Transmission method and apparatus, and terminal and network-side device

Abstract

The present application belongs to the technical field of communications. Disclosed are a transmission method and apparatus, and a terminal and a network-side device. The transmission method in the embodiments of the present application comprises: a terminal sending first information to a network-side device, wherein the first information is used for indicating an adjustment suggestion of the terminal regarding a first artificial intelligence (AI) unit.

Description

Transmission method, device, terminal and network side equipment

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to the Chinese patent application filed with the China Patent Office on December 21, 2023, with application number 202311773196.3 and invention name “Transmission method, device, terminal and network side equipment”. The entire contents of the Chinese patent application are incorporated herein by reference. Technical Field

[0003] The present application belongs to the field of communication technology, and specifically relates to a transmission method, apparatus, terminal and network-side equipment. Background Art

[0004] With the development of artificial intelligence (AI) technology, AI models (also known as AI units) have been applied to communication systems. Currently, in communication systems, model monitoring of AI models is typically based on monitoring indicators or monitoring data reported by terminals. The network adjusts the AI ​​model based on the monitoring indicators or monitoring data reported by multiple terminals. However, the AI ​​model adjusted by the network in this way is not applicable to all terminals, resulting in poor applicability of the AI ​​model adjustment method. Summary of the Invention

[0005] The embodiments of the present application provide a transmission method, apparatus, terminal, and network-side equipment, which can solve the problem of poor applicability of adjustment methods for AI models in related technologies.

[0006] In a first aspect, a transmission method is provided, which is performed by a terminal. The method includes:

[0007] The terminal sends first information to the network side device, where the first information is used to indicate the terminal's adjustment suggestion for the first artificial intelligence AI unit.

[0008] In a second aspect, a transmission method is provided, which is performed by a network-side device, and the method includes:

[0009] The network-side device receives first information sent by the terminal, where the first information is used to indicate an adjustment suggestion of the terminal to the first AI unit.

[0010] According to a third aspect, a transmission device is provided, the device comprising:

[0011] The first sending module is used to send first information to the network side device, where the first information is used to indicate the adjustment suggestion of the device to the first AI unit.

[0012] In a fourth aspect, a transmission device is provided, the device comprising:

[0013] The second receiving module is used to receive first information sent by the terminal, where the first information is used to indicate the terminal's adjustment suggestion for the first AI unit.

[0014] In a fifth aspect, a terminal is provided, comprising a processor and a memory, wherein 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, the steps of the method described in the first aspect are implemented.

[0015] In a sixth aspect, a terminal is provided, comprising a processor and a communication interface, wherein the communication interface is used to send first information to a network side device, and the first information is used to indicate the terminal's adjustment suggestions for the first AI unit.

[0016] In the seventh aspect, a network side device is provided, which includes a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the program or instructions are executed by the processor, the steps of the method described in the second aspect are implemented.

[0017] In an eighth aspect, a network side device is provided, comprising a processor and a communication interface, wherein the communication interface is used to receive first information sent by a terminal, and the first information is used to indicate the terminal's adjustment suggestions for the first AI unit.

[0018] In the ninth aspect, a readable storage medium is provided, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented, or the steps of the method described in the second aspect are implemented.

[0019] In the tenth aspect, a wireless communication system is provided, comprising: a terminal and a network side device, wherein the terminal can be used to execute the steps of the method described in the first aspect, and the network side device can be used to execute the steps of the method described in the second aspect.

[0020] In the eleventh aspect, a chip is provided, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the method as described in the first aspect, or to implement the method as described in the second aspect.

[0021] In a twelfth aspect, a computer program / program product is provided, wherein the computer program / program product is stored in a storage medium, and the 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.

[0022] In an embodiment of the present application, the terminal sends first information to the network-side device, where the first information is used to indicate the terminal's adjustment suggestion for the first AI unit. In this way, the network-side device adjusts the first AI unit based on the adjustment suggestion reported by the terminal, making the adjustment of the first AI unit more in line with the terminal's operating scenario, effectively improving the applicability of the AI ​​unit adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] FIG1 shows a block diagram of a wireless communication system to which embodiments of the present application may be applied;

[0024] FIG2 is a flow chart of a transmission method provided in an embodiment of the present application;

[0025] FIG3 is a flow chart of another transmission method provided in an embodiment of the present application;

[0026] FIG4 is a flow chart of another transmission method provided in an embodiment of the present application;

[0027] FIG5 is a structural diagram of a transmission device provided in an embodiment of the present application;

[0028] FIG6 is a structural diagram of another transmission device provided in an embodiment of the present application;

[0029] FIG7 is a structural diagram of a communication device provided in an embodiment of the present application;

[0030] FIG8 is a structural diagram of a terminal provided in an embodiment of the present application;

[0031] FIG9 is a structural diagram of a network-side device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0032] The following will be combined with the accompanying drawings in the embodiments of this application to clearly describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of this application.

[0033] The terms "first", "second", etc. in this application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way are interchangeable where appropriate, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same type, and do not limit the number of objects, for example, the first object can be one or more. In addition, "or" in this application represents at least one of the connected objects. For example, "A or B" covers three options, namely, Option 1: including A but not including B; Option 2: including B but not including A; Option 3: including both A and B. The character " / " generally indicates that the objects associated before and after are in an "or" relationship.

[0034] The term "indication" in this application can be either a direct indication (or explicit indication) or an indirect indication (or implicit indication). A direct indication can be understood as the sender explicitly informing the receiver of specific information, the operation to be performed, or the requested result, etc. in the instruction sent; an indirect indication can be understood as the receiver determining the corresponding information based on the instruction sent by the sender, or making a judgment and determining the operation to be performed or the requested result, etc. based on the judgment result.

[0035] It is worth noting that the technology described in the embodiments of the present application is not limited to the Long Term Evolution (LTE) / LTE-Advanced (LTE-A) system, but can also be used in other wireless communication systems, such as Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access (OFDMA), Single-carrier Frequency-Division Multiple Access (SC-FDMA) or other systems. The terms "system" and "network" in the embodiments of the present application are often used interchangeably, and the technology described can be used for the systems and radio technologies mentioned above, as well as for other systems and radio technologies. The following description describes a New Radio (NR) system for illustrative purposes, and NR terminology is used in most of the following description, but these technologies can also be applied to systems other than NR systems, such as 6th generation (6G) systems. thGeneration, 6G) communication system.

