Artificial intelligence-based processing method and apparatus, device and storage medium

Abstract

An artificial intelligence (AI)-based processing method and apparatus, a device, and a storage medium. The AI-based processing method is executed by a terminal, and comprises: on the basis of at least one first parameter, determining to activate at least one AI function or deactivate at least one AI function, wherein the first parameter is used for representing the confidence level of an AI-based inference result.

Description

Artificial intelligence-based processing method, device, equipment, and storage medium Technical Field

[0001] The present disclosure relates to the field of communication technologies, and in particular to processing methods, devices, equipment, and storage media based on artificial intelligence (AI). Background Art

[0002] Machine learning algorithms are one of the most important approaches to implementing artificial intelligence technology. Machine learning uses large amounts of training data to generate models that can then be used to predict events. In many fields, models trained using machine learning can produce highly accurate predictions.

[0003] Wireless communication networks can use AI for prediction and reasoning to improve system performance. During AI use and reasoning, multiple AI models or AI functionalities may be required for reasoning and prediction. An AI function can include one or more AI models to implement a specific function.

[0004] Summary of the Invention

[0005] How to activate or deactivate AI functions at the appropriate time.

[0006] The embodiments of the present disclosure provide a processing method, apparatus, device, and storage medium based on artificial intelligence.

[0007] In a first aspect, an embodiment of the present disclosure provides an AI-based processing method, executed by a terminal, the method comprising:

[0008] At least one AI function is activated or deactivated according to at least one first parameter, wherein the first parameter is used to represent the confidence level of an AI-based reasoning result.

[0009] In a second aspect, an embodiment of the present disclosure provides an AI-based processing method, performed by a network device, the method comprising:

[0010] Sending first indication information to the terminal, where the first indication information is used to indicate at least one second parameter, where the second parameter is a threshold corresponding to the first condition and / or the second condition, and the first condition is used by the terminal to determine whether to activate at least one AI function based on whether the at least one first parameter meets the first condition, and the second condition is used by the terminal to determine whether to deactivate at least one AI function based on whether the at least one first parameter meets the second condition, wherein the first parameter is used to represent the confidence level of the AI-based reasoning result.

[0011] In a third aspect, an embodiment of the present disclosure provides a terminal, including:

[0012] one or more processors;

[0013] The terminal is configured to implement the method described in the first aspect.

[0014] In a fourth aspect, an embodiment of the present disclosure provides a network device, including:

[0015] one or more processors;

[0016] The network device is configured to implement the method described in the second aspect.

[0017] In a fifth aspect, an embodiment of the present disclosure provides a communication system, including a terminal and a network device, wherein:

[0018] The terminal is configured to implement the method described in the first aspect.

[0019] The network device is configured to implement the method according to the second aspect.

[0020] In a seventh aspect, an embodiment of the present disclosure provides a storage medium, wherein the storage medium stores instructions, wherein:

[0021] When the instruction is executed on a communication device, the communication device is caused to execute the method as described in the first aspect or the second aspect.

[0022] In a ninth aspect, an embodiment of the present disclosure provides a program product, wherein:

[0023] When the program product is executed by a communication device, the communication device is caused to execute the method as described in the first aspect or the second aspect.

[0024] In the embodiment of the present disclosure, the AI ​​function can be activated or deactivated at a reasonable time, so that the AI ​​function can be used more effectively. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following drawings required for describing the embodiments are introduced. The following drawings are merely some embodiments of the present disclosure and do not impose specific limitations on the protection scope of the present disclosure.

[0026] FIG1 is an exemplary schematic diagram of the architecture of a communication system provided according to an embodiment of the present disclosure;

[0027] FIG2 is an exemplary interaction diagram of a method provided according to an embodiment of the present disclosure;

[0028] FIG3 is an exemplary flowchart of a method provided according to an embodiment of the present disclosure;

[0029] FIG4 is an exemplary flowchart of a method provided according to an embodiment of the present disclosure;

[0030] FIG5A is a schematic structural diagram of a terminal according to an embodiment of the present disclosure;

[0031] FIG5B is a schematic structural diagram of a network device according to an embodiment of the present disclosure;

[0032] FIG6A is a schematic diagram of a communication device according to an embodiment of the present disclosure;

[0033] FIG6B is a schematic diagram of a chip according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0034] The embodiments of the present disclosure provide a processing method, apparatus, device, and storage medium based on artificial intelligence.

[0035] In a first aspect, an embodiment of the present disclosure provides a processing method based on artificial intelligence (AI), which is executed by a terminal. The method includes:

[0036] At least one AI function is activated or deactivated according to at least one first parameter, wherein the first parameter is used to represent the confidence level of an AI-based reasoning result.

[0037] In the embodiment of the present disclosure, the AI ​​function can be activated or deactivated at a reasonable time, so that the AI ​​function can be used more effectively.

[0038] In combination with the embodiments of the first aspect, in some embodiments, the AI ​​function includes at least one AI model.

[0039] In conjunction with the embodiments of the first aspect, in some embodiments, activating at least one AI function according to at least one first parameter includes:

[0040] At least one AI function is activated if the at least one first parameter satisfies at least one of the following:

[0041] The first parameter is greater than a first threshold;

[0042] The first parameter is continuously greater than a first threshold within a first time period;

[0043] Within a second time period, the number of times that the first parameter is greater than or equal to the first threshold is greater than a first value;

[0044] Within a third time period, the number of times the first parameter is greater than or equal to the first threshold accounts for a number of times the first parameter is greater than or equal to the first threshold among all parameters that are greater than or equal to the first threshold that is greater than a first proportion; the most recent N1 first parameters are greater than or equal to the first threshold, and each first parameter corresponds to one of the inference results;

[0045] The number of first parameters greater than or equal to the first threshold among the most recent N2 first parameters is greater than a second value;

[0046] A ratio of the number of the most recent N3 first parameters that are greater than or equal to the first threshold to the number of all parameters that are greater than or equal to the first threshold is greater than a second ratio;

[0047] Among them, N1, N2, and N3 are positive integers.

[0048] In conjunction with the embodiments of the first aspect, in some embodiments, deactivating at least one AI function based on at least one first parameter includes:

[0049] At least one AI function is deactivated when at least one first parameter satisfies at least one of the following:

[0050] The first parameter is less than a second threshold;

[0051] The first parameter is continuously less than a second threshold within a fourth time period;

[0052] During a fifth time period, the number of times that the first parameter is less than or equal to the second threshold is greater than a third value;

[0053] During a sixth time period, the number of times the first parameter is less than or equal to the second threshold accounts for a greater proportion than a third proportion of the number of times all parameters are less than or equal to the second threshold.

