CN112162625A - Power consumption control method and device for electronic equipment, storage medium and terminal - Google Patents
Power consumption control method and device for electronic equipment, storage medium and terminal Download PDFInfo
- Publication number
- CN112162625A CN112162625A CN202011020925.4A CN202011020925A CN112162625A CN 112162625 A CN112162625 A CN 112162625A CN 202011020925 A CN202011020925 A CN 202011020925A CN 112162625 A CN112162625 A CN 112162625A
- Authority
- CN
- China
- Prior art keywords
- scene
- power consumption
- real
- electronic equipment
- time
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 85
- 238000003860 storage Methods 0.000 title claims abstract description 28
- 238000010801 machine learning Methods 0.000 claims abstract description 69
- 238000004364 calculation method Methods 0.000 claims description 60
- 238000012549 training Methods 0.000 claims description 50
- 238000012360 testing method Methods 0.000 claims description 43
- 238000004422 calculation algorithm Methods 0.000 claims description 35
- 238000004590 computer program Methods 0.000 claims description 23
- 230000007613 environmental effect Effects 0.000 claims description 10
- 230000000694 effects Effects 0.000 claims description 9
- 230000002040 relaxant effect Effects 0.000 claims description 7
- 238000013528 artificial neural network Methods 0.000 claims description 6
- 238000007477 logistic regression Methods 0.000 claims description 6
- 238000012706 support-vector machine Methods 0.000 claims description 6
- 238000009826 distribution Methods 0.000 abstract description 8
- 238000013473 artificial intelligence Methods 0.000 abstract description 6
- 238000005516 engineering process Methods 0.000 abstract description 6
- 230000002035 prolonged effect Effects 0.000 abstract description 6
- 230000008569 process Effects 0.000 description 11
- 238000010586 diagram Methods 0.000 description 6
- 238000012545 processing Methods 0.000 description 6
- 230000008901 benefit Effects 0.000 description 3
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 230000009467 reduction Effects 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011022 operating instruction Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F1/00—Details not covered by groups G06F3/00 - G06F13/00 and G06F21/00
- G06F1/26—Power supply means, e.g. regulation thereof
- G06F1/32—Means for saving power
- G06F1/3203—Power management, i.e. event-based initiation of a power-saving mode
- G06F1/3234—Power saving characterised by the action undertaken
- G06F1/3243—Power saving in microcontroller unit
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F1/00—Details not covered by groups G06F3/00 - G06F13/00 and G06F21/00
- G06F1/26—Power supply means, e.g. regulation thereof
- G06F1/32—Means for saving power
- G06F1/3203—Power management, i.e. event-based initiation of a power-saving mode
- G06F1/3234—Power saving characterised by the action undertaken
- G06F1/325—Power saving in peripheral device
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F1/00—Details not covered by groups G06F3/00 - G06F13/00 and G06F21/00
- G06F1/26—Power supply means, e.g. regulation thereof
- G06F1/32—Means for saving power
- G06F1/3203—Power management, i.e. event-based initiation of a power-saving mode
- G06F1/3234—Power saving characterised by the action undertaken
- G06F1/3287—Power saving characterised by the action undertaken by switching off individual functional units in the computer system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3058—Monitoring arrangements for monitoring environmental properties or parameters of the computing system or of the computing system component, e.g. monitoring of power, currents, temperature, humidity, position, vibrations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3058—Monitoring arrangements for monitoring environmental properties or parameters of the computing system or of the computing system component, e.g. monitoring of power, currents, temperature, humidity, position, vibrations
- G06F11/3062—Monitoring arrangements for monitoring environmental properties or parameters of the computing system or of the computing system component, e.g. monitoring of power, currents, temperature, humidity, position, vibrations where the monitored property is the power consumption
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computing Systems (AREA)
- Software Systems (AREA)
- Quality & Reliability (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Mathematical Physics (AREA)
- Computer Hardware Design (AREA)
- Power Sources (AREA)
Abstract
The invention discloses a power consumption control method, a power consumption control device, a storage medium and a terminal of electronic equipment, wherein the method comprises the following steps: identifying a real-time working scene of the electronic equipment by adopting a machine learning method; and controlling the power consumption of the electronic equipment by adopting a control method corresponding to the real-time working scene. According to the invention, through emerging artificial intelligence technologies such as machine learning, the working scene is judged according to the temperature distribution of main devices, the software running state, the operation instruction of a user and the like of the electronic equipment in various working scenes, and the strategy of increasing power consumption or reducing power consumption is timely and accurately executed when the working scene is switched, so that the endurance time of the electronic equipment is prolonged as much as possible in the scene where the endurance time is required to be increased according to the requirements of the user, the performance is improved as much as possible in the scene where the performance is required to be increased, the electronic equipment has long endurance and high performance on the premise of not increasing the cost, and the market competitiveness is greatly improved.
Description
[ technical field ] A method for producing a semiconductor device
The present invention relates to the field of intelligent devices, and in particular, to a power consumption control method and apparatus for an electronic device, a storage medium, and a terminal.
[ background of the invention ]
The endurance time and performance of electronic equipment such as a notebook computer are the most critical indexes for users, and have important influence on the normal use and experience of the users. The main methods for increasing the endurance time of the notebook computer are to increase the battery capacity and reduce the power consumption of the notebook computer, but the battery capacity cannot be greatly increased due to the space limitation of the notebook computer, especially for the ultra-thin notebook computer. On the other hand, the higher the performance of the notebook computer, the higher the power consumption, and the higher the surface temperature of the casing, the longer the endurance time is necessarily reduced. In the prior art, a power consumption control scheme for electronic equipment such as a notebook computer is simple, for example, under the condition that user requirements are not considered, the performance of hardware is actively reduced in order to ensure the endurance time, so that the user experience is poor, and the product competitiveness is insufficient.
[ summary of the invention ]
The invention provides a power consumption control method and device of electronic equipment, a storage medium and a terminal, and solves the technical problem that the electronic equipment in the prior art cannot have long endurance and high performance.