[0036] [Corrected 10.01.2025 according to Rule 91] Figure 1 shows a block diagram of a wireless communication system applicable to an embodiment of the present application. The wireless communication system includes a terminal 11 and a network-side device 12. The terminal 11 can be a mobile phone, a tablet computer (Tablet Personal Computer), a laptop computer (Laptop Computer), a notebook computer, a personal digital assistant (PDA), a handheld computer, a netbook, an ultra-mobile personal computer (UMPC), a mobile Internet device (MID), an augmented reality (AR), a virtual reality (VR) device, a robot, a wearable device (Wearable Device), an aircraft (flight vehicle), a vehicle user equipment (VUE), a ship-borne device, a pedestrian user equipment (PUE), a smart home (home appliance with wireless communication function, such as a refrigerator, a television, a washing machine or furniture, etc.), a game console, a personal computer (PC), a teller machine or a self-service machine, and other terminal-side devices. Wearable devices include: smart watches, smart bracelets, smart headphones, smart glasses, smart jewelry (smart bracelets, smart bracelets, smart rings, smart necklaces, smart anklets, smart anklets, etc.), smart wristbands, smart clothing, etc. Among them, the vehicle-mounted device can also be called a vehicle-mounted terminal, a vehicle-mounted controller, a vehicle-mounted module, a vehicle-mounted component, a vehicle-mounted chip or a vehicle-mounted unit, etc. It should be noted that the specific type of the terminal 11 is not limited in the embodiment of the present application. The network side device 12 may include an access network device or a core network device, wherein the access network device may also be called a radio access network (Radio Access Network, RAN) device, a radio access network function or a radio access network unit. The access network device may include a base station, a wireless local area network (WLAN) access point (AS) or a wireless fidelity (WiFi) node, etc.Among them, the base station can be referred to as Node B (NB), Evolved Node B (eNB), the next generation Node B (gNB), New Radio Node B (NR Node B), access point, Relay Base Station (RBS), Serving Base Station (SBS), Base Transceiver Station (BTS), radio base station, radio transceiver, Basic Service Set (BSS), Extended Service Set (ESS), Home Node B (HNB), Home Evolved Node B (home evolved Node B), Transmission Reception Point (TRP) or other appropriate terms in the field. As long as the same technical effect is achieved, the base station is not limited to specific technical vocabulary. It should be noted that in the embodiment of the present application, only the base station in the NR system is used as an example for introduction, and the specific type of the base station is not limited.

[0037] For better understanding, the following explains relevant concepts that may be involved in the embodiments of this application.

[0038] Artificial intelligence is currently being widely used in various fields. There are many ways to implement AI modules, such as neural networks, decision trees, support vector machines, and Bayesian classifiers. This application uses neural networks as an example, but does not limit the specific type of AI module.

[0039] Neural network parameters are optimized using optimization algorithms. An optimization algorithm is a type of algorithm that helps minimize or maximize an objective function (sometimes called a loss function). The objective function is often a mathematical combination of model parameters and data. For example, given data X and its corresponding label Y, we construct a neural network model f(.). With this model, we can obtain the predicted output f(x) based on the input x, and calculate the difference between the predicted value and the true value (f(x) - Y). This is the loss function. Our goal is to find appropriate weights (multiplicative coefficients) and biases (additive coefficients) to minimize the value of this loss function. The smaller the loss value, the closer our AI model is to the real world.

[0040] Currently, most common optimization algorithms are based on the back propagation (BP) algorithm. The basic idea of ​​the BP algorithm is that the learning process consists of two steps: forward propagation of signals and back propagation of errors. During forward propagation, input samples are passed from the input layer, processed layer by layer through each hidden layer, and then transmitted to the output layer. If the actual output of the output layer does not match the expected output, the error begins back propagation. Back propagation involves propagating the output error back through the hidden layers to the input layer layer by layer in some form, distributing the error to all units in each layer. This error signal is then generated for each unit in each layer, which serves as the basis for adjusting the weights of each unit. This process of adjusting the weights of each layer, through forward propagation of signals and back propagation of errors, is repeated over and over again. This process of continuous weight adjustment is the network's learning and training process. This process continues until the error in the network output is reduced to an acceptable level, or until a pre-set number of learning cycles is reached.

[0041] Generally speaking, the selected AI algorithms and models used vary depending on the type of problem being solved. Currently, the primary approach to improving 5G network performance through AI is to augment or replace existing algorithms or processing modules with neural network-based algorithms and AI models. In specific scenarios, neural network-based algorithms and AI models can achieve better performance than deterministic algorithms. Commonly used neural networks include deep neural networks, convolutional neural networks, and recurrent neural networks. Established AI tools can be used to build, train, and validate neural networks.

[0042] Replacing existing system modules with AI / machine learning (ML) methods can effectively improve system performance. For example, for Channel State Information (CSI) prediction, historical CSI is fed into an AI model, which analyzes the channel's time-domain variation characteristics and outputs future CSI. By monitoring and analyzing system performance, CSI prediction can significantly improve performance compared to solutions without prediction. Furthermore, the achievable prediction accuracy varies depending on the future time of the prediction.

[0043] While AI-based CSI prediction can achieve certain gains, it also has certain drawbacks, the most significant of which is the poor generalization of AI-based solutions. When the inference environment differs significantly from the training environment, the AI ​​model's performance will deteriorate, resulting in a mismatch. For example, a CSI prediction AI model trained on a channel with a terminal moving at 30 km / h will perform poorly in a scenario with a terminal moving at 60 km / h. Therefore, it is necessary to monitor the actual inference performance of CSI prediction and trigger a series of adjustment measures based on the monitoring results.

[0044] Model monitoring can be implemented based on a variety of indicators, such as monitoring based on the characteristics or distribution of input and output data, monitoring based on the intermediate results of model output (error indicators, accuracy indicators), monitoring based on final performance results, monitoring based on comparison results with other solutions, etc.

[0045] Monitoring based on model input or output distribution: The input or output information of an executing model (a model undergoing inference or an activated model) is statistically analyzed and calculated to obtain the distribution information of the model's input or output information (such as mean, mean vector, mean matrix, variance, variance vector, variance matrix, covariance, covariance vector, covariance matrix, maximum value, maximum value vector, maximum value matrix, minimum value, minimum value vector, minimum value matrix). When the model is trained, it also has a distribution information range for its applicable input or output. The terminal compares the calculated distribution information with the applicable distribution information range of the model to obtain the monitoring results. Alternatively, the terminal reports the calculated distribution information to the network, which compares the received distribution information with the applicable distribution information range of the model to obtain the monitoring results.

[0046] Monitoring intermediate results (error and accuracy indicators) calculated based on model output: The model output is compared with its corresponding true value to calculate intermediate results such as the model output error or accuracy. The terminal compares this intermediate result with a preset threshold to obtain the monitoring result. Alternatively, the terminal reports the calculated intermediate result to the network, which then compares it with a preset threshold to obtain the monitoring result.

[0047] Monitoring based on final performance results: The terminal or network side collects or calculates current communication system performance, such as throughput, spectral efficiency, signal-to-noise and interference ratio (SINR), signal-to-noise ratio (SNR), bit error rate, block error rate, packet loss rate, transmission rate (uplink / downlink), and peak rate (uplink / downlink). The terminal or network side compares the current communication system performance with preset thresholds to obtain monitoring results. If the current communication system performance is calculated or calculated by the terminal, it can also be reported to the network side.

[0048] Monitoring based on comparison results with other solutions: The terminal compares the intermediate or final performance results obtained based on the model with those obtained with other solutions to obtain monitoring results. Alternatively, the terminal reports the intermediate or final performance results obtained based on the model and those obtained with other solutions to the network, which then compares the intermediate or final performance results obtained based on the model with those obtained with other solutions to obtain monitoring results.

[0049] Currently, in communication systems, AI unit monitoring typically relies on terminal-reported monitoring indicators or data. The network then adjusts the AI ​​unit based on these indicators or data from multiple terminals. However, these adjustments to the AI ​​unit don't apply to all terminals, making this AI unit adjustment method less adaptable.

