CN117076006A - Method and system for configuring application in localization environment based on cloud platform - Google Patents
Method and system for configuring application in localization environment based on cloud platform Download PDFInfo
- Publication number
- CN117076006A CN117076006A CN202311337415.3A CN202311337415A CN117076006A CN 117076006 A CN117076006 A CN 117076006A CN 202311337415 A CN202311337415 A CN 202311337415A CN 117076006 A CN117076006 A CN 117076006A
- Authority
- CN
- China
- Prior art keywords
- application
- data
- module
- local
- function module
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 33
- 230000004807 localization Effects 0.000 title claims abstract description 7
- 238000009434 installation Methods 0.000 claims description 21
- 230000000694 effects Effects 0.000 abstract description 2
- 230000002349 favourable effect Effects 0.000 abstract 1
- 230000009286 beneficial effect Effects 0.000 description 6
- 230000009471 action Effects 0.000 description 2
- 230000006855 networking Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000010586 diagram 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
- G06F9/4451—User profiles; Roaming
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F8/00—Arrangements for software engineering
- G06F8/60—Software deployment
- G06F8/61—Installation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Theoretical Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Stored Programmes (AREA)
Abstract
The application provides a method and a system for configuring application based on a localization environment of a cloud platform, belongs to the technical field of application configuration, and is used for solving the problem of poor application service using effect on the cloud platform in the related technology. The cloud platform application is divided into a plurality of functional modules, user configuration preference of each functional module is determined according to user configuration big data of each application, the local recommendation score of each functional module is determined according to a preset local recommendation rule in combination with the utilization frequency duration preference of a person, which is reflected by the use record of the application, of the user requesting configuration, the functional modules with higher local recommendation scores are installed locally according to a preset storage space, the preference configuration and the high-frequency utilization of the functional modules are installed locally, other functional modules are installed in the cloud, and the configuration scheme that the high-frequency bias good utilization part is installed locally and the other parts are installed in the cloud is favorable for the application to be used more efficiently.
Description
Technical Field
The application relates to the technical field of application configuration, in particular to a method and a system for configuring applications in a localized environment based on a cloud platform.
Background
As cloud computing evolves, more and more suppliers begin to deploy cloud platforms on cloud servers. The cloud platform is generally configured with application services, and the user terminal is connected with the cloud platform through a network, so that the application services on the cloud platform can be used. Because the application service on the cloud platform needs to be used in a networking manner, the cloud platform generally needs to occupy network bandwidth and has certain time-lag, and in order to facilitate the use of the application service, the cloud platform also allows the user terminal to download and deploy the application service to the local of the user terminal, while the application service deployed to the local of the user terminal occupies local storage resources of the user terminal. How to facilitate the use of application services on a cloud platform is a problem that those skilled in the art are always expecting to solve.
Disclosure of Invention
The application provides a method and a system for configuring applications based on a localization environment of a cloud platform, which are beneficial to better using application services on the cloud platform.
In a first aspect, the present application provides a method for configuring an application based on a localized environment of a cloud platform. The method is applied to a system formed by a cloud platform and a user terminal, wherein the cloud platform comprises an application formed by a plurality of functional modules;
the method comprises the following steps:
acquiring configuration request information of a user terminal, wherein the configuration request information carries an application identifier of an application, a function module identifier of the application and preset storage space data;
determining first proportion information according to each function module identifier of the application based on the user configuration big data, wherein the first proportion information is related to the ratio of the number of the user terminals of which the function modules corresponding to the function module identifiers are configured at the user terminals and the number of the user terminals of the application;
determining second proportion data associated with the use frequency data and third proportion data associated with the use duration data of each function module according to the identification of each function module of the application based on the use record data of the user terminal for the application;
calculating local necessary score data of each functional module of the application according to the first proportion information, the second proportion information and the third proportion information based on a local necessary calculation rule;
based on a preset local recommendation rule, a local recommendation scheme is determined according to preset storage space data and preset acquired space occupation data, carrying function module identifiers, of the application, wherein the local recommendation scheme comprises a plurality of function modules which are recommended to be installed locally, the sum of the space occupation data of the plurality of function modules is not more than the preset storage space data, and the sum of local necessary score data of the plurality of function modules is highest.
By adopting the technical scheme, the application is divided into the functional modules, the local necessary score data of each functional module is determined according to the use condition and the configuration condition of the functional modules, and the functional modules which are configured locally are recommended to be determined in combination with the consideration of the storage space.
