Model determination method and device and communication equipment

Abstract

The invention discloses a model determination method and device and communication equipment, and belongs to the technical field of communication, and the model determination method comprises the steps that first equipment determines a first model based on first information; the first device activates the first model; wherein the first information comprises any one of the following items: information of a scene where the terminal equipment is located, and a mapping relation between a machine learning model and the scene information; the first device is the terminal device or a network side device; and model information of a machine learning model associated with the information of the scene where the terminal device is located.

Description

Technical Field

[0001] This application belongs to the field of communication technologies, and particularly relates to a method, apparatus, and communication device for determining a model. Background Art

[0002] Currently, 3GPP discussions have confirmed that model supervision is an important part of the life cycle management of machine learning models. However, existing model supervision methods based on model input / output only indicate whether the current model is effective, but do not indicate which machine learning model should be applied to the environment where the current terminal device is located. When the environment where the terminal device is located changes, some data information corresponding to the terminal device, such as location information, communication data, etc., will change, and the data characteristics will also change accordingly. If the environment where the current terminal device is located does not match the machine learning model running in the terminal device or the network-side device, it will affect the data processing efficiency and accuracy, and further affect the communication quality of the terminal device. Summary of the Invention

[0003] Embodiments of this application provide a method, apparatus, and communication device for determining a model, which can solve the problem in related technologies that it is impossible to indicate which machine learning model should be applied to the environment where the current terminal device is located.

[0004] In a first aspect, a method for determining a model is provided, including:

[0005] A first device determines a first model based on first information;

[0006] The first device activates the first model;

[0007] Wherein, the first information includes any one of the following:

[0008] Scene information where the terminal device is located, and a mapping relationship between the machine learning model and the scene information; the first device is the terminal device or the network-side device;

[0009] Model information of a machine learning model associated with the scene information where the terminal device is located.

[0010] In a second aspect, a data transmission method is provided, including:

[0011] A second device sends sixth information to the first device, and the sixth information includes at least one of the following:

[0012] Third information, where the third information is used to indicate an association relationship between a location coordinate and scene information;

[0013] A first indication, where the first indication is used to indicate the scene information of the terminal device;

[0014] A second indication for indicating the mapping relationship between the machine learning model and the scenario information;

[0015] A third indication for indicating the machine learning model associated with the scenario information where the terminal device is located.

[0016] In a third aspect, a model determination device is provided, which is applied to a first device. The device includes:

[0017] A model determination module for determining a first model based on first information;

[0018] A model activation module for activating the first model;

[0019] Wherein, the first information includes any one of the following:

[0020] The scenario information where the terminal device is located, and the mapping relationship between the machine learning model and the scenario information; the first device is the terminal device or the network side device;

[0021] The model information of the machine learning model associated with the scenario information where the terminal device is located.

[0022] In a fourth aspect, a data transmission device is provided, which is applied to a second device. The device includes:

[0023] An information sending module for sending sixth information to the first device. The sixth information includes at least one of the following:

[0024] A third information for indicating the association relationship between the position coordinates and the scenario information;

[0025] A first indication for indicating the scenario information of the terminal device;

[0026] A second indication for indicating the mapping relationship between the machine learning model and the scenario information;

[0027] A third indication for indicating the machine learning model associated with the scenario information where the terminal device is located.

[0028] In a fifth aspect, a communication device is provided. The communication device includes a processor and a memory. The memory stores a program or instruction that can run on the processor. When the program or instruction is executed by the processor, the steps of the model determination method described in the first aspect are implemented, or the steps of the data transmission method described in the second aspect are implemented.

[0029] In a sixth aspect, a model determination system is provided, including: a first device and a second device. 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.

[0030] In a seventh aspect, a readable storage medium is provided. Programs or instructions are stored on the readable storage medium, and when the programs or instructions are executed by a processor, the steps of the model determination method described in the first aspect are implemented, or the steps of the data transmission method described in the second aspect are implemented.

[0031] In an eighth aspect, a chip is provided. The chip includes a processor and a communication interface. The communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the method described in the first aspect, or to implement the method described in the second aspect.

[0032] In a ninth aspect, a computer program / program product is provided. The computer program / program product is stored in a storage medium, and the computer program / program product is executed by at least one processor to implement the steps of the method described in the first aspect or the second aspect.

[0033] In the embodiments of the present application, the machine learning model is associated with the scenario. The first device can determine which model should be applied in the current environment according to the scenario information of the terminal device and the mapping relationship between the machine learning model and the scenario information; alternatively, the first device can directly determine the machine learning model associated with the scenario information of the current terminal device according to the model information in the first information and activate the model. In the embodiments of the present application, the first device can determine which model should be applied according to the first information, so as to ensure that during the movement of the terminal device, the machine learning model running in the terminal device or the network-side device can always be adapted to the scenario where the terminal device is located, ensuring the accuracy and processing efficiency of data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 is a block diagram of a wireless communication system applicable to the embodiments of the present application;

[0035] Figure 2 is a flowchart of a model determination method in the embodiments of the present application;

[0036] Figure 3 is a schematic structural diagram of a neural network model in the embodiments of the present application;

[0037] Figure 4 is a schematic diagram of a neuron in the embodiments of the present application;

[0038] Figure 5It is a schematic flowchart of a model determination method in an embodiment of the present application;

[0039] Figure 6 It is a schematic flowchart of another model determination method in an embodiment of the present application;

[0040] Figure 7 A schematic flowchart of a model determination method in an embodiment of the present application;

[0041] Figure 8 A schematic flowchart of another model determination method in an embodiment of the present application;

[0042] Figure 9 It is a flowchart of a data transmission method in an embodiment of the present application;

[0043] Figure 10 It is a structural block diagram of a model determination device in an embodiment of the present application;

[0044] Figure 11 It is a structural block diagram of a data transmission device in an embodiment of the present application;

[0045] Figure 12 It is a structural block diagram of a communication device in an embodiment of the present application;

[0046] Figure 13 It is a structural block diagram of a terminal device in an embodiment of the present application;

[0047] Figure 14 It is a structural block diagram of a network side device in an embodiment of the present application;

[0048] Figure 15 It is a structural block diagram of another network side device in an embodiment of the present application. Detailed implementation manners

[0049] Next, the technical solutions in the embodiments of the present application will be clearly described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application belong to the scope of protection of the present application.

[0050] The terms "first", "second", etc. in the description and claims of this application are used to distinguish similar objects, rather than to describe a specific order or sequence. It should be understood that the terms used in this way can be interchanged under appropriate circumstances, so that the embodiments of this application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first" and "second" are usually of the same type, and do not limit the number of objects. For example, the first object can be one or more. In addition, "and / or" in the description and claims means at least one of the connected objects, and the character " / " generally means that the objects before and after are in an "or" relationship.

