CN115344786A - Cloud resource recommendation system, method, equipment and storage medium - Google Patents
Cloud resource recommendation system, method, equipment and storage medium Download PDFInfo
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Abstract
The disclosure provides a cloud resource recommendation system, method, device and storage medium, and relates to the technical field of computers, in particular to the technical field of intelligent recommendation. The specific implementation scheme is as follows: a cloud resource recommendation system comprising: a cloud platform and a server; the cloud platform acquires state information of a resource product to be recommended, acquires target attribute data corresponding to the resource product to be recommended by using a data acquisition mode corresponding to the state information, and sends the target attribute data to the server, wherein the target attribute data represent data related to the use of the resource product to be recommended; the server receives the target attribute data, analyzes the target attribute data to obtain target cloud resource data corresponding to the target attribute data, and feeds the target cloud resource data back to the cloud platform; the cloud platform receives the target cloud resource data fed back by the server, and recommends the target cloud resource package corresponding to the resource product to be recommended to the user, so that automatic recommendation of cloud resources is achieved.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and further relates to the field of intelligent recommendation technologies, and in particular, to a cloud resource recommendation system, method, device, and storage medium.
Background
With the development of cloud technology, more and more cloud resource products are popularized, and how to select a proper cloud resource product is particularly important in a use scene of the cloud resource product.
Disclosure of Invention
The disclosure provides a cloud resource recommendation system, method, device and storage medium.
According to an aspect of the present disclosure, there is provided a cloud resource recommendation system including: a cloud platform and a server;
the cloud platform is used for acquiring state information of a resource product to be recommended, acquiring target attribute data corresponding to the resource product to be recommended by using a data acquisition mode corresponding to the state information, and sending the target attribute data to the server, wherein the target attribute data represent data related to the use of the resource product to be recommended; receiving target cloud resource data fed back by the server, and recommending a target cloud resource package corresponding to the resource product to be recommended to a user;
the server is used for receiving the target attribute data, analyzing the target attribute data to obtain target cloud resource data corresponding to the target attribute data, and feeding the target cloud resource data back to the cloud platform.
According to another aspect of the present disclosure, a cloud resource recommendation method is provided, which is applied to a cloud platform of a cloud resource recommendation system, and the cloud resource recommendation system further includes: a server; the method comprises the following steps:
acquiring state information of a resource product to be recommended;
acquiring target attribute data corresponding to the resource product to be recommended by using a data acquisition mode corresponding to the state information, wherein the target attribute data represent data related to the use of the resource product to be recommended;
sending the target attribute data to the server;
and receiving target cloud resource data corresponding to the target attribute data obtained by analyzing the target attribute data by the server, and recommending a target cloud resource package corresponding to the resource product to be recommended to a user.
According to another aspect of the present disclosure, a cloud resource recommendation method is provided, which is applied to a server of a cloud resource recommendation system, where the cloud resource recommendation system further includes: a cloud platform; the method comprises the following steps:
receiving target attribute data corresponding to the resource product to be recommended, which is sent by the cloud platform; the target attribute data is: the cloud platform is obtained by using a data obtaining mode corresponding to the obtained state information of the resource product to be recommended, and the target attribute data represent data related to the use of the resource product to be recommended;
analyzing the target attribute data to obtain target cloud resource data corresponding to the target attribute data;
and feeding back the target cloud resource data to the cloud platform so that the cloud platform recommends the target cloud resource package corresponding to the resource product to be recommended to the user.
According to another aspect of the present disclosure, there is provided a cloud platform device including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,
the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the method of the first aspect of the disclosure.
According to another aspect of the present disclosure, there is provided a server apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the second aspect of the disclosure.
According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of the present disclosure.
According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the method of any one of the present disclosure.
According to the embodiment of the disclosure, automatic recommendation of cloud resources is realized.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is a schematic diagram of a cloud resource recommendation system according to the present disclosure;
FIG. 2 is a schematic illustration of a data acquisition mode selection according to the present disclosure;
FIG. 3 is a schematic diagram of recommending cloud resource packages according to the present disclosure;
FIG. 4 is a schematic illustration of data acquisition according to the present disclosure;
FIG. 5 is a schematic illustration of cloud resource recommendation in accordance with the present disclosure;
FIG. 6 is another schematic illustration of data acquisition according to the present disclosure;
FIG. 7 is another schematic illustration of cloud resource recommendation in accordance with the present disclosure;
FIG. 8 is a schematic diagram of recommending cloud resource package purchase information in accordance with the present disclosure;
FIG. 9 is a schematic diagram of a cloud resource recommendation method according to the present disclosure;
FIG. 10 is another schematic diagram of a cloud resource recommendation method according to the present disclosure;
fig. 11 is a block diagram of an electronic device for implementing a cloud resource recommendation method according to an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
With the development of cloud technology, more and more cloud resource products are popularized, and in a purchase and use scene of a cloud resource product, if a product developer needs to use cloud resources in the process of developing the product, a proper cloud resource product is selected, and great effort is often needed.
