CN107567083B - Method and device for performing power-saving optimization processing - Google Patents
Method and device for performing power-saving optimization processing Download PDFInfo
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- CN107567083B CN107567083B CN201710959480.8A CN201710959480A CN107567083B CN 107567083 B CN107567083 B CN 107567083B CN 201710959480 A CN201710959480 A CN 201710959480A CN 107567083 B CN107567083 B CN 107567083B
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Abstract
The disclosure relates to a method and a device for power saving optimization processing, and belongs to the technical field of electronics. The method comprises the following steps: acquiring a parameter value of a preset state parameter detected by a target terminal and a parameter value of a preset historical state parameter of the target terminal, which is stored in advance; inputting the parameter values of the preset state parameters and the parameter values of the preset historical state parameters into a pre-trained user sleep state recognition model to obtain current user sleep state information corresponding to the target terminal, wherein the user sleep state information comprises sleep or no sleep; and if the user sleep state information is sleep, executing preset power saving optimization processing on the target terminal. By adopting the method and the device, the power consumption of the terminal can be reduced.
Description
Technical Field
The present disclosure relates to the field of electronic technologies, and in particular, to a method and an apparatus for performing power saving optimization processing.
Background
With the development of electronic technology, the functions that a terminal (such as a smart phone, a tablet computer, etc.) can implement are increasingly powerful, such as a bluetooth function and a WiFi (Wireless-Fidelity) function.
The terminal can generate certain electricity consumption in the state of starting any function. Thus, the terminal power consumption is high.
Disclosure of Invention
To overcome the problems in the related art, the present disclosure provides a method and apparatus for performing power saving optimization processing. The technical scheme is as follows:
according to a first aspect of the embodiments of the present disclosure, there is provided a method of performing power saving optimization processing, the method including:
acquiring a parameter value of a preset state parameter detected by a target terminal and a parameter value of a preset historical state parameter of the target terminal, which is stored in advance;
inputting the parameter values of the preset state parameters and the parameter values of the preset historical state parameters into a pre-trained user sleep state recognition model to obtain current user sleep state information corresponding to the target terminal, wherein the user sleep state information comprises sleep or no sleep;
and if the user sleep state information is sleep, executing preset power saving optimization processing on the target terminal.
Optionally, the method further includes:
acquiring a parameter value of a preset state parameter detected by any terminal at a target moment and a parameter value of a preset historical state parameter of the any terminal, which is stored in advance;
acquiring user sleep state information corresponding to the arbitrary terminal at the target moment;
and taking the parameter value of the preset state parameter detected by the arbitrary terminal at the target moment, the parameter value of the preset historical state parameter of the arbitrary terminal and the user sleep state information corresponding to the arbitrary terminal at the target moment as samples to train the user sleep state recognition model.
Optionally, the obtaining a parameter value of a preset state parameter detected by a target terminal and a parameter value of a preset historical state parameter of the target terminal, which is stored in advance, includes:
when the time is in a preset sleep detection period and the target terminal is in a screen-off state, acquiring a parameter value of a preset state parameter detected by the target terminal and a parameter value of a preset historical state parameter of the target terminal, wherein the parameter value is stored in advance.
Optionally, the preset state parameters include any one or more of the following parameters: the identification of the currently running application program, the current WiFi connection state, the current moment, the current environmental light intensity, the current environmental sound intensity and the current terminal motion state;
when the preset state parameters include the identifier of the currently running application program, the preset historical state parameters include: the identification of the application program with the running probability exceeding a preset threshold value during historical sleep;
when the preset state parameters include a current WiFi connection state, the preset historical state parameters include: the WiFi connection state with the highest probability appears during historical sleep, wherein the WiFi connection state comprises connection or disconnection;
when the preset state parameters include the current moment, the preset historical state parameters include: historical average sleep time;
when the preset state parameters include the current ambient light intensity, the preset historical state parameters include: average ambient light intensity during historical sleep;
when the preset state parameters include the current environmental sound intensity, the preset historical state parameters include: average ambient sound intensity during historical sleep.
Optionally, the inputting the parameter value of the preset state parameter and the parameter value of the preset historical state parameter into a pre-trained user sleep state recognition model to obtain the current user sleep state information corresponding to the target terminal includes:
acquiring the date type of the current date;
and inputting the parameter values of the preset state parameters and the parameter values of the preset historical state parameters into a pre-trained user sleep state recognition model corresponding to the date type to obtain the current user sleep state information corresponding to the target terminal.
Optionally, the preset power saving optimization processing includes any one or more of the following processing:
when the Bluetooth function is in an open state, the Bluetooth function is closed;
when the WiFi function is in an on state, the WiFi function is turned off;
and clearing the application program cache data.
According to a second aspect of the embodiments of the present disclosure, there is provided an apparatus for performing power saving optimization processing, the apparatus including:
the first acquisition module is used for acquiring a parameter value of a preset state parameter detected by a target terminal and a parameter value of a preset historical state parameter of the target terminal, which is stored in advance;
a first determining module, configured to input a parameter value of the preset state parameter and a parameter value of the preset historical state parameter into a pre-trained user sleep state recognition model, so as to obtain current user sleep state information corresponding to the target terminal, where the user sleep state information includes sleep or no sleep;
and the execution module is used for executing preset power saving optimization processing on the target terminal if the user sleep state information is sleep.