[0051] It should be noted that the technology described in the embodiments of this application is not limited to the Long Term Evolution (LTE) / LTE-Advanced (LTE-A) system, and 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 this application are often used interchangeably, and the described technology can be used in the above-mentioned systems and radio technologies, as well as in other systems and radio technologies. The following description describes the New Radio (NR) system for example purposes, and uses NR terms in most of the following descriptions, but these technologies can also be applied to applications other than NR system applications, such as the 6th Generation (6G) communication system. th Generation, 6G) communication system.

[0052] Figure 1Block diagram of a wireless communication system to which embodiments of the present application can be applied. The wireless communication system includes a terminal device 11 and a network-side device 12. Among them, the terminal device 11 can be a mobile phone, a tablet personal computer, a 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, a vehicle-mounted device (VUE), a pedestrian terminal (PUE), a smart home (home devices with wireless communication functions, such as refrigerators, TVs, washing machines or furniture, etc.), a game console, a personal computer (PC), a teller machine or a self-service machine, etc. Terminal-side devices. Wearable devices include: smart watches, smart bracelets, smart earphones, smart glasses, smart jewelry (smart bracelets, smart bracelets, smart rings, smart necklaces, smart anklets, smart ankle chains, etc.), smart wristbands, smart clothing, etc. It should be noted that in the embodiments of the present application, the specific type of the terminal device 11 is not limited. The network-side device 12 can include an access network device or a core network device. Among them, the access network device 12 can also be called 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 can include a base station, a WLAN access point or a WiFi node, etc. The base station can be called 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 some other suitable term in the art. As long as the same technical effect is achieved, the base station is not limited to a specific technical term. It should be noted that in the embodiments of the present application, only the base station in the NR system is taken as an example for introduction, and the specific type of the base station is not limited.The core network device may include, but is not limited to, at least one of the following: core network node, core network function, Mobility Management Entity (MME), Access and Mobility Management Function (AMF), Session Management Function (SMF), User Plane Function (UPF), Policy Control Function (PCF), Policy and Charging Rules Function (PCRF), Edge Application Server Discovery Function (EASDF), Unified Data Management (UDM), Unified Data Repository (UDR), Home Subscriber Server (HSS), Centralized network configuration (CNC), Network Repository Function (NRF), Network Exposure Function (NEF), Local NEF (L-NEF), Binding Support Function (BSF), Application Function (AF), etc. It should be noted that in the embodiments of this application, only the core network devices in the NR system are taken as examples for introduction, and the specific types of core network devices are not limited.

[0053] The following will, with reference to the accompanying drawings, detail the model determination method provided in the embodiments of this application through some embodiments and their application scenarios.

[0054] The embodiments of this application provide a model determination method. Refer to Figure 2 , which shows a flowchart of a model determination method provided in the embodiments of this application. This method is applied to a first device. As Figure 2 shown, this method may specifically include:

[0055] Step 201, the first device determines a first model based on first information.

[0056] Step 202: The first device activates the first model.

[0057] Wherein, the first information includes any one of the following:

[0058] The scenario information of the terminal device and the mapping relationship between the machine learning model and the scenario information; the first device is the terminal device or the network-side device;

[0059] The model information of the machine learning model associated with the scenario information of the terminal device.

[0060] It should be noted that, in the embodiments of this 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 Figure 1 the terminal device 11 in. The positioning reference unit (Positioning Reference Unit, PRU) may perform positioning measurements, such as reference signal time difference (Reference signal time difference, RSTD), reference signal receiving power (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 may send a positioning reference signal (Positioning reference signal, PRS) to the transmission and receiving point (Transmission and Receiving Point, TRP), so that the TRP can measure and report the uplink (Up-Link, UL) positioning measurement values from the PRU at a known position, such as RTOA, UL-AoA, gNB Rx-Tx time difference, etc. The position server may compare the PRU measurement values with the expected measurement values at the known PRU position to determine the correction terms for other target devices in the vicinity, and then correct the DL and / or UL position measurement values of other target devices based on these correction terms.

[0061] The network-side device may be Figure 1 the access network device in, such as a base station or a newly defined artificial intelligence processing node on the access network side, or may also be Figure 1 the core network device in, such as a network data analytics function (Network Data Analytics Function, NWDAF), a location management function (Location Management Function, LMF), or a newly defined processing node on the core network side, or may also be a combination of the above multiple nodes.

[0062] In the embodiments of the present application, the machine learning model can be trained by a network-side device. 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 relationship between the model identifier of each machine learning model and the scenario information.

[0063] Alternatively, the machine learning model is trained by a third-party server. The third-party server sends the trained machine learning model to the terminal device and / or the network-side device, and sends the association relationship between the model identifier of the machine learning model and the scenario information to the terminal device and / or the network-side device.

[0064] It should be noted that the machine learning model in the embodiments 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 the neural network model as an example, its schematic diagram can be as Figure 3 shown. As Figure 3 shown, the neural network can include one or more input layers, one or more hidden layers, and one output layer. The data to be processed [X1, X2... Xn] are respectively input into the neural network from the corresponding input layer, and after being processed by the input layer, the hidden layer, and the output layer, the output result Y is obtained. In addition, the neural network is composed of neurons, and the schematic diagram of the neuron is as Figure 4 shown. Among them, in Figure 4 , a1, a2,... aK represent inputs, w represents the weight (i.e., the multiplicative coefficient), b represents the bias (i.e., the additive coefficient), and σ(.) represents the activation function. Common activation functions include Sigmoid (mapping the variable to between 0 and 1), tanh (translation and contraction of Sigmoid), Rectified Linear Unit (ReLU), etc.

[0065] In addition, taking the neural network model as an example, the process of model training is introduced as follows:

[0066] Among them, the parameters of the neural network can be optimized by gradient optimization algorithms. Gradient optimization algorithms are a class of algorithms that minimize or maximize an objective function (sometimes also called a loss function), and the objective function 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. Then, according to the input x, the predicted output f(x) can be obtained, and the difference between the predicted value and the true value (f(x) - Y) can be calculated, which is the loss function. Among them, the optimization goal of the gradient optimization algorithm is to find the appropriate w (i.e., weights) and b (i.e., biases) to minimize the value of the above loss function. The smaller the loss value, the closer the model is to the real situation.

[0067] Currently, common optimization algorithms are basically based on the error backpropagation (BP) algorithm. The basic idea of the BP algorithm is that the learning process consists of two processes: the forward propagation of signals and the backpropagation of errors. During forward propagation, the input samples are input 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, it enters the stage of error backpropagation. Error backpropagation is to backpropagate the output error layer by layer through the hidden layer to the input layer in a certain form, and allocate the error to all units of each layer, so as to obtain the error signals of each layer of units. This error signal is used as the basis for correcting the weights of each unit. This process of adjusting the weights of each layer during the forward propagation of signals and the backpropagation of errors is carried out cyclically. Among them, the process of continuously adjusting the weights is also the learning and training process of the network. This process continues until the error of the network output is reduced to an acceptable level, or until a pre-set number of learning times is reached.

