WO2020114339A1 - Procédé de sauvegarde de données et dispositif terminal - Google Patents

Procédé de sauvegarde de données et dispositif terminal Download PDF

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Publication number
WO2020114339A1
WO2020114339A1 PCT/CN2019/122301 CN2019122301W WO2020114339A1 WO 2020114339 A1 WO2020114339 A1 WO 2020114339A1 CN 2019122301 W CN2019122301 W CN 2019122301W WO 2020114339 A1 WO2020114339 A1 WO 2020114339A1
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Prior art keywords
application
terminal device
parameter
backup
probability
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PCT/CN2019/122301
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English (en)
Chinese (zh)
Inventor
贺明天
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华为技术有限公司
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Publication of WO2020114339A1 publication Critical patent/WO2020114339A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
    • G06F11/14—Error detection or correction of the data by redundancy in operation

Definitions

  • the present application relates to the technical field of terminals, in particular to a data backup method and terminal equipment.
  • terminal devices such as mobile phones and tablets
  • the user completes daily tasks (such as image shooting, sending and receiving emails, communication chat, etc.) through the terminal device.
  • daily tasks such as image shooting, sending and receiving emails, communication chat, etc.
  • a large amount of personal data will be generated during the use of the terminal device, such as captured pictures and videos.
  • the amount of personal data also increases. If data backup is not performed in time, on the one hand, personal data may be lost, on the other hand, the memory on the terminal equipment may gradually decrease, reducing the operating speed of the terminal equipment. , Affecting the user experience.
  • the software for backing up data is installed on the terminal device, and when the user turns on the data backup function in the software, the terminal device backs up the data. It can be seen that, in the prior art, when data is backed up by such software, the user needs to manually perform the data backup, the operation is too cumbersome, and the user experience is poor.
  • Embodiments of the present application provide a data backup method and a terminal device, which are used to automatically back up personal data in the terminal device, reduce user operations, and improve user experience.
  • an embodiment of the present application provides a data backup method, which can be executed by a terminal device (such as a mobile phone, an iPad, etc.).
  • the method includes: the terminal device obtains a first usage record of a user using the first application in the terminal device, and calculates a backup probability; the first usage record includes how frequently the first application is used, and the backup probability It is used to indicate the probability of backing up the data of the first application; the frequency with which the first application is used is positively correlated with the backup probability; when the backup probability is greater than a probability threshold, the terminal device backs up the first One application data.
  • the terminal device can calculate the backup probability based on the usage record of the first application, and then back up the data of the first application based on the backup probability, without the user's manual operation, which is convenient for operation, and an application is frequently used
  • the degree is positively related to the backup probability, that is, the more frequently used the application, the greater the backup probability, which helps to improve the accuracy of data backup.
  • the terminal device calculates the backup probability according to a preset model, and the preset model is as follows:
  • x is the input parameter
  • w is the parameter coefficient
  • b is the parameter offset
  • n is the number of input parameters
  • f is the preset activation function
  • y is the output parameter; where, when the value of x is the first When applying the value of the parameter related to the frequency of use, the obtained y represents the backup probability.
  • the terminal device may calculate the backup probability according to the model. Wherein, when the value of the input parameter in the model is the value of the parameter related to the frequency with which the first application is used, the obtained output result represents the backup probability. This method helps to improve the accuracy of data backup, and does not require manual operation by the user, which is convenient for operation and improves the user experience.
  • the frequency with which the first application is used is related to at least one of the following parameters:
  • the first parameter indicates the duration of use of the first application
  • the second parameter indicates the number of times the first application is used
  • the third parameter indicates the highest frequency of the first application in a single day
  • the fourth parameter indicates the longest single-day use of the first application
  • Fifth parameter represents the time interval since the first application was last used.
  • the frequency of use of the first application and related parameters include: the duration and number of uses of the first application; the highest frequency of daily use of the first application; the maximum daily use of the first application ; One or more of the time intervals since the first application was last used.
  • the terminal device can calculate the backup probability of the first application according to the relevant parameters, and then back up the data of the first application based on the backup probability, which helps to improve the accuracy of data backup, and does not require manual operation by the user, which is convenient for operation and improves the user experience.
  • the frequency with which the first application is used is related to the first parameter to the fifth parameter, and then the terminal device calculates the backup probability according to the following formula:
  • the first parameter is represented by x1
  • the second parameter is represented by x2
  • the third parameter is represented by x3
  • the fourth parameter is represented by x4
  • the fifth parameter is represented by x5, and y represents the backup probability.
  • Calculating the backup probability through the above formula helps to improve the accuracy of data backup, and does not require manual operation by the user, which is convenient for operation and improves the user experience.
  • the frequency with which the first application is used is related to at least one of the following parameters:
  • Sixth parameter indicates the application category of the first application.
  • Seventh parameter indicates the movement trajectory of the terminal device
  • Eighth parameter the proportion of holidays in the time period from the last backup of the data of the first application
  • Tenth parameter time interval since the last backup of the first application
  • Eleventh parameter the number of times the first application has been shared.
  • the frequency of use of the first application and related parameters include: the application category of the first application; the movement trajectory of the terminal device; the time period since the last time the data of the first application was backed up Proportion of holidays within the period; the user manually backs up history; the time interval since the last backup of the first application; the number of times the first application has been shared.
  • the terminal device can calculate the backup probability of the first application according to the relevant parameters, and then back up the data of the first application based on the backup probability, which helps to improve the accuracy of data backup, and does not require manual operation by the user, which is convenient for operation and improves the user experience.
  • the frequency with which the first application is used is related to the sixth parameter to the eleventh parameter, and then the terminal device calculates the backup probability according to the following formula:
  • the sixth parameter is represented by x6, the seventh parameter is represented by x7, the eighth parameter is represented by x8, the ninth parameter is represented by x9, the tenth parameter is represented by x10, the eleventh parameter is represented by x11, and y represents the backup probability.
  • Calculating the backup probability through the above formula helps to improve the accuracy of data backup, and does not require manual operation by the user, which is convenient for operation and improves the user experience.
  • the frequency with which the first application is used is related to at least one of the following parameters:
  • the first parameter indicates the duration of use of the first application
  • the second parameter indicates the number of times the first application is used
  • the third parameter indicates the highest frequency of the first application in a single day
  • the fourth parameter indicates the longest single-day use of the first application
  • Fifth parameter represents the time interval since the first application was last used
  • Sixth parameter indicates the application category of the first application.
  • Seventh parameter indicates the movement trajectory of the terminal device
  • Eighth parameter the proportion of holidays in the time period from the last backup of the data of the first application
  • Tenth parameter time interval since the last backup of the first application
  • Eleventh parameter the number of times the first application has been shared.
  • the frequency with which the first application is used is related to the first parameter to the eleventh parameter, and then the terminal device calculates the backup probability according to the following formula:
  • the first parameter is represented by x1
  • the second parameter is represented by x2
  • the third parameter is represented by x3
  • the fourth parameter is represented by x4
  • the fifth parameter is represented by x5
  • the sixth parameter is represented by x6, and the seventh parameter is represented by x7
  • the eighth parameter is represented by x8,
  • the ninth parameter is represented by x9
  • the tenth parameter is represented by x10
  • the eleventh parameter is represented by x11
  • y represents the backup probability.
