CN112904991A - Power consumption optimization method, device and equipment based on folding screen and storage medium - Google Patents
Power consumption optimization method, device and equipment based on folding screen and storage medium Download PDFInfo
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Abstract
The invention discloses a power consumption optimization method, a power consumption optimization device, power consumption optimization equipment and a power consumption optimization storage medium based on a folding screen, wherein the method comprises the following steps: acquiring streaming media playing data of a first split screen and a second split screen in a folding screen; extracting detail information in the streaming media playing data, and performing complexity analysis according to the detail information to obtain complexity information; carrying out grade quantization on the complexity information to obtain a corresponding complexity interval; searching a corresponding brightness adjustment threshold according to the mapping relation between the complexity interval and the brightness interval; the method comprises the steps of obtaining a current screen set brightness value, and dynamically adjusting the brightness of the folding screen according to the current screen set brightness value and the brightness adjustment threshold value, so that the dynamic brightness adjustment can be realized according to simple calculation by using streaming media playing data of a first split screen and a second split screen in the folding screen as brightness adjustment parameters, and the purpose of reducing the power consumption of the folding screen is achieved.
Description
Technical Field
The invention relates to the technical field of performance optimization, in particular to a power consumption optimization method, device, equipment and storage medium based on a folding screen.
Background
In recent years, various large mobile phone manufacturers gradually develop towards large-screen mobile phones, some mobile phones with a folding screen concept come to the fore, and compared with a straight-panel touch screen machine, the folding screen mobile phone has the greatest characteristic of variable display area, can keep portability, brings larger visual field and touch area, and has the advantage of obvious display effect.
However, the cruising ability of the mobile terminal is always an important index for evaluating the product performance, the folding screen mobile phone has higher requirement on power consumption, and the screen is turned off in a user using state under a general condition, so that the purpose of reducing the power consumption is achieved.
Disclosure of Invention
The invention mainly aims to provide a power consumption optimization method, a power consumption optimization device, power consumption optimization equipment and a power consumption optimization storage medium based on a folding screen, and aims to solve the technical problem that the power consumption cannot be reduced more effectively in the prior art.
In order to achieve the above object, the present invention provides a power consumption optimization method based on a folding screen, which includes the following steps:
acquiring streaming media playing data of a first split screen and a second split screen in a folding screen;
extracting detail information in the streaming media playing data, and performing complexity analysis according to the detail information to obtain complexity information;
carrying out grade quantization on the complexity information to obtain a corresponding complexity interval;
searching a corresponding brightness adjustment threshold according to the mapping relation between the complexity interval and the brightness interval;
and acquiring a current screen set brightness value, and dynamically adjusting the brightness of the folding screen according to the current screen set brightness value and the brightness adjustment threshold value.
Optionally, the extracting detail information in the streaming media playing data, and performing complexity analysis according to the detail information to obtain complexity information includes:
analyzing the details of the streaming media playing data to decompose the detail information at least containing video, audio or data continuous code streams;
analyzing the data of the network abstract layer according to the continuous code stream;
extracting the zone bit information of the transport stream in the network abstraction layer data, and obtaining the corresponding frame type according to the zone bit information;
dividing according to the frame type to obtain corresponding frame data;
and determining the frame length of a video I frame according to the frame data, and determining the complexity information according to the frame length of the video I frame.
Optionally, the performing level quantization on the complexity information to obtain a corresponding complexity interval includes:
generating a record array by the streaming media playing data according to a playing sequence so as to quantize the streaming media playing data;
in the process of playing the streaming media, carrying out complexity information according to continuously updated streaming media playing data to obtain updated streaming media playing data so as to update and correct the streaming media playing data;
updating the record array according to the corrected streaming media playing data to obtain an updated record array;
and carrying out interval division on the updated record array according to the frame length value to obtain a corresponding complexity interval.
Optionally, before searching for the corresponding brightness adjustment threshold according to the mapping relationship between the complexity interval and the brightness interval, the method further includes:
acquiring a screen brightness range, and dividing the screen brightness range according to preset grades to obtain a plurality of brightness grades and corresponding brightness intervals;
obtaining corresponding brightness interval demarcation point information and a weighted value according to the brightness grade and the corresponding brightness interval;
and establishing a mapping relation between the complexity interval and the brightness interval according to the weighted value, the complexity interval and the corresponding brightness interval demarcation point information.
Optionally, the obtaining a current screen set brightness value, and dynamically adjusting the brightness of the folding screen according to the current screen set brightness value and the brightness adjustment threshold includes:
acquiring a current screen set brightness value;
obtaining the deviation, deviation integral and deviation differential of a set value and actual output according to the set brightness value of the current screen and the brightness adjustment threshold;
acquiring a preset weighting coefficient, and establishing a control corresponding relation among the preset weighting coefficient, the set value, the actually output deviation, the deviation integral and the deviation differential and the control brightness value;
and obtaining a control brightness value according to the control corresponding relation, and dynamically adjusting the brightness of the folding screen according to the control brightness value.