[0054] During a seventh time period, the number of times that the first parameter is greater than or equal to the second threshold is less than a fourth value;

[0055] During an eighth time period, the number of times the first parameter is greater than or equal to the second threshold accounts for a number of times the number of times all parameters are greater than or equal to the second threshold that is greater than a fourth proportion;

[0056] The most recent M1 first parameters are less than or equal to a second threshold, and each first parameter corresponds to one of the inference results;

[0057] The number of the first parameters less than or equal to the second threshold among the most recent M2 first parameters is greater than a fifth value;

[0058] a ratio of the number of the most recent M3 first parameters that are smaller than or equal to the second threshold to the number of all parameters that are smaller than or equal to the second threshold is greater than a fifth ratio;

[0059] The number of the first parameters greater than or equal to the second threshold among the most recent M4 first parameters is less than a sixth value;

[0060] a ratio of the number of the most recent M5 first parameters that are greater than or equal to the second threshold to the number of all parameters that are greater than or equal to the second threshold is less than a sixth ratio;

[0061] Among them, M1, M2, M3, M4, and M5 are positive integers.

[0062] In conjunction with the embodiments of the first aspect, in some embodiments, each of the AI ​​functions corresponds to one of the first conditions, and the terminal determines whether to activate at least one AI function based on whether the at least one first parameter meets the first condition;

[0063] Each of the AI ​​functions corresponds to a second condition, and the terminal determines whether to deactivate at least one AI function according to whether the at least one first parameter meets the second condition.

[0064] In conjunction with the embodiments of the first aspect, in some embodiments, deactivating at least one AI function based on at least one first parameter includes:

[0065] According to at least one first parameter, it is determined that there is no activatable AI function, and a first number of AI functions are deactivated, or at least one set AI function is deactivated, or all AI functions are deactivated.

[0066] In conjunction with the embodiments of the first aspect, in some embodiments, activating at least one AI function according to at least one first parameter includes:

[0067] When it is determined according to at least one first parameter that no AI function can be activated, the AI ​​function with the best performance is activated.

[0068] In conjunction with the embodiments of the first aspect, in some embodiments, the AI ​​function with the best performance is determined based on at least one of the following:

[0069] The first parameter corresponding to the AI ​​function is the largest;

[0070] During the seventh time period, the number of times that the first parameter corresponding to the AI ​​function is greater than or equal to the third threshold is greater than a seventh value;

[0071] During the eighth time period, the ratio of the number of times the first parameter corresponding to the AI ​​function is greater than or equal to the third threshold to the total number of times is greater than the fifth ratio; during the eighth time period, the ratio of the number of times the first parameter corresponding to the AI ​​function is greater than or equal to the third threshold to the number of times all parameters are greater than or equal to the third threshold is greater than the fifth ratio;

[0072] The number of first parameters greater than or equal to the third threshold among the first parameters corresponding to the most recent K1 AI functions is greater than an eighth value;

[0073] The ratio of the number of first parameters greater than or equal to the third threshold among the most recent K3 first parameters to the K3 is greater than a sixth ratio. The ratio of the number of first parameters less than or equal to the second threshold among the most recent K2 first parameters to the number of parameters less than or equal to the third threshold among all parameters is greater than a fifth ratio.

[0074] In conjunction with the embodiments of the first aspect, in some embodiments, the method includes:

[0075] Receive first indication information sent by a network device, where the first indication information is used to indicate at least one second parameter, where the second parameter is a threshold corresponding to the first condition and / or the second condition, and determine, by the terminal, whether to activate at least one AI function based on whether the at least one first parameter meets the first condition, and determine, by the terminal, whether to deactivate at least one AI function based on whether the at least one first parameter meets the second condition.

[0076] In combination with the embodiments of the first aspect, in some embodiments, the first indication information is used to indicate at least one group of information, each group of information corresponding to one or more AI functions.

[0077] In conjunction with the embodiments of the first aspect, in some embodiments, the method includes:

[0078] An AI function that is in an inactivated state is periodically activated to obtain a first parameter of the AI ​​function.

[0079] In conjunction with the embodiments of the first aspect, in some embodiments, the method includes:

[0080] After deactivating at least one AI function, the confidence level of the AI ​​function acquired last time is used as the confidence level of the AI ​​function in the inactivated state.

[0081] In conjunction with the embodiments of the first aspect, in some embodiments, the method includes:

[0082] First indication information is sent to the network device, where the first indication information indicates to deactivate a first number of AI functions, or to deactivate at least one set AI function, or to deactivate all AI functions.

[0083] In a second aspect, an embodiment of the present disclosure provides an artificial intelligence (AI)-based processing method, which is executed by a network device. The method includes:

[0084] Sending first indication information to the terminal, where the first indication information is used to indicate at least one second parameter, where the second parameter is a threshold corresponding to the first condition and / or the second condition, and the first condition is used by the terminal to determine whether to activate at least one AI function based on whether the at least one first parameter meets the first condition, and the second condition is used by the terminal to determine whether to deactivate at least one AI function based on whether the at least one first parameter meets the second condition, wherein the first parameter is used to represent the confidence level of the AI-based reasoning result.

[0085] In conjunction with the embodiments of the first aspect, in some embodiments, the method includes:

[0086] The first indication information sent by the receiving terminal is instructing to deactivate a first number of AI functions, or to deactivate at least one set AI function, or to deactivate all AI functions.

[0087] In a third aspect, an embodiment of the present disclosure provides a communication device, configured in a terminal, the device comprising:

[0088] The processing module is configured to: activate at least one AI function or deactivate at least one AI function according to at least one first parameter, wherein the first parameter is used to represent the confidence of the AI-based reasoning result.

[0089] In a fourth aspect, an embodiment of the present disclosure provides a communication device, configured in a network device, comprising:

[0090] The transceiver module is configured to: send first indication information to the terminal, where the first indication information is used to indicate at least one second parameter, where the second parameter is a threshold corresponding to the first condition and / or the second condition, where the first condition is used by the terminal to determine whether to activate at least one AI function based on whether the at least one first parameter meets the first condition, and the second condition is used by the terminal to determine whether to deactivate at least one AI function based on whether the at least one first parameter meets the second condition, wherein the first parameter is used to represent the confidence level of the AI-based reasoning result.