Further, the determining, based on the user configuration big data, a first scale information according to each function module identifier of the application includes:
the user configuration big data comprises user configuration data, wherein the user configuration data comprises an application identifier and installation attribution information carrying a function module identifier, and the installation attribution information is installed on a cloud or locally;
for each function module identification of the application,
searching and determining module configuration quantity data carrying user configuration data of the function module identifier in the user configuration big data;
searching and determining user configuration data which carries the function module identifier and is locally installed as installation attribution information from the user configuration big data as module local configuration data;
the first scale information of the functional module is equal to the module local configuration data divided by the module configuration quantity data.
Further, the determining, based on the usage record data of the application by the user terminal, the second proportion data associated with the module usage frequency data and the third proportion data associated with the module usage duration data of each function module according to each function module identifier of the application includes:
determining module use frequency data of each function module in a preset duration according to each function module identifier of the application based on the use record data of the user terminal on the application;
determining application use frequency data of the application within a preset time period according to the application identifier of the application;
the second ratio data of the functional module is equal to the module usage frequency data divided by the application usage frequency data.
Further, the determining, based on the usage record data of the application by the user terminal, the second proportion data associated with the module usage frequency data and the third proportion data associated with the module usage duration data of each function module according to each function module identifier of the application further includes:
determining module use duration data of each function module within a preset duration according to each function module identifier of the application based on the use record data of the user terminal on the application;
determining application use duration data of the application within a preset duration according to the application identifier of the application;
said third proportion data of the functional module is equal to the module usage time length data divided by the application usage time length data.
Further, the calculating the local necessary score data of each functional module of the application according to the first scale information, the second scale information and the third scale information based on the local necessary calculation rule includes:
wherein,for a first preset coefficient of the ith functional module,for a second preset coefficient of the ith functional module,and a third preset coefficient for the ith functional module.
Further, the determining, based on the preset local recommendation rule, a local recommendation scheme according to preset storage space data and the pre-acquired space occupation data carrying the function module identifier of the application includes:
determining a local feasible scheme according to preset storage space data and pre-acquired space occupation data of the application, which carries with function module identifiers, wherein the local feasible scheme comprises a plurality of function modules, and the sum of the space occupation data of the plurality of function modules is not more than the preset storage space data;
determining a local essential score for each local feasible solution based on the local essential score for each functional module of the application;
and comparing and determining the local feasible scheme with the highest local necessary score as the local recommended scheme.
In a second aspect, the present application provides a system for configuring an application in a localized environment based on a cloud platform, where the system includes a cloud platform and a user terminal, where the cloud platform includes an application composed of a plurality of functional modules;
the cloud platform is configured to:
acquiring configuration request information of a user terminal, wherein the configuration request information carries an application identifier of an application, a function module identifier of the application and preset storage space data;
determining first proportion information according to each function module identifier of the application based on the user configuration big data, wherein the first proportion information is related to the ratio of the number of the user terminals of which the function modules corresponding to the function module identifiers are configured at the user terminals and the number of the user terminals of the application;
determining second proportion data associated with the use frequency data and third proportion data associated with the use duration data of each function module according to the identification of each function module of the application based on the use record data of the user terminal for the application;
calculating local necessary score data of each functional module of the application according to the first proportion information, the second proportion information and the third proportion information based on a local necessary calculation rule;
based on a preset local recommendation rule, a local recommendation scheme is determined according to preset storage space data and preset acquired space occupation data, carrying function module identifiers, of the application, wherein the local recommendation scheme comprises a plurality of function modules which are recommended to be installed locally, the sum of the space occupation data of the plurality of function modules is not more than the preset storage space data, and the sum of local necessary score data of the plurality of function modules is highest.
Further, the cloud platform is further configured to,
the determining, based on the user configuration big data, first scale information according to each function module identifier of the application includes:
the user configuration big data comprises user configuration data, wherein the user configuration data comprises an application identifier and installation attribution information carrying a function module identifier, and the installation attribution information is installed on a cloud or locally;
for each function module identification of the application,
searching and determining module configuration quantity data carrying user configuration data of the function module identifier in the user configuration big data;
searching and determining user configuration data which carries the function module identifier and is locally installed as installation attribution information from the user configuration big data as module local configuration data;
the first proportion information of the functional module is equal to the result of dividing module local configuration data by module configuration quantity data;
determining module use frequency data of each function module in a preset duration according to each function module identifier of the application based on the use record data of the user terminal on the application;
determining application use frequency data of the application within a preset time period according to the application identifier of the application;
the second ratio data of the functional module is equal to the module usage frequency data divided by the application usage frequency data.
