Cloud user management method and related device

Abstract

The present application provides a cloud user management method and related devices, belonging to the field of cloud technologies. The method includes: obtaining user verification information for the cloud user's current access to the cloud, where the user verification information includes at least one of the cloud user's historical cloud usage records, the user type of the cloud user, and the target cloud usage data for the cloud user's current access to the cloud; obtaining evaluation data from a data source that matches the user verification information, where the data source includes the historical cloud usage data of the cloud user, a cloud user of the same user type as the cloud user, or a cloud user meeting a predetermined standard; generating cloud usage cost prediction data for the cloud user's current access to the cloud using the evaluation data, so as to predict the cloud usage cost of the cloud user based on the cloud usage cost prediction data. The embodiments of the present application achieve precise and personalized management of cloud users.

Description

Technical Field

[0001] This application relates to the field of cloud technology. Specifically, it relates to a cloud user management method and related devices. Background Art

[0002] Cloud technology refers to a hosted cloud service technology that unifies a series of resources such as hardware, software, and networks within a wide area network or a local area network to achieve data computing, storage, processing, and sharing. The Content Delivery Network (CDN) is an important part of cloud services, providing fast, stable, intelligent, and reliable global content distribution acceleration services to support the distribution of diverse content such as pictures, audio, and video. Currently, more and more cloud users choose to use cloud services, and precise and personalized management of cloud users is very important.

[0003] In the management of cloud users, it is usually necessary to determine cloud usage cost measurement data when a cloud user goes online, and based on the cloud usage cost measurement data, determine the cloud usage cost of the cloud user. Currently, some fixed parameter combinations are usually obtained when a cloud user goes online as cloud usage cost measurement data, resulting in basically the same cloud usage cost measurement data for different users, making it difficult to achieve precise and personalized management of cloud users, and further leading to unreasonable cloud usage cost measurement results.

[0004] It should be noted that the information disclosed in the above background art section is only used to enhance the understanding of the background of the present disclosure, and thus may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0005] The purpose of the embodiments of this application is to provide a cloud user management method and device, which can achieve precise and personalized management of cloud users.

[0006] According to an embodiment of this application, a cloud user management method includes: obtaining user verification information for the cloud user's current online access, where the user verification information includes at least one of the cloud user's historical cloud usage records, the cloud user's user type, and the target cloud usage data for the cloud user's current online access; obtaining evaluation data from a data source that matches the user verification information, where the data source includes the historical cloud usage data of the cloud user, cloud users of the same user type as the cloud user, and cloud users of a predetermined standard; using the evaluation data to generate cloud usage cost prediction data for the cloud user's current online access, so as to predict the cloud user's cloud usage cost based on the cloud usage cost prediction data.

[0007] According to an embodiment of the present application, a cloud user management device includes: a first acquisition module configured to acquire user authentication information for the cloud user's current cloud access, where the user authentication information includes at least one of the cloud user's historical cloud usage records, the user type of the cloud user, and the target cloud usage data for the cloud user's current cloud access; a second acquisition module configured to acquire evaluation data from a data source that matches the user authentication information, where the data source includes the historical cloud usage data of the cloud user, cloud users of the same user type as the cloud user, and cloud users of a predetermined standard; and a prediction module configured to generate predicted cloud usage cost data for the cloud user's current cloud access using the evaluation data, so as to predict the cloud usage cost of the cloud user based on the predicted cloud usage cost data.

[0008] In some embodiments of the present application, the second acquisition module includes: a source determination unit configured to determine the data source that matches the user authentication information; a first quantity acquisition unit configured to acquire a first quantity of cloud usage data samples required to determine the predicted cloud usage cost data for the cloud user's current cloud access from the data source; and an evaluation data acquisition unit configured to acquire the first quantity of cloud usage data samples from the data source as the evaluation data.

[0009] In some embodiments of the present application, the evaluation data acquisition unit includes: a second quantity determination subunit configured to determine a second quantity of the cloud usage data samples included in the data source; a target quantity determination subunit configured to determine the smaller value of the first quantity and the second quantity, and use the smaller value as the target quantity of the cloud usage data samples required to determine the predicted cloud usage cost data for the cloud user's current cloud access; and an evaluation data acquisition subunit configured to acquire the target quantity of cloud usage data samples from the data source as the evaluation data.

[0010] In some embodiments of the present application, the device further includes a preset configuration module, where the first quantity is derived from preset configuration parameters in the preset configuration module. The preset configuration module includes: a difference calculation unit configured to calculate the difference between the first quantity and the target quantity; and an update unit configured to correct the first quantity according to the magnitude of the difference to obtain a third quantity, and use the third quantity to update the first quantity in the preset configuration parameters.

[0011] In some embodiments of the present application, the source determination unit includes: a first source determination subunit, configured to determine the historical cloud usage data of the cloud user as the data source when the user authentication information includes the usage records of the cloud user's historical cloud usage; a second source determination subunit, configured to determine the historical cloud usage data of cloud users of the same user type as the cloud user as the data source when the user authentication information includes the user type of the cloud user; and a third source determination subunit, configured to determine the historical cloud usage data of cloud users meeting a predetermined standard as the data source when the user authentication information only includes the target cloud usage data of the cloud user's current cloud access.

[0012] In some embodiments of the present application, the data source is the historical cloud usage data of the cloud user; the evaluation data acquisition unit includes: an effective data determination subunit, configured to determine whether there is effective data in the historical cloud usage data of the cloud user, where the effective data is a cloud usage data sample that meets the target evaluation requirements; an effective data acquisition subunit, configured to, when there is effective data in the historical cloud usage data of the cloud user, obtain a fourth quantity of cloud usage data samples from the effective data as the evaluation data; and a non-effective data acquisition subunit, configured to, when there is no effective data in the historical cloud usage data of the cloud user, obtain a fifth quantity of cloud usage data samples from the historical cloud usage data of the cloud user as the evaluation data.

[0013] In some embodiments of the present application, the prediction module includes: a first prediction unit, configured to generate cloud usage cost prediction data for the cloud user's current cloud access by comparing the target cloud usage data with the evaluation data when the user authentication information includes the target cloud usage data of the cloud user's current cloud access; and a second prediction unit, configured to normalize the evaluation data to obtain cloud usage cost prediction data for the cloud user's current cloud access when the user authentication information does not include the target cloud usage data of the cloud user's current cloud access.

