CN116257692B - Asset sharing and recommending method and system based on cloud edge collaboration - Google Patents
Asset sharing and recommending method and system based on cloud edge collaboration Download PDFInfo
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Abstract
The application discloses an asset sharing and recommending method and system based on cloud edge collaboration, wherein the method comprises the following steps: the cloud end builds an asset integrated value based on the asset basic attributes, the dynamic attributes, the side user search and the evaluation activities, wherein the asset integrated value comprises: asset base value, asset user value, and asset dynamic value; the side end obtains user attributes, working positions, department asset searching and evaluation records, and performs personalized recommendation to obtain a personalized recommendation catalog, and meanwhile, the cloud end performs asset value recommendation to obtain an asset value recommendation catalog; and forming a combined recommendation directory based on the personalized recommendation directory and the asset value recommendation directory, recommending to a side user based on the combined recommendation directory, and carrying out cloud side asset sharing. According to the cloud-edge collaborative recommendation method, the cloud end adopts value recommendation and the edge adopts personalized recommendation, so that the cloud-edge collaborative recommendation method is beneficial to realizing an intelligent thousand-person and thousand-face recommendation mechanism.
Description
Technical Field
The application relates to the technical field of asset recommendation, in particular to an asset sharing and recommending method and system based on cloud edge collaboration.
Background
With the rapid development of computer related software and hardware technology, related applications of big data have been deep into various corners of production and life. Along with the expansion of the data application field, the demand for data is increased along with the water-rise ship, and the analysis by utilizing the data and the application of analysis results to operation decisions become new power for enterprise development. In the digital economic era, data is taken as a production element to be inserted into an economic system, and the data is taken as a key production element for connecting innovation, activating funds, cultivating talents, promoting industrial upgrading and economic growth by the characteristic that the replicable, sharable, infinitely-increased and infinitely-supplied marginal cost is almost zero. In this context, data as a new production element for which sharing and recommendation would be an essential part of the data flow application process.
The data asset has application value, and the required assets in different application scenes are different, so how the data asset breaks down islands to form asset sharing, the assets are available and better matched with the application? The main mode at present is to build asset portals, such as provincial data sharing portals, digital resource systems and the like, which are classified according to catalogs of fields, topics, industries and the like, provide sharing formats of data, APIs, files and the like, and improve the liquidity of assets under the framework of data standards.
The application and popularization of asset portals and data portals, the continuous evolution of a digital resource system, various assets in the forms of application, data, calculation and the like exert greater value through sharing, the sharing recommendation is always one direction of continuous optimization of the informatization process, and new value signs are continuously provided to improve the sharing efficiency through the searching process of global searching, accurate searching and simulated searching. However, the existing asset recommendation method has low efficiency, and intelligent personalized asset recommendation cannot be realized.
Accordingly, there is a need for improvement and advancement in the art.
Disclosure of Invention
The application aims to solve the technical problems that the asset recommendation method in the prior art is low in efficiency and cannot realize intelligent personalized asset recommendation.
In a first aspect, the present application provides an asset sharing and recommending method based on cloud edge collaboration, where the method includes:
the cloud end builds an asset integrated value based on the asset basic attributes, the dynamic attributes, the side user search and the evaluation activities, wherein the asset integrated value comprises: asset base value, asset user value, and asset dynamic value;
the side end obtains user attributes, working positions, department asset searching and evaluation records, and performs personalized recommendation to obtain a personalized recommendation catalog, and meanwhile, the cloud end performs asset value recommendation to obtain an asset value recommendation catalog;
and forming a combined recommendation directory based on the personalized recommendation directory and the asset value recommendation directory, recommending to a side user based on the combined recommendation directory, and carrying out cloud side asset sharing.
In one implementation, the asset base value includes a business property value, a data scale value, a data quality value, a data inventory value, a data openness value.
In one implementation, the asset user value includes a user search value, a search view value, a user collection value, a user evaluation value, and a user sharing value, and the higher the asset user value is, the greater the association of the asset and the user is, and the higher the recommendation is.
In one implementation, the dynamic value of the asset includes an asset search times value, an asset endorsement number value, an asset sharing number value, an asset valuation value.
