CN113128695A - Device control method, device, electronic device and storage medium - Google Patents

Device control method, device, electronic device and storage medium Download PDF

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Publication number
CN113128695A
CN113128695A CN201911415966.0A CN201911415966A CN113128695A CN 113128695 A CN113128695 A CN 113128695A CN 201911415966 A CN201911415966 A CN 201911415966A CN 113128695 A CN113128695 A CN 113128695A
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application
information
model
preset
operated
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CN201911415966.0A
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Chinese (zh)
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吴建文
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Guangdong Oppo Mobile Telecommunications Corp Ltd
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Guangdong Oppo Mobile Telecommunications Corp Ltd
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Priority to CN201911415966.0A priority Critical patent/CN113128695A/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/48—Program initiating; Program switching, e.g. by interrupt
    • G06F9/4806—Task transfer initiation or dispatching

Abstract

The application discloses a device control method, a device, an electronic device and a storage medium, wherein the device control method is applied to the electronic device and comprises the following steps: acquiring current use information of the electronic equipment, wherein the current use information at least comprises application information of a first application currently running on the electronic equipment; determining a target model from a plurality of preset models according to the application information of the first application, wherein each preset model is obtained by training based on historical use information of the electronic equipment when the application is operated for multiple times and application information of a next application operated after the application is operated each time when the application is operated for multiple times; inputting the current use information into the target model, and obtaining application information of a second application output by the target model; and performing preprocessing operation before the second application runs according to the application information of the second application. The method can better realize the application prediction and improve the efficiency of application operation.

Description

Device control method, device, electronic device and storage medium
Technical Field
The present application relates to the field of computer technologies, and in particular, to a device control method and apparatus, an electronic device, and a storage medium.
Background
With the rapid development of electronic equipment, various application programs can be installed on the electronic equipment, so that different requirements of users are met. The configuration of the current electronic device is also increasing, and the electronic device can install more applications, for example, dozens or even hundreds of applications. As more and more functions are implemented by current applications, more and more resources are needed for running the applications, and thus the applications may be stuck during running and starting the applications.
Disclosure of Invention
In view of the foregoing, the present application provides a device control method, apparatus, electronic device, and storage medium.
In a first aspect, an embodiment of the present application provides an apparatus control method, which is applied to an electronic apparatus, and the method includes: acquiring current use information of the electronic equipment, wherein the current use information at least comprises application information of a first application currently running on the electronic equipment; determining a target model from a plurality of preset models according to the application information of the first application, wherein each preset model is obtained by training based on historical use information of the electronic equipment when the application is operated for multiple times and application information of a next application operated after the application is operated each time when the application is operated for multiple times; inputting the current use information into the target model, and obtaining application information of a second application output by the target model; and performing preprocessing operation before the second application runs according to the application information of the second application.
In a second aspect, an embodiment of the present application provides an apparatus control device, which is applied to an electronic device, and the apparatus includes: the electronic device comprises an information acquisition module, a model determination module, an application prediction module and a preprocessing module, wherein the information acquisition module is used for acquiring current use information of the electronic device, and the current use information at least comprises application information of a currently running first application; the model determining module is used for determining a target model from a plurality of preset models according to the application information of the first application, wherein each preset model is obtained by training based on historical use information of the electronic equipment during multiple times of application running and application information of a next application running after the application is run each time during multiple times of application running; the application prediction module is used for inputting the current use information into the target model and obtaining application information of a second application output by the target model; the preprocessing module is used for performing preprocessing operation before the second application runs according to the application information of the second application.
In a third aspect, an embodiment of the present application provides an electronic device, including: one or more processors; a memory; one or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs configured to perform the device control method provided by the first aspect above.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where a program code is stored in the computer-readable storage medium, and the program code may be called by a processor to execute the apparatus control method provided in the first aspect.
According to the scheme, by acquiring the current use information of the electronic equipment, the current use information at least comprises application information of a first application currently running on the electronic equipment, a target model is determined from a plurality of preset models according to the application information of the first application, each preset model is obtained based on historical use information of the electronic equipment when the application is run for a plurality of times and application information of a next application running after the application is run each time through training, then the current use information is input into the target model, application information of a second application output by the target model is acquired, and finally preprocessing operation before the second application runs is performed according to the application information of the second application. Therefore, the model for application prediction is selected according to the current application scene, the running application is predicted according to the selected model, and the predicted application is subjected to preprocessing operation before rerun, so that the application prediction can be accurately realized, and the efficiency of application running is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 shows a flow chart of a device control method according to an embodiment of the present application.
Fig. 2 shows a flow chart of a device control method according to another embodiment of the present application.
Fig. 3 shows a flow chart of a device control method according to yet another embodiment of the present application.
Fig. 4 shows a flow chart of a device control method according to yet another embodiment of the present application.
FIG. 5 shows a block diagram of a device control apparatus according to an embodiment of the present application.
Fig. 6 is a block diagram of an electronic device according to an embodiment of the present application, configured to execute a device control method according to an embodiment of the present application.
Fig. 7 is a storage unit according to an embodiment of the present application, configured to store or carry program code for implementing an apparatus control method according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.
At present, with the rapid development of the technology level, the functions of electronic devices such as smart phones and tablet computers become more and more powerful, and people can install various application programs on the electronic devices to watch movies, play games, browse webpages, chat videos and the like. Moreover, the functions that can be realized by the application program are more and more, and the quality of each aspect in the application is higher and higher, so that the resource occupied by the application program in the running process is higher and higher.
Therefore, in order to meet the requirements of users for installing and running more and more applications, the configuration of electronic devices is also higher and higher. However, when the electronic device runs a large number of applications, the phenomena of delay, jamming and the like still occur. For example, the system memory of the electronic device, i.e. a random-access memory (RAM), which is a main area for temporarily storing program instructions and data, is insufficient, and the running of the application program is stalled.
