WO2017206107A1 - 一种充电的方法及终端 - Google Patents

一种充电的方法及终端 Download PDF

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Publication number
WO2017206107A1
WO2017206107A1 PCT/CN2016/084334 CN2016084334W WO2017206107A1 WO 2017206107 A1 WO2017206107 A1 WO 2017206107A1 CN 2016084334 W CN2016084334 W CN 2016084334W WO 2017206107 A1 WO2017206107 A1 WO 2017206107A1
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WO
WIPO (PCT)
Prior art keywords
user
terminal
charging
charging mode
unit
Prior art date
Application number
PCT/CN2016/084334
Other languages
English (en)
French (fr)
Inventor
霍大伟
丁杰
王平华
李慧
马金博
Original Assignee
华为技术有限公司
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by 华为技术有限公司 filed Critical 华为技术有限公司
Priority to EP16903492.3A priority Critical patent/EP3454448A4/en
Priority to CN202110294496.8A priority patent/CN113131565A/zh
Priority to PCT/CN2016/084334 priority patent/WO2017206107A1/zh
Priority to CN201680086263.6A priority patent/CN109690900B/zh
Publication of WO2017206107A1 publication Critical patent/WO2017206107A1/zh
Priority to US16/205,418 priority patent/US11545703B2/en
Priority to US18/069,709 priority patent/US11862773B2/en

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Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/00032—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries characterised by data exchange
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/44—Methods for charging or discharging
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
    • H02J7/0048—Detection of remaining charge capacity or state of charge [SOC]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/007—Regulation of charging or discharging current or voltage
    • H02J7/0071—Regulation of charging or discharging current or voltage with a programmable schedule
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/007—Regulation of charging or discharging current or voltage
    • H02J7/00712—Regulation of charging or discharging current or voltage the cycle being controlled or terminated in response to electric parameters
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/007—Regulation of charging or discharging current or voltage
    • H02J7/007188—Regulation of charging or discharging current or voltage the charge cycle being controlled or terminated in response to non-electric parameters
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/02—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries for charging batteries from ac mains by converters
    • H02J7/04—Regulation of charging current or voltage
    • 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
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/10—Energy storage using batteries

Definitions

  • the present invention relates to charging technology, and more particularly to a charging method and terminal.
  • the functions of the terminal become more and more powerful, and the user can work and entertain through the terminal, so that the terminal has become an indispensable part of people's daily life.
  • the terminal's battery life is limited, requiring the user to continuously charge the terminal.
  • fast charging is a trend that can be convenient for users to use the terminal, but charging the terminal for a long time and fast will shorten the battery life.
  • the embodiment of the invention provides a charging method and a terminal, which can reduce the loss of the battery while completing the charging of the terminal and facilitating the use of the user.
  • a first aspect of the present invention discloses a charging method, the method comprising: when detecting that a terminal establishes a connection with a charger, acquiring a current time point and a user usage habit model; and using the current time point with the user Customizing the model to match the charging intent of the user; determining a charging mode corresponding to the charging intent; charging the terminal in accordance with the determined charging mode.
  • the historical data of the user using the terminal in a preset time period is acquired; and the terminal is used by the user by using a preset machine learning algorithm.
  • Historical data for training is acquired; and the user is trained to use the historical data of the terminal by using a preset machine learning algorithm, including: analyzing the historical data by using the preset machine learning algorithm, and acquiring an analysis result; The analysis results are corrected, and the corrected analysis results are used as the user usage habit model.
  • the historical data includes, but is not limited to, a time period in which the user uses the terminal, a location in which the user uses the terminal, an activity category of the user corresponding to the time and location, and the time Paragraph An environment state corresponding to the location, a peak consumption period, and an application whose usage frequency is greater than a third preset threshold;
  • the location where the terminal is located in order to obtain the charging intention of the user more accurately, optionally, the location where the terminal is located; according to the current time point, the location where the terminal is located, and the user usage habit model Determining the charging intent of the user.
  • the current time point and the location where the terminal is located are input as parameters to the user usage habit model to determine the charging intention of the user.
  • information about the environment in which the terminal is located may be acquired; according to the current time point, the location where the terminal is located, and the location of the terminal.
  • the information of the environment and the user usage habit model determine the charging intent of the user.
  • the current time point, the location where the terminal is located, and the environment where the terminal is located are input as parameters to the user usage habit model to determine the charging intention of the user.
  • the user in order to ensure that the charging mode meets the requirements of the user, the user can be confirmed.
  • the charging mode confirmation request is used to indicate whether the user performs charging according to the charging mode;
  • the terminal is charged according to the charging mode.
  • the model can be modified according to the information corrected by the user.
  • receiving the instruction of the user input to change the charging mode prompting the user to select a new charging mode;
  • the charging mode is determined by considering the current time period, the current terminal power, the current terminal location, the terminal running the application, and the like;
  • the user usage habit model fails to be obtained, determining whether the current time point is located in a preset sleep time period; and when the current time point is in the preset sleep time period, according to the current time
  • the point and the preset sleep period calculate a length of time available for charging; the charging mode is determined based on the length of time available for charging.
  • the charging mode is a fast charging mode.
  • the user usage habit model fails to be obtained, acquiring the remaining power of the terminal and the current location; when the remaining power is less than the first preset threshold, and the current location does not belong to the preset location set It is determined that the charging mode is a fast charging mode.
  • the user usage habit model fails to be obtained, acquiring the remaining power of the terminal, and detecting whether the terminal has a positive The running application; when the remaining power is less than the second preset threshold and there is a running application, determining that the charging mode is a fast charging mode.
  • historical data may be stored in a database, stored in a storage medium, or stored in the cloud.
  • the terminal can locate the current position through a GPS system (Globle Positioning System), obtain the peripheral temperature through the temperature sensor, obtain the peripheral humidity by the humidity sensor, obtain the current altitude by the altitude sensor, and obtain the current light intensity by the light sensor, and determine the operation light trace.
  • the user uses the duration of the terminal, the frequency of using the application, etc., and then stores the acquired information in a log.
  • a second aspect of the present invention discloses a terminal, where the terminal includes: an acquiring unit, configured to acquire a current time point and a user usage habit model when detecting that the terminal establishes a connection with the charger; and a matching unit, configured to: The current time point is matched with the user usage habit model to obtain the charging intention of the user; the determining unit is configured to determine a charging mode corresponding to the charging intention; and the charging unit is configured to perform the charging mode according to the determined charging mode The terminal is charged.
  • the terminal further includes a training unit; the acquiring unit is further configured to acquire historical data of the user using the terminal in a preset time period; and the training unit is configured to utilize at least one pre- And the training unit is configured to use the preset machine learning algorithm to perform the historical data by using the preset machine learning algorithm.
  • the analysis is further configured to correct the analysis result and set the corrected analysis result to the user usage habit model.
  • the historical data includes, but is not limited to, a time period in which the user uses the terminal, a location in which the user uses the terminal, an activity category of the user corresponding to the time and location, and the time The environment state corresponding to the segment and the location, the peak consumption period, and the application whose usage frequency is greater than the third preset threshold;
  • the acquiring unit is further configured to acquire a location where the terminal is located, and the matching unit is configured to use, according to the current time point, The location at which the terminal is located, and the user usage habit model determine the charging intent of the user.
  • the acquiring unit is further configured to acquire information about an environment in which the terminal is located
  • the matching unit is configured to: according to the current time point, the The location at which the terminal is located, the information of the environment in which the terminal is located, and the usage habit model of the user determine the charging intent of the user.
  • the terminal further includes a prompting unit; the prompting unit is configured to send a charging mode confirmation request to the user, the charging mode confirmation The request is for requesting whether the user performs charging according to the charging mode; and the charging unit is configured to: when receiving the instruction that the user confirms charging according to the charging mode, perform the terminal according to the charging mode Charging.
  • the user modified data may be utilized to correct the user usage habit model.
  • the terminal further includes a receiving unit and a correction unit; the prompting unit is further configured to prompt the user to select a new charging mode when receiving an instruction of the user input to change the charging mode; a receiving unit, configured to receive a charging mode selected by the user; the charging unit is configured to perform charging according to the charging mode selected by the user; and the correcting unit is configured to use the custom mode according to the charging mode selected by the user Make corrections.
  • the charging mode may be determined according to the time period, the terminal power, the status of the terminal running application, and the location where the terminal is located;
  • the terminal further includes a determining unit and a calculating unit, where the determining unit is configured to determine, when the user usage habit model fails, whether the current time point is located in a preset sleep time period; a unit, configured to calculate a length of time available for charging according to the current time point and a preset sleep time period when the current time point is located in the preset sleep time period; the determining unit is further configured to perform The length of time available for charging determines the charging mode.
  • the obtaining unit is further configured to: when the user usage habit model fails to be obtained, take the remaining power of the terminal and the current location; and the determining unit is configured to: when the remaining power is less than the first When the preset threshold and the current location do not belong to the preset location set, it is determined that the charging mode is a fast charging mode.
  • the terminal further includes a detecting unit, where the acquiring unit is configured to acquire a remaining power of the terminal when the user usage habit model fails, and the detecting unit is configured to acquire the user When the usage habit model fails, detecting whether the terminal has a running application; the determining unit, configured to determine the charging mode when the remaining power is less than a second preset threshold and there is a running application It is a fast charging mode.
  • a third aspect of the present invention discloses a terminal including a CPU (Central Processing Unit), a memory, a display, and a bus. Among them, the CPU is used to run the save The code stored in the memory performs the method described in the first aspect.
  • a CPU Central Processing Unit
  • the memory stores the code stored in the memory performs the method described in the first aspect.
  • a fourth aspect of the invention discloses a storage medium storing code for performing the method of the first aspect.
  • the technical solution of the present invention provides a charging method and a terminal.
  • a self-learning of historical data can be performed by using a machine learning algorithm to establish a user habit model, through the current time point and the user.