[0050] It should be noted that the AI ​​unit described in this application may also be referred to as an AI model, AI structure, etc., or the AI ​​unit may also refer to a processing unit that can implement specific AI-related algorithms, formulas, processing flows, capabilities, etc., or the AI ​​unit may be a processing method, algorithm, function, module or unit for a specific data set, or the AI ​​unit may be a processing method, algorithm, function, module or unit running on AI-related hardware such as a GPU, NPU, TPU, ASIC, etc., and this application does not make specific restrictions on this. Optionally, the specific data set includes the input and / or output of the AI ​​unit.

[0051] Optionally, the identifier of the AI ​​unit may be an AI model identifier, an AI structure identifier, an AI algorithm identifier, a functional identifier (functionality ID), a physical identifier, a logical identifier, a global identifier, a local identifier, or an identifier of a specific data set associated with the AI ​​unit, or an identifier of a specific AI-related scenario, environment, channel feature, or device, or an identifier of an AI-related function, feature, capability, or module. This application does not make any specific limitations on this.

[0052] The transmission method, apparatus, and communication equipment provided in the embodiments of the present application are described in detail below with reference to some embodiments and their application scenarios in conjunction with the accompanying drawings.

[0053] Please refer to FIG. 2 , which is a flow chart of a transmission method provided in an embodiment of the present application. As shown in FIG. 2 , the method includes the following steps:

[0054] Step 201: The terminal sends first information to a network-side device, where the first information is used to indicate an adjustment suggestion of the terminal to a first AI unit.

[0055] The first AI unit may refer to a specific AI unit. Optionally, the terminal may determine the first AI unit by an identifier of the AI ​​unit, so that the network-side device knows which AI unit the first information is directed to.

[0056] Exemplarily, the first AI unit is an AI unit running (or used) by the terminal. The terminal can then determine, based on the operating status of the first AI unit, adjustment recommendations for the first AI unit, such as model parameters that need to be adjusted for the first AI unit, monitoring configuration of the first AI unit, etc. The recommendations may also include recommendations on whether to adjust the first AI unit, such as maintaining the first AI unit running with its current configuration or switching the first AI unit. Based on these adjustment recommendations, the terminal generates first information and sends the first information to the network-side device, i.e., sending the terminal's adjustment recommendations for the first AI unit to the network-side device. This helps the network-side device make a decision on whether and how to adjust the first AI unit based on the adjustment recommendations.

[0057] In an embodiment of the present application, a terminal sends first information to a network-side device. The first information is used to indicate the terminal's adjustment recommendations for the first AI unit. The terminal can then derive adjustment recommendations based on its own understanding of the operation of the first AI unit, thereby helping the network-side device make decisions based on the adjustment recommendations on whether and how to adjust the first AI unit. This allows the network-side device to adjust the first AI unit based on the adjustment recommendations reported by the terminal, making the adjustment of the first AI unit more tailored to the terminal's operating scenario and making the adjusted first AI unit more suitable for operation in the terminal, thereby helping to improve the inference performance of the first AI unit.

[0058] Optionally, before step 201, the method further includes:

[0059] The terminal receives configuration information sent by the network-side device, where the configuration information is used to instruct the terminal to report an adjustment suggestion for the first AI unit.

[0060] In an embodiment of the present application, before the terminal sends the first information to the network side device, the network side device sends configuration information to the terminal, and the configuration information is used to instruct the terminal to report adjustment suggestions for the first AI unit. That is, the network side device can use the configuration information to inform the terminal which adjustment suggestions can be reported, so that the terminal can clearly determine which adjustment suggestions need to be collected based on the configuration information, which helps to better generate the first information.

[0061] Optionally, the first information or the configuration information includes at least one of the following adjustment suggestions:

[0062] Keep the first AI unit running in its current configuration, i.e., do not make any adjustments to the first AI unit;

[0063] Switching to a second AI unit, where the second AI unit is another AI unit different from the first AI unit, and the first information or the configuration information may include an identifier for switching to the second AI unit;

[0064] Falling back to a non-AI mode, for example, stopping the first AI unit;

[0065] Disabling the function corresponding to the first AI unit. For example, if the first AI unit is an AI unit that implements CSI prediction, the CSI prediction function may be disabled. For another example, if the first AI unit is an AI unit that implements CSI prediction in a specific scenario (such as a specific speed range), the CSI prediction function for the specific scenario may be disabled, while the CSI prediction functions for other scenarios remain unchanged.

[0066] Adjust the parameters of the first AI unit;

[0067] Request a new AI unit;

[0068] Cancel the first AI unit;

[0069] Adjust the monitoring configuration of the first AI unit, including information related to the monitoring window (monitoring duration, number of monitoring samples, etc.), monitoring trigger conditions, and monitoring result reporting conditions.

[0070] Request training for the first AI unit;

[0071] Requesting data collection for training the first AI unit;

[0072] Requesting multiple AI units to monitor in parallel or multiple AI units, wherein the multiple AI units may include the first AI unit;

[0073] Adjust the auxiliary information in the input of the first AI unit. The auxiliary information is part of the input information of the first AI unit and can be of various types. For example, the auxiliary information of the first AI unit for implementing CSI prediction can include speed information, location information, perception information, Doppler information, cell identification information, etc.

[0074] In an embodiment of the present application, the first information sent by the terminal to the network-side device includes at least one adjustment suggestion as described above, and the network-side device then obtains these adjustment suggestions based on the first information reported by the terminal, thereby helping the network-side device to make a decision on whether to adjust the first AI unit and how to adjust it based on the adjustment suggestion. In addition, the configuration information sent by the network-side device to the terminal may also include at least one adjustment suggestion as described above, so that the terminal can clearly identify which adjustment suggestions need to be reported based on the configuration information, making the terminal's reporting behavior more targeted.

[0075] Optionally, the first information or the configuration information further includes at least one of the following:

[0076] Reporting conditions for each adjustment suggestion in the first information or the configuration information, for example, determining to report a certain adjustment suggestion if a certain adjustment suggestion or a related indicator meets a preset threshold condition;

[0077] The reporting priority of each adjustment suggestion in the first information or the configuration information, that is, which adjustment suggestions can be reported first;

[0078] the order in which the adjustment suggestions in the first information or the configuration information are reported, that is, the order in which the adjustment suggestions are reported, for example, which adjustment suggestions are reported first and which adjustment suggestions are reported later;

[0079] The dependencies between the adjustment suggestions in the first information or the configuration information, for example, which adjustment suggestions need to be reported together, such as the adjustment suggestions for canceling the first AI unit and falling back to a non-AI mode, or the request for training the first AI unit and the request for data collection for training the first AI unit, etc.

[0080] In an embodiment of the present application, by limiting the reporting conditions, reporting priority, reporting order or dependency relationship of each adjustment suggestion in the first information or the configuration information, the reporting of the adjustment suggestion is made more orderly.

[0081] Optionally, before the terminal sends the first information to the network-side device, the method further includes:

[0082] The terminal receives second information sent by the network-side device, where the second information is used to indicate a monitoring type of the first AI unit by the terminal, where the monitoring type includes at least one of the following:

[0083] Monitoring based on monitoring indicators;

[0084] Monitoring based on raw data, where the raw data is the data used to calculate monitoring indicators;

[0085] Monitoring based on endpoint recommendations.