In the conventional technology, usually after an application program runs and when a checkpoint is found, a user can manually optimize a system or an application to meet the requirement of the user for smooth use of the application program, or automatically optimize when the system detects the checkpoint phenomenon, but the normal use of the application by the user is influenced.
In view of the above problems, the inventor proposes a device control method, an apparatus, an electronic device, and a storage medium provided in the embodiments of the present application, which can select a model for application prediction according to a current application scenario, predict an application to be executed according to the selected model, and perform a preprocessing operation before the rerun of the predicted application, so as to implement application prediction more accurately, and further improve efficiency of application execution. The specific device control method is described in detail in the following embodiments.
Referring to fig. 1, fig. 1 is a schematic flowchart illustrating an apparatus control method according to an embodiment of the present application. The device control method is used for selecting a model for application prediction according to the current application scene, predicting the running application according to the selected model, and performing preprocessing operation before the rerun of the predicted application, so that the application prediction can be accurately realized, and the efficiency of application running is further improved. In a specific embodiment, the device control method is applied to the device control apparatus 400 shown in fig. 5 and the electronic device 100 (fig. 6) configured with the device control apparatus 400. The following will describe a specific flow of the embodiment by taking an electronic device as an example, and it is understood that the electronic device applied in the embodiment may be a smart phone, a tablet computer, a smart watch, a PC computer, and the like, which is not limited herein. As will be described in detail with respect to the flow shown in fig. 1, the apparatus control method may specifically include the following steps:
step S110: acquiring current use information of the electronic equipment, wherein the current use information at least comprises application information of a first application currently running on the electronic equipment.
Various applications can be installed in the current electronic equipment to meet the use requirements of users. Although the performance and configuration of the electronic device are continuously improved and advanced, when the electronic device runs an application, a delay and a jam may still occur due to more processes of the currently running application or due to more resources required by the running application.
In order to avoid delay and blockage during starting and running of the application, the application to be started can be predicted, corresponding processing and preparation are carried out before the application is started, so that blockage during starting and running of the subsequent application can not occur, and the running efficiency of the application is improved.
In the embodiment of the application, the electronic device may obtain the current usage information, so as to predict the application to be run according to the current usage information of the electronic device. The current usage information may include application information of a first application currently running on the electronic device and state information of the electronic device, and the state information of the electronic device may include current time, date, week, charging state, peripheral connection state, network connection state, and the like, where the charging state indicates that the electronic device is in a charging or non-charging state, the peripheral connection state indicates whether the electronic device is connected to a peripheral (such as an earphone, a sound, and the like), and the network connection state indicates whether the electronic device is connected to a network. Of course, the specific usage information may not be limited, and for example, information of one or more previous applications run by the electronic device before the first application is run may also be included. The application information may be information for identifying an identity of the application, such as a package name of the application, and is not limited herein.
In some embodiments, the electronic device may acquire current usage information of the electronic device to predict an application to be run when a prediction condition for predicting the application is satisfied. The prediction condition may include: the currently running application is a set application, the current time is set time, the currently running application is switched to background running, the currently running application is exited, or the duration of running the currently running application reaches the set duration, and of course, the specific prediction condition may not be limited. That is to say, the electronic device may obtain the current usage information only when the prediction condition is satisfied, and perform the prediction of the application, without performing the prediction of the application all the time, thereby saving power consumption.
Step S120: and determining a target model from a plurality of preset models according to the application information of the first application, wherein each preset model is obtained by training based on historical use information of the electronic equipment when the application is operated for multiple times and application information of a next application operated after the application is operated each time when the application is operated for multiple times.
In this embodiment of the application, after the electronic device obtains the current usage information including the application information of the currently running first application, a target model may be determined from a plurality of preset models according to the application information of the first application, where the target model is a model used for predicting an application to be run according to the current usage information of the electronic device. Each preset model is trained in advance, so that the application to be run by the electronic equipment can be determined according to the input use information of the electronic equipment.
In some embodiments, the plurality of preset models may be stored locally in the terminal device, and the electronic device may directly read the determined target model locally; the plurality of preset models may also be stored in the server, and the electronic device may send a request to the server to invoke the target model when the application to be run needs to be predicted, which is not limited herein.
In the embodiment of the present application, the preset model can be obtained by training a large number of training samples. The training sample may include historical usage information for the electronic device when running the application multiple times, and application information for a next application that runs after each running of the application when running the application multiple times. The historical usage information may be the same as the usage information obtained by the electronic device, and it can be understood that the obtained current usage information is input data for inputting to the target model, which is obtained when the electronic device actually needs to predict, and the historical usage information in the training sample is training data in training.
In some embodiments, the plurality of different preset models may include training samples for the electronic device, and the obtained models are trained by using different machine learning algorithms. The type of machine learning algorithm may not be limiting, and may be, for example, a neural network, logistic regression, decision tree, random forest, or the like. The plurality of preset models may also include models obtained by training with the same machine learning algorithm for different training samples of the electronic device. The specific manner of obtaining the plurality of different preset models may not be limited.
In the embodiment of the application, when a plurality of different preset models predict the applications to be run in different application scenes, the corresponding accuracy rates may be different. Therefore, for the first application running at present, the preset model matched with the current application scene is selected from the multiple preset models to serve as the target model for prediction of subsequent applications, and the application to be run can be predicted more accurately. The application scene may refer to an application currently running on the electronic device. It can be understood that, due to differences of different models (for example, different models for machine learning are adopted), and there is usually a relationship between the currently running application and the application to be subsequently run, when the trained model is used in different application scenarios (when the currently running application is different), the accuracy of the trained model is different, and therefore, the model is selected according to the currently running first application, and the application to be run can be accurately predicted.