[0091] In a fifth aspect, an embodiment of the present disclosure provides a terminal, including:

[0092] one or more processors;

[0093] The terminal is configured to implement the method described in the first aspect.

[0094] In a sixth aspect, an embodiment of the present disclosure provides a network device, including:

[0095] one or more processors;

[0096] The network device is configured to implement the method described in the second aspect.

[0097] In a seventh aspect, an embodiment of the present disclosure provides a communication system, including a terminal and a network device, wherein:

[0098] The terminal is configured to implement the method described in the first aspect.

[0099] The network device is configured to implement the method according to the second aspect.

[0100] In an eighth aspect, an embodiment of the present disclosure provides a storage medium, wherein the storage medium stores instructions, wherein:

[0101] When the instruction is executed on a communication device, the communication device is caused to execute the method according to the first aspect or the second aspect.

[0102] In a ninth aspect, an embodiment of the present disclosure provides a program product, wherein:

[0103] When the program product is executed by a communication device, the communication device is caused to execute the method according to the first aspect or the second aspect.

[0104] In a tenth aspect, an embodiment of the present disclosure proposes a computer program, which, when executed on a computer, enables the computer to execute the method described in the optional implementation of any one of the first to second aspects.

[0105] In an eleventh aspect, an embodiment of the present disclosure provides a chip or a chip system, wherein the chip or chip system includes a processing circuit configured to execute the method described in the optional implementation of any one of the first to second aspects above.

[0106] It is understandable that the above-mentioned terminals, network devices, communication systems, storage media, program products, computer programs, chips, or chip systems are all used to perform the methods proposed in the embodiments of the present disclosure. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects of the corresponding methods and will not be repeated here.

[0107] The embodiments of the present disclosure are not exhaustive and are merely illustrative of some embodiments, and are not intended to be a specific limitation on the scope of protection of the present disclosure. In the absence of contradiction, each step in a certain embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a certain embodiment can also be implemented as an independent embodiment, and the order of the steps in a certain embodiment can be arbitrarily exchanged. In addition, the optional implementation methods in a certain embodiment can be arbitrarily combined; in addition, the embodiments can be arbitrarily combined. For example, some or all steps of different embodiments can be arbitrarily combined, and a certain embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.

[0108] In each embodiment of the present disclosure, unless otherwise specified or provided for by logic, the terms and / or descriptions between the embodiments are consistent and can be referenced by each other. The technical features in different embodiments can be combined to form a new embodiment based on their inherent logical relationships.

[0109] The terms used in the embodiments of the present disclosure are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure.

[0110] In the embodiments of the present disclosure, unless otherwise specified, elements expressed in the singular, such as "a", "an", "the", "above", "said", "the", "the", etc., may mean "one and only one", or "one or more", "at least one", etc. For example, when using articles such as "a", "an", "the" in English in translation, the noun following the article may be understood as a singular expression or a plural expression.

[0111] In the embodiments of the present disclosure, “plurality” refers to two or more.

[0112] In some embodiments, the terms "at least one," "one or more," "a plurality of," "multiple," etc. may be used interchangeably.

[0113] In some embodiments, descriptions such as "at least one of A and B," "A and / or B," "A in one case, B in another case," or "in response to one case A, in response to another case B" may include the following technical solutions depending on the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed); and in some embodiments, A and B (both A and B are executed). The above is also applicable when there are more branches such as A, B, and C.

[0114] In some embodiments, "A or B" and other descriptions may include the following technical solutions depending on the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed). The above is also applicable when there are more branches such as A, B, C, etc.

[0115] The prefixes such as "first" and "second" in the embodiments of the present disclosure are only used to distinguish different description objects and do not constitute any restriction on the position, order, priority, quantity or content of the description objects. For the statement of the description object, please refer to the description in the context of the claims or embodiments, and no unnecessary restriction should be constituted due to the use of prefixes. For example, if the description object is a "field", the ordinal number before the "field" in the "first field" and the "second field" does not limit the position or order between the "fields". "First" and "second" do not limit whether the "fields" they modify are in the same message, nor do they limit the order of the "first field" and the "second field". For another example, if the description object is a "level", the ordinal number before the "level" in the "first level" and the "second level" does not limit the priority between the "levels". For another example, the number of description objects is not limited by the ordinal number and can be one or more. Taking "first device" as an example, the number of "devices" can be one or more. In addition, the objects modified by different prefixes can be the same or different. For example, if the description object is "device", then the "first device" and the "second device" can be the same device or different devices, and their types can be the same or different; for another example, if the description object is "information", then the "first information" and the "second information" can be the same information or different information, and their contents can be the same or different.

[0116] In some embodiments, “including A,” “comprising A,” “used to indicate A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0117] In some embodiments, terms such as "in response to...", "in response to determining...", "in the case of...", "at the time of...", "when...", "if...", "if...", etc. can be used interchangeably.

[0118] In some embodiments, terms such as "greater than", "greater than or equal to", "not less than", "more than", "more than or equal to", "not less than", "higher than", "higher than or equal to", "not less than", and "above" can be replaced with each other, and terms such as "less than", "less than or equal to", "not greater than", "less than", "less than or equal to", "not more than", "lower than", "lower than or equal to", "not higher than", and "below" can be replaced with each other.

[0119] In some embodiments, devices and equipment can be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. In some cases, they can also be understood as "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", "subject", etc.

[0120] In some embodiments, "network" can be interpreted as devices included in the network, such as access network equipment, core network equipment, etc.

[0121] In some embodiments, "access network device (AN device)" may also be referred to as "radio access network device (RAN device)", "base station (BS)", "radio base station", "fixed station", and in some embodiments may also be understood as "node", "access point", "transmission point (TP)", "reception point (RP)", "transmission and / or reception point (TRP)" "panel", "antenna panel", "antenna array", "cell", "macro cell", "small cell", "femto cell", "pico cell", "sector", "cell group", "serving cell", "carrier", "component carrier", "bandwidth part (BWP)", etc.

[0122] In some embodiments, "terminal" or "terminal device" may be referred to as "user equipment (UE)", "user terminal" "mobile station (MS)", "mobile terminal (MT)", subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, etc.

[0123] In some embodiments, obtaining data, information, etc. may comply with the laws and regulations of the country where the data is obtained.

[0124] In some embodiments, data, information, etc. may be obtained with the user's consent.

[0125] FIG1 is a schematic diagram showing the architecture of a communication system according to an embodiment of the present disclosure.

[0126] As shown in FIG. 1 , a communication system 100 includes a terminal 101 and a network device 102 .