[0086] It should be noted that the monitoring based on monitoring indicators refers to monitoring indicators obtained based on the input and / or output data characteristics or distribution of the AI ​​unit, monitoring based on the intermediate results output by the AI ​​unit, monitoring based on the final performance results of the AI ​​unit, etc., such as accuracy indicators, error indicators, distribution indicators, scoring indicators, etc. These indicators can be one or a group of numerical values.

[0087] The monitoring based on raw data refers to the raw data of monitoring indicators obtained by the terminal based on the input and / or output data characteristics or distribution of the AI ​​unit, the intermediate results output by the AI ​​unit, the final performance results of the AI ​​unit, and other schemes, such as the output of the first AI unit and the true value corresponding to the output of the first AI unit.

[0088] The monitoring based on terminal suggestions is the monitoring of the terminal reporting the adjustment suggestions for the first AI unit to the network side device.

[0089] In an embodiment of the present application, the network-side device sends the second information to the terminal, so that the terminal can clarify which monitoring type to adopt for the first AI unit based on the second information, which helps the terminal monitor the reasoning performance of the first AI unit and also helps the terminal collect relevant data during the operation of the first AI unit to obtain the monitoring data or adjustment suggestions that need to be reported.

[0090] Optionally, the terminal sending the first information to the network side device includes:

[0091] The terminal generates first information based on at least one of the second information, the configuration message, and the reasoning performance of the first AI unit;

[0092] The terminal sends the first information to the network side device.

[0093] Exemplarily, when the terminal receives the second information and configuration information sent by the network-side device, it may generate the first information based on at least one of the second information, the configuration message, and the reasoning performance of the first AI unit. For example, the terminal generates the first information based on the configuration information and the reasoning performance of the first AI unit, that is, generates the terminal's adjustment suggestions for the first AI unit; or, when the second information includes terminal-based suggestions, the terminal generates the first information based on the second information, the configuration message, and the reasoning performance of the first AI unit, that is, generates the terminal's adjustment suggestions for the first AI unit. The reasoning performance of the first AI unit may include a reasoning performance monitoring indicator obtained by the terminal based on the operation of the first AI unit.

[0094] In an embodiment of the present application, the terminal can generate the first information based on at least one of the second information, the configuration message and the reasoning performance of the first AI unit, that is, generate the terminal's adjustment suggestion for the first AI unit, thereby clarifying the method of generating the adjustment suggestion.

[0095] Optionally, the sending of the second information and the configuration information satisfies any one of the following:

[0096] The second information and the configuration information are carried in the same message and sent. For example, the second information and the configuration information can be two contents or two sub-information in one message. Thus, the second information and the configuration information are sent together, which helps save transmission resources.

[0097] The second information and the configuration information are carried and sent in the same signaling. For example, the second information and the configuration information are two independent pieces of information, but can be carried and sent in the same signaling, which also helps to save transmission resources;

[0098] The second information and the configuration information are respectively carried in different signalings and sent, that is, the second information and the configuration information are two independent pieces of information and are respectively carried in different signalings and sent.

[0099] Optionally, before the terminal receives the second information sent by the network-side device, the method further includes at least one of the following:

[0100] The terminal sends first capability information to the network-side device, where the first capability information is used to indicate a monitoring type of the AI ​​unit supported by the terminal;

[0101] The terminal sends second capability information to the network-side device, where the second capability information is used to indicate whether the terminal supports the monitoring based on the terminal suggestion;

[0102] The terminal sends third capability information to the network-side device, where the third capability information is used to indicate the adjustment suggestion that the terminal can make.

[0103] For example, the terminal sends first capability information to the network side device, and uses the first capability information to indicate the monitoring type of the AI ​​unit supported by the terminal, that is, to indicate which type or types of monitoring the terminal supports, including monitoring based on monitoring indicators, monitoring based on raw data, and monitoring based on terminal suggestions. This enables the network side device to clearly understand the monitoring type of the AI ​​unit supported by the terminal, which helps the network side device to clarify subsequent behaviors, such as whether to send the configuration information to the terminal.

[0104] For another example, the terminal may send second capability information to the network side device, and use the second capability information to indicate whether the terminal supports the monitoring based on terminal suggestions, so that the network side device can clearly determine whether the terminal supports the monitoring based on terminal suggestions, and thus whether the terminal can report adjustment suggestions for the first AI unit, which also helps the network side device decide whether to send the configuration information to the terminal.

[0105] For example, the terminal can also send third capability information to the network side device, and use the third capability information to indicate the adjustment suggestions that the terminal can make, so that the network side device can know which adjustment suggestions for the first AI unit can be obtained from the terminal, thereby helping the network side device to make adjustment decisions for the first AI unit.

[0106] Optionally, the first capability information, the second capability information and the third capability information are sent separately or carried on the same signaling or information.

[0107] In the embodiment of the present application, after the terminal sends the first information to the network-side device, the method further includes:

[0108] The terminal receives third information sent by the network-side device, where the third information includes an adjustment decision for the first AI unit.

[0109] It is understood that after the terminal reports the adjustment suggestion for the first AI unit to the network device, the network device can make an adjustment decision based on the adjustment suggestion as to whether and how to adjust the first AI unit, and send the third information to the terminal, the third information including the adjustment decision for the first AI unit. Thus, the terminal can adjust the first AI unit based on the adjustment decision, making the adjusted first AI unit more suitable for operation on the terminal, thereby helping to improve the reasoning performance of the first AI unit.

[0110] Please refer to Figure 3, which is a flow chart of another transmission method provided by an embodiment of the present application. As shown in Figure 3, the method includes the following steps:

[0111] Step 301: A network-side device receives first information sent by a terminal, where the first information is used to indicate an adjustment suggestion of the terminal to a first AI unit.

[0112] Optionally, before the network-side device receives the first information sent by the terminal, the method further includes:

[0113] The network-side device sends configuration information to the terminal, where the configuration information is used to instruct the terminal to report an adjustment suggestion for the first AI unit.

[0114] Optionally, the first information or the configuration information includes at least one of the following adjustment suggestions:

[0115] Keep the first AI unit running in its current configuration;

[0116] Switch to the second AI unit;

[0117] Fall back to non-AI mode;

[0118] Turn off the function corresponding to the first AI unit;

[0119] Adjust the parameters of the first AI unit;

[0120] Request a new AI unit;

[0121] Cancel the first AI unit;

[0122] Adjust the monitoring configuration of the first AI unit;

[0123] Request training for the first AI unit;

[0124] Requesting data collection for training the first AI unit;

[0125] Request multiple AI units to run in parallel or monitor multiple AI units;

[0126] Adjust auxiliary information in the input of the first AI unit.

[0127] Optionally, the first information or the configuration information further includes at least one of the following:

[0128] reporting conditions for each adjustment suggestion in the first information or the configuration information;

[0129] a reporting priority of each adjustment suggestion in the first information or the configuration information;

[0130] a reporting order of the adjustment suggestions in the first information or the configuration information;

[0131] The dependency relationship between the adjustment suggestions in the first information or the configuration information.

