Resource determination method and device, equipment, readable storage medium and program product

Abstract

The invention relates to a resource determination method and device, equipment, a readable storage medium and a program product. The method comprises the following steps: firstly, acquiring computing power resource information of a target user for indicating resource information of computing power resources used by the target user in the process of using a computing power function provided by a computing power platform, then determining an algorithm model from a preset database according to the computing power resource information, and determining parameter values of algorithm parameters in the algorithm model according to the user information of the target user, and finally, determining a virtual resource value matched with the computing power resource used by the target user according to the parameter values of the algorithm model, the algorithm model and the computing power resource information. By adopting the method, the virtual resource values corresponding to the computing power resources used by the users can be obtained by different computing modes through different algorithm models and parameter values of the algorithm models for different users, the determination mode of the virtual resource values is more flexible, and the method can adapt to different users and different service scenes.

Description

Technical Field

[0001] The present application relates to the field of computing resource service technology, and in particular to a resource determination method, apparatus, device, readable storage medium and program product. Background Art

[0002] In cloud computing services, when providing users with virtual servers, cloud databases, or object storage services, computing power resource billing is usually based on the computing resources actually used by the user. The computing power resource billing model can encourage users to use computing resources more rationally and help promote the green and low-carbon development of computing power infrastructure.

[0003] At present, the commonly used computing resource billing model usually adopts a fixed calculation method for calculation, but this method is not flexible enough and cannot adapt to the special requirements of different business scenarios. Summary of the invention

[0004] Based on this, it is necessary to provide a resource determination method, device, equipment, readable storage medium and program product that can be flexibly configured to address the above technical issues.

[0005] In a first aspect, the present application provides a resource determination method, comprising:

[0006] Obtain the target user's computing power resource information, which is used to indicate the resource information of the computing power resources used by the target user in the process of using the computing power function provided by the computing power platform;

[0007] According to the computing power resource information, the algorithm model is determined from the preset database;

[0008] Determine parameter values ​​of algorithm parameters in the algorithm model according to user information of the target user;

[0009] According to the parameter values ​​of the algorithm model, the algorithm model and the computing power resource information, the virtual resource value that matches the computing power resources used by the target user is determined.

[0010] In the above embodiment, first, computing power resource information of the target user is obtained, which is used to indicate the resource information of the computing power resources used by the target user in the process of using the computing power function provided by the computing power platform. Then, according to the computing power resource information, the algorithm model is determined from a preset database, and the parameter values ​​of the algorithm parameters in the algorithm model are determined according to the user information of the target user. Finally, according to the parameter values ​​of the algorithm model, the algorithm model and the computing power resource information, the virtual resource value matching the computing power resources used by the target user is determined. In this way, when determining the virtual resource value corresponding to the computing power resources used by the target user, the algorithm model can be determined according to the computing power resource information of the target user and the parameter value of the algorithm model can be determined according to the user information. In this way, for different users, different calculation methods can be used through different algorithm models and parameter values ​​of the algorithm model to obtain the virtual resource value corresponding to the computing power resources used by the user. Therefore, the method for determining the virtual resource value is more flexible and can adapt to different users and different business scenarios.

[0011] In one embodiment, the computing power resource information includes type information of various computing power resources, and the algorithm model is determined from a preset database according to the computing power resource information, including:

[0012] Determine, according to the type information of various computing resources, first algorithm models and second algorithm models corresponding to various computing resources used by the target user;

[0013] The first algorithm model is used to determine the amount of computing resources used by the target user; the second algorithm model is used to obtain the virtual resource value based on the amount of computing resources.

[0014] In the above embodiment, the first algorithm model and the second algorithm model corresponding to various computing resources are determined by the type information of computing resources, so that different computing resources can be flexibly calculated. At the same time, when the algorithm model is changed, it can be changed through the database, which is more convenient for maintenance.

[0015] In one embodiment, determining the second algorithm models corresponding to various computing resources used by the target user includes:

[0016] Query the database based on the type information of various computing resources. The database includes the type information of computing resources, candidate algorithm models, and the corresponding relationship between model priorities.

[0017] According to the query results, multiple candidate algorithm models corresponding to various computing resources used by the target user are determined;

[0018] According to the model priorities corresponding to the candidate algorithm models, the second algorithm models corresponding to the various computing resources used by the target user are determined.

[0019] In the above embodiment, for various computing power resources, different model effectiveness conditions and model priorities can be set for different candidate algorithm models, so that different second algorithm models can be obtained according to different situations to calculate the virtual resource value of the computing power resources, and the computing power resource calculation method is more flexible.

[0020] In one embodiment, the method further comprises:

[0021] If the candidate algorithm model meets the preset priority change conditions, the model priority corresponding to the candidate algorithm model is updated.