In one implementation, the method further comprises:
based on accumulation of user browsing data, the cloud dynamically updates the dynamic value of the asset and records operation data.
In one implementation, the performing cloud edge asset sharing includes:
the cloud shares assets through APIs;
the method comprises the steps that an edge user applies for using assets, and applies for updating the assets to obtain asset updating results;
and distributing the new version of the asset based on the asset updating result.
In one implementation, the assigning a new version of the asset based on the asset update result includes:
if the asset updating result is that the asset updating verification is passed, the cloud end distributes a new version for the asset, and a cloud end user uses the new version of the asset;
and if the asset updating result is that the asset updating verification fails, the side end user applies for updating the asset again.
In a second aspect, an embodiment of the present application further provides an asset sharing and recommending system based on cloud edge collaboration, where the system includes:
the asset value evaluation module is used for constructing an asset comprehensive value based on the asset basic attribute, the dynamic attribute, the side user search and the evaluation activity by the cloud, wherein the asset comprehensive value comprises: asset base value, asset user value, and asset dynamic value;
the cloud side collaborative recommendation module is used for acquiring user attributes, working posts, department asset searching and evaluation records by the side end, performing personalized recommendation to obtain a personalized recommendation catalog, and performing asset value recommendation by the cloud side to obtain an asset value recommendation catalog;
and the asset sharing module is used for forming a combined recommendation directory based on the personalized recommendation directory and the asset value recommendation directory, recommending the side users based on the combined recommendation directory and carrying out cloud side asset sharing.
In a third aspect, an embodiment of the present application further provides a terminal device, where the terminal device includes a memory, a processor, and an asset sharing and recommending program based on cloud edge coordination stored in the memory and capable of running on the processor, and when the processor executes the asset sharing and recommending program based on cloud edge coordination, the processor implements the steps of the asset sharing and recommending method based on cloud edge coordination described in any one of the above schemes.
In a fourth aspect, an embodiment of the present application further provides a computer readable storage medium, where the computer readable storage medium stores an asset sharing and recommending program based on cloud edge collaboration, and when the asset sharing and recommending program based on cloud edge collaboration is executed by a processor, the method for sharing and recommending assets based on cloud edge collaboration according to any one of the above schemes is implemented.
The beneficial effects are that: compared with the prior art, the application provides an asset sharing and recommending method based on cloud edge collaboration, and the cloud end of the application constructs an asset comprehensive value based on asset basic attributes, dynamic attributes, side user searching and evaluation activities, wherein the asset comprehensive value comprises: asset base value, asset user value, and asset dynamic value. The side end obtains user attributes, working positions, department asset searching and evaluation records, and performs personalized recommendation to obtain a personalized recommendation directory, and meanwhile, the cloud end performs asset value recommendation to obtain an asset value recommendation directory. And forming a combined recommendation directory based on the personalized recommendation directory and the asset value recommendation directory, recommending to a side user based on the combined recommendation directory, and carrying out cloud side asset sharing. According to the cloud-edge collaborative recommendation method, the cloud end adopts value recommendation and the edge end adopts personalized recommendation, so that the cloud-edge collaborative realization of an intelligent thousand-person and thousand-face recommendation mechanism is facilitated, and the asset recommendation efficiency is improved.
Drawings
Fig. 1 is a flowchart of a specific implementation of an asset sharing and recommending method based on cloud edge collaboration according to an embodiment of the present application.
Fig. 2 is a schematic diagram of cloud edge collaborative asset recommendation in an asset sharing and recommending method based on cloud edge collaboration according to an embodiment of the present application.
Fig. 3 is a schematic diagram of cloud edge gray level sharing in an asset sharing and recommending method based on cloud edge collaboration according to an embodiment of the present application.
Fig. 4 is a functional schematic diagram of an asset sharing and recommending system based on cloud edge collaboration according to an embodiment of the present application.
