Energy efficiency safety management method and system for informatization machine room equipment

Abstract

The invention relates to the technical field of machine room management, in particular to an energy efficiency safety management method and system for informatization machine room equipment. The method comprises the steps that a machine room layout is obtained, and a plurality of cooling areas are determined according to the machine room layout; analyzing the plurality of cooling areas, and determining a current cooling influence factor of each cooling area; establishing a dynamic temperature field according to the current cooling influence factor; historical temperature data are obtained and analyzed, and the cooling requirement of each cooling area in the load peak period is determined; and according to the cooling requirement, the current cooling influence factor and the dynamic temperature field, a waste cold distribution scheme is generated, and dynamic scheduling is conducted according to the waste cold distribution scheme. The cooling equipment is dynamically dispatched through the residual cold distribution scheme, the cooling equipment can be flexibly adjusted according to real-time data and prediction results, utilization of cooling resources is optimized, meanwhile, energy consumption is reduced, energy waste is reduced, and the cooling effect is ensured.

Description

Technical Field

[0001] This application relates to the technical field of computer room management, and in particular to an energy efficiency and security management method and system for information-based computer room equipment. Background Art

[0002] With the rapid development of information technology, the scale of data centers and information-based computer rooms has gradually expanded, the equipment density has been continuously increased, and the operating load has also become larger and larger. The cooling equipment in the computer room consumes a large amount of energy, and its energy efficiency directly affects the energy consumption efficiency of the entire computer room, and may even affect the stability and operation safety of the equipment.

[0003] In the usage environment of large IT equipment and servers, the cooling equipment is an important link that occupies a large amount of energy consumption. Its reasonable design and effective scheduling directly affect the overall energy efficiency of the computer room. Therefore, how to optimize the energy efficiency of the cooling equipment while ensuring that the system can still maintain sufficient cooling capacity during peak load periods has become an important challenge in the current technological development. Summary of the Invention

[0004] This application provides an energy efficiency and security management method and system for information-based computer room equipment to solve the above problems.

[0005] In a first aspect, this application provides an energy efficiency and security management method for information-based computer room equipment. The method includes: obtaining the computer room layout, and determining a plurality of cooling areas according to the computer room layout; analyzing the plurality of cooling areas to determine the current cooling influencing factors for each cooling area; establishing a dynamic temperature field according to the current cooling influencing factors; obtaining and analyzing historical temperature data to determine the cooling requirements for each cooling area during peak load periods; generating a surplus cooling allocation plan according to the cooling requirements, the current cooling influencing factors, and the dynamic temperature field, and performing dynamic scheduling according to the surplus cooling allocation plan.

[0006] Through this solution, it is ensured that the internal structure of the computer room is accurately identified. By reasonably dividing the cooling areas, personalized cooling management can be carried out according to the characteristics of different areas, improving the cooling efficiency. By analyzing the energy consumption density and real-time load heat load conditions of each cooling area, the cooling requirements can be predicted more accurately, avoiding over-cooling or under-cooling. The dynamic temperature field can reflect the temperature distribution inside the computer room in real time, providing a basis for adjusting the cooling strategy to ensure that the equipment operates in a suitable temperature environment. By analyzing historical temperature data and real-time load conditions, the cooling requirements during peak periods can be predicted, and preparations can be made in advance to avoid equipment failures caused by insufficient cooling. The generation of the surplus cooling allocation plan helps to optimize the utilization of cooling resources, reduce energy waste, and ensure the cooling effect at the same time. Dynamic scheduling can flexibly adjust the cooling equipment according to real-time data and prediction results, ensuring the stable operation of the equipment during peak load periods while reducing energy consumption.

[0007] Optionally, determining a plurality of cooling areas according to the computer room layout includes: obtaining computer room scan data, and determining an air duct structure according to the computer room scan data; analyzing the computer room scan data to determine a three-dimensional coordinate set of the cabinets; determining the distribution density of the cabinets according to the three-dimensional coordinate set; determining the spatial heat conduction condition according to the air duct structure and the distribution density; and determining a plurality of cooling areas according to the spatial heat conduction condition.

[0008] Through this solution, the physical structure and spatial layout of the computer room can be comprehensively identified through scan data. Identifying the air duct structure helps analyze the path and direction of air flow, providing a basis for optimizing the cooling air flow. Precise cabinet position information helps analyze the impact of equipment distribution on cooling requirements, providing a basis for dividing cooling areas. Analyzing the cabinet distribution density helps identify high heat load areas, so that more attention can be given to these areas when dividing cooling areas. Identifying the spatial heat conduction condition helps predict the heat transfer mode in the computer room, providing a thermodynamic basis for dividing cooling areas. Reasonably dividing cooling areas enables each area to be effectively cooled while avoiding unnecessary energy waste.

[0009] Optionally, analyzing the plurality of cooling areas to determine the current cooling influencing factors for each cooling area includes: for each cooling area, obtaining the cabinet operation data of each cold area; analyzing the cabinet operation data to determine the power time series data of each cabinet; analyzing the power time series data to determine the real-time energy consumption density of each cooling area; and using the real-time energy consumption density as the current cooling influencing factor for each cooling area.

