CN113262467A

CN113262467A - Application control method, device and storage medium

Info

Publication number: CN113262467A
Application number: CN202110546498.1A
Authority: CN
Inventors: 黄帅
Original assignee: Beijing Xiaomi Mobile Software Co Ltd; Beijing Xiaomi Pinecone Electronic Co Ltd
Current assignee: Beijing Xiaomi Mobile Software Co Ltd; Beijing Xiaomi Pinecone Electronic Co Ltd
Priority date: 2021-05-19
Filing date: 2021-05-19
Publication date: 2021-08-17

Abstract

The disclosure relates to an application control method, an application control device and a storage medium. The application control method is applied to the cloud and comprises the following steps: acquiring characteristic data sent by a terminal, wherein the characteristic data is matched with preset parameter indexes when the terminal runs an application; inputting the characteristic data into a stuck prediction model to obtain stuck prediction data, wherein the stuck prediction data comprises stuck occurrence timestamp information, stuck occurrence probability value and operation parameter adjustment information; and sending the katton prediction data to the terminal. The application seizure prediction can be realized through the method and the device. The terminal receives the jamming prediction data which are determined and sent by the cloud based on the characteristic data, adjusts the application operation parameters and reduces the jamming occurrence probability.

Description

Application control method, device and storage medium

Technical Field

The present disclosure relates to the field of terminal technologies, and in particular, to an application control method, an application control apparatus, and a storage medium.

Background

With the development of terminal technology, applications running in the terminal become more and more diverse and complicated.

In the process of running an application in a terminal, the stuck phenomenon is a phenomenon that is often encountered in the running process of the application. For example, a game pause phenomenon may occur during a game such as a hand game or the like on the terminal. In the related art, aiming at the stuck phenomenon of application operation, application data subjected to stuck is analyzed through software, the reason for the stuck is determined, and application operation parameters are adjusted manually. However, in this way, the karton phenomenon cannot be prevented in a real sense, and the user experience is relatively poor.

Disclosure of Invention

To overcome the problems in the related art, the present disclosure provides an application control method, apparatus, and storage medium.

According to a first aspect of the embodiments of the present disclosure, an application control method is provided, which is applied to a cloud, and the application control method includes:

acquiring characteristic data sent by a terminal, wherein the characteristic data is matched with preset parameter indexes when the terminal runs an application; inputting the characteristic data into a stuck prediction model to obtain stuck prediction data, wherein the stuck prediction data comprises stuck occurrence timestamp information, stuck occurrence probability value and operation parameter adjustment information; and sending the katton prediction data to the terminal.

In one embodiment, the katon prediction model is determined as follows:

acquiring a dotting data set of an application generating a stuck phenomenon; cleaning the dotting data set to obtain a sample data set; extracting feature data included in the sample data set, wherein the feature data includes one or more of a terminal type, an application type, a stuck occurrence probability, a stuck occurrence time and an application operation parameter; and training based on the characteristic data to obtain a stuck prediction model, wherein the input of the stuck prediction model is the characteristic data of application operation, and the stuck prediction data is output.

In one embodiment, before the sending the katton prediction data to the terminal, the application control method further includes: determining that a stuck occurrence probability value included in the stuck prediction data is greater than or equal to a stuck occurrence probability threshold.

According to a second aspect of the embodiments of the present disclosure, there is provided an application control method applied to a terminal, the application control method including:

responding to the fact that an application runs in the terminal, and acquiring feature data matched with a preset parameter index based on the preset parameter index; sending the characteristic data to a cloud end; the method comprises the steps of receiving pause prediction data which are determined and sent by the cloud based on the characteristic data, wherein the pause prediction data comprise pause occurrence timestamp information, pause occurrence probability value and operation parameter adjustment information; adjusting an operating parameter of the application based on the katton prediction data.

In one embodiment, adjusting the operating parameter of the application based on the katton prediction data comprises:

determining a time at which a stuck event is imminent based on the stuck event timestamp information; and adjusting the operation parameters of the application according to the operation parameters indicated by the operation parameter adjustment information before the time when the Kanton is about to occur.

In one embodiment, adjusting the operation parameter of the application according to the operation parameter indicated by the operation parameter adjustment information includes: and responding to the operation parameter indicated by the operation parameter adjustment information, wherein the operation parameter comprises a plurality of operation parameters, and the operation parameters are respectively adjusted according to the plurality of operation parameters.

