Data center low-carbon control method, system and storage medium

Abstract

The present invention provides a data center low-carbon control method, system and storage medium, belonging to the field of data center technology, and specifically includes: determining the energy consumption control possibility and energy consumption control period of different time periods based on historical processing conditions; determining when a time period can be energy-controlled by using the energy consumption control possibilities of different future adjacent time periods and the time intervals with the time period; determining the minimum number of running servers in the time period based on the data processing fluctuation probability and energy consumption control possibility of the time period; determining the matching temperature control range of the data center based on the processing conditions of information data of different servers; and determining the energy consumption control method of the time period with the minimum number of running servers and the temperature control range as constraints and the lowest carbon emissions as the goal, thereby realizing low-carbon control of the data center.

Description

Technical Field

[0001] The present invention belongs to the technical field of data centers, and in particular relates to a low-carbon control method, system and storage medium for a data center. Background Art

[0002] With the rapid development of informatization and digitalization, data centers, as the central nodes of data transmission and analysis, play a key role in the entire information system. However, due to the high power consumption of data centers, how to achieve low-carbon and energy-saving control of data centers has become a technical problem that needs to be solved urgently.

[0003] To solve the above technical problems, the invention patent application CN202111443556.4 "A data center energy consumption prediction optimization method, system, medium and computing device" aims to minimize the overall energy consumption of the data center, solves the data center energy consumption optimization model to obtain the optimization execution strategy, and realizes lean control of data center energy consumption based on the joint regulation of the cooling system, servers and their loads. However, it is not difficult to find the following technical problems through analysis:

[0004] When performing energy consumption control in the existing technical solutions, the determination of differentiated energy consumption control modes based on the reliability requirements of the data processed by the data center is not considered. In different time periods, the amount of data processed by the data center varies greatly. If the same energy consumption control mode is adopted, the reliability of data processing cannot be guaranteed during the period when the amount of data processed is large.

[0005] In response to the above technical problems, the present invention provides a data center low-carbon control method, system and storage medium. Summary of the Invention

[0006] To achieve the purpose of the present invention, the present invention adopts the following technical solutions:

[0007] According to one aspect of the present invention, a low-carbon control method for a data center is provided.

[0008] A low-carbon control method for a data center, characterized by comprising:

[0009] S1 obtains historical processing status of information data of the data center in different time periods, and determines the possibility of energy consumption control in different time periods and the energy consumption control period based on the historical processing status. When the time period is the energy consumption control period, proceeds to the next step;

[0010] S2 determines the deviation between different adjacent operating periods and historical processing conditions based on the processing conditions of the information data of the adjacent operating periods, and proceeds to the next step when it is determined based on the deviation that the data processing fluctuation probability of the period meets the requirements;

[0011] S3: When it is determined that the energy consumption control of the time period can be performed based on the energy consumption control possibilities of different future adjacent time periods and the time interval between the time period and the time period, the process proceeds to the next step;

[0012] S4 determines the minimum number of servers running in the period based on the probability of data processing fluctuations and the possibility of energy consumption control in the period, determines the temperature control range of the matching data center based on the processing of information data of different servers, and uses the minimum number of servers and temperature control range as constraints to determine the energy consumption control method for the period with the goal of minimizing carbon emissions.

[0013] The beneficial effects of the present invention are:

[0014] 1. The possibility of energy consumption control in different time periods and the determination of energy consumption control time periods are carried out based on historical processing conditions, thereby realizing the evaluation of the possibility of energy consumption control in different time periods from the perspective of historical processing data volume, avoiding the technical problem of data processing reliability failing to meet requirements caused by energy consumption control in time periods with a large amount of processing data, and realizing accurate control of energy consumption in different time periods.

[0015] 2. With the minimum number of operations and the temperature control range as constraints, and the lowest carbon emissions as the goal, the energy consumption control method for the time period is determined. This not only takes into account the differences in temperature control requirements due to the differences in the amount of data processed by the servers, but also further combines the constraint of the minimum number of operations of the servers to ensure the reliability of data processing in the said time period while ensuring the lowest carbon emissions.