[0127] In some embodiments, the terminal 101 includes, for example, a mobile phone, a wearable device, an Internet of Things device, a car with communication function, a smart car, a tablet computer, a computer with wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device in industrial control, a wireless terminal device in self-driving, a wireless terminal device in remote medical surgery, a wireless terminal device in a smart grid, a wireless terminal device in transportation safety, a wireless terminal device in a smart city, and at least one of a wireless terminal device in a smart home, but is not limited thereto.

[0128] In some embodiments, the network device 102 may include at least one of an access network device and a core network device.

[0129] Optionally, the access network device is, for example, a node or device that accesses the terminal to the wireless network. The access network device may include an evolved NodeB (eNB), a next generation evolved NodeB (ng-eNB), a next generation NodeB (gNB), a node B (NB), a home node B (HNB), a home evolved nodeB (HeNB), a wireless backhaul device, a radio network controller (RNC), a base station controller (BSC), a base transceiver station (BTS), a base band unit (BBU), a mobile switching center, a base station in a 6G communication system, an open base station (Open RAN), a cloud base station (Cloud RAN), a base station in other communication systems, and at least one of an access node in a wireless fidelity (WiFi) system, but is not limited thereto.

[0130] In the disclosed embodiments, AI model training requires the collection of a large amount of data, and different application scenarios require different data. Application scenarios may include mobile communication system processes such as beam management, channel state information (CSI) reporting, CSI compression, positioning, handover, mobility management, and radio resource management.

[0131] During beam management, terminal 101 can reduce the number of measured beams. Terminal 101 or network device 102 uses AI inference to determine the optimal beam. Beam prediction includes spatial and temporal beam prediction. In spatial beam prediction, terminal 101 measures a small number of beams and predicts the measurement results of other beams. In temporal beam prediction, terminal 101 predicts future beam measurement results based on historical beam measurement results.

[0132] During CSI reporting, terminal 101 can compress CSI measurement results using AI and report the compressed CSI measurement results to network device 102. After receiving the compressed CSI measurement results, network device 102 can restore the original CSI measurement results using AI. This reduces the number of signaling bits required during the reporting process. Network device 102 can also predict future CSI based on historical CSI measurement results reported by terminal 101.

[0133] During the positioning process, the terminal 101 can predict an accurate position based on limited measurement results.

[0134] During mobility, terminal 101 can predict cell measurement results, handover target cells, or mobility events. Terminal 101 can predict future cell measurement results, which is called time-domain prediction. Alternatively, terminal 101 can predict measurement results for unmeasured cells, which is called spatial-domain prediction. Mobility events include measurement reporting condition satisfaction, handover failure, cell dwell time, and radio link failure.

[0135] The inference result output by AI can correspond to an indicator, which indicates the degree to which the AI ​​inference result is consistent with the true value. The indicator can be confidence, accuracy, or probability.

[0136] Among them, confidence refers to the probability corresponding to the degree to which the true value of the parameter falls around the inference result.

[0137] Accuracy refers to how similar the inference result is to the true value.

[0138] Likelihood refers to the probability that the inference result is similar to the true value.

[0139] The reasoning of the AI ​​model or function can be run on the terminal 101 side or on the network device 102 side.

[0140] When the inference of an AI model or AI function is executed on terminal 101, terminal 101 may need to manage resources based on the inference output, or report the inference output to network device 102, which then manages resources based on the inference result. If the confidence level of the inference result is low, the inference result may be incorrect. Using such inference result for resource management may result in reduced system performance.

[0141] Therefore, it is necessary to consider how to activate or deactivate AI functions at a reasonable time.

[0142] FIG2 is an interactive diagram of an artificial intelligence (AI)-based process according to an embodiment of the present disclosure. As shown in FIG2 , an embodiment of the present disclosure relates to an artificial intelligence (AI)-based process, and the method includes:

[0143] Step S2101 , the network device 102 sends first indication information to the terminal 101 .

[0144] In some embodiments, the terminal 101 receives first indication information sent by the network device 102 .

[0145] In some embodiments, the first indication information is used to indicate at least one second parameter.

[0146] In some embodiments, the second parameter includes at least one of the following:

[0147] Threshold, duration, and value.

[0148] In some embodiments, the second parameter is a threshold corresponding to the first condition and / or the second condition.

[0149] In some embodiments, the first condition is used for the terminal 101 to determine whether to activate at least one AI function based on whether the at least one first parameter meets the first condition.

[0150] In some embodiments, the second condition is used by the terminal 101 to determine whether to deactivate at least one AI function based on whether the at least one first parameter meets the second condition, wherein the first parameter is used to represent the confidence of the AI-based reasoning result.

[0151] In some embodiments, when the threshold corresponding to the first condition and the threshold corresponding to the second condition are the same, the second parameter is applicable to both the first condition and the second condition.

[0152] For example, if the first condition is that the first parameter is less than a first threshold, and the second condition is that the first parameter is greater than the first threshold, then the second parameter only needs to include the first threshold, and the terminal can obtain the first and second conditions based on the first threshold.

[0153] For example: the first condition is that the value is less than the first threshold value within the first time period, and the second condition is that the value is greater than the first threshold value within the second time period. The second parameter only needs to include the first time period and the first threshold value, and the terminal can obtain the first condition and the second condition based on the first time period and the first threshold value.

[0154] For example, the first condition is that the first parameter is less than the first threshold, and the second condition is that the first parameter is greater than the second threshold. The second parameter includes the first threshold and the second threshold. The terminal obtains the first condition based on the first threshold and obtains the second condition based on the second threshold.

[0155] In some embodiments, the network device 102 distinguishes which of the second parameters are thresholds corresponding to the first condition and which are thresholds corresponding to the second condition according to an agreed method.

[0156] In some embodiments, the terminal distinguishes which of the second parameters are thresholds corresponding to the first condition and which are thresholds corresponding to the second condition according to an agreed method.

[0157] In some embodiments, the first parameter is used to represent the confidence level of the AI-based reasoning result.

[0158] In some embodiments, the AI ​​functionality includes at least one AI model.

[0159] In some embodiments, the first indication information is used to indicate at least one set of information, each set of information corresponding to one or more AI functions.

[0160] In step S2102, the terminal 101 activates or deactivates at least one AI function according to at least one first parameter.

[0161] In some embodiments, the first parameter is used to represent the confidence level of the AI-based reasoning result.

[0162] In some embodiments, each AI function corresponds to a first condition.

