CN118590938A - Method, device, equipment and storage medium for alleviating delay - Google Patents

Abstract

A method, device, equipment and storage medium for delay mitigation. The method is executed by a terminal, and the method includes: obtaining a communication delay-related data group at a first time; inputting the communication delay-related data group at the first time into an artificial intelligence (AI) model to obtain a communication delay duration at a second time; and performing a delay mitigation operation when the communication delay duration is greater than a preset delay duration. The present disclosure predicts the delay that will occur through an artificial intelligence model, and performs a delay mitigation operation in advance when the delay has not occurred, so as to avoid data loss, communication jams and other problems caused by delays during the communication process.

Description

Method, device, equipment and storage medium for alleviating delay

Technical Field

The present disclosure relates to the field of communications, and in particular to a method, apparatus, device, and computer-readable storage medium for alleviating delays.

Background Art

When a terminal communicates with a base station, communication delay may occur. In the related art, when a network delay has occurred, an operation to alleviate the delay can be performed, so that the delay of subsequent communication between the terminal and the base station is reduced or alleviated. However, this operation to alleviate the delay is performed only when a delay has already occurred, which can only avoid subsequent delays, but cannot solve the problems such as data loss or obvious communication jams caused by the delay that has occurred.

Summary of the invention

In order to overcome the problems existing in the related art, the present disclosure provides a method, apparatus, device and computer-readable storage medium for alleviating delay, which can solve the above problems.

According to a first aspect of an embodiment of the present disclosure, a method for alleviating delay is provided, the method being performed by a terminal, the method comprising:

Acquire a communication delay related data group at the first time;

Inputting the communication delay related data group at the first time into an artificial intelligence AI model to obtain the communication delay duration at the second time;

In the case where the communication delay duration is greater than the preset delay duration, an operation to alleviate the delay is performed.

According to a second aspect of an embodiment of the present disclosure, a device for alleviating delay is provided, including:

An acquisition module, used to acquire a communication delay related data group at a first time;

A prediction module, used to input the communication delay related data group at the first time into an artificial intelligence AI model to obtain the communication delay duration at the second time;

The mitigation module is used to perform a delay mitigation operation when the communication delay duration is greater than a preset delay duration.

According to a third aspect of an embodiment of the present disclosure, there is provided an electronic device, including: a processor and a memory;

The memory is used to store computer programs;

The processor is used to execute the delay mitigation method as described in the first aspect by calling the computer program.

According to a fourth aspect of an embodiment of the present disclosure, there is provided a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for alleviating delays as described in the first aspect.

The technical solution provided by the embodiments of the present disclosure may have the following beneficial effects:

According to an embodiment of the present disclosure, a communication delay-related data group at a first time can be obtained, and the communication delay-related data group can be input into an artificial intelligence model to obtain the communication delay duration at a second time, and when a jam is predicted to occur at the second time, an operation to mitigate the delay is performed. The present disclosure predicts the upcoming delay through an artificial intelligence model, and performs a delay mitigation operation in advance when the delay has not yet occurred, so as to avoid data loss, communication jams, and other problems caused by delays during the communication process.

It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

Fig. 1 is a schematic flow chart of a method for alleviating delay according to an exemplary embodiment of the present disclosure.

Fig. 2 is a schematic flow chart of a method for alleviating delay according to an exemplary embodiment of the present disclosure.

Fig. 3 is a schematic flow chart of a method for alleviating delay according to an exemplary embodiment of the present disclosure.

Fig. 4 is a schematic flow chart of a method for alleviating delay according to an exemplary embodiment of the present disclosure.

Fig. 5 is an interactive schematic diagram of a method for alleviating delay according to an exemplary embodiment of the present disclosure.

Fig. 6 is a schematic block diagram of a device for alleviating delay according to an exemplary embodiment of the present disclosure.

Fig. 7 is a schematic block diagram of a device for alleviating delay according to an exemplary embodiment of the present disclosure.

DETAILED DESCRIPTION

Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

The terms used in this disclosure are for the purpose of describing specific embodiments only and are not intended to limit the disclosure. The singular forms of "a", "said" and "the" used in this disclosure and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and/or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

It should be understood that although the terms first, second, third, etc. may be used in the present disclosure to describe various information, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present disclosure, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

To solve the above problems, the present disclosure proposes a method for alleviating delays. FIG1 is a schematic flow chart of a method for alleviating delays according to an embodiment of the present disclosure, and the method for alleviating delays is executed by a terminal. The terminal includes but is not limited to communication devices such as mobile phones, tablet computers, wearable devices, sensors, and Internet of Things devices. The terminal can communicate with a network device, and the network device includes but is not limited to a network device in a communication system such as 4G and 5G, such as a base station, a network element in a core network, etc.