[0132] Optionally, before the network-side device receives the first information sent by the terminal, the method further includes:

[0133] The network-side device sends second information to the terminal, where the second information is used to indicate a monitoring type of the first AI unit by the terminal, where the monitoring type includes at least one of the following:

[0134] Monitoring based on monitoring indicators;

[0135] Monitoring based on raw data, where the raw data is the data used to calculate monitoring indicators;

[0136] Monitoring based on endpoint recommendations.

[0137] Optionally, the sending of the second information and the configuration information satisfies any one of the following:

[0138] The second information and the configuration information are carried in the same message and sent;

[0139] The second information and the configuration information are carried and sent in the same signaling;

[0140] The second information and the configuration information are respectively carried and sent in different signalings.

[0141] Optionally, before the network-side device sends the second information to the terminal, the method further includes at least one of the following:

[0142] The network-side device receives first capability information sent by the terminal, where the first capability information is used to indicate a monitoring type of the AI ​​unit supported by the terminal;

[0143] The network-side device receives second capability information sent by the terminal, where the second capability information is used to indicate whether the terminal supports the monitoring based on the terminal suggestion;

[0144] The network-side device receives third capability information sent by the terminal, where the third capability information is used to indicate the adjustment suggestion that the terminal can make.

[0145] Optionally, the first capability information, the second capability information and the third capability information are sent separately or carried on the same signaling or information.

[0146] Optionally, after the network-side device receives the first information sent by the terminal, the method further includes:

[0147] The network-side device generates third information based on the first information and sends the third information to the terminal, where the third information includes an adjustment decision for the first AI unit.

[0148] For example, the network-side device may receive the first information reported by multiple terminals and generate third information based on the adjustment suggestions in the first information reported by the multiple terminals, that is, generate an adjustment decision for the first AI unit. In this way, the network-side device can generate an adjustment decision for the first AI unit based on the adjustment suggestions reported by the multiple terminals, so that the adjustment decision generated by the network-side device incorporates the adjustment suggestions reported by the multiple terminals. This makes the adjustment decision for the first AI unit more comprehensive, more conducive to improving the performance of the first AI unit, and also makes the adjustment decision applicable to different terminals.

[0149] It should be noted that the transmission method applied to the network side device provided in the embodiment of the present application corresponds to the above-mentioned transmission method applied to the terminal side. The specific implementation process and related concepts involved in the embodiment of the present application can refer to the description in the above-mentioned terminal side method embodiment, and this embodiment will not be further described.

[0150] In an embodiment of the present application, a network-side device receives first information sent by a terminal, where the first information is used to indicate the terminal's adjustment recommendations for the first AI unit. The terminal can then derive adjustment recommendations based on its own understanding of the operation of the first AI unit, thereby helping the network-side device make decisions based on the adjustment recommendations regarding whether and how to adjust the first AI unit. This allows the network-side device to adjust the first AI unit based on the adjustment recommendations reported by the terminal, making the adjustment to the first AI unit more tailored to the terminal's operating scenario and effectively improving the applicability of the AI ​​unit adjustment.

[0151] Please refer to FIG. 4 , which is a flow chart of another transmission method provided in an embodiment of the present application. As shown in FIG. 4 , the method includes the following steps:

[0152] Step 401: The terminal sends first capability information, second capability information, and third capability information to a network device.

[0153] Step 402: The network side device sends configuration information and second information to the terminal;

[0154] Step 403: The terminal generates first information based on the configuration information and the second information, where the first information includes an adjustment suggestion of the terminal for the first AI unit.

[0155] Step 404: The terminal sends first information to the network side device;

[0156] Step 405: The network-side device generates an adjustment decision for the first AI unit based on the first information, and sends third information to the terminal, where the third information includes the adjustment decision.

[0157] The specific implementation process and related concepts involved in the embodiments of the present application can refer to the description in the above method embodiments, and will not be repeated in this embodiment.

[0158] The transmission method provided in the embodiment of the present application can be executed by a transmission device. In the embodiment of the present application, the transmission device provided in the embodiment of the present application is described by taking the transmission method executed by the transmission device as an example.

[0159] Please refer to FIG5 , which is a structural diagram of a transmission device provided in an embodiment of the present application. As shown in FIG5 , the transmission device 500 includes:

[0160] The first sending module 501 is used to send first information to the network side device, where the first information is used to indicate the adjustment suggestion of the device to the first AI unit.

[0161] Optionally, the device further comprises:

[0162] The first receiving module is used to receive configuration information sent by the network side device, where the configuration information is used to instruct the device to report adjustment suggestions for the first AI unit.

[0163] Optionally, the first information or the configuration information includes at least one of the following adjustment suggestions:

[0164] Keep the first AI unit running in its current configuration;

[0165] Switch to the second AI unit;

[0166] Fall back to non-AI mode;

[0167] Turn off the function corresponding to the first AI unit;

[0168] Adjust the parameters of the first AI unit;

[0169] Request a new AI unit;

[0170] Cancel the first AI unit;

[0171] Adjust the monitoring configuration of the first AI unit;

[0172] Request training for the first AI unit;

[0173] Requesting data collection for training the first AI unit;

[0174] Request multiple AI units to run in parallel or monitor multiple AI units;

[0175] Adjust auxiliary information in the input of the first AI unit.

[0176] Optionally, the first information or the configuration information further includes at least one of the following:

[0177] reporting conditions for each adjustment suggestion in the first information or the configuration information;

[0178] a reporting priority of each adjustment suggestion in the first information or the configuration information;

[0179] a reporting order of the adjustment suggestions in the first information or the configuration information;

[0180] The dependency relationship between the adjustment suggestions in the first information or the configuration information.

[0181] Optionally, the first receiving module is further configured to:

[0182] Receive second information sent by the network-side device, where the second information is used to indicate a monitoring type of the first AI unit by the apparatus, where the monitoring type includes at least one of the following:

[0183] Monitoring based on monitoring indicators;

[0184] Monitoring based on raw data, where the raw data is the data used to calculate monitoring indicators;

[0185] Monitoring based on endpoint recommendations.

[0186] Optionally, the first sending module 501 is further configured to:

[0187] generating first information based on at least one of the second information, the configuration message, and the reasoning performance of the first AI unit;

[0188] Send the first information to the network side device.

[0189] Optionally, the sending of the second information and the configuration information satisfies any one of the following:

[0190] The second information and the configuration information are carried in the same message and sent;

[0191] The second information and the configuration information are carried and sent in the same signaling;

[0192] The second information and the configuration information are respectively carried and sent in different signalings.

[0193] Optionally, the first sending module 501 is further configured to:

[0194] Sending first capability information to the network-side device, where the first capability information is used to indicate a monitoring type of the AI ​​unit supported by the apparatus;

[0195] Sending second capability information to the network-side device, where the second capability information is used to indicate whether the apparatus supports the monitoring based on the terminal suggestion;

[0196] Sending third capability information to the network side device, where the third capability information is used to indicate the adjustment suggestion that the apparatus can make.

[0197] Optionally, the first capability information, the second capability information and the third capability information are sent separately or carried on the same signaling or information.

[0198] Optionally, the device further comprises:

[0199] A third receiving module is used to receive third information sent by the network side device, where the third information includes an adjustment decision for the first AI unit.