Further, the cloud platform is further configured to,
the determining, based on the usage record data of the application by the user terminal, the second proportion data associated with the module usage frequency data and the third proportion data associated with the module usage duration data of each function module according to each function module identifier of the application further includes:
determining module use duration data of each function module within a preset duration according to each function module identifier of the application based on the use record data of the user terminal on the application;
determining application use duration data of the application within a preset duration according to the application identifier of the application;
said third proportion data of the functional module is equal to the module usage time length data divided by the application usage time length data.
Further, the cloud platform is further configured to,
the calculating the local necessary score data of each functional module of the application according to the first proportion information, the second proportion information and the third proportion information based on the local necessary calculation rule comprises:
let the first proportional information of the ith function module of the application beThe second proportion information isThe third proportion information isLocal essential score isThen
Wherein,for a first preset coefficient of the ith functional module,for a second preset coefficient of the ith functional module,and a third preset coefficient for the ith functional module.
In summary, the application at least comprises the following beneficial effects:
1. the method and the system divide the application into functional modules, recommend which functional modules are installed locally according to the use and configuration conditions of the application, and are beneficial to the efficient application of the application;
2. the determination modes of the first proportion information, the second proportion information and the third proportion information are objective and reasonable, so that the accurate determination of the local necessary score data of the finally determined functional module is facilitated;
3. under the condition that the storage space is allowed, the sum of local necessary score data of the function module which is recommended to be installed locally is as high as possible, and the local recommendation scheme is more reasonable.
It should be understood that the description in this summary is not intended to limit the critical or essential features of the embodiments of the application, nor is it intended to limit the scope of the application. Other features of the present application will become apparent from the description that follows.
Drawings
The above and other features, advantages and aspects of embodiments of the present application will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, wherein like or similar reference numerals denote like or similar elements, in which:
FIG. 1 illustrates a flow chart of a method of configuring an application based on a localized environment of a cloud platform in an embodiment of the application;
FIG. 2 illustrates a block diagram of a system for a cloud platform based localization environment configuration application in an embodiment of the application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In addition, the term "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
The application provides a method and a system for configuring applications based on a localization environment of a cloud platform, which can divide the applications of the cloud platform into a plurality of functional modules, and recommend some of the functional modules to be installed locally based on objective and reasonable rules so as to facilitate the overall use effect of the applications.
In a first aspect, an embodiment of the application discloses a method for configuring an application in a localized environment based on a cloud platform. The method is applied to a system formed by a cloud platform and a user terminal, wherein the cloud platform comprises an application formed by a plurality of functional modules.
Referring to fig. 1, the method includes:
s110: and acquiring configuration request information of the user terminal.
The configuration request information carries an application identifier of an application, a function module identifier of the application and preset storage space data.
When a user needs to determine how an application of the cloud platform is configured, the user sends configuration request information to the cloud platform through the user terminal, namely, the user terminal requests the cloud platform to assist in configuring the application. The configuration request information should include an application identification of the application, a function module identification of a plurality of function modules of the application selected by the user, and storage space data selected by the user.
It should be understood that it is known which application services are provided in the cloud platform and into which functional modules each application service is divided in advance, that is, the cloud platform may pre-construct an application module database, where the application module database includes all applications provided by the cloud platform and all functional modules included in each application, the applications are determined based on application identifiers, the functional modules are determined based on the functional module identifiers, and an association relationship between the functional module identifiers and the application identifiers is predetermined.
S120: based on the user configuration big data, a first scale information is determined according to each function module identification of the application.
Each of the function module identifiers referred to herein refers to a function module identifier of a plurality of function modules of the application selected by the user. The first proportion information is related to the ratio of the number of the user terminals of which the function modules corresponding to the function module identifiers are configured locally to the user terminals to the number of the user terminals of the application.
The method comprises the following steps: the user configuration big data comprises user configuration data, wherein the user configuration data comprises an application identifier and installation attribution information carrying a function module identifier, and the installation attribution information is installed on a cloud or locally; for each function module identifier of the application, searching and determining module configuration quantity data carrying user configuration data of the function module identifier in the user configuration big data; searching and determining user configuration data which carries the function module identifier and is locally installed as installation attribution information from the user configuration big data as module local configuration data; the first scale information of the functional module is equal to the module local configuration data divided by the module configuration quantity data.
