Methods, apparatus, equipment, media and products for predicting water use efficiency

Abstract

This application relates to the field of data processing technology, providing a method, apparatus, equipment, medium, and product for predicting water use efficiency. This method incorporates parameter values ​​from multiple influencing factors, including outdoor dynamic air parameters, indoor air design parameters, building design information, chilled water information, and cooling water information. It considers the water consumption requirements of air humidification under different regional meteorological conditions, thus resulting in more accurate water use efficiency calculations and higher reference value. Simultaneously, it can realize the water consumption and drainage volume of air conditioning fresh air throughout the year, thereby more accurately assessing the water use efficiency of the actual system under ideal conditions throughout the year.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically to a method, apparatus, equipment, medium, and product for predicting water use efficiency. Background Technology

[0002] With the booming development of the data center industry, the demand for water resources in data center server rooms is also constantly increasing. Currently, data center water systems often use chilled water centralized air conditioning systems, and water is also required for supporting humidification systems and other functions such as fresh air treatment.

[0003] Water scarcity can lead to data center infrastructure failures and overheating in server rooms, but excessive water consumption can exacerbate water shortages. To assess the water consumption of data centers, water use efficiency is used to characterize water consumption per unit of energy. Current technologies only determine the water use efficiency of existing facilities at the operational level, failing to accurately assess the water use efficiency under ideal conditions for the actual system. Summary of the Invention

[0004] This application provides a method, apparatus, device, medium, and product for predicting water use efficiency, in order to solve the problem of low accuracy in water use efficiency assessment of data centers in the prior art.

[0005] In a first aspect, embodiments of this application provide a method for predicting water use efficiency in a data center, comprising:

[0006] Based on the annual outdoor dynamic air parameters and indoor air design parameters of the region where the data center is located, as well as the building design information of the building on which the data center is located, the annual water consumption and annual drainage volume of the humidification system of the data center are determined.

[0007] Based on the configuration and selection information of the chiller units, cooling water information, and chilled water information of the data center, determine the annual cooling water system makeup water volume and the annual chilled water system makeup water volume of the data center.

[0008] The predicted total water consumption of the data center is determined based on the annual water consumption of the humidification system, the annual drainage of the humidification system, the annual water replenishment of the cooling water system, the annual water replenishment of the chilled water system, the annual wastewater recycling volume of the data center, and the annual rainwater collection volume.

[0009] The water utilization efficiency of the data center is determined based on the predicted total water consumption and annual power consumption of IT equipment.

[0010] According to an embodiment of this application, a method for predicting the water utilization efficiency of a data center includes determining the annual water consumption and annual wastewater discharge of the data center's humidification system based on the annual outdoor dynamic air parameters and indoor air design parameters of the region where the data center is located, as well as the building design information of the building on which the data center is located.

[0011] Based on the annual outdoor dynamic air parameters and the indoor air design parameters, determine the unit mass air conditioning fresh air humidification load and unit mass air conditioning fresh air dehumidification load of the data center;

[0012] Based on the architectural design information, determine the annual fresh air volume for the air conditioning system of the data center;

[0013] The annual water consumption of the humidification system is determined based on the unit mass air conditioning fresh air humidification load and the annual air conditioning fresh air volume.

[0014] The annual drainage volume of the humidification system is determined based on the unit mass air conditioning fresh air dehumidification load and the annual air conditioning fresh air volume.

[0015] According to an embodiment of this application, a method for predicting water utilization efficiency in a data center includes an hourly dry-bulb temperature and a first relative humidity; the indoor air design parameters include a dry-bulb temperature and a second relative humidity.

[0016] The step of determining the unit mass air conditioning humidification load and unit mass air conditioning dehumidification load of the data center based on the annual outdoor dynamic air parameters and the indoor air design parameters includes:

[0017] The hourly state humidity of the outdoor air of the data center is determined based on the hourly dry-bulb temperature and the first relative humidity.

[0018] The indoor air design humidity level of the data center is determined based on the dry-bulb temperature and the second relative humidity.

[0019] If the hourly humidity of the outdoor air is less than the design humidity of the indoor air, then the first difference between the design humidity of the indoor air and the hourly humidity of the outdoor air shall be used as the humidification load of the unit mass air conditioning fresh air.

[0020] If the hourly humidity of the outdoor air is greater than the design humidity of the indoor air, then the second difference between the hourly humidity of the outdoor air and the design humidity of the indoor air will be used as the dehumidification load of the unit mass air conditioning fresh air.

[0021] According to an embodiment of this application, a method for predicting water utilization efficiency in a data center is provided, wherein the building design information includes building area and floor height;

[0022] Determining the annual fresh air volume for the data center based on the building design information includes:

[0023] The design value for the number of fresh air exchanges for the data center is determined based on the building area and the floor height.

[0024] The fresh air volume for each room in the data center is determined based on the building area, floor height, and design value of fresh air exchange rate.

[0025] The annual fresh air volume of the data center is determined based on the fresh air volume of all the rooms.

[0026] According to an embodiment of this application, a method for predicting the water utilization efficiency of a data center is provided, wherein the cooling water information includes the cooling water circulation temperature difference and the cooling water makeup rate; and the chilled water information includes the chilled water circulation temperature difference and the chilled water makeup rate.

[0027] The step of determining the annual cooling water system makeup water volume and the annual chilled water system makeup water volume of the data center based on the configuration and selection information, cooling water information, and chilled water information of the data center includes:

[0028] The chilled water circulation heat load of the data center is determined based on the heat load of the IT equipment, the heat load of the building, and other heat loads.

[0029] Based on the chilled water circulation heat load, determine the configuration and selection information of the chiller unit;

[0030] Based on the configuration and selection information of the chiller unit, determine the performance coefficient of the chiller unit;

[0031] The cooling water circulation heat load of the data center is determined based on the performance coefficient and the chilled water circulation heat load.

[0032] The ratio of the cooling water circulation heat load to the cooling water circulation temperature difference is used as the cooling water circulation volume of the data center.

[0033] The ratio of the chilled water circulation heat load to the chilled water circulation temperature difference is used as the chilled water circulation volume of the data center.