As shown in Figure 1, delay mitigation methods include:

In step S101, a communication delay related data group at a first time is obtained;

In step S102, the communication delay related data group at the first time is input into an artificial intelligence (AI) model to obtain the communication delay duration at the second time;

In step S103, when the communication delay time is longer than the preset delay time, an operation of alleviating the delay is performed.

In one embodiment, the terminal can establish a data collection module to obtain a communication delay related data group at a first time. The first time can be a first moment or a first time period. Correspondingly, the second time can be a second moment or a second time period.

The first time is before the second time. For example, the first time is the current moment, the second time is the next moment, and the time interval between the current moment and the next moment can be predetermined; for example, the first time is the current time period, the second time is the next time period, and the time interval between the current time period and the next time period can be predetermined.

In one embodiment, the artificial intelligence model may be predetermined, wherein the artificial intelligence model may be determined by the terminal, or may be determined by a device other than the terminal and indicated to the terminal. The manner of determining the artificial intelligence model will be described in subsequent embodiments and will not be expanded here.

According to an embodiment of the present disclosure, a communication delay-related data group at a first time can be obtained, and the communication delay-related data group can be input into an artificial intelligence model to obtain the communication delay duration at a second time, and when a jam is predicted to occur at the second time, an operation to mitigate the delay is performed. The present disclosure predicts the upcoming delay through an artificial intelligence model, and performs a delay mitigation operation in advance when the delay has not yet occurred, so as to avoid data loss, communication jams, and other problems caused by delays during the communication process.

In one embodiment, the communication delay related data group may be a combination of communication delay related data that can cause delay in the communication process.

In one embodiment, the communication delay related data group includes at least one communication delay related data, and the communication delay related data includes at least one of the following: packet loss rate, domain name system (Domain Name System, DNS) success rate, buffer size, number of packet losses, round-trip time (Round-Trip Time, RTT) delay, received signal strength indicator (Received Signal Strength Indicator, RSSI), reference signal received power (Reference Signal Received Power, RSRP), and reference signal received quality (Reference Signal Received Quality, RSRQ).

In one embodiment, performing delay mitigation operations includes at least one of the following:

Reduce the congestion window;

Reduce the sending rate;

A weighted penalty is applied to the reference signal received power RSRP of the cell where the terminal is located.

In one embodiment, the congestion window is a window used for communication, and the size of the congestion window depends on the degree of network congestion and changes dynamically, wherein when the network congestion degree is high, the congestion window is small, and when the network congestion degree is low, the congestion window is large. In addition, the length of the congestion window is less than or equal to the uplink transmission resources configured by the network device for the terminal.

Among them, when there is no congestion, the terminal can expand the congestion window so that the terminal can send more data packets in the expanded congestion window; when congestion occurs, the congestion window can be reduced so that the terminal can only send fewer data packets in the reduced congestion window, which is conducive to alleviating subsequent congestion in the congestion window.

Therefore, the present invention determines the delay duration of the second time through an AI model, so as to predict in advance before the second time that a delay will be caused by congestion at the second time based on the delay duration, and then alleviate the congestion that will occur at the second time by executing an operation of reducing the size of the congestion window, so that the communication process remains unobstructed, and there will be no problems such as freezes or data loss caused by delays.

In one embodiment, the terminal may measure the reference signal received power RSRP of the cell in which it is located, and trigger cell switching when the RSRP reaches a predetermined switching value. For example, the terminal may switch to another cell with a relatively high RSRP among neighboring cells.

Therefore, the present invention determines the delay duration of the second time through an AI model, so as to predict in advance according to the delay duration that there will be a delay due to congestion in the cell where the terminal is located at the second time before the second time, and then by weighted penalty on the RSRP of the cell where the terminal is located, the cell switching can be triggered in advance, so that the terminal can switch to other cells at the second time, thereby alleviating the delay to a certain extent.

