CN111479314A - Terminal power consumption adjusting method and terminal equipment - Google Patents
Terminal power consumption adjusting method and terminal equipment Download PDFInfo
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- H—ELECTRICITY
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- H04W52/02—Power saving arrangements
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
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- H04W52/20—TPC being performed according to specific parameters using error rate
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Abstract
The application discloses a terminal power consumption adjusting method, which comprises the following steps: collecting performance key characteristic data of a communication assembly of the terminal; carrying out artificial intelligence AI identification according to the performance key characteristic data, and determining the performance grade of the communication assembly in the current scene; and adjusting the power consumption of the communication assembly according to the performance grade of the communication assembly in the current scene. Corresponding to the method, the application also discloses the terminal equipment. By applying the technical scheme disclosed by the application, the purpose of prolonging the service life of the terminal can be achieved while the performance requirement of the terminal is met.
Description
Technical Field
The present application relates to the field of communications devices, and in particular, to a method for adjusting power consumption of a terminal and a terminal device.
Background
The mobile communication technology is subject to the evolution of 2G, 3G, 4G and 5G, the functions of communication components are more and more powerful, the network rate of the user intelligent terminal is also increased rapidly at the speed of being visible by naked eyes, but the network rate is greatly improved, and meanwhile, higher requirements are also put forward on the power consumption of terminal equipment. For example, in the currently emerging 5G terminal, the power consumption required by the communication component is increased greatly, which greatly affects the service life of the battery of the terminal.
The power consumption of the terminal communication component is mainly the power consumption of the baseband chip and the power consumption of the receiving and sending parts of the terminal radio frequency. The waves need to be radiated and the frequency must reach a certain level. If the frequency is low, for example, the frequency of sound is only 20HZ-20KHZ, the signal of this frequency cannot be directly transmitted, and must be modulated to a high frequency, i.e., a radio frequency, to be transmitted.
In order to achieve good communication between the terminal and the base station, the transmission power of the terminal must have sufficient strength. As shown in fig. 1, which is a schematic diagram of the variation of the terminal transmission power with the distance between the terminal and the base station, when the terminal is close to the base station, the communication can be maintained with less power; when the terminal is far away from the base station, the terminal must increase its own transmission power to maintain a good communication level with the base station. Thus, the power of the radio frequency in the terminal is adjustable.
In the prior art, there are various technical schemes for adjusting the transmission power of a terminal:
for example, a radio frequency circuit is introduced in detail in an existing radio frequency circuit and terminal scheme, and the radio frequency circuit achieves stability of radio frequency signals through multiple filtering and switch control, so that the purpose of stability of terminal equipment is achieved. However, this scheme does not consider the power consumption performance of the terminal device.
For another example, an existing method and apparatus for adjusting terminal transmit power enable inter-channel interference to meet a requirement that communication quality is not affected by cyclically adjusting signal transmit power, solve the inter-channel interference problem in a multi-channel system, and ensure signal quality. However, the scheme does not consider the actual user scenario, for example, in the 5G mode, if the traffic speed of the terminal is small, the transmission power is large, which causes waste of power consumption.
For another example, a method for controlling power consumption, a wireless terminal and a wireless communication network device in the prior art are provided, which are intended to solve the problem that the power consumption is too much because the wireless terminal always interacts with the network when receiving and sending network data. The adopted method is to obtain and store the network background information of the communication negotiation between the wireless terminal and the wireless communication network, and close the preset function module of the wireless terminal, so that the terminal enters a Power Saving Mode (PSM). The method can ensure that the terminal is connected with the network only during data interaction and enters the PSM mode when no data interaction exists, so as to reduce power consumption. The scheme solves the problem of excessive power consumption loss in the data interaction process, and does not provide a corresponding solution for reducing the power consumption of the terminal for other processes.
For another example, an existing rf control method and terminal achieve the purpose of saving power and reducing radiation by reducing the rf power of the terminal or directly turning off the rf function of the terminal when the signal of the terminal is not good. The scheme has the advantages of simple operation and power consumption saving to a certain extent, but the power consumption cannot be reduced from the perspective of communication quality, so that terminal signals can be lost, and the communication of a user is not timely.