[0010] Through this solution, it is ensured that the operation status of each cabinet can be monitored in real time. By analyzing the change of power over time, the energy consumption of each cabinet at different time periods can be identified, providing a basis for predicting and planning cooling requirements. The real-time energy consumption density reflects the energy consumption level of each cabinet at the current time point, helping to more accurately evaluate the cooling requirements and avoid excessive or insufficient cooling. By analyzing the real-time energy consumption density, areas with high energy consumption can be identified, so as to adjust the cooling strategy targeted and improve the cooling efficiency. Combining factors such as real-time energy consumption density, equipment distribution, and environmental temperature can comprehensively evaluate the heat load and environmental conditions of the cooling area, providing a decision-making basis for optimizing the cooling strategy.

[0011] Optionally, establishing a dynamic temperature field according to the current cooling influencing factors includes: obtaining initial temperature distribution data in the computer room and wind speed data of the air-conditioning outlet; constructing a three-dimensional non-steady-state heat conduction model according to the distribution density, real-time energy consumption density and the initial temperature distribution data; using a finite volume method to spatially discretize the three-dimensional non-steady-state heat conduction model to generate a grid node temperature prediction model; and establishing a dynamic temperature field according to the power time series data and the grid node temperature prediction model.

[0012] This solution provides a benchmark for establishing a dynamic temperature field and identifies the temperature distribution of the computer room under no-load or low-load conditions. It provides data for simulating the impact of air conditioning on the temperature field, which helps predict the distribution and effect of cooling airflow in the computer room. By establishing a mathematical model, it is possible to simulate the change of heat in the computer room over time and space, providing a theoretical basis for predicting the temperature field. The continuous temperature field model is converted into a discrete node model to facilitate numerical calculation and simulation by the computer. Through the discretized model, the temperature change of each node is predicted to provide data support for the dynamic temperature field. The temperature changes of each node in the computer room are updated and predicted in real time to form a dynamic temperature field model, providing a basis for precise control and optimization of cooling.

[0013] Optionally, determining the spatial heat conduction situation according to the air duct structure and the distribution density includes: dividing the computer room into a grid according to the computer room scanning data to obtain a plurality of computer room units; analyzing the air duct structure based on the computer room scanning data to determine the air duct space coordinate set; determining the geometric parameters of the air duct structure according to the air duct space coordinate set; determining the convective heat transfer coefficient and air flow rate of each computer room unit according to the geometric parameters; determining the heat source intensity change of each computer room unit according to the distribution density; generating a heat flux density matrix for each computer room unit according to the convective heat transfer coefficient, the heat source intensity change and the air flow rate to obtain the spatial heat conduction situation.

[0014] Through this solution, the computer room is divided into several regular grid units, which helps to simplify complex heat conduction problems and facilitates calculation and simulation. By analyzing the duct structure, the path and direction of air flow can be identified, providing a basis for optimizing the cooling airflow. Determining the geometric parameters of the duct helps to calculate the air flow characteristics such as flow velocity and flow rate in the duct. By determining the convective heat transfer coefficient and air flow rate, the heat exchange efficiency between air and equipment can be evaluated. By analyzing the changes in heat source intensity, the heat distribution in the computer room at different time points can be predicted, providing a basis for the dynamic adjustment of cooling. The heat flux density matrix can reflect the heat flow of each unit in the computer room and provide data support for the optimal allocation of cooling resources. By analyzing the heat flux density matrix, the heat transfer between the units in the computer room can be determined, providing a scientific basis for the design and optimization of cooling.

[0015] Optionally, generating a remaining cooling distribution plan according to the cooling demand, the current cooling influencing factors, and the dynamic temperature field includes: determining a temperature transition region between any adjacent cooling regions according to the dynamic temperature field; determining the total energy consumption of each cabinet within a preset time period according to the power timing data; sorting the total energy consumption of each cabinet within the preset time period to obtain a sorting result, and determining the minimum energy consumption within the preset time period according to the sorting result; determining a cooling threshold for each cold zone region according to the cooling demand; obtaining the cooling temperature of each cooling region, and calculating the deviation difference between the cooling threshold and the cooling temperature of each cooling region; determining pipeline constraint conditions according to the computer room scan data; and generating a remaining cooling distribution plan based on the cooling demand, the minimum energy consumption, the deviation difference, the temperature transition region, and the pipeline constraint conditions.

[0016] Through this solution, identifying the temperature change between adjacent cooling regions helps to optimize the cooling air flow distribution and avoid heat loss caused by too large a temperature gradient. Identifying the energy consumption of each cabinet within a preset time period provides a basis for the reasonable allocation of cooling resources. Through energy consumption sorting, cabinets with high energy consumption can be identified, providing key targets for adjusting the cooling strategy. Determining the minimum energy consumption helps to reduce unnecessary energy consumption while ensuring the cooling effect. Setting a cooling threshold ensures that the equipment operates within a safe temperature range and avoids failures caused by overheating. Calculating the deviation difference helps to evaluate the gap between the cooling effect and the cooling demand and provides a reference for adjusting the cooling strategy. Analyzing the pipeline constraint conditions helps to consider the physical limitations of the equipment when allocating the remaining cooling and ensures the stable operation of the cooling equipment.

[0017] Optionally, determining the convective heat transfer coefficient and air velocity of each computer room unit according to the geometric parameters includes: analyzing the geometric parameters to determine the air duct width, air duct height, and air duct length; determining the air inflow rate of the air duct per unit time under different air output rates according to the air duct width and the air duct height; determining the air output rate at the current moment according to the cabinet operation data; determining the air flow state at the current moment according to the air inflow rate, the air output rate, and the wind speed data; and determining the convective heat transfer coefficient and air velocity of each computer room unit according to the air flow state.