According to a third aspect of the embodiments of the present disclosure, there is provided an application control apparatus applied to a cloud, the application control apparatus including:

the terminal comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring characteristic data sent by the terminal, and the characteristic data is matched with preset parameter indexes when the terminal runs an application; the prediction unit is used for inputting the characteristic data into a stuck prediction model to obtain stuck prediction data, and the stuck prediction data comprises stuck occurrence timestamp information, stuck occurrence probability value and operation parameter adjustment information; and the sending unit is used for sending the pause prediction data to the terminal.

In one embodiment, the prediction unit is further configured to determine the katon prediction model by:

In one embodiment, the sending unit is further configured to: determining that a stuck occurrence probability value included in the stuck prediction data is greater than or equal to a stuck occurrence probability threshold before transmitting the stuck prediction data to the terminal.

According to a fourth aspect of the embodiments of the present disclosure, there is provided an application control apparatus, which is applied to a terminal, the application control apparatus including:

the terminal comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring characteristic data matched with a parameter index based on a preset parameter index under the condition that an application runs in the terminal; the sending unit is used for sending the characteristic data to a cloud end; the receiving unit is used for receiving pause prediction data which is determined and sent by the cloud based on the characteristic data, and the pause prediction data comprises pause occurrence timestamp information, pause occurrence probability value and operation parameter adjustment information; and the processing unit is used for adjusting the running parameters of the application based on the katon prediction data.

In one embodiment, the processing unit adjusts the operating parameters of the application based on the katon prediction data in the following manner:

In one embodiment, the processing unit adjusts the operation parameter of the application according to the operation parameter indicated by the operation parameter adjustment information in the following manner: and responding to the operation parameter indicated by the operation parameter adjustment information, wherein the operation parameter comprises a plurality of operation parameters, and the operation parameters are respectively adjusted according to the plurality of operation parameters.

According to a fifth aspect of the embodiments of the present disclosure, there is provided an application control apparatus including:

a processor; a memory for storing processor-executable instructions;

wherein the processor is configured to: the method for controlling the application is implemented according to the first aspect or any one of the embodiments of the first aspect.

According to a sixth aspect of the embodiments of the present disclosure, there is provided an application control apparatus including:

a processor; a memory for storing processor-executable instructions;

wherein the processor is configured to: executing the application control method described in the second aspect or any one of the embodiments of the second aspect.

According to a seventh aspect of the embodiments of the present disclosure, a storage medium is provided, where instructions are stored in the storage medium, and when the instructions in the storage medium are executed by a processor of a cloud, the cloud is enabled to execute the application control method described in the first aspect or any one of the implementation manners of the first aspect.

According to an eighth aspect of the embodiments of the present disclosure, there is provided a storage medium having instructions stored therein, where the instructions stored in the storage medium, when executed by a processor of a terminal, enable the terminal to execute the application control method described in the second aspect or any one of the embodiments of the second aspect.

The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects: when the terminal runs the application, the characteristic data are collected and sent to the cloud end, the cloud end obtains the pause prediction data based on the pause prediction model and sends the pause prediction data to the terminal, and the pause prediction of the application can be achieved. The terminal receives the jamming prediction data which are determined and sent by the cloud based on the characteristic data, adjusts the application operation parameters and reduces the jamming occurrence probability.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

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

Fig. 1 is a schematic diagram illustrating a process of performing video stuck single-index analysis in the related art.

Fig. 2 illustrates a flow chart of an application control method shown in an exemplary embodiment of the present disclosure.

FIG. 3 illustrates a flow chart for determining a stuck prediction model in an exemplary embodiment of the present disclosure.

Fig. 4 is a schematic diagram illustrating a process of building a katon prediction model according to an exemplary embodiment of the present disclosure.

Fig. 5 shows a flowchart of an application control method shown in an exemplary embodiment of the present disclosure.

Fig. 6 illustrates a flow chart of an application control method shown in an exemplary embodiment of the present disclosure.

Fig. 7 illustrates a flow chart of an application control method shown in an exemplary embodiment of the present disclosure.

FIG. 8 is a block diagram illustrating an application control device according to an exemplary embodiment.

FIG. 9 is a block diagram illustrating an application control device according to an exemplary embodiment.

FIG. 10 is a block diagram illustrating an apparatus for application control in accordance with an exemplary embodiment.

FIG. 11 is a block diagram illustrating an apparatus for application control in accordance with an exemplary embodiment.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.

