Method and device for calculating energy efficiency and carbon emission of data center and electronic equipment

Abstract

The embodiment of the specification provides a data center energy efficiency and carbon emission benchmark value establishing method, device, electronic equipment, storage medium, data center and computer program product, wherein the data center energy efficiency benchmark value establishing method comprehensively considers three dimensions of data center resource use efficiency, computing power use rate and computing power energy efficiency, realizes comprehensive evaluation of all links of the data center from resource input to performance output, can scientifically and accurately reflect the energy consumption-output state of the data center, helps scientific formulation of the carbon emission benchmark and free carbon emission quota of the data center, thereby effectively guiding the data center energy efficiency and carbon emission related optimization and improving the performance of the data center.

Description

Technical Field

[0001] This specification relates to energy and carbon emission management technologies in the field of computer applications, and more specifically, to a method, apparatus, electronic equipment, storage medium, data center, and computer program product for calculating energy efficiency and carbon emissions in a data center. Background Technology

[0002] On July 16, 2021, the national carbon emission trading market opened, and carbon emission parameters (referred to as carbon emissions) have gradually become a production indicator of concern to various production participants.

[0003] Internet Data Centers (IDCs) consume significant amounts of energy and emit substantial amounts of carbon. Currently, some cities have already piloted inclusion in the carbon emission market, and in the future, they may be incorporated into a unified national carbon emission market. A crucial step in including data centers in the carbon emission market is establishing carbon emission benchmarks and allocating carbon emission quotas. The current related systems and rules have considerable room for improvement; therefore, it is necessary to propose energy efficiency and carbon emission calculation methods specifically for data centers. This is of great significance for assisting in the carbon emission management of data centers and promoting their green and low-carbon development. Summary of the Invention

[0004] This specification provides a method, apparatus, and electronic device for calculating the energy efficiency and carbon emissions of a data center. This enables a comprehensive evaluation of the entire chain of energy input to performance output in the data center energy efficiency and carbon emission parameter calculation process, providing guidance for optimizing data center carbon emission parameters and contributing to the healthy development of the data center industry.

[0005] To achieve the above technical objectives, the embodiments of this specification provide the following technical solutions:

[0006] Firstly, embodiments of this specification provide a method for calculating the energy efficiency and carbon emissions of a data center, the method comprising:

[0007] Obtain the energy efficiency parameters of the data center, including resource utilization efficiency, computing power utilization rate, and computing power energy efficiency;

[0008] The weights corresponding to the energy efficiency parameters of the data center are determined. Based on the energy efficiency parameters of the data center and their respective weights, the comprehensive energy efficiency of the data center is determined. The comprehensive energy efficiency is used to calculate the carbon emissions of the data center.

[0009] Secondly, embodiments of this specification provide a method for calculating the energy efficiency and carbon emissions of a data center, the method comprising:

[0010] Obtain the energy efficiency parameters of the data center, which are used to characterize the resource utilization efficiency and computing power utilization and output status of the data center;

[0011] The overall energy efficiency of the data center is determined by considering its energy efficiency parameters.

[0012] Thirdly, embodiments of this specification provide an energy efficiency and carbon emission calculation device for a data center, comprising:

[0013] The first energy efficiency acquisition module is used to acquire the energy efficiency parameters of the data center, including resource utilization efficiency, computing power utilization rate and computing power efficiency.

[0014] The first energy efficiency determination module is used to determine the weights corresponding to the energy efficiency parameters of the data center, and to determine the comprehensive energy efficiency of the data center based on the energy efficiency parameters and their respective weights. The comprehensive energy efficiency is used to calculate the carbon emissions of the data center.

[0015] Fourthly, embodiments of this specification provide an energy efficiency and carbon emission calculation device for a data center, comprising:

[0016] The second energy efficiency acquisition module is used to acquire the energy efficiency parameters of the data center, which are used to characterize the resource utilization efficiency and computing power utilization and output status of the data center.

[0017] The second energy efficiency determination module is used to determine the overall energy efficiency of the data center by integrating the energy efficiency parameters of the data center. The overall energy efficiency is used to calculate the carbon emissions of the data center.

[0018] Fifthly, embodiments of this specification provide an electronic device, including: a memory and a processor;

[0019] The memory is connected to the processor and is used to store programs;

[0020] The processor is configured to implement the energy efficiency and carbon emission calculation method for a data center as described above by running a program stored in the memory.

[0021] Sixthly, embodiments of this specification provide a storage medium storing a computer program, which, when executed by a processor, implements the energy efficiency and carbon emission calculation method for a data center as described in any of the preceding claims.

[0022] Seventhly, embodiments of this specification also provide a data center, including: IT equipment and a computing device connected to the IT equipment, the computing device being used to implement the energy efficiency and carbon emission calculation method of the data center described in any of the above embodiments.

[0023] Eighthly, embodiments of this specification provide a computer program product or computer program, the computer program product or computer program including computer instructions stored in a computer-readable storage medium; the processor of the computer device reads the computer instructions from the computer-readable storage medium, and when the processor executes the computer instructions, it implements the above-described method for calculating the energy efficiency and carbon emissions of a data center.

[0024] As can be seen from the above technical solutions, the embodiments of this specification provide a method, device, electronic device, storage medium, data center, and computer program product for calculating the energy efficiency and carbon emissions of a data center. The energy efficiency and carbon emission calculation method for the data center comprehensively considers resource utilization efficiency and computing power utilization and output status as energy efficiency parameters during the determination of carbon emission parameters. This achieves a comprehensive efficiency evaluation system for the entire chain of energy input and performance output of the data center, taking into account data center resource efficiency, computing power, and computing efficiency. It comprehensively reflects the overall efficiency level of the data center, overcoming the problem that traditional carbon emission quota determination methods cannot reflect the comprehensive efficiency of the entire process of data center energy utilization and computing power output. This provides guidance for optimizing data center carbon emission parameters and also helps the healthy development of the data center industry. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments or prior art of this specification, the drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this specification. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0026] Figure 1 A schematic diagram illustrating a scenario example provided for one embodiment of this specification;

[0027] Figure 2 A flowchart illustrating a method for calculating energy efficiency and carbon emissions of a data center, provided as an embodiment of this specification;

[0028] Figure 3 A flowchart illustrating a method for calculating energy efficiency and carbon emissions of a data center, provided as another embodiment of this specification;

[0029] Figure 4 A flowchart illustrating a method for calculating the energy efficiency and carbon emissions of a data center, provided as another embodiment of this specification;

[0030] Figure 5A flowchart illustrating a method for calculating the energy efficiency and carbon emissions of a data center, provided as another embodiment of this specification;

[0031] Figure 6 A flowchart illustrating a method for calculating energy efficiency and carbon emissions of a data center, provided as an optional embodiment of this specification;

[0032] Figure 7 A flowchart illustrating a method for calculating energy efficiency and carbon emissions in a data center, provided as another optional embodiment of this specification;

[0033] Figure 8 A schematic diagram of an energy efficiency optimization report display interface provided as an embodiment of this specification;

[0034] Figure 9 A schematic diagram of an energy efficiency optimization report display interface provided for another embodiment of this specification;

[0035] Figure 10 A flowchart illustrating a method for calculating energy efficiency and carbon emissions of a data center, provided as another optional embodiment of this specification.