Fig. 5 is a schematic block diagram of a terminal device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and effects of the present application clearer and more specific, the present application will be described in further detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The embodiment provides an asset sharing and recommending method based on cloud-edge collaboration, which is characterized in that in specific implementation, the cloud end of the embodiment constructs an asset comprehensive value based on asset basic attributes, dynamic attributes, edge user searching and evaluation activities, and the asset comprehensive value comprises: asset base value, asset user value, and asset dynamic value. The side end obtains user attributes, working positions, department asset searching and evaluation records, and performs personalized recommendation to obtain a personalized recommendation directory, and meanwhile, the cloud end performs asset value recommendation to obtain an asset value recommendation directory. And finally, forming a combined recommendation directory based on the personalized recommendation directory and the asset value recommendation directory, recommending to the side user based on the combined recommendation directory, and carrying out cloud side asset sharing. According to the cloud-edge collaborative recommendation method, the cloud end adopts value recommendation and the edge end adopts personalized recommendation, so that the cloud-edge collaborative implementation of an intelligent thousand-person and thousand-face recommendation mechanism is facilitated, and the asset recommendation efficiency is improved.
Exemplary method
The asset sharing and recommending method based on Yun Bian collaboration in this embodiment may be applied to a terminal device, where the terminal device may serve as a cloud end or a side end (i.e., a user end), as shown in fig. 1, and the asset sharing and recommending method based on cloud-side collaboration in this embodiment includes the following steps:
step S100, the cloud end builds an asset comprehensive value based on the asset basic attribute, the dynamic attribute, the side user search and the evaluation activity, wherein the asset comprehensive value comprises: asset base value, asset user value, and asset dynamic value.
The data portal is an asset recommending and sharing platform, and the core is to circulate more assets at the cloud end and the side end so as to realize asset sharing. The cloud end mainly refers to a platform management side and is responsible for cataloging, asset evaluation, asset recommendation and continuous asset service of all assets; the side end refers to a platform user side and comprises department-level asset release, asset use and the like; the method emphasizes that the cloud end recommends assets to the side asset use user based on asset value evaluation, and achieves asset sharing in an API gray version mode.
In the application, the asset polymorphism value assessment is based on the theoretical basis of combining the data value and the application scene, the most basic factors influencing the data value comprise asset basic attributes, user attributes and dynamic attribute numbers, the economic value contributed by the data is different in different application scenes, a cost method is used for applying a data asset value index to data asset metering, a polymorphic asset assessment analysis framework is provided, the data asset value influence factors are standardized and calculated, and the value assessment of the data asset is carried out. Data asset value index formula: p=c×u×d×f. Wherein P represents the target data asset value index, C represents the data basic attribute coefficient, U represents the data user attribute coefficient, D represents the data dynamic attribute coefficient, and F represents the positive and negative influence coefficient. And (3) setting specific indexes and weights for quantitative evaluation on the basis, the users and the dynamic data value influencing factors, and summarizing to obtain a percentage evaluation score after standardized processing.
In this embodiment, the asset base value includes a combined value of business property value, data scale value, data quality value, data inventory value, data openness value, and the like. The level of the business property value factor depends on whether the asset describes useful information in a field, the more useful information the more valuable the business property is to represent, and vice versa, the lower the value. The data size value factor depends on the asset data size, and only data reaching a certain size is valuable, but the larger the data size is, the higher the value is; the level of the data quality value factor depends on the quality of the asset in terms of null rate, repetition rate and standardability, the higher the quality the higher the value. The data cataloging value factors are clear in cataloging, the layers are ordered, the data corresponding categories are clear, the layers are more standard, and the value is higher. The data openness value factor depends on the sharing condition of the data, and the higher the sharing degree is, the higher the value is.
In this embodiment, the property value of the asset user is used to reflect the association between the asset and the user, and mainly includes a combination of a user search value, a search view value, a user collection value, a user evaluation value, a user sharing value, etc. to reflect the user value, and the higher the value, the greater the association, and the higher the recommendation. The searching value of the user is mainly that the searching relation between the user and the asset is recorded by recording the searching content and the searching result; the search viewing is mainly to record the viewing of asset information by a user, so as to add the browsing relationship between the user and the asset. The user collection value is mainly to record the information of the user collection assets, so that the attention relationship between the user and the assets is added. The user evaluation value is mainly to record user evaluation asset information so as to add the evaluation relationship between the user and the asset. The user sharing value is mainly recorded to share the asset information by the user, so that the sharing relationship between the user and the asset is added. The user value of the asset is formed by adding various relations between the user and the asset, and the closer the relation is, the higher the user value of the asset is.
