CN109600825A - A kind of mobile terminal electricity operation reserve generation method and its device - Google Patents
A kind of mobile terminal electricity operation reserve generation method and its device Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 65
- 230000005611 electricity Effects 0.000 title claims description 174
- 238000013486 operation strategy Methods 0.000 claims abstract description 31
- 238000013135 deep learning Methods 0.000 claims abstract description 30
- 230000008859 change Effects 0.000 claims description 16
- 238000011156 evaluation Methods 0.000 claims description 15
- 230000006870 function Effects 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 6
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- 238000012549 training Methods 0.000 description 26
- 230000007246 mechanism Effects 0.000 description 10
- 238000010586 diagram Methods 0.000 description 9
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- 238000005516 engineering process Methods 0.000 description 4
- 230000003993 interaction Effects 0.000 description 3
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- 230000009471 action Effects 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 230000002452 interceptive effect Effects 0.000 description 2
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- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 1
- 235000003140 Panax quinquefolius Nutrition 0.000 description 1
- 230000004913 activation Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 238000013499 data model Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 230000000779 depleting effect Effects 0.000 description 1
- 230000005059 dormancy Effects 0.000 description 1
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- 235000013399 edible fruits Nutrition 0.000 description 1
- 235000008434 ginseng Nutrition 0.000 description 1
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Classifications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. Transmission Power Control [TPC] or power classes
- H04W52/02—Power saving arrangements
- H04W52/0209—Power saving arrangements in terminal devices
- H04W52/0261—Power saving arrangements in terminal devices managing power supply demand, e.g. depending on battery level
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M1/00—Substation equipment, e.g. for use by subscribers
- H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
- H04M1/724—User interfaces specially adapted for cordless or mobile telephones
- H04M1/72448—User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions
- H04M1/72454—User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions according to context-related or environment-related conditions
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/06—Testing, supervising or monitoring using simulated traffic
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. Transmission Power Control [TPC] or power classes
- H04W52/02—Power saving arrangements
- H04W52/0209—Power saving arrangements in terminal devices
- H04W52/0261—Power saving arrangements in terminal devices managing power supply demand, e.g. depending on battery level
- H04W52/0267—Power saving arrangements in terminal devices managing power supply demand, e.g. depending on battery level by controlling user interface components
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. Transmission Power Control [TPC] or power classes
- H04W52/02—Power saving arrangements
- H04W52/0209—Power saving arrangements in terminal devices
- H04W52/0261—Power saving arrangements in terminal devices managing power supply demand, e.g. depending on battery level
- H04W52/0267—Power saving arrangements in terminal devices managing power supply demand, e.g. depending on battery level by controlling user interface components
- H04W52/027—Power saving arrangements in terminal devices managing power supply demand, e.g. depending on battery level by controlling user interface components by controlling a display operation or backlight unit
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- 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
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
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- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Human Computer Interaction (AREA)
- Environmental & Geological Engineering (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
本发明提供了一种移动终端电量运行策略生成方法及其装置，其中所述方法包括：获取移动终端的充电使用习惯数据；根据充电使用习惯数据，通过预先训练的充电时间点预测模型，获得移动终端对应的下一次充电时间点；根据移动终端当前电量，获取系统预估电量耗尽时间点；根据下一次充电时间点和系统预估电量耗尽时间点生成电量运行策略，以便于根据电量运行策略控制移动终端的省电模式的运行。本发明实现了基于AI的深度学习方式学习用户不同的充电行为，预测下一次充电时间，进而针对现有电量进行动态省电规划，生成电量运行策略，在保证维持用户使用体验前提下把电量维持到预测的充电时间点，提供更加贴合用户习惯的省电策略。
The present invention provides a method and a device for generating a power operation strategy for a mobile terminal, wherein the method includes: acquiring charging usage habit data of the mobile terminal; The next charging time point corresponding to the terminal; according to the current power level of the mobile terminal, obtain the estimated power exhaustion time point of the system; generate the power operation strategy according to the next charging time point and the system estimated power exhaustion time point, so as to operate according to the power level The policy controls the operation of the power saving mode of the mobile terminal. The invention realizes the AI-based deep learning method to learn different charging behaviors of users, predicts the next charging time, and then performs dynamic power saving planning according to the existing power, generates power operation strategies, and maintains the power on the premise of maintaining the user experience. At the predicted charging time point, a power saving strategy that is more in line with user habits is provided.
Description
Technical field
The present invention relates to electricity control technology fields, raw more specifically to a kind of mobile terminal electricity operation reserve
At method and device thereof.
Background technique
In different mobile terminal, electricity operation reserve generally comprises power saving strategy, electricity-saving mechanism or for power saving mould
Formula, for example, make mobile terminal within a certain period of time without any operation by control under the mode, it can be automatically into not
The power-consuming components frequency reducings or power-off etc. such as dormancy, standby, CPU more keep the endurance of battery for a long time in this way and use the time.