[0036] Figure 11 A schematic diagram of the structure of a data center energy efficiency and carbon emission calculation device provided as an embodiment of this specification;

[0037] Figure 12 A schematic diagram of the structure of a data center energy efficiency and carbon emission calculation device provided for another embodiment of this specification;

[0038] Figure 13 A schematic diagram of a data center structure is provided for one embodiment of this specification;

[0039] Figure 14 This is a schematic diagram of an electronic device provided as an embodiment of the present specification. Detailed Implementation

[0040] Unless otherwise defined, the technical or scientific terms used in the embodiments of this specification shall have the ordinary meaning understood by one of ordinary skill in the art to which this specification pertains. The terms "first," "second," and similar terms used in the embodiments of this specification do not indicate any order, quantity, or importance, but are merely used to avoid confusion of constituent elements.

[0041] Unless the context otherwise requires, throughout this specification, "a plurality of" means "at least two," and "including" is interpreted as open-ended or encompassing, that is, "including, but not limited to." In the description of this specification, terms such as "one embodiment," "some embodiments," "exemplary embodiment," "example," "specific example," or "some examples" are intended to indicate that a particular feature, structure, material, or characteristic associated with that embodiment or example is included in at least one embodiment or example of this specification. The illustrative representations of the above terms do not necessarily refer to the same embodiment or example.

[0042] The technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this specification.

[0043] Data centers, as entities for storing, computing, and interacting with massive amounts of data, include equipment such as computers, cooling, power supply, lighting, and machinery. They also serve as data hubs and computing carriers for technologies such as 5G, artificial intelligence (AI), the Internet of Things, and cloud computing, and are an important support for the development of "new infrastructure."

[0044] Data centers, including server clusters and auxiliary cooling systems, are high-energy-consuming devices that rely heavily on uninterrupted power supply. According to the "Data Center White Paper (2022)" released by the China Academy of Information and Communications Technology in April 2022, in 2021, the energy consumption of data centers nationwide was approximately 216.6 billion kilowatt-hours, accounting for 2.6% of the country's total electricity consumption. Indirect greenhouse gas emissions from electricity consumption reached approximately 135 million tons, accounting for about 1.14% of the country's total carbon emissions. Over the past decade, the overall electricity consumption of my country's data center industry has increased at an average annual rate of over 10%. With the increase in overall energy demand, the energy consumption of data centers has received significant attention. To promote energy conservation and carbon reduction in data centers, the government plans to include data centers in a unified national carbon market to facilitate emissions data monitoring, reporting, and verification, promote the implementation of industry energy efficiency standards, and minimize the overall emission reduction costs of the industry through market mechanisms.

[0045] Therefore, given the industry's impending inclusion of data centers in the carbon emissions market, and the critical issues surrounding carbon emission quota allocation within the market's mechanism, accurately and reasonably calculating energy efficiency and carbon emissions for data centers has become an urgent problem to solve. Accurate and reasonable energy efficiency and carbon emission calculations can provide data center companies with carbon emission references, guide them in optimizing equipment emissions, and improve the scientific rigor of carbon emission benchmarks and the carbon emission allocation process for the data center industry.

[0046] In one scenario example in this specification, refer to Figure 1 The energy consumption and carbon emissions generated by data centers during operation are currently typically evaluated using utilization efficiency indicators. These indicators include: PUE (Power Usage Effectiveness), WUE (Water Use Efficiency), CUE (Carbon Use Efficiency), IUE (Infrastructure Use Efficiency), and GUE (Resource Efficiency). The following section introduces the commonly used PUE indicator:

[0047] PUE = P total / P IT (1)

[0048] Among them, P total This refers to the total power consumption of the data center, expressed in kilowatt-hours.

[0049] P IT This refers to the power consumption of IT (Information Technology) equipment in a data center, measured in kilowatt-hours.

[0050] IT equipment is the basic equipment that generates computing power in a data center, and can include servers, storage devices, and network equipment.

[0051] In addition, the reciprocal of PUE is called Data Center Infrastructure Efficiency (DCiE) = 1 / PUE. DCiE reflects the proportion of IT equipment in the total energy efficiency of the data center, and its value is less than 1. The closer it is to 1, the better.

[0052] The derived efficiencies of PUE include pPUE (partial power usage efficiency), dPUE (design power usage efficiency), and iPUE (period power usage efficiency). Among them, dPUE is the expected PUE determined by the data center design goals, while iPUE refers to the PUE measured at a specified time, not the annual value.

[0053] pPUE = P total_sub / P IT_sub (2)

[0054] Among them, P total_sub It refers to the total power consumption within a subsystem of a data center (power distribution system, network equipment, cooling system, etc.), expressed in kilowatt-hours.

[0055] P IT_subThis refers to the power consumption of data center IT equipment, measured in kilowatt-hours.

[0056] In addition, domestic standards related to data center energy consumption assessment include: EEUE-R (Electric Energy Usage Effectiveness), EEUE-X (Electric Energy Usage Effectiveness Correction), WUE (Water Usage Effectiveness), CUE (Carbon Usage Effectiveness), IUE (Infrastructure Usage Effectiveness), GUE (Grid Usage Effectiveness), REF (Renewable Energy Factor), ERF (Energy Reuse Factor), and SEUR (Storage Energy Use Ratio).

[0057] These metrics focus on the consumption, conversion, and utilization structure of data center power and water resources. Except for PUE, most of these metrics are complex to calculate and difficult to be widely accepted. Furthermore, none of these metrics can reflect the computing power output supported by the data center during resource consumption, nor can they cover the entire chain of energy conversion and application in data centers, thus failing to provide a comprehensive perspective on the overall efficiency of data centers.

[0058] In view of this, the embodiments of this specification provide a method for calculating the energy efficiency and carbon emissions of data centers. In the process of determining comprehensive energy efficiency, this method takes into account resource utilization efficiency and computing power utilization and output status as energy efficiency parameters, realizing a comprehensive efficiency evaluation system for the entire chain of energy input and performance output of data centers. It takes into account the resource efficiency, computing power and computing efficiency of data centers, and comprehensively reflects the comprehensive efficiency level of data centers. It overcomes the problem that traditional carbon emission quota determination methods cannot reflect the comprehensive efficiency of the entire process of energy utilization and computing power output of data centers, and provides certain guidance for the optimization of data center energy efficiency benchmark values, and also helps the healthy development of the data center industry.