In the related art, the purchase of cloud resources and products of cloud resource application are disjointed, a cloud resource purchase platform (cloud platform for short) does not know the use condition of sold cloud resources, and then a developer cannot know which cloud resources are used for the development of which products through the cloud resource purchase platform in a product implementation scheme, so that the developer can only purchase the cloud resources according to a use guide of the cloud resources or own experience when needing to purchase and use the cloud resources. However, the existing cloud resource products are more and more, the application scenarios are different, and for developers, especially novice developers, greater learning cost is undoubtedly needed to select and purchase cloud resources by means of a use guide of the cloud resources or self experience. After the cloud resources are selected, when the cloud resources are purchased, tedious forms are usually required to be filled, most of the forms are composed of the number of partitions, issuing throughput and other acerbity fields related to product use, and therefore learning cost for purchasing the cloud resources is further increased.
The cloud resource recommendation system provided by the embodiment of the disclosure comprises: the cloud platform is used for acquiring state information of the resource product to be recommended, acquiring target attribute data corresponding to the resource product to be recommended by using a data acquisition mode corresponding to the state information, and sending the target attribute data to the server, wherein the target attribute data represent data related to the use of the resource product to be recommended; receiving target cloud resource data fed back by a server, and recommending a target cloud resource package corresponding to a resource product to be recommended to a user; and the server is used for receiving the target attribute data, analyzing the target attribute data to obtain target cloud resource data corresponding to the target attribute data, and feeding the target cloud resource data back to the cloud platform.
In the embodiment of the disclosure, the state information of the resource product to be recommended is acquired through the cloud platform, the target attribute data corresponding to the resource product to be recommended is acquired by further utilizing a data acquisition mode corresponding to the state information, the target attribute data represents data related to the use of the resource product to be recommended, that is, the data related to the use of the resource product to be recommended can be known, then, the target attribute data is analyzed through the server to obtain target cloud resource data corresponding to the target attribute data, and then, a target cloud resource package corresponding to the resource product to be recommended is recommended to a user through the cloud platform, so that the intelligent recommendation of the cloud resource package is realized, the user is helped to select appropriate cloud resources more quickly, the learning cost of the user for selecting the cloud resources is reduced, and the purchase efficiency of the cloud resources is improved.
In an embodiment of the present disclosure, a cloud resource recommendation system is provided, and referring to fig. 1, a cloud resource recommendation system 100 includes: cloud platform 110, and server 120.
In one example, the cloud platform 110 is a platform for purchasing cloud resource products, the server 120 is a device for analyzing product data, and the cloud platform 110 and the server 120 are connected through a network.
The cloud platform 110 is configured to obtain state information of a resource product to be recommended, obtain target attribute data corresponding to the resource product to be recommended by using a data obtaining manner corresponding to the state information, and send the target attribute data to the server, where the target attribute data represents data related to usage of the resource product to be recommended.
The status information of the resource product to be recommended may include a product non-online status, a product online status, and the like, where the product non-online status indicates that the current status of the resource product to be recommended is a non-online status, and the product online status indicates that the current status of the resource product to be recommended is an online status.
In an example, after the user logs in the cloud platform 110 for the first time, the cloud platform 110 may display a selection item of the product status information to the user, so that the user can select the status information of the resource product to be recommended, and after the user selects, the cloud platform 110 may obtain the status information of the resource product to be recommended. Further, the cloud platform 110 may also show detailed information of a data acquisition manner corresponding to the selected state information to the user, so as to acquire target attribute data corresponding to the resource product to be recommended.
In an example, after a user logs in the cloud platform 110 for the first time, the cloud platform 110 may present, to the user, a data obtaining manner for a resource product to be recommended, as shown in fig. 2, for the user to select, for example, the user may select to obtain an SDK (Software Development Kit), or select estimate data, and the user may be any user that needs to purchase cloud resources for the resource product to be recommended, for example, a developer of the resource product to be recommended, or the like. The user selects and obtains the SDK to represent that the state information of the resource product to be recommended is that the product is on-line, and the user selects the estimated data to represent that the state information of the resource product to be recommended is that the product is not on-line.
Under the condition that the user selects the data acquisition mode of the resource product to be recommended, the cloud platform 110 may know the state information of the resource product to be recommended, and may further display detailed information of the data acquisition mode corresponding to the state information to the user, for example, display, to the user, a specific data acquisition mode corresponding to an online state of the product for acquiring an SDK, or display, to the user, a specific data acquisition mode corresponding to estimated data corresponding to an offline state of the product for acquiring target attribute data corresponding to the resource product to be recommended. In one example, the target attribute data may be the user quantity, the visit quantity, the message quantity, and the like of the resource product to be recommended, and represents data related to the use of the resource product to be recommended.
And the server 120 is configured to receive the target attribute data, analyze the target attribute data to obtain target cloud resource data corresponding to the target attribute data, and feed the target cloud resource data back to the cloud platform.