[0014] In some embodiments of the present application, the device further includes: a feedback module, configured to send the cloud usage cost to an artificial management platform when the cloud usage cost meets a predetermined data condition, so that the artificial management platform verifies whether the cloud usage cost is correct.

[0015] According to another embodiment of the present application, an electronic device may include: a memory storing computer-readable instructions; and a processor reading the computer-readable instructions stored in the memory to execute the method as described above.

[0016] According to another embodiment of the present application, a computer program medium stores computer-readable instructions thereon. When the computer-readable instructions are executed by a processor of a computer, the computer is caused to execute the method as described above.

[0017] According to another embodiment of the present application, a computer program product or a computer program includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the method provided in the various alternative implementations as described above.

[0018] According to an embodiment of the present application, first, user verification information for the cloud user's current cloud access is obtained, where the user verification information includes at least one of the cloud user's historical cloud usage records, the cloud user's user type, and the target cloud usage data for the cloud user's current cloud access. Then, evaluation data is obtained from a data source that matches the user verification information, and the data source includes the historical cloud usage data of the cloud user, cloud users of the same user type as the cloud user, and cloud users meeting a predetermined standard. Finally, cloud usage cost prediction data for the cloud user's current cloud access is generated using the evaluation data to predict the cloud user's cloud usage cost based on the cloud usage cost prediction data.

[0019] In this way, by obtaining user verification information, the cloud access status of the cloud user can be reflected. Then, by obtaining evaluation data from a data source that matches the user verification information, corresponding evaluation data can be obtained according to different cloud access statuses. And since the data source has various types of historical cloud usage data, the evaluation data reflects the historical cloud usage situations under different cloud access statuses. Furthermore, accurate and personalized cloud usage cost prediction data for the cloud user's current cloud access can be generated using the evaluation data, effectively ensuring the rationality of predicting the cloud user's cloud usage cost based on the cloud usage cost prediction data.

[0020] Other features and advantages of the present application will become apparent from the following detailed description in conjunction with the accompanying drawings, or will be learned in part through the practice of the present application.

[0021] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and are not intended to limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic diagram of a system to which embodiments of the present application can be applied is shown.

[0023] Figure 2 A flowchart of a cloud user management method according to an embodiment of the present application is shown.

[0024] Figure 3 The flowchart of a method for obtaining evaluation data according to an embodiment of the present application is shown.

[0025] Figure 4 The flowchart of a method for determining a matching data source according to an embodiment of the present application is shown.

[0026] Figure 5 The schematic diagram of a method for obtaining evaluation data according to another embodiment of the present application is shown.

[0027] Figure 6 The schematic diagram of a method for obtaining evaluation data according to still another embodiment of the present application is shown.

[0028] Figure 7 The schematic diagram of a processing module in a cloud user management system in an application scenario applying the embodiment of the present application is shown.

[0029] Figure 8 is shown according to Figure 7 The flowchart of cloud user management in the shown application scenario is shown.

[0030] Figure 9 The block diagram of a cloud user management device according to an embodiment of the present application is shown.

[0031] Figure 10 The block diagram of an electronic device according to an embodiment of the present application is shown. Detailed implementation manners

[0032] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more complete and comprehensive, and will fully convey the concept of the example embodiments to those skilled in the art.

[0033] In addition, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the embodiments of the present application. However, those skilled in the art will realize that the technical solutions of the present application can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the present application. The block diagrams shown in the drawings are only functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0034] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0035] Figure 1 A schematic diagram of a system 100 to which embodiments of the present application can be applied is shown.

[0036] like Figure 1 As shown, the system 100 may include a server 101 , a server 102 and a terminal 103 .

[0037] Server 101 may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. Server 101 may manage cloud users.

[0038] Server 102 may be a node server in a content delivery network (CDN). By publishing site content to a large number of acceleration node servers around the world, CDN enables users to obtain required content nearby, avoiding network instability and high access latency caused by network congestion, cross-operator, cross-region, cross-border and other factors, effectively improving download speed, reducing response time, and providing a smooth user experience. Content delivery network (CDN) is an important part of cloud services. Through CDN's fast, stable, intelligent and reliable global content distribution acceleration service, it supports the distribution of multiple content such as pictures, audio and video.

[0039] The terminal 103 may be an edge device, such as a smart phone, a computer, etc. The terminal 103 may apply for cloud user cloud usage cost prediction.

[0040] The server 101, the server 102 and the terminal 103 may be connected directly or indirectly via wireless communication, and this application does not impose any special limitation thereto.

[0041] In one implementation of this example, Figure 1As shown, cloud user management can be performed in server 101. The cloud user management method includes: obtaining user verification information for the cloud user's current cloud access, where the user verification information includes at least one of the cloud user's historical cloud usage records, the cloud user's user type, and the target cloud usage data for the cloud user's current cloud access; obtaining evaluation data from a data source that matches the user verification information, where the data source includes the historical cloud usage data of the cloud user, cloud users of the same user type as the cloud user, and cloud users that meet a predetermined standard; and generating predicted cloud usage cost data for the cloud user's current cloud access using the evaluation data to predict the cloud user's cloud usage cost based on the predicted cloud usage cost data.

[0042] Figure 2 Schematically shows a flowchart of a cloud user management method according to an embodiment of the present application. The execution subject of the cloud user management method can be an electronic device with computing and processing capabilities, such as Figure 1 the server 101 or the terminal 103 shown in

[0043] The cloud user management method can include steps S210 to S230.

[0044] Step S210: Obtain user verification information for the cloud user's current cloud access, where the user verification information includes at least one of the cloud user's historical cloud usage records, the cloud user's user type, and the target cloud usage data for the cloud user's current cloud access.

[0045] Step S220: Obtain evaluation data from a data source that matches the user verification information, where the data source includes the historical cloud usage data of the cloud user, cloud users of the same user type as the cloud user, and cloud users that meet a predetermined standard.