[0200] In an embodiment of the present application, the device (e.g., a terminal) can send first information to a network-side device, where the first information is used to indicate the device's adjustment recommendations for the first AI unit. This allows the device to derive adjustment recommendations based on its own operational status of the first AI unit, thereby helping the network-side device make decisions on whether to adjust the first AI unit and how to adjust it based on the adjustment recommendations. This allows the network-side device to adjust the first AI unit based on the adjustment recommendations reported by the terminal, making the adjustment to the first AI unit more tailored to the terminal's operational scenario and effectively improving the applicability of the AI ​​unit adjustment.

[0201] The transmission device in the embodiments of the present application can 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 chip. The electronic device can be a terminal, or it can be other devices other than a terminal. For example, the terminal can include but is not limited to the types of terminal 11 listed above, and other devices can be servers, network attached storage (NAS), etc., which are not specifically limited in the embodiments of the present application.

[0202] The transmission device provided in the embodiment of the present application can implement each process implemented in the method embodiment of Figure 2 and achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0203] Please refer to FIG. 6 , which is a structural diagram of another transmission device provided in an embodiment of the present application. As shown in FIG. 6 , the transmission device 600 includes:

[0204] The second receiving module 601 is configured to receive first information sent by a terminal, where the first information is used to indicate an adjustment suggestion of the terminal to the first AI unit.

[0205] Optionally, the device further comprises:

[0206] The second sending module is used to send configuration information to the terminal, where the configuration information is used to instruct the terminal to report adjustment suggestions for the first AI unit.

[0207] Optionally, the first information or the configuration information includes at least one of the following adjustment suggestions:

[0208] Keep the first AI unit running in its current configuration;

[0209] Switch to the second AI unit;

[0210] Fall back to non-AI mode;

[0211] Turn off the function corresponding to the first AI unit;

[0212] Adjust the parameters of the first AI unit;

[0213] Request a new AI unit;

[0214] Cancel the first AI unit;

[0215] Adjust the monitoring configuration of the first AI unit;

[0216] Request training for the first AI unit;

[0217] Requesting data collection for training the first AI unit;

[0218] Request multiple AI units to run in parallel or monitor multiple AI units;

[0219] Adjust auxiliary information in the input of the first AI unit.

[0220] Optionally, the first information or the configuration information further includes at least one of the following:

[0221] reporting conditions for each adjustment suggestion in the first information or the configuration information;

[0222] a reporting priority of each adjustment suggestion in the first information or the configuration information;

[0223] a reporting order of the adjustment suggestions in the first information or the configuration information;

[0224] The dependency relationship between the adjustment suggestions in the first information or the configuration information.

[0225] Optionally, the second sending module is further configured to:

[0226] Sending second information to the terminal, where the second information is used to indicate a monitoring type of the first AI unit by the terminal, where the monitoring type includes at least one of the following:

[0227] Monitoring based on monitoring indicators;

[0228] Monitoring based on raw data, where the raw data is the data used to calculate monitoring indicators;

[0229] Monitoring based on endpoint recommendations.

[0230] Optionally, the sending of the second information and the configuration information satisfies any one of the following:

[0231] The second information and the configuration information are carried in the same message and sent;

[0232] The second information and the configuration information are carried and sent in the same signaling;

[0233] The second information and the configuration information are respectively carried and sent in different signalings.

[0234] Optionally, the second receiving module 601 is further configured to:

[0235] receiving first capability information sent by the terminal, where the first capability information is used to indicate a monitoring type of the AI ​​unit supported by the terminal;

[0236] receiving second capability information sent by the terminal, where the second capability information is used to indicate whether the terminal supports the monitoring based on the terminal suggestion;

[0237] Receive third capability information sent by the terminal, where the third capability information is used to indicate the adjustment suggestion that the terminal can make.

[0238] Optionally, the first capability information, the second capability information and the third capability information are sent separately or carried on the same signaling or information.

[0239] Optionally, the device further comprises:

[0240] A third sending module is configured to generate third information based on the first information and send the third information to the terminal, where the third information includes an adjustment decision for the first AI unit.

[0241] In the embodiment of the present application, the device receives the first information sent by the terminal, thereby enabling the device to make a decision on whether and how to adjust the first AI unit based on the adjustment suggestion in the first information. This allows the device to adjust the first AI unit based on the adjustment suggestion reported by the terminal, making the adjustment of the first AI unit more tailored to the terminal's operating scenario and effectively improving the applicability of the AI ​​unit adjustment.

[0242] The transmission device provided in the embodiment of the present application can implement each process implemented in the method embodiment of Figure 3 and achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0243] As shown in Figure 7, an embodiment of the present application further provides a communication device 700, including a processor 701 and a memory 702. The memory 702 stores a program or instruction that can be run on the processor 701. For example, when the communication device 700 is a terminal, the program or instruction, when executed by the processor 701, implements the various steps of the above-mentioned transmission method embodiment and can achieve the same technical effect. When the communication device 700 is a network-side device, the program or instruction, when executed by the processor 701, implements the various steps of the above-mentioned transmission method embodiment and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0244] The present application also provides a terminal including a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is configured to execute a program or instruction to implement the steps of the method embodiment shown in FIG2 . This terminal embodiment corresponds to the aforementioned terminal-side method embodiment, and each implementation process and implementation method of the aforementioned method embodiment is applicable to this terminal embodiment and can achieve the same technical effects. Specifically, FIG8 is a schematic diagram of the hardware structure of a terminal implementing an embodiment of the present application.

[0245] The terminal 800 includes but is not limited to: a radio frequency unit 801, a network module 802, an audio output unit 803, an input unit 804, a sensor 805, a display unit 806, a user input unit 807, an interface unit 808, a memory 809 and at least some of the components of the processor 810.

[0246] Those skilled in the art will appreciate that the terminal 800 may also include a power supply (such as a battery) to power various components. The power supply may be logically connected to the processor 810 via a power management system, thereby enabling the power management system to manage charging, discharging, and power consumption. The terminal structure shown in FIG8 does not limit the terminal. The terminal may include more or fewer components than shown, or may combine certain components, or have different component arrangements, which will not be described in detail here.

[0247] It should be understood that in an embodiment of the present application, the input unit 804 may include a graphics processing unit (GPU) 8041 and a microphone 8042, and the graphics processor 8041 processes the image data of a static picture or video obtained by an image capture device (such as a camera) in a video capture mode or an image capture mode. The display unit 806 may include a display panel 8061, and the display panel 8061 may be configured in the form of a liquid crystal display, an organic light emitting diode, etc. The user input unit 807 includes a touch panel 8071 and at least one of other input devices 8072. The touch panel 8071 is also called a touch screen. The touch panel 8071 may include two parts: a touch detection device and a touch controller. Other input devices 8072 may include, but are not limited to, a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which will not be repeated here.

[0248] In the embodiment of the present application, after receiving downlink data from a network-side device, the radio frequency unit 801 may transmit the data to the processor 810 for processing. Furthermore, the radio frequency unit 801 may send uplink data to the network-side device. Typically, the radio frequency unit 801 includes, but is not limited to, an antenna, an amplifier, a transceiver, a coupler, a low-noise amplifier, a duplexer, and the like.

[0249] The memory 809 can be used to store software programs or instructions and various data. The memory 809 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, applications or instructions required for at least one function (such as a sound playback function, an image playback function, etc.). In addition, the memory 809 may include a volatile memory or a non-volatile memory. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct RAM bus random access memory (DRRAM). The memory 809 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.

