Computing power transaction charging method and system based on cloud service

Abstract

The invention provides a computing power transaction charging method and system based on cloud service, and relates to the technical field of computing power charging. The method comprises the following steps: acquiring the total computing power of the cloud service, cloud service computing power node data and cloud service computing power allocation data, performing cloud service computing power resource use analysis, and establishing a cloud service computing power pricing system; computing power demand information is obtained, the computing power demand is subjected to pricing analysis in combination with cloud service computing power node data and a cloud service computing power pricing system, and computing power demand charging result data is formed; and charging is carried out according to the computing power demand charging result data, resource allocation is carried out on the cloud service computing power, the change of the computing power demand is monitored in real time, and the computing power demand charging result data is adjusted. According to the method, reasonable and equivalent charging of the cloud service computing power transaction can be effectively realized.

Description

Technical Field

[0001] The present application relates to the field of computing power billing technology, and more specifically, to a computing power transaction billing method and system based on cloud services. Background Art

[0002] Computing power is the information processing capability of a computer device or computing / data center. It is the ability of computer hardware and software to work together to perform a specific computational task. With the development of science and technology and the progress of society, the demand for computing power is gradually increasing to meet the growing demand for intelligent technology. The more complex and extensive the intelligent information needs, the greater the computing power required. Often, a single source of computing power is insufficient to support this demand, leading to the emergence of computing power trading.

[0003] Cloud-based computing power trading is becoming a popular method for trading computing power. By integrating computing power resources across the network, it can meet diverse computing power needs of varying types and amounts. However, computing power trading differs from conventional transactions. Conventional trading methods cannot effectively transfer computing power value. Therefore, a more efficient method of computing power trading is needed. Currently, computing power coins quantify computing power by anchoring it to a standard computing power, providing a new trading method that effectively ensures equal billing of computing power.

[0004] Therefore, designing a cloud service-based computing power transaction billing method and system that can effectively realize reasonable equivalent billing for cloud service computing power transactions is an urgent problem to be solved. Summary of the Invention

[0005] The purpose of this application is to provide a computing power transaction billing method based on cloud services. By analyzing the inherent characteristics of cloud service computing power and the historical scheduling of cloud service computing power, a reasonable cloud service computing power pricing system is established, which can more reasonably realize the use of cloud service computing power and fully realize the pricing method for the use of cloud service computing power based on the demand for cloud service computing power. After the pricing system is established, a pricing analysis is conducted based on the computing power demand and the current cloud service computing power situation. On the one hand, based on the pricing aspect, the cloud service computing power is guaranteed and satisfied for the computing power demand, so as to reasonably allocate the computing power to be allocated for the current cloud service. On the other hand, it can also ensure that the billing of the computing power demander is more reasonable while fully meeting the computing power demand. In addition, after determining the pricing of the computing power demand based on the pricing analysis, the changes in real-time computing power are tracked in the process of allocating computing power to meet the computing power demand, so as to achieve accurate computing power usage pricing.

[0006] The purpose of this application is also to provide an equivalent billing device for computing power based on cloud services, which uses a computing power resource collection unit to collect statistics and data on the computing power on the cloud service, and provide reliable data information for the subsequent establishment of a pricing system. The computing power pricing unit can reasonably and accurately complete equivalent billing based on the pricing system. The computing power allocation unit completes the actual computing power allocation based on the equivalent billing. Each device structure has a clear division of labor, realizing the overall automation of computing power allocation and computing power equivalent billing.

[0007] In the first aspect, the present application provides a cloud service-based computing power transaction billing method, including obtaining the total computing power of the cloud service, cloud service computing power node data, and cloud service computing power allocation data, conducting cloud service computing power resource usage analysis, and establishing a cloud service computing power pricing system; obtaining computing power demand information, combining cloud service computing power node data and cloud service computing power pricing system, conducting pricing analysis on computing power demand, and forming computing power demand billing result data; billing according to the computing power demand billing result data, allocating cloud service computing power resources, and monitoring changes in computing power demand in real time, and adjusting the computing power demand billing result data.

[0008] In this application, the method establishes a reasonable cloud service computing power pricing system by analyzing the inherent characteristics of cloud service computing power and the historical scheduling of cloud service computing power, which can more reasonably realize the use of cloud service computing power and fully realize the pricing method for the use of cloud service computing power based on the demand for cloud service computing power. After the pricing system is established, a pricing analysis is conducted based on the computing power demand and the current cloud service computing power situation. On the one hand, based on the pricing aspect, the cloud service computing power is guaranteed and satisfied for the computing power demand, so as to reasonably allocate the computing power to be allocated for the current cloud service. On the other hand, it can also fully meet the computing power demand while ensuring that the billing of the computing power demander is more reasonable. In addition, after determining the pricing of the computing power demand based on the pricing analysis, the changes in real-time computing power are tracked in the process of allocating computing power to meet the computing power demand, so as to achieve accurate computing power usage pricing.