  • the habit model can be used to determine the user's current charging intent, and then to determine the charging mode based on the charging intent.
  • the technical solution can effectively identify the user's charging demand, realize charging on demand, and improve the user experience while avoiding the problem of reduced battery life caused by frequent fast charging.
  • FIG. 1 is a schematic structural diagram of a terminal according to an embodiment of the present invention.
  • FIG. 3 is a schematic structural diagram of a terminal according to another embodiment of the present invention.
  • FIG. 4 is a physical structural diagram of a terminal according to an embodiment of the present invention.
  • FIG. 5 is a schematic structural diagram of a terminal according to another embodiment of the present invention.
  • FIG. 6 is a flow chart of a charging method in accordance with an embodiment of the present invention.
  • the terminal has developed rapidly and the hardware technology has been rapidly improved. At the same time, the battery technology of the terminal has not changed much in recent years. The power problem has always restricted the use of terminals (for example, smart phones). However, the emergence of fast charging technology provides a new way to solve the charging problem of the terminal.
  • the fast charging technology can effectively solve the problem of long charging time, it is too frequent and fast.
  • Speed charging can cause irreversible damage to the battery and reduce its service life.
  • the damage of the battery basically comes from two aspects: on the one hand, when the battery is charged and discharged, the cathode and anode of the battery will shrink and expand with the release and absorption of ions, and the rapid charging will destroy the battery for a long time. The chemical on the battery leads to a shortened battery life.
  • the thermal effect of the current is exacerbated due to the relatively large current, which causes the battery to generate high temperatures, and the high temperature also causes a sudden drop in capacity and permanent damage to the battery cells.
  • the invention provides a charging method and a terminal, which can self-learn historical data by using a machine learning algorithm to establish a user habit model, and can judge the current charging intention of the user through the current time point and the user using the habit model, and then according to the The charging intention determines the charging mode; the technical solution can effectively identify the charging demand of the user, realize charging on demand, and improve the user experience while avoiding the problem of reduced battery life caused by frequent fast charging.
  • FIG. 1 is a terminal 10 including an obtaining unit 110 , a matching unit 120 , a determining unit 130 , a charging unit 140 , and a training unit 150 .
  • the terminal 10 may be an electronic device such as a mobile phone, a tablet computer or a smart wearable device.
  • the terminal 10 trains the historical data in the preset time period according to the machine learning algorithm to obtain the user usage habit model. It can be understood that the more historical data, the more favorable the user is to training using the habit model.
  • the historical data in the terminal 10 includes, but is not limited to, a time period in which the user uses the terminal 10, a location where the user uses the terminal 10, an activity category of the user corresponding to the time and location, and the time period and location.
  • user X continues to play the mobile phone every night from 9:00 to 11:00, and the location is at home, and the activity category of the user corresponding to the time and place is entertainment, and the environment corresponding to the time period and location.
  • the state is quiet, the light is dark, and the power consumption peak period is 9:00-11:00.
  • an application running for more than 20 minutes per day can be considered as an application whose frequency is greater than a third preset threshold, that is, the third preset threshold is It is used for more than 20 minutes.
  • the third preset threshold is not limited here.
  • the third preset threshold may be set by the terminal by default, or may be user setting.
  • an application that runs more than 3 times a day can be considered to be an application that uses a frequency greater than a third predetermined threshold. That is to say, the third preset threshold is that the number of runs is three.
  • the common application frequency that is greater than the third preset threshold may be a certain game application, or a social application or a news application.
  • the terminal 10 stores a process in which the user uses the terminal 10 as a log through various types of sensors such as a temperature sensor, a gyroscope, a camera, an acceleration sensor, and a positioning sensor.
  • sensors such as a temperature sensor, a gyroscope, a camera, an acceleration sensor, and a positioning sensor.
  • the log can chronologically record which applications the user is using today, and how long each application has been used; then, based on the log, an application that uses a frequency greater than a third predetermined threshold can be determined.
  • the log can record the user charging several times a day, how long each time, and whether the user will use the terminal every time charging, then the user can determine the fixed charging time period of the user and the terminal according to the log. Power consumption speed, etc.;
  • the log can also record the location of the terminal 10 today, and the duration of the terminal 10 staying at each location. Then, according to the log, the user's main activity place can be determined. For example, an area in which the terminal 10 stays for more than 8 hours (the area may be a certain building, a certain community, or a company, etc.) defaults to a home or an office;
  • the log can also record the time period during which the terminal 10 consumes power faster;
  • the terminal 10 can obtain a lot of data information through various sensors, for example, obtaining a peripheral temperature by a temperature sensor, obtaining a peripheral humidity by a humidity sensor, acquiring an current altitude by an altitude sensor, positioning a current position by a GPS system, and acquiring a current light intensity by a light sensor. Wait. The terminal can then store the acquired information in a log.
  • the machine learning algorithm includes but is not limited to a classification algorithm, a clustering algorithm, a regression algorithm, an enhanced learning algorithm, a migration learning algorithm, and a deep learning algorithm.
  • the obtaining unit 10 is configured to acquire historical data stored by the terminal 10; for example, it may be historical data acquired from the foregoing log.
  • the training unit 150 is configured to train the historical data acquired by the obtaining unit 10 to obtain a user usage habit model according to at least one machine learning algorithm.
  • the training unit 150 may analyze the historical data by using at least one machine learning algorithm, and then correct the analysis result, and set the modified analysis result to the user usage habit model.
  • the terminal further includes a detecting unit 160;
  • the detecting unit 160 is configured to detect whether the terminal 10 establishes a connection with the charger;
  • the detecting unit 160 When the detecting unit 160 detects that the terminal 10 establishes a connection with the charger, the detecting unit 160 sends an indication to the acquiring unit 110;
  • the obtaining unit 110 is configured to acquire a current time point and a user usage habit model according to the indication; wherein the acquiring unit 110 may acquire a current time point by using a clock of the terminal;
  • the matching unit 120 is configured to match the current time point with the user usage habit model to obtain the charging intention of the user when the acquiring unit 110 succeeds in acquiring the user usage habit model;
  • the charging intention includes but is not limited to 2 hours full, 8 hours full, extreme speed charging, and no charging time.
  • User B is a commuter, 5 days a week, 2 days a week, rest, life schedule, and life activities. Based on the time and space history of the user using the smart terminal in a period of one month, the user's intelligence is acquired.
  • the terminal usage habits are as follows:
  • the obtaining unit 110 is further configured to acquire a location where the terminal 10 is located;
  • the position of the terminal can be located by GPS.
  • Common location package This includes, but is not limited to, home, work location, bar, library, restaurant, etc.
  • the terminal 10 can be connected to each sensor through the acquiring unit 110, and can be directly connected to each sensor through a bus.
  • the obtaining unit 110 sends a matching instruction to the matching unit 120.
  • the matching unit 120 is configured to: according to the matching instruction sent by the obtaining unit 110, the current time point and the terminal 10 are located. The location matches the user usage habit model to obtain the user's charging intent; it can be understood that inputting the current time point and the location of the terminal into the user usage habit model can more accurately confirm the user's charging. intention.
  • the obtaining unit 110 is further configured to obtain information about an environment in which the terminal 10 is located. It is understood that the acquiring unit 110 may obtain the temperature of the environment in which the terminal 10 is located through a temperature sensor, and may also obtain the temperature through the humidity sensor.
  • the humidity of the environment of the terminal 10 can also obtain the altitude of the location of the terminal 10 by using the altitude sensor; the light intensity of the environment where the terminal 10 is located can also be obtained by the light sensor; and the sound information of the environment where the terminal 10 is located can also be obtained by the microphone.
  • the motion state of the user can also be obtained by the acceleration sensor; the state of the terminal 10 can also be obtained by the level meter.
  • the activity performed by the user can be determined by combining the humidity, temperature, altitude, light, sound, and the state of motion of the user in the environment in which the terminal 10 is located.
  • the user may be working in the office
  • the user may be in the car.
  • the user may be in a sleep state if the surroundings are quiet, there is no light, the user is at rest, and the time is between 1 am and 5 am, then the user may be in a sleep state.
  • the user may climb the mountain outdoors and the like.
  • the obtaining unit 110 When the obtaining unit 110 acquires the user usage habit model successfully, the obtaining unit 110 sends a matching instruction to the matching unit 120;
  • the matching unit 120 is configured to match the current time point, the location where the terminal 10 is located, the environment where the terminal 10 is located, and the user usage habit model according to the matching instruction sent by the obtaining unit 110 to obtain the charging intention of the user. ;
  • the matching unit 120 is further configured to send a charging intention to the determining unit 130.
  • the determining unit 130 is configured to determine a charging mode according to the charging intention.
  • the correspondence between the charging intent and the charging mode may be stored in the terminal in advance.
  • the charging scheme may also be determined according to the charging intention. If there are multiple charging schemes, one of the plurality of charging schemes that meets the user's usage habits is selected. It should be noted that the charging scheme may include a charging mode, and may also include multiple charging modes (for example, fast charging and slow charging combined charging). Among them, there are many ways to fast charge, and here is not limited to the fast charge mode (such as open loop fast charge, closed loop fast charge, etc.).
  • the fast charge can be performed during this time period, and the fast charge and slow charge can be combined, and the charge can be directly charged.
  • time permits, you can charge as much as possible for a long time and fast charge for a short time.
  • the charging is as fast as possible.
  • the terminal 10 further includes a prompting unit 170 and a receiving unit 180.
  • the prompting unit 170 is configured to prompt the user to perform charging according to the charging mode after determining the charging mode. Specifically, the prompting unit 170 is configured to send a charging mode confirmation request to the user, where The charging mode confirmation request is used to indicate whether the user is charging according to the charging mode.
  • the charging unit 140 charges the terminal according to the charging mode.
  • the reminder unit 170 to perform reminders, including but not limited to text reminders, voice reminders, and the like.
  • FIG. 2 is a specific reminding manner.
  • a prompt interface pops up in Figure 2, which shows the charging mode, whether it is in the machine learning state (self-learning state) and the optional charging mode.
  • the prompting unit 170 is further configured to prompt the user to select a new charging mode when receiving an instruction of the user input to change the charging mode;
  • the receiving unit 180 is further configured to receive a charging mode selected by the user, and the charging unit 140 is configured to perform charging in the charging mode selected by the user.
  • the terminal 10 further includes a correction unit 190.
  • the correcting unit 190 is configured to correct the user usage habit model according to a charging mode selected by the user.
  • determining a time period to which the current time belongs determining a charging mode corresponding to the time period in the user usage habit model, and modifying the time period in a corresponding charging mode in the user usage habit model
  • the charging mode selected for the user is 3 am, which belongs to the time range from 1 am to 6 am.
  • the slow charge is corresponding from 1 am to 6 am, and the user changes the charging mode to fast.
  • the correction unit 190 modifies the charging mode corresponding to the user's use of the custom model from 1 am to 6 am to fast charge.
  • the user is prompted to confirm whether the charging mode meets the user's request, and then the user's usage habit model is corrected according to the information fed back by the user. It should be pointed out that the user can set whether or not the reminder option appears. If the user thinks that the previous charging correction or the charging mode corresponding to the charging intention can meet the charging requirement of the user after the previous several corrections, the user is considered to use the custom model. For accurate models, the user can set the option "No longer reminder to confirm charging mode" on the settings page.
  • the charging unit 140 performs charging according to the charging mode.
  • the battery in the terminal 10 includes, but is not limited to, a lithium battery, a lithium ion battery, an air battery, a lead acid battery, and a super capacitor.
  • the charging unit 140 is further configured to acquire battery state parameters such as battery voltage, current, internal resistance, battery capacity, battery temperature, and internal battery pressure, so as to adjust the charging mode according to the battery state parameter.
  • battery state parameters such as battery voltage, current, internal resistance, battery capacity, battery temperature, and internal battery pressure
  • the charging mode when the battery temperature is above the temperature threshold (40 ° C) or the battery voltage is above the voltage threshold (4.0 V), the charging mode cannot or does not recommend switching to the fast charging mode; when the battery capacity is below the capacity threshold (20%) or battery When the voltage is below the voltage threshold (3.3V), the charging mode preferably selects or suggests switching to the fast charging mode.
  • the threshold of the battery state parameter is related to the battery type.
  • the threshold may be a factory default setting or a user-defined setting; the setting of the battery state parameter threshold may further confirm the security performance of the battery charging mode switching.
  • the present invention provides a charging method and a terminal, which can self-learn historical data by using a machine learning algorithm to establish a user habit model, and can determine the current user's current time through the current time point and the user using the habit model.
  • the charging intention is further determined according to the charging intention; the technical solution can effectively identify the charging demand of the user, realize charging on demand, and improve the user experience while avoiding the problem of reduced battery life caused by frequent fast charging.
  • the terminal 10 also including a judging unit 210 and a calculating unit 220;
  • the detecting unit 160 is configured to detect whether the terminal 10 establishes a connection with the charger;
  • the acquiring unit 110 is configured to acquire a current time point and a user usage habit model when the detecting unit 210 detects that the terminal 10 establishes a connection with the charger;
  • the determining unit 210 is configured to determine, when the acquiring unit 220 fails to acquire the usage habit model, whether the current time point is within a preset sleep time period;
  • the calculating unit 220 is configured to calculate a length of time available for charging according to the current time point and the preset sleep time period when the current time point is within the preset sleep time period;
  • the current time point is 1 am
  • the preset sleep time period is from 0:00 am to 6:00 am; that is, the current time point is within the preset sleep time period.
  • the length of time available for charging is 5 hours.
  • the determining unit 130 is further configured to determine a charging mode according to the length of time.
  • the charging requirement faced by the terminal 10 is: Charge 50% in 5 hours.
  • the slow charging can charge 10% per hour, and the fast charging can charge 40% per hour, then the first solution can be used for five consecutive hours of slow charging; One hour, slow charging for two hours; Option three can be charged directly for one and a half hours.
  • the charging mode is the slow charging mode.
  • the charging unit 140 performs charging according to the charging mode.
  • the terminal 10 since the terminal 10 has not trained the user to use the habit model, or the storage medium has a problem such that the user habit model fails to be acquired, then the current power needs to be considered at this time. And whether there is currently a charging condition. details as follows:
  • the obtaining unit 110 is further configured to acquire, when the user usage habit model fails, acquire the remaining power of the terminal and the current location;
  • the determining unit 130 is configured to determine that the charging mode is a fast charging mode when the remaining power is less than the first preset threshold and the current location does not belong to the preset location set.
  • the first preset threshold may be the default of the terminal or may be set by the user.
  • the preset position set can be understood as a place that can provide long-term charging, such as a home, an office place.
  • the first preset threshold is 20%.
  • the preset location collection is home and office. Then, when the terminal 10 is connected to the charger, it is determined that the location where the terminal 10 is located is a shopping mall and the power of the terminal 10 is 15%, then it is determined that the charging mode is fast charging.
  • the reminder is whether the user performs fast charging, and if the user has sufficient time, the user can select slow charging. If you are in a hurry, you can also choose to charge quickly.
  • the terminal 10 since the terminal 10 has not trained the user to use the habit model, or the storage medium has a problem such that the user habit model fails to be acquired, then the current power needs to be considered at this time. And the current usage of the terminal 10. details as follows:
  • the obtaining unit 110 is further configured to acquire, when the user usage habit model fails, acquire the remaining power of the terminal;
  • the detecting unit 160 is configured to detect, when the user usage habit model fails, whether the terminal has a running application
  • the determining unit 130 is configured to determine that the charging mode is a fast charging mode when the remaining power is less than a second preset threshold and there is an running application.
  • the second preset threshold may be the default of the terminal or may be set by the user.
  • the second preset threshold is 30%. Then, when the terminal 10 is connected to the charger and detects that there is a running application in the terminal 10, then the user has the need to use the terminal 10, then the terminal should be quickly charged to meet the user's use requirements.
  • the terminal 10 since the terminal 10 has not trained the user to use the habit model, or the storage medium has a problem such that the user habit model fails to be acquired, then the current power needs to be considered at this time.
  • the terminal When the current power is less than the second preset threshold, the terminal is in a state of running multiple applications, and the charging condition is met, the terminal 10 Charge in the fast charge mode.
  • the user habits obtained by analyzing the relationship between the user's usual entertainment, sports, sleep, and the like and the use of the smart terminal are understood.
  • the user is When using the terminal entertainment, there is no power suddenly. At this time, it can be judged that the user's charging intention needs to be quickly charged, and there is no need to be full to continue the entertainment activities; the user likes to run outdoors and records the sports data.
  • the smart terminal has no power, and the user can be judged.
  • the charging speed is determined according to the duration of the user's lunch break.
  • the terminal provided in the embodiment of the present invention can establish a user usage habit model through self-learning (using machine learning algorithms and historical data), and then the current time point, the location of the terminal, the environment in which the terminal is located, and the usage habit of the user.
  • the model matches to determine the user's charging intention, and then determines the charging mode according to the charging intention; the technical solution can effectively identify the user's fast charging and charging demand, realize fast charging on demand, improve user experience, and avoid unnecessary frequent fast charging.
  • the problem of reduced battery life is brought about.
  • a terminal 30 is provided.
  • the terminal 30 includes a CPU 310 (Central Processing Unit), a memory 320, a display 330, and a bus 340.
  • CPU 310 Central Processing Unit
  • the CPU 310 is configured to run the code stored in the memory 320 to start the charging process.
  • the charging process includes:
  • the terminal is charged in accordance with the determined charging mode.
  • the execution process further includes:
  • the user is trained to use the historical data of the terminal to obtain the user usage habit model by using a preset machine learning algorithm.
  • the machine learning algorithm and the historical data may be pre-stored in the memory 320.
  • the execution process further includes:
  • the data includes, but is not limited to, a time period in which the user uses the terminal, a location in which the user uses the terminal, and an activity category of the user corresponding to the time and location, corresponding to the time period and location
  • the training by using the preset machine learning algorithm, the historical data of the user using the terminal, includes:
  • the historical data is analyzed by using the preset machine learning algorithm
  • the analysis result is corrected, and the corrected analysis result is set as the user usage habit model.
  • the terminal 30 further includes a temperature sensor 410, a humidity sensor 420, a light sensor 430, a positioning sensor 440, a camera 450, a gyroscope 460, an acceleration sensor 470, and the like.
  • the terminal 30 acquires data of the user through the above-described sensor and stores the data in the memory 320.
  • the stored data can be considered as historical data.
  • the performing process before the matching the current time point with the user usage habit model to obtain the charging intention of the user, the performing process further includes:
  • the matching the current time point with the user usage habit model to obtain the charging intention of the user includes:
  • the charging intent of the user is determined according to the current time point, the location where the terminal is located, and the user usage habit model.
  • the performing process before the matching the current time point with the user usage habit model to obtain the charging intention of the user, the performing process further includes:
  • the matching the current time point with the user usage habit model to obtain the charging intention of the user includes:
  • Determining the charging intention of the user according to the current time point, the location where the terminal is located, the environment of the terminal, and the user usage habit model.
  • the performing process further includes:
  • the execution process further includes:
  • the execution process further includes:
  • the charging mode is determined according to the length of time.
  • the execution process further includes:
  • the charging mode is a fast charging mode.
  • the execution process further includes:
  • the charging mode is a fast charging mode.
  • the present invention provides a method of charging, the method comprising:
  • the execution body of the method is a terminal, and the terminal may be an electronic device such as a mobile phone, a tablet computer or a smart wearable device.
  • the terminal uses a preset machine learning algorithm to train the user to use the historical data of the terminal to obtain the user usage habit model.
  • the machine learning algorithm includes but is not limited to a classification algorithm and a clustering algorithm. Method, regression algorithm, enhanced learning algorithm, migration learning algorithm, deep learning algorithm.
  • the terminal acquires historical data of the terminal from a database, a storage medium, or a cloud, and then analyzes the historical data by using the preset machine learning algorithm and obtains an analysis result; and then summarizes, converges, and Corrected to obtain the user usage habit model.
  • the historical data includes, but is not limited to, a time period in which the user uses the terminal, a location in which the user uses the terminal (eg, may be located by using a GPS), and the corresponding to the time period and location.
  • the user's activity category (such as sleeping, working, entertainment, outdoor sports, etc.), the environmental status (such as temperature, humidity, light intensity, altitude) corresponding to the time period and location, the peak consumption period and the frequency of use are greater than the first Three preset threshold applications (for example, an application that runs at least 20 minutes per day or an application that runs at least three times a day).
  • the environmental status such as temperature, humidity, light intensity, altitude
  • the charging intention of the user may also consider acquiring the location where the terminal is located; and then inputting the current time point and the location where the terminal is located as a parameter to the user usage habit
  • the model is used to determine the charging intent of the user.
  • the charging intention of the user may also consider acquiring the location where the terminal is located; and then inputting the current time point and the location where the terminal is located as a parameter to the user usage habit
  • the model is used to determine the charging intent of the user.
  • information about the environment in which the terminal is located may also be considered; and then information about the current time point and the environment in which the terminal is located is input as a parameter.
  • the user uses a custom model to determine the user's charging intent.
  • the information about the environment in which the terminal is located and the location where the terminal is located may also be considered; then the current time point, the location where the terminal is located, and the location Information about the environment in which the terminal is located is input as a parameter to the user usage habit model to determine the charging intent of the user.
  • Common modes include fast charge, slow charge, standard charge or combination of speed (such as fast charge in slow charge or slow charge first in fast charge, etc.).
  • the determined charging mode may also be displayed on the screen for confirmation by the user; when the user is confirmed to press the button When the charging is performed in the charging mode, the terminal is charged according to the charging mode.
  • the user usage habit model fails to be obtained, determining whether the current time point is within a preset sleep time period; and when the current time point is within a preset sleep time period, according to the current time
  • the point and the preset sleep period calculate a length of time required to charge the terminal such that the power of the terminal reaches a preset value; and determine a charging mode according to the length of time.
  • the charging mode is a fast charging mode.
  • the user usage habit model fails to be obtained, acquiring the remaining power of the terminal, and detecting whether the terminal has a running application; when the remaining power is less than a second preset threshold and the presence is When the application is running, it is determined that the charging mode is the fast charging mode.
  • the present invention provides a charging method for a terminal, which can self-learn historical data by using a machine learning algorithm to establish a user habit model, and can judge the current user through the current time point and the user using the habit model.
  • the charging intention is further determined according to the charging intention; the technical solution can effectively identify the charging demand of the user, realize charging on demand, and improve the user experience while avoiding the problem of reduced battery life caused by frequent fast charging.
  • the disclosed systems, devices, and methods may be implemented in other manners.
  • the device embodiments described above are merely illustrative.
  • the division of the unit is only a logical function division.
  • there may be another division manner for example, multiple units or components may be combined or Can be integrated into another system, or some features can be ignored or not executed.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, and may be in an electrical, mechanical or other form.
  • the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of the embodiment.
  • each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
  • the functions may be stored in a computer readable storage medium if implemented in the form of a software functional unit and sold or used as a standalone product.
  • the technical solution of the present invention which is essential or contributes to the prior art, or a part of the technical solution, may be embodied in the form of a software product, which is stored in a storage medium, including
  • the instructions are used to cause a computer device (which may be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention.
  • the foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and the like. .

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Abstract

一种充电的方法及终端（10、30）。所述方法包括：该终端（10、30）能够利用机器学习算法对历史数据进行自学习以建立用户习惯模型，将当前时间点和该用户使用习惯模型匹配可以确定用户当前的充电意图，进而根据充电意图确定充电模式。可有效识别出用户的充电需求，按需实现充电，在提升用户体验的同时又避免频繁的快充带来的电池寿命降低的问题。

Description

一种充电的方法及终端 技术领域
本发明涉及充电技术，尤其涉一种充电的方法及终端。
背景技术
随着科技的发展，终端的功能变得越来越强大，用户可以通过终端进行办公、娱乐，以至于终端已经成为人们日常生活中不可或缺的一部分。然而，终端的续航能力是有限的，需要用户不断的给终端充电。
目前，快速充电是一种趋势，可以方便用户使用终端，但是长期快速的对终端进行充电，会缩短电池使用年限。
但是，由于应用程序的来源众多，且控制器无法获知应用程序的来源是否可靠，这样容易导致非安全的应用程序对控制器的攻击，并造成对网络的恶意的破坏，从而可能会带来网络安全威胁。
发明内容
本发明实施例提供了一种充电的方法和终端，能够在完成对终端充电且方便用户使用的同时，还可以降低电池的损耗。
本发明第一方面公开了一种充电的方法，所述方法包括：当检测到终端与充电器建立连接时，获取当前时间点以及用户使用习惯模型；将所述当前时间点与所述用户使用习惯模型匹配以获取所述用户的充电意图；确定与所述充电意图对应的充电模式；按照所述确定的充电模式对所述终端进行充电。
结合第一方面，需要指出的是，所述获取用户使用习惯模型之前，获取预设时间段内所述用户使用所述终端的历史数据；利用预设机器学习算法对所述用户使用所述终端的历史数据进行训练。其中，具体的，利用预设机器学习算法对所述用户使用所述终端的历史数据进行训练，包括：利用所述预设机器学习算法对所述历史数据进行分析并获取分析结果；对所述分析结果进行修正，并将修正过的分析结果作为所述用户使用习惯模型。其中，所述历史数据包括但不限于所述用户使用所述终端的时间段、所述用户使用所述终端的地点、与所述时间和地点对应的所述用户的活动类别，与所述时间段 和地点对应的环境状态、耗电高峰时间段以及使用频率大于第三预设阈值的应用程序；
结合第一方面，为了更准确的获取用户的充电意图，可选的，获取所述终端所处的位置；根据所述当前时间点、所述终端所处的位置，以及所述用户使用习惯模型确定所述用户的充电意图。可以理解的是，将所述当前时间点和所述终端所处的位置作为参数输入到所述用户使用习惯模型以确定所述用户的充电意图。进一步，在获取当前时间点、终端所处位置的基础上，还可以获取所述终端所处的环境的信息；根据所述当前时间点、所述终端所处的位置、所述终端所处的环境的信息以及所述用户使用习惯模型确定所述用户的充电意图。具体的，将所述当前时间点、所述终端所处的位置以及所述终端所处环境的信息作为参数输入到所述用户使用习惯模型以确定所述用户的充电意图。
结合第一方面，为了能够保证充电模式符合用户的要求，因此可以让用户进行确认。可选的，所述根据所述充电意图确定充电模式之后，向所述用户发送充电模式确认请求，所述充电模式确认请求用于请示用户是否按照所述充电模式进行充电；；当接收到所述用户确认按照所述充电模式进行充电的指令时，根据所述充电模式对所述终端进行充电。进一步，还可以根据用户修正的信息，对模型进行修改。可选的，当接收到所述用户输入的更改充电模式的指令时，提示所述用户选择新的充电模式；
接收用户选择的充电模式，按照所述用户选择的充电模式进行充电，并且根据用户选择的充电模式对所述用户使用习惯模式进行修正。
结合第一方面，需要指出的是，如果获取用户使用习惯模型失败，就要考虑当前时间段、当前终端的电量、当前终端所处的位置、终端运行应用程序的情况等情况来确定充电模式；可选的，当获取所述用户使用习惯模型失败时，判断所述当前时间点是否位于预设睡眠时间段；当所述当前时间点位于所述预设睡眠时间段时，根据所述当前时间点和预设睡眠时间段计算可用于充电的时间长度；根据所述可用于充电的时间长度确定充电模式。可选的，当获取所述用户使用习惯模型失败时，获取所述终端的剩余电量以及当前的位置；当所述剩余电量小于第一预设阈值以及所述当前的位置不属于预设位置集合时，确定所述充电模式是快速充电模式。可选的，当获取所述用户使用习惯模型失败时，获取所述终端的剩余电量，并检测所述终端是否存在正 在运行的应用程序；当所述剩余电量小于第二预设阈值且存在正在运行的应用程序时，确定所述充电模式是快速充电模式。
结合第一方面，需要指出的是，历史数据可以存储在数据库中，也可以存储在存储介质中，还可以存储在云端。其中，终端可以通过GPS***(Globle Positioning System,全球定位***)定位当前位置，通过温度传感器获取周边温度，湿度传感器获取周边湿度，海拔传感器获取当前海拔，光线传感器获取当前光线强度，通过操作痕迹确定用户使用终端的时长、使用应用程序的频率等等，然后将获取的信息存储到日志中。
本发明第二方面公开了一种终端，所述终端包括：获取单元，用于当检测到终端与充电器建立连接时，获取当前时间点以及用户使用习惯模型；匹配单元，用于将所述当前时间点与所述用户使用习惯模型匹配以获取所述用户的充电意图；确定单元，用于确定与所述充电意图对应的充电模式；充电单元，用于按照所述确定的充电模式对所述终端进行充电。
结合第二方面，所述终端还包括训练单元；所述获取单元，还用于获取预设时间段内所述用户使用所述终端的历史数据；所述训练单元，用于利用至少一种预设机器学习算法对所述用户使用所述终端的历史数据进行训练以获取所述用户使用习惯模型；具体的，所述训练单元，用于利用所述预设机器学习算法对所述历史数据进行分析；还用于对所述分析结果进行修正，并将修正过的分析结果设置为所述用户使用习惯模型。其中，所述历史数据包括但不限于所述用户使用所述终端的时间段、所述用户使用所述终端的地点、与所述时间和地点对应的所述用户的活动类别，与所述时间段和地点对应的环境状态、耗电高峰时间段以及使用频率大于第三预设阈值的应用程序；
结合第二方面，为了更加准确的获取用户的充电意图；可选的，所述获取单元，还用于获取所述终端所处的位置；所述匹配单元，用于根据所述当前时间点、所述终端所处的位置，以及所述用户使用习惯模型确定所述用户的充电意图。
结合第二方面，为了更加准确的获取用户的充电意图；所述获取单元，还用于获取所述终端所处的环境的信息；所述匹配单元，用于根据所述当前时间点、所述终端所处的位置、所述终端所处的环境的信息以及所述用户使用习惯模型确定所述用户的充电意图。
结合第二方面，为了确保确定的充电意图复合用户的要求，可选的，所述终端还包括提示单元；所述提示单元，用于向所述用户发送充电模式确认请求，所述充电模式确认请求用于请示用户是否按照所述充电模式进行充电；；所述充电单元，用于当接收到所述用户确认按照所述充电模式进行充电的指令时，根据所述充电模式对所述终端进行充电。
进一步，为了确保用户使用习惯模型的准确性，可以利用用户修改的数据去修正所述用户使用习惯模型。可选的，所述终端还包括接收单元和修正单元；所述提示单元，还用于当接收到所述用户输入的更改充电模式的指令时，提示所述用户选择新的充电模式；所述接收单元，用于接收用户选择的充电模式；所述充电单元，用于按照所述用户选择的充电模式进行充电；所述修正单元，用于根据用户选择的充电模式对所述用户使用习惯模式进行修正。
结合第一方面需要指出的，当获取单元获取用户使用习惯模型失败时，还可以结合时间段、终端电量、终端运行应用程序的状况以及终端所处的位置确定充电模式；
可选的，所述终端还包括判断单元和计算单元；所述判断单元，用于当获取所述用户使用习惯模型失败时，判断所述当前时间点是否位于预设睡眠时间段；所述计算单元，用于当所述当前时间点位于所述预设睡眠时间段时，根据所述当前时间点和预设睡眠时间段计算可用于充电的时间长度；所述确定单元，还用于根据所述可用于充电的时间长度确定充电模式。
可选的，所述获取单元，还用于当获取所述用户使用习惯模型失败时，取所述终端的剩余电量以及当前的位置；所述确定单元，用于当所述剩余电量小于第一预设阈值以及所述当前的位置不属于预设位置集合时，确定所述充电模式是快速充电模式。
可选的，所述终端还包括检测单元；所述获取单元，用于当获取所述用户使用习惯模型失败时，获取所述终端的剩余电量；所述检测单元，用于当获取所述用户使用习惯模型失败时，检测所述终端是否存在正在运行的应用程序；所述确定单元，用于当所述剩余电量小于第二预设阈值且存在正在运行的应用程序时，确定所述充电模式是快速充电模式。
本发明第三方面公开了一种终端，终端包括CPU(Central Processing Unit，中央处理单元)、存储器、显示器以及总线。其中，CPU用于运行存 储在存储器中的代码以执行第一方面所述的方法。
本发明第四方面公开了一种存储介质，所述存储介质存储了用于执行第一方面所述的方法的代码。
从上可知，本发明技术方案提供了一种充电方法和终端，通过使用本发明提供的充电方法，能够利用机器学习算法对历史数据进行自学习以建立用户习惯模型，通过当前时间点和该用户使用习惯模型可以判断用户当前的充电意图，进而根据充电意图确定充电模式。该技术方案可有效识别出用户的充电需求，按需实现充电，在提升用户体验的同时又避免频繁的快充带来的电池寿命降低的问题。
附图说明
为了更清楚地说明本发明实施例的技术方案，下面将对本发明实施例中所需要使用的附图作简单地介绍，显而易见地，下面所描述的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是本发明实施例提供的终端结构示意图；
图2是本发明实施例提供的一种用户提示界面；
图3是本发明另一实施例提供的终端结构示意图；
图4是本发明实施例提供的终端物理结构图；
图5是本发明另一实施例提供的终端物理结构图；
图6是根据本发明实施实例的充电方法的流程图。
具体实施方式
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明的一部分实施例，而不是全部实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动的前提下所获得的所有其他实施例，都应属于本发明保护的范围。
终端迅猛发展，硬件技术快速提升的同时，终端的电池技术最近几年却没有多大变化，电量问题一直制约着终端(例如：智能手机)的使用。但是，快速充电技术的出现，为解决终端的充电问题提供了一个新途径。
虽然快速充电技术可以有效解决充电时间长的问题，但是过于频繁的快 速充电会给电池带来不可逆的损害，降低其使用寿命。从原理上来看，电池的损害基本上来源于两方面：一方面是在电池充放电的时候，电池的阴极与阳极会随着离子的释放和吸收而缩小和膨胀，长时间快速充电会破坏电池上的化学物质，导致电池寿命缩短。另一方面在快速充电时，由于电流的比较大，电流的热效应会加剧，导致电池会产生高温，高温也会让容量骤减和电芯永久性损坏的现象。
普通充电状态下(一般10W以下的认为是普通充电，例如5V1A或5V1.5A)，由于充电电流很小，给电池造成的损害很小，但是快速充电状态下，由于充电电流时普通充电电流的数倍，过大的电流充电时电池中的化学反应强度递增，对电池电极材料、电极结构的损害也加倍，从而导致电池的使用寿命缩短。
本发明提供了一种充电方法及终端，该终端能够利用机器学习算法对历史数据进行自学习以建立用户习惯模型，通过当前时间点和该用户使用习惯模型可以判断用户当前的充电意图，进而根据充电意图确定充电模式；该技术方案可有效识别出用户的充电需求，按需实现充电，在提升用户体验的同时又避免频繁的快充带来的电池寿命降低的问题。
如图1所示，图1为一种终端10，该终端10包括获取单元110、匹配单元120、确定单元130、充电单元140以及训练单元150。
其中，终端10可以是手机、平板电脑或智能穿戴式设备等电子设备。
其中，可以理解的是，终端10根据机器学习算法对预设时间段内的历史数据进行训练，以获得用户使用习惯模型。可以理解的，越多的历史数据越有利于用户使用习惯模型的训练。
具体的，终端10中的历史数据包括但不限于用户使用终端10的时间段、用户使用终端10的地点、与所述时间和地点对应的所述用户的活动类别，与所述时间段和地点对应的环境状态、耗电高峰时间段以及使用频率大于第三预设阈值的应用程序。
举例来说，用户X每天晚上9：00-11:00持续玩手机、地点在家里、与所述时间和地点对应的所述用户的活动类别是娱乐，与所述时间段和地点对应的环境状态是安静、光线暗，耗电高峰时间段9：00-11：00。
其中，需要指出的是，例如每天运行时长超过20分钟的应用程序可以认为是使用频率大于第三预设阈值的应用程序，也就是说，第三预设阈值就 是使用时间大于20分钟。在此不对第三预设阈值做限制。第三预设阈值可以是终端默认设置的，也可以是用户设置。
再例如，每天运行次数大于3次的应用程序就可以视为使用频率大于第三预设阈值的应用程序。也就是说，第三预设阈值就是运行次数是3次。
其中，常见的使用频率大于第三预设阈值的应用程序可以是某款游戏应用程序，也可以是某款社交应用程序还可以是某款新闻类应用程序。
其中，获取历史数据的方法有很多，可以直接从终端的存储介质或者数据库中获取，还可以从与该终端连接的云数据中心获取。
例如，终端10通过各类传感器(例如温度传感器、陀螺仪、摄像头、加速度传感器以及定位传感器等)将用户使用终端10的过程存储为日志。
举例来说，该日志可以按照时间顺序记录用户今天都使用哪些应用程序，每个应用程序使用了多久；那么根据该日志就可以确定使用频率大于第三预设阈值的应用程序。
举例来说，该日志可以记录用户每天充电几次，每次充多久，以及每次充电的时候用户是否会使用该终端，那么就可以根据该日志就可以确定用户的固定充电时间段以及终端的电量消耗速度等；
举例来说，该日志还可以记录该终端10今天处的位置，以及该终端10在每个位置的停留的时长，那么，根据该日志就可以确定用户的主要活动场所。比如，将终端10停留的时长超过8个小时的区域(该区域可以是某个大厦，某个小区，或某个公司等)默认为家里或办公室；
举例来说，该日志还可以记录终端10耗电较快的时间段；
综上所述，终端10可以通过各种传感器获取很多数据信息，例如通过温度传感器获取周边温度，湿度传感器获取周边湿度，海拔传感器获取当前海拔，GPS***定位当前位置，光线传感器获取当前光线强度等等。然后终端就可以将获取的信息存储到日志中。
其中，所述机器学习算法包括但不限于分类算法、聚类算法、回归算法、增强学习算法、迁移学习算法、深度学习算法。
具体的，获取单元10，用于获取终端10存储的历史数据；例如，可以是是从上述日志中获取的历史数据。
训练单元150，用于根据至少一种机器学习算法，对获取单元10获取的历史数据进行训练以获取用户使用习惯模型。
其中，具体的，训练单元150可以利用至少一种机器学习算法对所述历史数据进行分析，然后对分析结果修正，并将修正过的分析结果设置为所述用户使用习惯模型。
在本发明的一个实施例中，终端要还包括检测单元160；
检测单元160，用于检测终端10与充电器是否建立连接；
当检测单元160检测到终端10与充电器建立连接时，检测单元160向获取单元110发送指示；
获取单元110，用于根据所述指示获取当前时间点以及用户使用习惯模型；其中，获取单元110可以通过终端的时钟获取当前的时间点；
匹配单元120，用于当获取单元110获取所述用户使用习惯模型成功时，将所述当前时间点与所述用户使用习惯模型匹配以获取所述用户的充电意图；
其中，需要指出的是，充电意图包括但不限于2小时充满即可、8小时充满即可、极速充电、不在乎充电时间等。
举例说明：用户B为上班族，一周5天上班2天周末休息，生活作息时间和生活活动规律，基于该用户在时间周期为一个月内的使用智能终端时空历史使用数据，获取的用户的智能终端使用习惯如下表：
Figure PCTCN2016084334-appb-000001
其中，可选的，获取单元110，还用于获取终端10所处的位置；
其中，可以理解的是，可以通过GPS定位终端的位置。常见的位置包 括但不限于家里、工作地点、酒吧、图书馆、饭店等位置。其中，终端10可以通过获取单元110与各个传感器连接，可以是直接通过总线与各个传感器连接。
当获取单元110获取所述用户使用习惯模型成功时，获取单元110向匹配单元120发送匹配指令；匹配单元120，用于根据获取单元110发送的匹配指令将所述当前时间点、终端10所处的位置与所述用户使用习惯模型匹配以获取所述用户的充电意图；可以理解的是，将当前时间点以及终端的位置输入到所述用户使用习惯模型中，可以更精确的确认用户的充电意图。
其中，可选的，获取单元110，还用于获取终端10所处的环境的信息；可以理解的是，获取单元110可以通过温度传感器获取终端10所处环境的温度，还可以通过湿度传感器获取终端10所述环境的湿度，还可以通过海拔传感器获取终端10所述位置的海拔；还可以通过光线传感器获取终端10所处环境的光线强度；还可以通过话筒获取终端10所处环境的声音信息；还可以通过加速度传感器获取用户的运动状态；还可以通过水平仪获取终端10所处的状态。通过结合终端10所处环境的湿度、温度、海拔、光线、声音、用户的运动状态可以确定用户所进行的活动。
举例来说，周围光线明亮、安静、用户没有处于运动状态，那么用户就有可能在办公室办公；
又例如，周围有噪声、用户处于快速运动状态、周围湿度大，那么用户就可能在车里。
再例如，周围安静、没有光线、用户处于静止状态、时间处于凌晨1点至凌晨5点，那么用户就有可能处于睡眠状态。
再例如，当海拔高、湿度大、温度低、用户处于运动状态，那么用户可能在户外爬山等等。
当获取单元110获取所述用户使用习惯模型成功时，获取单元110向匹配单元120发送匹配指令；
匹配单元120，用于根据获取单元110发送的匹配指令将所述当前时间点、终端10所处的位置、终端10所处的环境与所述用户使用习惯模型匹配以获取所述用户的充电意图；
可以理解的是，将当前时间点、终端的位置以及终端所处的环境的信息输入到所述用户使用习惯模型中，可以更精确的确认用户的充电意图。
其中，匹配单元120，还用于向确定单元130发送充电意图；
确定单元130用于根据所述充电意图确定充电模式。
其中，可以预先将充电意图与充电模式的对应关系存储在终端中。也可以根据充电意图确定充电方案，如果充电方案有多个的话，从多个中选择一个符合用户使用习惯的充电方案。需要指出的是，充电方案中可以包含一种充电模式，也可以包含多种充电模式(例如快充慢充结合充电)。其中，快充的方式有很多，在此不对快充模式做限定(例如开环快充、闭环快充等)。
例如，当用户认为凌晨1：00-凌晨7：00之间充好就可以，那么在这个时间段可以进行快充，也可以使快充慢充相结合，还可以直接慢充。其中，需要指出的是，如果时间允许的话，可以尽可能的长时间慢充、短时间快充。
再例如，根据时间段以及用户使用习惯模型确定当前时间段用户可能在玩游戏，那么就尽可能的快速充电。
其中，可选的，终端10还包括提示单元170和接收单元180。
其中，提示单元170，用于在确定充电模式之后，提示所述用户是否按照所述充电模式进行充电；其中，具体的，提示单元170，用于向所述用户发送充电模式确认请求，所述充电模式确认请求用于请示用户是否按照所述充电模式进行充电。
当接收单元180接收到所述用户确认按照所述充电模式进行充电的指令时，充电单元140根据所述充电模式对所述终端进行充电。需要指出的是，提醒单元170进行提醒的方式有很多，包括但不限于文字提醒、语音提醒等方式。具体的，如图2所示，图2为一种具体的提醒方式。图2中会弹出一个提示界面，该界面显示了充电模式，是否处于机器学习状态(自学习状态)以及可选的充电模式。
可选的，提示单元170，还用于当接收到所述用户输入的更改所述充电模式的指令时，提示所述用户选择新的充电模式；
接收单元180，还用于接收用户选择的充电模式；充电单元140，用于所述用户选择的充电模式进行充电。
进一步的，终端10还包括修正单元190。其中，修正单元190，用于根据用户选择的充电模式对所述用户使用习惯模型进行修正。
例如，确定当前时刻所属的时间段；确定该时间段在用户使用习惯模型中对应的充电模式，将该时间段在用户使用习惯模型中对应的充电模式修改 为用户选择的充电模式。具体的，当前时刻是凌晨3点，隶属于凌晨1点至凌晨6点这个时间段，在用户使用习惯模型中凌晨1点到凌晨6点对应的是慢充，用户将充电模式修改成了快充，那么修正单元190将用户使用习惯模型中的凌晨1点到凌晨6点对应的充电模式修改为快充。
可以理解的是，每次根据根据充电意图确定充电模式后，在提示用户确认该充电模式是否符合用户的要求，然后根据用户反馈的信息对用户使用习惯模型进行修正。需要指出的是，用户可以设置是否出现提醒这个选项，如果用户认为经过前面几次的修正，后续出现的充电意图或者充电意图对应的充电模式能够符合用户的充电要求，就认为该用户使用习惯模型为准确模型，那么用户就可以在设置页面设置“不再提醒确认充电模式”的选项。
其中，当确定单元130确定充电模式时，充电单元140根据所述充电模式进行充电。
其中，终端10中的电池包括但不限于锂电池、锂离子电池、空气电池、铅酸电池、超级电容器。
充电单元140，还用于获取电池电压、电流、内阻、电池容量、电池温度、电池内部压力等电池状态参数，以便根据电池状态参数调整充电模式。