[0022] In one embodiment, if the candidate algorithm model meets the preset priority change condition, the model priority corresponding to the candidate algorithm model is updated, including:

[0023] If the effective time of the model priority corresponding to the candidate algorithm model indicates that the model priority has timed out, then updating the model priority corresponding to the candidate algorithm model;

[0024] Alternatively, the total usage information of various computing resources in the computing platform is obtained. If the total usage information meets the preset usage conditions, the model priority corresponding to the candidate algorithm model is updated according to the total usage information;

[0025] Alternatively, if a priority adjustment instruction is received, the model priority corresponding to the candidate algorithm model is updated according to the priority adjustment instruction.

[0026] In the above embodiment, the model priority of each candidate algorithm model is changed by judging whether each candidate algorithm model meets the preset priority change conditions. The model priority of each candidate algorithm model can be adjusted according to the actual usage of computing resources to achieve adaptive adjustment of the model priority.

[0027] In one embodiment, determining a virtual resource value matching the computing resources used by a target user according to the parameter value of the algorithm model, the algorithm model, and the computing resource information includes:

[0028] Analyze the algorithm model to obtain multiple target dimensions;

[0029] The conversion coefficient of the algorithm model is determined by querying the database according to each target dimension;

[0030] According to the conversion coefficient, the parameter value of the algorithm model, the algorithm model and the computing power resource information, the virtual resource value that matches the computing power resources used by the target user is determined.

[0031] In the above embodiment, multiple dimensions are obtained by analyzing the algorithm model, and then the conversion coefficient of the algorithm model is determined according to each dimension, so that the algorithm model can adapt to units of various dimensions without the need for manual conversion, and the calculation efficiency is higher.

[0032] In a second aspect, the present application further provides a resource determination device, including:

[0033] An acquisition module is used to acquire computing power resource information of a target user, where the computing power resource information is used to indicate resource information of computing power resources used by the target user in the process of using the computing power function provided by the computing power platform;

[0034] A first determination module is used to determine an algorithm model from a preset database according to computing power resource information;

[0035] A second determination module, used to determine parameter values ​​of algorithm parameters in the algorithm model according to user information of the target user;

[0036] The third determination module is used to determine the virtual resource value that matches the computing power resources used by the target user based on the parameter value of the algorithm model, the algorithm model and the computing power resource information.

[0037] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the resource determination method described in any one of the first aspects is implemented.

[0038] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the resource determination method described in any one of the first aspects above is implemented.

[0039] In a fifth aspect, the present application further provides a computer program product, including a computer program, which, when executed by a processor, implements any resource determination method described in the first aspect above.

[0040] The resource determination method, device, equipment, readable storage medium and program product described above first obtain the computing power resource information of the target user, which is used to indicate the resource information of the computing power resources used by the target user in the process of using the computing power function provided by the computing power platform. Then, according to the computing power resource information, the algorithm model is determined from a preset database, and the parameter values ​​of the algorithm parameters in the algorithm model are determined according to the user information of the target user. Finally, according to the parameter values ​​of the algorithm model, the algorithm model and the computing power resource information, the virtual resource value matching the computing power resources used by the target user is determined. In this way, when determining the virtual resource value corresponding to the computing power resources used by the target user, the algorithm model can be determined according to the computing power resource information of the target user and the parameter value of the algorithm model can be determined according to the user information. In this way, for different users, different calculation methods can be used through different algorithm models and parameter values ​​of the algorithm model to obtain the virtual resource value corresponding to the computing power resources used by the user. Therefore, the method for determining the virtual resource value is more flexible and can adapt to different users and different business scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 A schematic diagram of a flow chart of a resource determination method in one embodiment;

[0043] Figure 2 A schematic diagram of a flow chart of a resource determination method in another embodiment;

[0044] Figure 3 A schematic diagram of a flow chart of a resource determination method in another embodiment;

[0045] Figure 4 A schematic diagram of a flow chart of a resource determination method in another embodiment;

[0046] Figure 5 A schematic diagram of a flow chart of a resource determination method in another embodiment;

[0047] Figure 6 is a structural block diagram of a resource determination device in one embodiment;

[0048] Figure 7 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0050] In an exemplary embodiment, Figure 1 As shown, a resource determination method is provided, and the method is applied to a server as an example for explanation. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. It can be understood that the method can also be applied to a terminal, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server, and includes the following steps:

[0051] Step 101, obtain computing resource information of the target user.

[0052] Among them, the computing power resource information is used to indicate the resource information of the computing power resources used by the target user in the process of using the computing power function provided by the computing power platform. Optionally, the computing power platform can be integrated on the server that determines the resources, or it can be deployed on other servers or server clusters. The computing power resources can be resources such as the CPU (Central Processing Unit), memory or hard disk provided by the computing power platform used. The computing power resource information may include information such as the resource type, usage amount and duration of use of at least one computing power resource used by the target user. For example, taking the resource type of the computing power resource as CPU as an example, the usage amount can be expressed by the number of cores corresponding to the CPU resource, and the duration of use is the running time of the CPU used by the target user.

[0053] Step 102: determine the algorithm model from a preset database based on the computing resource information.