The technical scheme for solving the technical problems is as follows: a power consumption control method of an electronic device, comprising the steps of:
identifying a real-time working scene of the electronic equipment by adopting a machine learning method;
and controlling the power consumption of the electronic equipment by adopting the control method corresponding to the real-time working scene.
In a preferred embodiment, the working scene of the electronic device comprises a long endurance scene and a high performance scene, wherein the long endurance scene comprises a webpage browsing scene, a local video playing scene and a low power consumption software running scene; the high-performance scene comprises a high-power-consumption software running scene, a high-power-consumption game running scene and a multi-software simultaneous running scene.
In a preferred embodiment, the controlling the power consumption of the electronic device by using the control method corresponding to the real-time working scenario includes:
if the real-time working scene is a long-endurance scene, reducing the power consumption of the electronic equipment by adopting a first control method;
if the real-time working scene is a high-performance scene, increasing the power consumption of the electronic equipment by adopting a second control method;
the first control method comprises one or more of reminding a user to turn off an LED lamp effect and a touch panel, reducing the CPU overclocking power and overclocking time, limiting the maximum power consumption of the CPU, controlling the CPU to work at the lowest rated power, controlling a charging chip to enter a sleep mode and reducing the rotating speed of a fan;
the second control method includes one or more of increasing the CPU over-frequency power and time, increasing the CPU maximum power consumption, relaxing the temperature limit of the CPU for the lowering frequency after the high temperature, and increasing the fan speed.
In a preferred embodiment, the identifying the real-time working scene of the electronic device by using the machine learning method includes:
acquiring historical temperature data of a plurality of preset positions on a mainboard in different working scenes, and establishing a first training set and a first testing set;
establishing a first target calculation model for identifying the real-time working scene of the electronic equipment by adopting a machine learning method based on the first training set and the first testing set;
and acquiring real-time temperature data of the plurality of preset positions on the mainboard, and calling the first target calculation model to generate a real-time working scene of the electronic equipment.
In a preferred embodiment, the identifying the real-time working scenario of the electronic device by using the machine learning method further includes:
establishing the first training set and the first testing set corresponding to different environmental temperature ranges;
establishing the first target calculation model corresponding to each environment temperature range by adopting a machine learning method;
and acquiring real-time environment temperature, calling a corresponding first target calculation model according to the environment temperature range where the real-time environment temperature is located, and generating a real-time working scene of the electronic equipment.
In a preferred embodiment, the machine learning method comprises at least one of a conditional random field algorithm, a maximum entropy model algorithm, a bagging algorithm, a boosting algorithm, a neural network algorithm, a logistic regression algorithm, and a support vector machine algorithm.
In a preferred embodiment, the identifying the real-time working scene of the electronic device by using the machine learning method includes:
acquiring historical positioning information, historical time information, historical software running state information and corresponding user operation instructions of the electronic equipment in different working scenes, and establishing a second training set and a second testing set;
establishing a second target calculation model for identifying the real-time working scene of the electronic equipment by adopting a machine learning method based on the second training set and the second testing set;
and identifying current positioning information, current time information, current software running state information and a current operation instruction of a user of the electronic equipment, and calling the second target calculation model to generate a real-time working scene of the electronic equipment.
A second aspect of embodiments of the present invention provides a power consumption control apparatus for an electronic device, including a scene recognition module and a control module,
the scene recognition module is used for recognizing the real-time working scene of the electronic equipment by adopting a machine learning method;
the control module is used for controlling the power consumption of the electronic equipment by adopting the control method corresponding to the real-time working scene.
In a preferred embodiment, when the real-time working scene is a long endurance scene, the control module is configured to reduce power consumption of the electronic device by using a first control method;
when the real-time working scene is a high-performance scene, the control module is used for improving the power consumption of the electronic equipment by adopting a second control method;
the first control method comprises one or more of reminding a user to turn off an LED lamp effect and a touch panel, reducing the CPU overclocking power and overclocking time, limiting the maximum power consumption of the CPU, controlling the CPU to work at the lowest rated power, controlling a charging chip to enter a sleep mode and reducing the rotating speed of a fan;
the second control method includes one or more of increasing the CPU over-frequency power and time, increasing the CPU maximum power consumption, relaxing the temperature limit of the CPU for the lowering frequency after the high temperature, and increasing the fan speed.
In a preferred embodiment, the scene recognition module includes a first machine learning unit, a first storage unit, a first temperature collection unit, and a first recognition unit,
the first machine learning unit is used for acquiring historical temperature data of a plurality of preset positions on the mainboard under different working scenes, establishing a first training set and a first testing set, and establishing a first target calculation model for identifying the real-time working scene of the electronic equipment by adopting a machine learning method based on the first training set and the first testing set;
the first storage unit is used for storing the first target calculation model;
the first temperature acquisition unit is used for acquiring real-time temperature data of the plurality of preset positions on the mainboard;
the first identification unit is used for calling the first target calculation model and generating a real-time working scene of the electronic equipment according to the real-time temperature data.
In a preferred embodiment, the scene recognition module further comprises a second temperature acquisition unit,
the second temperature acquisition unit is used for acquiring real-time environment temperature;
the first machine learning unit is further configured to establish the first training set and the first test set corresponding to different environmental temperature ranges, and establish the first target calculation model corresponding to each environmental temperature range by using a machine learning method;
the first identification unit is further used for calling a corresponding first target calculation model according to the environment temperature range where the real-time environment temperature is located, and generating a real-time working scene of the electronic equipment.
In a preferred embodiment, the scene recognition module further comprises a second machine learning unit, a second storage unit, a state acquisition unit and a second recognition unit,
the second machine learning unit is used for acquiring historical positioning information, historical time information, historical software running state information and corresponding user operation instructions of the electronic equipment in different working scenes, establishing a second training set and a second test set, and establishing a second target calculation model for identifying the real-time working scene of the electronic equipment by adopting a machine learning method based on the second training set and the second test set;
the second storage unit is used for storing the second target calculation model;
the state acquisition unit is used for acquiring current positioning information, current time information, current software running state information and a current operation instruction of a user of the electronic equipment;
the second identification unit is used for calling the second target calculation model and generating a real-time working scene of the electronic equipment according to the current positioning information, the current time information, the current software running state information and the current operation instruction of the user of the electronic equipment.