[0016] A further technical solution is that the historical processing status of the information data includes the historical processing data volume of the information data on different dates and the variation of the historical processing data volume between different adjacent dates.

[0017] A further technical solution is that the method for determining the possibility of energy consumption control in the time period is:

[0018] Determining a historical data processing volume of the information data of the period on different dates based on historical processing conditions of the information data of the period, and determining a data processing difficulty of the information data of the period based on the historical data processing volume;

[0019] Obtaining a change in the amount of historical processed data between different adjacent dates in the time period, and determining a processing change in the information data for the time period based on the change in the amount of historical processed data between different adjacent dates;

[0020] The possibility of energy consumption control in the time period is determined according to the processing variation of the information data in the time period and the difficulty of data processing.

[0021] A further technical solution is that when the energy consumption control possibility of the time period is within a set range, the time period is determined to be an energy consumption control time period.

[0022] A further technical solution is that, when the time period does not belong to the energy consumption control time period, there is no need to perform energy consumption control processing in the time period.

[0023] A further technical solution is that the temperature control range of the matching data center is determined according to the amount of data processed by the information data of the server.

[0024] A further technical solution is to determine the energy consumption control method for the time period, specifically including:

[0025] Determining a predicted processing data volume of the server's information data for the period based on historical processing of the information data;

[0026] Based on the preset processing data volume and the minimum operating number, determining the temperature control range of the data center under different operating numbers of servers, and determining the energy consumption under different operating numbers of servers in combination with the processing data volume of different servers;

[0027] Based on the energy consumption under different numbers of running servers, the carbon emissions under different numbers of running servers are determined according to the corresponding function of energy consumption and carbon emissions, and the energy consumption control method of the time period is determined with the lowest carbon emissions as the goal.

[0028] A further technical solution is that the predicted processing data volume of the information data of the server in the period is determined based on the average value of the historical processing data volume of the information data in the period on different dates.

[0029] In a second aspect, the present invention provides a computer system comprising: a memory and a processor in communication connection, and a computer program stored in the memory and capable of running on the processor, characterized in that: when the processor runs the computer program, it executes the above-mentioned low-carbon control method for a data center.

[0030] In a third aspect, the present invention provides a computer storage medium having a computer program stored thereon. When the computer program is executed in a computer, the computer is caused to execute the above-mentioned low-carbon control method for a data center.

[0031] Other features and advantages will be described in the following description. The objectives and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0032] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the accompanying drawings.

[0034] Figure 1 It is a flow chart of a low-carbon control method for a data center;

[0035] Figure 2 is a flow chart of a method for determining the possibility of energy consumption control in a time period;

[0036] Figure 3 It is a flow chart of a method for determining the probability of data processing fluctuations in a time period meeting the requirements;

[0037] Figure 4 It is a flow chart of a method for determining a time period in which energy consumption control can be performed;

[0038] Figure 5 is a flow chart of a method for determining a minimum number of running servers for a time period;

[0039] Figure 6 It is a framework diagram of a computer storage medium. DETAILED DESCRIPTION

[0040] To help those skilled in the art better understand the technical solutions in this specification, the following will provide a clear and complete description of the technical solutions in the embodiments of this specification, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this specification without creative work should fall within the scope of protection of this specification.

[0041] The following will further elaborate from two perspectives: method-based embodiments and system-based embodiments.

[0042] Method Example

[0043] To solve the above problems, according to one aspect of the present invention, Figure 1 As shown, a data center low-carbon control method is provided, which is characterized by specifically including:

[0044] S1 obtains historical processing status of information data of the data center in different time periods, and determines the possibility of energy consumption control in different time periods and the energy consumption control period based on the historical processing status. When the time period is the energy consumption control period, proceeds to the next step;

[0045] Furthermore, the historical processing status of the information data includes the historical processing data volume of the information data on different dates and the variation of the historical processing data volume between different adjacent dates.