It should be noted that, according to the current usage information of the electronic device, the determined target model may be one preset model or multiple preset models, which is not limited herein.
Step S130: and inputting the current use information into the target model, and obtaining the application information of the second application output by the target model.
In the embodiment of the application, the electronic device may input the obtained current usage information into the target model, and the target model may determine, according to the input current usage information, a second application to be run by the electronic device after the first application is run, and output application information of the second application, thereby implementing prediction of the application to be run. Wherein the content of the first and second substances,
the number of applications to be started predicted may be one or more, and the specific number is not limited.
Step S140: and performing preprocessing operation before the second application runs according to the application information of the second application.
In this embodiment of the application, after predicting that the application information of the second application to be executed is obtained, the electronic device may perform a preprocessing operation before the second application is executed according to the application information of the second application. The preprocessing operation is used for increasing the speed of the second application in running so as to avoid the jamming when the second application is started.
In some embodiments, the electronic device may perform the preprocessing operation before the second application runs, where the preprocessing operation is to optimize a memory resource of the electronic device, for example, to clean a residual process after the application runs, to clean a process with a lower priority (that is, a lower importance), or to clean a process of an application that is not operated for a long time.
Further, the electronic device may obtain a memory resource required by the first application when running, and then perform optimization of the memory resource according to the memory resource required by the first application when running.
As a specific implementation manner, the electronic device may compare the memory resource required by the first application when running with the remaining memory resource currently remaining in the electronic device; if the current remaining memory resources of the electronic device are not less than (greater than or equal to) the memory resources required by the first application operation, the memory resources may not be optimized; if the current remaining memory resource of the electronic device is smaller than the memory resource required by the first application operation, the memory resource can be released, so that the remaining memory resource is not smaller than the memory resource required by the first application operation.
As another specific implementation manner, the electronic device may calculate a difference between the currently remaining memory resource of the electronic device and a memory resource required by the first application when running; if the difference is smaller than the set threshold, the memory resources can be released, so that the difference between the residual memory resources and the memory resources required by the first application operation is not smaller than the set threshold; if the difference is not less than the set threshold, the memory resources may not be optimized. Therefore, certain memory resources can be ensured to be remained after the first application is run, and the electronic equipment is prevented from being blocked. The specific set threshold may not be limited, and may be, for example, 100 mega (Mb), 200Mb, or the like.
Of course, the strategy for optimizing the memory resource of the electronic device may not be limited.
In other embodiments, the electronic device may also preload the second application, and specifically, the electronic device may preload a resource required by the second application. For example, when the second application is social software, a start screen, a contact list, a message record and the like in the second application may be preloaded; for example, when the second application is a game, a game loading screen or the like may be preloaded. The specific preloading procedure and the loaded data resources may not be limiting.
In still other embodiments, the electronic device may perform preprocessing before the second application runs, and display a control corresponding to the second application, where the control is used to trigger entry into the second application. The electronic device displays the control of the second application, which may be that the control corresponding to the second application is popup-displayed in the currently displayed interface. The control of the second application may be an application icon corresponding to the second application, or may be an application link for entering the second application, and a specific control form may not be limited.
In still other embodiments, the electronic device performs the preprocessing before the second application runs, and the electronic device may allocate a network resource of the electronic device. It can be understood that, if the second application is an application that needs to use the network, the electronic device may pre-adjust the network resource according to the network resource needed by the second application when running, and prepare the network resource allocated to the second application. For example, the electronic device may reduce the network resources allocated to the idle process in advance to free up the network resources allocated to the second application. Of course, the specific way of allocating network resources may not be limited.
In this embodiment of the application, a specific manner of the preprocessing operation performed by the electronic device may not be limited, and the manners of performing the memory resource optimization, preloading the second application, allocating the network resource, and the like may be performed alternatively, or may be performed in two or all manners. Of course, the electronic device may also perform other hardware resource allocation, and is not limited herein.
According to the device control method provided by the embodiment of the application, the current use information of the electronic device is obtained, the current use information at least comprises application information of a first application currently running on the electronic device, a target model is determined from a plurality of preset models according to the application information of the first application, each preset model is trained in advance, the application to be run by the electronic device is determined according to the input use information of the electronic device, the current use information is input into the target model, application information of a second application output by the target model is obtained, and finally preprocessing operation before running of the second application is carried out according to the application information of the second application. Therefore, the model for application prediction is selected according to the current application scene, the application to be operated is predicted according to the selected model, and the preprocessing operation before the operation of the predicted application is carried out, so that the prediction of the application to be operated can be accurately realized, the efficiency of the application in operation is improved, and the blockage during the operation of the application is avoided.
Referring to fig. 2, fig. 2 is a schematic flow chart illustrating an apparatus control method according to another embodiment of the present application. The device control method is applied to the electronic device, and will be described in detail with respect to the flow shown in fig. 2, and the device control method may specifically include the following steps:
step S210: acquiring current use information of the electronic equipment, wherein the current use information at least comprises application information of a first application currently running on the electronic equipment.
In the embodiment of the present application, step S210 may refer to the contents of the foregoing embodiments, which are not described herein again.
Step S220: and acquiring the prediction accuracy of each preset model in the plurality of preset models, wherein the prediction accuracy is the accuracy of the application information output by the preset model according to the input data comprising the application information of the first application.
In the embodiment of the application, when the electronic device determines the target model from the plurality of preset models according to the application information of the currently running first application, the accuracy of each preset model in the plurality of preset models may be obtained, so as to select the target model according to the accuracy of the preset model. The prediction accuracy is the accuracy of the output application information when each preset model is based on the input data including the application information of the first application, and the prediction accuracy can also be understood as the prediction accuracy of each preset model in the application scene of the first application.
