A low-carbon control method and system for data centers

Abstract

The present invention provides a low-carbon control method and system for a data center, which belongs to the technical field of data centers, and specifically comprises: based on weather data and predicted operation data of servers in different time periods of the data center, the predicted temperature of the data center at different times under different temperature control strategies is determined, the temperature anomaly coefficients in different divided time periods are determined based on the distribution data of temperature anomaly moments in different divided time periods, when it is determined based on the temperature anomaly coefficient that there is no temperature anomaly time period under the temperature control strategy, the predicted operation data of the servers in different divided time periods are obtained, and the matching control strategy of the data center is determined in combination with the temperature anomaly coefficients and energy consumption in different divided time periods under the temperature control strategy, the temperature of the data center is controlled by using the matching control strategy, and the energy consumption of the temperature control of the data center is reduced.

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 and system for data centers. Background Art

[0002] With the rapid development of big data technology, data centers have been developed on a large scale. At the same time, how to control the energy consumption of data centers and reduce the carbon emissions of data centers has become a technical problem that needs to be solved urgently. Specifically, in the invention patent application CN201610912215.X "A memory alloy spring fault indicator for detecting transmission line connection points", there are the following technical problems:

[0003] When conducting low-carbon control in a data center, if a fixed temperature control strategy is used to adjust the temperature of cooling fans and air conditioners, it will inevitably lead to larger carbon emissions from the data center. Therefore, if a differentiated energy consumption control strategy cannot be determined, it will be impossible to reduce the energy consumption of the data center while ensuring the operational reliability of the data center's servers.

[0004] In response to the above technical problems, the present application specifically provides a low-carbon control method and system for a data center. Summary of the invention

[0005] To achieve the purpose of the present invention, the present invention adopts the following technical solutions:

[0006] According to one aspect of the present invention, a low-carbon control method for a data center is provided.

[0007] A low-carbon control method for a data center, specifically comprising:

[0008] S1 determines the deviation between the temperature regulation energy consumption of the data center using a preset temperature control strategy and the power generation of the new energy equipment on a similar historical date to the current date based on the weather data of the current date, and uses it as the energy consumption deviation;

[0009] S2, when it is determined that the data center cannot adopt the preset temperature control strategy according to the energy consumption deviation, based on the weather data and the predicted operation data of the servers in the data center in different time periods, determines the predicted temperature of the data center at different times under different temperature control strategies;

[0010] S3 determines the temperature anomaly moments in different divided time periods based on the predicted temperatures at different moments in different divided time periods, and determines the temperature anomaly coefficients in different divided time periods based on the distribution data of the temperature anomaly moments in different divided time periods, and when it is determined based on the temperature anomaly coefficient that there is no temperature anomaly period under the temperature control strategy, proceeds to the next step;

[0011] S4 obtains the predicted operation data of the server in different divided time periods, and determines the matching control strategy of the data center in combination with the temperature anomaly coefficient and energy consumption in different divided time periods under the temperature control strategy, and uses the matching control strategy to perform temperature control of the data center.

[0012] The beneficial effects of the present invention are:

[0013] The distribution data of temperature abnormality moments in different divided time periods is used to determine whether there is a temperature abnormality period under the temperature control strategy, thereby realizing the determination of the temperature control strategy for the temperature abnormality period based on the number of temperature abnormality moments in the divided time periods and the degree of temperature abnormality at the temperature abnormality moments, avoiding the technical problem of insufficient operation reliability of the server due to the high degree of temperature abnormality under the temperature control strategy, and improving the reliability and safety of the server operation.

[0014] The matching control strategy of the data center is determined based on the predicted operating data, temperature anomaly coefficient and energy consumption of the server in different divided time periods. It not only takes into account the differences in the requirements for ambient temperature due to the differences in the amount of data processed by the servers in different divided time periods, but also takes into account the degree of temperature anomaly in different divided time periods and the energy consumption under the temperature control strategy, thereby realizing the determination of the optimal temperature control strategy from multiple dimensions, and improving the reliability of temperature control on the basis of reducing energy consumption.

