CN117458661A

CN117458661A - Charging control method and device, equipment and storage medium

Info

Publication number: CN117458661A
Application number: CN202311427278.2A
Authority: CN
Inventors: 佀佳梁
Original assignee: Shanghai Wingtech Electronic Technology Co Ltd
Current assignee: Shanghai Wingtech Electronic Technology Co Ltd
Priority date: 2023-10-30
Filing date: 2023-10-30
Publication date: 2024-01-26

Abstract

The embodiment of the application discloses a charging control method and device, equipment and a storage medium. In the technical scheme, the electronic equipment firstly acquires battery state data at the current moment and the starting moment of charging, then inputs the battery state data at the current moment and the starting moment of charging into a preset machine learning model to obtain predicted plug-in duration and predicted charge duration, and finally charges the battery according to the predicted plug-in duration, the predicted charge duration and a preset charging strategy so that the voltage of the battery is smaller than a preset safety voltage threshold. According to the charging control method, the predicted plug-in time length and the predicted full-charge time length are obtained by using the preset machine learning model, and the battery is charged by combining the preset charging strategy, so that different charging habits and service situations of a user can be adapted, the service life of the battery is prolonged, and the battery loss is reduced.

Description

Charging control method and device, equipment and storage medium

Technical Field

The embodiment of the application relates to the technical field of machine learning, and relates to a charging control method, a charging control device, charging control equipment and a storage medium.

Background

With the continuous popularity of portable electronic devices, rechargeable batteries are also becoming increasingly popular. When the battery is low in electric quantity, the battery of the electronic device needs to be charged through the data line. For example, users often connect a cell phone to a charger to charge a battery in the cell phone. During charging of the battery, if the battery is full but still connected to the charger, the battery still continues to receive current, which may cause the voltage of the battery to exceed a safe range, resulting in "overcharging". If the battery is often overcharged, it is easily damaged, resulting in a shortened life.

In the related art, the charging protection method for the battery is generally based on a fixed rule or static setting, and the personalized requirement or charging behavior difference of the user is not considered, so that a 'one-cut' strategy is caused, and the service life of the battery cannot be fully optimized.

Therefore, how to extend the life of the battery to the maximum extent and reduce the battery loss is a problem to be solved.

Disclosure of Invention

In view of this, the method, device, equipment and storage medium for controlling charging provided in the embodiments of the present application can extend the service life of the battery to the greatest extent and reduce the battery loss. The charging control method, device, equipment and storage medium provided by the embodiment of the application are realized in the following way:

The charging control method provided by the embodiment of the application is applied to electronic equipment, and comprises the following steps: acquiring battery state data of the electronic equipment at the current moment and the starting moment of charging, wherein the battery state data are used for indicating current data, voltage data and the residual quantity of a battery in a charging circuit of the electronic equipment; inputting the battery state data at the current moment and the charging starting moment into a preset machine learning model to obtain a predicted power-on duration and a predicted full-charge duration, wherein the predicted power-on duration is used for indicating the duration of the connection state of the electronic equipment and the charger; charging the battery according to the predicted power-on duration, the predicted full-charge duration and a preset charging strategy so that the voltage of the battery is smaller than a preset safety voltage threshold; the preset machine learning model is obtained by training an initial deep neural network model based on training data corresponding to a plurality of historical time periods, and the training data in each historical time period comprises historical battery state data, historical plug-in duration, historical full-charge duration and historical charging starting time.

In some embodiments, before the battery state data at the current time and the start time of charging are input into a preset machine learning model to obtain the predicted plug-in duration and the predicted full-charge duration, the method further includes: acquiring training data of the electronic device in each of the plurality of historical time periods; and training the initial deep neural network model based on the training data in each historical time period to obtain the preset machine learning model.

In some embodiments, the training the initial deep neural network model based on the training data in each historical time period to obtain the preset machine learning model includes: preprocessing the current data and the voltage data in each historical time period to obtain preprocessed current data and preprocessed voltage data, wherein the preprocessing comprises at least one of denoising and filtering; calculating statistical characteristics corresponding to the preprocessed current data and the preprocessed voltage data respectively according to the preprocessed current data and the preprocessed voltage data; and respectively inputting the corresponding statistical characteristics, the corresponding residual quantity of the battery, the historical power-on time, the historical full-charge time and the starting time of the historical charging of the preprocessed current data and the preprocessed voltage data in each historical time period into the initial deep neural network model for training to obtain the preset machine learning model.

In some embodiments, the preset charging policy includes a correspondence between a plug-in duration, a full-charge duration, and a charging policy of the battery, and charging the battery according to the predicted plug-in duration, the predicted full-charge duration, and the preset charging policy includes: determining a target charging strategy from the preset charging strategy according to the predicted plug-in time length and the predicted full-charge time length; and charging the electronic equipment based on the target charging strategy.

In some embodiments, in the case that the predicted plug-in time period is greater than or equal to the predicted full-charge time period, the target charging strategy is to charge the battery with a first current, or the target charging strategy is to charge the battery with a second current, and after the charging time period reaches the predicted full-charge time period, the charging circuit is controlled to be in an off state, where the first current is less than the second current.

