Computing power resource pricing method, device, equipment and medium

Abstract

The invention provides a computing power resource pricing method and device, equipment and a medium, and relates to the technical field of computers, and the method comprises the steps: obtaining resource demand information and identity information of a resource demand side; acquiring historical behavior information corresponding to the resource demander according to the identity information; determining a preference coefficient corresponding to the resource demand side according to resource demand parameters and historical behavior information in the resource demand information; determining a calculation model corresponding to the resource demand side according to a charging mode in the resource demand information; and inputting the tenant level, the preferential coefficient and the resource demand parameter into a calculation model corresponding to the resource demand side to obtain a calculation power resource pricing result corresponding to the resource demand side. According to the technical scheme provided by the embodiment of the invention, the computing power resource requirements of different resource demanders are met through multiple charging modes, and meanwhile, the computing power resource utilization rate is improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a computing resource pricing method, apparatus, device and medium. Background Art

[0002] Existing pricing methods for computing power resources are relatively rigid, lacking flexibility and dynamic adjustment capabilities. However, computing power resource prices fluctuate significantly due to differences in demand during busy and off-peak hours. This rigid billing model weakens computing power providers' competitiveness in the market, leading to higher equipment idle rates and reducing their overall economic benefits. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a computing power resource pricing method, device, equipment and medium, which can improve the competitiveness of the pricing mechanism of the computing power resource supplier through a variety of flexible and dynamically adjustable billing modes, thereby improving the absorption rate and yield rate of computing power resources and maximizing the operational benefits of computing power resources.

[0004] In a first aspect, an embodiment of the present invention provides a computing resource pricing method, including:

[0005] Obtain resource demand information and identity information of resource demanders; wherein the identity information includes terminal demanders and intermediate demanders;

[0006] Based on identity information, obtain historical behavior information corresponding to the resource demander;

[0007] Determine the preferential coefficient corresponding to the resource demander based on the resource demand parameters and historical behavior information in the resource demand information;

[0008] Determine the calculation model corresponding to the resource demander based on the billing mode in the resource demand information; the billing mode includes a monthly billing mode, a busy hour billing mode, and / or a non-busy hour billing mode;

[0009] Input the preferential coefficient and resource demand parameters into the calculation model corresponding to the resource demander to obtain the computing power resource pricing result corresponding to the resource demander.

[0010] In a second aspect, an embodiment of the present invention further provides a computing power resource pricing device, including:

[0011] An information acquisition module is used to obtain resource demand information and identity information of resource demanders; wherein the identity information includes terminal demanders and intermediate demanders;

[0012] The historical information acquisition module is used to obtain the historical behavior information corresponding to the resource demander based on the identity information;

[0013] A preferential determination module is used to determine the preferential coefficient corresponding to the resource demander based on the resource demand parameters and historical behavior information in the resource demand information;

[0014] A calculation model determination module is used to determine the calculation model corresponding to the resource demander according to the billing mode in the resource demand information; the billing mode includes a monthly billing mode, a busy hour billing mode and / or a non-busy hour billing mode;

[0015] The pricing module is used to input the preferential coefficient and resource demand parameters into the calculation model corresponding to the resource demander to obtain the computing power resource pricing result corresponding to the resource demander.

[0016] In a third aspect, an embodiment of the present invention further provides an electronic device comprising a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement the computing power resource pricing method of the first aspect mentioned above.

[0017] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the computing power resource pricing method of the first aspect mentioned above.

[0018] Other features and advantages of the present invention will be described in the following description, or some of them may be inferred or clearly determined from the description, or may be learned by practicing the above-mentioned techniques of the present invention. To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following is a detailed description of preferred embodiments with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the specific embodiments or the description of the prior art. The drawings described below are some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0020] Figure 1 A flowchart of a computing resource pricing method provided by an embodiment of the present invention;

[0021] Figure 2 A schematic diagram of the structure of a computing power resource pricing device provided by an embodiment of the present invention;

[0022] Figure 3 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. To facilitate understanding of this embodiment, a computing power resource pricing method disclosed in an embodiment of the present invention is first introduced in detail.

[0024] Example 1

[0025] The embodiment of the present invention provides a method for pricing computing power resources. Figure 1 A flowchart of a computing power resource pricing method provided in an embodiment of the present invention includes the following steps:

[0026] Step S101: Obtain resource demand information and identity information of a resource demander.

[0027] A resource demander is a user who needs to use computing resources. Identity information describes the type of resource demander. This includes both end-user and intermediary demanders. An end-user is a resource demander who acquires computing resources for their own use. An intermediary demander is a resource demander who redistributes acquired computing resources for use by more resource demanders. Resource demand information refers to the resource demander's specific demand for computing resources. Exemplarily, this information includes at least the resource usage duration, resource type, resource demand volume, and the resource demander's billing model. Resource usage duration refers to the duration the computing resource is required to use. Resource type refers to the type of device used to provide computing resources, which can also be understood as a type of computing resource. There is at least one resource type, and exemplary resource types include bare metal servers and cloud servers. Resource demand volume refers to the amount of computing resources required for each resource type. The resource demander's billing model refers to the billing method selected by the resource demander for computing resource usage duration when renting computing resources. This model clearly stipulates the calculation rules for the fees that the demander should bear for using computing resources for a specific period of time.

[0028] Specifically, in an embodiment of the present invention, multiple computing resource suppliers can provide computing resources, which can be integrated to form a computing network and a unified computing resource pool. Based on this computing resource pool, resource demanders can initiate computing resource requests to the computing resource pool, including the resource demander's resource demand information and identity information. After receiving the computing resource request, the computing resource pool can obtain the resource request information and identity information included in the computing resource request to allocate computing resources to the resource demander and determine the price of the computing resources. The computing resource pool can simultaneously receive computing resource requests from multiple resource demanders.

[0029] Step S102: Obtain historical behavior information corresponding to the resource demander based on the identity information.

[0030] Historical behavior information refers to the historical usage of computing resources by resource demanders. Historical behavior information includes at least historical cumulative usage, historical billing model information, and historical demander level information. Historical cumulative usage describes the cumulative results of computing resource usage by resource demanders before sending a computing resource request. Historical cumulative usage includes cumulative resource usage duration, historical resource types, and their corresponding historical resource usage quantities. Cumulative resource usage duration refers to the cumulative duration of computing resource usage by resource demanders before sending a computing resource request. Historical resource types refer to the resource types used by resource demanders before sending a computing resource request. Historical resource usage quantity refers to the cumulative amount of computing resources used by resource demanders for each resource type before sending a computing resource request.

[0031] Specifically, when the identity information indicates that the resource demander is a terminal demander, the resource supplier obtains the terminal demander's historical behavior information. When the identity information indicates that the resource demander is an intermediate demander, the resource supplier obtains the intermediate demander's historical behavior information. After obtaining computing power resources, the intermediate demander will redistribute the acquired computing power resources for use by more terminal demanders. Therefore, the historical behavior information corresponding to the intermediate demander can also be the historical behavior information of at least one other terminal demander.