In the embodiment of the present application, the prediction accuracy of each preset model may be obtained by:
acquiring a test data set, wherein the test data set comprises historical use information of the electronic equipment when the first application is operated for multiple times and application information of a next application which is operated after the first application is operated each time when the first application is operated for multiple times; and testing each preset model in the plurality of preset models according to the test data set to obtain the prediction accuracy of each preset model.
In some embodiments, the above test data set may be a set composed of partial sample data in a plurality of sample data obtained by the electronic device before training to obtain each preset model. Wherein the part of the sample data comprises historical use information of the application information of the first application and application information of a next application which is operated after the first application is operated.
In some embodiments, when the electronic device tests the preset model, the electronic device may input the historical usage information in the test data to the preset model, obtain application information output by the preset model, and compare the output application information with application information of a next application in the test data, if the two are the same, it indicates that the prediction of the preset model is accurate, and if the two are different, it indicates that the prediction of the preset model is not accurate. After the preset model is tested by using a plurality of pieces of test data, the ratio of the number of times of accurate prediction of the preset model to the total number of times of testing can be obtained, so that the accuracy of the preset model is obtained.
In some embodiments, the prediction accuracy of the preset model may be obtained by pre-calculation by the electronic device, and the electronic device may directly read the prediction accuracy of the preset model from the local when the electronic device needs to use the prediction accuracy of the preset model. The prediction accuracy of the preset model can be calculated and obtained by the server, and the electronic equipment can obtain the prediction accuracy from the server when the prediction accuracy of the preset model is required to be used.
Step S230: and selecting at least one preset model with the prediction accuracy meeting the specified accuracy condition as a target model according to the prediction accuracy corresponding to each preset model.
In the embodiment of the application, the electronic device may select, according to the prediction accuracy corresponding to each preset model, at least one model with the prediction accuracy meeting the specified accuracy condition as the target model. Because the prediction accuracy of the preset model is the prediction accuracy of the preset model in the application scene of the first application (when the first application is operated), the target model is determined according to the prediction accuracy, the target model which is more accurately predicted in the currently operated first application can be determined, and the subsequent application prediction is more accurate.
In some embodiments, specifying the accuracy condition comprises: the prediction accuracy of the preset model is maximum; or the prediction accuracy of the preset model is greater than the specified accuracy. It can be understood that the electronic device may select the preset model with the maximum prediction accuracy as the target model, or may select the preset model with the prediction accuracy higher than the specified accuracy as the target model. The specific value of the designated accuracy may not be limited, and may be, for example, 90% or 95%.
Of course, the specific prediction condition may not be limited, for example, the prediction condition may also be that the prediction accuracy rates of all the preset models are sorted from large to small, and then the first N prediction accuracy rates are selected according to the sorting result, that is, the preset models corresponding to the first N prediction accuracy rates are selected, where N is a positive integer.
In the embodiment of the present application, one or more preset models satisfying the specified accuracy condition may be selected from the plurality of preset models. For example, when the specified accuracy condition is that the prediction accuracy is the maximum, the highest accuracy among the prediction accuracies of the plurality of preset models is 95%, and the prediction accuracies of two preset models are 95%, so that the plurality of preset models satisfying the prediction accuracy condition are provided. When a plurality of preset models meeting the specified accuracy condition are available, the electronic device may select one of the preset models as the target model, or may use all of the preset models meeting the specified accuracy condition as the target model.
In some embodiments, when the electronic device determines that the number of the preset models with the prediction accuracy meeting the specified accuracy condition is at least two according to the prediction accuracy corresponding to each preset model, the current occupancy rate of the processor of the electronic device may be obtained; then the electronic equipment can compare the current occupancy rate with the specified occupancy rate, and if the current occupancy rate is smaller than the specified occupancy rate, at least two preset models are used as target models; and if the current occupancy rate is greater than or equal to the specified occupancy rate, acquiring a preset model with the minimum model size from at least two preset models as a target model. It can be understood that, generally, the prediction model has a larger size, and occupies more resources when being operated, the specified occupancy rate may be used as a criterion for determining that the electronic device may be stuck when operating a plurality of preset models, and if the current occupancy rate is greater than the specified occupancy rate, it indicates that the electronic device operates the plurality of preset models, the sticking may occur, and the electronic device may be crashed when operating, so that one preset model with the smallest model size may be selected from the at least two preset models as the target model, so as to avoid the subsequent electronic device from being stuck when operating the target model. If the current occupancy rate is less than or equal to the specified occupancy rate, the electronic equipment is represented to not cause jamming when running a plurality of preset models.
In this embodiment, if the prediction condition is that the prediction accuracy of the preset model is greater than the specified accuracy, it is determined that the number of the conditions satisfying the specified accuracy is at least two, and the current occupancy of the processor is greater than the specified occupancy, the preset model with the largest prediction accuracy may be selected from the at least two preset models as the target model.
In some embodiments, when the electronic device records the target model selected each time, the electronic device may further update the prediction accuracy of the preset model according to whether the electronic device really runs the predicted application after predicting the application to be run by using the selected target model. Therefore, the prediction accuracy of the preset model can be continuously updated, and the running application can be conveniently and accurately predicted in the follow-up process.
Step S240: and inputting the current use information into the target model, and obtaining the application information of the second application output by the target model.
Step S250: and performing preprocessing operation before the second application runs according to the application information of the second application.
In the embodiment of the present application, step S240 and step S250 may refer to the contents of the foregoing embodiments, and are not described herein again.