[0009] As a possible implementation method, the total computing power of the cloud service, cloud service computing power node data, and cloud service computing power allocation data are obtained, and cloud service computing power resource usage analysis is conducted to establish a cloud service computing power pricing system, including: obtaining cloud service computing power node data and cloud service computing power scheduling data, conducting computing power type analysis, and determining a first pricing factor; obtaining the total computing power of the cloud service and cloud service computing power allocation data, conducting computing power utilization analysis, and determining a second pricing factor; obtaining cloud service computing power node data, conducting computing power type combination analysis, and determining a third pricing factor; obtaining the computing power value of the computing power coin to form a basic billing computing power; and establishing a cloud service computing power pricing system by combining the basic billing computing power, the first pricing factor, the second pricing factor, and the third pricing factor.

[0010] In this application, when scheduling cloud service computing power to meet computing power demand, it is considered that the impact of using different computing power types on cloud service computing power is different. At the same time, the amount of resources consumed by allocating different computing power types is also different. For example, based on the classification of computing power types based on hardware, CPU computing power, GPU computing power, ASIC computing power, etc. have different computing power capabilities, and the resources consumed for allocation and the consumption of hardware are also different. Therefore, when performing equivalent pricing on cloud service computing power, it is necessary to consider the situation of scheduling different computing power types and establish an impact factor for selecting computing power types to measure the impact of the choice of computing power type on pricing. For the total computing power of cloud services, the utilization rate of computing power is different in different periods. When the computing power demand is high, the computing power is tight, and when the computing power demand is low, the computing power is abundant. This results in more resources used to allocate computing power when the computing power demand is high. Therefore, it is necessary to consider the impact of the total computing power utilization of cloud services on computing power pricing. This is also a reasonable floating pricing for the supply and demand of computing power to achieve optimization of computing power allocation. Of course, when the demand for a single computing power is large and a single matching computing power type cannot be provided, different computing power types need to be deployed to meet the computing power demand. This requires resource integration of different computing power types, which consumes additional resources. It is also necessary to consider the impact of using different computing power types simultaneously on the computing power pricing. In addition, after the computing power is priced, the demander needs to provide an approved exchange item to trade in exchange for computing power according to the priced amount. This application uses computing power coins to carry out computing power pricing transactions, thereby forming a reasonable pricing system to ensure the order of cloud service computing power transactions.

[0011] As a possible implementation method, cloud service computing power node data and cloud service computing power scheduling data are obtained, computing power type analysis is performed, and a first pricing factor is determined, including: obtaining different cloud service computing power node data, identifying computing power categories, determining the computing power type of each node, and forming a computing power type set A for the cloud service; based on the cloud service computing power scheduling data, the scheduling usage frequency of different computing power types in the computing power type set A is counted to form computing power type statistical data, and the first pricing factor is determined based on the computing power type statistical data.

[0012] In this application, when determining the first pricing factor that considers the impact of computing power type on pricing, it is understood that the utilization rate of computing power type will, to a certain extent, cause a loss of computing power, especially a loss of hardware resources. Therefore, considering the impact of using different computing power types on pricing based on their usage frequency can reasonably control the allocation and use of different computing power types, which is conducive to improving the matching of computing power types.

[0013] As a possible implementation method, according to the cloud service computing power scheduling data, the scheduling usage frequency of different computing power types in the computing power type set A is counted to form computing power type statistical data, and the first pricing factor is determined based on the computing power type statistical data, including: obtaining the scheduling usage frequency of different computing power types in the computing power type set A, and arranging the different computing power types in descending order according to the scheduling usage frequency, to form a computing power type frequency data set B = [b1, b2, ..., b n ], n is the order number of different computing power types according to the frequency of scheduling; set the first basic pricing parameter α1, and determine the first pricing factor C of different computing power types according to the following formula n :

[0014] In the present application, in the process of quantifying the first pricing factor, after sorting the different computing power types using the statistical frequency of deployment of different computing power types on the cloud service, the computing power type with the highest usage frequency is determined and used as the reference basis for the quantification of the first pricing factor. In this way, on the one hand, the first basic pricing parameter can be reasonably set according to the frequency of use, and the quantification of the first pricing factor is stable. On the other hand, compared with using the computing power type with the lowest deployment frequency as the reference for the quantification of the first pricing factor, it can avoid the instability of the reference for the quantification of the first pricing factor due to the large change of the computing power type with the lowest usage frequency, and it can also avoid the unreasonable quantification of the first pricing factor due to the large difference in the relative value of the usage frequency caused by the small amount of computing power. In addition, the first basic pricing parameter can be determined based on the computing power pricing data of the same level, and it also provides an adjustable amount setting for the cloud service computing power when using the computing power type, and tailor-made reasonable equivalent pricing values for the cloud service computing power itself. It should be noted that for computing power types with higher usage frequencies, the hardware resources behind them are consumed relatively quickly. Therefore, when performing level pricing for computing power allocation, it is necessary to consider that the computing power types with lower usage frequencies have a higher first pricing factor.

[0015] As a possible implementation method, the total computing power and cloud service computing power allocation data of the cloud service are obtained, computing power utilization analysis is performed, and the second pricing factor is determined, including: obtaining cloud service computing power allocation data and determining the average cloud service allocated computing power Q1; obtaining the total computing power Q0 of the cloud service, and setting the second basic pricing parameter α2, and determining the second pricing factor D according to the following formula: Among them, Q2 represents the cloud service allocation computing power obtained during pricing.