[0046] Step S230: Generate predicted cloud usage cost data for the cloud user's current cloud access using the evaluation data to predict the cloud user's cloud usage cost based on the predicted cloud usage cost data.

[0047] The following describes the specific processes of each step when performing cloud user management.

[0048] In step S210, obtain user verification information for the cloud user's current cloud access, where the user verification information includes at least one of the cloud user's historical cloud usage records, the cloud user's user type, and the target cloud usage data for the cloud user's current cloud access.

[0049] In the implementation of this example, the cloud user's historical cloud usage records can include the existence of usage records and the non - existence of usage records. The cloud user's user type can be an enterprise type. The target cloud usage data for the cloud user's current cloud access can include various data such as content delivery network regions, usage data, billing methods, and calculation methods.

[0050] The user verification information includes at least one of the cloud usage records of the cloud user in history, the user type of the cloud user, and the target cloud usage data of the cloud user for this cloud access, which can reflect different cloud access states of the cloud user. For example, when the user verification information only includes the user type of the cloud user, it can only reflect the user type of the cloud user.

[0051] Obtaining the user verification information of the cloud user for this cloud access may include receiving the incoming parameters when the cloud user accesses the cloud this time. The incoming parameters include at least one of the user information of the cloud user, the user type of the cloud user, and the target cloud usage data of the cloud user for this cloud access; then, query the usage records of the cloud user in history and the historical cloud usage data of the cloud user from the cloud database according to the user information.

[0052] In step S220, obtain the evaluation data from the data source that matches the user verification information. The data source includes the historical cloud usage data of one of the cloud user, cloud users of the same user type as the cloud user, and cloud users of a predetermined standard.

[0053] In the implementation manner of this example, the data source includes the historical cloud usage data of one of the cloud user, cloud users of the same user type as the cloud user, and cloud users of a predetermined standard. These several different types of data sources respectively reflect the historical cloud usage situation of the cloud user from different perspectives.

[0054] These several data sources respectively match the user verification information in different cloud access states, and the evaluation data in the cloud access state of the cloud user for this time can be obtained from the data source that matches the user verification information obtained in step S210. Moreover, since the evaluation data is derived from the historical cloud usage data, it can then reflect the cloud usage situation of the cloud user in the cloud access state of this time in a personalized and accurate manner.

[0055] In one embodiment, refer to Figure 3 , step S220, obtaining the evaluation data from the data source that matches the user verification information, includes:

[0056] Step S310, determine the data source that matches the user verification information;

[0057] Step S320, obtain the first quantity of cloud usage data samples required to determine the cloud usage cost prediction data of the cloud user for this cloud access from the data source;

[0058] Step S330, obtain the first quantity of cloud usage data samples from the data source as the evaluation data.

[0059] Determining the data source that matches the user verification information can be determined according to a preset mapping table (which contains the mapping relationship between the user verification information and the data source).

[0060] The cloud usage data samples can include the cloud usage data of the cloud user each time the user accesses the cloud, that is, the time nodes at which each cloud usage data sample of the cloud user is generated are different.

[0061] Each data source usually includes many data sets (cloud usage data sample sets). For determining the cloud usage cost prediction data of the cloud user's current cloud access, the number of cloud usage data samples required when facing different data sources is different. By obtaining this first number (which can be the number obtained from the preset configuration parameters or the number input by the user in real time), and then obtaining this first number of cloud usage data samples from the data source as the evaluation data, the effectiveness of the obtained cloud usage data samples can be effectively guaranteed for different data sources.

[0062] In one embodiment, refer to Figure 4 , step S310, determining the data source matched by the user authentication information includes:

[0063] Step S410, when the user authentication information includes the usage record of the cloud user's historical cloud usage, determining the historical cloud usage data of the cloud user as the data source;

[0064] Step S420, when the user authentication information includes the user type of the cloud user, determining the historical cloud usage data of the cloud users belonging to the same user type as the cloud user as the data source;

[0065] Step S430, when the user authentication information only includes the target cloud usage data of the cloud user's current cloud access, determining the historical cloud usage data of the cloud users meeting the predetermined criteria as the data source.

[0066] When the user authentication information includes the usage record of the cloud user's historical cloud usage, it indicates that there is historical cloud usage data of the cloud user, and thus the historical cloud usage data of the cloud user can be directly determined as the data source.

[0067] When the user authentication information includes the user type of the cloud user, determining the historical cloud usage data of the cloud users belonging to the same user type as the cloud user as the data source can analyze the cloud usage situation of the cloud user based on the historical cloud usage data of the cloud users belonging to the same user type as the cloud user. In particular, the user type is information that is relatively easy to obtain. When no other information is obtained and only the user type is obtained, the cloud usage situation of the cloud user can be easily analyzed through the historical cloud usage data of the cloud users belonging to the same user type as the cloud user.

[0068] When the user verification information only includes the target cloud usage data of the cloud user for this cloud migration, that is, the usage records and user types of the cloud user's historical cloud usage are not determined. At this time, the historical cloud usage data of cloud users with a predetermined standard is determined as the data source. In such cases, the cloud usage situation of the cloud user under the predetermined standard can be analyzed based on the historical cloud usage data of cloud users with a predetermined standard.

[0069] In one embodiment, refer to Figure 5 , step S330, obtain the first quantity of cloud usage data samples from the data source as evaluation data, including:

[0070] Step S510, determine the second quantity of cloud usage data samples included in the data source;

[0071] Step S520, determine the smaller value between the first quantity and the second quantity, and use the smaller value as the target quantity of cloud usage data samples required to determine the cloud usage cost prediction data for the cloud user's current cloud migration;

[0072] Step S530, obtain the target quantity of cloud usage data samples from the data source as evaluation data.

[0073] The second quantity of cloud usage data samples included in the data source is, for example, the number of cloud usage data samples included in the historical cloud usage data of the cloud user.

[0074] Determine the smaller value between the first quantity and the second quantity, and use the smaller value as the target data to obtain cloud usage data samples, so as to reasonably obtain evaluation data according to the situation of the data source. It can be understood that when there is no smaller value between the first quantity and the second quantity, that is, the first quantity is equal to the second quantity, obtain the first quantity or the second quantity of cloud usage data samples from the data source as evaluation data.