Optionally, the obtaining a control brightness value according to the control correspondence, and dynamically adjusting the brightness of the folding screen according to the control brightness value includes:
acquiring a proportional learning rate parameter, an integral learning rate parameter and a differential learning rate parameter of the control corresponding relation;
establishing a corresponding relation of the set value weighting coefficient according to the proportional learning rate parameter, the integral learning rate parameter and the differential learning rate parameter;
establishing a neural network training model according to the corresponding relation of the set value weighting coefficients;
acquiring an initial value of a neural network coefficient, and acquiring a proportional learning rate value, an integral learning rate value and a differential learning rate value according to the initial value of the neural network coefficient and the neural network training model;
and obtaining a control brightness value according to the proportional learning rate value, the integral learning rate value and the differential learning rate value, and dynamically adjusting the brightness of the folding screen according to the control brightness value.
Optionally, before the dynamically adjusting the brightness of the foldable screen according to the current screen set brightness value and the brightness adjustment threshold, the method further includes:
generating dynamic brightness adjustment reminding information;
judging whether feedback information of the dynamic brightness adjustment reminding information is received or not;
and when receiving the feedback information of the dynamic brightness adjustment reminding information, executing a step of dynamically adjusting the brightness of the folding screen according to the set brightness value of the current screen and the brightness adjustment threshold value.
In addition, in order to achieve the above object, the present invention further provides a power consumption optimization method apparatus based on a foldable screen, where the power consumption optimization method apparatus based on the foldable screen includes:
the acquisition module is used for acquiring streaming media playing data of a first split screen and a second split screen in the folding screen;
the extracting module is used for extracting detail information in the streaming media playing data and carrying out complexity analysis according to the detail information to obtain complexity information;
the quantization module is used for carrying out grade quantization on the complexity information to obtain a corresponding complexity interval;
the searching module is used for searching a corresponding brightness adjustment threshold according to the mapping relation between the complexity interval and the brightness interval;
and the adjusting module is used for acquiring the set brightness value of the current screen and dynamically adjusting the brightness of the folding screen according to the set brightness value of the current screen and the brightness adjusting threshold value.
Furthermore, to achieve the above object, the present invention also proposes an apparatus comprising: the power consumption optimization method comprises the following steps of a memory, a processor and a folded screen-based power consumption optimization program stored on the memory and capable of running on the processor, wherein the folded screen-based power consumption optimization program is configured to realize the steps of the folded screen-based power consumption optimization method.
In addition, to achieve the above object, the present invention further provides a storage medium, where a folded-screen-based power consumption optimization program is stored, and when executed by a processor, the storage medium implements the steps of the folded-screen-based power consumption optimization method as described above.
The power consumption optimization method based on the folding screen, provided by the invention, comprises the steps of obtaining streaming media playing data of a first split screen and a second split screen in the folding screen; extracting detail information in the streaming media playing data, and performing complexity analysis according to the detail information to obtain complexity information; carrying out grade quantization on the complexity information to obtain a corresponding complexity interval; searching a corresponding brightness adjustment threshold according to the mapping relation between the complexity interval and the brightness interval; the method comprises the steps of obtaining a current screen set brightness value, and dynamically adjusting the brightness of the folding screen according to the current screen set brightness value and the brightness adjustment threshold value, so that the dynamic brightness adjustment can be realized according to simple calculation by using streaming media playing data of a first split screen and a second split screen in the folding screen as brightness adjustment parameters, and the purpose of reducing the power consumption of the folding screen is achieved.
Drawings
FIG. 1 is a schematic structural diagram of a folded screen-based power consumption optimization method for a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a power consumption optimization method based on a foldable screen according to a first embodiment of the present invention;
FIG. 3 is a flowchart illustrating a power consumption optimization method based on a foldable screen according to a second embodiment of the present invention;
FIG. 4 is a flowchart illustrating a power consumption optimization method based on a foldable screen according to a third embodiment of the present invention;
fig. 5 is a functional module diagram of a power consumption optimization method and apparatus based on a folding screen according to a first embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic device structure diagram of a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the apparatus may include: a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may comprise a Display screen (Display), an input unit such as keys, and the optional user interface 1003 may also comprise a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the folded screen based power consumption optimization method apparatus configuration shown in fig. 1 does not constitute a limitation of the folded screen based power consumption optimization method apparatus, and may include more or fewer components than those shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a storage medium, may include therein an operating system, a network communication module, a user interface module, and a power consumption optimization method program based on a folding screen.
