Model determination method and apparatus, and communication device

Abstract

The present application relates to the technical field of communications, and discloses a model determination method and apparatus, and a communication device. The model determination method in embodiments of the present application comprises: a first device determines a first model on the basis of first information; and the first device activates the first model. The first information comprises any one of the following: scenario information where a terminal device is located, and a mapping relationship between a machine learning model and the scenario information; the first device being the terminal device or a network side device; and model information of the machine learning model associated with the scenario information where the terminal device is located.

Description

Model determination method, device and communication equipment

[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 202311550325.2 and titled “Model determination method, device and communication equipment,” 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 determination method, device and communication equipment. Background Art

[0004] Current 3GPP discussions have confirmed that model supervision is a key component of machine learning model lifecycle management. However, existing model supervision methods based on model input / output only indicate whether the current model is valid, without specifying which machine learning model should be applied to the current terminal device environment. Depending on the environment in which a terminal device operates, certain data information, such as location information and communication data, will change, and the data characteristics will also change accordingly. If the current terminal device environment does not match the machine learning model running on the terminal device or network-side equipment, it will affect data processing efficiency and accuracy, and thus the terminal device's communication quality. Summary of the Invention

[0005] The embodiments of the present application provide a model determination method, apparatus, and communication device, which can solve the problem in related technologies that it is impossible to indicate which machine learning model should be applied to the environment in which the current terminal device is located.

[0006] In a first aspect, a model determination method is provided, comprising:

[0007] The first device determines a first model based on the first information;

[0008] activating the first model by the first device;

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

[0010] Scenario information of the terminal device, and a mapping relationship between the machine learning model and the scenario information; the first device is the terminal device or a network-side device;

[0011] Model information of a machine learning model associated with scene information in which the terminal device is located.

[0012] In a second aspect, a data transmission method is provided, comprising:

[0013] The second device sends sixth information to the first device, where the sixth information includes at least one of the following:

[0014] third information, where the third information is used to indicate an association relationship between the position coordinates and the scene information;

[0015] A first indication, where the first indication is used to indicate scenario information of a terminal device;

[0016] a second indication, where the second indication is used to indicate a mapping relationship between the machine learning model and the scene information;

[0017] A third indication is used to indicate a machine learning model associated with the scene information in which the terminal device is located.

[0018] In a third aspect, a model determination apparatus is provided, applied to a first device, the apparatus comprising:

[0019] A model determination module, configured to determine a first model based on the first information;

[0020] A model activation module, configured to activate the first model;

[0021] The first information includes any one of the following:

[0022] Scenario information of the terminal device, and a mapping relationship between the machine learning model and the scenario information; the first device is the terminal device or a network-side device;

[0023] Model information of a machine learning model associated with scene information in which the terminal device is located.

[0024] In a fourth aspect, a data transmission apparatus is provided, applied to a second device, the apparatus comprising:

[0025] An information sending module is configured to send sixth information to the first device, where the sixth information includes at least one of the following:

[0026] third information, where the third information is used to indicate an association relationship between the position coordinates and the scene information;

[0027] A first indication, where the first indication is used to indicate scenario information of a terminal device;

[0028] a second indication, where the second indication is used to indicate a mapping relationship between the machine learning model and the scene information;

[0029] A third indication is used to indicate a machine learning model associated with the scene information in which the terminal device is located.

[0030] In a fifth aspect, a communication device is provided, which includes a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the program or instructions are executed by the processor, the steps of the model determination method as described in the first aspect are implemented, or the steps of the data transmission method as described in the second aspect are implemented.

[0031] In the sixth aspect, a model determination system is provided, comprising: a first device and a second device, wherein the first device can be used to execute the steps of the model determination method as described in the first aspect above, and the second device can be used to execute the steps of the data transmission method as described in the second aspect above.

[0032] In the seventh 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 model determination method as described in the first aspect are implemented, or the steps of the data transmission method as described in the second aspect are implemented.

[0033] In an eighth 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 a program or instruction to implement the method described in the first aspect, or to implement the method described in the second aspect.

[0034] In a ninth aspect, a computer program / program product is provided, wherein the computer program / program product is stored in a storage medium and is executed by at least one processor to implement the steps of the method described in the first aspect or the second aspect.

[0035] In a tenth aspect, a model determination apparatus / device is provided, which includes the apparatus / device (configured to) be used to execute to implement the steps of the model determination method as described in the first aspect.

[0036] In the eleventh aspect, a data transmission apparatus / device is provided, which includes the apparatus / device (configured to) be used to execute to implement the steps of the data transmission method as described in the first aspect.

[0037] The embodiment of the present application associates the machine learning model with the scene. The first device can determine which model should be applied in the current environment based on the scene information of the terminal device and the mapping relationship between the machine learning model and the scene information; or the first device can directly determine the machine learning model associated with the scene information of the current terminal device based on the model information in the first information and activate the model. In the embodiment of the present application, the first device can determine which model should be applied based on the first information, thereby ensuring that the machine learning model running in the terminal device or the network-side device can always adapt to the scene of the terminal device during the movement of the terminal device, thereby ensuring the accuracy and efficiency of data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] FIG1 is a block diagram of a wireless communication system to which embodiments of the present application may be applied;

[0039] FIG2 is a flow chart of a model determination method in an embodiment of the present application;

[0040] FIG3 is a schematic diagram of the structure of a neural network model in an embodiment of the present application;

[0041] FIG4 is a schematic diagram of a neuron in an embodiment of the present application;

[0042] FIG5 is a flow chart of a model determination method in an embodiment of the present application;

[0043] FIG6 is a flow chart of another model determination method in an embodiment of the present application;

[0044] FIG7 is a flow chart of a model determination method according to an embodiment of the present application;

[0045] FIG8 is a flow chart of another model determination method in an embodiment of the present application;

[0046] FIG9 is a flow chart of a data transmission method according to an embodiment of the present application;

[0047] FIG10 is a structural block diagram of a model determination device in an embodiment of the present application;

[0048] FIG11 is a structural block diagram of a data transmission device according to an embodiment of the present application;

[0049] FIG12 is a structural block diagram of a communication device in an embodiment of the present application;

[0050] FIG13 is a block diagram of a terminal device according to an embodiment of the present application;

[0051] FIG14 is a structural block diagram of a network-side device in an embodiment of the present application;

[0052] FIG15 is a structural block diagram of another network-side device in an embodiment of the present application. DETAILED DESCRIPTION

[0053] 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.

[0054] The terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of this application can be implemented in an order other than that illustrated or described herein, and that the objects distinguished by "first" and "second" are generally of the same type, and do not limit the number of objects. For example, the first object can be one or more. In addition, the term "and / or" in the specification and claims refers to at least one of the connected objects, and the character " / " generally indicates that the objects connected are in an "or" relationship.

[0055] 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) and 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 the NR terminology is used in most of the following description, but these technologies can also be applied to applications other than NR system applications, such as 6th generation (6G) systems. th Generation, 6G) communication system.

[0056] FIG1 shows a block diagram of a wireless communication system applicable to embodiments of the present application. The wireless communication system includes a terminal device 11 and a network-side device 12 . The terminal device 11 may be a mobile phone, a tablet computer (Tablet Personal Computer), a laptop computer (Laptop Computer) or 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) / virtual reality (VR) device, a robot, a wearable device (Wearable Device), a vehicle-mounted device (VUE), a pedestrian terminal (PUE), a smart home (home appliances with wireless communication functions, such as refrigerators, televisions, washing machines, or furniture, etc.), a game console, a personal computer (PC), an ATM or a self-service machine, and 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. It should be noted that the specific type of the terminal device 11 is not limited in the embodiments of the present application. The network side device 12 may include an access network device or a core network device, wherein the access network device 12 may also be referred to as a radio access network device, a radio access network (RAN), a radio access network function, or a radio access network unit. The access network device 12 may include a base station, a WLAN access point, or a WiFi node, etc. The base station may be referred to as a node B, an evolved node B (eNB), an access point, a base transceiver station (BTS), a radio base station, a radio transceiver, a basic service set (BSS), an extended service set (ESS), a home node B, a home evolved node B, a transmitting and receiving point (TRP), or other appropriate terms in the field. As long as the same technical effect is achieved, the base station is not limited to a 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.The core network equipment 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 service discovery function (EASDF), unified data management (UDM), unified data storage (UDR), home subscriber server (HSS), centralized network configuration (CNC), network storage function (NRF), network exposure function (NEF), local NEF (L-NEF), binding support function (BSF), application function ( It should be noted that in the embodiments 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.