[0015] A further technical solution is that the weather data includes ambient temperature, wind speed and light intensity.

[0016] A further technical solution is that the similar historical dates are historical dates whose weather data in different dimensions from the current date are within a preset deviation range.

[0017] A further technical solution is that the temperature regulation energy consumption includes the temperature regulation energy consumption of a cooling fan and an air conditioner.

[0018] A further technical solution is that the power generation of the new energy equipment is determined according to the power generation of the new energy equipment installed in the data center.

[0019] A further technical solution is that the method for determining the matching control strategy of the data center is:

[0020] Determine the predicted processing data volume of the servers in different divided time periods based on the predicted operation data of the servers in different divided time periods, and regard the servers whose predicted processing data volume is greater than a preset data volume threshold as busy servers;

[0021] Determine the reliability weight coefficients in different divided time periods according to the proportion of the number of busy servers in different divided time periods, and determine the operation abnormality coefficient under the temperature control strategy in combination with the temperature abnormality coefficients in different divided time periods under the temperature control strategy;

[0022] The temperature control optimization coefficient of the temperature control strategy is determined based on the operation abnormality coefficient and the energy consumption, and the matching control strategy of the data center is determined based on the temperature control optimization coefficient.

[0023] A further technical solution is that the operation abnormality coefficient under the temperature control strategy is determined according to the sum of the products of the reliability weight coefficient and the temperature abnormality coefficient in different divided time periods.

[0024] A further technical solution is that the temperature control optimization coefficient of the temperature control strategy is determined according to the average value of the ratio of the operation abnormality coefficient and the energy consumption to the preset energy consumption.

[0025] A further technical solution is that the matching control strategy is a temperature control strategy with the smallest temperature control optimization coefficient.

[0026] In a second aspect, the present invention provides a computer system comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned low-carbon control method for a data center when running the computer program.

[0027] Other features and advantages will be described in the following description. The objects and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0028] 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

[0029] 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 attached drawings.

[0030] Figure 1 is a flow chart of a low carbon control method for a data center;

[0031] Figure 2 is a flow chart for determining that a data center cannot adopt a preset temperature control strategy;

[0032] Figure 3 is a flow chart of a method for determining a temperature anomaly coefficient within a divided time period;

[0033] Figure 4is a flow chart of a method for determining a matching control strategy for a data center;

[0034] Figure 5 It is a framework diagram of a computer system. DETAILED DESCRIPTION

[0035] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0036] When conducting low-carbon control in a data center, if a fixed temperature control strategy is used to adjust the temperature of cooling fans and air conditioners, it will inevitably lead to larger carbon emissions from the data center. Therefore, weather data and power generation data of new energy equipment installed in the data center are used to generate differentiated energy consumption control strategies.

[0037] Energy consumption deviation: the energy consumption deviation between the power generation and temperature control energy consumption of new energy equipment on similar historical dates.

[0038] Determine that the data center cannot adopt the preset temperature control strategy: determine the energy consumption deviation amounts of different similar historical dates based on the energy consumption deviation situations in different similar historical dates, and use the dates when the power generation of new energy equipment cannot meet the temperature regulation energy consumption as energy consumption deviation dates. When the number of energy consumption deviation dates accounts for a larger proportion of the number of similar historical dates than the preset number, it is determined that the data center cannot adopt the preset temperature control strategy.

[0039] Predicted temperature: Based on weather data, predicted operation data of servers in the data center at different time periods, and temperature control strategies, the predicted temperature at different times is determined using a preset prediction model.

[0040] Abnormal temperature moment: the moment when the predicted temperature is greater than the preset temperature threshold.

[0041] Temperature anomaly coefficient within the divided time period: The temperature anomaly coefficient within the divided time period is taken as the ratio of the number of temperature anomaly moments within the divided time period.

[0042] Abnormal temperature period: the period when the temperature anomaly coefficient is greater than 0.6.