[0075] In one embodiment, the first quantity is derived from preset configuration parameters. After step S520, determine the smaller value between the first quantity and the second quantity, and use the smaller value as the target quantity of cloud usage data samples required to determine the cloud usage cost prediction data for the cloud user's current cloud migration, it may further include: calculating the difference between the first quantity and the target quantity; correcting the first quantity according to the magnitude of the difference to obtain a third quantity, and using the third quantity to update the first quantity in the preset configuration parameters.

[0076] The smaller value between the first quantity and the second quantity is used as the target quantity, indicating that there is instability in obtaining the first quantity set in the preset configuration parameters. By calculating the difference between the first quantity and the target quantity, the instability of the first quantity set in the preset configuration parameters can be determined. Then, based on the magnitude of the difference, the degree of instability can be determined. By correcting the first quantity to obtain a third quantity and using the third quantity to update the first quantity in the preset configuration parameters, the stability of the first quantity set in the preset configuration parameters can be ensured.

[0077] Correcting the first quantity according to the magnitude of the difference to obtain a third quantity may be to first determine the numerical range where the difference is located, and then obtain the preset correction coefficient corresponding to the data range. The product of the first quantity and the preset correction coefficient is used as the obtained third quantity.

[0078] In one embodiment, refer to Figure 6 , when the data source is the historical cloud usage data of the cloud user; step S330, obtaining a cloud usage data sample of the first quantity from the data source as evaluation data, including:

[0079] Step S610, determining whether there is valid data in the historical cloud usage data of the cloud user, where the valid data is a cloud usage data sample that meets the target evaluation requirement;

[0080] Step S620, when there is valid data in the historical cloud usage data of the cloud user, obtaining a cloud usage data sample of the fourth quantity from the valid data as evaluation data;

[0081] Step S630, when there is no valid data in the historical cloud usage data of the cloud user, obtaining a cloud usage data sample of the fifth quantity from the historical cloud usage data of the cloud user as evaluation data.

[0082] The target evaluation requirement may be a target cloud usage cost evaluation requirement or a data requirement specified by a professional. The valid data is a cloud usage data sample that meets the target evaluation requirement. When there is valid data, obtaining a cloud usage data sample of the fourth quantity from the valid data as evaluation data can effectively reflect the cloud usage cost prediction data of this time through only a small number of cloud usage data samples and can reliably predict the cloud usage cost of the cloud user.

[0083] On the contrary, when there is no valid data, a cloud usage data sample of the fifth quantity is obtained from the overall historical cloud usage data of the cloud user as evaluation data.

[0084] In step S230, the cloud usage cost prediction data for the cloud user's current cloud migration is generated using the evaluation data to predict the cloud user's cloud usage cost based on the cloud usage cost prediction data.

[0085] In the implementation of this example, the cloud usage cost prediction data for the cloud user's current cloud migration is generated using the evaluation data, which can be to generate data that meets the cost prediction requirements from the evaluation data according to a predetermined data screening criterion. Among them, the predetermined data screening criterion can include a data category criterion that meets the cloud usage cost prediction requirements, and can also include a data error criterion that meets the cloud usage cost prediction requirements, etc.

[0086] According to the predetermined data screening criterion, operations such as comparison and screening, merging (for example, merging multiple data with the same parameters into one), and cleaning (filtering duplicate data) can be performed on the cloud usage cost prediction data to generate the cloud usage cost prediction data for the cloud user's current cloud migration.

[0087] The evaluation data can reflect the cloud usage situation of the cloud user in the current cloud migration in a personalized and accurate manner, and thus can generate personalized and accurate cloud usage cost prediction data for the cloud user's current cloud migration. Based on the cloud usage cost prediction data, the cloud usage cost of the cloud user can be reasonably predicted according to a cost prediction formula or a cost prediction model (a trained machine learning model).

[0088] In this way, by simply passing in a part of simple parameters (such as the user information and user type of the cloud user), the cloud usage cost data of the cloud user after using the cloud acceleration service to distribute the content delivery network can be predicted in real time.

[0089] In one embodiment, in step S230, generating the cloud usage cost prediction data for the cloud user's current cloud migration using the evaluation data includes: when the user verification information includes the target cloud usage data of the cloud user's current cloud migration, generating the cloud usage cost prediction data for the cloud user's current cloud migration by comparing the target cloud usage data with the evaluation data; when the user verification information does not include the target cloud usage data of the cloud user's current cloud migration, normalizing the evaluation data to obtain the cloud usage cost prediction data for the cloud user's current cloud migration.

[0090] When the user verification information includes the target cloud usage data of the cloud user's current cloud migration, comparing the target cloud usage data with the evaluation data to generate the cloud usage cost prediction data for the cloud user's current cloud migration can generate the cloud usage cost prediction data by integrating the current target usage data and historical usage data.

[0091] Among them, the target cloud usage data is compared with the evaluation data to generate the cloud usage cost prediction data for the cloud user's current cloud migration. For example, the evaluation data includes the historical data parameters of three cloud usage data samples A, B, and C, and the target cloud usage data includes the data parameters of the sample of X; A corresponds to data A1, A2, A3, B corresponds to B1, B2, B3, C corresponds to C1, C2, C3; X corresponds to data X1, X2, X3; First, sort the cloud usage cost data corresponding to the three cloud usage data samples A, B, and C, and then remove the values with relatively large cost data deviations. For example, A1 = 5, B1 = 5, C1 = 50, and here the relatively large deviation C1 is removed; Then, calculate the average value of A2, B2, and C2 to obtain the average value, compare the average value with X2, and determine one of the average value and X2 as the data of parameter 2 in the cloud usage cost prediction data according to the screening criteria of parameter 2. Furthermore, the cloud usage cost of the cloud user can be predicted based on the cloud usage cost prediction data using a preset cost formula.