In order to trigger the cell switching earlier, the modem (Modulator and Demodulator, Modem) can be notified to perform a weighted penalty on the RSRP of the cell where the terminal is located. For example, if the RSRP of the cell where the terminal is located is measured to be -100 decibels relative to one milliwatt, the RSRP is subjected to a weighted penalty of 3 decibels through the Modem, that is, the actual RSRP is compared with the preset switching value as -103dbm, so that the terminal can trigger the cell switching earlier, so that it can be switched to other cells at the second time, thereby alleviating the delay to a certain extent.

In one embodiment, the higher the sending rate of the terminal sending data packets, the more data packets are sent per unit time, and the more likely congestion occurs. Correspondingly, the lower the sending rate of the terminal sending data packets, the fewer data packets are sent per unit time, and the easier it is to relieve congestion.

Therefore, the present invention determines the delay duration of the second time through an AI model, so as to predict in advance before the second time that there will be a delay due to congestion at the second time based on the delay duration, and then alleviate the congestion that will occur at the second time by performing an operation of reducing the terminal sending rate, so that the communication process remains unobstructed, and there will be no problems such as freezes or data loss caused by delays.

It should be noted that the above embodiments provide examples for three delay mitigation operations: reducing the congestion window, reducing the transmission rate, and performing a weighted penalty on the reference signal received power RSRP of the cell where the terminal is located. In practical applications, when performing delay mitigation operations, only any one of the above delay mitigation operations may be performed, or multiple delay mitigation operations may be performed. And the delay mitigation operations are not limited to the above three, and can be implemented as needed.

In one embodiment, the method further comprises:

The uplink jamming level is determined according to the communication delay duration, wherein when the communication delay duration is greater than the preset delay duration, an operation to alleviate the delay is performed, including: when the jamming level is greater than the preset jamming level, an operation to alleviate the delay is performed.

In one embodiment, since the communication delay duration may have many values in actual applications, directly comparing the communication delay duration with the preset delay duration to determine whether the communication delay duration is relatively large may cause trouble to the calculation logic of the processor of the terminal.

Therefore, this embodiment determines the uplink jamming level according to the communication delay duration, converts the multiple possible numerical values corresponding to the communication delay into a small number of uplink jamming levels, and then compares the uplink jamming level with the preset jamming level, which helps the terminal processor to determine whether the delay is large more quickly and efficiently.

For example, the delay time can be set as jamming level 1 when it is less than 0.3 seconds, jamming level 2 when it is between 0.3-0.5 seconds, jamming level 3 when it is between 0.5-1 second, jamming level 4 when it is between 1-1.5 seconds, and jamming level 5 when it is more than 1.5 seconds. The jamming level can be preset to 4. Then, when the jamming level is greater than or equal to 4, it is determined that the communication delay time is relatively large, and operations to alleviate the delay are performed.

In one embodiment, the performing the delay mitigation operation includes: performing the delay mitigation operation according to the communication delay duration.

In one embodiment, an operation to alleviate the delay may be performed according to the communication delay duration. Taking the example of performing the operation to alleviate the delay including reducing the congestion window, when the communication delay duration is greater than the preset delay duration, the congestion window may be reduced according to a first ratio when the communication delay duration is equal to a first duration, and the congestion window may be reduced according to a second ratio when the communication delay duration is equal to a second duration, wherein the first duration is less than the second duration, and the first ratio is greater than the second ratio.

For example, when the delay time is 1 second, the congestion window at the second time can be reduced to two-thirds of the size of the congestion window at the first time; when the delay time is 1.5 seconds, the congestion window at the second time can be reduced to one-third of the size of the congestion window at the first time. Accordingly, when the delay time is relatively long, the delay that will occur at the second time can be more effectively avoided.

FIG2 is a schematic flow chart of a method for alleviating delay according to an embodiment of the present disclosure, wherein the artificial intelligence model includes a first model and a second model, and a communication delay-related data group at a first time is input into the artificial intelligence AI model to obtain a communication delay duration at a second time, including:

In step S201, a communication delay related data group at a first time is input into a first model to obtain a communication delay related data group at a second time;

In step S202, the communication delay related data group at the second time is input into the second model to obtain the communication delay duration at the second time.

In one embodiment, the correlation between the communication delay related data group at the first time and the communication delay duration at the second time is relatively weak, while the correlation between the communication delay related data group at the second time and the communication delay duration at the second time is relatively strong. Moreover, the communication delay related data group changes smoothly over a period of time, so the communication delay related data group at the first time and the communication delay related data group at the second time are relatively strong.