In summary, the terminal transmission power is an important technical index, and is also a double-edged sword, on one hand, a user wants that the transmission power is large enough to overcome the loss of the radio wave propagation path and the loss of transmission or refraction, so as to overcome the interference of other radio waves; on the other hand, it is desirable that the transmission power is small enough to interfere with the communication of others as little as possible. The solution is to control the transmission power of the terminal according to the need, and to reduce the transmission power of all terminals as much as possible under the condition of ensuring the normal communication of all users.
Disclosure of Invention
The application provides a terminal power consumption adjusting method and terminal equipment, which aim to achieve the purpose of prolonging the service life of a terminal while meeting the performance requirement of the terminal.
The application discloses a terminal power consumption adjusting method, which comprises the following steps:
collecting performance key characteristic data of a communication assembly of the terminal;
carrying out artificial intelligence AI identification according to the performance key characteristic data, and determining the performance grade of the communication assembly in the current scene;
and adjusting the power consumption of the communication assembly according to the performance grade of the communication assembly in the current scene.
Preferably, the method may further include: setting at least two communication component performance levels according to performance requirements of different scenes on the communication components, wherein at least one of the at least two communication component performance levels is used for correspondingly reducing the power consumption of the communication components of the terminal;
the adjusting the power consumption of the communication component according to the performance level of the communication component of the current scene comprises: and if the performance level of the communication assembly in the current scene is at least one, reducing the power consumption of the communication assembly of the terminal.
Preferably, the reducing the power consumption of the communication component of the terminal may include at least one of:
reducing the transmission power of a communication component of the terminal;
and reducing the network system of the terminal.
Preferably, when reducing the power consumption of the communication component of the terminal, the method may further include:
monitoring a communication quality index, wherein the communication quality index comprises at least one of a bit error rate and a packet error rate;
and when any one of the communication quality indexes exceeds a set threshold value, adjusting the reduced power consumption of the communication component back to the original value.
Preferably, before performing artificial intelligence AI identification according to the performance critical feature data, the method may further include:
judging whether AI model training is needed;
and if the AI model training is needed, inputting the acquired performance key characteristic data of the communication assembly as historical data into the selected AI model, and training by taking the set performance grade of the communication assembly as an identified classification label to train the parameters of the selected AI model.
Preferably, the determining whether AI model training is required may include:
if the current AI model parameters are not trained, the AI model needs to be trained;
or, when the power consumption of the communication component of the terminal is reduced according to the performance level of the communication component determined by the AI identification, and then the number of times of adjusting the reduced power consumption of the communication component back to the original value exceeds a set threshold, the AI model training is required.
Preferably, the performance critical characteristic data of the communication component may include at least one of: maximum network speed, average network speed, network delay, signal strength, network type and received power;
the acquiring may include at least one of: and collecting during switching between the foreground and the background according to a set period.
The application also discloses a terminal device, including: scene data acquisition module, AI identification module and communication subassembly power consumption configuration module, wherein:
the scene data acquisition module is used for acquiring performance key characteristic data of the communication component of the terminal equipment and providing the performance key characteristic data to the AI identification module;
the AI identification module is used for carrying out AI identification according to the performance key characteristic data, determining the performance grade of the communication assembly in the current scene and providing the performance grade to the power consumption configuration module of the communication assembly;
and the communication component power consumption configuration module is used for adjusting the power consumption of the communication component according to the performance grade of the communication component in the current scene.
Preferably, the performance levels of the communication components are set according to performance requirements of different scenes on the communication components, at least two performance levels of the communication components are set, and at least one of the performance levels of the communication components is used for correspondingly reducing the power consumption of the communication components of the terminal;
and the communication component power consumption configuration module is used for reducing the power consumption of the communication component of the terminal equipment when the performance level of the communication component in the current scene is at least one.
Preferably, the communication component power consumption configuration module may be specifically configured to: reducing the transmission power of the communication component of the terminal equipment; or, the network system of the terminal equipment is reduced.