After obtaining the target attribute data corresponding to the resource product to be recommended, the cloud platform 110 sends the target attribute data to the server 120 through a network connected to the server 120, and the server 120 receives the target attribute data and may analyze the target attribute data through a data analysis method, a data analysis model, or the like, to obtain the target cloud resource data corresponding to the target attribute data. The target cloud resource data is cloud resource data required by the resource product to be recommended, and then the server 120 feeds back the obtained target cloud resource data to the cloud platform through the network.
The cloud platform 110 is further configured to receive target cloud resource data fed back by the server, and recommend a target cloud resource package corresponding to a resource product to be recommended to the user.
Illustratively, as shown in fig. 3, the cloud platform 110 recommends, to the user, a target cloud resource package corresponding to the resource product XXX to be recommended, where the target cloud resource package includes a resource package 1-economic edition and a resource package 2-luxury edition, where the resource package 1-economic edition includes: EKS (Elastic kubernets Service) cloud products, datahhub (aristoloc) and monitoring (Prometheus) services, etc., and the resource package 2-luxury version includes: EKS cloud products, datahhub, monitoring services, and MNS (Message Service), among others.
In the embodiment of the disclosure, the state information of the resource product to be recommended is acquired through the cloud platform, the target attribute data corresponding to the resource product to be recommended is further acquired by using a data acquisition mode corresponding to the state information, the target attribute data represents data related to the use of the resource product to be recommended, that is, the data related to the use of the resource product to be recommended can be known, then, the target attribute data is analyzed through the server to obtain target cloud resource data corresponding to the target attribute data, then, a target cloud resource package corresponding to the resource product to be recommended is recommended to a user through the cloud platform, so that the intelligent recommendation of the cloud resource package is realized, the user is helped to select appropriate cloud resources more quickly, the learning cost of selecting cloud resources by the user is reduced, and the purchase efficiency of the cloud resources is improved.
In a possible implementation manner, in a case that the state information of the resource product to be recommended is a product non-online state, the cloud platform 110 includes: a data collection module and a cloud resource recommendation module,
the data collection module is configured to receive product current status data and pre-estimated data of preset attributes, which are input by a user for a resource product to be recommended, obtain target attribute data corresponding to the resource product to be recommended, and send the target attribute data to the server 120.
And the cloud resource recommendation module is configured to receive the target cloud resource data fed back by the server 120, package the target cloud resource data into a target cloud resource package, and recommend the target cloud resource package corresponding to the resource product to be recommended to the user.
In an example, when the user selects the estimated data in fig. 2, that is, the state information of the resource product to be recommended is the product offline state, the cloud platform 110 displays a data acquisition page shown in fig. 4 to the user to collect data of the resource product to be recommended. Through the data obtaining page shown in fig. 4, the user inputs the product name of the resource product to be recommended, selects the product type of the resource product to be recommended, such as APP (Application), applet, mini game, web page, or privatized deployment, and inputs or selects information such as the operation region, the estimated user number (i.e., user amount), the estimated message number, and the estimated data amount (i.e., access amount) of the resource product to be recommended, and after clicking the submission function option, successfully submits the estimated attribute data of the resource product to be recommended to the cloud platform 110, where the sum of fig. 4 indicates that the item must be filled, and the others are not limited.
The data collection module of the cloud platform 110 receives the product current data (for example, the product name of the resource product to be recommended, the product type of the selected resource product to be recommended, and the operation region) input by the user for the resource product to be recommended through the data acquisition page shown in fig. 4, and the estimated data of the preset attribute (for example, the user inputs or selects the user amount, the estimated message amount, and the estimated data amount of the resource product to be recommended through the data acquisition page shown in fig. 4), obtains target attribute data corresponding to the resource product to be recommended, and sends the obtained target attribute data to the server 120. The server 120 analyzes the target attribute data and returns target cloud resource data corresponding to the target attribute data.
The cloud resource recommendation module of the cloud platform 110 receives the target cloud resource data fed back by the server 120, packages the target cloud resource data into a target cloud resource package, and recommends the target cloud resource package corresponding to the resource product to be recommended to the user.
Illustratively, as shown in fig. 5, when a developer develops an offline product, that is, the state information of a resource product to be recommended is in an offline state of the product, the developer interacts with the cloud platform 110, specifically, the developer interacts with the cloud platform 110 in a manner of filling a data form on the cloud platform 110, and the cloud platform 110 receives product current state data and estimated data of preset attributes, which are input by the developer for the resource product to be recommended, so as to obtain target attribute data corresponding to the resource product to be recommended, as shown in fig. 2 and 4. The cloud platform 110 interacts with the server 120, specifically, the cloud platform 110 sends the obtained target attribute data corresponding to the resource product to be recommended to the server 120, and the server 120 performs big data operation analysis on the target attribute data to obtain target cloud resource data corresponding to the target attribute data, and feeds the target cloud resource data back to the cloud platform 110. The cloud platform 110 further automatically recommends the target cloud resource package corresponding to the resource product to be recommended to the developer.