[0034] The product of the cooling water circulation rate and the cooling water replenishment rate is taken as the annual cooling water system replenishment amount;

[0035] The product of the chilled water circulation volume and the chilled water replenishment rate is taken as the annual chilled water system replenishment volume.

[0036] According to an embodiment of this application, a method for predicting the water utilization efficiency of a data center includes determining the water utilization efficiency of the data center based on the predicted total water consumption and the annual power consumption of IT equipment.

[0037] The ratio of the predicted total water consumption of the data center to the annual power consumption of the IT equipment is used as the water utilization efficiency of the data center.

[0038] According to an embodiment of this application, a method for predicting the water utilization efficiency of a data center, after determining the water utilization efficiency of the data center based on the predicted total water consumption and the annual power consumption of IT equipment, further includes:

[0039] Determine the classification level of the water use efficiency;

[0040] Based on the aforementioned grading level, an optimization strategy for the data center is determined;

[0041] Based on the optimization strategy, the baseline value of the water use efficiency is optimized.

[0042] Secondly, embodiments of this application provide a water use efficiency prediction device for a data center, comprising:

[0043] The first data processing module is used to determine the annual water consumption and annual drainage volume of the humidification system of the data center based on the annual outdoor dynamic air parameters and indoor air design parameters of the region where the data center is located, as well as the architectural design information of the building where the data center is located.

[0044] The second data processing module is used to determine the annual cooling water system makeup water volume and the annual chilled water system makeup water volume of the data center based on the configuration and selection information, cooling water information and chilled water information of the chiller units of the data center.

[0045] The predicted total water consumption determination module is used to determine the predicted total water consumption of the data center based on the annual water consumption of the humidification system, the annual drainage of the humidification system, the annual water replenishment of the cooling water system, the annual water replenishment of the chilled water system, the annual wastewater recycling volume of the data center, and the annual rainwater collection volume.

[0046] The water use efficiency determination module is used to determine the water use efficiency of the data center based on the predicted total water consumption and the annual power consumption of IT equipment.

[0047] Thirdly, embodiments of this application provide an electronic device, including a processor and a memory storing a computer program, wherein the processor executes the program to implement the steps of the data center water utilization efficiency prediction method described in the first aspect.

[0048] Fourthly, embodiments of this application provide a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the data center water utilization efficiency prediction method described in the first aspect.

[0049] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the steps of the data center water utilization efficiency prediction method described in the first aspect.

[0050] The water use efficiency prediction method, apparatus, equipment, medium, and product provided in this application incorporate parameter values ​​from multiple influencing factors, including outdoor dynamic air parameters, indoor air design parameters, building design information, chilled water information, and cooling water information. This considers the water consumption requirements of air humidification under different regional meteorological conditions. Therefore, the calculated water use efficiency is more accurate and has higher reference value. Simultaneously, it can realize the water consumption and drainage volume of air conditioning fresh air throughout the year, thereby more accurately assessing the water use efficiency of the actual system under ideal conditions throughout the year. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This is a flowchart illustrating the water use efficiency prediction method for data centers provided in an embodiment of this application.

[0053] Figure 2 This is a flowchart illustrating the rapid prediction method for water use efficiency in data centers based on meteorological data provided in this application embodiment;

[0054] Figure 3 This is a schematic diagram of the water use efficiency optimization strategy framework provided in the embodiments of this application;

[0055] Figure 4 This is a schematic diagram of the structure of the data center water use efficiency prediction device provided in the embodiments of this application;

[0056] Figure 5 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0058] With the rapid development of IoT, cloud computing, big data, and AI technologies, and the continuous advancement of information technology, data storage and network communication volumes are experiencing explosive growth, leading to a rapid increase in both the scale of data center construction and water consumption. The rational utilization and planning of water resources is a key consideration in the construction of every data center. Therefore, quickly predicting the annual water consumption and water utilization efficiency of a data center using known external meteorological conditions and design parameters can provide a reference for site selection and design in determining the water utilization efficiency of other large-scale data centers. Currently, the assessment / prediction of water utilization efficiency in data centers mainly faces the following problems:

[0059] (1) Determining the water utilization efficiency of existing facilities only at the operation and maintenance level is affected by multiple actual conditions such as the current outdoor dynamics and indoor air design parameters, the coefficient of performance (COP) of the selected chiller unit, the chilled water makeup rate and the cooling water makeup rate, and cannot accurately assess the water utilization efficiency of the actual system under ideal conditions.

[0060] (2) The determination method is based on monitoring and analysis, but does not consider the water consumption requirements of air humidification under different meteorological conditions in different regions. Therefore, meteorological conditions vary greatly in different regions. For example, in the northwest, the outdoor air is mostly dry and hot in summer; in the southeast, the outdoor air is warm and humid in summer. Therefore, for the entire life cycle of data center planning, design, construction, and operation, the determination of water use efficiency has an important impact on planning site selection, equipment configuration, and selection, thereby avoiding the occurrence of mismatch between water resources and equipment configuration. However, existing technologies cannot provide reference and evaluation in this regard in the early stages.

[0061] (3) Water use efficiency changes dynamically with infrastructure conditions, leaving considerable room for optimization. Therefore, quickly and accurately predicting water use efficiency under ideal conditions helps optimize design results and reduce initial investment waste and operating costs. However, existing technologies cannot reflect the optimal relationship between input conditions and the final water use efficiency.

[0062] Based on the above problems, this application proposes a method for predicting water use efficiency in data centers.

[0063] Figure 1This is a flowchart illustrating the water use efficiency prediction method for data centers provided in an embodiment of this application. (Refer to...) Figure 1 This application provides a method for predicting water use efficiency in a data center, which may include:

[0064] Step 100: Based on the annual outdoor dynamic air parameters and indoor air design parameters of the region where the data center is located, as well as the building design information of the building where the data center is located, determine the annual water consumption and annual drainage volume of the data center's humidification system.

[0065] A data center is a facility used for centralized storage, management, and processing of large amounts of data. It includes hardware such as servers, network equipment, and storage systems, and is equipped with professional infrastructure such as air conditioning, power supply, and security to ensure the safe, stable, and efficient operation of the data.

[0066] Obtain the year-round outdoor dynamic air parameters for the region where the data center is located. These parameters include hourly dry-bulb temperature. and first relative humidity Hourly dry-bulb temperature can be understood as the dry-bulb temperature measured hourly, that is, the dry-bulb temperature recorded sequentially throughout the hour. In air conditioning engineering, dry-bulb temperature refers to the actual temperature of the air, measured under conditions where there is no moisture evaporation or condensation. Furthermore, based on hourly dry-bulb temperature... and first relative humidity Determine the hourly humidity of outdoor air in the data center. For example, the relationship between hourly dry-bulb temperature, first relative humidity, and hourly state moisture content of outdoor air is stored in the form of a chart. Therefore, after determining the hourly dry-bulb temperature and first relative humidity, the hourly state moisture content of outdoor air can be determined by looking up the chart.

[0067] Obtain the indoor air design parameters for the data center, including the dry-bulb temperature T. design Second relative humidity RH design Furthermore, based on the dry-bulb temperature T... design Second relative humidity RH design Determine the indoor air design humidity level d for the data center. design For example, the relationship between dry-bulb temperature, second relative humidity, and indoor air design humidity is stored in the form of a chart. Therefore, after determining the dry-bulb temperature and second relative humidity, the indoor air design humidity can be determined by consulting the chart.

[0068] If the hourly humidity of outdoor air is less than the design humidity of indoor air, then the first difference between the design humidity of indoor air and the hourly humidity of outdoor air will be used as the humidification load per unit mass of air conditioning fresh air. If the hourly humidity of outdoor air is greater than the design humidity of indoor air, then the second difference between the hourly humidity of outdoor air and the design humidity of indoor air will be used as the dehumidification load per unit mass of air conditioning fresh air.

[0069]

[0070] Obtain the architectural design information of the building housing the data center, including the building area S and floor height H. Further, based on the building area and floor height, determine the design value α for the fresh air exchange rate of the data center, where the fresh air exchange rate refers to the number of times indoor air is completely replaced per unit time. For example, calculate the total air volume of the building housing the data center based on the building area and floor height, and determine the design value for the fresh air exchange rate based on the correspondence between the total air volume and the fresh air exchange rate. Alternatively, the design value for the fresh air exchange rate can also be determined using a correlation table among the building area, floor height, and design value for the fresh air exchange rate.

[0071] Based on the building area, floor height, and design values ​​for fresh air exchange rate, determine the fresh air volume for each room in the data center, and then determine the annual fresh air volume for the data center's air conditioning system based on the fresh air volume of all rooms.

[0072] The fresh air volume for each room is the product of the building area, floor height, and the design value for the number of fresh air exchanges.

[0073] The annual fresh air volume of the air conditioner is calculated by summing up the fresh air volumes of each room. The calculation formula is as follows:

[0074]

[0075] Among them, L w The annual fresh air volume for air conditioning, where i represents the number of rooms, N represents the number of rooms, and S represents the annual fresh air volume for air conditioning. i Let H be the building area of ​​the i-th room. i Let α be the floor height of the i-th room, and α be the design value for the number of fresh air exchanges.

[0076] Furthermore, based on the unit mass air conditioning fresh air humidification load and the annual air conditioning fresh air volume, the annual water consumption of the humidification system is determined, and based on the unit mass air conditioning fresh air dehumidification load and the annual air conditioning fresh air volume, the annual drainage volume of the humidification system is determined.

[0077] Among them, the annual water consumption of the humidification system The calculation formula is:

[0078]

[0079] Annual drainage volume of humidification system The calculation formula is:

[0080]

[0081] Where t represents time and M represents the number of hours in a year.

[0082] This application's embodiments are based on the fundamental calculation principles of air conditioning design and dynamic outdoor air parameters throughout the year. By using hourly outdoor air humidity data throughout the year, it can infer whether the fresh air needs humidification or dehumidification at a given moment, and calculate the hourly water consumption or drainage volume per unit mass of fresh air. Simultaneously, by combining building conditions and the design value of fresh air exchange rate, the air conditioning fresh air volume for each room is obtained, and the annual air conditioning fresh air volume is calculated by summing the hourly values. Multiplying the hourly water consumption or drainage volume per unit mass of fresh air by the annual air conditioning fresh air volume yields the annual water consumption or annual drainage volume for air conditioning fresh air. Based on this, the accuracy and efficiency of water use efficiency assessment are improved.

[0083] Step 200: Based on the configuration and selection information of the chiller units, cooling water information, and chilled water information of the data center, determine the annual cooling water system makeup water volume and the annual chilled water system makeup water volume of the data center.

[0084] Obtain the heat load of IT equipment, building heat load, and other heat loads in the data center. Other heat loads may include personnel heat load, lighting heat load, and external environmental heat load. Then, based on the IT equipment heat load, building heat load, and other heat loads, determine the chilled water circulation heat load of the data center. The formula for calculating the chilled water circulation heat load is as follows:

[0085] Q CHW =Q IT +Q Arc +Q other ;

[0086] Among them, Q CHW For the chilled water circulation heat load, Q IT For the heat load of IT equipment, Q Arci For building heat load, Q other For other heat loads.

[0087] Based on the chilled water circulation heat load, determine the configuration and selection information for chiller units. For example, calculate the configuration and selection of chiller units for data centers based on values ​​and relevant methods from HVAC technical manuals.

[0088] Based on the configuration and selection information of the chiller unit, determine the coefficient of performance (COP) of the chiller unit. For example, obtain the chiller unit's cooling capacity and energy consumption from the configuration and selection information, and then calculate the COP based on these parameters. Alternatively, after determining the configuration and selection information, you can directly consult the COP provided by the manufacturer.

[0089] The cooling water circulation heat load of the data center is determined based on the performance coefficient and the chilled water circulation heat load. The calculation formula for the cooling water circulation heat load is as follows:

[0090]

[0091] Among them, Q CW The cooling water circulation heat load is represented by COP, which is the coefficient of performance.

[0092] Furthermore, the ratio of cooling water circulation heat load to cooling water circulation temperature difference is calculated, and this ratio is used as the cooling water circulation volume of the data center; and the ratio of chilled water circulation heat load to chilled water circulation temperature difference is calculated, and this ratio is used as the chilled water circulation volume of the data center.

[0093] Among them, the cooling water circulation volume L CW The calculation formula is:

[0094]

[0095] chilled water circulation volume (L) CHW The calculation formula is:

[0096]

[0097] Where c is the specific heat capacity of water, Δt CW Δt represents the temperature difference in the cooling water circulation. CHW This refers to the temperature difference in the chilled water circulation.

[0098] Furthermore, calculate the product of cooling water circulation volume and cooling water makeup rate, and use this product as the annual cooling water system makeup volume; calculate the product of chilled water circulation volume and chilled water makeup rate, and use this product as the annual chilled water system makeup volume.

[0099] Among them, the annual cooling water system makeup water volume The calculation formula is:

[0100]

[0101] Annual chilled water system makeup water volume The calculation formula is:

[0102]

[0103] Among them, the cooling water makeup rate is β CW ,β CHW This refers to the chilled water makeup rate.

[0104] chilled water makeup rate β CHW It is generally a fixed value, ranging from 1% to 1.5%. Cooling water makeup rate β CWWith cooling tower evaporation loss L vapor,loss It is related to the concentration factor γ, where the cooling water makeup rate β CW The calculation formula is:

[0105]

[0106] Step 300: Determine the predicted total water consumption of the data center based on the annual water consumption of the humidification system, the annual drainage volume of the humidification system, the annual water replenishment volume of the cooling water system, the annual water replenishment volume of the chilled water system, the annual wastewater recycling volume of the data center, and the annual rainwater collection volume.

[0107] First, calculate the sum of the annual cooling water system makeup water volume, the annual chilled water system makeup water volume, and the annual humidification system water consumption. Then, calculate the difference between this sum and the annual humidification system drainage volume, the annual wastewater recycling volume of the data center, and the annual rainwater collection volume. This difference is then used to determine the predicted total water consumption of the current data center. The formula for calculating the predicted total water consumption is as follows:

[0108]

[0109] Among them, L Total To predict total water consumption, For the annual makeup water volume of the cooling water system, For the annual makeup water volume of the chilled water system, This represents the annual water consumption of the humidification system. The annual drainage volume of the humidification system, L se,ra This refers to the annual wastewater recycling volume and the annual rainwater collection volume.

[0110] Step 400: Determine the water utilization efficiency of the data center based on the predicted total water consumption of the data center and the annual power consumption of IT equipment.

[0111] The annual power consumption of IT equipment in the data center is obtained. Then, the ratio of the predicted total water consumption of the data center to the annual power consumption of the IT equipment is calculated. This ratio is taken as the data center's water use efficiency. The formula for calculating water use efficiency is:

[0112] WUE=L Total / q IT ;

[0113] Where WUE is water use efficiency, q IT The annual power consumption of IT equipment.

[0114] The water use efficiency prediction method for data centers provided in this application has the following advantages compared with the prior art:

[0115] (1) Effectively assess the annual water utilization efficiency of data centers. Existing technologies only measure the water consumption of water systems deployed within a preset time period, and their determination methods cannot effectively assess the annual water utilization efficiency of the air conditioning system of data center infrastructure. This application can realize the water consumption and drainage of air conditioning fresh air throughout the year, thereby more accurately assessing the water utilization efficiency of the actual system under ideal conditions throughout the year.

[0116] (2) This application incorporates the parameter values ​​of multiple influencing factors, such as outdoor dynamic air parameters, indoor air design parameters, building design information, chilled water information and cooling water information, to consider the water consumption requirements of air humidification under different meteorological conditions in different regions. Therefore, the calculation results of this application are more accurate and have higher reference value.

[0117] (3) Existing technologies only measure and determine the water utilization efficiency of existing facilities at the operation and maintenance level, and cannot reflect the water utilization efficiency under different scales, regions and design conditions. However, the water utilization efficiency determined in this application can quickly and accurately predict the water utilization efficiency under ideal conditions throughout the entire life cycle of the data center, thereby helping to optimize design results, reduce investment waste and operating costs.

[0118] To further explain the data center water use efficiency prediction method proposed in this application, refer to... Figure 2 and the following examples.

[0119] This application specifically proposes a method for rapid prediction of water use efficiency in data centers based on meteorological data, including the following steps:

[0120] (1) Obtain the annual outdoor dynamic air parameters of the area where the data center is located, including hourly dry-bulb temperature. and first relative humidity Based on hourly dry-bulb temperature and first relative humidity Determine the hourly humidity of outdoor air

[0121] (2) Obtain the indoor air design parameters of the data center, including the dry-bulb temperature T. design Second relative humidity RH design According to the dry-bulb temperature T design Second relative humidity RH design Determine the indoor air design humidity level d for the data center. design .

[0122] (3) Obtain the architectural design information of the building where the data center is located, including the building area S and floor height H. Determine the design value α of the fresh air exchange rate of the data center based on the building area S and floor height H.

[0123] (4) When the hourly state moisture content of outdoor air is determined Less than the indoor air design humidity level d design At this point, the difference between the two is calculated as the humidification load of the fresh air conditioning unit mass. Conversely, when the hourly state humidity of outdoor air is determined... Greater than the indoor air design humidity level d design At this point, the difference between the two values ​​is calculated as the dehumidification load per unit mass of air conditioning fresh air.

[0124]

[0125] (5) Calculate the annual fresh air volume of the air conditioner (L) w The required parameters include the building area S of each room. i Floor height H i The design value for fresh air exchange rate α is used to calculate the fresh air volume for each room based on the parameters mentioned above. The annual fresh air volume (L) of the air conditioner is calculated by summing up the fresh air volume of each room. w .

[0126]

[0127]

[0128] (6) Determine the annual water consumption of the humidification system based on the unit mass air conditioning fresh air humidification load and the annual air conditioning fresh air volume, and determine the annual drainage volume of the humidification system based on the unit mass air conditioning fresh air dehumidification load and the annual air conditioning fresh air volume.

[0129] Among them, the annual water consumption of the humidification system The calculation formula is:

[0130]

[0131] Annual drainage volume of humidification system The calculation formula is:

[0132]

[0133] (7) Obtain the heat load Q of the IT equipment in the data center. IT Building heat load Q Arci Other heat loads Q other Then, based on the heat load of IT equipment, building heat load, and other heat loads, the chilled water circulation heat load of the data center is determined. The chilled water circulation heat load Q is... CHW The calculation formula is:

[0134] Q CHW =Q IT +QArci +Q other ;

[0135] (8) Determine the coefficient of performance (COP) of the chiller units based on their configuration and selection information. Based on the COP and the chilled water circulation heat load, determine the cooling water circulation heat load of the data center. The calculation formula for the cooling water circulation heat load is as follows:

[0136]

[0137] (9) Calculate the cooling water circulation heat load and the cooling water circulation temperature difference Δt CW The ratio of these values ​​is used as the cooling water circulation volume for the data center; and the ratio of the chilled water circulation heat load to the chilled water circulation temperature difference Δt is calculated. CHW The ratio of the two values ​​is used as the chilled water circulation volume of the data center.

[0138] Among them, the cooling water circulation volume L CW The calculation formula is:

[0139]

[0140] chilled water circulation volume (L) CHW The calculation formula is:

[0141]

[0142] (10) Calculate the cooling water circulation rate and cooling water makeup rate β CW The product of these factors is used as the annual cooling water system makeup water volume; the chilled water circulation volume and chilled water makeup rate β are calculated. CHW The product of these two values ​​is used as the annual makeup water volume for the chilled water system.

[0143] Among them, the annual cooling water system makeup water volume The calculation formula is:

[0144]

[0145] Annual chilled water system makeup water volume The calculation formula is:

[0146]

[0147] (11) First, calculate the sum of the annual cooling water system makeup water volume, the annual chilled water system makeup water volume, and the annual humidification system water consumption. Then, calculate the difference between this sum and the annual humidification system drainage volume, the annual wastewater recovery volume of the data center, and the annual rainwater collection volume. Determine this difference as the current predicted total water consumption of the data center. The formula for calculating the predicted total water consumption is as follows:

[0148]

[0149] (12) Obtain the annual power consumption q of IT equipment in the data center IT Then, the ratio of the predicted total water consumption of the data center to the annual power consumption of IT equipment is calculated, and this ratio is taken as the water use efficiency of the data center. The formula for calculating water use efficiency is:

[0150] WUE=L Tota / q IT .

[0151] This application's embodiments incorporate parameter values ​​from multiple influencing factors, including outdoor dynamic air parameters, indoor air design parameters, building design information, chilled water information, and cooling water information. By considering the water consumption requirements for air humidification under different regional meteorological conditions, the calculated water use efficiency is more accurate and has higher reference value. Simultaneously, it can realize the water consumption and drainage volume of air conditioning fresh air throughout the year, thereby enabling a more accurate assessment of the water use efficiency of the actual system under ideal conditions throughout the year.

[0152] Based on the above embodiments, after determining the water utilization efficiency of the data center based on the predicted total water consumption and annual power consumption of IT equipment, the method further includes:

[0153] Step 411: Determine the classification level of water use efficiency;

[0154] Step 412: Determine the optimization strategy for the data center based on the hierarchical level;

[0155] Step 413: Optimize the baseline value of water use efficiency according to the optimization strategy.

[0156] In the planning and construction guidelines for data centers in different regions, benchmark values ​​for water use efficiency are usually specified. However, due to the lack of systematic calculation guidelines and specific optimization strategy frameworks, it is impossible to specifically explore the optimization space of the solution, which leads to potential investment waste and increased operating costs of the implemented solution. More importantly, high water use efficiency results in the waste of water resources.

[0157] The water utilization efficiency prediction method for data centers described in the above embodiments clarifies the basic relationships between various design parameters based on meteorological conditions, building conditions, and equipment selection and configuration conditions during the design process of data center air conditioning systems. This method can accurately assess the current optimization level of water utilization efficiency in data centers, and therefore can be applied to establish an optimization strategy framework. For example... Figure 3 As shown, a specific hierarchical evaluation is conducted, and targeted optimization measures are proposed to guide the planning and design of the scheme. Specific optimization methods and measures include:

[0158] (1) Hierarchical evaluation of water utilization efficiency level.

[0159] According to the different water resource conditions in different regions, the benchmark values of the indicators vary. Since the equipment has a relatively economical operating load rate range, the initial load rate of the newly built data center is not high. Usually, it is required to be no higher than 1.4 in the first year, no higher than 1.3 in the second year, and no higher than 1.2 in water-scarce regions. The predicted value (i.e., water utilization efficiency) obtained by the current method can be used to quickly evaluate the deviation level of the current design scheme relative to the benchmark value. When the deviation level is too large, it means that the current scheme does not meet the planning requirements for building a data center in the local area, and it is necessary to optimize some parameters of the scheme针对性地. Based on the above benchmark level, this application example corresponds to two water utilization efficiency levels according to the hierarchical principle, including the first level: 1.4 < WUE; the second level: 1.3 < WUE < 1.4, where WUE is the water utilization efficiency.

[0160] When the finally determined water utilization efficiency is within the range higher than the first level, the first-level optimization strategy and the auxiliary optimization strategy are preferably adopted to adjust the overall parameters, and the level value under the optimization strategy is recalculated until the scheme meets the first-level range or the regulations for the planning and construction of the local data center. When the finally determined water utilization efficiency, or the water utilization efficiency optimized by the first-level optimization strategy, is within the second level range, the second-level optimization strategy and the auxiliary optimization strategy are preferably adopted to adjust the local parameters, and the level value under the optimization strategy is recalculated until the scheme meets the regulations for the planning and construction of the local data center.

[0161] (2) The optimization strategies include: the first-level optimization strategy, the second-level optimization strategy, and the general auxiliary optimization strategy.

[0162] 2.1) The first-level optimization strategy includes: optimizing the COP of the selection and configuration of the chiller and optimizing the building design conditions.

[0163] Optimizing the COP of the selection and configuration of the chiller: In the above calculation method, it can be clarified that the heat load of the chilled water circulation is determined according to the air-conditioning load; the heat load of the cooling water circulation is closely related to the COP of the selection and configuration of the chiller. Selecting a chiller with a better coefficient of performance COP helps to reduce the heat load of the cooling water circulation, thereby reducing the cooling water circulation volume and also reducing the makeup water volume of the cooling water system. <0000​​2.2) Secondary optimization strategies include: increasing the temperature difference between chilled water circulation and cooling water circulation, and increasing the concentration factor of cooling water.

[0166] Increase the temperature difference between chilled water circulation and cooling water circulation: If the temperature difference between chilled water circulation and cooling water circulation is between 5-10℃ and between 4-6℃, as the technology of chiller units continues to improve and become more sophisticated, the technology for supplying chilled water with large temperature differences and small flow rates is becoming increasingly mature. As the temperature difference increases, the chilled water circulation volume will also decrease under the same cooling capacity. Under the condition that other factors remain unchanged, the annual chilled water makeup volume will also decrease at any time.

[0167] Increasing the concentration ratio of cooling water: The main components of circulating water consumption in a cooling water system include cooling tower evaporation losses and blowdown losses. The concentration ratio is the ratio of the concentration of substances in the circulating water to the concentration of substances in the makeup water. As the cooling tower evaporates and dissipates heat, water evaporates away, leaving behind calcium and magnesium ions and impurities in the water. Therefore, regular blowdown is necessary to control the concentration ratio of the cooling water, thereby controlling the content and conductivity of calcium and magnesium ions in the cooling water, and delaying scaling on pipes and equipment. Generally, the volume of blowdown is second only to the volume of evaporation water, accounting for approximately 23% of the total water consumption. Therefore, increasing the cooling water concentration ratio can reduce blowdown water consumption.

[0168] The national standard generally recommends a concentration ratio of 3 to 5. In actual operation, the concentration ratio of cooling water can be gradually optimized to the upper limit of 5 times, and the amount of sewage discharged can be reduced by nearly 50% compared to a concentration ratio of 3 times.

[0169] 2.3) General auxiliary optimization strategies include adding wastewater recycling and reuse devices and rainwater harvesting and utilization devices:

[0170] For data centers with high water resource utilization rates, various wastewater treatment and secondary utilization measures can be considered, such as recycling and reusing wastewater discharged from data centers, and rainwater collection and utilization in areas with abundant rainfall, thus making comprehensive and efficient use of water resources.

[0171] This application embodiment optimizes the baseline value of water use efficiency by predicting the water use efficiency of the data center, thereby realizing the optimization space of the targeted solution, reducing potential investment waste and operating costs of the implementation solution, and at the same time, reducing water waste.

[0172] The following describes the data center water use efficiency prediction device provided in the embodiments of this application. The data center water use efficiency prediction device described below and the data center water use efficiency prediction method described above can be referred to each other.

[0173] refer to Figure 4 , Figure 4This is a schematic diagram of the structure of the data center water use efficiency prediction device provided in this application embodiment. The data center water use efficiency prediction device provided in this application embodiment includes:

[0174] The first data processing module 401 is used to determine the annual water consumption and annual drainage volume of the humidification system of the data center based on the annual outdoor dynamic air parameters and indoor air design parameters of the region where the data center is located, as well as the building design information of the building where the data center is located.

[0175] The second data processing module 402 is used to determine the annual cooling water system makeup water volume and the annual chilled water system makeup water volume of the data center based on the configuration and selection information of the chiller units, cooling water information and chilled water information of the data center.

[0176] The total water consumption prediction module 403 is used to determine the total water consumption of the data center based on the annual water consumption of the humidification system, the annual drainage of the humidification system, the annual water replenishment of the cooling water system, the annual water replenishment of the chilled water system, the annual wastewater recycling volume of the data center, and the annual rainwater collection volume.

[0177] The water use efficiency determination module 404 is used to determine the water use efficiency of the data center based on the predicted total water consumption of the data center and the annual power consumption of IT equipment.

[0178] The data center water utilization efficiency prediction device provided in this application incorporates parameter values ​​from multiple influencing factors, including outdoor dynamic air parameters, indoor air design parameters, building design information, chilled water information, and cooling water information. It considers the water consumption requirements of air humidification under different regional meteorological conditions. Therefore, the calculated water utilization efficiency is more accurate and has higher reference value. Simultaneously, it can realize the water consumption and drainage volume of air conditioning fresh air throughout the year, thereby more accurately assessing the water utilization efficiency of the actual system under ideal conditions throughout the year.

[0179] In one embodiment, the first data processing module 401 is further configured to:

[0180] Based on the annual outdoor dynamic air parameters and indoor air design parameters, determine the unit mass air conditioning fresh air humidification load and unit mass air conditioning fresh air dehumidification load of the data center;

[0181] Based on the building design information, determine the annual fresh air volume for the data center's air conditioning system;

[0182] The annual water consumption of the humidification system is determined based on the unit mass air conditioning fresh air humidification load and the annual air conditioning fresh air volume.

[0183] The annual drainage volume of the humidification system is determined based on the unit mass air conditioning fresh air dehumidification load and the annual air conditioning fresh air volume.

[0184] In one embodiment, the first data processing module 401 is further configured to:

[0185] The hourly state humidity of the outdoor air in the data center is determined based on the hourly dry-bulb temperature and the first relative humidity.

[0186] Determine the indoor air design humidity level for the data center based on dry-bulb temperature and second relative humidity.

[0187] If the hourly humidity of outdoor air is less than the design humidity of indoor air, the first difference between the design humidity of indoor air and the hourly humidity of outdoor air will be used as the humidification load of the fresh air per unit mass of air conditioning.

[0188] If the hourly humidity of outdoor air is greater than the design humidity of indoor air, then the second difference between the hourly humidity of outdoor air and the design humidity of indoor air will be used as the dehumidification load per unit mass of air conditioning fresh air.

[0189] In one embodiment, the first data processing module 401 is further configured to:

[0190] The design value for the number of fresh air exchanges in the data center is determined based on the building area and floor height.

[0191] The fresh air volume for each room in the data center is determined based on the building area, floor height, and design values ​​for fresh air exchange rate.

[0192] Determine the annual fresh air volume for the data center's air conditioning system based on the fresh air volume of all rooms.

[0193] In one embodiment, the second data processing module 402 is further configured to:

[0194] Determine the chilled water circulation heat load of the data center based on the heat load of the IT equipment, the heat load of the building, and other heat loads.

[0195] The configuration and selection information of the chiller unit is determined based on the chilled water circulation heat load;

[0196] Determine the performance coefficient of the chiller unit based on the configuration and selection information of the chiller unit;

[0197] The cooling water circulation heat load of the data center is determined based on the performance coefficient and the chilled water circulation heat load.

[0198] The ratio of cooling water circulation heat load to cooling water circulation temperature difference is used as the cooling water circulation volume of the data center.

[0199] The ratio of chilled water circulation heat load to chilled water circulation temperature difference is used as the chilled water circulation volume of the data center.

[0200] The product of the cooling water circulation volume and the cooling water makeup rate is taken as the annual cooling water system makeup volume.

[0201] The product of the chilled water circulation volume and the chilled water makeup rate is taken as the annual chilled water system makeup volume.

[0202] In one embodiment, the water use efficiency determination module 404 is further configured to:

[0203] The ratio of the predicted total water consumption of a data center to the annual power consumption of IT equipment is used as the water utilization efficiency of the data center.

[0204] In one embodiment, the water use efficiency determination module 404 is further configured to:

[0205] Determine the classification level of water use efficiency;

[0206] Based on the hierarchical level, determine the optimization strategy for the data center;

[0207] Based on the optimization strategy, optimize the baseline value of water use efficiency.

[0208] Figure 5 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 5 As shown, the electronic device may include a processor 510, a communication interface 520, a memory 530, and a communication bus 540, wherein the processor 510, the communication interface 520, and the memory 530 communicate with each other via the communication bus 540. The processor 510 can call a computer program in the memory 530 to execute the steps of a water use efficiency prediction method for a data center, such as including:

[0209] Based on the annual outdoor dynamic air parameters and indoor air design parameters of the area where the data center is located, as well as the building design information of the building on which the data center is located, determine the annual water consumption and annual drainage volume of the data center's humidification system.

[0210] Based on the configuration and selection information of the chiller units, cooling water information, and chilled water information of the data center, determine the annual cooling water system makeup water volume and the annual chilled water system makeup water volume of the data center;

[0211] The predicted total water consumption of the data center is determined based on the annual water consumption of the humidification system, the annual drainage volume of the humidification system, the annual water replenishment volume of the cooling water system, the annual water replenishment volume of the chilled water system, the annual wastewater recycling volume of the data center, and the annual rainwater collection volume.

[0212] The water utilization efficiency of a data center is determined based on its projected total water consumption and annual power consumption of IT equipment.

[0213] Furthermore, the logical instructions in the aforementioned memory 530 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0214] On the other hand, embodiments of this application also provide a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the steps of the data center water use efficiency prediction method provided in the above embodiments, including, for example:

[0215] Based on the annual outdoor dynamic air parameters and indoor air design parameters of the area where the data center is located, as well as the building design information of the building on which the data center is located, determine the annual water consumption and annual drainage volume of the data center's humidification system.

[0216] Based on the configuration and selection information of the chiller units, cooling water information, and chilled water information of the data center, determine the annual cooling water system makeup water volume and the annual chilled water system makeup water volume of the data center;

[0217] The predicted total water consumption of the data center is determined based on the annual water consumption of the humidification system, the annual drainage volume of the humidification system, the annual water replenishment volume of the cooling water system, the annual water replenishment volume of the chilled water system, the annual wastewater recycling volume of the data center, and the annual rainwater collection volume.

[0218] The water utilization efficiency of a data center is determined based on its projected total water consumption and annual power consumption of IT equipment.

[0219] On the other hand, embodiments of this application also provide a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can perform the steps of the data center water use efficiency prediction method provided in the above embodiments, such as including:

[0220] Based on the annual outdoor dynamic air parameters and indoor air design parameters of the area where the data center is located, as well as the building design information of the building on which the data center is located, determine the annual water consumption and annual drainage volume of the data center's humidification system.

[0221] Based on the configuration and selection information of the chiller units, cooling water information, and chilled water information of the data center, determine the annual cooling water system makeup water volume and the annual chilled water system makeup water volume of the data center;

[0222] The predicted total water consumption of the data center is determined based on the annual water consumption of the humidification system, the annual drainage volume of the humidification system, the annual water replenishment volume of the cooling water system, the annual water replenishment volume of the chilled water system, the annual wastewater recycling volume of the data center, and the annual rainwater collection volume.

[0223] The water utilization efficiency of a data center is determined based on its projected total water consumption and annual power consumption of IT equipment.

[0224] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0225] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0226] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for predicting water use efficiency in a data center, characterized in that, include: Based on the annual outdoor dynamic air parameters and indoor air design parameters of the region where the data center is located, as well as the building design information of the building on which the data center is located, the annual water consumption and annual drainage volume of the humidification system of the data center are determined. Based on the configuration and selection information of the chiller units, cooling water information, and chilled water information of the data center, determine the annual cooling water system makeup water volume and the annual chilled water system makeup water volume of the data center. The predicted total water consumption of the data center is determined based on the annual water consumption of the humidification system, the annual drainage of the humidification system, the annual water replenishment of the cooling water system, the annual water replenishment of the chilled water system, the annual wastewater recycling volume of the data center, and the annual rainwater collection volume. The water utilization efficiency of the data center is determined based on the predicted total water consumption and annual power consumption of IT equipment. The process of determining the annual water consumption and drainage volume of the data center's humidification system based on the yearly outdoor dynamic air parameters and indoor air design parameters of the region where the data center is located, as well as the building design information of the building on which the data center is situated, includes: Based on the annual outdoor dynamic air parameters and the indoor air design parameters, determine the unit mass air conditioning fresh air humidification load and unit mass air conditioning fresh air dehumidification load of the data center; Based on the architectural design information, determine the annual fresh air volume for the air conditioning system of the data center; The annual water consumption of the humidification system is determined based on the unit mass air conditioning fresh air humidification load and the annual air conditioning fresh air volume. The annual drainage volume of the humidification system is determined based on the unit mass air conditioning fresh air dehumidification load and the annual air conditioning fresh air volume. The annual outdoor dynamic air parameters include hourly dry-bulb temperature and first relative humidity; the indoor air design parameters include dry-bulb temperature and second relative humidity. The step of determining the unit mass air conditioning humidification load and unit mass air conditioning dehumidification load of the data center based on the annual outdoor dynamic air parameters and the indoor air design parameters includes: The hourly state humidity of the outdoor air of the data center is determined based on the hourly dry-bulb temperature and the first relative humidity. The indoor air design humidity level of the data center is determined based on the dry-bulb temperature and the second relative humidity. If the hourly humidity of the outdoor air is less than the design humidity of the indoor air, then the first difference between the design humidity of the indoor air and the hourly humidity of the outdoor air shall be used as the humidification load of the unit mass air conditioning fresh air. If the hourly humidity of the outdoor air is greater than the design humidity of the indoor air, then the second difference between the hourly humidity of the outdoor air and the design humidity of the indoor air will be used as the dehumidification load of the unit mass air conditioning fresh air.

2. The method for predicting water use efficiency in data centers according to claim 1, characterized in that, The architectural design information includes building area and floor height; Determining the annual fresh air volume for the data center based on the building design information includes: The design value for the number of fresh air exchanges for the data center is determined based on the building area and the floor height. The fresh air volume for each room in the data center is determined based on the building area, floor height, and design value of fresh air exchange rate. The annual fresh air volume of the data center is determined based on the fresh air volume of all the rooms.

3. The method for predicting water use efficiency in data centers according to claim 1, characterized in that, The cooling water information includes the cooling water circulation temperature difference and the cooling water makeup rate; the chilled water information includes the chilled water circulation temperature difference and the chilled water makeup rate. The step of determining the annual cooling water system makeup water volume and the annual chilled water system makeup water volume of the data center based on the configuration and selection information, cooling water information, and chilled water information of the data center includes: The chilled water circulation heat load of the data center is determined based on the heat load of the IT equipment, the heat load of the building, and other heat loads. Based on the chilled water circulation heat load, determine the configuration and selection information of the chiller unit; Based on the configuration and selection information of the chiller unit, determine the performance coefficient of the chiller unit; The cooling water circulation heat load of the data center is determined based on the performance coefficient and the chilled water circulation heat load. The ratio of the cooling water circulation heat load to the cooling water circulation temperature difference is used as the cooling water circulation volume of the data center. The ratio of the chilled water circulation heat load to the chilled water circulation temperature difference is used as the chilled water circulation volume of the data center. The product of the cooling water circulation rate and the cooling water replenishment rate is taken as the annual cooling water system replenishment amount; The product of the chilled water circulation rate and the chilled water makeup rate is taken as the annual chilled water system makeup amount; The formula for calculating the heat load of the cooling water circulation is: Among them, Q CW For the cooling water circulation heat load, Q CHW The heat load for the chilled water circulation is represented by COP, which is the coefficient of performance.

4. The method for predicting water use efficiency in data centers according to claim 1, characterized in that, The determination of the data center's water utilization efficiency based on the data center's predicted total water consumption and annual power consumption of IT equipment includes: The ratio of the predicted total water consumption of the data center to the annual power consumption of the IT equipment is used as the water utilization efficiency of the data center.

5. The method for predicting water use efficiency in data centers according to claim 1, characterized in that, After determining the water utilization efficiency of the data center based on the predicted total water consumption and annual power consumption of IT equipment, the process further includes: Determine the classification level of the water use efficiency; Based on the aforementioned grading level, an optimization strategy for the data center is determined; Based on the optimization strategy, the baseline value of the water use efficiency is optimized.

6. A water use efficiency prediction device for a data center, characterized in that, include: The first data processing module is used to determine the annual water consumption and annual drainage volume of the humidification system of the data center based on the annual outdoor dynamic air parameters and indoor air design parameters of the region where the data center is located, as well as the architectural design information of the building where the data center is located. The second data processing module is used to determine the annual cooling water system makeup water volume and the annual chilled water system makeup water volume of the data center based on the configuration and selection information, cooling water information and chilled water information of the chiller units of the data center. The predicted total water consumption determination module is used to determine the predicted total water consumption of the data center based on the annual water consumption of the humidification system, the annual drainage of the humidification system, the annual water replenishment of the cooling water system, the annual water replenishment of the chilled water system, the annual wastewater recycling volume of the data center, and the annual rainwater collection volume. A water use efficiency determination module is used to determine the water use efficiency of the data center based on the predicted total water consumption and the annual power consumption of IT equipment. The first data processing module is further configured to: determine the unit mass air conditioning humidification load and unit mass air conditioning dehumidification load of the data center based on the annual outdoor dynamic air parameters and the indoor air design parameters; determine the annual air conditioning fresh air volume of the data center based on the building design information; determine the annual water consumption of the humidification system based on the unit mass air conditioning humidification load and the annual air conditioning fresh air volume; and determine the annual drainage volume of the humidification system based on the unit mass air conditioning dehumidification load and the annual air conditioning fresh air volume. The annual outdoor dynamic air parameters include hourly dry-bulb temperature and a first relative humidity; the indoor air design parameters include dry-bulb temperature and a second relative humidity. The first data processing module is further configured to: determine the hourly state humidity of the outdoor air of the data center based on the hourly dry-bulb temperature and the first relative humidity; determine the indoor air design humidity of the data center based on the dry-bulb temperature and the second relative humidity; if the hourly state humidity of the outdoor air is less than the indoor air design humidity, then the first difference between the indoor air design humidity and the hourly state humidity of the outdoor air is used as the humidification load of the air conditioning fresh air per unit mass; if the hourly state humidity of the outdoor air is greater than the indoor air design humidity, then the second difference between the hourly state humidity of the outdoor air and the indoor air design humidity is used as the dehumidification load of the air conditioning fresh air per unit mass.

7. An electronic device comprising a processor and a memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the data center water use efficiency prediction method according to any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the data center water use efficiency prediction method according to any one of claims 1 to 5.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the data center water use efficiency prediction method according to any one of claims 1 to 5.

Images