A third aspect of embodiments of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described power consumption control method for an electronic device.
A fourth aspect of the embodiments of the present invention provides a terminal, including the computer-readable storage medium and a processor, where the processor implements the steps of the power consumption control method of the electronic device when executing the computer program on the computer-readable storage medium.
The invention provides a power consumption control method, a device, a storage medium and a terminal of electronic equipment, which judge working scenes according to temperature distribution of main devices, software running states, operation instructions of users and the like of the electronic equipment in various working scenes through emerging artificial intelligence technologies such as machine learning and the like, and timely and accurately execute strategies of increasing power consumption or reducing power consumption when the working scenes are switched, so that the endurance time of the electronic equipment is prolonged as much as possible in scenes needing to increase the endurance time according to user requirements, the performance is improved as much as possible in scenes needing to improve the performance, the electronic equipment has long endurance and high performance on the premise of not increasing the cost, and the market competitiveness is greatly improved.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
[ description of the drawings ]
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is an application scenario diagram of a power consumption control method of an electronic device in an embodiment;
FIG. 2 is a flowchart illustrating a method of controlling power consumption of an electronic device according to an embodiment;
FIG. 3 is a flow diagram that illustrates a process for identifying real-time operational scenarios of an electronic device using a machine learning approach, under an embodiment;
FIG. 4 is a flow chart illustrating a method for identifying a real-time working scenario of an electronic device using a machine learning method according to another embodiment;
FIG. 5 is a schematic diagram showing a configuration of a power consumption control apparatus of an electronic device in one embodiment;
FIG. 6 is a diagram of the internal structure of an electronic device in one embodiment.
[ detailed description ] embodiments
In order to make the objects, technical solutions and advantageous effects of the present invention more clearly apparent, the present invention is further described in detail below with reference to the accompanying drawings and the detailed description. It should be understood that the detailed description and specific examples, while indicating the preferred embodiment of the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.
The power consumption control method of the electronic device provided by the application can be applied to the application environment shown in fig. 1. The power consumption control method of the electronic device is applied to the terminal 102, wherein the terminal 102 may be, but not limited to, a computer, a notebook computer, a palm computer, a Personal Digital Assistant (PDA), a Portable Media Player (PMP), a navigation terminal, a wearable device, a smart band, a pedometer, and other mobile terminals, and fixed terminals such as a Digital TV, a desktop computer, and the like.
In one embodiment, as shown in fig. 2, a method for controlling power consumption of an electronic device is provided, which is described by taking the method as an example for being applied to the notebook computer in fig. 1, and includes the following steps:
step S1: the notebook computer adopts a machine learning method to identify the real-time working scene.
In this embodiment, the working scene of the notebook computer can be divided into a long endurance scene and a high performance scene according to different requirements of the user. The long-endurance scene comprises a webpage browsing scene, a local video playing scene and a low-power-consumption software operation scene, such as an office, pdf reader and other small program operation scenes, and the scenes can meet the requirements of a user on high response speed and short delay without too high hardware performance. The high-performance scenes comprise high-power-consumption software such as a run-out software, a CAD drawing software and a video clip software operation scene, high-power-consumption games such as a large-scale single-machine game operation scene and a plurality of multi-software simultaneous operation scenes, and the scenes have high requirements on hardware performance and can normally operate only when the performance is met, so that the improvement of the hardware performance of the machine can be replaced by the endurance time.
For a notebook computer, the following two modes can be selected for the work scene judgment. A real-time working scene is identified through temperature distribution of a mainboard on a notebook computer, and the method comprises the following steps:
s101, the notebook computer obtains historical temperature data of a plurality of preset positions on the mainboard in different working scenes, and a first training set and a first testing set are established. In a specific embodiment, a plurality of high-precision wide-working-temperature sensors arranged on a main board can acquire temperature data corresponding to a preset position, a notebook computer records the temperature data to form historical temperature data, then one part of the historical temperature data can be set as a first training set, the other part of the historical temperature data is set as a first test set, the first training set is used for training a calculation model of a machine learning method, and the first test set is used for testing the classification effect of the trained calculation model and optimizing parameters of the calculation model. In a specific implementation Process, temperature sensors of the notebook computer are distributed near a mainboard high-power-consumption device, such as a Central Processing Unit (CPU), a Solid State Disk (SSD), a WIFI module, and the like, and heat radiated and conducted by the high-power-consumption device can be quickly sensed by the temperature sensors.
S102, under a constant temperature environment, the notebook computer establishes a first target calculation model for identifying the real-time working scene of the notebook computer by adopting a machine learning method based on a first training set and a first testing set, and the first target calculation model comprises the following steps: and Q is 1-T1 + 2-T2 + … + (n) -T (n), wherein T represents the temperature value collected by the temperature sensor and represents the weighted value of the temperature sensor corresponding to each preset position, and n represents the number of the temperature sensors. In the process of training the calculation model by adopting a machine learning method, deep learning is carried out on a large amount of temperature data of the first training set, so that the optimal first target calculation model is converged, namely, the optimal value is adjusted, and different working scenes have stable Q value ranges. If the Q value continuously fluctuates in a larger range or different working scenes cannot be distinguished obviously, the learned historical temperature data is continuously increased, namely the data volume of the first training set is increased, and the cycle is repeated continuously to adjust the value until more ideal Q value distribution appears.
In a specific embodiment, the notebook computer may efficiently execute the machine learning process by using an Embedded Controller (EC) or a chip dedicated to machine learning, and obtain the first target calculation model. In other embodiments, the notebook computer may further only have a storage module storing the first target calculation model, and the training process of the first target calculation model is preprocessed by an external embedded controller or a machine learning dedicated device.