[0016] In the present application, the second pricing factor is established by taking into account the supply and demand relationship between the demand for computing power and the usage of the total computing power of the cloud service. Compared with the first pricing factor, it has a stronger fixedness, that is, the use of resources is determined under the usage of a certain cloud service computing power, and as the utilization rate of the cloud service computing power increases, the usage of resources also gradually increases. At the same time, as the utilization rate of the cloud service computing power increases, the remaining computing power to be allocated gradually decreases, and the competition for computing power demand to be allocated also gradually increases. Therefore, the second pricing factor can be obtained relatively stably by the ratio of the relative usage of the cloud service computing power to the total computing power of the cloud service. In this way, the setting of the second pricing factor is in line with the usage trend of cloud service computing power resources on the one hand, and on the other hand, it fully reflects the market law of the impact of changes in supply and demand on pricing. Of course, the second basic pricing parameter is to adjust the equivalent pricing level of the second pricing factor as a whole to conform to the current equivalent billing characteristics and ensure that the second pricing factor based on the computing power resource occupancy analysis of the total computing power of the cloud service can perform equivalent billing more reasonably.

[0017] As a possible implementation method, cloud service computing power node data is obtained, computing power type combination analysis is performed, and the third pricing factor is determined, including: obtaining the computing power G of each cloud service computing power type determined to meet the computing power demand; i , where i represents the number of the computing power type on the cloud service determined to meet the computing power demand; set the third basic pricing parameter α3, and determine the third pricing factor E according to the following formula: E=α3*|(G x ―G y )|, where x, y≤i.

[0018] In this application, for the third pricing factor, a reasonable pricing point is set based on the consumption of allocated resources for combining different types of computing power when different types of computing power are combined to meet the computing power requirements. Since there are differences in the degree of matching between different types of computing power, resources need to be consumed for matching when combining, and then an efficient computing power as a whole is formed to meet the computing power requirements. Therefore, the resources consumed by the integrated matching of the computing power are required for pricing. The matching differences between different types of computing power are often determined by the level of computing power or the total computing power gap. Therefore, the third pricing factor is determined based on the total computing power of different types of computing power, which can fix the impact on pricing. It should be noted that in the process of using computing power, since it is impossible to know the matching method of different types of computing power, all possible matching between the different types of computing power deployed are considered during pricing.

[0019] As a possible implementation method, computing power demand information is obtained, and computing power demand is analyzed by combining cloud service computing power node data and cloud service computing power pricing system to form computing power demand billing result data, including: determining the required computing power H0 based on the computing power demand information; obtaining the allocated computing power Q3 of the current cloud service computing power, and determining the second pricing factor D in combination with the required computing power H0, where Q2=Q3+H0; obtaining the computing power nodes to be allocated on the current cloud service, and planning the computing power nodes to be used on the cloud service to meet the computing power demand in combination with the required computing power H0 and the required computing power type, to form a required computing power node cluster; determining all computing power types in the required computing power node cluster, obtaining the first pricing factor corresponding to the computing power type, and determining it as the first pricing factor C of the required computing power v，t , v is the number of the computing power type determined in the computing power node cluster, and v≤n, t represents the number of the computing power node with computing power type v; determine the computing power H to be allocated to each computing power node in the required computing power node cluster v，t , obtain the third pricing factor E, and determine it as the demand third pricing factor E v ; Use the following formula to calculate the computing power demand and determine the number of computing power coins S: Among them, P0 represents the computing power value of the computing power coin.

[0020] In this application, pricing analysis is performed based on a pricing system determined by the first, second, and third pricing factors. This needs to be implemented in conjunction with actual computing power requirements. This pricing method, tailored to different computing power requirements, can fully achieve reasonable and equivalent billing for different computing power requirements. It is understood that the use of the first pricing factor is determined by the type and amount of computing power to be scheduled to meet the required computing power, while the second pricing factor is entirely determined by the total computing power required. The third pricing factor is determined based on the total computing power of different computing power types after the type and amount of computing power to be scheduled are determined. It is understood that after considering the overall pricing of the required computing power, how to achieve a substantive transaction between the computing power demander and the computing power provider is the key to equivalent billing. This application measures the total computing power provided by cloud services to meet the required computing power in the form of computing power coins, thereby presenting the demand for computing power in the form of computing power coins. Taking into account the versatility of computing power coins, a trading method in which computing power coins are equivalent to computing power is formed. Since computing power coins have the characteristics of currency-like liquidity, computing power demanders and providers have a stable and reasonable basis for equivalent exchange in computing power transactions.

[0021] As a possible implementation method, billing is performed based on the computing power demand billing result data, cloud service computing power is allocated resources, and changes in computing power demand are monitored in real time, and the computing power demand billing result data is adjusted, including: if the current computing power demand on the cloud service is met, the current remaining computing power demand is priced and analyzed, and the computing power demand billing result data is updated.