[0054] Among them, the preset database can be integrated on the server, or it can be placed on the cloud or other network servers, and the algorithm model in the database can be obtained through communication. The preset database can include multiple algorithm models corresponding to different computing resources. For example, based on the computing resource information, the computing resources used by the target user can be determined, so as to further determine the algorithm model for calculating the computing resources. Different computing resources correspond to different algorithm models. The computing resources used by the target user can include multiple types, and the corresponding algorithm models are determined according to various computing resources. Among them, the algorithm model can be a computing model obtained by training a machine learning algorithm, or it can be a function calculated according to the information of the computing resources.

[0055] Step 103: Determine parameter values ​​of algorithm parameters in the algorithm model according to the user information of the target user.

[0056] The user information of the target user may include identification information of the target user and type information of the target user. By setting different parameter values ​​of the algorithm parameters in the algorithm model for different users, different users can use the same algorithm model. Optionally, a preset database may be queried according to the identification information of the target user or the type information of the target user, and the parameter value of the algorithm parameter corresponding to the target user may be determined according to the query result.

[0057] Step 104, determining a virtual resource value that matches the computing resources used by the target user based on the parameter values ​​of the algorithm model, the algorithm model, and the computing resource information.

[0058] Among them, the virtual resource value that matches the computing power resources used by the target user is the billing result of the computing power resources used by the target user. The virtual resource value can be the amount determined by the billing of the computing power resources, or it can be a virtual asset issued by the computing power platform. According to the virtual resource values ​​that match the various computing power resources used by the target user, the virtual resource values ​​corresponding to all the computing power resources used by the target user can be obtained. For example, each computing power resource used by the target user can be used as a billing item. The virtual resource value that matches the computing power resources used by the target user can be obtained by summing up all the computing power resources used by the target user, that is, all the billing items.

[0059] Optionally, the method for determining the virtual resource value may include inputting parameter values ​​of the algorithm model and computing power resource information into the computing power model, and obtaining a virtual resource value that matches the computing power resources used by the target user based on the output result of the computing power model.

[0060] In the above embodiment, first, computing power resource information of the target user is obtained, which is used to indicate the resource information of the computing power resources used by the target user in the process of using the computing power function provided by the computing power platform. Then, according to the computing power resource information, the algorithm model is determined from a preset database, and the parameter values ​​of the algorithm parameters in the algorithm model are determined according to the user information of the target user. Finally, according to the parameter values ​​of the algorithm model, the algorithm model and the computing power resource information, the virtual resource value matching the computing power resources used by the target user is determined. In this way, when determining the virtual resource value corresponding to the computing power resources used by the target user, the algorithm model can be determined according to the computing power resource information of the target user and the parameter value of the algorithm model can be determined according to the user information. In this way, for different users, different calculation methods can be used through different algorithm models and parameter values ​​of the algorithm model to obtain the virtual resource value corresponding to the computing power resources used by the user. Therefore, the method for determining the virtual resource value is more flexible and can adapt to different users and different business scenarios.

[0061] In an embodiment of the present application, the computing power resource information includes type information of various computing power resources. The above step 102 may include: determining the first algorithm model and the second algorithm model corresponding to the various computing power resources used by the target user according to the type information of the various computing power resources.

[0062] Among them, the first algorithm model is used to determine the amount of computing power resources used by the target user; the second algorithm model is used to obtain the virtual resource value based on the amount of computing power resources. The amount of computing power resources used by the target user is the total amount of a type of computing power resources used by the target user. Taking the computing power resource as the CPU as an example, the amount of computing power resources of the CPU is the product of the number of CPU cores and the running time. According to the type information of various computing power resources, the database is queried to determine the first algorithm models corresponding to various computing power resources. Optionally, the database may include multiple sets of correspondences between the types of computing power resources and the first algorithm models.

[0063] It can be understood that the database also includes multiple sets of correspondences between the types of computing power resources and the second algorithm model. The database is queried through the type information of various computing power resources to determine the second algorithm models corresponding to various computing power resources. According to the second algorithm model, the virtual resource values ​​matching each computing power resource can be obtained. By summing up the virtual resource values ​​of various computing power resources, the virtual resource values ​​matching all computing power resources used by the target user can be obtained.

[0064] In the above embodiment, the first algorithm model and the second algorithm model corresponding to various computing resources are determined by the type information of computing resources, so that different computing resources can be flexibly calculated. At the same time, when the algorithm model is changed, it can be changed through the database, which is more convenient for maintenance.

[0065] In one embodiment, the steps of determining the second algorithm models corresponding to various computing resources used by the target user are as follows: Figure 2 As shown, including:

[0066] Step 201, query the database according to the type information of various computing resources.

[0067] The database includes the type information of computing power resources, candidate algorithm models, and the correspondence between model priorities. One type of computing power resource can correspond to multiple candidate algorithm models, and each candidate algorithm model also includes a model priority. Optionally, the candidate algorithm model can also include a model effectiveness condition, which can be time information, that is, the candidate algorithm model is valid within the time period indicated by the model effectiveness condition and invalid outside the time period.

[0068] Step 202: Determine, based on the query results, a plurality of candidate algorithm models corresponding to various computing resources used by the target user.