In the embodiment of the disclosure, for a resource product to be recommended in a non-online state of a product, current product data and estimated data of preset attributes input by a user for the resource product to be recommended are received through a data collection module of a cloud platform to obtain target attribute data corresponding to the resource product to be recommended, namely, data related to the use of the resource product to be recommended in a non-online state can be estimated, then, the target attribute data are analyzed through a server to obtain target cloud resource data corresponding to the target attribute data, the target cloud resource data are packaged into a target cloud resource package through a cloud resource recommendation module of the cloud platform, the target cloud resource package corresponding to the resource product to be recommended is recommended to the user, intelligent recommendation of the cloud resource package is achieved, the user is helped to select appropriate cloud resources more quickly, the learning cost of selecting cloud resources by the user is reduced, and the purchase efficiency of the cloud resources is improved.
In a possible implementation manner, in a case that the state information of the resource product to be recommended is a state that the product is online, the cloud platform 110 includes: the system comprises an SDK module, a data collection module and a cloud resource recommendation module.
The SDK module is used for receiving and responding to an instruction of a user for acquiring the SDK code of the resource product to be recommended, and acquiring the SDK code corresponding to the resource product to be recommended.
And the data collection module is configured to obtain target attribute data corresponding to the resource product to be recommended from the SDK code obtained by the SDK module, and send the target attribute data to the server 120.
And the cloud resource recommendation module is configured to receive the target cloud resource data fed back by the server 120, package the target cloud resource data into a target cloud resource package, and recommend the target cloud resource package corresponding to the resource product to be recommended to the user.
In an example, when the user selects the SDK acquisition shown in fig. 2, that is, the state information of the resource product to be recommended is the product online state, the cloud platform 110 displays a data acquisition page shown in fig. 6 to the user to collect data of the resource product to be recommended. The cloud platform 110 provides an SDK interface, so that the cloud platform 110 can access an online product through the SDK interface.
The user inputs the product name of the resource product to be recommended, selects the product type of the resource product to be recommended, and selects the SDK download function option through the data acquisition page shown in fig. 6, after selecting the SDK download function option, the cloud platform 110 acquires an instruction of acquiring the SDK code for the resource product to be recommended from the user, and further the cloud platform 110 accesses the online resource product to be recommended through the SDK interface to acquire the SDK code of the resource product to be recommended. Where the plus one of fig. 6 indicates that items must be selected, others are not limiting.
Specifically, the SDK module of the cloud platform 110 receives and responds to the instruction of obtaining the SDK code for the resource product to be recommended by the user (that is, the user inputs the product name of the resource product to be recommended, selects the product type of the resource product to be recommended, and selects the SDK download function option through the data obtaining page shown in fig. 6), and obtains the SDK code corresponding to the resource product to be recommended through the SDK interface.
Further, the data collection module reads target attribute data corresponding to the resource product to be recommended and including the user amount, the access amount, the message amount, and the like of the resource product to be recommended from the SDK code acquired by the SDK module, and sends the target attribute data to the server 120. The server 120 analyzes the target attribute data and returns target cloud resource data corresponding to the target attribute data.
The cloud resource recommendation module of the cloud platform 110 receives the target cloud resource data fed back by the server 120, packages the target cloud resource data into a target cloud resource package, and recommends the target cloud resource package corresponding to the resource product to be recommended to the user.
Exemplarily, as shown in fig. 7, when a developer develops an online product, that is, the state information of a resource product to be recommended is the product online state, the cloud platform 110 can access the online product developed by the developer through an SDK interface, the developer interacts with the cloud platform 110, and with reference to fig. 2 and fig. 6, an SDK module of the cloud platform 110 receives and responds to an instruction of the developer for acquiring an SDK code of the resource product to be recommended, and acquires the SDK code corresponding to the resource product to be recommended through the SDK interface, and a data collection module of the cloud platform 110 reads target attribute data corresponding to the resource product to be recommended from the SDK code. The cloud platform 110 interacts with the server 120, a data collection module of the cloud platform 110 sends the target attribute data to the server 120, and the server 120 performs big data operation analysis on the target attribute data to obtain target cloud resource data corresponding to the target attribute data and feeds the target cloud resource data back to the cloud platform 110. The cloud platform 110 further automatically recommends the target cloud resource package corresponding to the resource product to be recommended to the developer.
In the embodiment of the disclosure, for a resource product to be recommended in an online state of a product, an SDK code corresponding to the resource product to be recommended is acquired through an SDK interface of a cloud platform, a data collection module reads target attribute data corresponding to the resource product to be recommended from the acquired SDK code, that is, data related to usage of the resource product to be recommended that is online can be known, then, a server is used for analyzing the target attribute data to obtain target cloud resource data corresponding to the target attribute data, the target cloud resource data is packaged into a target cloud resource package through a cloud resource recommendation module of the cloud platform, the target cloud resource package corresponding to the resource product to be recommended is recommended to a user, intelligent recommendation of the cloud resource package is achieved, the user is helped to select appropriate cloud resources more quickly, learning cost of the user for selecting cloud resources is reduced, and purchase efficiency of the cloud resources is improved.