[0250] Processor 810 may include one or more processing units. Optionally, processor 810 integrates an application processor and a modem processor. The application processor primarily handles operations related to the operating system, user interface, and application programs, while the modem processor primarily processes wireless communication signals, such as a baseband processor. It is understood that the modem processor may not be integrated into processor 810.

[0251] The radio frequency unit 801 is configured to send first information to a network-side device, where the first information is configured to indicate an adjustment suggestion of the terminal to the first AI unit.

[0252] In an embodiment of the present application, a terminal sends first information to a network-side device. The first information is used to indicate the terminal's adjustment suggestion for the first AI unit. The terminal can then derive adjustment suggestions based on its own understanding of the operation of the first AI unit, thereby helping the network-side device make decisions based on the adjustment suggestions on whether and how to adjust the first AI unit. This allows the network-side device to adjust the first AI unit based on the adjustment suggestions reported by the terminal, making the adjustment of the first AI unit more tailored to the terminal's operating scenario and effectively improving the applicability of the AI ​​unit adjustment.

[0253] It can be understood that the implementation process of each implementation method mentioned in this embodiment can refer to the relevant description of the above-mentioned terminal side method embodiment and achieve the same or corresponding technical effect. To avoid repetition, it will not be repeated here.

[0254] The present application also provides a network-side device, including a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is configured to execute a program or instruction to implement the steps of the method embodiment shown in FIG3 . This network-side device embodiment corresponds to the aforementioned network-side device method embodiment, and each implementation process and implementation method of the aforementioned method embodiment are applicable to this network-side device embodiment and can achieve the same technical effects.

[0255] Specifically, embodiments of the present application also provide a network-side device. As shown in Figure 9, the network-side device 900 includes an antenna 91, a radio frequency device 92, a baseband device 93, a processor 94, and a memory 95. Antenna 91 is connected to radio frequency device 92. In the uplink direction, radio frequency device 92 receives information via antenna 91 and sends the received information to baseband device 93 for processing. In the downlink direction, baseband device 93 processes the information to be transmitted and sends it to radio frequency device 92. Radio frequency device 92 processes the received information and then sends it through antenna 91.

[0256] The method executed by the network-side device in the above embodiment may be implemented in the baseband device 93 , which includes a baseband processor.

[0257] The baseband device 93 may include, for example, at least one baseband board, on which multiple chips are arranged, as shown in Figure 9, one of the chips is, for example, a baseband processor, which is connected to the memory 95 through a bus interface to call the program in the memory 95 and execute the network device operations shown in the above method embodiment.

[0258] The network side device may further include a network interface 96, which is, for example, a Common Public Radio Interface (CPRI).

[0259] Specifically, the network side device 900 of an embodiment of the present invention also includes: instructions or programs stored in the memory 95 and executable on the processor 94. The processor 94 calls the instructions or programs in the memory 95 to execute the methods executed by the modules shown in FIG6 and achieve the same technical effect. To avoid repetition, they will not be elaborated here.

[0260] An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the various processes of the above-mentioned transmission method embodiment are implemented and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0261] The processor is the processor in the terminal described in the above embodiment. The readable storage medium includes a computer-readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk. In some examples, the readable storage medium may be a non-transitory readable storage medium.

[0262] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, the communication interface and the processor are coupled, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned transmission method embodiment and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0263] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0264] An embodiment of the present application further provides a computer program / program product, which is stored in a storage medium. The computer program / program product is executed by at least one processor to implement the various processes of the above-mentioned transmission method embodiment and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0265] An embodiment of the present application further provides a .... system, comprising: a terminal and a network-side device, wherein the terminal can be used to execute the steps of the transmission method described above, and the network-side device can be used to execute the steps of the transmission method described above.

[0266] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the opposite order according to the functions involved. For example, the described method may be performed in an order different from that described, and various steps may also be added, omitted or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0267] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of a computer software product plus a necessary general-purpose hardware platform, or of course, by hardware. The computer software product is stored in a storage medium (such as ROM, RAM, magnetic disk, optical disk, etc.) and includes a number of instructions for enabling a terminal or network-side device to execute the methods described in each embodiment of the present application.

[0268] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms of implementation methods without departing from the purpose of this application and the scope of protection of the claims. These implementation methods are all within the protection of this application.

Claims

1. A transmission method, comprising: The terminal sends first information to the network side device, where the first information is used to indicate the terminal's adjustment suggestion for the first artificial intelligence AI unit.

2. The method according to claim 1, wherein: Before the terminal sends the first information to the network side device, the method further includes: The terminal receives configuration information sent by the network side device, where the configuration information is used to instruct the terminal to report adjustment suggestions for the first AI unit.

3. The method according to claim 2, wherein: The first information or the configuration information includes at least one of the following adjustment suggestions: Keep the first AI unit running in the current configuration; Switch to the second AI unit; Fall back to non-AI mode; Turn off the function corresponding to the first AI unit; Adjust the parameters of the first AI unit; Request new AI units; Deregister the first AI unit; Adjust the monitoring configuration of the first AI unit; Request training for the first AI unit; Requesting data collection for training the first AI unit; Request multiple AI units to run in parallel or monitor multiple AI units; Auxiliary information in the input of the first AI unit is adjusted.

4. The method according to claim 3, wherein: The first information or the configuration information further includes at least one of the following: a reporting condition for each adjustment suggestion in the first information or the configuration information; a reporting priority of each adjustment suggestion in the first information or the configuration information; a reporting order of each adjustment suggestion in the first information or the configuration information; The dependency relationship between the adjustment suggestions in the first information or the configuration information.

5. The method according to claim 2, wherein: Before the terminal sends the first information to the network side device, the method further includes: The terminal receives second information sent by the network side device, where the second information is used to indicate a monitoring type of the first AI unit by the terminal, where the monitoring type includes at least one of the following: Monitoring based on monitoring indicators; Monitoring based on raw data, where the raw data is the data used to calculate monitoring indicators; Monitoring based on endpoint recommendations.

6. The method according to claim 5, wherein: The terminal sends first information to the network side device, including: The terminal generates first information based on at least one of the second information, the configuration message, and the reasoning performance of the first AI unit; The terminal sends the first information to a network side device.

7. The method according to claim 5, wherein: The sending of the second information and the configuration information satisfies any one of the following: The second information and the configuration information are carried in the same information and sent; The second information and the configuration information are carried in the same signaling and sent; The second information and the configuration information are respectively carried in different signalings and sent.

8. The method according to claim 5, wherein: Before the terminal receives the second information sent by the network side device, the method further includes at least one of the following: The terminal sends first capability information to the network side device, where the first capability information is used to indicate a monitoring type of the AI ​​unit supported by the terminal; The terminal sends second capability information to the network side device, where the second capability information is used to indicate whether the terminal supports the monitoring based on the terminal suggestion; The terminal sends third capability information to the network side device, where the third capability information is used to indicate the adjustment suggestion that the terminal can make.

9. The method according to claim 8, wherein: The first capability information, the second capability information and the third capability information are sent separately or carried on the same signaling or information.