[0043] Matching control strategy of data center: determine the predicted processing data volume of servers in different divided time periods based on the predicted operation data of servers in different divided time periods, and regard servers with predicted processing data volume greater than a preset data volume threshold as busy servers, determine the reliability weight coefficients in different divided time periods based on the proportion of the number of busy servers in different divided time periods, determine the operation abnormality coefficient under the temperature control strategy based on the sum of the products of the reliability weight coefficients and the temperature abnormality coefficients in different divided time periods, determine the temperature control optimization coefficient of the temperature control strategy based on the average value of the operation abnormality coefficient and the ratio of the energy consumption to the preset energy consumption, and use the temperature control optimization coefficient to determine the matching control strategy of the data center.

[0044] Embodiment 1 To solve the above problem, according to one aspect of the present invention, Figure 1 As shown, a first aspect is provided. The present invention provides a low-carbon control method for a data center, specifically comprising:

[0045] S1 determines the deviation between the temperature regulation energy consumption of the data center using a preset temperature control strategy and the power generation of the new energy equipment on a similar historical date to the current date based on the weather data of the current date, and uses it as the energy consumption deviation;

[0046] Furthermore, the weather data includes ambient temperature, wind speed and sunlight.

[0047] Specifically, the similar historical date is a historical date whose weather data in different dimensions from the current date are within a preset deviation range.

[0048] It should be noted that the temperature adjustment energy consumption includes the temperature adjustment energy consumption of the cooling fan and the air conditioner.

[0049] It can be understood that the power generation of the new energy equipment is determined according to the power generation of the new energy equipment installed in the data center.

[0050] S2, when it is determined that the data center cannot adopt the preset temperature control strategy according to the energy consumption deviation, based on the weather data and the predicted operation data of the servers in the data center in different time periods, determines the predicted temperature of the data center at different times under different temperature control strategies;

[0051] Specifically, Figure 2 As shown, determining that the data center cannot adopt a preset temperature control strategy specifically includes:

[0052] Determine the energy consumption deviation amount of different similar historical dates based on the energy consumption deviation conditions in different similar historical dates;

[0053] Determine the energy consumption deviation date in the similar historical date by using the energy consumption deviation amount;

[0054] Whether the data center adopts a preset temperature control strategy is determined according to the proportion of the number of the energy consumption deviation dates in the number of similar historical dates.

[0055] Furthermore, the energy consumption deviation date is a date when the power generation of the new energy equipment cannot meet the temperature regulation energy consumption.

[0056] It can be understood that when the proportion of the number of the energy consumption deviation dates in the number of similar historical dates is greater than the preset proportion, it is determined that the data center cannot adopt the preset temperature control strategy.

[0057] Optionally, determining that the data center cannot adopt a preset temperature control strategy specifically includes:

[0058] Determine the energy consumption deviation amount of different similar historical dates based on the energy consumption deviation conditions in different similar historical dates;

[0059] Determine the energy consumption deviation date in the similar historical date by using the energy consumption deviation amount;

[0060] Whether the data center adopts a preset temperature control strategy is determined according to the maximum value of the energy consumption deviation in the energy consumption deviation date.

[0061] Optionally, determining that the data center cannot adopt a preset temperature control strategy specifically includes:

[0062] S11 determines the energy consumption deviation amounts of different similar historical dates based on the energy consumption deviation conditions in different similar historical dates, and divides the similar historical dates into energy consumption deviation dates and energy consumption reliable dates using the energy consumption deviation amounts;

[0063] S12 determines the energy consumption deviation coefficient of the energy consumption deviation date and the supply reliability coefficient of the energy consumption reliability date by using the number of energy consumption deviation dates and the energy consumption deviation amounts of different energy consumption deviation dates, the number of energy consumption reliability dates and the energy consumption deviation amounts of different energy consumption reliability dates respectively;

[0064] S13 determines an energy consumption deviation evaluation amount according to the ratio of the energy consumption deviation coefficient of the energy consumption deviation date to the supply reliability coefficient of the energy consumption reliability date, and uses the energy consumption deviation evaluation amount to determine whether the data center can adopt a preset temperature control strategy.