In one example, when the state information of the resource product to be recommended is the online state of the product, the cloud platform 110 automatically recommends the target cloud resource package corresponding to the resource product to be recommended to the developer, which is helpful for the user to intelligently complete operations such as product expansion, product configuration promotion or reduction, and does not need to actively operate on the cloud platform by the user.
In a possible implementation, the server 120 includes: the data receiving module, the data storage module and the data operation module;
the data receiving module is used for receiving the target attribute data and respectively sending the target attribute data to the data storage module and the data operation module;
the data storage module is used for storing the target attribute data;
and the data operation module is used for analyzing the target attribute data to obtain target cloud resource data corresponding to the target attribute data and feeding the target cloud resource data back to the cloud platform.
In the embodiment of the disclosure, the data receiving module of the server can receive the target attribute data and distribute and process the target attribute data, the data storage module can store the target attribute data so as to further analyze and process the target attribute data subsequently, and the data operation module can analyze the target attribute data to obtain the target cloud resource data corresponding to the target attribute data, so that the big data analysis of the resource product to be recommended is realized, and the cloud platform can recommend reasonable cloud resource data required to be used by the resource product to be recommended to the user.
In a possible implementation manner, the data operation module is specifically configured to: inputting the target attribute data into a pre-trained resource prediction model to obtain target cloud resource data required by the target attribute data corresponding to the resource product to be recommended; the pre-trained resource prediction model is obtained by training according to the attribute data of the sample product and the cloud resource data required by the sample product.
The data operation module of the server 120 may store a pre-trained resource prediction model, and further, when analyzing the target attribute data, the target attribute data may be directly input into the pre-trained resource prediction model to perform prediction of cloud resources, so as to obtain target cloud resource data required by the target attribute data corresponding to the resource product to be recommended.
In the embodiment of the disclosure, the resource prediction model is trained in advance according to the attribute data of the sample product and the cloud resource data required by the sample product, so that when the target attribute data of the resource product to be recommended is analyzed, the pre-trained resource prediction model is directly utilized to predict the target cloud resource data required by the resource product to be recommended, and accurate and intelligent recommendation of the target cloud resource data required by the resource product to be recommended is realized.
In a possible implementation manner, the data operation module may be further configured to: and updating the training data set of the resource prediction model and updating the training resource prediction model by using the target attribute data of the resource product to be recommended stored in the data storage module and the cloud resource data corresponding to the cloud resource packet selected by the user.
In an example, after the cloud platform 110 recommends the target cloud resource package corresponding to the resource product to be recommended to the user, the cloud platform 110 may further store cloud resource data corresponding to the cloud resource package finally selected by the user, and forward the cloud resource data to the server 120. And then the data receiving module of the server 120 sends the received data to the data storage module and the data operation module respectively, the data storage module stores the received cloud resource data corresponding to the cloud resource package selected by the user, and the data operation module updates the training data set of the resource prediction model and updates the training resource prediction model by using the target attribute data of the resource product to be recommended stored in the data storage module and the cloud resource data corresponding to the cloud resource package selected by the user.
In the embodiment of the disclosure, the training data set of the resource prediction model is updated by using the target attribute data of the resource product to be recommended stored in the data storage module and the cloud resource data corresponding to the cloud resource packet selected by the user, and the training resource prediction model is updated, so that the resource prediction model can more accurately predict the target cloud resource data required by the resource product to be recommended.
In a possible implementation, the cloud platform 110 is further configured to: and recommending the purchase information of the target cloud resource package to the user.
Illustratively, when the user selects the resource package 1-economic edition shown in fig. 3, the cloud platform 110 may show the user the related functions and purchase information of each cloud product included in the resource package 1-economic edition shown in fig. 8, and the user may select each cloud product in the resource package 1, may also perform operations such as replacement or deletion on the selected cloud product (e.g., the EKS cloud product shown by the solid line frame in fig. 8), and may select to purchase all or part of the cloud products, thereby implementing convenient purchase of the cloud resource package by the user.
In the embodiment of the disclosure, the cloud platform recommends the purchase information of the target cloud resource package to the user, so that the user can purchase the cloud resource package conveniently, and the efficiency of cloud resource purchase is improved.
The embodiment of the present disclosure further provides a cloud resource recommendation method, which is applied to a cloud platform of a cloud resource recommendation system, where the cloud resource recommendation system further includes: a server; referring to fig. 9, the method includes:
s901, acquiring state information of a resource product to be recommended;
s902, acquiring target attribute data corresponding to the resource product to be recommended by using a data acquisition mode corresponding to the state information, wherein the target attribute data represents data related to the use of the resource product to be recommended;
s903, sending the target attribute data to a server;
and S904, receiving target cloud resource data corresponding to the target attribute data obtained by analyzing the target attribute data by the server, and recommending a target cloud resource package corresponding to the resource product to be recommended to the user.