In existing mobile terminal, the operation method of battery saving mode generally comprises two kinds: first method, for when terminal
When electricity reaches a preset threshold (such as electricity no more than 15%), start battery saving mode；Second method is device power
Battery saving mode is run after starting always, except non-user is voluntarily closed or is adjusted, otherwise as full-time operation.Wherein, the first side
The problem of method, is, can only open (when electricity is lower than 15%) under rigid condition, this mechanism can't consider making for user
With habit, preceding 85% electricity is perhaps rapidly depleting without limitation, causes user to be in experience for a long time very poor
In battery saving mode, battery finally may be still exhausted.The problem of second method, is that the electricity-saving mechanism of full-time running is most likely
The problem in experience is brought, such as: processor, which is limited, causes performance to decline, and excessively radical limitation leads to dysfunction etc..
The use habit of each user is different, is not suitable for being adapted to proprietary operation mode with single mechanism (parameter), perhaps someone is solid
It just fills primary electricity within fixed every 6 hours, does not need any electricity-saving mechanism at all, the electricity-saving mechanism of excessively standard causes the body of the user
Test variation.
In short, the operation method of existing battery saving mode is in the presence of the use habit that can't consider user, it is a certain reaching
When fixed value, that is, force start battery saving mode or full-time operation battery saving mode, the use of mobile terminal is brought for user
Inconvenience, poor user experience.
Summary of the invention
In view of this, the present invention provides a kind of mobile terminal electricity operation reserve generation method and its device is existing to solve
The deficiency of technology.
To solve the above problems, the present invention provides a kind of mobile terminal electricity operation reserve generation method, comprising:
Obtain the charging use habit data of mobile terminal；
The shifting is obtained by charging time point prediction model trained in advance according to the charging use habit data
The dynamic corresponding point of charging time next time of terminal；
According to the mobile terminal current electric quantity, acquisition system estimates electricity and exhausts time point；
Electricity, which is estimated, according to the point of charging time next time and the system exhausts time point generation electricity operation reserve,
In order to controlled according to the electricity operation reserve mobile terminal battery saving mode operation.
Preferably, before described " according to the mobile terminal current electric quantity, acquisition system estimates electricity and exhausts time point ",
Further include:
When the battery capacity of the mobile terminal changes, the current electric quantity delta data of the battery is obtained；
The update for carrying out the electricity operation reserve is determined according to the current electric quantity delta data.
Preferably, described " update carried out to the electricity operation reserve is determined according to the current electric quantity delta data "
It include: to judge whether the current electric quantity delta data of the battery is greater than default electric quantity change data；
If so, carrying out the update to the electricity operation reserve.
Preferably, described " according to the mobile terminal current electric quantity, acquisition system estimates electricity and exhausts time point " includes:
According to the mobile terminal current electric quantity, corresponding remaining capacity is calculated；
Obtain the mobile terminal applies power consumption statistical data；
According to the residual electric quantity with the application power consumption statistical data, when the system is calculated estimating electricity and exhaust
Between point.
Preferably, the acquisition methods of the charging time point prediction model include:
Run deep learning frame；
Obtain the history consumption habit data and corresponding expected annotation results data of the mobile terminal；
The history consumption habit data are input to the deep learning frame to calculate, and are based on the loss function pair
The expected annotation results data are compared with calculated result, are corrected the calculating parameter of the deep learning frame, are obtained institute
State charging time point prediction model.
Preferably, the electricity operation reserve includes the electricity operation of the electricity operation reserve of common grade, danger level
Strategy, the electricity operation reserve of warning level and the power-off other electricity operation reserve of critical level；
It is described " electricity to be estimated according to the point of charging time next time and the system and exhausts time point generation electricity operation
Strategy, in order to controlled according to the electricity operation reserve mobile terminal battery saving mode operation." include:
Charging time point subtracts the system and estimates the difference that electricity exhausts time point next time described in calculating, as current
Battery saving mode evaluation of estimate；
If the current battery saving mode evaluation of estimate generates the electricity operation reserve of the general mode less than 0；
If the current battery saving mode evaluation of estimate is not less than 2, the electricity operation reserve of the danger level is generated；
If the current battery saving mode evaluation of estimate is not less than 4, the electricity operation reserve of the warning level is generated；
If the current battery saving mode evaluation of estimate is not less than 6, the other electricity operation reserve of power-off critical level is generated.
In addition, to solve the above problems, being wrapped the present invention also provides a kind of mobile terminal electricity operation reserve generating means
It includes:
Module is obtained, for obtaining the charging use habit data of mobile terminal；
Computing module, for passing through charging time point prediction mould trained in advance according to the charging use habit data
Type obtains the corresponding point of charging time next time of the mobile terminal；
The acquisition module is also used to according to the mobile terminal current electric quantity, and acquisition system estimates electricity and exhausts the time
Point；
Generation module exhausts time point generation for estimating electricity according to charging time point and the system next time
Electricity operation reserve, in order to controlled according to the electricity operation reserve mobile terminal battery saving mode operation.
In addition, to solve the above problems, the present invention also provides a kind of user terminal, the user terminal include memory with
And processor, the memory generate program for memory mobile terminal electricity operation reserve, the processor runs the shifting
Dynamic terminal power operation reserve generates program so that mobile terminal execution mobile terminal electricity operation reserve as described above
Generation method.
In addition, to solve the above problems, the present invention also provides a kind of computer readable storage medium, it is described computer-readable
It is stored with mobile terminal electricity operation reserve on storage medium and generates program, the mobile terminal electricity operation reserve generates program
Mobile terminal electricity operation reserve generation method as described above is realized when being executed by processor.