[0022] In this application, it is understood that after cloud service computing power is scheduled based on computing power demand, the remaining computing power to be allocated on the cloud service can be used to meet other computing power demands. In this way, cloud service computing power is always in a dynamic use process. Therefore, as cloud service computing power meets computing power demands, the satisfaction of different computing power demands can be dynamically considered based on the pricing system. In particular, when the computing power demand can still be met by the remaining computing power to be allocated, the pricing system can be used to determine the equivalent cost of the new computing power demand.

[0023] As a possible implementation method, billing is performed based on the computing power demand billing result data, cloud service computing power is allocated resources, and changes in computing power demand are monitored in real time, and the computing power demand billing result data is adjusted, including: if there is an increase in the current computing power demand on the cloud service, new computing power demand billing result data analysis is performed on the increased part, and the demand billing result data before the computing power demand increased is combined with the currently obtained demand result data.

[0024] In this application, of course, when dynamically meeting different computing power requirements for cloud service computing power, some discrepancies may occur in the required computing power that has been equivalently billed. Here, the deviation is directly analyzed under the pricing system for equivalent billing to determine the increased equivalent fee. Finally, when billing and settling the required computing power, the original equivalent billing result and the equivalent billing result of the increased part need to be combined to determine the final accurate equivalent fee.

[0025] In the second aspect, the present application provides an equivalent billing device for computing power based on cloud services, which adopts the equivalent billing method for computing power based on cloud services of the first aspect, including a computing power resource collection unit for obtaining the total computing power of the cloud service, cloud service computing power node data and cloud service computing power allocation data; a computing power pricing unit for obtaining the total computing power of the cloud service, cloud service computing power node data and cloud service computing power allocation data from the computing power resource collection unit, and obtaining computing power demand billing result data; a computing power allocation unit for allocating the computing power on the cloud service according to the computing power demand billing result data.

[0026] In this application, the computing power resource collection unit is used to collect statistics and data on the computing power of the cloud service, providing reliable data information for the subsequent establishment of the pricing system. The computing power pricing unit can reasonably and accurately complete equivalent billing based on the pricing system. The computing power allocation unit adds the equivalent billing to complete the actual computing power allocation. Each device structure has a clear division of labor, realizing the overall automation of computing power allocation and computing power equivalent billing.

[0027] The beneficial effects of the cloud service-based computing power transaction billing method and system provided by the present invention are:

[0028] This method establishes a reasonable cloud service computing power pricing system by analyzing the inherent characteristics of cloud service computing power and the historical scheduling of cloud service computing power. It can more reasonably realize the use of cloud service computing power and fully realize the pricing method of cloud service computing power usage based on the demand for cloud service computing power. After the pricing system is established, the pricing analysis is conducted based on the computing power demand and the current cloud service computing power situation. On the one hand, the cloud service computing power is guaranteed and satisfied based on the pricing aspect, so as to reasonably allocate the computing power to be allocated for the current cloud service. On the other hand, it can fully meet the computing power demand while ensuring that the billing of the computing power demander is more reasonable. In addition, after determining the pricing of the computing power demand based on the pricing analysis, the changes in real-time computing power are tracked in the process of allocating computing power to meet the computing power demand to achieve accurate computing power usage pricing.

[0029] This device uses a computing resource collection unit to collect statistics and data on the computing power of cloud services, providing reliable data for the subsequent establishment of a pricing system. The computing power pricing unit accurately and reasonably implements equivalent billing based on the pricing system. The computing power allocation unit then allocates computing power based on equivalent billing. Each device has a clearly defined division of labor, enabling automated computing power allocation and equivalent billing. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0031] Figure 1 A step diagram of the method and device for equivalent billing of computing power based on cloud services provided in an embodiment of the present application. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0033] Computing power is the information processing capability of a computer device or computing / data center. It is the ability of computer hardware and software to work together to perform a specific computational task. With the development of science and technology and the progress of society, the demand for computing power is gradually increasing to meet the growing demand for intelligent technology. The more complex and extensive the intelligent information needs, the greater the computing power required. Often, a single source of computing power is insufficient to support this demand, leading to the emergence of computing power trading.

[0034] Cloud-based computing power trading is becoming a popular method for trading computing power. By integrating computing power resources across the network, it can meet diverse computing power needs of varying types and amounts. However, computing power trading differs from conventional transactions. Conventional trading methods cannot effectively transfer computing power value. Therefore, a more efficient method of computing power trading is needed. Currently, computing power coins quantify computing power by anchoring it to a standard computing power, providing a new trading method that effectively ensures equal billing of computing power.

[0035] refer to Figure 1 , the embodiment of the present application provides a computing power transaction billing method based on cloud services. This method establishes a reasonable cloud service computing power pricing system by analyzing the inherent characteristics of the cloud service computing power and the historical scheduling of the cloud service computing power. It can more reasonably realize the use of cloud service computing power and fully realize the pricing method for the use of cloud service computing power based on the demand for cloud service computing power. After the pricing system is established, a pricing analysis is conducted based on the computing power demand and the current cloud service computing power situation. On the one hand, based on the pricing aspect, the cloud service computing power is guaranteed and satisfied for the computing power demand, so as to reasonably allocate the computing power to be allocated for the current cloud service. On the other hand, it can also ensure that the billing of the computing power demander is more reasonable while fully meeting the computing power demand. In addition, after determining the pricing of the computing power demand based on the pricing analysis, the changes in real-time computing power are tracked in the process of allocating computing power to meet the computing power demand, so as to achieve accurate computing power usage pricing.