[0046] Specifically, such as Figure 2 As shown, the method for determining the possibility of energy consumption control in the time period is:

[0047] Determining a historical data processing volume of the information data of the period on different dates based on historical processing conditions of the information data of the period, and determining a data processing difficulty of the information data of the period based on the historical data processing volume;

[0048] Obtaining a change in the amount of historical processed data between different adjacent dates in the time period, and determining a processing change in the information data for the time period based on the change in the amount of historical processed data between different adjacent dates;

[0049] The possibility of energy consumption control in the time period is determined according to the processing variation of the information data in the time period and the difficulty of data processing.

[0050] Furthermore, when the energy consumption control possibility of the time period is within a set range, the time period is determined to be an energy consumption control time period.

[0051] Specifically, when the time period does not belong to the energy consumption control time period, there is no need to perform energy consumption control processing in the time period.

[0052] The possibility of energy consumption control in different time periods and the determination of energy consumption control time periods based on historical processing conditions

[0053] In another embodiment, the method for determining the possibility of energy consumption control during the time period is:

[0054] S11 determines the historical data processing volume of the information data of the time period on different dates based on the historical processing status of the information data of the time period, and uses the dates on which the historical data processing volume of the information data is greater than the preset processing volume as screening processing dates, and determines whether the number of the screening processing dates meets the requirement. If so, proceeds to the next step; if not, determines that the time period does not belong to the energy consumption control period;

[0055] S12: obtaining the number of screening processing dates for the time period, and determining the screening processing difficulty of the time period based on the historical data processing volume of information data of different screening processing dates, and judging whether the screening processing difficulty of the time period is within a preset range. If so, proceeding to the next step; if not, determining that the time period does not belong to the energy consumption control period;

[0056] S13 determines the data processing difficulty of the information data of the period based on the historical data processing volume of different dates in the period and the screening processing difficulty of the period, and judges whether the data processing difficulty of the information data of the period is greater than the preset processing difficulty. If so, proceeds to the next step; if not, proceeds to step S15;

[0057] S14 obtains a change in the amount of historical processed data between different adjacent dates in the time period, and determines a processing change in the information data for the time period based on the change in the amount of historical processed data between different adjacent dates, and determines whether the processing change in the information data meets the requirement. If so, proceeds to the next step; if not, determines that the time period does not belong to the energy consumption control period.

[0058] S15 determines the energy consumption control possibility of the time period according to the processing variation of the information data in the time period and the data processing difficulty.

[0059] In another embodiment, the method for determining the possibility of energy consumption control during the time period is:

[0060] S21 determines the historical data processing volume of the information data of the period on different dates based on the historical processing status of the information data of the period, and uses the dates on which the historical data processing volume of the information data is greater than the preset processing volume as screening processing dates, and determines whether the number of the screening processing dates is greater than the preset number of dates. If so, proceeds to step S23; if not, proceeds to the next step;

[0061] S22: obtaining the number of screening processing dates in the period, and determining the screening processing difficulty of the period based on the historical data processing volume of information data on different screening processing dates. The data processing difficulty of the information data in the period is determined based on the historical data processing volume of different dates in the period and the screening processing difficulty of the period. It is determined whether the data processing difficulty of the information data in the period is greater than a preset processing difficulty. If so, proceed to the next step; if not, proceed to step S25.

[0062] S23 obtains the change in the amount of historical processed data between different adjacent dates in the time period, and takes the date whose change does not meet the requirement as the abnormal change date, and determines whether the number of the abnormal change dates meets the requirement. If so, proceeds to the next step; if not, determines that the time period does not belong to the energy consumption control period;

[0063] S24 determines the processing change of the information data in the period based on the change of the historical processing data volume in the period between different adjacent dates, and determines whether the processing change of the information data meets the requirements. If so, proceed to the next step; if not, determine that the period does not belong to the energy consumption control period;

[0064] S25 determines the energy consumption control possibility of the time period according to the processing variation of the information data in the time period and the data processing difficulty.