Therefore, according to the first model, the communication delay related data group at the first time can be relatively accurately predicted based on the communication delay related data group at the second time, and according to the second model, the communication delay duration at the second time can be relatively accurately predicted based on the communication delay related data group at the second time.

FIG3 is a schematic flow chart of a method for alleviating delay according to an embodiment of the present disclosure, the method further comprising:

In step S301, a delay-related data group is collected as a first sample, and a first sample set is determined according to a plurality of first samples;

In step S302, model training is performed according to the first sample set to obtain a first model.

In one embodiment, the terminal can establish a data collection module to collect delay-related data groups. Furthermore, the delay-related data groups can be collected every preset time, and the collected delay-related data groups can be stored in the data collection document. When a certain amount of data has been collected, data cleaning can be performed to remove some invalid data to obtain a first sample set, wherein the first sample set should contain a large number of continuous delay-related data groups. Using the first sample set, the AI model is trained through a long short-term memory recursive neural network (LSTM) so that the obtained first model can predict the delay-related data group at the second time based on the input delay-related data group at the first time.

The present disclosure obtains a first model based on a plurality of collected delay-related data sets, so that the model has a higher accuracy when predicting the delay-related data set at a second time.

FIG4 is a schematic flow chart of a method for alleviating delay according to an embodiment of the present disclosure, the method further comprising:

In step S401, delay-related data groups are collected, and the communication delay duration corresponding to each delay-related data group is determined;

In step S402, a second sample is determined according to the delay-related data group and the corresponding delay duration communication;

In step S403, a second sample set is determined according to the plurality of second samples;

In step S404, model training is performed according to the second sample set to obtain a second model.

In one embodiment, the terminal can establish a data collection module to collect delay-related data groups, determine the communication delay duration corresponding to each delay-related data group, combine a group of delay-related data groups with the corresponding communication delay duration into a second sample, and store it in a data collection document. When a certain amount of data is collected in the document, data cleaning can be performed to remove some invalid data to obtain a second sample set, wherein the second sample set should contain a large number of continuous delay-related data groups and their corresponding delay durations. Using the second sample set, the AI model is trained through a convolutional neural network (CNN) so that the obtained second model can determine the delay duration corresponding to the data group based on the input delay-related data group.

In one embodiment, determining the communication delay duration corresponding to each delay-related data group includes: triggering a data packet of a network diagnostic tool to a server when the delay-related data group is collected; and determining the communication delay duration corresponding to the data packet. Specifically, when the data collection module collects the delay-related data group, a data packet of a network diagnostic tool (Packet Internet Groper, ping) can be triggered to a fixed server, and the delay duration corresponding to the group of delay-related data groups can be determined based on the data packet returned by the fixed server.

The present disclosure obtains a second model based on the collected multiple delay-related data groups and the corresponding delay durations, so that the model has a higher accuracy when determining the delay duration of the second time.

FIG5 is an interactive schematic diagram of a method for alleviating delay according to an embodiment of the present disclosure, wherein the terminal includes an AI Data Collection Service, an Indicator forecast (IF) module, a Delay mapping (DM) module, an ease delay execution (EDE) module, a Modem, and an operating system kernel, wherein the functions of each module are as follows:

The data collection module 510 is used to obtain a communication delay related data group at the first time and pass it to the parameter IF module;

The IF module 520 is used to input the communication delay related data group at the first time into the first model, obtain the communication delay related data group at the second time, and pass it to the DM module;

The DM module 530 is used to input the communication delay related data group at the second time into the second model, obtain the communication delay duration at the second time, and pass it to the EDE module;

The EDE module 540 is used to determine whether to perform an operation to alleviate the delay according to the communication delay duration at the second time and the preset delay duration, and issue instructions to the modem and the kernel if the operation to alleviate the delay needs to be performed;

Modem 550 is used to perform weighted penalty on the RSRP of the cell where the terminal is located.

Kernel 560 is used to reduce the congestion window and reduce the sending rate.

The implementation process of the functions and effects of the above-mentioned modules is specifically described in the implementation process of the corresponding steps in the above-mentioned method, which will not be repeated here.

Corresponding to the method embodiment of the present disclosure, the present disclosure also provides an embodiment of a corresponding device for alleviating delay.