[0036] The equivalent billing method based on computing power of cloud services includes the following main steps:

[0037] S1: Obtain the total computing power of the cloud service, cloud service computing power node data, and cloud service computing power allocation data, conduct cloud service computing power resource usage analysis, and establish a cloud service computing power pricing system.

[0038] This step includes: obtaining cloud service computing power node data and cloud service computing power scheduling data, analyzing the computing power type, and determining the first pricing factor; obtaining the total computing power of the cloud service and the cloud service computing power allocation data, analyzing the computing power utilization rate, and determining the second pricing factor; obtaining cloud service computing power node data, analyzing the computing power type combination, and determining the third pricing factor; obtaining the computing power value of the computing power coin to form the basic billing computing power; and establishing a cloud service computing power pricing system by combining the basic billing computing power, the first pricing factor, the second pricing factor, and the third pricing factor.

[0039] When scheduling cloud service computing power to meet demand, consider the varying impacts of different computing power types on the computing power. Furthermore, the resource allocation requirements for different computing power types vary. For example, computing power types are categorized by hardware. CPU, GPU, and ASIC computing power each have different computing capabilities, resulting in varying resource and hardware requirements for allocation. Therefore, when pricing cloud service computing power, it's necessary to consider the scheduling of different computing power types and establish a factor to measure the impact of computing power type selection on pricing. The utilization of the total computing power of a cloud service varies over time. When demand for computing power is high, computing power is limited, while when demand is low, computing power is abundant. This results in more resources being consumed for allocating computing power during periods of high demand. Therefore, it's important to consider the impact of total computing power utilization on computing power pricing. This is also a way to optimize computing power allocation by implementing reasonable floating pricing based on the supply and demand of computing power. Of course, when the demand for a single computing power is large and a single matching computing power type cannot be provided, different computing power types need to be deployed to meet the computing power demand. This requires resource integration of different computing power types, which consumes additional resources. It is also necessary to consider the impact of using different computing power types simultaneously on the computing power pricing. In addition, after the computing power is priced, the demander needs to provide an approved exchange item to trade in exchange for computing power according to the priced amount. This application uses computing power coins to carry out computing power pricing transactions, thereby forming a reasonable pricing system to ensure the order of cloud service computing power transactions.

[0040] Among them, cloud service computing power node data and cloud service computing power scheduling data are obtained, computing power type analysis is performed, and the first pricing factor is determined, including: obtaining different cloud service computing power node data, and identifying computing power categories, determining the computing power type of each node, and forming a computing power type set A for the cloud service; according to the cloud service computing power scheduling data, the scheduling usage frequency of different computing power types in the computing power type set A is counted to form computing power type statistical data, and the first pricing factor is determined based on the computing power type statistical data.

[0041] Regarding the determination of the first pricing factor that considers the impact of computing power type on pricing, it is understandable that the utilization rate of computing power type will, to a certain extent, cause a loss of computing power, especially hardware resources. Therefore, considering the impact of different computing power types on pricing based on their usage frequency can reasonably control the allocation and use of different computing power types, which is conducive to improving the matching of computing power types.

[0042] According to the cloud service computing power scheduling data, the scheduling usage frequencies of different computing power types in the computing power type set A are counted to form computing power type statistical data, and the first pricing factor is determined based on the computing power type statistical data, including: obtaining the scheduling usage frequencies of different computing power types in the computing power type set A, and arranging the different computing power types in descending order according to the scheduling usage frequencies, to form a computing power type frequency data set B = [b1, b2, ..., b n ], n is the order number of different computing power types according to the frequency of scheduling; set the first basic pricing parameter α1, and determine the first pricing factor C of different computing power types according to the following formula n :

[0043] During the quantification of the first pricing factor, the different computing power types are ranked using the statistically analyzed frequency of deployment and usage of different computing power types on the cloud service. The most frequently used computing power type is then determined and used as the reference for quantifying the first pricing factor. This allows the first basic pricing parameter to be reasonably set based on the frequency of usage, providing a stable reference for quantifying the first pricing factor. Furthermore, compared to using the computing power type with the lowest frequency of deployment as the reference for quantifying the first pricing factor, this avoids the possibility of significant changes in the computing power type with the lowest frequency of deployment, which could cause the reference for quantifying the first pricing factor to be unstable and affect the quantification. It also avoids the situation where a large difference in relative frequency of usage due to a small amount of computing power results in an unreasonable quantification of the first pricing factor. Furthermore, the first basic pricing parameter can be determined based on computing power pricing data at the same level, providing an adjustable pricing setting for the cloud service computing power when using computing power types, thereby tailoring the reasonable equivalent pricing value for the cloud service computing power. It should be noted that for computing power types with higher usage frequencies, the hardware resources behind them are consumed relatively quickly. Therefore, when performing level pricing for computing power allocation, it is necessary to consider that the computing power types with lower usage frequencies have a higher first pricing factor.

