CN111580883A

CN111580883A - Application program starting method, device, computer system and medium

Info

Publication number: CN111580883A
Application number: CN202010369861.2A
Authority: CN
Inventors: 戎修凯; 肖夏; 贾新冬; 杨晓蕾
Original assignee: Industrial and Commercial Bank of China Ltd ICBC
Current assignee: Industrial and Commercial Bank of China Ltd ICBC
Priority date: 2020-04-30
Filing date: 2020-04-30
Publication date: 2020-08-25
Anticipated expiration: 2040-04-30
Also published as: CN111580883B

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 including: the method comprises the steps of responding to a received request for starting an application program, obtaining state information of terminal equipment, inputting the state information into a prediction model, obtaining a function identification set, determining a function module to be started based on the function identification set, and starting the function module to be started, wherein the function identification set comprises at least one function identification, and the at least one function identification corresponds to the at least one function module.

Description

Application program starting method, device, computer system and medium

Technical Field

The present disclosure relates to the field of computer technologies, and in particular, to an application program starting method, an application program starting device, a computer system, and a computer-readable medium.

Background

With the rapid development of information technology and electronic technology, various mobile terminals (e.g., smart phones, tablet computers, etc.) have become an indispensable part of people's work and life. Through the mobile terminal, a user can make a call, receive and send 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 an online shopping mall and the like. The mobile terminal has become an indispensable tool for various people to work, live and socialize. With the increasing development of the functions of the mobile terminal, various applications and application functions on the mobile terminal are also in the endlessly.

In carrying out the inventive concept, the inventors have found that there are at least the following problems in the related art. In the related art, when an application is started, all functions included in the application are usually started for a user to operate. For example, a mobile banking application, as an important customer platform for banks, carries most of the business of banks. Currently, mobile banking functions are numerous and only a few of them are often used or may be used by each user. Furthermore, the functions that may be used by different users are also different. However, when the current mobile banking application program is started, all functions included in the application program can be started indiscriminately, which wastes a large amount of starting time and hardware resources.

Disclosure of Invention

In view of the above, the present disclosure provides an application program starting method, apparatus, computer system and computer readable medium.

One aspect of the present disclosure provides an application starting method, applied to a terminal device, where the application includes a plurality of functional modules, and the method includes: the method comprises the steps of responding to a received request for starting an application program, obtaining state information of the terminal device, inputting the state information to 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, determining a function module to be started based on the function identification set, and starting the function module to be started.

According to an embodiment of the present disclosure, the determining a function module to be started based on the function identifier set includes: and increasing the priority corresponding to the function module corresponding to the function identification set by one level, wherein each function module in a 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 the starting the function modules to be started includes: and sequentially starting the plurality of functional modules to be started based on the priorities respectively corresponding to the plurality of functional modules to be started.

According to an embodiment of the present disclosure, the method further comprises: obtaining sample data, wherein the sample data comprises state information and user operation information of the terminal equipment, and training the prediction 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, operating 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: and 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 as to enable the background server to store the prediction model.

Another aspect of the present disclosure provides an application starting apparatus of a terminal device, where the application includes a plurality of functional modules, and the apparatus includes a first obtaining module, a predicting module, a determining module, and a starting module. The first obtaining module is used for responding to a request for starting the application program and obtaining 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 at least one function module. The determining module is used for determining the functional 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 present disclosure, the apparatus further includes a second acquisition module and a training module. The second obtaining module is used for obtaining sample data, and the sample data comprises state information of the terminal equipment and user operation information. And a training module for training the predictive model based on the sample data.

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, a storage device to store 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 for implementing the method as described above when executed.

Another aspect of the disclosure provides a computer program comprising computer executable instructions for implementing the method as described above when executed.

According to the embodiment of the disclosure, the problem that all functions included in an application program can be started indiscriminately and a large amount of starting time and hardware resources are wasted when the application program is started in the related art can be at least partially solved, and therefore, the 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.