[0059] Furthermore, this energy efficiency calculation method also determines the corresponding weights for the energy efficiency parameters of data centers based on their attributes. This allows the determination of the energy efficiency benchmark value of data centers to be based on the different attributes of the data centers, laying the foundation for determining the energy efficiency benchmark value of data centers with different attributes and improving the targeting of the energy efficiency and carbon emission calculation method for data centers.

[0060] The following describes the energy efficiency and carbon emission calculation method for data centers provided in the embodiments of this specification, with reference to feasible exemplary models.

[0061] An exemplary embodiment of this specification provides a method for calculating the energy efficiency and carbon emissions of a data center, such as... Figure 2 As shown, it includes:

[0062] S101: Obtain the energy efficiency parameters of the data center, which are used to characterize the resource utilization efficiency and computing power utilization and output status of the data center.

[0063] In step S101, in addition to resource utilization efficiency, computing power utilization and output status are also included as part of the energy efficiency parameters. That is, in the process of evaluating the comprehensive energy efficiency of a data center, not only the energy consumption of the data center is considered, but also the computing power output supported by the energy consumption. This makes the energy efficiency parameters used to determine comprehensive energy efficiency cover the entire process from energy utilization to performance output. The comprehensive energy efficiency of the data center is considered from multiple dimensions, so that the determination of comprehensive energy efficiency fully reflects the comprehensive energy efficiency level of the data center. This effectively overcomes the problem that the current comprehensive energy efficiency determination method cannot fully reflect the comprehensive efficiency of the entire process of energy utilization and computing power output of the data center.

[0064] In one embodiment of this specification, "resources" in resource utilization efficiency refers to the resources used by the data center. These resources include at least one of the following: energy, water, cooling, and space occupied by rack units. Water resources may include water used for server cooling and data center humidification. Cooling resources may include coolant other than cooling water. Energy resources include at least one of the following forms: electrical energy, light energy, and thermal energy. Accordingly, resource utilization efficiency refers to the efficiency of resource utilization in the data center. Optionally, taking electrical energy used by the data center as an example, resource utilization efficiency can be the PUE described above, or indicators reflecting energy utilization efficiency such as pPUE (partial energy utilization efficiency), dPUE (design energy utilization efficiency), or iPUE (period energy utilization efficiency). Specific definitions and calculation methods for pPUE, dPUE, and iPUE can be found in the description above. Furthermore, resource utilization efficiency can also be other indicators reflecting resource utilization efficiency described above; this specification does not limit this.

[0065] Parameters used to characterize the state of computing power usage and output can be the computing power utilization rate and computing power efficiency of the data center themselves, or parameters after further processing (such as normalization, standardization, etc.) based on the computing power utilization rate and computing power efficiency. This specification does not limit this. In a data center, IT equipment is divided into computing, storage, and network equipment. Among them, the computing part consumes the most energy, accounting for more than 90% of the total energy consumption of IT equipment. Therefore, computing power efficiency (CEUE), which characterizes the energy consumption efficiency of the computing part, can evaluate the energy efficiency of the data center from the perspective of computing power efficiency.

[0066] For computing power utilization, the average utilization rate of computing power in a data center within a specific time period can be used as a representative value, as can the average idle rate of computing power within a data center within a specific time period. Computing power refers to the computational capability to process information data and achieve target output results. In a data center, computing power can be represented by the CPU (Central Processing Unit) resources of servers. Computing power utilization can evaluate the energy efficiency of a data center from the perspective of computing power usage. That is, when the representative value of computing power utilization refers to the average utilization rate of computing power in a data center within a specific time period, it can be represented by the average utilization rate η of the server CPUs in the data center within that specific time period. CPU The unit %, can be expressed as:

[0067] η CPU =CPU _U / CPU _tot (3)

[0068] Among them, CPU _U This refers to the CPU resources already in use, measured in Hz. _tot This refers to total CPU resources, measured in Hz. When the computing power utilization rate represents the average idle rate of computing power in a data center over a specific period of time, the computing power utilization rate can be expressed as: 1 - η CPU .

[0069] S102: Based on the energy efficiency parameters of the data center, determine the overall energy efficiency of the data center, which is used to calculate the carbon emissions of the data center.

[0070] In step S102, the comprehensive energy efficiency is determined by comprehensively considering three dimensions: data center resource utilization efficiency, computing power utilization rate, and computing power energy efficiency. This achieves a multi-dimensional comprehensive evaluation of the entire process of data center from energy utilization to performance output, making the determined comprehensive energy efficiency more scientific and accurate in reflecting the energy efficiency-output status of data center. This lays a solid foundation for scientifically formulating carbon emission benchmarks and free carbon emission allowances for data centers.

[0071] When determining the overall energy efficiency of a data center, the energy efficiency parameters can be directly superimposed, weighted and summed, or calculated according to a preset formula. This manual does not limit this method; the specific method depends on the actual situation.

[0072] In addition, scientific and accurate comprehensive energy efficiency can guide data centers to optimize energy efficiency, contributing to energy conservation, emission reduction, and improved energy efficiency in data centers.

[0073] Since the overall energy efficiency of a data center is related to its carbon emissions, once the overall energy efficiency of a data center is determined, its carbon emissions can be calculated based on that overall energy efficiency.

[0074] The weights of various energy efficiency parameters may differ for data centers with different attributes. To achieve accurate calculation of energy efficiency and carbon emissions for data centers with different attributes, this specification provides an exemplary embodiment of another method for calculating the energy efficiency and carbon emissions of data centers, such as... Figure 3 As shown, it includes:

[0075] S201: Obtain the energy efficiency parameters of the data center, including resource utilization efficiency, computing power utilization rate, and computing power efficiency.

[0076] In step S201, computing power utilization and computing power efficiency are used as parameters characterizing the state of computing power utilization and output. Computing power efficiency can be calculated based on computing power efficiency, which characterizes the energy efficiency of the computing power portion of IT equipment, or it can be calculated based on parameters characterizing the energy efficiency of the storage portion or the network portion of IT equipment. This specification does not limit this calculation.

[0077] Besides being characterized by the computing power efficiency itself, computing power efficiency can also be characterized by parameters after normalizing the computing power efficiency. For example, computing power efficiency can be expressed as:

[0078] η CEUE = (actual CEUE value - baseline CEUE value) / baseline CEUE value (4)

[0079] CEUE refers to the power consumption of IT equipment required per unit of computing power in a data center, measured in W / TFLOPS. The calculation formula is shown in formula (5).