It can be understood that the user configuration big data includes a large amount of and comprehensive user configuration data, and covers all the functional modules of all applications, and each user configuration data includes installation attribution of each module in each application, that is, installation attribution information carrying an application identifier and a functional module identifier, where the installation attribution information is installed in the cloud or locally. The user configuration big data can embody which modules of which applications are configured locally on the user terminal by the user and which modules are configured on the cloud end of the cloud platform by the user.
In the method of this step, since the user wants to configure the application determination of the user terminal, the application identification is determined, and the function module identification of each function module under the application can be determined. For the function module identifier of each function module of the application selected by the user, the number of the user configuration data configuring the function module to the local user terminal can be obtained by searching in the user configuration big data, or the total number of the user configuration data configuring the function module to the local user terminal and the cloud platform cloud end can be obtained by searching in the user configuration data, and then the first proportion information of each function module of the application selected by the user can be obtained, wherein the first proportion information is equal to the result of dividing the number of the user configuration data configuring the function module to the local user terminal by the total number of the user configuration data configuring the function module to the local user terminal and the cloud platform cloud end.
S130: based on the usage record data of the application by the user terminal, second proportion data associated with the usage frequency data and third proportion data associated with the usage duration data of each function module are determined according to the identification of each function module of the application.
The method comprises the following steps: determining module use frequency data of each function module in a preset duration according to each function module identifier of the application based on the use record data of the user terminal on the application; determining application use frequency data of the application within a preset time period according to the application identifier of the application; the second ratio data of the functional module is equal to the module usage frequency data divided by the application usage frequency data.
The method of this step further comprises: determining module use duration data of each function module within a preset duration according to each function module identifier of the application based on the use record data of the user terminal on the application; determining application use duration data of the application within a preset duration according to the application identifier of the application; said third proportion data of the functional module is equal to the module usage time length data divided by the application usage time length data.
Specifically, when the user terminal does not configure the application of the cloud platform, the user terminal can also use the application service by networking to the cloud platform, that is, the user terminal not configuring the application can also have the use record data of the application.
The usage record data of the user terminal is determined based on the application identifier and the function module identifier, that is, the user terminal can record which function module of which application starts to be used at the moment and stops to be used at the moment, and a continuous time from the moment of starting to the moment of stopping to be recorded as one time, that is, the usage record data can determine the usage frequency and the usage duration of each function module in each application. The preset duration is preset, and in the embodiment of the present application, the preset duration taking the current time as the end time, that is, the preset duration before the current time, where the current time is the time when the configuration request information is acquired.
Thus, the use record data carrying the application identifier can be determined according to the use record data of the user and the application identifier in the configuration request information, and further the use record data of each function module identifier of the application is determined. And further, the use duration and the use frequency of each functional module in the preset duration are determined, and accordingly, the total use duration and the total use frequency of the application in the preset duration can also be determined. The total duration of the application in the preset duration is equal to the sum of the duration of the application of all the functional modules in the preset duration, and the total frequency of the application is equal to the sum of the total frequency of the application of all the functional modules in the preset duration.
Dividing the frequency of use of the functional modules in the preset time period by the total frequency of use of all the functional modules in the preset time period to obtain second proportion data. Dividing the use time length data of the function modules in the preset time length by the use time length data of all the function modules in the preset time length to obtain third proportion data.
S140: based on the local necessary calculation rule, local necessary score data of each functional module of the application is calculated according to the first scale information, the second scale information and the third scale information.
Let the first proportional information of the ith function module of the application beThe second proportion information isThe third proportion information isLocal essential score isThen
Wherein,for a first preset coefficient of the ith functional module,for a second preset coefficient of the ith functional module,and a third preset coefficient for the ith functional module.
S150: based on a preset local recommendation rule, a local recommendation scheme is determined according to preset storage space data and preset acquired space occupation data, carrying function module identifiers, of the application.
The local recommendation scheme comprises a plurality of function modules which are recommended to be installed locally, the sum of space occupation data of the plurality of function modules is not larger than the preset storage space data, and the sum of local necessary score data of the plurality of function modules is highest.