[0163] In some embodiments, each AI function corresponds to a second condition.

[0164] In some embodiments, terminal 101 stores a plurality of AI functions, each AI function corresponding to a first condition.

[0165] In some embodiments, terminal 101 stores a plurality of AI functions, each AI function corresponding to a second condition.

[0166] In some embodiments, the correspondence between the AI ​​function and the first condition, and the correspondence between the AI ​​function and the second condition, are configured by the network device 102, and may also be configured by an application layer device.

[0167] In some embodiments, the terminal 101 activates at least one AI function when at least one of the following six conditions is met:

[0168] The first parameter is greater than a first threshold;

[0169] The first parameter is continuously greater than the first threshold within a first time period;

[0170] During the second time period, the number of times the first parameter is greater than or equal to the first threshold is greater than the first value;

[0171] Within the third time length, the number of times the first parameter is greater than or equal to the first threshold accounts for the number of times the first parameter is greater than or equal to the first threshold among all parameters that are greater than or equal to the first threshold is greater than the first proportion; the most recent N1 first parameters are greater than or equal to the first threshold, and each first parameter corresponds to one of the inference results; for example: each inference result corresponds to 5 parameters, the first parameter is one of the 5 parameters, the number of times the first parameter is greater than or equal to the first threshold within the third time length is X1, and the sum of the number of times each of the 5 parameters is greater than or equal to the first threshold within the third time length is X2, then the above proportion refers to the ratio of X1 to X2.

[0172] The number of first parameters greater than or equal to the first threshold in the most recent N2 first parameters is greater than the second value; wherein the most recent N2 first parameters are the N2 first parameters obtained in the most recent N2 reasoning processes, and each reasoning process corresponds to 1 first parameter.

[0173] The ratio of the number of the most recent N3 first parameters that are greater than or equal to the first threshold to the number of all parameters that are greater than or equal to the first threshold is greater than a second ratio; wherein the most recent N2 first parameters are the N2 first parameters obtained in the most recent N2 inference processes, with each inference process corresponding to one first parameter. In one example, each inference result corresponds to five parameters, the first parameter is one of the five parameters, the number of the most recent N3 first parameters that are greater than or equal to the first threshold is Y1, and the sum of the number of times each parameter in the most recent N3 five parameters is greater than or equal to the first threshold is Y2. In this case, the above ratio refers to the ratio of Y1 to Y2.

[0174] Among them, N1, N2, and N3 are positive integers.

[0175] In some embodiments, the terminal 101 activates at least one AI function when an activation condition is met.

[0176] In some embodiments, the activation condition includes at least one of the six.

[0177] In some embodiments, the terminal 101 determines to deactivate at least one AI function when at least one of the following eleven conditions is met:

[0178] The first parameter is less than a second threshold;

[0179] The first parameter is continuously less than a second threshold within a fourth time period;

[0180] During a fifth time period, the number of times that the first parameter is less than or equal to the second threshold is greater than a third value;

[0181] During the sixth time period, the number of times the first parameter is less than or equal to the second threshold accounts for a greater proportion than the third proportion among all parameters. For example, each inference result corresponds to five parameters, and the first parameter is one of the five parameters. During the sixth time period, the number of times the first parameter is less than or equal to the second threshold is G1. During the sixth time period, the sum of the number of times each of the five parameters is less than or equal to the second threshold is G2. The above proportion refers to the ratio of G1 to G2.

[0182] During a seventh time period, the number of times that the first parameter is greater than or equal to the second threshold is less than a fourth value;

[0183] During an eighth time period, the number of times the first parameter is greater than or equal to the second threshold accounts for a number of times the number of times all parameters are greater than or equal to the second threshold that is greater than a fourth proportion;

[0184] The most recent M1 first parameters are less than or equal to a second threshold, and each first parameter corresponds to one of the inference results;

[0185] The number of the first parameters less than or equal to the second threshold among the most recent M2 first parameters is greater than a fifth value;

[0186] a ratio of the number of the most recent M3 first parameters that are smaller than or equal to the second threshold to the number of all parameters that are smaller than or equal to the second threshold is greater than a fifth ratio;

[0187] The number of the first parameters greater than or equal to the second threshold among the most recent M4 first parameters is less than a sixth value;

[0188] a ratio of the number of the most recent M5 first parameters that are greater than or equal to the second threshold to the number of all parameters that are greater than or equal to the second threshold is less than a sixth ratio;

[0189] Among them, M1, M2, M3, M4, and M5 are positive integers.

[0190] In some embodiments, the terminal 101 activates at least one AI function when a deactivation condition is met.

[0191] In some embodiments, the deactivation condition includes at least one of the eleven.

[0192] In some embodiments, deactivating at least one AI function based on at least one first parameter includes:

[0193] When the terminal 101 determines, based on at least one first parameter, that there is no activatable AI function, it deactivates a first number of AI functions, or deactivates at least one set AI function, or deactivates all AI functions.

[0194] In some embodiments, activating at least one AI function based on at least one first parameter includes:

[0195] When it is determined according to at least one first parameter that the number of activatable AI functions is greater than 1, the AI ​​function with the best performance is activated.

[0196] In some embodiments, the best performing AI function is determined based on at least one of the following:

[0197] The first parameter corresponding to the AI ​​function is the largest;

[0198] During the seventh time period, the number of times that the first parameter corresponding to the AI ​​function is greater than or equal to the third threshold is greater than a seventh value;

[0199] During an eighth time period, the number of times the first parameter corresponding to the AI ​​function is greater than or equal to the third threshold accounts for a number of times the number of times the first parameter corresponding to the AI ​​function is greater than or equal to the third threshold among all parameters that are greater than the fifth proportion;

[0200] The number of first parameters greater than or equal to the third threshold among the first parameters corresponding to the most recent K1 AI functions is greater than an eighth value;

[0201] The ratio of the number of the most recent K2 first parameters that are smaller than or equal to the second threshold to the number of all parameters that are smaller than or equal to the third threshold is greater than a fifth ratio.

[0202] Step S2103 , the terminal 101 sends second indication information to the network device 102 .

[0203] In some embodiments, the network device 102 receives the second indication information sent by the terminal 101 .

[0204] In some embodiments, the second indication information indicates deactivation of a first number of AI functions.

[0205] In some embodiments, the second indication information indicates deactivation of at least one set AI function.

[0206] In some embodiments, the second indication information indicates deactivation of all AI functions.