[0057] The model determination method provided in the embodiments of the present application is described in detail below through some embodiments and their application scenarios in conjunction with the accompanying drawings.

[0058] The present application provides a model determination method. Referring to FIG2 , a flow chart of a model determination method provided by the present application is shown. The method is applied to a first device, as shown in FIG2 , and the method may specifically include:

[0059] Step 201: A first device determines a first model based on first information.

[0060] Step 202: The first device activates the first model.

[0061] The first information includes any one of the following:

[0062] Scenario information of the terminal device, and a mapping relationship between the machine learning model and the scenario information; the first device is the terminal device or a network-side device;

[0063] Model information of a machine learning model associated with scene information in which the terminal device is located.

[0064] It should be noted that, in an embodiment of the present application, the first device may be a terminal device or a network side device. The terminal device may include a conventional terminal device and / or a positioning reference unit. Among them, the conventional terminal device may be the terminal device 11 in Figure 1. The positioning reference unit (PRU) can perform positioning measurements, such as reference signal time difference (RSTD), reference signal receiving power (RSRP), UE Rx-Tx time difference measurement, etc., and report these measurement results to the positioning server. In addition, the PRU can send a positioning reference signal (PRS) to a transmission and receiving point (TRP), so that the TRP can measure and report uplink (UL) positioning measurement values ​​from a PRU at a known location, such as RTOA, UL-AoA, gNB Rx-Tx time difference, etc. The location server may compare the PRU measurements with expected measurements at known PRU locations to determine correction terms for other nearby target devices, and then correct the DL and / or UL location measurements of the other target devices based on the correction terms.

[0065] The network side device can be the access network device in Figure 1, such as a base station or a newly defined artificial intelligence processing node on the access network side, or it can be the core network device in Figure 1, such as a network data analysis function (Network Data Analytics Function, NWDAF), a positioning management function (Location Management Function, LMF), or a newly defined processing node on the core network side, or it can be a combination of the above multiple nodes.

[0066] In an embodiment of the present application, the machine learning model can be trained by a network-side device, and the network-side device sends the trained machine learning model to the terminal device through model transfer / delivery. The network-side device records the association between the model identification of each machine learning model and the scenario information.

[0067] Alternatively, the machine learning model is trained by a third-party server, which sends the trained machine learning model to the terminal device and / or network side device, and sends the association between the model identifier of the machine learning model and the scenario information to the terminal device and / or network side device.

[0068] It should be noted that the machine learning model in the embodiment of the present application can be an artificial intelligence (AI) model, such as any one of a fully connected neural network, a convolutional neural network, a decision tree, a support vector machine, and a Bayesian classifier. Taking a neural network model as an example, its schematic diagram can be shown in Figure 3. As shown in Figure 3, the neural network may include one or more input layers, one or more hidden layers, and an output layer. The data to be processed [X1, X2…Xn] are respectively input into the neural network from the corresponding input layer, and after processing the input layer, hidden layer, and output layer, the output result Y is obtained. In addition, the neural network is composed of neurons, and a schematic diagram of the neuron is shown in Figure 4. In Figure 4, a1, a2,…aK represents input, w represents weight (i.e., multiplicative coefficient), b represents bias (i.e., additive coefficient), and σ (.) represents activation function. Common activation functions include Sigmoid (mapping variables between 0 and 1), tanh (translation and contraction of Sigmoid), linear rectification function / rectified linear unit (Rectified Linear Unit, ReLU), etc.

[0069] In addition, taking the neural network model as an example, the model training process is introduced as follows:

[0070] The parameters of a neural network can be optimized using a gradient optimization algorithm. A gradient optimization algorithm is a type of algorithm that minimizes or maximizes an objective function (sometimes also called a loss function), which is often a mathematical combination of model parameters and data. For example, given data X and its corresponding label Y, a neural network model f(.) can be constructed. Based on the input x, the predicted output f(x) can be obtained, and the difference between the predicted value and the true value (fx-Y) can be calculated. This is the loss function. The optimization goal of the gradient optimization algorithm is to find the appropriate w (i.e., weight) and b (i.e., bias) to minimize the value of the aforementioned loss function. The smaller the loss value, the closer the model is to the true situation.

[0071] Currently, most common optimization algorithms are based on the back propagation (BP) algorithm. The basic idea of ​​the BP algorithm is that the learning process consists of two steps: forward propagation of signals and back propagation of errors. During forward propagation, input samples are passed from the input layer, processed layer by layer through each hidden layer, and then transmitted to the output layer. If the actual output of the output layer does not match the expected output, the error begins back propagation. Back propagation involves propagating the output error back through the hidden layers to the input layer layer by layer in some form, distributing the error to all units in each layer. This error signal is then generated for each unit in each layer, and used as the basis for correcting the weights of each unit. This process of adjusting the weights of each layer, including forward propagation of signals and back propagation of errors, is repeated over and over again. This continuous adjustment of weights is the network's learning and training process. This process continues until the error in the network output is reduced to an acceptable level, or until a pre-set number of learning cycles has been completed.

[0072] In addition, common optimization algorithms include gradient descent, stochastic gradient descent (SGD), mini-batch gradient descent, momentum method (Momentum), Nesterov (the name of the inventor, specifically stochastic gradient descent with momentum), adaptive gradient descent (ADAptive GRADient descent, Adagrad), Adagrad's extended algorithm (Adadelta), root mean square error deceleration (root mean square prop, RMSprop), Adaptive Moment Estimation (Addam), etc.

[0073] When these optimization algorithms backpropagate errors, they all calculate the derivative / partial derivative of the current neuron based on the error / loss obtained by the loss function, add the influence of the learning rate, the previous gradient / derivative / partial derivative, etc., obtain the gradient, and pass the gradient to the previous layer.

[0074] In the embodiments of the present application, the machine learning model may also be referred to as an AI unit, an AI model, an ML (machine learning) model, an ML unit, an AI structure, an AI function, an AI characteristic, a neural network, a neural network function, a neural network function, etc., or the AI ​​unit / AI model may also refer to a processing unit capable of implementing specific algorithms, formulas, processing flows, capabilities, etc. related to AI, or the AI ​​unit / AI model may be a processing method, algorithm, function, module or unit for a specific data set, or the AI ​​unit / AI model may be a processing method, algorithm, function, module or unit running on AI / ML related hardware such as a GPU, NPU, TPU, ASIC, etc., and the present invention does not make specific limitations on this. Optionally, the specific data set includes the input and / or output of the AI ​​unit / AI model.

[0075] Optionally, the identifier of the AI ​​unit / AI model may be an AI model identifier, an AI structure identifier, an AI algorithm identifier, or an identifier of a specific data set associated with the AI ​​unit / AI model, or an identifier of a specific scenario, environment, channel feature, or device related to the AI / ML, or an identifier of a function, feature, capability, or module related to the AI / ML. This embodiment of the present application does not specifically limit this.