In the embodiment of the disclosure, the state information of the resource product to be recommended is acquired through the cloud platform, the target attribute data corresponding to the resource product to be recommended is further acquired by using a data acquisition mode corresponding to the state information, the target attribute data represents data related to the use of the resource product to be recommended, that is, the data related to the use of the resource product to be recommended can be known, then, the target attribute data is analyzed through the server to obtain target cloud resource data corresponding to the target attribute data, then, a target cloud resource package corresponding to the resource product to be recommended is recommended to a user through the cloud platform, so that the intelligent recommendation of the cloud resource package is realized, the user is helped to select appropriate cloud resources more quickly, the learning cost of selecting cloud resources by the user is reduced, and the purchase efficiency of the cloud resources is improved.
In a possible implementation manner, in a case that the status information is a product offline status, the cloud platform includes: the system comprises a data collection module and a cloud resource recommendation module;
the above obtaining target attribute data corresponding to the resource product to be recommended by using the data obtaining mode corresponding to the state information includes:
the data collection module receives current product data and estimated data of preset attributes input by a user for the resource product to be recommended, and target attribute data corresponding to the resource product to be recommended are obtained;
the receiving of the target cloud resource data fed back by the server and the recommending of the target cloud resource package corresponding to the resource product to be recommended to the user include:
the cloud resource recommending module receives target cloud resource data fed back by the server, packs the target cloud resource data into a target cloud resource package, and recommends the target cloud resource package corresponding to the resource product to be recommended to the user.
In a possible implementation manner, in a case that the status information is a product online status, the cloud platform includes: the system comprises a Software Development Kit (SDK) module, a data collection module and a cloud resource recommendation module;
the above obtaining target attribute data corresponding to the resource product to be recommended by using the data obtaining mode corresponding to the state information includes:
the SDK module receives and responds to an instruction of a user for acquiring an SDK code of a resource product to be recommended, and the SDK code corresponding to the resource product to be recommended is acquired;
the data collection module acquires target attribute data corresponding to the resource product to be recommended from the SDK code acquired by the SDK module;
the receiving of the target cloud resource data fed back by the server and the recommending of the target cloud resource package corresponding to the resource product to be recommended to the user include:
the cloud resource recommending module receives target cloud resource data fed back by the server, packs the target cloud resource data into a target cloud resource package, and recommends the target cloud resource package corresponding to the resource product to be recommended to the user.
In a possible embodiment, the method further includes:
and recommending the purchase information of the target cloud resource package to the user.
The embodiment of the present disclosure further provides a cloud resource recommendation method, which is applied to a server of a cloud resource recommendation system, where the cloud resource recommendation system further includes: a cloud platform; referring to fig. 10, the method includes:
s1001, receiving target attribute data corresponding to a resource product to be recommended, which are sent by a cloud platform; the target attribute data is: the cloud platform is obtained by using a data obtaining mode corresponding to the obtained state information of the resource product to be recommended, and the target attribute data represent data related to the use of the resource product to be recommended;
s1002, analyzing the target attribute data to obtain target cloud resource data corresponding to the target attribute data;
s1003, feeding the target cloud resource data back to the cloud platform, so that the cloud platform recommends the target cloud resource package corresponding to the resource product to be recommended to the user.
According to the cloud resource package intelligent recommendation method and device, the target attribute data corresponding to the resource product to be recommended is obtained through a data obtaining mode corresponding to the obtained state information of the resource product to be recommended through the cloud platform, the target attribute data represent data relevant to the resource product to be recommended when the resource product to be recommended is used, namely the data relevant to the resource product to be recommended when the resource product to be recommended is used can be known, then the target attribute data are analyzed through the server to obtain target cloud resource data corresponding to the target attribute data, then a target cloud resource package corresponding to the resource product to be recommended is recommended to a user through the cloud platform, intelligent recommendation of the cloud resource package is achieved, the user is helped to select appropriate cloud resources more quickly, the learning cost of the user for selecting the cloud resources is reduced, and meanwhile the purchase efficiency of the cloud resources is improved.
In one possible embodiment, the server includes: the device comprises a data receiving module, a data storage module and a data operation module;
the receiving of the target attribute data corresponding to the resource product to be recommended sent by the cloud platform includes:
the data receiving module receives target attribute data corresponding to the resource product to be recommended, which are sent by the cloud platform, and sends the target attribute data to the data storage module and the data operation module respectively;
the analyzing the target attribute data to obtain target cloud resource data corresponding to the target attribute data includes:
the data operation module inputs the target attribute data into a pre-trained resource prediction model to obtain target cloud resource data required by the target attribute data corresponding to the resource product to be recommended; the pre-trained resource prediction model is obtained by training according to the attribute data of the sample product and the cloud resource data required by the sample product;
the method further comprises the following steps:
and the data storage module stores the target attribute data.
In a possible embodiment, the method further includes:
and the data operation module updates the training data set of the resource prediction model and updates the training resource prediction model by using the target attribute data of the resource product to be recommended stored in the data storage module and the cloud resource data corresponding to the cloud resource packet selected by the user.