Drawings

The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of embodiments of the present disclosure with reference to the accompanying drawings, in which:

fig. 1 schematically illustrates an application scenario of an application starting method and apparatus according to an embodiment of the present disclosure;

FIG. 2 schematically shows a flow chart of an application launching method according to an embodiment of the present disclosure;

FIG. 3 schematically shows a block diagram of an application launching device according to an embodiment of the present disclosure; and

FIG. 4 schematically shows 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 illustrative only and is not intended to limit the scope of the present disclosure. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not 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" and the like as used herein are also intended to include the meanings of "a plurality" and "the" unless the context clearly dictates 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 is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.

Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have 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 convention analogous to "A, B or at least one of C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B or C" would include but not be limited to systems that have 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 will be further understood by those within the art that virtually any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. 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: the method comprises the steps of responding to a received request for starting an application program, obtaining state information of terminal equipment, inputting the state information into a prediction model, obtaining a function identification set, determining a function module to be started based on the function identification set, and starting the function module to be started, wherein the function identification set comprises at least one function identification, and the at least one function identification corresponds to the at least one function module.

Fig. 1 schematically illustrates an application scenario 100 of an application launching 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 the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, and does not mean that the embodiments of the present disclosure may not be applied to 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. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

The user may use the

terminal devices

101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The

terminal devices

101, 102, 103 may have installed thereon various communication client applications, such as shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).

The

terminal devices

101, 102, 103 may be various electronic devices having a display screen and supporting wireless communication, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.

The server 105 may be a server that provides various services, and may be, for example, a background management server that provides support for applications in the

terminal devices

101, 102, 103. The background management server may analyze and perform other processing on the received data such as the user request, and feed back a processing result (e.g., a webpage, information, or data obtained or generated according to the user request) to the terminal device.

It should be noted that the application starting method provided by the embodiment of the present disclosure may be generally executed by a terminal device installed with the application. Accordingly, the application starting apparatus of the terminal device provided by the embodiment of the present disclosure may be generally disposed in the terminal device installed with the application.

For example, any of the

terminal devices

101, 102, 103 (e.g., terminal device 101, but not limited thereto) may have an application installed, and server 105 may be, for example, a backend server for the application. The terminal device 101 may respond to a start request of a user, obtain current state information of the terminal device 101, input the state information to the prediction model, 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 end devices, networks, servers, and database clusters in fig. 1 is merely illustrative. There may be any number of end devices, networks, servers, and database clusters, as desired for implementation.

Fig. 2 schematically shows a flow chart of an application launching 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 devices installed with the application program, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers and the like.

In the disclosed embodiment, the application program may include a plurality of functional modules. For example, a login function module, a financing function module, a loan function module, a credit card function module, and the like may be included. It is to be understood that the embodiments of the present disclosure do not limit the functions that can be implemented by each functional module of the application program, and those skilled in the art can 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, the user can initiate the request for starting the application program 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 present disclosure, the current state information of the terminal device may be acquired in response to receiving a request for starting an application. The status information may include, for example, at least one of time information, motion information, location information, environment information, network connection information, identification information, operational 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 device can be acquired. For example, the current motion information of the terminal device is obtained through 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 (gyroscopic), a Gravity sensor (Gravity), a linear acceleration sensor (linear acceleration), or a Rotation vector sensor (Rotation) of the terminal device.

According to the embodiment of the disclosure, the current position information of the terminal equipment can be acquired. For example, the current location information of the terminal device may also be acquired by a location sensor of the terminal device. For example, the current position information of the terminal device may be acquired by a geomagnetic sensor (Magnetometer), an Orientation sensor (Orientation), a Proximity sensor (Proximity), or the like. The current location information of the terminal device may also be obtained through a Global Positioning System (GPS) of the terminal device. For example, the current location information may be obtained by the user's authorization to access the GPS of the terminal device.

According to the embodiment of the disclosure, the current environment information of the terminal equipment can be acquired. For example, the current environment information of the terminal device may also be acquired by an environment sensor of the terminal device. For example, the current environment information of the terminal device may be acquired by a sensor such as a Light sensor (Light), a Temperature sensor (Temperature), and a Pressure sensor (Pressure).

According to the embodiment of the disclosure, network connection information of the terminal device can be acquired. For example, WIFI information, such as WIFI MAC or WIFI SSID, is obtained. Bluetooth information, such as, for example, Bluename or BlueMAC information, may also be obtained. For example, the WIFI information and the bluetooth information may be obtained through authorization of the user.