[0032] Step S103: determining the preferential coefficient corresponding to the resource demander according to the resource demand parameters and historical behavior information in the resource demand information.

[0033] Resource demand parameters describe the type and quantity of computing power currently required by resource demanders. Exemplarily, these parameters include at least the resource usage duration, resource type, and resource usage quantity as described in the resource demand information. Specifically, the resource demand parameters and historical behavior information can be input into a preferential coefficient calculation model to obtain the corresponding preferential coefficient for the resource demander.

[0034] Specifically, the preferential coefficient can be determined through steps A1 to A3.

[0035] Step A1: Input the resource demand parameters and historical behavior information in the resource demand information into the tenant scoring model to obtain the tenant score.

[0036] The tenant scoring model is a pre-built model that can be constructed using statistical analysis, machine learning algorithms, and other methods. The tenant scoring model comprehensively considers the resource demand parameters and historical behavior information of the resource demander, quantitatively evaluates them, and assigns a score. The tenant scoring model is used to evaluate different resource demanders. Specifically, the tenant scoring model can be expressed as follows:

[0037]

[0038] Among them, P is the tenant score; α is the weight value corresponding to the historical behavior information; β is the weight value corresponding to the resource demand parameter; ε ij is the cumulative usage time of computing power resources of type i corresponding to resource demanders of type j; ij is the historical resource usage of the i-th type of computing resources corresponding to the j-th type of resource demander, The resource usage time of the i-th type of computing resources corresponding to the j-th type of resource demander; ω ij The amount of computing power used by the jth type of resource demander for the i-th type of computing resource. i refers to a bare metal server or cloud server, j refers to the end user or intermediate user, and α and β can be pre-set based on actual conditions.

[0039] Step A2: According to the tenant score, the tenant level corresponding to the resource demander is obtained from a preset demander level table.

[0040] Tenant levels are used to group different resource demanders. The preset demander level table records the correspondence between tenant scores and tenant levels, and clarifies the tenant levels corresponding to different tenant score ranges. By querying the preset demander level table, the tenant level corresponding to the tenant score can be queried based on the tenant score of the resource demander, and used as the tenant level corresponding to the resource demander. Specifically, in the architectural design of the demander level table, a tenant score and tenant level correspondence system is constructed for two types of resource demanders with identity information: terminal demanders and intermediate demanders. Through this system, the tenant level can be accurately defined based on the level of the tenant score. For example, when the identity information is the terminal demander, the tenant level corresponding to the tenant score can be queried from the tenant score and tenant level correspondence system corresponding to the terminal demander based on the tenant score. When the identity information is the intermediate demander, the tenant level corresponding to the tenant score can be queried from the tenant score and tenant level correspondence system corresponding to the intermediate demander based on the tenant score. For example, when the identity information is the terminal demander (or intermediate demander), the tenant score is a. From the tenant score and tenant grade corresponding system of the terminal demander (or intermediate demander), it is found that the tenant score is a, and the corresponding tenant grade is the third-level customer of the terminal demander (or intermediate demander).

[0041] Step A3: Determine the preferential coefficient corresponding to the tenant level based on the correspondence between the preset level and the preferential coefficient.

[0042] The correspondence between the preset level and the preferential coefficient is used to describe the preferential coefficient corresponding to different tenant levels. This coefficient is used to give corresponding discounts to resource demanders of different levels in the computing power resource pricing process. Higher-level tenants usually correspond to a larger preferential coefficient, which means that they can enjoy a larger price discount. Specifically, for intermediate demanders and terminal demanders, a correspondence between the preset level and the preferential coefficient can be set respectively. Based on the identity information of the resource demander, the preferential coefficient corresponding to the tenant level can be queried from the correspondence between the corresponding preset level and the preferential coefficient. By comprehensively considering the resource demand parameters and historical behavior information in the resource demand information to determine the tenant level and preferential coefficient, it is possible to more comprehensively and accurately evaluate the value and demand characteristics of the resource demander, and provide accurate pricing and high-quality services.

[0043] Step S104: determining a calculation model corresponding to the resource demander according to the billing mode in the resource demand information.

[0044] Billing modes include monthly, busy-hour, and / or idle-hour billing. Monthly billing refers to a billing mode used by resource demanders for long-term computing resource usage based on a calendar month. In this application, the computing resource usage period is one month, meaning the computing resource usage fee is calculated once every month. Busy-hour billing refers to a billing mode used by resource demanders for short-term computing resource usage. The computing resource usage period can be one day, one hour, or one minute, meaning the computing resource usage fee is calculated once every day, hour, or minute. Idle-hour billing refers to a billing mode where the resource supplier sets a resource load warning threshold based on the type of computing resource. Below this warning threshold, the resource demander can use idle resource billing. For example, if the supplier sets a CPU usage warning threshold of 70%, and the supplier monitors that the current CPU usage is 30%, the idle-hour billing mode is offered to the demander. If the supplier monitors that the current CPU usage is 70%, the supplier disables the idle-hour billing mode, and the demander cannot choose the idle-hour billing mode. Based on different computing resource types, preset thresholds are set to determine whether the user can choose the off-peak billing model. This ensures high computing resource utilization while avoiding the adverse effects of over-utilization, such as performance degradation, hardware loss, and increased energy consumption. The off-peak billing cycle refers to the period from the start of computing resource use to the end of use. In other words, when using the off-peak billing model, computing resource usage fees are settled only once within the current usage cycle. Different billing models correspond to different calculation models to accurately calculate the fees payable by end users or intermediate users. The calculation model is a pricing calculation model constructed by the resource supplier based on the billing model in the resource demand information. It is understood that the resource supplier can provide billing models corresponding to multiple billing models. The resource demander can select one of the billing models provided by the resource supplier based on their needs. The resource supplier then determines the pricing result for the resource demander's computing resources based on the corresponding billing model. It should be noted that in this application, the monthly billing mode is independent of the other two billing modes and has the highest priority for the corresponding supply of computing resources. Except for the monthly billing, the other two billing modes are based on the utilization rate of the supplier's computing resources to distinguish between idle time (low priority) and busy time (second highest priority).

[0045] In step S105, the preferential coefficient and resource demand parameters are input into the calculation model corresponding to the resource demander to obtain the computing power resource pricing result corresponding to the resource demander.

[0046] The computing power resource pricing result refers to the fee that the resource demander needs to pay when using computing power resources for one cycle. Specifically, after the preferential coefficient and resource demand parameters are input into the calculation model corresponding to the resource demander, the calculation model calculates the pricing of computing power resources corresponding to each resource type for one cycle based on the resource type and resource demand in the resource demand parameters, and sums them up. The product of the summation result and the preferential coefficient is used as the computing power resource pricing result corresponding to the resource demander. Exemplarily, the pricing of each resource type can be determined based on the product of the resource demand, resource usage time and resource unit price of the resource type, wherein the resource unit price of each resource type can be pre-set according to actual conditions.

