CN108076224B - Application program control method and device, storage medium and mobile terminal - Google Patents
Application program control method and device, storage medium and mobile terminal Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M1/00—Substation equipment, e.g. for use by subscribers
- H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
- H04M1/724—User interfaces specially adapted for cordless or mobile telephones
- H04M1/72403—User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/048—Interaction techniques based on graphical user interfaces [GUI]
- G06F3/0484—Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/048—Interaction techniques based on graphical user interfaces [GUI]
- G06F3/0487—Interaction techniques based on graphical user interfaces [GUI] using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser
- G06F3/0488—Interaction techniques based on graphical user interfaces [GUI] using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser using a touch-screen or digitiser, e.g. input of commands through traced gestures
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M1/00—Substation equipment, e.g. for use by subscribers
- H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
- H04M1/724—User interfaces specially adapted for cordless or mobile telephones
- H04M1/72403—User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality
- H04M1/72406—User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality by software upgrading or downloading
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M1/00—Substation equipment, e.g. for use by subscribers
- H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
- H04M1/724—User interfaces specially adapted for cordless or mobile telephones
- H04M1/72403—User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality
- H04M1/7243—User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality with interactive means for internal management of messages
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M1/00—Substation equipment, e.g. for use by subscribers
- H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
- H04M1/724—User interfaces specially adapted for cordless or mobile telephones
- H04M1/72469—User interfaces specially adapted for cordless or mobile telephones for operating the device by selecting functions from two or more displayed items, e.g. menus or icons
Abstract
The embodiment of the application discloses an application program control method, an application program control device, a storage medium and a mobile terminal, wherein the method comprises the following steps: acquiring current behavior characteristics at set time intervals in a set period; acquiring a preset behavior prediction model generated based on a machine learning method, wherein the preset behavior prediction model is obtained by training a plurality of behavior characteristic samples of known control operation and is used for predicting the control operation based on the behavior characteristic; inputting the current behavior characteristics into the preset behavior prediction model to obtain a prediction result output by the preset behavior prediction model; and automatically executing control operation according with the behavior habit of the user according to the prediction result, wherein the control operation comprises data cleaning and data updating operation of the application program. According to the technical scheme, the application program control operation which accords with the behavior habits of the user can be automatically executed, and the intelligence of application program control is improved.
Description
Technical Field
The embodiment of the application relates to the technical field of mobile terminals, in particular to an application program control method, an application program control device, a storage medium and a mobile terminal.
Background
The functions of mobile terminals such as mobile phones are more and more, convenience is provided for life and work of people, various application software can be installed in the mobile terminals to support the realization of different functions of the mobile terminals, and people can make and receive calls, listen to music, watch videos, chat, play games and the like by using the mobile phones. When the user uses the mobile terminal, the user has some behavior habits, and the behavior habits of the user are not well utilized when the application program is controlled in the prior art, and need to be improved.
Disclosure of Invention
The embodiment of the application program control method and device, the storage medium and the mobile terminal can automatically execute application program control operation according with user behavior habits.
In a first aspect, an embodiment of the present application provides an application program control method, including:
acquiring current behavior characteristics at set time intervals in a set period;
acquiring a preset behavior prediction model generated based on a machine learning method, wherein the preset behavior prediction model is obtained by training a plurality of behavior characteristic samples of known control operation and is used for predicting the control operation based on the behavior characteristic;
inputting the current behavior characteristics into the preset behavior prediction model to obtain a prediction result output by the preset behavior prediction model;
and automatically executing control operation according with the behavior habit of the user according to the prediction result, wherein the control operation comprises data cleaning and data updating operation of the application program.
In a second aspect, an embodiment of the present application provides an application control apparatus, including:
the current behavior characteristic acquisition module acquires current behavior characteristics at set time intervals in a set period;
the device comprises a preset behavior prediction model acquisition module, a control module and a control module, wherein the preset behavior prediction model acquisition module is used for acquiring a preset behavior prediction model generated based on a machine learning method, and the preset behavior prediction model is obtained by training a plurality of behavior characteristic samples of known control operation and is used for predicting the control operation based on the behavior characteristics;
the control operation prediction module is used for inputting the current behavior characteristics into the preset behavior prediction model to obtain a prediction result output by the preset behavior prediction model;
and the control operation execution module is used for automatically executing the control operation according with the user behavior habit, and the control operation comprises data cleaning and data updating operation of an application program.
In a third aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, and the program, when executed by a processor, implements the application control method provided in the first aspect.
In a fourth aspect, an embodiment of the present application provides a mobile terminal, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor, when executing, implements the application control method provided in the first aspect.
According to the control scheme of the application program, the current behavior characteristics are acquired at set intervals in the set period, the current behavior characteristics are input into the preset behavior prediction model generated based on the machine learning method, the prediction result output by the preset behavior prediction model is obtained, and the control operation according with the user behavior habit is automatically executed according to the prediction result, wherein the control operation comprises data cleaning and data updating operations of the application program, so that the control operation of the application program according with the user behavior habit can be automatically executed, and the intelligence of the application program control is improved.
Drawings
Fig. 1 is a flowchart of an application control method provided in an embodiment of the present application;
FIG. 2 is a flow chart of another method for controlling an application provided in an embodiment of the present application;
FIG. 3 is a flow chart of another method for controlling an application provided by an embodiment of the present application;
fig. 4 is a schematic structural diagram of an application control device according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a mobile terminal according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of another mobile terminal according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, specific embodiments of the present application will be described in detail with reference to the accompanying drawings. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. It should be further noted that, for the convenience of description, only some but not all of the relevant portions of the present application are shown in the drawings. Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the operations (or steps) as a sequential process, many of the operations can be performed in parallel, concurrently or simultaneously. In addition, the order of the operations may be re-arranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, and the like.