According to the device control method provided by the embodiment of the application, the current use information of the electronic device is obtained, the current use information at least comprises application information of a first application currently running on the electronic device, the prediction accuracy of each preset model in a plurality of preset models is obtained according to the application information of the first application, at least one preset model with the prediction accuracy meeting the specified accuracy condition is selected as a target model, the current use information is input into the target model, the application information of a second application output by the target model is obtained, and finally preprocessing operation before the running of the second application is carried out according to the application information of the second application. Therefore, the model with high prediction accuracy in the application scene is selected according to the current application scene, the application to be operated is predicted according to the selected model, and the preprocessing operation before the predicted application is operated again is performed, so that the prediction of the application to be operated can be accurately realized, the efficiency of the application in operation is improved, and the blockage during the operation of the application is avoided.
Referring to fig. 3, fig. 3 is a schematic flow chart illustrating a device control method according to another embodiment of the present application. The device control method is applied to the electronic device, and will be described in detail with respect to the flow shown in fig. 3, and the device control method may specifically include the following steps:
step S310: the method comprises the steps of obtaining a training data set, wherein the training data set comprises historical use information of the electronic equipment when the application is operated for multiple times and application information of a next application operated after the application is operated each time when the application is operated for multiple times.
For the preset model in the foregoing embodiment, the embodiment of the present application further includes a training method for the preset model, and it should be noted that training for the preset model may be performed in advance according to the acquired training data set, and then prediction may be performed according to the preset model each time prediction is performed on an application to be run, without training the preset model each time the application to be run is predicted.
In some embodiments, an electronic device may collect usage records of application programs for a period of time (e.g., 30 days) for the electronic device, the usage records including usage information for the application when it is running and application information for the next application to run after the application is running. Specifically, the usage record includes x and y, x representing usage information when an application is running, and is defined as x ═ t, d, ac,A1,A2,c,e,n]，y＝An. Wherein t represents the current time and has a value range of [0,23 ]]In units of one hour; d represents week and has a value range of [0,6 ]]7 values are taken from Monday to Sunday; a. thecIndicates the currentRunning applications, A1,A2Respectively representing the first two application programs running before the currently running application is run; c represents the current charging state, the value is {0,1}, 0 represents uncharged, and 1 represents charging; e represents the current earphone state, the value is {0,1}, 0 represents that the earphone is not connected, and 1 represents that the earphone is connected; n represents the current network state, the value is {0,1}, 0 represents the unconnected network, and 1 represents the connected network; a. thenIndicating the next application to be used after the currently running application is run.
Further, the usage record of the application collected above may be used as sample data, where x may be used as a label and y is the label result. That is, each piece of usage information is labeled as application information of the next application, and each set of sample data includes one input sample x and one output sample y.
Specifically, the training data that can be used to train the preset model may be part of the above sample data, thereby constituting a training data set; and the test data may be another portion of the above sample data, thereby constituting a test data set. For example, the training data may be 70% of the sample data, the test data may be 30% of the sample data, and the specific proportion may not be limiting.
Step S320: and training by using various machine learning algorithms to obtain a plurality of different preset models according to the training data set by using each piece of use information as input data and the application information of the next application corresponding to each piece of use information as output data, wherein each preset model can determine the application to be operated by the electronic equipment according to the input use information of the electronic equipment.
In the embodiment of the application, different machine learning algorithms can be adopted for training the training data set of the electronic device, so that a plurality of different preset models are obtained. Wherein, the adopted machine learning algorithm can comprise: a neural network, a Long Short-Term Memory (LSTM) network, a threshold cycle unit, a simple cycle unit, an automatic encoder, a decision tree, a random forest, and featuresValue classification, classification regression tree, hidden markov, K-nearest neighbor (KNN) algorithm, logistic regression model, bayesian model, gaussian model, and KL divergence (Kullback-Leibler divergence), among others. The specific machine learning algorithm may not be limiting. For example, when training using a logistic regression algorithm, the loss function may be
Figure BDA0002351203570000151
Where m may be the total number of training data, yiAnd the real label of the ith data is represented, and y' represents the label calculated by the logistic regression algorithm. For another example, when the application prediction learning is performed by using the random forest algorithm, the number of the policy trees is moderate, for example, N is 50, and for the generation process of the trees, the training samples are divided by calculating the information entropy, and the formula is as follows:
Figure BDA0002351203570000152
where n denotes the number of classes of labels in the training set data, piRepresenting the probability that the training sample label belongs to class i.
The training of the initial model based on the training data set is described below using a neural network as an example.
The use information x of the electronic device in one group of data in the training data set is used as input data of the neural network, and the application information y of the next application, which is executed by the electronic device after the currently executed application, is used as output data of the neural network. The neurons in the input layer are fully connected with the neurons in the hidden layer, and the neurons in the hidden layer are fully connected with the neurons in the output layer, so that potential features with different granularities can be effectively extracted. And the number of the hidden layers can be multiple, so that the nonlinear relation can be better fitted, and the trained preset model is more accurate.
It is understood that the training process for the preset model may or may not be performed by the electronic device. When the training process is not completed by the electronic device, the electronic device may be used as a direct user or an indirect user, that is, the electronic device may send the current usage information of the electronic device to a server storing a preset model, and obtain the predicted second application to be run from the server.
In some embodiments, the trained preset model may be stored locally in the electronic device, and the trained preset model may also be stored in a server in communication connection with the electronic device, so that the storage space occupied by the electronic device may be reduced, and the operating efficiency of the electronic device may be improved.
In some embodiments, the preset model may be periodically or aperiodically trained and updated by acquiring new training data.
In some embodiments, the electronic device may further test the trained multiple models according to the test data set, and count different preset models in different application scenarios acPrediction accuracy in the following (different currently running applications). Wherein can be defined
Figure BDA0002351203570000161
Representing a preset model M in an application scenario AcThe prediction accuracy of (1).