The machine learning method adopted by the invention comprises at least one of a Conditional random field algorithm (Conditional random field), a Maximum entropy model algorithm (Maximum entropy model), a Bagging algorithm (Bagging), a Boosting algorithm (Boosting), a Neural network algorithm (Neural network), a Logistic regression algorithm (Logistic regression) and a Support vector machine algorithm (Support vector machine), and a first training set and a first testing set can be constructed in a vector mode in the machine learning process so as to obtain a first target calculation model represented by a vector.
S103, the notebook computer collects real-time temperature data of a plurality of preset positions on the mainboard and calls the first target calculation model to generate a real-time working scene of the notebook computer. In a specific embodiment, after the notebook computer continuously works in a certain working scene for a period of time, parameters such as machine temperature and power consumption reach a stable state, the temperature sensor reads real-time temperature data and reports the real-time temperature data to an embedded controller or a machine learning special chip of the notebook computer, and then the embedded controller or the machine learning special chip calls a first target calculation model to calculate a stable Q value, so that the real-time working scene of the notebook computer is accurately judged.
In another preferred embodiment, in consideration of the influence of the ambient temperature on the weighted value of the first target calculation model, so as to reduce the accuracy of the judgment of the real-time working scene, the embedded controller of the notebook computer or the machine learning dedicated chip may further establish a first training set and a first test set corresponding to different ambient temperature ranges, and establish the first target calculation model corresponding to each ambient temperature range based on the first training set and the first test set. And then, acquiring the real-time environment temperature, and calling a corresponding first target calculation model according to the environment temperature range where the real-time environment temperature is located, so that the real-time working scene where the notebook computer is located is generated more accurately.
Another scheme of the embodiment of the present invention is to identify a real-time working scene through the positioning information, the time information, the software running state information and the user operation instruction of the notebook computer, as shown in fig. 4, including the following steps:
and S105, the notebook computer acquires historical positioning information, historical time information, historical software running state information and corresponding user operation instructions under different working scenes, and establishes a second training set and a second testing set.
And S106, establishing a second target calculation model for identifying the real-time working scene by the notebook computer by adopting a machine learning method based on the second training set and the second testing set.
S107, the notebook computer identifies the current positioning information, the current time information, the current software running state information and the current operation instruction of the user, and calls a second target calculation model to generate the real-time working scene.
In a specific embodiment, an embedded controller or a machine learning dedicated chip of a notebook computer may perform machine learning training by using all historical positioning information, historical time information, historical software running state information, and corresponding user operation instructions in a period of time as 4-type tensor data, so as to determine a working scene in which the notebook computer is located. The software running state information can be detected through a Dynamic Platform and a hot frame program (DPTF) of the notebook computer, or through an operating system of the notebook computer. The dynamic platform and the hot framework program are originally basic programs of a computer released by Intel corporation, can monitor basic software running on the computer under the condition that adaptive policy is started, or directly use an operating system for detection, and can more specifically and accurately acquire software running state information. And the user operation instruction comprises information such as operation instruction content, operation time, operation frequency and the like. And training through a second training set, and performing parameter optimization through a second testing set, so as to obtain a second target calculation model through calculating the relation between the working scene predicted value and the multivariate. This has the advantage that it can be inferred that the laptop is in a long endurance scenario or a high performance scenario based on the user's historical operating instructions (e.g., periodically at 8 pm, at a fixed geographic area such as home, turning on video software to watch local video).
Of course, in other embodiments, the temperature data and the positioning information, the time information, the software running state information and the user operation instruction at the preset position may be combined to perform machine learning training, so as to obtain a better third target calculation model, and further improve the speed and accuracy of the work scene recognition.
Then, step S2 is executed, and the notebook computer controls its own power consumption by using the control method corresponding to the real-time working scenario. In a specific embodiment, if the real-time working scene is a long-endurance scene, the embedded controller or the machine learning dedicated chip of the notebook computer reduces the power consumption of the notebook computer by using a first control method, wherein the first control method comprises one or more of reminding a user to turn off an LED lamp effect and a touch panel, reducing the over-frequency power and the over-frequency time of a CPU, limiting the maximum power consumption of the CPU, controlling the CPU to work at the lowest rated power, controlling a charging chip to enter a sleep mode, and reducing the rotating speed of a fan.
When the real-time working scene is a high-performance scene, the embedded controller or the machine learning special chip of the notebook computer adopts a second control method to improve the power consumption of the notebook computer, and the second control method comprises one or more of improving the over-frequency power and the over-frequency time of the CPU, improving the maximum power consumption of the CPU, relaxing the temperature limit of the reduction frequency of the CPU after high temperature and increasing the rotating speed of the fan.
In the process of reducing power consumption by using the first control method or increasing power consumption by using the second control method, the control means can be sequentially adopted according to a preset sequence, for example, firstly considering changing the rotating speed of the fan, secondly considering changing the power consumption or rated power of a CPU (central processing unit), and the like, so that the power consumption of the notebook computer is controlled step by step. And various control means can be adopted at the same time, so that the power consumption adjusting speed and efficiency are further improved.
The embodiment provides a power consumption control method for electronic equipment, which judges a working scene according to temperature distribution of main devices, software running state, operation instructions of a user and the like of the electronic equipment in various working scenes through emerging artificial intelligence technologies such as machine learning and the like, and timely and accurately executes a strategy of increasing power consumption or reducing power consumption when the working scenes are switched, so that the endurance time of the electronic equipment is prolonged as much as possible in scenes needing to improve the endurance time according to the requirements of the user, the performance is improved as much as possible in scenes needing to improve the performance, the electronic equipment has long endurance and high performance on the premise of not increasing the cost, and the market competitiveness is greatly improved.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Fig. 5 is a schematic structural diagram of a power consumption control apparatus of an electronic device according to another embodiment of the present invention, as shown in fig. 5, including a scene recognition module 100 and a control module 200,
the scene recognition module 100 is configured to recognize a real-time working scene of the electronic device by using a machine learning method;
the control module 200 is configured to control power consumption of the electronic device by using a control method corresponding to a real-time working scene.
