Energy consumption optimization method, control system, device and medium for cloud computing data center

Abstract

The present invention relates to the field of energy management technology, and more particularly to a method, control system, device, and medium for optimizing energy consumption in a cloud computing data center. The method provided by the present invention establishes an energy consumption correlation model between industrial production equipment and data center computing equipment, calculates a heat conversion coefficient based on the operating status of the industrial production equipment and the heat generation power of the data center computing equipment, determines the energy consumption of the cooling equipment of the data center computing equipment based on the energy consumption correlation model and the heat conversion coefficient, and adjusts the parameters of the cooling equipment, so that each computing device in the data center can dynamically adjust the equipment cooling situation. The method provided by the present invention is simple to calculate, occupies few resources, does not require additional hardware equipment, and realizes personalized cooling for each computing device in the data center, avoiding problems such as equipment overheating and excessive cooling.

Description

Technical Field

[0001] The present invention relates to the field of energy management technology, and in particular to a method, a management and control system, a device and a medium for optimizing energy consumption in a cloud computing data center. Background Art

[0002] With the rapid development of Internet of Things and cloud technologies, energy management platform systems have evolved from traditional display platforms to smart management platforms that use multiple devices and technologies such as various information sensors, radio frequency identification technology, global positioning systems, infrared sensors, laser scanners, etc. to collect energy information in real time, realize the monitoring, connection and interaction of energy equipment, and can realize energy data calculation, storage, processing and sharing. It has achieved the mutual isolation of energy management software and energy management underlying hardware, and adopts distributed storage for energy management data based on a large number of servers, using redundant storage to ensure the reliability of energy data. Therefore, smart energy management platforms based on Internet of Things and cloud computing are widely used in industry to coordinate energy configuration in various fields and realize energy data processing and sharing.

[0003] As one of my country's traditionally energy-intensive industries, the cement industry requires the integration of smart energy management platforms based on IoT and cloud computing technologies to coordinate energy allocation and optimize energy structure, thereby achieving energy conservation, emission reduction, and low-carbon production. Integrating IoT and cloud computing into cement production requires the construction of data centers for cement manufacturers. These data centers require a large number of servers and storage devices to coordinate and optimize energy usage across cement production equipment, generating significant heat. Experiments have shown that the probability of computer equipment failure is positively correlated with the temperature of the operating environment. Therefore, cooling measures for data center servers and storage devices are essential for stable operation. Furthermore, cooling equipment consumes electricity to provide cooling, and excessive cooling increases energy consumption, resulting in wasteful energy consumption.

[0004] Some existing solutions achieve cooling in computer rooms by installing temperature sensors in the room and setting an appropriate standard temperature. However, due to the different heat levels of different computer equipment in the industrial production process, a unified temperature setting cannot achieve the optimal cooling energy consumption of all equipment. Moreover, further optimization measures of this technical solution can only be achieved by adding temperature sensor hardware equipment. For large computer rooms, the hardware and software costs of cooling will increase rapidly.

[0005] Other technical solutions in the existing technology adopt predictive cooling strategies. By using the heating cycle of computer equipment in the industrial production process, the cooling capacity required by each computer equipment in the current time period is predicted, and the parameters of each cooling equipment are further periodically adjusted. However, this method requires more accurate periodic modeling, the calculation process is complex, and it cannot adapt to the cooling situation of computer equipment after the industrial production conditions are adjusted. There is a delay in updating the cooling strategy. Summary of the Invention

[0006] In view of this, the first object of the present invention is to provide a method for optimizing energy consumption of a cloud computing data center by establishing an energy consumption correlation model between industrial production equipment and data center computing equipment, calculating the heat conversion coefficient based on the operating status of the industrial production equipment and the heating power of the data center computing equipment, judging the energy consumption of the cooling equipment of the data center computing equipment based on the energy consumption correlation model and the heat conversion coefficient, and adjusting the parameters of the cooling equipment so that each computing equipment in the data center can dynamically adjust the equipment cooling situation. The method provided by the present invention is simple to calculate, occupies few resources, does not require additional hardware equipment, and realizes personalized cooling for each computing equipment in the data center, avoiding the problems of equipment overheating and excessive cooling.

[0007] Based on the same inventive concept, the second object of the present invention is to provide a cloud computing data center energy consumption optimization and management system.

[0008] Based on the same inventive concept, the third object of the present invention is to provide an energy consumption optimization device for a cloud computing data center.

[0009] Based on the same inventive concept, the fourth object of the present invention is to provide a storage medium.

[0010] The first object of the present invention can be achieved by the following technical solutions:

[0011] A method for optimizing energy consumption in a cloud computing data center comprises the following steps:

[0012] Based on the industrial production process, establish the energy consumption correlation model of industrial production equipment and data center computing equipment corresponding to the process;

[0013] Calculate the heat conversion coefficient based on the operating conditions of the industrial production equipment corresponding to the process and the heating power of the data center computing equipment;

[0014] Calculate the energy consumption of cooling equipment for computing equipment in data centers based on the heat conversion coefficient and energy consumption correlation model;

[0015] According to the energy consumption of the cooling equipment of the computing equipment in the data center, the operating parameters of the cooling equipment of the computing equipment in the data center are adjusted.

[0016] Furthermore, based on the industrial production process, an energy consumption correlation model of the industrial production equipment and data center computing equipment corresponding to the process is established, including the following steps:

[0017] Screening of energy consumption related parameters;

[0018] Based on energy consumption-related parameters, collect energy consumption data sets of industrial production equipment and data center computing equipment corresponding to the process;

[0019] According to the data characteristics of energy consumption related parameters, the energy consumption correlation model is established using the regression method.

[0020] Furthermore, the heat conversion coefficient is calculated based on the operating status of the industrial production equipment corresponding to the process and the heat generation power of the data center computing equipment, including the following steps:

[0021] Obtain the full-load heat generation power of computing equipment in the data center;

[0022] Obtain a dataset of the number of running and total number of industrial production equipment corresponding to the process from the data center, and collect a dataset of the heat generation power of computing equipment in the data center;

[0023] Calculate the heat conversion coefficient regression model based on the full-load heat output of the data center computing equipment, the data set of the number of operating and total number of industrial production equipment corresponding to the process, and the heat output data set of the data center computing equipment;

[0024] The number of operating units and the total number of industrial production equipment corresponding to the process are obtained in real time from the data center, and the heat conversion coefficient is calculated using the heat conversion coefficient regression model.

[0025] Furthermore, the energy consumption of the cooling equipment of the computing equipment in the data center is calculated based on the heat conversion coefficient and the energy consumption correlation model, including the following steps:

[0026] Collect and calculate historical data on energy consumption of multiple groups of data center computing equipment, energy consumption of cooling equipment, and heat conversion coefficients;

[0027] The energy consumption and heat conversion coefficient of the data center computing equipment are used as the independent variable matrix, and the energy consumption of the cooling equipment is used as the dependent variable matrix. The transformation matrix T is obtained based on the independent variable matrix and the dependent variable matrix.

[0028] Obtain real-time energy consumption data of industrial production equipment corresponding to the process from the data center;

[0029] Use energy consumption correlation models to calculate the energy consumption of computing equipment in data centers;

[0030] Calculate the heat conversion coefficient based on the operating status of the industrial production equipment corresponding to the process and the real-time heating power of the data center computing equipment;

[0031] Calculate the energy consumption E of cooling equipment Z , whose expression is:

[0032] E Z =λ*x*E

[0033] Where x is the heat conversion coefficient, λ is the characteristic solution of the transformation matrix T, and E is the energy consumption of the computing equipment in the data center.

[0034] The second object of the present invention can be achieved by the following technical solutions:

[0035] A cloud computing data center energy consumption optimization and control system includes industrial production equipment, data center computing equipment, and a cooling equipment group, wherein:

[0036] Industrial production equipment, used to implement industrial production processes;

[0037] Data center computing equipment is used to control the operating status and energy consumption of industrial production equipment. Data center computing equipment is spatially partitioned according to industrial production processes.

[0038] The cooling equipment group includes a plurality of cooling equipment, each of which is used to cool the computing equipment in the data center according to the above-mentioned energy consumption optimization method for the cloud computing data center.

[0039] Furthermore, the industrial production equipment is industrial production equipment for cement production, and the industrial production process is divided into a first process, a second process and a third process, wherein:

[0040] The first process is the cement raw material crushing and pre-homogenization process;

[0041] The second process is the cement raw material preparation, homogenization and preheating decomposition process;

[0042] The third process is the cement clinker burning and grinding process;

[0043] Data center computing equipment is divided into first data center computing equipment, second data center computing equipment, and third data center computing equipment according to the industrial process of cement production, among which:

[0044] The computing equipment in the first data center is used to control the operating conditions and energy consumption of the cement raw material crushing and pre-homogenization equipment;

[0045] The computing equipment in the second data center is used to control the operating status and energy consumption of the cement raw meal preparation homogenization and preheating decomposition equipment;

[0046] The computing equipment in the third data center is used to control the operating status and energy consumption of cement clinker burning and grinding equipment;

[0047] The cooling equipment group includes a first cooling unit, a second cooling unit and a third cooling unit, wherein:

[0048] The first cooling unit is used to provide cooling for computing equipment in the first data center;

[0049] The second cooling unit is used to provide cooling for computing equipment in the second data center;

[0050] The third cooling unit is used to provide cooling for computing equipment in the third data center.

[0051] Furthermore, in the energy consumption correlation model of the industrial production equipment and the data center computing equipment corresponding to the first process, the energy consumption-related parameters include the energy consumption of the first data center computing equipment, the electricity consumption of the cement raw material crushing and pre-homogenizing equipment, and the coal consumption of the cement raw material crushing and pre-homogenizing equipment;

[0052] In the energy consumption correlation model of the industrial production equipment and data center computing equipment corresponding to the second process, the energy consumption-related parameters include the energy consumption of the second data center computing equipment, the electricity consumption of the cement raw meal preparation, homogenization and preheating decomposition equipment, and the coal consumption of the cement raw meal preparation, homogenization and preheating decomposition equipment;

[0053] In the energy consumption correlation model of industrial production equipment and data center computing equipment corresponding to the third process, energy consumption-related parameters include the energy consumption of the third data center computing equipment, the electricity consumption of cement clinker burning and grinding equipment, and the coal consumption of cement clinker burning and grinding equipment.

[0054] Furthermore, the cooling equipment is an air conditioner, which is provided with an air supply outlet, and the air supply outlet is provided with an electric air valve; the operating parameters of the air conditioner are adjusted by the air conditioner inverter and the electric air valve.

[0055] The third object of the present invention can be achieved by the following technical solutions:

[0056] A cloud computing data center energy consumption optimization device, comprising:

[0057] The energy consumption correlation model unit is used to establish an energy consumption correlation model of industrial production equipment and data center computing equipment corresponding to the industrial production process according to the industrial production process;

[0058] A heat conversion unit is used to calculate the heat conversion coefficient based on the operating conditions of the industrial production equipment corresponding to the process and the heat generation power of the data center computing equipment;

[0059] An energy consumption calculation unit, used to calculate the energy consumption of cooling equipment for computing equipment in the data center based on a heat production conversion coefficient and an energy consumption correlation model;

[0060] The regulating unit is used to regulate the operating parameters of the cooling equipment of the computing equipment in the data center according to the energy consumption of the cooling equipment of the computing equipment in the data center.

[0061] The fourth object of the present invention can be achieved by the following technical solutions:

[0062] A storage medium stores a program, which, when executed by a computer, implements the above-mentioned method for optimizing energy consumption of a cloud computing data center.

[0063] The present invention has the following beneficial effects compared to the prior art:

[0064] (1) The cloud computing data center energy consumption optimization method provided by the present invention can be applied to a cloud computing data center based on functional zoning. It can realize personalized cooling for each computing device in the cloud computing data center according to the heat generated by each computing device in the cloud computing data center, thereby avoiding the problems of equipment overheating and excessive cooling.

[0065] (2) The cloud computing data center energy consumption optimization method provided by the present invention can adjust the operating parameters of the cooling equipment in real time and dynamically, and the calculation is simple, occupies few resources, and does not require additional hardware equipment;

[0066] (3) The present invention provides a cloud computing data center energy consumption optimization and control system for the cement production process, which conforms to the characteristics of the cement production process and further optimizes the energy consumption of the cloud computing data center in cement industrial production. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 This is a flow chart of a method for optimizing energy consumption in a cloud computing data center according to Embodiment 1 of the present invention;

[0068] Figure 2 This is a schematic diagram of the structure of the cloud computing data center energy consumption optimization and control system according to Example 2 of the present invention. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0070] Example 1:

[0071] like Figure 1 As shown, this embodiment provides a method for optimizing energy consumption in a cloud computing data center, which is applied to cement production and includes the following steps:

[0072] S100, establishing an energy consumption correlation model of industrial production equipment and data center computing equipment corresponding to the industrial production process according to the industrial production process;

[0073] In this embodiment, the industrial production equipment is industrial production equipment for cement production, and the industrial production process is divided into a first process, a second process, and a third process, wherein:

[0074] The first process is the cement raw material crushing and pre-homogenization process;

[0075] The second process is the cement raw material preparation, homogenization and preheating decomposition process;

[0076] The third process is the cement clinker burning and grinding process;

[0077] Data center computing equipment is divided into first data center computing equipment, second data center computing equipment, and third data center computing equipment according to the industrial process of cement production, among which:

[0078] The computing equipment in the first data center is used to control the operating conditions and energy consumption of the cement raw material crushing and pre-homogenization equipment;

[0079] The computing equipment in the second data center is used to control the operating status and energy consumption of the cement raw meal preparation homogenization and preheating decomposition equipment;

[0080] The computing equipment in the third data center is used to control the operating status and energy consumption of cement clinker burning and grinding equipment;

[0081] In this embodiment, step S100 specifically includes the following steps:

[0082] S110, screening energy consumption related parameters;

[0083] In this embodiment, energy consumption related parameters are screened according to the cement industry production process, and the selected energy consumption related parameters are specifically:

[0084] In the energy consumption correlation model of the industrial production equipment and data center computing equipment corresponding to the first process, the energy consumption-related parameters include the energy consumption of the first data center computing equipment, the electricity consumption of the cement raw material crushing and pre-homogenizing equipment, and the coal consumption of the cement raw material crushing and pre-homogenizing equipment;

[0085] In the energy consumption correlation model of the industrial production equipment and data center computing equipment corresponding to the second process, the energy consumption-related parameters include the energy consumption of the second data center computing equipment, the electricity consumption of the cement raw meal preparation, homogenization and preheating decomposition equipment, and the coal consumption of the cement raw meal preparation, homogenization and preheating decomposition equipment;

[0086] In the energy consumption correlation model of industrial production equipment and data center computing equipment corresponding to the third process, energy consumption-related parameters include the energy consumption of the third data center computing equipment, the electricity consumption of cement clinker burning and grinding equipment, and the coal consumption of cement clinker burning and grinding equipment.

[0087] S120. Collect energy consumption data sets of industrial production equipment and data center computing equipment corresponding to the process based on energy consumption related parameters;

[0088] S130: Based on the data characteristics of the energy consumption related parameters, an energy consumption correlation model is established using a regression method, specifically including the following steps:

[0089] S131. Establish an energy consumption correlation model for industrial production equipment and data center computing equipment corresponding to the first process.

[0090] In this embodiment, the energy consumption of the computing equipment in the first data center is linearly correlated with the electricity consumption of the cement raw material crushing and pre-homogenization equipment, and the energy consumption of the computing equipment in the first data center is linearly correlated with the coal consumption of the cement raw material crushing and pre-homogenization equipment. Therefore, the linear regression method is used to establish an energy consumption correlation model, specifically:

[0091] E1=λ a1 ∑E a1 +λ b1 ∑E b1 +c1

[0092] Among them, E1 is the energy consumption of computing equipment in the first data center, E a1 is the power consumption of cement raw material crushing and pre-homogenization equipment, E b1 is the coal consumption of cement raw material crushing and pre-homogenization equipment; a1 ,λ b1 , c1 is the energy consumption correlation model coefficient.

[0093] S132. Establish an energy consumption correlation model for industrial production equipment and data center computing equipment corresponding to the second process.

[0094] In this embodiment, the energy consumption of the computing equipment in the second data center is linearly correlated with the electricity consumption of the cement raw material preparation, homogenization, and preheating decomposition equipment, and the energy consumption of the computing equipment in the second data center is linearly correlated with the coal consumption of the cement raw material preparation, homogenization, and preheating decomposition equipment. Therefore, a linear regression method is used to establish an energy consumption correlation model, specifically:

[0095] E2=λ a2 ∑E a2 +λ b2 ∑E b2 +c2

[0096] Among them, E2 is the energy consumption of computing equipment in the first data center, E a2E is the power consumption of the cement raw material preparation homogenization and preheating decomposition equipment, b2 Coal consumption for cement raw meal preparation, homogenization and preheating decomposition equipment; a2 ,λ b2 , c2 is the energy consumption correlation model coefficient.

[0097] S133. Establish an energy consumption correlation model for industrial production equipment and data center computing equipment corresponding to the third process.

[0098] In this embodiment, the energy consumption of the computing equipment in the third data center is linearly correlated with the electricity consumption of the cement clinker burning and grinding equipment, and the energy consumption of the computing equipment in the third data center is linearly correlated with the coal consumption of the cement clinker burning and grinding equipment. Therefore, a linear regression method is used to establish an energy consumption correlation model, specifically:

[0099] E3=λ a3 ∑E a3 +λ b3 ∑E b3 +c3

[0100] Among them, E3 is the energy consumption of computing equipment in the third data center, E a3 is the power consumption of cement clinker burning and grinding equipment, E b3 is the coal consumption of cement clinker burning and grinding equipment; a3 ,λ b3 , c3 is the energy consumption correlation model coefficient.

[0101] S200, calculating a heat conversion coefficient based on the operating status of the industrial production equipment corresponding to the process and the heating power of the data center computing equipment;

[0102] In this embodiment, step S200 specifically includes the following steps:

[0103] S210: Obtain full-load heating power of the data center computing devices, including the full-load heating power of the first data center computing device, the full-load heating power of the second data center computing device, and the full-load heating power of the third data center computing device;

[0104] S220. Obtaining a data set of the number of running units and the total number of industrial production equipment corresponding to the process from the data center, and collecting a data set of the heat generation power of computing equipment in the data center;

[0105] S230: Calculate a heat conversion coefficient regression model based on the full-load heat output of the data center computing equipment, a dataset of the number of running and total number of industrial production equipment corresponding to the process, and a dataset of the heat output of the data center computing equipment, including the following steps:

[0106] S231. Calculate a first heat conversion coefficient regression model based on the full-load heat generation power of the computing equipment in the first data center, the heat generation power dataset of the computing equipment in the first data center, the number of operating cement raw material crushing and pre-homogenizing equipment, and the total number of cement raw material crushing and pre-homogenizing equipment.

[0107] In this embodiment, the first heat conversion coefficient x1 satisfies the following constraints:

[0108] x1*E1=P1′

[0109] Wherein P1′ is the heat generation power of the computing equipment in the first data center.

[0110] S232. Calculate a second heat conversion coefficient regression model based on the full-load heating power of the computing equipment in the second data center, the heating power data set of the computing equipment in the second data center, the number of operating cement raw meal preparation, homogenization, and preheating decomposition equipment, and the total number of cement raw meal preparation, homogenization, and preheating decomposition equipment.

[0111] In this embodiment, the second heat conversion coefficient x2 satisfies the following constraints:

[0112] x2*E2=P2′

[0113] Wherein P2′ is the heat generation power of the computing equipment in the second data center.

[0114] S233. Calculate a third heat conversion coefficient regression model based on the full-load heat generation power of the computing equipment in the third data center, the heat generation power dataset of the computing equipment in the third data center, the number of operating cement clinker burning and grinding equipment, and the total number of cement clinker burning and grinding equipment.

[0115] In this embodiment, the third heat conversion coefficient x3 satisfies the following constraints:

[0116] x3*E3=P3′

[0117] Wherein P3′ is the heat generation power of the computing equipment in the second data center.

[0118] S240: Obtain the number of operating and total number of industrial production equipment corresponding to the process from the data center in real time, and calculate the heat conversion coefficient using a heat conversion coefficient regression model, specifically including the following steps:

[0119] S241. Calculate the first heat conversion coefficient x1 according to the first heat conversion coefficient regression model. The expression is:

[0120]

[0121] Among them, λ x1 、c x1is the regression model coefficient, P1 is the full-load heating power of the computing equipment in the first data center, n1 is the number of cement raw material crushing and pre-homogenization equipment in operation, and N1 is the total number of cement raw material crushing and pre-homogenization equipment.

[0122] In this embodiment, λ x1 The value of c is 0.052, x1 The value of is 0.

[0123] S242. Calculate the second heat conversion coefficient x2 according to the second heat conversion coefficient regression model. The expression is:

[0124]

[0125] Among them, λ x2 、c x2 is the regression model coefficient, P2 is the full-load heating power of the computing equipment in the second data center, n2 is the number of cement raw meal preparation homogenization and preheating decomposition equipment in operation, and N2 is the total number of cement raw meal preparation homogenization and preheating decomposition equipment.

[0126] In this embodiment, λ x2 The value of is 0.028, c x2 The value of is 0.

[0127] S243. Calculate the third heat conversion coefficient x3 according to the third heat conversion coefficient regression model. The expression is:

[0128]

[0129] Among them, λ x3 、c x3 is the regression model coefficient, P3 is the full-load heating power of the computing equipment in the second data center, n3 is the number of cement raw meal preparation homogenization and preheating decomposition equipment in operation, and N3 is the total number of cement raw meal preparation homogenization and preheating decomposition equipment.

[0130] In this embodiment, λ x3 The value of is 0.016, c x3 The value of is 0.

[0131] S300, calculating the energy consumption of cooling equipment for computing equipment in the data center based on the heat production conversion coefficient and the energy consumption correlation model;

[0132] In this embodiment, the cooling equipment for the computing equipment in the data center is a cooling equipment group, which includes a first cooling unit, a second cooling unit, and a third cooling unit, wherein:

[0133] The first cooling unit is used to provide cooling for computing equipment in the first data center;

[0134] The second cooling unit is used to provide cooling for computing equipment in the second data center;

[0135] The third cooling unit is used to provide cooling for computing equipment in the third data center.

[0136] In this embodiment, step S300 specifically includes the following steps:

[0137] S310, collecting and calculating historical data on energy consumption of multiple groups of data center computing equipment, energy consumption of cooling equipment, and heat conversion coefficients;

[0138] S320: Using the energy consumption and heat conversion coefficient of the computing equipment in the data center as the independent variable matrix and the energy consumption of the cooling equipment as the dependent variable matrix, and obtaining a transformation matrix based on the independent variable matrix and the dependent variable matrix, specifically including the following steps:

[0139] S321, using the energy consumption of the computing equipment of the first data center and the first heat conversion coefficient as the independent variable matrix, and the energy consumption of the first cooling unit as the dependent variable matrix, and calculating the transformation matrix T1 based on the independent variable matrix and the dependent variable matrix;

[0140] S322, using the energy consumption of the computing equipment in the second data center and the second heat conversion coefficient as the independent variable matrix, and the energy consumption of the second cooling unit as the dependent variable matrix, and calculating the transformation matrix T2 based on the independent variable matrix and the dependent variable matrix;

[0141] S323, using the energy consumption of the computing equipment in the third data center and the third heat conversion coefficient as the independent variable matrix, and the energy consumption of the third cooling unit as the dependent variable matrix, and calculating the transformation matrix T3 based on the independent variable matrix and the dependent variable matrix;

[0142] S330, obtaining real-time energy consumption data of industrial production equipment corresponding to the process from the data center;

[0143] S340: Calculate the energy consumption of the data center computing devices using the energy consumption correlation model, including the energy consumption of the first data center computing device, the energy consumption of the second data center computing device, and the energy consumption of the third data center computing device.

[0144] S350, calculating a heat conversion coefficient based on the operating status of the industrial production equipment corresponding to the process and the real-time heating power of the data center computing equipment, specifically including the following steps:

[0145] S351, calculate the energy consumption E of the first cooling equipment Z1 , whose expression is:

[0146] E Z1 =λ1*x1*E1

[0147] Wherein, x1 is the first heat conversion coefficient, λ1 is the characteristic solution of the transformation matrix T1, and E1 is the energy consumption of the computing equipment of the first data center.

[0148] In this embodiment, the value of λ1 is 1.2.

[0149] S352: Calculate the energy consumption E of the second cooling equipment Z2 , whose expression is:

[0150] E Z2 =λ2*x2*E2

[0151] Wherein, x2 is the second heat conversion coefficient, λ2 is the characteristic solution of the transformation matrix T2, and E2 is the energy consumption of the computing equipment in the second data center.

[0152] In this embodiment, the value of λ2 is 2.7.

[0153] S353, calculate the energy consumption E of the third cooling equipment Z1 , whose expression is:

[0154] E Z3 =λ3*x3*E3

[0155] Among them, x3 is the third heat conversion coefficient, λ3 is the characteristic solution of the transformation matrix T3, and E3 is the energy consumption of the computing equipment in the third data center.

[0156] In this embodiment, the value of λ3 is 2.9.

[0157] S400: Adjust operating parameters of the cooling equipment for the computing equipment in the data center according to the energy consumption of the cooling equipment for the computing equipment in the data center.

[0158] Example 2:

[0159] like Figure 2 As shown, this embodiment provides a cloud computing data center energy consumption optimization management and control system, including industrial production equipment, data center computing equipment, and a cooling equipment group, wherein:

[0160] Industrial production equipment, used to implement industrial production processes;

[0161] In this embodiment, the industrial production equipment is industrial production equipment for cement production;

[0162] Data center computing equipment is used to control the operating status and energy consumption of industrial production equipment. Data center computing equipment is spatially partitioned according to industrial production processes.

[0163] In this embodiment, the data center computing equipment is divided into a first data center computing equipment, a second data center computing equipment, and a third data center computing equipment according to the industrial process of cement production, wherein:

[0164] The computing equipment in the first data center is used to control the operating conditions and energy consumption of the cement raw material crushing and pre-homogenization equipment;

[0165] The computing equipment in the second data center is used to control the operating status and energy consumption of the cement raw meal preparation homogenization and preheating decomposition equipment;

[0166] The computing equipment in the third data center is used to control the operating status and energy consumption of cement clinker burning and grinding equipment;

[0167] In this embodiment, the first data center computing device, the second data center computing device, and the third data center computing device all include multiple servers and memories. The multiple servers and memories of the first data center computing device are located in similar spatial areas of the computing center computer room; the multiple servers and memories of the second data center computing device are located in similar spatial areas of the computing center computer room; and the multiple servers and memories of the third data center computing device are located in similar spatial areas of the computing center computer room.

[0168] The cooling equipment group includes a plurality of cooling equipment, each of which is used to cool the computing equipment in the data center using the energy consumption optimization method for a cloud computing data center according to Embodiment 1 of the present invention, specifically:

[0169] Based on the industrial production process, establish the energy consumption correlation model of industrial production equipment and data center computing equipment corresponding to the process;

[0170] Calculate the heat conversion coefficient based on the operating conditions of the industrial production equipment corresponding to the process and the heating power of the data center computing equipment;

[0171] Calculate the energy consumption of cooling equipment for computing equipment in data centers based on the heat conversion coefficient and energy consumption correlation model;

[0172] According to the energy consumption of the cooling equipment of the computing equipment in the data center, the operating parameters of the cooling equipment of the computing equipment in the data center are adjusted.

[0173] In this embodiment, the cooling device is an air conditioner, which is provided with an air outlet, and an electric air valve is provided on the air outlet; the operating parameters of the air conditioner are adjusted by the air conditioner inverter and the electric air valve.

[0174] In this embodiment, the cooling equipment group includes a first cooling unit, a second cooling unit and a third cooling unit, wherein:

[0175] The first cooling unit is used to provide cooling for computing equipment in the first data center;

[0176] The second cooling unit is used to provide cooling for computing equipment in the second data center;

[0177] The third cooling unit is used to provide cooling for computing equipment in the third data center.

[0178] In this embodiment, the air-conditioning operating frequency can be regulated by the air-conditioning inverter through the setting of the air-conditioning main unit, the air supply duct and the electric air valve. If the effect required by the cold channel air-conditioning cannot be achieved, the size of the air outlet can be adjusted by the electric air valve to achieve partial regulation of the cooling capacity.

[0179] In summary, the cloud computing data center energy consumption optimization method provided by the embodiment of the present invention can be applied to cloud computing data centers based on functional zoning. It can realize personalized cooling for each computing device in the cloud computing data center according to the heat generation of each computing device in the cloud computing data center, thereby avoiding the problems of equipment overheating and excessive cooling. The cloud computing data center energy consumption optimization method provided by the embodiment of the present invention can adjust the operating parameters of the cooling equipment in real time and dynamically, and the calculation is simple, occupies few resources, and does not require additional hardware equipment. The embodiment of the present invention provides a cloud computing data center energy consumption optimization and control system for the cement production process, which conforms to the characteristics of the cement production process, so that the energy consumption of the cloud computing data center in the cement industry production is further optimized.

[0180] Example 3:

[0181] This embodiment provides a cloud computing data center energy consumption optimization device, including:

[0182] The energy consumption correlation model unit is used to establish an energy consumption correlation model of industrial production equipment and data center computing equipment corresponding to the industrial production process according to the industrial production process;

[0183] A heat conversion unit is used to calculate the heat conversion coefficient based on the operating conditions of the industrial production equipment corresponding to the process and the heat generation power of the data center computing equipment;

[0184] An energy consumption calculation unit, used to calculate the energy consumption of cooling equipment for computing equipment in the data center based on a heat production conversion coefficient and an energy consumption correlation model;

[0185] The regulating unit is used to regulate the operating parameters of the cooling equipment of the computing equipment in the data center according to the energy consumption of the cooling equipment of the computing equipment in the data center.

[0186] That is to say, the energy consumption association model unit of this embodiment is used to implement step S100 of embodiment 1 of the present invention, the heat conversion unit is used to implement step S200 of embodiment 1 of the present invention, the energy consumption calculation unit is used to implement step S300 of embodiment 1 of the present invention, and the adjustment unit is used to implement step S400 of embodiment 1 of the present invention. Steps S100-S400 have been described in detail in embodiment 1 of the present invention and will not be repeated here.

[0187] Example 4:

[0188] This embodiment provides a storage medium storing a program. When the program is executed by a computer, the method for optimizing energy consumption of a cloud computing data center according to Embodiment 1 of the present invention is implemented, specifically:

[0189] Based on the industrial production process, establish the energy consumption correlation model of industrial production equipment and data center computing equipment corresponding to the process;

[0190] Calculate the heat conversion coefficient based on the operating conditions of the industrial production equipment corresponding to the process and the heating power of the data center computing equipment;

[0191] Calculate the energy consumption of cooling equipment for computing equipment in data centers based on the heat conversion coefficient and energy consumption correlation model;

[0192] According to the energy consumption of the cooling equipment of the computing equipment in the data center, the operating parameters of the cooling equipment of the computing equipment in the data center are adjusted.

[0193] It should be noted that the computer-readable storage medium of the present embodiment may be a computer-readable signal medium or a computer-readable storage medium or any combination thereof. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0194] In this embodiment, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. Furthermore, in this embodiment, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable program. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable storage medium other than a computer-readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device. The computer program contained on a computer-readable storage medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0195] The computer readable storage medium can be written in one or more programming languages ​​or a combination thereof to execute the computer program for the present embodiment, including object-oriented programming languages ​​such as Java, Python, C++, and conventional procedural programming languages ​​such as C or similar programming languages. The program can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect via the Internet).

[0196] Obviously, the embodiments described above are only part of the embodiments of the present invention, rather than all the embodiments. The present invention is not limited to the details of the above embodiments. Any appropriate changes or modifications made by ordinary technicians in the relevant technical field are deemed to be within the patent scope of the present invention.

Claims

1. A method for optimizing energy consumption in a cloud computing data center, characterized in that: The following steps are involved: Based on the industrial production process, establish the energy consumption correlation model of industrial production equipment and data center computing equipment corresponding to the process; The heat conversion coefficient is calculated based on the operating conditions of the industrial production equipment corresponding to the process and the heating power of the data center computing equipment. Specifically, it is: Obtain the full-load heat generation power of computing equipment in the data center; Obtain a dataset of the number of running and total number of industrial production equipment corresponding to the process from the data center, and collect a dataset of the heat generation power of computing equipment in the data center; Calculate the heat conversion coefficient regression model based on the full-load heat output of the data center computing equipment, the data set of the number of operating and total number of industrial production equipment corresponding to the process, and the heat output data set of the data center computing equipment; Obtain the number of operating and total industrial production equipment corresponding to the process from the data center in real time, and calculate the heat conversion coefficient using the heat conversion coefficient regression model; Based on the heat conversion coefficient and energy consumption correlation model, the energy consumption of the cooling equipment of the data center computing equipment is calculated as follows: Collect and calculate historical data on energy consumption of multiple groups of data center computing equipment, energy consumption of cooling equipment, and heat conversion coefficients; The energy consumption and heat conversion coefficient of the data center computing equipment are used as the independent variable matrix, and the energy consumption of the cooling equipment is used as the dependent variable matrix. The transformation matrix is ​​obtained based on the independent variable matrix and the dependent variable matrix. T ; Obtain real-time energy consumption data of industrial production equipment corresponding to the process from the data center; Use energy consumption correlation models to calculate the energy consumption of computing equipment in data centers; Calculate the heat conversion coefficient based on the operating status of the industrial production equipment corresponding to the process and the real-time heating power of the data center computing equipment; Calculating energy consumption of cooling equipment E z , whose expression is: ； in, x is the heat conversion coefficient, λ is the transformation matrix T The characteristic solution of E Calculate the energy consumption of equipment for data centers; According to the energy consumption of the cooling equipment of the computing equipment in the data center, the operating parameters of the cooling equipment of the computing equipment in the data center are adjusted.

2. The cloud computing data center energy consumption optimization method according to claim 1, characterized in that: Based on the industrial production process, an energy consumption correlation model for industrial production equipment and data center computing equipment corresponding to the process is established, including the following steps: Screening of energy consumption related parameters; Based on energy consumption-related parameters, collect energy consumption data sets of industrial production equipment and data center computing equipment corresponding to the process; According to the data characteristics of energy consumption related parameters, the energy consumption correlation model is established using the regression method.

3. A cloud computing data center energy consumption optimization management and control system, characterized in that: Including industrial production equipment, data center computing equipment, and cooling equipment groups, including: Industrial production equipment, used to implement industrial production processes; Data center computing equipment is used to control the operating status and energy consumption of industrial production equipment. Data center computing equipment is spatially partitioned according to industrial production processes. The cooling equipment group includes a plurality of cooling equipment, each of which is used to cool the computing equipment of the data center according to the energy consumption optimization method of the cloud computing data center according to any one of claims 1-2.

4. The cloud computing data center energy consumption optimization management and control system according to claim 3 is characterized in that: Industrial production equipment is industrial production equipment used for cement production. The industrial production process is divided into the first process, the second process and the third process, among which: The first process is the cement raw material crushing and pre-homogenization process; The second process is the cement raw material preparation, homogenization and preheating decomposition process; The third process is the cement clinker burning and grinding process; Data center computing equipment is divided into first data center computing equipment, second data center computing equipment, and third data center computing equipment according to the industrial process of cement production, among which: The computing equipment in the first data center is used to control the operating conditions and energy consumption of the cement raw material crushing and pre-homogenization equipment; The computing equipment in the second data center is used to control the operating status and energy consumption of the cement raw meal preparation homogenization and preheating decomposition equipment; The computing equipment in the third data center is used to control the operating status and energy consumption of cement clinker burning and grinding equipment; The cooling equipment group includes a first cooling unit, a second cooling unit and a third cooling unit, wherein: The first cooling unit is used to provide cooling for computing equipment in the first data center; The second cooling unit is used to provide cooling for computing equipment in the second data center; The third cooling unit is used to provide cooling for computing equipment in the third data center.

5. The cloud computing data center energy consumption optimization management and control system according to claim 4 is characterized in that: In the energy consumption correlation model of the industrial production equipment and data center computing equipment corresponding to the first process, the energy consumption-related parameters include the energy consumption of the first data center computing equipment, the electricity consumption of the cement raw material crushing and pre-homogenizing equipment, and the coal consumption of the cement raw material crushing and pre-homogenizing equipment; In the energy consumption correlation model of the industrial production equipment and data center computing equipment corresponding to the second process, the energy consumption-related parameters include the energy consumption of the second data center computing equipment, the electricity consumption of the cement raw meal preparation, homogenization and preheating decomposition equipment, and the coal consumption of the cement raw meal preparation, homogenization and preheating decomposition equipment; In the energy consumption correlation model of industrial production equipment and data center computing equipment corresponding to the third process, energy consumption-related parameters include the energy consumption of the third data center computing equipment, the electricity consumption of cement clinker burning and grinding equipment, and the coal consumption of cement clinker burning and grinding equipment.

6. The cloud computing data center energy consumption optimization management and control system according to claim 3 is characterized in that: The cooling equipment is an air conditioner, which is provided with an air supply outlet and an electric air valve; the operating parameters of the air conditioner are adjusted by the air conditioner inverter and the electric air valve.

7. A cloud computing data center energy consumption optimization device, characterized in that: include: The energy consumption correlation model unit is used to establish an energy consumption correlation model of industrial production equipment and data center computing equipment corresponding to the industrial production process according to the industrial production process; The heat conversion unit is used to calculate the heat conversion coefficient based on the operating status of the industrial production equipment corresponding to the process and the heat generation power of the data center computing equipment. Specifically, it is: Obtain the full-load heat generation power of computing equipment in the data center; Obtain a dataset of the number of running and total number of industrial production equipment corresponding to the process from the data center, and collect a dataset of the heat generation power of computing equipment in the data center; Calculate the heat conversion coefficient regression model based on the full-load heat output of the data center computing equipment, the data set of the number of operating and total number of industrial production equipment corresponding to the process, and the heat output data set of the data center computing equipment; Obtain the number of operating and total industrial production equipment corresponding to the process from the data center in real time, and calculate the heat conversion coefficient using the heat conversion coefficient regression model; The energy consumption calculation unit is used to calculate the energy consumption of the cooling equipment of the computing equipment in the data center based on the heat conversion coefficient and the energy consumption correlation model, specifically: Collect and calculate historical data on energy consumption of multiple groups of data center computing equipment, energy consumption of cooling equipment, and heat conversion coefficients; The energy consumption and heat conversion coefficient of the data center computing equipment are used as the independent variable matrix, and the energy consumption of the cooling equipment is used as the dependent variable matrix. The transformation matrix is ​​obtained based on the independent variable matrix and the dependent variable matrix. T ; Obtain real-time energy consumption data of industrial production equipment corresponding to the process from the data center; Use energy consumption correlation models to calculate the energy consumption of computing equipment in data centers; Calculate the heat conversion coefficient based on the operating status of the industrial production equipment corresponding to the process and the real-time heating power of the data center computing equipment; Calculating energy consumption of cooling equipment E z , whose expression is: ； in, x is the heat conversion coefficient, λ is the transformation matrix T The characteristic solution of E Calculate the energy consumption of equipment for data centers; The regulating unit is used to regulate the operating parameters of the cooling equipment of the computing equipment in the data center according to the energy consumption of the cooling equipment of the computing equipment in the data center.

8. A storage medium storing a program, characterized in that: When the program is executed by a computer, the cloud computing data center energy consumption optimization method according to any one of claims 1 to 2 is implemented.

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