Preferably, the communication component power consumption configuration module may be further configured to:
monitoring a communication quality index, wherein the communication quality index comprises at least one of a bit error rate and a packet error rate;
and when any one of the communication quality indexes exceeds a set threshold value, adjusting the reduced power consumption of the communication component back to the original value.
Preferably, the AI identification module is further configured to:
judging whether AI model training is needed;
and if the AI model training is needed, inputting the acquired performance key characteristic data of the communication assembly as historical data into the selected AI model, and training by taking the set performance grade of the communication assembly as an identified classification label to train the parameters of the selected AI model.
Preferably, the AI identification module is specifically configured to:
judging whether the trained AI model parameters exist at present, and if not, judging that the AI model training is needed;
or judging whether the power consumption of the communication assembly of the terminal is reduced according to the performance grade of the communication assembly determined by the AI identification, and then adjusting the reduced power consumption of the communication assembly back to the original value for a time exceeding a set threshold value, and if the power consumption exceeds the set threshold value, judging that the AI model training is needed.
Preferably, the performance key feature data of the communication component collected by the scene data obtaining module includes at least one of the following: maximum network speed, average network speed, network delay, signal strength, network type and received power;
the scene data acquisition module performs acquisition in a manner of at least one of: and collecting during switching between the foreground and the background according to a set period.
According to the technical scheme, the performance level of the communication assembly in the current scene is determined by acquiring the performance key characteristic data of the communication assembly of the terminal and carrying out AI identification according to the performance key characteristic data; and according to the performance grade of the communication assembly in the current scene, the power consumption of the communication assembly is adjusted, so that when the performance requirement of the user scene is lower, the power consumption of the communication assembly can be reduced, the power consumption of the terminal communication assembly is adjusted according to the requirement of practical application, and the purpose of prolonging the service life of the terminal is achieved while the performance requirement is met.
On this basis, this application has realized the guarantee to communication quality through adopting the communication quality after the feedback mode monitoring reduces the communication subassembly consumption to guarantee to adjust the consumption of terminal communication subassembly as required in real time under the prerequisite that does not influence communication quality, can satisfy the performance requirement, can prolong long time again in the terminal use.
Drawings
FIG. 1 is a diagram illustrating the variation of the transmission power of a terminal with the distance between the terminal and a base station;
fig. 2 is a schematic flow chart illustrating a preferred method for adjusting power consumption of a terminal according to the present application;
FIG. 3 is a schematic diagram of a preferred terminal device according to the present application;
FIG. 4 is a schematic diagram illustrating data acquisition by an exemplary scene data acquisition module according to the present application;
FIG. 5 is a schematic diagram of a random forest model;
FIG. 6 is a schematic diagram illustrating the operation of an exemplary AI identification module of the present application;
FIG. 7 is a schematic diagram illustrating an exemplary power consumption configuration module of the communication assembly of the present application;
fig. 8 is a schematic diagram illustrating an application of the technical solution in a video playing scene according to an embodiment of the present application;
fig. 9 is a schematic diagram of an application of the technical solution of the second embodiment of the present application in a game scene.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is further described in detail below by referring to the accompanying drawings and examples.
The inventor of the application discovers that: in the prior art, the adjustment of the power of the terminal communication assembly is mainly considered from the communication, the communication performance is preferably ensured, and the power consumption is not saved from the actual user scene. If the sending frequency of the terminal can be adjusted as required according to the actual use scene of the user terminal, the stability of the terminal can be ensured, and the use duration of the equipment can be prolonged to a certain extent.
Therefore, the application provides a method for adjusting power consumption of a terminal, which comprises the following steps:
firstly, collecting performance key characteristic data of a communication assembly of the terminal;
then, carrying out Artificial Intelligence (AI) identification according to the collected performance key characteristic data, and determining the performance grade of the communication assembly in the current scene;
and finally, adjusting the power consumption of the communication assembly according to the performance grade of the communication assembly in the current scene.
In this application, the performance levels of the communication components may be set according to performance requirements of different scenarios on the communication components, specifically, at least two performance levels of the communication components may be set, and at least one of the performance levels of the communication components may be corresponding to a reduction in power consumption of the communication components of the terminal.