  • the terminal device before calculating the backup probability according to a preset model, the terminal device also determines an appropriate value of w from a plurality of values of w and a plurality of values of b Appropriate value of b.
  • the terminal device uses the model to calculate the backup probability, it is possible to determine the appropriate values of w and b, which improves the accuracy of determining the values of the parameters (w and b) in the model, and thus helps to improve The accuracy of data backup, and no manual operation by the user is required, which is convenient for operation and improves the user experience.
  • the terminal device determines a suitable value of w from a plurality of values of w, and determines a suitable value of b from a plurality of values of b, including: the terminal device Determining a first value of w from a plurality of values of w, and determining a second value of b from a plurality of values of b; the terminal device according to a second usage record of the user using the first application, The first value, the second value, and the preset model are calculated to obtain an output result; the second usage record includes how frequently the first application is used, and the second usage The record is generated before the first usage record; if the output result is consistent with the actual result, the terminal device determines that the first value and the second value are appropriate.
  • the terminal device may determine appropriate values of w and b. Specifically, the terminal device may use training to determine whether the values of w and b are appropriate. For example, the terminal device brings the determined value of w and the value of b into the formula of the model, and also brings the second usage record into the formula of the model to obtain the output result, and compares the output result with the actual result. If they are consistent, the values of w and b are appropriate. In this way, the accuracy of determining the values of the parameters (w and b) in the model is improved, which in turn helps to improve the accuracy of data backup, and does not require manual operation by the user, which is convenient for operation and improves the user experience.
  • the terminal device determines a suitable value of w from a plurality of values of w, and determines a suitable value of b from a plurality of values of b, including: the terminal device
  • the values of w and b corresponding to the first application are determined according to the application category of the first application.
  • the terminal device may determine the values of w and b according to the application category of the first application. In this way, the accuracy of determining the values of the parameters (w and b) in the model is improved, which in turn helps to improve the accuracy of data backup, and does not require manual operation by the user, which is convenient for operation and improves the user experience.
  • the terminal device after the terminal device obtains the first usage record of the user using the first application in the terminal device, calculates the backup probability, and when the preset period is exceeded, the terminal device re-determines w and b The value of w; or the terminal device uses the preset model to perform calculations more than a preset number of times, and then re-determines the values of w and b; the terminal device according to the re-determined values of w and b, the first Three usage records and the preset model, a backup probability is calculated, the third usage record includes the frequency with which the first application is used, and the third usage record is generated after the first usage record.
  • the values of w and b may be re-determined. In this way, the terminal device can continuously optimize the values of w and b in the model, which helps to improve the accuracy of data backup, and does not require manual operation by the user, which is convenient for operation and improves the user experience.
  • the terminal device before the terminal device obtains the first usage record of the user using the first application in the terminal device and calculates the backup probability, the terminal device detects one or more of the following trigger conditions Conditions: the terminal device detects the insertion of the power supply; the terminal device detects the insertion of the memory card; the terminal device detects that the display screen has been extinguished for a first preset time period; the terminal device detects the distance from the last backup The time reaches the second preset duration; the terminal device detects that the current power is greater than the preset power.
  • the terminal device may calculate the backup probability only after detecting the trigger condition, which helps to save the calculation amount.
  • the terminal device displays the first interface in response to the user operation ,
  • the first interface includes a first control, a second control, and a third control; when the first control is triggered, the terminal device starts to acquire the first use of the first application in the terminal device by the user The function of recording and calculating the backup probability; when the second control is triggered, the terminal device starts the function of periodically backing up data of all applications; when the third control is triggered, the terminal device displays A second interface, where the second interface includes multiple applications, and when the terminal device detects that the user selects the second application among the multiple applications, the terminal device backs up data of the second application.
  • the function of backing up data of the terminal device may be executed after the user activates the function. In this way, users are provided with choice opportunities to enhance the user experience.
  • the terminal device displays a third interface in response to the operation, and the third interface includes a history of backed up data; the terminal device detects that During the operation of a history record, the backup data corresponding to the first history record is restored to the terminal device.
  • the terminal device may also restore the backed up data to the terminal device, which helps to improve the user experience.
  • an embodiment of the present application further provides a terminal device, including: a touch screen, a memory, and a processor; wherein, the touch screen is used by a user to collect user touch operations; the memory is used to store data; and the processor is based on the user
  • a touch operation obtains a first usage record of a user using the first application in the terminal device, and calculates a backup probability; the first usage record includes how frequently the first application is used, and the backup probability is used for Indicates the probability of backing up the data of the first application; the frequency with which the first application is used is positively correlated with the backup probability; when the backup probability is greater than a probability threshold, the terminal device backs up the first application The data.
  • the processor is specifically configured to calculate the backup probability according to a preset model, and the preset model is as follows:
  • x is the input parameter
  • w is the parameter coefficient
  • b is the parameter offset
  • n is the number of input parameters
  • f is the preset activation function
  • y is the output parameter; where, when the value of x is the first When applying the value of the parameter related to the frequency of use, the obtained y represents the backup probability.
  • the frequency with which the first application is used is related to at least one of the following parameters:
  • the first parameter indicates the duration of use of the first application
  • the second parameter indicates the number of times the first application is used
  • the third parameter indicates the highest frequency of the first application in a single day
  • the fourth parameter indicates the longest single-day use of the first application
  • Fifth parameter represents the time interval since the first application was last used.
  • the frequency with which the first application is used is related to the first parameter to the fifth parameter
  • the processor is specifically configured to calculate the backup probability according to the following formula:
  • the first parameter is represented by x1
  • the second parameter is represented by x2
  • the third parameter is represented by x3
  • the fourth parameter is represented by x4
  • the fifth parameter is represented by x5, and y represents the backup probability.
  • the frequency with which the first application is used is related to at least one of the following parameters:
  • Sixth parameter indicates the application category of the first application.
  • Seventh parameter indicates the movement trajectory of the terminal device
  • Eighth parameter the proportion of holidays in the time period from the last backup of the data of the first application
  • Tenth parameter time interval since the last backup of the first application
  • Eleventh parameter the number of times the first application has been shared.
  • the frequency with which the first application is used is related to the sixth parameter to the eleventh parameter
  • the processor is specifically configured to calculate the backup probability according to the following formula:
  • the sixth parameter is represented by x6, the seventh parameter is represented by x7, the eighth parameter is represented by x8, the ninth parameter is represented by x9, the tenth parameter is represented by x10, the eleventh parameter is represented by x11, and y represents the backup probability.
  • the frequency with which the first application is used is related to at least one of the following parameters:
  • the first parameter indicates the duration of use of the first application
  • the second parameter indicates the number of times the first application is used
  • the third parameter indicates the highest frequency of the first application in a single day
  • the fourth parameter indicating that the first application is used for the longest single day
  • Fifth parameter represents the time interval since the first application was last used
  • Sixth parameter indicates the application category of the first application.
  • Seventh parameter indicates the movement trajectory of the terminal device
  • Eighth parameter the proportion of holidays in the time period from the last backup of the data of the first application
  • Tenth parameter time interval since the last backup of the first application
  • Eleventh parameter the number of times the first application has been shared.