[0065] Specifically, the method for determining the energy consumption deviation coefficient of the energy consumption deviation date is:

[0066] Determine the energy consumption deviation proportional factors for different energy consumption deviation dates according to the ratio of the energy consumption deviation amount on different energy consumption deviation dates to the power generation amount of the new energy equipment installed in the data center;

[0067] The energy consumption deviation coefficient of the energy consumption deviation date is determined by multiplying the quantity ratio of the energy consumption deviation dates by the average value of the energy consumption deviation proportional factors of different energy consumption deviation dates.

[0068] It should be noted that when the energy consumption deviation assessment value cannot meet the requirement, it is determined that the data center cannot adopt the preset temperature control strategy.

[0069] It can be understood that the energy reliability date is a similar historical date excluding the energy consumption deviation date.

[0070] Furthermore, the method for determining the predicted temperature of the data center at different times is:

[0071] Based on weather data, predicted operation data of servers in the data center at different time periods, and temperature control strategies, the predicted temperature at different times is determined using a preset prediction model.

[0072] Specifically, the temperature control strategy is to control the cooling fan and the air conditioner and the air volume of the air conditioner under different temperature thresholds.

[0073] S3 determines the temperature anomaly moments in different divided time periods based on the predicted temperatures at different moments in different divided time periods, and determines the temperature anomaly coefficients in different divided time periods based on the distribution data of the temperature anomaly moments in different divided time periods, and when it is determined based on the temperature anomaly coefficient that there is no temperature anomaly period under the temperature control strategy, proceeds to the next step;

[0074] Furthermore, the abnormal temperature moment is the moment when the predicted temperature is greater than a preset temperature threshold.

[0075] It should be noted that if Figure 3 As shown, the method for determining the temperature anomaly coefficient within the divided time period is:

[0076] Determine the number ratio of the abnormal temperature moments in the divided time period based on the distribution data of the abnormal temperature moments in the divided time period;

[0077] The ratio of the number of temperature anomaly moments within the divided time period is used as the temperature anomaly coefficient within the divided time period.

[0078] Furthermore, the temperature anomaly period is a divided period in which the temperature anomaly coefficient is greater than a preset anomaly coefficient.

[0079] It can be understood that when there is a period of abnormal temperature, it is determined that the temperature control strategy does not belong to the matching control strategy.

[0080] In another embodiment, the method for determining the temperature anomaly coefficient within the divided time periods is:

[0081] Determine the number ratio of the abnormal temperature moments in the divided time period based on the distribution data of the abnormal temperature moments in the divided time period;

[0082] Determine the abnormal coefficients at different temperature abnormal moments according to the deviations between the temperatures at different temperature abnormal moments and the preset temperature thresholds;

[0083] The product of the number ratio of the temperature abnormal moments in the divided time period and the average value of the abnormal coefficients of different temperature abnormal moments is used as the temperature abnormal coefficient in the divided time period.

[0084] Furthermore, the abnormal coefficient at the temperature abnormality moment is determined according to the ratio of the deviation between the temperature at the temperature abnormality moment and a preset temperature threshold and the preset temperature threshold.

[0085] In another embodiment, the method for determining the temperature anomaly coefficient within the divided time periods is:

[0086] When it is determined that there is no temperature abnormal moment in the divided time period based on the distribution data of the temperature abnormal moment in the divided time period, the temperature abnormal coefficient in the divided time period is determined using the preset abnormal coefficient;

[0087] When there is an abnormal temperature moment within the divided time period:

[0088] Acquire the number of abnormal temperature moments in the divided time period, and when the number of abnormal temperature moments in the divided time period is greater than the preset number of abnormal moments, determine that the divided time period is a abnormal temperature period;

[0089] When the number of abnormal temperature moments within the divided time period is not greater than the preset number of abnormal moments:

[0090] Obtaining a number ratio of abnormal temperature moments within the divided time period, and when the number ratio of abnormal temperature moments within the divided time period is greater than a preset number ratio, determining that the divided time period is a temperature abnormality time period;

[0091] When the proportion of abnormal temperature moments in the divided time period is within a preset proportion range:

[0092] Obtaining deviations between the temperatures at different temperature anomaly moments and a preset temperature threshold, and when an average value of the deviations between the temperatures at different temperature anomaly moments and the preset temperature threshold is greater than the preset deviation threshold, determining that the divided time period is a temperature anomaly period;

[0093] When the number ratio of abnormal temperature moments in the divided time period is not within the preset number ratio interval or the average value of the deviation between the temperature at different abnormal temperature moments and the preset temperature threshold is not greater than the preset deviation threshold:

[0094] Determine the abnormal coefficients of different temperature abnormal moments according to the deviations between the temperatures at different temperature abnormal moments and the preset temperature threshold, and when the number of temperature abnormal moments at which the abnormal coefficients are greater than the preset abnormal coefficient threshold does not meet the requirement, determine that the divided time period is a temperature abnormal time period;

[0095] When the number of temperature anomaly moments with an anomaly coefficient greater than the preset anomaly coefficient threshold meets the requirement:

[0096] The product of the number ratio of the temperature abnormal moments in the divided time period and the average value of the abnormal coefficients of different temperature abnormal moments is used as the temperature abnormal coefficient in the divided time period.

[0097] S4 obtains the predicted operation data of the server in different divided time periods, and determines the matching control strategy of the data center in combination with the temperature anomaly coefficient and energy consumption in different divided time periods under the temperature control strategy, and uses the matching control strategy to perform temperature control of the data center.

[0098] It should be noted that the predicted operation data of the server includes the predicted processing data volume of the server, wherein the predicted processing data volume of the server is determined according to the average value of the processing data volume on different dates in the time period.

[0099] Specifically, Figure 4 As shown, the method for determining the matching control strategy of the data center is:

[0100] Determine the predicted processing data volume of the servers in different divided time periods based on the predicted operation data of the servers in different divided time periods, and regard the servers whose predicted processing data volume is greater than a preset data volume threshold as busy servers;

[0101] Determine the reliability weight coefficients in different divided time periods according to the proportion of the number of busy servers in different divided time periods, and determine the operation abnormality coefficient under the temperature control strategy in combination with the temperature abnormality coefficients in different divided time periods under the temperature control strategy;

[0102] The temperature control optimization coefficient of the temperature control strategy is determined based on the operation abnormality coefficient and the energy consumption, and the matching control strategy of the data center is determined based on the temperature control optimization coefficient.

[0103] Furthermore, the operation abnormality coefficient under the temperature control strategy is determined according to the sum of the products of the reliability weight coefficient and the temperature abnormality coefficient in different divided time periods.

[0104] It can be understood that the temperature control optimization coefficient of the temperature control strategy is determined according to the average value of the ratio of the operation abnormality coefficient and the energy consumption to the preset energy consumption.

[0105] Specifically, the matching control strategy is a temperature control strategy with the smallest temperature control optimization coefficient.

[0106] In another embodiment, the method for determining the matching control strategy of the data center is:

[0107] Based on the temperature anomaly coefficients in different divided time periods under the temperature control strategy, when the average value of the temperature anomaly coefficients in different divided time periods is greater than a preset temperature anomaly coefficient threshold, it is determined that the temperature control strategy does not belong to the matching control strategy;

[0108] When the average value of the temperature anomaly coefficient in different divided time periods is not greater than the preset temperature anomaly coefficient threshold:

[0109] Obtaining the divided time periods of the temperature anomaly coefficient within the preset anomaly coefficient range, and using them as the screening anomaly divided time periods, and when the number of the screening anomaly divided time periods is greater than the preset divided time period number threshold, determining that the temperature control strategy does not belong to the matching control strategy;