For example: the set level of performance of the communication component may include a "low" level, and the "low" level may be set so as to reduce power consumption of the communication component of the terminal. Then, when the power consumption of the communication component is adjusted according to the performance level of the communication component of the current scene, if the performance level of the communication component of the current scene is "low", the power consumption of the communication component of the terminal is reduced.
The method for reducing the power consumption of the terminal communication component may be to reduce the transmission power of the terminal communication component, or to reduce the network type of the terminal, for example: from 5G to 4G.
In order to ensure the communication quality, the power consumption of the communication component of the terminal is reduced, and meanwhile, the communication quality index can be further monitored, for example: detecting at least one of a bit error rate and a packet error rate; when any one of the communication quality indexes exceeds a set threshold value, the reduced power consumption of the communication component is adjusted back to the original value. Therefore, when the communication quality is reduced due to the fact that the power consumption of the communication assembly is reduced, the power consumption of the communication assembly which is adjusted downwards is adjusted to the original value, and therefore the communication quality is improved.
The AI model of the application does not need to be trained every time, and only needs to be trained when needed. For example, if there are no currently trained AI model parameters, AI model training is required; or, when the power consumption of the communication component of the terminal is reduced according to the performance level of the communication component determined by the AI identification, and then the number of times of adjusting the reduced power consumption of the communication component back to the original value exceeds a set threshold, the AI model training is required.
And if the AI model training is required, inputting the acquired performance key characteristic data of the communication assembly as historical data into the selected AI model, training by taking the set performance grade of the communication assembly as an identified classification label, and training parameters of the selected AI model.
In the present application, the performance critical characteristic data of the communication component may include at least one of the following: maximum network speed, average network speed, network delay, signal strength, network type and received power;
the method for acquiring the characteristic data comprises at least one of the following steps: and collecting when the foreground and the background are switched, or collecting according to a set period.
Fig. 2 is a schematic flow chart of a preferred method for adjusting power consumption of a terminal according to the present application, where the method includes:
first, scene data is collected and data processing is performed.
Then, whether an AI model training is needed is judged, if the AI model training is needed, the AI model training is performed according to the method of the present application, and if the AI model training is not needed, AI identification is performed according to the collected scene data to determine a communication component performance level (also referred to as a communication module performance level).
Then, judging whether the current scene is a scene with low performance requirement on the communication assembly according to the determined performance level of the communication assembly, and if not, transmitting according to the original transmission power; and if the current scene is a scene with low requirement on the performance of the communication assembly, the sending power of the terminal communication assembly is down-regulated, and meanwhile, the bit error rate and the packet error rate are monitored.
If the bit error rate or the packet error rate is monitored to be larger than the set threshold value, the down-regulation of the terminal sending power is stopped, otherwise, the terminal sending power is down-regulated, and the process is ended.
According to the technical scheme, the communication component performance requirements corresponding to the scenes are identified by utilizing AI training according to the actual use scenes of the user, and when the performance requirements of the user scenes are lower, the power consumption of the communication component is reduced, so that the power consumption of the terminal communication component is adjusted according to the requirements of actual application, and the purpose of prolonging the service life of the terminal is achieved while the performance requirements are met. On this basis, this application has realized the guarantee to communication quality through adopting the communication quality after the feedback mode monitoring reduces the communication subassembly consumption to guarantee to adjust the consumption of terminal communication subassembly as required in real time under the prerequisite that does not influence communication quality, can satisfy the performance requirement, can prolong long time again in the terminal use.
Corresponding to the above method, the present application further provides a terminal device, whose composition structure is shown in fig. 3, and mainly includes: the device comprises a scene data acquisition module, an AI identification module and a communication component power consumption configuration module. The three modules are described in detail below.
1. A scene data acquisition module:
the module is mainly used for collecting performance key characteristic data (also referred to as characteristic data for short) related to the communication assembly and preparing for AI identification and classification of the next AI identification module. A schematic diagram of the scene data acquisition module for data acquisition is shown in fig. 4:
the feature data (i.e., "data source" shown in the figure) that the scene data acquisition module needs to collect includes but is not limited to: maximum network speed, average network speed, network delay, signal strength, network type, received power, etc.
The occasion of acquiring the feature data by the scene data acquisition module may be when switching between the foreground and the background, and in addition, the feature data may be acquired periodically, that is, the related feature data is acquired periodically.
After the characteristic data are collected, the scene data acquisition module stores the characteristic data in a local database, and sends the characteristic data to the AI identification module for use after data sorting and standardization of the characteristic data.
2. An AI identification module:
the module is used for identifying according to the collected performance key characteristic data of the communication assembly and determining the performance grade of the communication assembly in the current scene.
The performance grades can be divided according to actual needs, at least two grades are divided, and at least one of the performance grades of the at least two communication components is used for reducing the power consumption of the communication components of the terminal.
In a preferred example of the present application, the performance level of the communication component is divided into 5 levels, as shown in table 1:
| l evel 1: Very L ow (Very low) |
| L evel 2: L ow (Low) |
| |
| L evel 4: High |
| L evel 5: Very High (Very High) |
TABLE 1
L evel 1-L evel 5 in table 1 sequentially indicate that the requirements of the corresponding scenes on the performance levels of the communication components are gradually increased, and specifically L evel 1-L evel 5 sequentially indicate that the requirements of the corresponding scenes on the performance levels of the communication components are very low, normal, high and very high.
For the AI identification module, the type of AI model needs to be selected first. Here, an appropriate model may be selected according to the characteristics of the AI model, and a random forest model is selected in the embodiment of the present application. The random forest model is shown in fig. 5: and performing decision fusion by the random forest algorithm according to the decision trees to obtain a random forest decision. When the random forest algorithm works, the occupation ratios of all attributes of the input feature vectors do not need to be manually formulated, and the determination of the occupation ratios is completed by model training. That is, the importance of each input parameter is evaluated after the training model is obtained by the random forest model.
After the type of AI model is selected, model parameters need to be trained on the selected AI model. Fig. 6 is a schematic diagram of an operating principle of the AI identification module of the present application, specifically: the AI identification module takes performance key characteristic data of the communication assembly, which is acquired by the scene data acquisition module before, as historical data to be input into the AI model through machine learning, and takes the set performance grade of the communication assembly as an identified classification label to train, so as to train parameters of the AI model. The training of the model need not be performed many times, and is performed only when needed, for example, when the number of performance losses caused by the performance level determined by AI recognition to the power consumption configuration of the communication component exceeds a threshold value, that is to say: when the power consumption of the communication component of the terminal is reduced according to the performance level of the communication component determined by AI identification, and then the number of times of adjusting the reduced power consumption of the communication component back to the original value exceeds the set threshold value, the AI model training needs to be carried out again.
And finally, after the AI model is trained, AI identification can be carried out on the current scene. And inputting the characteristic data of the current scene, which is acquired by the scene data acquisition module, into the AI model as new data, identifying by using the AI model, determining the performance grade of the communication assembly of the current scene, and providing the determined performance grade of the communication assembly for the power consumption configuration module of the communication assembly.
3. Communication subassembly consumption configuration module:
the module is used for correspondingly adjusting the power consumption of the communication assembly according to the performance grade of the communication assembly identified by the AI identification module so as to meet the performance requirement and simultaneously achieve the purpose of reducing the power consumption.
In the prior art, the determination of the transmission power is often based on the level of the received power, the higher the transmission power. However, there is a certain adjustable range for determining the magnitude of the transmission power on the premise of determining the reception power. Therefore, the AI identification module is used for identifying the performance requirement of the terminal on the communication component (namely, the performance grade of the communication component) in the current scene, and the transmission power is properly adjusted downwards in the scene with the performance requirement lower than the set level. Meanwhile, other methods for reducing the transmission power can be adopted, and under the condition that a user agrees, the network system can be reduced, for example, under the scene that the network speed and the delay requirement are low, the current 5G with high power consumption is reduced to 4G.