[0080] CEUE=P IT / CP (5)

[0081] Among them, P IT CEUE refers to the total power of IT equipment, measured in kW. CP refers to Computing Power, measured in FLOPS (Floating-point Operations Per Second), used to evaluate the general computing power and high-performance computing power of data centers. The CEUE benchmark value can be determined by the most recently published national data center overall computing power energy efficiency value, or by the average of the national data center overall computing power energy efficiency values ​​published within the past two years.

[0082] S202: Determine the weights corresponding to the energy efficiency parameters of the data center, and determine the comprehensive energy efficiency of the data center based on the energy efficiency parameters and their respective weights. The comprehensive energy efficiency is used to calculate the carbon emissions of the data center.

[0083] After determining the weights corresponding to each energy efficiency parameter, the sum of the products of the energy efficiency parameters and their respective weights can be used as the overall energy efficiency of the data center. Since there are multiple ways to represent the various energy efficiency parameters of a data center, more specifically, the sum of the products of the representative values ​​of resource utilization efficiency, computing power efficiency, and computing power utilization rate, and their respective weights, can be used as the overall energy efficiency of the data center. If Power Usage Effectiveness (PUE) is used as the representative value of resource utilization efficiency, and the representative value of computing power utilization rate is expressed as 1-η... CPU This indicates the computing power efficiency η of using IT equipment. CEUE As a numerical representation of computing power efficiency, the overall energy efficiency can be calculated according to the following formula (6):

[0084] S tot = k1·(PUE)+k2·(1-η) CPU )+k3·η CEUE (6)

[0085] Among them, S tot PUE represents overall energy efficiency, while η represents the efficiency of electricity use. CPU η represents the average utilization rate of computing power in a data center over a specific period of time. CEUEThis represents the computing power efficiency of IT equipment. k1 represents the weight corresponding to resource utilization efficiency (electrical energy utilization efficiency in this embodiment), k2 represents the weight corresponding to the numerical value representing computing power utilization rate, and k3 represents the weight corresponding to computing power efficiency (computing power efficiency of IT equipment in this embodiment).

[0086] Optionally, determining the weights corresponding to the respective energy efficiency parameters of the data center includes:

[0087] S2021: Based on the attributes of the data center, determine the weights corresponding to the energy efficiency parameters of the data center. The attributes of the data center are used to characterize the type, operating environment, and operating status of the data center.

[0088] As mentioned earlier, due to the different attributes of data centers, the contribution of resource utilization efficiency, computing power utilization rate, and computing power efficiency to the overall energy efficiency of data centers also varies. Therefore, it is necessary to determine the weight of each of the energy efficiency parameters of the data center according to its attributes.

[0089] The types of data centers include, but are not limited to, ultra-large data centers, large data centers, medium-sized data centers and small data centers classified by size, enterprise self-use data centers and third-party hosted data centers classified by operation, and Class A data centers, Class B data centers and Class C data centers classified according to GB50174-2008 standard.

[0090] The operating environment of the data center may include the region where the data center is located, the current season, etc. In some embodiments, the operating environment of the data center may also include incentive measures for the region where the data center is located.

[0091] The operational status of the data center includes, but is not limited to, its purpose and type of server room. The purpose of the data center includes, but is not limited to, data storage, data processing and analysis, and product services.

[0092] The contribution of various energy efficiency parameters to overall energy efficiency differs depending on the characteristics of the data centers. For example, if data center 1 and data center 2 are located in city A and city B respectively, and city A suffers from power shortages and relies on electricity from other cities year-round, while city B is rich in water, wind, and solar resources, with well-developed hydroelectric, wind, and solar power plants, capable of not only meeting its own electricity needs but also transmitting large amounts of electricity to other cities, then in this scenario, the resource utilization efficiency weight of data center 1 in city A can be greater than that of data center 2 in city B. This is because the resources and costs (such as transmission costs) required for data center 1 to use one kilowatt-hour of electricity are greater than those required for data center 2 to use one kilowatt-hour of electricity.

[0093] For example, data center 3 is located in city C in northern China. City C experiences very cold winters, requiring significant electricity and thermal power to meet heating needs. However, city C has pleasant summers and does not require the same level of electricity for air conditioning and other cooling equipment as other cities. Therefore, the resource utilization efficiency weight for data center 3 during winter can be greater than its weight during summer. This specification does not exhaustively list all possible scenarios; the specific weighting of various energy efficiency parameters depends on the actual situation.

[0094] After establishing a comprehensive energy efficiency determination method similar to formula (6), a comprehensive data center performance evaluation system can be established based on this method. Specifically, such as... Figure 4 As shown, the method for calculating the energy efficiency and carbon emissions of the data center also includes:

[0095] S301: Obtain the first typical value, second typical value, and third typical value corresponding to each of the energy efficiency parameters. The first typical value represents the typical value of the corresponding energy efficiency parameter when it is in the first energy efficiency range. The second typical value represents the typical value of the corresponding energy efficiency parameter when it is in the second energy efficiency range. The energy efficiency represented by the energy efficiency parameter when it is in the first energy efficiency range, the second energy efficiency range, and the third energy efficiency range decreases sequentially.

[0096] The first typical value, the second typical value, and the third typical value refer to the typical values ​​of data centers whose energy efficiency parameters are in the advanced range (i.e., the first energy efficiency range, which can be considered as the high energy efficiency range), the general range (i.e., the second energy efficiency range, which can be considered as the general energy efficiency range), and the backward range (i.e., the third energy efficiency range, which can be considered as the low energy efficiency range). These typical values ​​can be the median or average of various data centers in the data center industry within the above ranges, but this specification does not limit them.

[0097] Referring to Table 1, which shows a feasible first typical value, second typical value, and third typical value for PUE, computing power utilization, and computing power efficiency, respectively.

[0098] Table 1 Typical Values ​​of Data Center Energy Efficiency Parameters

[0099]

[0100] S302: The sum of the products of the first typical value corresponding to each of the energy efficiency parameters and the weight corresponding to each of the energy efficiency parameters is taken as the first typical energy efficiency value.

[0101] Taking the first typical value in Table 1 as an example, and taking k1 = 50, k2 = 100, k3 = 50 in formula (6) as an example, the first typical energy efficiency value = 1.2 × 50 + 100 × 30% + (-0.6) × 50 = 60.

[0102] S303: The sum of the products of the second typical value corresponding to each of the energy efficiency parameters and the weight corresponding to each of the energy efficiency parameters is taken as the second typical energy efficiency value.