Optionally, the apparatus further comprises:
the second acquisition module is used for acquiring a parameter value of a preset state parameter detected by any terminal at a target moment and a parameter value of a preset historical state parameter of the any terminal, which is stored in advance;
a third obtaining module, configured to obtain user sleep state information corresponding to the arbitrary terminal at the target time;
and the training module is used for training the user sleep state recognition model by taking the parameter value of the preset state parameter detected by the any terminal at the target moment, the parameter value of the preset historical state parameter of the any terminal and the user sleep state information corresponding to the any terminal at the target moment as samples.
Optionally, the first obtaining module is configured to:
when the time is in a preset sleep detection period and the target terminal is in a screen-off state, acquiring a parameter value of a preset state parameter detected by the target terminal and a parameter value of a preset historical state parameter of the target terminal, wherein the parameter value is stored in advance.
Optionally, the preset state parameters include any one or more of the following parameters: the identification of the currently running application program, the current WiFi connection state, the current moment, the current environmental light intensity, the current environmental sound intensity and the current terminal motion state;
when the preset state parameters include the identifier of the currently running application program, the preset historical state parameters include: the identification of the application program with the running probability exceeding a preset threshold value during historical sleep;
when the preset state parameters include a current WiFi connection state, the preset historical state parameters include: the WiFi connection state with the highest probability appears during historical sleep, wherein the WiFi connection state comprises connection or disconnection;
when the preset state parameters include the current moment, the preset historical state parameters include: historical average sleep time;
when the preset state parameters include the current ambient light intensity, the preset historical state parameters include: average ambient light intensity during historical sleep;
when the preset state parameters include the current environmental sound intensity, the preset historical state parameters include: average ambient sound intensity during historical sleep.
Optionally, the first determining module is configured to:
acquiring the date type of the current date;
and inputting the parameter values of the preset state parameters and the parameter values of the preset historical state parameters into a pre-trained user sleep state recognition model corresponding to the date type to obtain the current user sleep state information corresponding to the target terminal.
Optionally, the execution module is configured to execute any one or more of the following processes:
when the Bluetooth function is in an open state, the Bluetooth function is closed;
when the WiFi function is in an on state, the WiFi function is turned off;
and clearing the application program cache data.
According to a third aspect of the embodiments of the present disclosure, there is provided a server, the server including a processor and a memory, the memory storing therein at least one instruction, the instruction being loaded and executed by the processor to implement the method for performing power saving optimization processing according to the first aspect.
According to a fourth aspect of embodiments of the present disclosure, there is provided a computer-readable storage medium having at least one instruction stored therein, where the instruction is loaded and executed by a processor to implement the method for power saving optimization processing according to the first aspect.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:
in the embodiment of the disclosure, a parameter value of a preset state parameter detected by a target terminal and a parameter value of a preset historical state parameter of the target terminal, which are stored in advance, are obtained, the parameter value of the preset state parameter and the parameter value of the preset historical state parameter are input into a pre-trained user sleep state recognition model, and current user sleep state information corresponding to the target terminal is determined, wherein the user sleep state information includes sleep or non-sleep. And if the user sleep state information is sleep, executing preset power saving optimization processing on the target terminal. Thus, when it is determined that the user is sleeping, the target terminal can perform power saving optimization processing, and thus, power consumption can be reduced.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. In the drawings:
FIG. 1 is a flow diagram illustrating a method of performing a power saving optimization process in accordance with an exemplary embodiment;
FIG. 2 is a diagram illustrating a user sleep state recognition model in accordance with an exemplary embodiment;
FIG. 3 is a diagram illustrating a server receiving a plurality of terminal messages in accordance with an illustrative embodiment;
FIG. 4 is a labeled diagram illustrating a user sleep state information according to an exemplary embodiment;
FIG. 5 is a system architecture diagram illustrating a method of performing a power saving optimization process in accordance with an example embodiment;
FIG. 6 is a schematic diagram illustrating an apparatus for performing power saving optimization in accordance with an exemplary embodiment;
FIG. 7 is a schematic diagram illustrating an apparatus for performing power saving optimization in accordance with an exemplary embodiment;
fig. 8 is a schematic diagram illustrating a configuration of a server according to an example embodiment.
With the foregoing drawings in mind, certain embodiments of the disclosure have been shown and described in more detail below. These drawings and written description are not intended to limit the scope of the disclosed concepts in any way, but rather to illustrate the concepts of the disclosure to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
An exemplary embodiment of the present disclosure provides a method for performing power saving optimization processing, which may be implemented by a server and a terminal together. The terminal can be a smart phone, a tablet computer and the like.
The server may include a processor, memory, transceiver, etc. The processor, which may be a Central Processing Unit (CPU), may be configured to obtain parameter values of preset state parameters, train a sleep state identification model of the user, determine a current sleep state, and the like. The Memory may be a RAM (Random Access Memory), a Flash Memory, and the like, and may be configured to store received data, data required by a processing procedure, data generated in the processing procedure, and the like, such as a parameter value of a preset state parameter, a parameter value of a preset historical state parameter, sleep state information of a current user, and the like. A transceiver, which may be used for data transmission with a terminal or other server (e.g., a positioning server), for example, to send a notification to perform power saving optimization processing to the terminal, and may include an antenna, a matching circuit, a modem, and the like.
The terminal may include components such as a processor, memory, screen, etc. The processor, which may be a CPU or the like, may be configured to perform power saving optimization processing, and the like. The memory may be a RAM, a Flash, or the like, and may be configured to store received data, data required by the processing procedure, data generated in the processing procedure, or the like, such as parameter values of preset state parameters. The screen may be a touch screen, may be used to display device lists, control pages, and may also be used to detect touch signals, etc. The terminal may further include a transceiver, an audio input part, a light sensor, an acceleration sensor, and the like. The transceiver may be configured to perform data transmission with other devices, for example, receive a notification of performing power saving optimization processing sent by the server, and may include an antenna, a matching circuit, a modem, and the like. The audio input means may be a microphone or the like for detecting the sound intensity of the environment surrounding the terminal. And the light sensor can be used for detecting the light intensity of the environment around the terminal. And the acceleration sensor can be used for detecting the current motion state of the terminal.
The process flow shown in fig. 1 will be described in detail below with reference to the embodiments, and the contents may be as follows:
in step 101, a parameter value of a preset state parameter detected by a target terminal and a parameter value of a preset historical state parameter of the target terminal, which are stored in advance, are obtained.
The preset state parameters may include any one or more of the following parameters: the identification of the currently running application program, the current WiFi connection state, the current moment, the current environmental light intensity, the current environmental sound intensity and the current terminal motion state.
Correspondingly, when the preset state parameters include the identifier of the currently running application program, the preset historical state parameters include: the identification of the application program with the running probability exceeding a preset threshold value during historical sleep; when the preset state parameters include the current WiFi connection state, the preset historical state parameters include: the WiFi connection state with the highest probability appears during historical sleep, wherein the WiFi connection state comprises connection or disconnection; when the preset state parameters include the current moment, the preset historical state parameters include: historical average sleep time; when the preset state parameters include the current ambient light intensity, the preset historical state parameters include: average ambient light intensity during historical sleep; when the preset state parameters include the current environment sound intensity, the preset historical state parameters include: average ambient sound intensity during historical sleep.
In implementation, the target terminal detects a parameter value of the preset state parameter at the current time, for example, one or more of an identifier of an application program running at the current terminal, a current WiFi connection state, the current time, a current environmental light intensity, a current environmental sound intensity, a current terminal motion state, and the like may be detected, and sends the detected parameter value of the preset state parameter and the identifier of the target terminal to the server for subsequent processing. After receiving the parameter value of the preset state parameter, the server may read a parameter value of a preset historical state parameter corresponding to the target terminal identifier, where the preset historical state parameter corresponds to the received preset state parameter.
Optionally, the processing procedure in this embodiment may be performed in the sleep detection period and when the target terminal is in the off-screen state, and the corresponding processing in step 101 may be as follows: and when the time is in a preset sleep detection period and the target terminal is in a screen-off state, acquiring a parameter value of a preset state parameter detected by the target terminal and a parameter value of a preset historical state parameter of the target terminal, which is stored in advance.
The preset sleep detection time period can be 9 pm to 3 am the next day. The screen closing state of the target terminal, that is, the screen closing state, may be a state in which the user actively closes the screen of the target terminal, or may be a state in which the target terminal automatically closes the screen when the user does not use the target terminal for a long time. When the target terminal is in the off-screen state, the user may not perform any operation on the target terminal.
In implementation, the starting time of the terminal in the longest period of the screen off time in one day is between 9 pm and 3 am of the next day, which can be obtained based on statistics of the screen on-off state information of all terminals. And it can be considered that the user is in a sleep state within a period of time in which the screen is turned off for the longest time. Therefore, it may be considered that the user starts sleeping between 9 pm and 3 am on the next day, that is, 9 pm to 3 am on the next day may be set as the sleep detection period in advance. When the target terminal detects that the current time is within the preset sleep detection period and the target terminal is in the off-screen state, the target terminal and the server may perform the processing in step 101, which is not described herein again.
In step 102, the parameter values of the preset state parameters and the parameter values of the preset historical state parameters are input into a pre-trained user sleep state recognition model, and current user sleep state information corresponding to the target terminal is determined.
Wherein, the sleep state information of the user comprises sleeping or not sleeping. As shown in fig. 2, the user sleep state identification model is a calculation model for determining the user sleep state based on the parameter values of the preset state parameters and the preset historical state parameters, the input parameters are the parameter values of the preset state parameters and the preset historical state parameters, and the output parameters are the user sleep state information of the user to which the terminal belongs.
Optionally, as shown in fig. 3, the user sleep state recognition model may be trained based on parameter values of preset state parameters and parameter values of preset historical state parameters acquired by a large number of terminals, and the corresponding processing may be as follows: acquiring a parameter value of a preset state parameter detected by any terminal at a target moment and a parameter value of a preset historical state parameter of any terminal stored in advance; determining user sleep state information corresponding to any terminal at the target moment according to the screen switch state information in the preset night period to which the target moment belongs; and taking the parameter value of the preset state parameter detected by any terminal at the target moment, the parameter value of the preset historical state parameter of any terminal and the user sleep state information corresponding to any terminal at the target moment as a sample, and training the user sleep state recognition model.
The preset night time period may be a time period in which the user sleeps every night preset by the technician, such as 9 pm to 9 pm. The screen switch state information may include a time when the screen is turned off and a time when the screen is turned on.
In implementation, any terminal can record the screen closing time (namely screen switch state information) when the screen is closed in a preset sleep detection time period (such as 9 pm to 3 am in the next day), and detect and store the parameter value of the preset state parameter. And the preset state parameters can be detected again at preset time intervals (such as 4 minutes) until the time exceeds the preset sleep detection time period, or the screen is started again, and the moment when the screen is started (namely the screen switch state information) is recorded. After any terminal is connected with the server, the screen switch state information in the preset night time period, the parameter value of the preset state parameter in the preset sleep detection time period and the corresponding terminal identification can be sent to the server for subsequent processing.
The server may process the received screen on-off state information, determine a time period in which the screen off duration of the corresponding terminal is longest, mark the user sleep state information corresponding to the terminal in the time period as sleep, mark the user sleep state information corresponding to the terminal in other time periods in the preset night time period as not sleep, and indicate the marking result as shown in fig. 4. Furthermore, the parameter value of the preset state parameter in the period when the user sleep state information is in sleep can be screened out from the received parameter values of the preset state parameters of the corresponding terminal, and the parameter value of the preset historical state parameter corresponding to the terminal identifier can be read out. The parameter value of the preset state parameter detected by any screened terminal at the corresponding moment in the period when the user sleep state information is in sleep, the parameter value of the preset historical state parameter of any screened terminal, and the user sleep state information (i.e. sleep) corresponding to any screened terminal at the corresponding moment are used as samples, and the samples can be divided into training samples and testing samples according to the proportion of 3: 1. Further, the user sleep state recognition model may be trained based on the training samples.
The sleep state recognition model of the user can be a decision tree model, and specifically, can be a random gradient descent decision tree. In the training process, probability statistics can be carried out on various input parameters and output parameters of the training sample to obtain the conditional entropy H (Y | X) of various input parametersi) Where Y is the output parameter (i.e. sleep or not), XiThe parameters are input for various types. For example, the input parameter is the current ambient light intensity, the number of samples whose output parameters are "sleep" may be counted in all training samples to obtain the conditional probability corresponding to "sleep", the number of samples whose output parameters are "not sleep" may be counted to obtain the conditional probability corresponding to "not sleep", and then the conditional entropy corresponding to the current ambient light intensity may be calculated. The conditional entropy represents the uncertainty of the input parameter, and the larger the value of the conditional entropy is, the larger the uncertainty of the parameter is; the smaller the value of the conditional entropy, the smaller the uncertainty of the parameter. After the conditional entropies of various input parameters are obtained, the input parameter with the minimum conditional entropy can be selected as a root node of the decision tree.
And judging whether the value of the root node can be regarded as continuous, for example, the current environmental light intensity can be a random value between 0 and 100 or can contain two decimal numbers, and the continuity is strong, namely the root node can be regarded as continuous. If the value of the root node can be regarded as continuous, then the values of all input parameters with the output parameters of "sleep" and the values of all input parameters with the output parameters of "not sleep" can be counted to obtain a value which can roughly distinguish the samples with "sleep" from "not sleep", and the value can be used as a judgment basis for dividing branches of the decision tree, for example, if most output parameters in the samples with the current ambient light intensity of more than 30 are "sleep", and most output parameters in the samples with the current ambient light intensity of less than or equal to 30 are "not sleep", the value 30 can be used as a judgment basis. That is, samples with the current ambient light intensity greater than 30 can be classified into one type of branch, and samples with the current ambient light intensity less than or equal to 30 can be classified into another type of branch. If the value of the root node is judged to be discrete, for example, the value of the current WiFi connection state can only be 'connected' and 'unconnected', and the discreteness is strong, the sample taking the value of 'connected' can be directly divided into one type of branches, and the sample taking the value of 'unconnected' can be divided into another type of branches. And storing the input parameters corresponding to the nodes and the branch conditions as the information of the nodes.
Furthermore, in the divided samples of each branch, input parameters appearing in a superior node are not considered any more, the conditional entropies corresponding to the remaining various input parameters are counted, the input parameter with the minimum conditional entropy is selected as the next node, and the process is repeated until the node with the conditional entropy of 0 is obtained, the fact that the sample in the node has no uncertainty is shown, namely the output parameters are all 'sleeping' or all 'not sleeping', leaf nodes are found, the leaf nodes can correspond to the unique output parameters, and the branches are not divided any more. After all branches determine leaf nodes, a complete decision tree can be obtained, and the trained user sleep state recognition model can be obtained.
The user sleep state recognition model can be input to perform testing based on the parameter value of the preset state parameter of any terminal and the parameter value of the preset historical state parameter of any terminal in the test sample. The output result is compared with the user sleep state information (i.e. sleep) in the test sample, and the accuracy and recall rate of the user sleep state identification model can be calculated based on the confusion matrix. When the accuracy is greater than the preset accuracy threshold and the recall rate is greater than the preset recall rate threshold, the trained user sleep state recognition model can be used for judging the current user sleep state information. In addition, the server can recalculate the updated parameter value of the preset historical state parameter of any terminal based on the received parameter value of the preset state parameter of any terminal.
Inputting the parameter value of the preset state parameter of the target terminal and the parameter value of the preset historical state parameter obtained in the step 101 into the trained user sleep state recognition model, determining an input parameter and a branch condition corresponding to the root node according to the information of the root node from the root node, judging which branch condition the value of the input parameter meets according to the value of the input parameter obtained in the step 101, and if the condition of a certain branch is met, obtaining a next-level node corresponding to the branch. Furthermore, the next-level node of the node is determined continuously by the same method as the above method, which is not described herein again. And obtaining the output parameters corresponding to the leaf node until the leaf node is reached, namely obtaining the current user sleep state information corresponding to the target terminal.
Optionally, different date types may correspond to different user sleep state recognition models, and the corresponding processing of step 102 may be as follows: acquiring the date type of the current date; and inputting the parameter values of the preset state parameters and the parameter values of the preset historical state parameters into a user sleep state recognition model corresponding to a pre-trained date type to obtain the current user sleep state information corresponding to the target terminal.
The date type may include weekdays (monday-friday), weekends (saturday, sunday), holidays (spring festival, tomb-sweeping festival, afternoon festival, etc.), among others.
In implementation, when the terminal sends the parameter value of the preset state parameter to the server, the sent information may also carry a date type. In the process of training the user sleep state recognition model, the user sleep state recognition model corresponding to different date types can be trained based on samples corresponding to different date types. In the process of determining the current user sleep state information by using the user sleep state identification model, the target terminal may acquire a date type to which the current date belongs, and include the date type in the information sent to the server, that is, a parameter value of a preset state parameter sent to the server has a corresponding date type. Furthermore, the server may call the user sleep state identification model corresponding to the date type to perform corresponding processing, which is not described herein again.
Correspondingly, when the preset state parameters include the identifier of the currently running application program, the preset historical state parameters include: historically, the identification of the application program with the operation probability exceeding a preset threshold value during sleeping in each date of the date type; when the preset state parameters include the current WiFi connection state, the preset historical state parameters include: historically, the WiFi connection state with the highest probability of appearing during sleep in each date of the date type comprises connection or disconnection; when the preset state parameters include the current moment, the preset historical state parameters include: historically, the average sleep time in each date of that date type; when the preset state parameters include the current ambient light intensity, the preset historical state parameters include: historically the average ambient light intensity while sleeping in each date for that date type; when the preset state parameters include the current environment sound intensity, the preset historical state parameters include: historically the average ambient sound intensity while sleeping in each date of that date type.
In step 103, if the user sleep state information is sleep, a preset power saving optimization process is performed on the target terminal.
Optionally, the preset power saving optimization process may include any one or more of the following processes: when the Bluetooth function is in an open state, the Bluetooth function is closed; when the WiFi function is in an on state, the WiFi function is turned off; and clearing the application program cache data.
In implementation, when the server determines that the current user sleep state information corresponding to the target terminal is sleep, a notification message for performing power saving optimization processing may be sent to the target terminal. Further, the target terminal can perform corresponding power saving optimization processing. For example, the target terminal may detect whether the bluetooth function is currently in an on state, and when detecting that the bluetooth function is in the on state, close the bluetooth function; detecting whether the WiFi function is in an on state at present, and closing the WiFi function when detecting that the WiFi function is in the on state; whether application program cache data exist is detected, and when the application program cache data exist, the application program cache data are cleared. The power saving optimization process may include the above-described process or another process conceivable by a skilled person, and is not limited herein.
The system framework and the execution flow based on the system framework of the embodiment of the disclosure can be as shown in fig. 5.
In the embodiment of the disclosure, the server obtains the parameter value of the preset state parameter detected by the target terminal and the parameter value of the preset historical state parameter of the target terminal, and inputs the parameter value of the preset state parameter and the parameter value of the preset historical state parameter into the pre-trained user sleep state recognition model to obtain the current user sleep state information corresponding to the target terminal. And if the user sleep state information is sleep, executing preset power saving optimization processing on the target terminal. Thus, when it is determined that the user is likely to sleep, the target terminal can perform power saving optimization processing, and thus, power consumption can be reduced.
Yet another exemplary embodiment of the present disclosure provides an apparatus for performing power saving optimization processing, as shown in fig. 6, the apparatus including:
a first obtaining module 610, configured to obtain a parameter value of a preset state parameter detected by a target terminal, and a parameter value of a preset historical state parameter of the target terminal, which is stored in advance;
a first determining module 620, configured to input a parameter value of the preset state parameter and a parameter value of the preset historical state parameter into a pre-trained user sleep state recognition model, and determine current user sleep state information corresponding to the target terminal, where the user sleep state information includes sleep or non-sleep;
an executing module 630, configured to execute a preset power saving optimization process on the target terminal if the user sleep state information is sleep.
Optionally, as shown in fig. 7, the apparatus further includes:
a second obtaining module 640, configured to obtain a parameter value of a preset state parameter detected by any terminal at a target time, and a parameter value of a preset historical state parameter of the any terminal, which is stored in advance;
a third obtaining module 650, configured to obtain user sleep state information corresponding to the arbitrary terminal at the target time;
a training module 660, configured to train the user sleep state recognition model by using, as a sample, a parameter value of a preset state parameter detected by the arbitrary terminal at a target time, a parameter value of a preset historical state parameter of the arbitrary terminal, and user sleep state information corresponding to the arbitrary terminal at the target time.
Optionally, the first obtaining module 610 is configured to:
when the time is in a preset sleep detection period and the target terminal is in a screen-off state, acquiring a parameter value of a preset state parameter detected by the target terminal and a parameter value of a preset historical state parameter of the target terminal, wherein the parameter value is stored in advance.
Optionally, the preset state parameters include any one or more of the following parameters: the identification of the currently running application program, the current WiFi connection state, the current moment, the current environmental light intensity, the current environmental sound intensity and the current terminal motion state;
when the preset state parameters include the identifier of the currently running application program, the preset historical state parameters include: the identification of the application program with the running probability exceeding a preset threshold value during historical sleep;
when the preset state parameters include a current WiFi connection state, the preset historical state parameters include: the WiFi connection state with the highest probability appears during historical sleep, wherein the WiFi connection state comprises connection or disconnection;
when the preset state parameters include the current moment, the preset historical state parameters include: historical average sleep time;
when the preset state parameters include the current ambient light intensity, the preset historical state parameters include: average ambient light intensity during historical sleep;
when the preset state parameters include the current environmental sound intensity, the preset historical state parameters include: average ambient sound intensity during historical sleep.
Optionally, the first determining module 620 is configured to:
acquiring the date type of the current date;
and inputting the parameter values of the preset state parameters and the parameter values of the preset historical state parameters into a pre-trained user sleep state recognition model corresponding to the date type, and determining the current user sleep state information corresponding to the target terminal.
Optionally, the executing module 630 is configured to execute any one or more of the following processes:
when the Bluetooth function is in an open state, the Bluetooth function is closed;
when the WiFi function is in an on state, the WiFi function is turned off;
and clearing the application program cache data.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
In the embodiment of the disclosure, the server obtains the parameter value of the preset state parameter detected by the target terminal and the parameter value of the preset historical state parameter of the target terminal, and inputs the parameter value of the preset state parameter and the parameter value of the preset historical state parameter into the pre-trained user sleep state recognition model to obtain the current user sleep state information corresponding to the target terminal. And if the user sleep state information is sleep, executing preset power saving optimization processing on the target terminal. Thus, when it is determined that the user is likely to sleep, the target terminal can perform power saving optimization processing, and thus, power consumption can be reduced.
It should be noted that: in the device for performing power saving optimization processing according to the above embodiment, when performing power saving optimization processing, only the division of the above functional modules is illustrated, and in practical applications, the above function distribution may be completed by different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the above described functions. In addition, the apparatus for performing power saving optimization processing and the method embodiment for performing power saving optimization processing provided in the foregoing embodiments belong to the same concept, and specific implementation processes thereof are detailed in the method embodiment and are not described herein again.
Yet another exemplary embodiment of the present disclosure shows a schematic structural diagram of a server.
Referring to FIG. 8, server 800 includes a processing component 822, which further includes one or more processors and memory resources, represented by memory 832, for storing instructions, such as applications, that are executable by processing component 822. The application programs stored in memory 832 may include one or more modules that each correspond to a set of instructions. Further, the processing component 822 is configured to execute instructions to perform the above-described method of performing power saving optimization.
The server 800 may also include a power component 826 configured to perform power management of the server 800, a wired or wireless network interface 850 configured to connect the server 800 to a network, and an input/output (I/O) interface 858. The server 800 may operate based on an operating system stored in memory 832, such as Windows Server, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, or the like.
The server 800 may include memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing the above-described method embodiments.
In the embodiment of the disclosure, the server obtains the parameter value of the preset state parameter detected by the target terminal and the parameter value of the preset historical state parameter of the target terminal, and inputs the parameter value of the preset state parameter and the parameter value of the preset historical state parameter into the pre-trained user sleep state recognition model to obtain the current user sleep state information corresponding to the target terminal. And if the user sleep state information is sleep, executing preset power saving optimization processing on the target terminal. Thus, when it is determined that the user is likely to sleep, the target terminal can perform power saving optimization processing, and thus, power consumption can be reduced.
Yet another embodiment of the present disclosure provides a non-transitory computer readable storage medium having instructions that, when executed by a processor of a terminal, enable the terminal to perform:
acquiring a parameter value of a preset state parameter detected by a target terminal and a parameter value of a preset historical state parameter of the target terminal, which is stored in advance;
inputting the parameter values of the preset state parameters and the parameter values of the preset historical state parameters into a pre-trained user sleep state recognition model, and determining the current user sleep state information corresponding to the target terminal, wherein the user sleep state information comprises sleep or no sleep;
and if the user sleep state information is sleep, executing preset power saving optimization processing on the target terminal.
Optionally, the method further includes:
acquiring a parameter value of a preset state parameter detected by any terminal at a target moment and a parameter value of a preset historical state parameter of the any terminal, which is stored in advance;
acquiring user sleep state information corresponding to the arbitrary terminal at the target moment;
and taking the parameter value of the preset state parameter detected by the arbitrary terminal at the target moment, the parameter value of the preset historical state parameter of the arbitrary terminal and the user sleep state information corresponding to the arbitrary terminal at the target moment as samples to train the user sleep state recognition model.
Optionally, the obtaining a parameter value of a preset state parameter detected by a target terminal and a parameter value of a preset historical state parameter of the target terminal, which is stored in advance, includes:
when the time is in a preset sleep detection period and the target terminal is in a screen-off state, acquiring a parameter value of a preset state parameter detected by the target terminal and a parameter value of a preset historical state parameter of the target terminal, wherein the parameter value is stored in advance.
Optionally, the preset state parameters include any one or more of the following parameters: the identification of the currently running application program, the current WiFi connection state, the current moment, the current environmental light intensity, the current environmental sound intensity and the current terminal motion state;
when the preset state parameters include the identifier of the currently running application program, the preset historical state parameters include: the identification of the application program with the running probability exceeding a preset threshold value during historical sleep;
when the preset state parameters include a current WiFi connection state, the preset historical state parameters include: the WiFi connection state with the highest probability appears during historical sleep, wherein the WiFi connection state comprises connection or disconnection;
when the preset state parameters include the current moment, the preset historical state parameters include: historical average sleep time;
when the preset state parameters include the current ambient light intensity, the preset historical state parameters include: average ambient light intensity during historical sleep;
when the preset state parameters include the current environmental sound intensity, the preset historical state parameters include: average ambient sound intensity during historical sleep.
Optionally, the inputting the parameter value of the preset state parameter and the parameter value of the preset historical state parameter into a pre-trained user sleep state recognition model, and determining the current user sleep state information corresponding to the target terminal includes:
acquiring the date type of the current date;
and inputting the parameter values of the preset state parameters and the parameter values of the preset historical state parameters into a pre-trained user sleep state recognition model corresponding to the date type, and determining the current user sleep state information corresponding to the target terminal.
Optionally, the preset power saving optimization processing includes any one or more of the following processing:
when the Bluetooth function is in an open state, the Bluetooth function is closed;
when the WiFi function is in an on state, the WiFi function is turned off;
and clearing the application program cache data.
In the embodiment of the disclosure, the server obtains the parameter value of the preset state parameter detected by the target terminal and the parameter value of the preset historical state parameter of the target terminal, and inputs the parameter value of the preset state parameter and the parameter value of the preset historical state parameter into the pre-trained user sleep state recognition model to obtain the current user sleep state information corresponding to the target terminal. And if the user sleep state information is sleep, executing preset power saving optimization processing on the target terminal. Thus, when it is determined that the user is likely to sleep, the target terminal can perform power saving optimization processing, and thus, power consumption can be reduced.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements 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.
Claims (8)
1. A method of performing power saving optimization, the method comprising:
acquiring a parameter value of a preset state parameter detected by any terminal at a target moment and a parameter value of a preset historical state parameter of the any terminal, which is stored in advance;
acquiring user sleep state information corresponding to the arbitrary terminal at the target moment according to screen switch state information in a preset night period to which the target moment belongs;
taking the parameter value of the preset state parameter detected by the arbitrary terminal at the target moment, the parameter value of the preset historical state parameter of the arbitrary terminal, and the user sleep state information corresponding to the arbitrary terminal at the target moment as samples, and training the user sleep state recognition model;
calculating the accuracy and the recall rate of the user sleep state identification model, and when the accuracy rate is greater than a preset accuracy rate threshold and the recall rate is greater than a preset recall rate threshold, using the user sleep state identification model for judging the current user sleep state information;
the method comprises the steps of obtaining a parameter value of a preset state parameter detected by a target terminal and a parameter value of a preset historical state parameter of the target terminal, wherein the preset state parameter comprises any one or more of the following parameters: the identification of the currently running application program, the current WiFi connection state, the current moment, the current environmental light intensity, the current environmental sound intensity and the current terminal motion state; when the preset state parameters include the identifier of the currently running application program, the preset historical state parameters include: the identification of the application program with the running probability exceeding a preset threshold value during historical sleep; when the preset state parameters include the current WiFi connection state, the preset historical state parameters include: the WiFi connection state with the highest probability appears during historical sleep, wherein the WiFi connection state comprises connection or disconnection; when the preset state parameters include the current time, the preset historical state parameters include: historical average sleep time; when the preset state parameters include the current ambient light intensity, the preset historical state parameters include: average ambient light intensity during historical sleep; when the preset state parameters include the current environment sound intensity, the preset historical state parameters include: average ambient sound intensity during historical sleep;
acquiring the date type of the current date;
inputting a pre-trained user sleep state recognition model corresponding to the date type into the parameter values of the preset state parameters and the parameter values of the preset historical state parameters to obtain current user sleep state information corresponding to the target terminal, wherein the user sleep state information comprises sleep or no sleep, the user sleep state recognition model is a calculation model for determining the user sleep state information based on the parameter values of the preset state parameters and the parameter values of the preset historical state parameters, the input parameters are the parameter values of the preset state parameters and the parameter values of the preset historical state parameters, and the output parameters are the user sleep state information of the user to which the terminal belongs;
if the user sleep state information is sleep, executing preset power saving optimization processing on the target terminal;
the step of inputting the parameter values of the preset state parameters and the parameter values of the preset historical state parameters into the pre-trained user sleep state recognition model corresponding to the date type to obtain the current user sleep state information corresponding to the target terminal includes: determining an input parameter and a branch condition corresponding to a root node according to information of the root node from the root node, judging which branch condition the value of the input parameter meets according to the value of the obtained input parameter, if the condition of a certain branch is met, obtaining a next-level node corresponding to the branch, continuously determining the next-level node by adopting the same method until a leaf node is reached, obtaining an output parameter corresponding to the leaf node, and obtaining current user sleep state information corresponding to the target terminal.
2. The method according to claim 1, wherein the obtaining of the parameter value of the preset state parameter detected by the target terminal and the parameter value of the preset historical state parameter of the target terminal stored in advance comprises:
when the time is in a preset sleep detection period and the target terminal is in a screen-off state, acquiring a parameter value of a preset state parameter detected by the target terminal and a parameter value of a preset historical state parameter of the target terminal, wherein the parameter value is stored in advance.
3. The method according to claim 1, wherein the preset power saving optimization process comprises any one or more of the following processes:
when the Bluetooth function is in an open state, the Bluetooth function is closed;
when the WiFi function is in an on state, the WiFi function is turned off;
and clearing the application program cache data.
4. An apparatus for performing power saving optimization processing, the apparatus comprising:
the second acquisition module is used for acquiring a parameter value of a preset state parameter detected by any terminal at a target moment and a parameter value of a preset historical state parameter of the any terminal, which is stored in advance;
the third acquisition module is used for acquiring the sleep state information of the user corresponding to the any terminal at the target moment according to the screen switch state information in the preset night period to which the target moment belongs;
a training module, configured to train the user sleep state recognition model by using, as a sample, a parameter value of a preset state parameter detected by the arbitrary terminal at a target time, a parameter value of a preset historical state parameter of the arbitrary terminal, and user sleep state information corresponding to the arbitrary terminal at the target time;
the training module is further used for calculating the accuracy and the recall rate of the user sleep state identification model, and when the accuracy is greater than a preset accuracy threshold and the recall rate is greater than a preset recall rate threshold, the user sleep state identification model is used for judging the current user sleep state information;
the first obtaining module is configured to obtain a parameter value of a preset state parameter detected by a target terminal and a parameter value of a preset historical state parameter of the target terminal, where the preset state parameter includes any one or more of the following parameters: the identification of the currently running application program, the current WiFi connection state, the current moment, the current environmental light intensity, the current environmental sound intensity and the current terminal motion state; when the preset state parameters include the identifier of the currently running application program, the preset historical state parameters include: the identification of the application program with the running probability exceeding a preset threshold value during historical sleep; when the preset state parameters include the current WiFi connection state, the preset historical state parameters include: the WiFi connection state with the highest probability appears during historical sleep, wherein the WiFi connection state comprises connection or disconnection; when the preset state parameters include the current time, the preset historical state parameters include: historical average sleep time; when the preset state parameters include the current ambient light intensity, the preset historical state parameters include: average ambient light intensity during historical sleep; when the preset state parameters include the current environment sound intensity, the preset historical state parameters include: average ambient sound intensity during historical sleep;
the first determining module is used for acquiring the date type of the current date;
inputting a pre-trained user sleep state recognition model corresponding to the date type into the parameter values of the preset state parameters and the parameter values of the preset historical state parameters to obtain current user sleep state information corresponding to the target terminal, wherein the user sleep state information comprises sleep or no sleep, the user sleep state recognition model is a calculation model for determining the user sleep state information based on the parameter values of the preset state parameters and the parameter values of the preset historical state parameters, the input parameters are the parameter values of the preset state parameters and the parameter values of the preset historical state parameters, and the output parameters are the user sleep state information of the user to which the terminal belongs;
the execution module is used for executing preset power saving optimization processing on the target terminal if the user sleep state information is sleep;
the step of inputting the parameter values of the preset state parameters and the parameter values of the preset historical state parameters into the pre-trained user sleep state recognition model corresponding to the date type to obtain the current user sleep state information corresponding to the target terminal includes: determining an input parameter and a branch condition corresponding to a root node according to information of the root node from the root node, judging which branch condition the value of the input parameter meets according to the value of the obtained input parameter, if the condition of a certain branch is met, obtaining a next-level node corresponding to the branch, continuously determining the next-level node by adopting the same method until a leaf node is reached, obtaining an output parameter corresponding to the leaf node, and obtaining current user sleep state information corresponding to the target terminal.
5. The apparatus of claim 4, wherein the first obtaining module is configured to:
when the time is in a preset sleep detection period and the target terminal is in a screen-off state, acquiring a parameter value of a preset state parameter detected by the target terminal and a parameter value of a preset historical state parameter of the target terminal, wherein the parameter value is stored in advance.
6. The apparatus of claim 4, wherein the execution module is configured to perform any one or more of the following:
when the Bluetooth function is in an open state, the Bluetooth function is closed;
when the WiFi function is in an on state, the WiFi function is turned off;
and clearing the application program cache data.
7. A server, comprising a processor and a memory, wherein the memory stores at least one instruction, and the instruction is loaded and executed by the processor to implement the method for power saving optimization according to any one of claims 1 to 3.
8. A computer-readable storage medium having stored thereon at least one instruction, which is loaded and executed by a processor to implement the method for power saving optimization according to any one of claims 1 to 3.
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| CN108255543A (en) * | 2018-01-17 | 2018-07-06 | 北京小米移动软件有限公司 | Application operation method, apparatus and computer readable storage medium |
| CN108944052B (en) * | 2018-07-27 | 2021-11-09 | 北京小米移动软件有限公司 | Printer maintenance method and device |
| CN113164056A (en) * | 2019-02-18 | 2021-07-23 | 深圳市欢太科技有限公司 | Sleep prediction method, device, storage medium and electronic equipment |
| WO2020168445A1 (en) * | 2019-02-18 | 2020-08-27 | 深圳市欢太科技有限公司 | Sleep recognition method, apparatus, storage medium, and electronic device |
| CN112997148A (en) * | 2019-02-18 | 2021-06-18 | 深圳市欢太科技有限公司 | Sleep prediction method, device, storage medium and electronic equipment |
| WO2020168451A1 (en) * | 2019-02-18 | 2020-08-27 | 深圳市欢太科技有限公司 | Sleep prediction method and apparatus, storage medium, and electronic device |
| CN109831817B (en) * | 2019-02-27 | 2020-09-11 | 北京达佳互联信息技术有限公司 | Terminal control method, device, terminal and storage medium |
| CN110362184A (en) * | 2019-07-05 | 2019-10-22 | 深圳前海微众银行股份有限公司 | A kind of method and wearable device of controlling electronic devices |
| CN113138656A (en) * | 2021-05-14 | 2021-07-20 | 上海传英信息技术有限公司 | Control method, mobile terminal and storage medium |
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