In this embodiment, the dynamic property value of the asset includes a combination value such as an asset search number value, an asset praise value, an asset share value, and an asset evaluation value. The value of the number of searching times of the asset is higher, and conversely, the value is lower when the number of searching times is higher by recording the searching sum of each searching entrance, including direct searching, associated searching, recommended searching and the like. The value of the number of times of praying of the asset mainly refers to the number of times of praying of the asset by a user, and the higher the number of times of praying, the higher the value of the embodiment is, and the lower the value is otherwise. The value of the sharing times of the assets is mainly the number of times that the assets are shared by users, and the higher the sharing times, the higher the value of the assets is, and the lower the value of the assets is, conversely. The asset evaluation value mainly refers to the evaluation condition of the asset, the evaluation times and average scores of the asset are comprehensively considered according to a 0-5 evaluation mechanism, and the higher the evaluation value is, the higher the value is, and otherwise, the lower the value is. The value factors of the attributes need to be carried out in modes of buried point recording, summarizing statistics and the like in the operation process of the platform. And adding all the attributes on the basis of acquiring the value of all the attributes to obtain the dynamic state of the data asset.
Asset polymorphic value assessment is a continuous process, and besides basic value, user value and dynamic value are comprehensively considered, the acting force chemotaxis of all indexes on the data asset value assessment result can be ensured by setting a positive and negative influence coefficient F. The positive and negative influence coefficient F may be provided by a system configuration, and is mainly set in connection with validity of data, evaluation of data, importance of data, and the like. When data is not available, the data score is low, or the data is no longer important, the value of the high value asset can be reduced by the factor F, otherwise the value is increased.
And step 200, acquiring user attributes, working posts, department asset searching and evaluation records by the side, performing personalized recommendation to obtain a personalized recommendation directory, and performing asset value recommendation by the cloud to obtain an asset value recommendation directory.
As shown in fig. 2, the asset portal shows an overview, a theme and a classification of assets, which are the entrance for a user to acquire the assets, and based on the assets evaluated by polymorphic values, uses a cloud-side collaborative recommendation engine, and on one hand, the cloud side outputs a cloud-side asset recommendation catalog based on the latest asset comprehensive value; on the other hand, the personalized recommendation of the side user is based on the side user attribute, the working post, the department and the asset searching and evaluating record, and a personalized recommendation catalog is output; the portal combination two-aspect recommendation catalogs form a recommendation asset TopN list, an intelligent thousand-person thousand-face recommendation mechanism is realized, and the matching efficiency of assets and users is improved. A user at one side enters an asset portal, and as the user belongs to portal browsing data for the first time or only has a small amount, the recommendation method mainly recommends based on cloud value and outputs an asset TopN list classified by theme, classification and the like; along with the accumulation of the browsing data of the user, including the activities of searching, browsing, using, evaluating, sharing and the like of the user, the cloud platform dynamically updates the asset value and records the operation data. When the side user enters the asset portal again, the recommendation method cooperates with cloud value recommendation and side personalized recommendation, and according to the characteristics of each user, the comprehensive value of the asset is combined, the hot asset list of interest is matched for the user, and the hot asset list is combined to form a recommendation catalog, so that the recommendation efficiency of the asset is improved, and the circulation of the asset is realized.
And step 300, forming a combined recommendation directory based on the personalized recommendation directory and the asset value recommendation directory, recommending to a side user based on the combined recommendation directory, and carrying out cloud side asset sharing.