例如，当电池温度高于温度阈值(40℃)或电池电压高于电压阈值(4.0V)，充电模式不能或不建议切换到快速充电模式；当电池容量低于容量阈值(20％)或电池电压低于电压阈值(3.3V)时，充电模式优选选择或建议选择切换到快速充电模式。
其中，可以理解的是，电池状态参数的阈值与电池类型相关。具体的，所述阈值可以是出厂默认设置，也可以用户自定义设置；通过电池状态参数阈值的设置，可以进一步确认电池充电模式切换的安全性能。
可以理解的是，本发明提供了一种充电方法及终端，该终端能够利用机器学习算法对历史数据进行自学习以建立用户习惯模型，通过当前时间点和该用户使用习惯模型可以判断用户当前的充电意图，进而根据充电意图确定充电模式；该技术方案可有效识别出用户的充电需求，按需实现充电，在提升用户体验的同时又避免频繁的快充带来的电池寿命降低的问题。
如图3所示，基于上述实施例，在本发明的另一个实施例中，终端10 还包括判断单元210和计算单元220；
检测单元160，用于检测终端10与充电器是否建立连接；
获取单元110，用于当检测单元210检测到终端10与充电器建立连接时，获取当前时间点以及用户使用习惯模型；
判断单元210，用于当获取单元220获取所述用户使用习惯模型失败时，判断所述当前时间点是否位于预设睡眠时间段内；
计算单元220，用于当所述当前时间点位于预设睡眠时间段内时，根据所述当前时间点和预设睡眠时间段计算可用于充电的时间长度；
举例来说，比如当前时间点是凌晨1点；预设睡眠时间段是凌晨0点至凌晨6点；那么也就是说当前时间点是位于预设睡眠时间段内的。进一步，还可计算出可用于充电的时间长度为5个小时。
确定单元130，还用于根据所述时间长度确定充电模式。
举例来说，如果当前终端的电量为40％，而电量的预设值是90％(可以是终端默认的，也可以是用户设置的)，那么也就是说，终端10面临的充电要求就是：要在5个小时内充电50％。
针对该要求，可以有多种充电方式，假如慢充每小时可以充10％，快充每小时可以充40％，那么方案一：可以进行连续五个小时的慢充；方案二：可以快充一小时，慢充俩小时；方案三可以直接快充一个半小时。但是处于尽可能满足用户的使用需求的角度和延长电池使用寿命的角度来说，由于睡眠时间段用户基本不用该终端10，那么就可以确定充电模式为慢充模式。
充电单元140根据所述充电模式进行充电。
如图1所示，在本发明的另一个实施例中，由于终端10还未训练好用户使用习惯模型，或者存储介质出现问题以至于获取用户习惯模型失败，那么此时就需要考虑当前的电量以及当前是否具备充电的条件。具体如下：
获取单元110，还用于当获取所述用户使用习惯模型失败时，获取所述终端的剩余电量以及当前的位置；
确定单元130，用于当所述剩余电量小于第一预设阈值以及所述当前的位置不属于预设位置集合时，确定所述充电模式是快速充电模式。
其中，需要指出的是，第一预设阈值可以是终端默认的，也可以是用户设置的。
其中，预设位置集合可以理解为能够提供长时间充电的地方，例如家里，办公室地方。
举例来说，比如第一预设阈值为20％。预设位置集合为家里和办公室。那么，当终端10连接上充电器时，确定终端10所在的位置是商场且终端10电量为15％，那么就确定充电模式为快速充电。
再例如，当终端10连接上充电器时，确定终端10所在的位置是家里且终端10电量为30％，那么就提醒是用户是否进行快速充电，用户如果时间充裕，就可以选择慢速充电，如果赶时间也可以选择快速充电。
如图1所示，在本发明的另一个实施例中，由于终端10还未训练好用户使用习惯模型，或者存储介质出现问题以至于获取用户习惯模型失败，那么此时就需要考虑当前的电量以及该终端10当前的使用情况。具体如下：
获取单元110，还用于当获取所述用户使用习惯模型失败时，获取所述终端的剩余电量；
检测单元160，用于当获取所述用户使用习惯模型失败时，检测所述终端是否存在正在运行的应用程序；
确定单元130，用于当所述剩余电量小于第二预设阈值且存在正在运行的应用程序时，确定所述充电模式是快速充电模式。
其中，需要指出的是，第二预设阈值可以是终端默认的，也可以是用户设置的。
举例来说，比如第二预设阈值为30％。那么，当终端10连接上充电器时，检测终端10中存在正在运行的应用程序，那么就说明用户有使用终端10的需求，那么就应该快速为终端充电，以满足用户的使用需求。
如图1所示，在本发明的另一个实施例中，由于终端10还未训练好用户使用习惯模型，或者存储介质出现问题以至于获取用户习惯模型失败，那么此时就需要考虑当前的电量、该终端10当前的使用情况以及该终端10当前所处的地方是否具备充电的条件，当当前电量小于第二预设阈值、终端处于运行多个应用程序的状态以及具备充电条件时，终端10按照快充模式进行充电。
在本发明的另一实施例中，可以理解的是，通过分析用户平时娱乐、运动、睡眠等活动与使用智能终端的关系以获取的用户习惯。比如说，用户正 在使用终端娱乐时突然没电了，此时可判断用户充电意图需要快速充电，无需充满可继续娱乐活动；用户爱好户外跑步并记录运动数据，此时智能终端没电了，即可判断用户需要快速充电；用户的睡眠习惯一般都比较稳定，如果是晚上睡眠即可判断用户不需使用智能终端，此时选择尽可能慢速充电；如果是午休即可判断用户需要补充智能终端电量，则可根据用户午休的时长来决定充电快慢。
本发明实施例中所提供的终端，能够通过自学习(利用机器学习算法和历史数据)建立用户使用习惯模型，进而将当前时间点、终端所处位置，终端所处环境与所述用户使用习惯模型匹配以确定用户充电意图，然后根据充电意图确定充电模式；该技术方案可有效识别出用户的快速充电充电需求，按需实现快速充电，提升用户体验，同时又避免不必要的频繁的快速充电带来的电池寿命降低的问题。
如图4所示，在本发明的另一实施例中提供了一种终端30，终端30包括CPU310(Central Processing Unit，中央处理单元)、存储器320、显示器330、总线340。
其中，CPU310，用于运行存储在存储器320中的代码以启动充电程序，具体的，充电的过程包括：
当检测到终端30与充电器建立连接时，获取当前时间点以及用户使用习惯模型；
将所述当前时间点与所述用户使用习惯模型匹配以获取所述用户的充电意图；
确定与所述充电意图对应的充电模式；
按照所述确定的充电模式对所述终端进行充电。
其中，需要指出的是，所述获取用户使用习惯模型之前，执行过程还包括：
利用预设机器学习算法对所述用户使用所述终端的历史数据进行训练以获取所述用户使用习惯模型。其中，需要指出的是，机器学习算法以及历史数据可以是预先存储在存储器320中。
其中，需要指出的是，所述利用预设机器学习算法对所述用户使用所述终端的历史数据进行训练之前，执行过程还包括：
获取预设时间段内所述用户使用所述终端的历史数据；其中，所述历史 数据包括但不限于所述用户使用所述终端的时间段、所述用户使用所述终端的地点、与所述时间和地点对应的所述用户的活动类别，与所述时间段和地点对应的环境状态、耗电高峰时间段以及使用频率大于第三预设阈值的应用程序；
所述利用预设机器学习算法对所述用户使用所述终端的历史数据进行训练，包括：
利用所述预设机器学习算法对所述历史数据进行分析；
对所述分析结果进行修正，并将所述修正过的分析结果设置为所述用户使用习惯模型。
其中，如图5所示，终端30还包括温度传感器410、湿度传感器420、光线传感器430、定位传感器440、摄像头450、陀螺仪460、加速度传感器470等。终端30通过上述传感器获取用户的数据，并将所述数据存储到存储器320中。该存储的数据可认为是历史数据。
可选的，所述将所述当前时间点与所述用户使用习惯模型匹配以获取所述用户的充电意图之前，所述执行过程还包括：
获取所述终端所处的位置；
所述将所述当前时间点与所述用户使用习惯模型匹配以获取所述用户的充电意图，包括：
根据所述当前时间点、所述终端所处的位置，以及所述用户使用习惯模型确定所述用户的充电意图。
可选的，所述将所述当前时间点与所述用户使用习惯模型匹配以获取所述用户的充电意图之前，所述执行过程还包括：
获取所述终端所处的环境的信息；
所述将所述当前时间点与所述用户使用习惯模型匹配以获取所述用户的充电意图，包括：
根据所述当前时间点、所述终端所处的位置、所述终端所处的环境的信息以及所述用户使用习惯模型确定所述用户的充电意图。
可选的，所述根据所述充电意图确定充电模式之后，所述执行过程还包括：
提示所述用户是否按照所述充电模式进行充电；
当接收到所述用户确认按照所述充电模式进行充电的指令时，根据所述 充电模式对所述终端进行充电。
可选的，执行过程还包括：
当接收到所述用户输入的更改所述充电模式的指令时，提示所述用户选择新的充电模式；
接收用户选择的充电模式，按照所述用户选择的充电模式进行充电，并且根据用户选择的充电模式对所述用户使用习惯模式进行修正。
可选的，执行过程还包括：
当获取所述用户使用习惯模型失败时，判断所述当前时间点是否位于预设睡眠时间段内；
当所述当前时间点位于预设睡眠时间段内时，根据所述当前时间点和预设睡眠时间段计算对所述终端充电以使得所述终端的电量达到预设值所需的时间长度；
根据所述时间长度确定充电模式。
可选的，执行过程还包括：
当获取所述用户使用习惯模型失败时，获取所述终端的剩余电量以及当前的位置；
当所述剩余电量小于第一预设阈值以及所述当前的位置不属于预设位置集合时，确定所述充电模式是快速充电模式。
可选的，执行过程还包括：
当获取所述用户使用习惯模型失败时，获取所述终端的剩余电量，并检测所述终端是否存在正在运行的应用程序；
当所述剩余电量小于第二预设阈值且存在正在运行的应用程序时，确定所述充电模式是快速充电模式。
如图6所示，本发明提供一种充电的方法，所述方法包括：
S501、当检测到终端与充电器建立连接时，获取当前时间点以及用户使用习惯模型；
其中，该方法的执行主体为终端，该终端可以是手机、平板电脑或智能穿戴式设备等电子设备。
其中，可以理解的是，在获取用户使用习惯模型之前，终端会利用预设机器学习算法对所述用户使用所述终端的历史数据进行训练以获取所述用户使用习惯模型。其中，所述机器学习算法包括但不限于分类算法、聚类算 法、回归算法、增强学习算法、迁移学习算法、深度学习算法。
具体的，终端从数据库、存储介质或云端获取该终端的历史数据，然后利用所述预设机器学习算法对所述历史数据进行分析并获取分析结果；接着对所述分析结果进行归纳、收敛以及修正，以获取所述用户使用习惯模型。其中，所述历史数据包括但不限于所述用户使用所述终端的时间段、所述用户使用所述终端的地点(例如，可以用GPS定位)、与所述时间段和地点对应的所述用户的活动类别(例如睡觉、工作、娱乐、户外运动等)，与所述时间段和地点对应的环境状态(例如温度、湿度、光线强度、海拔)、耗电高峰时间段以及使用频率大于第三预设阈值的应用程序(例如每天至少运行20分钟的应用程序或者每天至少运行三次的应用程序)。
S502、将所述当前时间点与所述用户使用习惯模型匹配以获取所述用户的充电意图；
可选的，为了更准确的理解用户的充电意图，还可以考虑获取所述终端所处的位置；然后将所述当前时间点和所述终端所处的位置作为参数输入到所述用户使用习惯模型中以确定所述用户的充电意图。
可选的，为了更准确的理解用户的充电意图，还可以考虑获取所述终端所处的位置；然后将所述当前时间点和所述终端所处的位置作为参数输入到所述用户使用习惯模型中以确定所述用户的充电意图。
可选的，为了更准确的理解用户的充电意图，还可以考虑获取所述终端所处的环境的信息；然后将所述当前时间点和所述终端所处的环境的信息作为参数输入到所述用户使用习惯模型中以确定所述用户的充电意图。
可选的，为了更准确的理解用户的充电意图，还可以考虑获取所述终端所处的环境的信息和所处的位置；然后将所述当前时间点、所处终端所处的位置和所述终端所处的环境的信息作为参数输入到所述用户使用习惯模型中以确定所述用户的充电意图。