In the power consumption optimization method device based on the folding screen shown in fig. 1, the network interface 1004 is mainly used for connecting a server and performing data communication with the server; the user interface 1003 is mainly used for connecting a user terminal and performing data communication with the terminal; the power consumption optimization method based on the folding screen calls a power consumption optimization method program based on the folding screen stored in the memory 1005 through the processor 1001, and executes the power consumption optimization method based on the folding screen provided by the embodiment of the invention.
Based on the hardware structure, the embodiment of the power consumption optimization method based on the folding screen is provided.
Referring to fig. 2, fig. 2 is a schematic flow chart of a power consumption optimization method based on a folding screen according to a first embodiment of the present invention.
In a first embodiment, the folding screen based power consumption optimization method includes the following steps:
step S10, acquiring streaming media playing data of a first split screen and a second split screen in the folding screen.
It should be noted that, in this embodiment, the execution main body may be a power consumption optimization device based on a folding screen, the power consumption optimization device based on the folding screen is provided with a power consumption optimization method program based on the folding screen, and may also be other devices that can achieve the same or similar functions.
In this embodiment, the streaming media playing data includes a played audio or video clip, and may further include other types of playing clips, which is not limited in this embodiment, a video playing example is taken as an example for explanation, and this embodiment is based on that the mobile terminal performs power consumption optimization according to the played video, where, for a folding screen, since the mobile terminal is a folding screen, as for the folding screen, since the folding screen includes a first split screen and a second split screen, a requirement on power consumption is higher during video playing, and power consumption is higher during video playing, therefore, this embodiment focuses on dynamically adjusting brightness of the folding screen during video playing, so as to achieve performance optimization.
And step S20, extracting detail information in the streaming media playing data, and performing complexity analysis according to the detail information to obtain complexity information.
It is understood that the detail information may include information of a video frame and information of video playing content, and may further include other information, which is not limited in this embodiment.
In the specific implementation, a frame type and corresponding length information are obtained by analyzing video frames in streaming media playing data, and the number and the length information corresponding to the frame type are obtained by analyzing the video frames in the streaming media playing data, wherein the more the number of the frames is, the more complex the streaming media playing data is, the less the number of the frames is, the simpler the streaming media playing data is, the brightness can be reduced for the simple streaming media playing data, and the brightness can be increased for the complex streaming media playing data, so that the brightness adjustment is performed according to the complexity of the streaming media playing data, that is, the dynamic brightness adjustment of the stacked screen is performed according to the streaming media playing data.
And step S30, carrying out grade quantization on the complexity information to obtain a corresponding complexity interval.
In this embodiment, the level quantization may be divided into five levels according to the complexity information, or may be divided into other number of level information, which is not limited in this embodiment, five levels are taken as an example for illustration, and the division into five sections according to the complexity information can be represented by P1, P2, P3, P4, and P5, or can be represented by other manners, which is not limited in this embodiment.
In a specific implementation, an array is recorded according to the lengths of key frames in the complexity information in sequence, { L1, L2, L3 … … }, and the frame length values in the array are divided into five sections, P1, P2, P3, P4, and P5, so that the complexity information is subjected to level quantization to obtain corresponding complexity sections, so as to obtain corresponding brightness adjustment information.
Step S40, finding a corresponding brightness adjustment threshold according to the mapping relationship between the complexity interval and the brightness interval.
It should be noted that a mapping relationship between a complexity interval and a brightness interval is pre-established, and a corresponding brightness interval, for example, the brightness intervals corresponding to the complexity interval P1, P2, P3, P4, and P5 are 0 to 50, 50 to 100, 100 to 150, 150 to 200, and 200 to 255 through the complexity interval by querying the mapping relationship between the complexity interval and the brightness interval, for example, when the complexity is a, the corresponding complexity interval is P2, the mapping relationship between the complexity interval and the brightness interval is queried according to P2, and the corresponding brightness interval is 50 to 100, so as to implement conversion between the complexity and the brightness, and implement dynamic adjustment of the brightness of the folding screen according to the currently played streaming media playing data.
In the specific implementation, in order to obtain the mapping relationship between the complexity interval and the brightness interval, sample complexity information and corresponding reasonable brightness information are collected, the corresponding complexity interval and the corresponding brightness interval are obtained according to the sample complexity information and the corresponding reasonable brightness information, and the mapping relationship between the complexity interval and the brightness interval is established according to the complexity interval and the brightness interval, so that the brightness interval is quickly inquired according to the mapping relationship between the complexity interval and the brightness interval.
Step S50, obtaining the current screen set brightness value, and dynamically adjusting the brightness of the folding screen according to the current screen set brightness value and the brightness adjustment threshold value.
In this embodiment, the current screen setting brightness value can be obtained according to the light parameter acquired by the light sensor, and the brightness information input by the user can be obtained through the preset brightness setting interface, so that the current screen setting brightness value can be obtained according to the brightness information input by the user.
