CN110806908A - Application software pre-starting method, terminal and computer readable storage medium - Google Patents
Application software pre-starting method, terminal and computer readable storage medium Download PDFInfo
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- CN110806908A CN110806908A CN201911065003.2A CN201911065003A CN110806908A CN 110806908 A CN110806908 A CN 110806908A CN 201911065003 A CN201911065003 A CN 201911065003A CN 110806908 A CN110806908 A CN 110806908A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/445—Program loading or initiating
- G06F9/44505—Configuring for program initiating, e.g. using registry, configuration files
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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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- 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
- H04M1/72448—User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions
- H04M1/72451—User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions according to schedules, e.g. using calendar applications
Abstract
The invention provides an application software pre-starting method, a terminal and a computer readable storage medium. The application software pre-starting method provided by the invention comprises the following steps: s10: detecting whether the current scene number meets a pre-starting condition; s20: if yes, acquiring pre-starting software corresponding to the current scene parameters; s30: and starting the pre-starting software according to a preset rule. According to the method and the device, the habit of using the mobile phone equipment by the user is analyzed, the use scene of the mobile phone equipment by the user is associated with the pre-starting software, and the application software to be started by the user in different use scenes is accurately predicted, so that the prediction is more targeted, and the prediction accuracy is improved.
Description
Technical Field
The present invention relates to the field of mobile communications, and in particular, to a method for pre-starting application software, a terminal, and a computer-readable storage medium.
Background
In order to facilitate a user to directly enter an application program (APP) and reduce waiting time when the APP is started, in the prior art, the use relevance of a private APP of the user is extracted from a historical APP use sequence of the user through the behavior characteristics of the user, and the APP to be used by the user is predicted in an active period when the user uses a mobile phone; or by mining the cooperative competition relationship among the APPs, and coordinating the Nowcasting and the forecasting (Forcasting) to realize the forecasting and the speculation of the APP flow by combining the APP related feature selection, the context feature and the current application state. However, in the prior art, a calculation model for predicting an application program is complex, so that the prediction of the APP is inaccurate, and the model lacks efficiency.
Disclosure of Invention
The invention mainly aims to provide an application software pre-starting method, and aims to solve the problems that in the prior art, a calculation model is complex and the prediction accuracy is low when an application program is predicted.
In order to achieve the above object, the present invention provides an application software pre-starting method, which comprises the following steps:
s10: detecting whether the current scene parameters meet a pre-starting condition;
s20: if yes, acquiring pre-starting software corresponding to the current scene parameters;
s30: and starting the pre-starting software according to a preset rule.
Optionally, the application software pre-starting method further includes:
acquiring use information and/or scene parameters of each piece of software within a preset time interval;
and determining a pre-starting condition and/or corresponding pre-starting software according to the use information and/or the scene parameters.
Optionally, the scene parameter includes at least one of time information, location information, a networking access point, a connection device, and environment information, and the usage information includes at least one of a starting time point, a starting number, and a usage duration.
Optionally, the step of determining a pre-boot condition and/or corresponding pre-boot software according to the usage information and/or the scene parameter includes:
acquiring each piece of starting software in a preset time interval according to the starting time point, and acquiring scene parameters of each piece of starting software;
determining the frequency of using the starting software in each preset time interval according to the use duration of each starting software;
and determining the corresponding pre-starting software in each preset time interval according to the frequency, and determining the pre-starting condition according to the scene parameters of the pre-starting software.
Optionally, the step of determining the pre-boot condition according to the scene parameter of the pre-boot software further includes:
acquiring the occurrence frequency of the scene parameters of the pre-starting software;
and determining the pre-starting condition according to the occurrence frequency.
Optionally, the preset rule includes at least one of the following rules:
starting the pre-starting software in a background;
starting the pre-starting software in a foreground;
starting the pre-starting software at fixed time;
prompting to start the pre-starting software;
starting the software in background or foreground or at regular time or prompt according to the starting time of the software
The software is pre-started.