A kind of mobile terminal electricity operation reserve generation method provided by the invention and its device.Wherein, the present invention is mentioned
The method of confession obtains charging time point next time by charging time point prediction model, goes forward side by side according to charging use habit data
One step synthesis estimates electricity according to charging time point and system next time and exhausts time point generation electricity operation reserve, and according to this
Electricity operation reserve controls the operation of battery saving mode.The present invention realizes the deep learning mode based on AI, learns user not
Same charging behavior, predicts the charging time next time, and then carries out dynamic power saving planning for existing electricity, generates electricity
Operation reserve is measured, under the premise of guaranteeing to maintain user experience, electricity is maintained the charging time point to prediction, is provided more
Stick on the power saving strategy for sharing family habit, the normal use of mobile terminal is provided convenience for user, substantially increases use
Family experience.
Detailed description of the invention
Fig. 1 is the hardware running environment that mobile terminal electricity operation reserve generation method example scheme of the present invention is related to
Structural schematic diagram；
Fig. 2 is the flow diagram of mobile terminal electricity operation reserve generation method first embodiment of the present invention；
Fig. 3 is the flow diagram of mobile terminal electricity operation reserve generation method second embodiment of the present invention；
It includes step S60 refinement step that Fig. 4, which is in mobile terminal electricity operation reserve generation method second embodiment of the present invention,
Rapid overall flow schematic diagram；
Fig. 5 is the refinement of step S30 described in mobile terminal electricity operation reserve generation method 3rd embodiment kind of the present invention
Flow diagram；
Fig. 6 is charging time point prediction model in mobile terminal electricity operation reserve generation method fourth embodiment of the present invention
Acquisition methods flow diagram；
Fig. 7 is the flow diagram of the 5th embodiment of mobile terminal electricity operation reserve generation method of the present invention；
Fig. 8 is the functional block diagram of mobile terminal electricity operation reserve generating means of the present invention.
The embodiments will be further described with reference to the accompanying drawings for the realization, the function and the advantages of the object of the present invention.
Specific embodiment
The embodiment of the present invention is described below in detail, in which the same or similar labels are throughly indicated same or like
Element or element with the same or similar functions.
In addition, term " first ", " second " are used for descriptive purposes only and cannot be understood as indicating or suggesting relative importance
Or implicitly indicate the quantity of indicated technical characteristic.Define " first " as a result, the feature of " second " can be expressed or
Implicitly include one or more of the features.In the description of the present invention, the meaning of " plurality " is two or more,
Unless otherwise specifically defined.
In the present invention unless specifically defined or limited otherwise, term " installation ", " connected ", " connection ", " fixation " etc.
Term shall be understood in a broad sense, for example, it may be being fixedly connected, may be a detachable connection, or integral；It can be mechanical connect
It connects, is also possible to be electrically connected；It can be directly connected, can also can be in two elements indirectly connected through an intermediary
The interaction relationship of the connection in portion or two elements.It for the ordinary skill in the art, can be according to specific feelings
Condition understands the concrete meaning of above-mentioned term in the present invention.
It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not intended to limit the present invention.
As shown in Figure 1, being the structural schematic diagram of the hardware running environment for the terminal that the embodiment of the present invention is related to.
The PC that the terminal of that embodiment of the invention can be is also possible to smart phone, tablet computer or has certain calculate
Ability and include that E-book reader, MP3 player, MP4 player, portable computer of image capture device etc. are removable
Dynamic formula terminal device.As shown in Figure 1, the terminal may include: processor 1001, such as CPU, network interface 1004, Yong Hujie
Mouth 1003, memory 1005, communication bus 1002.Wherein, communication bus 1002 is logical for realizing the connection between these components
Letter.User interface 1003 may include display screen, input unit such as keyboard, remote controler, and optional user interface 1003 can be with
Including standard wireline interface and wireless interface.Network interface 1004 optionally may include standard wireline interface and wireless interface
(such as WI-FI interface).Memory 1005 can be high speed RAM memory, be also possible to stable memory, such as disk storage
Device.Memory 1005 optionally can also be the storage device independently of aforementioned processor 1001.Optionally, terminal can also wrap
Include RF (Radio Frequency, radio frequency) circuit, voicefrequency circuit, WiFi module etc..In addition, mobile terminal can also configure top
The other sensors such as spiral shell instrument, barometer, hygrometer, thermometer, infrared sensor, details are not described herein.
It will be understood by those skilled in the art that the restriction of the not structure paired terminal of terminal shown in Fig. 1, may include ratio
More or fewer components are illustrated, certain components or different component layouts are perhaps combined.As shown in Figure 1, as a kind of meter
It may include operating system, data-interface control program, network attachment procedure in the memory 1005 of calculation machine readable storage medium storing program for executing
And mobile terminal electricity operation reserve generates program.
A kind of mobile terminal electricity operation reserve generation method provided by the invention and its device.Wherein, the method is real
The deep learning mode based on AI is showed, has learnt the different charging behaviors of user, predict charging time next time, Jin Erzhen
Dynamic power saving planning is carried out to existing electricity, generates electricity operation reserve, in the premise for guaranteeing maintenance user experience
Under, electricity is maintained the charging time point to prediction, the power saving strategy of more fitting user habit is provided, is user for movement
The normal use of terminal provides convenience, and substantially increases user experience.