In this embodiment, the carrier of the asset includes library table, file, API, etc., where sharing is performed in API manner, and making call using HTTP protocol is a common method. The embodiment emphasizes that an API sharing method is selected, and on the basis, the gray version management is used to realize continuous calling of the assets and promote the asset sharing service. The method is characterized in that the method is used by a user after the asset is put on shelf, once the asset is connected with the user, sustainable service of the asset needs to be ensured, the coexistence of an asset stable version and a tested version to be distributed is realized by introducing a gray level management mechanism of the cloud edge of the asset, gray level distribution is completed once the tested version passes, a plurality of stable versions exist on the asset, the availability of the asset is continuously ensured, and the asset sharing service is provided. As shown in fig. 3, the cloud end of the embodiment shares the asset with the API, and the end user applies to use the asset, and the end user applies to update the asset, so as to obtain the asset update result. Then, based on the asset update results, a new version of the asset is assigned. In addition, when the asset is updated, if the asset updating result is that the asset updating verification is passed, the cloud end distributes a new version for the asset, and the cloud end user uses the new version of the asset; and if the asset updating result is that the asset updating verification fails, the side end user applies for updating the asset again.
Specifically, in the stable version of the asset in this embodiment, the user responsible for putting the asset on shelf first issues the asset, acquires the asset ID, uses the ID all the time as the unique identifier of the asset, defines the version number V1 by default on the basis of the asset information, and is available corresponding to one stable version information on behalf of the asset on shelf, and supports maintenance of multiple version information on the basis of the asset. For the assets needing to be updated and maintained, the assets can be directly maintained, new version numbers V2 are generated after the maintenance is completed and released, at the moment, V1 and V2 are bound to the same asset, and when the asset is successfully put on the V2 version, the asset supports the use of the version V2; and an asset taken failure may return to version V1. The user who has acquired the asset can choose to continue to use the original asset, and can also switch different versions to use through a version parameterization mechanism; and multiple versions can be applied for use at one time for new users.
In summary, the cloud end of the embodiment constructs an asset comprehensive value based on the asset basic attribute, the dynamic attribute, the side user search and the evaluation activity, where the asset comprehensive value includes: asset base value, asset user value, and asset dynamic value. The side end obtains user attributes, working positions, department asset searching and evaluation records, and performs personalized recommendation to obtain a personalized recommendation directory, and meanwhile, the cloud end performs asset value recommendation to obtain an asset value recommendation directory. And finally, forming a combined recommendation directory based on the personalized recommendation directory and the asset value recommendation directory, recommending to the side user based on the combined recommendation directory, and carrying out cloud side asset sharing. According to the cloud-edge collaborative recommendation method, the cloud end adopts value recommendation and the edge end adopts personalized recommendation, so that the cloud-edge collaborative implementation of an intelligent thousand-person and thousand-face recommendation mechanism is facilitated, and the asset recommendation efficiency is improved.
Exemplary System
Based on the above embodiment, the present embodiment further provides an asset sharing and recommending system based on cloud edge collaboration, as shown in fig. 4. The system of the present embodiment includes: asset value assessment module 10, cloud edge collaborative recommendation module 20, asset sharing module 30. In this embodiment, the asset value evaluation module 10 is configured to construct an asset integrated value based on the asset basic attribute, the dynamic attribute, the side user search and the evaluation activity, where the asset integrated value includes: asset base value, asset user value, and asset dynamic value. The cloud-edge collaborative recommendation module 20 is configured to obtain user attributes, work posts, department asset search and evaluation records from an edge, perform personalized recommendation to obtain a personalized recommendation directory, and perform asset value recommendation to obtain an asset value recommendation directory. The asset sharing module 30 is configured to form a combined recommendation directory based on the personalized recommendation directory and the asset value recommendation directory, recommend to a side user based on the combined recommendation directory, and perform cloud-edge asset sharing.
The working principle of each module in the asset sharing and recommending system based on Yun Bian cooperation in this embodiment is the same as that of each step in the above method embodiment, and will not be repeated here.
Based on the above embodiment, the present application also provides a terminal device, and a schematic block diagram of the terminal device may be shown in fig. 5. The terminal device may include one or more processors 100 (only one shown in fig. 5), a memory 101, and a computer program 102 stored in the memory 101 and executable on the one or more processors 100, e.g., an asset sharing and recommendation program based on Yun Bian collaboration. The functions of the modules/units in embodiments of the cloud-based collaborative asset sharing and recommendation system may be implemented by one or more processors 100 when executing computer program 102, without limitation.