Optionally, after the step S30, the method further includes:
comparing the pre-starting software started in the background under the current scene parameters with the application software started in the foreground of the user to obtain the matching degree of the pre-starting software and the application software;
and if the matching degree exceeds the preset matching degree, closing the pre-starting software.
Optionally, after the step of closing the pre-boot software, the method further includes:
acquiring use information of each software under current scene parameters within a preset time interval;
and determining the pre-starting software corresponding to the current scene parameters according to the use information.
In order to achieve the above object, the present invention further provides a terminal device, which is characterized in that the terminal device includes a memory, a processor, and a control program of an application software pre-boot method stored in the memory and executable on the processor, and when the control program of the application software pre-boot method is executed by the processor, the steps of the control method of application software pre-boot are implemented.
In order to achieve the above object, the present invention further provides a computer-readable storage medium, wherein a control program of an application software pre-boot method is stored on the computer-readable storage medium, and when the control program of the application software pre-boot method is executed by a processor, the steps of the control method of application software pre-boot are implemented as described above.
According to the technical scheme, when the current scene parameters meet the pre-starting conditions, the pre-starting software corresponding to the current scene parameters is obtained, and the pre-starting software is started in the background. The habit of using the mobile phone equipment by the user is analyzed, the use scene of the mobile phone equipment by the user is associated with the pre-starting software, and the application software to be started by the user in different use scenes is accurately predicted, so that the prediction is more targeted, and the prediction accuracy is improved.
Drawings
Fig. 1 is a schematic terminal structure diagram of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a method for pre-starting application software according to a first embodiment of the present invention;
FIG. 3 is a flowchart illustrating a pre-boot method of application software according to a second embodiment of the present invention;
FIG. 4 is a flowchart illustrating a method for pre-starting application software according to a third embodiment of the present invention;
FIG. 5 is a detailed diagram of step S53 in the third embodiment of the method for pre-starting application software according to the present invention;
FIG. 6 is a flowchart illustrating a method for pre-starting application software according to a fourth embodiment of the present invention;
FIG. 7 is a flowchart illustrating a method for pre-starting application software according to a fifth embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that, if directional indications (such as up, down, left, right, front, and back) are involved in the embodiment of the present invention, the directional indications are only used for explaining the relative positional relationship, the motion situation, and the like between the components in a certain posture, and if the certain posture is changed, the directional indications are changed accordingly.
In addition, technical solutions between various embodiments may be combined with each other, but must be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present invention.
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The main solution of the embodiment of the invention is as follows: detecting whether the current scene parameters meet a pre-starting condition; if yes, acquiring pre-starting software corresponding to the current scene parameters; and starting the pre-starting software according to a preset rule.
In the prior art, the calculation model is complex when the application program is predicted, and the prediction accuracy is not high.
The invention provides an application software pre-starting method, which comprises the following steps: s10: detecting whether the current scene parameters meet a pre-starting condition; s20: if yes, acquiring pre-starting software corresponding to the current scene parameters; s30: and starting the pre-starting software according to a preset rule. The method solves the technical problems that the existing calculation model for predicting the application program is complex, so that the APP prediction is inaccurate, and the model lacks efficiency.
As shown in fig. 1, fig. 1 is a schematic terminal structure diagram of a hardware operating environment according to an embodiment of the present invention.
The terminal of the embodiment of the invention can be a smart phone and can also be a mobile intelligent terminal such as a tablet personal computer.
As shown in fig. 1, the terminal may include: a processor 1001, such as a CPU, a user interface 1003, a memory 1005, a communication bus 1002. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The memory 1005 may be a high-speed RAM memory, or may be an NVM (non-volatile memory), such as a disk memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the terminal structure shown in fig. 1 is not intended to be limiting and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a kind of computer-readable storage medium, may include a program of an operating system and an application software pre-boot method therein.
In the terminal shown in fig. 1, the user interface 1003 is mainly used for connecting a client (user side) and performing data communication with the client; and the processor 1001 may be configured to call a program of the application software pre-boot method stored in the memory 1005 and perform the following operations:
s10: detecting whether the current scene parameters meet a pre-starting condition;
s20: if yes, acquiring pre-starting software corresponding to the current scene parameters;
s30: and starting the pre-starting software according to a preset rule.
Further, the processor 1001 may call the program of the application software pre-boot method stored in the memory 1005, and also perform the following operations:
acquiring use information and/or scene parameters of each piece of software within a preset time interval;
and determining a pre-starting condition and/or corresponding pre-starting software according to the use information and/or the scene parameters.
Further, the processor 1001 may call the program of the application software pre-boot method stored in the memory 1005, and also perform the following operations:
acquiring each piece of starting software in a preset time interval according to the starting time point, and acquiring scene parameters of each piece of starting software;
determining the frequency of using the starting software in each preset time interval according to the use duration of each starting software;
and determining the corresponding pre-starting software in each preset time interval according to the frequency, and determining the pre-starting condition according to the scene parameters of the pre-starting software.
Further, the processor 1001 may call the program of the application software pre-boot method stored in the memory 1005, and also perform the following operations:
acquiring the occurrence frequency of the scene parameters of the pre-starting software;
and determining the pre-starting condition according to the occurrence frequency.
Further, the processor 1001 may call the program of the application software pre-boot method stored in the memory 1005, and also perform the following operations:
comparing the pre-starting software started in the background under the current scene parameters with the application software started in the foreground of the user to obtain the matching degree of the pre-starting software and the application software;
and if the matching degree exceeds the preset matching degree, closing the pre-starting software.
Further, the processor 1001 may call the program of the application software pre-boot method stored in the memory 1005, and also perform the following operations:
acquiring use information of each software under current scene parameters within a preset time interval;
and determining the pre-starting software corresponding to the current scene parameters according to the use information.
Based on the hardware architecture, the embodiment of the application software pre-starting method is provided.
Referring to fig. 2, fig. 2 is a first embodiment of the application software pre-starting method of the present invention, and the application software pre-starting method includes the following steps:
step S10, detecting whether the current scene parameter meets the pre-starting condition;
step S20, if yes, acquiring a pre-starting condition corresponding to the current scene parameter;
in this embodiment, the system automatically obtains scene parameters in the current scene, where the scene parameters include time information, location information, a networking access point, a connection device, a charging line plugging state, an earphone plugging state, and the like of the mobile terminal, and determines whether the pre-start software needs to be started in the background in the current scene according to the scene parameters.
In this embodiment, before the system automatically obtains the scene parameters in the current scene, the system performs statistical calculation on the condition that the APP application is started by the mobile terminal loaded by the system, and obtains the pre-start software corresponding to different scene parameters through calculation, and the process is implemented through the established deep neural network model. Specifically, the system obtains a history record of using the APP by the mobile terminal in a specific time period, where the specific time period may be a time period from activation to current use of the mobile terminal, or a relatively short time period therein, but to obtain the most recent history record, a time period from activation to a current time node is generally adopted, where the history record includes characteristic information such as APP use sequence, APP use duration, APP use frequency, and the number of times that the APP is opened in the specific time period, and further includes corresponding scenario information when using the APP, such as location information of the terminal device when the APP is opened, plug-pull state of a charging wire when the APP is opened, plug-pull state of an earphone when the APP is opened, and the above-mentioned scenario information is introduced into the deep neural network model for calculation to obtain a corresponding relationship between the APP use situation and the scenario, the application software with the highest use frequency under different scenes is used, so that pre-starting software corresponding to different scene parameters is obtained, and habits and preferences of users in using the APP are obtained through analysis. And matching the scene parameters under the current scene acquired by the system with the known scene parameters, and acquiring the pre-starting software corresponding to the current scene if the pre-starting software is acquired.
In this embodiment, the number of the pre-boot software corresponding to different scene parameters is not limited to one, and in order to adapt to the usage habits of the user and improve the prediction accuracy of the pre-boot software and widen the time dimension of the current scene, the pre-boot software corresponding to different scene parameters is ranked according to the matching degree of the calculation result, and several application software ranked the top is taken as the pre-boot software, and the number of the pre-boot software also needs to consider the operating memory of the mobile terminal, and generally can be set to 2, 3, or 4, but the specific number is determined according to the specific condition of the mobile terminal, and is not limited to the number. If the running memory capacity is large, the number of the pre-starting software can be increased, and if the running memory capacity is small, the number of the pre-starting software can be correspondingly reduced in order to avoid the phenomena of blockage and the like of the mobile phone.
And step S30, starting the pre-starting software according to a preset rule.
In this embodiment, after acquiring the pre-boot software corresponding to the current scene parameter, the system may start the pre-boot software according to a preset rule, where the preset rule includes at least one of the following rules: (1) starting the pre-starting software in a background; the pre-starting software is started in the background in advance, and when a user clicks the application software which is the same as the pre-starting software, the user can directly enter a use interface of the application software, so that the time of waiting for the application software to be started by the user is shortened. (2) Starting the pre-starting software in a foreground; (3) starting the pre-starting software at fixed time; (4) prompting to start the pre-starting software; (5) and starting the pre-starting software in a background or foreground or at regular time or prompt according to the starting time of the pre-starting software, acquiring the starting time of the pre-starting software after the system acquires the corresponding pre-starting software according to the current scene parameters, and starting the pre-starting software in the background or foreground or at regular time or prompt according to the starting time. In the time of a day, the mobile terminal may have the same scene parameters, for example, in a day, the user may have the situations of repeatedly plugging and unplugging the headset, repeatedly charging, holding the mobile terminal at the same position, and the like, but when the same scene occurs each time, the information of the frequency, the duration, and the like of the APP used by the user may be different. Therefore, the time point of the background starting of the pre-starting software is also determined by combining the historical starting time of the pre-starting software. The historical starting time comprises a time period or a time point when the pre-starting software is started most frequently in one day, and if the predicted frequency of the pre-starting software starting at 8 am and 12 am is the highest, the system starts the pre-starting software at 8 am and 12 am in the background.
In the embodiment, the pre-boot software is started in the background under the condition that the time and the scene are matched simultaneously, so that the pertinence of a prediction algorithm of the system to the use habits of users and the accuracy of prediction are improved, the situation that the pre-boot software is repeatedly started in the background of the system due to the fact that a single scene parameter is used as a reference condition is avoided, the frequency of starting the pre-boot software in the background is reduced, and the running memory of the system is saved.
In the embodiment, the system correlates the use scene of the mobile phone device of the user with the pre-starting software by analyzing the habit of the user using the mobile phone device, and accurately predicts the application software to be started by the user in different use scenes, so that the prediction is more targeted, and the prediction accuracy is improved.
Further, referring to fig. 3, a second embodiment of the application software pre-starting method according to the present invention is based on the first embodiment, and the application software pre-starting method further includes:
step S40, acquiring the use information and/or scene parameters of each software in a preset time interval;
step S50, determining a pre-boot condition and/or corresponding pre-boot software according to the usage information and/or the scene parameters.
In this embodiment, a day is divided into a plurality of time periods, for example, a time period of 5 minutes is divided into 284 time periods, a system obtains a history of using APPs of a user in each time period, and counts usage information and scenario parameters of each APP in different time periods, where the scenario parameters include at least one of time information, location information, networking access points, connection devices, and environment information, and the usage information includes at least one of a starting time point, a starting number of times, and a usage duration. And importing the APP use information and the scene parameters into a deep neural network model for calculation to obtain the corresponding relation between the APP use condition and the scene parameters in each time period. Meanwhile, the system also counts the total times of starting the APP or the total duration of the APP in different periods according to the acquired user history records, takes the partial periods with higher times as high-frequency using periods, and if 40 periods are taken as the high-frequency using periods, if the conditions that the front and the back of some high-frequency using periods are adjacent, the high-frequency using periods are combined. The method comprises the steps of taking APP with more starting times or using duration in a high-frequency using time period as pre-starting software, and taking scene parameters corresponding to the pre-starting software as pre-starting conditions.
In this embodiment, the system may automatically adjust the division standard of the high-frequency time period, which is not limited to the above embodiment using 5 minutes as one time period, and the adjustment method is based on the calculation result of the deep neural network model. The user can also set the division standard of the time period in the corresponding functional area of the terminal equipment.
In the embodiment, the time of a day is divided into a plurality of time periods, the time periods of the high-frequency using equipment are counted, the pre-starting condition and the corresponding pre-starting software are determined according to the APP use information and the scene parameters in the high-frequency time periods, the prediction model is started in the time periods of the high-frequency using equipment to calculate to obtain the pre-starting software, the time range of calculating by the system starting prediction model is shortened, and the pertinence and the accuracy of prediction are further improved.
Further, referring to fig. 4 and 5, a third embodiment of the application software pre-starting method according to the present invention, based on the third embodiment, step S50 includes:
step S51, acquiring each starting software in a preset time interval according to the starting time point, and acquiring the scene parameter of each starting software;
in this embodiment, after the system acquires the use information and the scene parameters of each software in the preset time interval, the APP started in each preset time interval is obtained according to the starting time point of the historical use APP, and then the scene parameters corresponding to different types of APPs in each time period are acquired, so as to subdivide the use condition of the APPs.
Step S52, determining the frequency of using the starting software in each preset time interval according to the use duration of each starting software;
in this embodiment, after obtaining the APP types started in each preset time interval, the usage duration of different types of APPs in the time period is also counted, so as to obtain the usage frequency of different types of APPs in each preset time interval.
Step S53, determining the corresponding pre-starting software in each preset time interval according to the frequency, and determining the pre-starting condition according to the scene parameters of the pre-starting software.
In this embodiment, the APP use frequencies of different types in each preset time interval are sequenced to obtain one or more APPs with the use frequencies ranked at the top, and the APPs are used as the pre-start software in the time interval. Meanwhile, the scene parameters corresponding to the APP in the time period are used as pre-starting conditions, and when the system detects that the mobile terminal is in the same scene parameters in the same time period of the next day, the background starts the APP corresponding to the scene parameters.
Further, referring to fig. 5, the determining the pre-boot condition according to the scene parameter of the pre-boot software in step S53 includes:
step S53a, acquiring the occurrence frequency of the scene parameters of the pre-starting software;
and step S53b, determining the pre-starting condition according to the occurrence frequency.
In this embodiment, when the corresponding scene parameters of the APP in each time interval are used as the pre-start conditions, situations may arise with multiple context parameters, such as, for example, within a certain time interval, a user initiating a WeChat, during the period, the mobile terminal has charging condition, the mobile terminal is connected with the earphone intermittently, and the user holds the mobile terminal to appear in a plurality of places, the system carries out statistics and sequencing on charging, connecting the earphone and using different places of the mobile terminal, takes a scene with higher frequency as a scene condition for judging the pre-starting software, if the statistics and analysis result shows that the frequency of using the WeChat by the mobile terminal equipment held by the user in the office is high, or the frequency of using the earphone when the user uses the WeChat is high, the scene that the user holds the mobile terminal in the office or the scene that the user connects the earphone is used as the pre-starting condition for starting the WeChat.
In this embodiment, the above scenario parameters are not limited to the plug state of the charging line of the mobile terminal, the plug state of the earphone of the mobile terminal, and the location information of the user using the mobile terminal, but also include other parameters that can reflect the time, place, and environment when the user uses the APP, which is not exemplified herein.
In the embodiment, the scene parameters which can most reflect the APP used by the user are determined by counting the occurrence frequency of different scene parameters, and the pre-starting condition for starting the pre-starting software is determined according to the scene parameters, so that the degree of grasp of the prediction model on the user preference is enhanced, and the prediction accuracy is further improved.
Further, referring to fig. 6, a fourth embodiment of the application software pre-starting method according to the present invention, based on the first embodiment, after step S30, further includes:
step S60, comparing the pre-boot software started in the background under the current scene parameters with the application software started in the foreground of the user, and acquiring the matching degree of the pre-boot software and the application software;
in this embodiment, the process of starting the pre-boot software by the system background does not have any influence on the actual operation of the user, the APP started by the user in the time interval may be completely consistent with the pre-boot software, or may be partially consistent with the pre-boot software, or after the system background starts the pre-boot software, the user does not start any APP in the time interval, or other possible situations occur so that the pre-boot software started by the system background is inconsistent with the APP actually started by the user. Therefore, the system can count the actual APP starting condition in the time interval, compare the actual APP starting condition with the pre-starting software started by the system in the background in the time interval, and calculate the matching degree of the actual APP starting condition and the pre-starting software.
Specifically, if 3 pieces of pre-start software are started in the system background within a certain time interval, and 2 pieces of APPs actually started by the user within the time interval are the same as the pre-start software, the matching degree of the prediction is two thirds.
And step S70, if the matching degree exceeds the preset matching degree, closing the pre-starting software.
In this embodiment, a preset matching degree value is set in the system, the actually obtained matching degree is compared with the preset matching degree, and if the actually measured matching degree exceeds the preset matching degree, the pre-start software is closed; and if the measured matching degree does not exceed the preset matching degree, starting the same pre-starting software by the background when the next same scene appears. Specifically, if the preset matching degree is four fifths, two thirds of the actual matching degree exceeds the preset matching degree, and the system immediately closes the pre-starting software.
In this embodiment, the preset matching degree value is not limited to four fifths of the above, and the user can set the corresponding functional area in the system according to actual needs.
In this embodiment, the actual matching degree is compared with the preset matching degree, and a corresponding processing mode is obtained according to the comparison result, so that the system adjusts the prediction model in time, and repeated errors are avoided.
Further, referring to fig. 7, a fifth embodiment of the application software pre-starting method according to the present invention, based on the first embodiment, after step S70, further includes:
step S80, acquiring the use information of each software under the current scene parameters within a preset time interval;
and step S90, determining the pre-starting software corresponding to the current scene parameters according to the use information.
In this embodiment, when the measured matching degree exceeds the preset matching degree, the system closes the pre-boot software, re-acquires the scene parameters and the use information of each software within the preset time interval, introduces the updated history into the deep neural network model for calculation, obtains new pre-boot software and corresponding scene parameters, and then starts the pre-boot software in the background according to the recently calculated pre-boot software and corresponding scene parameters.
In this embodiment, through the degree of matching of the APP of contrast prediction and the APP of in-service use, measure the rate of accuracy of system prediction, user's use habit is probably changed, consequently, when the prediction rate of accuracy is lower, reacquires up-to-date APP and uses historical record, obtains new prediction software according to up-to-date APP and uses historical record to this adapts to user's use habit, improves the rate of accuracy of prediction, and then promotes user experience.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention, and all modifications and equivalents of the present invention, which are made by the contents of the present specification and the accompanying drawings, or directly/indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. The application software pre-starting method is characterized by comprising the following steps:
s10: detecting whether the current scene parameters meet a pre-starting condition;
s20: if yes, acquiring pre-starting software corresponding to the current scene parameters;
s30: and starting the pre-starting software according to a preset rule.
2. The method of claim 1, further comprising:
acquiring use information and/or scene parameters of each piece of software within a preset time interval;
and determining a pre-starting condition and/or corresponding pre-starting software according to the use information and/or the scene parameters.
3. The application software pre-boot method of claim 2, wherein the scenario parameter includes at least one of time information, location information, networking access point, connected device, and environment information, and the usage information includes at least one of a boot time point, a boot number, and a usage duration.
4. The method for pre-starting application software according to claim 3, wherein the step of determining pre-starting conditions and/or corresponding pre-starting software according to the usage information and/or scene parameters comprises:
acquiring each piece of starting software in a preset time interval according to the starting time point, and acquiring scene parameters of each piece of starting software;
determining the frequency of using the starting software in each preset time interval according to the use duration of each starting software;
and determining the corresponding pre-starting software in each preset time interval according to the frequency, and determining the pre-starting condition according to the scene parameters of the pre-starting software.
5. The method for pre-starting application software according to claim 4, wherein the step of determining the pre-starting condition according to the scene parameter of the pre-starting software further comprises:
acquiring the occurrence frequency of the scene parameters of the pre-starting software;
and determining the pre-starting condition according to the occurrence frequency.
6. The method according to any one of claims 1 to 5, wherein the preset rules comprise at least one of the following rules:
starting the pre-starting software in a background;
starting the pre-starting software in a foreground;
starting the pre-starting software at fixed time;
prompting to start the pre-starting software;
and starting the pre-starting software in a background or foreground or at regular time or in a prompt mode according to the starting time of the pre-starting software.
7. The application software pre-boot method of claim 6, further comprising, after the step of S30:
comparing the pre-starting software started in the background under the current scene parameters with the application software started in the foreground to obtain the matching degree of the pre-starting software and the application software;
and if the matching degree exceeds the preset matching degree, closing the pre-starting software.
8. The application software pre-boot method of claim 7, further comprising, after said step of shutting down said pre-boot software:
acquiring use information of each software under current scene parameters within a preset time interval;
and determining the pre-starting software corresponding to the current scene parameters according to the use information.
9. A terminal device, characterized in that the terminal device comprises a memory, a processor and a control program of an application software pre-boot method stored on the memory and executable on the processor, the control program of the application software pre-boot method realizing the steps of the control method of application software pre-boot as claimed in any one of claims 1 to 8 when executed by the processor.
10. A computer-readable storage medium, on which a control program of an application software pre-boot method is stored, which, when executed by a processor, implements the steps of the control method of application software pre-boot as claimed in any one of claims 1 to 8.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112416132A (en) * | 2020-11-27 | 2021-02-26 | 上海影创信息科技有限公司 | Method and system for detecting starting condition of VR (virtual reality) glasses application program and VR glasses |
| WO2022032668A1 (en) * | 2020-08-14 | 2022-02-17 | 深圳传音控股股份有限公司 | Application management method and apparatus, and storage medium |
| CN116033431A (en) * | 2022-08-18 | 2023-04-28 | 荣耀终端有限公司 | Connection method and device of wearable device |
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2019
- 2019-11-01 CN CN201911065003.2A patent/CN110806908A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022032668A1 (en) * | 2020-08-14 | 2022-02-17 | 深圳传音控股股份有限公司 | Application management method and apparatus, and storage medium |
| CN112416132A (en) * | 2020-11-27 | 2021-02-26 | 上海影创信息科技有限公司 | Method and system for detecting starting condition of VR (virtual reality) glasses application program and VR glasses |
| CN116033431A (en) * | 2022-08-18 | 2023-04-28 | 荣耀终端有限公司 | Connection method and device of wearable device |
| CN116033431B (en) * | 2022-08-18 | 2023-10-31 | 荣耀终端有限公司 | Connection method and device of wearable device |
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