[0047] Furthermore, after obtaining the computing power resource pricing result corresponding to the resource demander, it also includes: according to the billing model, querying the billing cycle corresponding to the billing model; judging whether the billing duration of the resource demander has reached the billing cycle, and if so, settling the resource bill based on the computing power resource pricing result and resource demand corresponding to the resource demander, and sending the resource bill to the resource demander. If not, the billing duration is updated in real time and it is judged whether the billing cycle has been reached. It is judged whether the current billing cycle has reached the preset price adjustment time, and if so, it returns to step S102. If not, the billing unit price of the previous cycle is used as the billing unit price of the current cycle, and it returns to the step of judging whether the billing duration of the resource demander has reached the billing cycle.

[0048] Specifically, a billing cycle refers to the settlement period for a resource demander's use of computing resources. For example, a monthly subscription billing model uses the total number of days in a calendar month as a billing cycle; a busy-hour billing model uses days, hours, or minutes as a billing cycle. The billing duration refers to the duration used to settle the resource bill for the resource demander's use of computing resources. This duration is calculated from the last resource bill settlement to the current time. For example, in a monthly subscription billing model, resource bills are typically settled monthly. If the billing duration is greater than or equal to the billing cycle, the resource demander's billing duration has reached the billing cycle. At this point, the resource bill is settled and sent to the resource demander. If the billing duration is less than the billing cycle, the billing duration is updated in real time to determine whether the billing cycle has been reached. The billing bill refers to the total charges for all types of computing resources used by the resource demander during a billing cycle.

[0049] For example, resource bills can be settled using the following formula:

[0050]

[0051] Where X is the resource bill; is the resource unit price corresponding to the mth type of resource, κ m is the resource demand corresponding to the mth type of resource, and m is the total number of resource types required by the demander.

[0052] The preset price adjustment time refers to the time set in advance for adjusting the pricing result of the computing power resources corresponding to the resource demander. It can be understood that in the monthly billing mode and the busy hour billing mode, the duration of the resource demander's use of computing power resources may include multiple billing cycles. The current billing cycle refers to the billing cycle in which the current time is located. If the current time is the same as the preset price adjustment time, or after the preset price adjustment time, it is determined that the current billing cycle has reached the preset price adjustment time, and the process returns to step S102. If the current time is before the preset price adjustment time, it is determined that the current billing cycle has not reached the preset price adjustment time, then the computing power resource pricing result of the previous billing cycle will be used as the computing power resource pricing result of the current cycle, and the process returns to step S102 to determine whether the billing duration of the resource demander has reached the billing cycle.

[0053] Querying the corresponding billing cycle based on different billing modes allows adapting to diverse billing needs. Different billing modes correspond to different billing cycle settings, allowing for flexible pricing management for various resource demanders. When the billing duration reaches the billing cycle, resource bills are settled based on the computing power resource pricing results and resource demand. This approach ensures accurate fee calculations, comprehensively considering pricing results and actual resource usage, avoiding overcharging or undercharging, and safeguarding the interests of both resource demanders and resource suppliers. The system determines whether the current billing cycle has reached the preset price adjustment time. If so, historical behavior information is retrieved based on identity information and subsequent operations are performed, enabling dynamic price adjustment. This allows resource suppliers to adjust pricing strategies in a timely manner based on market changes, user behavior changes, and other factors, optimizing resource allocation and economic benefits.

[0054] The present invention provides a computing power resource pricing method based on a computing power supplier's computing power resource pool, which adopts a hybrid direct sales and distribution sales model to provide computing power service products. The direct sales model pricing mechanism for end-users primarily utilizes a personalized, diversified, and dynamically adjusted pricing design through monthly billing, busy-hour billing, and off-hour billing models. This allows the computing power supplier's pricing to adapt to market price changes in real time, forming a competitive pricing system. This not only helps computing power suppliers quickly sell computing power products and increase their equipment availability, but also helps them expand their market share and improve the competitiveness of computing power operation services. The distribution model pricing mechanism for intermediate users, based on the diversified and dynamic pricing model corresponding to the direct sales model, provides intermediate users with more favorable discounts based on distributor preferential parameters and rebate performance points. This not only helps intermediate users quickly and cost-effectively build a nationwide sales agency network, but also facilitates their consumption of computing power resources and facilitates increasing the availability of computing power suppliers' equipment, thereby increasing their profitability. Furthermore, by obtaining resource demand information, identity information, and corresponding historical behavior from resource demanders, and integrating multi-dimensional data to determine tenant tiers and discount coefficients, the system can dynamically, scientifically, rationally, and objectively assess the characteristics and needs of resource demanders with different identities in real time, making tenant tiering more rational and discount coefficients more targeted. By differentiating resource demanders with different identities, the system can better meet the personalized needs of both end-users and intermediary demanders, enhancing the system's versatility and adaptability. Accurate tenant tiering and a reasonable pricing mechanism help allocate computing resources more efficiently to users with different needs, avoiding idle computing resources and wasting them, improving overall resource utilization, and ensuring more efficient use of computing resources. The calculation model is determined based on the billing mode in the resource demand information. The availability of multiple billing modes allows resource demanders to select the most appropriate billing method based on their actual situation, increasing the dynamic flexibility of pricing.

[0055] Example 2

[0056] The embodiment of the present invention also provides another computing power resource pricing method; this method is implemented on the basis of the method of the above embodiment, and the computing power resource pricing method may include the following steps:

[0057] Step S201: Obtain resource demand information and identity information of a resource demander.

[0058] Step S202: Obtain historical behavior information corresponding to the resource demander based on the identity information.

[0059] Step S203: determining the preferential coefficient corresponding to the resource demander according to the resource demand parameters and historical behavior information in the resource demand information.

[0060] Step S204: determining a calculation model corresponding to the resource demander according to the billing mode in the resource demand information.

[0061] Step S205 , when the billing mode is a monthly billing mode, the resource pricing corresponding to each resource type is calculated according to at least one resource type included in the resource demand parameter and the resource demand corresponding to each resource type.

[0062] Resource pricing refers to the fees that resource demanders pay when using a particular type of computing resource. It's understood that the resource demand parameter can include at least one computing resource of a resource type, and each computing resource of that resource type corresponds to a resource demand. In the case of a monthly billing model, for each computing resource type, the resource price corresponding to that resource type is calculated by multiplying the resource demand for that computing resource type by the unit price.

[0063] In step S206, the product of the sum of the resource prices corresponding to each resource type and the discount coefficient is used as the computing power resource pricing result corresponding to the resource demander under the monthly billing model.

