CN114385257A - Program preheating method, device, electronic equipment and computer readable storage medium - Google Patents
Program preheating method, device, electronic equipment and computer readable storage medium Download PDFInfo
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- 238000010586 diagram Methods 0.000 description 10
- 238000010438 heat treatment Methods 0.000 description 5
- 230000003796 beauty Effects 0.000 description 2
- 239000002699 waste material Substances 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/445—Program loading or initiating
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2474—Sequence data queries, e.g. querying versioned data
Abstract
The application discloses a program preheating method, a device, an electronic device and a computer readable storage medium, wherein the program preheating method comprises the following steps: starting a target program and acquiring historical data of the target program; analyzing the historical data based on an MRU algorithm to obtain at least one target module in the target program; at least one target module is preloaded. Based on the mode, the user experience can be effectively improved.
Description
Technical Field
The present application relates to the field of data loading technologies, and in particular, to a method and an apparatus for preheating a program, an electronic device, and a computer-readable storage medium.
Background
In the prior art, when a user opens an application, the application needs to load various modules (such as a sports information module and a makeup information module) in the application, so that the user can view the contents of the various modules from the application.
The prior art has the defects that the application program needs to consume a long time for loading the module, and a user can only view a blank picture before the module is completely loaded after opening the module which is still loaded, so that the user experience is poor.
Disclosure of Invention
The technical problem mainly solved by the application is how to improve user experience.
In order to solve the above technical problem, the first technical solution adopted by the present application is: a program warm-up method comprising: starting a target program and acquiring historical data of the target program; analyzing the historical data based on an MRU algorithm to obtain at least one target module in the target program; at least one target module is preloaded.
In order to solve the above technical problem, the second technical solution adopted by the present application is: a program preheating device comprising: the acquisition module is used for starting the target program and acquiring historical data of the target program; the analysis module is used for analyzing the historical data based on an MRU algorithm to obtain at least one target module in the target program; and the loading module is used for preloading at least one target module.
In order to solve the above technical problem, a third technical solution adopted by the present application is: an electronic device, comprising: a memory and a processor; the memory is used for storing program instructions and the processor is used for executing the program instructions to realize the program preheating method.
In order to solve the above technical problem, a fourth technical solution adopted by the present application is: a computer readable storage medium having stored thereon program instructions which, when executed by a processor, implement the program warm-up method described above.
The beneficial effect of this application lies in: different from the prior art, the method and the device have the advantages that the historical data are obtained when the target program is started, the corresponding analysis is carried out on the historical data based on the MRU algorithm, so that at least one commonly used target module in the target program is determined, the at least one target module is preloaded, a user can check the commonly used module more quickly after the target program is started, and the user experience is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic flow chart diagram of a first embodiment of a pre-heating method of the present application;
FIG. 2 is a schematic flow chart diagram of a second embodiment of the pre-heating method of the present application;
FIG. 3 is a schematic flow chart diagram of a third embodiment of the pre-heating method of the present application;
FIG. 4 is a schematic flow chart diagram illustrating a fourth embodiment of the pre-heating method of the present application;
FIG. 5 is a schematic structural diagram of an embodiment of a pre-heating apparatus of the present application;
FIG. 6 is a schematic diagram of an embodiment of an electronic device of the present application;
FIG. 7 is a schematic structural diagram of an embodiment of a computer-readable storage medium of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first" and "second" in this application are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless explicitly specifically limited otherwise. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
The present application first proposes a program preheating method, as shown in fig. 1, where fig. 1 is a schematic flow chart of a first embodiment of the program preheating method of the present application, and the program preheating method may include:
step S11: and starting the target program to acquire the historical data of the target program.
The target program can be started, and meanwhile, historical data used for recording the use information of each module in the target program is obtained for subsequent corresponding analysis according to the historical data.
The historical data may be a program usage record corresponding to the target program, and the program usage record may include the number of times each module in the target program is used, the time length of each time each module is used, and other types of usage records, which may be determined according to actual needs, and is not limited herein.
Step S12: the historical data is analyzed based on the MRU algorithm to obtain at least one target module in the target program.
The MRU (most recently used) algorithm can calculate a plurality of target modules which are most frequently used by a user in the target program based on the MRU algorithm. The most frequently used target modules may refer to a plurality of target modules used most frequently in the target program, may also refer to a plurality of target modules used most frequently within a preset time duration in the target program, may also refer to a plurality of target modules used for the longest time duration in the target program, and may also refer to a plurality of target modules satisfying other conditions, which may be determined according to actual needs, and is not limited herein.