FIG6 is a schematic block diagram of a device for alleviating delay according to an embodiment of the present disclosure. As shown in FIG6 , the device includes:

An acquisition module 610 is used to acquire a communication delay related data group at a first time;

Prediction module 620, used to input the communication delay related data group at the first time into the artificial intelligence AI model to obtain the communication delay duration at the second time;

The mitigation module 630 is used to perform a delay mitigation operation when the communication delay duration is greater than a preset delay duration.

In one embodiment, the communication delay related data group includes at least one communication delay related data, and the communication delay related data includes at least one of the following:

Packet loss rate, Domain Name System DNS success rate, buffer size, number of packet losses, round-trip time RTT delay, received signal strength indication, reference signal received power, reference signal reception quality.

In one embodiment, performing delay mitigation operations includes at least one of the following:

Reduce the congestion window;

Reduce the sending rate;

A weighted penalty is applied to the reference signal received power RSRP of the cell where the terminal is located.

In one embodiment, it further includes:

The uplink jam level is determined according to the communication delay duration, wherein when the communication delay duration is greater than the preset delay duration, an operation to alleviate the delay is performed, including:

When the jam level is greater than the preset jam level, an operation to alleviate the delay is performed.

In one embodiment, operations to mitigate delays are performed, including:

Based on the duration of the communication delay, an action to alleviate the delay is performed.

In one embodiment, the artificial intelligence model includes a first model and a second model, and the communication delay-related data group at the first time is input into the artificial intelligence AI model to obtain the communication delay duration at the second time, including:

Inputting the communication delay related data group at the first time into the first model to obtain the communication delay related data group at the second time;

The communication delay related data group at the second time is input into the second model to obtain the communication delay duration at the second time.

In one embodiment, the method further comprises:

collecting a delay-related data group as a first sample, and determining a first sample set according to a plurality of first samples;

Model training is performed according to the first sample set to obtain a first model.

In one embodiment, the method further comprises:

Collect delay-related data groups and determine the communication delay duration corresponding to each delay-related data group;

Determine a second sample based on the delay-related data group and the corresponding delay duration communication;

determining a second sample set based on the plurality of second samples;

Model training is performed according to the second sample set to obtain a second model.

In one embodiment, determining the communication delay duration corresponding to each delay-related data group includes:

triggering a data packet of a network diagnostic tool to the server when a delay-related data group is collected;

Determine the communication delay duration corresponding to the data packet.

The implementation process of the functions and effects of each module in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, which will not be repeated here.

An embodiment of the present disclosure further proposes an electronic device, comprising: a processor and a memory; the memory is used to store a computer program; the processor is used to execute the delay mitigation method as described in any of the above embodiments by calling the computer program.

An embodiment of the present disclosure further provides a computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the method for alleviating delay as described in any of the above embodiments is implemented.

7 is a schematic block diagram of an apparatus 700 for receiving information according to an embodiment of the present disclosure. For example, the apparatus 700 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

7 , device 700 may include one or more of the following components: a processing component 702 , a memory 704 , a power component 706 , a multimedia component 708 , an audio component 710 , an input/output (I/O) interface 712 , a sensor component 714 , and a communication component 716 .

The processing component 702 generally controls the overall operation of the device 700, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 702 may include one or more processors 720 to execute instructions to complete all or part of the steps of the above-mentioned information receiving method. In addition, the processing component 702 may include one or more modules to facilitate the interaction between the processing component 702 and other components. For example, the processing component 702 may include a multimedia module to facilitate the interaction between the multimedia component 708 and the processing component 702.

The memory 704 is configured to store various types of data to support operations on the device 700. Examples of such data include instructions for any application or method operating on the device 700, contact data, phone book data, messages, pictures, videos, etc. The memory 704 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

The power supply component 706 provides power to the various components of the device 700. The power supply component 706 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device 700.

The multimedia component 708 includes a screen that provides an output interface between the device 700 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 708 includes a front camera and/or a rear camera. When the device 700 is in an operating mode, such as a shooting mode or a video mode, the front camera and/or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

The audio component 710 is configured to output and/or input audio signals. For example, the audio component 710 includes a microphone (MIC), and when the device 700 is in an operating mode, such as a call mode, a recording mode, and a speech recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 704 or sent via the communication component 716. In some embodiments, the audio component 710 also includes a speaker for outputting audio signals.

I/O interface 712 provides an interface between processing component 702 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, a home button, a volume button, a start button, and a lock button.