The method comprises the following steps: determining a local feasible scheme according to preset storage space data and pre-acquired space occupation data of the application, which carries with function module identifiers, wherein the local feasible scheme comprises a plurality of function modules, and the sum of the space occupation data of the plurality of function modules is not more than the preset storage space data; determining a local essential score for each local feasible solution based on the local essential score for each functional module of the application; and comparing and determining the local feasible scheme with the highest local necessary score as the local recommended scheme.
If there are a plurality of local viable schemes with highest local necessary scores, one of the local viable schemes is selected as a local recommended scheme.
Based on the method, firstly, the application of the cloud platform is divided into a plurality of functional modules, then, the user configuration preference of each functional module is determined according to the user configuration big data of each application, then, the frequency duration preference of the application is utilized by the person reflected by the use record of the application by the user requesting configuration, the local recommendation score of each functional module installed locally is determined according to the preset local recommendation rule, and then, the functional module with higher local recommendation score is installed locally according to the preset storage space, so that the preference configuration and the functional module with high frequency utilization are installed locally, other functional modules are installed in the cloud, and the configuration scheme that the high frequency good utilization part is installed locally and the other parts are installed in the cloud is beneficial to the overall more efficient use of the application.
It should be noted that, for simplicity of description, the foregoing method embodiments are all expressed as a series of action combinations, but it should be understood by those skilled in the art that the present application is not limited by the described action sequences, as some steps may be performed in other sequences or simultaneously, according to the embodiments of the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are alternative embodiments, and that the acts and modules referred to are not necessarily required for the present application.
The foregoing describes a method embodiment, and the following further describes a scheme according to an embodiment of the present application through a system embodiment.
In a second aspect, the present application provides a system for configuring an application based on a localized environment of a cloud platform.
Referring to fig. 2, the system includes a cloud platform 210 and a user terminal 220, the cloud platform 210 containing an application composed of a plurality of functional modules.
The cloud platform 210 is configured to:
acquiring configuration request information of the user terminal 220, wherein the configuration request information carries an application identifier of an application, a function module identifier of the application and preset storage space data;
determining first scale information according to each function module identifier of the application based on the user configuration big data, wherein the first scale information is related to the ratio of the number of the user terminals 220 of which the function modules corresponding to the function module identifiers are configured locally to the user terminals 220 to the number of the user terminals 220 of the application;
determining second proportion data associated with the usage frequency data and third proportion data associated with the usage duration data of each function module according to each function module identification of the application based on usage record data of the application by the user terminal 220;
calculating local necessary score data of each functional module of the application according to the first proportion information, the second proportion information and the third proportion information based on a local necessary calculation rule;
based on a preset local recommendation rule, a local recommendation scheme is determined according to preset storage space data and preset acquired space occupation data, carrying function module identifiers, of the application, wherein the local recommendation scheme comprises a plurality of function modules which are recommended to be installed locally, the sum of the space occupation data of the plurality of function modules is not more than the preset storage space data, and the sum of local necessary score data of the plurality of function modules is highest.
Further, the cloud platform 210 is further configured to,
the determining, based on the user configuration big data, first scale information according to each function module identifier of the application includes:
the user configuration big data comprises user configuration data, wherein the user configuration data comprises an application identifier and installation attribution information carrying a function module identifier, and the installation attribution information is installed on a cloud or locally;
for each function module identification of the application,
searching and determining module configuration quantity data carrying user configuration data of the function module identifier in the user configuration big data;
searching and determining user configuration data which carries the function module identifier and is locally installed as installation attribution information from the user configuration big data as module local configuration data;
the first proportion information of the functional module is equal to the result of dividing module local configuration data by module configuration quantity data;
determining module use frequency data of each function module within a preset duration according to each function module identifier of the application based on the use record data of the user terminal 220 on the application;
determining application use frequency data of the application within a preset time period according to the application identifier of the application;
the second ratio data of the functional module is equal to the module usage frequency data divided by the application usage frequency data.
Further, the cloud platform 210 is further configured to,
the determining, based on the usage record data of the application by the user terminal 220, the second proportion data associated with the module usage frequency data and the third proportion data associated with the module usage duration data of each function module according to each function module identifier of the application further includes:
based on the usage record data of the user terminal 220 for the application, determining module usage duration data of each function module within a preset duration according to each function module identifier of the application;
determining application use duration data of the application within a preset duration according to the application identifier of the application;
said third proportion data of the functional module is equal to the module usage time length data divided by the application usage time length data.