[0064] Specifically, the resource prices corresponding to each resource type are added together to obtain the sum of the resource prices corresponding to each resource type, and the sum of the resource prices corresponding to each resource type is multiplied by the discount coefficient to obtain the computing power resource pricing result corresponding to the resource demander under the monthly billing model.

[0065] Step S207: When the billing mode is the busy hour billing mode, the current resource usage rate, the first historical price increase coefficient, and the second historical price increase coefficient of the resource supplier are obtained.

[0066] The current resource utilization rate refers to the utilization rate of the computing power resources provided by the resource supplier at the current moment. The first historical price increase coefficient refers to the average of all time-based price increase coefficients of the resource supplier within a preset period. The second historical price increase coefficient refers to the average of all time-based price increase coefficients of other resource suppliers within a preset period. There are multiple second historical price increase coefficients. The preset period refers to multiple consecutive billing periods before the current moment. The length of the preset period can be set according to actual circumstances. Other resource suppliers refer to other suppliers that can provide computing power resources, and can also be understood as friendly companies of the resource supplier. The time-based price increase coefficient is used to adjust the pricing results of computing power resources. It can be understood that if the current resource utilization rate is low, it means that less computing power resources are being used among the computing power resources provided by the current supplier. In this case, the time-based price increase coefficient can be reduced to provide resource demanders with lower computing power resource pricing results, thereby increasing their demand for computing power resources.

[0067] Specifically, when the billing mode is the busy hour billing mode, the resource supplier will provide the current resource usage rate, the first historical price increase coefficient and the second historical price increase coefficient.

[0068] Step S208: Determine the current price increase coefficient of the resource supplier based on the current resource usage rate, the first historical price increase coefficient, and the second historical price increase coefficient.

[0069] The current price increase coefficient refers to the timed price increase coefficient determined at the current moment. Specifically, the current price increase coefficient is the weighted average of the current resource utilization rate, the first historical price increase coefficient, and the second historical price increase coefficient. It is understood that the weights corresponding to the current resource utilization rate, the first historical price increase coefficient, and the second historical price increase coefficient can be calculated based on actual circumstances.

[0070] Step S209: input the preferential coefficient and resource requirement parameters into a calculation model corresponding to the monthly billing mode to obtain the monthly billing unit price.

[0071] The monthly billing unit price refers to the pricing result of computing power resources corresponding to the resource demander under the monthly billing model. Specifically, after the preferential coefficient and resource demand parameters are input into the calculation model corresponding to the monthly billing model, steps S205 and S206 are executed to obtain the monthly billing unit price.

[0072] For example, the monthly billing price can be calculated using the following formula:

[0073]

[0074] Where Y is the monthly billing unit price; n is the total number of resource types; m is the total number of component types that constitute a computing resource; q m is the unit price of the mth component; r m is the number of the mth element; δ n is the resource demand of the nth computing resource; ε i This is the discount coefficient corresponding to the resource demander. It is understood that a computing resource can be composed of multiple types of components, including CPU cores, GPUs, memory, system disks, and data disks. The unit price and quantity of components can be determined based on actual conditions.

[0075] In other embodiments, when the resource demander is an intermediate demander, in addition to the unit price of the computing resource element in the calculation formula of the monthly billing unit price (ie, q m ) can be determined by user, and can also be determined through steps C1-C3 m The method is as follows:

[0076] Step C1: Obtain sales data sets from at least two computing power suppliers, where each sales data set includes at least the computing power resource supply quantity, computing power resource component supply type, and the corresponding unit price of each computing power resource component type.

[0077] Sales data sets refer to the data sets generated by resource suppliers when allocating computing resources. The "computing resource supply quantity" refers to the amount of computing resources that the resource supplier can provide for each type of computing resource. The "computing resource supply type" refers to the type of computing resource that the resource supplier can provide. The "unit price" corresponding to a computing resource type refers to the price determined by the computing resource supplier for each type of computing resource.

[0078] Step C2: Calculate the price weight value based on all sales data sets. The price weight value is the relative proportion of the supply of computing power resource components corresponding to different computing power resource suppliers to the total supply of computing power resource components corresponding to all computing power resource suppliers.

[0079] Specifically, the price weight value is calculated according to the following formula:

[0080]

[0081] Where: X i is the price weight of the i-th computing resource supplier; Y i is the supply of computing power resource components by the i-th computing power resource supplier; i is the total number of computing power resource suppliers.

[0082] Step C3: Based on the price weight value, the computing power resource supply type, and each computing power resource type, the unit price of the computing power resource component is obtained.

[0083] Specifically, the unit price of computing power resource components is calculated according to the following formula:

[0084]

[0085] Where: q m is the unit price of the mth computing resource component; im is the price weight of the mth computing resource component corresponding to the i-th computing resource supplier; ρ im is the price of the mth computing resource component for the i-th computing resource supplier; i is the total number of computing resource suppliers. It can be understood that the unit price of each component is determined based on the prices of multiple suppliers and the size of their computing resource supply, making the pricing more reasonable, scientific, and close to market prices.

[0086] Step S210: Input the monthly billing unit price and the current price increase coefficient into the calculation model corresponding to the busy hour billing mode to obtain the computing power resource pricing result corresponding to the resource demander under the busy hour billing mode.

[0087] Specifically, the pricing result of computing power resources corresponding to the resource demander under the busy hour billing mode can be determined by the following calculation formula: The pricing result of computing power resources corresponding to the resource demander under the busy hour billing mode = (monthly billing unit price / 24 / 30) * current price increase coefficient.

[0088] The computing power resource pricing method provided by the present invention employs separate calculation processes for monthly and busy-hour billing models. In the monthly billing model, resource pricing is calculated based on resource type and demand, and then combined with a discount factor to arrive at the final pricing result. In the busy-hour billing model, the current resource usage rate and historical price increase factor are used to determine the current price increase factor, which is then combined with the monthly unit price to calculate the final price. This refined calculation method, tailored to different billing models, can better meet the billing needs of resource demanders in different usage scenarios and provide more practical services. In the busy-hour billing model, the current resource usage rate, the first historical price increase factor, and the second historical price increase factor are obtained in real time to determine the current price increase factor. This enables the platform to dynamically adjust prices based on the resource demander's real-time resource usage, effectively addressing peaks and troughs in resource usage, rationally allocating resources, avoiding cost waste caused by excessive resource idleness, and, to a certain extent, controlling the risks associated with resource price fluctuations. By utilizing historical behavior information to determine the discount factor and the first and second historical price increase factors to determine the current price increase factor, the value of historical data is fully exploited. By analyzing historical data, we can predict the future resource usage trends and price tolerance of resource demanders, provide strong data support for the platform in resource allocation and price setting, and help the platform optimize resource allocation and reduce operating costs.