[0092] When the user verification information does not include the target cloud usage data of the cloud user's current cloud migration, normalize the evaluation data. For example, the evaluation data includes the historical data parameters of three cloud usage data samples A, B, and C; A corresponds to data A1, A2, A3, B corresponds to B1, B2, B3, C corresponds to C1, C2, C3; X corresponds to data X1, X2, X3; First, sort the cloud usage cost data corresponding to the three cloud usage data samples A, B, and C, and then remove the values with relatively large cost data deviations. For example, A1 = 5, B1 = 5, C1 = 50, and here the relatively large deviation C1 is removed; Then, randomly obtain one from A1 and B1 or calculate the average value of A1 and B1 as the cloud usage cost prediction data.

[0093] In one embodiment, in step S230, after predicting the cloud usage cost of the cloud user based on the cloud usage cost prediction data, it further includes:

[0094] When the cloud usage cost meets the predetermined data conditions, send the cloud usage cost to the manual management platform so that the manual management platform can verify whether the cloud usage cost is correct.

[0095] The predetermined data conditions are preset cost data monitoring conditions. When the cloud usage cost meets the predetermined data conditions, it indicates that there are special situations in the predicted cloud usage cost. Send the cloud usage cost to the manual management platform so that the manual management platform can verify whether the cloud usage cost is correct. For the calculation results of special cloud usage costs, secondary manual intervention can be performed to ensure the reliability of cloud user management.

[0096] Figure 7Schematic diagram of a processing module in a cloud user management system under an application scenario of the embodiments of the present application. Figure 8 According to Figure 7 Flowchart of cloud user management under the application scenario shown.

[0097] Referring to Figure 7 and Figure 8 As shown, this application scenario is a method for real-time generating content delivery network (CDN) cloud usage cost prediction data. When a cloud user applies to use the acceleration service on the CDN cloud (i.e., goes to the cloud), through this method, relevant personnel can provide some simple parameters such as the user type of the cloud user and relevant parameters (such as user information, target cloud usage data of the cloud user for this cloud access) in the "start" step S810, and then can perform real-time cloud usage cost prediction analysis for the cloud user based on subsequent steps.

[0098] The processing module in the cloud user management system under this application scenario may include: a user historical data backtracking calculation module 710, a user data calculation and estimation module 720, a market parameter estimation module 730, and a reference estimation module for model users 740. The module configuration parameters in these several modules are different. For example, the first quantity in each module is configured according to the characteristics of the data source corresponding to the module and is different for each.

[0099] Among them, the user historical data backtracking calculation module 710 is used to process the effective data of the cloud user. That is, the user historical data backtracking calculation module 710 can process:

[0100] Step S820, determine whether there is a usage record of the cloud user using the cloud in history and historical cloud usage data according to the incoming user information;

[0101] Step S830, when there is a usage record and historical cloud usage data, at this time, the user verification information obtained at least includes the usage record of the cloud user using the cloud in history. At this time, it means that the historical cloud usage data of this cloud user can be used as the initial data source, and then, further determine whether there is effective data in the historical cloud usage data of this cloud user;

[0102] Step S840, if there is effective data, use the effective data as the final data source;

[0103] Step S890, read the existing module configuration parameters (i.e., preset configuration parameters) in the user historical data backtracking calculation module 710, which include the first quantity of cloud usage data samples required to determine the cloud usage cost prediction data of the cloud user for this cloud access in this data source. The first quantity is specifically the fourth quantity in the foregoing embodiments when the effective data is used as the data source, and obtain the first quantity of cloud usage data samples from this effective data as evaluation data.

[0104] The user data calculation and estimation module 720 is used to process the historical cloud usage data of cloud users when it is determined in step S830 that there is no effective data. Specifically, the user data calculation and estimation module 720 can process:

[0105] Step S850, uniformly take out the historical cloud usage data of the cloud user as the data source;

[0106] Step S890, read the module configuration parameters of the user data calculation and estimation module 720 (i.e., preset configuration parameters, including the first quantity of cloud usage data samples required to determine the cloud usage cost prediction data of the cloud user's current cloud access in this data source and the data conditions for data that can participate in historical calculations. Specifically, this first quantity is the fifth quantity in the foregoing embodiments, and the data conditions may include the target time period and the cost range corresponding to the cloud usage data samples, etc.), and obtain the cloud usage data samples with the first quantity and meeting the data conditions from the historical cloud usage data of the cloud user as evaluation data.

[0107] The market parameter estimation module 730 is used to process the situation where the user type is uncertain when it is determined in step S820 that there is no usage record of the cloud user's historical cloud usage and historical cloud usage data. Specifically, the market parameter estimation module 730 can process:

[0108] Step S860, when there is no usage record of the cloud user's historical cloud usage and historical cloud usage data, determine whether the user type of the cloud user can be determined. For example, determine whether there is a user type in the parameters passed in at the start of step S810.

[0109] Step S870, when the user type of the cloud user is uncertain, if the target cloud usage data of the cloud user's current cloud access can be obtained, it means that the user authentication information only includes the target cloud usage data of the cloud user's current cloud access. Determine the historical cloud usage data of cloud users with a predetermined standard as the data source;

[0110] Step S890, read the module configuration parameters in the market parameter estimation module 730 (i.e., preset configuration parameters, including the first quantity of cloud usage data samples required to determine the cloud usage cost prediction data of the cloud user's current cloud access in this data source. In particular, in this scenario, the first quantity is specifically the number of cloud users with a predetermined standard that have generated historical cloud usage data), and obtain the cloud usage data samples corresponding to the first quantity of cloud users with a predetermined standard according to the configuration parameters as evaluation data.

[0111] The template user reference and estimation module 740 is used to handle the situation where the user type can be determined when it is determined in step S820 that there is no usage record and historical cloud usage data of the cloud user's historical cloud usage. Specifically, the template user reference and estimation module 740 can handle:

[0112] Step S880, when determining the user type of the cloud user, if the target cloud usage data of the cloud user's current cloud access cannot be obtained, it means that the user authentication information only includes the user type. The historical cloud usage data of the cloud users belonging to the same user type as this cloud user is determined as the data source;

[0113] Step S890, read the module configuration parameters in the template user reference and estimation module 740 (i.e., preset configuration parameters, including the first quantity of cloud usage data samples required to determine the cloud usage cost prediction data of the cloud user's current cloud access in this data source), and obtain the first quantity of cloud usage data samples from this data source as the evaluation data according to the configuration parameters.