Specifically, when the electronic device obtains the prediction accuracy of each preset model in different application scenarios (different currently running applications), the same application scenario a may be screened out from the test data setcCorresponding test data, i.e. screening out the use information including the same currently running application AcThe application information of the application information is input into the preset model by using the use information of the test data respectively to obtain the application information output by the preset model, the output application information is compared with the application information of the next application in the test data, if the output application information is the same as the application information of the next application in the test data, the prediction of the preset model is accurate, and if the output application information is different from the application information of the next application in the test data, the prediction of the preset model is inaccurate. After the preset model is tested by using a plurality of pieces of test data, the ratio of the accurate prediction times of the preset model to the total test times can be obtained, so that the ratio is obtainedAnd presetting the prediction accuracy of the model. The electronic equipment can repeatedly perform the above steps according to a plurality of different application scenes, so that the prediction accuracy of the preset model in the plurality of different application scenes can be obtained, and the target model can be selected for predicting the running application according to the prediction accuracy of the preset model in the different application scenes.
Step S330: acquiring current use information of the electronic equipment, wherein the current use information at least comprises application information of a first application currently running on the electronic equipment.
Step S340: and determining a target model from a plurality of preset models according to the application information of the first application, wherein each preset model is trained in advance to determine the application to be run by the electronic equipment according to the input use information of the electronic equipment.
Step S350: and inputting the current use information into the target model, and obtaining the application information of the second application output by the target model.
Step S360: and performing preprocessing operation before the second application runs according to the application information of the second application.
In the embodiment of the present application, steps S330 to S360 may refer to the contents of the foregoing embodiments, and are not described herein again.
The device control method provided by the embodiment of the application provides a method for training a preset model, and the initial model is trained through training data marked with application information of the next application which runs after the current running application of the electronic device runs, so that the preset model is obtained. The preset model may be used to output application information of a next application to be run by the electronic device according to the input usage information of the electronic device. And the prediction accuracy of different preset models in different application scenes is tested, so that the electronic equipment can select a target model with high accuracy according to the application information of the currently running first application in the current use information, predict the running second application according to the target model, and realize the prediction of the running application more accurately. In addition, preprocessing before application operation is performed according to the predicted application to be operated, so that the speed of the application in operation can be increased, blockage is avoided, and the user experience is improved.
Referring to fig. 4, fig. 4 is a schematic flowchart illustrating an apparatus control method according to another embodiment of the present application. The device control method is applied to the electronic device, and will be described in detail with respect to the flow shown in fig. 4, and the device control method may specifically include the following steps:
step S410: acquiring current use information of the electronic equipment, wherein the current use information at least comprises application information of a first application currently running on the electronic equipment.
In the embodiment of the present application, step S410 may refer to the contents of the foregoing embodiments, and is not described herein again.
Step S420: and when the electronic equipment predicts the application, selecting a selection record of a target model from the plurality of preset models.
In the embodiment of the application, when the electronic device determines the target model from the plurality of preset models according to the application information of the currently running first application, the electronic device may also select the preset model according to a past selection record. It can be understood that the electronic device may record a preset model selected when the prediction of the next application to be run is performed in different application scenarios (when different applications are run) each time, and form a selection record, and the electronic device may read the selection record when the preset model needs to be selected, so as to select the preset model accordingly.
Step S430: and selecting the target model selected when the first application is operated from the plurality of preset models according to the selection record.
In this embodiment of the application, after the electronic device obtains the selection record, the electronic device may obtain, according to the selection record, a target model selected when the electronic device runs the first application, and select the target model from the plurality of preset models, so as to predict a running application in the following.
In the above embodiment, when the electronic device predicts an application to be run in an application scene for the first time, the preset model with the prediction accuracy meeting the specified accuracy condition in the foregoing embodiment may be selected as the target model according to the prediction accuracy of different preset models in the application scene, so as to predict the application. Therefore, a selection record of the selected model in the application scene can be formed, and the target model can be selected according to the selection record when the application to be operated needs to be predicted in the application scene subsequently, so that the target model can be selected quickly when the application prediction is carried out each time.
In some embodiments, when the electronic device records each selected target model, it may also record whether the electronic device actually runs the predicted application after the selected target model is used for predicting the application to be run, if the predicted application is not run by the electronic device after M consecutive predictions in the application scenario, or for a period of time, after the prediction in the application scenario, when the number of times that the electronic device does not run the predicted application is greater than the specified number of times, the electronic device may perform the application prediction in the application scenario, and selecting other preset models for application prediction according to the prediction accuracy of the preset models, for example, if the prediction accuracy of the preset models recorded before is the highest, selecting the preset model with the second highest prediction accuracy for application prediction.
In some embodiments, the electronic device may also record application information for the next application that the electronic device is actually running after each prediction of an application is made, as well as current usage information used at each prediction. If the next application really running by the electronic equipment continuously and repeatedly is inconsistent with the predicted application, the electronic equipment can also perform correction training on the preset model according to the recorded current use information and the application information of the next application really running so as to improve the accuracy of the preset model.
Step S440: and inputting the current use information into the target model, and obtaining the application information of the second application output by the target model.
Step S450: and performing preprocessing operation before the second application runs according to the application information of the second application.
In the embodiment of the present application, step S440 and step S450 may refer to the contents of the foregoing embodiments, and are not described herein again.
According to the device control method provided by the embodiment of the application, the current use information of the electronic device is obtained, the current use information at least comprises application information of a first application currently running on the electronic device, a target model selected in the past when the first application is running is selected according to the application information of the first application and records of models selected by the electronic device from a plurality of preset models, the current use information is input into the target model, application information of a second application output by the target model is obtained, and finally preprocessing operation before the second application runs is carried out according to the application information of the second application. Therefore, the model with high prediction accuracy in the application scene is selected according to the current application scene, the application to be operated is predicted according to the selected model, and the preprocessing operation before the predicted application is operated again is performed, so that the prediction of the application to be operated can be accurately realized, the efficiency of the application in operation is improved, and the blockage during the operation of the application is avoided.
Referring to fig. 5, a block diagram of a device control apparatus 400 according to an embodiment of the present disclosure is shown. The device control apparatus 400 applies the above-described electronic device, and the device control apparatus 400 includes: an information acquisition module 410, a model determination module 420, an application prediction module 430, and a pre-processing module 440. The information obtaining module 410 is configured to obtain current usage information of the electronic device, where the current usage information at least includes application information of a currently running first application; the model determining module 420 is configured to determine a target model from a plurality of preset models according to the application information of the first application, where each preset model is obtained by training based on historical usage information of the electronic device when the application is repeatedly run and application information of a next application that runs after the application is run each time when the application is repeatedly run; the application prediction module 430 is configured to input the current usage information into the target model, and obtain application information of a second application output by the target model; the preprocessing module 440 is configured to perform a preprocessing operation before the second application runs according to the application information of the second application.
In some implementations, the model determination module 420 can include: an accuracy obtaining unit, configured to obtain a prediction accuracy of each of the plurality of preset models, where the prediction accuracy is an accuracy of application information output by the preset model according to input data including application information of the first application; and the first model selection unit is used for selecting at least one preset model with the prediction accuracy meeting the specified accuracy condition as a target model according to the prediction accuracy corresponding to each preset model.
In this embodiment, the specified accuracy condition includes: the prediction accuracy of the preset model is maximum; or the prediction accuracy of the preset model is greater than the specified accuracy.
In this embodiment, the first model selecting unit may include: the occupancy rate acquiring subunit is used for acquiring the current occupancy rate of the processor of the electronic equipment when at least two preset models with the prediction accuracy rates meeting the specified accuracy rate condition are determined according to the prediction accuracy rate corresponding to each preset model; and the first selecting subunit is used for taking the at least two preset models as target models if the current occupancy rate is less than the specified occupancy rate.
Further, the first model selecting unit may further include: and the second selecting subunit is used for acquiring a preset model with the minimum model size from the at least two preset models as a target model if the current occupancy rate is greater than or equal to the specified occupancy rate.
In some embodiments, the device control apparatus 400 may further include: the device comprises a test set acquisition module and a model test module. The test set acquisition module is used for acquiring a test data set before the prediction accuracy of each preset model in the plurality of preset models is acquired, wherein the test data set comprises historical use information of the electronic equipment when the first application is operated for multiple times and application information of a next application operated after the first application is operated each time when the first application is operated for multiple times; the model testing module is used for testing each preset model in the plurality of preset models according to the test data set to obtain the prediction accuracy of each preset model.
In some implementations, the model determination module 420 can include: the record acquisition unit is used for acquiring a selection record of a target model selected from the plurality of preset models when the electronic equipment predicts the application; and the second model selecting unit is used for selecting the target model selected when the first application is operated from the plurality of preset models according to the selection record.
In some embodiments, the device control apparatus 400 may further include: a training set acquisition module and a model training module. The training set acquisition module is used for acquiring a training data set before determining a target model from a plurality of preset models according to the application information of the first application, wherein the training data set comprises historical use information of the electronic equipment when the application is operated for multiple times and application information of a next application operated after the application is operated each time when the application is operated for multiple times; and the model training module is used for training by using various machine learning algorithms to obtain a plurality of different preset models according to the training data set by using each piece of use information as input data and the application information of the next application corresponding to each piece of use information as output data, wherein each preset model can determine the application to be operated by the electronic equipment according to the input use information of the electronic equipment.
In some embodiments, the preprocessing module 440 performs the preprocessing operation before the second application is executed, and may include one or more of the following manners:
optimizing the memory resource of the electronic equipment according to the memory resource required by the second application in operation;
preloading the second application;
displaying a control corresponding to the second application according to network resources required by the running of the second application, wherein the control is used for triggering to enter the second application;
and allocating network resources of the electronic equipment.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses and modules may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, the coupling between the modules may be electrical, mechanical or other type of coupling.
In addition, functional modules in the embodiments of the present application may be integrated into one processing module, or each of the modules may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode.
In summary, according to the scheme provided by the application, by obtaining current usage information of an electronic device, where the current usage information at least includes application information of a first application currently running on the electronic device, determining a target model from a plurality of preset models according to the application information of the first application, where each preset model is pre-trained to determine an application to be run by the electronic device according to input usage information of the electronic device, inputting the current usage information to the target model to obtain application information of a second application output by the target model, and finally performing pre-processing operation before running the second application according to the application information of the second application. Therefore, the model for application prediction is selected according to the current application scene, the running application is predicted according to the selected model, and the predicted application is subjected to preprocessing operation before rerun, so that the application prediction can be accurately realized, and the efficiency of application running is improved.
Referring to fig. 6, a block diagram of an electronic device according to an embodiment of the present application is shown. The electronic device 100 may be a smart phone, a tablet computer, a smart watch, a PC computer, or other electronic devices capable of running an application. The electronic device 100 in the present application may include one or more of the following components: a processor 110, a memory 120, and one or more applications, wherein the one or more applications may be stored in the memory 120 and configured to be executed by the one or more processors 110, the one or more programs configured to perform a method as described in the aforementioned method embodiments.
Processor 110 may include one or more processing cores. The processor 110 connects various parts within the overall electronic device 100 using various interfaces and lines, and performs various functions of the electronic device 100 and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 120 and calling data stored in the memory 120. Alternatively, the processor 110 may be implemented in hardware using at least one of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). The processor 110 may integrate one or more of a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), a modem, and the like. Wherein, the CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing display content; the modem is used to handle wireless communications. It is understood that the modem may not be integrated into the processor 110, but may be implemented by a communication chip.