  • the frequency with which the first application is used is related to the first parameter to the eleventh parameter, and then the terminal device calculates the backup probability according to the following formula:
  • the first parameter is represented by x1
  • the second parameter is represented by x2
  • the third parameter is represented by x3
  • the fourth parameter is represented by x4
  • the fifth parameter is represented by x5
  • the sixth parameter is represented by x6, and the seventh parameter is represented by x7
  • the eighth parameter is represented by x8,
  • the ninth parameter is represented by x9
  • the tenth parameter is represented by x10
  • the eleventh parameter is represented by x11
  • y represents the backup probability.
  • the processor before the processor is used to calculate the backup probability according to a preset model, the processor further specifically:
  • the processor is specifically used to:
  • the second usage record includes the first How frequently an application is used, and the second usage record is generated before the first usage record;
  • the processor is specifically configured to determine the values of w and b corresponding to the first application according to the application category of the first application.
  • the processor is also used to: when the preset period is exceeded, re-determine the values of w and b; or
  • the terminal device calculates a backup probability according to the re-determined values of w and b, a third usage record, and the preset model, and the third usage record includes how frequently the first application is used, and The third usage record is generated after the first usage record.
  • the processor before the processor is used for the terminal device to obtain the first usage record of the user using the first application in the terminal device, and the backup probability is calculated, the processor is also used to:
  • One or more of the following trigger conditions are detected:
  • the terminal device further includes:
  • the processor starts the function of acquiring the first usage record of the user using the first application in the terminal device, and calculating the backup probability
  • the processor starts a function of periodically backing up data of all applications
  • the display screen displays a second interface, and the second interface includes multiple applications.
  • the terminal device detects that the user selects the second application among the multiple applications, the The terminal device backs up the data of the second application.
  • the terminal device further includes:
  • the display screen is used to display a third interface, which includes a history of backed up data
  • the processor detects an operation with respect to the first history record in the history record, the backup data corresponding to the first history record is restored to the terminal device.
  • an embodiment of the present application further provides a terminal device, including a memory and a processor,
  • the memory is used to store one or more computer programs; when the one or more computer programs stored in the memory are executed by the processor, the terminal device can implement the first aspect or the first aspect Any possible design method.
  • an embodiment of the present application further provides a terminal device, where the terminal device includes a module/unit that performs the method of the first aspect or any one of the possible designs of the first aspect; these modules/units may be implemented by hardware It can also be implemented by hardware executing corresponding software.
  • a computer-readable storage medium is also provided in an embodiment of the present application.
  • the computer-readable storage medium includes a computer program, and when the computer program runs on a terminal, the terminal executes the first aspect or the above-mentioned first On the one hand, any possible design method.
  • an embodiment of the present application further provides a computer program product, which, when the computer program product runs on a terminal device, causes the terminal device to perform the first aspect or any one of the foregoing possible aspects Design method.
  • FIG. 1 is a schematic diagram of a model provided by an embodiment of this application.
  • FIG. 2 is a schematic diagram of a model and model parameters provided by an embodiment of this application.
  • FIG. 3 is a schematic structural diagram of a mobile phone 100 provided by an embodiment of the present application.
  • FIG. 4 is a schematic diagram of switching between model training and model use provided by an embodiment of the present application.
  • FIG. 5 is a schematic flowchart of a model training method provided by an embodiment of this application.
  • FIG. 6 is a schematic diagram of training data provided by an embodiment of the present application.
  • FIG. 7 is a schematic flowchart of a data backup method provided by an embodiment of this application.
  • FIG. 8 is a schematic diagram of a characteristic parameter provided by an embodiment of the present application.
  • FIG. 9 is a schematic diagram of another characteristic parameter provided by an embodiment of the present application.
  • FIG. 10 is a schematic diagram of application and model parameters provided by embodiments of the present application.
  • FIG. 11 is a schematic diagram of a display interface of a mobile phone 100 provided by an embodiment of this application.
  • FIG. 12 is a schematic diagram of a display interface of the mobile phone 100 provided by an embodiment of the present application.
  • the application (app) involved in the embodiments of the present application is a computer program capable of realizing a specific function or multiple specific functions.
  • multiple applications can be installed in the terminal device.
  • camera applications For example, camera applications, SMS applications, MMS applications, various mailbox applications, WeChat, Tencent chat software (QQ), WhatsApp Messenger, Line (Me), photo sharing (instagram), Kakao Talk, Nail, etc.
  • the application program mentioned below may be an application program that comes with the terminal when it is shipped from the factory, or an application program that the user downloads from the network side during the use of the terminal.
  • the model involved in the embodiments of the present application includes one or more functions/equations.
  • the model mentioned in the embodiment of the present application may be an existing algorithm, such as logistic regression (LR), naive bayes (NB) classification algorithm, random forest (RF) algorithm, support Vector machine (support vector machine, SVM) algorithm and so on.
  • LR logistic regression
  • NB naive bayes
  • RF random forest
  • SVM support Vector machine
  • a model is an equation including model parameters, input parameters, and output parameters. Given the specific values of the model parameters and input parameters, the specific values of the output parameters can be obtained by calculating the equations.
  • the following uses the neural network unit as an example to introduce the model parameters, input parameters, and output parameters of the model.
  • the model is a neural unit.
  • the neural unit is calculated as follows:
  • x 1 -x n are multiple input parameters of the neural unit;
  • w is the coefficient of the input parameter (also called weight);
  • b is the offset of the input parameter (used to indicate the intercept of u from the origin of the coordinate) ;
  • F is a function (such as Sigmoid function, tanh function, etc.) used to ensure that the value range of the output result is within the interval [0, 1].
  • the model parameters are the weight w and the offset b, and the input parameter is x.
  • the output result y can be obtained by the above formula.
  • the model shown in FIG. 1 is only an example. In the embodiment of the present application, other models may also be used, and different models have different model parameters. It should be noted that from the above formula, the terminal device needs to know the specific values of the model parameters (such as weight w and offset b) and input parameters (such as x) to determine the output parameter y. value.
  • the model parameters such as weight w and offset b
  • input parameters such as x
  • the terminal device needs to determine the values of model parameters (such as w and b) before using the model.
  • This process is the model training process, which will be described later.
  • the model training process involved in the embodiments of the present application is a process of determining appropriate model parameter values of a model.
  • the terminal device may store the values of multiple sets of model parameters of the model.
  • FIG. 2 shows the correspondence between the model and model parameter values provided in the embodiments of the present application.
  • the model training process is a process in which the terminal device determines a suitable set of model parameter values from multiple sets of model parameter values.
  • the value of the model parameter may be determined by the designer according to existing experience or experiments before the mobile phone 100 is shipped from the factory, and stored in the mobile phone 100 for use.
  • the values of the model parameters shown in FIG. 2 are only examples for the convenience of readers to understand, and are not limitations on the values of the model parameters.
  • the values of w and b can be independent, for example, w has multiple values, b also has multiple values, and the terminal device can Choose one of the multiple values of w, select one of the multiple values of b, and use them randomly.