[0076] In an embodiment of the present application, the first device may determine a first model based on the first information. For example, the first device may determine a machine learning model associated with the scene information of the terminal device based on the scene information of the terminal device and the mapping relationship between the machine learning model and the scene information, and determine the model as the first model. Alternatively, if the first information includes model information of a machine learning model associated with the scene information of the terminal device, the first device may directly determine the machine learning model indicated by the model information as the first model.

[0077] After determining the first model, the first device can activate the first model, thereby using the first model to process data generated by the terminal device in the current scenario.

[0078] It is understandable that, in the case where the first device is on the network side, the machine learning model running in the first device can be used to process data corresponding to the terminal device, such as determining the location information of the terminal device, analyzing the communication quality of the cell where the terminal device is located, performing access control on the terminal device, etc. When the scenario in which the terminal device is located changes, the machine learning model running in the first device may not meet the data processing requirements of the current scenario in which the terminal device is located. In this case, the terminal device can determine the first model that matches the current scenario in which the terminal device is located based on the first information and activate it.

[0079] It should be noted that, in an embodiment of the present application, the scene information of the terminal device may include but is not limited to the scene identifier (scenario ID), scene information (scenario information), scenario category (scenario category), area identifier (area ID), area information (area information), area category (area category), data set identifier (dataset ID), data set information (dataset information), data set category (dataset category), etc. of the scene in which the terminal device is located. Among them, the granularity of the scene or area or data set may be a cell. In one possible implementation, the scene ID, area ID, and data set ID may be associated with the physical cell identifier (Physical Cell Identifier, PCI) of one or more cells, thereby determining the scene ID, area ID, and data set ID corresponding to the first device based on the cell in which the first device is located. In another possible implementation, the granularity of the scene or area or data set may also be smaller than the cell. For example, in AI positioning, a scene may be a factory building, a building, or even a floor in a building within a cell.

[0080] A machine learning model can correspond to one or more scenarios, regions, or datasets.

[0081] In an embodiment of the present invention, if the first model determined by the first device based on the first information is different from the machine learning model currently running in the first device, the first device can determine that the currently running machine learning model can no longer meet the computing requirements of the current scenario. In other words, in the scenario currently located by the first device, the machine learning model currently running in the first device is an invalid model. In this case, the first device can switch the currently running machine learning model to the first model.

[0082] The embodiment of the present application associates the machine learning model with the scene. The first device can determine which model should be applied in the current environment based on the scene information of the terminal device and the mapping relationship between the machine learning model and the scene information; or the first device can directly determine the machine learning model associated with the scene information of the current terminal device based on the model information in the first information and activate the model. In the embodiment of the present application, the first device can determine which model should be applied based on the first information, thereby ensuring that during the movement of the terminal device, the machine learning model running in the terminal device or the network-side device can always adapt to the scene of the terminal device, thereby ensuring the accuracy and efficiency of data processing.

[0083] Optionally, before the first device determines the first model based on the first information, the method further includes:

[0084] The first device obtains scene information of the terminal device;

[0085] The first device obtains a mapping relationship between the machine learning model and the scene information.

[0086] In an embodiment of the present application, the mapping relationship between the machine learning model and the scene information can be generated by a network-side device or a third-party server that trains the model. For example, if the first device is a terminal device, the mapping relationship between the machine learning model and the scene information can be sent to the first device by the network-side device or the third-party server that trains the model; if the first device is a network-side device, then when the network-side device performs model training, the network-side device can locally generate the mapping relationship between the machine learning model and the scene information based on the model training process; when the model training is performed by a third-party server, the network-side device can read the mapping relationship between the machine learning model and the scene information from the third-party server.

[0087] Similarly, the first device can also obtain the scene information of the terminal device in a variety of ways.

[0088] As an example, the first device obtains the scene information of the terminal device, including:

[0089] Step S11: The first device obtains second information, where the second information is used to indicate communication information of the terminal device, and the second information is associated with scene information of the terminal device;

[0090] Step S12: The first device determines the scenario information of the terminal device based on the second information.

[0091] The second information is associated with the scene in which the first device is located. For example, the second information is associated with information such as the scene ID, area ID, data set ID, scene category, area category, and data set category that the first device is currently located in. As an example, the second information may include a cell ID, reference signal ID, transmitting and receiving point ID, area ID, and tracking area ID corresponding to the first device.

[0092] In an embodiment of the present application, the first device can determine the scene information of the terminal device based on the second information.

[0093] Optionally, when the first device is a terminal device, the first device acquiring the second information includes:

[0094] The first device measures a reference signal and determines second information based on the measurement result.

[0095] 5 , a flow chart of a model determination method provided by an embodiment of the present application is shown. As shown in FIG5 , if the first device is a terminal device, the first device can determine the second information by measuring a reference signal.

[0096] Optionally, when the first device is a network-side device, the first device acquiring the second information includes:

[0097] The first device receives second information sent by the terminal device.

[0098] Referring to Figure 6 , a flow chart illustrating another model determination method provided by an embodiment of the present application is shown. As shown in Figure 6 , if the first device is a network-side device, the second information can be generated by the terminal device based on the reference signal measurement results and then sent to the network-side device. The network-side device itself does not need to perform any measurement operations on the reference signal.

[0099] Optionally, the first device determining the scenario information of the terminal device according to the second information includes:

[0100] The first device acquires an association relationship between the communication information and the scene information;

[0101] The first device determines the scene information of the terminal device based on the second information and the association between the communicated information and the scene information.

[0102] The association between the communication information and the scenario can be sent by the second device to the first device or specified by the protocol. If the first device is a network-side device, such as an access network device, the second device can be a core network device. If the first device is a terminal device, the second device can be a network-side device or a higher-level layer of the terminal device.

[0103] After the first device obtains the second information, it can determine the scene information of the terminal device based on the communication information indicated by the second information and the association between the communication information and the scene information.

[0104] Exemplarily, as shown in Figure 6, if the first device is a network side device, after the network side device receives the second information reported by the terminal device, it can determine the scene information of the terminal device based on the communication information indicated by the second information, and the association between the communication information and the scene information, and then further combine the association between the machine learning model and the scene information to determine the first model and activate it.

[0105] As shown in Figure 5, if the first device is a terminal device, after the terminal device determines the second information by measuring the reference signal, it can determine the scene information of the terminal device based on the communication information indicated by the second information and the association between the communication information and the scene information. Then, it can further determine the first model and activate it by combining the association between the machine learning model and the scene information. Alternatively, the terminal device can also send the second information to the network-side device, and the network-side device can determine the scene information of the terminal device based on the second information and indicate it to the terminal device.

[0106] Optionally, the first device determining the scenario information of the terminal device according to the second information includes:

[0107] Step S21: The first device sends the second information to a network-side device;

[0108] Step S22: The first device receives a first indication sent by the network side device; the first indication is used to indicate scenario information of the first device.

[0109] As shown in Figure 5, in one possible application scenario of the present application, the first device is a terminal device. The terminal device can determine second information by measuring a reference signal and send the second information to a network-side device. Based on the second information and the association between the communication information and the scenario information, the network-side device determines the scenario information currently located by the terminal device and indicates the scenario information to the terminal device via a first indication. The terminal device then determines and activates the first model based on the scenario information indicated by the first indication and the association between the machine learning model and the scenario information.

[0110] Alternatively, the network side device determines the scene information in which the terminal device is currently located based on the communication information indicated by the second information and the association between the communication information and the scene information, and further determines the machine learning model associated with the scene information in which the terminal device is currently located based on the association between the machine learning model and the scene information, that is, the first model, and indicates the first model to the terminal device through a third indication.

[0111] In an optional embodiment of the present application, the first device obtains the scene information of the terminal device, including:

[0112] The first device receives a first indication and a second indication sent by a second device, where the first indication is used to indicate the scene information of the terminal device; and the second indication is used to indicate a mapping relationship between a machine learning model and the scene information.

[0113] In another possible application scenario of the present application, the scene information of the terminal device and the mapping relationship between the machine learning model and the scene information can also be indicated to the first device by the second device.