[0114] The user historical data backtracking calculation module 710, the user data calculation and estimation module 720, the overall market parameter estimation module 730, and the template user reference and estimation module 740. These modules perform the pull of the configuration parameters of the corresponding modules in step S890 to obtain the first quantity of cloud usage data samples as the evaluation data. In step S8100, the evaluation data is specifically obtained according to the configuration parameters. First, determine the second quantity of cloud usage data samples included in the data source; then, determine the smaller value between the first quantity and the second quantity, and use the smaller value as the target quantity of the cloud usage data samples required to determine the cloud usage cost prediction data of the cloud user's current cloud access; finally, obtain the target quantity of cloud usage data samples from the corresponding data source as the evaluation data.

[0115] Specifically, when the user historical data backtracking calculation module 710 obtains the first quantity n1 of cloud usage data samples from the corresponding data source (effective data), if the existing cloud usage data samples m1 in the data source are greater than n1 in the configuration parameters, then select the "last expired" n1 (target quantity) cloud usage data samples in m1 as the evaluation data; if the existing cloud usage data samples m1 in the data source are less than n1 in the configuration parameters, then directly use the m1 (target quantity) existing cloud usage data samples as the evaluation data.

[0116] When the user data calculation estimation module 720 obtains the cloud usage data samples of the first quantity n2 from the corresponding data source (the historical cloud usage data of cloud users), it filters part of the data in the data source according to the preset data conditions that can participate in the calculation, and performs operations such as merging (for example, merging multiple data with the same parameters into one) and cleaning (filtering duplicate data) on the remaining data to generate m2 cloud usage data samples that can participate in the calculation; then, when m2 is greater than n2 in the configuration parameters, it selects the n2 (target quantity) cloud usage data samples that are "last generated" from m2 as the evaluation data; if m2 is less than n2 in the configuration parameters, it directly uses the m2 (target quantity) generated cloud usage data samples as the evaluation data.

[0117] When the market index estimation module 730 obtains the cloud usage data samples of the first quantity n3 (specifically, the number of cloud users meeting the predetermined standard) from the corresponding data source (the historical cloud usage data of cloud users meeting the predetermined standard), it reads the number m3 of cloud users meeting the predetermined standard in the historical cloud usage data within the "target time period (the predetermined length time period corresponding to the relevant parameters passed in during this calculation)" in the data source. If m3 is greater than n3 in the configuration parameters, it selects the cloud usage data samples corresponding to the n3 (target quantity) cloud users meeting the predetermined standard with the "largest generated usage data (such as usage cost)" from m3 as the evaluation data; if m3 is less than n3, it directly uses the cloud usage data samples corresponding to the m3 (target quantity) cloud users meeting the predetermined standard as the evaluation data.

[0118] When the reference estimation module 740 for model users obtains the cloud usage data samples of the first quantity n4 (specifically, the number of cloud users of the same user type as this cloud user) from the corresponding data source (the historical cloud usage data of cloud users of the same user type as this cloud user), it reads the number m4 of "valid cloud users" among the cloud users of the same user type as this cloud user in the data source, where a valid cloud user is a cloud user that has generated valid cloud usage data (such as generated cloud usage cost) during the specified time period. If m4 is greater than n4 in the configuration parameters, it selects the cloud usage data samples corresponding to the n4 (target quantity) "most valid" (that is, the largest time of valid cloud usage data, for example, user A has 20 time points of valid cloud usage data within the specified time period, and user B has 30 time points of valid cloud usage data within the specified time period, then user B is more valid than user A) valid cloud users from m4 as the evaluation data; if m4 is less than n4, it directly uses the cloud usage data samples corresponding to the m4 (target quantity) valid cloud users as the evaluation data.

[0119] The user historical data backtracking calculation module 710, the user data calculation and estimation module 720, the overall market parameter estimation module 730, and the reference estimation module 740 for model users. In step S8100, after specifically obtaining the evaluation data according to the configuration parameters, when there may be the target cloud usage data of the cloud user for this cloud migration, the cloud usage cost prediction data for this cloud migration of the cloud user is generated by comparing the target cloud usage data with the evaluation data; when there is no target cloud usage data of the cloud user for this cloud migration, the evaluation data is normalized to obtain the cloud usage cost prediction data for this cloud migration of the cloud user. Finally, the cloud usage cost of the cloud user (i.e., the initial quotation) is predicted based on the cloud usage cost prediction data.

[0120] In step S8110, the target quantity (the number of samples required for this calculation) is returned to the "Dynamic Calculation Sample Quantity Configuration Update Module", and the difference between the first quantity in the configuration parameters of each processing module and the target quantity is calculated; the first quantity is corrected according to the size of the difference to obtain the third quantity, and the third quantity is used to update the first quantity in the preset configuration parameters of each processing module. The first quantity in the preset configuration parameters will tend to a stable order of magnitude value as more and more data is calculated.

[0121] In step S8120, according to the predetermined data conditions in each processing module, it is judged whether the cloud usage cost corresponding to each module meets the predetermined data conditions. When it does not meet the predetermined data conditions, the cloud usage cost (quotation) is returned to the relevant personnel in step S8130; when the cloud usage cost meets the predetermined data conditions, the cloud usage cost is sent to the manual management platform in step S8140, so that the manual management platform can verify whether the cloud usage cost is correct and perform manual intervention.

[0122] Based on the foregoing embodiments, this application scenario can achieve: 1. Multiple processing modules are set, and different processing modules can process cloud users of different types (different cloud migration states). 2. Each processing module can accurately and effectively backtrack historical cloud usage data according to different situations, and dynamically adjust the first quantity participating in the calculation, ensuring that the calculation time does not take too long and the quotation data of the cloud usage cost can be returned in real time. 3. For new cloud users, the cloud usage cost data corresponding to the user type or the overall market parameter estimation module can be estimated, and the number of cloud users meeting the predetermined standards participating in the measurement or the number of cloud users belonging to the same user type as the cloud user is also dynamically adjusted. 4. There is a cost measurement manual intervention module. For special calculation results, secondary manual intervention can be performed, and the result is immediately returned to the relevant personnel after the intervention is completed, without the need for the relevant personnel to perform secondary operations.