[0069] For ease of understanding, this embodiment is described by taking a computing power resource as an example. According to the type information of the computing power resource, such as a CPU, multiple candidate algorithm models corresponding to the CPU are queried from the database, and the effective candidate algorithm model among the multiple candidate algorithm models corresponding to the queried CPU is determined according to the model effectiveness condition. It can be understood that for other types of computing power resources, the steps for determining the candidate algorithm models corresponding to other types of computing power resources are the same as the above steps.

[0070] Step 203: Determine the second algorithm models corresponding to the various computing resources used by the target user according to the model priorities corresponding to the candidate algorithm models.

[0071] Then, according to the model priority corresponding to each valid candidate algorithm model, screening is performed, and the candidate algorithm model with the highest model priority among multiple candidate algorithm models is selected as the second algorithm model of the CPU. It can be understood that the steps for determining the corresponding second algorithm model for other types of computing power resources are the same as above, so as to obtain the second algorithm models corresponding to various computing power resources used by the target user. That is, a computing power resource determines the corresponding second algorithm model, and the virtual resource value matching the computing power resource can be obtained through the second algorithm model, that is, the billing result of the computing power resource.

[0072] In the above embodiment, for various computing power resources, different model effectiveness conditions and model priorities can be set for different candidate algorithm models, so that different second algorithm models can be obtained according to different situations to calculate the virtual resource value of the computing power resources, and the computing power resource calculation method is more flexible.

[0073] In an optional embodiment, the method further includes: if the candidate algorithm model satisfies a preset priority change condition, updating the model priority corresponding to the candidate algorithm model.

[0074] Among them, judgments can be made regularly to determine whether each candidate algorithm model meets the preset priority change conditions. If the preset priority change conditions are met, the model priority corresponding to the candidate algorithm model is changed.

[0075] Among them, the methods of determining whether the candidate algorithm model meets the preset priority change conditions may include the following.

[0076] In the first way, if the effective time of the model priority corresponding to the candidate algorithm model indicates that the model priority has timed out, the model priority corresponding to the candidate algorithm model is updated.

[0077] Optionally, the model priority corresponding to the candidate model algorithm may include an effective time. For example, the effective time may be 30 days, that is, the model priority is valid within 30 days. When it exceeds 30 days, the model priority of the candidate model algorithm times out, and the model priority corresponding to the candidate algorithm model needs to be updated. The model priority can be lowered or increased according to the usage of computing resources.

[0078] Alternatively, the second method is to obtain the total usage information of various computing resources in the computing platform. If the total usage information meets the preset usage conditions, the model priority corresponding to the candidate algorithm model is updated according to the total usage information.

[0079] Optionally, total usage information of various computing resources in the computing power platform is obtained. Taking the hard disk as an example, the total usage information of the hard disk by different users of the computing power platform is obtained. The total usage information may include the total amount of resources used by the hard disk of the computing power platform. The preset usage condition may be that the ratio of the hard disk used exceeds a first preset threshold, such as the hard disk usage rate exceeds 80%. At this time, if the total usage information meets the preset usage condition, that is, the hard disk usage rate of the computing power platform has exceeded 80%, the load of the computing power platform is large, which may affect the normal use of the computing power resources. Therefore, the model priority corresponding to each candidate algorithm model corresponding to the hard disk can be updated. For example, different candidate algorithm models can correspond to different discount information. The model priority corresponding to the candidate algorithm model with a low discount is increased, and the model priority corresponding to the algorithm model with a high discount is lowered, thereby improving the computing power billing result of the hard disk computing power resources, so that users can save the use of computing power resources.

[0080] It is understandable that the preset usage condition can also be that the ratio of hard disk usage is lower than a second preset threshold, such as the hard disk usage rate is lower than 20%. At this time, if the total usage information meets the preset usage condition, that is, the hard disk usage rate of the computing power platform is lower than 20%, the load of the computing power platform is small. Therefore, the model priority corresponding to each candidate algorithm model corresponding to the hard disk can be updated. For example, the model priority corresponding to the high-discount candidate algorithm model can be increased, and the model priority corresponding to the low-discount algorithm model can be lowered, thereby reducing the computing power billing result of the hard disk computing power resources, so that users can improve the use of computing power resources and improve the utilization rate of hard disk resources.

[0081] Alternatively, in a third manner, if a priority adjustment instruction is received, the model priority corresponding to the candidate algorithm model is updated according to the priority adjustment instruction.

[0082] Optionally, if a priority adjustment instruction is received, the priority adjustment instruction may include the type of computing resources, the identifier of the candidate algorithm model, and the target model priority. The candidate algorithm model whose model priority is to be modified is determined based on the type of computing resources and the identifier of the candidate algorithm model, and then the model priority corresponding to the candidate algorithm model is adjusted according to the target model priority.

[0083] In the above embodiment, the model priority of each candidate algorithm model is changed by judging whether each candidate algorithm model meets the preset priority change conditions. The model priority of each candidate algorithm model can be adjusted according to the actual usage of computing resources to achieve adaptive adjustment of the model priority.