[0018] Through this solution, by analyzing the geometric parameters of the air duct, the physical characteristics of the air duct can be analyzed. According to the geometric dimensions of the air duct, the air intake volume of the air duct under different air output volumes can be calculated, which helps to evaluate the air flow capacity and pressure loss of the cooling equipment. Obtaining the current air output volume in real time helps to analyze the actual operating state of the cooling equipment and provides a basis for dynamically adjusting the cooling strategy. By analyzing the air intake volume, air output volume and air velocity data, the flow state of air in the computer room can be determined, providing data support for the convective heat transfer analysis. Calculating the convective heat transfer coefficient helps to evaluate the heat exchange efficiency between air and equipment and provides a reference for optimizing the design of the cooling equipment. Determining the air velocity can evaluate the cooling effect of the cooling equipment and provide a basis for adjusting the air duct design or cooling equipment parameters.

[0019] Optionally, the determining the heat source intensity change of each computer room unit according to the distribution density includes: determining the heat source intensity change of each computer room unit according to the air flow state and the distribution density.

[0020] Through this solution, it is ensured that the distribution of cabinets in the computer room can be identified. By calculating the distribution density, high heat load areas can be identified, providing a basis for the optimal allocation of cooling resources. Real-time monitoring of the operating state of the cabinets helps to predict and evaluate the heat generation of the equipment. Calculating the heat source intensity helps to analyze the heat output of each cabinet and provides data support for adjusting the cooling strategy. By analyzing the change of the heat source intensity, the heat distribution in the computer room at different time points can be predicted, providing a basis for the dynamic adjustment of the cooling equipment.

[0021] Optionally, the generating a surplus cooling distribution plan based on the cooling demand according to the minimum energy consumption, the deviation difference, the temperature transition region and the pipeline constraint conditions includes: determining the high-load area and the low-load area according to the temperature transition region; determining the tolerable capacity of the high-load area per unit time according to the pipeline constraint conditions; determining the release speed of the remaining cooling capacity of the low-load area and the released cooling capacity per unit time according to the minimum energy consumption, the deviation difference and the tolerable capacity.

[0022] Through this solution, identifying areas with large temperature variations in the computer room helps analyze and predict heat distribution and flow patterns, distinguish areas with different cooling requirements, provide a basis for targeted cooling strategies, identify the limitations of pipeline equipment, ensure the design and operation of cooling equipment within a safe range, evaluate the maximum cooling requirements of high-load areas, provide a design basis for cooling equipment, identify the most energy-efficient cooling configuration, help reduce the operating costs of the data center, analyze the gap between the actual cooling effect and the target cooling threshold, provide a reference for adjusting the cooling strategy, identify the cooling potential of low-load areas, provide data for optimizing the allocation of cooling resources, plan the effective utilization of surplus cooling capacity, improve the overall efficiency of cooling equipment, and quantify the cooling capacity of low-load areas to provide specific indicators for the operation and management of cooling equipment.

[0023] In a second aspect, the present application provides an energy efficiency and safety management system for information-based computer room equipment. The system includes: An area determination module for obtaining the computer room layout and determining several cooling areas according to the computer room layout; An area analysis module for analyzing the several cooling areas and determining the current cooling influencing factors for each cooling area; A temperature field establishment module for establishing a dynamic temperature field according to the current cooling influencing factors; A demand determination module for obtaining and analyzing historical temperature data and determining the cooling requirements of each cooling area during the peak load period; A solution generation module for generating a surplus cooling allocation solution according to the cooling requirements, the current cooling influencing factors, and the dynamic temperature field and performing dynamic scheduling according to the surplus cooling allocation solution.

[0024] Optionally, when the area determination module determines several cooling areas according to the computer room layout, it is used for: Obtaining computer room scan data and determining the air duct structure according to the computer room scan data; Analyzing the computer room scan data and determining the three-dimensional coordinate set of the cabinets; Determining the distribution density of the cabinets according to the three-dimensional coordinate set; Determining the spatial heat conduction situation according to the air duct structure and the distribution density; Determining several cooling areas according to the spatial heat conduction situation.

[0025] Optionally, when the area analysis module analyzes the several cooling areas and determines the current cooling influencing factors for each cooling area, it is used for: For each cooling area, obtaining the cabinet operation data of each cold area; Analyzing the cabinet operation data and determining the power time series data of each cabinet; Analyze the power timing data to determine the real-time energy consumption density of each cooling area; Use the real-time energy consumption density as the current cooling influencing factor for each cooling area.

[0026] Optionally, when the temperature field establishment module establishes a dynamic temperature field according to the current cooling influencing factor, it is used for: Obtain the initial temperature distribution data in the computer room and the wind speed data at the air outlet of the air conditioner; Construct a three-dimensional unsteady heat conduction model according to the distribution density, real-time energy consumption density and the initial temperature distribution data; Use the finite volume method to discretize the three-dimensional unsteady heat conduction model in space to generate a grid node temperature prediction model; Establish a dynamic temperature field according to the power timing data and the grid node temperature prediction model.

[0027] Optionally, when the area determination module determines the spatial heat conduction situation according to the air duct structure and the distribution density, it is used for: Divide the computer room into grids according to the computer room scan data to obtain a number of computer room units; Analyze the air duct structure based on the computer room scan data to determine the air duct space coordinate set; Determine the geometric parameters of the air duct structure according to the air duct space coordinate set; Determine the convective heat transfer coefficient and air velocity of each computer room unit according to the geometric parameters; Determine the change in heat source intensity of each computer room unit according to the distribution density; Generate a heat flux density matrix for each computer room unit according to the convective heat transfer coefficient, the change in heat source intensity and the air velocity to obtain the spatial heat conduction situation.