[0068] In addition, common optimization algorithms include Gradient Descent, Stochastic Gradient Descent (SGD), mini-batch gradient descent, Momentum, Nesterov (the name of the inventor, specifically Stochastic Gradient Descent with Momentum), ADAptive GRADient descent (Adagrad), the extended algorithm of Adagrad (Adadelta), root mean square prop (RMSprop), Adaptive Moment Estimation (Adam), etc.

[0069] When these optimization algorithms perform backpropagation of errors, they all calculate the derivative / partial derivative of the current neuron based on the error / loss obtained from the loss function, and then, considering factors such as the learning rate and the previous gradient / derivative / partial derivative, obtain the gradient, which is then passed to the previous layer.

[0070] 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 feature, a neural network, a neural network function, a neural network capability, 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 GPUs, NPUs, TPUs, ASICs, etc. The present invention does not make specific limitations in this regard. Optionally, the specific data set includes the input and / or output of the AI unit / AI model.

[0071] Optionally, the identifier of the AI unit / AI model may be an AI model identifier, an AI structure identifier, an AI algorithm identifier, or the identifier of a specific data set associated with the AI unit / AI model, or the identifier of a specific scenario, environment, channel feature, or device related to AI / ML, or the identifier of a function, feature, capability, or module related to AI / ML. The embodiments of the present application do not make specific limitations in this regard.

[0072] In the embodiments of the present application, the first device may determine a first model based on the first information. For example, the first device determines a machine learning model associated with the scenario information of the terminal device according to the scenario information of the terminal device and the mapping relationship between the machine learning model and the scenario information, and determines this model as the first model. Or, when the first information includes the model information of a machine learning model associated with the scenario information of the terminal device, the first device may directly determine the machine learning model indicated by this model information as the first model.

[0073] After determining the first model, the first device can activate the first model, so as to process the data generated by the terminal device in the current scenario using the first model.

[0074] It can be understood that when 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, and so on. When the scenario where the terminal device is located changes, the machine learning model running in the first device may not be able to meet the data processing requirements of the current scenario where the terminal device is located. In this case, the terminal device can determine and activate a first model that matches the current scenario where the terminal device is located according to the first information.

[0075] It should be noted that in the embodiments of the present application, the scenario information of the terminal device may include, but is not limited to, the scenario identifier (scenario ID), scenario information, scenario category, area identifier (area ID), area information, area category, dataset identifier (dataset ID), dataset information, dataset category, etc. of the scenario where the terminal device is located. Among them, the granularity of the scenario or area or dataset can be a cell. In one possible implementation, the scenario ID, area ID, and dataset ID can be associated with the physical cell identifier (Physical Cell Identifier, PCI) of one or more cells, so as to determine the scenario ID, area ID, and dataset ID corresponding to the first device according to the cell where the first device is located. In another possible implementation, the granularity of the scenario or area or dataset can also be smaller than a cell. For example, in AI positioning, a scenario can be a workshop, a building, or even a certain floor in a building within a cell.

[0076] A machine learning model can correspond to one or more scenarios, areas, or datasets.

[0077] In the embodiments 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 current scenario where the first device is located, the currently running machine learning model in the first device is an invalid model. In this case, the first device can switch the running machine learning model to the first model.

[0078] In the embodiments of the present application, a machine learning model is associated with a scenario. The first device can determine which model should be applied in the current environment according to the scenario information of the terminal device and the mapping relationship between the machine learning model and the scenario information; alternatively, the first device can directly determine the machine learning model associated with the scenario information of the current terminal device according to the model information in the first information and activate the model. In the embodiments of the present application, the first device can determine which model should be applied according to the first information, so as to ensure that the machine learning model running in the terminal device or the network-side device can always be adapted to the scenario where the terminal device is located during the movement of the terminal device, and ensure the accuracy and processing efficiency of data processing.

[0079] Optionally, before the first device determines the first model based on the first information, the method further includes:

[0080] The first device obtains the scenario information of the terminal device;

[0081] The first device obtains the mapping relationship between the machine learning model and the scenario information.

[0082] In the embodiments of the present application, the mapping relationship between the machine learning model and the scenario information can be generated by the network-side device or the third-party server that trains the model. Exemplarily, if the first device is a terminal device, the mapping relationship between the machine learning model and the scenario information can be sent by the network-side device or the third-party server that trains the model to the first device; if the first device is a network-side device, in the case where the network-side device performs model training, the network-side device can locally generate the mapping relationship between the machine learning model and the scenario information based on the model training process; in the case where the third-party server performs model training, the network-side device can read the mapping relationship between the machine learning model and the scenario information from the third-party server.

[0083] Similarly, the first device can also obtain the scenario information of the terminal device in various ways.

[0084] As an example, the first device obtaining the scenario information of the terminal device includes:

[0085] Step S11, the first device obtains second information, the second information is used to indicate the communication information of the terminal device, and the second information is associated with the scenario information of the terminal device;

[0086] Step S12, the first device determines the scenario information of the terminal device according to the second information.

[0087] Among them, the second information is associated with the scenario where the first device is located. For example, the second information is associated with information such as the scenario ID, area ID, dataset ID, scenario category, area category, dataset category, etc. where the first device is currently located. As an example, the second information may include the cell ID, reference signal ID, transmit-receive point ID, area ID, Tracking Area ID, etc. corresponding to the first device.

[0088] In the embodiments of the present application, the first device may determine the scenario information where the terminal device is located according to the second information.

[0089] Optionally, when the first device is a terminal device, the first device obtaining the second information includes:

[0090] The first device measures the reference signal and determines the second information based on the measurement result.

[0091] Refer to Figure 5 , which shows a schematic flowchart of a model determination method provided by an embodiment of the present application. As Figure 5 shown, if the first device is a terminal device, then the first device may determine the second information by measuring the reference signal.

[0092] Optionally, when the first device is a network-side device, the first device obtaining the second information includes:

[0093] The first device receives the second information sent by the terminal device.

[0094] Refer to Figure 6 , which shows a schematic flowchart of another model determination method provided by an embodiment of the present application. As Figure 6 shown, if the first device is a network-side device, then after the terminal device generates the second information according to the measurement result of the reference signal, the second information is sent to the network-side device. The network-side device itself does not need to perform any measurement operations on the reference signal.

[0095] Optionally, the first device determining the scenario information of the terminal device according to the second information includes:

[0096] The first device obtains the association relationship between the communication information and the scenario information;

[0097] The first device determines the scenario information of the terminal device according to the second information and the association relationship between the communication information and the scenario information.

[0098] Among them, the association relationship between the communication information and the scenario can be sent by the second device to the first device, or can be specified by the protocol. When the first device is a network-side device, for example, when the first device is an access network device, 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 higher layer of the terminal device.