[0207] In step S2104, the terminal 101 periodically activates the AI ​​function that is in an inactivated state and obtains a first parameter of the AI ​​function.

[0208] In step S2105 , after the terminal 101 deactivates at least one AI function, the confidence level of the AI ​​function acquired last time is used as the confidence level of the AI ​​function in the inactivated state.

[0209] In some embodiments, the processing method based on artificial intelligence (AI) includes some of the steps from step S2101 to step S2105. For example, in one embodiment, it includes step S2102, in another embodiment, it includes step S2101 and step S2102, and in another embodiment, it includes step S2101, step S2102, and step S2103.

[0210] FIG3 is a flow chart of a method for processing based on artificial intelligence (AI) according to an embodiment of the present disclosure. As shown in FIG3 , an embodiment of the present disclosure relates to a method for processing based on artificial intelligence (AI), which is executed by terminal 101 and includes:

[0211] Step S3101: Receive first indication information sent by the network device 102.

[0212] In some embodiments, the implementation of step S3101 can refer to the implementation of step S2101 and will not be repeated here.

[0213] Step S3102: Activate at least one AI function or deactivate at least one AI function according to at least one first parameter.

[0214] In some embodiments, the implementation of step S3102 can refer to the implementation of step S2102 and will not be repeated here.

[0215] Step S3103: Send second indication information to the network device 102.

[0216] In some embodiments, the implementation of step S3103 can refer to the implementation of step S2103 and will not be repeated here.

[0217] Step S3104: Periodically activate the inactivated AI function and obtain a first parameter of the AI ​​function.

[0218] In some embodiments, the implementation of step S3104 can refer to the implementation of step S2104 and will not be repeated here.

[0219] Step S3105: After deactivating at least one AI function, the confidence level of the AI ​​function acquired last time is used as the confidence level of the AI ​​function in the inactivated state.

[0220] In some embodiments, the implementation of step S3105 can refer to the implementation of step S2105 and will not be repeated here.

[0221] In some embodiments, the processing method based on artificial intelligence (AI) includes some of the steps from step S3101 to step S3105. For example, in one embodiment, it includes step S3102, in another embodiment, it includes step S3101 and step S3102, and in another embodiment, it includes step S3101, step S3102, and step S3103.

[0222] FIG4 is a flow chart of a method for processing based on artificial intelligence (AI) according to an embodiment of the present disclosure. As shown in FIG4 , an embodiment of the present disclosure relates to a method for processing based on artificial intelligence (AI), which is executed by a network device 102 and includes:

[0223] Step S4101: Send first indication information to terminal 101.

[0224] In some embodiments, the implementation of step S4101 can refer to the implementation of step S2101 and will not be repeated here.

[0225] Step S4102: Send second indication information to terminal 101.

[0226] In some embodiments, the implementation of step S4102 can refer to the implementation of step S2103 and will not be repeated here.

[0227] The embodiments of the present disclosure further provide an apparatus for implementing any of the above methods. For example, an apparatus is provided, comprising units or modules for implementing each step performed by a terminal in any of the above methods. For another example, another apparatus is provided, comprising units or modules for implementing each step performed by a network device (e.g., an access network device, a core network function node, a core network device, etc.) in any of the above methods.

[0228] It should be understood that the division of the various units or modules in the above device is merely a division of logical functions. In actual implementation, they may be fully or partially integrated into a physical entity, or they may be physically separated. In addition, the units or modules in the device may be implemented in the form of a processor calling software: for example, the device includes a processor, the processor is connected to a memory, and the memory stores instructions. The processor calls the instructions stored in the memory to implement any of the above methods or implement the functions of the various units or modules of the above device, wherein the processor is, for example, a general-purpose processor, such as a central processing unit (CPU) or a microprocessor, and the memory is a memory within the device or a memory outside the device. Alternatively, the units or modules in the device can be implemented in the form of hardware circuits, and the functions of some or all of the units or modules can be realized by designing the hardware circuits. The above-mentioned hardware circuits can be understood as one or more processors; for example, in one implementation, the above-mentioned hardware circuit is an application-specific integrated circuit (ASIC), which realizes the functions of some or all of the above units or modules by designing the logical relationship of the components in the circuit; for example, in another implementation, the above-mentioned hardware circuit can be realized by a programmable logic device (PLD). Taking a field programmable gate array (FPGA) as an example, it can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured by configuring the configuration file, thereby realizing the functions of some or all of the above units or modules. All units or modules of the above devices can be realized in the form of software called by the processor, or in the form of hardware circuits, or in part by the form of software called by the processor, and the rest by hardware circuits.

[0229] In the embodiments of the present disclosure, the processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction reading and execution capabilities, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a microprocessor), or a digital signal processor (DSP). In another implementation, the processor can implement certain functions through the logical relationship of the hardware circuit. The logical relationship of the above-mentioned hardware circuit is fixed or reconfigurable. For example, the processor is a hardware circuit implemented by an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document and implementing the hardware circuit configuration can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units or modules. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DPU), etc.

[0230] Figure 5A is a schematic diagram of the structure of a terminal proposed in an embodiment of the present disclosure. As shown in Figure 5A, terminal 5100 may include at least one of a transceiver module 5101 and a processing module 5102. In some embodiments, processing module 5102 is configured to activate or deactivate at least one AI function based on at least one first parameter, where the first parameter is used to indicate the confidence level of an AI-based reasoning result.

[0231] Optionally, the transceiver module 5101 is configured to execute at least one of the communication steps of sending and / or receiving performed by the terminal 101 in any of the above methods, which are not described in detail here. Optionally, the processing module 5102 is configured to execute at least one of the other steps performed by the terminal 101 in any of the above methods, which are not described in detail here.

[0232] Figure 5B is a schematic diagram of the structure of a network device proposed in an embodiment of the present disclosure. As shown in Figure 5B, the network device 5200 may include: at least one of a transceiver module 5201, a processing module 5202, etc. The above-mentioned transceiver module 5201 is used to send a first indication message to the terminal, where the first indication message is used to indicate at least one second parameter, where the second parameter is a threshold value corresponding to the first condition and / or the second condition, where the first condition is used for the terminal to determine whether to activate at least one AI function based on whether the at least one first parameter meets the first condition, and the second condition is used for the terminal to determine whether to deactivate at least one AI function based on whether the at least one first parameter meets the second condition, wherein the first parameter is used to represent the confidence level of the AI-based reasoning result.

[0233] Optionally, the transceiver module 5201 is configured to execute at least one of the communication steps of sending and / or receiving performed by the network device 102 in any of the above methods, which are not described in detail here. Optionally, the processing module 5202 is configured to execute at least one of the other steps performed by the network device 102 in any of the above methods, which are not described in detail here.

[0234] Figure 6A is a schematic diagram of the structure of a communication device 6100 proposed in an embodiment of the present disclosure. Communication device 6100 can be a network device (e.g., an access network device, a core network device, etc.), a terminal (e.g., a user equipment, etc.), a chip, a chip system, or a processor that supports a network device to implement any of the above methods, or a chip, a chip system, or a processor that supports a terminal to implement any of the above methods. Communication device 6100 can be used to implement the methods described in the above method embodiments. For details, please refer to the description of the above method embodiments.

[0235] As shown in Figure 6A, the communication device 6100 includes one or more processors 6101. The processor 6101 can be a general-purpose processor or a dedicated processor, for example, a baseband processor or a central processing unit. The baseband processor can be used to process the communication protocol and communication data, and the central processing unit can be used to control the communication device (such as a base station, a baseband chip, a terminal device, a terminal device chip, a DU or a CU, etc.), execute programs, and process program data. Optionally, the communication device 6100 is used to perform any of the above methods. Optionally, one or more processors 6101 are used to call instructions to enable the communication device 6100 to perform any of the above methods.

[0236] In some embodiments, the communication device 6100 further includes one or more transceivers 6102. When the communication device 6100 includes one or more transceivers 6102, the transceiver 6102 performs at least one of the communication steps, such as sending and / or receiving, in the above-described method, and the processor 6101 performs at least one of the other steps. In an optional embodiment, the transceiver may include a receiver and / or a transmitter, and the receiver and transmitter may be separate or integrated. Optionally, the terms transceiver, transceiver unit, transceiver, transceiver circuit, interface circuit, and interface may be used interchangeably; the terms transmitter, transmitting unit, transmitter, and transmitting circuit may be used interchangeably; and the terms receiver, receiving unit, receiver, and receiving circuit may be used interchangeably.

[0237] In some embodiments, the communication device 6100 further includes one or more memories 6103 for storing data. Alternatively, all or part of the memories 6103 may be located outside the communication device 6100. In alternative embodiments, the communication device 6100 may include one or more interface circuits 6104. Optionally, the interface circuits 6104 are connected to the memories 6103 and may be configured to receive data from the memories 6103 or other devices, or to send data to the memories 6103 or other devices. For example, the interface circuits 6104 may read data stored in the memories 6103 and send the data to the processor 6101.

[0238] The communication device 6100 described in the above embodiment may be a network device or a terminal, but the scope of the communication device 6100 described in the present disclosure is not limited thereto, and the structure of the communication device 6100 may not be limited to FIG6A. The communication device may be an independent device or may be part of a larger device. For example, the communication device may be: 1) an independent integrated circuit IC, or a chip, or a chip system or subsystem; (2) a collection of one or more ICs, optionally, the above IC collection may also include a storage component for storing data and programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, a terminal device, an intelligent terminal device, a cellular phone, a wireless device, a handheld device, a mobile unit, an in-vehicle device, a network device, a cloud device, an artificial intelligence device, etc.; (6) others, etc.

[0239] 6B is a schematic diagram of the structure of a chip 6200 according to an embodiment of the present disclosure. If the communication device 6100 can be a chip or a chip system, reference can be made to the schematic diagram of the structure of the chip 6200 shown in FIG6B , but the present disclosure is not limited thereto.

[0240] The chip 6200 includes one or more processors 6201. The chip 6200 is configured to execute any of the above methods.

[0241] In some embodiments, chip 6200 further includes one or more interface circuits 6202. Terms such as interface circuit, interface, and transceiver pins may be used interchangeably. In some embodiments, chip 6200 further includes one or more memories 6203 for storing data. Alternatively, all or part of memory 6203 may be located external to chip 6200. Optionally, interface circuit 6202 is connected to memory 6203 and may be used to receive data from memory 6203 or other devices, or may be used to send data to memory 6203 or other devices. For example, interface circuit 6202 may read data stored in memory 6203 and send the data to processor 6201.

[0242] In some embodiments, the interface circuit 6202 performs at least one of the communication steps, such as sending and / or receiving, in the above-described method. For example, the interface circuit 6202 performing the communication steps, such as sending and / or receiving, in the above-described method means that the interface circuit 6202 performs data exchange between the processor 6201, the chip 6200, the memory 6203, or the transceiver device. In some embodiments, the processor 6201 performs at least one of the other steps.

[0243] The modules and / or devices described in various embodiments, such as virtual devices, physical devices, and chips, can be arbitrarily combined or separated according to circumstances. Optionally, some or all steps can also be performed collaboratively by multiple modules and / or devices, which is not limited here.

[0244] The present disclosure also proposes a storage medium having instructions stored thereon. When the instructions are executed on the communication device 6100, the communication device 6100 executes any of the above methods. Optionally, the storage medium is an electronic storage medium. Optionally, the storage medium is a computer-readable storage medium, but is not limited thereto and may also be a storage medium readable by other devices. Optionally, the storage medium may be a non-transitory storage medium, but is not limited thereto and may also be a transient storage medium.

[0245] The present disclosure also provides a program product, which, when executed by the communication device 6100, enables the communication device 6100 to perform any of the above methods. Optionally, the program product is a computer program product.

[0246] The present disclosure also proposes a computer program, which, when executed on a computer, causes the computer to perform any one of the above methods. Industrial Applicability

[0247] The AI ​​function can be activated or deactivated at a reasonable time to use the AI ​​function more effectively.

Claims

1. A processing method based on artificial intelligence (AI), executed by a terminal, comprising: At least one AI function is activated or deactivated according to at least one first parameter, wherein the first parameter is used to represent the confidence level of an AI-based reasoning result.

2. The method according to claim 1, wherein The AI ​​function includes at least one AI model.

3. The method according to claim 1, wherein The activating at least one AI function according to at least one first parameter includes: At least one AI function is activated when at least one of the following conditions is met: The first parameter is greater than a first threshold; The first parameter is continuously greater than a first threshold within a first time period; Within a second time period, the number of times that the first parameter is greater than or equal to the first threshold is greater than a first value; Within a third time period, the number of times the first parameter is greater than or equal to the first threshold accounts for a number of times the number of times all parameters are greater than or equal to the first threshold that is greater than a first proportion; the most recent N1 first parameters are greater than or equal to the first threshold, and each first parameter corresponds to one of the inference results; The number of first parameters greater than or equal to the first threshold among the most recent N2 first parameters is greater than a second value; A ratio of the number of the most recent N3 first parameters that are greater than or equal to the first threshold to the number of all parameters that are greater than or equal to the first threshold is greater than a second ratio; Among them, N1, N2, and N3 are positive integers.