[0084] In one embodiment, the process of determining the virtual resource value in step 104 is as follows: Figure 3 As shown, including:

[0085] Step 301, analysing the algorithm model to obtain multiple target dimensions.

[0086] As described above, after determining the algorithm model according to the computing power resource information, taking the first algorithm model as an example, the first algorithm model for calculating the CPU resource amount is determined according to the type information of the computing power resource such as CPU, the first algorithm model is parsed and processed to obtain multiple target dimensions, which are the model parameters in the first algorithm model. The target dimensions may include the variable values ​​of the model parameters and the units of the model parameters.

[0087] Step 302, querying a database according to each target dimension to determine a conversion coefficient of the algorithm model.

[0088] Since the target dimension includes the unit of the model parameter, the unit of the target dimension may not match the benchmark unit required by the first algorithm model. Therefore, the database is queried according to each target dimension. The database may include a unit configuration table. According to the unit configuration table, the conversion coefficient between the unit of the target dimension and the benchmark unit required by the first algorithm model can be determined.

[0089] Step 303, determining a virtual resource value that matches the computing resources used by the target user based on the conversion coefficient, the parameter value of the algorithm model, the algorithm model, and the computing resource information.

[0090] Optionally, the first algorithm model is modified according to the conversion coefficient to obtain a first algorithm model that meets the requirements of the benchmark unit, and then the resource amount of the computing power resource CPU is calculated based on the first algorithm model and information such as CPU usage and usage duration in the computing power resource information.

[0091] It is understandable that the second algorithm model calculation process also needs to be analyzed to obtain multiple dimensions corresponding to the second algorithm model, and then the conversion coefficient is determined by querying the database according to each dimension. According to the conversion coefficient, the second algorithm model, the parameter value of the algorithm model and the computing power resource information, the virtual resource value that matches the computing power resources used by the target user can be determined. The sum of the virtual resource values ​​corresponding to all computing power resources used by the target user is the virtual resource value corresponding to the total computing power resources used by the target user.

[0092] In the above embodiment, multiple dimensions are obtained by analyzing the algorithm model, and then the conversion coefficient of the algorithm model is determined according to each dimension, so that the algorithm model can adapt to units of various dimensions without the need for manual conversion, and the calculation efficiency is higher.

[0093] In the embodiments of the present application, Figure 4 As shown, a resource determination method is provided, comprising:

[0094] Step 401, obtaining computing resource information of the target user.

[0095] Step 402, determine the algorithm model from a preset database based on the computing resource information.

[0096] According to the type information of various computing resources, the first algorithm model and the second algorithm model corresponding to the various computing resources used by the target user are determined.

[0097] Step 403: Determine parameter values ​​of algorithm parameters in the algorithm model according to the user information of the target user.

[0098] Step 404, analysing the algorithm model to obtain multiple target dimensions.

[0099] Step 405, querying a database according to each target dimension to determine a conversion coefficient of the algorithm model.

[0100] Step 406, determining a virtual resource value that matches the computing resources used by the target user based on the conversion coefficient, the parameter value of the algorithm model, the algorithm model, and the computing resource information.

[0101] In the embodiment of the present application, for ease of understanding, the above resource determination method is illustrated by way of example. The flowchart of the resource determination method is as follows: Figure 5 The billing formula of all computing resources of the target user is abstracted into a function expression, as shown in the following formula. The virtual resource value, that is, the amount of all computing resources used by the target user, is the sum of the products of each billing item, the price and the corresponding discount. Among them, a billing item represents a type of computing resource.

[0102]

[0103] Optionally, expression a, ie, the second algorithm model, may be as shown below: The custom coefficient may be a parameter value of an algorithm parameter determined according to user information of different users.

[0104]

[0105] Optionally, for different conditions, the above formula can also be expressed as a piecewise function, as shown in the following formula. Condition 1 and condition 2 can be logical expressions, and the algorithm parameters are determined to be different parameter values ​​by judging whether the user information of the target user meets the corresponding conditions.

[0106]

[0107] Optionally, the price formula p of each billing item can be segmented according to different conditions such as linear billing and step billing. Taking step billing as an example, as shown below.

[0108]

[0109] Among them, k1, k2, k n Can correspond to different prices, x is the usage of the billing item, m1, m2, m n-1 Corresponding to different usage thresholds.

[0110] The billing item resource amount expression f is also the first algorithm model. Different billing items correspond to different first algorithm models. Taking the billing item with CPU as the computing resource as an example, the first algorithm model is shown as follows.

[0111] CPU resources = number of cores * running time

[0112]

[0113] The variable job.jobProcNum represents the core number variable corresponding to the computing resource CPU, which is a dimension. In addition to the numeric part, the variable job.jobProcNum includes the unit of "core·second".