Embodiment 1:
Referring to Fig. 2, first embodiment of the invention provides a kind of mobile terminal electricity operation reserve generation method, comprising:
Step S10 obtains the charging use habit data of mobile terminal；
Above-mentioned, in the present embodiment, mobile terminal can be mobile phone, or other can realize the electronics of portable functional
Equipment is provided with battery, and it is wherein mountable have different operating system, can be used provided in the present embodiment
Mobile terminal electricity operation reserve generation method.
Above-mentioned, the charging of mobile terminal needs a period of time, can obtain its corresponding electricity after each charging
The charging use habit data in pond.
Above-mentioned, charge use habit data, for charging habit and charged state of the user when using the mobile terminal, fills
The information such as electric mode.Charging beginning and ending time point, charging duration are carried out for example, may include but be not limited to user, charge power supply
Type, such as 1.2V, 2.1V, fast charge or trickle charge are mobile terminals when charging starts by common AC or USB interface
Battery capacity etc. data information.By the use habit data that charge, reflects use of the user using mobile terminal when and practise
It is used.
It can also include that (such as wall socket, insert row turn for the position location of charging in addition, the charging use habit data
It connects, forceful electric power light current etc.).
Step S20 is obtained according to the charging use habit data by charging time point prediction model trained in advance
The corresponding point of charging time next time of the mobile terminal；
In the present embodiment, using neural network learning technology, by being input to preparatory instruction using the use habit data that charge
In the model perfected, i.e., charging time point prediction model, progress operation predict the point of charging time next time of user.
Step S30, according to the mobile terminal current electric quantity, acquisition system estimates electricity and exhausts time point；
Above-mentioned, system estimates electricity and exhausts time point, can be to be obtained by real-time or timing acquisition in mobile terminal system
The data arrived, calculation basis are the current electric quantity of mobile terminal.For example, being got in ios system or Android system
The system estimates the function that electricity exhausts time point.Again for example in ios system, the operation of real-time statistics user's operation mobile phone is practised
It is used, i.e., using the time of different APP, using the time of the time of screen, reading and video etc. information, calculate mobile terminal
Remaining capacity to the complete depletion of timing node of electricity.In addition, the time point, it can also be by voluntarily being calculated in mobile terminal
Out, i.e., according to user to the statistics of the runing time of the different APP softwares in terminal, and according to the corresponding theoretical consumption of each APP
Theoretical power consumption corresponding to electricity, comprehensive screen present intensity and the corresponding hardware capability opened, COMPREHENSIVE CALCULATING are current out
System estimate electricity and exhaust time point.
Step S40 estimates electricity according to the point of charging time next time and the system and exhausts time point generation electricity
Operation reserve, in order to controlled according to the electricity operation reserve mobile terminal battery saving mode operation.
Above-mentioned, battery saving mode controls operational mode of battery capacity in the case where low battery, wherein can as in system
To include but is not limited to the frequency reducing operation for hardware such as CPU, GPU, reduction, hue adjustment of system UI of screen intensity etc.
Deng operation and adjustment mode.
In the present embodiment, charging time point is to user by charging time point prediction model to mobile terminal next time
The data of charging habit calculated and exported, that is, pass through the data of AI prediction；And system estimates electricity and exhausts time point,
To pass through prediction data acquired in system acquisition or the use habit based on user to mobile terminal.Electricity is estimated by system
It exhausts the time interval between time point and next time charging time point, user can be analyzed for the electricity of mobile terminal
Actual use habit and charging habit, and then according to two data, would know that it is corresponding need to take which type of means or
Strategy, if battery saving mode of activation system, etc., as generation electricity operation reserve.In electricity operation reserve, it may include
There is the setting of battery saving mode, also may include the setting of general mode, operation of the setting of different mode for hardware in system
State (such as frequency, switch etc.) and the operating status (backstage number, screen intensity) of software etc. are configured, thus
Realize be accustomed to according to practical application of the user for mobile terminal and charging habit carry out generation to electricity operation reserve and
Setting.
A kind of mobile terminal electricity operation reserve generation method provided in this embodiment.Wherein, side provided by the present invention
Method obtains charging time point next time according to charging use habit data, by charging time point prediction model, and further comprehensive
Conjunction estimates electricity according to charging time point and system next time and exhausts time point generation electricity operation reserve.The present embodiment realizes
Deep learning mode based on AI learns the different charging behaviors of user, predicts the charging time next time, and then for existing
Some electricity carry out dynamic power saving planning, generate electricity operation reserve, under the premise of guaranteeing to maintain user experience,
Electricity maintains the charging time point to prediction, provides the power saving strategy of more fitting user habit, is user for mobile terminal
Normal use provide convenience, substantially increase user experience.
Embodiment 2:
Referring to Fig. 3-4, second embodiment of the invention provides a kind of mobile terminal electricity operation reserve generation method, based on upper
First embodiment shown in Fig. 2 is stated, the step S20, " according to the mobile terminal current electric quantity, acquisition system estimates electricity
Exhaust time point " before,
Step S50 obtains the current electric quantity variation of the battery when the battery capacity of the mobile terminal changes
Data；
In the present embodiment, mobile terminal may be at any time among the use of user, such as open APP, game, view
Frequently, call etc., using different APP, using different sensors, and the brightness that its screen is shown may also be different, cause
It is dynamic change that the system estimated, which estimates electricity to exhaust this data of time point, estimates electricity due to system and exhausts time point
Do not stop to change, the further generation electricity operation reserve that frequently calculates is resulted in go to update existing electricity operation reserve, this
System can be caused additionally to increase a large amount of operations, lead to the generation of extra power consumption.