In the technical scheme of the disclosure, the collection, storage, use, processing, transmission, provision, disclosure and other processing of the personal information of the related user are all in accordance with the regulations of related laws and regulations and do not violate the good customs of the public order. It should be noted that the head model in this embodiment is not a head model for a specific user, and cannot reflect personal information of a specific user. It should be noted that the two-dimensional face image in the present embodiment is from a public data set.
According to an embodiment of the present disclosure, the present disclosure also provides a cloud platform device, a server device, a readable storage medium, and a computer program product.
Wherein, cloud platform equipment includes:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a cloud resource recommendation method of the present disclosure applied to a cloud platform of a cloud resource recommendation system.
A server device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a cloud resource recommendation method of the present disclosure applied to a server of a cloud resource recommendation system.
A non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method of any one of the present disclosure.
A computer program product comprising a computer program which, when executed by a processor, implements the method of any of the present disclosure.
The cloud platform devices and the server devices described above may both be referred to as electronic devices, and their structures may be the same, fig. 11 shows a schematic block diagram of an example electronic device 1100 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 11, the device 1100 comprises a computing unit 1101, which may perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 1102 or a computer program loaded from a storage unit 1108 into a Random Access Memory (RAM) 1103. In the RAM 1103, various programs and data necessary for the operation of the device 1100 may also be stored. The calculation unit 1101, the ROM 1102, and the RAM 1103 are connected to each other by a bus 1104. An input/output (I/O) interface 1105 is also connected to bus 1104.
A number of components in device 1100 connect to I/O interface 1105, including: an input unit 1106 such as a keyboard, mouse, or the like; an output unit 1107 such as various types of displays, speakers, and the like; a storage unit 1108 such as a magnetic disk, optical disk, or the like; and a communication unit 1109 such as a network card, a modem, a wireless communication transceiver, and the like. The communication unit 1109 allows the device 1100 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.
The computing unit 1101 can be a variety of general purpose and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 1101 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The computing unit 1101 performs the various methods and processes described above, such as a cloud resource recommendation method. For example, in some embodiments, the cloud resource recommendation method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 1108. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 1100 via ROM 1102 and/or communications unit 1109. When the computer program is loaded into RAM 1103 and executed by computing unit 1101, one or more steps of the cloud resource recommendation method described above may be performed. Alternatively, in other embodiments, the computing unit 1101 may be configured to perform the cloud resource recommendation method by any other suitable means (e.g., by way of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program code, when executed by the processor or controller, causes the functions/acts specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
Claims (18)
1. A cloud resource recommendation system comprising: a cloud platform and a server;
the cloud platform is used for acquiring state information of a resource product to be recommended, acquiring target attribute data corresponding to the resource product to be recommended by using a data acquisition mode corresponding to the state information, and sending the target attribute data to the server, wherein the target attribute data represent data related to the use of the resource product to be recommended; receiving target cloud resource data fed back by the server, and recommending a target cloud resource package corresponding to the resource product to be recommended to a user;
the server is used for receiving the target attribute data, analyzing the target attribute data to obtain target cloud resource data corresponding to the target attribute data, and feeding the target cloud resource data back to the cloud platform.
2. The system of claim 1, wherein in the case that the status information is a product not-on-line status, the cloud platform comprises: a data collection module and a cloud resource recommendation module,
the data collection module is used for receiving the current product data and the estimated data of the preset attribute input by the user aiming at the resource product to be recommended, acquiring target attribute data corresponding to the resource product to be recommended and sending the target attribute data to the server;
the cloud resource recommendation module is used for receiving the target cloud resource data fed back by the server, packaging the target cloud resource data into a target cloud resource package, and recommending the target cloud resource package corresponding to the resource product to be recommended to a user.
3. The system of claim 1, wherein in case the status information is that the product is on-line, the cloud platform comprises: a Software Development Kit (SDK) module, a data collection module and a cloud resource recommendation module,
the SDK module is used for receiving and responding to an SDK code acquisition instruction of a user for the resource product to be recommended, and acquiring an SDK code corresponding to the resource product to be recommended;
the data collection module is used for acquiring target attribute data corresponding to the resource product to be recommended from the SDK code acquired by the SDK module and sending the target attribute data to the server;
the cloud resource recommendation module is used for receiving the target cloud resource data fed back by the server, packaging the target cloud resource data into a target cloud resource package, and recommending the target cloud resource package corresponding to the resource product to be recommended to a user.
4. The system of any of claims 1-3, wherein the server comprises: the data receiving module, the data storage module and the data operation module;
the data receiving module is used for receiving the target attribute data and respectively sending the target attribute data to the data storage module and the data operation module;
the data storage module is used for storing the target attribute data;
the data operation module is used for analyzing the target attribute data to obtain target cloud resource data corresponding to the target attribute data and feeding the target cloud resource data back to the cloud platform.
5. The system of claim 4, wherein the data operation module is specifically configured to:
inputting the target attribute data into a pre-trained resource prediction model to obtain target cloud resource data required by the target attribute data corresponding to a resource product to be recommended; the pre-trained resource prediction model is obtained by training according to the attribute data of the sample product and the cloud resource data required by the sample product.