[0114] In order to realize the above-mentioned resource determination, the database is configured according to the above-mentioned algorithm model, and the database may include a dimensional configuration table, a unit configuration table, a billing item configuration table and a billing model configuration table, wherein the dimensional configuration table may include multiple groups of correspondences between dimensional identifiers and dimensional units, the unit configuration table may include multiple groups of correspondences between unit identifiers, unit categories, whether they are basic units, and conversion coefficients, the billing model configuration table includes multiple groups of correspondences between billing item identifiers, effective conditions of billing items, first algorithm models, and base units, and the billing configuration table includes multiple groups of correspondences between product identifiers, billing item identifiers, model effective conditions, model priorities, and second algorithm models.

[0115] like Figure 5 The steps of the resource determination method shown include:

[0116] 1. Obtain the target user's computing power resource information, including the type, usage, and usage duration of the computing power resources used by the target user.

[0117] 2. Match the billing item configuration table in the database with the billing item validity conditions. The validity conditions may be that the computing power resource information includes the type information corresponding to the computing power resource billing item. Determine the list of all billing items that need to be billed based on the matching results.

[0118] 3. Get a target billing item in the billing item list.

[0119] 4. Query the database according to the target billing item, obtain the first algorithm model for calculating the resource amount of the target billing item, decompose the first algorithm model into each independent dimension, obtain the unit corresponding to each dimension, obtain the conversion coefficient with the basic unit according to the unit of each dimension, and modify the first algorithm model of the target billing item according to the conversion coefficient. After modification, calculate through the script interpreter, and calculate according to the first algorithm model and the resource information of the computing power resources to obtain the resource amount of the target billing item.

[0120] 5. Determine the second algorithm model with the highest priority for calculating the amount of the target billing item based on user information and model priority, decompose the second algorithm model into independent dimensions, obtain the unit corresponding to each dimension, obtain the conversion coefficient with the basic unit based on the unit of each dimension, and modify the second algorithm model of the target billing item based on the conversion coefficient. After modification, calculate through the script interpreter, and calculate the amount of the target billing item based on the second algorithm model, the parameter values ​​of the algorithm parameters determined by the user information, and the resource information of the computing power resources.

[0121] 6. Repeat the above steps until the billing amounts of all billing items in the billing item list are obtained. The sum of the amounts of all billing items is the final billing amount, which is the virtual resource value corresponding to all computing resources used by the target user.

[0122] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0123] Based on the same inventive concept, the embodiment of the present application also provides a resource determination device for implementing the resource determination method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more resource determination device embodiments provided below can refer to the limitations on the resource determination method above, and will not be repeated here.

[0124] In an exemplary embodiment, Figure 6 As shown, a resource determination device 600 is provided, comprising: an acquisition module 601, a first determination module 602, a second determination module 603 and a third determination module 604, wherein:

[0125] The acquisition module 601 is used to acquire computing power resource information of the target user, where the computing power resource information is used to indicate resource information of computing power resources used by the target user in the process of using the computing power function provided by the computing power platform;

[0126] A first determination module 602 is used to determine an algorithm model from a preset database according to computing power resource information;

[0127] A second determination module 603 is used to determine parameter values ​​of algorithm parameters in the algorithm model according to user information of the target user;

[0128] The third determination module 604 is used to determine the virtual resource value that matches the computing power resources used by the target user based on the parameter values ​​of the algorithm model, the algorithm model and the computing power resource information.

[0129] In one embodiment, the computing power resource information includes type information of various computing power resources. The first determination module 602 is specifically used to determine the first algorithm model and the second algorithm model corresponding to the various computing power resources used by the target user according to the type information of the various computing power resources; the first algorithm model is used to determine the amount of computing power resources used by the target user; the second algorithm model is used to obtain the virtual resource value according to the amount of computing power resources.

[0130] In one embodiment, the first determination module 602 is specifically used to query a database based on type information of various computing power resources, where the database includes the type information of computing power resources, candidate algorithm models, and the correspondence between model priorities; based on the query results, determine multiple candidate algorithm models corresponding to various computing power resources used by the target user; and based on the model priorities corresponding to the candidate algorithm models, determine second algorithm models corresponding to various computing power resources used by the target user.

[0131] In one of the embodiments, the device includes an updating module for updating the model priority corresponding to the candidate algorithm model if the candidate algorithm model meets the preset priority change condition.

[0132] In one of the embodiments, the update module is specifically used to update the model priority corresponding to the candidate algorithm model if the effective time of the model priority corresponding to the candidate algorithm model indicates that the model priority has timed out; or, obtain the total usage information of various computing resources in the computing power platform, and if the total usage information meets the preset usage conditions, update the model priority corresponding to the candidate algorithm model according to the total usage information; or, if a priority adjustment instruction is received, update the model priority corresponding to the candidate algorithm model according to the priority adjustment instruction.

[0133] In one embodiment, the third determination module 604 is specifically used to parse the algorithm model to obtain multiple target dimensions; determine the conversion coefficient of the algorithm model by querying the database according to each target dimension; and determine the virtual resource value that matches the computing power resources used by the target user based on the conversion coefficient, the parameter value of the algorithm model, the algorithm model, and the computing power resource information.

[0134] Each module in the above resource determination device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0135] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 7As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store algorithm model data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a resource determination method is implemented.