[0065] S2 determines the deviation between different adjacent operating periods and historical processing conditions based on the processing conditions of the information data of the adjacent operating periods, and proceeds to the next step when it is determined based on the deviation that the data processing fluctuation probability of the period meets the requirements;

[0066] Specifically, such as Figure 3 As shown, determining that the probability of data processing fluctuations during the period meets the requirements specifically includes:

[0067] Determining, based on historical processing conditions of information data in different adjacent operating periods, deviations between processing conditions of information data in different adjacent operating periods and historical processing conditions on different dates, and determining fluctuations of processing data in different adjacent operating periods based on the deviations;

[0068] Determine the basic weight values ​​of different adjacent operating time periods by the interval lengths between different adjacent operating time periods and the time period, and determine the data processing fluctuation probability of the time period in combination with the processing data fluctuation amount of different adjacent operating time periods;

[0069] A preset probability threshold and the data processing fluctuation probability of the time period are used to determine whether the data processing fluctuation probability of the time period meets the requirements.

[0070] Furthermore, determining whether the data processing fluctuation probability of the time period meets the requirements by using a preset probability threshold and the data processing fluctuation probability of the time period specifically includes:

[0071] When the data processing fluctuation probability of the time period is less than the preset probability threshold, it is determined that the data processing fluctuation probability of the time period meets the requirement.

[0072] It should be noted that when the data processing fluctuation probability in the time period does not meet the requirements, there is no need to perform energy consumption control processing in the time period.

[0073] In another embodiment, determining whether the data processing fluctuation probability during the time period meets the requirement specifically includes:

[0074] Determine, based on historical processing conditions of information data in different adjacent operating periods, deviations between the processing conditions of information data in different adjacent operating periods and historical processing conditions on different dates, and determine, based on the deviations, fluctuations in processed data in different adjacent operating periods, and determine whether there are adjacent operating periods in which the fluctuations in processed data do not meet the requirements. If so, proceed to the next step; if not, determine whether the data processing fluctuation probability of the period meets the requirements.

[0075] The adjacent operating time periods in which the processing data fluctuation amount does not meet the requirement are regarded as adjacent fluctuation time periods, and whether the number of the adjacent fluctuation time periods meets the requirement is determined. If so, the next step is performed; if not, it is determined that the data processing fluctuation probability of the time period does not meet the requirement;

[0076] Determine the basic weight values ​​of different adjacent operating time periods by the interval lengths between different adjacent operating time periods and the time period, and judge whether the sum of the basic weight values ​​of the adjacent fluctuation time periods meets the requirements. If so, proceed to the next step; if not, determine that the data processing fluctuation probability of the time period does not meet the requirements;

[0077] Determine a comprehensive fluctuation amount based on the basic weight values ​​of different adjacent fluctuation periods, the fluctuation amount of processed data, and the number of adjacent fluctuation periods, and judge whether the comprehensive fluctuation amount meets the requirements. If so, proceed to the next step; if not, determine that the data processing fluctuation probability of the period does not meet the requirements;

[0078] The data processing fluctuation probability of the time period is determined according to the basic weight values ​​of different adjacent operating time periods and the processing data fluctuation amounts of different adjacent operating time periods, and the preset probability threshold and the data processing fluctuation probability of the time period are used to determine whether the data processing fluctuation probability of the time period meets the requirements.

[0079] In another embodiment, determining whether the data processing fluctuation probability during the time period meets the requirement specifically includes:

[0080] Determine, based on historical processing conditions of information data in different adjacent operating periods, deviations between the processing conditions of information data in different adjacent operating periods and historical processing conditions on different dates, and determine, based on the deviations, fluctuations in processed data in different adjacent operating periods, and determine whether there are adjacent operating periods in which the fluctuations in processed data do not meet the requirements. If so, proceed to the next step; if not, determine whether the data processing fluctuation probability of the period meets the requirements.