In some embodiments, the preset charging strategy includes a first strategy for charging the battery with a third current and a second strategy for charging the battery with a fourth current, the third current being less than the fourth current, the charging the battery according to the predicted plug-in duration, the predicted charge duration, and the preset charging strategy including: judging the sizes of the predicted plug-in time length and the predicted full-charge time length; charging the battery by adopting the first strategy under the condition that the predicted power-on time length is greater than or equal to the predicted full-charge time length; and under the condition that the predicted plug-in time length is smaller than the predicted full-charge time length, charging the battery by adopting the second strategy.

In some embodiments, before the acquiring the battery state data of the electronic device at the current time and the start time of charging, the method further comprises: and determining that the electronic equipment is in a charging protection state, wherein the charging protection state is a state that the electronic equipment charges the battery according to the predicted plug-in time length, the predicted full-charge time length and a preset charging strategy.

The embodiment of the application provides a control device that charges is applied to electronic equipment, and the device includes: the device comprises an acquisition module, a charging module and a control module, wherein the acquisition module is used for acquiring battery state data of the electronic equipment at the current moment and the starting moment of charging, and the battery state data are used for indicating current data, voltage data and the residual quantity of a battery in a charging circuit of the electronic equipment; the processing module is used for inputting the battery state data at the current moment and the charging starting moment into a preset machine learning model to obtain a predicted power-on duration and a predicted full-charge duration, wherein the predicted power-on duration is used for indicating the duration of the electronic equipment in a connection state with a charger; the charging module is used for charging the battery according to the predicted plug-in time length, the predicted full-charge time length and a preset charging strategy so that the voltage of the battery is smaller than a preset safety voltage threshold value; the preset machine learning model is obtained by training an initial deep neural network model based on training data corresponding to a plurality of historical time periods, and the training data in each historical time period comprises historical battery state data, historical plug-in duration, historical full-charge duration and historical charging starting time.

In some embodiments, the charging control device further includes: and a training module. The acquisition module is further used for acquiring training data of the electronic device in each historical time period in the plurality of historical time periods; and the training module is used for training the initial deep neural network model based on the training data in each historical time period to obtain the preset machine learning model.

In some embodiments, the training module is specifically configured to: preprocessing the current data and the voltage data in each historical time period to obtain preprocessed current data and preprocessed voltage data, wherein the preprocessing comprises at least one of denoising and filtering; calculating statistical characteristics corresponding to the preprocessed current data and the preprocessed voltage data respectively according to the preprocessed current data and the preprocessed voltage data; and respectively inputting the corresponding statistical characteristics, the corresponding residual quantity of the battery, the historical power-on time, the historical full-charge time and the starting time of the historical charging of the preprocessed current data and the preprocessed voltage data in each historical time period into the initial deep neural network model for training to obtain the preset machine learning model.

In some embodiments, the preset charging strategy includes a first strategy for charging the battery with a third current and a second strategy for charging the battery with a fourth current, the third current being less than the fourth current, and the charging module is specifically configured to: judging the sizes of the predicted plug-in time length and the predicted full-charge time length; charging the battery by adopting the first strategy under the condition that the predicted power-on time length is greater than or equal to the predicted full-charge time length; and under the condition that the predicted plug-in time length is smaller than the predicted full-charge time length, charging the battery by adopting the second strategy.

In some embodiments, the charging control device further includes: and a determining module. The determining module is configured to determine that the electronic device is in a charging protection state, where the charging protection state is a state in which the electronic device charges the battery according to the predicted plug-in duration, the predicted full-charge duration, and a preset charging policy.

The computer device provided by the embodiment of the application comprises a memory and a processor, wherein the memory stores a computer program capable of running on the processor, and the processor realizes the charging control method described by the embodiment of the application when executing the program.

The computer readable storage medium provided in the embodiments of the present application stores a computer program thereon, which when executed by a processor implements the method for controlling charging provided in the embodiments of the present application.

In the charging control method, the device, the computer equipment and the computer readable storage medium provided by the embodiment of the application, the predicted plug-in time length and the predicted full-charge time length are obtained by using the preset machine learning model, and the battery is charged by combining with the preset charging strategy, so that the charging control method is more intelligent than the traditional fixed charging mode, can adapt to different charging habits and use situations of users, further prolongs the service life of the battery, reduces the battery loss, and solves the technical problem proposed in the background art.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and, together with the description, serve to explain the technical aspects of the application.

FIG. 1 is a schematic diagram of a system architecture of an electronic device according to one embodiment of the present application;

fig. 2 is a schematic implementation flow chart of a charging control method according to an embodiment of the present application;

fig. 3 is a schematic implementation flow chart of a charging control method according to another embodiment of the present application;

FIG. 4 is a schematic diagram of a display interface of a charging application according to one embodiment of the present application;

fig. 5 is a schematic structural diagram of a charging control device according to an embodiment of the present application;

fig. 6 is a schematic structural diagram of a computer device according to an embodiment of the present application.

Detailed Description

For the purposes, technical solutions and advantages of the embodiments of the present application to be more apparent, the specific technical solutions of the present application will be described in further detail below with reference to the accompanying drawings in the embodiments of the present application. The following examples are illustrative of the present application, but are not intended to limit the scope of the present application.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the present application.

In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is to be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments and can be combined with one another without conflict.

It should be noted that the term "first/second/third" in reference to the embodiments of the present application is used to distinguish similar or different objects, and does not represent a specific ordering of the objects, it being understood that the "first/second/third" may be interchanged with a specific order or sequence, as permitted, to enable the embodiments of the present application described herein to be implemented in an order other than that illustrated or described herein.