In a specific implementation, the brightness of the folded screen is dynamically adjusted through a neural network model according to the set brightness value of the current screen and the brightness adjustment threshold, and the brightness can also be dynamically adjusted through other deep learning models, which is not limited in this embodiment.
In the embodiment, the streaming media playing data of a first split screen and a second split screen in a folding screen are acquired; extracting detail information in the streaming media playing data, and performing complexity analysis according to the detail information to obtain complexity information; carrying out grade quantization on the complexity information to obtain a corresponding complexity interval; searching a corresponding brightness adjustment threshold according to the mapping relation between the complexity interval and the brightness interval; the method comprises the steps of obtaining a current screen set brightness value, and dynamically adjusting the brightness of the folding screen according to the current screen set brightness value and the brightness adjustment threshold value, so that the dynamic brightness adjustment can be realized according to simple calculation by using streaming media playing data of a first split screen and a second split screen in the folding screen as brightness adjustment parameters, and the purpose of reducing the power consumption of the folding screen is achieved.
In an embodiment, as shown in fig. 3, a second embodiment of the power consumption optimization method based on a foldable screen according to the present invention is proposed based on the first embodiment, and the step S20 includes:
step S201, performing detail analysis on the streaming media playing data, and resolving detail information at least containing video, audio, or data continuous code stream.
In specific implementation, the mobile streaming media client decodes the data packet and then analyzes the data packet to extract the complexity information of the video content. The key frame is judged firstly because the complexity of the image frame takes the length of the key frame as a judgment basis. Decomposing ES (Elementary Stream), analyzing NAL (Network Abstract Layer), dividing frame, and analyzing slice type.
A Packet Identifier (PID) in a Transport Stream (TS) header finds a Program Association Table (PAT) of the TS, a payload _ unit _ start _ indicator flag in the TS Packet is 1 and includes picture header information, and the frame type is determined according to the picture _ coding _ type. And when the Nal _ unit _ type value is 5 and is represented as an IDR frame, and when the slice _ type is 2, 4, 7 and 9, the frame is represented as an I frame, so that the detailed analysis of the streaming media playing data is realized.
And S202, analyzing the data of the network abstract layer according to the continuous code stream.
In this embodiment, the network abstraction layer data is a NAL layer, and the network abstraction layer data analyzed according to the continuous code stream can realize identification of video frames, such as I frames.
Step S203, extracting the zone bit information of the transport stream in the network abstraction layer data, and obtaining the corresponding frame type according to the zone bit information.
In this embodiment, the flag bit information may be 1, and may also be other parameters, which are not limited in this embodiment, for example, a Nal _ unit _ type value is 5, which is denoted as an IDR frame, and when a slice _ type is 2, 4, 7, and 9, which is denoted as an I frame, so as to implement frame type identification.
And step S204, dividing according to the frame types to obtain corresponding frame data.
Step S205, determining the frame length of the video I frame according to the frame data, and determining the complexity information according to the frame length of the video I frame.
In this embodiment, the frame length of the video I frame is taken as the complexity information, and the complexity information, such as contrast information, may also be obtained through other parameters.
In one embodiment, the step S30 includes:
generating a record array by the streaming media playing data according to a playing sequence so as to quantize the streaming media playing data; in the process of playing the streaming media, carrying out complexity information according to continuously updated streaming media playing data to obtain updated streaming media playing data so as to update and correct the streaming media playing data; updating the record array according to the corrected streaming media playing data to obtain an updated record array; and carrying out interval division on the updated record array according to the frame length value to obtain a corresponding complexity interval.
In this embodiment, the streaming media play data is generated into a record array, for example, { L1, L2, L3 … … } according to a play sequence, so as to quantize the streaming media play data, for example, at the start of video playing, the luminance dynamic adjustment is not performed first, the frame length of the video I frame is recorded, the video play luminance adjustment threshold is selected, and updating and correcting are performed continuously according to newly collected data. The length of the key frame appearing first at the beginning of video playing is recorded into an array, { L1, L2, L3 … … }, and the frame length values in the array are divided into five sections, P1, P2, P3, P4 and P5, so that corresponding complexity sections are obtained.
In an embodiment, before the step S40, the method further includes:
acquiring a screen brightness range, and dividing the screen brightness range according to preset grades to obtain a plurality of brightness grades and corresponding brightness intervals; obtaining corresponding brightness interval demarcation point information and a weighted value according to the brightness grade and the corresponding brightness interval; and establishing a mapping relation between the complexity interval and the brightness interval according to the weighted value, the complexity interval and the corresponding brightness interval demarcation point information.