The Memory 120 may include a Random Access Memory (RAM) or a Read-Only Memory (Read-Only Memory). The memory 120 may be used to store instructions, programs, code sets, or instruction sets. The memory 120 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing various method embodiments described below, and the like. The storage data area may also store data created by the terminal 100 in use, such as a phonebook, audio-video data, chat log data, and the like.
Referring to fig. 7, a block diagram of a computer-readable storage medium according to an embodiment of the present application is shown. The computer-readable medium 800 has stored therein a program code that can be called by a processor to execute the method described in the above-described method embodiments.
The computer-readable storage medium 800 may be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read only memory), an EPROM, a hard disk, or a ROM. Alternatively, the computer-readable storage medium 800 includes a non-volatile computer-readable storage medium. The computer readable storage medium 800 has storage space for program code 810 to perform any of the method steps of the method described above. The program code can be read from or written to one or more computer program products. The program code 810 may be compressed, for example, in a suitable form.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not necessarily depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.

Claims (12)

1. An apparatus control method applied to an electronic apparatus, the method comprising:
acquiring current use information of the electronic equipment, wherein the current use information at least comprises application information of a first application currently running on the electronic equipment;
determining a target model from a plurality of preset models according to the application information of the first application, wherein each preset model is obtained by training based on historical use information of the electronic equipment when the application is operated for multiple times and application information of a next application operated after the application is operated each time when the application is operated for multiple times;
inputting the current use information into the target model, and obtaining application information of a second application output by the target model;
and performing preprocessing operation before the second application runs according to the application information of the second application.
2. The method of claim 1, wherein determining the target model from a plurality of preset models according to the application information of the first application comprises:
acquiring the prediction accuracy of each preset model in the plurality of preset models, wherein the prediction accuracy is the accuracy of the application information output by the preset model according to the input data comprising the application information of the first application;
and selecting at least one preset model with the prediction accuracy meeting the specified accuracy condition as a target model according to the prediction accuracy corresponding to each preset model.
3. The method of claim 2, wherein the specified accuracy condition comprises:
the prediction accuracy of the preset model is maximum; or
The prediction accuracy of the preset model is greater than the specified accuracy.
4. The method according to claim 2, wherein the selecting, as the target model, at least one preset model with a prediction accuracy meeting a specified accuracy condition according to the prediction accuracy corresponding to each preset model comprises:
when at least two preset models with the prediction accuracy meeting the specified accuracy condition are determined according to the prediction accuracy corresponding to each preset model, the current occupancy rate of a processor of the electronic equipment is obtained;
and if the current occupancy rate is less than the specified occupancy rate, taking the at least two preset models as target models.
5. The method according to claim 4, wherein the selecting at least one preset model with a prediction accuracy meeting a specified accuracy condition as a target model according to the prediction accuracy corresponding to each preset model further comprises:
and if the current occupancy rate is greater than or equal to the specified occupancy rate, acquiring a preset model with the minimum model size from the at least two preset models as a target model.
6. The method of claim 2, wherein prior to said obtaining a prediction accuracy for each of said plurality of predetermined models, said method further comprises:
acquiring a test data set, wherein the test data set comprises historical use information of the electronic equipment when the first application is operated for multiple times and application information of a next application operated after the first application is operated each time when the first application is operated for multiple times;
and testing each preset model in the plurality of preset models according to the test data set to obtain the prediction accuracy of each preset model.
7. The method of claim 1, wherein determining the target model from a plurality of preset models according to the application information of the first application comprises:
acquiring a selection record of a target model selected from the plurality of preset models when the electronic equipment predicts the application;
and selecting the target model selected when the first application is operated from the plurality of preset models according to the selection record.
8. The method of claim 1, wherein prior to said determining a target model from a plurality of preset models according to the application information of the first application, the method further comprises:
acquiring a training data set, wherein the training data set comprises historical use information of the electronic equipment when the application is operated for multiple times and application information of a next application operated after the application is operated each time when the application is operated for multiple times;
and training by using various machine learning algorithms to obtain a plurality of different preset models according to the training data set by using each piece of use information as input data and the application information of the next application corresponding to each piece of use information as output data, wherein each preset model can determine the application to be operated by the electronic equipment according to the input use information of the electronic equipment.
9. The method according to any one of claims 1 to 8, wherein the performing the pre-processing operation before the second application is executed comprises one or more of the following:
optimizing the memory resource of the electronic equipment according to the memory resource required by the second application in operation;
preloading the second application;
displaying a control corresponding to the second application, wherein the control is used for triggering to enter the second application;
and distributing the network resources of the electronic equipment according to the network resources required by the running of the second application.
10. An apparatus control device, applied to an electronic apparatus, the apparatus comprising: an information acquisition module, a model determination module, an application prediction module, and a pre-processing module, wherein,
the information acquisition module is used for acquiring current use information of the electronic equipment, wherein the current use information at least comprises application information of a first application which is currently running;
the model determining module is used for determining a target model from a plurality of preset models according to the application information of the first application, wherein each preset model is obtained by training based on historical use information of the electronic equipment during multiple times of application running and application information of a next application running after the application is run each time during multiple times of application running;
the application prediction module is used for inputting the current use information into the target model and obtaining application information of a second application output by the target model;
the preprocessing module is used for performing preprocessing operation before the second application runs according to the application information of the second application.
11. An electronic device, comprising:
one or more processors;
a memory;
one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more programs configured to perform the method of any of claims 1-9.
12. A computer-readable storage medium, having stored thereon program code that can be invoked by a processor to perform the method according to any one of claims 1 to 9.
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