S503、确定与所述充电意图对应的充电模式；
常见的模式包括快充、慢充、标准充电或快慢结合(比如先快充在慢充或先慢充在快充等)。
S504、按照所述确定的充电模式对所述终端进行充电。
其中需要指出的是，根据所述充电模式对所述终端进行充电之前，还可以将确定的充电模式显示在屏幕上以供用户确认；当接收到所述用户确认按 照所述充电模式进行充电的指令时，根据所述充电模式对所述终端进行充电。当接收到所述用户输入的更改所述充电模式的指令时，提示所述用户选择新的充电模式；接收用户选择的充电模式，按照所述用户选择的充电模式进行充电，并且根据用户选择的充电模式对所述用户使用习惯模式进行修正。
另外，需要指出的是，也存在获取用户使用习惯模型失败的可能，例如终端还没有训练好用户使用习惯模型，也可能是存储介质损坏，终端无法从介质中获取用户使用习惯模型。此时，还有几种判断充电模式的方法。
可选的，当获取所述用户使用习惯模型失败时，判断所述当前时间点是否位于预设睡眠时间段内；当所述当前时间点位于预设睡眠时间段内时，根据所述当前时间点和预设睡眠时间段计算对所述终端充电以使得所述终端的电量达到预设值所需的时间长度；根据所述时间长度确定充电模式。
可选的，当获取所述用户使用习惯模型失败时，获取所述终端的剩余电量以及当前的位置；当所述剩余电量小于第一预设阈值以及所述当前的位置不属于预设位置集合时，确定所述充电模式是快速充电模式。
可选的，当获取所述用户使用习惯模型失败时，获取所述终端的剩余电量，并检测所述终端是否存在正在运行的应用程序；当所述剩余电量小于第二预设阈值且存在正在运行的应用程序时，确定所述充电模式是快速充电模式。
从上可知，本发明提供了一种针对终端的充电方法，该终端能够利用机器学习算法对历史数据进行自学习以建立用户习惯模型，通过当前时间点和该用户使用习惯模型可以判断用户当前的充电意图，进而根据充电意图确定充电模式；该技术方案可有效识别出用户的充电需求，按需实现充电，在提升用户体验的同时又避免频繁的快充带来的电池寿命降低的问题。
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本发明的范围。
所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的***、装置和单元的具体工作过程，可以参考前述方法实施例中的对应 过程，在此不再赘述。
在本申请所提供的几个实施例中，应该理解到，所揭露的***、装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个***，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本发明各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。
所述功能如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本发明的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本发明各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。
以上所述，仅为本发明的具体实施方式，但本发明的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本发明揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本发明的保护范围之内。因此，本发明的保护范围应以所述权利要求的保护范围为准。

Claims (20)

  1. 一种充电的方法，其特征在于，所述方法包括：
    当检测到终端与充电器建立连接时，获取当前时间点以及用户使用习惯模型；
    将所述当前时间点与所述用户使用习惯模型匹配以获取所述用户的充电意图；
    确定与所述充电意图对应的充电模式；
    按照所述确定的充电模式对所述终端进行充电。
  2. 根据权利要求1所述的方法，其特征在于，所述获取用户使用习惯模型之前，所述方法还包括：
    利用预设机器学习算法对所述用户使用所述终端的历史数据进行训练以获取所述用户使用习惯模型。
  3. 根据权利要求2所述的方法，其特征在于，所述利用预设机器学习算法对所述用户使用所述终端的历史数据进行训练之前，所述方法还包括：
    获取预设时间段内所述用户使用所述终端的历史数据；所述利用预设机器学习算法对所述用户使用所述终端的历史数据进行训练，包括：
    利用所述预设机器学习算法对所述历史数据进行分析；
    对分析结果进行修正，并将所述修正过的分析结果设置为所述用户使用习惯模型。
  4. 根据权利要求1至3任一所述的方法，其特征在于，所述将所述当前时间点与所述用户使用习惯模型匹配以获取所述用户的充电意图之前，所述方法还包括：
    获取所述终端所处的位置；
    所述将所述当前时间点与所述用户使用习惯模型匹配以获取所述用户的充电意图，包括：
    根据所述当前时间点、所述终端所处的位置以及所述用户使用习惯模型确定所述用户的充电意图。
  5. 根据权利要求4所述的方法，其特征在于，所述将所述当前时间点与所述用户使用习惯模型匹配以获取所述用户的充电意图之前，所述方法还包括：
    获取所述终端所处的环境的信息；
    所述将所述当前时间点与所述用户使用习惯模型匹配以获取所述用户的充电意图，包括：
    根据所述当前时间点、所述终端所处的位置、所述终端所处的环境的信息以及所述用户使用习惯模型确定所述用户的充电意图。
  6. 根据权利要求1至5任一所述的方法，其特征在于，所述根据所述充电意图确定充电模式之后，所述方法还包括：
    向所述用户发送充电模式确认请求，所述充电模式确认请求用于请示用户是否按照所述充电模式进行充电；
    当接收到所述用户确认按照所述充电模式进行充电的指令时，根据所述充电模式对所述终端进行充电。
  7. 根据权利要求6所述的方法，其特征在于，所述方法还包括：
    当接收到所述用户输入的更改充电模式的指令时，提示所述用户选择新的充电模式；
    接收用户选择的充电模式，按照所述用户选择的充电模式进行充电，并且根据用户选择的充电模式对所述用户使用习惯模式进行修正。
  8. 根据权利要求1至7任一所述的方法，其特征在于，所述方法还包括：
    当获取所述用户使用习惯模型失败时，判断所述当前时间点是否位于预设睡眠时间段；
    当所述当前时间点位于所述预设睡眠时间段时，根据所述当前时间点和预设睡眠时间段计算可用于充电的时间长度；
    根据所述可用于充电的时间长度确定充电模式。
  9. 根据权利要求1至7任一所述的方法，其特征在于，所述方法还包括：
    当获取所述用户使用习惯模型失败时，获取所述终端的剩余电量以及当前的位置；
    当所述剩余电量小于第一预设阈值以及所述当前的位置不属于预设位置集合时，确定所述充电模式是快速充电模式。
  10. 根据权利要求1至7任一所述的方法，其特征在于，所述方法还包括：
    当获取所述用户使用习惯模型失败时，获取所述终端的剩余电量，并检测所述终端是否存在正在运行的应用程序；
    当所述剩余电量小于第二预设阈值且存在正在运行的应用程序时，确定所述充电模式是快速充电模式。
  11. 一种终端，其特征在于，所述终端包括：
    获取单元，用于当检测到终端与充电器建立连接时，获取当前时间点以及用户使用习惯模型；
    匹配单元，用于将所述当前时间点与所述用户使用习惯模型匹配以获取所述用户的充电意图；
    确定单元，用于确定与所述充电意图对应的充电模式；
    充电单元，用于按照所述确定的充电模式对所述终端进行充电。
  12. 根据权利要求11所述的终端，其特征在于，所述终端还包括训练单元；
    所述训练单元，用于利用预设机器学习算法对所述用户使用所述终端的历史数据进行训练以获取所述用户使用习惯模型；
    所述获取单元，用于获取所述训练单元训练出来的所述用户使用习惯模型。
  13. 根据权利要求12所述的终端，其特征在于，
    所述获取单元，还用于获取预设时间段内所述用户使用所述终端的历史数据；
    所述训练单元，用于利用所述预设机器学习算法对所述历史数据进行分析并获取分析结果；
    所述训练单元，还用于对所述分析结果进行修正，并将所述修正过的分析结果设置为所述用户使用习惯模型。
  14. 根据权利要求11至13任一所述的终端，其特征在于，
    所述获取单元，还用于获取所述终端所处的位置；
    所述匹配单元，用于根据所述当前时间点、所述终端所处的位置以及所述用户使用习惯模型确定所述用户的充电意图。
  15. 根据权利要求14所述的终端，其特征在于，
    所述获取单元，还用于获取所述终端所处的环境的信息；
    所述匹配单元，用于根据所述当前时间点、所述终端所处的位置、所述终端所处的环境的信息以及所述用户使用习惯模型确定所述用户的充电意图。
  16. 根据权利要求11至15任一所述的终端，其特征在于，所述终端还包括提示单元；
    所述提示单元，用于向所述用户发送充电模式确认请求，所述充电模式确认请求用于请示用户是否按照所述充电模式进行充电；所述充电单元，用于当接收到所述用户确认按照所述充电模式进行充电的指令时，根据所述充电模式对所述终端进行充电。
  17. 根据权利要求16所述的终端，其特征在于，所述终端还包括接收单元和修正单元；
    所述提示单元，还用于当接收到所述用户输入的更改充电模式的指令时，提示所述用户选择新的充电模式；
    所述接收单元，用于接收用户选择的充电模式；
    所述充电单元，用于按照所述用户选择的充电模式进行充电；
    所述修正单元，用于根据用户选择的充电模式对所述用户使用习惯模式进行修正。
  18. 根据权利要求11至17任一所述的终端，其特征在于，所述终端还包括判断单元和计算单元；
    所述判断单元，用于当获取所述用户使用习惯模型失败时，判断所述当前时间点是否位于预设睡眠时间段；
    所述计算单元，用于当所述当前时间点位于所述预设睡眠时间段时，根据所述当前时间点和预设睡眠时间段计算可用于充电的时间长度；
    所述确定单元，还用于根据所述可用于充电的时间长度确定充电模式。
  19. 根据权利要求11至17任一所述的终端，其特征在于，
    所述获取单元，还用于当获取所述用户使用习惯模型失败时，获取所述终端的剩余电量以及当前的位置；
    所述确定单元，用于当所述剩余电量小于第一预设阈值以及所述当前的位置不属于预设位置集合时，确定所述充电模式是快速充电模式。
  20. 根据权利要求11至17任一所述的终端，其特征在于，所述终端还包括检测单元；
    所述获取单元，用于当获取所述用户使用习惯模型失败时，获取所述终端的剩余电量；
    所述检测单元，用于当获取所述用户使用习惯模型失败时，检测所述终端是否存在正在运行的应用程序；
    所述确定单元，用于当所述剩余电量小于第二预设阈值且存在正在运行的应用程序时，确定所述充电模式是快速充电模式。
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EP3454448A1 (en) 2019-03-13
EP3454448A4 (en) 2019-08-07
CN113131565A (zh) 2021-07-16
CN109690900B (zh) 2021-03-23
US20190109483A1 (en) 2019-04-11

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