[0123] Figure 9 The block diagram of a cloud user management device according to an embodiment of the present application is shown.

[0124] AsFigure 9 As shown in Figure 9 , the cloud user management device 900 may include a first acquisition module 910, a second acquisition module 920, and a prediction module 930.

[0125] The first acquisition module 910 may be configured to acquire user authentication information for the cloud user's current cloud access, where the user authentication information includes at least one of the cloud user's historical cloud usage records, the cloud user's user type, and the target cloud usage data for the cloud user's current cloud access.

[0126] The second acquisition module 920 may be configured to acquire evaluation data from a data source that matches the user authentication information, where the data source includes the historical cloud usage data of the cloud user, cloud users of the same user type as the cloud user, and cloud users of a predetermined standard.

[0127] The prediction module 930 may be configured to generate predicted cloud usage cost data for the cloud user's current cloud access using the evaluation data, and predict the cloud usage cost of the cloud user based on the predicted cloud usage cost data.

[0128] In some embodiments of the present application, the second acquisition module includes: a source determination unit configured to determine the data source that matches the user authentication information; a first quantity acquisition unit configured to acquire a first quantity of cloud usage data samples required to determine the predicted cloud usage cost data for the cloud user's current cloud access from the data source; and an evaluation data acquisition unit configured to acquire the first quantity of cloud usage data samples from the data source as the evaluation data.

[0129] In some embodiments of the present application, the evaluation data acquisition unit includes: a second quantity determination subunit configured to determine a second quantity of cloud usage data samples included in the data source; a target quantity determination subunit configured to determine the smaller value of the first quantity and the second quantity, and use the smaller value as the target quantity of cloud usage data samples required to determine the predicted cloud usage cost data for the cloud user's current cloud access; and an evaluation data acquisition subunit configured to acquire the target quantity of cloud usage data samples from the data source as the evaluation data.

[0130] In some embodiments of the present application, the device further includes a preset configuration module, where the first quantity is derived from a preset configuration parameter in the preset configuration module. The preset configuration module includes: a difference calculation unit configured to calculate the difference between the first quantity and the target quantity; and an update unit configured to correct the first quantity according to the magnitude of the difference to obtain a third quantity, and use the third quantity to update the first quantity in the preset configuration parameter.

[0131] In some embodiments of the present application, the source determination unit includes: a first source determination subunit, configured to determine the historical cloud usage data of the cloud user as the data source when the user authentication information includes the usage record of the cloud user's historical cloud usage; a second source determination subunit, configured to determine the historical cloud usage data of cloud users belonging to the same user type as the cloud user as the data source when the user authentication information includes the user type of the cloud user; a third source determination subunit, configured to determine the historical cloud usage data of cloud users meeting the predetermined criteria as the data source when the user authentication information only includes the target cloud usage data of the cloud user's current cloud access.

[0132] In some embodiments of the present application, the data source is the historical cloud usage data of the cloud user; the evaluation data acquisition unit includes: an effective data determination subunit, configured to determine whether there is effective data in the historical cloud usage data of the cloud user, where the effective data is a cloud usage data sample that meets the target evaluation requirements; an effective data acquisition subunit, configured to, when there is effective data in the historical cloud usage data of the cloud user, obtain a fourth quantity of cloud usage data samples from the effective data as the evaluation data; a non-effective data acquisition subunit, configured to, when there is no effective data in the historical cloud usage data of the cloud user, obtain a fifth quantity of cloud usage data samples from the historical cloud usage data of the cloud user as the evaluation data.

[0133] In some embodiments of the present application, the prediction module includes: a first prediction unit, configured to generate cloud usage cost prediction data for the cloud user's current cloud access by comparing the target cloud usage data with the evaluation data when the user authentication information includes the target cloud usage data of the cloud user's current cloud access; a second prediction unit, configured to normalize the evaluation data to obtain cloud usage cost prediction data for the cloud user's current cloud access when the user authentication information does not include the target cloud usage data of the cloud user's current cloud access.

[0134] In some embodiments of the present application, the device further includes: a feedback module, configured to send the cloud usage cost to the manual management platform when the cloud usage cost meets the predetermined data conditions, so that the manual management platform verifies whether the cloud usage cost is correct.

[0135] It should be noted that although several modules or units of a device for action execution are mentioned in the above detailed description, such a division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more of the above-described modules or units can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0136] Figure 10 A block diagram of an electronic device according to an embodiment of the present application is schematically shown.

[0137] It should be noted that Figure 10 the illustrated electronic device 1000 is merely an example and should not impose any limitation on the functions and the scope of use of the embodiments of the present application.

[0138] As Figure 10 shown, the electronic device 1000 includes a central processing unit (CPU) 1001, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage section 1008 into a random access memory (RAM) 1003. In the RAM 1003, various programs and data required for system operations are also stored. The CPU 1001, the ROM 1002, and the RAM 1003 are connected to each other via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0139] The following components are connected to the I / O interface 1005: an input section 1006 including a keyboard, a mouse, etc.; an output section 1007 including, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN (local area network) card, a modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to the I / O interface 1005 as required. A removable medium 1011, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1010 as required so that a computer program read therefrom can be installed into the storage section 1008 as required.

[0140] Specifically, according to an embodiment of the present application, the processes described below with reference to the flowcharts can be implemented as computer software programs. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program codes for performing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 1009, and / or installed from the removable medium 1011. When the computer program is executed by the central processing unit (CPU) 1001, various functions defined in the system of the present application are executed.

[0141] It should be noted that the computer-readable medium shown in this application can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this application, a computer-readable storage medium can be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device. And in this application, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium can be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF (radio frequency), etc., or any suitable combination of the above.

[0142] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram can represent a module, a program segment, or a part of code, and the above-mentioned module, program segment, or part of code contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, as well as the combination of blocks in a block diagram or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0143] The units involved in the embodiments described in this application can be implemented in software or in hardware, and the described units can also be provided in a processor. Among them, the names of these units do not, in some cases, constitute a limitation on the units themselves.