According to the embodiment of the disclosure, the identification information of the terminal device can be acquired. Such as device ID information, device brand and signal information, or user encoded information on the device, etc.

According to the embodiment of the disclosure, the working state information of the terminal device can be acquired. Such as whether the prison is broken, whether the root is root, etc.

According to the embodiment of the disclosure, fingerprint information of the terminal device can be acquired. The fingerprint information may be, for example, unique identification information of the terminal device. For example, the terminal device may include at least one of serial, hardware, board, device, build ID, display, finger print, android ID, imei, version, sdk, radioversion, resolution, imsi, number, simserial, simcountryiso, simcorountryiso, simmoperator, simmoperatame, networkcorountryio, networkkoperator, networkkoperame, phoneType, networkType, simState, mccmnc, gclac, gsmccid, cpuinfo, and cpuprich information.

In operation S202, the state information is input to the prediction model, and a function identifier set is obtained, where the function identifier set includes at least one function identifier, and each of the at least one function identifier corresponds to at least one function module.

The prediction model in the embodiments of the present disclosure may be obtained by: and acquiring sample data, wherein the sample data comprises state information and user operation information of the terminal equipment, and training a prediction model based on the sample data.

According to the embodiment of the disclosure, the sample data can be acquired in the process that the user uses the application program through the terminal device each time. 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 current use are acquired.

For example, the prediction model may be trained based on the terminal device state information and the user operation information of each time the user uses the application program and the function module used by the user in the use process, so that the trained model may predict the function module that may be used by the user based on the current terminal device state information.

In embodiments of the present disclosure, the predictive model may output a set of identifications 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: and at least one of operation interface information, operation action information and operation object information.

According to the embodiment of the disclosure, the operation interface information may include, for example, a current interface identifier. For example, home page is 1, transfer interface is 2, and my payee interface is 3. Alternatively, the interface information may be represented by using an interface identifier currently existing in the application.

According to the embodiment of the present disclosure, the operation action information may include, for example, an input action (e.g., character input), a screen sliding action (e.g., up and down sliding, left and right sliding), a click operation (e.g., clicking a shortcut key), a shaking action, a fingerprint pressing, or a face recognition. The fingerprint pressing and the face recognition can only acquire whether the user performs the operation without acquiring specific data.

According to the embodiment of the present disclosure, acquiring the operation target information may be acquiring coordinate information of the user operation position, for example. Or also the starting state of the current user operation action, for example, the start is identified by 0 and the rest is identified by 1.

According to the embodiment of the disclosure, the current interface operation state identifier can be obtained. E.g., whether the operation on the current interface was successful, etc.

It can be appreciated that different users may use different functions in different scenarios. For example, in a work scenario, a user may use functions such as money transfer, loan, etc. In a shopping scenario, the user may use a scan code payment, a scan, etc. function. In a home scenario, the user may use functions such as financing, funding, and the like. The disclosed embodiments train a predictive model by learning the state information of the electronic device and the operational information of the user and the functional modules used by the user each time the user uses an application. Therefore, the function module to be used by the user can be predicted based on the trained prediction model and the current state information of the electronic equipment.

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 function module corresponding to the function identifier can be determined based on the function identifier contained in the function identifier set output by the prediction model.

In the embodiment of the present disclosure, the prediction model may output one function identifier set or may output a plurality of function identifier sets. And the plurality of function identification sets may or may not have an intersection.

According to the embodiment of the present disclosure, determining a function module to be started based on the function identifier set may include: and the priority corresponding to the function module corresponding to the function identification set is increased by one level, wherein each function module in a 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 as 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 function module may be defaulted to 1, and the higher the number, the higher the activation priority.

For example, if the predicted function identifier set is { function 1, function 2, and 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 priority of the function module 1, the function module 2, and the function module 3 is 2. Then, the function module 1, the function module 2, and the function module 3 may be determined as function modules to be started.

In operation S204, a function module to be started is started.

According to the embodiment of the disclosure, when the function module to be started includes a plurality of function modules to be started, the plurality of function modules to be started may be sequentially started based on priorities respectively corresponding to the plurality of function modules to be started.

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 function 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 priority of all the function modules is 1. All functional modules may be activated.

In another embodiment of the present disclosure, if the prediction model outputs a plurality of function identifier sets. The priority of the function module corresponding to each set may be sequentially increased by 1, and then each function module having a priority higher than the initial priority may be sequentially started based on the priority order of each function module.

For example, if the prediction model output set 1{ function 1, function 2, function 3, function 4, function 5}, the priorities of the function block 1, function block 2, function block 3, function block 4, and function block 5 are respectively increased by 1, and the priorities are all changed to 2. The prediction model also outputs a set 2{ function 1, function 2, function 6, function 7, function 8}, and the priorities of the function module 1, the function module 2, the function module 6, the function module 7, and the 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 start 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. The function module 1 and the function module 2 may be started first, and then the function module 3, the function module 4, the function module 5, the function module 6, the function module 7, and the function module 8 may be started.

According to the embodiment of the present disclosure, if the highest priority of each functional module is greater than the initial priority, all functional modules having a priority higher than the initial priority are sequentially started according to the priority order. And if the highest priority of each functional module is the initial priority, starting all the functional modules of the application program.

In the embodiment of the disclosure, in the starting process of the application program, each functional module may judge whether the application program can be started according to its own priority, and start its own function if its own priority is higher than the initial priority, otherwise, it may not start.

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 once every preset time, or may be updated once in response to a change such as a user moving home.

For example, sample data may be collected continuously as the user uses the application, and the predictive model may be 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 and re-downloads the application program, the background server can send the prediction model corresponding to the user to the terminal equipment of the user, so that the terminal equipment can predict the functional module to be started according to the prediction model.

According to the embodiment of the invention, 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 function module to be started can be determined according to the output result of the prediction model, and only the function module to be started is started, thereby accelerating the starting speed and reducing the energy consumption.

The prediction model of the embodiment of the disclosure is determined according to the use habits of each user, the actual needs of each user are compounded, and the individualized starting of the application program is met.

In the process of starting the application program, each functional module can judge whether the application program is started according to the priority of the functional module, and the self-adaptive starting of the application program can be quickly realized without developing an additional starting management function.

Fig. 3 schematically shows a block diagram of an application launching device 300 according to an embodiment of the present disclosure.

According to the embodiment of the present disclosure, the apparatus 300 may be disposed in a terminal device installed 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.

As shown in fig. 3, the apparatus 300 may include a first obtaining module 310, a predicting module 320, a determining module 330, and an initiating module 340.

The first obtaining module 310 is configured to obtain the status information of the terminal device in response to receiving the request for starting the application program. According to the embodiment of the present disclosure, the first obtaining module 310 may, for example, perform operation S201 described above with reference to fig. 2, which is not described herein again.

The prediction module 320 is configured to input the state information into a prediction model to obtain a function identifier set, where the function identifier set includes at least one function identifier, and each of 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, for example, perform operation S202 described above with reference to fig. 2, which is not described herein again.

The determining module 330 is configured to determine a function module to be started based on the set of function identifiers. According to the embodiment of the present disclosure, the determining module 330 may, for example, perform operation S203 described above with reference to fig. 2, which is not described herein again.

The starting module 340 is used for starting the functional module to be started. According to the embodiment of the present disclosure, the starting module 340 may, for example, perform the operation S204 described above with reference to fig. 2, which is not described herein again.

Any number of modules, sub-modules, units, sub-units, or at least part of the functionality of any number thereof according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, sub-modules, units, and sub-units according to the embodiments of the present disclosure may be implemented by being split into a plurality of 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 a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in any other reasonable manner of hardware or firmware by integrating or packaging a circuit, or in any one of or a suitable combination of software, hardware, and firmware implementations. Alternatively, one or more of the modules, sub-modules, units, sub-units according to embodiments of the disclosure may be at least partially implemented as a computer program module, which when executed may perform the corresponding functions.

For example, any of the first obtaining module 310, the predicting module 320, the determining module 330, and the initiating module 340 may be combined into one module to be implemented, or any one of the modules may be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the first obtaining module 310, the predicting module 320, the determining module 330, and the initiating module 340 may be implemented at least partially as a hardware circuit, 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 manner of integrating or packaging a circuit, or in any one of three implementations of software, hardware, and firmware, or in any suitable combination of any of them. Alternatively, at least one of the first obtaining module 310, the predicting module 320, the determining module 330 and the initiating module 340 may be at least partially implemented as a computer program module, which when executed may perform a corresponding function.

FIG. 4 schematically shows a block diagram of a computer system according to an embodiment of the disclosure. The computer system illustrated in FIG. 4 is only one example and should not impose any limitations on the scope of use or functionality of embodiments of the 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. Processor 401 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 401 may also include onboard memory for caching purposes. Processor 401 may include a single processing unit or multiple processing units for performing the different actions of the method flows described with reference to fig. 2 in accordance with embodiments of the present disclosure.

In the RAM 403, various programs and data necessary for the operation of the system 400 are stored. The processor 401, ROM 402 and 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 programs may also be stored in one or more memories other than the ROM 402 and RAM 403. The processor 401 may also perform the methods described above by executing programs stored in the one or more memories.

According to an embodiment of the present disclosure, system 400 may also include an input/output (I/O) interface 405, input/output (I/O) interface 405 also connected to 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 section 407 including a display device such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 408 including a hard disk and 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. A driver 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 mounted on the drive 410 as necessary, so that a computer program read out therefrom is mounted into the storage section 408 as necessary.

According to an embodiment of the present disclosure, the method described above with reference to the flow chart 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 illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 409, and/or installed from the removable medium 411. The computer program, when executed by the processor 401, performs the above-described functions defined in the system of the embodiments of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.

It should be noted that the computer readable media shown in the present disclosure may be computer readable signal media or computer readable storage media or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination 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 present 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 contrast, in the present disclosure, a computer-readable signal medium may include a propagated data signal with computer-readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. 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, a computer-readable medium may include the ROM 402 and/or RAM 403 and/or one or more memories other than the ROM 402 and RAM 403 described above.

The flowchart 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, which may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated 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 have been 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 separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.

Claims

1. An application program starting method is applied to terminal equipment, the application program comprises a plurality of functional modules, and the method comprises the following steps:

responding to a received request for starting the application program, and acquiring state information of the terminal equipment;

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;

determining a function module to be started based on the function identification set; and

and starting the functional module to be started.

2. The method of claim 1, wherein the determining a functional module to be started based on the set of functional identifiers comprises:

increasing the priority corresponding to the functional module corresponding to the functional identification set by one level, wherein each functional module in a plurality of functional modules contained in the application program corresponds to an initial priority; and

and determining the functional module with the priority higher than the initial priority in the plurality of functional modules as the functional module to be started.

3. The method of claim 2, wherein the functional module to be started comprises a plurality of functional modules to be started, the starting the functional module to be started comprising:

and sequentially starting the plurality of functional modules to be started based on the priorities respectively corresponding to the plurality of functional modules to be started.

4. The method of claim 1, further comprising:

acquiring sample data, wherein the sample data comprises state information and user operation information of terminal equipment; and

training the predictive model based on the sample data.

5. 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, operational state information, and fingerprint information.

6. The method of claim 4, wherein the user operation information includes at least one of operation interface information, operation action information, and operation object information.

7. The method of claim 1, further comprising: and updating the prediction model.

8. The method of claim 1, further comprising: and 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.

9. An application starting apparatus of a terminal device, the application 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 at least one function module;

the determining module is used for determining a function module to be started based on the function identification set; and

and the starting module is used for starting the functional module to be started.

10. The apparatus of claim 9, wherein the determining a functional module to be launched based on the set of functional identifications comprises:

11. The apparatus of claim 10, wherein the function module to be activated comprises a plurality of function modules to be activated, the activating the function module to be activated comprising:

12. The apparatus of claim 9, further comprising:

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 to train the predictive model based on the sample data.

13. The apparatus of claim 9, 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, operational state information, and fingerprint information.

14. The apparatus according to claim 12, wherein the user operation information includes at least one of operation interface information, operation action information, and operation object information.

15. The apparatus of claim 9, further comprising:

and the updating module is used for updating the prediction model.

16. The apparatus of claim 15, 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.

17. A computer system, comprising:

one or more processors;

a storage device 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 1-8.

18. A computer readable medium having stored thereon executable instructions which, when executed by a processor, cause the processor to perform the method of any one of claims 1 to 8.