Fig. 1 is a flowchart of an application control method provided in an embodiment of the present application, where the method of this embodiment may be performed by an application control device, which may be implemented by hardware and/or software, and the device may be disposed inside a mobile terminal as a part of the mobile terminal. The mobile terminal described in the embodiment of the present application includes, but is not limited to, a smart phone, a tablet computer, or a notebook computer.
As shown in fig. 1, the application control method provided in this embodiment includes the following steps:
In the process of using the mobile terminal, different users have behavior habits aiming at the mobile terminal used by the users due to different types and performances of the mobile terminal, for example, the memory of the mobile terminal is not very large, the users may frequently clear cache data of some application programs, and for example, the users can set and use different alarm clocks according to actual needs of the users. In the prior art, the behavior habits are all manually operated by a user, are not intelligent enough and need to be repeatedly operated. An applicable scenario of the technical scheme provided by the embodiment of the application is as follows: the method comprises the steps that historical behavior habits of a user are learned through a mobile terminal or a preset server, a preset behavior prediction model is generated, behavior characteristics of the mobile terminal within a period of time are obtained in the subsequent use process of the mobile terminal, the control operation of an application program to be executed is predicted based on the preset behavior prediction model, and the control operation is automatically executed.
Wherein, the set period can be one month, and the set time can be one week; the set period can be one week, and the set time can be one day; the set period may be one day, the set time may be 1 hour, or any other condition that the set time is less than the set period may be satisfied, which is not limited in this embodiment.
The behavior characteristics may include user behavior characteristics and mobile terminal performance parameters, and the mobile terminal performance parameters may include memory, power, network conditions, and the like. The current behavior feature may be a behavior feature possessed during a period from a previous current setting time to a beginning of the setting period.
Optionally, if the preset behavior prediction model is established on the mobile terminal, before step 101, the following steps may be further included: during the operation of the mobile terminal, collecting a first user behavior habit of a user for operating the mobile terminal; analyzing the first user behavior habit to obtain a first basic behavior characteristic; and training the first basic behavior characteristics serving as training samples based on a machine learning method to generate a preset behavior prediction model. The user behavior habits comprise the behavior habits of the user and performance parameters of the mobile terminal in the process of the behavior habits of the user.
Wherein the basic behavior characteristics comprise at least one of performance parameters of the mobile terminal, application program identification, behavior occurrence time, behavior end time, behavior occurrence place, behavior content and behavior frequency. The current behavior feature may be one or more of the basic behavior features. Illustratively, the basic behavior characteristics are application identification, behavior occurrence time, behavior end time, behavior occurrence location, behavior content and behavior frequency, and the current behavior characteristics are application identification, behavior occurrence time, behavior occurrence location and behavior content.
And 102, acquiring a preset behavior prediction model generated based on a machine learning method. The preset behavior prediction model is obtained by training a plurality of behavior feature samples of known control operation and is used for predicting the control operation based on the behavior feature.
The training generation and updating process of the preset behavior prediction model generated based on the machine learning method can be carried out locally on the mobile terminal or in a preset server, and when the training generation of the preset behavior prediction model is finished or the updating is finished, the preset behavior prediction model can be directly sent to the mobile terminal for storage, or the preset behavior prediction model can be stored in the preset server for waiting for the active acquisition of the mobile terminal. Accordingly, this step 102 may include: and acquiring a preset behavior prediction model generated based on a machine learning method from a preset server or a mobile terminal locally.
Optionally, the machine learning method includes a neural network method, a support vector machine method, a decision tree method, a logistic regression method, a bayesian method, and a random forest method. Among them, the Neural Network (NNs) system refers to an artificial Neural network, a biological Neural network inspired from the human brain to process information, which includes an input layer, a hidden layer, and an output layer, and correspondingly includes three kinds of nodes (basic units of the Neural network): the system comprises an input node, a hidden node and an output node, wherein the input node acquires information from the outside world; the hidden nodes are not directly connected with the outside world, and the nodes are calculated by using the activation function and transmit information from the input nodes to the output nodes; the output nodes are used to communicate information to the outside world.
In this embodiment, the source and the number of the user behavior habit samples are not limited, and may be historical behavior habits of the mobile terminal user on the application program, historical behavior habits of the target user group on the application program, or a combination of the two, and preferably are historical behavior habits of the mobile terminal user on the application program. Wherein the target user group may be a plurality of users having the same user attribute of the mobile terminal user, and the user attribute may include at least one of age, gender, hobby and occupation. In the embodiment, the historical behavior habits of the mobile terminal user or the target user group with the same user attributes as the mobile terminal user are used as samples for training, and the control operation of the application program according with the actual behavior habits of the user can be automatically performed.
Training basic behavior characteristics in historical user behavior habits as samples based on a machine learning method to obtain a preset behavior prediction model, wherein the input of the preset behavior prediction model is the behavior characteristics, and the output is the control operation to be executed on the application program under the behavior characteristics.
Optionally, after the generating the preset behavior prediction model, the method further includes: the system automatically acquires the second user behavior habit in the current set time period every set period; analyzing the second user behavior habit to obtain a second basic behavior characteristic; inputting the second basic behavior characteristics into the preset behavior prediction model, and obtaining an output result of the preset behavior prediction model; and if the difference value of the actual control operation corresponding to the output result and the second basic behavior characteristic exceeds a preset error range, the second basic behavior characteristic is used as a new training sample to train and update the preset behavior prediction model again. For example, the set period may be one day, that is, after the preset behavior prediction model is generated, the daily system automatically acquires the behavior habits of the user in the day.