[0110] When the number of the abnormal screening time periods is greater than the preset time period number threshold:

[0111] Determine the predicted processing data volume of the servers in different divided time periods based on the predicted operation data of the servers in different divided time periods, and regard the servers whose predicted processing data volume is greater than the preset data volume threshold as busy servers, and determine the reliability weight coefficients in different divided time periods according to the proportion of the number of busy servers in different divided time periods, and when the sum of the reliability weight coefficients of the abnormal divided time periods is greater than the preset weight coefficient threshold, determine that the temperature control strategy does not belong to the matching control strategy;

[0112] When the sum of the reliable weight coefficients of the abnormal screening time periods is not greater than the preset weight coefficient threshold:

[0113] Determine the corrected abnormal coefficients in different divided time periods under the temperature control strategy by using the reliable weight coefficients in different divided time periods and the temperature abnormal coefficients in different divided time periods, and when the number of divided time periods in which the corrected abnormal coefficients are within the preset coefficient range is greater than the preset time period number setting value, determine that the temperature control strategy does not belong to the matching control strategy;

[0114] When the number of time periods divided by the correction abnormal coefficient within the preset coefficient range is not greater than the preset time period setting value:

[0115] Determine the reliability weight coefficients in different divided time periods according to the proportion of the number of busy servers in different divided time periods, and determine the operation abnormality coefficient under the temperature control strategy in combination with the temperature abnormality coefficients in different divided time periods under the temperature control strategy;

[0116] The temperature control optimization coefficient of the temperature control strategy is determined based on the operation abnormality coefficient and the energy consumption, and the matching control strategy of the data center is determined based on the temperature control optimization coefficient.

[0117] Embodiment 2 In the second aspect, as Figure 5 As shown, the present invention provides a computer system, comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned low-carbon control method for a data center when running the computer program.

[0118] Optionally, the above step S11 includes the following contents:

[0119] S111 determines the energy consumption deviation of different similar historical dates based on the energy consumption deviation in different similar historical dates. When it is determined by using the energy consumption deviation that there is no energy consumption reliable date in the similar historical dates, it is determined that the data center cannot adopt the preset temperature control strategy. When there is an energy consumption reliable date in the similar historical dates, the process proceeds to step S112.

[0120] S112 obtains the energy consumption deviation amount of the energy consumption deviation date. When the maximum value of the energy consumption deviation amount of the energy consumption deviation date does not meet the requirement, it is determined that the data center cannot adopt the preset temperature control strategy. When the maximum value of the energy consumption deviation amount of the energy consumption deviation date meets the requirement, the process proceeds to step S113;

[0121] S113 obtains the ratio of the energy consumption deviation date to the similar historical dates. When the ratio of the energy consumption deviation date to the similar historical dates is greater than the preset ratio, it is determined that the data center cannot adopt the preset temperature control strategy. When the ratio of the energy consumption deviation date to the similar historical dates is not greater than the preset ratio, the process proceeds to step S114.

[0122] S114 obtains the number of energy consumption deviation dates within the preset energy consumption deviation range. When the number of energy consumption deviation dates within the preset energy consumption deviation range does not meet the requirements, it is determined that the data center cannot adopt the preset temperature control strategy. When the number of energy consumption deviation dates within the preset energy consumption deviation range meets the requirements, proceed to step S12.

[0123] Optionally, the above step S12 includes the following contents:

[0124] S121 determines the energy consumption deviation coefficient of the energy consumption deviation date by using the number of energy consumption deviation dates and the energy consumption deviation amounts of different energy consumption deviation dates. When the energy consumption deviation coefficient of the energy consumption deviation date is greater than a preset deviation coefficient threshold, it is determined that the data center cannot adopt a preset temperature control strategy. When the energy consumption deviation coefficient of the energy consumption deviation date is not greater than the preset deviation coefficient threshold, the process proceeds to step S122.

[0125] S122 determines the supply reliability coefficient of the energy consumption reliable date by using the number of the energy consumption reliable dates and the energy consumption deviation of different energy consumption reliable dates. When the supply reliability coefficient of the energy consumption reliable date is greater than the preset reliability coefficient threshold, the process proceeds to step S123. When the supply reliability coefficient of the energy consumption reliable date is not greater than the preset reliability coefficient threshold, the process proceeds to step S13.

[0126] S123 When the energy consumption deviation coefficient of the energy consumption deviation date is within the preset deviation coefficient range, it is determined that the data center can adopt the preset temperature control strategy; when the energy consumption deviation coefficient of the energy consumption deviation date is not within the preset deviation coefficient range, proceed to step S13.

[0127] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0128] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0129] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A low-carbon control method for a data center, characterized in that: Specifically include: Based on the weather data of the current date, determine the deviation between the temperature regulation energy consumption of the data center using a preset temperature control strategy and the power generation of the new energy equipment on similar historical dates to the current date, and use it as the energy consumption deviation; When it is determined that the data center cannot adopt the preset temperature control strategy according to the energy consumption deviation, the predicted temperature of the data center at different times under different temperature control strategies is determined based on weather data and predicted operation data of the servers in the data center in different time periods; Determine the temperature anomaly moments in different divided time periods based on the predicted temperatures at different moments in different divided time periods, and determine the temperature anomaly coefficients in different divided time periods based on the distribution data of the temperature anomaly moments in different divided time periods, and proceed to the next step when it is determined based on the temperature anomaly coefficient that there is no temperature anomaly period under the temperature control strategy; Acquire the predicted operation data of the server in different divided time periods, and determine the matching control strategy of the data center in combination with the temperature anomaly coefficient and energy consumption in different divided time periods under the temperature control strategy, and use the matching control strategy to perform temperature control of the data center; Determining that the data center cannot adopt a preset temperature control strategy includes: Based on the energy consumption deviations in different similar historical dates, determine the energy consumption deviation amounts in different similar historical dates; Determine the energy consumption deviation date in the similar historical date by using the energy consumption deviation amount; Determining whether the data center adopts a preset temperature control strategy according to the proportion of the number of energy consumption deviation dates in the number of similar historical dates; The method for determining the matching control strategy of the data center is: Determine the predicted processing data volume of the servers in different divided time periods based on the predicted operation data of the servers in different divided time periods, and regard the servers whose predicted processing data volume is greater than a preset data volume threshold as busy servers; Determine the reliability weight coefficients in different divided time periods according to the proportion of the number of busy servers in different divided time periods, and determine the operation abnormality coefficient under the temperature control strategy in combination with the temperature abnormality coefficients in different divided time periods under the temperature control strategy; The temperature control optimization coefficient of the temperature control strategy is determined based on the operation abnormality coefficient and the energy consumption, and the matching control strategy of the data center is determined based on the temperature control optimization coefficient.

2. The low-carbon control method for a data center according to claim 1, characterized in that: The weather data includes ambient temperature, wind speed, and sunlight.

3. The low-carbon control method for a data center according to claim 1, characterized in that: The similar historical dates are historical dates whose weather data in different dimensions from the current date are within a preset deviation range.

4. The low-carbon control method for a data center according to claim 1, characterized in that: The temperature regulation energy consumption includes the temperature regulation energy consumption of the cooling fan and the air conditioner.

5. The low-carbon control method for a data center according to claim 1, characterized in that: The power generation capacity of the new energy equipment is determined according to the power generation capacity of the new energy equipment installed in the data center.

6. The low-carbon control method for a data center according to claim 1, characterized in that: The energy consumption deviation date is the date when the power generation of the new energy equipment cannot meet the temperature regulation energy consumption.

7. The low-carbon control method for a data center according to claim 1, characterized in that: The operation abnormality coefficient under the temperature control strategy is determined according to the sum of the products of the reliability weight coefficient and the temperature abnormality coefficient in different divided time periods.

8. 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 low-carbon control method for a data center as described in any one of claims 1-7 when running the computer program.

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