In one embodiment, when the real-time working scene is a long endurance scene, the control module 200 is configured to reduce the power consumption of the electronic device by using the first control method;
when the real-time working scene is a high-performance scene, the control module 200 is configured to increase the power consumption of the electronic device by using the second control method;
the first control method comprises one or more of reminding a user to turn off an LED lamp effect and a touch panel, reducing the CPU overclocking power and overclocking time, limiting the maximum power consumption of the CPU, controlling the CPU to work at the lowest rated power, controlling a charging chip to enter a sleep mode and reducing the rotating speed of a fan;
the second control method includes one or more of increasing the CPU over-frequency power and time, increasing the CPU maximum power consumption, relaxing the temperature limit of the CPU for the lowering frequency after high temperature, and increasing the fan speed.
In one embodiment, the scene recognition module 100 includes a first machine learning unit, a first storage unit, a first temperature collection unit, and a first recognition unit,
the first machine learning unit is used for acquiring historical temperature data of a plurality of preset positions on the mainboard under different working scenes, establishing a first training set and a first testing set, and establishing a first target calculation model for identifying the real-time working scene of the electronic equipment by adopting a machine learning method based on the first training set and the first testing set;
the first storage unit is used for storing a first target calculation model;
the first temperature acquisition unit is used for acquiring real-time temperature data of a plurality of preset positions on the mainboard;
the first identification unit is used for calling the first target calculation model and generating a real-time working scene of the electronic equipment according to the real-time temperature data.
In one embodiment, the scene recognition module 100 further comprises a second temperature acquisition unit,
the second temperature acquisition unit is used for acquiring real-time environment temperature;
the first machine learning unit is also used for establishing a first training set and a first testing set corresponding to different environmental temperature ranges, and establishing a first target calculation model corresponding to each environmental temperature range by adopting a machine learning method;
the first identification unit is further used for calling the corresponding first target calculation model according to the environment temperature range where the real-time environment temperature is located, and generating a real-time working scene of the electronic equipment.
In one embodiment, the scene recognition module 100 further includes a second machine learning unit, a second storage unit, a state acquisition unit and a second recognition unit,
the second machine learning unit is used for acquiring historical positioning information, historical time information, historical software running state information and corresponding user operation instructions of the electronic equipment in different working scenes, establishing a second training set and a second test set, and establishing a second target calculation model for identifying the real-time working scene of the electronic equipment by adopting a machine learning method based on the second training set and the second test set;
the second storage unit is used for storing a second target calculation model;
the state acquisition unit is used for acquiring current positioning information, current time information, current software running state information and a current operation instruction of a user of the electronic equipment;
the second identification unit is used for calling the second target calculation model and generating a real-time working scene of the electronic equipment according to the current positioning information, the current time information, the current software running state information and the current operation instruction of the user of the electronic equipment.
The embodiment provides a power consumption control device of an electronic device, which judges a working scene according to temperature distribution of main devices, software running state, operation instructions of a user and the like of the electronic device under various working scenes through emerging artificial intelligence technologies such as machine learning and the like, and timely and accurately executes a strategy of increasing power consumption or reducing power consumption when the working scenes are switched, so that the endurance time of the electronic device is prolonged as much as possible in a scene needing to improve the endurance time according to the requirements of the user, the performance is improved as much as possible in the scene needing to improve the performance, the electronic device has long endurance and high performance on the premise of not increasing the cost, and the market competitiveness is greatly improved.
In one embodiment, the invention also provides a computer-readable storage medium having a computer program stored thereon, the computer program, when executed by a processor, implementing the steps of:
s1, identifying the real-time working scene of the electronic equipment by adopting a machine learning method;
and S2, controlling the power consumption of the electronic equipment by adopting a control method corresponding to the real-time working scene.
In one embodiment, the computer program when executed by the processor further performs the steps of:
s101, acquiring historical temperature data of a plurality of preset positions on a mainboard in different working scenes, and establishing a first training set and a first testing set;
s102, establishing a first target calculation model for identifying the real-time working scene of the electronic equipment by adopting a machine learning method based on a first training set and a first testing set;
s103, collecting real-time temperature data of a plurality of preset positions on the mainboard, and calling the first target calculation model to generate a real-time working scene of the electronic equipment.
In one embodiment, the computer program when executed by the processor further performs the steps of: establishing a first training set and a first test set corresponding to different environmental temperature ranges, and establishing a first target calculation model corresponding to each environmental temperature range based on the first training set and the first test set. And then, acquiring the real-time environment temperature, and calling a corresponding first target calculation model according to the environment temperature range where the real-time environment temperature is located, so that the real-time working scene where the notebook computer is located is generated more accurately.
In one embodiment, the computer program when executed by the processor further performs the steps of:
s105, acquiring historical positioning information, historical time information, historical software running state information and corresponding user operation instructions of the electronic equipment in different working scenes, and establishing a second training set and a second testing set;
s106, establishing a second target calculation model for identifying the real-time working scene of the electronic equipment by adopting a machine learning method based on the second training set and the second testing set;
s107, identifying the current positioning information, the current time information, the current software running state information and the current operation instruction of the user of the electronic equipment, and calling a second target calculation model to generate a real-time working scene of the electronic equipment.
In one embodiment, the computer program, when executed by the processor, includes at least one of a conditional random field algorithm, a maximum entropy model algorithm, a bagging algorithm, a boosting algorithm, a neural network algorithm, a logistic regression algorithm, and a support vector machine algorithm.
In one embodiment, when the computer program is executed by the processor, the working scenes of the electronic equipment comprise a long endurance scene and a high performance scene, wherein the long endurance scene comprises a webpage browsing scene, a local video playing scene and a low power consumption software running scene; the high-performance scene comprises a high-power-consumption software running scene, a high-power-consumption game running scene and a multi-software simultaneous running scene.