  • the terminal device can determine whether it is appropriate to select the second group of model parameter values. Specifically, the terminal device can substitute the value of the known input parameters x 1 -x n into formula (2) to obtain the value of y. If the value is consistent with the actual value (described later), then The second set of model parameters is appropriate. If the value is not consistent with the actual value, the second set of model parameters is not suitable. The terminal device can reselect a set of model parameters until the selected model parameter is selected. Until the value is appropriate (the specific process will be described later).
  • the training data designed in the embodiment of the present application is used to train the model data, including: the above-mentioned process of determining whether the second set of model parameter values are appropriate, the values of known input parameters x 1 -x n used , and The preset value.
  • the model After the model training is completed, that is, after the values of the model parameters w and b are determined, the model can be used, that is, the process of using the model.
  • the characteristic parameters involved in the embodiments of the present application include behavior data of a user's process of using a terminal device (such as a mobile phone).
  • the feature parameters are also input parameters of the model, which can be one or more.
  • the characteristic parameter may be an input parameter x 1 -x n .
  • the characteristic parameters include the time when the user last backed up WeChat data, the duration of WeChat usage (for example, the duration of the time since the last backup), and the number of usages of WeChat (such as the time since the last backup) Within the number of uses) and so on.
  • the terminal device can set the user's last time to backup WeChat data as x 1 , the use time of WeChat as x 2 , and the number of uses of WeChat as x 3 .
  • the terminal device substitutes x 1 -x 3 into formula (2) to obtain the output result y.
  • the characteristic parameter is an input parameter of the model
  • the terminal device runs the model to perform calculation to obtain an output result
  • the output result represents a backup probability. Since the behavior data of the user operating each application in the terminal device is different, the characteristic parameters of each application are different. Therefore, when the terminal device uses the characteristic parameters corresponding to different applications as the input parameters of the model, it will obtain different output results, and the output results are used to indicate the backup probability of the application.
  • the personal data involved in the embodiments of the present application are data generated by users during the use of terminal devices.
  • Different applications produce different personal data. For example, pictures and videos taken by the camera music of the terminal device; communication records (such as chat records) generated by the user using the communication applications (such as WeChat and QQ) in the terminal device; information stored in the terminal device memo application, etc.; terminal Contacts stored in the phone application in the device, contact information of the contact, call logs, etc.
  • the terminal device uses the detected characteristic parameters of an application (such as a WeChat application) as input parameters of the model, and runs the model to obtain an output result, which represents the backup probability of the personal data of the WeChat application.
  • the backup probability is large, the terminal device backs up the personal data (chat records, etc.) of the WeChat application; when the backup probability is small, the terminal device does not back up the personal data of the WeChat application.
  • Multiple involved in the embodiments of the present application means greater than or equal to two.
  • the terminal device may be a portable terminal, such as a mobile phone, a tablet computer, a wearable device (such as a smart watch) with wireless communication function, and the like.
  • the portable terminal includes a device capable of detecting characteristic parameters such as a touch sensor, and a device (such as a processor) that runs a formula of a model for calculation.
  • Exemplary embodiments of portable terminals include but are not limited to piggybacking Or portable terminals of other operating systems.
  • the above-mentioned portable terminal may also be other portable terminals, as long as it can detect the characteristic parameters and run the formula of the model for calculation. It should also be understood that in some other embodiments of the present application, the above terminal device may not be a portable terminal, but a device capable of detecting characteristic parameters and a desktop computer that calculates by running a formula of a model.
  • the terminal device may not need to have the ability to calculate the formula of the running model, but only need to have the communication capability, and can send the detected characteristic parameters to others having the formula of the running model for the calculation capability.
  • the output result is calculated by the formula of the model of other devices running, and the terminal only needs to receive the output result sent by the cloud server.
  • the terminal device detects the characteristic parameters by itself and runs the model for calculation as an example.
  • FIG. 3 shows a schematic structural diagram of the mobile phone 100.
  • the mobile phone 100 may include a processor 110, an external memory interface 120, an internal memory 121, an antenna 1, an antenna 2, a mobile communication module 151, a wireless communication module 152, a sensor module 180, a key 190, a display screen 194, a positioning module 160, and the like.
  • the sensor module 180 may include a pressure sensor 180A, a touch sensor 180K, etc. (the mobile phone 100 may also include other sensors such as a distance sensor, a fingerprint sensor, a temperature sensor, an ambient light sensor, a gyro sensor, etc., not shown in the figure).
  • the structure illustrated in the embodiment of the present application does not constitute a specific limitation on the mobile phone 100.
  • the mobile phone 100 may include more or fewer components than shown, or combine some components, or split some components, or arrange different components.
  • the illustrated components can be implemented in hardware, software, or a combination of software and hardware.
  • the components of the mobile phone 100 shown in FIG. 3 are described below.
  • the processor 110 may include one or more processing units, for example, the processor 110 may include an application processor (application processor, AP), a modem processor, a graphics processor (graphics processing unit, GPU), and an image signal processor (image)signal processor (ISP), controller, memory, video codec, digital signal processor (DSP), baseband processor, and/or neural-network processing unit (NPU) Wait.
  • application processor application processor
  • AP application processor
  • GPU graphics processor
  • ISP image signal processor
  • controller memory
  • video codec digital signal processor
  • DSP digital signal processor
  • NPU neural-network processing unit
  • the controller may be the nerve center and command center of the mobile phone 100.
  • the controller can generate the operation control signal according to the instruction operation code and the timing signal to complete the control of fetching instructions and executing instructions.
  • the processor 110 may also be provided with a memory for storing instructions and data.
  • the memory in the processor 110 is a cache memory.
  • the memory may store instructions or data that the processor 110 has just used or recycled. If the processor 110 needs to use the instruction or data again, it can be directly called from the memory. Avoid repeated access, reduce the waiting time of the processor 110, thus improving the efficiency of the system.
  • the processor 110 can run the formula of the model to calculate.
  • the NPU can run the formula of the model for calculation.
  • the formula of the model may be run on one of the at least two devices. For example, for a model with a more complicated operation process (such as a more complex formula), it can be run by a device with a relatively strong calculation capability, and for a model with a simpler operation process (such as a simpler formula), it can be operated by a relatively weaker one. Device to run.
  • the model when the model is an RF algorithm, the model may be run by the NPU, and when the model is an LR algorithm, the model may be run by the application processor.
  • the model when the model is an RF algorithm, the model may be run by the NPU, and when the model is an LR algorithm, the model may be run by the application processor.
  • the detection of the characteristic parameter may be performed by the processor 110.
  • the characteristic parameter is the behavior data of the user operating the mobile phone 100, such as the usage record of the app. Therefore, the processor 110 can record the usage of the application. Taking WeChat as an example, the processor 110 can record the current usage time, duration (for example, the duration of the time since the last backup) and the number of usage (for the duration of the time since the last backup) Wait.
  • the internal memory 121 may be used to store computer executable program code, where the executable program code includes instructions.
  • the processor 110 executes instructions stored in the internal memory 121 to execute various functional applications and data processing of the mobile phone 100.
  • the internal memory 121 may include a storage program area and a storage data area.
  • the storage program area can store software codes of the operating system and application programs (such as WeChat applications, camera applications, etc.).