[0114] It should be noted that the second device in the embodiments of the present application can be a network-side device or a higher-level device of a terminal device. For example, if the first device is a network-side device, such as an access network device, the second device can be a core network device; if the first device is a terminal device, the second device can be a network-side device or a higher-level device of the terminal device.

[0115] As an example, the first indication and the second indication may be carried in the same signaling, and the second device may send the first indication and the second indication to the first device at the same time through a certain signaling, and the first device may determine the first model and activate it based on the received first indication and second indication. Alternatively, the second device may send the first indication to the first device through one signaling and send the second indication to the first device through other signaling. The signaling carrying the first indication and / or the second indication may include but is not limited to: Radio Resource Control Protocol (RRC) signaling, Radio Link Control Protocol (RLA) signaling, Media Access Control (MAC) signaling, LTE Positioning Protocol (LPP) signaling, NR Positioning Protocol A (NRPPa) signaling, downlink control information (DCI), etc.

[0116] As another example, the first indication is sent by the second device to the first device, the machine learning model is trained by a third-party server, and the third-party server sends the second indication to the first device.

[0117] Optionally, before the first device determines the first model based on the first information, the method further includes:

[0118] The first device receives a third indication sent by the second device, where the third indication is used to indicate a machine learning model associated with the scene information in which the terminal device is located.

[0119] In an embodiment of the present application, the third indication may be sent by the second device to the first device. After receiving the third indication, the first device may directly determine the machine learning model indicated by the third indication as the first model to be activated.

[0120] It is understandable that the second device can determine the scene information of the terminal device based on the location information of the terminal device, and then determine the machine learning model that matches the scene currently located by the terminal device based on the association between the scene information and the machine learning model, generate a third indication, and send it to the first device. Alternatively, the first device can send the scene information of the terminal device to the second device, and the second device can determine the machine learning model associated with the scene currently located by the terminal device based on the scene information and the association between the machine learning model and the scene information, generate a third indication, and send it to the first device.

[0121] Optionally, the first device measures the reference signal, and determines the second information based on the measurement result, including:

[0122] Step S31: Upon receiving a fourth instruction, the first device measures a first reference signal; the fourth instruction is used to instruct the first device to measure at least one reference signal;

[0123] Step S32: The first device determines the second information based on a first measurement result of the first reference signal.

[0124] In a possible application scenario, the first device is a terminal device. The terminal device may measure the first reference signal upon receiving the fourth indication, and determine the second information based on a first measurement result of the first reference signal.

[0125] It should be noted that the fourth indication may be sent by the second device to the first device, or may be sent by another device to the first device. In another possible application scenario, the fourth indication may also be automatically triggered by a higher layer of the first device when certain measurement conditions are met.

[0126] Among them, the first reference signal may include but is not limited to: positioning reference signal, downlink channel sounding reference signal (Channel-State-Information Reference Signal, CSI-RS), uplink sounding signal (Sounding Reference Signal, SRS), synchronization signal block (Synchronization Signal Block, SSB), time-frequency tracking reference signal (Tracking Reference Signal, TRS), etc.

[0127] Optionally, the first device measures the reference signal, and determines the second information based on the measurement result, including:

[0128] Step S41: The first device receives a second reference signal sent by a reference point;

[0129] Step S42: The first device measures the second reference signal, and determines second information based on a second measurement result of the second reference signal.

[0130] In an embodiment of the present application, the first device is a terminal device, and the terminal device may also measure a second reference signal from a reception reference point and determine second information based on a second measurement result of the second reference signal. As an example, the second information may include a cell ID, a reference signal ID, a reception reference point ID, a scenario ID, an area ID, a tracking area ID, etc. corresponding to the terminal device.

[0131] Optionally, the second information includes at least one of the following:

[0132] First communication information, the first parameter being used to indicate a communication resource of the terminal device;

[0133] The second communication information, the fifth parameter is used to indicate the communication area where the terminal device is located.

[0134] Optionally, the first communication information includes at least one of the following:

[0135] reference signal information of the first device;

[0136] Communication indicator information of the first device.

[0137] Optionally, the reference signal information includes at least one of the following:

[0138] a first parameter, wherein the second parameter is used to indicate a reference signal resource;

[0139] The second parameter, the third parameter is used to indicate reference signal measurement information;

[0140] The third parameter, the fourth parameter is used to indicate reference signal reporting information.

[0141] The first parameter may be a reference signal resource ID, a reference signal resource set ID, etc. The second parameter may be a reference signal measurement ID, a reference signal measurement configuration ID, etc. The third parameter may be a reference signal reporting ID, a reference signal reporting configuration ID.

[0142] Optionally, the communication indicator information includes at least one of the following:

[0143] a fourth parameter, wherein the first parameter is used to indicate channel quality;

[0144] Beam information;

[0145] Channel state information;

[0146] Multipath average delay;

[0147] Multipath delay spread.

[0148] Among them, the fourth parameter can be a statistical value or representation of signal quality, such as signal-to-noise ratio (SNR), signal to interference plus noise ratio (SINR), RSRP, reference signal received quality (RSRQ), signal power, noise power, interference power, etc.; or such as L1-RSRP, L1-SINR, L1-RSRP, L1-RSRQ, L3-RSRP, L3-SINR, L3-RSRP, L3-RSRQ, etc.

[0149] The beam information may include information such as a beam index and a beam direction.

[0150] In an optional embodiment of the present application, the first device measures the reference signal and determines the second information based on the measurement result, including:

[0151] Step S51: The first device measures a reference signal to obtain a measurement result;

[0152] Step S52: The first device determines a target reference signal resource according to the measurement result, and determines second information according to resource information of the target reference signal resource.

[0153] The target reference signal resource includes at least one of the following:

[0154] A1, N first target reference signal resources among the reference signal resources configured for each transmission / reception point; the reference signal received power of the N first target reference signal resources is greater than the reference signal received power of other reference signal resources at the same transmission / reception point; N is a positive integer;

[0155] A2. A second target reference signal resource selected from the reference signal resources configured for each transmitting and receiving point; the reference signal received power of the second target reference signal resource being greater than or equal to a preset threshold;

[0156] A3. Reference signal resources configured at each transmitting and receiving point.

[0157] In an embodiment of the present application, the first device is a terminal device, which can screen out N first target reference signal resources whose reference signal receiving power is greater than other reference signal resources of the same transmitting and receiving point from the reference signal resources configured for each transmitting and receiving point, and determine the second information based on the resource information of the first target reference signal resource, such as the reference signal ID, the reference signal measurement ID, the reference signal reporting ID and other information.

[0158] Alternatively, the terminal device may also filter out, from the reference signal resources configured for each transmitting and receiving point, a second target reference signal resource having a reference signal received power greater than or equal to a preset threshold, thereby determining the second information based on resource information of the second target reference signal resource, such as a reference signal ID, a reference signal measurement ID, a reference signal reporting ID, etc. The preset threshold may be indicated by a network-side device or specified by a protocol, and this embodiment of the present application does not specifically limit this.

[0159] Alternatively, the terminal device determines the second information based on the reference signal resources configured for each transmitting and receiving point, without screening the reference signal resources.

[0160] In an embodiment of the present application, the terminal device can screen the target reference signal resources based on any one of items A1 to A3, and determine the second information based on the screened target reference signal resources, and then determine the scene information of the terminal device. The determined scene information is adapted to the reference signal resources configured at the sending and receiving points, and meets the specific reference signal receiving power, thereby ensuring the reliability of the determined scene information, which is conducive to improving the reliability of the first model finally determined, thereby ensuring that during the movement of the terminal device, the machine learning model running in the terminal device can always adapt to the reference signal resources configured at the sending and receiving points.