Since portable electronic devices generally use rechargeable batteries as power sources, the use of rechargeable batteries is becoming increasingly widespread with the increasing popularity of portable electronic devices. When the battery is low in electric quantity, the battery of the electronic device needs to be charged through the data line. For example, users often connect a cell phone to a charger to charge a battery in the cell phone.

In general, the charging process of a battery is generally divided into three stages: restorative charging, constant-current charging, and constant-voltage charging. In the constant voltage charging phase, the battery voltage of the electronic device is maintained at a fixed value, the current is gradually reduced, and when the battery is full, the voltage is reduced and then rises again, and the "recharging" phase is entered. That is, when the battery is full but still connected to the charger, the battery still continues to receive current, which may cause the voltage of the battery to exceed a safe range, known as "overcharging". If the battery is often overcharged, it is easily damaged, resulting in a shortened life.

In the related art, a charge protection method for a battery is generally based on a fixed rule or a static setting, such as setting a time charge, or continuously charging the battery with a fixed voltage or current, etc. However, the battery may undergo charge and discharge cycles during its lifetime, and the charging method in the related art does not take into account the individual demands of the user or the difference of charge behaviors, resulting in a "one-cut" strategy, which generally cannot sufficiently optimize the lifetime of the battery. Therefore, how to extend the life of the battery to the maximum extent and reduce the battery loss is a problem to be solved.

In view of this, an embodiment of the present application provides a method for controlling charging, where the method is applied to an electronic device, and the method specifically includes: the method comprises the steps that firstly, the electronic equipment obtains battery state data at the current moment and the initial charging moment, wherein the battery state data are used for indicating current data, voltage data and the residual quantity of a battery in a charging circuit of the electronic equipment; then, the battery state data at the current moment and the initial charging moment are input into a preset machine learning model to obtain a predicted power-on duration and a predicted full-charge duration, wherein the predicted power-on duration is used for indicating the duration of the connection state of the electronic equipment and the charger; finally, charging the battery according to the predicted plug-in time length, the predicted full-charge time length and a preset charging strategy so that the voltage of the battery is smaller than a preset safety voltage threshold; the machine learning model is obtained by training an initial deep neural network model based on training data corresponding to a plurality of historical time periods, and the training data in each historical time period comprises historical battery state data, historical power-on time duration, historical full-charge time duration and historical charging starting time. According to the charging control method, the predicted plug-in time length and the predicted full-charge time length are obtained by using the preset machine learning model, and the battery is charged by combining the preset charging strategy, so that the charging control method is more intelligent than the traditional fixed charging mode, can adapt to different charging habits and use situations of a user, further prolongs the service life of the battery, and reduces battery loss.

It should be appreciated that the electronic device to which the embodiments of the present application relate may be a mobile phone (mobile phone), a tablet computer, a notebook computer, a palm computer, a mobile internet device (mobile internet device, MID), a wearable device, a Virtual Reality (VR) device, an augmented reality (augmented reality, AR) device, a smart screen, an artificial intelligence (artificial intelligence, AI) sound, headphones, a terminal in industrial control (industrial control), a terminal in unmanned (self driving), a terminal in teleoperation (remote medical surgery), a terminal in smart grid (smart grid), a terminal in transportation security (transportation safety), a terminal in smart city (smart city), a terminal in smart home (smart home), a personal digital assistant (personal digital assistant, PDA), and the like, to which the embodiments of the present application are not limited.

Illustratively, fig. 1 is a schematic system architecture of an electronic device according to an embodiment of the present application. As shown in fig. 1, the electronic device includes a processor 110, a memory 120, a transceiver 130, a display unit 140, an input unit 150, a sensor 160, an audio circuit 170, and a power module 180.

The processor 110 is a control center of the electronic device, connects various parts of the entire electronic device using various interfaces and lines, and performs various functions of the electronic device and processes data by running or executing software programs and/or modules stored in the memory 120, and calling data stored in the memory 120, thereby performing overall monitoring of the electronic device. Optionally, the processor 110 may include one or more processing units; alternatively, the processor 110 may be an integrated application processor, where the application processor primarily processes operating devices, user interfaces, application programs, etc., although other processors may be included, as is not explicitly recited herein.

The memory 120 may be used to store software programs and modules, and the processor 110 may execute various functional applications and data processing of the electronic device by executing the software programs and modules stored in the memory 120. The memory 120 mainly includes a storage program area that can store an operating device, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and a storage data area; the storage data area may store data created according to the use of the electronic device (such as audio data, phonebooks, etc.), and the like. In addition, memory 120 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.

The transceiver 130 may provide solutions for wireless communication including wireless local area network (wireless local area networks, WLAN) (e.g., wireless fidelity (wireless fidelity, wi-Fi) network), bluetooth (BT), global navigation satellite system (global navigation satellite system, GNSS), frequency modulation (frequency modulation, FM), near field wireless communication technology (near field communication, NFC), infrared technology (IR), etc., as applied on electronic devices. The transceiver 130 may be one or more devices that integrate at least one communication processing module, such as integrating an antenna with a baseband processor into the transceiver 130, or integrating an antenna with a modem processor into the transceiver 130, etc., without limitation.

