments. The readable storage medium includes a computer-readable storage medium, such as a computer read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disk. In some examples, the readable storage medium may be a non-transitory readable storage medium.

[0619] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the model reasoning method embodiment in the above-mentioned communication system, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0620] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0621] An embodiment of the present application further provides a computer program / program product, which is stored in a storage medium. The computer program / program product is executed by at least one processor to implement the various processes of the model reasoning method embodiment in the above-mentioned communication system, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0622] An embodiment of the present application also provides a communication system, including: a first device, a second device and a third device, wherein the first device can be used to execute the steps performed by the first device in the model inference method in the communication system as described above, the second device can be used to execute the steps performed by the second device in the model inference method in the communication system as described above, and the third device can be used to execute the steps performed by the third device in the model inference method in the communication system as described above.

[0623] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the opposite order according to the functions involved. For example, the described method may be performed in an order different from that described, and various steps may also be added, omitted or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0624] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of a computer software product plus a necessary general-purpose hardware platform, or of course, by hardware. The computer software product is stored in a storage medium (such as ROM, RAM, magnetic disk, optical disk, etc.) and includes a number of instructions for enabling a terminal or network-side device to execute the methods described in each embodiment of the present application.

[0625] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms of implementation methods without departing from the purpose of this application and the scope of protection of the claims. These implementation methods are all within the protection of this application.

Claims

1. A model reasoning method in a communication system, comprising: A first device receives first information sent by a second device, wherein at least one of knowledge information and calling interface information of knowledge information is stored on the first device; wherein the knowledge information includes at least one of the following: a knowledge graph and a knowledge vector library; The first device sends second information to a third device, the first model is deployed on the third device, and the second information is determined according to the first information and knowledge information stored or called on the first device; The first information includes at least one of the following: Auxiliary information used for reasoning with the first model; Requirement information for performing reasoning on the first model; input information of the first model; Calling interface information of knowledge information; an indication of a receiving device of the second information; An indication of samples corresponding to the input information of the first model.

2. The method according to claim 1, wherein: The auxiliary information includes at least one of the following: Artificial intelligence AI model identification information, AI model function indication, AI model feature indication, target knowledge graph indication, cell identification, bandwidth part BWP identification.

3. The method according to claim 1 or 2, wherein: The demand information includes at least one of the following: Indication of target tasks that the AI ​​model needs to adapt to; Service quality of experience (QoE) requirements for AI models; Business processing latency of AI models; The accuracy of AI model’s business processing; The business computing volume of AI models; The data processing scale of AI models.

4. The method according to any one of claims 1 to 3, wherein: The second information includes at least one of the following: Hidden variables, where the hidden variables are feature vectors after mapping the input information of the first model; Prompt information; Configuration information of network-side devices; Scene type information; A sample indication corresponding to the input information of the first model; The prompt information includes at least one of the following: A chain of thoughts related to the target task adapted by the first model; Prompt text related to the target task; Prompt files related to the target task; a prompt code associated with the target task; Identification of thought chains related to the target task; Prompt text labels related to the target task; The reminder file identifier related to the target task; A prompt code identifier related to the target task; The address of the prompt file related to the target task; The configuration information of the network side device includes at least one of the following: The number of transmitting antennas of the network side equipment; The number of transmit beams of the network-side device; Number of antenna ports on network-side equipment; The transmit power of the network-side device; Antenna gain of network-side equipment; The beam 3dB bandwidth of the network-side equipment; The distance between network-side devices; Frequency of network-side equipment; System bandwidth; Terminal distribution characteristics under the coverage of network-side equipment; The scene type information includes at least one of the following: Line-of-sight LOS, non-line-of-sight NLOS, indoor, outdoor, urban microcell, urban macrocell, suburban microcell, high speed, low speed.

5. The method according to any one of claims 1 to 4, wherein: The method further comprises: The first device determines the second information according to the first information and knowledge information stored on the first device; or, The first device receives the second information from a target device, where the target device is a device accessed using the calling interface information.

6. The method according to claim 5, wherein: The method further comprises: The first device receives fifth information sent by the target device; The first device registers at least one calling interface information according to the fifth information; The first device sends sixth information to the target device; The fifth information includes at least one of the following: a calling interface name, a calling interface parameter list, a calling interface description, a calling interface usage example, and a calling interface instruction set; The sixth information includes at least one of the following: Whether the calling interface is successfully registered, whether the calling interface is refused to be registered, the list of registered calling interfaces, and the list of failed registration calling interfaces.

7. The method according to any one of claims 1 to 6, wherein: The method further comprises: The first device sends the first information to the third device.

8. The method according to any one of claims 1 to 7, wherein: The calling interface information includes at least one of the following: Calling interface name, calling interface input data format, calling interface output data format, calling interface input data.

9. A model reasoning method in a communication system, comprising: The second device sends the first information to the first device, wherein the first device stores at least one of knowledge information and calling interface information of the knowledge information, wherein the knowledge information includes at least one of the following: a knowledge graph and a knowledge vector library; The first information includes at least one of the following: Auxiliary information used for reasoning by the first model; Requirement information for the first model to perform reasoning; Input information of the first model; Calling interface information of knowledge information; a receiving device indication of the second information; An indication of samples corresponding to the input information of the first model.