[0028] Optionally, when the solution generation module generates a redundant cooling distribution solution according to the cooling demand, the current cooling influencing factor and the dynamic temperature field, it is used for: Determine the temperature transition area between any adjacent cooling areas according to the dynamic temperature field; Determine the total energy consumption of each cabinet within a preset time period according to the power timing data; Sort the total energy consumption of each cabinet within the preset time period to obtain a sorting result, and determine the minimum energy consumption within the preset time period according to the sorting result; Determine the cooling threshold of each cold area according to the cooling demand; Obtain the cooling temperature of each cooling area, and calculate the deviation difference between the cooling threshold and the cooling temperature of each cooling area; Determine the pipeline constraint conditions according to the computer room scan data; Generate a remaining cooling distribution plan based on the cooling demand, the minimum energy consumption, the deviation difference, the temperature transition region, and the pipeline constraint conditions.

[0029] Optionally, when the area determination module determines the convective heat transfer coefficient and air velocity of each computer room unit according to the geometric parameters, it is used for: Analyze the geometric parameters to determine the air duct width, air duct height, and air duct length; Determine the air inflow volume of the air duct per unit time under different air output volumes according to the air duct width and the air duct height; Determine the air output volume at the current moment according to the cabinet operation data; Determine the air flow state at the current moment according to the air inflow volume, the air output volume, and the wind speed data; Determine the convective heat transfer coefficient and air velocity of each computer room unit according to the air flow state.

[0030] Optionally, when the area determination module determines the change in heat source intensity of each computer room unit according to the distribution density, it is used for: Determine the change in heat source intensity of each computer room unit according to the air flow state and the distribution density.

[0031] Optionally, when the plan generation module generates a remaining cooling distribution plan based on the cooling demand, the minimum energy consumption, the deviation difference, the temperature transition region, and the pipeline constraint conditions, it is used for: Determine the high-load area and the low-load area according to the temperature transition region; Determine the tolerable capacity of the high-load area per unit time according to the pipeline constraint conditions; Determine the release speed of the remaining cooling capacity and the released cooling capacity per unit time in the low-load area according to the minimum energy consumption, the deviation difference, and the tolerable capacity. Description of the Drawings

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0033] Figure 1 It is a schematic diagram of an application scenario provided by an embodiment of the present application; Figure 2Flowchart of an energy efficiency and security management method for information-based computer room equipment provided by an embodiment of the present application; Figure 3 Schematic structural diagram of an energy efficiency and security management system for information-based computer room equipment provided by an embodiment of the present application. Detailed implementation manners

[0034] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without making creative efforts shall fall within the protection scope of the present application.

[0035] In addition, the term "and / or" in this document is merely an association relationship describing associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally represents an "or" relationship between the associated objects before and after, unless otherwise specified.

[0036] The embodiments of the present application will be further described in detail below with reference to the accompanying drawings of the specification.

[0037] In the usage environment of large IT devices and servers, cooling equipment is an important link that consumes a large amount of energy, and its reasonable design and effective scheduling directly affect the overall energy efficiency of the computer room. Therefore, how to optimize the energy efficiency of cooling equipment while ensuring that the system can still maintain sufficient cooling capacity during peak load periods has become an important challenge in the current technological development.

[0038] Based on this, the present application provides an energy efficiency and security management method and system for information-based computer room equipment. Obtain the computer room layout, and determine several cooling areas according to the computer room layout; analyze the several cooling areas to determine the current cooling influencing factors for each cooling area; establish a dynamic temperature field according to the current cooling influencing factors; obtain and analyze historical temperature data to determine the cooling requirements of each cooling area during the peak load period; generate a surplus cooling distribution plan according to the cooling requirements, current cooling influencing factors and dynamic temperature field, and perform dynamic scheduling according to the surplus cooling distribution plan. Ensure accurate identification of the internal structure of the computer room. By reasonably dividing the cooling areas, personalized cooling management can be carried out according to the characteristics of different areas, improving the cooling efficiency. By analyzing the energy consumption density and real-time load heat load conditions of each cooling area, the cooling requirements can be predicted more accurately, avoiding excessive or insufficient cooling. The dynamic temperature field can reflect the temperature distribution inside the computer room in real time, providing a basis for adjusting the cooling strategy and ensuring that the equipment operates in a suitable temperature environment. By analyzing historical temperature data and real-time load conditions, the cooling requirements during the peak period can be predicted, and preparations can be made in advance to avoid equipment failures caused by insufficient cooling. The generation of the surplus cooling distribution plan helps to optimize the utilization of cooling resources, reduce energy waste, and ensure the cooling effect at the same time. Dynamic scheduling can flexibly adjust the cooling equipment according to real-time data and prediction results, ensuring the stable operation of the equipment during the peak load period and reducing energy consumption at the same time.