The display unit 140 may be used to display information input by a user or information provided to the user and various menus of the electronic device. The display unit 140 may be configured in the form of a liquid crystal display (liquid crystal display, LCD), an organic light-emitting diode (OLED), or the like, and is not limited herein.

The input unit 150 may be used to receive input numeric or character information and to generate key signal inputs related to user settings and function controls of the electronic device. Specifically, the input unit 150 may collect operations on or near the user and drive the corresponding connection device according to a preset program. In addition, the input unit 150 may include a touch panel, and the touch panel may be implemented in various types such as resistive, capacitive, infrared, and surface acoustic wave. The input unit 150 may include other input devices in addition to the touch panel. In particular, other input devices may include, but are not limited to, one or more of function keys (such as volume control keys, switch keys, etc.), trackballs, joysticks, and the like.

The electronic device may also include at least one sensor 160, such as a gyroscopic sensor, a motion sensor, and other sensors. The motion sensor can comprise an acceleration sensor, is used for detecting the acceleration in all directions, can detect the gravity and the direction when the motion sensor is static, and can be used for recognizing the application of the gesture of the electronic equipment, such as horizontal and vertical screen switching, related games, magnetometer gesture calibration and the like; other sensors such as pressure gauge, barometer, hygrometer, thermometer, infrared sensor, fingerprint sensor, etc. that may be further configured for the electronic device are not described herein.

The audio circuitry 170 may include a speaker and microphone, which may provide an audio interface between a user and the electronic device. The audio circuit 170 may transmit the received electrical signal converted from audio data to a speaker, and convert the electrical signal into a sound signal for output; on the other hand, the microphone converts the collected sound signals into electrical signals, which are received by the audio circuit 170 and converted into audio data, which are processed by the audio data output processor 110 for transmission via a video circuit to, for example, another electronic device, or which are output to the memory 120 for further processing.

The electronic device further comprises a power module 180 for powering the various components, optionally, the power module 180 may be logically connected to the processor 110 by a power management device, so as to implement functions of managing charging, discharging, and power consumption management by the power management device.

Although not shown, the electronic device may also include a camera. Optionally, the position of the camera on the electronic device may be front-mounted or rear-mounted, which is not limited in the embodiment of the present application.

It should be understood that the structures illustrated in the embodiments of the present application do not constitute a specific limitation on the electronic device. In other embodiments of the present application, the electronic device may include more or less components than illustrated, or certain components may be combined, or certain components may be split, or different arrangements of components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

In order to make the purpose and technical solution of the present application clearer and more intuitive, the method, device, equipment and storage medium for controlling charging provided in the embodiments of the present application will be described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.

Referring to fig. 2, a schematic implementation flow chart of a charging control method according to an embodiment of the present application is shown. The method may be applied to an electronic device as shown in fig. 1, and as shown in fig. 2, the method may include the following steps 201 to 203:

step 201, acquiring battery state data of the electronic device at the current moment and a charging start moment, wherein the battery state data is used for indicating current data, voltage data and the residual quantity of a battery in a charging circuit of the electronic device.

In some embodiments, the current data in the charging circuit is obtained through a current sensor, the voltage data is obtained through a voltage sensor, and the current sensor and the voltage sensor can be built-in or externally accessed by an electronic device, which is not limited in the application.

It should be understood that the remaining capacity of a battery refers to the proportion of the available capacity of the battery within the current time, typically expressed as a percentage, while the remaining capacity of the battery, i.e., the state of charge of the battery, is reflected.

Optionally, the electronic device has a clock built in, and time data, such as the start time of charging, etc., may be recorded.

Step 202, inputting battery state data at the current moment and a charging start moment into a preset machine learning model to obtain a predicted plug-in time length and a predicted full-charge time length, wherein the predicted plug-in time length is used for indicating the time length of the connection state of the electronic equipment and the charger.

The machine learning model is obtained by training an initial deep neural network model based on training data corresponding to a plurality of historical time periods, and the training data in each historical time period comprises historical battery state data, historical power-on time duration, historical full-charge time duration and historical charging starting time.

It should be appreciated that the plug-in duration may be obtained by monitoring voltage data statistics in the charging circuit, for example, when the electronic device is in a connected state with the charger, there is a voltage in the charging circuit, and when the electronic device is in a disconnected state with the charger, there is no voltage in the charging circuit. The duration of the fill can be obtained by monitoring current data statistics in the charging circuit.

In some embodiments, the historical time period refers to a time period before the current time, the preset machine learning model is obtained by training the initial deep neural network model according to training data in each historical time period in a plurality of historical time periods, the preset machine learning model may include a plurality of hidden layers and appropriate activation functions, and the application is not limited in this particular.

And 203, charging the battery according to the predicted plug-in time, the predicted full-charge time and the preset charging strategy so that the voltage of the battery is smaller than a preset safety voltage threshold.

Optionally, the preset charging policy may be a preset charging policy in a table form, for example, the preset charging policy may include a corresponding relationship between a plug-in duration, a full-charge duration, and a charging policy of the battery, and the electronic device searches for the corresponding charging policy according to the predicted plug-in duration and the predicted full-charge duration, and charges the battery; or based on the judgment of the electronic equipment on the predicted plug-in time and the predicted full-charge time, further selecting proper current or voltage to charge the battery; or may be in other ways, and this is not limited in particular by the present application.

It should be appreciated that when the voltage of the battery is less than the preset safe voltage threshold, the battery can be prevented from being overcharged, and thus battery loss can be reduced.