10. The method according to any one of claims 1 to 9, wherein: After the terminal sends the first information to the network side device, the method further includes: The terminal receives third information sent by the network side device, where the third information includes an adjustment decision for the first AI unit.

11. A transmission method, comprising: The network side device receives first information sent by the terminal, where the first information is used to indicate an adjustment suggestion of the terminal to the first AI unit.

12. The method according to claim 11, wherein: Before the network side device receives the first information sent by the terminal, the method further includes: The network side device sends configuration information to the terminal, where the configuration information is used to instruct the terminal to report adjustment suggestions for the first AI unit.

13. The method according to claim 12, wherein: The first information or the configuration information includes at least one of the following adjustment suggestions: Keep the first AI unit running in the current configuration; Switch to the second AI unit; Fall back to non-AI mode; Turn off the function corresponding to the first AI unit; Adjust the parameters of the first AI unit; Request new AI units; Deregister the first AI unit; Adjust the monitoring configuration of the first AI unit; Request training for the first AI unit; Requesting data collection for training the first AI unit; Request multiple AI units to run in parallel or monitor multiple AI units; Auxiliary information in the input of the first AI unit is adjusted.

14. The method according to claim 13, wherein: The first information or the configuration information further includes at least one of the following: a reporting condition for each adjustment suggestion in the first information or the configuration information; a reporting priority of each adjustment suggestion in the first information or the configuration information; a reporting order of each adjustment suggestion in the first information or the configuration information; The dependency relationship between the adjustment suggestions in the first information or the configuration information.

15. The method according to claim 12, wherein: Before the network side device receives the first information sent by the terminal, the method further includes: The network-side device sends second information to the terminal, where the second information is used to indicate a monitoring type of the first AI unit by the terminal, where the monitoring type includes at least one of the following: Monitoring based on monitoring indicators; Monitoring based on raw data, where the raw data is the data used to calculate monitoring indicators; Monitoring based on endpoint recommendations.

16. The method according to claim 15, wherein: The sending of the second information and the configuration information satisfies any one of the following: The second information and the configuration information are carried in the same information and sent; The second information and the configuration information are carried in the same signaling and sent; The second information and the configuration information are respectively carried in different signalings and sent.

17. The method according to claim 15, wherein: Before the network side device sends the second information to the terminal, the method further includes at least one of the following: The network side device receives first capability information sent by the terminal, where the first capability information is used to indicate a monitoring type of the AI ​​unit supported by the terminal; The network side device receives second capability information sent by the terminal, where the second capability information is used to indicate whether the terminal supports the monitoring based on the terminal suggestion; The network side device receives third capability information sent by the terminal, where the third capability information is used to indicate the adjustment suggestion that the terminal can make.

18. The method according to claim 17, wherein: The first capability information, the second capability information and the third capability information are sent separately or carried on the same signaling or information.

19. The method according to any one of claims 11 to 18, wherein: After the network side device receives the first information sent by the terminal, the method further includes: The network side device generates third information according to the first information, and sends the third information to the terminal, where the third information includes an adjustment decision for the first AI unit.

20. A transmission device, comprising: The first sending module is used to send first information to the network side device, where the first information is used to indicate the adjustment suggestion of the device to the first AI unit.

21. The device according to claim 20, wherein: The device also includes: The first receiving module is used to receive configuration information sent by the network side device, where the configuration information is used to instruct the device to report adjustment suggestions for the first AI unit.

22. The device according to claim 21, wherein The first information or the configuration information includes at least one of the following adjustment suggestions: Keep the first AI unit running in the current configuration; Switch to the second AI unit; Fall back to non-AI mode; Turn off the function corresponding to the first AI unit; Adjust the parameters of the first AI unit; Request new AI units; Deregister the first AI unit; Adjust the monitoring configuration of the first AI unit; Request training for the first AI unit; Requesting data collection for training the first AI unit; Request multiple AI units to run in parallel or monitor multiple AI units; Auxiliary information in the input of the first AI unit is adjusted.

23. The device according to claim 21, wherein The first receiving module is also used for: Receive second information sent by the network side device, where the second information is used to indicate a monitoring type of the first AI unit by the apparatus, where the monitoring type includes at least one of the following: Monitoring based on monitoring indicators; Monitoring based on raw data, where the raw data is the data used to calculate monitoring indicators; Monitoring based on endpoint recommendations.

24. The device according to claim 23, wherein: The first sending module is further used for: generating first information based on at least one of the second information, the configuration message, and the reasoning performance of the first AI unit; The first information is sent to a network side device.

25. The device according to claim 23, wherein: The first sending module is further used for any of the following: Sending first capability information to the network side device, where the first capability information is used to indicate a monitoring type of the AI ​​unit supported by the apparatus; Sending second capability information to the network side device, where the second capability information is used to indicate whether the device supports the monitoring based on the terminal suggestion; Sending third capability information to the network side device, where the third capability information is used to indicate the adjustment suggestion that the apparatus can make.

26. A transmission device, comprising: The second receiving module is used to receive first information sent by the terminal, where the first information is used to indicate the terminal's adjustment suggestion for the first AI unit.

27. The device according to claim 26, wherein: The device also includes: The second sending module is used to send configuration information to the terminal, where the configuration information is used to instruct the terminal to report adjustment suggestions for the first AI unit.

28. The device according to claim 27, wherein The first information or the configuration information includes at least one of the following adjustment suggestions: Keep the first AI unit running in the current configuration; Switch to the second AI unit; Fall back to non-AI mode; Turn off the function corresponding to the first AI unit; Adjust the parameters of the first AI unit; Request new AI units; Deregister the first AI unit; Adjust the monitoring configuration of the first AI unit; Request training for the first AI unit; Requesting data collection for training the first AI unit; Request multiple AI units to run in parallel or monitor multiple AI units; Auxiliary information in the input of the first AI unit is adjusted.

29. The device according to claim 27, wherein: The second sending module is also used for: Sending second information to the terminal, where the second information is used to indicate a monitoring type of the first AI unit by the terminal, where the monitoring type includes at least one of the following: Monitoring based on monitoring indicators; Monitoring based on raw data, where the raw data is the data used to calculate monitoring indicators; Monitoring based on endpoint recommendations.

30. The device according to claim 29, wherein: The second receiving module is further used for any of the following: receiving first capability information sent by the terminal, where the first capability information is used to indicate a monitoring type of the AI ​​unit supported by the terminal; receiving second capability information sent by the terminal, where the second capability information is used to indicate whether the terminal supports the monitoring based on terminal suggestion; The third capability information sent by the terminal is received, where the third capability information is used to indicate the adjustment suggestion that the terminal can make.

31. The device according to any one of claims 26 to 30, wherein: The device also includes: A third sending module is used to generate third information according to the first information, and send the third information to the terminal, where the third information includes an adjustment decision for the first AI unit.

32. A terminal comprising a processor and a memory, wherein 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, the steps of the transmission method according to any one of claims 1 to 10 are implemented.

33. A network side device, comprising a processor and a memory, wherein 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, the steps of the transmission method as described in any one of claims 11 to 19 are implemented.

34. A readable storage medium storing a program or instruction, wherein the program or instruction, when executed by a processor, implements the steps of the transmission method according to any one of claims 1 to 10, or implements the steps of the transmission method according to any one of claims 11 to 19.

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