So in the present embodiment setting one first judges the shifting for the update precondition of electricity operation reserve first
Whether dynamic terminal reaches electricity operation reserve more new state, and further data acquisition and electricity can be carried out if reaching the state
Measure the update of operation reserve.
Above-mentioned, current electric quantity delta data is delta data of the electricity of the battery of mobile terminal relative to original state.
When in use, whether in call or browsing information, the variation of electricity can occur for mobile terminal, as long as generation electricity changes
Become, current electric quantity delta data can be obtained.For example, current electric quantity be 100%, by user screen intensity be 70% state
When browsing webpage after five minutes, electricity has reached 99%, and current electric quantity delta data at this time is 1%.
Step S60 determines the update for carrying out the electricity operation reserve according to the current electric quantity delta data.
Above-mentioned, the update of electricity operation reserve, calculation basis is current electric quantity delta data, i.e., is become according to current electric quantity
Change data, determines whether electricity operation reserve needs to be updated.For example, the amplitude of the corresponding variation of current electric quantity delta data
It is smaller, then without carrying out the update for electricity operation reserve, conversely, then needing to be updated, run using different electricity
Strategy.
Further, the step S60 " is determined according to the current electric quantity delta data and run to the electricity
Strategy update " include:
Step S61, judges whether the current electric quantity delta data of the battery is greater than default electric quantity change data；
Step S62, if so, carrying out the update to the electricity operation reserve.
In addition, judging whether the current electric quantity delta data of the battery is greater than default electric quantity change in the step S61
After data, further includes:
If it is not, then determining that the battery of the mobile terminal does not enter the more new state of electricity operation reserve, do not need to update,
And do not need to carry out further " according to the charging use habit data, by the charging time point prediction model trained in advance,
Obtain the corresponding point of charging time next time of the mobile terminal " the step of, the step S50 can be returned to, and re-start
Judgement.
It is above-mentioned, the current electric quantity delta data of battery is monitored, if by statistic record, battery power down reaches certain journey
Degree then can determine that the power saving of triggering that the state of battery at this time reaches refers to when being as greater than default electric quantity change data
Target updates, and as triggering is primary mobile terminal electricity operation reserve is generated and is updated.
Above-mentioned, the operation of electricity operation reserve more new state, as electricity reaches can further progress adjustment electricity operation plan
Standard slightly.Specifically, its standard can reach certain percentage or SOC values for electricity power down, that is, it can reach the state,
Into after the state, can further progress by charging time point prediction model trained in advance to charging use habit data pair
The prediction for the point of charging time next time answered.
It is above-mentioned, it is preferred that default electric quantity change data provided in the present embodiment can be 2%.
In the present embodiment, by the way that the update precondition for electricity operation reserve is arranged, i.e., the movement is first judged first
Whether terminal reaches electricity operation reserve more new state, and further data acquisition and electricity can be carried out if reaching the state
The update of operation reserve is avoided since electricity does not stop to change, and system frequent progress is caused to calculate, and is generated and is updated electricity operation
The problem of strategy, avoids additional power consumption.
Embodiment 3:
Referring to Fig. 5, third embodiment of the invention provides a kind of mobile terminal electricity operation reserve generation method, based on above-mentioned
First embodiment shown in Fig. 2, the step S30, " according to the mobile terminal current electric quantity, acquisition system estimates electricity consumption
Use up time point " include:
Corresponding remaining capacity is calculated according to the mobile terminal current electric quantity in step S31；
Step S32, obtain the mobile terminal applies power consumption statistical data；
Step S33 is calculated the system and estimates electricity according to the residual electric quantity with the application power consumption statistical data
Amount exhausts time point.
It is above-mentioned, remaining capacity, for the remaining electricity that can be used according to current electric quantity, being calculated.
It is above-mentioned, using power consumption statistical data, it is the statistical data of real-time update built-in in system, is practised including user
The used all APP's used uses duration, and accordingly in the mobile terminal institute's power consumption statistical data, by the data,
It would know that, user gets used to duration and corresponding power consumption using which APP, using the APP.The data can be system
It is built-in, or the calculating of method provided in the present embodiment is got, when use by obtaining all APP in real time from the background
Between, using duration, power consumption is calculated, in order to further calculate.
It is above-mentioned, according to remaining capacity, and it is based on obtained application power consumption statistical data, can further calculate and be
System estimates electricity and exhausts time point.Wherein, system estimates electricity and exhausts time point, as system-computed obtain in remaining capacity
On the basis of, according to user for the APP that is installed in terminal, estimating using duration under the use habit of user.
In the present embodiment, by the current electric quantity of mobile terminal, remaining capacity can be obtained, and get using power consumption statistical data,
And then according to power consumption statistical data and remaining capacity is applied, COMPREHENSIVE CALCULATING show that system estimates electricity and exhausts time point, so as to
The mobile terminal is estimated out based under the use habit of user it is expected that using the time.