[0089] Example 3

[0090] An embodiment of the present invention also provides another computing power resource pricing method, which may include the following steps:

[0091] Step S301: Obtain resource demand information and identity information of the resource demander.

[0092] Step S302: Obtain historical behavior information corresponding to the resource demander based on the identity information.

[0093] Step S303: determining the preferential coefficient corresponding to the resource demander according to the resource demand parameters and historical behavior information in the resource demand information.

[0094] Step S304: determining a calculation model corresponding to the resource demander according to the billing mode in the resource demand information.

[0095] Step S305 : When the billing mode is the off-peak billing mode, the bidding values ​​and bidding interval values ​​of multiple resource demanders are obtained.

[0096] The bid value refers to the desired computing resource pricing result for the resource demander. The bid range value refers to the range of values ​​corresponding to the bid value. The bid range value includes an upper limit and a lower limit, which define the range of values ​​corresponding to the bid value. The lower limit of the bid range value can be determined by multiplying the computing resource cost of the computing power supplier by a preset price increase ratio. The computing resource cost includes at least the cabinet power operating cost of the computing power resource and the share of the operation and maintenance personnel expenses. The price increase ratio can be pre-set based on actual conditions. The upper limit value is based on the computing resource pricing result corresponding to the monthly billing model, multiplied by a preset discount ratio to determine the price.

[0097] Specifically, when determining the upper limit value, the preferential coefficient and resource demand parameters can be input into the calculation model corresponding to the monthly billing mode. The calculation model corresponding to the monthly billing mode will calculate the resource pricing corresponding to each resource type based on at least one resource type included in the resource demand parameters and the resource demand corresponding to each resource type. The sum of the resource pricing corresponding to each resource type and the product of the preferential coefficient are used as the computing power resource pricing result corresponding to the resource demander under the monthly billing mode. The computing power resource pricing result corresponding to the monthly billing mode is used as the basis, and then multiplied by the preset discount ratio to determine the price value, and the price value is used as the upper limit value.

[0098] It can be understood that in the computing power resource requests of multiple resource demanders, when the billing mode in the resource request information is the off-peak billing mode, the computing power resource requests of multiple resource demanders can be processed at the same time to obtain the bidding value and bidding range value provided by each resource requester.

[0099] Step S306: Determine the first authority of each resource demander based on the bid values, upper limit and lower limit.

[0100] The first permission is used to describe whether the resource demander is eligible to bid. For each resource requester, the bid value provided by the resource requester is compared with the bid interval value to obtain a comparison result. When the comparison result is that the bid value is less than or equal to the lower limit value, the first permission is prohibited, indicating that the resource demander is not eligible to bid, and a first prompt message is sent to the resource demander to remind the resource demander that the bid value is too low and is not eligible to bid. When the comparison result is that the bid value is greater than the upper limit value, the first permission is prohibited, and a second prompt message is sent to the resource demander to remind the resource demander that the current off-peak billing mode is less cost-effective and it is recommended to change to a more favorable billing mode to reduce the cost of obtaining computing power resources. When the comparison result is that the bid value is greater than the lower limit value and less than or equal to the upper limit value, the first permission is allowed, indicating that the resource demander is eligible to bid.

[0101] Step S307: When the first authority is allowed, the highest bid value among the bid values ​​is determined as the current bid value.

[0102] From at least one resource demander for which the first authority is allowed, the bid values ​​provided by the resource demanders are compared, and the highest bid value is used as the current bid value.

[0103] Step S308: Obtain the computing power resource type corresponding to the current bidding value from the resource demand information of the resource demander corresponding to the current bidding value.

[0104] The resource demander that provides the highest bid value will be used as the resource demander corresponding to the highest bid value. The resource type will be extracted from the resource demand information of the resource demander corresponding to the highest bid value as the computing power resource type corresponding to the current bid value.

[0105] Step S309: Determine the second permission of the resource demander corresponding to the current bid value based on the computing power resource type.

[0106] The second permission is used to describe whether the resource demander's demand for computing power resources is applicable to the off-peak pricing model when the bid price provided is appropriate. If the computing power resource type includes bare metal servers, it indicates that the resource demander is not applicable to the off-peak pricing model, and the second permission is prohibited. At this time, a third prompt message is sent to the resource demander to prompt the resource demander that there are resource types that are not applicable to the off-peak pricing model among the resource types of the resource demander, and it is recommended to change the billing model, and the resource demander with the second lowest bid value is used as the new evaluation object, that is, the second lowest bid value is used as the current bid value, and the execution returns to step S306. If the computing power resource type does not include computing power resources such as bare metal servers that are not applicable to the off-peak pricing model, the second permission is allowed.

[0107] Understandably, cloud host servers offer technical advantages such as fast client system switching, low switching costs, and accurate billing. Therefore, this computing resource billing model is applicable not only to off-peak billing, but also to monthly and busy-hour billing. However, computing resources such as bare metal servers, which suffer from disadvantages such as slow client system switching, high switching costs, and complex billing logic, are not suitable for off-peak billing. Therefore, it is necessary to determine whether the resource demand information participating in the off-peak billing model contains resource types that are not applicable to this billing model. If so, a third prompt message is sent to the resource demander, indicating that the computing resources required by the resource demander are not applicable to the off-peak billing model. If not, a determination is made as to whether the off-peak resource quantity can meet the current computing resource demand, thereby determining whether to allocate or reclaim resources.

[0108] Step S310: When the second permission is allowed, obtain the idle time resource amount.

[0109] The idle time resource amount refers to the amount of unused computing resources in the resource pool. Specifically, when the second permission is allowed, the idle time resource amount of the resource pool is counted.

[0110] Step S311, determining the computing power resource pricing result corresponding to the resource demander in the off-peak billing mode according to the off-peak resource quantity and the computing power resource demand in the resource demand information.

[0111] The amount of off-peak resources and the demand for computing resources can be compared. When the amount of off-peak resources is greater than or equal to the demand for computing resources, it indicates that the resource supplier has idle computing resources that can meet the resource demander's demand for computing resources. At this time, the bidding price provided by the resource demander can be directly used as the computing resource pricing result corresponding to the resource demander under the off-peak billing model. When the amount of idle resources is less than the demand for computing resources, it indicates that the idle computing resources of the resource supplier cannot meet the demand for computing resources of the resource demander. At this time, the computing resources in use can be recycled (a recycling request can be sent to the resource demander who is using the computing resources. When the resource demander responds and agrees to the recycling, it indicates that the resource demander will end the use of the computing resources, and the computing resources used by the resource demander can be recycled). When the amount of idle resources obtained after recycling is greater than or equal to the demand for computing resources, the resource supplier can meet the demand for computing resources of the resource demander, and then use the bidding price provided by the resource demander as the pricing result of the computing resources corresponding to the resource demander under the idle billing mode. Alternatively, a prompt message can be directly sent to the resource demander to remind the resource demander that the computing resources are insufficient and it is recommended to change the billing mode.