[0161] Optionally, the resource information includes at least one of the following:

[0162] Reference signal received power;

[0163] Reference signal resource identifier;

[0164] Beam identification;

[0165] Beam direction.

[0166] In an embodiment of the present application, the terminal device can determine the second information based on resource information such as the reference signal receiving power, reference signal resource representation, beam identifier, beam direction, etc. of the target reference signal resource (including at least one item from A1 to A3).

[0167] In another optional embodiment of the present application, the first device obtains the scene information of the terminal device, including:

[0168] Step S61: The first device obtains location information of the terminal device, where the location information is associated with scene information of the terminal device.

[0169] Step S62: The first device determines the scene information of the terminal device based on the location information and the association between the location coordinates and the scene information.

[0170] In an embodiment of the present application, in addition to determining the scene information of the terminal device based on the second information, the first device can also determine the scene information of the terminal device based on the location information of the terminal device and the association between the location coordinates and the scene information.

[0171] It is understandable that the location information of the terminal device can be determined by the terminal device based on an AI model or other positioning methods, such as satellite positioning, GPS positioning system, Beidou positioning system, Bluetooth positioning, radar positioning, and other positioning methods based on mobile communication networks, such as positioning methods based on NR systems and LTE systems.

[0172] The association relationship between the location coordinates and the scene information can be determined by a network-side device, specified by a protocol, or sent by a second device to the first device. This embodiment of the present application does not specifically limit this. It should be noted that the second device can be a network-side device or a high-level terminal device. For example, when the first device is an access network device, such as a base station, the second device can be a core network device; when the first device is a terminal device, the second device can be a network-side device or a high-level terminal device.

[0173] Optionally, before the first device determines the scene information of the terminal device based on the location information and the association between the location coordinates and the scene information, the method further includes:

[0174] The first device receives third information sent by the second device, where the third information is used to indicate an association relationship between the position coordinates and the scene information.

[0175] In a possible application scenario of the present application, the second device may also indicate the association relationship between the location coordinates and the scene information to the first device through the third information. After the first device receives the third information, it can determine the scene information of the terminal device based on the location information of the terminal device and the association relationship between the location coordinates and the scene information, and then determine the first model based on the scene information and the association relationship between the machine learning model and the scene information.

[0176] Alternatively, the first device sends the determined scene information, such as scene ID, area ID, data set ID, etc., to the second device, and the second device determines a machine learning model that matches the scene information reported by the first device and indicates it to the first device.

[0177] Optionally, the first device acquiring the location information of the terminal device includes:

[0178] In the case where the first device is a terminal device, the first device determines current location information based on positioning technology;

[0179] In the case where the first device is a network-side device, the first device receives fourth information sent by the terminal device, where the fourth information is used to indicate location information of the terminal device.

[0180] Referring to Figure 7, a flowchart of a model determination method provided by an embodiment of the present application is shown. As shown in Figure 7, if the first device is a terminal device, then the location information of the terminal device can be determined by the terminal device itself based on an AI model or other positioning methods, such as satellite positioning, GPS positioning system, Beidou positioning system, Bluetooth positioning, radar positioning, and other positioning methods based on mobile communication networks, such as positioning methods based on NR systems and LTE systems.

[0181] After the terminal device determines the location information, it can determine the scene information by combining the association between the location coordinates and the scene information. Alternatively, the terminal device reports the location information to the network device via the fourth information. The network device determines the scene information of the terminal device based on the location information of the terminal device and the association between the location coordinates and the scene, and indicates the determined scene information to the terminal device via the first indication.

[0182] After the terminal device determines the scene information, it can further determine the first model based on the association between the machine learning model and the scene information. Alternatively, the network-side device can determine the scene information of the terminal device based on the location information reported by the terminal device, and further determine the machine learning model associated with the scene information of the terminal device based on the association between the machine learning model and the scene information, i.e., the first model, and indicate the first model to the terminal device through a third indication.

[0183] Referring to Figure 8 , a flow chart illustrating another model determination method provided in an embodiment of the present application is shown. As shown in Figure 8 , if the first device is a network-side device, the terminal device's location information may be reported to the first device by the terminal device via fourth information. Optionally, if the first device is a network-side device, the terminal device may simultaneously report the method for obtaining the location information and the reliability or confidence level to the first device.

[0184] After receiving the location information reported by the terminal device, the network-side device can determine the scene information of the terminal device based on the location information and the association between the location coordinates and the scene information. Furthermore, based on the association between the machine learning model and the scene information, the network-side device can determine a first model associated with the scene information of the terminal device.

[0185] Optionally, the first device activating the first model includes:

[0186] When a currently running second model does not match the first model, the first device deactivates the second model and activates the first model.

[0187] In an embodiment of the present application, if the second model currently running in the first device does not match the first model, it means that the second model currently running can no longer meet the data processing requirements of the first scene in which the first device is currently located. In this case, the first device can deactivate the second model and activate the first model.

[0188] It should be noted that the first model and the second model in this application are not limited to a certain AI model. In other words, the first model and the second model in this application may include one or more AI models, or may be an AI function, and an AI function may be associated with one or more AI models. Accordingly, deactivating the second model may be to simultaneously deactivate one or more AI models contained in the second model, or to simultaneously deactivate one or more AI functions referred to by the second model. Similarly, activating the first model may be to simultaneously activate one or more AI models contained in the first model, or to simultaneously activate one or more AI functions referred to by the first model.

[0189] In addition, the deactivation operation and the activation operation can be independent of each other. For example, if the first model determined by the first device based on the first information includes the machine learning model currently running in the first device, then the machine learning model currently running in the first device is valid and there is no need to perform a deactivation operation. In this case, the normal operation of the currently running machine learning model can be maintained, and then the AI ​​models and / or AI functions included in the first model, except for the currently running machine learning model, can be activated. Alternatively, if the AI ​​models and / or AI functions included in the first model only include the AI ​​models and / or AI functions currently running in the first device, then there is no need to perform the deactivation operation and the activation operation. Alternatively, if there is no AI model and / or AI function in the first device that matches the AI ​​model and / or AI function included in the first model, then the activation operation cannot be performed. Alternatively, if no AI model or AI function is currently running in the first device, then there is no need to perform a deactivation operation.

[0190] It should be noted that in the embodiment of the present application, if a certain AI function is deactivated, all AI models associated with the AI ​​function will become invalid; similarly, if a certain AI function is activated, all AI models associated with the AI ​​function will become valid models.

[0191] The embodiment of the present application deactivates the second model and activates the first model when the currently running second model does not match the first model, thereby ensuring that the machine learning model running in the first device can always adapt to the scenario in which the terminal device is located during the movement of the terminal device, thereby ensuring the accuracy and efficiency of data processing.

[0192] Optionally, when the first device is a terminal device, the method further includes:

[0193] The first device sends fifth information to the network side device.

[0194] The fifth information includes at least one of the following:

[0195] a model identifier of the first model;

[0196] a model identifier of the second model;

[0197] The activation time of the first model.

[0198] In an embodiment of the present application, after the terminal device determines the first model to be activated, it can send at least one of the model identifier of the deactivated second model, the model identifier of the first model to be activated, and the activation time of the first model to the network-side device via fifth information. The activation time of the first model is used to indicate the time of model switching. For example, model switching is performed after M time units, including deactivating the second model and activating the first model.

[0199] In summary, the embodiment of the present application provides a model determination method that associates a machine learning model with a scene. The first device can determine which model should be applied in the current environment based on the scene information of the terminal device and the mapping relationship between the machine learning model and the scene information; or the first device can directly determine the machine learning model associated with the scene information of the current terminal device based on the model information in the first information, and activate the model. In the embodiment of the present application, the first device can determine which model should be applied based on the first information, thereby ensuring that during the movement of the terminal device, the machine learning model running in the terminal device or the network-side device can always adapt to the scene in which the terminal device is located, thereby ensuring the accuracy and efficiency of data processing.