Fig. 7 is a schematic diagram illustrating an operation principle of an exemplary power consumption configuration module of a communication module according to the present application, where the operation principle includes:
when the AI identification module identifies that the performance level of the communication assembly of the terminal in the current scene is low, the transmission power is properly adjusted downwards, and meanwhile, the communication quality is monitored as feedback under the condition of adjusting the transmission power downwards. In the present application, a bit Error Rate (BitError Rate) and a Packet Error Rate (Packet Error Rate) are used as monitoring targets, and when any one of the two monitoring targets exceeds a set threshold, the down-regulation of the transmission power is abandoned, that is: and adjusting the down-regulated transmission power back to the original transmission power to ensure the communication quality. If the network system is reduced, the original network system is adjusted.
The technical scheme provided by the application can be suitable for various actual use scenes, such as: the method comprises the steps of collecting scene data, utilizing an AI model to train and identify the performance of a communication assembly in a specific scene to determine the performance grade of the communication assembly, and configuring the communication assembly to be in a low power consumption mode when the performance requirement of a user scene is low, so that the power of the communication assembly of the terminal can be adjusted in real time according to the requirement, and the purposes of meeting the performance requirement and prolonging the service life of the terminal are achieved.
The following describes a specific implementation of the technical solution of the present application with reference to specific application scenarios through several preferred embodiments.
The first embodiment is as follows:
in this embodiment, a video playing scene is taken as an example for explanation, and a schematic diagram of applying the technical solution of the present application to the video playing scene is shown in fig. 8, which includes:
And 2, if the model needs to be trained, the AI identification module utilizes historical data stored in the equipment to train, taking the video scene model training related to the embodiment as an example, for a high-definition video, the communication component performance level is set to be Normal level due to high network speed, and for a low-definition video, the communication component performance level is set to be L ow level due to low network speed.
The trained models are stored locally at the terminal.
If no model training is needed, step 3 is performed directly.
And 3, carrying out AI identification by the AI identification module according to the current characteristic data acquired by the scene data acquisition module by using the trained model:
if the current terminal plays a high-definition video, the network speed of the terminal is higher, and the network speed characteristic data acquired by the scene data acquisition module is higher, so that the AI identification module identifies that the performance requirement of the current communication component is Normallevel after performing model matching according to the characteristic data, and the power consumption configuration module of the communication component accordingly determines not to adjust the sending power of the terminal;
if the current terminal plays a low-definition video, the network speed of the low-definition video is low, and the network speed characteristic data acquired by the scene data acquisition module is low, so that the AI identification module identifies that the performance requirement of the current communication component is identified as L ow level after performing model matching according to the characteristic data, and the communication component power consumption configuration module determines that the sending power of the terminal should be properly adjusted downwards according to the identification, thereby saving the power consumption.
When the transmitting power of the terminal is adjusted downwards, the error rate and the packet error rate can be monitored, if the error rate or the packet error rate exceeds a set threshold, the transmitting power is not adjusted downwards, namely: the transmission power that has been down-regulated is adjusted back to the original value. When the number of times of not performing down-regulation on the transmission power exceeds a set threshold, the training of the AI model needs to be performed again to improve the accuracy of AI identification.
Therefore, the embodiment adopts the technical scheme of the application to realize that the power consumption of the terminal is reduced as much as possible while the communication quality is ensured.
Example II,
In this embodiment, a game scene is taken as an example for explanation, and a schematic diagram of applying the technical solution of the present application to a game scene is shown in fig. 9, which includes:
And 2, in the process of playing the game by the user, the scene data acquisition module acquires data and acquires the maximum network speed, the signal intensity, the network type, the receiving power and the like of the terminal equipment.
And 3, the scene data acquisition module performs data standardization, performs data sorting and localization processing on the acquired characteristic data, and prepares for the next AI module.
And 4, when model training is needed, the AI identification module utilizes historical data stored in the equipment to train and generate a standby scene model, if the game is a stand-alone game, the requirement on network performance is low, the performance grade of the communication assembly corresponding to the scene is defined as Very L ow, and if the game is a networking game, the requirement on network performance, particularly network delay, is high, and the performance grade of the communication assembly corresponding to the scene is defined as L ow.