In one embodiment, the computer program when executed by the processor further performs the steps of:
if the real-time working scene is a long-endurance scene, the power consumption of the notebook computer is reduced by adopting a first control method, and the first control method comprises one or more of reminding a user to close an LED lamp effect and a touch panel, reducing the over-frequency power and the over-frequency time of a CPU, limiting the maximum power consumption of the CPU, controlling the CPU to work at the lowest rated power, controlling a charging chip to enter a sleep mode and reducing the rotating speed of a fan.
And when the real-time working scene is a high-performance scene, the power consumption of the electronic equipment is improved by adopting a second control method, wherein the second control method comprises one or more of improving the overtone power and the overtone time of the CPU, improving the maximum power consumption of the CPU, relaxing the temperature limit of the reduction frequency of the CPU after high temperature and increasing the rotating speed of the fan.
The above embodiment provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to execute a power consumption control method of an electronic device, so that a working scenario can be determined according to temperature distribution of main devices, software running states, and user operation instructions in various working scenarios of the electronic device through a new artificial intelligence technology such as machine learning, and a policy of increasing power consumption or reducing power consumption is timely and accurately executed when the working scenario is switched, so that the duration of the electronic device is prolonged as much as possible in a scenario in which the duration needs to be increased according to user requirements, the performance of the electronic device is increased as much as possible in a scenario in which the performance needs to be increased, the electronic device has both long duration and high performance without increasing cost, and market competitiveness is greatly increased.
Fig. 6 is an internal structure diagram of an electronic device in an embodiment, where the electronic device may be a notebook computer, or may be another mobile terminal or fixed terminal. As shown in fig. 6, the apparatus comprises a memory 81 and a processor 80, the memory 81 stores a computer program 82, and the processor 80 implements the following steps when executing the computer program 82:
s1, identifying the real-time working scene of the electronic equipment by adopting a machine learning method;
and S2, controlling the power consumption of the electronic equipment by adopting a control method corresponding to the real-time working scene.
In one embodiment, the processor 80, when executing the computer program 82, further performs the steps of:
s101, acquiring historical temperature data of a plurality of preset positions on a mainboard in different working scenes, and establishing a first training set and a first testing set;
s102, establishing a first target calculation model for identifying the real-time working scene of the electronic equipment by adopting a machine learning method based on a first training set and a first testing set;
s103, collecting real-time temperature data of a plurality of preset positions on the mainboard, and calling the first target calculation model to generate a real-time working scene of the electronic equipment.
In one embodiment, the processor 80, when executing the computer program 82, further performs the steps of: establishing a first training set and a first test set corresponding to different environmental temperature ranges, and establishing a first target calculation model corresponding to each environmental temperature range based on the first training set and the first test set. And then, acquiring the real-time environment temperature, and calling a corresponding first target calculation model according to the environment temperature range where the real-time environment temperature is located, so that the real-time working scene where the notebook computer is located is generated more accurately.
In one embodiment, the processor 80, when executing the computer program 82, further performs the steps of:
s105, acquiring historical positioning information, historical time information, historical software running state information and corresponding user operation instructions of the electronic equipment in different working scenes, and establishing a second training set and a second testing set;
s106, establishing a second target calculation model for identifying the real-time working scene of the electronic equipment by adopting a machine learning method based on the second training set and the second testing set;
s107, identifying the current positioning information, the current time information, the current software running state information and the current operation instruction of the user of the electronic equipment, and calling a second target calculation model to generate a real-time working scene of the electronic equipment.
In one embodiment, the machine learning method includes at least one of a conditional random field algorithm, a maximum entropy model algorithm, a bagging algorithm, a boosting algorithm, a neural network algorithm, a logistic regression algorithm, and a support vector machine algorithm when the computer program 82 is executed by the processor 80.
In one embodiment, when the processor 80 executes the computer program 82, the operating scenarios of the electronic device include a long endurance scenario and a high performance scenario, the long endurance scenario includes a web browsing scenario, a local video playing scenario, and a low power software running scenario; the high-performance scene comprises a high-power-consumption software running scene, a high-power-consumption game running scene and a multi-software simultaneous running scene.
In one embodiment, the processor 80, when executing the computer program 82, further performs the steps of:
if the real-time working scene is a long-endurance scene, the power consumption of the notebook computer is reduced by adopting a first control method, and the first control method comprises one or more of reminding a user to close an LED lamp effect and a touch panel, reducing the over-frequency power and the over-frequency time of a CPU, limiting the maximum power consumption of the CPU, controlling the CPU to work at the lowest rated power, controlling a charging chip to enter a sleep mode and reducing the rotating speed of a fan.
And when the real-time working scene is a high-performance scene, the power consumption of the electronic equipment is improved by adopting a second control method, wherein the second control method comprises one or more of improving the overtone power and the overtone time of the CPU, improving the maximum power consumption of the CPU, relaxing the temperature limit of the reduction frequency of the CPU after high temperature and increasing the rotating speed of the fan.
According to the embodiment, the working scene is judged according to the temperature distribution of main devices of the electronic equipment in various working scenes, the software running state, the operation instruction of a user and the like through emerging artificial intelligence technologies such as machine learning and the like, and the strategy of increasing power consumption or reducing power consumption is timely and accurately executed when the working scene is switched, so that the endurance time of the electronic equipment is prolonged as far as possible in the scene where the endurance time is required to be increased according to the requirements of the user, the performance is improved as far as possible in the scene where the performance is required to be increased, the electronic equipment has long endurance and high performance on the premise of not increasing the cost, and the market competitiveness is greatly improved.
It will be understood by those skilled in the art that fig. 6 is only one example of the terminal of the present invention, and is not limited to the terminal, and may include more or less components than those shown, or combine some components, or different components, for example, the terminal may further include a power management module, an arithmetic processing module, an input/output device, a network access device, a bus, etc.