[0200] The present application provides a data transmission method. Referring to FIG9 , a flow chart of a data transmission method provided by the present application is shown. The method is applied to a second device, as shown in FIG9 , and the method may specifically include:

[0201] Step 501: The second device sends sixth information to the first device.

[0202] The six pieces of information include at least one of the following:

[0203] third information, where the third information is used to indicate an association relationship between the position coordinates and the scene information;

[0204] A first indication, where the first indication is used to indicate scene information in which the terminal device is located;

[0205] a second indication, where the second indication is used to indicate a mapping relationship between the machine learning model and the scene information;

[0206] A third indication is used to indicate a machine learning model associated with the scene information in which the terminal device is located.

[0207] It should be noted that the second device in the embodiments of the present application can be a network-side device or a higher-level device of a terminal device. For example, if the first device is a network-side device, such as an access network device, the second device can be a core network device; if the first device is a terminal device, the second device can be a network-side device or a higher-level device of the terminal device.

[0208] The third information is used to indicate the association between the location coordinates and the scene information. In one possible application scenario of the present application, the second device can indicate the association between the location coordinates and the scene information to the first device through the third information. After the first device receives the third information, it can determine the scene information of the terminal device based on the location information of the terminal device and the association between the location coordinates and the scene information, and then determine the first model based on the scene information and the association between the machine learning model and the scene information.

[0209] The first indication may include, but is not limited to, a scenario ID, scenario information, scenario category, area ID, area information, area category, dataset ID, dataset information, dataset category, etc. of the scenario in which the first device is located. Among them, the granularity of the scene, area, or dataset may be a cell. In one possible implementation, the scene ID, area ID, and dataset ID may be associated with the physical cell identifier (PCI) of one or more cells, thereby determining the scene ID, area ID, and dataset ID corresponding to the first device based on the cell in which the first device is located. In another possible implementation, the granularity of the scene, area, or dataset may also be smaller than a cell. For example, in AI positioning, a scene may be a factory building, a building, or even a floor in a building within a cell. A machine learning model may correspond to one or more scenes, areas, or datasets.

[0210] In an embodiment of the present application, the machine learning model can be trained by a second device, and the second device records the association between the model identifier of each machine learning model and the scene information. Alternatively, the machine learning model is trained by a third-party server, which sends the trained machine learning model to the first device and sends the association between the machine learning model and the scene information to the first device and / or the second device.

[0211] The second device can send the association relationship between the machine learning model and the scene information to the first device through the second indication, so that the first device determines the first model based on the second indication.

[0212] Alternatively, the second device may also determine a machine learning model associated with the scene information in which the terminal device is located based on the scene information in which the terminal device is located and the positional relationship between the machine learning model and the scene information, and indicate the model information of the model to the first device through a third indication. The third indication may include model information of the machine learning model associated with the scene information in which the terminal device is located, such as a model identifier.

[0213] In summary, an embodiment of the present application provides a data transmission method, whereby the second device can send at least one of the association relationship between the location coordinates and the scene information, the scene information where the terminal device is located, the mapping relationship between the machine learning model and the scene information, and the model information of the machine learning model associated with the scene information where the terminal device is located to the first device through the sixth information, so that the first device can determine which model should be applied in the scene where the terminal device is currently located based on the received sixth information.

[0214] The model determination method provided in the embodiment of the present application can be executed by a model determination device. In the embodiment of the present application, the model determination device provided in the embodiment of the present application is described by taking the execution of the model determination method by the model determination device as an example.

[0215] The embodiment of the present application provides a model determination device. Referring to FIG10 , a structural block diagram of a model determination device provided by the embodiment of the present application is shown, and the device can be applied to a first device. As shown in FIG10 , the device may specifically include:

[0216] A model determination module 601 is configured to determine a first model based on the first information;

[0217] A model activation module 602 is used to activate the first model;

[0218] The first information includes any one of the following:

[0219] Scenario information of the terminal device, and a mapping relationship between the machine learning model and the scenario information; the first device is the terminal device or a network-side device;

[0220] Model information of a machine learning model associated with scene information in which the terminal device is located.

[0221] Optionally, the device further comprises:

[0222] A scene information acquisition module, used to obtain scene information of the terminal device;

[0223] The first relationship acquisition module is used to obtain the mapping relationship between the machine learning model and the scene information.

[0224] Optionally, the scene information acquisition module includes:

[0225] A first acquisition submodule is configured to acquire second information, where the second information is used to indicate communication information of the terminal device, and the second information is associated with scene information of the terminal device;

[0226] The first determining submodule is configured to determine the scenario information of the terminal device according to the second information.

[0227] Optionally, when the first device is a terminal device, the first acquiring submodule includes:

[0228] The measuring unit is configured to measure the reference signal and determine the second information based on the measurement result.

[0229] Optionally, when the first device is a network-side device, the first acquiring submodule includes:

[0230] The first receiving unit is used to receive second information sent by the terminal device.

[0231] Optionally, the first determining submodule includes:

[0232] A first acquiring unit, configured for the first device to acquire an association relationship between the communication information and the scene information;

[0233] The first determining unit is used to determine the scene information of the terminal device according to the second information and the association relationship between the communicated information and the scene information.

[0234] Optionally, the first determining submodule includes:

[0235] A first sending unit, configured to send the second information to a network-side device;

[0236] The second receiving unit is used to receive a first indication sent by the network side device; the first indication is used to indicate scenario information of the first device.

[0237] Optionally, the scene information acquisition module includes:

[0238] A second acquisition submodule is used to acquire location information of the terminal device, where the location information is associated with scene information where the terminal device is located;

[0239] The second determining submodule is used to determine the scene information of the terminal device based on the position information and the association relationship between the position coordinates and the scene information.

[0240] Optionally, the scene information acquisition module further includes:

[0241] The first receiving submodule is used to receive third information sent by the second device, where the third information is used to indicate an association relationship between the position coordinates and the scene information.

[0242] Optionally, the second acquisition submodule includes:

[0243] a second determining unit, configured to determine current location information based on positioning technology when the first device is a terminal device;

[0244] The third receiving unit is used to receive fourth information sent by the terminal device when the first device is a network side device, and the fourth information is used to indicate the location information of the terminal device.

[0245] Optionally, the scene information acquisition module includes:

[0246] The second receiving submodule is used to receive a first indication and a second indication sent by a second device, where the first indication is used to indicate the scene information of the terminal device; and the second indication is used to indicate the mapping relationship between the machine learning model and the scene information.

[0247] Optionally, the device further comprises:

[0248] A third indication receiving module is used to receive a third indication sent by a second device, where the third indication is used to indicate a machine learning model associated with the scene information in which the terminal device is located.

[0249] Optionally, the measuring unit is specifically configured to:

[0250] Upon receiving a fourth indication, measuring the first reference signal; the fourth indication is used to instruct the first device to measure at least one reference signal;

[0251] The second information is determined based on a first measurement result of the first reference signal.

[0252] Optionally, the measuring unit is specifically configured to:

[0253] receiving a second reference signal sent by the reference point;

[0254] The second reference signal is measured, and the second information is determined based on a second measurement result of the second reference signal.

[0255] Optionally, the second information includes at least one of the following:

[0256] First communication information, the first parameter being used to indicate a communication resource of the terminal device;

[0257] The second communication information, the fifth parameter is used to indicate the communication area where the terminal device is located.

[0258] Optionally, the first communication information includes at least one of the following:

[0259] reference signal information of the first device;

[0260] Communication indicator information of the first device.

[0261] Optionally, the reference signal information includes at least one of the following:

[0262] a first parameter, wherein the second parameter is used to indicate a reference signal resource;

[0263] The second parameter, the third parameter is used to indicate reference signal measurement information;

[0264] The third parameter, the fourth parameter is used to indicate reference signal reporting information.