  • the storage data area may store personal data created during the use of the mobile phone 100 (such as pictures and videos taken by the camera application).
  • the internal memory 121 can also be used to store the values of the model and model parameters.
  • the internal memory can store the correspondence between the model and the values of the model parameters shown in FIG. 2.
  • the correspondence between the model shown in FIG. 2 and the values of model parameters can be stored in the storage program area or the storage data area.
  • the internal memory 121 may also store characteristic parameters, probability thresholds (described later), and the like.
  • the internal memory 121 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, a universal flash memory (universal flash storage, UFS), and so on.
  • a non-volatile memory such as at least one magnetic disk storage device, a flash memory device, a universal flash memory (universal flash storage, UFS), and so on.
  • the positioning module 160 is used to locate the current geographic location of the mobile phone 100.
  • the positioning method adopted by the positioning module 160 may be one or more methods such as GPS, base station, WiFi hotspot, Bluetooth (iBacon), assisted GPS (assisted GPS, AGPS) and so on.
  • the positioning module 160 may send the current geographic location information to the processor 110.
  • the processor 100 uses the current geographic location information as the input parameter of the model, and runs the formula of the model for calculation (to be described later).
  • the external memory interface 120 is used to connect the external memory with the mobile phone 100, where the external memory includes an external memory card (SD memory card) and a NAS storage device, which are not limited in the embodiments of the present application.
  • the processor 110 backs up data according to the data backup method provided in the embodiment of the present application, the personal data to be backed up may be stored in the external storage through the external storage interface 120 (or the personal data may be backed up to the cloud such as Baidu network disk, etc.).
  • the functions of the sensor module 180 are described below.
  • Touch sensor 180K also known as "touch panel”.
  • the touch sensor 180K may be provided on the display screen 194, and the touch sensor 180K and the display screen 194 constitute a touch screen, also called a "touch screen”.
  • the touch sensor 180K is used to detect a touch operation acting on or near it.
  • the touch sensor can pass the detected touch operation to the application processor to determine the type of touch event.
  • the visual output related to the touch operation may be provided through the display screen 194.
  • the touch sensor 180K may also be disposed on the surface of the mobile phone 100, which is different from the location where the display screen 194 is located.
  • the touch sensor 180K may assist the processor 110 to detect characteristic parameters.
  • the main interface includes icons of multiple applications, such as a camera application, a phone application, a WeChat application, and a QQ application.
  • the touch sensor 180K can detect the user's touch operation on the touch screen and send the touch operation to the processor 110, and the processor 100 can determine the icon corresponding to the touch operation based on the touch operation, that is, determine the application that the user wants to click.
  • the processor 110 records the operation of the application clicked by the user. For example, the processor 100 records that the user clicked on WeChat once at 10:00, and clicks on WeChat again at 12:00, and the processor 100 records that the user used WeChat twice in the morning.
  • the touch sensor 180K detects a touch operation to start the first application at the first moment (such as an operation of clicking an icon of the first application in the main interface), and a touch operation to exit the first application at the second moment .
  • the touch sensor 180K sends the two touch operations and the time corresponding to each touch operation to the processor 110.
  • the processor 110 records the use duration of the first application as the time difference between the first moment and the second moment. It can be seen that the processor 110 determines the user's use history of the application through the touch operation detected by the touch-sensitive sensor 180K.
  • the mobile phone 100 can also receive an input operation through the key 190 and send the input operation to the processor 110, and the processor 110 determines icons corresponding to the input operation, such as a camera application, a phone application, a WeChat application, a QQ application, and the like.
  • the display screen 194 is used to display images, videos and the like.
  • the display screen 194 includes a display panel.
  • the display panel may use a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active matrix organic light-emitting diode or an active matrix organic light-emitting diode (active-matrix organic light) emitting diode, AMOLED, flexible light-emitting diode (FLED), Miniled, MicroLed, Micro-oLed, quantum dot light emitting diode (QLED), etc.
  • the mobile phone 100 may include 1 or N display screens 194, where N is a positive integer greater than 1.
  • the display screen 194 is used to display a display interface of an application, such as an interface of a WeChat application or Alipay.
  • the display interface is also used to display historical backup records, which include records of data backup performed by the mobile phone 100 using the data backup method provided by the embodiment of the present application.
  • the wireless communication function of the mobile phone 100 can be realized by the antenna 1, the antenna 2, the mobile communication module 150, the wireless communication module 160, the modem processor and the baseband processor.
  • Antenna 1 and antenna 2 are used to transmit and receive electromagnetic wave signals.
  • Each antenna in the electronic device 100 may be used to cover a single or multiple communication frequency bands. Different antennas can also be reused to improve antenna utilization.
  • the antenna 1 can be multiplexed as a diversity antenna of a wireless local area network. In other embodiments, the antenna may be used in conjunction with a tuning switch.
  • the mobile communication module 150 may provide a wireless communication solution including 2G/3G/4G/5G and the like applied to the electronic device 100.
  • the mobile communication module 150 may include at least one filter, switch, power amplifier, low noise amplifier (LNA), and so on.
  • the mobile communication module 150 can receive electromagnetic waves from the antenna 1 and filter, amplify, etc. the received electromagnetic waves, and transmit them to the modem processor for demodulation.
  • the mobile communication module 150 can also amplify the signal modulated by the modulation and demodulation processor and convert it to electromagnetic wave radiation through the antenna 1.
  • at least part of the functional modules of the mobile communication module 150 may be provided in the processor 110.
  • at least part of the functional modules of the mobile communication module 150 and at least part of the modules of the processor 110 may be provided in the same device.
  • the modem processor may include a modulator and a demodulator.
  • the modulator is used to modulate the low-frequency baseband signal to be transmitted into a high-frequency signal.
  • the demodulator is used to demodulate the received electromagnetic wave signal into a low-frequency baseband signal.
  • the demodulator then transmits the demodulated low-frequency baseband signal to the baseband processor for processing.
  • the low-frequency baseband signal is processed by the baseband processor and then passed to the application processor.
  • the application processor outputs a sound signal through an audio device (not limited to a speaker 170A, a receiver 170B, etc.), or displays an image or video through a display screen 194.
  • the modem processor may be an independent device.
  • the modem processor may be independent of the processor 110, and may be set in the same device as the mobile communication module 150 or other functional modules.
  • the wireless communication module 160 can provide wireless local area networks (wireless local area networks, WLAN) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (bluetooth, BT), and global navigation satellites that are applied to the electronic device 100.
  • System global navigation satellite system, GNSS
  • frequency modulation frequency modulation, FM
  • near field communication technology near field communication, NFC
  • infrared technology infrared, IR
  • the wireless communication module 160 may be one or more devices integrating at least one communication processing module.
  • the wireless communication module 160 receives electromagnetic waves via the antenna 2, frequency-modulates and filters electromagnetic wave signals, and transmits the processed signals to the processor 110.
  • the wireless communication module 160 may also receive the signal to be transmitted from the processor 110, frequency-modulate it, amplify it, and convert it to electromagnetic wave radiation through the antenna 2.
  • the detected characteristic parameters can be sent through the mobile communication module 151 or the wireless communication module 152
  • the other devices perform the calculations and get the output results.