In one embodiment, the processor 100 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
In one embodiment, the memory 101 may be an internal storage unit of the electronic device, such as a hard disk or a memory of the electronic device. The memory 101 may also be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash card (flash card) or the like, which are provided on the electronic device. Further, the memory 101 may also include both an internal storage unit and an external storage device of the electronic device. The memory 101 is used to store computer programs and other programs and data required by the terminal device. The memory 101 may also be used to temporarily store data that has been output or is to be output.
It will be appreciated by persons skilled in the art that the functional block diagram shown in fig. 5 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the terminal device to which the present inventive arrangements are applied, and that a particular terminal device may include more or fewer components than shown, or may combine some of the components, or may have a different arrangement of components.
Those skilled in the art will appreciate that implementing all or part of the above-described methods may be accomplished by way of a computer program, which may be stored on a non-transitory computer readable storage medium and which, when executed, may comprise the steps of the above-described embodiments of the methods. Any reference to memory, storage, operational databases, or other media used in the various embodiments provided herein may include non-volatile and volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual operation data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (4)
1. An asset sharing and recommending method based on cloud edge collaboration is characterized by comprising the following steps:
the cloud end builds an asset integrated value based on the asset basic attributes, the dynamic attributes, the side user search and the evaluation activities, wherein the asset integrated value comprises: asset base value, asset user value, and asset dynamic value;
the side end obtains user attributes, working positions, department asset searching and evaluation records, and performs personalized recommendation to obtain a personalized recommendation catalog, and meanwhile, the cloud end performs asset value recommendation to obtain an asset value recommendation catalog;
forming a combined recommendation directory based on the personalized recommendation directory and the asset value recommendation directory, recommending to a side user based on the combined recommendation directory, and performing cloud side asset sharing;
the asset basic value comprises business property value, data scale value, data quality value, data cataloging value and data openness value;
the asset user value comprises a user search value, a search viewing value, a user collection value, a user evaluation value and a user sharing value, and the higher the asset user value is, the higher the association between the asset and the user is, and the higher the recommendation degree is;
the dynamic value of the asset comprises an asset searching frequency value, an asset praise value, an asset sharing value and an asset evaluating value; the dynamic value of the asset needs to be carried out in a buried point recording and summarizing statistics mode in the running process of the platform, and on the basis of acquiring the value of each attribute, all the values are added up to obtain the dynamic of the data asset;
the method further comprises the steps of:
by setting a positive and negative influence coefficient F, the acting force of all indexes on the data asset value evaluation result is ensured to be chemotactic; the positive and negative influence coefficient F is provided through system configuration, is mainly set by combining the validity of data, the evaluation of the data and the importance of the data, and reduces the value of high-value assets through the positive and negative influence coefficient F when the data is unavailable, the data score is low or the data is not important any more, otherwise improves the value;
the method further comprises the steps of:
based on accumulation of browsing data of users, the cloud dynamically updates the dynamic value of the asset and records operation data;
the cloud edge asset sharing method comprises the following steps:
the cloud end shares assets by using an API, and realizes coexistence of an asset stable version and a tested version to be distributed by using gray version management;
the method comprises the steps that an edge user applies for using assets, and applies for updating the assets to obtain asset updating results;
distributing new versions of the assets based on the asset update results;
the assigning new versions of the assets based on the asset update results includes:
if the asset updating result is that the asset updating verification is passed, the cloud end distributes a new version for the asset, and a cloud end user uses the new version of the asset;
if the asset updating result is that the asset updating verification fails, the side end user applies for updating the asset again;
the user in charge of the asset stable version puts up firstly releases the asset, acquires the asset ID, uses the asset ID all the time as a unique identifier of the asset, simultaneously defaults to define a version number V1, and represents that the put up asset corresponds to one stable version information and is available, in addition, the asset supports maintenance of a plurality of version information, the maintenance is directly carried out on the asset which needs to be upgraded and maintained, a new version number V2 is generated after the maintenance is completed and released, at the moment, the V1 and the V2 are both bound to the same asset, and when the asset is smoothly put up with the version V2, the asset supports the use of the version V2; and the asset is returned to the V1 version after the failure of taking the asset from the shelf; the user who has acquired the asset selects to continue to use the original asset, or through the version parameterization mechanism, switch over different version uses; and applies for multiple versions at a time for a new user.