[0099] After the first device obtains the second information, it can determine the scenario information of the terminal device based on the communication information indicated by the second information and the association relationship between the communication information and the scenario information.

[0100] Exemplarily, as Figure 6 shown, 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 scenario information of the terminal device according to the communication information indicated by the second information and the association relationship between the communication information and the scenario information, and then further determine and activate the first model in combination with the association relationship between the machine learning model and the scenario information.

[0101] As Figure 5 shown, 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 scenario information of the terminal device according to the communication information indicated by the second information and the association relationship between the communication information and the scenario information, and then further determine and activate the first model in combination with the association relationship between the machine learning model and the scenario information. Alternatively, the terminal device can also send the second information to the network-side device, and the network-side device determines the scenario information of the terminal device based on the second information and indicates it to the terminal device.

[0102] Optionally, the first device determines the scenario information of the terminal device according to the second information, including:

[0103] Step S21, the first device sends the second information to the network-side device;

[0104] Step S22, the first device receives a first indication sent by the network-side device; the first indication is used to indicate the scenario information of the first device.

[0105] As Figure 5As shown, in a possible application scenario of the present application, the first device is a terminal device. The terminal device can determine the second information by measuring the reference signal and send the second information to the network-side device. The network-side device determines the scenario information where the terminal device is currently located based on the second information and the association relationship between the communication information and the scenario information, and indicates the scenario information to the terminal device through a first indication. Then, the terminal device determines and activates the first model according to the scenario information indicated by the first indication and the association relationship between the machine learning model and the scenario information.

[0106] Alternatively, the network-side device determines the scenario information where the terminal device is currently located based on the communication information indicated by the second information and the association relationship between the communication information and the scenario information, and further determines the machine learning model associated with the scenario information where the terminal device is currently located, that is, the first model, based on the association relationship between the machine learning model and the scenario information, and indicates the first model to the terminal device through a third indication.

[0107] In an alternative embodiment of the present application, the first device obtaining the scenario information where the terminal device is located includes:

[0108] The first device receives a first indication and a second indication sent by a second device. The first indication is used to indicate the scenario information of the terminal device; the second indication is used to indicate the mapping relationship between the machine learning model and the scenario information.

[0109] In another possible application scenario of the present application, the scenario information where the terminal device is located and the mapping relationship between the machine learning model and the scenario information can also be indicated to the first device by the second device.

[0110] It should be noted that the second device in the embodiments of the present application can be a network-side device or a higher layer of the terminal device. For example, when the first device is a network-side device, such as the first device being an access network device, then 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 higher layer of the terminal device.

[0111] As an example, the first indication and the second indication may be carried in the same signaling. The second device sends the first indication and the second indication to the first device simultaneously through a certain signaling, and the first device determines and activates the first model according to 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 (RRC) signaling, Radio Link Control (RLA) signaling, Media Access Control (MAC) signaling, LTE Positioning Protocol (LPP) signaling, NR Positioning Protocol A (NRPPa) signaling, Downlink Control Information (DCI), etc.

[0112] As another example, the first indication is sent from 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.

[0113] Optionally, before the first device determines the first model based on the first information, the method further includes:

[0114] The first device receives a third indication sent by the second device, and the third indication is used to indicate a machine learning model associated with the scenario information where the terminal device is located.

[0115] In the embodiment of the present application, the third indication may be sent from 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.

[0116] It can be understood that the second device may determine the scenario information where the terminal device is located according to the location information of the terminal device, and then determine the machine learning model matching the current scenario of the terminal device according to the association relationship between the scenario information and the machine learning model, generate the third indication and send it to the first device. Alternatively, the first device may send the scenario information where the terminal device is located to the second device, and the second device determines the machine learning model associated with the current scenario of the terminal device according to the scenario information and the association relationship between the machine learning model and the scenario information, generates the third indication and sends it to the first device.

[0117] Optionally, the first device measures the reference signal and determines the second information based on the measurement result, including:

[0118] Step S31: When the first device receives a fourth indication, it measures a first reference signal; the fourth indication is used to instruct the first device to measure at least one reference signal.

[0119] Step S32: The first device determines the second information based on the first measurement result of the first reference signal.

[0120] In a possible application scenario, the first device is a terminal device. The terminal device can measure the first reference signal when receiving the fourth indication and determine the second information based on the first measurement result of the first reference signal.

[0121] It should be noted that the fourth indication can be sent from the second device to the first device, or from other devices to the first device. In another possible application scenario, the fourth indication can also be automatically triggered by the upper layer of the first device when certain measurement conditions are met.

[0122] Among them, the first reference signal can include but is not limited to: positioning reference signal, downlink channel sounding reference signal (Channel-State-Information Reference Signal, CSI-RS), uplink sounding signal (SoundingReference Signal, SRS), synchronization signal block (Synchronization Signal Block, SSB), time-frequency tracking reference signal (Tracking Reference Signal, TRS), etc.

[0123] Optionally, the first device measures the reference signal and determines the second information based on the measurement result, including:

[0124] Step S41: The first device receives a second reference signal sent by a reference point.

[0125] Step S42: The first device measures the second reference signal and determines the second information based on the second measurement result of the second reference signal.

[0126] In the embodiments of the present application, the first device is a terminal device. The terminal device can also measure the second reference signal from the receiving reference point and determine the second information based on the second measurement result of the second reference signal. As an example, the second information can include the cell ID corresponding to the terminal device, reference signal ID, receiving reference point ID, scenario ID, area ID, Tracking Area ID, etc.

[0127] Optionally, the second information includes at least one of the following:

[0128] The first communication information, where the first parameter is used to indicate the communication resources of the terminal device;

[0129] The second communication information, where the fifth parameter is used to indicate the communication area where the terminal device is located.

[0130] Optionally, the first communication information includes at least one of the following:

[0131] The reference signal information of the first device;

[0132] The communication metric information of the first device.

[0133] Optionally, the reference signal information includes at least one of the following:

[0134] The first parameter, where the second parameter is used to indicate the reference signal resources;

[0135] The second parameter, where the third parameter is used to indicate the reference signal measurement information;

[0136] The third parameter, where the fourth parameter is used to indicate the reference signal reporting information.

[0137] Among them, the first parameter can be the reference signal resource ID, reference signal resource set ID, etc. The second parameter can be the reference signal measurement ID, reference signal measurement configuration ID, etc. The third parameter can be the reference signal reporting ID, reference signal reporting configuration ID.

[0138] Optionally, the communication metric information includes at least one of the following:

[0139] The fourth parameter, where the first parameter is used to indicate the channel quality;

[0140] Beam information;

[0141] Channel state information;

[0142] The multipath average delay;

[0143] The multipath delay spread.

[0144] Among them, the fourth parameter may 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.

[0145] The beam information may include information such as beam index and beam direction.