[0265] Optionally, the communication indicator information includes at least one of the following:

[0266] a fourth parameter, wherein the first parameter is used to indicate channel quality;

[0267] Beam information;

[0268] Channel state information;

[0269] Multipath average delay;

[0270] Multipath delay spread.

[0271] Optionally, the measuring unit is specifically configured to:

[0272] measuring a reference signal to obtain a measurement result;

[0273] determining a target reference signal resource according to the measurement result, and determining second information according to resource information of the target reference signal resource;

[0274] The target reference signal resource includes at least one of the following:

[0275] N first target reference signal resources among the reference signal resources configured for each transmission / reception point; the reference signal received power of the N first target reference signal resources is greater than the reference signal received power of other reference signal resources at the same transmission / reception point; N is a positive integer;

[0276] A second target reference signal resource selected from the reference signal resources configured for each transmitting and receiving point; the reference signal received power of the second target reference signal resource being greater than or equal to a preset threshold;

[0277] Reference signal resources configured for each transmitting and receiving point.

[0278] Optionally, the resource information includes at least one of the following:

[0279] Reference signal received power;

[0280] Reference signal resource identifier;

[0281] Beam identification;

[0282] Beam direction.

[0283] Optionally, the model activation module includes:

[0284] The model activation submodule is used to deactivate the second model and activate the first model when the currently running second model does not match the first model.

[0285] Optionally, the device further comprises:

[0286] A fifth information sending module is configured to send fifth information to the network-side device, where the fifth information includes at least one of the following:

[0287] a model identifier of the first model;

[0288] a model identifier of the second model;

[0289] The activation time of the first model.

[0290] The model determination device in the embodiment of the present application can be an electronic device, such as an electronic device with an operating system, or a component in the electronic device, such as an integrated circuit or a chip.

[0291] The model determination device provided in the embodiment of the present application can implement the various processes implemented in the aforementioned method embodiment and achieve the same technical effect. To avoid repetition, it will not be described here.

[0292] The embodiment of the present application provides a data transmission device. Referring to FIG11 , a block diagram of a data transmission device provided by the embodiment of the present application is shown, which can be applied to a second device. As shown in FIG11 , the device may specifically include:

[0293] The information sending module 701 is configured to send sixth information to the first device.

[0294] The six pieces of information include at least one of the following:

[0295] third information, where the third information is used to indicate an association relationship between the position coordinates and the scene information;

[0296] A first indication, where the first indication is used to indicate scenario information of a terminal device;

[0297] a second indication, where the second indication is used to indicate a mapping relationship between the machine learning model and the scene information;

[0298] A third indication is used to indicate a machine learning model associated with the scene information in which the terminal device is located.

[0299] The data transmission device in the embodiment of the present application can be an electronic device, such as an electronic device with an operating system, or a component in the electronic device, such as an integrated circuit or a chip.

[0300] The data transmission device provided in the embodiment of the present application can implement the various processes implemented in the aforementioned method embodiment and achieve the same technical effect. To avoid repetition, it will not be described here.

[0301] Optionally, as shown in Figure 12, an embodiment of the present application further provides a communication device 900, including a processor 901 and a memory 902, wherein the memory 902 stores a program or instruction that can be run on the processor 901. For example, when the communication device 900 is a network side device, the program or instruction is executed by the processor 901 to implement the various steps of the aforementioned model determination method embodiment, or to implement the various steps of the aforementioned data transmission method embodiment, and can achieve the same technical effect. When the communication device 900 is a terminal device, the program or instruction is executed by the processor 901 to implement the various steps of the aforementioned model determination method embodiment, or to implement the various steps of the aforementioned data transmission method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0302] As shown in FIG13 , it is a schematic diagram of the hardware structure of a terminal device implementing an embodiment of the present application.

[0303] The terminal device 1000 includes but is not limited to: a radio frequency unit 1001, a network module 1002, an audio output unit 1003, an input unit 1004, a sensor 1005, a display unit 1006, a user input unit 1007, an interface unit 1008, a memory 1009 and at least some of the components of the processor 1010.

[0304] Those skilled in the art will appreciate that the terminal device 1000 may also include a power supply (such as a battery) to power various components. The power supply may be logically connected to the processor 1010 via a power management system, thereby enabling the power management system to manage charging, discharging, and power consumption. The terminal device structure shown in FIG13 does not limit the terminal device. The terminal device may include more or fewer components than shown, or may combine certain components, or have different component arrangements, which will not be described in detail here.

[0305] It should be understood that in an embodiment of the present application, the input unit 1004 may include a graphics processing unit (GPU) 10041 and a microphone 10042, and the graphics processor 10041 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 1006 may include a display panel 10061, and the display panel 10061 may be configured in the form of a liquid crystal display, an organic light emitting diode, etc. The user input unit 1007 includes a touch panel 10071 and at least one of other input devices 10072. The touch panel 10071 is also called a touch screen. The touch panel 10071 may include two parts: a touch detection device and a touch controller. Other input devices 10072 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 an operating stick, which will not be repeated here.

[0306] In the embodiment of the present application, after receiving downlink data from a network-side device, the RF unit 1001 may transmit the data to the processor 1010 for processing. Furthermore, the RF unit 1001 may send uplink data to the network-side device. Typically, the RF unit 1001 includes, but is not limited to, an antenna, an amplifier, a transceiver, a coupler, a low-noise amplifier, a duplexer, and the like.

[0307] The memory 1009 can be used to store software programs or instructions and various data. The memory 1009 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 1009 may include a volatile memory or a non-volatile memory, or the memory 1009 may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), a synchronous dynamic random access memory (SDRAM), a double data rate synchronous dynamic random access memory (DDRSDRAM), an enhanced synchronous dynamic random access memory (ESDRAM), a synchronous link dynamic random access memory (SLDRAM), and a direct memory bus random access memory (DRRAM). The memory 1009 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.

[0308] Processor 1010 may include one or more processing units. Optionally, processor 1010 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 1010.

[0309] 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 aforementioned method embodiment. This network-side device embodiment corresponds to the aforementioned network-side device method embodiment, and each implementation process and implementation method of the network-side device in the aforementioned method embodiment is applicable to this network-side device embodiment and can achieve the same technical effects.

[0310] Specifically, an embodiment of the present application further provides a network-side device. As shown in FIG14 , the network-side device 1100 includes an antenna 111, a radio frequency device 112, a baseband device 113, a processor 114, and a memory 115. Antenna 111 is connected to radio frequency device 112. In the uplink direction, radio frequency device 112 receives information via antenna 111 and sends the received information to baseband device 113 for processing. In the downlink direction, baseband device 113 processes the information to be transmitted and sends it to radio frequency device 112. Radio frequency device 112 processes the received information and then sends it through antenna 111.

[0311] The method executed by the network-side device in the above embodiment may be implemented in the baseband device 113 , which includes a baseband processor.

[0312] The baseband device 113 may include, for example, at least one baseband board, on which multiple chips are arranged, as shown in Figure 14, one of which is, for example, a baseband processor, which is connected to the memory 115 through a bus interface to call the program in the memory 115 and execute the network device operations shown in the above method embodiment.

[0313] The network side device may further include a network interface 116, which is, for example, a common public radio interface (CPRI).

[0314] Specifically, the network side device 1100 of an embodiment of the present invention also includes: instructions or programs stored in the memory 115 and executable on the processor 114. The processor 114 calls the instructions or programs in the memory 115 to execute the methods executed by the modules in FIG10 or FIG11 and achieve the same technical effect. To avoid repetition, they will not be elaborated here.

[0315] The embodiment of the present application further provides a network side device. As shown in FIG15 , the network side device 1200 includes: a processor 1201, a network interface 1202, and a memory 1203. The network interface 1202 is, for example, a common public radio interface (CPRI).