Embodiment 3:
Referring to Fig. 6, third embodiment of the invention provides a kind of mobile terminal electricity operation reserve generation method, based on above-mentioned
First embodiment shown in Fig. 2, before the step S20, the charging time acquisition methods of point prediction model include:
Step S70 runs deep learning frame；
Above-mentioned, charging time point prediction model is based on depth learning technology, by run corresponding deep learning frame into
Row training obtains.
Step S80, obtain the mobile terminal history consumption habit data and corresponding expected annotation results number
According to；
It can make the training of different depth learning method according to the data collected, in the present embodiment, using is prison
Superintend and direct formula study, the answer (namely so-called Ground truth) for the data that supervised study needs to be ready in advance, knot
Aforementioned collected data are closed, it can be with N data as input, when Ground truth is the charging of next record data
Between, carry out training pattern through the combination of such data.
It is above-mentioned, history consumption habit data, what the use point of the as mobile terminal within the scope of a period of time of user was accustomed to
Statistical data (the as cut-and-dried target data that need to be trained).Wherein it is possible to include the position of user's charging, charging
The fast charge trickle charge charger that time, charging use etc. project.
It is expected that annotation results data, are as obtained by operation early period or user are known or calculated by other means
The corresponding intended result of one obtained (as corresponding Ground truth), the result and the history point habit inputted
Data are corresponding.
The history consumption habit data are input to the deep learning frame and calculated by step S90, and based on damage
Consumption function is compared the expected annotation results data with calculated result, corrects the calculating ginseng of the deep learning frame
Number, obtains the charging time point prediction model.
It is above-mentioned, based on the supervised learning algorithm in neural network algorithm, using history consumption habit data as input, and
And using its corresponding expected annotation results data as Ground truth；History consumption habit data are input to deep learning
In frame, after being calculated based on the loss function, obtained calculated result is compared with expected annotation results data, thus
The calculating parameter of obtained deep learning frame is further corrected, so that it is pre- to obtain charging time point under by constantly correcting
Survey model.In the present embodiment, by utilizing supervised learning algorithm, by preparing history consumption habit data in advance, and
Ground truth, and the amendment obtained calculated result of history consumption habit data is gone by Ground truth, thus instead
Multiple corrected parameter improves the accuracy of model calculating, computational efficiency to obtain more accurate model.
Embodiment 4:
Referring to Fig. 7, fourth embodiment of the invention provides a kind of mobile terminal electricity operation reserve generation method, based on above-mentioned
First embodiment shown in Fig. 2,
The electricity operation reserve includes the electricity operation reserve of common grade, the electricity operation reserve of danger level, police
Accuse the electricity operation reserve and the power-off other electricity operation reserve of critical level of rank；
The step S40 " estimates electricity according to the point of charging time next time and the system and exhausts time point generation
Electricity operation reserve, in order to controlled according to the electricity operation reserve mobile terminal battery saving mode operation." packet
It includes:
Step S41, the calculating charging time point next time subtract the system and estimate the difference that electricity exhausts time point,
As current battery saving mode evaluation of estimate；
Step S42, if the current battery saving mode evaluation of estimate less than 0, generates the electricity operation plan of the general mode
Slightly；
Step S43 generates the electricity operation of the danger level if the current battery saving mode evaluation of estimate is not less than 2
Strategy；
Step S44 generates the electricity operation of the warning level if the current battery saving mode evaluation of estimate is not less than 4
Strategy；
Step S45 generates the other electricity of the power-off critical level if the current battery saving mode evaluation of estimate is not less than 6
Operation reserve.
Above-mentioned, pass through formula: charging time point subtracts the system and estimates the difference that electricity exhausts time point next time, can
The time difference for embodying the power-off time of prediction and the charging time of prediction would know that user at this time by the time difference
Which kind of need to take for the control strategy of electricity, it is safe to reach filling next time thus in the case where guaranteeing user experience
The time point of electricity.
In the present embodiment, provided electricity operation reserve can be divided into four modes, that is, be respectively according to for current
The difference of situation be set as the electricity operation reserve of common grade, the electricity operation reserve of danger level, warning level electricity fortune
Row strategy and the power-off other electricity operation reserve of critical level.Wherein, the power saving of electricity operation reserve, alternatively referred to as different stage refers to
Mark.In the present embodiment, corresponding it can be divided into from light to heavy corresponding Normal/Dangerous/ according to situation
Tetra- kinds of grades of Warning/Critical, power saving pointer provide reference of all types of electricity-saving mechanism of system as control intensity,
It is heavier to indicate to suggest that power saving dynamics is stronger (closer to Critical), it is exactly to be saved as index adjusts for electricity-saving mechanism
The control (being shown in Table 1) of motor.
1 electricity operation reserve table of table
| Difference | Power saving index | Electricity operation reserve |
| >=6 hours | output:Critical level | Power off the other electricity operation reserve of critical level |
| >=4 hours | output:Warning level | The electricity operation reserve of warning level |
| >=2 hours | output:Dangerous level | The electricity operation reserve of danger level |
| < 0 | output:Normal level | The electricity operation reserve of general mode |
Four-stage among the above, Normal are not needed then to apply or start electricity-saving mechanism and (are avoided dropping as general mode
Frequently, brightness of display screen etc. is adjusted), in addition three stages are just as different degrees of set uses the operation reserve (power saver of associative mode
System).