[0146] In an alternative embodiment of the present application, the first device measures a reference signal and determines second information based on the measurement result, including:

[0147] Step S51, the first device measures the reference signal to obtain a measurement result;

[0148] Step S52, the first device determines a target reference signal resource according to the measurement result, and determines second information according to the resource information of the target reference signal resource.

[0149] Among them, the target reference signal resource includes at least one of the following:

[0150] A1. N first target reference signal resources in the reference signal resources configured for each transmit-receive point; the reference signal reception power of the N first target reference signal resources is greater than the reference signal reception power of other reference signal resources at the same transmit-receive point; N is a positive integer;

[0151] A2. Second target reference signal resources screened from the reference signal resources configured for each transmit-receive point; the reference signal reception power of the second target reference signal resources is greater than or equal to a preset threshold;

[0152] A3. The reference signal resources configured for each transmit-receive point.

[0153] In an embodiment of the present application, the first device is a terminal device, and the terminal device can screen out N first target reference signal resources with reference signal reception power greater than other reference signal resources at the same transmit-receive point from the respective reference signal resources configured for each transmit-receive point, and determine second information according to the resource information of the first target reference signal resources, such as reference signal ID, reference signal measurement ID, reference signal reporting ID, etc.

[0154] Alternatively, the terminal device may also screen out second target reference signal resources with a reference signal received power greater than or equal to a preset threshold from the respective reference signal resources configured for each transmission and reception point, and then determine second information based on the resource information of the second target reference signal resources, such as reference signal ID, reference signal measurement ID, reference signal reporting ID, etc. Herein, the preset threshold may be indicated by the network-side device or specified by the protocol, and the embodiments of the present application do not make specific limitations thereto.

[0155] Alternatively, the terminal device determines the second information based on the reference signal resources configured for each transmission and reception point without screening the reference signal resources.

[0156] In the embodiments of the present application, the terminal device may screen the target reference signal resources based on any one of items A1 to A3, determine the second information based on the screened target reference signal resources, and then determine the scenario information where the terminal device is located. The determined scenario information is adapted to the reference signal resources configured for the transmission and reception point and satisfies a specific reference signal received power, ensuring the reliability of the determined scenario information, which is beneficial to improving the reliability of the finally determined first model, and thus ensuring that during the movement of the terminal device, the machine learning model running in the terminal device can always be adapted to the reference signal resources configured for the transmission and reception point.

[0157] Optionally, the resource information includes at least one of the following:

[0158] Reference signal received power;

[0159] Reference signal resource identifier;

[0160] Beam identifier;

[0161] Beam direction.

[0162] In the embodiments of the present application, the terminal device may determine the second information based on the resource information such as the reference signal received power, reference signal resource representation, beam identifier, and beam direction of the target reference signal resources (including at least one of A1 to A3).

[0163] In another alternative embodiment of the present application, the first device obtaining the scenario information where the terminal device is located includes:

[0164] Step S61, the first device obtains the location information of the terminal device, and the location information is associated with the scenario information where the terminal device is located;

[0165] Step S62, the first device determines the scenario information of the terminal device according to the location information and the association relationship between the location coordinates and the scenario information.

[0166] In the embodiments of the present application, in addition to determining the scenario information of the terminal device according to the second information, the first device may also determine the scenario information of the terminal device according to the location information of the terminal device and the association relationship between the location coordinates and the scenario information.

[0167] It can be understood that the location information of the terminal device can be determined by the terminal device according to 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 a mobile communication network, such as positioning methods based on an NR system, an LTE system, etc.

[0168] The association relationship between the location coordinates and the scenario information can be determined by a network-side device, can be specified by a protocol, or can be sent by a second device to the first device. In this regard, the embodiments of the present application do not make specific limitations. It should be noted that the second device can be a network-side device or a higher layer of the 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 higher layer of the terminal device.

[0169] Optionally, before the first device determines the scenario information of the terminal device according to the location information and the association relationship between the location coordinates and the scenario information, the method further includes:

[0170] The first device receives third information sent by a second device, and the third information is used to indicate the association relationship between the location coordinates and the scenario information.

[0171] In a possible application scenario of the present application, the second device can also indicate the association relationship between the location coordinates and the scenario information to the first device through the third information. After receiving the third information, the first device can determine the scenario information of the terminal device according to the location information of the terminal device and the association relationship between the location coordinates and the scenario information, and then determine the first model according to the scenario information and the association relationship between the machine learning model and the scenario information.

[0172] Alternatively, the first device sends the determined scenario information, such as scenario ID, area ID, dataset ID, etc. to the second device, and the second device determines the machine learning model matching the scenario information reported by the first device and indicates it to the first device.

[0173] Optionally, the first device obtaining the location information of the terminal device includes:

[0174] When the first device is a terminal device, the first device determines the current location information based on a positioning technology;

[0175] When the first device is a network - side device, the first device receives the fourth information sent by the terminal device, and the fourth information is used to indicate the location information of the terminal device.

[0176] Refer to Figure 7 , which shows the flowchart of a model determination method provided by an embodiment of the present application. As Figure 7 shown, if the first device is a terminal device, the location information of the terminal device can be determined by the terminal device itself according to 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 a mobile communication network, such as positioning methods based on the NR system, LTE system, etc.

[0177] After the terminal device determines the location information, combining the association relationship between the location coordinates and the scene information, the scene information can be determined. Alternatively, the terminal device reports the location information to the network - side device through the fourth information, and the network - side device determines the scene information of the terminal device according to the location information of the terminal device and the association relationship between the location coordinates and the scene, and indicates the determined scene information to the terminal device through the first indication.

[0178] After the terminal device determines the scene information, it can further determine the first model by combining the association relationship between the machine - learning model and the scene information. Alternatively, the network - side device determines the scene information of the terminal device based on the location information reported by the terminal device, and further combines the association relationship between the machine - learning model and the scene information to determine the machine - learning model associated with the scene information of the terminal device, that is, the first model, and indicates the first model to the terminal device through the third indication.

[0179] Refer to Figure 8 , which shows the schematic flowchart of another model determination method provided by an embodiment of the present application. As Figure 8 shown, if the first device is a network - side device, the location information of the terminal device can be reported by the terminal device to the first device through the fourth information. Optionally, when the first device is a network - side device, the terminal device can simultaneously report the acquisition method and reliability or confidence level of the location information to the first device.

[0180] After the network - side device receives the location information reported by the terminal device, according to the location information and the association relationship between the location coordinates and the scene information, the network - side device can determine the scene information of the terminal device. Further, based on the association relationship between the machine - learning model and the scene information, the network - side device can determine the first model associated with the scene information where the terminal device is located.

[0181] Optionally, the activation of the first model by the first device includes:

[0182] When the second model currently running on the first device does not match the first model, the second model is deactivated and the first model is activated.