[0316] Specifically, the network side device 1200 of an embodiment of the present invention also includes: instructions or programs stored in the memory 1203 and executable on the processor 1201. The processor 1201 calls the instructions or programs in the memory 1203 to execute the methods executed by the modules shown in FIG10 or FIG11 and achieve the same technical effect. To avoid repetition, they will not be elaborated here.

[0317] 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 aforementioned method embodiment are implemented and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0318] The processor is the processor in the terminal device described in the above embodiment. The readable storage medium includes a computer-readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0319] 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 aforementioned method embodiment and achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0320] 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.

[0321] 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 aforementioned method embodiment and can achieve the same technical effect. To avoid repetition, it will not be described here.

[0322] An embodiment of the present application also provides a model determination system, including: a first device and a second device, wherein the first device can be used to execute the steps of the model determination method described in the first aspect above, and the second device can be used to execute the steps of the data transmission method described in the second aspect above.

[0323] 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.

[0324] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0325] 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 without departing from the purpose of this application and the scope of protection of the claims, all of which are within the protection of this application.

Claims

1. A model determination method, wherein: include: The first device determines a first model based on the first information; The first device activates the first model; The first information includes any one of the following: Scene information of the terminal device, and a mapping relationship between the machine learning model and the scene information; the first device is the terminal device or a network side device; Model information of the machine learning model associated with the scene information in which the terminal device is located.

2. The method according to claim 1, wherein: Before the first device determines the first model based on the first information, the method further includes: The first device obtains scene information where the terminal device is located; The first device obtains a mapping relationship between a machine learning model and scene information.

3. The method according to claim 2, wherein: The first device obtains scene information of the terminal device, including: The first device acquires second information, where the second information is used to indicate communication information of the terminal device, and the second information is associated with scene information where the terminal device is located; The first device determines the scene information of the terminal device according to the second information.

4. The method according to claim 3, wherein: In the case where the first device is a terminal device, the first device acquiring the second information includes: The first device measures the reference signal and determines second information based on the measurement result.

5. The method according to claim 3, wherein: In the case where the first device is a network side device, the first device acquires the second information, including: The first device receives second information sent by the terminal device.

6. The method according to claims 3 to 5, wherein: The first device determines the scene information of the terminal device according to the second information, including: The first device acquires an association relationship between the communication information and the scene information; The first device determines the scene information of the terminal device according to the second information and the association between the communicated information and the scene information.

7. The method according to claim 4, wherein: The first device determines the scene information of the terminal device according to the second information, including: The first device sends the second information to a network side device; The first device receives a first indication sent by the network side device; the first indication is used to indicate scenario information of the first device.

8. The method according to claim 2, wherein: The first device obtains scene information of the terminal device, including: The first device acquires location information of the terminal device, where the location information is associated with scene information where the terminal device is located; The first device determines the scene information of the terminal device based on the location information and the association between the location coordinates and the scene information.

9. The method according to claim 8, wherein: Before the first device determines the scene information of the terminal device according to the location information and the association relationship between the location coordinates and the scene information, the method further includes: The first device receives third information sent by the second device, where the third information is used to indicate an association relationship between the location coordinates and the scene information.

10. The method according to claim 8, wherein: The first device obtains the location information of the terminal device, including: In the case where the first device is a terminal device, the first device determines current location information based on positioning technology; In the case where the first device is a network side device, the first device receives fourth information sent by the terminal device, where the fourth information is used to indicate location information of the terminal device.

11. The method according to claim 2, wherein: The first device obtains scene information of the terminal device, including: The first device receives a first indication and a second indication sent by a second device, wherein the first indication is used to indicate scene information of the terminal device; and the second indication is used to indicate a mapping relationship between a machine learning model and the scene information.

12. The method according to claim 1, wherein: Before the first device determines the first model based on the first information, the method further includes: The first device receives a third indication sent by the second device, where the third indication is used to indicate a machine learning model associated with scene information in which the terminal device is located.

13. The method according to claim 4, wherein: The first device measures the reference signal and determines second information based on the measurement result, including: When receiving the fourth indication, the first device measures the first reference signal; the fourth indication is used to instruct the first device to measure at least one reference signal; The first device determines the second information based on a first measurement result of the first reference signal.

14. The method according to claim 4, wherein: The first device measures the reference signal and determines second information based on the measurement result, including: The first device receives a second reference signal sent by a reference point; The first device measures the second reference signal, and determines the second information based on a second measurement result of the second reference signal.

15. The method according to claims 3 to 7, 13 and 14, wherein: The second information includes at least one of the following: first communication information, the first parameter being used to indicate a communication resource of the terminal device; The second communication information, the fifth parameter is used to indicate the communication area where the terminal device is located.

16. The method according to claim 15, wherein: The first communication information includes at least one of the following: reference signal information of the first device; Communication indicator information of the first device.

17. The method according to claim 16, wherein: The reference signal information includes at least one of the following: a first parameter, wherein the second parameter is used to indicate a reference signal resource; The second parameter, the third parameter is used to indicate reference signal measurement information; The third parameter, the fourth parameter is used to indicate reference signal reporting information.

18. The method according to claim 4, wherein: The first device measures the reference signal and determines second information based on the measurement result, including: The first device measures the reference signal to obtain a measurement result; The first device determines a target reference signal resource according to the measurement result, and determines second information according to resource information of the target reference signal resource; The target reference signal resource includes at least one of the following: N first target reference signal resources among the reference signal resources configured for each transmission / reception point; the reference signal received power of the N first target reference signal resources is greater than the reference signal received power of other reference signal resources of the same transmission / reception point; N is a positive integer; A second target reference signal resource selected from the reference signal resources configured at each transmitting and receiving point; the reference signal received power of the second target reference signal resource is greater than or equal to a preset threshold; Reference signal resources configured for each transmitting and receiving point.

19. The method according to claim 1, wherein: The first device activating the first model includes: When a currently running second model does not match the first model, the first device deactivates the second model and activates the first model.

20. The method according to claim 19, wherein: In the case where the first device is a terminal device, the method further includes: The first device sends fifth information to the network side device, where the fifth information includes at least one of the following: a model identifier of the first model; a model identifier of the second model; The activation time of the first model.

21. A data transmission method, wherein: include: The second device sends sixth information to the first device, where the sixth information includes at least one of the following: third information, where the third information is used to indicate an association relationship between the position coordinates and the scene information; A first indication, where the first indication is used to indicate scene information of a terminal device; a second indication, wherein the second indication is used to indicate a mapping relationship between the machine learning model and the scene information; A third indication is used to indicate a machine learning model associated with the scene information in which the terminal device is located.

22. A model determination device, wherein: Applied to a first device, the apparatus comprises: A model determination module, configured to determine a first model based on the first information; A model activation module, used for activating the first model; The first information includes any one of the following: Scene information of the terminal device, and a mapping relationship between the machine learning model and the scene information; the first device is the terminal device or a network side device; Model information of the machine learning model associated with the scene information in which the terminal device is located.

23. A data transmission device, wherein: Applied to a second device, the apparatus comprises: The information sending module is used to send sixth information to the first device, where the sixth information includes at least one of the following: third information, where the third information is used to indicate an association relationship between the position coordinates and the scene information; A first indication, where the first indication is used to indicate scene information of a terminal device; a second indication, wherein the second indication is used to indicate a mapping relationship between the machine learning model and the scene information; A third indication is used to indicate a machine learning model associated with the scene information in which the terminal device is located.

24. A communication device, wherein: It includes a processor and a memory, the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, it implements the steps of the model determination method as described in any one of claims 1 to 20, or implements the steps of the data transmission method as described in claim 21.

25. A readable storage medium, wherein: The readable storage medium stores a program or instruction, and when the program or instruction is executed by the processor, it implements the model determination method according to any one of claims 1 to 20, or implements the steps of the data transmission method according to claim 21.

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