In the present embodiment, by being set as different electricity operation reserves, for calculate difference evaluate, not
It, can be in operating process of the user to mobile terminal, according to user using different electricity-saving mechanisms under same electricity operation reserve
Actual conditions it is corresponding using different power saving strategies, avoid the unalterable not province centered on user's use habit
The setting of power mode, influences for caused by the real experiences of user.
In addition, in order to better illustrate the application, the present embodiment also provides a kind of mobile terminal electricity operation reserve generation
Method is based on above-mentioned first embodiment shown in Fig. 2, the mobile terminal electricity operation reserve generation method, further includes:
Obtain the current accumulative charging times of mobile terminal；
The acquisition of charging time point prediction model needs to carry out deep learning training and obtains.User is for mobile terminal
Operating habit, it is possible to change for a period of time, so charging time point prediction model, needs the reality according to user
Application habit carries out the adjustment of adaptability, this just needs re -training, in order to which the closer practical application with user is accustomed to.
In the present embodiment, setting one carries out the opportunity point of deep learning, i.e., statistics currently adds up charging times, when the system
Metering number reaches certain preset times, that is, can determine that can currently carry out the training for model again.
It is above-mentioned, in mobile terminal during using and charge, charging every time is recorded, current accumulative charging time can be obtained
Number.That is, every charging once just updates the charging times of a bulk registration.
If the currently accumulative charging times reach default training triggering times, described will currently add up charging times into
Row zeros data, and obtain the charging use habit number that every time charges of the mobile terminal before this secondary zeros data
According to.
Using the charging use habit data, AI deep learning is carried out, the charging time point prediction model is established.
It is above-mentioned, it presets and trains triggering times, preferably can be 100 times in the present embodiment.For example, whenever user uses
Mobile terminal carries out charging and adds up 100 times, that is, can determine that the training for model that can be carried out under current state Yi Ci again.
Also, the current accumulative charging times counted are zeroed out, to carry out the statistics again to the data.
When being trained, all charging use habit data before this zeros data need to be obtained, carry out for
The re -training of model.
The present embodiment is determining that currently accumulative charging times reach one by the statistics for currently adding up charging times
After default training triggering times, i.e., current accumulative charging times are reset, need to be obtained all before this zeros data
Charge use habit data, learns to the training of model, according to the application habit of user and charges habit not with implementation model
With change and the purpose that adjusts accordingly, thus make charging time point prediction model more close to, close to the reality of user
Application habit and charging habit, make the mobile device electricity operation reserve ultimately generated be more suitable for the practical habit of the user
It is used, personalization and conveniently is provided for the use of mobile terminal for user.
It is above-mentioned, it is described " to utilize the charging use habit data, carry out AI deep learning, establish the charging time point
Prediction model ", comprising:
The charging use habit data are sent to server-side by the mobile terminal；
After the server-side receives the charging use habit data, it is deep that AI is carried out to the charging use habit data
Degree study, obtains the charging time point prediction model.
In the present embodiment, deep learning can be the training of long-distance cloud end, remotely i.e. cloud is equipped with a server-side, at this
After trained condition for study is reached on ground, i.e., will reset twice between time point locally new data (charging every time obtained
Charging use habit data) be uploaded to server-side and be updated (cloud), being utilized by server-side includes that original charging makes
It is trained movement collectively as training data with habit data and the charging use habit data of update, after the completion of training, then
It is downloaded to local mobile terminal and carries out further inference and analysis.The present embodiment carries out the training for model by cloud
Movement receives the charging use habit data between zeros data time point adjacent twice transmitted by mobile terminal, thus
It greatly reduces local progress and added burden is caused for system for the operand of the training of model, so as to avoid due to volume
The outer training action for carrying out model leads to the influence of operation fluency and system resource for system.
In addition, the present embodiment the training action of model can also be carried out in local mobile terminal, it is local
Training can input the data of collection, then training generates depanning for by local running of mobile terminal deep learning frame
Type carrys out the charging time of inference (inference) out next time with regard to permeable model later.Depth is carried out by local mobile terminal
The operation of learning framework, and it is trained generation or more new model, it avoids a large amount of between terminal and cloud due to data
Security risk brought by interaction.
Before the step " the charging use habit data are sent to server-side ", further includes:
The mobile terminal sends long-range deep learning request to the server-side；Wherein, the server-side is according to
Deep learning request, generates public key and private key in correspondence with each other, and the public key is sent to the mobile terminal；
After the mobile terminal receives the public key, using the public key to the charging use habit data encryption,
In order to execute " the charging use habit data are sent to server-side by the mobile terminal "；
The step is " after the server-side receives the charging use habit data, to the charging use habit number
According to AI deep learning is carried out, the charging time point prediction model is obtained ", comprising:
After the server-side receives the charging use habit data, using private key corresponding with the public key, to adding
The charging use habit data deciphering after close；Also, AI depth is carried out to the charging use habit data after decryption
Study, obtains the charging time point prediction model.
The charging use habit data include charging sart point in time, charging time length, charge power supply type, charging
The remaining battery power of mobile device and charging place when beginning.
Above-mentioned, the charging use habit data encryption of each mobile terminal belongs to individual privacy for using for user
Data, wherein include personal application habit and charging habit etc. data, it is personal during being that cloud interacts
The safety of private data is extremely important, so needing in the data exchange process between mobile terminal and cloud server
Data are encrypted.