  • the mobile phone 100 receives the output result sent by other devices through the mobile communication module 151 or the wireless communication module 152.
  • the mobile phone 100 may further include a camera, such as a front camera and a rear camera; it may also include a motor for generating a vibration prompt (such as a call vibration prompt); and may also include an indicator such as an indicator light. It can also be used for indicating messages, missed calls, notifications, etc. for indicating the state of charge and the change in power.
  • the mobile phone 100 may also include an audio module (speaker, receiver, microphone, headphone jack) and so on.
  • FIG. 4 is a schematic flowchart of a data backup method provided by an embodiment of the present application.
  • the mobile phone 100 trains the model.
  • the mobile phone 100 determines a suitable set of model parameter values from multiple sets of model parameter values of the model (for example, from the two groups shown in FIG. 2) Select a set of model parameter values among the model parameter values), based on this set of model parameter values, train the model to obtain the training result. If the training result meets the conditions (described later), the model training process ends. If the training result does not satisfy the condition, the mobile phone 100 continues to determine another set of model parameter values from multiple sets of model parameter values. Therefore, during the model training process, the mobile phone 100 determines the values of the model parameters.
  • the mobile phone 100 uses the model (the model whose value of the model parameter has been determined) to obtain the backup probability.
  • the mobile phone 100 performs data backup based on the backup probability (for example, when the backup probability is small, the mobile phone 100 does not back up data, and when the backup probability is large, the mobile phone 100 backs up data).
  • the model if the period arrives or the model does not meet the conditions (described later), the mobile phone 100 performs model training again and repeats the foregoing process.
  • the mobile phone 100 uses the characteristic parameter as the input parameter of the model to obtain the backup probability. In other words, the mobile phone 100 can determine the backup probability of each application according to the user's habit of using the application. If the backup probability is large, the mobile phone 100 backs up the data of the application, and if the backup probability is small, the mobile phone 100 does not back up the application The data does not need to be manually operated by the user, which is convenient for the user to operate.
  • FIG. 5 is a schematic flowchart of the model training process provided by the embodiment of the present application.
  • the model is the neural unit shown in FIG. 1 as an example.
  • the process includes:
  • the mobile phone 100 selects a set of model parameter values from multiple sets of model parameter values of the model, and brings them into the formula (1) of the model to obtain a model with known model parameter values.
  • the model with known model parameter values is model A.
  • Formula (2) is a model with known values of model parameters, and hereinafter referred to as formula (2) is model A.
  • the mobile phone 100 obtains training data, which includes known values and actual results of input parameters, where the input parameters include application usage records and actual results; the actual results reflect the user's actual backup results.
  • FIG. 6 is a schematic diagram of training data provided by an embodiment of the present application.
  • the training data shown in Figure 6 includes different applications (such as application AC), the duration of use of each application (the duration of the time from the last backup to the time of use), the number of uses, the highest frequency of use per day, and the most frequent use per day Long time, interval from last use to present, application category, etc.
  • the training data also includes the actual result a corresponding to each application.
  • the actual result a may be the result of the user manually backing up data. For example, taking application A as an example, if the user manually backs up the data of application A within the preset time, the actual result a is 1, if the user does not manually back up the data of application A within the preset time, the actual result a is 0. In this way, the actual result a is updated dynamically. For example, the actual result a corresponding to the application A in the previous period of time is 1, and the actual result a corresponding to the next period of time is 0.
  • the actual result a may also be preset manually, and stored in the mobile phone 100 after the mobile phone 100 leaves the factory. In this way, the actual result a is a fixed value.
  • the actual result a may also be stored in the cloud server, for example, a person on the cloud server side manually sets the value of the actual result a.
  • the process of training the model on the mobile phone 100 may be performed by the cloud server.
  • the cloud server trains the model
  • the trained model (the model with the determined parameter values) is pushed to the mobile phone 100 uses. Therefore, the cloud server can collect records of user usage applications (for example, the mobile phone 100 sends the user usage records to the cloud server, etc.), and the staff on the cloud server side manually sets the actual result a.
  • the cloud server trains the model based on the process shown in FIG. 5 to obtain the output result y. If the output result y is compared with the actual result set manually, if they are consistent, the model training is successful. If they are inconsistent, the model training is unsuccessful. Select model parameters.
  • the mobile phone 100 takes the usage record of the application in the training data as the input parameter of the model A, runs the model A to perform calculation, and obtains the output result y.
  • the duration is the first input parameter, which is represented by x1, for example, the value of x1 is 5min.
  • the time interval last used so far as the fifth input parameter, expressed as x5, for example, the value of x5 is 50min.
  • the application category is represented by x6.
  • the value of x6 is 3.
  • the user's usage records of the applications in the mobile phone 100 change in real time, and the usage records of the applications used in the model training process may be historical usage records.
  • the mobile phone 100 uses the trained model and the current usage record to calculate the backup probability. For example, the mobile phone 100 uses the WeChat usage record training model two days ago, and then uses the WeChat usage record and the trained model today to calculate the backup probability.
  • the mobile phone 100 can set application categories of different applications, for example, phone and memo are system applications, music players and video players are multimedia applications, and QQ and WeChat are social applications.
  • the mobile phone 100 is set to encode different application categories, for example, the system application is encoded as 1, the multimedia application is encoded as 2, and the social application is encoded as 3. Since application A is WeChat and belongs to a social application, the value of x6 is 3.
  • the mobile phone 100 brings the values of x1-x6 into the above formula (2), and taking f in the formula (2) as a tanh function as an example, the following formula is obtained:
  • the value of the output result y can be obtained. It is assumed that the output result y obtained by the mobile phone 100 is 0.6, that is, the backup probability of application A, that is, WeChat, is 0.6.
  • S504 The mobile phone 100 determines the training result b according to the output result y.
  • the training result b may be determined based on the backup probability. For example, if the output result y (that is, backup probability) is greater than the probability threshold (such as 0.5), you need to back up the data, that is, the training result b is 1. When the output result y (backup probability) is less than or equal to the probability threshold, no backup data is needed, that is, the training result b is 0. Assuming that the mobile phone 100 obtains a backup probability of 0.6, which is greater than the preset probability of 0.5, the mobile phone 100 determines to train the model A using the usage record of the application A, and the obtained training result b is 1.
  • the probability threshold such as 0.5
  • S505 The mobile phone 100 judges whether the training result b is consistent with the actual result a. If the training result is consistent, the model training is ended; if not, the mobile phone 100 executes S501.
  • the mobile phone 100 obtains the training result b based on the output result y, so in S504, the mobile phone 100 can compare whether the training result b is consistent with the actual result a.
  • the mobile phone 100 restarts from Choose another set of model parameter values among multiple sets of model parameter values.
  • the model A is more accurate and the model training process ends.
  • the above probability threshold may be determined by the designer based on experiments before the mobile phone 100 is shipped from the factory, stored in the mobile phone 100, may be user-defined, or may be determined by the mobile phone 100 in other ways ( For example, the mobile phone 100 draws the ROC curve according to the training result and the actual result, and then determines the probability threshold according to the ROC curve.
  • the process of drawing the ROC curve and the process of determining the probability threshold on the ROC curve are the same as the prior art, and there are not many embodiments of the present application Repeat).
  • the model training process is a process for the mobile phone 100 to determine a suitable model parameter value. After the mobile phone 100 determines the appropriate model parameter value, the model can be used to calculate the backup probability. The following describes the process by which the mobile phone 100 uses this model to calculate the backup probability.
  • FIG. 7 is a schematic flowchart of a data backup method provided by an embodiment of the present application. As shown in Figure 7, the process includes the following:
  • the mobile phone 100 collects characteristic parameters, and the characteristic parameters include a usage record of a user using an application in the mobile phone 100. Taking WeChat as an example, specifically, the characteristic parameters include one or more of the following parameters:
  • Parameter 1 (x1): duration of use (unit min)
  • parameter 2 (x2) number of uses (unit times/day)
  • parameter 3 (x3): highest frequency of use in a single day (unit times/day)
  • parameter 4 (x4) :Maximum use time in a single day (unit min)
  • parameter 5 (x5): time interval (unit min) since the last use
  • parameter 6 (x6): application category.
  • FIG. 8 is an example of values of different parameters among the characteristic parameters provided by the embodiments of the present application. Taking WeChat as an example, the mobile phone 100 collects the values of x1-x6.
  • S702 The mobile phone 100 brings the value of x1-x6 into the formula of the model, performs calculation, and obtains the backup probability.
  • the mobile phone 100 brings the values of x1-x6 corresponding to the WeChat application into formula (2) to obtain an output result y.
  • the Meituan application and memo in FIG. 8 are calculated in a similar manner (for example, the mobile phone 100 determines that the backup probability of the Meituan application is 0.3 and the backup probability of the memo is 0.6). Repeat.
  • S703 The mobile phone 100 judges whether the backup probability is greater than the probability threshold, if so, the data is backed up, if not, the data is not backed up.
  • the mobile phone 100 obtains the backup probability of each application.
  • the backup probability when the backup probability is large, the mobile phone 100 backs up the data of the application, and when the backup probability is small, the mobile phone 100 does not back up the data of the application.
  • the mobile phone 100 may compare the obtained output result y (that is, the backup probability) with a probability threshold. If the calculated backup probability is greater than the probability threshold, the mobile phone 100 backs up data, and if the calculated backup probability is less than or equal to the probability threshold, the mobile phone 100 does not back up the data.
  • the mobile phone 100 calculates the backup probability of the WeChat application as 0.9, and the backup probability is greater than the probability threshold (such as 0.5), then the mobile phone 100 backs up the data of the WeChat application.
  • the mobile phone 100 calculates that the backup probability of the Meituan application is 0.3, and the backup probability is less than the probability threshold (such as 0.5), and the mobile phone 10 does not back up the data of the Meituan application.
  • the characteristic parameters include the six parameters of the application shown in FIG. 8, that is, x1-x6 as an example.
  • the characteristic parameters may further include more parameters, for example, the current geographic location of the mobile phone 100, the last backup interval of the application, and the proportion of holidays.
  • the characteristic parameters of the WeChat application include x7-x11 in addition to the above x1-x6 as an example.
  • Parameter 7 (x7) indicates the movement trajectory.
  • the mobile phone 100 can record a collection of places the user has visited during the period from the last backup to the WeChat application.
  • the mobile phone 100 may be divided into different locations for different locations, for example, homes are classified as living places, offices are classified as workplaces, shopping malls are classified as entertainment venues, and parks are classified as tourist venues.
  • the workplace category is represented by 1
  • the residence category is represented by 2
  • the tourism venue category is represented by 3
  • the entertainment venue category is represented by 4.
  • the movement trajectory during this period is [1,1,1,1,2,2,2,2,2,2,3,4]. There are 4 1s in the movement track that have been to the workplace 4 times.
  • Parameter 9 indicates that the user manually backs up history. Generally, when a user manually backs up data, the backup software is opened to select the application to be backed up, and then the data of the application selected by the user is backed up. The mobile phone 100 sets a number for each application in the application list in the backup software, such as contact-501, memo-502, short message-503, alarm clock-504, WeChat-505, and so on. If the user manually backs up, select multiple applications such as SMS, alarm clock, and WeChat. Then x9 is [503,504,505].
  • Parameter 10 indicates the time interval since the last backup, the unit can be days, hours or minutes.
  • Parameter 11 indicates the number of sharing. Taking the WeChat application as an example, x11 represents the number of times that the WeChat application has shared data in the WeChat application since the last backup to the current time, such as the number of times the friend circle shared pictures.
  • the training data adopted by the mobile phone 100 during the model training process also includes more parameters (that is, the training data shown in FIG. 7 also includes more parameters).
  • the mobile phone 100 runs the model, more parameters are used as input parameters to run the model. Specifically, the mobile phone 100 brings x1-x11 shown in FIG. 9 into formula (3) to obtain an output result y.
  • the above describes the process of the mobile phone 100 from training the model to using the model.
  • the process of switching from using the model to training the model of the mobile phone 100 is described below.
  • the mobile phone 100 may set a certain period, which represents the duration of use of a model, and the period may be set by the mobile phone 100 when it is shipped from the factory, or may be user-defined. For example, after the mobile phone 100 trains the model, when the duration of using the model reaches a preset period (for example, one day), the mobile phone 100 automatically performs model training.
  • a preset period for example, one day
  • the mobile phone 100 may train the model after each power-on; or, when the mobile phone 100 detects that the user actively triggers the operation of training data, retrain the model.
  • the model is the neural unit shown in FIG. 1 and the weight w of each input parameter in the formula (1) of the neural unit is the same.
  • Another embodiment is described below. In this embodiment, the weights of different input parameters may be different.
  • the mobile phone 100 can divide applications into different categories, such as Taobao, Tmall, JD.com, Alipay, etc., which belong to the shopping category, and WeChat, QQ, etc. belong to the social category; applications such as the memo, phone, etc., which come with the mobile phone 100 when shipped from the factory are system categories.
  • Different types of applications have different weights. As shown in FIG. 10, the weight value of the social application is w0 or w1, the weight value of the shopping application is w2 or w3, and the weight value of the system application is w4. It should be noted that w0-w4 is not a variable, but a specific value. For simplicity, this article only emphasizes that each value in w0-w4 is different, and does not give specific values.
  • the weights of different input parameters in the characteristic parameters of an application may be different. Please continue to refer to FIG. 10. Taking social applications as an example, the weight of the duration and number of times (the duration and number of times during the period since the last backup) can be higher than other parameters.
  • the weight of each application is different. Therefore, in this embodiment, during the model training of the mobile phone 100, different types of applications can be trained separately. For example, for social applications or system applications, the models shown in FIG. 5 can be used for training respectively.
  • the mobile phone 100 substitutes the weights corresponding to different input parameters into the formula (1), and obtains the following formula:
  • the mobile phone 100 uses the above formula (5) to train the model.
  • the model parameters used in the model training process are different, that is, different types of applications have different model parameters determined through the training process. Therefore, in the process of using the model, for different types of applications, the mobile phone 100 can use different models (different model parameters) for calculation.