The sensor assembly 714 includes one or more sensors for providing various aspects of status assessment for the device 700. For example, the sensor assembly 714 can detect the open/closed state of the device 700, the relative positioning of components, such as the display and keypad of the device 700, and the sensor assembly 714 can also detect the position change of the device 700 or a component of the device 700, the presence or absence of user contact with the device 700, the orientation or acceleration/deceleration of the device 700, and the temperature change of the device 700. The sensor assembly 714 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 714 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 714 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 716 is configured to facilitate wired or wireless communication between the device 700 and other devices. The device 700 can access a wireless network based on a communication standard, such as WiFi, 2G, 3G, 4GLTE, 5G NR, or a combination thereof. In an exemplary embodiment, the communication component 716 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 716 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

In an exemplary embodiment, the apparatus 700 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above-mentioned information receiving method.

In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 704 including instructions, and the instructions can be executed by the processor 720 of the device 700 to complete the above-mentioned information receiving method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the disclosure disclosed herein. The present disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including 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, the elements defined by the statement "comprises a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

The method and device provided in the embodiments of the present disclosure are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present disclosure. The description of the above embodiments is only used to help understand the method of the present disclosure and its core idea. At the same time, for those skilled in the art, according to the idea of the present disclosure, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present disclosure.

Claims (12)

1. A method for alleviating delay, characterized in that the method is performed by a terminal, and the method comprises: Acquire a communication delay related data group at the first time; Inputting the communication delay related data group at the first time into an artificial intelligence AI model to obtain the communication delay duration at the second time; In the case where the communication delay duration is greater than the preset delay duration, an operation to alleviate the delay is performed. 2. The method according to claim 1, characterized in that the communication delay related data group includes at least one communication delay related data, and the communication delay related data includes at least one of the following: Packet loss rate, Domain Name System DNS success rate, buffer size, number of packet losses, round-trip time RTT delay, received signal strength indication, reference signal received power, reference signal reception quality. 3. The method according to claim 1, wherein the operation of performing delay mitigation comprises at least one of the following: Reduce the congestion window; Reduce the sending rate; A weighted penalty is performed on the reference signal received power RSRP of the cell where the terminal is located. 4. The method according to claim 1, characterized in that the method further comprises: Determining the uplink jam level according to the communication delay duration, wherein, when the communication delay duration is greater than a preset delay duration, performing an operation to alleviate the delay includes: When the jam level is greater than a preset jam level, an operation to alleviate the delay is performed. 5. The method according to claim 1, wherein the performing of the delay mitigation operation comprises: An operation to alleviate the delay is performed according to the duration of the communication delay. 6. The method according to any one of claims 1 to 5, characterized in that the artificial intelligence model includes a first model and a second model, and the step of inputting the communication delay related data group at the first time into the artificial intelligence AI model to obtain the communication delay duration at the second time comprises: inputting the communication delay related data group at the first time into the first model to obtain the communication delay related data group at the second time; The communication delay related data group of the second time is input into the second model to obtain the communication delay duration of the second time. 7. The method according to claim 6, characterized in that the method further comprises: collecting a delay-related data group as a first sample, and determining a first sample set according to a plurality of the first samples; Model training is performed according to the first sample set to obtain the first model. 8. The method according to claim 6, characterized in that the method further comprises: Collect delay-related data groups, and determine the communication delay duration corresponding to each of the delay-related data groups; Determine a second sample based on the delay-related data group and the corresponding delay duration communication; determining a second sample set according to the plurality of the second samples; Model training is performed according to the second sample set to obtain the second model. 9. The method according to claim 8, characterized in that the determining of the communication delay duration corresponding to each of the delay-related data groups comprises: triggering a data packet of a network diagnostic tool to a server when the delay-related data group is collected; Determine the communication delay duration corresponding to the data packet. 10. A device for alleviating delay, comprising: An acquisition module, used to acquire a communication delay related data group at a first time; A prediction module, used to input the communication delay related data group at the first time into an artificial intelligence AI model to obtain the communication delay duration at the second time; The mitigation module is used to perform a delay mitigation operation when the communication delay duration is greater than a preset delay duration. 11. An electronic device, comprising: a processor and a memory; The memory is used to store computer programs; The processor is configured to execute the delay mitigation method according to any one of claims 1 to 9 by calling the computer program. 12. A computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the method for alleviating delays as described in any one of claims 1 to 9 is implemented.