[0081] The adjacent operation time periods in which the processing data fluctuation amount does not meet the requirements are regarded as adjacent fluctuation time periods, and the adjacent operation time periods with an interval length less than a preset time length are regarded as adjacent operation time periods. It is determined whether there is an adjacent fluctuation time period in the adjacent operation time periods. If so, it is determined that the data processing fluctuation probability of the time period does not meet the requirements. If not, proceed to the next step.

[0082] Determine the basic weight values ​​of different adjacent operating time periods by the interval lengths between different adjacent operating time periods and the time period, obtain the number of adjacent fluctuation time periods and the proportion of the number of adjacent operating time periods, and determine the fluctuation amount evaluation value of the adjacent fluctuation time period in combination with the basic weight values ​​of the different adjacent fluctuation time periods, and judge whether the fluctuation amount evaluation value of the adjacent fluctuation time period meets the requirements. If not, determine that the data processing fluctuation probability of the time period does not meet the requirements. If so, proceed to the next step.

[0083] The comprehensive fluctuation amount is determined by the basic weight values ​​of different adjacent fluctuation periods, the processing data fluctuation amount, and the number of adjacent fluctuation periods. The data processing fluctuation probability of the period is determined according to the basic weight values ​​of different adjacent operating time periods and the processing data fluctuation amount of different adjacent operating time periods. The preset probability threshold and the data processing fluctuation probability of the period are used to determine whether the data processing fluctuation probability of the period meets the requirements.

[0084] S3: When it is determined that the energy consumption control of the time period can be performed based on the energy consumption control possibilities of different future adjacent time periods and the time interval between the time period and the time period, the process proceeds to the next step;

[0085] Furthermore, the future adjacent time period is a time period within a preset time threshold after the time period.

[0086] Specifically, such as Figure 4 As shown, determining that energy consumption control can be performed during the time period specifically includes:

[0087] dividing the future adjacent time periods into energy consumption control time periods and energy consumption uncontrollable time periods based on energy consumption control possibilities of the future adjacent time periods;

[0088] Determine the basic running number of servers in different energy consumption control periods according to the energy consumption control possibilities of different energy consumption control periods, and determine the fluctuation amount of the number of servers started in the energy consumption control period in combination with the time interval between different energy consumption control periods and the period;

[0089] Determine the fluctuation amount of the number of servers started during the energy-uncontrollable period based on the number of different energy-uncontrollable periods and the time intervals between the different energy-uncontrollable periods and the period;

[0090] The fluctuation amount of the number of servers turned on during the energy consumption uncontrollable period and the fluctuation amount of the number of servers turned on during the energy consumption control period are used to calculate the comprehensive fluctuation amount of the number of servers turned on, and whether energy consumption control can be performed in the period is determined based on the comprehensive fluctuation amount.

[0091] Furthermore, determining whether energy consumption control can be performed in the time period based on the comprehensive fluctuation amount of the quantity specifically includes:

[0092] When the comprehensive fluctuation amount of the quantity does not meet the requirement, it is determined that energy consumption control cannot be performed during the time period.

[0093] In another embodiment, determining that the time period is capable of energy consumption control specifically includes:

[0094] Based on the energy consumption control possibility of the future adjacent time periods, the future adjacent time periods are divided into energy consumption control time periods and energy consumption uncontrollable time periods, and whether the number of the energy consumption uncontrollable time periods meets the requirement is determined. If so, the process proceeds to the next step; if not, it is determined that energy consumption control cannot be performed in the time periods;

[0095] Determine the fluctuation amount of the number of servers started during the uncontrollable energy consumption period based on the number of different uncontrollable energy consumption periods and the time interval between the different uncontrollable energy consumption periods and the period, and judge whether the fluctuation amount of the number of servers started during the uncontrollable energy consumption period meets the requirement; if so, proceed to the next step; if not, determine that energy consumption control cannot be performed during the period;