[0112] In another possible implementation, the computing power resource pricing result corresponding to the resource demander in the off-peak billing mode can be determined through steps B1 to B6.

[0113] Step B1: When the amount of idle resources is less than the required amount of computing resources, the resources to be released are obtained.

[0114] When the amount of off-peak resources is greater than or equal to the computing resource demand, it indicates that the off-peak resources can meet the computing resource needs of the resource demander. Unused computing resources from the computing resources provided by the resource supplier will be allocated according to the resource type and computing resource demand of the resource demander for use by the resource demander. When the amount of off-peak resources is less than the computing resource demand, it indicates that the off-peak resources cannot meet the computing resource needs of the resource demander. In this case, the computing resources currently in use under the off-peak billing model need to be reclaimed. Resource demanders who are using computing resources in the resource pool and are billed under the off-peak billing model are considered to be "to-be-released" resources.

[0115] Step B2: Determine the resource requirement difference based on the idle time resource amount and the computing power resource requirement.

[0116] The resource demand gap describes the difference between idle resources and the required computing resources. Specifically, for each computing resource type, the difference between the idle resources and the required computing resources is calculated to determine the resource demand gap for that computing resource type.

[0117] Step B3: Determine the target resource party from the resource parties to be released based on the resource demand difference.

[0118] The target resource party refers to the resource party to be released whose computing power resources need to be recovered. Specifically, the resource party to be released whose corresponding resource demand differences based on different computing power resource types are all greater than zero is selected as the first releaser (that is, it is ensured that all types of computing power resources occupied by the first releaser can cover the resource demand of the resource demand party); then the computing power resource occupancy of the first releaser (that is, the sum of the computing power resources currently being used by the resource party to be released) is compared respectively, and the second releaser with the smallest resource occupancy (that is, the resource party to be released currently occupies the least total amount of resources) is selected as the target resource party. After the resource demand of the target resource party is recovered, the idle resource amount obtained can not only cover the resource demand party's requirements for computing power resources, but also ensure the minimization of the recovered resource demand, so that the recovered resource demand can obtain the maximum utilization rate.

[0119] Furthermore, a notification message of resource recovery is sent to the target resource party, and it is confirmed that the billing cycle of the target resource party has reached the settlement cycle. The bill is obtained by settlement and sent to the corresponding target demand party. For example, the time of sending the notification is the end of the settlement period, and the time of allocating computing resources to the target resource party is the beginning of the settlement period. The length of the settlement period is based on the length of the settlement period and the end of the settlement period as a whole settlement cycle, and the bill is obtained by settlement according to the formula of the settlement resource bill and sent to the target demand party to recover the computing resources of the target resource party. The computing resources and idle resources of the recovered target resource party are allocated to the resource demand party as needed.

[0120] Step B4: Compare the computing power resource pricing result corresponding to the target resource party with the current bidding value to obtain a comparison result.

[0121] The comparison results include that the computing power resource pricing result corresponding to the target resource party is less than the current bid value, and the computing power resource pricing result corresponding to the target resource party is greater than or equal to the current bid value.

[0122] In step B5, when the comparison result shows that the computing power resource pricing result corresponding to the target resource party is less than the current bid value, a value update notification is sent to the target resource party.

[0123] When the computing power resource pricing result corresponding to the target resource party is lower than the current bid value (that is, the target resource party uses the computing power resources at a lower price), a price update notice is sent to all target resource parties to confirm whether they want to continue using the computing power resources they need at the current bid value.

[0124] When the computing power resource pricing result corresponding to the target resource party is greater than or equal to the current bidding value (that is, the resource demander bids for the right to use computing power resources at a lower price), a bidding result notification will be sent to the resource demander who provided the current bidding value to inform it that the current bidding value does not have a competitive advantage.

[0125] Step B6: Determine the computing power resource pricing result corresponding to the resource demander in the off-peak billing mode based on the feedback information of the target resource party on the value update notification.

[0126] Feedback information includes confirmation and cancellation. Confirmation indicates that the target resource party confirms to continue using the computing power resources it requires at the current bid value, and cancellation indicates that the target resource party refuses to continue using the computing power resources it requires at the current bid value.

[0127] Based on the feedback from each target resource party regarding the price update notification, if the target resource party confirms that it will continue to use the computing power resources it requires at the current bid value, the current bid value will be used as the pricing result for the computing power resources corresponding to the target resource party under the off-peak billing mode. At the same time, it will be confirmed that the target resource party has reached the current settlement period, and a bill will be sent to the target resource party. The billing duration will be updated based on the updated computing power resource pricing result and a new billing period will be started. At the same time, a bidding result notification will be sent to the resource demander, informing them that the current bid value does not have a competitive advantage. In other words, given the same price, the resource occupation rights of old customers will be prioritized to increase user stickiness. If the target resource party refuses to continue using the computing power resources it requires at the current bid value, a resource reclaim notification will be sent to the target resource party, and it will be confirmed that the target resource party's billing period has reached the settlement period. The bill will be settled and sent to the target resource party. Exemplarily, the time for sending the notification is the end of the settlement period, and the time for allocating computing resources to the target resource party is the beginning of the settlement period. The duration of the beginning and end of the settlement period is used as a whole settlement cycle, and the settlement is performed according to the formula of the settlement resource bill to obtain the bill and send it to the party to release the resources.

[0128] When the amount of idle resources is less than the demand for computing power, the system proactively searches for potential resource providers. By calculating the difference in resource demand, it accurately determines the amount of resources to be acquired from the potential resource providers. It then identifies target resource providers from these potential resource providers. This approach maximizes the potential utilization of available resources, allocating low-cost potential resource providers to high-cost resource providers with minimal computational resource recovery. This improves computing power providers' profitability while maintaining high computing power utilization, optimizing the allocation of idle resources among different resource providers while maintaining high returns. By comparing the target resource provider's corresponding computing power pricing with the current bid value, if the target resource provider's pricing is lower than the current bid value, a value update notification is sent to the potential resource provider. This process encourages the potential resource provider to dynamically adjust its resource pricing based on market prices, resulting in more reasonable resource prices. Implementing a bidding and dynamic recovery mechanism ensures high computing power utilization while meeting the computing power needs of a wider range of resource providers.