In a specific implementation, the screen brightness range of the mobile phone is 0-255, 0 represents the darkest, and 255 represents 100% brightness. The screen brightness range is divided into five grades which respectively correspond to screen brightness values of 0-50, 50-100, 100-150, 150-200 and 200-255. The frame length values in the array are divided into five sections P1, P2, P3, P4 and P5, which respectively correspond to five brightness section demarcation points: 50, 100, 150, 200 and 250.
In this embodiment, the complexity information is obtained by extracting the detail information in the streaming media playing data and performing complexity analysis according to the detail information; carrying out grade quantization on the complexity information to obtain a corresponding complexity interval; and searching a corresponding brightness adjustment threshold according to the mapping relation between the complexity interval and the brightness interval, and obtaining the brightness interval by establishing the mapping relation between the complexity interval and the brightness interval so as to realize the dynamic adjustment of the brightness of the folding screen.
In an embodiment, as shown in fig. 4, a third embodiment of the power consumption optimization method based on a foldable screen according to the present invention is provided based on the first embodiment or the second embodiment, and the step S50 includes:
step S501, obtaining the set brightness value of the current screen.
In a specific implementation, the set value of the illumination intensity is set as r (k), the output of the brightness is y (k), and the output is the state quantity x required by the neuron learning control deviation quantity1(k)、x2(k)、x3(k) Respectively representing the deviation of the set value from the actual output, the deviation integral and the deviation derivative.
Step S502, obtaining the deviation, deviation integral and deviation differential of the set value and the actual output according to the set brightness value of the current screen and the brightness adjusting threshold value.
Acquiring a preset weighting coefficient, and establishing a control corresponding relation among the preset weighting coefficient, the set value, the actually output deviation, the deviation integral and the deviation differential and the control brightness value as follows:
wherein x is1(k)＝yr(k) -y (k) ═ e (k) is a performance index.
Step S503, obtaining a preset weighting coefficient, and establishing a control corresponding relation among the preset weighting coefficient, the set value, the actually output deviation, the deviation integral and the deviation differential and the control brightness value.
And step S504, obtaining a control brightness value according to the control corresponding relation, and dynamically adjusting the brightness of the folding screen according to the control brightness value.
In a particular implementation of the method of the invention,
wherein, wi(k) Denotes xi(k) U (K) represents a control luminance value, and K represents a scale factor of the neuron.
In an embodiment, the step S504 includes:
acquiring a proportional learning rate parameter, an integral learning rate parameter and a differential learning rate parameter of the control corresponding relation; establishing a corresponding relation of the set value weighting coefficient according to the proportional learning rate parameter, the integral learning rate parameter and the differential learning rate parameter; establishing a neural network training model according to the corresponding relation of the set value weighting coefficients; acquiring an initial value of a neural network coefficient, and acquiring a proportional learning rate value, an integral learning rate value and a differential learning rate value according to the initial value of the neural network coefficient and the neural network training model; and obtaining a control brightness value according to the proportional learning rate value, the integral learning rate value and the differential learning rate value, and dynamically adjusting the brightness of the folding screen according to the control brightness value.
In this embodiment, wi(k) The convergence and robustness of the Hebb supervised learning algorithm are better ensured, and the control brightness value is obtained through the following formula:
wherein eta isP(k)、ηI(k)、ηD(k) The proportional learning rate parameter, the integral learning rate parameter, and the differential learning rate parameter, respectively.
It is understood that the initial values of the neural network coefficients are calculated as:
the control brightness value is obtained from the above formula.
In an embodiment, before the step S50, the method further includes:
generating dynamic brightness adjustment reminding information; judging whether feedback information of the dynamic brightness adjustment reminding information is received or not; and when receiving the feedback information of the dynamic brightness adjustment reminding information, executing a step of dynamically adjusting the brightness of the folding screen according to the set brightness value of the current screen and the brightness adjustment threshold value.
In this embodiment, the brightness dynamic adjustment reminding information is generated to obtain the feedback information of the user, and whether brightness adjustment is performed is judged according to the feedback information of the user, so that adjustment is performed according to the requirement of the user, interaction with the user is realized, and user experience is improved.
The invention further provides a power consumption optimization method and device based on the folding screen.
Referring to fig. 5, fig. 5 is a functional module schematic diagram of a power consumption optimization method and apparatus based on a folding screen according to a first embodiment of the present invention.
In a first embodiment of a power consumption optimization method based on a folding screen, the power consumption optimization method based on the folding screen includes:
the acquiring module 10 is configured to acquire streaming media playing data of a first split screen and a second split screen in a folding screen.
In this embodiment, the streaming media playing data includes a played audio or video clip, and may further include other types of playing clips, which is not limited in this embodiment, a video playing example is taken as an example for explanation, and this embodiment is based on that the mobile terminal performs power consumption optimization according to the played video, where, for a folding screen, since the mobile terminal is a folding screen, as for the folding screen, since the folding screen includes a first split screen and a second split screen, a requirement on power consumption is higher during video playing, and power consumption is higher during video playing, therefore, this embodiment focuses on dynamically adjusting brightness of the folding screen during video playing, so as to achieve performance optimization.