[0096] Determine the basic running number of servers in different energy consumption control periods according to the energy consumption control possibilities of different energy consumption control periods, and determine the fluctuation amount of the number of servers started in the energy consumption control period in combination with the time interval between different energy consumption control periods and the period;

[0097] The fluctuation amount of the number of servers turned on during the energy consumption uncontrollable period and the fluctuation amount of the number of servers turned on during the energy consumption control period are used to calculate the comprehensive fluctuation amount of the number of servers turned on, and whether energy consumption control can be performed in the period is determined based on the comprehensive fluctuation amount.

[0098] S4 determines the minimum number of servers running in the period based on the probability of data processing fluctuations and the possibility of energy consumption control in the period, determines the temperature control range of the matching data center based on the processing of information data of different servers, and uses the minimum number of servers and temperature control range as constraints to determine the energy consumption control method for the period with the goal of minimizing carbon emissions.

[0099] Specifically, such as Figure 5 As shown, the method for determining the minimum number of running servers in the time period is:

[0100] Determine a preset running quantity corresponding to the data processing fluctuation probability of the time period according to the data processing fluctuation probability of the time period, and use the preset running quantity as the fluctuation preset quantity;

[0101] Determining a preset number of operations corresponding to the energy consumption control possibility of the time period based on the energy consumption control possibility of the time period, and using the preset number of operations as the energy consumption preset number;

[0102] The maximum value of the fluctuation preset number and the energy consumption preset number is used as the minimum operating number of the server in the time period.

[0103] Furthermore, the temperature control range of the matched data center is determined according to the amount of data processed by the information data of the server.

[0104] In another embodiment, determining the energy consumption control method for the time period specifically includes:

[0105] Determining a predicted processing data volume of the server's information data for the period based on historical processing of the information data;

[0106] Based on the preset processing data volume and the minimum operating number, determining the temperature control range of the data center under different operating numbers of servers, and determining the energy consumption under different operating numbers of servers in combination with the processing data volume of different servers;

[0107] Based on the energy consumption under different numbers of running servers, the carbon emissions under different numbers of running servers are determined according to the corresponding function of energy consumption and carbon emissions, and the energy consumption control method of the time period is determined with the lowest carbon emissions as the goal.

[0108] Furthermore, the predicted processing data volume of the information data of the server in the time period is determined based on an average value of the historical processing data volume of the information data in the time period on different dates.

[0109] Example 2

[0110] In a second aspect, the present invention provides a computer system comprising: a memory and a processor in communication connection, and a computer program stored in the memory and capable of running on the processor, characterized in that: when the processor runs the computer program, it executes the above-mentioned low-carbon control method for a data center.

[0111] Example 3

[0112] Thirdly, as Figure 6 As shown, the present invention provides a computer storage medium on which a computer program is stored. When the computer program is executed in a computer, the computer is caused to execute the above-mentioned low-carbon control method for a data center.

[0113] Through the above embodiments, the present invention achieves the following beneficial effects:

[0114] 1. The possibility of energy consumption control in different time periods and the determination of energy consumption control time periods are carried out based on historical processing conditions, thereby realizing the evaluation of the possibility of energy consumption control in different time periods from the perspective of historical processing data volume, avoiding the technical problem of data processing reliability failing to meet requirements caused by energy consumption control in time periods with a large amount of processing data, and realizing accurate control of energy consumption in different time periods.

[0115] 2. With the minimum number of operations and the temperature control range as constraints, and the lowest carbon emissions as the goal, the energy consumption control method for the time period is determined. This not only takes into account the differences in temperature control requirements due to the differences in the amount of data processed by the servers, but also further combines the constraint of the minimum number of operations of the servers to ensure the reliability of data processing in the said time period while ensuring the lowest carbon emissions.

[0116] The various embodiments in this specification are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the device, apparatus, and non-volatile computer storage medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simplified. For relevant details, refer to the descriptions of the method embodiments.