[0136] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0137] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the following steps when executing the computer program: obtaining computing power resource information of a target user, the computing power resource information being used to indicate resource information of computing power resources used by the target user in the process of using computing power functions provided by a computing power platform; determining an algorithm model from a preset database based on the computing power resource information; determining parameter values ​​of algorithm parameters in the algorithm model based on user information of the target user; and determining a virtual resource value that matches the computing power resources used by the target user based on the parameter values ​​of the algorithm model, the algorithm model, and the computing power resource information.

[0138] In one embodiment, the computing power resource information includes type information of various computing power resources, and the processor also implements the following steps when executing the computer program: determining the first algorithm model and the second algorithm model corresponding to the various computing power resources used by the target user according to the type information of the various computing power resources; the first algorithm model is used to determine the amount of computing power resources used by the target user; the second algorithm model is used to obtain the virtual resource value based on the amount of computing power resources.

[0139] In one embodiment, when the processor executes the computer program, the following steps are also implemented: querying a database based on type information of various computing power resources, the database including the correspondence between type information of computing power resources, candidate algorithm models, and model priority; determining, based on the query results, multiple candidate algorithm models corresponding to various computing power resources used by the target user; determining, based on the model priorities corresponding to the candidate algorithm models, second algorithm models corresponding to various computing power resources used by the target user.

[0140] In one embodiment, when the processor executes the computer program, the following steps are also implemented: if the candidate algorithm model meets the preset priority change condition, the model priority corresponding to the candidate algorithm model is updated.

[0141] In one embodiment, the processor further implements the following steps when executing the computer program: if the effective time of the model priority corresponding to the candidate algorithm model indicates that the model priority has timed out, then the model priority corresponding to the candidate algorithm model is updated; or, the total usage information of various computing resources in the computing power platform is obtained, and if the total usage information meets the preset usage conditions, then the model priority corresponding to the candidate algorithm model is updated according to the total usage information; or, if a priority adjustment instruction is received, then the model priority corresponding to the candidate algorithm model is updated according to the priority adjustment instruction.

[0142] In one embodiment, when the processor executes the computer program, the following steps are also implemented: the algorithm model is parsed to obtain multiple target dimensions; the conversion coefficient of the algorithm model is determined by querying the database according to each target dimension; and the virtual resource value that matches the computing power resources used by the target user is determined according to the conversion coefficient, the parameter value of the algorithm model, the algorithm model and the computing power resource information.

[0143] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: obtaining computing power resource information of a target user, the computing power resource information being used to indicate resource information of computing power resources used by the target user in the process of using computing power functions provided by a computing power platform; determining an algorithm model from a preset database based on the computing power resource information; determining parameter values ​​of algorithm parameters in the algorithm model based on the user information of the target user; and determining a virtual resource value that matches the computing power resources used by the target user based on the parameter values ​​of the algorithm model, the algorithm model, and the computing power resource information.

[0144] In one embodiment, the computing power resource information includes type information of various computing power resources, and the computer program also implements the following steps when executed by the processor: determining the first algorithm model and the second algorithm model corresponding to the various computing power resources used by the target user according to the type information of the various computing power resources; the first algorithm model is used to determine the amount of computing power resources used by the target user; the second algorithm model is used to obtain the virtual resource value based on the amount of computing power resources.

[0145] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: querying a database based on type information of various computing power resources, the database including the correspondence between type information of computing power resources, candidate algorithm models, and model priority; determining, based on the query results, multiple candidate algorithm models corresponding to various computing power resources used by the target user; determining, based on the model priorities corresponding to the candidate algorithm models, second algorithm models corresponding to various computing power resources used by the target user.

[0146] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: if the candidate algorithm model meets the preset priority change condition, the model priority corresponding to the candidate algorithm model is updated.

[0147] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: if the effective time of the model priority corresponding to the candidate algorithm model indicates that the model priority has timed out, then the model priority corresponding to the candidate algorithm model is updated; or, the total usage information of various computing resources in the computing power platform is obtained, and if the total usage information meets the preset usage conditions, the model priority corresponding to the candidate algorithm model is updated according to the total usage information; or, if a priority adjustment instruction is received, the model priority corresponding to the candidate algorithm model is updated according to the priority adjustment instruction.

[0148] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: the algorithm model is parsed to obtain multiple target dimensions; the conversion coefficient of the algorithm model is determined by querying the database according to each target dimension; and the virtual resource value that matches the computing power resources used by the target user is determined according to the conversion coefficient, the parameter value of the algorithm model, the algorithm model and the computing power resource information.

[0149] In one embodiment, a computer program product is provided, including a computer program, which implements the following steps when executed by a processor: obtaining computing power resource information of a target user, the computing power resource information being used to indicate resource information of computing power resources used by the target user in the process of using computing power functions provided by a computing power platform; determining an algorithm model from a preset database based on the computing power resource information; determining parameter values ​​of algorithm parameters in the algorithm model based on user information of the target user; and determining a virtual resource value that matches the computing power resources used by the target user based on the parameter values ​​of the algorithm model, the algorithm model, and the computing power resource information.