And the extracting module 20 is configured to extract detail information in the streaming media playing data, and perform complexity analysis according to the detail information to obtain complexity information.
It is understood that the detail information may include information of a video frame and information of video playing content, and may further include other information, which is not limited in this embodiment.
In the specific implementation, a frame type and corresponding length information are obtained by analyzing video frames in streaming media playing data, and the number and the length information corresponding to the frame type are obtained by analyzing the video frames in the streaming media playing data, wherein the more the number of the frames is, the more complex the streaming media playing data is, the less the number of the frames is, the simpler the streaming media playing data is, the brightness can be reduced for the simple streaming media playing data, and the brightness can be increased for the complex streaming media playing data, so that the brightness adjustment is performed according to the complexity of the streaming media playing data, that is, the dynamic brightness adjustment of the stacked screen is performed according to the streaming media playing data.
And a quantization module 30, configured to perform level quantization on the complexity information to obtain a corresponding complexity interval.
In this embodiment, the level quantization may be divided into five levels according to the complexity information, or may be divided into other number of level information, which is not limited in this embodiment, five levels are taken as an example for illustration, and the division into five sections according to the complexity information can be represented by P1, P2, P3, P4, and P5, or can be represented by other manners, which is not limited in this embodiment.
In a specific implementation, an array is recorded according to the lengths of key frames in the complexity information in sequence, { L1, L2, L3 … … }, and the frame length values in the array are divided into five sections, P1, P2, P3, P4, and P5, so that the complexity information is subjected to level quantization to obtain corresponding complexity sections, so as to obtain corresponding brightness adjustment information.
And the searching module 40 is configured to search a corresponding brightness adjustment threshold according to the mapping relationship between the complexity interval and the brightness interval.
It should be noted that a mapping relationship between a complexity interval and a brightness interval is pre-established, and a corresponding brightness interval, for example, the brightness intervals corresponding to the complexity interval P1, P2, P3, P4, and P5 are 0 to 50, 50 to 100, 100 to 150, 150 to 200, and 200 to 255 through the complexity interval by querying the mapping relationship between the complexity interval and the brightness interval, for example, when the complexity is a, the corresponding complexity interval is P2, the mapping relationship between the complexity interval and the brightness interval is queried according to P2, and the corresponding brightness interval is 50 to 100, so as to implement conversion between the complexity and the brightness, and implement dynamic adjustment of the brightness of the folding screen according to the currently played streaming media playing data.
In the specific implementation, in order to obtain the mapping relationship between the complexity interval and the brightness interval, sample complexity information and corresponding reasonable brightness information are collected, the corresponding complexity interval and the corresponding brightness interval are obtained according to the sample complexity information and the corresponding reasonable brightness information, and the mapping relationship between the complexity interval and the brightness interval is established according to the complexity interval and the brightness interval, so that the brightness interval is quickly inquired according to the mapping relationship between the complexity interval and the brightness interval.
And the adjusting module 50 is configured to obtain a current screen set brightness value, and dynamically adjust the brightness of the folding screen according to the current screen set brightness value and the brightness adjustment threshold.
In this embodiment, the current screen setting brightness value can be obtained according to the light parameter acquired by the light sensor, and the brightness information input by the user can be obtained through the preset brightness setting interface, so that the current screen setting brightness value can be obtained according to the brightness information input by the user.
In a specific implementation, the brightness of the folded screen is dynamically adjusted through a neural network model according to the set brightness value of the current screen and the brightness adjustment threshold, and the brightness can also be dynamically adjusted through other deep learning models, which is not limited in this embodiment.
In the embodiment, the streaming media playing data of a first split screen and a second split screen in a folding screen are acquired; extracting detail information in the streaming media playing data, and performing complexity analysis according to the detail information to obtain complexity information; carrying out grade quantization on the complexity information to obtain a corresponding complexity interval; searching a corresponding brightness adjustment threshold according to the mapping relation between the complexity interval and the brightness interval; the method comprises the steps of obtaining a current screen set brightness value, and dynamically adjusting the brightness of the folding screen according to the current screen set brightness value and the brightness adjustment threshold value, so that the dynamic brightness adjustment can be realized according to simple calculation by using streaming media playing data of a first split screen and a second split screen in the folding screen as brightness adjustment parameters, and the purpose of reducing the power consumption of the folding screen is achieved.