6. The system of claim 5, the data operation module further to:
and updating a training data set of the resource prediction model and updating and training the resource prediction model by using the target attribute data of the resource product to be recommended stored in the data storage module and the cloud resource data corresponding to the cloud resource packet selected by the user.
7. The system of claim 1, the cloud platform further to:
recommending the purchase information of the target cloud resource package to the user.
8. A cloud resource recommendation method is applied to a cloud platform of a cloud resource recommendation system, and the cloud resource recommendation system further comprises: a server; the method comprises the following steps:
acquiring state information of a resource product to be recommended;
acquiring target attribute data corresponding to the resource product to be recommended by using a data acquisition mode corresponding to the state information, wherein the target attribute data represent data related to the use of the resource product to be recommended;
sending the target attribute data to the server;
and receiving target cloud resource data corresponding to the target attribute data obtained by analyzing the target attribute data by the server, and recommending a target cloud resource package corresponding to the resource product to be recommended to a user.
9. The method of claim 8, wherein in the case that the status information is a product not-on-line status, the cloud platform comprises: a data collection module and a cloud resource recommendation module,
the obtaining of the target attribute data corresponding to the resource product to be recommended by using the data obtaining mode corresponding to the state information includes:
the data collection module receives the current product data and the estimated data of preset attributes, which are input by a user aiming at the resource product to be recommended, and acquires target attribute data corresponding to the resource product to be recommended;
the receiving of the target cloud resource data fed back by the server and the recommending of the target cloud resource package corresponding to the resource product to be recommended to the user include:
and the cloud resource recommending module receives target cloud resource data fed back by the server, packages the target cloud resource data into a target cloud resource package, and recommends the target cloud resource package corresponding to the resource product to be recommended to a user.
10. The method of claim 8, wherein in the case that the status information is that the product is online, the cloud platform comprises: a Software Development Kit (SDK) module, a data collection module and a cloud resource recommendation module,
the obtaining of the target attribute data corresponding to the resource product to be recommended by using the data obtaining mode corresponding to the state information includes:
the SDK module receives and responds to an SDK code acquisition instruction of a user for the resource product to be recommended, and an SDK code corresponding to the resource product to be recommended is acquired;
the data collection module acquires target attribute data corresponding to the resource product to be recommended from the SDK code acquired by the SDK module;
the receiving of the target cloud resource data fed back by the server and the recommending of the target cloud resource package corresponding to the resource product to be recommended to the user include:
and the cloud resource recommending module receives target cloud resource data fed back by the server, packages the target cloud resource data into a target cloud resource package, and recommends the target cloud resource package corresponding to the resource product to be recommended to a user.
11. The method of claim 8, further comprising:
recommending the purchase information of the target cloud resource package to the user.
12. A cloud resource recommendation method is applied to a server of a cloud resource recommendation system, and the cloud resource recommendation system further comprises: a cloud platform; the method comprises the following steps:
receiving target attribute data corresponding to the resource product to be recommended, which is sent by the cloud platform; the target attribute data is: the cloud platform is obtained by using a data obtaining mode corresponding to the obtained state information of the resource product to be recommended, and the target attribute data represent data related to the use of the resource product to be recommended;
analyzing the target attribute data to obtain target cloud resource data corresponding to the target attribute data;
and feeding back the target cloud resource data to the cloud platform so that the cloud platform recommends the target cloud resource package corresponding to the resource product to be recommended to the user.
13. The method of claim 12, wherein the server comprises: the data receiving module, the data storage module and the data operation module;
the receiving of the target attribute data corresponding to the resource product to be recommended, which is sent by the cloud platform, includes:
the data receiving module receives target attribute data corresponding to the resource product to be recommended, which are sent by the cloud platform, and sends the target attribute data to the data storage module and the data operation module respectively;
the analyzing the target attribute data to obtain target cloud resource data corresponding to the target attribute data includes:
the data operation module inputs the target attribute data into a pre-trained resource prediction model to obtain target cloud resource data required by the target attribute data corresponding to the resource product to be recommended; the pre-trained resource prediction model is obtained by training according to the attribute data of the sample product and the cloud resource data required by the sample product;
the method further comprises the following steps:
and the data storage module stores the target attribute data.
14. The method of claim 13, further comprising:
and the data operation module updates a training data set of the resource prediction model and updates and trains the resource prediction model by using the target attribute data of the resource product to be recommended, which is stored in the data storage module, and the cloud resource data corresponding to the cloud resource packet selected by the user.
15. A cloud platform device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 8-11.
16. A server device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 12-14.
17. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 8-14.
18. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 8-14.
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| CN116600020A (en) * | 2023-07-13 | 2023-08-15 | 支付宝(杭州)信息技术有限公司 | Protocol generation method, terminal cloud collaborative recommendation method and device |
| CN116600020B (en) * | 2023-07-13 | 2023-10-10 | 支付宝(杭州)信息技术有限公司 | Protocol generation method, terminal cloud collaborative recommendation method and device |
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