[0103] Taking the second typical value in Table 1 as an example, and taking k1 = 50, k2 = 100, k3 = 50 in formula (6) as an example, the second typical energy efficiency value = 1.4 × 50 + 100 × 70% + 0 × 50 = 140.

[0104] S304: The sum of the products of the third typical value corresponding to each of the energy efficiency parameters and the weight corresponding to each of the energy efficiency parameters shall be used as the third typical energy efficiency value.

[0105] Taking the third typical value in Table 1 as an example, and taking k1 = 50, k2 = 100, k3 = 50 in formula (6) as an example, the third typical energy efficiency value = 1.8 × 50 + 100 × 90% + 0.6 × 50 = 210.

[0106] Referring again to Table 1, which also shows the typical energy efficiency values ​​calculated based on the acquired energy efficiency parameters,... t .

[0107] It is not difficult to understand that S tot The smaller the value, the better the data center's resource utilization and computing power output.

[0108] S305: Determine a first critical value between the first typical energy efficiency value and the second typical energy efficiency value.

[0109] S306: Determine a second critical value between the second typical energy efficiency value and the third typical energy efficiency value.

[0110] S307: Based on the relationship between the overall energy efficiency of the data center and the first and second threshold values, determine the energy efficiency status of the data center, wherein the energy efficiency status is used to characterize the excellent level of the overall energy efficiency of the data center.

[0111] The first critical value can be between the first typical energy efficiency value and the second typical energy efficiency value, serving as the dividing line between excellent and good levels of comprehensive energy efficiency for data centers. The second critical value can be between the second typical energy efficiency value and the third typical energy efficiency value, serving as the dividing line between good and poor levels of comprehensive energy efficiency for data centers.

[0112] Referring to Table 2, which illustrates a feasible comprehensive performance rating system for data centers, the first threshold value is the average of the first typical energy efficiency value (e.g., 60) and the second typical energy efficiency value (e.g., 140) (e.g., 100), and the second threshold value is the average of the second typical energy efficiency value (e.g., 140) and the third typical energy efficiency value (e.g., 210) (e.g., 190).

[0113] Table 2 Data Center Comprehensive Performance Level Classification System

[0114]

[0115] As shown in Table 2, after determining the first and second critical values, the energy efficiency status of a data center can be evaluated based on the relationship between its overall energy efficiency and these values. This energy efficiency status characterizes the level of excellence in carbon emissions. Using Table 2 as an example again, when the overall energy efficiency of a data center is less than or equal to the first critical value, it can be considered to be at an excellent level, indicating excellent energy efficiency and computing power output. When the overall energy efficiency is greater than the first critical value and less than or equal to the second critical value, it can be considered to be at a good level, indicating good energy efficiency and computing power output. When the overall energy efficiency is greater than the second critical value, it can be considered to be at a poor level, indicating poor energy efficiency and computing power output, requiring optimization to improve overall energy efficiency.

[0116] Of course, in Table 2, when the overall energy efficiency of a data center equals the first threshold, the excellent level of its overall energy efficiency is classified as "Good," and when the overall energy efficiency of a data center equals the second threshold, the good level is classified as "Fair." In some embodiments, when the overall energy efficiency of a data center equals the first threshold, the excellent level of its overall energy efficiency can also be classified as "Fair." Similarly, when the overall energy efficiency of a data center equals the second threshold, the excellent level of its overall energy efficiency can also be classified as "Poor." This specification does not limit this; the specific classification depends on the actual situation.

[0117] In addition to evaluating the energy efficiency of a data center based on the relationship between its overall energy efficiency and the first and second threshold values, one can also refer to... Figure 5 The energy efficiency and carbon emission calculation method for the data center also includes:

[0118] S401: Determine the energy efficiency benchmark value of the data center based on the first threshold value and the second threshold value, and determine the carbon emission benchmark value of the data center based on the energy efficiency benchmark value of the data center.

[0119] Once the dividing point between excellent and poor overall energy efficiency levels of data centers (i.e., the first and second critical values) is determined, an energy efficiency benchmark value representing the energy efficiency level of mainstream data centers can be determined based on this critical value. Furthermore, a carbon emission benchmark value can be determined based on the energy efficiency benchmark value. This carbon emission benchmark value can serve as the basis for determining free carbon emission allowances, thereby promoting the inclusion of data centers in the carbon emission market.

[0120] Optionally, the energy efficiency benchmark value can fall between the first and second critical values, meaning the benchmark value is greater than the first critical value and less than the second critical value. Alternatively, the energy efficiency benchmark value can be equal to the average of the sum of the first and second critical values. Taking the example above where the first critical value is 100 and the second critical value is 190, an energy efficiency benchmark value greater than 100 and less than 190 can characterize the carbon emission level of mainstream data centers. Of course, the energy efficiency benchmark value can be set as the average of the sum of 100 and 190 (i.e., 145), or it can be set closer to the second critical value (e.g., 150, 155, or 160), allowing more data centers to have annual energy efficiency benchmark values ​​within the free carbon emission allowance, thus encouraging the development of the data center industry.

[0121] Data center carbon emission benchmarks can also be obtained by mapping data center energy efficiency benchmarks, such as a direct proportional relationship.

[0122] As mentioned above, the first typical value, second typical value, and third typical value corresponding to each energy efficiency parameter in Table 1 may change with the continuous development of the data center industry. Therefore, in one embodiment of this specification, reference is made to... Figure 6 The energy efficiency and carbon emission calculation method for the data center also includes:

[0123] S501: Every preset time interval, update the first typical value, the second typical value, and the third typical value corresponding to each of the energy efficiency parameters, and return the step of using the sum of the products of the first typical value corresponding to each energy efficiency parameter and the weight corresponding to each energy efficiency parameter as the first typical value of energy efficiency.

[0124] By updating the first, second, and third typical values ​​corresponding to each energy efficiency parameter at preset intervals, the comprehensive energy efficiency and carbon emission accounting of data centers can be more closely aligned with the current development status of the data center industry, keeping pace with industry development and making the comprehensive energy efficiency and carbon emission accounting more scientific and accurate.

[0125] The preset time can be 6 months, 1 year, 2 years, etc. The selection of the preset time can be determined according to the development speed of the data center industry, and this manual does not limit it.

[0126] In addition, in the process of determining the overall energy efficiency of a data center, the relationship between the product of each energy efficiency parameter and its corresponding weight and the typical values ​​of the first, second, and third energy efficiency levels can guide the optimization direction of the overall energy efficiency of the data center. This optimization direction can be reflected in the form of an energy efficiency optimization report.

[0127] Specifically, refer to Figure 7 The data update methods for calculating energy efficiency and carbon emissions also include:

[0128] S601: Generate an energy efficiency optimization report based on the relationship between the energy efficiency parameters of the data center and the first typical value, the second typical value and the third typical value corresponding to each energy efficiency parameter.