In an embodiment, the extracting module 20 is further configured to perform detail analysis on the streaming media playing data to resolve detail information at least containing a video, audio, or data continuous code stream; analyzing the data of the network abstract layer according to the continuous code stream; extracting the zone bit information of the transport stream in the network abstraction layer data, and obtaining the corresponding frame type according to the zone bit information; dividing according to the frame type to obtain corresponding frame data; and determining the frame length of a video I frame according to the frame data, and determining the complexity information according to the frame length of the video I frame.
In an embodiment, the quantizing module 30 is further configured to generate a record array from the streaming media playing data according to a playing sequence, so as to quantize the streaming media playing data;
in the process of playing the streaming media, carrying out complexity information according to continuously updated streaming media playing data to obtain updated streaming media playing data so as to update and correct the streaming media playing data;
updating the record array according to the corrected streaming media playing data to obtain an updated record array;
and carrying out interval division on the updated record array according to the frame length value to obtain a corresponding complexity interval.
In an embodiment, the search module 40 is further configured to obtain a screen brightness range, and divide the screen brightness range according to preset levels to obtain a plurality of brightness levels and corresponding brightness intervals;
obtaining corresponding brightness interval demarcation point information and a weighted value according to the brightness grade and the corresponding brightness interval;
and establishing a mapping relation between the complexity interval and the brightness interval according to the weighted value, the complexity interval and the corresponding brightness interval demarcation point information.
In an embodiment, the adjusting module 50 is further configured to obtain a current screen set brightness value;
obtaining the deviation, deviation integral and deviation differential of a set value and actual output according to the set brightness value of the current screen and the brightness adjustment threshold;
acquiring a preset weighting coefficient, and establishing a control corresponding relation among the preset weighting coefficient, the set value, the actually output deviation, the deviation integral and the deviation differential and the control brightness value;
and obtaining a control brightness value according to the control corresponding relation, and dynamically adjusting the brightness of the folding screen according to the control brightness value.
In an embodiment, the adjusting module 50 is further configured to obtain a proportional learning rate parameter, an integral learning rate parameter, and a differential learning rate parameter of the control correspondence;
establishing a corresponding relation of the set value weighting coefficient according to the proportional learning rate parameter, the integral learning rate parameter and the differential learning rate parameter;
establishing a neural network training model according to the corresponding relation of the set value weighting coefficients;
acquiring an initial value of a neural network coefficient, and acquiring a proportional learning rate value, an integral learning rate value and a differential learning rate value according to the initial value of the neural network coefficient and the neural network training model;
and obtaining a control brightness value according to the proportional learning rate value, the integral learning rate value and the differential learning rate value, and dynamically adjusting the brightness of the folding screen according to the control brightness value.
In an embodiment, the adjusting module 50 is further configured to generate a dynamic brightness adjustment reminding message;
judging whether feedback information of the dynamic brightness adjustment reminding information is received or not;
and when feedback information of the dynamic brightness adjustment reminding information is received, dynamically adjusting the brightness of the folding screen according to the set brightness value of the current screen and the brightness adjustment threshold value.
In addition, in order to achieve the above object, the present invention further provides a power consumption optimization device based on a foldable screen, including: a memory, a processor, and a folded screen based power consumption optimization program stored on the memory and executable on the processor, the folded screen based power consumption optimization program configured to implement the steps of the folded screen based power consumption optimization method as described above.
In addition, an embodiment of the present invention further provides a storage medium, where a power consumption optimization program based on a folded screen is stored on the storage medium, and when being executed by a processor, the power consumption optimization program based on the folded screen implements the steps of the power consumption optimization method based on the folded screen.
Since the storage medium adopts all technical solutions of all the embodiments, at least all the beneficial effects brought by the technical solutions of the embodiments are achieved, and no further description is given here.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be substantially or partially embodied in the form of a software product, which is stored in a computer-readable storage medium (such as ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling an intelligent terminal (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A power consumption optimization method based on a folding screen is characterized by comprising the following steps:
acquiring streaming media playing data of a first split screen and a second split screen in a folding screen;
extracting detail information in the streaming media playing data, and performing complexity analysis according to the detail information to obtain complexity information;
carrying out grade quantization on the complexity information to obtain a corresponding complexity interval;
searching a corresponding brightness adjustment threshold according to the mapping relation between the complexity interval and the brightness interval;
and acquiring a current screen set brightness value, and dynamically adjusting the brightness of the folding screen according to the current screen set brightness value and the brightness adjustment threshold value.
2. The folding-screen-based power consumption optimization method of claim 1, wherein the extracting detail information from the streaming media playing data and performing complexity analysis according to the detail information to obtain complexity information comprises:
analyzing the details of the streaming media playing data to decompose the detail information at least containing video, audio or data continuous code streams;
analyzing the data of the network abstract layer according to the continuous code stream;
extracting the zone bit information of the transport stream in the network abstraction layer data, and obtaining the corresponding frame type according to the zone bit information;
dividing according to the frame type to obtain corresponding frame data;
and determining the frame length of a video I frame according to the frame data, and determining the complexity information according to the frame length of the video I frame.