  • model training process and model use process shown in FIG. 4 are introduced above.
  • the model training process and/or model use process may be performed by other devices (such as cloud servers).
  • the model training process is performed by other devices, and the model usage process is performed by the mobile phone 100.
  • the mobile phone 100 can send training data to other devices, and the other devices use the process shown in FIG. 5 to perform model training based on the training data, that is, other devices determine appropriate model parameters.
  • Other devices send the determined model parameters to the mobile phone 100, and then the mobile phone 100 can determine the appropriate model parameters after the model training process, which helps to save the calculation amount.
  • the above data backup function may be a function that starts to be used after the mobile phone 100 is activated; or it may be used after the user triggers the activation of this function in the process of using the mobile phone 100 Function.
  • three controls are displayed on the display interface 1101 of the mobile phone 100, namely: "Automatic Backup All” 1102, “Manual Backup” 1103, and “Automatic Backup According to Habit” 1104.
  • the mobile phone 100 periodically and automatically backs up all application data.
  • the mobile phone 100 displays a backup list, and the user can select an application to be backed up in the list, and the mobile phone 100 only backs up data of the application selected by the user.
  • the "automatic backup according to habit" 1104 is activated, the mobile phone 100 backs up data according to the data backup method provided by the embodiment of the present application.
  • the display interface 1101 shown in FIG. 11 may be an application interface of an application (such as a setting application) in the mobile phone 100; or, the display interface 1101 may automatically pop up, such as the mobile phone 100 detecting that data has not been backed up for a long time or the current When the mobile phone 100 stores more data, the display interface 1101 may be automatically popped up.
  • an application such as a setting application
  • the display interface 1201 shown in FIG. 12 may be displayed.
  • the application list is displayed on the display interface 1201, and the user can select one or more applications in the application list.
  • the selected application such as WeChat
  • the mobile phone 100 can be backed up according to the data backup method provided in the embodiment of the present application
  • the unselected applications such as Weibo
  • the mobile phone 100 can be not backed up or periodically automatically Backup.
  • the mobile phone 100 uses the data backup method provided by the embodiment of the present application to back up data, it can be automatically backed up periodically or automatically when one of the following trigger conditions is detected.
  • Condition 1 The mobile phone 100 detects that the power supply is plugged in (for example, the mobile phone 100 detects that the charging state is entered).
  • Condition 2 The mobile phone 100 detects the insertion of an external memory card (such as an SD memory card).
  • Condition 3 The time when the mobile phone 100 detects that the screen is turned off is greater than or equal to the preset time (for example, 5 min).
  • Condition 4 The time period when the mobile phone 100 detects that the time since the last backup was successful is greater than or equal to the preset time period;
  • Condition 5 The mobile phone 100 detects that the current power level is greater than the preset power level (such as 75%).
  • the backup may be interrupted for some reason.
  • the mobile phone 100 detects that the available space on the SD card is insufficient (notification in the notification bar); for another example, when the mobile phone 100 detects that the power supply is unplugged during the backup process, the backup is interrupted, and when the mobile phone 100 detects the reinsertion of the power supply again, continue Backup.
  • the mobile phone 100 detects that the current power is less than the preset power (for example, 5%).
  • the user when there are at least two external memories connected to the mobile phone 100, the user can select which memory to back up data to.
  • the mobile phone 100 displays an option of at least two external memories (such as two SD cards).
  • the mobile phone 100 detects that an external storage is selected, the mobile phone 100 backs up data to the external storage.
  • the external storage can also be a network-attached storage device (Network Attached Storage, NAS), such as Western Digital (WD).
  • NAS Network Attached Storage
  • WD Western Digital
  • the triggering condition may include Condition 6 in addition to the above-mentioned conditions, and the mobile phone 100 detects that the Western Digital is currently logged in.
  • the method provided by the embodiments of the present application is introduced from the perspective of the terminal device (mobile phone 100) as an execution subject.
  • the terminal may include a hardware structure and/or a software module, and implement the above functions in the form of a hardware structure, a software module, or a hardware structure plus a software module. Whether one of the above functions is executed in a hardware structure, a software module, or a hardware structure plus a software module depends on the specific application of the technical solution and design constraints.
  • An embodiment of the present invention also provides a computer-readable storage medium.
  • the storage medium may include a memory, and the memory may store a program.
  • the program When executed, the computer execution includes the execution as before.
  • FIG. 5 and FIG. 7 All or part of the steps performed by the terminal device described in the illustrated method embodiment.
  • An embodiment of the present invention also provides a computer program product that, when the computer program product runs on a terminal, causes the terminal device to execute the terminal described in the method embodiments shown in FIG. 5 and FIG. 7 All or part of the steps performed by the device.
  • Computer-readable media includes computer storage media and communication media, where communication media includes any medium that facilitates transfer of a computer program from one place to another.
  • a storage medium may be any available medium that can be accessed by a computer.
  • computer readable media may include RAM, ROM, electrically erasable programmable read-only memory (electrically erasable programmable read only memory, EEPROM), compact disc read-only (memory, CD- ROM) or other optical disk storage, magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer. Also. Any connection can become a computer-readable medium as appropriate.
  • disks and discs include compact discs (CDs), laser discs, optical discs, digital video discs (DVDs), floppy disks, and Blu-ray discs, wherein Disks usually copy data magnetically, while disks use lasers to copy data optically.
  • CDs compact discs
  • DVDs digital video discs
  • floppy disks floppy disks
  • Blu-ray discs wherein Disks usually copy data magnetically, while disks use lasers to copy data optically.

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Quality & Reliability (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Telephone Function (AREA)
  • Stored Programmes (AREA)
  • User Interface Of Digital Computer (AREA)

Abstract

L'invention concerne un procédé de sauvegarde de données et un dispositif terminal. Le procédé comprend les étapes suivantes : un dispositif terminal acquiert un enregistrement d'utilisation d'un utilisateur à l'aide d'une première application dans le dispositif terminal, et calcule et obtient une probabilité de sauvegarde, la probabilité de sauvegarde étant utilisée pour représenter la probabilité de sauvegarde de données de la première application, et la fréquence de la première application utilisée étant en corrélation positive avec la probabilité de sauvegarde ; et lorsque la probabilité de sauvegarde est supérieure à une valeur seuil de probabilité, le dispositif terminal sauvegarde les données de la première application. Le procédé n'a pas besoin d'une opération manuelle d'un utilisateur et est facilement mis en oeuvre. De plus, la fréquence d'une application utilisée est en corrélation positive avec une probabilité de sauvegarde, c'est-à-dire plus l'application est utilisée fréquemment, plus la probabilité de sauvegarde est élevée, facilitant ainsi l'amélioration de la précision de sauvegarde de données.
PCT/CN2019/122301 2018-12-03 2019-12-02 Procédé de sauvegarde de données et dispositif terminal WO2020114339A1 (fr)

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CN111198785B (zh) * 2019-12-27 2023-05-09 宇龙计算机通信科技(深圳)有限公司 一种数据备份方法、装置、存储介质及终端
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CN116680118A (zh) * 2022-12-23 2023-09-01 荣耀终端有限公司 一种应用备份的方法、电子设备、存储介质及程序产品

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