[0129] Taking Table 1 as an example again, assuming that resource utilization efficiency is represented by PUE, and the value representing computing power utilization is 1-η CPU Characterized by computing power efficiency in terms of η CEUE (The calculation method can refer to formula (4)) The first typical value, the second typical value and the third typical value of PUE can be 1.2, 1.4 and 1.8 respectively. The first typical value, the second typical value and the third typical value of the computing power utilization rate can be 30%, 70% and 90% respectively. The first typical value, the second typical value and the third typical value of computing power efficiency can be 1.2, 1.4 and 1.8 respectively.

[0130] Accordingly, assuming the measured PUE of data center α is 1.9, the representative value of computing power utilization is 20%, and the computing power efficiency is 1.3, by comparing the PUE, the representative values ​​of computing power utilization, and the computing power efficiency with the corresponding first, second, and third typical values ​​mentioned above, it can be found that data center α has a poor PUE, while the representative values ​​of computing power utilization and computing power efficiency are excellent. Figure 8 The energy efficiency optimization report can indicate that the PUE of data center α is poor and recommend optimizing the PUE (optimization methods include but are not limited to improving the data center cooling method, etc., which are not limited in this manual) to improve the overall energy efficiency of data center α.

[0131] Assuming the measured PUE of data center β is 1.4, the representative value of computing power utilization is 85%, and the computing power efficiency is 1.9, a comparison of the PUE, representative values ​​of computing power utilization, and computing power efficiency with their respective first, second, and third typical values ​​reveals that data center β has poor computing power utilization and computing power efficiency, while its PUE is excellent. Figure 9The energy efficiency optimization report can indicate that the computing power utilization and computing power efficiency of data center β are poor, and it is recommended to optimize the computing power efficiency and computing power utilization of data center β to improve the overall energy efficiency of data center β.

[0132] This specification also illustrates feasible methods for obtaining energy efficiency parameters through its embodiments. Optionally, refer to... Figure 10 The energy efficiency and carbon emission calculation methods for the data center include:

[0133] S901: Obtain the operating parameters of the data center, which are used to characterize the resources used by the data center, the computing power usage status, and the computing power generated by the data center.

[0134] S902: Calculate the energy efficiency parameters of the data center based on the operating parameters.

[0135] S903: Based on the attributes of the data center, determine the weights corresponding to the energy efficiency parameters of the data center, wherein the attributes of the data center are used to characterize the type, operating environment and operating status of the data center.

[0136] S904: Determine the overall energy efficiency of the data center based on its energy efficiency parameters and the weights corresponding to each parameter.

[0137] Specifically, step S901 may include:

[0138] S9011: Obtain the total power consumption of the data center, the power consumption of the IT equipment, the total processor resources of the data center, the used processor resources of the data center, the total power of the IT equipment, and the computing power of the data center.

[0139] The total power consumption of the data center can be expressed as the total power P. total The power consumption of IT equipment is expressed in kilowatt-hours (kWh). Correspondingly, the power consumption of IT equipment can be expressed as the power P of the data center IT equipment. IT The unit is kilowatt-hour. Of course, in some embodiments, the total power consumption of the data center can also be the actual total power consumption Q. total The power consumption of IT equipment is expressed in kilowatts (Q), and the unit is kilowatts. IT This indicates that the unit is kilowatt.

[0140] The total processor resources in a data center can be categorized by CPU clock speed (CPU frequency). tot This is expressed in Hertz (Hz). Correspondingly, the used processor resources in a data center can be represented by the CPU's used clock speed. U It is indicated by the unit Hertz (Hz).

[0141] Data center computing power can be expressed as the number of floating-point operations performed per second, measured in FLOPS.

[0142] Step S902 may specifically include:

[0143] S9021: The ratio of the power consumption of the IT equipment to the total power consumption of the data center is used as the resource utilization efficiency of the data center.

[0144] When resource utilization efficiency is expressed as PUE, the power consumption of the IT equipment is expressed as the power P of the data center IT equipment. IT This indicates that the total power consumption of the data center is expressed in terms of total power P. total When expressed in a certain way, step S9021 can be expressed as formula (7).

[0145] PUE = P total / P IT (7)

[0146] S9022: The ratio of the difference between the total processor resources and the used processor resources in the data center to the total processor resources in the data center is used as a numerical value representing the computing power utilization rate of the data center.

[0147] Step S9022 can be represented by formula (8).

[0148] 1-η CPU =(CPU_ tot -CPU_ U ) / CPU_ tot (8)

[0149] S9023: The ratio of the total power of the IT equipment to the computing power of the data center is taken as the computing power efficiency of the data center, and the ratio of the difference between the computing power efficiency of the data center and the benchmark computing power efficiency to the benchmark computing power efficiency is taken as the computing power efficiency of the data center.

[0150] Step S9023 can be represented by formulas (9) and (10).

[0151] η CEUE = (actual CEUE value - baseline CEUE value) / baseline CEUE value (9)

[0152] Actual CEUE value = P IT / CP (10)

[0153] Where, η CEUE CEUE represents the computing power efficiency, with the actual CEUE value representing the computing power efficiency and the baseline CEUE value representing the baseline computing power efficiency. The baseline computing power efficiency can be determined by calculating the overall computing power efficiency value of data centers nationwide, which has been recently published.

[0154] The method described above for calculating the energy efficiency parameters of the data center has the advantages of simple calculation method and convenient measurement and statistics of each parameter, which helps to simplify the calculation method of energy efficiency and carbon emissions of data centers and improve the efficiency of method execution.

[0155] refer to Figure 11 Corresponding to the above-mentioned data center energy efficiency and carbon emission calculation methods, this specification also provides a data center energy efficiency and carbon emission calculation device, including:

[0156] The first energy efficiency acquisition module 100 is used to acquire the energy efficiency parameters of the data center, including resource utilization efficiency, computing power utilization rate and computing power efficiency.

[0157] The first energy efficiency determination module 200 is used to determine the weights corresponding to the energy efficiency parameters of the data center, and to determine the comprehensive energy efficiency of the data center based on the energy efficiency parameters of the data center and the weights corresponding to the energy efficiency parameters. The comprehensive energy efficiency is used to calculate the carbon emissions of the data center.

[0158] refer to Figure 12 Corresponding to the above-mentioned data center energy efficiency and carbon emission calculation methods, this specification also provides another data center energy efficiency and carbon emission calculation device, including:

[0159] The second energy efficiency acquisition module 300 is used to acquire energy efficiency parameters of the data center. The energy efficiency parameters are used to characterize the resource utilization efficiency and computing power utilization and output status of the data center. The computing power utilization and output status are used to characterize the computing power utilization rate and computing power efficiency of the data center.