[0039] Figure 1 FIG. is a schematic diagram of an application scenario provided by the present application. When optimizing the energy efficiency of the cooling equipment in the information computer room, the method provided by the present application is applied. Specifically, the method provided by the present application is applied to any server, and the server interacts with the database and the cooling equipment. By retrieving data on the computer room layout, such as on-site measurements or design drawings related to the computer room layout, from the database; Ensure accurate identification of the internal structure of the computer room. By reasonably dividing the cooling areas, personalized cooling management can be carried out according to the characteristics of different areas, improving the cooling efficiency. By analyzing the energy consumption density and real-time load heat load conditions of each cooling area, the cooling requirements can be predicted more accurately, avoiding excessive or insufficient cooling. The dynamic temperature field can reflect the temperature distribution inside the computer room in real time, providing a basis for adjusting the cooling strategy and ensuring that the equipment operates in a suitable temperature environment. By analyzing historical temperature data and real-time load conditions, the cooling requirements during the peak period can be predicted, and preparations can be made in advance to avoid equipment failures caused by insufficient cooling. The generation of the surplus cooling distribution plan helps to optimize the utilization of cooling resources, reduce energy waste, and ensure the cooling effect at the same time. By dynamically scheduling the cooling equipment according to the surplus cooling distribution plan, the cooling equipment can be flexibly adjusted according to real-time data and prediction results, ensuring the stable operation of the equipment during the peak load period and reducing energy consumption at the same time.

[0040] Specific implementation manners can refer to the following embodiments.

[0041] Figure 2 The flowchart of an energy efficiency and security management method for information-based computer room equipment provided by an embodiment of the present application. The method of this embodiment can be applied to the servers in the above scenarios. As Figure 2 shown, the method includes: S201. Obtain the computer room layout, and determine several cooling areas according to the computer room layout; The computer room layout can be the spatial arrangement and configuration of the internal equipment and facilities in the computer room.

[0042] The cooling area can be an area divided in the computer room that requires independent temperature control and management.

[0043] Specifically, the energy consumption of the cooling equipment in the computer room is very high. Optimizing the energy consumption of the cooling equipment can significantly improve the energy efficiency of the computer room; excessive pursuit of energy efficiency may lead to insufficient cooling capacity of the cooling equipment, especially during peak load periods, which may cause overheating problems, affect the stable operation of the equipment, and even result in failures; therefore, data on the computer room layout such as on-site measurements or design drawings related to the computer room layout are retrieved from the database. The collected data on the computer room layout is analyzed in detail to identify equipment and structures such as server cabinets, storage devices, network devices, cable channels, and air ducts in the computer room. According to the physical layout of the computer room and the distribution of the equipment, the computer room is divided into several cooling areas.

[0044] S202. Analyze several cooling areas and determine the current cooling influencing factors for each cooling area; The current cooling influencing factors can refer to various real-time factors such as equipment power consumption, ambient temperature, humidity, equipment load rate, and air duct blockage that affect the cooling effect of the computer room.

[0045] Specifically, data such as temperature, humidity, air flow velocity, and equipment power in each cooling area are collected in real time. The temperature distribution in each cooling area is analyzed. The influence of humidity on the cooling effect is evaluated. The air flow velocity in each cooling area is calculated, and it is analyzed whether the air flow is uniform and whether there are dead air zones. The power consumption of the equipment in each cooling area is analyzed to determine the heat source intensity and heat load distribution. The operating parameters, coolant temperature, cooling capacity, and other operating states of the cooling equipment are evaluated. Combining the temperature distribution, humidity, air flow velocity, equipment power, and cooling operating state, the current cooling influencing factors for each cooling area are determined.

[0046] S203. Establish a dynamic temperature field according to the current cooling influencing factors; The dynamic temperature field can be a temperature distribution model used to simulate and predict the real-time temperature conditions at different positions inside the computer room.

[0047] Specifically, analyze the current cooling influencing factors, identify problems such as hot spots, cold spots, and uneven air flow distribution in the cooling area. According to the principles of thermodynamics and fluid mechanics, establish a mathematical model describing heat transfer and air flow. Use the finite difference numerical method to discretize the continuous mathematical model, and perform simulation calculations on the discretized model to obtain the dynamic temperature field.

[0048] S204. Obtain and analyze historical temperature data to determine the cooling demand of each cooling area during the peak load period; The historical temperature data can be the temperature information of the computer room recorded over a period of time in the past.

[0049] The peak load period can be the time period when the power consumption and heat load of the equipment in the computer room reach the maximum value.

[0050] The cooling demand can be the amount of heat removal that the cooling equipment needs to provide to maintain the operation of the computer room equipment in a suitable temperature environment.

[0051] Specifically, for energy-saving of redundant cooling and reducing energy expenditure, conduct regionalized management and energy consumption analysis to achieve a joint optimization plan for energy efficiency - cooling effect of the cooling equipment that meets the stable operation of the equipment. Obtain historical temperature data sets such as daily time-series temperature, cabinet load rate, and external environmental temperature and humidity from the database. Decompose the temperature sequence using wavelet transform, and extract the daily cycle and weekly cycle components as feature vectors. Construct a long short-term memory (LSTM) neural network prediction model, and input the feature vectors to train to obtain the load-temperature response relationship. Identify load mutation events in the historical data through the sliding time window detection method, and mark the corresponding cooling demand increment. Combine with the analysis of the heat dissipation margin of the current dynamic temperature field to output the cooling power demand during the peak period.

[0052] S205. Generate a redundant cooling distribution plan according to the cooling demand, current cooling influencing factors and dynamic temperature field, and perform dynamic scheduling according to the redundant cooling distribution plan.

[0053] The redundant cooling distribution plan can be a plan that reasonably distributes the redundant cooling generated by the cooling equipment to each cooling area according to the temperature distribution and cooling demand in the computer room to improve the cooling efficiency and reduce energy consumption.