In the embodiment, the predicted plug-in time length and the predicted full-charge time length are obtained by using the preset machine learning model, and the battery is charged by combining a preset charging strategy, so that the device is more intelligent than the traditional fixed charging mode, can adapt to different charging habits and service situations of users, further prolongs the service life of the battery, and reduces the battery loss.

Based on the above embodiments, fig. 3 is a schematic implementation flow chart of a charging control method according to another embodiment of the present application, as shown in fig. 3, the method may include the following steps 301 to 305:

step 301, acquiring battery state data of the electronic device at the current moment and a starting moment of charging, where the battery state data is used to indicate current data, voltage data and a remaining capacity of a battery in a charging circuit of the electronic device.

Step 302, acquiring training data of an electronic device during each of a plurality of historical time periods.

The training data in each historical time period comprises historical battery state data, historical power-on time, historical full-charge time and historical charging starting time, wherein the historical battery state data are used for indicating historical current data, historical voltage data and residual electric quantity of a historical battery in a charging circuit of the electronic equipment.

In some embodiments, high precision voltage sensors, such as INA219, may be used in collecting voltage data; in acquiring current data, a high-precision current sensor, such as ACS712, may be employed; the electronic device periodically records the current time stamp by using a built-in clock module of the mobile phone at the same time, for example, zhong Caiyang battery current data and voltage data per second, so as to ensure the time precision and consistency of the data, and the clock module can use a Real Time Clock (RTC) module, for example.

Optionally, the electronic device may also communicate with the charger through a universal serial bus (universal serial bus, USB) communication interface to obtain characteristic parameters of the charger, such as maximum output current and voltage specifications.

Step 303, training an initial deep neural network model based on training data in each historical time period to obtain a preset machine learning model.

In some embodiments, the electronic device first pre-processes the current data and the voltage data for each historical time period to obtain pre-processed current data and voltage data, the pre-processing including at least one of denoising and filtering. That is, the electronic device denoises and filters the current data and the voltage data in each historical period to eliminate noise interference and ensure accuracy of the data.

Further, according to the preprocessed current data and the preprocessed voltage data, calculating statistical characteristics corresponding to the preprocessed current data and the preprocessed voltage data respectively, wherein the specific implementation process is as follows: the continuous charge data stream is divided into discrete time periods, for example, one time period per minute, which is taken as a basic unit of feature extraction, and then statistical features of current and voltage, such as mean, standard deviation, kurtosis, skewness, current waveform shape, and the like, of each time period are calculated.

Optionally, the starting time of charging refers to the time when the user connects the electronic device with the charger, and may be used to indicate the charging habit of the user, for example, the user often starts charging at 10 pm, plugs in for 8 hours each time, or charges in noon for 2 hours each time.

For example, the time of day may be divided into different time periods, for example, divided in 24 hours, 6 to 8 to 59 divided into a first time period, 9 to 11 to 59 divided into a second time period, 12 to 14 to 59 divided into a third time period, 15 to 17 to 59 divided into a fourth time period, 18 to 20 to a fifth time period, 21 to 23 to 59 divided into a sixth time period, and zero to 5 to 59 divided into a seventh time period, and the starting time of charging may be divided into different time periods for training at the time of model training.

And finally, inputting the statistical characteristics, the residual quantity of the corresponding battery, the historical plug-in time length, the historical full-charge time length and the starting time of the historical charging which are respectively corresponding to the preprocessed current data and the preprocessed voltage data in each historical time period into an initial deep neural network model for training to obtain a preset machine learning model. The initial deep neural network model is provided with a plurality of hidden layers and proper activation functions (such as ReLU), a back propagation algorithm can be adopted to train the model, errors between the predicted ending time and the actual ending time are minimized, and then an optimization algorithm such as random gradient descent and the like can be adopted to optimize.

Step 304, inputting the battery state data at the current moment and the initial charging moment into a preset machine learning model to obtain a predicted plug-in time length and a predicted full-charge time length, wherein the predicted plug-in time length is used for indicating the time length of the connection state of the electronic equipment and the charger.

In some embodiments, the electronic device inputs the battery state data of the electronic device at the current time and the start time of charging obtained in step 301 into a preset machine learning model, so as to obtain a predicted plug-in duration and a predicted full-charge duration which are predicted in real time.

In step 305, the battery is charged according to the predicted plug-in duration, the predicted full-charge duration, and the preset charging strategy, so that the voltage of the battery is less than the preset safe voltage threshold.

In one possible implementation manner, the preset charging policy may include a corresponding relationship between a plug-in duration, a full-charge duration, and a charging policy of the battery, and the electronic device may determine, from the preset charging policy, a target charging policy according to the predicted plug-in duration and the predicted full-charge duration, and then charge the electronic device based on the target charging policy.

As an example, in the case where the predicted plug-in duration is greater than or equal to the predicted full-charge duration, the target charging strategy is to charge the battery with a first current, or the target charging strategy is to charge the battery with a second current and to control the charging circuit to be in an off state after the charging duration reaches the full-charge duration, the first current being less than the second current. For example, in the case where the predicted plug-in duration is 8 hours and the predicted charge duration is 3 hours, the corresponding preset charging strategy may be to charge with a small current so that the battery is charged within 8 hours, or may be to charge with a large current, and to disconnect the charging circuit after reaching 3 hours of charging.

As another example, in the case where the predicted plug-in duration is less than the predicted charge duration, the target charging strategy may be to charge the battery with a large current, that is, assuming that the predicted plug-in duration is 2 hours, but the battery takes 4 hours to charge, the maximum capacity of the charger may be used to charge the battery.

In another possible embodiment, the preset charging strategy includes a first strategy for charging the battery with a third current and a second strategy for charging the battery with a fourth current, the third current being less than the fourth current. Accordingly, when the electronic device charges the battery according to the predicted plug-in time length, the predicted full-charge time length and the preset charging strategy, the size of the predicted plug-in time length and the predicted full-charge time length can be judged first, and the battery is charged by adopting the first strategy under the condition that the predicted plug-in time length is greater than or equal to the predicted full-charge time length; and under the condition that the predicted plug-in time is smaller than the predicted full-charge time, charging the battery by adopting a second strategy.

That is, after the predicted plug-in time length and the predicted full-charge time length are predicted according to the preset machine learning model, the electronic device can directly compare the predicted plug-in time length with the predicted full-charge time length, and then charge the battery by adopting a corresponding first strategy or a corresponding second strategy according to the comparison result of the predicted plug-in time length and the predicted full-charge time length so as to avoid overcharging the battery.

In some embodiments, before acquiring the battery state data at the current time and the starting time of charging, the electronic device may further determine that the electronic device is in a charging protection state, where the charging protection state is a state in which the electronic device charges the battery according to the predicted plug-in duration, the predicted full-charge duration, and the preset charging policy. That is, the electronic device includes a switch for opening the charging protection state, which may be a physical switch or a button switch displayed on a screen, allowing a user to manually activate and supporting automatic triggering of the system, and the electronic device may be charged by using the charging control method in the present application when the switch for opening the charging protection state is opened, so as to prolong the service life of the battery and reduce the battery loss.

In this embodiment, the electronic device first obtains battery state data at a current time and a charging start time, then obtains training data of the electronic device in each of a plurality of historical time periods, trains an initial deep neural network model based on the training data in each historical time period to obtain a preset machine learning model, further, inputs the battery state data at the current time and the charging start time into the preset machine learning model to obtain a predicted plug-in time and a predicted charging time, and finally charges the battery according to the predicted plug-in time, the predicted charging time and a preset charging strategy so that the voltage of the battery is smaller than a preset safety voltage threshold. The charging control method is different from the traditional timing or simple current/voltage control method, a machine learning model is utilized, the charging state and the state of the electronic equipment are monitored in real time, the charging time length and the full-charge time length are predicted according to real-time data, and the charging current is dynamically adjusted according to the predicted charging time length and full-charge time length.

In some embodiments, the electronic device further performs closed-loop control to continuously monitor the battery voltage, current and temperature, ensure safe and stable charging of the battery, and periodically update the machine learning model according to the real-time data of the user to adapt to the changing charging habit of the user, and analyze the performance and prediction accuracy of the model at a later stage to perform optimization and improvement.

In other embodiments, the electronic device further includes a corresponding charging application program, where the charging application program may display a charging state and a charging preference setting of the battery, allow a user to perform personalized setting according to own needs, for example, set a maximum charging time or manually control a charging current, and timely pop-up a window to remind the user when an abnormality occurs in charging, so that flexibility and user friendliness are good, and user experience is enhanced.

For example, fig. 4 is a schematic diagram of a display interface of a charging application provided in an embodiment of the present application, as shown in fig. 4, where a current battery level (e.g. 67%), a current value, a voltage value, and further setting buttons may be displayed in the interface to further set a user charging preference.

In some embodiments, a multi-level safety protection mechanism is further provided in the electronic device, including overvoltage protection, over-temperature protection, current limiting protection, and the like, while monitoring battery states, such as voltage, temperature, and internal resistance, in real time, and implementing real-time fault detection.

Optionally, an accelerometer, such as MPU6050, may be further disposed in the electronic device, and during the charging process of the electronic device, the accelerometer detects whether the electronic device moves, and if the movement range exceeds a certain threshold, the charging is stopped in time to protect the battery. Wherein the real-time sensor data is obtained through an electronic device operating system application program interface (application programinterface, API).

In summary, according to the charging control method provided by the embodiment of the application, the machine learning model is trained by using the historical user charging data, so that a data-driven charging decision is realized, the charging mode is dynamically adjusted by the intelligent control according to actual conditions, and the intelligent property and efficiency of charging are improved; meanwhile, the charging state, the state of the electronic equipment and the intelligent charging control driven by data are monitored in real time, so that the service life of the battery can be prolonged to the greatest extent, and the battery loss is reduced; through the charging application program, a user interaction interface can be provided, the user is allowed to set the charging preference in a personalized way, for example, the maximum charging time is set, the user can customize the charging mode according to the requirement of the user, and the user experience is enhanced. And the electronic equipment supports an energy-saving mode, is beneficial to reducing unnecessary charging and battery loss, prolongs the service life of the battery, reduces the consumption of resources and increases the sustainability. In addition, a multi-level safety protection mechanism is implemented, including overvoltage protection, over-temperature protection, current limiting protection and the like, and real-time fault detection is carried out, so that the safety of the battery is ensured. At the same time, monitoring battery conditions, such as voltage, temperature, and internal resistance, helps to discover problems and take action in advance.