And 5, carrying out AI identification analysis on the current real-time acquired data by the AI identification module, identifying that the performance grade of the communication assembly is Very L ow if the game is a stand-alone game, and reducing the sending power of the terminal by the communication assembly power consumption configuration module to save power consumption, and identifying that the performance grade of the communication assembly is L ow if the game is a networking game, wherein the requirement on network performance, especially network delay, is higher, and the sending power of the terminal can be properly reduced to save power consumption.
And 6, while reducing the transmitting power of the terminal, the power consumption configuration module of the communication assembly continuously monitors the error rate and the packet error rate, feeds back the result and finally determines whether the transmitting power of the terminal needs to be adjusted downwards or not by continuously checking. When the number of times of not performing the down-regulation exceeds the set threshold, the training of the AI model needs to be performed again to improve the accuracy of the AI identification.
And 7, informing the final decision to the configuration module.
Through the processing, the embodiment realizes that the power consumption of the terminal is reduced as much as possible while the communication quality is ensured.
Example III,
The embodiment takes an online music scene as an example to illustrate the application of the technical scheme of the application, which includes:
And 2, when model training is needed, the AI identification module utilizes historical data stored in the equipment to train, and because the requirement on the internet speed for listening to music is Very low and the requirements on other parameters are also Very low, the AI identification module identifies that the model of which the performance level of the communication component is determined to be Very L ow. and is trained is stored locally at the terminal.
And 3, because the requirement on the network speed for listening to music is Very low and the requirements on other parameters are also Very low, the scene data acquisition module acquires the characteristic data, and the AI identification module identifies that the performance grade of the communication assembly of the current scene is Very L ow.
And 4, after receiving the AI identification result, the communication component power consumption configuration module down-regulates the sending power of the terminal equipment, and appropriately reduces the network standard under the condition that the user agrees, wherein if 5G is reduced to 4G, the power consumption is reduced to be lower.
Similar to the previous embodiment, while reducing the power consumption of the terminal communication component, the communication quality needs to be continuously monitored, and the communication quality is continuously checked to finally determine whether the transmission power of the terminal needs to be adjusted down, so that the power consumption of the terminal is reduced as much as possible while the communication quality is ensured.
Example four,
In this embodiment, an application of the technical solution of the present application is described by taking a standby (standby mode) scenario as an example, including:
And 2, when model training is needed, the AI identification module utilizes historical data stored in the equipment to train, and because the terminal has low requirements on the network speed in the standby mode and the other parameters, the AI identification module identifies that the model with the communication component performance level of Very L ow. trained is stored locally in the terminal.
And 3, because the terminal has low requirement on the network speed in the standby mode and the requirements on other parameters are also low, the scene data acquisition module acquires the characteristic data, and the AI identification module identifies that the performance level of the communication assembly of the current scene is Very L ow.
And 4, after receiving the AI identification result, the communication component power consumption configuration module down-regulates the sending power of the terminal equipment, and appropriately reduces the network standard under the condition that the user agrees, wherein if 5G is reduced to 4G, the power consumption is reduced to the lowest.
Similar to the previous embodiment, while reducing the power consumption of the terminal communication component, the communication quality needs to be continuously monitored, and the communication quality is continuously checked to finally determine whether the transmission power of the terminal needs to be adjusted down, so that the power consumption of the terminal is reduced as much as possible while the communication quality is ensured.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the scope of protection of the present application.
Claims (14)
1. A method for adjusting power consumption of a terminal is characterized by comprising the following steps:
collecting performance key characteristic data of a communication assembly of the terminal;
carrying out artificial intelligence AI identification according to the performance key characteristic data, and determining the performance grade of the communication assembly in the current scene;
and adjusting the power consumption of the communication assembly according to the performance grade of the communication assembly in the current scene.
2. The method of claim 1, wherein;
the method further comprises the following steps: setting at least two communication component performance levels according to performance requirements of different scenes on the communication components, wherein at least one of the at least two communication component performance levels is used for correspondingly reducing the power consumption of the communication components of the terminal;
the adjusting the power consumption of the communication component according to the performance level of the communication component of the current scene comprises: and if the performance level of the communication assembly in the current scene is at least one, reducing the power consumption of the communication assembly of the terminal.
3. The method of claim 2, wherein reducing the power consumption of the communication component of the terminal comprises at least one of:
reducing the transmission power of a communication component of the terminal;
and reducing the network system of the terminal.