10. The method according to claim 9, wherein: The second device is a terminal, and the second device sends first information to the first device, including: When at least one of the following events occurs, sending the first information to the first device: receiving a first indication, wherein the first indication is used to indicate activation or switching to the first model; The second device determines to activate or switch to the first model; The second device determines that the first model does not meet the requirements; A second indication is received, where the second indication is used to indicate that the first model does not meet the requirements.

11. The method according to claim 9 or 10, wherein: The method further comprises: The second device sends the first information to a third device, and the first model is deployed on the third device.

12. A model reasoning method in a communication system, comprising: The third device obtains the second information from the first device or the target device, wherein the first model is deployed on the third device, and at least one of knowledge information and calling interface information of knowledge information is stored on the first device or the target device, wherein the target device is a device accessed using the calling interface information, and the knowledge information includes at least one of the following: a knowledge graph and a knowledge vector library; Wherein, the second information is determined according to the first information and the knowledge information stored or called on the first device; The first information includes at least one of the following: Auxiliary information used for reasoning with the first model; Requirement information for performing reasoning on the first model; input information of the first model; Calling interface information of knowledge information; an indication of a receiving device of the second information; An indication of samples corresponding to the input information of the first model.

13. The method according to claim 12, wherein: The third device obtains second information from the target device, including: The third device receives fourth information sent by the first device, where the fourth information includes calling interface information of the knowledge information, or the fourth information includes the first information and calling interface information of the knowledge information; The third device sends seventh information to the target device, where the seventh information includes calling interface information of the knowledge information; The third device receives the second information sent by the target device.

14. The method according to claim 12 or 13, wherein: The third device is a terminal, and the method further includes: The third device sends at least one of the eighth information and the first capability information to the network side device, wherein the first model is deployed on the third device, the eighth information is used to indicate the auxiliary information required for the third device to perform reasoning based on the first model, and the first capability information is used to indicate the capability information of the third device to perform reasoning based on the first model.

15. The method according to claim 14, wherein: The eighth information includes at least one of the following: The third device indicates a network element where knowledge information required for reasoning based on the first model is located; The third device indicates the knowledge information required for reasoning based on the first model; A calling interface indication of knowledge information required for the third device to perform reasoning based on the first model; an indication of a preprocessing method of the second information; The format of the second information is indicated.

16. The method according to claim 14 or 15, wherein: The first capability information includes at least one of the following: The third device indicates the knowledge information supported by reasoning based on the first model; A calling interface indication of knowledge information supported by reasoning performed by a third device based on the first model; an indication of a supported preprocessing method of the second information; An indication of a format of the second information supported.

17. The method according to any one of claims 14 to 16, wherein: The third device sends at least one of the eighth information and the first capability information to the network side device, including: The third device sends at least one of the eighth information and the first capability information to the network side device during the model registration process or the capability reporting process.

18. The method according to any one of claims 12 to 17, wherein: The method further comprises: The third device receives the first information sent by the second device, and deploys the first model on the third device.

19. The method according to any one of claims 12 to 18, wherein: The method further comprises: The third device uses the first model to perform inference based on the second information and input information of the first model.

20. The method according to any one of claims 12 to 19, wherein: The calling interface information includes at least one of the following: Calling interface name, calling interface input data format, calling interface output data format, calling interface input data.

21. A communication device, comprising: A communication unit, configured to receive first information sent by a second device, wherein the communication device stores at least one of knowledge information and calling interface information of the knowledge information; wherein the knowledge information includes at least one of the following: a knowledge graph, a knowledge vector library; and Sending second information to a third device, on which the first model is deployed, wherein the second information is determined according to the first information and knowledge information stored or called on the communication device; The first information includes at least one of the following: Auxiliary information used for reasoning with the first model; Requirement information for performing reasoning on the first model; input information of the first model; Calling interface information of knowledge information; an indication of a receiving device of the second information; An indication of samples corresponding to the input information of the first model.

22. A communication device, comprising: A communication unit, configured to send first information to a first device, wherein the first device stores at least one of knowledge information and calling interface information of the knowledge information, wherein the knowledge information includes at least one of the following: a knowledge graph and a knowledge vector library; The first information includes at least one of the following: Auxiliary information used for reasoning by the first model; Requirement information for the first model to perform reasoning; Input information of the first model; Calling interface information of knowledge information; a receiving device indication of the second information; An indication of samples corresponding to the input information of the first model.

23. A communication device, comprising: A communication unit, configured to obtain second information from a first device or a target device, wherein a first model is deployed on the communication apparatus 700, and at least one of knowledge information and calling interface information of knowledge information is stored on the first device or the target device, wherein the target device is a device accessed using the calling interface information, and the knowledge information includes at least one of the following: a knowledge graph and a knowledge vector library; Wherein, the second information is determined according to the first information and the knowledge information stored or called on the first device; The first information includes at least one of the following: Auxiliary information used for reasoning with the first model; Requirement information for performing reasoning on the first model; input information of the first model; Calling interface information of knowledge information; an indication of a receiving device of the second information; An indication of samples corresponding to the input information of the first model.

24. A communication device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the steps in the method according to any one of claims 1 to 8, or the steps in the method according to any one of claims 9 to 11, or the steps in the method according to any one of claims 12 to 20 are implemented.

25. A readable storage medium storing a program or instruction, wherein the program or instruction, when executed by a processor, implements the steps in the method as claimed in any one of claims 1 to 8, or the steps in the method as claimed in any one of claims 9 to 11, or the steps in the method as claimed in any one of claims 12 to 20.

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