[0129] The computing power resource pricing method provided by embodiments of the present invention, under an off-peak billing model, obtains bid values ​​and bid ranges from multiple resource demanders and determines a first permission based on these values. This bidding mechanism enables off-peak resources to be preferentially allocated to resource demanders with more urgent resource needs and relatively high bids. The second permission is determined based on the computing power resource type corresponding to the resource demander's current bid value, further ensuring accurate resource allocation. Subsequent resource allocation operations are only performed if the resource type matches and the second permission allows. This ensures that off-peak resources accurately meet the needs of specific resource demanders for specific computing power resource types, improves the matching of resources and needs, and optimizes the overall resource allocation structure. By setting the first and second permissions, resource demanders' eligibility to access off-peak resources is strictly controlled. Only when the permissions allow can resource demanders participate in bidding and obtain resources. This effectively avoids confusion and unfairness in the resource allocation process and maintains the operational order of the platform. In other embodiments, when the computing power demander corresponding to the idle billing mode and the computing power demander corresponding to the monthly or busy-hour billing mode make requests at the same time, and the current idle computing power resources cannot meet their required resource quantities, the computing power resources will be preferentially recovered through steps B1-B6 and allocated to the computing power demanders corresponding to the monthly billing mode and the busy-hour billing mode (that is, the demander with the higher bid will be preferentially satisfied).

[0130] The idle-hour billing model provided by the embodiments of the present invention effectively complements the monthly and busy-hour billing models. Its core value lies in increasing the utilization rate of computing resources. While the highest bidder wins, it also maintains the right of resource providers to reclaim computing resources from low-revenue users at any time and dispatch them to higher-revenue customers. Idle (idle) resources are dynamically reclaimed based on a bidding mechanism, ensuring increased equipment utilization, maximizing corporate profits, and meeting the needs of a wider range of customers.

[0131] Example 4

[0132] An embodiment of the present invention further provides a method for pricing computing power resources when a computing power resource supplier is an intermediate demander, which may include the following steps:

[0133] Step S401: Obtain resource demand information and identity information of the resource demander.

[0134] Step S402: Obtain historical behavior information corresponding to the resource demander based on the identity information.

[0135] Step S403: determining the preferential coefficient corresponding to the resource demander according to the resource demand parameters and historical behavior information in the resource demand information.

[0136] Step S404: determining a calculation model corresponding to the resource demander according to the billing mode in the resource demand information.

[0137] In step S405, the preferential coefficient and resource demand parameters are input into the calculation model corresponding to the resource demander to obtain the computing power resource pricing result corresponding to the resource demander.

[0138] Step S406: When the identity information indicates that the resource demander is an intermediate demander, the computing power resource pricing result corresponding to the resource demander is determined as the initial pricing result.

[0139] Step S407: Acquire specific preferential parameters of multiple resource suppliers and preferential parameters of distributors.

[0140] Specific discount parameters are determined based on peak computing resource utilization rates at different times (i.e., the current time period and current computing resource utilization rate) and tenant tiers. Generally speaking, the higher the computing resource utilization rate, the smaller the specific discount coefficient. These discount parameters can be determined by looking up a table. Distributor discount parameters can be provided directly by resource suppliers or determined by looking up a table based on the computing power requirements of intermediary demanders.

[0141] Step S408: Determine the updated computing power resource pricing result corresponding to the resource demander based on the initial pricing result, the specific discount coefficient and the distributor discount parameter.

[0142] The product of the initial pricing result, the specific discount coefficient and the distributor discount parameter is used as the updated computing power resource pricing result corresponding to the resource demander.

[0143] Furthermore, the distributor preferential parameters also include rebate parameters; after determining the computing power resource pricing result corresponding to the resource demander based on the initial pricing result, specific preferential coefficient and distributor preferential parameters, it also includes: determining the rebate performance points based on the computing power resource volume and preset standards of the resource demander; updating the computing power resource pricing result corresponding to the resource demander based on the initial pricing result, specific preferential coefficient, distributor preferential parameters and rebate performance points.

[0144] The preset standard refers to the pre-set relationship between computing power and commission points. By looking up the table, the corresponding commission points for the resource demander's computing power are found from the preset standard. The product of the base price, the specific discount coefficient, the distributor's discount parameters, and the commission points is used to update the computing power pricing result for the resource demander.

[0145] When the identity information indicates that the resource demander is an intermediate demander, it means that the resource demander can redistribute the acquired computing power resources. At this time, parameters such as distributor discounts and rebate points are used to give the resource demander a more favorable discount. This is not only beneficial for the resource demander to quickly and cost-effectively sell the agency network, but also beneficial for its absorption of computing power resources from resource suppliers, which is easy to increase the equipment shelf rate of resource suppliers, thereby increasing the rate of return.

[0146] The computing resource pricing method provided by the embodiments of the present invention comprehensively considers multiple parameters, making pricing more accurate and reasonable. It can fully reflect market conditions, the characteristics of different resource demanders, and various factors in business relationships. Using market-related data such as sales numerical parameters to calculate the base price helps to make pricing closer to the true market value. This can avoid the adverse impact of over- or under-pricing on business, promote the rational allocation of resources and the healthy development of the market.

[0147] Example 5

[0148] Corresponding to the above method embodiment, the embodiment of the present invention provides a computing power resource pricing device, Figure 2 A schematic diagram of a computing resource pricing device provided by an embodiment of the present invention is shown in FIG. Figure 2 As shown, the computing power resource pricing device may include:

[0149] The information acquisition module 201 is used to obtain resource demand information and identity information of resource demanders; wherein the identity information includes terminal demanders and intermediate demanders;

[0150] The historical information acquisition module 202 is used to obtain the historical behavior information corresponding to the resource demander based on the identity information;

[0151] A preferential determination module 203 is configured to determine a preferential coefficient corresponding to a resource demander based on resource demand parameters and historical behavior information in the resource demand information;

[0152] The calculation model determination module 204 is used to determine the calculation model corresponding to the resource demander according to the billing mode in the resource demand information; the billing mode includes a monthly billing mode, a busy hour billing mode and / or a non-busy hour billing mode;

[0153] The pricing module 205 is used to input the preferential coefficient and resource demand parameters into the calculation model corresponding to the resource demander to obtain the computing power resource pricing result corresponding to the resource demander.

[0154] The device provided in the embodiment of the present invention has the same implementation principle and technical effects as those in the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference can be made to the corresponding content in the aforementioned method embodiment.

[0155] Example 6

[0156] The embodiment of the present invention also provides an electronic device for executing the above-mentioned computing resource pricing method; see Figure 3 A structural diagram of an electronic device is shown, which includes a memory 300 and a processor 301, wherein the memory 300 is used to store one or more computer instructions, and the one or more computer instructions are executed by the processor 301 to implement the above-mentioned computing power resource pricing method.

[0157] Further, Figure 3 The electronic device shown further includes a bus 302 and a communication interface 303 , and the processor 301 , the communication interface 303 and the memory 300 are connected via the bus 302 .

[0158] The memory 300 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk storage. The communication connection between the system network element and at least one other network element is achieved through at least one communication interface 303 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used. The bus 302 may be an ISA bus, a PCI bus, or an EISA bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0159] The processor 301 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by hardware integrated logic circuits in the processor 301 or by software instructions. The above processor 301 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in conjunction with the embodiments of the present invention can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in memory 300, and processor 301 reads the information in memory 300 and, in conjunction with its hardware, completes the steps of the method of the aforementioned embodiment.