4. The method of claim 3, further comprising, while reducing power consumption of a communication component of the terminal:
monitoring a communication quality index, wherein the communication quality index comprises at least one of a bit error rate and a packet error rate;
and when any one of the communication quality indexes exceeds a set threshold value, adjusting the reduced power consumption of the communication component back to the original value.
5. The method according to any of claims 2 to 4, further comprising, prior to performing artificial intelligence AI identification from the performance critical characteristics data:
judging whether AI model training is needed;
and if the AI model training is needed, inputting the acquired performance key characteristic data of the communication assembly as historical data into the selected AI model, and training by taking the set performance grade of the communication assembly as an identified classification label to train the parameters of the selected AI model.
6. The method of claim 5, wherein the determining whether AI model training is required comprises:
if the current AI model parameters are not trained, the AI model needs to be trained;
or, when the power consumption of the communication component of the terminal is reduced according to the performance level of the communication component determined by the AI identification, and then the number of times of adjusting the reduced power consumption of the communication component back to the original value exceeds a set threshold, the AI model training is required.
7. The method according to any one of claims 1 to 4, characterized in that:
the performance critical characteristic data of the communication assembly comprises at least one of the following: maximum network speed, average network speed, network delay, signal strength, network type and received power;
the acquiring comprises at least one of: and collecting during switching between the foreground and the background according to a set period.
8. A terminal device, comprising: scene data acquisition module, AI identification module and communication subassembly power consumption configuration module, wherein:
the scene data acquisition module is used for acquiring performance key characteristic data of the communication component of the terminal equipment and providing the performance key characteristic data to the AI identification module;
the AI identification module is used for carrying out AI identification according to the performance key characteristic data, determining the performance grade of the communication assembly in the current scene and providing the performance grade to the power consumption configuration module of the communication assembly;
and the communication component power consumption configuration module is used for adjusting the power consumption of the communication component according to the performance grade of the communication component in the current scene.
9. The terminal device of claim 8, wherein:
the performance levels of the communication components are set according to the performance requirements of different scenes on the communication components, at least two performance levels of the communication components are provided, and at least one of the performance levels of the communication components is correspondingly used for reducing the power consumption of the communication components of the terminal;
and the communication component power consumption configuration module is used for reducing the power consumption of the communication component of the terminal equipment when the performance level of the communication component in the current scene is at least one.
10. The terminal device of claim 9, wherein the communication component power consumption configuration module is specifically configured to:
reducing the transmission power of the communication component of the terminal equipment;
or, the network system of the terminal equipment is reduced.
11. The terminal device of claim 10, wherein the communication component power consumption configuration module is further configured to:
monitoring a communication quality index, wherein the communication quality index comprises at least one of a bit error rate and a packet error rate;
and when any one of the communication quality indexes exceeds a set threshold value, adjusting the reduced power consumption of the communication component back to the original value.
12. The terminal device of any one of claims 9 to 11, wherein the AI identification module is further configured to:
judging whether AI model training is needed;
and if the AI model training is needed, inputting the acquired performance key characteristic data of the communication assembly as historical data into the selected AI model, and training by taking the set performance grade of the communication assembly as an identified classification label to train the parameters of the selected AI model.
13. The terminal device of claim 12, wherein the AI identification module is specifically configured to:
judging whether the trained AI model parameters exist at present, and if not, judging that the AI model training is needed;
or judging whether the power consumption of the communication assembly of the terminal is reduced according to the performance grade of the communication assembly determined by the AI identification, and then adjusting the reduced power consumption of the communication assembly back to the original value for a time exceeding a set threshold value, and if the power consumption exceeds the set threshold value, judging that the AI model training is needed.
14. The terminal device according to any of claims 8 to 11, characterized by:
the performance key characteristic data of the communication assembly collected by the scene data acquisition module comprises at least one of the following data: maximum network speed, average network speed, network delay, signal strength, network type and received power;
the scene data acquisition module performs acquisition in a manner of at least one of: and collecting during switching between the foreground and the background according to a set period.
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Application publication date: 20200731 |