[0117] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0118] The foregoing description is merely one or more embodiments of this specification and is not intended to limit this specification. It will be apparent to those skilled in the art that various modifications and variations may be made to one or more embodiments of this specification. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of one or more embodiments of this specification are intended to be within the scope of the claims of this specification.

Claims

1. A data center low-carbon control method, characterized in that: Specifically include: Obtaining historical processing of information data of the data center at different time periods, and determining the possibility of energy consumption control at different time periods and the energy consumption control period based on the historical processing situation, and proceeding to the next step when the time period is an energy consumption control period; Determine the deviation between different adjacent operating periods and historical processing conditions based on the processing conditions of the information data of the adjacent operating periods, and proceed to the next step when it is determined based on the deviation conditions that the data processing fluctuation probability of the period meets the requirements; When it is determined that energy consumption control can be performed in the time period through energy consumption control possibilities of different future adjacent time periods and the time interval between the time period and the time period, proceed to the next step; The minimum number of running servers in the time period is determined based on the probability of data processing fluctuations and the possibility of energy consumption control in the time period. The temperature control range of the matching data center is determined based on the processing of information data of different servers. The energy consumption control method for the time period is determined with the minimum number of running servers and the temperature control range as constraints and the lowest carbon emissions as the goal.

2. The data center low-carbon control method according to claim 1, characterized in that: The historical processing status of the information data includes the historical processing data volume of the information data on different dates and the variation of the historical processing data volume between different adjacent dates.

3. The data center low-carbon control method according to claim 1, characterized in that: The method for determining the energy consumption control possibility during the time period is: Determining a historical data processing volume of the information data of the period on different dates based on historical processing conditions of the information data of the period, and determining a data processing difficulty of the information data of the period based on the historical data processing volume; Obtaining a change in the amount of historical processed data between different adjacent dates in the time period, and determining a processing change in the information data for the time period based on the change in the amount of historical processed data between different adjacent dates; The possibility of energy consumption control in the time period is determined according to the processing variation of the information data in the time period and the difficulty of data processing.

4. The data center low-carbon control method according to claim 3, characterized in that: When the energy consumption control possibility of the time period is within a set interval, the time period is determined to be an energy consumption control time period.

5. The data center low-carbon control method according to claim 1, characterized in that: When the time period does not belong to the energy consumption control time period, there is no need to perform energy consumption control processing in the time period.

6. The data center low-carbon control method according to claim 1, characterized in that: Determining that the probability of data processing fluctuations during the period meets the requirements, specifically including: Determining, based on historical processing conditions of information data in different adjacent operating periods, deviations between processing conditions of information data in different adjacent operating periods and historical processing conditions on different dates, and determining fluctuations of processing data in different adjacent operating periods based on the deviations; Determine the basic weight values ​​of different adjacent operating time periods by the interval lengths between different adjacent operating time periods and the time period, and determine the data processing fluctuation probability of the time period in combination with the processing data fluctuation amount of different adjacent operating time periods; A preset probability threshold and the data processing fluctuation probability of the time period are used to determine whether the data processing fluctuation probability of the time period meets the requirements.

7. The data center low-carbon control method according to claim 6, characterized in that: Determining whether the data processing fluctuation probability of the time period meets the requirements by using the preset probability threshold and the data processing fluctuation probability of the time period specifically includes: When the data processing fluctuation probability of the time period is less than the preset probability threshold, it is determined that the data processing fluctuation probability of the time period meets the requirement.

8. The data center low-carbon control method according to claim 1, characterized in that: The future adjacent time period is a time period within a preset time threshold after the time period.

9. A computer system comprising: A memory and a processor in communication connection, and a computer program stored in the memory and capable of running on the processor, characterized in that the processor executes a data center low-carbon control method according to any one of claims 1 to 8 when running the computer program.

10. A computer storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to execute the data center low-carbon control method according to any one of claims 1 to 8.

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