Further, the cloud platform 210 is further configured to,
the calculating the local necessary score data of each functional module of the application according to the first proportion information, the second proportion information and the third proportion information based on the local necessary calculation rule comprises:
let the first proportional information of the ith function module of the application beThe second proportion information isThe third proportion information isLocal essential score isThen
Wherein,for a first preset coefficient of the ith functional module,for a second preset coefficient of the ith functional module,and a third preset coefficient for the ith functional module.
It will be clear to those skilled in the art that, for convenience and brevity of description, reference may be made to the corresponding process in the foregoing method embodiment for the specific working process of the described system, which is not described herein again.
In summary, the application at least comprises the following beneficial effects:
1. the method and the system divide the application into functional modules, recommend which functional modules are installed locally according to the use and configuration conditions of the application, and are beneficial to the efficient application of the application;
2. the determination modes of the first proportion information, the second proportion information and the third proportion information are objective and reasonable, so that the accurate determination of the local necessary score data of the finally determined functional module is facilitated;
under the condition that the storage space is allowed, the sum of local necessary score data of the function module which is recommended to be installed locally is as high as possible, and the local recommendation scheme is more reasonable.
The above description is only illustrative of the preferred embodiments of the present application and of the principles of the technology employed. It will be appreciated by persons skilled in the art that the scope of the disclosure referred to in the present application is not limited to the specific combinations of technical features described above, but also covers other technical features which may be formed by any combination of the technical features described above or their equivalents without departing from the spirit of the disclosure. Such as the above-mentioned features and the technical features disclosed in the present application (but not limited to) having similar functions are replaced with each other.
Claims (10)
1. The method for configuring the application based on the localization environment of the cloud platform is characterized by being applied to a system formed by the cloud platform and a user terminal, wherein the cloud platform comprises the application formed by a plurality of functional modules;
the method comprises the following steps:
acquiring configuration request information of a user terminal, wherein the configuration request information carries an application identifier of an application, a function module identifier of the application and preset storage space data;
determining first proportion information according to each function module identifier of the application based on the user configuration big data, wherein the first proportion information is related to the ratio of the number of the user terminals of which the function modules corresponding to the function module identifiers are configured at the user terminals and the number of the user terminals of the application;
determining second proportion data associated with the use frequency data and third proportion data associated with the use duration data of each function module according to the identification of each function module of the application based on the use record data of the user terminal for the application;
calculating local necessary score data of each functional module of the application according to the first proportion information, the second proportion information and the third proportion information based on a local necessary calculation rule;
based on a preset local recommendation rule, a local recommendation scheme is determined according to preset storage space data and preset acquired space occupation data, carrying function module identifiers, of the application, wherein the local recommendation scheme comprises a plurality of function modules which are recommended to be installed locally, the sum of the space occupation data of the plurality of function modules is not more than the preset storage space data, and the sum of local necessary score data of the plurality of function modules is highest.
2. The method of claim 1, wherein determining a first scale information based on the user configuration profile based on each function module identification of the application comprises:
the user configuration big data comprises user configuration data, wherein the user configuration data comprises an application identifier and installation attribution information carrying a function module identifier, and the installation attribution information is installed on a cloud or locally;
for each function module identification of the application,
searching and determining module configuration quantity data carrying user configuration data of the function module identifier in the user configuration big data;
searching and determining user configuration data which carries the function module identifier and is locally installed as installation attribution information from the user configuration big data as module local configuration data;
the first scale information of the functional module is equal to the module local configuration data divided by the module configuration quantity data.
3. The method according to claim 2, wherein determining the second proportion data associated with the module usage frequency data and the third proportion data associated with the module usage duration data of each function module according to each function module identification of the application based on the usage record data of the application by the user terminal comprises:
determining module use frequency data of each function module in a preset duration according to each function module identifier of the application based on the use record data of the user terminal on the application;
determining application use frequency data of the application within a preset time period according to the application identifier of the application;
the second ratio data of the functional module is equal to the module usage frequency data divided by the application usage frequency data.
4. The method according to claim 2, wherein determining the second proportion data associated with the module usage frequency data and the third proportion data associated with the module usage duration data of each function module according to each function module identification of the application based on the usage record data of the application by the user terminal further comprises:
determining module use duration data of each function module within a preset duration according to each function module identifier of the application based on the use record data of the user terminal on the application;
determining application use duration data of the application within a preset duration according to the application identifier of the application;
said third proportion data of the functional module is equal to the module usage time length data divided by the application usage time length data.
5. The method of claim 4, wherein calculating the local necessary score data of each functional module of the application based on the local necessary calculation rule according to the first scale information, the second scale information, and the third scale information comprises:
let the first proportional information of the ith function module of the application beThe second proportion information is->The third proportion information is->The local essential score is +.>Then
；
Wherein,for the first preset coefficient of the ith functional module,/->For a second preset coefficient of the ith functional module,and a third preset coefficient for the ith functional module.
6. The method of claim 5, wherein the determining the local recommendation scheme based on the preset local recommendation rule according to the preset storage space data and the pre-acquired space occupation data carrying the function module identifier of the application includes:
determining a local feasible scheme according to preset storage space data and pre-acquired space occupation data of the application, which carries with function module identifiers, wherein the local feasible scheme comprises a plurality of function modules, and the sum of the space occupation data of the plurality of function modules is not more than the preset storage space data;
determining a local essential score for each local feasible solution based on the local essential score for each functional module of the application;
and comparing and determining the local feasible scheme with the highest local necessary score as the local recommended scheme.
7. A system for configuring applications based on a localized environment of a cloud platform, comprising a cloud platform (210) and a user terminal (220), the cloud platform (210) comprising an application consisting of a plurality of functional modules;
the cloud platform (210) is configured to:
acquiring configuration request information of a user terminal (220), wherein the configuration request information carries an application identifier of an application, a function module identifier of the application and preset storage space data;
determining first scale information according to each function module identifier of the application based on the user configuration big data, wherein the first scale information is related to the ratio of the number of user terminals (220) of which the function module corresponding to the function module identifier is configured at the local of the user terminal (220) to the number of the user terminals (220) of the application;
determining, based on usage record data of the application by the user terminal (220), second proportion data associated with usage frequency data and third proportion data associated with usage duration data for each function module according to each function module identification of the application;
calculating local necessary score data of each functional module of the application according to the first proportion information, the second proportion information and the third proportion information based on a local necessary calculation rule;
based on a preset local recommendation rule, a local recommendation scheme is determined according to preset storage space data and preset acquired space occupation data, carrying function module identifiers, of the application, wherein the local recommendation scheme comprises a plurality of function modules which are recommended to be installed locally, the sum of the space occupation data of the plurality of function modules is not more than the preset storage space data, and the sum of local necessary score data of the plurality of function modules is highest.
8. The system of claim 7, wherein the cloud platform (210) is further configured to,
the determining, based on the user configuration big data, first scale information according to each function module identifier of the application includes:
the user configuration big data comprises user configuration data, wherein the user configuration data comprises an application identifier and installation attribution information carrying a function module identifier, and the installation attribution information is installed on a cloud or locally;
for each function module identification of the application,
searching and determining module configuration quantity data carrying user configuration data of the function module identifier in the user configuration big data;
searching and determining user configuration data which carries the function module identifier and is locally installed as installation attribution information from the user configuration big data as module local configuration data;
the first proportion information of the functional module is equal to the result of dividing module local configuration data by module configuration quantity data;
determining module use frequency data of each function module within a preset duration according to each function module identifier of the application based on the use record data of the user terminal (220) on the application;
determining application use frequency data of the application within a preset time period according to the application identifier of the application;
the second ratio data of the functional module is equal to the module usage frequency data divided by the application usage frequency data.
9. The system of claim 8, wherein the cloud platform (210) is further configured to,
the determining, based on the usage record data of the application by the user terminal (220), the second proportion data associated with the module usage frequency data and the third proportion data associated with the module usage duration data of each function module according to each function module identifier of the application further includes:
determining module use duration data of each function module within a preset duration according to each function module identifier of the application based on the use record data of the user terminal (220) on the application;
determining application use duration data of the application within a preset duration according to the application identifier of the application;
said third proportion data of the functional module is equal to the module usage time length data divided by the application usage time length data.
10. The system of claim 9, wherein the cloud platform (210) is further configured to,
the calculating the local necessary score data of each functional module of the application according to the first proportion information, the second proportion information and the third proportion information based on the local necessary calculation rule comprises:
let the first proportional information of the ith function module of the application beThe second proportion information is->The third proportion information is->The local essential score is +.>Then
；
Wherein,for the first preset coefficient of the ith functional module,/->For a second preset coefficient of the ith functional module,for the ith functional moduleAnd a third preset coefficient.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311337415.3A CN117076006A (en) | 2023-10-17 | 2023-10-17 | Method and system for configuring application in localization environment based on cloud platform |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311337415.3A CN117076006A (en) | 2023-10-17 | 2023-10-17 | Method and system for configuring application in localization environment based on cloud platform |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117076006A true CN117076006A (en) | 2023-11-17 |
Family
ID=88719854
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311337415.3A Pending CN117076006A (en) | 2023-10-17 | 2023-10-17 | Method and system for configuring application in localization environment based on cloud platform |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117076006A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109345324A (en) * | 2018-02-07 | 2019-02-15 | 深圳壹账通智能科技有限公司 | Application function recommended method, device, computer equipment and storage medium |
| CN111427628A (en) * | 2020-03-27 | 2020-07-17 | 李琦 | Software function module configuration method, device, software product and storage medium |
| CN112328318A (en) * | 2020-09-27 | 2021-02-05 | 北京华胜天成科技股份有限公司 | Method and device for automatic planning of proprietary cloud platform and storage medium |
| CN115495106A (en) * | 2022-09-26 | 2022-12-20 | 北京奇艺世纪科技有限公司 | Application program generation method and device, server and storage medium |
| WO2023009170A1 (en) * | 2021-07-27 | 2023-02-02 | UiPath, Inc. | A common platform for implementing rpa services on customer premises |
-
2023
- 2023-10-17 CN CN202311337415.3A patent/CN117076006A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109345324A (en) * | 2018-02-07 | 2019-02-15 | 深圳壹账通智能科技有限公司 | Application function recommended method, device, computer equipment and storage medium |
| CN111427628A (en) * | 2020-03-27 | 2020-07-17 | 李琦 | Software function module configuration method, device, software product and storage medium |
| CN112328318A (en) * | 2020-09-27 | 2021-02-05 | 北京华胜天成科技股份有限公司 | Method and device for automatic planning of proprietary cloud platform and storage medium |
| WO2023009170A1 (en) * | 2021-07-27 | 2023-02-02 | UiPath, Inc. | A common platform for implementing rpa services on customer premises |
| CN115495106A (en) * | 2022-09-26 | 2022-12-20 | 北京奇艺世纪科技有限公司 | Application program generation method and device, server and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112714178B (en) | Task unloading method and device based on vehicle-mounted edge calculation | |
| US20200004785A1 (en) | Automatic grouping based on user behavior | |
| CN116691645A (en) | Control method and device of range extender based on navigation information and storage medium | |
| CN110035128B (en) | Live broadcast scheduling method and device, live broadcast system and storage medium | |
| CN117076006A (en) | Method and system for configuring application in localization environment based on cloud platform | |
| CN111414528B (en) | Method and device for determining equipment identification, storage medium and electronic equipment | |
| CN113377817A (en) | Data processing method, system, device and storage medium | |
| CN109582829B (en) | Processing method, device, equipment and readable storage medium | |
| CN111444074A (en) | Data monitoring method and device, electronic equipment and readable storage medium | |
| CN113839888B (en) | Current limiting method, device, equipment and readable storage medium | |
| CN112148320A (en) | Application program upgrading method, device, equipment and storage medium | |
| CN114584565B (en) | Application protection method and system, electronic equipment and storage medium | |
| CN116303418A (en) | Mass rule processing method, system and storage medium based on Internet of things platform | |
| CN109842482A (en) | A kind of information synchronization method, system and terminal device | |
| CN112256420B (en) | Task allocation method and device and electronic equipment | |
| CN112929923B (en) | Uplink resource acquisition method and device, mobile terminal and readable storage medium | |
| CN111586160A (en) | Data processing method, data processing device, data processing equipment and storage medium | |
| KR101591595B1 (en) | Method for predicting link in big database | |
| CN111787102A (en) | Service processing method and device for multiple application platforms, electronic equipment and storage medium | |
| CN111104528A (en) | Picture obtaining method and device and client | |
| CN110912820A (en) | Distributed routing method, distributed routing apparatus, and computer-readable storage medium | |
| CN114710450B (en) | Flow control method, device, equipment and computer storage medium | |
| CN115098793B (en) | User portrait analysis method and system based on big data | |
| CN109039801B (en) | Package overuse detection method and device of distributed cluster and computing equipment | |
| CN113613184B (en) | Flow package determining 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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20231117 |
|
| RJ01 | Rejection of invention patent application after publication |