It should be understood that, although the steps in the flowcharts described above are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described above may include a plurality of sub-steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of execution of the sub-steps or stages is not necessarily sequential, but may be performed alternately or alternately with at least a part of the sub-steps or stages of other steps or other steps.

Based on the foregoing embodiments, the embodiments of the present application provide a charging control device, where the device includes each module included, and each unit included in each module may be implemented by a processor; of course, the method can also be realized by a specific logic circuit; in an implementation, the processor may be a Central Processing Unit (CPU), a Microprocessor (MPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), or the like.

Fig. 5 is a schematic structural diagram of a charging control device according to an embodiment of the present application, as shown in fig. 5, the device 500 includes an obtaining module 501, a processing module 502, and a charging module 503, where:

an obtaining module 501, configured to obtain battery state data of an electronic device at a current time and a start time of charging, where the battery state data is used to indicate current data, voltage data, and a remaining power of a battery in a charging circuit of the electronic device; the processing module 502 is configured to input the battery state data at the current time and the start time of charging into a preset machine learning model to obtain a predicted power-on duration and a predicted full-charge duration, where the predicted power-on duration is used to indicate a duration that the electronic device is in a connection state with a charger; a charging module 503, configured to charge the battery according to the predicted plug-in duration, the predicted full-charge duration, and a preset charging policy, so that a voltage of the battery is less than a preset safe voltage threshold; the preset machine learning model is obtained by training an initial deep neural network model based on training data corresponding to a plurality of historical time periods, and the training data in each historical time period comprises historical battery state data, historical plug-in duration, historical full-charge duration and historical charging starting time.

In some embodiments, the charging control device further includes: and a training module. An obtaining module 501, configured to obtain training data of the electronic device in each of the plurality of historical time periods; and the training module is used for training the initial deep neural network model based on the training data in each historical time period to obtain the preset machine learning model.

In some embodiments, the preset charging strategy includes a first strategy for charging the battery with a third current and a second strategy for charging the battery with a fourth current, the third current being less than the fourth current, and the charging module 503 is specifically configured to: judging the sizes of the predicted plug-in time length and the predicted full-charge time length; charging the battery by adopting the first strategy under the condition that the predicted power-on time length is greater than or equal to the predicted full-charge time length; and under the condition that the predicted plug-in time length is smaller than the predicted full-charge time length, charging the battery by adopting the second strategy.

The description of the apparatus embodiments above is similar to that of the method embodiments above, with similar advantageous effects as the method embodiments. For technical details not disclosed in the device embodiments of the present application, please refer to the description of the method embodiments of the present application for understanding.

It should be noted that, in the embodiment of the present application, the division of the modules by the charging control device shown in fig. 5 is schematic, and is merely a logic function division, and there may be another division manner in actual implementation. In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units. Or in a combination of software and hardware.

It should be noted that, in the embodiment of the present application, if the method is implemented in the form of a software functional module, and sold or used as a separate product, the method may also be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially or part contributing to the related art, and the computer software product may be stored in a storage medium, including several instructions for causing an electronic device to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read Only Memory (ROM), a magnetic disk, an optical disk, or other various media capable of storing program codes. Thus, embodiments of the present application are not limited to any specific combination of hardware and software.

The embodiment of the application provides a computer device, which may be a server, and an internal structure diagram thereof may be shown in fig. 6. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is for storing data. The network interface of the computer device is used for communicating with an external terminal through a network connection. Which computer program, when being executed by a processor, carries out the above-mentioned method.

The present embodiment provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of the method provided in the above embodiment.

The present application provides a computer program product comprising instructions which, when run on a computer, cause the computer to perform the steps of the method provided by the method embodiments described above.

It will be appreciated by those skilled in the art that the structure shown in fig. 6 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.

In one embodiment, the resource pooling device provided herein may be implemented in the form of a computer program that is executable on a computer device as shown in fig. 6. The memory of the computer device may store the various program modules that make up the apparatus. The computer program of each program module causes a processor to perform the steps in the methods of each embodiment of the present application described in the present specification.

It should be noted here that: the description of the storage medium and apparatus embodiments above is similar to that of the method embodiments described above, with similar benefits as the method embodiments. For technical details not disclosed in the storage medium, storage medium and device embodiments of the present application, please refer to the description of the method embodiments of the present application for understanding.

It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" or "some embodiments" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" or "in some embodiments" in various places throughout this specification are not necessarily referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application. The foregoing embodiment numbers of the present application are merely for describing, and do not represent advantages or disadvantages of the embodiments. The foregoing description of various embodiments is intended to highlight differences between the various embodiments, which may be the same or similar to each other by reference, and is not repeated herein for the sake of brevity.

The term "and/or" is herein merely an association relation describing associated objects, meaning that there may be three relations, e.g. object a and/or object B, may represent: there are three cases where object a alone exists, object a and object B together, and object B alone exists.

It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments are merely illustrative, and the division of the modules is merely a logical function division, and other divisions may be implemented in practice, such as: multiple modules or components may be combined, or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or modules, whether electrically, mechanically, or otherwise.

The modules described above as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules; can be located in one place or distributed to a plurality of network units; some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional module in each embodiment of the present application may be integrated in one processing unit, or each module may be separately used as one unit, or two or more modules may be integrated in one unit; the integrated modules may be implemented in hardware or in hardware plus software functional units.

Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read Only Memory (ROM), a magnetic disk or an optical disk, or the like, which can store program codes.

Alternatively, the integrated units described above may be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially or part contributing to the related art, and the computer software product may be stored in a storage medium, including several instructions for causing an electronic device to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a removable storage device, a ROM, a magnetic disk, or an optical disk.

The methods disclosed in the several method embodiments provided in the present application may be arbitrarily combined without collision to obtain a new method embodiment.

The features disclosed in the several product embodiments provided in the present application may be combined arbitrarily without conflict to obtain new product embodiments.

The features disclosed in the several method or apparatus embodiments provided in the present application may be arbitrarily combined without conflict to obtain new method embodiments or apparatus embodiments.

The foregoing is merely an embodiment of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered in the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A control method of charging, characterized by being applied to an electronic device, the method comprising:

acquiring battery state data of the electronic equipment at the current moment and the starting moment of charging, wherein the battery state data are used for indicating current data, voltage data and the residual quantity of a battery in a charging circuit of the electronic equipment;

inputting the battery state data at the current moment and the charging starting moment into a preset machine learning model to obtain a predicted power-on duration and a predicted full-charge duration, wherein the predicted power-on duration is used for indicating the duration of the connection state of the electronic equipment and the charger;

charging the battery according to the predicted power-on duration, the predicted full-charge duration and a preset charging strategy so that the voltage of the battery is smaller than a preset safety voltage threshold;

The preset machine learning model is obtained by training an initial deep neural network model based on training data corresponding to a plurality of historical time periods, and the training data in each historical time period comprises historical battery state data, historical plug-in duration, historical full-charge duration and historical charging starting time.

2. The method of claim 1, wherein prior to said inputting the battery state data at the current time and the start time of the charge into a preset machine learning model to obtain a predicted plug-in duration and a predicted fill-in duration, the method further comprises:

acquiring training data of the electronic device in each of the plurality of historical time periods;

and training the initial deep neural network model based on the training data in each historical time period to obtain the preset machine learning model.

3. The method of claim 2, wherein training the initial deep neural network model based on the training data for each historical time period to obtain the preset machine learning model comprises:

preprocessing the current data and the voltage data in each historical time period to obtain preprocessed current data and preprocessed voltage data, wherein the preprocessing comprises at least one of denoising and filtering;

Calculating statistical characteristics corresponding to the preprocessed current data and the preprocessed voltage data respectively according to the preprocessed current data and the preprocessed voltage data;

and respectively inputting the corresponding statistical characteristics, the corresponding residual quantity of the battery, the historical power-on time, the historical full-charge time and the starting time of the historical charging of the preprocessed current data and the preprocessed voltage data in each historical time period into the initial deep neural network model for training to obtain the preset machine learning model.

4. The method of claim 1, wherein the preset charging strategy includes a correspondence between a plug-in time period, a full-charge time period, and a charging strategy of the battery, and wherein charging the battery according to the predicted plug-in time period, the predicted full-charge time period, and the preset charging strategy includes:

determining a target charging strategy from the preset charging strategy according to the predicted plug-in time length and the predicted full-charge time length;

and charging the electronic equipment based on the target charging strategy.

5. The method of claim 4, wherein the target charging strategy is to charge the battery with a first current or the target charging strategy is to charge the battery with a second current and to control the charging circuit to be in an off state after the charging period reaches the predicted full period, the first current being less than the second current, if the predicted power-on period is greater than or equal to the predicted full period.

6. The method of claim 1, wherein the preset charging strategy comprises a first strategy for charging the battery with a third current and a second strategy for charging the battery with a fourth current, the third current being less than the fourth current, the charging the battery according to the predicted plug-in duration, the predicted charge duration, and the preset charging strategy comprising:

judging the sizes of the predicted plug-in time length and the predicted full-charge time length;

charging the battery by adopting the first strategy under the condition that the predicted power-on time length is greater than or equal to the predicted full-charge time length;

and under the condition that the predicted plug-in time length is smaller than the predicted full-charge time length, charging the battery by adopting the second strategy.

7. The method of claim 1, wherein prior to the acquiring battery state data of the electronic device at the current time and the start time of charging, the method further comprises:

and determining that the electronic equipment is in a charging protection state, wherein the charging protection state is a state that the electronic equipment charges the battery according to the predicted plug-in time length, the predicted full-charge time length and a preset charging strategy.

8. A control apparatus for charging, characterized by being applied to an electronic device, the apparatus comprising:

the device comprises an acquisition module, a charging module and a control module, wherein the acquisition module is used for acquiring battery state data of the electronic equipment at the current moment and the starting moment of charging, and the battery state data are used for indicating current data, voltage data and the residual quantity of a battery in a charging circuit of the electronic equipment;

the processing module is used for inputting the battery state data at the current moment and the charging starting moment into a preset machine learning model to obtain a predicted power-on duration and a predicted full-charge duration, wherein the predicted power-on duration is used for indicating the duration of the electronic equipment in a connection state with a charger;

the charging module is used for charging the battery according to the predicted plug-in time length, the predicted full-charge time length and a preset charging strategy so that the voltage of the battery is smaller than a preset safety voltage threshold value;

9. A computer device comprising a memory and a processor, the memory storing a computer program executable on the processor, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the program is executed.

10. A computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the method according to any one of claims 1 to 7.