The Processor 80 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 81 may be an internal storage unit of the terminal, such as a hard disk or a memory. The memory 81 may also be an external storage device of the terminal, such as a plug-in hard disk provided on the compass calibration terminal, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the memory 81 may also include both an internal storage unit of the compass calibration terminal and an external storage device. The memory 81 is used to store computer programs and other programs and data needed for compass calibration of the terminal. The memory 81 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and simplicity of description, the foregoing functional units and modules are merely illustrated in terms of division, and in practical applications, the foregoing functional allocation may be performed by different functional units and modules as needed, that is, the internal structure of the terminal is divided into different functional units or modules to perform all or part of the above described functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the above-mentioned apparatus refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and method steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed terminal/terminal device and method can be implemented in other ways. For example, the above-described terminal/terminal device embodiments are merely illustrative, and for example, a module or a unit may be divided into only one logic function, and may be implemented in other ways, for example, a plurality of units or components may be combined or integrated into another device, or some features may be omitted or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, terminals or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The invention is not limited solely to that described in the specification and embodiments, and additional advantages and modifications will readily occur to those skilled in the art, so that the invention is not limited to the specific details, representative apparatus, and illustrative examples shown and described herein, without departing from the spirit and scope of the general concept as defined by the appended claims and their equivalents.
Claims (10)
1. A power consumption control method of an electronic device, comprising the steps of:
identifying a real-time working scene of the electronic equipment by adopting a machine learning method;
and controlling the power consumption of the electronic equipment by adopting the control method corresponding to the real-time working scene.
2. The power consumption control method of the electronic device according to claim 1, wherein the operating scene of the electronic device includes a long endurance scene and a high performance scene, the long endurance scene includes a web browsing scene, a local video playing scene, and a low power consumption software running scene; the high-performance scene comprises a high-power-consumption software running scene, a high-power-consumption game running scene and a multi-software simultaneous running scene.
3. The power consumption control method of the electronic device according to claim 2, wherein the controlling the power consumption of the electronic device by using the control method corresponding to the real-time working scene comprises:
if the real-time working scene is a long-endurance scene, reducing the power consumption of the electronic equipment by adopting a first control method;
if the real-time working scene is a high-performance scene, increasing the power consumption of the electronic equipment by adopting a second control method;
the first control method comprises one or more of reminding a user to turn off an LED lamp effect and a touch panel, reducing the CPU overclocking power and overclocking time, limiting the maximum power consumption of the CPU, controlling the CPU to work at the lowest rated power, controlling a charging chip to enter a sleep mode and reducing the rotating speed of a fan;
the second control method includes one or more of increasing the CPU over-frequency power and time, increasing the CPU maximum power consumption, relaxing the temperature limit of the CPU for the lowering frequency after the high temperature, and increasing the fan speed.
4. The power consumption control method of the electronic device according to any one of claims 1 to 3, wherein the identifying the real-time working scene of the electronic device by using the machine learning method includes:
acquiring historical temperature data of a plurality of preset positions on a mainboard in different working scenes, and establishing a first training set and a first testing set;
establishing a first target calculation model for identifying the real-time working scene of the electronic equipment by adopting a machine learning method based on the first training set and the first testing set;
and acquiring real-time temperature data of the plurality of preset positions on the mainboard, and calling the first target calculation model to generate a real-time working scene of the electronic equipment.
5. The method for controlling power consumption of an electronic device according to claim 4, wherein the identifying the real-time operating scenario of the electronic device by using the machine learning method further comprises:
establishing the first training set and the first testing set corresponding to different environmental temperature ranges;
establishing the first target calculation model corresponding to each environment temperature range by adopting a machine learning method;
and acquiring real-time environment temperature, calling a corresponding first target calculation model according to the environment temperature range where the real-time environment temperature is located, and generating a real-time working scene of the electronic equipment.
6. The power consumption control method of the electronic device according to claim 5, wherein the machine learning method includes at least one of a conditional random field algorithm, a maximum entropy model algorithm, a bagging algorithm, a boosting algorithm, a neural network algorithm, a logistic regression algorithm, and a support vector machine algorithm.
7. The power consumption control method of the electronic device according to any one of claims 1 to 3, wherein the identifying the real-time working scene of the electronic device by using the machine learning method includes:
acquiring historical positioning information, historical time information, historical software running state information and corresponding user operation instructions of the electronic equipment in different working scenes, and establishing a second training set and a second testing set;
establishing a second target calculation model for identifying the real-time working scene of the electronic equipment by adopting a machine learning method based on the second training set and the second testing set;
and identifying current positioning information, current time information, current software running state information and a current operation instruction of a user of the electronic equipment, and calling the second target calculation model to generate a real-time working scene of the electronic equipment.
8. A power consumption control device of an electronic device is characterized by comprising a scene recognition module and a control module,
the scene recognition module is used for recognizing the real-time working scene of the electronic equipment by adopting a machine learning method;
the control module is used for controlling the power consumption of the electronic equipment by adopting the control method corresponding to the real-time working scene.
9. A computer-readable storage medium, in which a computer program is stored, which, when executed by a processor, implements a power consumption control method of an electronic device according to any one of claims 1 to 7.