[0183] In the embodiments of the present application, if the second model currently running in the first device does not match the first model, it indicates that the currently running second model can no longer meet the data processing requirements of the first scenario where the first device is currently located. In this case, the first device can deactivate the second model and activate the first model.

[0184] It should be noted that the first model and the second model in the present application are not limited to a certain AI model. In other words, both the first model and the second model in the present application can include one or more AI models, or can be an AI function, and an AI function can be associated with one or more AI models. Correspondingly, deactivating the second model can be deactivating one or more AI models included in the second model simultaneously, or deactivating one or more AI functions represented by the second model simultaneously. Similarly, activating the first model can be activating one or more AI models included in the first model simultaneously, or activating one or more AI functions represented by the first model simultaneously.

[0185] 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, it means that the currently running machine learning model in the first device is effective 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 other than the currently running machine learning model in the AI models and / or AI functions included in the first model can be activated. Or, 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. Or, if there are no AI models and / or AI functions in the first device that match the AI models and / or AI functions included in the first model, then the activation operation cannot be performed. Or, if there are no AI models or AI functions currently running in the first device, then there is no need to perform the deactivation operation.

[0186] It should be noted that in the embodiments of the present application, if an AI function is deactivated, all AI models associated with the AI function become invalid; similarly, if an AI function is activated, all AI models associated with the AI function become valid models.

[0187] In the embodiment of the present application, when the second model currently running does not match the first model, the second model is deactivated and the first model is activated, so as to ensure that during the movement of the terminal device, the machine learning model running in the first device can always adapt to the scenario where the terminal device is located, guaranteeing the accuracy and processing efficiency of data processing.

[0188] Optionally, when the first device is a terminal device, the method further includes:

[0189] The first device sends fifth information to the network-side device.

[0190] Wherein, the fifth information includes at least one of the following:

[0191] The model identifier of the first model;

[0192] The model identifier of the second model;

[0193] The activation time of the first model.

[0194] In the 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 through the fifth information. Among them, 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.

[0195] In summary, the embodiment of the present application provides a model determination method, which associates the machine learning model with the scenario. The first device can determine which model should be applied in the current environment according to the scenario information of the terminal device and the mapping relationship between the machine learning model and the scenario information; or, the first device can directly determine the machine learning model associated with the scenario information of the current terminal device according to 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 according to the first information, so as to ensure 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 scenario where the terminal device is located, guaranteeing the accuracy and processing efficiency of data processing.

[0196] The embodiment of the present application provides a data transmission method. Refer to Figure 9 , which shows a flowchart of a data transmission method provided by the embodiment of the present application. This method is applied to the second device. As Figure 9 shown, this method may specifically include:

[0197] Step 501, the second device sends sixth information to the first device.

[0198] Among them, the six pieces of information include at least one of the following:

[0199] The third piece of information, which is used to indicate the association relationship between the position coordinates and the scene information;

[0200] The first indication, which is used to indicate the scene information where the terminal device is located;

[0201] The second indication, which is used to indicate the mapping relationship between the machine learning model and the scene information;

[0202] The third indication, which is used to indicate the machine learning model associated with the scene information where the terminal device is located.

[0203] It should be noted that the second device in the embodiments of the present application may be a network-side device or a higher layer of the terminal device. For example, when the first device is a network-side device, such as the first device being an access network device, then the second device may be a core network device; when the first device is a terminal device, the second device may be a network-side device or a higher layer of the terminal device.

[0204] The third piece of information is used to indicate the association relationship between the position coordinates and the scene information. In a possible application scenario of the present application, the second device may, through the third piece of information, indicate the association relationship between the position coordinates and the scene information to the first device. After receiving the third piece of information, the first device can, based on the location information of the terminal device and the association relationship between the position coordinates and the scene information, determine the scene information where the terminal device is located, and then, based on the scene information and the association relationship between the machine learning model and the scene information, determine the first model.

[0205] The first indication may include, but is not limited to, the scenario ID, scenario information, scenario category, area ID, area information, area category, dataset ID, dataset information, dataset category, etc. of the scenario where the first device is located. Among them, the granularity of the scenario or area or dataset may be a cell. In a possible implementation, the scenario ID, area ID, and dataset ID may be associated with the physical cell identifier (PCI) of one or more cells, so as to determine the scenario ID, area ID, and dataset ID corresponding to the first device according to the cell where the first device is located. In another possible implementation, the granularity of the scenario or area or dataset may also be smaller than a cell. For example, in AI positioning, a scenario may be a workshop, a building, or even a certain floor in a building within a cell. A machine learning model may correspond to one or more scenarios, areas, or datasets.

[0206] In the embodiments of the present application, the machine learning model may be trained by a second device, and the association relationship between the model ID of each machine learning model and the scenario information is recorded in the second device. Alternatively, the machine learning model is trained by a third-party server. The third-party server sends the trained machine learning model to the first device and sends the association relationship between the machine learning model and the scenario information to the first device and / or the second device.

[0207] The second device may send the association relationship between the machine school model and the scenario information to the first device through a second indication, so that the first device determines the first model based on the second indication.

[0208] Alternatively, the second device may also determine the machine learning model associated with the scenario information of the terminal device according to the scenario information of the terminal device and the positional relationship between the machine learning model and the scenario information, and indicate the model information of the model to the first device through a third indication. The third indication may include the model information of the machine learning model associated with the scenario information of the terminal device, such as the model ID.

[0209] In summary, the embodiment of the present application provides a data transmission method. The second device may send at least one of the association relationship between the position 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 determines which model should be applied in the scene where the terminal device is currently located based on the received sixth information.

[0210] For the model determination method provided by the embodiment of the present application, the execution subject may be a model determination device. In the embodiment of the present application, taking the model determination device executing the model determination method as an example, the model determination device provided by the embodiment of the present application is described.

[0211] The embodiment of the present application provides a model determination device. Referring to Figure 10 , a structural block diagram of a model determination device provided by the embodiment of the present application is shown. The device can be applied to the first device. As Figure 10 shown, the device may specifically include:

[0212] A model determination module 601, configured to determine a first model based on the first information;

[0213] A model activation module 602, configured to activate the first model;

[0214] Wherein, the first information includes any one of the following:

[0215] The scene information where the terminal device is located, and the mapping relationship between the machine learning model and the scene information; the first device is the terminal device or the network-side device;

[0216] The model information of the machine learning model associated with the scene information where the terminal device is located.

[0217] Optionally, the device further includes:

[0218] A scene information acquisition module, configured to acquire the scene information where the terminal device is located;

[0219] A first relationship acquisition module, configured to acquire the mapping relationship between the machine learning model and the scene information.

[0220] Optionally, the scene information acquisition module includes:

[0221] A first acquisition sub-module, configured to acquire second information, where the second information is used to indicate the communication information of the terminal device, and the second information is associated with the scene information where the terminal device is located;

[0222] The first determination sub-module is configured to determine the scenario information of the terminal device according to the second information.

[0223] Optionally, when the first device is a terminal device, the first acquisition sub-module includes:

[0224] A measurement unit configured to measure a reference signal and determine second information based on the measurement result.

[0225] Optionally, when the first device is a network-side device, the first acquisition sub-module includes:

[0226] A first receiving unit configured to receive the second information sent by the terminal device.

[0227] Optionally, the first determination sub-module includes:

[0228] A first acquisition unit configured to acquire the association relationship between communication information and scenario information for the first device;

[0229] A first determination unit configured to determine the scenario information of the terminal device according to the second information and the association relationship between the communication information and the scenario information.

[0230] Optionally, the first determination sub-module includes:

[0231] A first sending unit configured to send the second information to a network-side device;

[0232] A second receiving unit configured to receive a first indication sent by the network-side device; the first indication is used to indicate the scenario information of the first device.

[0233] Optionally, the scenario information acquisition module includes:

[0234] A second acquisition sub-module configured to acquire the location information of the terminal device, where the location information is associated with the scenario information of the terminal device;

[0235] A second determination sub-module configured to determine the scenario information of the terminal device according to the location information and the association relationship between the location coordinates and the scenario information.

[0236] Optionally, the scenario information acquisition module further includes:

[0237] A first receiving sub-module configured to receive third information sent by a second device, where the third information is used to indicate the association relationship between location coordinates and scenario information.

[0238] Optionally, the second acquisition sub-module includes:

[0239] A second determination unit, configured to determine current location information based on a positioning technology when the first device is a terminal device;

[0240] A third receiving unit, configured to receive fourth information sent by the terminal device when the first device is a network-side device, where the fourth information is used to indicate the location information of the terminal device.

[0241] Optionally, the scenario information acquisition module includes:

[0242] A second receiving sub-module, configured to receive a first indication and a second indication sent by a second device, where the first indication is used to indicate the scenario information of the terminal device; the second indication is used to indicate a mapping relationship between a machine learning model and the scenario information.

[0243] Optionally, the apparatus further includes:

[0244] A third indication receiving module, configured 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 scenario information where the terminal device is located.

[0245] Optionally, the measurement unit is specifically configured to:

[0246] Measure a first reference signal when receiving a fourth indication, where the fourth indication is used to indicate that the first device measures at least one reference signal;

[0247] Determine the second information based on a first measurement result of the first reference signal.

[0248] Optionally, the measurement unit is specifically configured to:

[0249] Receive a second reference signal sent by a reference point;

[0250] Measure the second reference signal and determine the second information based on a second measurement result of the second reference signal.

[0251] Optionally, the second information includes at least one of the following:

[0252] First communication information, where the first parameter is used to indicate communication resources of the terminal device;

[0253] Second communication information, where the fifth parameter is used to indicate a communication area where the terminal device is located.

[0254] Optionally, the first communication information includes at least one of the following:

[0255] Reference signal information of the first device;

[0256] The communication metric information of the first device.

[0257] Optionally, the reference signal information includes at least one of the following:

[0258] A first parameter, where the second parameter is used to indicate the reference signal resource;

[0259] A second parameter, where the third parameter is used to indicate the reference signal measurement information;

[0260] A third parameter, where the fourth parameter is used to indicate the reference signal reporting information.

[0261] Optionally, the communication metric information includes at least one of the following:

[0262] A fourth parameter, where the first parameter is used to indicate the channel quality;

[0263] Beam information;

[0264] Channel state information;

[0265] Multipath average delay;

[0266] Multipath delay spread.

[0267] Optionally, the measurement unit is specifically configured to:

[0268] Measure the reference signal to obtain a measurement result;

[0269] Determine a target reference signal resource according to the measurement result, and determine second information according to the resource information of the target reference signal resource;

[0270] Wherein, the target reference signal resource includes at least one of the following:

[0271] N first target reference signal resources in the reference signal resources configured for each transmit-receive point; the reference signal reception power of the N first target reference signal resources is greater than the reference signal reception power of other reference signal resources at the same transmit-receive point; N is a positive integer;

[0272] Second target reference signal resources selected from the reference signal resources configured for each transmit-receive point; the reference signal reception power of the second target reference signal resources is greater than or equal to a preset threshold;

[0273] The reference signal resources configured for each transmit-receive point.

[0274] Optionally, the resource information includes at least one of the following:

[0275] Reference signal reception power;

[0276] Reference signal resource identifier;

[0277] Beam identifier;

[0278] Beam direction.

[0279] Optionally, the model activation module includes:

[0280] A model activation sub-module, configured to deactivate the second model and activate the first model when the currently running second model does not match the first model.

[0281] Optionally, the apparatus further includes:

[0282] A fifth information sending module, configured to send fifth information to a network-side device, where the fifth information includes at least one of the following:

[0283] The model identifier of the first model;

[0284] The model identifier of the second model;

[0285] The activation time of the first model.

[0286] The model determination apparatus in the embodiments of the present application may be an electronic device, such as an electronic device with an operating system, or a component in an electronic device, such as an integrated circuit or a chip.

[0287] The model determination apparatus provided in the embodiments of the present application can implement each process implemented in the foregoing method embodiments and achieve the same technical effects. To avoid repetition, details are not described herein again.

[0288] The embodiments of the present application provide a data transmission apparatus. Referring to Figure 11 , a structural block diagram of a data transmission apparatus provided in the embodiments of the present application is shown. The apparatus may be applied to a second device. As Figure 11 shown, the apparatus may specifically include:

[0289] An information sending module 701, configured to send sixth information to a first device.

[0290] Wherein, the sixth information includes at least one of the following:

[0291] Third information, where the third information is used to indicate an association relationship between position coordinates and scene information;

[0292] A first indication, where the first indication is used to indicate the scene information of a terminal device;

[0293] A second indication, where the second indication is used to indicate a mapping relationship between a machine learning model and scene information;

[0294] A third indication, which is used to indicate a machine learning model associated with the scenario information where the terminal device is located.

[0295] The data transmission device in the embodiments of the present application may be an electronic device, such as an electronic device with an operating system, or a component in an electronic device, such as an integrated circuit or a chip.

[0296] The data transmission device provided in the embodiments of the present application can implement each process implemented by the foregoing method embodiments and achieve the same technical effects. To avoid repetition, details are not described herein again.

[0297] Optionally, as Figure 12 shown, the embodiments of the present application further provide a communication device 900, including a processor 901 and a memory 902. A program or instruction that can run on the processor 901 is stored on the memory 902. For example, when the communication device 900 is a network-side device, when the program or instruction is executed by the processor 901, it implements each step of the foregoing model determination method embodiment, or implements each step of the foregoing data transmission method embodiment, and can achieve the same technical effects. When the communication device 900 is a terminal device, when the program or instruction is executed by the processor 901, it implements each step of the foregoing model determination method embodiment, or implements each step of the foregoing data transmission method embodiment, and can achieve the same technical effects. To avoid repetition, details are not described herein again.

[0298] As Figure 13 shown, it is a schematic diagram of the hardware structure of a terminal device according to an embodiment of the present application.

[0299] The terminal device 1000 includes, but is not limited to: at least some components such as 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 a processor 1010.

[0300] Those skilled in the art can understand that the terminal device 1000 may further include a power supply (such as a battery) for supplying power to each component. The power supply can be logically connected to the processor 1010 through a power management system, so as to implement functions such as management of charging, discharging, and power consumption management through the power management system. Figure 13 The structure of the terminal device shown in

[0301] It should be understood that in the embodiments of the present application, the input unit 1004 may include a Graphics Processing Unit (GPU) 10041 and a microphone 10042. The graphics processor 10041 processes the image data of static pictures or videos 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, for example, a liquid crystal display, an organic light-emitting diode, etc. The user input unit 1007 includes at least one of a touch panel 10071 and other input devices 10072. The touch panel 10071 is also referred to as a touch screen. The touch panel 10071 may include two parts: a touch detection device and a touch controller. The 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 a joystick, which will not be elaborated here.

[0302] In the embodiments of the present application, after receiving downlink data from a network-side device, the radio frequency unit 1001 may transmit it to the processor 1010 for processing; in addition, the radio frequency unit 1001 may send uplink data to the network-side device. Generally, the radio frequency unit 1001 includes, but is not limited to, an antenna, an amplifier, a transceiver, a coupler, a low-noise amplifier, a duplexer, etc.

[0303] 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. Among them, the first storage area may store an operating system, application programs 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 a volatile memory and a non-volatile memory. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), a synchronous dynamic random access memory (Synchronous DRAM, SDRAM), a double data rate synchronous dynamic random access memory (Double Data Rate SDRAM, DDR SDRAM), an enhanced synchronous dynamic random access memory (Enhanced SDRAM, ESDRAM), a synchronous link dynamic random access memory (Synch link DRAM, SLDRAM), and a direct rambus random access memory (Direct Rambus RAM, DRRAM). The memory 1009 in the embodiments of the present application includes but is not limited to these and any other suitable types of memories.

[0304] The processor 1010 may include one or more processing units; optionally, the processor 1010 integrates an application processor and a modem processor. Among them, the application processor mainly processes operations related to the operating system, user interface, and application programs, etc., and the modem processor mainly processes wireless communication signals, such as a baseband processor. It can be understood that the above modem processor may not be integrated into the processor 1010.

[0305] The embodiments of the present application further provide a network-side device, including a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the steps of the foregoing method embodiments. This network-side device embodiment corresponds to the above network-side device method embodiment. All the implementation processes and implementation manners of the network-side device in the above method embodiments can be applied to this network-side device embodiment and can achieve the same technical effects.

[0306] Specifically, an embodiment of the present application further provides a network-side device, such as Figure 14 As shown, 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. The antenna 111 is connected to the radio frequency device 112. In the uplink direction, the radio frequency device 112 receives information through the antenna 111 and sends the received information to the baseband device 113 for processing. In the downlink direction, the baseband device 113 processes the information to be sent and sends it to the radio frequency device 112. After processing the received information, the radio frequency device 112 sends it out through the antenna 111.

[0307] In the above embodiments, the method executed by the network-side device can be implemented in the baseband device 113, and the baseband device 113 includes a baseband processor.

[0308] The baseband device 113 may include, for example, at least one baseband board, and a plurality of chips are arranged on the baseband board, such as Figure 14 As shown, one of the chips 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 embodiments.

[0309] The network-side device may further include a network interface 116, and this interface is, for example, a common public radio interface (CPRI).

[0310] Specifically, the network-side device 1100 in the embodiment of the present invention further includes: instructions or programs stored on the memory 115 and executable on the processor 114. The processor 114 calls the instructions or programs in the memory 115 to execute Figure 10 or Figure 11 the methods executed by the modules in, and achieve the same technical effects. To avoid repetition, they are not described herein again.

[0311] An embodiment of the present application further provides a network-side device. As Figure 15 shown, the network-side device 1200 includes: a processor 1201, a network interface 1202, and a memory 1203. Among them, the network interface 1202 is, for example, a common public radio interface (CPRI).

[0312] Specifically, the network-side device 1200 in the embodiment of the present invention further includes: instructions or programs stored on the memory 1203 and executable on the processor 1201. The processor 1201 calls the instructions or programs in the memory 1203 to execute Figure 10 orFigure 11 The methods executed by the modules shown above achieve the same technical effects. To avoid repetition, they will not be elaborated here.

[0313] The embodiments of the present application further provide a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, it implements each process of the foregoing method embodiments and can achieve the same technical effects. To avoid repetition, they will not be elaborated here.

[0314] Wherein, the processor is the processor in the terminal device described in the foregoing embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory ROM, random access memory RAM, magnetic disks, or optical discs, etc.

[0315] The embodiments of the present application further provide a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement each process of the foregoing method embodiments and can achieve the same technical effects. To avoid repetition, they will not be elaborated here.

[0316] It should be understood that the chip mentioned in the embodiments of the present application may also be referred to as a system-on-chip, system chip, chip system, or system-on-chip, etc.

[0317] The embodiments of the present application further provide 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 each process of the foregoing method embodiments and can achieve the same technical effects. To avoid repetition, they will not be elaborated here.

[0318] The embodiments of the present application further provide a model determination system, including: a first device and a second device. 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.

[0319] It should be noted that, in this text, the term "comprise", "include" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or apparatus comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or apparatus. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or apparatus comprising that element. In addition, it should be pointed out that the scope of the methods and apparatuses 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 reverse order according to the functions involved. For example, the described methods may be performed in an order different from that described, and various steps may be added, omitted, or combined. Additionally, the features described with reference to certain examples may be combined in other examples.

[0320] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described example methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disc) and includes several instructions for causing a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present application.

[0321] The embodiments of the present application have been described above in conjunction with the accompanying drawings. However, the present application is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Those of ordinary skill in the art, under the inspiration of the present application and without departing from the spirit and scope protected by the claims of the present application, can still make many forms, all of which fall within the protection scope of the present application.

Claims

1. A model determination method, characterized in that: 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, characterized in that 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, characterized in that 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, characterized in that 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, characterized in that: 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, characterized in that 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, characterized in that 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, characterized in that: 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, characterized in that 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, characterized in that 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, characterized in that 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, characterized in that 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, characterized in that 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, characterized in that 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, characterized in that 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, characterized in that 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, characterized in that 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, characterized in that 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, characterized in that 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, characterized in that 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, characterized in that: 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, characterized in that: 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, characterized in that: 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, characterized in that: 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, characterized in that: 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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