In addition, in order to avoid the leakage of personal data, improving safety in the data exchange process of terminal and server-side
Property, anonymization processing can be carried out for the charging use habit data that mobile terminal is issued in the present embodiment, to be hidden
The charging use habit data of nameization processing.It should be noted that the charging use habit data of anonymization processing can not let out
It is counter after dew to find data source or corresponding user, further increase the safety in data exchange process.
In the present embodiment, by utilizing rivest, shamir, adelman, the encryption of data interaction between progress cloud and local, from
And substantially increase safety of the data in interactive process.
It is above-mentioned, it should be noted that rivest, shamir, adelman, principle are to need two keys: public-key cryptography
(publickey) and private cipher key (privatekey).Public-key cryptography and private cipher key are a pair, if with public-key cryptography logarithm
According to being encrypted, could only be decrypted with corresponding private cipher key；If encrypted with private cipher key pair data, only have
It could be decrypted with corresponding public-key cryptography.Because encryption and decryption use two different keys, this algorithm
Make rivest, shamir, adelman.Rivest, shamir, adelman realizes that the basic process of confidential information exchange is: Party A generates a pair of secret keys
And one therein is disclosed as Public key to other sides；The Party B for obtaining the Public key is believed using the key pair secret
Breath is then forwarded to Party A after being encrypted；Party A solves private key to encrypted information with oneself the another of preservation again
It is close.
In the present embodiment, what it is by the transmission of rivest, shamir, adelman opposite direction cloud includes the privacies numbers such as personal use habit
According to charging use habit data prevent letting out for individual privacy to substantially increase safety of the data in interactive process
Dew prevents leaking for the data such as personal application habit and charging use habit, provides safety guarantee for user.
Further, the step " utilizes the charging use habit data, carries out AI deep learning, fill described in foundation
Electric time point prediction model ", comprising:
After the acquisition for mobile terminal to the charging use habit data, the charging use habit data are saved as
Training data source；
Detect the operating status of presently described mobile terminal；
If the operating status of the mobile terminal is to put out screen charged state, using the training data source in local progress
AI deep learning establishes the charging time point prediction model.
Above-mentioned, for the use habit data that charge training, can occupy certain system resource, cause to make user
It is impacted with experience.In the present embodiment, the AI training for the use habit data that charge is carried out by local mobile terminal, is
It avoids occupying influence of a large amount of system resources for user experience, the current operating status of mobile terminal is examined
It surveys.Wherein, operating status may include judging whether the mobile terminal is in charged state and screen is in screen extinguishing mode.Such as
Fruit is in the state, then can determine that the AI training that can be carried out under current state for the use habit data that charge, so as to avoid
Due to carrying out influence caused by operation of the system resource of AI data training occupancy for user.
In addition, if determining that operating status is that can also carry out AI deep learning, also, in operation shape when putting out screen state
When state changes into screen and lights and program operation occur, stop carrying out AI deep learning, improves the efficiency of AI deep learning, and
The problem of avoiding the poor user experience caused by system resource occupies when operating for user to mobile terminal.
In addition, the present invention also provides a kind of mobile terminal electricity operation reserve generating means with reference to Fig. 8, comprising: obtain mould
Block 10, computing module 20 and generation module 30；
Module 10 is obtained, for obtaining the charging use habit data of mobile terminal；
Computing module 20, for passing through charging time point prediction trained in advance according to the charging use habit data
Model obtains the corresponding point of charging time next time of the mobile terminal；
The acquisition module 10 is also used to according to the mobile terminal current electric quantity, when acquisition system is estimated electricity and exhausted
Between point；
Generation module 30 exhausts time point life for estimating electricity according to charging time point and the system next time
At electricity operation reserve, in order to controlled according to the electricity operation reserve mobile terminal battery saving mode operation.
In addition, the present invention also provides a kind of user terminal, the user terminal includes memory and processor, described to deposit
Reservoir generates program for memory mobile terminal electricity operation reserve, and the processor runs the mobile terminal electricity and runs plan
Program is slightly generated so that mobile terminal execution mobile terminal electricity operation reserve generation method as described above.
In addition, being stored on the computer readable storage medium the present invention also provides a kind of computer readable storage medium
There is mobile terminal electricity operation reserve to generate program, when the mobile terminal electricity operation reserve generation program is executed by processor
Realize mobile terminal electricity operation reserve generation method as described above.
The serial number of the above embodiments of the invention is only for description, does not represent the advantages or disadvantages of the embodiments.
Through the above description of the embodiments, those skilled in the art can be understood that above-described embodiment side
Method can be realized by means of software and necessary general hardware platform, naturally it is also possible to by hardware, but in many cases
The former is more preferably embodiment.Based on this understanding, technical solution of the present invention substantially in other words does the prior art
The part contributed out can be embodied in the form of software products, which is stored in one as described above
In storage medium (such as ROM/RAM, magnetic disk, CD), including some instructions are used so that terminal device (it can be mobile phone,
Computer, server or network equipment etc.) execute method described in each embodiment of the present invention.The above is only of the invention
Preferred embodiment is not intended to limit the scope of the invention, all using made by description of the invention and accompanying drawing content
Equivalent structure or equivalent flow shift is applied directly or indirectly in other relevant technical fields, and is similarly included in this hair
In bright scope of patent protection.
Claims (9)
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