3. The folded-screen-based power consumption optimization method of claim 1, wherein the performing level quantization on the complexity information to obtain a corresponding complexity interval comprises:
generating a record array by the streaming media playing data according to a playing sequence so as to quantize the streaming media playing data;
in the process of playing the streaming media, carrying out complexity information according to continuously updated streaming media playing data to obtain updated streaming media playing data so as to update and correct the streaming media playing data;
updating the record array according to the corrected streaming media playing data to obtain an updated record array;
and carrying out interval division on the updated record array according to the frame length value to obtain a corresponding complexity interval.
4. The power consumption optimization method based on the folding screen as claimed in claim 1, wherein before searching for the corresponding brightness adjustment threshold according to the mapping relationship between the complexity interval and the brightness interval, the method further comprises:
acquiring a screen brightness range, and dividing the screen brightness range according to preset grades to obtain a plurality of brightness grades and corresponding brightness intervals;
obtaining corresponding brightness interval demarcation point information and a weighted value according to the brightness grade and the corresponding brightness interval;
and establishing a mapping relation between the complexity interval and the brightness interval according to the weighted value, the complexity interval and the corresponding brightness interval demarcation point information.
5. The method for optimizing power consumption based on a folding screen according to any one of claims 1 to 4, wherein the obtaining a current screen setting brightness value and dynamically adjusting brightness of the folding screen according to the current screen setting brightness value and the brightness adjustment threshold value comprises:
acquiring a current screen set brightness value;
obtaining the deviation, deviation integral and deviation differential of a set value and actual output according to the set brightness value of the current screen and the brightness adjustment threshold;
acquiring a preset weighting coefficient, and establishing a control corresponding relation among the preset weighting coefficient, the set value, the actually output deviation, the deviation integral and the deviation differential and the control brightness value;
and obtaining a control brightness value according to the control corresponding relation, and dynamically adjusting the brightness of the folding screen according to the control brightness value.
6. The power consumption optimization method based on the folding screen according to claim 5, wherein the obtaining a control brightness value according to the control correspondence, and dynamically adjusting the brightness of the folding screen according to the control brightness value comprises:
acquiring a proportional learning rate parameter, an integral learning rate parameter and a differential learning rate parameter of the control corresponding relation;
establishing a corresponding relation of the set value weighting coefficient according to the proportional learning rate parameter, the integral learning rate parameter and the differential learning rate parameter;
establishing a neural network training model according to the corresponding relation of the set value weighting coefficients;
acquiring an initial value of a neural network coefficient, and acquiring a proportional learning rate value, an integral learning rate value and a differential learning rate value according to the initial value of the neural network coefficient and the neural network training model;
and obtaining a control brightness value according to the proportional learning rate value, the integral learning rate value and the differential learning rate value, and dynamically adjusting the brightness of the folding screen according to the control brightness value.
7. The method for optimizing power consumption based on a folding screen according to any one of claims 1 to 4, wherein before dynamically adjusting the brightness of the folding screen according to the current screen setting brightness value and the brightness adjustment threshold, the method further comprises:
generating dynamic brightness adjustment reminding information;
judging whether feedback information of the dynamic brightness adjustment reminding information is received or not;
and when receiving the feedback information of the dynamic brightness adjustment reminding information, executing a step of dynamically adjusting the brightness of the folding screen according to the set brightness value of the current screen and the brightness adjustment threshold value.
8. A power consumption optimization method device based on a folding screen is characterized in that the power consumption optimization method device based on the folding screen comprises the following steps:
the acquisition module is used for acquiring streaming media playing data of a first split screen and a second split screen in the folding screen;
the extracting module is used for extracting detail information in the streaming media playing data and carrying out complexity analysis according to the detail information to obtain complexity information;
the quantization module is used for carrying out grade quantization on the complexity information to obtain a corresponding complexity interval;
the searching module is used for searching a corresponding brightness adjustment threshold according to the mapping relation between the complexity interval and the brightness interval;
and the adjusting module is used for acquiring the set brightness value of the current screen and dynamically adjusting the brightness of the folding screen according to the set brightness value of the current screen and the brightness adjusting threshold value.
9. A power consumption optimization method device based on a folding screen is characterized in that the power consumption optimization device based on the folding screen comprises: a memory, a processor, and a folded screen based power consumption optimization program stored on the memory and executable on the processor, the folded screen based power consumption optimization program configured to implement the steps of the folded screen based power consumption optimization method of any one of claims 1 to 7.
10. A storage medium having stored thereon a folded-screen based power consumption optimization program, which when executed by a processor implements the steps of the folded-screen based power consumption optimization method according to any one of claims 1 to 7.
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