The application control method provided by the embodiment of the disclosure can be applied to a scene that the control terminal runs an application and is stuck. For example, the application may be in an application pause control scenario performed on a terminal having an operating system for controlling application execution, such as a big data field, an intelligent terminal field, and an artificial intelligence field. For example, the application may be controlled to be blocked on a terminal such as a smartphone or a personal computer having an operating system such as Android or ios.

In the related art, in order to prevent the applications running on the terminal from being stuck, a software analysis method is adopted to perform single index analysis on data. Fig. 1 is a schematic diagram illustrating a process of performing video stuck single-index analysis in the related art. Referring to fig. 1, the terminal reports the morton-related index data. The device running with the analysis software collects data reported by the terminal, stores the data (for example, stores the data to a cloud), and simply analyzes a single index corresponding to the data by the cloud to obtain the cause of the blockage. After the cause of the jamming is obtained through cloud analysis, the cause of the jamming can be fed back to the terminal, and then a user using the terminal can check the cause of the jamming and manually adjust application operation parameters to reduce the follow-up probability of the jamming.

In the technology, the sample size of a common software testing method is small, the accurate stuck abnormity early warning of million-level intelligent equipment cannot be objectively graded, the stuck abnormity cannot be timely fed back, the user experience is reduced, the abnormity occurrence is timely corrected through the stuck abnormity early warning, and the user experience is improved. And the stuck abnormal problem discovered by the common software testing method has limitation, and is only limited to the current application and/or terminal for data analysis. In addition, the implementation process of performing the jamming control is based on that when the terminal is jammed, the single index analysis is performed on the data of the terminal in which the jamming occurs, so as to reduce the probability of the subsequent jamming. However, this method needs to perfect the subsequent process of the katton, does not really prevent the katton phenomenon, and has relatively poor user experience. Therefore, the establishment of the stuck abnormal system is incomplete, not objective and not scientific, and the problem of stuck abnormality cannot be solved in time by simple statistical analysis of stuck data.

In view of this, an embodiment of the present disclosure provides an application control method, in which time when an application may be stuck is predicted in advance, so as to adjust an operation parameter before the application is stuck, thereby preventing the application from being stuck and improving user experience.

In an implementation manner, in the application control method provided by the embodiment of the present disclosure, the cloud collects a plurality of data of an application in which a stuck occurs, stores the data, extracts data features based on a feature extraction algorithm, and performs data analysis to establish a stuck prediction model for predicting the stuck occurrence of the application. For example, a stuck prediction model for predicting the occurrence of stuck in an application is obtained by learning and training based on feature data corresponding to the plurality of data. For example, the stuck prediction model predicts stuck prediction data such as time and probability of occurrence of stuck and operation parameters for parameter adjustment after the stuck occurs. The pause prediction model can effectively predict and early warn the game pause, judge the pause abnormal conditions of intelligent equipment such as a terminal and the like from the data science perspective and early warn in time. Under the condition that the cloud end determines that the terminal can be blocked in the large probability of running application, blocking prediction data are sent to the terminal, so that the terminal can adjust running parameters based on the blocking prediction data to prevent blocking, the probability of blocking is reduced, and user experience is improved.

Fig. 2 illustrates a flow chart of an application control method shown in an exemplary embodiment of the present disclosure. As shown in fig. 2, the application control method is applied to the cloud, and includes the following steps.

In step S11, the feature data transmitted by the terminal is acquired.

In the embodiment of the present disclosure, the feature data acquired by the cloud is feature data matched with a preset parameter index when the terminal runs and applies.

In step S12, the acquired feature data is input to the katton prediction model to obtain katton prediction data.

In the embodiment of the present disclosure, the stuck prediction data includes stuck occurrence timestamp information, stuck occurrence probability value, and operation parameter adjustment information.

In the embodiment of the disclosure, a set of katon prediction data may correspond to each feature data. For example, if there are a plurality of acquired feature data, the katton prediction data output by the katton prediction model may be katton occurrence timestamp information, a katton occurrence probability value, and operation parameter adjustment information corresponding to each of the plurality of feature data.

In step S13, the katton prediction data is transmitted to the terminal.

According to the application control method provided by the embodiment of the disclosure, the cloud obtains the stuck prediction data based on the stuck prediction model and sends the stuck prediction data to the terminal, so that the stuck prediction of the application can be realized. The terminal receives the jamming prediction data which are determined and sent by the cloud based on the characteristic data, adjusts the application operation parameters and reduces the jamming occurrence probability.