[0160] The second energy efficiency determination module 400 is used to determine the comprehensive energy efficiency of the data center by integrating the energy efficiency parameters of the data center. The comprehensive energy efficiency is used to calculate the carbon emissions of the data center.

[0161] The data center energy efficiency and carbon emission calculation device provided in this embodiment belongs to the same application concept as the data center energy efficiency and carbon emission calculation method provided in the above embodiments of this application. It can execute the data center energy efficiency and carbon emission calculation method provided in any of the above embodiments of this application, and has the corresponding functional modules and beneficial effects for executing the data center energy efficiency and carbon emission calculation method. Technical details not described in detail in this embodiment can be found in the specific processing content of the data center energy efficiency and carbon emission calculation method provided in the above embodiments of this application, and will not be repeated here.

[0162] Accordingly, an exemplary embodiment of this specification also provides a data center, such as Figure 13As shown, it includes: IT equipment and a computing device 30 connected to the IT equipment, the computing device 30 being used to implement the energy efficiency and carbon emission calculation method of the data center described in any of the above embodiments.

[0163] In a real data center, there are usually multiple IT devices, which may include server devices 10, etc. Server devices 10 and switches 20 can be connected by active optical cables.

[0164] exist Figure 13 In addition to server device 10 and switch 20, a router is also shown. Both switch 20 and router are types of network switching devices. These network switching devices can be interconnected, and through these network switching devices, server device 10 in the data center can be interconnected.

[0165] Figure 13 The architecture shown can also be described as a data center network. This network structure can be divided into a server layer, an edge switch layer, an aggregation switch layer, a core switch layer, a router layer, and an optical signal transmission layer. Active optical cables are primarily used to establish optical communication connections between servers and switches in the server layer.

[0166] The computing device provided in this embodiment belongs to the same concept as the data center energy efficiency and carbon emission calculation method provided in the above embodiments of this application. It can execute the data center energy efficiency and carbon emission calculation method provided in any of the above embodiments of this application, and has the corresponding functional modules and beneficial effects for executing the data center energy efficiency and carbon emission calculation method. Technical details not described in detail in this embodiment can be found in the specific processing content of the data center energy efficiency and carbon emission calculation method provided in the above embodiments of this application, and will not be repeated here.

[0167] Another embodiment of this application also provides an electronic device, see [link to relevant documentation] Figure 14 As shown, an exemplary embodiment of this specification also provides an electronic device, including: a memory and a processor, the memory storing a computer program, the processor executing the computer program to perform the steps in the data center energy efficiency and carbon emission calculation method according to various embodiments of this specification described above.

[0168] The internal structure of the electronic device can be as follows: Figure 14As shown, the electronic device includes a processor, memory, network interface, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory of the central control device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface of the electronic device is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it follows the steps in the data center energy efficiency and carbon emission calculation methods according to various embodiments of this specification as described in the above embodiments.

[0169] The processor may include the main processor, as well as baseband chips, modems, etc.

[0170] The memory stores a program that executes the technical solution of this invention, and may also store an operating system and other critical business functions. Specifically, the program may include program code, which includes computer operation instructions. More specifically, the memory may include read-only memory (ROM), other types of static storage devices capable of storing static information and instructions, random access memory (RAM), other types of dynamic storage devices capable of storing information and instructions, disk storage, flash memory, etc.

[0171] The processor can be a general-purpose processor, such as a general-purpose central processing unit (CPU), a microprocessor, etc., or an application-specific integrated circuit (ASIC), or one or more integrated circuits used to control the execution of the program of the present invention. It can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0172] Input devices may include devices that receive data and information input by the user, such as keyboards, mice, cameras, scanners, light pens, voice input devices, touch screens, pedometers, or gravity sensors.

[0173] Output devices may include devices that allow information to be output to a user, such as displays, printers, speakers, etc.

[0174] The communication interface may include any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, Radio Access Network (RAN), Wireless Local Area Network (WLAN), etc.

[0175] The processor executes the program stored in the memory and calls other devices, which can be used to implement the various steps of any of the data center energy efficiency and carbon emission calculation methods provided in the above embodiments of this application.

[0176] The electronic device may also include a display component and a voice component. The display component may be a liquid crystal display screen or an e-ink display screen. The input device of the electronic device may be a touch layer covering the display component, or a button, trackball or touchpad set on the casing of the electronic device, or an external keyboard, touchpad or mouse, etc.

[0177] Those skilled in the art will understand that Figure 14 The structures shown are merely block diagrams of a portion of the structure related to the scheme described in this specification, and do not constitute a limitation on the electronic devices to which the scheme described in this specification is applied. Specific electronic devices may include more or fewer components than those shown in the figures, or may combine certain components, or may have different component arrangements.

[0178] In addition to the methods and devices described above, the data center energy efficiency and carbon emission calculation methods provided in the embodiments of this specification can also be computer program products, which include computer program instructions. When the computer program instructions are run by a processor, the processor causes the processor to perform the steps in the data center energy efficiency and carbon emission calculation methods according to various embodiments of this specification as described in the "Exemplary Methods" section above.

[0179] The computer program product described herein can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments described herein. These programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0180] Furthermore, embodiments of this specification also provide a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor of the steps in the data center energy efficiency and carbon emission calculation methods according to various embodiments of this specification as described in the "Exemplary Methods" section above.

[0181] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this specification can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0182] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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.

[0183] The embodiments described above are merely illustrative of several implementation methods outlined in this specification. While the descriptions are specific and detailed, they should not be construed as limiting the scope of the solutions provided in this specification. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this specification, and these all fall within the scope of protection of this specification. Therefore, the scope of protection for this patent should be determined by the appended claims.

Claims

1. A method for energy efficiency and carbon emission calculation of a data center, characterized in that, The method comprises: obtaining energy efficiency parameters of the data center, the energy efficiency parameters comprising resource use efficiency, computing power use rate and computing power energy efficiency; determining respective weights of the energy efficiency parameters of the data center, and determining a comprehensive energy efficiency of the data center according to the energy efficiency parameters of the data center and the respective weights of the energy efficiency parameters, wherein the comprehensive energy efficiency is used to calculate carbon emissions of the data center. Further comprising: obtaining respective first typical values, second typical values and third typical values of the energy efficiency parameters, wherein the first typical value represents a typical value of the corresponding energy efficiency parameter when the corresponding energy efficiency parameter is in a first energy efficiency interval, the second typical value represents a typical value of the corresponding energy efficiency parameter when the corresponding energy efficiency parameter is in a second energy efficiency interval, and the third typical value represents a typical value of the corresponding energy efficiency parameter when the corresponding energy efficiency parameter is in a third energy efficiency interval; the energy efficiency represented by the first energy efficiency interval, the second energy efficiency interval and the third energy efficiency interval decreases in turn; generating an energy efficiency optimization report according to the size relationship between the energy efficiency parameters of the data center and the first typical values, the second typical values and the third typical values of the energy efficiency parameters.

2. The method of claim 1, wherein, The determination of the comprehensive energy efficiency of the data center according to the energy efficiency parameters of the data center and the respective weights of the energy efficiency parameters comprises: taking the sum of the products of the energy efficiency parameters of the data center and the respective weights as the comprehensive energy efficiency of the data center.

3. The method of claim 2, wherein, Further comprising: taking the sum of the products of the first typical values corresponding to each of the energy efficiency parameters and the weights corresponding to each of the energy efficiency parameters as a first energy efficiency typical value; taking the sum of the products of the second typical values corresponding to each of the energy efficiency parameters and the weights corresponding to each of the energy efficiency parameters as a second energy efficiency typical value; taking the sum of the products of the third typical values corresponding to each of the energy efficiency parameters and the weights corresponding to each of the energy efficiency parameters as a third energy efficiency typical value; determining a first critical value between the first energy efficiency typical value and the second energy efficiency typical value; determining a second critical value between the second energy efficiency typical value and the third energy efficiency typical value; determining an energy efficiency state of the data center according to the size relationship between the comprehensive energy efficiency of the data center and the first critical value and the second critical value, wherein the energy efficiency state is used to represent the excellent level of the comprehensive energy efficiency of the data center.

4. The method of claim 3, wherein, Further comprising: determining an energy efficiency reference value of the data center according to the first critical value and the second critical value; determining a carbon emission reference value of the data center according to the energy efficiency reference value of the data center.

5. The method of claim 3, wherein, Further comprising: updating the first typical values, the second typical values and the third typical values corresponding to each of the energy efficiency parameters every preset time, and returning to the step of taking the sum of the products of the first typical values corresponding to each of the energy efficiency parameters and the weights corresponding to each of the energy efficiency parameters as a first energy efficiency typical value.

6. The method of claim 1, wherein, The obtaining of the energy efficiency parameters of the data center comprises: obtaining operation parameters of the data center, wherein the operation parameters are used to represent resources used by the data center, computing power use state and computing power generated by the data center; calculating the energy efficiency parameters of the data center according to the operation parameters.

7. The method of claim 6, wherein, The obtaining the operation parameters of the data center comprises: obtaining the total power consumption of the data center, the IT equipment power consumption, the total processor resources of the data center, the used processor resources of the data center, the total power of the IT equipment and the computing power of the data center; The calculating the energy efficiency parameters of the data center according to the operation parameters comprises: taking the ratio of the IT equipment power consumption to the total power consumption of the data center as the resource use efficiency of the data center; taking the ratio of the difference between the total processor resources and the used processor resources of the data center to the total processor resources of the data center as the representation value of the computing power use rate of the data center; taking the ratio of the total power of the IT equipment to the computing power of the data center as the computing power energy efficiency of the data center, and taking the ratio of the difference between the computing power energy efficiency of the data center and the benchmark computing power energy efficiency to the benchmark computing power energy efficiency as the computing power energy efficiency of the data center.

8. The method according to any one of claims 1 to 7, characterized in that, The determining the respective weights of the energy efficiency parameters of the data center comprises: determining the respective weights of the energy efficiency parameters of the data center according to the attributes of the data center, wherein the attributes of the data center are used to represent the type, operating environment and operating state of the data center.

9. A method for energy efficiency and carbon emission calculation of a data center, characterized in that, comprises: obtaining the energy efficiency parameters of the data center, wherein the energy efficiency parameters are used to represent the resource use efficiency and the computing power use and output state of the data center; comprehensively determining the comprehensive energy efficiency of the data center based on the energy efficiency parameters of the data center. Further comprising: obtaining the first typical value, the second typical value and the third typical value corresponding to each of the energy efficiency parameters, wherein the first typical value represents the typical value when the corresponding energy efficiency parameter is in the first energy efficiency interval, the second typical value represents the typical value when the corresponding energy efficiency parameter is in the second energy efficiency interval, and the third typical value represents the typical value when the corresponding energy efficiency parameter is in the third energy efficiency interval; the energy efficiency represented by the first energy efficiency interval, the second energy efficiency interval and the third energy efficiency interval decreases in turn; generating an energy efficiency optimization report according to the size relationship between the energy efficiency parameters of the data center and the first typical value, the second typical value and the third typical value corresponding to each of the energy efficiency parameters.

10. A data center energy efficiency and carbon emission calculation device, characterized in that, comprises: a first energy efficiency obtaining module, configured to obtain the energy efficiency parameters of the data center, wherein the energy efficiency parameters comprise resource use efficiency, computing power use rate and computing power energy efficiency; a first energy efficiency determining module, configured to determine the respective weights of the energy efficiency parameters of the data center, and determine the comprehensive energy efficiency of the data center based on the energy efficiency parameters of the data center and the respective weights of the energy efficiency parameters, wherein the comprehensive energy efficiency is used to calculate the carbon emission of the data center; The first energy efficiency obtaining module is further configured to: obtaining respective first typical values, second typical values and third typical values of the energy efficiency parameters, the first typical value representing a typical value of the corresponding energy efficiency parameter when the energy efficiency parameter is in a first energy efficiency interval, the second typical value representing a typical value of the corresponding energy efficiency parameter when the energy efficiency parameter is in a second energy efficiency interval, and the third typical value representing a typical value of the corresponding energy efficiency parameter when the energy efficiency parameter is in a third energy efficiency interval; the energy efficiency represented by the first, second and third energy efficiency intervals decreases in turn; generating an energy efficiency optimization report according to the energy efficiency parameters of the data center and the size relationship between the energy efficiency parameters and the first, second and third typical values corresponding to the energy efficiency parameters.

11. An electronic device, comprising: comprising: a memory and a processor; wherein the memory is connected to the processor, and the memory is configured to store a program; the processor is configured to realize the energy efficiency and carbon emission calculation method of the data center according to any one of claims 1-9 by running the program stored in the memory.

12. A storage medium, characterized by The storage medium has a computer program stored thereon, and the computer program is run by the processor to realize the energy efficiency and carbon emission calculation method of the data center according to any one of claims 1-9.

13. A data center, characterized by, comprising: IT equipment and accounting equipment connected to the IT equipment, and the accounting equipment is configured to realize the energy efficiency and carbon emission calculation method of the data center according to any one of claims 1-9.

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