[0150] In one embodiment, the computing power resource information includes type information of various computing power resources, and the computer program also implements the following steps when executed by the processor: determining the first algorithm model and the second algorithm model corresponding to the various computing power resources used by the target user according to the type information of the various computing power resources; the first algorithm model is used to determine the amount of computing power resources used by the target user; the second algorithm model is used to obtain the virtual resource value based on the amount of computing power resources.

[0151] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: querying a database based on type information of various computing power resources, the database including the correspondence between type information of computing power resources, candidate algorithm models, and model priority; determining, based on the query results, multiple candidate algorithm models corresponding to various computing power resources used by the target user; determining, based on the model priorities corresponding to the candidate algorithm models, second algorithm models corresponding to various computing power resources used by the target user.

[0152] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: if the candidate algorithm model meets the preset priority change condition, the model priority corresponding to the candidate algorithm model is updated.

[0153] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: if the effective time of the model priority corresponding to the candidate algorithm model indicates that the model priority has timed out, then the model priority corresponding to the candidate algorithm model is updated; or, the total usage information of various computing resources in the computing power platform is obtained, and if the total usage information meets the preset usage conditions, the model priority corresponding to the candidate algorithm model is updated according to the total usage information; or, if a priority adjustment instruction is received, the model priority corresponding to the candidate algorithm model is updated according to the priority adjustment instruction.

[0154] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented: the algorithm model is parsed to obtain multiple target dimensions; the conversion coefficient of the algorithm model is determined by querying the database according to each target dimension; and the virtual resource value that matches the computing power resources used by the target user is determined according to the conversion coefficient, the parameter value of the algorithm model, the algorithm model and the computing power resource information.

[0155] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0156] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0157] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0158] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A resource determination method, characterized in that: The method comprises: Obtain computing power resource information of the target user, where the computing power resource information is used to indicate resource information of computing power resources used by the target user in the process of using the computing power function provided by the computing power platform; Determine an algorithm model from a preset database according to the computing power resource information; Determining parameter values ​​of algorithm parameters in the algorithm model according to the user information of the target user; According to the parameter values ​​of the algorithm model, the algorithm model and the computing power resource information, a virtual resource value matching the computing power resources used by the target user is determined.

2. The method according to claim 1, characterized in that The computing power resource information includes type information of various computing power resources, and determining the algorithm model from a preset database according to the computing power resource information includes: Determine, according to the type information of various computing resources, first algorithm models and second algorithm models corresponding to various computing resources used by the target user; The first algorithm model is used to determine the amount of computing resources used by the target user; the second algorithm model is used to obtain the virtual resource value based on the amount of computing resources.

3. The method according to claim 2, characterized in that The second algorithm model for determining the respective corresponding computing resources used by the target user includes: Querying the database according to the type information of various computing resources, wherein the database includes the type information of computing resources, candidate algorithm models, and the correspondence between model priorities; According to the query results, multiple candidate algorithm models corresponding to various computing resources used by the target user are determined; According to the model priorities corresponding to the candidate algorithm models, second algorithm models corresponding to various computing resources used by the target user are determined.

4. The method according to claim 3, characterized in that The method further comprises: If the candidate algorithm model meets the preset priority change condition, the model priority corresponding to the candidate algorithm model is updated.

5. The method according to claim 4, characterized in that If the candidate algorithm model satisfies the preset priority change condition, updating the model priority corresponding to the candidate algorithm model includes: If the effective time of the model priority corresponding to the candidate algorithm model indicates that the model priority has timed out, updating the model priority corresponding to the candidate algorithm model; Alternatively, the total usage information of various computing resources in the computing power platform is obtained, and if the total usage information meets the preset usage conditions, the model priority corresponding to the candidate algorithm model is updated according to the total usage information; Alternatively, if a priority adjustment instruction is received, the model priority corresponding to the candidate algorithm model is updated according to the priority adjustment instruction.

6. The method according to claim 1, characterized in that The determining, according to the parameter value of the algorithm model, the algorithm model and the computing resource information, a virtual resource value matching the computing resource used by the target user comprises: Analytically processing the algorithm model to obtain multiple target dimensions; Querying the database according to each of the target dimensions to determine the conversion coefficient of the algorithm model; A virtual resource value that matches the computing power resources used by the target user is determined based on the conversion coefficient, the parameter value of the algorithm model, the algorithm model, and the computing power resource information.

7. A resource determination device, characterized in that: The device comprises: An acquisition module, used to acquire computing power resource information of a target user, where the computing power resource information is used to indicate resource information of computing power resources used by the target user in the process of using the computing power function provided by the computing power platform; A first determination module, configured to determine an algorithm model from a preset database according to the computing power resource information; A second determination module, used to determine parameter values ​​of algorithm parameters in the algorithm model according to user information of the target user; The third determination module is used to determine the virtual resource value that matches the computing power resources used by the target user based on the parameter value of the algorithm model, the algorithm model and the computing power resource information.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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