[0160] An embodiment of the present invention also provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the above-mentioned computing power resource pricing method. The specific implementation can be found in the method embodiment, which will not be repeated here.

[0161] The computer program product for the computing power resource pricing method provided in the embodiment of the present invention includes a computer-readable storage medium storing non-volatile program code executable by a processor. The instructions included in the program code can be used to execute the method described in the previous method embodiment. The specific implementation can be found in the method embodiment and will not be repeated here.

[0162] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0163] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. There may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be through some communication interface, indirect coupling or communication connection of devices or units, which may be electrical, mechanical or other forms.

[0164] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0165] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0166] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0167] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A computing power resource pricing method, characterized in that: include: Obtain resource demand information and identity information of resource demanders; wherein the identity information includes terminal demanders and intermediate demanders; Obtaining historical behavior information corresponding to the resource demander based on the identity information; Determining a preferential coefficient corresponding to the resource demander based on the resource demand parameters in the resource demand information and the historical behavior information; Determining a calculation model corresponding to the resource demander according to a billing mode in the resource demand information; the billing mode includes a monthly billing mode, a busy hour billing mode, and / or an idle hour billing mode; The preferential coefficient and the resource demand parameter are input into the calculation model corresponding to the resource demander to obtain the computing power resource pricing result corresponding to the resource demander.

2. The method according to claim 1, characterized in that The determining, based on the resource demand parameters in the resource demand information and the historical behavior information, a preferential coefficient corresponding to the resource demander includes: Inputting the resource demand parameters in the resource demand information and the historical behavior information into a tenant scoring model to obtain a tenant score; According to the tenant score, the tenant level corresponding to the resource demander is obtained from a preset demander level table; According to the correspondence between the preset level and the preferential coefficient, the preferential coefficient corresponding to the tenant level is determined.

3. The method according to claim 1, characterized in that When the billing mode is a monthly billing mode, the preferential coefficient and the resource demand parameter are input into the calculation model corresponding to the resource demander to obtain the computing power resource pricing result corresponding to the resource demander, including: Calculating resource pricing corresponding to each resource type according to at least one resource type included in the resource demand parameter and the resource demand corresponding to each resource type; The product of the sum of the resource prices corresponding to each resource type and the discount coefficient is used as the computing power resource pricing result corresponding to the resource demander under the monthly billing model.

4. The method according to claim 1, wherein When the billing mode is the busy hour billing mode, the preferential coefficient and the resource demand parameter are input into the calculation model corresponding to the resource demander to obtain the computing power resource pricing result corresponding to the resource demander, including: Obtain the current resource usage rate, the first historical price increase coefficient, and the second historical price increase coefficient of the resource supplier; Determining a current price increase coefficient of the resource supplier based on the current resource usage rate, the first historical price increase coefficient, and the second historical price increase coefficient; Inputting the preferential coefficient and the resource demand parameter into a calculation model corresponding to the monthly billing mode to obtain a monthly billing unit price; The monthly billing unit price and the current price increase coefficient are input into the calculation model corresponding to the busy hour billing mode to obtain the computing power resource pricing result corresponding to the resource demander under the busy hour billing mode.

5. The method according to claim 3, characterized in that When the billing mode is the off-peak billing mode, the preferential coefficient and the resource demand parameter are input into the calculation model corresponding to the resource demander to obtain the computing power resource pricing result corresponding to the resource demander, including: Obtain bid values ​​and bid interval values ​​from multiple resource demanders; wherein the bid interval value includes an upper limit value and a lower limit value, wherein the upper limit value is a price value determined based on the computing power resource pricing result corresponding to the monthly billing model and a preset discount ratio; and the lower limit value is a price value determined based on the computing power resource cost and a preset price increase ratio; Determining the first authority of each resource demander according to each bid value, the upper limit and the lower limit; When the first permission is allowed, determining the highest bid value among the bid values ​​as the current bid value; Obtaining the computing power resource type corresponding to the current bid value from the resource demand information of the resource demander corresponding to the current bid value; Determining, based on the computing power resource type, a second permission of the resource demander corresponding to the current bid value; If the second permission is allowed, obtaining the idle time resource amount; Determine the computing power resource pricing result corresponding to the resource demander under the idle time billing mode according to the idle time resource amount and the computing power resource demand in the resource demand information.

6. The method according to claim 5, characterized in that The determining, according to the idle time resource amount and the computing power resource demand in the resource demand information, a computing power resource pricing result corresponding to the resource demander in the idle time billing mode includes: When the amount of idle resources is less than the required amount of computing resources, obtaining resources to be released; Determining a resource requirement difference based on the idle time resource amount and the computing power resource requirement; Determine the target resource party from the resource parties to be released according to the resource demand difference; Compare the computing power resource pricing result corresponding to the target resource party with the current bid value to obtain a comparison result; When the comparison result shows that the computing power resource pricing result corresponding to the target resource party is less than the current bid value, a value update notification is sent to the target resource party; Determine the computing power resource pricing result corresponding to the resource demander in the off-peak billing mode according to the feedback information of the target resource party to the value update notification.

7. The method according to claim 1, characterized in that After inputting the preferential coefficient and the resource demand parameter into a calculation model corresponding to the resource demander to obtain a computing power resource pricing result corresponding to the resource demander, the method further includes: When the identity information indicates that the resource demander is the intermediate demander, determining the computing power resource pricing result corresponding to the resource demander as the initial pricing result; Obtain sales numerical parameters, specific discount parameters and distributor discount parameters of multiple resource suppliers; Input the sales numerical parameters and the initial pricing result into a base price calculation model to obtain the base price corresponding to the resource demander; The computing power resource pricing result corresponding to the resource demander is determined based on the base price, specific discount coefficient and distributor discount parameters.

8. A computing power resource pricing device, characterized in that: include: An information acquisition module is used to acquire resource demand information and identity information of resource demanders; wherein the identity information includes terminal demanders and intermediate demanders; A historical information acquisition module, configured to acquire historical behavior information corresponding to the resource demander based on the identity information; A preferential determination module, configured to determine a preferential coefficient corresponding to the resource demander based on the resource demand parameters in the resource demand information and the historical behavior information; A calculation model determination module is used to determine the calculation model corresponding to the resource demander according to the billing mode in the resource demand information; the billing mode includes a monthly billing mode, a busy hour billing mode and / or an idle hour billing mode; The pricing module is used to input the preferential coefficient and the resource demand parameter into the calculation model corresponding to the resource demander to obtain the computing power resource pricing result corresponding to the resource demander.

9. An electronic device, characterized in that: It includes a processor and a memory, the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement the computing power resource pricing method described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and executed by the processor, the computer-executable instructions prompt the processor to implement the computing power resource pricing method described in any one of claims 1 to 7.

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