4. The method according to claim 1, wherein The deactivating at least one AI function according to at least one first parameter includes: At least one AI function is deactivated when at least one of the following conditions is met: The first parameter is less than a second threshold; The first parameter is continuously less than a second threshold within a fourth time period; During a fifth time period, the number of times that the first parameter is less than or equal to the second threshold is greater than a third value; During a sixth time period, the number of times the first parameter is less than or equal to the second threshold accounts for a greater proportion than a third proportion of the number of times all parameters are less than or equal to the second threshold. During a seventh time period, the number of times that the first parameter is greater than or equal to the second threshold is less than a fourth value; During an eighth time period, the number of times the first parameter is greater than or equal to the second threshold accounts for a number of times the number of times all parameters are greater than or equal to the second threshold that is greater than a fourth proportion; The most recent M1 first parameters are less than or equal to a second threshold, and each first parameter corresponds to one of the inference results; The number of the first parameters less than or equal to the second threshold among the most recent M2 first parameters is greater than a fifth value; a ratio of the number of the most recent M3 first parameters that are smaller than or equal to the second threshold to the number of all parameters that are smaller than or equal to the second threshold is greater than a fifth ratio; The number of the first parameters greater than or equal to the second threshold among the most recent M4 first parameters is less than a sixth value; a ratio of the number of the most recent M5 first parameters that are greater than or equal to the second threshold to the number of all parameters that are greater than or equal to the second threshold is less than a sixth ratio; Among them, M1, M2, M3, M4, and M5 are positive integers.

5. The method according to claim 1, wherein Each of the AI ​​functions corresponds to a first condition, and the terminal determines whether to activate at least one AI function according to whether the at least one first parameter meets the first condition; Each of the AI ​​functions corresponds to a second condition, and the terminal determines whether to deactivate at least one AI function according to whether the at least one first parameter meets the second condition.

6. The method of claim 1, wherein: The deactivating at least one AI function according to at least one first parameter includes: When it is determined according to at least one first parameter that no activatable AI function exists, a first number of AI functions are deactivated, or at least one set AI function is deactivated, or all AI functions are deactivated.

7. The method of claim 1, wherein: The activating at least one AI function according to at least one first parameter includes: When it is determined according to at least one first parameter that the number of activatable AI functions is greater than 1, the AI ​​function with the best performance is activated.

8. The method of claim 7, wherein: The AI ​​function with the best performance is determined based on at least one of the following: The first parameter corresponding to the AI ​​function is the largest; During the seventh time period, the number of times that the first parameter corresponding to the AI ​​function is greater than or equal to the third threshold is greater than a seventh value; During an eighth time period, the number of times the first parameter corresponding to the AI ​​function is greater than or equal to the third threshold accounts for a greater proportion than the fifth proportion among all parameters. The number of first parameters greater than or equal to the third threshold among the first parameters corresponding to the most recent K1 AI functions is greater than an eighth value; The ratio of the number of the most recent K2 first parameters that are smaller than or equal to the second threshold to the number of all parameters that are smaller than or equal to the third threshold is greater than a fifth ratio.

9. The method according to any one of claims 1 to 8, wherein: The method comprises: Receive first indication information sent by a network device, where the first indication information is used to indicate at least one second parameter, where the second parameter is a threshold corresponding to the first condition and / or the second condition, and determine, by the terminal, whether to activate at least one AI function based on whether the at least one first parameter meets the first condition, and determine, by the terminal, whether to deactivate at least one AI function based on whether the at least one first parameter meets the second condition.

10. The method of claim 9, wherein: The first indication information is used to indicate at least one group of information, each group of information corresponding to one or more AI functions.

11. The method according to any one of claims 1 to 10, wherein: The method comprises: An AI function that is in an inactivated state is periodically activated to obtain a first parameter of the AI ​​function.

12. The method according to any one of claims 1 to 10, wherein: The method comprises: After deactivating at least one AI function, the confidence level of the AI ​​function acquired last time is used as the confidence level of the AI ​​function in the inactivated state.

13. The method according to any one of claims 1 to 12, wherein: The method comprises: Second indication information is sent to the network device, where the second indication information instructs to deactivate a first number of AI functions, or to deactivate at least one set AI function, or to deactivate all AI functions.

14. A processing method based on artificial intelligence (AI), executed by a network device, comprising: Sending first indication information to the terminal, where the first indication information is used to indicate at least one second parameter, where the second parameter is a threshold corresponding to the first condition and / or the second condition, and the first condition is used by the terminal to determine whether to activate at least one AI function based on whether the at least one first parameter meets the first condition, and the second condition is used by the terminal to determine whether to deactivate at least one AI function based on whether the at least one first parameter meets the second condition, wherein the first parameter is used to represent the confidence level of the AI-based reasoning result.

15. The method of claim 14, wherein: The method comprises: The first indication information sent by the receiving terminal is instructing to deactivate a first number of AI functions, or to deactivate at least one set AI function, or to deactivate all AI functions.

16. A communication device, configured in a terminal, comprising: The processing module is configured to: activate at least one AI function or deactivate at least one AI function according to at least one first parameter, wherein the first parameter is used to represent the confidence of the AI-based reasoning result.

17. A communication device, configured in a network device, comprising: The transceiver module is configured to: send first indication information to the terminal, where the first indication information is used to indicate at least one second parameter, where the second parameter is a threshold corresponding to the first condition and / or the second condition, where the first condition is used by the terminal to determine whether to activate at least one AI function based on whether the at least one first parameter meets the first condition, and the second condition is used by the terminal to determine whether to deactivate at least one AI function based on whether the at least one first parameter meets the second condition, wherein the first parameter is used to represent the confidence level of the AI-based reasoning result.

18. A communication device comprising a processor and a memory, wherein: The memory is used to store computer programs; The processor is configured to execute the computer program to implement the method according to any one of claims 1 to 13 or the method according to any one of claims 14 to 15.

19. A computer-readable storage medium, wherein instructions are stored in the computer-readable storage medium. When the instructions are called and executed on a computer, the computer is caused to execute the method according to any one of claims 1 to 13, or the method according to any one of claims 14 to 15.

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