In the application control method provided by the embodiment of the present disclosure, the stuck prediction model may be determined based on a plurality of data of the application where the stuck occurs.

FIG. 3 illustrates a flow chart for determining a stuck prediction model in an exemplary embodiment of the present disclosure. As shown in fig. 3, the following steps are included.

In step S21, a dotting dataset of an application in which the stuck phenomenon occurs is acquired.

In the embodiment of the disclosure, dotting data can be acquired for applications in which various types of terminals generate the stuck phenomenon. The application in which the karton phenomenon occurs may be a specific type of application or may be a plurality of different types of applications. For example, in the embodiment of the present disclosure, the dotting data may be acquired for game applications such as game games.

In step S22, the dotting data set is cleaned to obtain a sample data set.

In the embodiment of the present disclosure, data cleaning may be performed on the obtained dotting data set to filter out garbage data that does not comply with the creating condition of the stuck model, for example, the garbage data may be data whose application type does not comply with the creating condition of the stuck model, may also be data whose terminal type does not comply with the creating condition of the stuck model, or may also be data whose time does not comply with the creating condition of the stuck model.

When data cleaning is performed, a distributed framework can be adopted for data cleaning. The distributed framework may be spark, live, mapreduce, flink, etc., for example.

In step S23, feature data included in the sample data set is extracted.

In the embodiment of the present disclosure, one or more of a terminal type, an application type, a stuck occurrence probability, a stuck occurrence time, and an application operation parameter may be included in the feature data.

In an example, the extracted feature data may include one or more of a terminal device identifier, a terminal system version type, terminal configuration information (memory size, storage space, touch screen, etc.), a package name of an application program, an application type classification identifier, application operation start and end time, application operation duration, a communication network type used by an application, an application start operation device temperature, an application end operation temperature, an application display frame rate, the number of times that the application is triggered, terminal power consumption every different time period, a stabilization rate and a standard deviation of the application display frame rate, a power down capacity of the terminal per hour, a temperature at an interval specified time after the application starts to operate, a background process start number, a stuck number, and the like.

In the embodiment of the present disclosure, the data features may also be extracted through frames such as TensorFlow, keras, logistic regression, linear regression, and K clustering.

In step S24, a katon prediction model is trained based on the feature data.

The input of the stuck prediction model obtained based on the characteristic data training is the characteristic data of application operation, and stuck prediction data is output.

In the embodiment of the present disclosure, the morton prediction model may be implemented by collecting and storing multi-type application data reported by a multi-type terminal through a cloud, and performing data feature extraction and model establishment based on a feature engineering of feature data extraction. Fig. 4 is a schematic diagram illustrating a process of building a katon prediction model according to an exemplary embodiment of the present disclosure. Referring to fig. 4, a plurality of types of terminals report data. And the cloud end acquires data and stores the data. And performing feature extraction on the stored data based on the feature engineering, and creating a Kanton prediction model based on the extracted feature data.

After the completion of the katton prediction model is created, the embodiments of the present disclosure may train the katton prediction model. In an example of the embodiment of the present disclosure, when training the katton prediction model, the following method may be used for training:

and reading a plurality of fields as features, and classifying different application types, network types, terminal equipment identifications and the like through K clustering so as to classify the terminal types and the application types and obtain different application types corresponding to different terminal types. After the classification of the terminal type and the application type is completed, the classified terminal type and application type can be converted into a multi-dimensional vector, and a large amount of sample data sets, cycle times, text sizes and the like are set. Each feature value is trained by the framework of TensorFlow, keras, logistic regression, linear regression, etc. And predicting the time of occurrence of the seizure, the probability value of the occurrence of the seizure, the operating parameters for preventing the occurrence of the seizure from being correspondingly adjusted and the like based on the characteristic values obtained by training.

In the application control method provided by the embodiment of the disclosure, in order to improve the accuracy of preventing the occurrence of the stuck state and reduce the resource waste caused by the cloud sending the stuck prediction data to the terminal under the condition that the stuck occurrence probability is low, a stuck occurrence probability threshold value may be preset. And the cloud sends the jamming prediction data to the terminal under the condition that the probability value of the jamming occurrence obtained through prediction is larger than or equal to the threshold value of the jamming occurrence probability.

Fig. 5 shows a flowchart of an application control method shown in an exemplary embodiment of the present disclosure. As shown in fig. 5, the application control method is applied to the cloud, and includes the following steps.

In step S31, a katon occurrence probability value is predicted based on the katon prediction model.