10. A terminal, characterized in that it comprises the computer-readable storage medium of claim 9 and a processor, which when executing the computer program on the computer-readable storage medium implements the steps of the power consumption control method of the electronic device of any of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011020925.4A CN112162625A (en) | 2020-09-25 | 2020-09-25 | Power consumption control method and device for electronic equipment, storage medium and terminal |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011020925.4A CN112162625A (en) | 2020-09-25 | 2020-09-25 | Power consumption control method and device for electronic equipment, storage medium and terminal |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112162625A true CN112162625A (en) | 2021-01-01 |
Family
ID=73863803
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011020925.4A Pending CN112162625A (en) | 2020-09-25 | 2020-09-25 | Power consumption control method and device for electronic equipment, storage medium and terminal |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112162625A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114706465A (en) * | 2022-04-25 | 2022-07-05 | 紫光计算机科技有限公司 | Method and device for adjusting performance of electronic equipment, electronic equipment and medium |
| WO2023160343A1 (en) * | 2022-02-25 | 2023-08-31 | 华为技术有限公司 | Water entrance detection method and related apparatus |
| CN117667605A (en) * | 2024-02-01 | 2024-03-08 | 深圳市智仁科技有限公司 | Performance optimization method and system for main board |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017002995A1 (en) * | 2015-07-02 | 2017-01-05 | 가온미디어 주식회사 | Scenario-based power consumption control method in electronic device |
| CN106339065A (en) * | 2016-09-23 | 2017-01-18 | 乐视控股（北京）有限公司 | Multi-core processor control method and device as well as mobile terminal |
| CN106774786A (en) * | 2016-11-22 | 2017-05-31 | 珠海市魅族科技有限公司 | A kind of power consumption control method and device |
| CN108093368A (en) * | 2017-12-13 | 2018-05-29 | 广东欧珀移动通信有限公司 | Control method, device, storage medium and mobile terminal based on locating module |
| CN108886548A (en) * | 2017-02-24 | 2018-11-23 | 华为技术有限公司 | A kind of method, electronic equipment and the storage medium of the device power consumption of determining electronic equipment |
| CN109960395A (en) * | 2018-10-15 | 2019-07-02 | 华为技术有限公司 | Resource regulating method and computer equipment |
| CN111132283A (en) * | 2019-11-11 | 2020-05-08 | 华为技术有限公司 | Power consumption control method and equipment |
| CN111479314A (en) * | 2020-06-02 | 2020-07-31 | 三星电子（中国）研发中心 | Terminal power consumption adjusting method and terminal equipment |
-
2020
- 2020-09-25 CN CN202011020925.4A patent/CN112162625A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017002995A1 (en) * | 2015-07-02 | 2017-01-05 | 가온미디어 주식회사 | Scenario-based power consumption control method in electronic device |
| CN106339065A (en) * | 2016-09-23 | 2017-01-18 | 乐视控股（北京）有限公司 | Multi-core processor control method and device as well as mobile terminal |
| CN106774786A (en) * | 2016-11-22 | 2017-05-31 | 珠海市魅族科技有限公司 | A kind of power consumption control method and device |
| CN108886548A (en) * | 2017-02-24 | 2018-11-23 | 华为技术有限公司 | A kind of method, electronic equipment and the storage medium of the device power consumption of determining electronic equipment |
| CN108093368A (en) * | 2017-12-13 | 2018-05-29 | 广东欧珀移动通信有限公司 | Control method, device, storage medium and mobile terminal based on locating module |
| CN109960395A (en) * | 2018-10-15 | 2019-07-02 | 华为技术有限公司 | Resource regulating method and computer equipment |
| CN111132283A (en) * | 2019-11-11 | 2020-05-08 | 华为技术有限公司 | Power consumption control method and equipment |
| CN111479314A (en) * | 2020-06-02 | 2020-07-31 | 三星电子（中国）研发中心 | Terminal power consumption adjusting method and terminal equipment |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023160343A1 (en) * | 2022-02-25 | 2023-08-31 | 华为技术有限公司 | Water entrance detection method and related apparatus |
| CN114706465A (en) * | 2022-04-25 | 2022-07-05 | 紫光计算机科技有限公司 | Method and device for adjusting performance of electronic equipment, electronic equipment and medium |
| CN114706465B (en) * | 2022-04-25 | 2024-05-10 | 紫光计算机科技有限公司 | Performance adjustment method and device of electronic equipment, electronic equipment and medium |
| CN117667605A (en) * | 2024-02-01 | 2024-03-08 | 深圳市智仁科技有限公司 | Performance optimization method and system for main board |
| CN117667605B (en) * | 2024-02-01 | 2024-04-30 | 深圳市智仁科技有限公司 | Performance optimization method and system for main board |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112162625A (en) | Power consumption control method and device for electronic equipment, storage medium and terminal | |
| JP6761134B2 (en) | Processor controllers, methods and devices | |
| Pasricha et al. | A survey on energy management for mobile and IoT devices | |
| CN112529301B (en) | Power consumption prediction method, device and storage medium | |
| CN105393187B (en) | Hot configuration file based on the temperature information for calculating device location | |
| US20170185457A1 (en) | Technologies for offloading and on-loading data for processor/coprocessor arrangements | |
| CN105144118A (en) | Application testing and analysis | |
| US9436266B2 (en) | Method and apparatus for controlling central processing unit in a group of central processing units by acquiring power consumption factors for each central processing unit and comparing a mean of power consumption factors with a threshold | |
| CN104007806A (en) | Method and device for controlling CPU frequency in Android system and equipment | |
| CN111783375A (en) | Chip system and related device | |
| CN110300959A (en) | Task management when dynamic operation | |
| CN108279019B (en) | Step counting method and device and intelligent wearable device | |
| CN107734618B (en) | Application program closing method and device, storage medium and electronic equipment | |
| Khan et al. | Advanced data analytics modeling for evidence-based data center energy management | |
| US11429178B2 (en) | Electronic device and method for determining operating frequency of processor | |
| CN115657570B (en) | Intelligent chip control method and device in low power consumption mode and storage medium | |
| CN114936715B (en) | Fruit freshness prediction method, system and storage medium based on smell information | |
| CN116184851A (en) | Sleep mode adjusting method, device, terminal and storage medium | |
| EP3489802B1 (en) | Information processing apparatus, information processing method, and program | |
| CN108803857A (en) | Application software energy consumption monitoring method, mobile terminal and readable storage medium storing program for executing | |
| CN109992369A (en) | Applied program processing method and device, electronic equipment, computer readable storage medium | |
| CN112306213A (en) | Display card mode switching method and device based on terminal, storage medium and terminal | |
| CN111248857A (en) | Sleep quality monitoring method and device, computer equipment and storage medium | |
| CN115167641B (en) | Inertial self-adaptive temperature control method and system | |
| CN115129093B (en) | Temperature control method, temperature control device and storage medium for power assembly |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |