CN111580883B - Application program starting method, device, computer system and medium - Google Patents
Application program starting method, device, computer system and medium Download PDFInfo
- Publication number
- CN111580883B CN111580883B CN202010369861.2A CN202010369861A CN111580883B CN 111580883 B CN111580883 B CN 111580883B CN 202010369861 A CN202010369861 A CN 202010369861A CN 111580883 B CN111580883 B CN 111580883B
- Authority
- CN
- China
- Prior art keywords
- function
- information
- module
- started
- application program
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 46
- 230000004044 response Effects 0.000 claims abstract description 8
- 230000009471 action Effects 0.000 claims description 13
- 238000012549 training Methods 0.000 claims description 8
- 230000003213 activating effect Effects 0.000 claims 1
- 230000006870 function Effects 0.000 description 175
- 238000004590 computer program Methods 0.000 description 11
- 230000015654 memory Effects 0.000 description 9
- 238000004891 communication Methods 0.000 description 8
- 238000010586 diagram Methods 0.000 description 8
- 230000008569 process Effects 0.000 description 6
- 230000007613 environmental effect Effects 0.000 description 4
- 230000003287 optical effect Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 238000005265 energy consumption Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000001133 acceleration Effects 0.000 description 2
- 238000013475 authorization Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 239000000835 fiber Substances 0.000 description 2
- 238000009472 formulation Methods 0.000 description 2
- 230000014509 gene expression Effects 0.000 description 2
- 230000005484 gravity Effects 0.000 description 2
- 230000000977 initiatory effect Effects 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 239000004065 semiconductor Substances 0.000 description 2
- 239000000758 substrate Substances 0.000 description 2
- 238000012546 transfer Methods 0.000 description 2
- 108010001267 Protein Subunits Proteins 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000005358 geomagnetic field Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000004806 packaging method and process Methods 0.000 description 1
- 238000000060 site-specific infrared dichroism spectroscopy Methods 0.000 description 1
Classifications
-
- 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
Abstract
The present disclosure provides an application program starting method, applied to a terminal device, where the application program includes a plurality of functional modules, the method includes: in response to receiving a request for starting an application program, acquiring state information of the terminal device, inputting the state information into a prediction model to obtain a function identification set, wherein the function identification set comprises at least one function identification, the at least one function identification corresponds to at least one function module respectively, determining the function module to be started based on the function identification set, and starting the function module to be started.
Description
Technical Field
The present disclosure relates to the field of computer technology, and more particularly, to an application program starting method, apparatus, computer system, and computer readable medium.
Background
With the rapid development of information technology and electronic technology, various mobile terminals (e.g., smartphones, tablet computers, etc.) have become an integral part of people's work and life. Through the mobile terminal, a user can dial a call, send and receive multimedia files such as short messages, pictures, mails, videos and the like, and can also realize surfing the internet, playing games, editing texts, shopping in online shopping malls and the like. Mobile terminals have become an indispensable tool for work, life and social contact of various groups. With the increasing development of mobile terminal functions, various application programs and application functions on the mobile terminal are also layered endlessly.
In the course of implementing the inventive concept, the inventors found that at least the following problems exist in the related art. In the related art, when an application program is started, all functions included in the application program are usually started for a user to operate. For example, mobile banking applications, which are important customer platforms for banks, carry most of the business of banks. Currently, mobile banking functions are numerous, and the functions that each user often uses or may use are only a few. Moreover, the functions that may be used by different users are not identical. However, when the current mobile banking application program is started, all functions contained in the application program can be started indiscriminately, and a large amount of starting time and hardware resources are wasted.
Disclosure of Invention
In view of this, the present disclosure provides an application program starting method, apparatus, computer system, and computer readable medium.
An aspect of the present disclosure provides an application program starting method, applied to a terminal device, where the application program includes a plurality of functional modules, and the method includes: in response to receiving a request for starting the application program, acquiring state information of the terminal equipment, inputting the state information into a prediction model to obtain a function identification set, wherein the function identification set comprises at least one function identification, the at least one function identification corresponds to at least one function module, a function module to be started is determined based on the function identification set, and the function module to be started is started.
According to an embodiment of the disclosure, the determining a function module to be started based on the function identification set includes: and increasing the priority corresponding to the function module corresponding to the function identification set by one level, wherein each of the plurality of function modules contained in the application program corresponds to an initial priority, and determining the function module with the priority higher than the initial priority in the plurality of function modules as the function module to be started.
According to an embodiment of the present disclosure, the function module to be started includes a plurality of function modules to be started, and starting the function module to be started includes: and sequentially starting the plurality of functional modules to be started based on priorities respectively corresponding to the plurality of functional modules to be started.
According to an embodiment of the present disclosure, the method further comprises: sample data is acquired, the sample data comprising state information of a terminal device and user operation information, and the predictive model is trained based on the sample data.
According to an embodiment of the present disclosure, the state information of the terminal device includes at least one of time information, motion information, location information, environment information, network connection information, identification information, operation state information, and fingerprint information.
According to an embodiment of the present disclosure, the user operation information includes at least one of operation interface information, operation action information, and operation object information.
According to an embodiment of the present disclosure, the method further comprises: updating the prediction model.
According to an embodiment of the present disclosure, the method further comprises: and sending the updated prediction model to a background server corresponding to the application program so that the background server stores the prediction model.
Another aspect of the present disclosure provides an application program starting apparatus of a terminal device, where the application program includes a plurality of functional modules, and the apparatus includes a first acquisition module, a prediction module, a determination module, and a starting module. The first acquisition module is used for responding to the received request for starting the application program and acquiring the state information of the terminal equipment. The prediction module is used for inputting the state information into the prediction model to obtain a function identifier set, wherein the function identifier set comprises at least one function identifier, and the at least one function identifier corresponds to the at least one function module respectively. The determining module is used for determining the function module to be started based on the function identification set. The starting module is used for starting the functional module to be started.
According to an embodiment of the disclosure, the determining a function module to be started based on the function identification set includes: and increasing the priority corresponding to the function module corresponding to the function identification set by one level, wherein each of the plurality of function modules contained in the application program corresponds to an initial priority, and determining the function module with the priority higher than the initial priority in the plurality of function modules as the function module to be started.
According to an embodiment of the present disclosure, the function module to be started includes a plurality of function modules to be started, and starting the function module to be started includes: and sequentially starting the plurality of functional modules to be started based on priorities respectively corresponding to the plurality of functional modules to be started.
According to an embodiment of the disclosure, the apparatus further comprises a second acquisition module and a training module. The second acquisition module is used for acquiring sample data, wherein the sample data comprises state information and user operation information of the terminal equipment. And a training module for training the predictive model based on the sample data.
According to an embodiment of the present disclosure, the state information of the terminal device includes at least one of time information, motion information, location information, environment information, network connection information, identification information, operation state information, and fingerprint information.
According to an embodiment of the present disclosure, the user operation information includes at least one of operation interface information, operation action information, and operation object information.
According to an embodiment of the present disclosure, the apparatus further comprises: and the updating module is used for updating the prediction model.
According to an embodiment of the present disclosure, the apparatus further comprises: and the sending module is used for sending the updated prediction model to a background server corresponding to the application program so as to enable the background server to store the prediction model.
Another aspect of the present disclosure provides a computer system comprising: one or more processors, storage means for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method as described above.
Another aspect of the present disclosure provides a computer-readable medium storing computer-executable instructions that, when executed, are configured to implement a method as described above.
Another aspect of the present disclosure provides a computer program comprising computer executable instructions which when executed are for implementing a method as described above.
According to the embodiment of the disclosure, the problems that a large amount of starting time and hardware resources are wasted because all functions contained in the application program are started indiscriminately when the application program is started in the related art can be at least partially solved, and the technical effects that part of functions in the application program are started differently according to different users can be achieved, so that the starting speed of the application program is increased, and the energy consumption is reduced can be achieved.
Drawings
The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of embodiments thereof with reference to the accompanying drawings in which:
FIG. 1 schematically illustrates an application scenario of an application launch method and apparatus according to an embodiment of the present disclosure;
FIG. 2 schematically illustrates a flow chart of an application launch method according to an embodiment of the present disclosure;
FIG. 3 schematically illustrates a block diagram of an application launch device according to an embodiment of the present disclosure; and
fig. 4 schematically illustrates a block diagram of a computer system according to an embodiment of the disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is only exemplary and is not intended to limit the scope of the present disclosure. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The words "a", "an", and "the" as used herein are also intended to include the meaning of "a plurality", etc., unless the context clearly indicates otherwise. Furthermore, the terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms used herein should be construed to have meanings consistent with the context of the present specification and should not be construed in an idealized or overly formal manner.
Where expressions like at least one of "A, B and C, etc. are used, the expressions should generally be interpreted in accordance with the meaning as commonly understood by those skilled in the art (e.g.," a system having at least one of A, B and C "shall include, but not be limited to, a system having a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). Where a formulation similar to at least one of "A, B or C, etc." is used, in general such a formulation should be interpreted in accordance with the ordinary understanding of one skilled in the art (e.g. "a system with at least one of A, B or C" would include but not be limited to systems with a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). It should also be appreciated by those skilled in the art that virtually any disjunctive word and/or phrase presenting two or more alternative items, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the items, either of the items, or both. For example, the phrase "a or B" should be understood to include the possibility of "a" or "B", or "a and B".
The embodiment of the disclosure provides an application program starting method which is applied to terminal equipment, wherein the application program comprises a plurality of functional modules. The method comprises the following steps: in response to receiving a request for starting an application program, acquiring state information of the terminal device, inputting the state information into a prediction model to obtain a function identification set, wherein the function identification set comprises at least one function identification, the at least one function identification corresponds to at least one function module respectively, determining the function module to be started based on the function identification set, and starting the function module to be started.
Fig. 1 schematically illustrates an application scenario 100 of an application program starting method and apparatus according to an embodiment of the present disclosure.
It should be noted that fig. 1 is only an example of a system architecture to which embodiments of the present disclosure may be applied to assist those skilled in the art in understanding the technical content of the present disclosure, but does not mean that embodiments of the present disclosure may not be used in other devices, systems, environments, or scenarios.
As shown in fig. 1, an application scenario 100 according to this embodiment may comprise terminal devices 101, 102, 103, a network 104, a server 105, a first database cluster 106 and a second database cluster 107. The network 104 may be a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as shopping class applications, web browser applications, search class applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only) may be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be a variety of electronic devices having a display screen and supporting wireless communications, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.
The server 105 may be a server providing various services, for example, a background management server providing support for applications in the terminal devices 101, 102, 103. The background management server may analyze and process the received data such as the user request, and feed back the processing result (e.g., the web page, information, or data obtained or generated according to the user request) to the terminal device.
It should be noted that, the application program starting method provided by the embodiment of the present disclosure may be generally executed by a terminal device in which the application program is installed. Accordingly, the application program starting apparatus of the terminal device provided by the embodiment of the present disclosure may be generally set in the terminal device in which the application program is installed.
For example, any one of the terminal apparatuses 101, 102, 103 (for example, but not limited to, the terminal apparatus 101) may be installed with an application program, and the server 105 may be a background server of the application program, for example. The terminal device 101 may respond to a start request of a user, obtain current state information of the terminal device 101, and then input the state information into a prediction model to obtain a function identifier set, determine a function module to be started based on the function identifier set, and start the function module to be started. Therefore, partial functions in the application program can be started differently according to different users, so that the starting speed of the application program is increased, and the technical effect of reducing energy consumption is achieved.
It should be understood that the number of terminal devices, networks, servers and database clusters in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, servers, and database clusters, as desired for implementation.
Fig. 2 schematically illustrates a flowchart of an application launch method according to an embodiment of the present disclosure.
As shown in fig. 2, the method includes operations S201 to S204.
According to the embodiment of the disclosure, the method can be applied to terminal equipment provided with the application program, including but not limited to a smart phone, a tablet computer, a laptop portable computer, a desktop computer and the like.
In the disclosed embodiments, the application may contain a plurality of functional modules. For example, a login function module, a financial function module, a loan function module, a credit card function module, and the like may be included. It will be appreciated that the embodiments of the present disclosure are not limited to the functions that each functional module of an application program can implement, and those skilled in the art may set the functions according to actual needs.
In operation S201, status information of a terminal device is acquired in response to receiving a request for starting an application.
According to the embodiment of the disclosure, a user may initiate a request to start the application through a specific operation. For example, the user may click on an icon of the application, initiating a request to launch the application.
In the embodiment of the disclosure, the current state information of the terminal device can be acquired in response to receiving a request for starting an application program. The status information may include, for example, at least one of time information, motion information, location information, environment information, network connection information, identification information, operation status information, and fingerprint information.
According to the embodiment of the disclosure, the current time information of the terminal equipment can be acquired. For example, a timestamp of the terminal device may be obtained.
According to the embodiment of the disclosure, the current motion information of the terminal equipment can also be obtained. For example, the current motion information of the terminal device is acquired by a motion sensor of the terminal device. For example, the current motion information of the terminal device may be acquired by a sensor such as an acceleration sensor (Accelerator), a Gyroscope (gyroscillope), a Gravity sensor (Gravity), a linear acceleration sensor (Linear Accelerator), a Rotation vector sensor (Rotation), or the like of the terminal device.
According to the embodiment of the disclosure, the current position information of the terminal equipment can also be acquired. For example, the current position information of the terminal device can also be acquired by a position sensor of the terminal device. For example, the current position information of the terminal device may be acquired by a sensor such as a geomagnetic field sensor (Magnetometer), a direction sensor (Orientation), a Proximity sensor (Proximity), or the like. The current location information of the terminal device may also be obtained by a global positioning system (Global Positioning System, abbreviated as GPS) of the terminal device. For example, the current location information may be obtained by a user's authorization, accessing the terminal device's GPS.
According to the embodiment of the disclosure, the current environmental information of the terminal equipment can also be obtained. For example, the current environmental information of the terminal device can also be acquired by an environmental sensor of the terminal device. For example, the current environmental information of the terminal device may be acquired by a sensor such as a Light sensor (Light), a Temperature sensor (Temperature), a Pressure sensor (Pressure), or the like.
According to the embodiment of the disclosure, the network connection information of the terminal device can also be obtained. For example, WIFI information, such as WIFI MAC or WIFI SSID, is obtained. Bluetooth information, such as Bluename or BlueMAC, may also be obtained. For example, WIFI information and bluetooth information may be obtained through authorization of a user.
According to the embodiment of the disclosure, the identification information of the terminal device can also be obtained. Such as device ID information, device brand and signal information, or user coded information on the device, etc.
According to the embodiment of the disclosure, the working state information of the terminal equipment can also be obtained. For example, whether to break a jail, whether to root, and the like.
According to the embodiment of the disclosure, fingerprint information of the terminal device can also be acquired. The fingerprint information may for example be unique identification information of the terminal device. For example, at least one of serial, hardware, board, device, build ID, display, fingerprint, android ID, imei, version, sdk, radioversion, resolution, imsi, number, simserial, simcountryiso, simoperator, simoperatorname, networkcountryios, networkoperator, networkoperatorname, phoneType, networkType, simState, mccmnc, gsmlac, gsmcid, cpuinfo, cpuarch of the terminal device.
In operation S202, the state information is input to the prediction model, resulting in a set of function identifiers, the set of function identifiers including at least one function identifier, the at least one function identifier each corresponding to at least one function module.
The predictive model in the embodiments of the present disclosure may be obtained by: sample data is acquired, the sample data including state information of the terminal device and user operation information, and a predictive model is trained based on the sample data.
According to the embodiment of the disclosure, the sample data can be acquired in the process of using the application program through the terminal equipment each time by a user. For example, each time the user uses the application program through the terminal device, the state information and the user operation information of the terminal device corresponding to the use are acquired.
For example, the prediction model may be trained based on terminal device state information and user operation information each time the user uses the application program and the functional modules used by the user during the use process, so that the trained model may predict the functional modules that the user may use based on the current terminal device state information.
In the disclosed embodiments, the predictive model may output a set of identities of predicted functional modules based on the input information. For example, the predictive model may output a set of function identifications { function 1, function 2, function 3}. Function 1 may correspond to function module 1, function 2 may correspond to function module 2, and function 3 may correspond to function module 3.
According to an embodiment of the present disclosure, the user operation information may include: at least one of operation interface information, operation action information, and operation object information.
According to embodiments of the present disclosure, the operator interface information may include, for example, a current interface identification. For example, the home page is 1, the transfer interface is 2, and the my payee interface is 3. Alternatively, the interface information may be represented using an interface identifier currently existing in the application.
According to embodiments of the present disclosure, the operation action information may include, for example, an input action (e.g., character input), a slide screen action (e.g., slide up and down, slide left and right), a click operation (e.g., click a shortcut key), a shake action, fingerprint pressing, or face recognition. The fingerprint pressing and face recognition can only acquire whether the user performs the operation without acquiring specific data.
According to the embodiment of the present disclosure, the acquisition of the operation object information may be, for example, acquisition of coordinate information of a user operation position. Or may also be the starting state of the current user action, e.g., start identified by 0 and the rest identified by 1.
According to the embodiment of the disclosure, the current interface operation state identification can also be obtained. E.g., whether the operation on the current interface was successful, etc.
It will be appreciated that the functions used by different users in different scenarios are not identical. For example, in a work scenario, a user may use transfer money, loan, etc. functions. In a shopping scenario, a user may use swipe code payment, swipe one, etc. functions. In a home scenario, a user may use functions such as financial, fund, etc. The embodiment of the disclosure trains a prediction model by learning state information of the electronic device and operation information of the user each time the user uses the application program and a functional module used by the user. So that the functional module to be used by the user can be predicted based on the state information of the current electronic device based on the trained prediction model.
In operation S203, a function module to be started is determined based on the function identification set.
According to the embodiment of the disclosure, the corresponding functional module can be determined based on the functional identifications contained in the functional identification set output by the prediction model.
In the embodiment of the disclosure, the prediction model may output one set of function identifiers or may output a plurality of sets of function identifiers. Wherein the plurality of sets of function identifiers may or may not have an intersection.
According to an embodiment of the present disclosure, determining a function module to be started based on a function identification set may include: the priority corresponding to the function module corresponding to the function identification set is increased by one level, wherein each of the plurality of function modules contained in the application program corresponds to an initial priority, and the function module with the priority higher than the initial priority in the plurality of function modules is determined to be the function module to be started.
In the disclosed embodiment, each functional module of the application has an initial priority. For example, the initial priority of each functional module may be defaulted to 1, with a higher number initiating a higher priority.
For example, if the predicted function identifier set is { function 1, function 2, function 3}, the priorities corresponding to function module 1, function module 2, and function module 3 are respectively increased by 1. At this time, the priorities of the function module 1, the function module 2, and the function module 3 are 2. Then, the function module 1, the function module 2, and the function module 3 can be determined as the function module to be started.
In operation S204, the function module to be started is started.
According to the embodiment of the disclosure, in the case that the to-be-started functional module includes a plurality of to-be-started functional modules, the plurality of to-be-started functional modules may be started in turn based on priorities respectively corresponding to the plurality of to-be-started functional modules.
In an embodiment of the present disclosure, if the prediction model outputs a set of function identifiers, the priority of the function module corresponding to each function identifier in the set may be increased to 2. All functional modules with priority 2 are started.
In another embodiment of the present disclosure, if the function set output by the prediction model is an empty set, the priorities of all the function modules are 1. All functional modules may be activated.
In yet another embodiment of the present disclosure, if the predictive model outputs multiple sets of functional identifications. The priority of the function modules corresponding to the respective sets may be sequentially increased by 1 level, and then the function modules having higher priority than the initial priority may be sequentially started based on the priority order of the function modules.
For example, if the prediction model output set 1{ function 1, function 2, function 3, function 4, function 5}, the priorities of function module 1, function 2 module, function module 3, function module 4, and function module 5 are each increased by 1, and the priorities are each changed to 2. The prediction model outputs the set 2{ function 1, function 2, function 6, function 7, function 8}, and the priorities of the function module 1, function module 2, function module 6, function module 7, and function module 8 are respectively increased by 1. At this time, the priority of the function module 1 is 3, the priority of the function module 2 is 3, the priority of the function module 3 is 2, the priority of the function module 4 is 2, the priority of the function module 5 is 2, the starting priority of the function module 6 is 2, the priority of the function module 7 is 2, and the priority of the function module 8 is 2. It is possible to activate the functional module 1 and the functional module 2 first and then activate the functional module 3, the functional module 4, the functional module 5, the functional module 6, the functional module 7 and the functional module 8.
According to the embodiment of the disclosure, if the highest priority of each functional module is greater than the initial priority, all functional modules with priorities higher than the initial priority are started in sequence according to the priority order. If the highest priority of each functional module is the initial priority, all functional modules of the application are started.
In the embodiment of the disclosure, during the starting process of the application program, each functional module may determine whether the application program can be started according to its own priority, if the own priority is higher than the initial priority, then start its own function, otherwise, it may not be started.
According to the embodiment of the disclosure, the prediction model can be updated, and the updated prediction model is sent to the background server corresponding to the application program, so that the background server stores the prediction model. For example, the prediction model may be updated every predetermined time, or may be updated in response to a change such as a movement of the user.
For example, sample data may be continuously collected during the use of the application by the user and the predictive model updated based on the sample data. After the update is completed, the latest prediction model may be sent to the background server. When the user deletes to download the application program again, the background server can send the prediction model corresponding to the user to the terminal equipment of the user so that the terminal equipment predicts the functional module to be started according to the prediction model.
According to the embodiment of the disclosure, the lightweight machine learning module is added, and the prediction model is continuously trained by collecting the client operation data and the terminal equipment state data, so that the functional module to be started can be determined according to the output result of the prediction model, and only the functional module to be started is started, so that the starting speed is increased, and the energy consumption is reduced.
The prediction model in the embodiment of the disclosure is determined according to the use habit of each user, and the actual needs of each user are compounded, so that the personalized starting of the application program is satisfied.
In the starting process of the application program, each functional module can judge whether to start according to the priority of the functional module, and the self-adaptive starting of the application program can be realized quickly without developing an additional starting management function.
Fig. 3 schematically illustrates a block diagram of an application launch device 300 according to an embodiment of the present disclosure.
According to embodiments of the present disclosure, the apparatus 300 may be disposed in a terminal device in which the application is installed, including but not limited to a smart phone, a tablet computer, a laptop portable computer, a desktop computer, and the like.
As shown in fig. 3, the apparatus 300 may include a first acquisition module 310, a prediction module 320, a determination module 330, and a startup module 340.
The first obtaining module 310 is configured to obtain, in response to receiving a request for starting the application, state information of the terminal device. According to an embodiment of the present disclosure, the first obtaining module 310 may, for example, perform the operation S201 described above with reference to fig. 2, which is not described herein.
The prediction module 320 is configured to input the state information into a prediction model, to obtain a set of function identifiers, where the set of function identifiers includes at least one function identifier, and the at least one function identifier corresponds to at least one function module. According to an embodiment of the present disclosure, the prediction module 320 may perform, for example, operation S202 described above with reference to fig. 2, which is not described herein.
The determining module 330 is configured to determine a function module to be started based on the set of function identifiers. According to an embodiment of the present disclosure, the determining module 330 may perform, for example, operation S203 described above with reference to fig. 2, which is not described herein.
The starting module 340 is configured to start the function module to be started. According to an embodiment of the present disclosure, the starting module 340 may perform, for example, operation S204 described above with reference to fig. 2, which is not described herein.
According to an embodiment of the disclosure, the determining a function module to be started based on the function identification set includes: and increasing the priority corresponding to the function module corresponding to the function identification set by one level, wherein each of the plurality of function modules contained in the application program corresponds to an initial priority, and determining the function module with the priority higher than the initial priority in the plurality of function modules as the function module to be started.
According to an embodiment of the present disclosure, the function module to be started includes a plurality of function modules to be started, and starting the function module to be started includes: and sequentially starting the plurality of functional modules to be started based on priorities respectively corresponding to the plurality of functional modules to be started.
According to an embodiment of the disclosure, the apparatus further comprises a second acquisition module and a training module. The second acquisition module is used for acquiring sample data, wherein the sample data comprises state information and user operation information of the terminal equipment. And a training module for training the predictive model based on the sample data.
According to an embodiment of the present disclosure, the state information of the terminal device includes at least one of time information, motion information, location information, environment information, network connection information, identification information, operation state information, and fingerprint information.
According to an embodiment of the present disclosure, the user operation information includes at least one of operation interface information, operation action information, and operation object information.
According to an embodiment of the present disclosure, the apparatus further comprises: and the updating module is used for updating the prediction model.
According to an embodiment of the present disclosure, the apparatus further comprises: and the sending module is used for sending the updated prediction model to a background server corresponding to the application program so as to enable the background server to store the prediction model.
Any number of modules, sub-modules, units, sub-units, or at least some of the functionality of any number of the sub-units according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented as split into multiple modules. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system-on-chip, a system-on-substrate, a system-on-package, an Application Specific Integrated Circuit (ASIC), or in any other reasonable manner of hardware or firmware that integrates or encapsulates the circuit, or in any one of or a suitable combination of three of software, hardware, and firmware. Alternatively, one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be at least partially implemented as computer program modules, which when executed, may perform the corresponding functions.
For example, any of the first acquisition module 310, the prediction module 320, the determination module 330, and the start module 340 may be combined in one module to be implemented, or any of the modules may be split into a plurality of modules. Alternatively, at least some of the functionality of one or more of the modules may be combined with at least some of the functionality of other modules and implemented in one module. According to embodiments of the present disclosure, at least one of the first acquisition module 310, the prediction module 320, the determination module 330, and the start-up module 340 may be implemented at least in part as hardware circuitry, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in hardware or firmware in any other reasonable way of integrating or packaging the circuitry, or in any one of or a suitable combination of any of the three. Alternatively, at least one of the first acquisition module 310, the prediction module 320, the determination module 330, and the start-up module 340 may be at least partially implemented as a computer program module, which when executed, may perform the corresponding functions.
Fig. 4 schematically illustrates a block diagram of a computer system according to an embodiment of the disclosure. The computer system illustrated in fig. 4 is merely an example, and should not be construed as limiting the functionality and scope of use of the embodiments of the present disclosure.
As shown in fig. 4, a computer system 400 according to an embodiment of the present disclosure includes a processor 401 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 402 or a program loaded from a storage section 408 into a Random Access Memory (RAM) 403. The processor 401 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or an associated chipset and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), or the like. Processor 401 may also include on-board memory for caching purposes. The processor 401 may comprise a single processing unit or a plurality of processing units for performing different actions of the method flow according to the embodiment of the disclosure described with reference to fig. 2.
In the RAM 403, various programs and data required for the operation of the system 400 are stored. The processor 401, the ROM 402, and the RAM 403 are connected to each other by a bus 404. The processor 401 performs various operations described above with reference to fig. 2 by executing programs in the ROM 402 and/or the RAM 403. Note that the program may be stored in one or more memories other than the ROM 402 and the RAM 403. The processor 401 may also perform the method described above by executing a program stored in the one or more memories.
According to an embodiment of the present disclosure, the system 400 may further include an input/output (I/O) interface 405, the input/output (I/O) interface 405 also being connected to the bus 404. The system 400 may also include one or more of the following components connected to the I/O interface 405: an input section 406 including a keyboard, a mouse, and the like; an output portion 407 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker, and the like; a storage section 408 including a hard disk or the like; and a communication section 409 including a network interface card such as a LAN card, a modem, or the like. The communication section 409 performs communication processing via a network such as the internet. The drive 410 is also connected to the I/O interface 405 as needed. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed on the drive 410 as needed, so that a computer program read therefrom is installed into the storage section 408 as needed.
According to embodiments of the present disclosure, the method described above with reference to the flowcharts may be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 409 and/or installed from the removable medium 411. The above-described functions defined in the system of the embodiments of the present disclosure are performed when the computer program is executed by the processor 401. The systems, devices, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.
It should be noted that the computer readable medium shown in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing. According to embodiments of the present disclosure, the computer-readable medium may include the ROM 402 and/or RAM 403 described above and/or one or more memories other than ROM 402 and RAM 403.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
As another aspect, the present disclosure also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be present alone without being fitted into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to perform the method as described above.
The embodiments of the present disclosure are described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the embodiments cannot be used advantageously in combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be made by those skilled in the art without departing from the scope of the disclosure, and such alternatives and modifications are intended to fall within the scope of the disclosure.
Claims (16)
1. An application program starting method is applied to a terminal device, wherein the application program comprises a plurality of functional modules, and the method comprises the following steps:
acquiring state information of the terminal equipment in response to receiving a request for starting the application program;
inputting the state information into a prediction model to obtain a function identifier set, wherein the function identifier set comprises at least one function identifier, and the at least one function identifier corresponds to at least one function module respectively;
determining a function module to be started based on the function identification set; and
starting the functional module to be started;
the prediction model is trained based on terminal equipment state information and user operation information when a user uses the application program each time; wherein the determining the function module to be started based on the function identification set comprises:
The priority corresponding to the function module corresponding to the function identification set is increased by one level, wherein each of the plurality of function modules contained in the application program corresponds to an initial priority; and
determining a function module with higher priority than the initial priority in the plurality of function modules as a function module to be started;
and under the condition that the plurality of function identification sets are determined, sequentially increasing the corresponding priority of the function modules in the plurality of function identification sets by one level.
2. The method of claim 1, wherein the functional module to be started comprises a plurality of functional modules to be started, and the starting the functional module to be started comprises:
and sequentially starting the plurality of functional modules to be started based on priorities respectively corresponding to the plurality of functional modules to be started.
3. The method of claim 1, further comprising:
acquiring sample data, wherein the sample data comprises state information of terminal equipment and user operation information; and
the predictive model is trained based on the sample data.
4. The method of claim 1, wherein the state information of the terminal device includes at least one of time information, motion information, location information, environment information, network connection information, identification information, operation state information, and fingerprint information.
5. The method of claim 3, wherein the user operation information includes at least one of operation interface information, operation action information, and operation object information.
6. The method of claim 1, further comprising: updating the prediction model.
7. The method of claim 1, further comprising: and sending the updated prediction model to a background server corresponding to the application program so that the background server stores the prediction model.
8. An application program starting apparatus of a terminal device, the application program including a plurality of function modules, the apparatus comprising:
the first acquisition module is used for responding to the received request for starting the application program and acquiring the state information of the terminal equipment;
the prediction module is used for inputting the state information into a prediction model to obtain a function identifier set, wherein the function identifier set comprises at least one function identifier, and the at least one function identifier corresponds to the at least one function module respectively;
the determining module is used for determining a function module to be started based on the function identification set; and
the starting module is used for starting the functional module to be started;
The prediction model is trained based on terminal equipment state information and user operation information when a user uses the application program each time;
wherein the determining the function module to be started based on the function identification set comprises:
the priority corresponding to the function module corresponding to the function identification set is increased by one level, wherein each of the plurality of function modules contained in the application program corresponds to an initial priority; and
determining a function module with higher priority than the initial priority in the plurality of function modules as a function module to be started;
and under the condition that the plurality of function identification sets are determined, sequentially increasing the corresponding priority of the function modules in the plurality of function identification sets by one level.
9. The apparatus of claim 8, wherein the functional module to be activated comprises a plurality of functional modules to be activated, the activating the functional module to be activated comprising:
and sequentially starting the plurality of functional modules to be started based on priorities respectively corresponding to the plurality of functional modules to be started.
10. The apparatus of claim 8, further comprising:
The second acquisition module is used for acquiring sample data, wherein the sample data comprises state information of terminal equipment and user operation information; and
and the training module is used for training the prediction model based on the sample data.
11. The apparatus of claim 8, wherein the state information of the terminal device comprises at least one of time information, motion information, location information, environment information, network connection information, identification information, operation state information, and fingerprint information.
12. The apparatus of claim 10, wherein the user operation information includes at least one of operation interface information, operation action information, and operation object information.
13. The apparatus of claim 8, further comprising:
and the updating module is used for updating the prediction model.
14. The apparatus of claim 13, further comprising:
and the sending module is used for sending the updated prediction model to a background server corresponding to the application program so as to enable the background server to store the prediction model.
15. A computer system, comprising:
one or more processors;
storage means for storing one or more programs,
Wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method of any of claims 1-7.
16. A computer readable medium having stored thereon executable instructions which, when executed by a processor, cause the processor to perform the method according to any of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010369861.2A CN111580883B (en) | 2020-04-30 | 2020-04-30 | Application program starting method, device, computer system and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010369861.2A CN111580883B (en) | 2020-04-30 | 2020-04-30 | Application program starting method, device, computer system and medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111580883A CN111580883A (en) | 2020-08-25 |
| CN111580883B true CN111580883B (en) | 2024-04-12 |
Family
ID=72127658
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010369861.2A Active CN111580883B (en) | 2020-04-30 | 2020-04-30 | Application program starting method, device, computer system and medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111580883B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114579196A (en) * | 2022-05-06 | 2022-06-03 | 成都前锋信息技术股份有限公司 | Self-learning-based computer starting disk starting sequence control method |
| CN115293753A (en) * | 2022-07-19 | 2022-11-04 | 中国银联股份有限公司 | Remote payment method, terminal, device, system and medium based on intelligent routing |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005258729A (en) * | 2004-03-11 | 2005-09-22 | Mitsubishi Electric Corp | Program, method and system for controlling terminal function |
| CN103309687A (en) * | 2012-03-09 | 2013-09-18 | 联想(北京)有限公司 | Electronic equipment and application program starting method thereof |
| CN106547556A (en) * | 2016-11-03 | 2017-03-29 | 广东欧珀移动通信有限公司 | The method and apparatus for starting function of application interface |
| CN108196892A (en) * | 2017-12-29 | 2018-06-22 | 北京安云世纪科技有限公司 | For the method, apparatus and mobile terminal being customized to system starting process |
| CN108829309A (en) * | 2018-04-23 | 2018-11-16 | 北京五八信息技术有限公司 | Navigation bar display methods, equipment, system and storage medium |
| CN110286954A (en) * | 2018-03-15 | 2019-09-27 | 腾讯科技（深圳）有限公司 | A kind of the starting method, apparatus and storage medium of application program |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10127023B2 (en) * | 2013-09-20 | 2018-11-13 | Oracle International Corporation | Computer-aided development of native mobile application code |
-
2020
- 2020-04-30 CN CN202010369861.2A patent/CN111580883B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005258729A (en) * | 2004-03-11 | 2005-09-22 | Mitsubishi Electric Corp | Program, method and system for controlling terminal function |
| CN103309687A (en) * | 2012-03-09 | 2013-09-18 | 联想(北京)有限公司 | Electronic equipment and application program starting method thereof |
| CN106547556A (en) * | 2016-11-03 | 2017-03-29 | 广东欧珀移动通信有限公司 | The method and apparatus for starting function of application interface |
| CN108196892A (en) * | 2017-12-29 | 2018-06-22 | 北京安云世纪科技有限公司 | For the method, apparatus and mobile terminal being customized to system starting process |
| CN110286954A (en) * | 2018-03-15 | 2019-09-27 | 腾讯科技（深圳）有限公司 | A kind of the starting method, apparatus and storage medium of application program |
| CN108829309A (en) * | 2018-04-23 | 2018-11-16 | 北京五八信息技术有限公司 | Navigation bar display methods, equipment, system and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111580883A (en) | 2020-08-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2019206213A1 (en) | Application program pre-loading method and apparatus, and storage medium and terminal | |
| CN110781373B (en) | List updating method and device, readable medium and electronic equipment | |
| US20220043898A1 (en) | Methods and apparatuses for acquiring information | |
| CN111597467A (en) | Display method and device and electronic equipment | |
| CN110990075A (en) | Starting method, device and equipment of fast application and storage medium | |
| CN111580883B (en) | Application program starting method, device, computer system and medium | |
| CN112036558A (en) | Model management method, electronic device, and medium | |
| CN111596991A (en) | Interactive operation execution method and device and electronic equipment | |
| US10433105B2 (en) | Geographically-driven group communications | |
| US10643252B2 (en) | Banner display method of electronic device and electronic device thereof | |
| CN110708238B (en) | Method and apparatus for processing information | |
| CN111460211A (en) | Audio information playing method and device and electronic equipment | |
| EP3374860B1 (en) | Communicating information about an update of an application | |
| CN111580882B (en) | Application program starting method, device, computer system and medium | |
| CN113992989A (en) | Content display method, device, system, equipment and storage medium | |
| KR20210009301A (en) | Methods and systems for dealing with chat rooms based on usage and non-transitory computer-readable recording media | |
| CN110908629A (en) | Electronic equipment operation method and device, electronic equipment and storage medium | |
| US11902340B2 (en) | Data processing method, apparatus, electronic device and storage medium | |
| CN112948127B (en) | Cloud platform container average load monitoring method, terminal equipment and readable storage medium | |
| CN109766246B (en) | Method and apparatus for monitoring applications | |
| CN110891120B (en) | Interface content display method and device and storage medium | |
| CN110704767B (en) | Page opening method and device, electronic equipment and storage medium | |
| CN114417782A (en) | Display method and device and electronic equipment | |
| CN117667047A (en) | Method, device, equipment and medium for generating function template | |
| CN113835697A (en) | Event response method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |