Data center machine room parameter adjustment method and device, storage medium and electronic equipment

Abstract

The present application relates to a kind of data center computer room parameter adjustment method, device, storage medium and electronic equipment, method includes: according to the actual building structure of data center computer room, equipment configuration, equipment space distribution characteristics and the physical interaction between equipment, construct three-dimensional physical model;Configuration parameters of each equipment in three-dimensional physical model and the environment parameter of data center computer room are obtained, the parameter assignment of three-dimensional physical model is carried out, the numerical simulation model is obtained by carrying out fluid dynamics numerical simulation to the three-dimensional physical model of assignment;The current operating parameter of data center computer room, temperature and humidity parameter and environmental parameter are obtained, input numerical simulation model, obtain numerical simulation prediction dataset, obtain computer room control strategy based on numerical simulation prediction dataset and pre-constructed mathematical model;According to the temperature and humidity of data center computer room is adjusted according to computer room control strategy.Can improve the operating efficiency of data center computer room.

Description

Technical Field

[0001] This invention relates to the field of data center energy-saving technology, and in particular to a method, apparatus, storage medium and electronic equipment for adjusting parameters in a data center computer room. Background Technology

[0002] With the development of the Internet and cloud computing businesses, the growth of hyperscale data centers has exploded. In order to ensure that the computers are in a better operating state in the densely deployed data center computer rooms, it is necessary to control the parameters of the data center computer room to make them run in a suitable environment, such as within a preset temperature and humidity range.

[0003] Currently, most data center server rooms rely on temperature and humidity sensors built into precision air conditioning systems to monitor the temperature and humidity conditions within the room. Based on the difference between preset temperature and humidity and the actual temperature and humidity detected by the sensors, a linear adjustment method is used to adjust key energy consumption parameters such as fan speed and compressor load of the air conditioning system to ensure that the actual temperature and humidity in the data center matches the preset temperature and humidity. However, this method of temperature and humidity control in data center server rooms only adjusts the temperature and humidity detected by the deployed sensors, failing to comprehensively consider various factors affecting the heat dissipation efficiency of the data center server room. For example, the density of computers in different areas will lead to different temperature and humidity data in different areas, resulting in uneven temperature distribution within the data center server room. This may prevent computers in some areas from operating within a suitable temperature and humidity range, reducing the operating efficiency of the data center server room. Furthermore, this adjustment method often causes the air conditioning system to operate under high load, which not only affects the stability of the data center server room and the lifespan of equipment but also leads to a significant waste of energy. Summary of the Invention

[0004] In view of this, the present invention provides a method, apparatus, storage medium and electronic device for adjusting parameters of a data center computer room.

[0005] Specifically, the present invention is achieved through the following technical solution:

[0006] According to a first aspect of the present invention, a method for adjusting parameters in a data center computer room is provided, the method comprising:

[0007] A three-dimensional physical model is constructed based on the actual building structure, equipment configuration, spatial distribution characteristics of equipment, and physical interaction relationships between equipment in the data center.

[0008] The configuration parameters of each device in the 3D physical model and the environmental parameters of the data center are obtained. The parameters of the 3D physical model are assigned, and the fluid dynamics numerical simulation is performed on the assigned 3D physical model to obtain the numerical simulation model.

[0009] Obtain the current operating parameters, temperature and humidity parameters, and environmental parameters of the data center computer room, input them into the numerical simulation model, obtain the numerical simulation prediction dataset, and obtain the computer room control strategy based on the numerical simulation prediction dataset and the pre-built mathematical model.

[0010] The temperature and humidity of the data center are adjusted according to the data center control strategy.

[0011] Optionally, the step of performing fluid dynamics numerical simulation on the assigned three-dimensional physical model to obtain a numerical simulation model includes:

[0012] Configure multiple operating parameter groups for each device in the data center computer room;

[0013] For each set of operating parameters, obtain the historical temperature and humidity parameters and historical environmental parameters of the data center within a preset time period;

[0014] The three-dimensional physical model is assigned values ​​for the same set of operating parameters, historical temperature and humidity parameters, and historical environmental parameters at the same time. The assigned three-dimensional physical model performs fluid dynamics numerical simulation based on the input parameters to obtain a numerical simulation dataset containing temperature and humidity parameters within a preset time period.

[0015] Based on the temperature and humidity parameters for the target time in the numerical simulation dataset, and the historical temperature and humidity parameters for that target time, the assigned three-dimensional physical model is subjected to reverse propagation calculation.

[0016] Optionally, after obtaining the numerical simulation prediction dataset and before obtaining the data center control strategy based on the numerical simulation prediction dataset and the pre-built mathematical model, the method further includes:

[0017] The process ends when the temperature and humidity parameter values ​​in the numerical simulation prediction dataset are within the preset range.

[0018] In response to the temperature and humidity parameter values ​​in the numerical simulation prediction dataset exceeding the preset temperature and humidity parameter value range, the step of obtaining the data center control strategy based on the numerical simulation prediction dataset and the pre-built mathematical model is executed.

[0019] Optionally, the response to temperature and humidity parameter values ​​in the numerical simulation prediction dataset exceeding a preset range of temperature and humidity parameter values ​​includes:

[0020] In response to the temperature and humidity parameter values ​​of any computer device in the numerical simulation prediction dataset exceeding the preset temperature and humidity parameter value range, confirm that the temperature and humidity parameter values ​​in the numerical simulation prediction dataset exceed the preset temperature and humidity parameter value range.

[0021] Optionally, the response to temperature and humidity parameter values ​​in the numerical simulation prediction dataset exceeding a preset range of temperature and humidity parameter values ​​includes:

[0022] Generate temperature and humidity distribution cloud maps inside the data center computer room based on numerical simulation prediction datasets;

[0023] Obtain the high temperature distribution value and low temperature distribution value corresponding to the highest temperature area and the lowest temperature area in the temperature distribution cloud map, respectively; and obtain the high humidity distribution value and low humidity distribution value corresponding to the highest humidity area and the lowest humidity area in the humidity distribution cloud map, respectively.

[0024] In response to the fact that the high and low temperature distribution values ​​are within the range of temperature parameter values, and the high and low humidity distribution values ​​are within the range of humidity parameter values, it is confirmed that the temperature and humidity parameter values ​​in the numerical simulation prediction dataset do not exceed the preset temperature and humidity parameter value range.

[0025] Optionally, obtaining the data center control strategy based on the numerical simulation prediction dataset and the pre-built mathematical model includes:

[0026] The numerical simulation prediction dataset is input into a pre-built mathematical model, and the numerical simulation prediction dataset is run according to a pre-set optimization algorithm to obtain the data center control strategy. The temperature and humidity adjustment parameter values ​​in the data center control strategy are within the pre-set temperature and humidity parameter value range.

[0027] Optionally, the configuration parameters include: the power range, electrical load, and heat dissipation efficiency of the computer; the chilled water temperature range and flow rate range of the air conditioning system; the air supply volume range of the fresh air system; the humidification volume range of the humidification system; the rated power of the automatic control system; and the rated power of the lighting system.

[0028] The data center parameter adjustment method in this technical solution constructs a three-dimensional physical model based on the actual building structure, equipment configuration, spatial distribution characteristics of equipment, and physical interaction relationships between equipment in the data center. It then acquires the configuration parameters of each device and the environmental parameters of the data center in the three-dimensional physical model, assigns parameter values ​​to the model, performs fluid dynamics numerical simulation on the assigned model, and obtains a numerical simulation model. Finally, it acquires the current operating parameters, temperature and humidity parameters, and environmental parameters of the data center, inputs them into the numerical simulation model, and obtains a numerical simulation prediction dataset. Based on the numerical simulation prediction dataset and the pre-constructed mathematical model, it obtains a data center control strategy. Finally, it adjusts the temperature and humidity of the data center according to the control strategy. In this way, by constructing a three-dimensional physical model of the data center, using fluid dynamics numerical simulation algorithms, and performing numerical simulations on the three-dimensional physical model based on the current parameters of the data center, and obtaining control strategies for the data center based on the mathematical model, the control strategies can be adaptively adjusted according to external disturbances, reducing abnormal operating states of the data center and thus improving the operating efficiency and stability of the data center. Furthermore, since it can prevent the temperature and humidity of the data center from exceeding the threshold, it can effectively reduce the energy consumption of the data center and achieve the overall energy-saving goal of the data center.

[0029] According to a second aspect of the present invention, a data center server room parameter adjustment device is provided, the data center server room parameter adjustment device comprising:

[0030] The model building module is used to build a three-dimensional physical model based on the actual building structure, equipment configuration, equipment spatial distribution characteristics, and physical interaction relationships between equipment in the data center.

[0031] The model training module is used to obtain the configuration parameters of each device in the 3D physical model and the environmental parameters of the data center, assign parameter values ​​to the 3D physical model, perform fluid dynamics numerical simulation on the assigned 3D physical model, and obtain the numerical simulation model.

[0032] The control strategy acquisition module is used to acquire the current operating parameters, temperature and humidity parameters and environmental parameters of the data center, input them into the numerical simulation model, obtain the numerical simulation prediction dataset, and acquire the control strategy of the data center based on the numerical simulation prediction dataset and the pre-built mathematical model.

[0033] The parameter control module is used to adjust the temperature and humidity of the data center according to the data center control strategy.

[0034] According to a third aspect of the present invention, a storage medium is provided having a computer program stored thereon, wherein when the program is executed by a processor, it implements the steps of the data center parameter adjustment method in any possible implementation of the first aspect.

[0035] According to a fourth aspect of the present invention, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the data center parameter adjustment method in any possible implementation of the first aspect. Attached Figure Description

[0036] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0038] Figure 1 A flowchart illustrating a method for adjusting data center parameters according to an embodiment of the present invention;

[0039] Figure 2 This is a schematic diagram of a data center parameter adjustment device provided in an embodiment of the present invention;

[0040] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] In related technologies, data center computer rooms rely on temperature and humidity sensors built into the air conditioning system to monitor the temperature and humidity conditions within the data center. Based on the difference between the preset temperature and humidity and the actual temperature and humidity detected by the sensors, key energy consumption parameters such as fan speed and compressor load of the air conditioning system are adjusted linearly to make the actual temperature and humidity consistent with the preset temperature and humidity. However, because this method only relies on the temperature detected by the temperature and humidity sensors deployed within the air conditioning system, it fails to comprehensively consider various factors affecting the heat dissipation efficiency of the data center computer room. For example, it does not consider the following important influencing factors:

[0043] Computer heat dissipation density: Different types of computers are deployed in data center computer rooms, and the heat dissipation requirements of different types of computers are different. The heat dissipation density directly affects the temperature distribution of the data center computer room, making the temperature distribution of the data center computer room uneven, which may cause the computers in some areas to not be in a suitable temperature and humidity range.

[0044] Real-time load changes: As the workload of data center computer rooms fluctuates, the computing load of each computer changes in real time, which in turn affects the heat dissipation requirements. The air conditioning system needs to respond to these changes in a timely manner.

[0045] Impact of humidification systems: Humidification operations will change the humidity level in the data center server room, indirectly affecting temperature sensing and heat dissipation efficiency, which needs to be fully considered in the adjustment strategy.

[0046] The role of the fresh air system: The introduction of fresh air is not only related to air quality, but may also have a significant impact on the temperature of the data center server room, and needs to be included in the current regulation logic.

[0047] Airflow organization in data center computer room: The airflow path and distribution inside the data center computer room are crucial for heat dissipation. An unreasonable airflow organization often leads to an uneven temperature field.

[0048] Because the aforementioned influencing factors were not considered during the temperature and humidity adjustment process, uneven temperature distribution often occurs in the data center, which may cause computers in some areas to be outside the suitable temperature and humidity range, resulting in low operating efficiency of the data center. Furthermore, the air conditioning system often operates under high load, which not only affects the stability of the data center and the lifespan of equipment, but also leads to a large amount of energy waste.

[0049] In this embodiment, a more intelligent and comprehensive temperature and humidity control strategy is explored to comprehensively optimize the heat dissipation management of the data center. A multi-dimensional environmental perception adaptive adjustment method based on the data center is proposed, which combines the environmental control systems such as the cooling system (air conditioning system), humidification system, and fresh air system in the data center with the automatic control system. Based on data acquisition, numerical simulation analysis, mathematical model iterative calculation, and automatic equipment control, the overall energy saving of the data center is achieved, reducing the energy consumption of the data center and improving the stability and operating efficiency of the data center.

[0050] In this embodiment, specifically, based on the data center's structure, equipment layout, and load, a comprehensive energy-saving method is designed, integrating the cooling, humidification, fresh air, and automatic control systems within the data center through a series of processes including data acquisition, numerical simulation analysis, mathematical iterative calculations, and automatic control. This approach addresses the complex and ever-changing structure and equipment layout of data centers by proposing a highly integrated and intelligent environmental optimization and energy efficiency management solution. The solution constructs a refined physical model and deeply integrates advanced computational fluid dynamics (CFD) numerical simulation technology to achieve accurate prediction and dynamic control of the microenvironment within the data center, thereby maximizing the overall energy efficiency and operational stability of the data center.

[0051] See Figure 1 This invention provides a method for adjusting parameters in a data center, which may include the following steps:

[0052] S101. Based on the actual building structure, equipment configuration, equipment spatial distribution characteristics, and physical interaction relationships between equipment in the data center, construct a three-dimensional physical model;

[0053] In this embodiment, as an optional implementation, the equipment configuration includes, but is not limited to: computers, air conditioning systems, humidification systems, fresh air systems, automatic control systems, and lighting systems. Based on the actual building structure, equipment configuration, spatial distribution characteristics, and physical interaction relationships between devices in the data center, a detailed three-dimensional physical model is constructed using high-precision three-dimensional modeling technology. This ensures that the constructed three-dimensional physical model not only accurately reflects the geometric shape of each device within the data center but also fully considers the physical interaction relationships between various devices (e.g., computers, air conditioning systems), making it consistent with the actual layout of the data center.

[0054] In this embodiment, as an optional implementation, a three-dimensional physical model containing the geometric shape of each device is constructed using computer-aided design (CAD), finite element method, finite difference method, and finite volume method.

[0055] S102. Obtain the configuration parameters of each device in the three-dimensional physical model and the environmental parameters of the data center computer room, assign parameter values ​​to the three-dimensional physical model, perform fluid dynamics numerical simulation on the assigned three-dimensional physical model, and obtain the numerical simulation model.

[0056] In this embodiment, as an optional implementation, the configuration parameters include, but are not limited to: the power range, electrical load, and heat dissipation efficiency of the computer; the chilled water temperature range and flow rate range of the air conditioning system; the air supply volume range of the fresh air system; the humidification capacity range of the humidification system; the rated power of the automatic control system; and the rated power of the lighting system. The environmental parameters of the data center include, but are not limited to: the temperature and humidity inside the data center and the temperature and humidity outside the data center.

[0057] In this embodiment, the three-dimensional physical model is initialized according to the configuration parameters and environmental parameters, that is, the parameters of each device contained in the three-dimensional physical model are assigned values.

[0058] In this embodiment, as an optional embodiment, a fluid dynamics numerical simulation is performed on the assigned three-dimensional physical model to obtain a numerical simulation model, including:

[0059] Configure multiple operating parameter groups for each device in the data center computer room;

[0060] For each set of operating parameters, obtain the historical temperature and humidity parameters and historical environmental parameters of the data center within a preset time period;

[0061] The three-dimensional physical model is assigned values ​​for the same set of operating parameters, historical temperature and humidity parameters, and historical environmental parameters at the same time. The assigned three-dimensional physical model performs fluid dynamics numerical simulation based on the input parameters to obtain a numerical simulation dataset containing temperature and humidity parameters within a preset time period.

[0062] Based on the temperature and humidity parameters for the target time in the numerical simulation dataset, and the historical temperature and humidity parameters for that target time, the assigned three-dimensional physical model is subjected to reverse propagation calculation.

[0063] In this embodiment, the assigned 3D physical model is iteratively trained by acquiring training datasets corresponding to different sets of operating parameters. Each set of operating parameters corresponds to a training dataset. By setting the operating parameters of the data center, each device in the data center runs for a pre-set time period according to its respective set operating parameters. During this time period, temperature, humidity, and environmental parameters are sampled according to a set sampling period. As an optional embodiment, historical operating parameters such as the power load of computer equipment, the chilled water temperature and flow rate of the air conditioning system, the air volume of the fresh air system, the humidification capacity of the humidification system, and the power of the lighting system are acquired, as well as historical environmental parameters such as temperature and humidity inside and outside the data center, and historical temperature and humidity parameters of each computer device. The historical operating parameters, historical environmental parameters, and historical temperature and humidity parameters are spliced ​​together to construct a key parameter set, which is used as the boundary condition input to the assigned 3D physical model. Based on a pre-set CFD numerical simulation algorithm, multi-dimensional and high-precision environmental simulation is performed to obtain a numerical simulation dataset for a future time period. As an optional embodiment, the numerical simulation dataset includes temperature and humidity parameters.

[0064] In this embodiment, as an optional example, the CFD numerical simulation algorithm includes, but is not limited to: the Semi-Implicit Method for Pressure-Linked Equations (SMPLE) algorithm, the SIMPLE-Consistent algorithm (SMPLEC) algorithm, and the Pressure Implicit with Splitting of Operators (PISO) algorithm.

[0065] S103. Obtain the current operating parameters, temperature and humidity parameters and environmental parameters of the data center computer room, input them into the numerical simulation model, obtain the numerical simulation prediction dataset, and obtain the computer room control strategy based on the numerical simulation prediction dataset and the pre-built mathematical model.

[0066] In this embodiment, as an optional implementation, the data center parameters include, but are not limited to, operating parameters, temperature and humidity parameters, and environmental parameters. Based on the data center parameters, a numerical simulation model is used to perform fluid dynamics numerical simulation to obtain a numerical simulation prediction dataset. The numerical simulation prediction dataset is input into a pre-built mathematical model, and the optimal data center control strategy is derived through the calculation and analysis of complex algorithms in the mathematical model.

[0067] In this embodiment, as an optional embodiment, after obtaining the numerical simulation prediction dataset and before obtaining the data center control strategy based on the numerical simulation prediction dataset and the pre-built mathematical model, the method further includes:

[0068] The process ends when the temperature and humidity parameter values ​​in the numerical simulation prediction dataset are within the preset range.

[0069] In response to the temperature and humidity parameter values ​​in the numerical simulation prediction dataset exceeding the preset temperature and humidity parameter value range, the step of obtaining the data center control strategy based on the numerical simulation prediction dataset and the pre-built mathematical model is executed.

[0070] In this embodiment, as an optional embodiment, responding to the temperature and humidity parameter values ​​in the numerical simulation prediction dataset exceeding the preset temperature and humidity parameter value range includes:

[0071] In response to the temperature and humidity parameter values ​​of any computer device in the numerical simulation prediction dataset exceeding the preset temperature and humidity parameter value range, confirm that the temperature and humidity parameter values ​​in the numerical simulation prediction dataset exceed the preset temperature and humidity parameter value range.

[0072] In this embodiment, as another optional embodiment, responding to the temperature and humidity parameter values ​​in the numerical simulation prediction dataset exceeding the preset temperature and humidity parameter value range includes:

[0073] Generate temperature and humidity distribution cloud maps inside the data center computer room based on numerical simulation prediction datasets;

[0074] Obtain the high temperature distribution value and low temperature distribution value corresponding to the highest temperature area and the lowest temperature area in the temperature distribution cloud map, respectively; and obtain the high humidity distribution value and low humidity distribution value corresponding to the highest humidity area and the lowest humidity area in the humidity distribution cloud map, respectively.

[0075] In response to the fact that the high and low temperature distribution values ​​are within the range of temperature parameter values, and the high and low humidity distribution values ​​are within the range of humidity parameter values, it is confirmed that the temperature and humidity parameter values ​​in the numerical simulation prediction dataset do not exceed the preset temperature and humidity parameter value range.

[0076] In this embodiment, based on the numerical simulation prediction dataset, an airflow organization vector map can also be generated. Thus, based on the temperature distribution cloud map, humidity distribution cloud map, and airflow organization vector map, the subtle differences and potential problems in the data center computer room environment can be intuitively displayed.

[0077] In this embodiment, as an optional implementation, a data center control strategy is obtained based on a numerical simulation prediction dataset and a pre-built mathematical model, including:

[0078] The numerical simulation prediction dataset is input into a pre-built mathematical model, and the numerical simulation prediction dataset is run according to a pre-set optimization algorithm to obtain the data center control strategy. The temperature and humidity adjustment parameter values ​​in the data center control strategy are within the pre-set temperature and humidity parameter value range.

[0079] In this embodiment, as an optional embodiment, the mathematical model can be obtained by retraining the numerical simulation model accordingly. For example, by setting the temperature and humidity adjustment parameter values ​​in the parameters output by the numerical simulation model to be within the range of temperature and humidity parameter values, numerical simulation can be performed based on the input numerical simulation prediction dataset to obtain other parameter values, such as the chilled water temperature and flow rate of the air conditioning system, the air supply volume of the fresh air system, and the humidification capacity of the humidification system. These other parameter values ​​do not include the operating parameters of each computer device and the power of the lighting system. Numerical simulation is performed based on this method to obtain the corresponding mathematical model.

[0080] S104. Adjust the temperature and humidity of the data center computer room according to the computer room control strategy.

[0081] In this embodiment, as an optional implementation, the data center control strategy is input into the server of the automated control system, so that the server adjusts the temperature and humidity of the data center data room according to the data center control strategy.

[0082] In this embodiment, the data center control strategy is imported into the server corresponding to the automated control system (automatic control system). Based on the calculation results of the data center control strategy, the server adaptively sends precise corresponding instructions to equipment such as the air conditioning system, the fresh air handling unit of the fresh air system, the chiller unit, and the humidifier of the humidification system, and dynamically adjusts their operating parameters, such as cooling capacity, fan speed, humidification capacity, cooling load, valve opening and air volume, so as to achieve precise control of environmental parameters.

[0083] The data center parameter adjustment method in this embodiment has a high degree of adaptability and feedback mechanism. Specifically, when external disturbances occur, such as fluctuations in the power load of computer equipment in the data center due to changes in computing tasks, or changes in water supply conditions caused by external environmental factors (such as outdoor temperature, fresh air temperature and humidity), the system captures and collects changes (current operating parameters, temperature and humidity parameters, and environmental parameters) in real time or according to a set collection cycle. This data is then input into the numerical simulation model to automatically adjust the boundary conditions of the numerical simulation, re-perform numerical simulation calculations, and generate new data center control schemes or strategies. This forms a closed-loop, self-circulating iterative optimization system, ensuring that the data center environment (temperature and humidity) is always maintained in an optimal state, effectively improving energy efficiency ratio and operating efficiency.

[0084] In this embodiment, a process of iterative loop is formed by establishing a physical model of the data center computer room, collecting boundary data for CFD numerical simulation, substituting the simulation results into the mathematical model for calculation, and using the calculated optimal computer room control strategy to guide the automatic control system to automatically control the power and environmental equipment.

[0085] The following is a specific embodiment to illustrate the method of this embodiment in detail.

[0086] The method in this embodiment was applied to two data center computer rooms with an area of ​​500 square meters and an IT load of about 600KW. After calculation and statistics, the auxiliary facility load of the two data center computer rooms was reduced by 23.1kW and 27.5.3kW respectively, and the average monthly energy saving was 18,749 kWh and 21,799 kWh respectively. It is estimated that the two data center computer rooms can save 477,000 kWh per year.

[0087] Based on the same inventive concept, such as Figure 2 As shown, this embodiment of the invention also provides a data center server room parameter adjustment device, the device comprising:

[0088] The model building module 201 is used to build a three-dimensional physical model based on the actual building structure, equipment configuration, equipment spatial distribution characteristics and physical interaction relationships between equipment in the data center.

[0089] In this embodiment, as an optional embodiment, the model building module 201 is specifically used for:

[0090] Configure multiple operating parameter groups for each device in the data center computer room;

[0091] For each set of operating parameters, obtain the historical temperature and humidity parameters and historical environmental parameters of the data center within a preset time period;

[0092] The three-dimensional physical model is assigned values ​​for the same set of operating parameters, historical temperature and humidity parameters, and historical environmental parameters at the same time. The assigned three-dimensional physical model performs fluid dynamics numerical simulation based on the input parameters to obtain a numerical simulation dataset containing temperature and humidity parameters within a preset time period.

[0093] Based on the temperature and humidity parameters for the target time in the numerical simulation dataset, and the historical temperature and humidity parameters for that target time, the assigned three-dimensional physical model is subjected to reverse propagation calculation.

[0094] The model training module 202 is used to obtain the configuration parameters of each device in the three-dimensional physical model and the environmental parameters of the data center computer room, assign parameters to the three-dimensional physical model, perform fluid dynamics numerical simulation on the assigned three-dimensional physical model, and obtain the numerical simulation model.

[0095] In this embodiment, as an optional embodiment, the configuration parameters include: the power range, electrical load, and heat dissipation efficiency of the computer; the chilled water temperature range and flow rate range of the air conditioning system; the air supply volume range of the fresh air system; the humidification volume range of the humidification system; the rated power of the automatic control system; and the rated power of the lighting system.

[0096] The control strategy acquisition module 203 is used to acquire the current operating parameters, temperature and humidity parameters and environmental parameters of the data center computer room, input them into the numerical simulation model, obtain the numerical simulation prediction dataset, and acquire the computer room control strategy based on the numerical simulation prediction dataset and the pre-built mathematical model.

[0097] In this embodiment, as an optional embodiment, the regulation strategy acquisition module 203 is specifically used for:

[0098] The numerical simulation prediction dataset is input into a pre-built mathematical model, and the numerical simulation prediction dataset is run according to a pre-set optimization algorithm to obtain the data center control strategy. The temperature and humidity adjustment parameter values ​​in the data center control strategy are within the pre-set temperature and humidity parameter value range.

[0099] In this embodiment, as an optional embodiment, the regulation strategy acquisition module 203 is further configured to:

[0100] The process ends when the temperature and humidity parameter values ​​in the numerical simulation prediction dataset are within the preset range.

[0101] In response to the temperature and humidity parameter values ​​in the numerical simulation prediction dataset exceeding the preset temperature and humidity parameter value range, the step of obtaining the data center control strategy based on the numerical simulation prediction dataset and the pre-built mathematical model is executed.

[0102] In this embodiment, as an optional embodiment, responding to the temperature and humidity parameter values ​​in the numerical simulation prediction dataset exceeding the preset temperature and humidity parameter value range includes:

[0103] In response to the temperature and humidity parameter values ​​of any computer device in the numerical simulation prediction dataset exceeding the preset temperature and humidity parameter value range, confirm that the temperature and humidity parameter values ​​in the numerical simulation prediction dataset exceed the preset temperature and humidity parameter value range.

[0104] In this embodiment, as another optional embodiment, the regulation strategy acquisition module 203 is further configured to:

[0105] Generate temperature and humidity distribution cloud maps inside the data center computer room based on numerical simulation prediction datasets;

[0106] Obtain the high temperature distribution value and low temperature distribution value corresponding to the highest temperature area and the lowest temperature area in the temperature distribution cloud map, respectively; and obtain the high humidity distribution value and low humidity distribution value corresponding to the highest humidity area and the lowest humidity area in the humidity distribution cloud map, respectively.

[0107] In response to the fact that the high and low temperature distribution values ​​are within the range of temperature parameter values, and the high and low humidity distribution values ​​are within the range of humidity parameter values, it is confirmed that the temperature and humidity parameter values ​​in the numerical simulation prediction dataset do not exceed the preset temperature and humidity parameter value range.

[0108] The parameter control module 204 is used to adjust the temperature and humidity of the data center according to the data center control strategy.

[0109] In this embodiment, as an optional implementation, adjusting the temperature and humidity of the data center according to the data center control strategy includes, but is not limited to: dynamically adjusting the operating parameters of the air conditioning system, humidification system, and fresh air system in the data center, such as cooling capacity, fan speed, humidification capacity, cooling load, valve opening, and air volume, to the corresponding parameter values ​​in the data center control strategy. For example, if the data center control strategy includes a first fan speed of 1500 rpm and a second fan speed of 1200 rpm, then the first fan speed will be adjusted from its current speed to 1500 rpm and the second fan speed will be adjusted from its current speed to 1200 rpm.

[0110] In this embodiment, by comprehensively collecting various parameters of the data center computer room, including but not limited to: data center computer room structure, equipment layout, power load, chilled water temperature and airflow organization, numerical simulation, mathematical iterative calculation and automatic control technology are used to realize the automatic adjustment of the power and environmental equipment (specifically covering equipment and facilities such as chiller units, air conditioning systems, humidification systems and fresh air systems) inside the data center computer room, so as to ensure that the power and environmental equipment inside the data center computer room can achieve precise dynamic control, thereby keeping the data center computer room environment in an optimal state.

[0111] Based on the same inventive concept, embodiments of the present invention also provide a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the data center server room parameter adjustment method in any of the above possible implementations.

[0112] Optionally, the storage medium may be a non-transitory computer-readable storage medium, such as a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device.

[0113] Based on the same inventive concept, see [link to inventive concept] Figure 3This invention also provides an electronic device, including a memory 101 (e.g., non-volatile memory), a processor 102, and a computer program stored on the memory 101 and executable on the processor 102. When the processor 102 executes the program, it implements the steps of the data center parameter adjustment method in any of the above possible implementations, which can be equivalent to the aforementioned data center parameter adjustment device. Of course, the processor can also be used to process other data or perform calculations. This electronic device can be a PC, server, terminal, or other similar device.

[0114] like Figure 3 As shown, the electronic device may also include: memory 103, network interface 104, and internal bus 105. In addition to these components, other hardware may also be included, which will not be described in detail here.

[0115] It should be noted that the aforementioned data center parameter adjustment device can be implemented through software. As a logical device, it is formed by the processor 102 of the electronic device in which it is located reading the computer program instructions stored in the non-volatile memory into the memory 103 for execution.

[0116] The embodiments of the subject matter and functional operation described in this specification can be implemented in the following ways: digital electronic circuits, tangibly embodied computer software or firmware, computer hardware including the structures disclosed in this specification and their structural equivalents, or combinations thereof. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible, non-transitory program carrier for execution by a data processing apparatus or for controlling the operation of a data processing apparatus. Alternatively or additionally, the program instructions may be encoded on artificially generated propagation signals, such as machine-generated electrical, optical, or electromagnetic signals, which are generated to encode information and transmit it to a suitable receiving device for execution by the data processing apparatus. The computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or combinations thereof.

[0117] The processing and logic flow described in this specification can be executed by one or more programmable computers that execute one or more computer programs to perform corresponding functions by operating on input data and generating output. The processing and logic flow can also be executed by special-purpose logic circuitry—such as FPGA (Field Programmable Gate Array) or ASIC (Application-Specific Integrated Circuit), and the device can also be implemented as special-purpose logic circuitry.

[0118] Suitable computers for executing computer programs include, for example, general-purpose and / or special-purpose microprocessors, or any other type of central processing unit. Typically, the central processing unit receives instructions and data from read-only memory and / or random access memory. The basic components of a computer include a central processing unit for implementing or executing instructions and one or more memory devices for storing instructions and data. Typically, a computer will also include one or more mass storage devices for storing data, such as disks, magneto-optical disks, or optical disks, or the computer will be operatively coupled to such mass storage devices to receive data from or transfer data to them, or both. However, a computer is not required to have such devices. Furthermore, a computer can be embedded in another device, such as a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device such as a universal serial bus (USB) flash drive, to name a few.

[0119] Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, such as semiconductor memory devices (e.g., EPROM, EEPROM, and flash memory devices), magnetic disks (e.g., internal hard disks or removable disks), magneto-optical disks, and CD-ROM and DVD-ROM disks. Processors and memory may be supplemented by or incorporated into dedicated logic circuitry.

[0120] While this specification contains numerous specific implementation details, these should not be construed as limiting the scope of any invention or the scope of the claims, but rather are primarily used to describe features of specific embodiments of a particular invention. Certain features described in the various embodiments herein may also be implemented in combination in a single embodiment. Conversely, various features described in a single embodiment may also be implemented separately in various embodiments or in any suitable sub-combination. Furthermore, while features may function in certain combinations as described above and even initially claimed in this way, one or more features from a claimed combination may be removed from that combination in some cases, and a claimed combination may refer to a sub-combination or a variation thereof.

[0121] Similarly, although the operations are depicted in a specific order in the accompanying drawings, this should not be construed as requiring these operations to be performed in the specific order shown or sequentially, or requiring all illustrated operations to be performed to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous. Furthermore, the separation of various system modules and components in the above embodiments should not be construed as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

[0122] Thus, specific embodiments of the subject matter have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions recited in the claims may be performed in a different order and still achieve the desired result. Furthermore, the processes depicted in the drawings are not necessarily shown in a specific order or sequence to achieve the desired result. In some implementations, multitasking and parallel processing may be advantageous.

[0123] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0124] The above are merely specific embodiments of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for adjusting parameters in a data center computer room, characterized in that, The method comprises the steps of: constructing a three-dimensional physical model according to the actual building structure, equipment configuration, equipment space distribution characteristics and physical interaction relationship between the equipment in the data center room; obtaining configuration parameters of each equipment in the three-dimensional physical model and environment parameters of the data center room, performing parameter assignment on the three-dimensional physical model, performing fluid dynamics numerical simulation on the assigned three-dimensional physical model, and obtaining a numerical simulation model; obtaining current operating parameters, temperature and humidity parameters and environment parameters of the data center room, inputting the numerical simulation model, obtaining a numerical simulation prediction data set, and obtaining a room control strategy based on the numerical simulation prediction data set and a pre-constructed mathematical model, wherein the obtaining of the room control strategy based on the numerical simulation prediction data set and the pre-constructed mathematical model comprises the following steps: inputting the numerical simulation prediction data set into the pre-constructed mathematical model, running the numerical simulation prediction data set according to a pre-set optimization algorithm, and obtaining the room control strategy, wherein temperature and humidity adjustment parameter values in the room control strategy are within a pre-set temperature and humidity parameter value range; adjusting the temperature and humidity of the data center room according to the room control strategy; the fluid dynamics numerical simulation on the assigned three-dimensional physical model to obtain the numerical simulation model comprises the following steps: setting a plurality of operating parameter groups of each equipment in the data center room; for each operating parameter group, obtaining historical temperature and humidity parameters of the data center room in a preset time period and historical environment parameters of the data center room; inputting the operating parameter group, the historical temperature and humidity parameters and the historical environment parameters at the same time into the assigned three-dimensional physical model, so that the assigned three-dimensional physical model performs fluid dynamics numerical simulation based on the input parameters to obtain a numerical simulation data set containing temperature and humidity parameters in the preset time period; performing a reverse transfer operation on the assigned three-dimensional physical model based on the temperature and humidity parameters for a target time in the numerical simulation data set and the obtained historical temperature and humidity parameters for the target time; after the numerical simulation prediction data set is obtained, before the room control strategy is obtained based on the numerical simulation prediction data set and the pre-constructed mathematical model, the method further comprises the following steps: ending the process in response to the temperature and humidity parameter values in the numerical simulation prediction data set being within the pre-set temperature and humidity parameter value range; in response to the temperature and humidity parameter values in the numerical simulation prediction data set exceeding the pre-set temperature and humidity parameter value range, performing the step of obtaining the room control strategy based on the numerical simulation prediction data set and the pre-constructed mathematical model.

2. The data center room parameter adjustment method of claim 1, wherein, the response to the temperature and humidity parameter values in the numerical simulation prediction data set exceeding the pre-set temperature and humidity parameter value range comprises: in response to the temperature and humidity parameter values of any computer equipment in the numerical simulation prediction data set exceeding the pre-set temperature and humidity parameter value range, confirming that the temperature and humidity parameter values in the numerical simulation prediction data set exceed the pre-set temperature and humidity parameter value range.

3. The method of claim 1, wherein, the response to the temperature and humidity parameter values in the numerical simulation prediction data set exceeding the pre-set temperature and humidity parameter value range comprises: generating a temperature distribution cloud map and a humidity distribution cloud map inside the data center room based on the numerical simulation prediction data set; The high temperature distribution value and the low temperature distribution value corresponding to the highest temperature area and the lowest temperature area in the temperature distribution cloud map are obtained, and the high humidity distribution value and the low humidity distribution value corresponding to the highest humidity area and the lowest humidity area in the humidity distribution cloud map are obtained. In response to the high temperature distribution value and the low temperature distribution value being within the temperature parameter value range, and the high humidity distribution value and the low humidity distribution value being within the humidity parameter value range, it is confirmed that the temperature and humidity parameter values in the numerical simulation prediction data set do not exceed the pre-set temperature and humidity parameter value range.

4. The method of claim 1 to 3, wherein, The configuration parameters include: the power range, power load, and heat dissipation efficiency of the computer, the chilled water temperature range, flow range of the air conditioning system, the supply air volume range of the fresh air system, the humidification amount range of the humidification system, the rated power of the automatic control system, and the rated power of the lighting system.

5. A data center room parameter adjustment apparatus, characterized by, The data center machine room parameter adjustment device is used to execute the data center machine room parameter adjustment method of claim 1, comprising: A model construction module is configured to construct a three-dimensional physical model based on the actual building structure, equipment configuration, equipment space distribution characteristics, and physical interaction relationship between equipment of the data center machine room. A model training module is configured to obtain configuration parameters of each equipment in the three-dimensional physical model and environmental parameters of the data center machine room, perform parameter assignment on the three-dimensional physical model, perform fluid dynamics numerical simulation on the assigned three-dimensional physical model, and obtain a numerical simulation model. A control strategy acquisition module is configured to obtain the current operating parameters, temperature and humidity parameters, and environmental parameters of the data center machine room, input the numerical simulation model, obtain a numerical simulation prediction data set, and obtain a machine room control strategy based on the numerical simulation prediction data set and a pre-constructed mathematical model. A parameter control module is configured to adjust the temperature and humidity of the data center machine room based on the machine room control strategy.

6. A storage medium, characterized by A storage medium stores programs or instructions, which are run by a processor to implement the steps of the data center machine room parameter adjustment method of any one of claims 1 to 4.

7. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the programs to implement the steps of the data center machine room parameter adjustment method of any one of claims 1 to 4.

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