The predicted result may be a control operation that conforms to the behavior habit of the user under the behavior feature, or may be no control operation.
And 104, automatically executing control operation to be executed according with the user behavior habit according to the prediction result, wherein the control operation comprises data cleaning and data updating operation of the application program.
And when the predicted result is the control operation which accords with the behavior habit of the user under the behavior characteristic, automatically executing the control operation.
Optionally, the data cleaning operation of the application program includes a cache data cleaning operation of the application program and/or a closing operation of the background application program, and the data updating operation of the application program includes a version updating operation of the application program and/or an operation parameter updating operation of the application program. For example, for an application program capable of playing audio and video, the operation parameter may be a headphone play and a speaker play, and for an alarm clock application program, the operation parameter may be an alarm clock setting time or an alarm clock ringing mode, and the like.
Illustratively, if the current behavior characteristic is that the memory of the mobile terminal is within the range of 500M-1G, the behavior occurrence time is 8 pm, the current behavior characteristic is input into a preset behavior prediction model, and the output result of the preset behavior prediction model is the cache data cleaning operation of the application program, the mobile terminal automatically executes the cleaning of the WeChat and the cache of the Temple application program which are used by the user to clean. Illustratively, if the current behavior characteristics are that the application program is identified as an alarm clock, the behavior occurrence time is 10 hours 30 minutes at night, the behavior end time is 10 hours 32 minutes, the behavior content is alarm clock time setting and background reserved running, the current behavior characteristics are input into the preset behavior prediction model, the output result of the preset behavior prediction model is the closing operation of the background application program, and then the alarm clock reserved running in the background is automatically closed.
According to the application program control method provided by the embodiment, the current behavior characteristics are acquired at set time intervals in a set period, the current behavior characteristics are input into the preset behavior prediction model generated based on the machine learning method, the prediction result output by the preset behavior prediction model is obtained, and the control operation according with the user behavior habit is automatically executed according to the prediction result, wherein the control operation comprises data cleaning and data updating operations of the application program, so that the application program control operation according with the user behavior habit can be automatically executed, and the intelligence of application program control is improved.
The following briefly describes a method for controlling an application program based on a preset behavior prediction model, taking the local establishment of the preset behavior prediction model in a mobile terminal as an example. Fig. 2 is a flowchart of another application control method according to an embodiment of the present application. As shown in fig. 2, the application control method provided in this embodiment includes the following steps:
By recording the behavior habit of the user operating the mobile terminal for a long time, illustratively, a series of continuous operations of the user operating the mobile terminal are recorded, and the control operation executed by the mobile terminal after the series of continuous operations is recorded.
Optionally, the first basic behavior feature includes at least one of a performance parameter of the mobile terminal, an application identifier, a behavior occurrence time, a behavior end time, a behavior occurrence location, a behavior content, and a behavior frequency.
And 203, taking the first basic behavior feature as a training sample, training based on a machine learning method, and generating a preset behavior prediction model.
Optionally, the method includes: taking the first basic behavior characteristics as training samples, training based on different machine learning methods, and generating a plurality of candidate behavior prediction models; and taking the candidate behavior prediction model with the highest prediction accuracy as a preset behavior prediction model.
And step 204, acquiring the current behavior characteristics at intervals of set time in a set period.
And step 206, automatically executing control operation according with the behavior habit of the user according to the prediction result, wherein the control operation comprises data cleaning and data updating operation of the application program.
In the method provided by the embodiment, during the operation of the mobile terminal, the first user behavior habit of the user for operating the mobile terminal is collected, the first user behavior habit is analyzed to obtain the first basic behavior feature, the first basic behavior feature is used as a training sample and is trained based on a machine learning method to generate the preset behavior prediction model, the control operation prediction model conforming to the user behavior habit is provided, the control operation of the mobile terminal can be accurately predicted according to the current behavior feature, and the control operation conforming to the user behavior habit is automatically executed.
In the following, a method of performing application control by using a preset behavior prediction model generated by a neural network method is described by taking machine learning as an example of the neural network method. The neural network method includes an input layer, a hidden layer, and an output layer. Fig. 3 is a flowchart of another application control method according to an embodiment of the present application. As shown in fig. 3, the application control method provided in this embodiment includes the following steps:
And 303, inputting the first basic behavior characteristics to the input layer, and outputting an intermediate control operation through calculation of an activation function corresponding to each node of the hidden layer.
The activation function refers to providing a non-linear modeling capability for the neural network system, and is a non-linear function in general. The activation function may include a relu function, a sigmoid function, a tanh function, or a maxout function.
sigmoid is a commonly used nonlinear activation function, and its mathematical form is as follows:
its output is a value between 0 and 1. tanh is also very similar to sigmoid, and in fact, tanh is a variant of sigmoid: tan (x) ═ 2sigmoid (2x) -1, unlike sigmoid, tan is 0-mean. In recent years relu has become more and more popular. Its mathematical expression is as follows: f (x) max (0, x), wherein the input signal<When 0, the outputs are all 0, the input signal>In the case of 0, the output equals the input. The expression of the maxout function is as follows: f. ofi(x)＝maxj∈[1,k]Zij. Assuming that the input nodes include x1 and x2, and the corresponding weights are w1 and w2, respectively, and further include a weight b, the output node Y ═ f (w1 × 1+ w2 × 2+ b), where f is the activation function. In addition, the number of input layers and output layers is usually one, and the hidden layer may be formed of a plurality of layers.
And 304, repeatedly correcting the weight in the activation function by using the difference between the intermediate control operation and the actual control operation corresponding to the first basic behavior characteristic and an optimization algorithm until the difference between the intermediate control operation and the actual control operation is within a preset error range, obtaining the activation function of each trained node, and generating a preset behavior prediction model.
The optimization algorithm includes a Stochastic Gradient Descent (SGD) algorithm, an adaptive moment estimation (adam) algorithm, or a Momentum algorithm.
And 305, acquiring the current behavior characteristics at set time intervals in a set period.
And step 306, inputting the current behavior characteristics into the preset behavior prediction model to obtain a prediction result output by the preset behavior prediction model.
And 307, automatically executing the control operation according with the user behavior habit according to the prediction result, wherein the control operation comprises data cleaning and data updating operations of an application program.
According to the method provided by the embodiment, the user behavior habits are trained by adopting a neural network method to generate the preset behavior prediction model, the current behavior characteristics are acquired at set intervals in the set period, the current behavior characteristics are input into the preset behavior prediction model to obtain the prediction result output by the preset behavior prediction model, and the control operation conforming to the user behavior habits is automatically executed according to the prediction result, so that the control operation of the application program conforming to the user behavior habits is automatically executed, and the intelligence of the application program control is improved.
Fig. 4 is a schematic structural diagram of an application control device according to an embodiment of the present disclosure, where the application control device may be implemented by software and/or hardware and integrated in a mobile terminal. As shown in fig. 4, the apparatus includes a present behavior feature obtaining module 41, a preset behavior prediction model obtaining module 42, a control operation prediction module 43, and a control operation execution module 44.
A current behavior feature obtaining module 41, which obtains current behavior features at set time intervals in a set period;
a preset behavior prediction model obtaining module 42, configured to obtain a preset behavior prediction model generated based on a machine learning method, where the preset behavior prediction model is obtained by training a plurality of behavior feature samples of known control operations and is used for predicting the control operations based on the behavior features;
a control operation prediction module 43, configured to input the current behavior feature to the preset behavior prediction model, so as to obtain a prediction result output by the preset behavior prediction model;
and the control operation execution module 44 is used for automatically executing the control operation according with the user behavior habit, wherein the control operation comprises data cleaning and data updating operations of the application program.
According to the device provided by the embodiment, the current behavior characteristics are acquired at set intervals in a set period, the current behavior characteristics are input into the preset behavior prediction model generated based on the machine learning method, the prediction result output by the preset behavior prediction model is obtained, and the control operation according with the user behavior habit is automatically executed according to the prediction result, wherein the control operation comprises data cleaning and data updating of the application program, so that the control operation of the application program according with the user behavior habit can be automatically executed, and the intelligence of the application program control is improved.
Optionally, the data cleaning operation of the application program includes a cache data cleaning operation of the application program and/or a closing operation of the background application program, and the data updating operation of the application program includes a version updating operation of the application program and/or an operation parameter updating operation of the application program.
Optionally, the apparatus further comprises:
the mobile terminal comprises a user behavior habit acquisition module, a first user behavior habit acquisition module and a second user behavior habit acquisition module, wherein the user behavior habit acquisition module is used for acquiring a first user behavior habit of a user operating the mobile terminal during the operation period of the mobile terminal;
the basic behavior feature acquisition module is used for analyzing the first user behavior habit to obtain a first basic behavior feature;
and the preset behavior prediction model generation module is used for training the first basic behavior characteristics as training samples based on a machine learning method to generate a preset behavior prediction model.
Optionally, the basic behavior feature includes at least one of a performance parameter of the mobile terminal, an application identifier, a behavior occurrence time, a behavior end time, a behavior occurrence location, a behavior content, and a behavior frequency.
Optionally, the machine learning method includes a neural network method, a support vector machine method, a decision tree method, a logistic regression method, a bayesian method, and a random forest method.
Optionally, the machine learning method includes a neural network method, the neural network method includes an input layer, a hidden layer, and an output layer, and the preset behavior prediction model generation module is specifically configured to:
inputting the first basic behavior characteristics to the input layer, and outputting intermediate control operation through calculation of an activation function corresponding to each node of the hidden layer;
repeatedly correcting the weight in the activation function by using the difference between the intermediate control operation and the actual behavior operation corresponding to the first basic behavior characteristic and an optimization algorithm until the difference between the intermediate control operation and the actual behavior operation is within a preset error range, obtaining the activation function of each trained node, and generating a preset behavior prediction model.
Optionally, the system further includes a preset behavior prediction model updating module, specifically configured to:
after a preset behavior prediction model is generated, automatically acquiring a second user behavior habit in the current set period by the system every other set period;
analyzing the second user behavior habit to obtain a second basic behavior characteristic;
inputting the second basic behavior characteristics into the preset behavior prediction model, and obtaining an output result of the preset behavior prediction model;
and if the difference value of the actual control operation corresponding to the output result and the second basic behavior characteristic exceeds a preset error range, the second basic behavior characteristic is used as a new training sample to train and update the preset behavior prediction model again.
Embodiments of the present application also provide a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform a method for application control, the method comprising:
acquiring current behavior characteristics at set time intervals in a set period;
acquiring a preset behavior prediction model generated based on a machine learning method, wherein the preset behavior prediction model is obtained by training a plurality of behavior characteristic samples of known control operation and is used for predicting the control operation based on the behavior characteristic;
inputting the current behavior characteristics into the preset behavior prediction model to obtain a prediction result output by the preset behavior prediction model;
and automatically executing control operation according with the behavior habit of the user according to the prediction result, wherein the control operation comprises data cleaning and data updating operation of the application program.
Storage medium-any of various types of memory devices or storage devices. The term "storage medium" is intended to include: mounting media such as CD-ROM, floppy disk, or tape devices; computer system memory or random access memory such as DRAM, DDR RAM, SRAM, EDO RAM, Lanbas (Rambus) RAM, etc.; non-volatile memory such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc. The storage medium may also include other types of memory or combinations thereof. In addition, the storage medium may be located in a first computer system in which the program is executed, or may be located in a different second computer system connected to the first computer system through a network (such as the internet). The second computer system may provide program instructions to the first computer for execution. The term "storage medium" may include two or more storage media that may reside in different locations, such as in different computer systems that are connected by a network. The storage medium may store program instructions (e.g., embodied as a computer program) that are executable by one or more processors.
Of course, the storage medium provided in the embodiments of the present application and containing computer-executable instructions is not limited to the above-described application control operation, and may also perform related operations in the application control method provided in any embodiment of the present application.
The embodiment of the application provides a mobile terminal, and the application control device provided by the embodiment of the application can be integrated in the mobile terminal. Fig. 5 is a schematic structural diagram of a mobile terminal according to an embodiment of the present application. The mobile terminal 500 may include: the application control method comprises a memory 501, a processor 502 and a computer program stored on the memory 501 and capable of being executed by the processor 502, wherein the processor 502 executes the computer program to realize the application control method according to the embodiment of the application.
According to the mobile terminal provided by the embodiment of the application, the current behavior characteristics are acquired at set intervals in the set period, the current behavior characteristics are input into the preset behavior prediction model generated based on the machine learning method, the prediction result output by the preset behavior prediction model is obtained, and the control operation according with the behavior habits of the user is automatically executed according to the prediction result, wherein the control operation comprises data cleaning and data updating operations of the application program, so that the control operation of the application program according with the behavior habits of the user can be automatically executed, and the intelligence of the application program control is improved.
Fig. 6 is a schematic structural diagram of another mobile terminal provided in the embodiment of the present application, and as shown in fig. 6, the mobile terminal may include: a memory 601, a Central Processing Unit (CPU) 602 (also called a processor, hereinafter referred to as CPU), and the memory 601, configured to store executable program codes; the processor 602 executes a program corresponding to the executable program code by reading the executable program code stored in the memory 601, for performing: acquiring current behavior characteristics at set time intervals in a set period; acquiring a preset behavior prediction model generated based on a machine learning method, wherein the preset behavior prediction model is obtained by training a plurality of behavior characteristic samples of known control operation and is used for predicting the control operation based on the behavior characteristic; inputting the current behavior characteristics into the preset behavior prediction model to obtain a prediction result output by the preset behavior prediction model; and automatically executing control operation according with the behavior habit of the user according to the prediction result, wherein the control operation comprises data cleaning and data updating operation of the application program.
The mobile terminal further includes: peripheral interfaces 603, RF (Radio Frequency) circuitry 605, audio circuitry 606, speakers 611, power management chip 608, input/output (I/O) subsystem 609, touch screen 612, other input/control devices 610, and external ports 604, which communicate via one or more communication buses or signal lines 607.
It should be understood that the illustrated mobile terminal 600 is merely one example of a mobile terminal and that the mobile terminal 600 may have more or fewer components than shown, may combine two or more components, or may have a different configuration of components. The various components shown in the figures may be implemented in hardware, software, or a combination of hardware and software, including one or more signal processing and/or application specific integrated circuits.
The following describes in detail a mobile terminal for controlling an application provided in this embodiment, where the mobile terminal is a mobile phone as an example.
A memory 601, the memory 601 being accessible by the CPU602, the peripheral interface 603, and the like, the memory 601 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic disk storage devices, flash memory devices, or other volatile solid state storage devices.
A peripheral interface 603, said peripheral interface 603 may connect input and output peripherals of the device to the CPU502 and the memory 601.
An I/O subsystem 609, the I/O subsystem 609 may connect input and output peripherals on the device, such as a touch screen 612 and other input/control devices 610, to the peripheral interface 603. The I/O subsystem 609 may include a display controller 6091 and one or more input controllers 6092 for controlling other input/control devices 610. Where one or more input controllers 6092 receive electrical signals from or transmit electrical signals to other input/control devices 610, the other input/control devices 610 may include physical buttons (push buttons, rocker buttons, etc.), dials, slide switches, joysticks, click wheels. It is noted that the input controller 6092 may be connected to any one of: a keyboard, an infrared port, a USB interface, and a pointing device such as a mouse.
A touch screen 612, which touch screen 612 is an input interface and an output interface between the user terminal and the user, displays visual output to the user, which may include graphics, text, icons, video, and the like.
The display controller 6091 in the I/O subsystem 609 receives electrical signals from the touch screen 612 or transmits electrical signals to the touch screen 612. The touch screen 612 detects a contact on the touch screen, and the display controller 6091 converts the detected contact into an interaction with a user interface object displayed on the touch screen 612, that is, to implement a human-computer interaction, where the user interface object displayed on the touch screen 612 may be an icon for running a game, an icon networked to a corresponding network, or the like. It is worth mentioning that the device may also comprise a light mouse, which is a touch sensitive surface that does not show visual output, or an extension of the touch sensitive surface formed by the touch screen.
The RF circuit 605 is mainly used to establish communication between the mobile phone and the wireless network (i.e., network side), and implement data reception and transmission between the mobile phone and the wireless network. Such as sending and receiving short messages, e-mails, etc. In particular, RF circuitry 605 receives and transmits RF signals, also referred to as electromagnetic signals, through which RF circuitry 605 converts electrical signals to or from electromagnetic signals and communicates with a communication network and other devices. RF circuitry 605 may include known circuitry for performing these functions including, but not limited to, an antenna system, an RF transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a CODEC (CODEC) chipset, a Subscriber Identity Module (SIM), and so forth.
The audio circuit 606 is mainly used to receive audio data from the peripheral interface 603, convert the audio data into an electric signal, and transmit the electric signal to the speaker 611.
The speaker 611 is used to convert the voice signal received by the handset from the wireless network through the RF circuit 605 into sound and play the sound to the user.
And a power management chip 608 for supplying power and managing power to the hardware connected to the CPU602, the I/O subsystem, and the peripheral interface 603.
The application control device, the storage medium and the mobile terminal provided in the above embodiments may execute the application control method provided in any embodiment of the present application, and have corresponding functional modules and beneficial effects for executing the method. For technical details that are not described in detail in the above embodiments, reference may be made to an application control method provided in any embodiment of the present application.
The foregoing is considered as illustrative of the preferred embodiments of the invention and the technical principles employed. The present application is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present application has been described in more detail with reference to the above embodiments, the present application is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present application, and the scope of the present application is determined by the scope of the claims.
Claims (5)
1. An application control method, comprising:
during the operation of the mobile terminal, collecting a first user behavior habit of a user for operating the mobile terminal;
analyzing the first user behavior habit to obtain a first basic behavior characteristic;
training the first basic behavior characteristics serving as training samples based on a machine learning method to generate a preset behavior prediction model;
every other set period, the system automatically acquires the second user behavior habit in the current set period;
analyzing the second user behavior habit to obtain a second basic behavior characteristic;
inputting the second basic behavior characteristics into the preset behavior prediction model, and obtaining an output result of the preset behavior prediction model;
if the difference value of the actual control operation corresponding to the output result and the second basic behavior characteristic exceeds a preset error range, the second basic behavior characteristic is used as a new training sample to train and update the preset behavior prediction model again; the first basic behavior characteristic and the second basic behavior characteristic respectively comprise at least one of performance parameters, application program identifications, behavior occurrence time, behavior ending time, behavior occurrence places, behavior contents and behavior frequency of the mobile terminal;
acquiring current behavior characteristics at set time intervals in the set period; acquiring a preset behavior prediction model generated based on a machine learning method, wherein the preset behavior prediction model is obtained by training a plurality of behavior characteristic samples of known control operation and is used for predicting the control operation based on the behavior characteristic; the first user behavior habit and the second user behavior habit are historical behavior habits of a target user group and a mobile terminal user on an application program, or the first user behavior habit and the second user behavior habit are historical behavior habits of the target user group on the application program, the target user group is a plurality of users with the same user attribute, the current behavior characteristics comprise user behavior characteristics and mobile terminal performance parameters, and the mobile terminal performance parameters comprise memory, electric quantity and network conditions;
inputting the current behavior characteristics into the preset behavior prediction model to obtain a prediction result output by the preset behavior prediction model;
according to the prediction result, automatically executing control operation according with the behavior habit of the user, wherein the control operation comprises data cleaning and data updating operation of the application program, and the data updating operation of the application program comprises version updating operation of the application program and/or operation parameter updating operation of the application program;
the machine learning method comprises a neural network method, the neural network method comprises an input layer, a hidden layer and an output layer, the training is carried out by taking the first basic behavior characteristics as training samples based on the machine learning method, and the step of generating a preset behavior prediction model comprises the following steps:
inputting the first basic behavior characteristics to the input layer, and outputting intermediate control operation through calculation of an activation function corresponding to each node of the hidden layer;
repeatedly correcting the weight in the activation function by using the difference between the intermediate control operation and the actual behavior operation corresponding to the first basic behavior characteristic and an optimization algorithm until the difference between the intermediate control operation and the actual behavior operation is within a preset error range, obtaining the activation function of each trained node, and generating a preset behavior prediction model.
2. The application control method of claim 1, wherein the machine learning method comprises a neural network method, a support vector machine method, a decision tree method, a logistic regression method, a bayesian method, and a random forest method.
3. An application control apparatus, comprising:
the mobile terminal comprises a user behavior habit acquisition module, a first user behavior habit acquisition module and a second user behavior habit acquisition module, wherein the user behavior habit acquisition module is used for acquiring a first user behavior habit of a user operating the mobile terminal during the operation period of the mobile terminal;
the basic behavior feature acquisition module is used for analyzing the first user behavior habit to obtain a first basic behavior feature;
the preset behavior prediction model generation module is used for training the first basic behavior characteristics serving as training samples based on a machine learning method to generate a preset behavior prediction model;
the preset behavior prediction model updating module is used for automatically acquiring the behavior habits of a second user in the current set period every set period after the preset behavior prediction model is generated;
analyzing the second user behavior habit to obtain a second basic behavior characteristic;
inputting the second basic behavior characteristics into the preset behavior prediction model, and obtaining an output result of the preset behavior prediction model;
if the difference value of the actual control operation corresponding to the output result and the second basic behavior characteristic exceeds a preset error range, the second basic behavior characteristic is used as a new training sample to train and update the preset behavior prediction model again;
the first basic behavior characteristic and the second basic behavior characteristic respectively comprise at least one of performance parameters, application program identifications, behavior occurrence time, behavior ending time, behavior occurrence places, behavior contents and behavior frequency of the mobile terminal;
the current behavior characteristic acquisition module acquires current behavior characteristics at intervals of set time in the set period;
the device comprises a preset behavior prediction model acquisition module, a control module and a control module, wherein the preset behavior prediction model acquisition module is used for acquiring a preset behavior prediction model generated based on a machine learning method, and the preset behavior prediction model is obtained by training a plurality of behavior characteristic samples of known control operation and is used for predicting the control operation based on the behavior characteristics; the first user behavior habit and the second user behavior habit are historical behavior habits of a target user group and a mobile terminal user on an application program, or the first user behavior habit and the second user behavior habit are historical behavior habits of the target user group on the application program, the target user group is a plurality of users with the same user attribute, the current behavior characteristics comprise user behavior characteristics and mobile terminal performance parameters, and the mobile terminal performance parameters comprise memory, electric quantity and network conditions;
the control operation prediction module is used for inputting the current behavior characteristics into the preset behavior prediction model to obtain a prediction result output by the preset behavior prediction model;
the control operation execution module is used for automatically executing control operation according with user behavior habits, the control operation comprises data cleaning and data updating operation of an application program, and the data updating operation of the application program comprises version updating operation of the application program and/or operation parameter updating operation of the application program;
the machine learning method comprises a neural network method, the neural network method comprises an input layer, a hidden layer and an output layer, and the preset behavior prediction model generation module is further used for inputting the first basic behavior characteristics to the input layer and outputting intermediate control operation through calculation of an activation function corresponding to each node of the hidden layer;
repeatedly correcting the weight in the activation function by using the difference between the intermediate control operation and the actual behavior operation corresponding to the first basic behavior characteristic and an optimization algorithm until the difference between the intermediate control operation and the actual behavior operation is within a preset error range, obtaining the activation function of each trained node, and generating a preset behavior prediction model.
4. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the application control method according to any one of claims 1-2.
5. A mobile terminal comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the application control method according to any of claims 1-2 when executing the computer program.
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Families Citing this family (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108595227A (en) | 2018-05-10 | 2018-09-28 | Oppo广东移动通信有限公司 | Application program preloads method, apparatus, storage medium and mobile terminal |
| CN108595228B (en) | 2018-05-10 | 2021-03-12 | Oppo广东移动通信有限公司 | Application program prediction model establishing method and device, storage medium and mobile terminal |
| CN108710513B (en) | 2018-05-15 | 2020-07-21 | Oppo广东移动通信有限公司 | Application program starting method and device, storage medium and terminal |
| CN108920156A (en) * | 2018-05-29 | 2018-11-30 | Oppo广东移动通信有限公司 | Application program prediction model method for building up, device, storage medium and terminal |
| CN108804157A (en) | 2018-06-05 | 2018-11-13 | Oppo广东移动通信有限公司 | Application program preloads method, apparatus, storage medium and terminal |
| JP7031502B2 (en) | 2018-06-07 | 2022-03-08 | オムロン株式会社 | Control system, control method, learning device, control device, learning method and learning program |
| CN108829863B (en) * | 2018-06-22 | 2021-07-30 | Oppo广东移动通信有限公司 | Information prediction method, information prediction device, storage medium and terminal |
| CN108958629B (en) * | 2018-07-05 | 2020-09-08 | Oppo广东移动通信有限公司 | Split screen quitting method and device, storage medium and electronic equipment |
| US10885905B2 (en) * | 2018-07-13 | 2021-01-05 | Samsung Electronics Co., Ltd. | Predicting user actions on ubiquitous devices |
| CN109120487B (en) * | 2018-09-06 | 2021-09-17 | 深圳市沃特沃德信息有限公司 | Intelligent equipment wireless control method and device |
| CN109324902B (en) * | 2018-09-21 | 2021-03-26 | 深圳市中科明望通信软件有限公司 | Method for adjusting working frequency of mobile terminal, mobile terminal and storage medium |
| CN110969249A (en) * | 2018-09-29 | 2020-04-07 | 北京国双科技有限公司 | Production well yield prediction model establishing method, production well yield prediction method and related device |
| CN111050385A (en) * | 2018-10-15 | 2020-04-21 | 中兴通讯股份有限公司 | Application cleaning method, device, equipment and readable storage medium |
| CN109408384B (en) * | 2018-10-16 | 2022-07-29 | 网易（杭州）网络有限公司 | Software application testing method and device, processor and electronic device |
| CN110517679B (en) * | 2018-11-15 | 2022-03-08 | 腾讯科技（深圳）有限公司 | Artificial intelligence audio data processing method and device and storage medium |
| CN111800537B (en) * | 2019-04-09 | 2021-07-20 | Oppo广东移动通信有限公司 | Terminal use state evaluation method and device, storage medium and electronic equipment |
| CN111798018A (en) * | 2019-04-09 | 2020-10-20 | Oppo广东移动通信有限公司 | Behavior prediction method, behavior prediction device, storage medium and electronic equipment |
| CN112783412A (en) * | 2019-11-04 | 2021-05-11 | 阿里巴巴集团控股有限公司 | Page control method, page control device, electronic book reader and electronic equipment |
| CN111651263B (en) * | 2020-02-12 | 2023-10-13 | 北京小米移动软件有限公司 | Resource processing method and device of mobile terminal, computer equipment and storage medium |
| CN112227007B (en) * | 2020-09-18 | 2023-08-25 | 张国政 | Control method for purchasing clothes treating agent for washing machine |
| CN112947264A (en) * | 2021-04-21 | 2021-06-11 | 苏州希盟科技股份有限公司 | Control method and device for dispenser, electronic equipment and medium |
| CN116040713B (en) * | 2023-02-27 | 2023-11-21 | 南栖仙策(南京)高新技术有限公司 | Aeration method, device, equipment and storage medium in sewage treatment |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102378156A (en) * | 2010-08-10 | 2012-03-14 | 鸿富锦精密工业（深圳）有限公司 | Client side equipment and method for updating equipment parameter |
| CN103677936A (en) * | 2013-12-23 | 2014-03-26 | 联想(北京)有限公司 | Application version upgrading method and electronic device |
| CN104317628A (en) * | 2014-11-13 | 2015-01-28 | 北京奇虎科技有限公司 | Mobile terminal and storage space cleaning method thereof |
| CN104375856A (en) * | 2014-09-24 | 2015-02-25 | 腾讯科技（深圳）有限公司 | Application updating method and device |
| CN105100144A (en) * | 2014-05-05 | 2015-11-25 | 腾讯科技（深圳）有限公司 | Application updating method and device |
| CN105224335A (en) * | 2015-10-22 | 2016-01-06 | 上海斐讯数据通信技术有限公司 | A kind of control method of Automatic clearance terminal device, system and terminal device |
| CN105487877A (en) * | 2014-09-16 | 2016-04-13 | 中兴通讯股份有限公司 | Version updating method, system and base station |
| CN105939416A (en) * | 2016-05-30 | 2016-09-14 | 努比亚技术有限公司 | Mobile terminal and application prestart method thereof |
| CN106203067A (en) * | 2016-06-22 | 2016-12-07 | 广东欧珀移动通信有限公司 | The method for cleaning of a kind of application program and device |
| CN106502747A (en) * | 2016-11-02 | 2017-03-15 | 武汉斗鱼网络科技有限公司 | A kind of method of application upgrade and mobile terminal |
| CN106775816A (en) * | 2016-10-28 | 2017-05-31 | 广州视源电子科技股份有限公司 | The automatic update method and system of application program in a kind of LAN |
| CN107203774A (en) * | 2016-03-17 | 2017-09-26 | 阿里巴巴集团控股有限公司 | The method and device that the belonging kinds of data are predicted |
Family Cites Families (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120324481A1 (en) * | 2011-06-16 | 2012-12-20 | Samsung Electronics Co. Ltd. | Adaptive termination and pre-launching policy for improving application startup time |
| CN102999368B (en) * | 2012-12-14 | 2017-10-03 | 广东欧珀移动通信有限公司 | Mobile terminal Program pre-actuates method, device and mobile terminal |
| CN104679743B (en) * | 2013-11-26 | 2019-10-29 | 阿里巴巴集团控股有限公司 | A kind of method and device of the preference pattern of determining user |
| CN103761306B (en) * | 2014-01-22 | 2017-08-04 | 广东欧珀移动通信有限公司 | A kind of intelligent terminal file clean-up method and device |
| CN105095251B (en) * | 2014-05-06 | 2019-12-20 | 中兴通讯股份有限公司 | Terminal automatic display method and device based on user habits |
| CN105094305B (en) * | 2014-05-22 | 2018-05-18 | 华为技术有限公司 | Identify method, user equipment and the Activity recognition server of user behavior |
| CN104360875B (en) * | 2014-10-22 | 2017-09-12 | 小米科技有限责任公司 | Privacy mode starts method and apparatus |
| CN105426036A (en) * | 2015-10-26 | 2016-03-23 | 努比亚技术有限公司 | Interface display upgrade method, apparatus and system for application |
| CN105893024A (en) * | 2016-01-15 | 2016-08-24 | 广东欧珀移动通信有限公司 | Intelligent interaction method and device of mobile terminal, and mobile terminal |
| CN105955741A (en) * | 2016-04-29 | 2016-09-21 | 乐视控股（北京）有限公司 | Application processing method and device |
| CN106230849B (en) * | 2016-08-22 | 2019-04-19 | 中国科学院信息工程研究所 | A kind of smart machine machine learning safety monitoring system based on user behavior |
| CN106339444A (en) * | 2016-08-23 | 2017-01-18 | 深圳市金立通信设备有限公司 | Method for instantly deleting file and terminal |
| CN106598571A (en) * | 2016-11-17 | 2017-04-26 | 珠海市魅族科技有限公司 | Terminal control method and device |
-
2017
- 2017-12-21 CN CN201711394447.1A patent/CN108076224B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102378156A (en) * | 2010-08-10 | 2012-03-14 | 鸿富锦精密工业（深圳）有限公司 | Client side equipment and method for updating equipment parameter |
| CN103677936A (en) * | 2013-12-23 | 2014-03-26 | 联想(北京)有限公司 | Application version upgrading method and electronic device |
| CN105100144A (en) * | 2014-05-05 | 2015-11-25 | 腾讯科技（深圳）有限公司 | Application updating method and device |
| CN105487877A (en) * | 2014-09-16 | 2016-04-13 | 中兴通讯股份有限公司 | Version updating method, system and base station |
| CN104375856A (en) * | 2014-09-24 | 2015-02-25 | 腾讯科技（深圳）有限公司 | Application updating method and device |
| CN104317628A (en) * | 2014-11-13 | 2015-01-28 | 北京奇虎科技有限公司 | Mobile terminal and storage space cleaning method thereof |
| CN105224335A (en) * | 2015-10-22 | 2016-01-06 | 上海斐讯数据通信技术有限公司 | A kind of control method of Automatic clearance terminal device, system and terminal device |
| CN107203774A (en) * | 2016-03-17 | 2017-09-26 | 阿里巴巴集团控股有限公司 | The method and device that the belonging kinds of data are predicted |
| CN105939416A (en) * | 2016-05-30 | 2016-09-14 | 努比亚技术有限公司 | Mobile terminal and application prestart method thereof |
| CN106203067A (en) * | 2016-06-22 | 2016-12-07 | 广东欧珀移动通信有限公司 | The method for cleaning of a kind of application program and device |
| CN106775816A (en) * | 2016-10-28 | 2017-05-31 | 广州视源电子科技股份有限公司 | The automatic update method and system of application program in a kind of LAN |
| CN106502747A (en) * | 2016-11-02 | 2017-03-15 | 武汉斗鱼网络科技有限公司 | A kind of method of application upgrade and mobile terminal |
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