[0044] Obtaining the total computing power of the cloud service and the cloud service computing power allocation data, performing computing power utilization analysis, and determining the second pricing factor, including: obtaining the cloud service computing power allocation data and determining the average cloud service allocated computing power Q1; obtaining the total computing power of the cloud service Q0, and setting the second basic pricing parameter α2, and determining the second pricing factor D according to the following formula: Among them, Q2 represents the cloud service allocation computing power obtained during pricing.

[0045] The second pricing factor is established based on the supply and demand relationship between computing power demand and the total computing power usage of the cloud service. Compared to the first pricing factor, it is more fixed. Specifically, resource usage is fixed for a given computing power usage, and resource usage gradually increases as the utilization rate of the cloud service computing power increases. Furthermore, as the utilization rate of the cloud service computing power increases, the remaining computing power to be allocated gradually decreases, and the competition for computing power to be allocated gradually increases. Therefore, the second pricing factor can be determined relatively stably based on the ratio of computing power usage to the total computing power of the cloud service. This setting of the second pricing factor not only conforms to the usage trend of cloud service computing power resources, but also fully reflects the market laws that influence pricing based on the changing supply and demand relationship. Furthermore, the second basic pricing parameter adjusts the overall equivalent pricing level of the second pricing factor to conform to the current equivalent billing characteristics, ensuring that the second pricing factor, which analyzes computing power resource utilization based on the total computing power of the cloud service, can achieve more reasonable equivalent billing.

[0046] Obtain cloud service computing power node data, perform computing power type combination analysis, and determine the third pricing factor, including: obtaining the computing power G of each cloud service computing power type determined to meet the computing power requirements i , where i represents the number of the computing power type on the cloud service determined to meet the computing power demand; set the third basic pricing parameter α3, and determine the third pricing factor E according to the following formula: E=α3*|(G x ―G y )|, where x, y≤i.

[0047] The third pricing factor considers the resource consumption associated with combining different computing power types to meet computing power requirements, setting a reasonable pricing point. Because different computing power types often differ in their compatibility, resources are consumed when combining them to form an efficient overall computing power to meet computing power requirements. Therefore, pricing is based on the resources consumed by the combined matching of computing power. The compatibility differences between different computing power types are often determined by the level of computing power or the difference in total computing power. Therefore, determining the third pricing factor based on the total computing power of different computing power types provides a fixed basis for determining pricing. It should be noted that since the matching methods of different computing power types are unknown during the use of computing power, pricing considers all possible matching methods between the different allocated computing power types.

[0048] S2: Obtain computing power demand information, combine cloud service computing power node data and cloud service computing power pricing system, conduct pricing analysis on computing power demand, and generate computing power demand billing result data.

[0049] Obtain computing power demand information, combine cloud service computing power node data and cloud service computing power pricing system, perform pricing analysis on computing power demand, and form computing power demand billing result data, including: determining the required computing power H0 based on the computing power demand information; obtaining the allocated computing power Q3 of the current cloud service computing power, and combining it with the required computing power H0 to determine the second pricing factor D, where Q2 = Q3 + H0; obtaining the computing power nodes to be allocated on the current cloud service, combining the required computing power H0 and the required computing power type, planning the computing power nodes to be used on the cloud service to meet the computing power demand, and forming a required computing power node cluster; determining all computing power types in the required computing power node cluster, obtaining the first pricing factor corresponding to the computing power type, and determining it as the first pricing factor C of the required computing power v，t , v is the number of the computing power type determined in the computing power node cluster, and v≤n, t represents the number of the computing power node with computing power type v; determine the computing power H to be allocated to each computing power node in the required computing power node cluster v，t , obtain the third pricing factor E, and determine it as the demand third pricing factor E v ; Use the following formula to calculate the computing power demand and determine the number of computing power coins S: Among them, P0 represents the computing power value of the computing power coin.

[0050] Pricing analysis based on the pricing system determined by the first, second, and third pricing factors requires actual integration with computing power requirements. This allows for pricing methods tailored to different computing power requirements to fully achieve reasonable and equivalent billing for different computing power requirements. It is understood that the use of the first pricing factor is determined by the type and amount of computing power to be scheduled to meet the required computing power, while the second pricing factor is entirely determined by the total computing power required. The third pricing factor is determined based on the total computing power of different computing power types after the type and amount of computing power to be scheduled are determined. It is understood that after considering the overall pricing of the required computing power, how to achieve a substantive transaction between the computing power demander and the computing power provider is the key to equivalent billing. This application measures the total computing power provided by cloud services to meet the required computing power in the form of computing power coins, thereby presenting the demand for computing power in the form of computing power coins. Taking into account the versatility of computing power coins, a trading method in which computing power coins are equivalent to computing power is formed. Since computing power coins have the characteristics of currency-like liquidity, computing power demanders and providers have a stable and reasonable basis for equivalent exchange in computing power transactions.

[0051] S3: Billing is performed based on the computing power demand billing result data, cloud service computing power resources are allocated, and changes in computing power demand are monitored in real time to adjust the computing power demand billing result data.

[0052] Billing is performed based on the computing power demand billing result data, cloud service computing power is allocated resources, and changes in computing power demand are monitored in real time. The computing power demand billing result data is adjusted, including: if the current computing power demand on the cloud service is met, the current remaining computing power demand is analyzed for pricing and the computing power demand billing result data is updated.

[0053] It's understandable that after cloud service computing power is scheduled based on computing power demand, the remaining computing power to be allocated on the cloud service can be used to meet other computing power needs. This way, cloud service computing power is always in a dynamic state of utilization. Therefore, as cloud service computing power meets computing power demands, the pricing system can dynamically consider the satisfaction of different computing power demands. In particular, when computing power demands can still be met by the remaining computing power to be allocated, the pricing system can be used to determine the equivalent cost of new computing power demands.

[0054] In addition, billing is performed based on the computing power demand billing result data, cloud service computing power is allocated resources, and changes in computing power demand are monitored in real time, and the computing power demand billing result data is adjusted, including: if there is an increase in the current computing power demand on the cloud service, the new computing power demand billing result data is analyzed for the increased part, and the demand billing result data before the computing power demand increased is combined with the currently obtained demand result data.

[0055] Of course, when cloud service computing power dynamically meets different computing power requirements, some discrepancies may occur in the required computing power that has been equivalently billed. In this case, the deviation is directly analyzed under the pricing system for equivalent billing to determine the increased equivalent fee. Finally, when billing and settling the required computing power, the original equivalent billing results and the equivalent billing results of the increased portion are combined to determine the final accurate equivalent fee.

[0056] The present application also provides an equivalent billing device based on computing power of cloud services, which adopts the equivalent billing method based on computing power of cloud services provided by the present application. The device includes a computing power resource collection unit, which is used to obtain the total computing power of the cloud service, cloud service computing power node data and cloud service computing power allocation data; a computing power pricing unit, which is used to obtain the total computing power of the cloud service, cloud service computing power node data and cloud service computing power allocation data from the computing power resource collection unit, and obtain computing power demand billing result data; a computing power allocation unit, which is used to allocate the computing power on the cloud service according to the computing power demand billing result data.

[0057] This device uses a computing resource collection unit to collect statistics and data on the computing power of cloud services, providing reliable data for the subsequent establishment of a pricing system. The computing power pricing unit accurately and reasonably implements equivalent billing based on the pricing system. The computing power allocation unit then allocates computing power based on equivalent billing. Each device has a clearly defined division of labor, enabling automated computing power allocation and equivalent billing.

[0058] In summary, the beneficial effects of the cloud service-based computing power equivalent billing method and device provided in the embodiments of the present application are:

[0059] This method indirectly obtains relative pressure data based on flow monitoring data. Since the pipeline pressure formula establishes an effective relationship with the square value of the flow rate, the pressure value can be reasonably amplified by determining the pressure parameter through the flow rate value. In particular, when analyzing the relative pressure changes between different monitoring points, the pressure change value can be amplified and analyzed, which greatly improves the accuracy of the pressure change analysis, makes it easier and more accurate to judge whether the pressure is normal, improves the accuracy of pressure monitoring, and effectively ensures the safety of pipeline production and use.

[0060] The device collects flow data through the flow sensor, thereby providing a material basis for the data analysis unit to perform pipeline pressure analysis based on the flow data, and efficiently and accurately monitors and analyzes pipeline pressure.

[0061] In this application, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0062] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0063] Those skilled in the art will appreciate that the units and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0064] 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.

[0065] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0066] 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.

[0067] In addition, each functional unit in each embodiment of the present application 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.

[0068] 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 computer-readable storage medium. Based on this understanding, the technical solution of the present application, 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, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. 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.

[0069] The technical features of the above embodiments can 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 specification.

[0070] The above embodiments merely illustrate several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art would be able to make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be determined by the appended claims.

Claims

1. A computing power transaction billing method based on cloud services, characterized in that: include: Obtain the total computing power of cloud services, cloud service computing power node data, and cloud service computing power allocation data, conduct cloud service computing power resource usage analysis, and establish a cloud service computing power pricing system; Obtain computing power demand information, combine the cloud service computing power node data and the cloud service computing power pricing system, perform pricing analysis on the computing power demand, and generate computing power demand billing result data; Billing is performed based on the computing power demand billing result data, resource allocation is performed on the cloud service computing power, and changes in computing power demand are monitored in real time to adjust the computing power demand billing result data.

2. The computing power transaction billing method for cloud services according to claim 1, characterized in that: The acquisition of the total computing power of the cloud service, cloud service computing power node data, and cloud service computing power allocation data, analysis of cloud service computing power resource usage, and establishment of a cloud service computing power pricing system include: Obtaining the cloud service computing power node data and the cloud service computing power scheduling data, analyzing the computing power type, and determining a first pricing factor; Obtaining the total computing power of the cloud service and the computing power allocation data of the cloud service, performing computing power utilization analysis, and determining a second pricing factor; Obtaining the cloud service computing power node data, performing computing power type combination analysis, and determining a third pricing factor; Obtain the computing power value of the computing power coin to form the basic billing computing power; The cloud service computing power pricing system is established by combining the basic billing computing power, the first pricing factor, the second pricing factor, and the third pricing factor.

3. The equivalent billing method based on computing power of cloud services according to claim 2 is characterized in that: The acquiring the cloud service computing power node data and the cloud service computing power scheduling data, analyzing the computing power type, and determining the first pricing factor includes: Obtain data on different cloud service computing nodes, identify the computing power category, determine the computing power type of each node, and form a computing power type set A for the cloud service; According to the cloud service computing power scheduling data, the scheduling usage frequency of different computing power types in the computing power type set A is counted to form computing power type statistical data, and the first pricing factor is determined based on the computing power type statistical data.

4. The cloud service-based computing power transaction billing method according to claim 3 is characterized in that: The step of counting the scheduling usage frequencies of different computing power types in the computing power type set A according to the cloud service computing power scheduling data to form computing power type statistical data, and determining the first pricing factor according to the computing power type statistical data includes: Obtain the scheduling usage frequency of different computing power types in the computing power type set A, and arrange the different computing power types in descending order according to the scheduling usage frequency to form a computing power type frequency data set B = [b1, b2, ..., b n ], n is the sequence number determined by arranging different computing power types according to the scheduling usage frequency; Set the first basic pricing parameter α1 and determine the first pricing factor C of different computing power types according to the following formula n :

5. The cloud service-based computing power transaction billing method according to claim 4 is characterized in that: The obtaining of the total computing power of the cloud service and the computing power allocation data of the cloud service, performing computing power utilization analysis, and determining the second pricing factor includes: Obtain the cloud service computing power allocation data and determine the average cloud service allocation computing power Q1; Obtain the total computing power Q0 of the cloud service, set the second basic pricing parameter α2, and determine the second pricing factor D according to the following formula: Among them, Q2 represents the cloud service allocation computing power obtained during pricing.

6. The cloud service-based computing power transaction billing method according to claim 5, characterized in that: The obtaining of the cloud service computing power node data, performing computing power type combination analysis, and determining the third pricing factor includes: Obtain the computing power G of each cloud service computing power type determined to meet the computing power requirements i , where i represents the number of the computing power type on the cloud service determined to meet the computing power requirements; Set the third basic pricing parameter α3 and determine the third pricing factor E according to the following formula: E=α3*|(G x ―G y )|, where x, y≤i.

7. The cloud service-based computing power transaction billing method according to claim 6, characterized in that: The computing power demand information is obtained, and a pricing analysis is performed on the computing power demand in combination with the cloud service computing power node data and the cloud service computing power pricing system to form computing power demand billing result data, including: Determine the required computing power H0 based on the computing power requirement information; Obtain the allocated computing power Q3 of the current cloud service computing power, and determine the second pricing factor D based on the required computing power H0, where Q2 = Q3 + H0; Obtain the computing power nodes to be deployed on the current cloud service, and plan the computing power nodes to be used on the cloud service to meet the computing power requirements based on the required computing power H0 and the required computing power type, to form a required computing power node cluster; Determine all the computing power types in the required computing power node cluster, obtain the first pricing factor corresponding to the computing power type, and determine it as the first pricing factor C of the required computing power v，t , v is the number of the computing power type determined in the computing power node cluster, and v≤n, t represents the number of the computing power node with computing power type v; Determine the computing power H to be allocated to each computing power node in the required computing power node cluster v，t , obtain the third pricing factor E, and determine it as the demand third pricing factor E v-1 ; Use the following formula to calculate the computing power demand and determine the number of computing power coins S: Among them, P0 represents the computing power value of the computing power coin.

8. The cloud service-based computing power transaction billing method according to claim 7, characterized in that: The billing according to the computing power demand billing result data, resource allocation of the cloud service computing power, and real-time monitoring of changes in computing power demand, and adjustment of the computing power demand billing result data include: If the current computing power demand on the cloud service is met, a pricing analysis is performed on the current remaining computing power demand, and the computing power demand billing result data is updated.

9. The cloud service-based computing power transaction billing method according to claim 8, characterized in that: Billing is performed based on the computing power demand billing result data, resource allocation is performed on the cloud service computing power, and changes in computing power demand are monitored in real time, and adjustments are made to the computing power demand billing result data, including: If there is an increase in the current computing power demand on the cloud service, the new computing power demand billing result data analysis is performed on the increased part, and the demand billing result data before the computing power demand increase is combined with the currently obtained demand result data.

10. A cloud service-based computing power transaction billing system, adopting the cloud service-based computing power transaction billing method according to any one of claims 1 to 9, characterized in that: include: The computing power resource collection unit is used to obtain the total computing power of the cloud service, the cloud service computing power node data, and the cloud service computing power allocation data; A computing power pricing unit is used to obtain the total computing power of the cloud service, cloud service computing power node data, and cloud service computing power allocation data from the computing power resource collection unit, and obtain computing power demand billing result data; The computing power allocation unit is used to allocate the computing power on the cloud service according to the computing power demand billing result data.

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