Step S13: at least one target module is preloaded.
The target module can be preloaded in the target program found based on the MRU algorithm, that is, the data in the target modules which are most frequently used by the user and found based on the MRU algorithm are preloaded, so that the situation that the data are loaded when the user selects to open one of the target modules is avoided, the waiting time of the user when the user selects to open one of the target modules is reduced or eliminated, the time of the user seeing blank pages is reduced or eliminated, and the user experience is improved.
Optionally, step S13 may specifically include:
and preloading at least one target module based on the sequence of the operating frequencies from large to small.
Specifically, after step S12, the operation frequency of each module in the at least one target module is obtained, the at least one target module is sorted according to the principle that the operation frequency is from large to small, and the at least one target module is preloaded according to the sorted order.
The larger the running frequency is, the more likely the user opens the corresponding target module at the first time, so that based on the above manner, the target module with the maximum running frequency can be loaded at the first time, the time length for the user to wait for loading the target module is reduced, and the user experience is improved.
It should be noted that, after the target program is started, the system where the target program is located needs to perform operations such as initialization and memory allocation on the target program, and at this time, the historical data of the target program is obtained and the subsequent steps executed based on the historical data are performed, which is difficult for the user to perceive, that is, the program preheating can be completed without being perceived by the user, so as to improve the speed of the module commonly used by the user entering the viewable state of the user.
Different from the prior art, the method and the device have the advantages that the historical data are obtained when the target program is started, the corresponding analysis is carried out on the historical data based on the MRU algorithm, so that at least one commonly used target module in the target program is determined, the at least one target module is preloaded, a user can check the commonly used module more quickly after the target program is started, and the user experience is improved.
The present application further provides a program preheating method, as shown in fig. 2, fig. 2 is a schematic flow chart of a second embodiment of the program preheating method of the present application, and the program preheating method may include:
step S21: and starting the target program to acquire the historical data of the target program.
Step S22: and analyzing the historical data based on the MRU algorithm, and determining the module with the maximum running frequency in the target program to obtain at least one target module in the target program.
Wherein, the module with the maximum running frequency in the target program can be determined based on the MRU algorithm and is recorded as at least one obtained target module. The module with the maximum operating frequency in the target program may refer to at least one module with the highest operating frequency in the target program, which is ranked in the order from the largest operating frequency to the smallest operating frequency, or may refer to at least one module with the maximum operating frequency and the same operating frequency in the target program.
The larger the running frequency is, the more likely the user opens the corresponding target module at the first time, so that based on the above manner, the target module with the maximum running frequency can be loaded at the first time, the time length for the user to wait for loading the target module is reduced, and the user experience is improved.
Step S23: at least one target module is preloaded.
Step S21 and step S23 correspond to step S11 and step S13, respectively, in the embodiment shown in fig. 1, and are not described herein again.
Optionally, step S22 may specifically include:
and analyzing the historical data based on the MRU algorithm, and determining the module with the maximum operating frequency in the preset time period in the target program to obtain at least one target module in the target program.
Specifically, the preset time period is a time period from the starting time to the current time, and the starting time is a time before the current time.
The step S21 may be executed to obtain historical data of the target program in a preset time period, and then, when the step S22 is executed, the historical data is analyzed based on the MRU algorithm, so that a module that determines that the operation frequency of the target program is the maximum in the preset time period may be obtained, so as to obtain at least one target module in the target program.
When step S22 is executed, the module that determines that the operating frequency is the maximum within the preset time period in the target program may be obtained by analyzing the partial data corresponding to the preset time period in the historical data based on the MRU algorithm, so as to obtain at least one target module in the target program.
The module with the maximum operating frequency in the target program within the preset time period may also be determined in other manners, so as to obtain at least one target module in the target program, which is not limited herein.
Further, before the step of analyzing the historical data based on the MRU algorithm to determine the module of the target program with the highest operating frequency within the preset time period to obtain at least one target module in the target program, step S22 may further include:
the current time is acquired, and a preset time period is determined based on the current time.
Specifically, the preset time period may be determined based on the characteristics of the current time. For example, the following steps are carried out: if the current time is morning, determining the morning time period in a preset day as a preset time period; if the current time is a working day, the working day time intervals in a preset week can be determined as preset time intervals; if the current time is the first quarter, the first quarter period within the preset year may be determined as the preset period.
Based on the mode, the more consistent preset time period can be determined based on the characteristics of the current time, so that at least one target module in a target program obtained by analyzing historical data based on the MRU algorithm is a module which is more likely to be opened and used by a user preferentially in the current time, and the waiting time of the user is more likely to be reduced by performing subsequent preloading on the modules, and the user experience is improved.
Optionally, step S22 may specifically include:
and analyzing the historical data based on an MRU algorithm, and determining a module with the maximum running frequency not less than a preset frequency threshold value in the target program to obtain at least one target module in the target program.
Specifically, the historical data can be analyzed based on an MRU algorithm to obtain at least one module with a front sequence in the target program according to the operation frequency in the descending order, and the modules with the operation frequency greater than or equal to a preset frequency threshold in the obtained at least one module are screened to obtain at least one target module.
Based on the above manner, the modules with extremely low operating frequency can be prevented from being preloaded subsequently, and the waste of resources is reduced, because the modules with extremely low operating frequency may be only modules used by users occasionally, even if the operating frequency of the modules belongs to the higher modules of all the modules, the modules cannot be regarded as modules commonly used by users, and the preloading of the modules can cause the waste of resources.
Different from the prior art, the method and the device have the advantages that the historical data are obtained when the target program is started, the corresponding analysis is carried out on the historical data based on the MRU algorithm, so that at least one commonly used target module in the target program is determined, the at least one target module is preloaded, a user can check the commonly used module more quickly after the target program is started, and the user experience is improved.
The present application further provides a program preheating method, as shown in fig. 3, where fig. 3 is a schematic flow chart of a third embodiment of the program preheating method of the present application, and the program preheating method may include:
step S311: and acquiring a program use record corresponding to the target program in the target device.
The program use record corresponding to the target program and stored in the target device can be acquired, the record corresponding to the program use record is a record of the target program used by the target device, and the record can embody the habit characteristics of a user when using the target device.
Step S321: the program usage record is analyzed based on the MRU algorithm to obtain at least one target module in the target program.
And analyzing by adopting an MRU algorithm based on the program use record of the target program acquired from the target equipment to obtain at least one most frequently used target module in the target program of the target equipment.
Step S33: at least one target module is preloaded.
Step S33 corresponds to step S13 in the embodiment shown in fig. 1, and is not described here again.
Specifically, based on the above manner, at least one target module in a target program most frequently used by a user through the target device can be acquired for the target device. For example, the following steps are carried out: the user A frequently accesses the football information module through the equipment A, the user B frequently accesses the make-up information module through the equipment B, the football information module can be obtained based on the rough rate after the MRU algorithm analyzes the program use record obtained from the equipment A, and the make-up information module can be obtained based on the rough rate after the MRU algorithm analyzes the program use record obtained from the equipment B, so that different preheating schemes can be executed aiming at different equipment by the program preheating method, different modules are preloaded, the possibility of reducing the waiting time of the user is improved, and the user experience is improved.
Different from the prior art, the method and the device have the advantages that the historical data are obtained when the target program is started, the corresponding analysis is carried out on the historical data based on the MRU algorithm, so that at least one commonly used target module in the target program is determined, the at least one target module is preloaded, a user can check the commonly used module more quickly after the target program is started, and the user experience is improved.
The present application further provides a program preheating method, as shown in fig. 4, fig. 4 is a schematic flow chart of a fourth embodiment of the program preheating method of the present application, and the program preheating method may include:
step S411: and acquiring a program use record corresponding to the target program in the target account.
The program usage record corresponding to the target program and stored in the target account can be acquired, the record corresponding to the program usage record is a record of using the target program through the target account, and the record can reflect the habit characteristics of a user when using the target account.
Step S421: the program usage record is analyzed based on the MRU algorithm to obtain at least one target module in the target program.
And analyzing by adopting an MRU algorithm based on the program use record of the target program acquired from the target account to obtain at least one most frequently used target module in the target program of the target account.
Step S43: at least one target module is preloaded.
Step S43 corresponds to step S13 in the embodiment shown in fig. 1, and is not described here again.
Specifically, based on the above manner, at least one target module in a target program most frequently used by a user through a target account may be acquired for the target account. For example, the following steps are carried out: the user A frequently accesses the football information module through the account A, the user B frequently accesses the beauty information module through the account B, the football information module can be obtained based on the rough rate of the MRU algorithm after analyzing the program use record obtained from the account A, the beauty information module can be obtained based on the rough rate of the MRU algorithm after analyzing the program use record obtained from the account B, different preheating schemes can be executed according to different accounts, different modules are preloaded, and therefore even if the user replaces other equipment, the most appropriate preheating scheme can be obtained as long as the user logs in the own account on the corresponding equipment, the waiting time of the user is shortened, and the user experience is improved.
Different from the prior art, the method and the device have the advantages that the historical data are obtained when the target program is started, the corresponding analysis is carried out on the historical data based on the MRU algorithm, so that at least one commonly used target module in the target program is determined, the at least one target module is preloaded, a user can check the commonly used module more quickly after the target program is started, and the user experience is improved.
The present application further provides a program preheating device, as shown in fig. 5, fig. 5 is a schematic structural diagram of an embodiment of the program preheating device of the present application, and the program preheating device 50 includes: an acquisition module 51, an analysis module 52 and a loading module 53.
The obtaining module 51 is used for starting the target program and obtaining the history data of the target program.
The analysis module 52 is configured to analyze the historical data based on the MRU algorithm to obtain at least one target module in the target program.
The loading module 53 is used to preload at least one target module.
Optionally, the analysis module 52 is specifically configured to: and analyzing the historical data based on the MRU algorithm, and determining the module with the maximum running frequency in the target program to obtain at least one target module in the target program.
Specifically, the analysis module 52 is further configured to: and analyzing the historical data based on an MRU algorithm, and determining a module with the maximum operating frequency in a preset time period in the target program to obtain at least one target module in the target program, wherein the preset time period is a time period from the starting time to the current time, and the starting time is a time before the current time.
Specifically, the analysis module 52 is further configured to: and analyzing the historical data based on an MRU algorithm, and determining a module with the maximum running frequency not less than a preset frequency threshold value in the target program to obtain at least one target module in the target program.
Optionally, the obtaining module 51 is specifically configured to: and acquiring a program use record corresponding to the target program in the target device.
The analysis module 52 is specifically configured to: the program usage record is analyzed based on the MRU algorithm to obtain at least one target module in the target program.
Optionally, the obtaining module 51 is specifically configured to: and acquiring a program use record corresponding to the target program in the target account.
The analysis module 52 is specifically configured to: the program usage record is analyzed based on the MRU algorithm to obtain at least one target module in the target program.
Optionally, the loading module 53 is specifically configured to: and preloading at least one target module based on the sequence of the operating frequencies from large to small.
Different from the prior art, the method and the device have the advantages that the historical data are obtained when the target program is started, the corresponding analysis is carried out on the historical data based on the MRU algorithm, so that at least one commonly used target module in the target program is determined, the at least one target module is preloaded, a user can check the commonly used module more quickly after the target program is started, and the user experience is improved.
The present application further provides an electronic device, as shown in fig. 6, fig. 6 is a schematic structural diagram of an embodiment of the electronic device of the present application, and the electronic device 60 includes: a processor 61, a memory 62, and a bus 63.
The processor 61 and the memory 62 are respectively connected to the bus 63, the memory 62 stores program instructions, and the processor 61 is configured to execute the program instructions to implement the program preheating method in the above-described embodiment.
In the present embodiment, the processor 61 may also be referred to as a CPU (Central Processing Unit). The processor 61 may be an integrated circuit chip having signal processing capabilities. The processor 61 may also be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. A general purpose processor may be a microprocessor or the processor 61 may be any conventional processor or the like.
Different from the prior art, the method and the device have the advantages that the historical data are obtained when the target program is started, the corresponding analysis is carried out on the historical data based on the MRU algorithm, so that at least one commonly used target module in the target program is determined, the at least one target module is preloaded, a user can check the commonly used module more quickly after the target program is started, and the user experience is improved.
The present application further provides a computer-readable storage medium, as shown in fig. 7, fig. 7 is a schematic structural diagram of an embodiment of the computer-readable storage medium of the present application, and the computer-readable storage medium 70 has stored thereon program instructions 71, and when the program instructions 71 are executed by a processor (not shown), the method for interaction in the foregoing embodiment is implemented.
The computer readable storage medium 70 of this embodiment may be, but is not limited to, a usb disk, an SD card, a PD optical drive, a removable hard disk, a high-capacity floppy drive, a flash memory, a multimedia memory card, a server, a storage unit in an FPGA or an ASIC, etc.
Different from the prior art, the method and the device have the advantages that the historical data are obtained when the target program is started, the corresponding analysis is carried out on the historical data based on the MRU algorithm, so that at least one commonly used target module in the target program is determined, the at least one target module is preloaded, a user can check the commonly used module more quickly after the target program is started, and the user experience is improved.
The above description is only for the purpose of illustrating embodiments of the present application and is not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application or are directly or indirectly applied to other related technical fields, are also included in the scope of the present application.
Claims (10)
1. A program warm-up method, comprising:
starting a target program and acquiring historical data of the target program;
analyzing the historical data based on an MRU algorithm to obtain at least one target module in the target program;
preloading the at least one target module.
2. The program warm-up method of claim 1, wherein the step of analyzing the historical data based on the MRU algorithm to obtain at least one target module in the target program comprises:
and analyzing the historical data based on an MRU algorithm, and determining a module with the maximum running frequency in the target program to obtain at least one target module in the target program.
3. The program preheating method according to claim 2, wherein the step of analyzing the historical data based on the MRU algorithm to determine a module of the target program having a highest operating frequency to obtain at least one target module of the target program comprises:
analyzing the historical data based on an MRU algorithm, and determining a module with the maximum operating frequency in a preset time period in the target program to obtain at least one target module in the target program, wherein the preset time period is a time period from a starting time to the current time, and the starting time is a time before the current time.
4. The program warm-up method according to claim 1, wherein the step of acquiring the history data of the target program includes:
acquiring a program use record corresponding to a target program in target equipment;
the step of analyzing the historical data based on the MRU algorithm to obtain at least one target module in the target program comprises:
analyzing the program usage record based on an MRU algorithm to obtain at least one target module in the target program.
5. The program warm-up method according to claim 1, wherein the step of acquiring the history data of the target program includes:
acquiring a program use record corresponding to a target program in a target account;
the step of analyzing the historical data based on the MRU algorithm to obtain at least one target module in the target program comprises:
analyzing the program usage record based on an MRU algorithm to obtain at least one target module in the target program.
6. The program preheating method according to claim 2, wherein the step of analyzing the historical data based on the MRU algorithm to determine a module of the target program having a highest operating frequency to obtain at least one target module of the target program comprises:
analyzing the historical data based on an MRU algorithm, and determining a module with the maximum running frequency in the target program and the running frequency not less than a preset frequency threshold value so as to obtain at least one target module in the target program.
7. The program warm-up method of claim 1, wherein the step of preloading the at least one target module comprises:
and preloading the at least one target module based on the sequence of the operating frequencies from large to small.
8. A program preheating device, comprising:
the acquisition module is used for starting a target program and acquiring historical data of the target program;
the analysis module is used for analyzing the historical data based on an MRU algorithm to obtain at least one target module in the target program;
and the loading module is used for preloading the at least one target module.
9. An electronic device, comprising: a memory and a processor;
the memory is for storing program instructions, and the processor is for executing the program instructions to implement the method of any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores program instructions which, when executed by a processor, implement the method according to any one of claims 1 to 7.
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| CN104333501A (en) * | 2013-07-22 | 2015-02-04 | 腾讯科技（深圳）有限公司 | Display method and device of contact list |
| CN108717364A (en) * | 2018-04-08 | 2018-10-30 | 努比亚技术有限公司 | Application acceleration open method, terminal and computer readable storage medium |
| CN108681474A (en) * | 2018-05-21 | 2018-10-19 | Oppo广东移动通信有限公司 | Using preloading method, apparatus, storage medium and terminal |
| CN111258676A (en) * | 2020-01-16 | 2020-06-09 | 中国平安财产保险股份有限公司 | Module preloading method, device, equipment and readable storage medium |
| CN111464690A (en) * | 2020-02-27 | 2020-07-28 | 华为技术有限公司 | Application preloading method and electronic equipment |
| CN113722023A (en) * | 2020-05-22 | 2021-11-30 | 华为技术有限公司 | Application data processing method and device |
| CN112073525A (en) * | 2020-09-11 | 2020-12-11 | 广州宸祺出行科技有限公司 | Advertisement pushing method and device and electronic equipment |
| CN112631689A (en) * | 2021-01-04 | 2021-04-09 | 北京字节跳动网络技术有限公司 | Application program loading method and device and computer storage medium |
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