2. An asset sharing and recommending system based on cloud edge collaboration, which is characterized by comprising:
the asset value evaluation module is used for constructing an asset comprehensive value based on the asset basic attribute, the dynamic attribute, the side user search and the evaluation activity by the cloud, wherein the asset comprehensive value comprises: asset base value, asset user value, and asset dynamic value;
the cloud side collaborative recommendation module is used for acquiring user attributes, working posts, department asset searching and evaluation records by the side end, performing personalized recommendation to obtain a personalized recommendation catalog, and performing asset value recommendation by the cloud side to obtain an asset value recommendation catalog;
the asset sharing module is used for forming a combined recommendation directory based on the personalized recommendation directory and the asset value recommendation directory, recommending to a side user based on the combined recommendation directory and carrying out cloud side asset sharing;
the asset basic value comprises business property value, data scale value, data quality value, data cataloging value and data openness value;
the asset user value comprises a user search value, a search viewing value, a user collection value, a user evaluation value and a user sharing value, and the higher the asset user value is, the higher the association between the asset and the user is, and the higher the recommendation degree is;
the dynamic value of the asset comprises an asset searching frequency value, an asset praise value, an asset sharing value and an asset evaluating value; the dynamic value of the asset needs to be carried out in a buried point recording and summarizing statistics mode in the running process of the platform, and on the basis of acquiring the value of each attribute, all the values are added up to obtain the dynamic of the data asset;
the system is also for:
by setting a positive and negative influence coefficient F, the acting force of all indexes on the data asset value evaluation result is ensured to be chemotactic; the positive and negative influence coefficient F is provided through system configuration, is mainly set by combining the validity of data, the evaluation of the data and the importance of the data, and reduces the value of high-value assets through the positive and negative influence coefficient F when the data is unavailable, the data score is low or the data is not important any more, otherwise improves the value;
the system is also for:
based on accumulation of browsing data of users, the cloud dynamically updates the dynamic value of the asset and records operation data;
the cloud edge asset sharing method comprises the following steps:
the cloud end shares assets by using an API, and realizes coexistence of an asset stable version and a tested version to be distributed by using gray version management;
the method comprises the steps that an edge user applies for using assets, and applies for updating the assets to obtain asset updating results;
distributing new versions of the assets based on the asset update results;
the assigning new versions of the assets based on the asset update results includes:
if the asset updating result is that the asset updating verification is passed, the cloud end distributes a new version for the asset, and a cloud end user uses the new version of the asset;
if the asset updating result is that the asset updating verification fails, the side end user applies for updating the asset again;
the user in charge of the asset stable version puts up firstly releases the asset, acquires the asset ID, uses the asset ID all the time as a unique identifier of the asset, simultaneously defaults to define a version number V1, and represents that the put up asset corresponds to one stable version information and is available, in addition, the asset supports maintenance of a plurality of version information, the maintenance is directly carried out on the asset which needs to be upgraded and maintained, a new version number V2 is generated after the maintenance is completed and released, at the moment, the V1 and the V2 are both bound to the same asset, and when the asset is smoothly put up with the version V2, the asset supports the use of the version V2; and the asset is returned to the V1 version after the failure of taking the asset from the shelf; the user who has acquired the asset selects to continue to use the original asset, or through the version parameterization mechanism, switch over different version uses; and applies for multiple versions at a time for a new user.
3. A terminal device comprising a memory, a processor, and a cloud-based collaborative asset sharing and recommendation program stored in the memory and operable on the processor, wherein the processor, when executing the cloud-based collaborative asset sharing and recommendation program, performs the steps of the cloud-based collaborative asset sharing and recommendation method of claim 1.
4. A computer readable storage medium, wherein the computer readable storage medium has stored thereon an asset sharing and recommending program based on cloud edge collaboration, which when executed by a processor, implements the steps of the asset sharing and recommending method based on cloud edge collaboration as claimed in claim 1.
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