In step S32, it is determined that the katon occurrence probability value included in the katon prediction data is greater than or equal to the katon occurrence probability threshold.

In step S33, the katton prediction data is transmitted to the terminal.

In the embodiment of the disclosure, after obtaining the applied stuck occurrence probability value based on the stuck prediction model, the cloud sends the stuck prediction data to the terminal under the condition that the stuck occurrence probability value is determined to be greater than or equal to the stuck occurrence probability threshold. That is, the katon prediction data sent to the terminal in the embodiment of the present disclosure is the katon prediction data having the corresponding katon occurrence probability value greater than or equal to the katon occurrence probability threshold.

In one example, it is assumed that M katon prediction data corresponding to M feature data are obtained based on a katon prediction model. The method comprises the following steps that the pause prediction data with the N pause occurrence probability values larger than or equal to the pause occurrence probability threshold are included. Wherein N is less than or equal to M, and M and N are both natural numbers. The cloud sends the N pieces of pause prediction data with the pause occurrence probability value larger than or equal to the pause occurrence probability threshold to the terminal.

In the embodiment of the disclosure, the katton prediction data may include timestamp information of the occurrence of the katton, and the timestamp information may represent a time distance between the time of the occurrence of the katton of the application and the current time. The stuck prediction data may further include operation parameter adjustment information for preventing the occurrence of the stuck. The terminal adjusts the operation parameters before the occurrence of the jamming based on the timestamp information and the operation parameter adjustment information of the occurrence of the jamming, so that the probability of the occurrence of the jamming can be reduced.

Based on the same concept, the embodiment of the disclosure also provides an application control method, which is executed by a terminal.

Fig. 6 illustrates a flow chart of an application control method shown in an exemplary embodiment of the present disclosure. As shown in fig. 6, the application control method is applied to a terminal, and includes the following steps.

In step S41, in response to an application running in the terminal, feature data matching the parameter index is collected based on a preset parameter index.

In the application control method provided by the embodiment of the present disclosure, the preset parameter index may be a parameter corresponding to feature data used when the cloud is used for the calton prediction model training. For example, the application program may be one or more of a terminal device identifier, a terminal system version type, terminal configuration information (memory size, storage space, touch screen, etc.), a package name of an application program, an application type classification identifier, application operation start and end time, application operation duration, a communication network type used by an application, a device temperature during application start operation, a temperature during application end operation, an application display frame rate, the number of times that the application is triggered, a terminal power amount every different time period, a stability rate and a standard deviation of the application display frame rate, a power-down capacity of the terminal per hour, a temperature at an interval specified time after the application starts operation, a background process starting number, a stuck number, and the like.

The terminal can acquire data corresponding to the parameter indexes aiming at the current application when acquiring the characteristic data based on the parameter indexes. For example, when the current application is a game and the terminal is a mobile phone, one or more of a device id of the mobile phone, a system version type, a system version, mobile phone configuration information (memory, storage size, cpu, etc.), app package name, game classification name, a game start time period, a game pause duration, a network type, a temperature of the mobile phone device, a game frame rate, a trigger frequency per 3 seconds, an electric quantity of the mobile phone, a power-down capacity per hour, a background process starting number, and a pause frequency may be acquired.

In step S42, the collected feature data is sent to the cloud.

In step S43, katton prediction data determined and transmitted by the cloud based on the feature data is received.

The characteristic data sent by the terminal is input to the stuck prediction model by the cloud end for prediction to obtain the stuck prediction data received in the embodiment of the disclosure.

The pause prediction data comprises pause occurrence timestamp information, pause occurrence probability value, operation parameter adjustment information and the like.

In step S44, the operating parameters of the application are adjusted based on the katon prediction data.

According to the application control method provided by the embodiment of the disclosure, when the terminal runs the application, the characteristic data is collected and sent to the cloud, the cloud obtains the stuck prediction data based on the stuck prediction model and sends the stuck prediction data to the terminal, and the stuck prediction of the application can be realized. The terminal receives the jamming prediction data which are determined and sent by the cloud based on the characteristic data, adjusts the application operation parameters and reduces the jamming occurrence probability.

Fig. 7 illustrates a flow chart of an application control method shown in an exemplary embodiment of the present disclosure. As shown in fig. 7, the application control method is applied to a terminal, and includes the following steps.

In step S51, the time at which the katton is about to occur is determined based on the katton occurrence time stamp information.

In step S52, the operation parameters of the application are adjusted according to the operation parameters indicated by the operation parameter adjustment information before the time when the katton is about to occur.

In the embodiment of the disclosure, the terminal adjusts the operation parameters based on the timestamp information and the operation parameter adjustment information of the occurrence of the stuck state before the stuck state occurs, so that the probability of the stuck state occurrence can be reduced.

In the application control method provided by the embodiment of the present disclosure, the stuck prediction data received by the terminal may be multiple sets, where the operation parameters indicated by the operation parameter adjustment information in the stuck prediction data may include one or more operation parameters. If the operation parameters indicated by the operation parameter adjustment information include a plurality of operation parameters, the operation parameters are respectively adjusted to reduce the probability of occurrence of seizure.

According to the application control method provided by the embodiment of the disclosure, a large amount of data of the terminal which is blocked is collected and reported to the cloud. And the cloud carries out model training based on the collected mass data to obtain a Canton prediction model. And in the application running process of the terminal, acquiring application characteristic data in real time, and sending the acquired data to a cloud terminal in real time. And the cloud end obtains the stuck prediction data of the current running application of the terminal based on the feature data obtained in real time and the pre-trained stuck prediction model. The cloud predicts the jamming occurrence probability value of the application running by the terminal, and determines whether the predicted jamming occurrence probability value exceeds a preset jamming occurrence probability threshold value. And if the predicted stuck occurrence probability value exceeds a preset stuck occurrence probability threshold value, the cloud end sends the predicted stuck prediction data to the terminal. The terminal receives the pause prediction data sent by the cloud, and adjusts the operation parameters of the application operated by the mobile phone based on the pause occurrence timestamp information and the operation parameter adjustment information in the pause prediction data so as to avoid the occurrence of pause. If the predicted stuck occurrence probability value does not exceed the preset stuck occurrence probability threshold value, the cloud end does not need to send the predicted stuck prediction data to the terminal.

Based on the same conception, the embodiment of the disclosure also provides an application control device.

It is understood that, in order to implement the above functions, the application control device provided in the embodiments of the present disclosure includes a hardware structure and/or a software module corresponding to each function. The disclosed embodiments can be implemented in hardware or a combination of hardware and computer software, in combination with the exemplary elements and algorithm steps disclosed in the disclosed embodiments. Whether a function is performed as hardware or computer software drives hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

FIG. 8 is a block diagram illustrating an application control device according to an exemplary embodiment. Referring to fig. 8, application control apparatus 100 includes acquisition unit 101, prediction unit 102, and transmission unit 103. The acquiring unit 101 is configured to acquire feature data sent by a terminal, where the feature data is feature data matched with a preset parameter index when the terminal runs an application. The prediction unit 102 is configured to input the feature data into a stuck prediction model to obtain stuck prediction data, where the stuck prediction data includes stuck occurrence timestamp information, stuck occurrence probability value, and operation parameter adjustment information. A sending unit 103, configured to send the kation prediction data to the terminal.

In one embodiment, the prediction unit 102 is further configured to determine the stuck prediction model by:

acquiring a dotting data set of an application generating a stuck phenomenon; cleaning the dotting data set to obtain a sample data set; extracting characteristic data included in the sample data set, wherein the characteristic data comprises one or more of a terminal type, an application type, a pause occurrence probability, a pause occurrence time and an application operation parameter; and training based on the characteristic data to obtain a stuck prediction model, wherein the input of the stuck prediction model is the characteristic data of application operation, and the stuck prediction data is output.

In one embodiment, the sending unit 103 is further configured to: before transmitting the katon prediction data to the terminal, it is determined that a katon occurrence probability value included in the katon prediction data is greater than or equal to a katon occurrence probability threshold value.

FIG. 9 is a block diagram illustrating an application control device according to an exemplary embodiment. Referring to fig. 9, the application control apparatus 200 includes an acquisition unit 201, a transmission unit 202, a reception unit 203, and a processing unit 204.

The acquisition unit 201 is configured to acquire feature data matching the parameter index based on a preset parameter index when an application runs in the terminal. A sending unit 202, configured to send the feature data to the cloud. The receiving unit 203 is configured to receive the pause prediction data determined and sent by the cloud based on the feature data, where the pause prediction data includes pause occurrence timestamp information, a pause occurrence probability value, and operation parameter adjustment information. The processing unit 204 is configured to adjust an operation parameter of the application based on the katon prediction data.

In one embodiment, the processing unit 204 determines an upcoming time of a stuck based on the stuck occurrence timestamp information; and before the time when the Kanton is about to occur, adjusting the operation parameters of the application according to the operation parameters indicated by the operation parameter adjustment information.

In one embodiment, in response to the operation parameter indicated by the operation parameter adjustment information including a plurality of operation parameters, the processing unit 204 respectively adjusts the plurality of operation parameters.

With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.

FIG. 10 is a block diagram illustrating an apparatus 300 for application control in accordance with an exemplary embodiment. The apparatus 300 may be provided as a terminal. For example, the apparatus 300 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, an exercise device, a personal digital assistant, and the like.

Referring to fig. 10, the apparatus 300 may include one or more of the following components: a processing component 302, a memory 304, a power component 306, a multimedia component 308, an audio component 310, an input/output (I/O) interface 312, a sensor component 314, and a communication component 316.

The processing component 302 generally controls overall operation of the device 300, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 302 may include one or more processors 320 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 302 can include one or more modules that facilitate interaction between the processing component 302 and other components. For example, the processing component 302 may include a multimedia module to facilitate interaction between the multimedia component 308 and the processing component 302.

The memory 304 is configured to store various types of data to support operations at the apparatus 300. Examples of such data include instructions for any application or method operating on device 300, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 304 may be implemented by any type or combination of volatile or non-volatile memory devices, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.

Power components 306 provide power to the various components of device 300. The power components 306 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for the apparatus 300.

The multimedia component 308 includes a screen that provides an output interface between the device 300 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 308 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the device 300 is in an operating mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

The audio component 310 is configured to output and/or input audio signals. For example, audio component 310 includes a Microphone (MIC) configured to receive external audio signals when apparatus 300 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 304 or transmitted via the communication component 316. In some embodiments, audio component 310 also includes a speaker for outputting audio signals.

The I/O interface 312 provides an interface between the processing component 302 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.

The sensor assembly 314 includes one or more sensors for providing various aspects of status assessment for the device 300. For example, sensor assembly 314 may detect an open/closed state of device 300, the relative positioning of components, such as a display and keypad of device 300, the change in position of device 300 or a component of device 300, the presence or absence of user contact with device 300, the orientation or acceleration/deceleration of device 300, and the change in temperature of device 300. Sensor assembly 314 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 314 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 314 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 316 is configured to facilitate wired or wireless communication between the apparatus 300 and other devices. The device 300 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 316 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 316 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

In an exemplary embodiment, the apparatus 300 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.

In an exemplary embodiment, a non-transitory computer-readable storage medium comprising instructions, such as the memory 304 comprising instructions, executable by the processor 320 of the apparatus 300 to perform the above-described method is also provided. For example, the non-transitory computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

Fig. 11 is a block diagram illustrating an apparatus 400 for application control in accordance with an example embodiment. For example, the apparatus 400 may be provided as a server. Referring to fig. 11, apparatus 400 includes a processing component 422, which further includes one or more processors, and memory resources, represented by memory 432, for storing instructions, such as applications, that are executable by processing component 422. The application programs stored in memory 432 may include one or more modules that each correspond to a set of instructions. Further, the processing component 422 is configured to execute instructions to perform the above-described methods.

The apparatus 400 may also include a power component 426 configured to perform power management of the apparatus 400, a wired or wireless network interface 450 configured to connect the apparatus 400 to a network, and an input output (I/O) interface 458. The apparatus 400 may operate based on an operating system stored in the memory 432, such as Windows Server, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, or the like.

In an exemplary embodiment, a non-transitory computer readable storage medium comprising instructions, such as the memory 432 comprising instructions, executable by the processing component 422 of the apparatus 400 to perform the above-described method is also provided. For example, the non-transitory computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

It is understood that "a plurality" in this disclosure means two or more, and other words are analogous. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. The singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

It will be further understood that the terms "first," "second," and the like are used to describe various information and that such information should not be limited by these terms. These terms are only used to distinguish one type of information from another and do not denote a particular order or importance. Indeed, the terms "first," "second," and the like are fully interchangeable. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present disclosure.

It is further to be understood that while operations are depicted in the drawings in a particular order, this is not to be understood as requiring that such operations be performed in the particular order shown or in serial order, or that all illustrated operations be performed, to achieve desirable results. In certain environments, multitasking and parallel processing may be advantageous.

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. An application control method is applied to a cloud, and comprises the following steps:

acquiring characteristic data sent by a terminal, wherein the characteristic data is matched with preset parameter indexes when the terminal runs an application;

inputting the characteristic data into a stuck prediction model to obtain stuck prediction data, wherein the stuck prediction data comprises stuck occurrence timestamp information, stuck occurrence probability value and operation parameter adjustment information;

and sending the katton prediction data to the terminal.

2. The application control method of claim 1, wherein the stuck prediction model is determined as follows:

acquiring a dotting data set of an application generating a stuck phenomenon;

cleaning the dotting data set to obtain a sample data set;

extracting feature data included in the sample data set, wherein the feature data includes one or more of a terminal type, an application type, a stuck occurrence probability, a stuck occurrence time and an application operation parameter;

and training based on the characteristic data to obtain a stuck prediction model, wherein the input of the stuck prediction model is the characteristic data of application operation, and the stuck prediction data is output.

3. The application control method according to claim 1 or 2, wherein before the sending of the katon prediction data to the terminal, the application control method further comprises:

determining that a stuck occurrence probability value included in the stuck prediction data is greater than or equal to a stuck occurrence probability threshold.

4. An application control method is applied to a terminal, and the application control method comprises the following steps:

responding to the fact that an application runs in the terminal, and acquiring feature data matched with a preset parameter index based on the preset parameter index;

sending the characteristic data to a cloud end;

the method comprises the steps of receiving pause prediction data which are determined and sent by the cloud based on the characteristic data, wherein the pause prediction data comprise pause occurrence timestamp information, pause occurrence probability value and operation parameter adjustment information;

adjusting an operating parameter of the application based on the katton prediction data.

5. The application control method of claim 4, wherein adjusting the operating parameters of the application based on the katon prediction data comprises:

determining a time at which a stuck event is imminent based on the stuck event timestamp information;

and adjusting the operation parameters of the application according to the operation parameters indicated by the operation parameter adjustment information before the time when the Kanton is about to occur.

6. The application control method according to claim 5, wherein adjusting the operation parameter of the application according to the operation parameter indicated by the operation parameter adjustment information includes:

and responding to the operation parameter indicated by the operation parameter adjustment information, wherein the operation parameter comprises a plurality of operation parameters, and the operation parameters are respectively adjusted according to the plurality of operation parameters.

7. An application control apparatus, applied to a cloud, the application control apparatus comprising:

the terminal comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring characteristic data sent by the terminal, and the characteristic data is matched with preset parameter indexes when the terminal runs an application;

the prediction unit is used for inputting the characteristic data into a stuck prediction model to obtain stuck prediction data, and the stuck prediction data comprises stuck occurrence timestamp information, stuck occurrence probability value and operation parameter adjustment information;

and the sending unit is used for sending the pause prediction data to the terminal.

8. The application control device of claim 7, wherein the prediction unit is further configured to determine the stuck prediction model by:

acquiring a dotting data set of an application generating a stuck phenomenon;

cleaning the dotting data set to obtain a sample data set;

9. The application control device according to claim 7 or 8, wherein the sending unit is further configured to:

determining that a stuck occurrence probability value included in the stuck prediction data is greater than or equal to a stuck occurrence probability threshold before transmitting the stuck prediction data to the terminal.

10. An application control apparatus, applied to a terminal, the application control apparatus comprising:

the terminal comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring characteristic data matched with a parameter index based on a preset parameter index under the condition that an application runs in the terminal;

the sending unit is used for sending the characteristic data to a cloud end;

the receiving unit is used for receiving pause prediction data which is determined and sent by the cloud based on the characteristic data, and the pause prediction data comprises pause occurrence timestamp information, pause occurrence probability value and operation parameter adjustment information;

and the processing unit is used for adjusting the running parameters of the application based on the katon prediction data.

11. The application control device of claim 10, wherein the processing unit adjusts the operating parameters of the application based on the katon prediction data in a manner that:

12. The application control device according to claim 11, wherein the processing unit adjusts the operation parameter of the application according to the operation parameter indicated by the operation parameter adjustment information in the following manner:

13. An application control apparatus, comprising:

a processor;

a memory for storing processor-executable instructions;

wherein the processor is configured to: -executing the application control method of any of claims 1 to 3, or-executing the application control method of any of claims 5 to 6.

14. A storage medium having instructions stored therein, wherein the instructions when executed by a processor of a cloud enable the cloud to perform the application control method of any one of claims 1 to 3, or

The instructions in the storage medium, when executed by a processor of a terminal, enable the terminal to perform the application control method of any one of claims 5 to 6.