[0144] As another aspect, the present application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the one or more programs are executed by an electronic device, the electronic device implements the method described in the above embodiments.

[0145] It should be noted that although several modules or units of the device for action execution are mentioned in the above detailed description, such division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of the two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0146] From the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented by software or by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (such as a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present application.

[0147] After considering the specification and practicing the disclosed embodiments herein, those skilled in the art will readily conceive of other embodiments of the present application. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include known common knowledge or conventional technical means in the technical field not disclosed in the present application.

[0148] It should be understood that the present application is not limited to the embodiments described above and shown in the drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A cloud user management method, characterized in that, Including: Obtain the user verification information of the cloud user for this cloud access. The user verification information includes at least one of the cloud user's historical cloud usage records, the user type of the cloud user, and the target cloud usage data of the cloud user for this cloud access; Obtain evaluation data from a data source that matches the user verification information. The data source includes the historical cloud usage data of the cloud user, cloud users of the same user type as the cloud user, and cloud users of a predetermined standard; Generate cloud usage cost prediction data for the cloud user's this cloud access using the evaluation data, so as to predict the cloud usage cost of the cloud user based on the cloud usage cost prediction data; Among them, the obtaining evaluation data from a data source that matches the user verification information includes: Determine the data source that matches the user verification information; Determine the first quantity of cloud usage data samples required for the cloud usage cost prediction data of the cloud user's this cloud access from the data source; Obtain the first quantity of cloud usage data samples from the data source as the evaluation data; Among them, the determining the data source that matches the user verification information includes: When the user verification information includes the cloud user's historical cloud usage records, determine the cloud user's historical cloud usage data as the data source; When the user verification information includes the user type of the cloud user, determine the historical cloud usage data of cloud users of the same user type as the cloud user as the data source; When the user verification information only includes the target cloud usage data of the cloud user for this cloud access, determine the historical cloud usage data of cloud users of the predetermined standard as the data source.

2. The method according to claim 1, characterized in that, The obtaining the first quantity of cloud usage data samples from the data source as the evaluation data includes: Determine the second quantity of cloud usage data samples included in the data source; Determine the smaller value between the first quantity and the second quantity, and use the smaller value as the target quantity of cloud usage data samples required for determining the cloud usage cost prediction data of the cloud user's this cloud access; Obtain the target quantity of cloud usage data samples from the data source as the evaluation data.

3. The method according to claim 2, wherein The first quantity is derived from preset configuration parameters. After determining the smaller value between the first quantity and the second quantity and using the smaller value as the target quantity of cloud usage data samples required for determining the cloud usage cost prediction data of the cloud user's this cloud access, the method further includes: Calculate the difference between the first quantity and the target quantity; Correct the first quantity according to the magnitude of the difference to obtain a third quantity, and use the third quantity to update the first quantity in the preset configuration parameters.

4. The method according to claim 1, wherein The data source is the cloud user's historical cloud usage data; the obtaining the first quantity of cloud usage data samples from the data source as the evaluation data includes: Determine whether there is valid data in the cloud user's historical cloud usage data. The valid data is a cloud usage data sample that meets the target evaluation requirements; When there is the effective data in the historical cloud usage data of the cloud user, obtain a fourth quantity of cloud usage data samples from the effective data as the evaluation data; When there is no such effective data in the historical cloud usage data of the cloud user, obtain a fifth quantity of cloud usage data samples from the historical cloud usage data of the cloud user as the evaluation data.

5. The method according to claim 1, wherein The generating the cloud usage cost prediction data for the cloud user's current cloud migration using the evaluation data includes: When the user verification information includes the target cloud usage data for the cloud user's current cloud migration, generate the cloud usage cost prediction data for the cloud user's current cloud migration by comparing the target cloud usage data with the evaluation data; When the user verification information does not include the target cloud usage data for the cloud user's current cloud migration, normalize the evaluation data to obtain the cloud usage cost prediction data for the cloud user's current cloud migration.

6. The method according to claim 1, wherein After predicting the cloud usage cost of the cloud user based on the cloud usage cost prediction data, the method further includes: When the cloud usage cost meets the predetermined data conditions, send the cloud usage cost to the manual management platform so that the manual management platform verifies whether the cloud usage cost is correct.

7. A cloud user management device, characterized in that, Including: A first acquisition module, configured to acquire user verification information for the cloud user's current cloud migration, where the user verification information includes at least one of the usage records of the cloud user's historical cloud usage, the user type of the cloud user, and the target cloud usage data for the cloud user's current cloud migration; A second acquisition module, configured to acquire evaluation data from a data source that matches the user verification information, where the data source includes the historical cloud usage data of the cloud user, cloud users of the same user type as the cloud user, and cloud users of a predetermined standard; A prediction module, configured to generate cloud usage cost prediction data for the cloud user's current cloud migration using the evaluation data, so as to predict the cloud usage cost of the cloud user based on the cloud usage cost prediction data; Among them, the acquiring the evaluation data from the data source that matches the user verification information includes: Determine the data source that matches the user verification information; Determine a first quantity of cloud usage data samples required for the cloud usage cost prediction data of the cloud user's current cloud migration from the data source; Acquire the first quantity of cloud usage data samples from the data source as the evaluation data; Among them, the determining the data source that matches the user verification information includes: When the user verification information includes the usage records of the cloud user's historical cloud usage, determine the historical cloud usage data of the cloud user as the data source; When the user verification information includes the user type of the cloud user, determine the historical cloud usage data of cloud users of the same user type as the cloud user as the data source; When the user verification information only includes the target cloud usage data for the cloud user's current cloud migration, determine the historical cloud usage data of cloud users of the predetermined standard as the data source.

8. A computer program medium having computer-readable instructions stored thereon, characterized in that, When the computer-readable instructions are executed by a processor of a computer, the computer is caused to perform the method according to any one of claims 1-6.

9. A computer program product, characterized in that, The computer program product includes computer instructions stored in a computer-readable storage medium; wherein, a processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the method according to any one of claims 1-6.

10. An electronic device, characterized in that, Comprising: a memory storing computer-readable instructions; a processor that reads the computer-readable instructions stored in the memory to perform the method according to any one of claims 1-6.

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