[0054] Specifically, analyze the cooling demand, considering factors such as equipment heat dissipation power, environmental temperature, and humidity. Analyze current cooling influencing factors such as the operating state of air conditioning equipment, air flow distribution, and equipment load changes. Based on real-time data, establish a dynamic temperature field describing the change of temperature distribution inside the cooling area. Use an optimization algorithm to generate a redundant cooling distribution plan according to the cooling demand, current cooling influencing factors and dynamic temperature field. Dynamically adjust the distribution of the redundant cooling generated by the cooling equipment according to the generated redundant cooling distribution plan.

[0055] Through this solution, it is ensured that the internal structure of the computer room is accurately identified. By reasonably dividing the cooling areas, personalized cooling management can be carried out according to the characteristics of different areas, improving the cooling efficiency. By analyzing the energy consumption density and real-time load heat load of each cooling area, the cooling demand can be predicted more accurately, avoiding excessive or insufficient cooling. The dynamic temperature field can reflect the temperature distribution inside the computer room in real time, providing a basis for adjusting the cooling strategy and ensuring that the equipment operates in a suitable temperature environment. By analyzing historical temperature data and real-time load conditions, the cooling demand during peak periods can be predicted, and preparations can be made in advance to avoid equipment failures caused by insufficient cooling. The generation of the surplus cooling distribution plan helps to optimize the utilization of cooling resources, reduce energy waste, and ensure the cooling effect at the same time. Dynamic scheduling can flexibly adjust the cooling equipment according to real-time data and prediction results, ensuring the stable operation of the equipment during peak load periods while reducing energy consumption.

[0056] In some embodiments, computer room scan data is obtained, and according to the computer room scan data, the air duct structure is determined; the computer room scan data is analyzed to determine the three-dimensional coordinate set of the cabinets; according to the three-dimensional coordinate set, the distribution density of the cabinets is determined; according to the air duct structure and the distribution density, the spatial heat conduction situation is determined; according to the spatial heat conduction situation, several cooling areas are determined.

[0057] The computer room scan data can be detailed data obtained by scanning the internal structure of the computer room using a laser scanner, three-dimensional modeling technology or other spatial measurement tools.

[0058] The air duct structure can be channels such as supply air ducts, return air ducts, fans and vents in the computer room for guiding and distributing cooling air.

[0059] The cabinet can be a metal frame used to store IT equipment such as servers, network equipment, and storage equipment.

[0060] The three-dimensional coordinate set can be the specific position information of each cabinet in three-dimensional space, usually represented by X, Y, and Z axis coordinates, used to accurately describe the spatial position of the cabinet in the computer room.

[0061] The distribution density can be the density of the cabinets within a certain space range.

[0062] The spatial heat conduction situation can be the situation of heat transfer and distribution in the computer room.

[0063] Specifically, use a laser scanner, 3D modeling technology or other spatial measurement tools to scan the computer room to obtain detailed data related to the internal structure of the computer room and the cooling system. Based on the scan data, identify and analyze the duct layout such as the direction, size and connection method of the duct in the computer room. Through the scan data, determine the three-dimensional coordinate set of each cabinet in the computer room on the X, Y, and Z axes. According to the three-dimensional coordinate set of the cabinet, analyze the distribution of each cabinet in the computer room and calculate the distribution density of the cabinet. Combined with the duct structure and the cabinet distribution density, analyze the airflow path, resistance, heat exchange efficiency and other spatial heat conduction conditions that affect air flow and heat transfer in the computer room. According to the duct structure, cabinet distribution density and spatial heat conduction conditions, divide a number of cooling areas that are independently managed according to the spatial heat conduction conditions and cooling needs.

[0064] Through this solution, the physical structure and spatial layout of the computer room can be fully identified by scanning data. Identifying the duct structure helps analyze the path and direction of air flow, providing a basis for optimizing cooling airflow. Accurate cabinet location information helps analyze the impact of equipment distribution on cooling needs and provides a basis for the division of cooling areas. Analyzing the cabinet distribution density helps identify high heat load areas, so that more attention can be paid to these areas when dividing cooling areas. Identifying the heat conduction situation in the space helps predict the heat transfer pattern in the computer room and provides a thermodynamic basis for the division of cooling areas. Reasonable division of cooling areas allows each area to be effectively cooled while avoiding unnecessary energy waste.

[0065] In some embodiments, for each cooling area, the cabinet operation data of each cold zone is obtained; the cabinet operation data is analyzed to determine the power timing data of each cabinet; the power timing data is analyzed to determine the real-time energy consumption density of each cooling area; and the real-time energy consumption density is used as the current cooling influencing factor of each cooling area.

[0066] The cabinet operation data may be data related to the operation status of the equipment in the cabinet, such as power consumption, temperature, operation time, fault information, etc.

[0067] Power time series data can be a data sequence of device power changes over time, which is used to analyze the energy consumption of the device at different time points.

[0068] Real-time energy consumption density can be the energy consumption of equipment per unit area or volume at the current time point.

[0069] Specifically, sensors, monitoring devices, and IT management software are used to collect real-time operating data such as power, temperature, and fan speed of each cabinet. Time series analysis is performed on the collected cabinet operating data to determine the change trend of the power of each cabinet over time. According to the power time series data of each cabinet, its energy consumption density at the current time point is calculated, that is, the energy consumption per unit area or volume per unit time. The real-time energy consumption density in each cooling area is analyzed to determine the overall energy consumption level and heat load distribution in that area. Combining factors such as real-time energy consumption density, equipment distribution, environmental temperature, and humidity, the current cooling requirements and environmental conditions of each cooling area are comprehensively evaluated to determine the influencing factors affecting the current cooling effect.

[0070] Through this solution, it is ensured that the operating status of each cabinet can be monitored in real time. By analyzing the change of power over time, the energy consumption of each cabinet in different time periods can be identified, providing a basis for predicting and planning cooling requirements. The real-time energy consumption density reflects the energy consumption level of each cabinet at the current time point, helping to more accurately evaluate the cooling requirements and avoid over-cooling or under-cooling. By analyzing the real-time energy consumption density, areas with high energy consumption can be identified, and thus the cooling strategy can be adjusted targeted to improve the cooling efficiency. Combining factors such as real-time energy consumption density, equipment distribution, and environmental temperature, the heat load and environmental conditions of the cooling area can be comprehensively evaluated, providing a decision-making basis for optimizing the cooling strategy.

[0071] In some embodiments, the initial temperature distribution data in the computer room and the wind speed data at the air outlet of the air conditioner are obtained; according to the distribution density, real-time energy consumption density, and initial temperature distribution data, a three-dimensional unsteady heat conduction model is constructed; the finite volume method is used to discretize the three-dimensional unsteady heat conduction model in space to generate a grid node temperature prediction model; according to the power time series data and the grid node temperature prediction model, a dynamic temperature field is established.

[0072] The initial temperature distribution data can be the initial temperature readings at various positions in the computer room before the temperature simulation of the cooling equipment, which is used to establish the initial state of the temperature field.

[0073] The air outlet of the air conditioner can be the opening or device in the air conditioner for sending air into the computer room.

[0074] The wind speed data can be the air flow velocity at the air outlet of the air conditioner, in meters per second (m / s).

[0075] The three-dimensional unsteady heat conduction model can be a mathematical equation describing the heat transfer in three-dimensional space over time, considering the changes in space and time, and is used to simulate and predict the dynamic changes of the temperature field.

[0076] The finite volume method can be a numerical analysis method that discretizes a continuous differential equation into a series of algebraic equations.

[0077] The grid node temperature prediction model can be obtained by discretizing using the finite volume method, and is a mathematical model used to predict and control the temperature of each node in the computer room, where each node represents a discrete temperature point.

[0078] Specifically, collect the initial temperature data of each point in the computer room through temperature sensors to establish an initial temperature distribution model. Use a wind speed detection device to measure the wind speed at the air outlet of the air conditioner to obtain wind speed data. Based on the initial temperature distribution data, the wind speed data at the air outlet of the air conditioner, the equipment distribution density, and the real-time energy consumption density, construct a three-dimensional unsteady heat conduction model that describes heat transfer and temperature change. Discretize the three-dimensional unsteady heat conduction model to convert the continuous mathematical model into a series of discrete equations. Divide the grid in the computer room space, where each grid node represents a discrete temperature point, and predict the temperature of each node according to the discretized equations. Input the cabinet power time series data as boundary conditions into the prediction model, and solve the temperature field distribution at each time step through an implicit time integration algorithm. Combine the hydrodynamic parameters of the air duct structure, and use computational fluid dynamics (CFD) to correct the convective heat transfer coefficient in the temperature field, and output the dynamic temperature field evolution map.

[0079] Through this solution, it provides a benchmark for establishing a dynamic temperature field and identifies the temperature distribution in the computer room under no-load or low-load conditions. It provides data for simulating the influence of the air conditioner on the temperature field, which helps to predict the distribution and effect of the cooling air flow in the computer room. By establishing a mathematical model, it can simulate the change of heat in the computer room over time and space, providing a theoretical basis for predicting the temperature field. Convert the continuous temperature field model into a discrete node model to facilitate numerical calculation and simulation by the computer. Through the discretized model, predict the temperature change of each node, providing data support for the dynamic temperature field. Real-time update and predict the temperature change of each node in the computer room to form a dynamic temperature field model, providing a basis for the precise control and optimization of cooling.

[0080] In some embodiments, according to the computer room scan data, divide the computer room into grids to obtain several computer room units; based on the computer room scan data, analyze the air duct structure to determine the air duct space coordinate set; according to the air duct space coordinate set, determine the geometric parameters of the air duct structure; according to the geometric parameters, determine the convective heat transfer coefficient and air velocity of each computer room unit; according to the distribution density, determine the change in heat source intensity of each computer room unit; according to the convective heat transfer coefficient, the change in heat source intensity, and the air velocity, generate the heat flux density matrix of each computer room unit to obtain the spatial heat conduction situation.

[0081] A computer room unit can be to divide the computer room into several regular grids or regions, and each unit represents a spatial part in the computer room.

[0082] The air duct space coordinate set can be a series of coordinate points describing the position of the air duct in three-dimensional space.

[0083] Geometric parameters can be parameters such as length, width, height, diameter, etc., which describe the shape and size of an object.

[0084] The convective heat transfer coefficient can be a physical quantity describing the heat transfer efficiency between a fluid and a solid surface.

[0085] The air velocity can be the velocity of air flowing in the air duct, measured in meters per second (m / s).

[0086] The change in heat source intensity can be the variation of the heat generated by the equipment in the computer room over time and space.

[0087] The heat flux density matrix can be a matrix containing the heat flux density data of each unit in the computer room.

[0088] Specifically, collect the scanning data such as the physical dimensions of the computer room, equipment layout, and air duct position. According to the size of the computer room and the distribution of the equipment, determine the size and quantity of the grid division. According to the determined grid division scheme, divide the computer room into several regular grids or regions, and each grid represents a computer room unit. Analyze the computer room scanning data to identify the characteristics such as the starting point, ending point, branch point, and inflection point of the air duct. Establish a three-dimensional model of the air duct based on the computer room scanning data to determine the space coordinate set of the air duct in three-dimensional space. Extract all the coordinate points from the air duct space coordinate set, and use the distance formula between the coordinate points to calculate the total length of the air duct and the lengths of each air duct segment. According to the cross-sectional shape of the air duct, use the area formula to calculate the cross-sectional area of the air duct. Through the length and cross-sectional area of the air duct, calculate the volume of the air duct. Analyze the angular change between the coordinate points to calculate the curvature of the air duct. According to parameters such as length, cross-sectional area, volume, and curvature, determine the geometric parameters of the air duct. According to the geometric parameters, calculate the air velocity of each computer room unit. Use the convective heat transfer formula to determine the convective heat transfer coefficient of each computer room unit. Collect the distribution density data of the equipment in each computer room unit. Analyze the operating states such as the real-time power consumption and operating time of each equipment. According to the power consumption and distribution density of the equipment, determine the change in heat source intensity of each computer room unit. Based on the principles of thermodynamics and fluid dynamics equations, establish a model describing the heat flux density. Use the heat flux density model to calculate the heat flux density of each computer room unit according to the collected parameter data. Organize the calculated heat flux density data into a matrix. Analyze the heat flux density matrix to determine the spatial heat conduction situation.

[0089] With this solution, the computer room is divided into several regular grid units, which helps to simplify complex heat conduction problems and facilitates calculation and simulation. By analyzing the air duct structure, the paths and directions of air flow can be identified, providing a basis for optimizing the cooling air flow. Determining the geometric parameters of the air duct helps to calculate the air flow characteristics such as the flow velocity and flow rate inside the air duct. By determining the convective heat transfer coefficient and air flow velocity, the heat exchange efficiency between the air and the equipment can be evaluated. Analyzing the change in heat source intensity can predict the heat distribution in the computer room at different time points, providing a basis for the dynamic adjustment of cooling. The heat flux density matrix can reflect the heat flow situation of each unit in the computer room, providing data support for the optimal allocation of cooling resources. By analyzing the heat flux density matrix, the heat transfer situation between each unit in the computer room can be determined, providing a scientific basis for the design and optimization of cooling.

[0090] In some embodiments, according to the dynamic temperature field, determine the temperature transition region between any adjacent cooling regions; according to the power time series data, determine the total energy consumption of each cabinet within a preset time period; sort the total energy consumption of each cabinet within the preset time period to obtain a sorting result, and according to the sorting result, determine the minimum energy consumption within the preset time period; according to the cooling demand, determine the cooling threshold of each cold zone area; obtain the cooling temperature of each cooling region, and calculate the deviation difference between the cooling threshold and the cooling temperature of each cooling region; according to the computer room scan data, determine the pipeline constraint conditions; based on the cooling demand, generate a remaining cooling allocation plan according to the minimum energy consumption, deviation difference, temperature transition region and pipeline constraint conditions.

[0091] The temperature transition region can be a region with a large temperature change between adjacent cooling regions.

[0092] The preset time period can be a pre-set time period for analyzing and evaluating the energy consumption and cooling demand of the equipment, which is pre-stored in the server and called when in use.

[0093] The total energy consumption can be the total energy consumed by the equipment within the preset time period, usually measured in kilowatt-hours (kWh).

[0094] The sorting result can be the result of arranging a set of data in ascending or descending order of energy consumption.

[0095] The minimum energy consumption can be the energy consumed by the equipment with the lowest energy consumption within the preset time period.

[0096] The cold zone area can be an area divided in the computer room according to the cooling demand.

[0097] The cooling threshold can be the minimum temperature limit required for the equipment to operate.

[0098] The cooling temperature can be the temperature of the cooling air or cooling liquid provided by the cooling equipment.

[0099] The deviation difference can be the difference between the cooling threshold and the cooling temperature of each cooling area.

[0100] The pipeline constraint conditions can be physical and performance limitations such as the maximum flow rate, pressure limit, and material temperature resistance of the pipeline in the cooling equipment.

[0101] Specifically, analyze the dynamic temperature field, identify the areas with large temperature changes in the computer room, that is, the temperature transition areas, so as to determine the temperature transition areas between any adjacent cooling areas. Collect the power time-series data of each cabinet. According to the operating requirements of the cooling equipment, determine a preset time period. Calculate the total energy consumption of each cabinet during the preset time period based on the collected power time-series data. Sort the total energy consumption of all cabinets, either from high to low or from low to high, to obtain the sorting result. Determine the minimum energy consumption during the preset time period according to the sorting result. Analyze the cooling requirements of each cooling area, such as equipment type, power, and heat dissipation requirements. Determine the cooling threshold of each cooling area according to the cooling requirements. Collect the cooling temperature data of each cooling area in real time. Calculate the deviation difference between the cooling threshold and the actual cooling temperature of each cooling area. Analyze the layout, size, material, etc. of the pipeline based on the computer room scan data to determine the pipeline constraint conditions. Analyze the energy consumption data to determine the cooling configuration with the minimum energy consumption under the condition of meeting the cooling requirements. Analyze the position and characteristics of the temperature transition area and consider its impact on the cooling effect. Based on the cooling requirements, the minimum energy consumption, the deviation difference, the temperature transition area, and the pipeline constraint conditions, use an optimization algorithm to generate a surplus cooling allocation plan.

[0102] Through this solution, identifying the temperature changes between adjacent cooling areas helps to optimize the cooling air flow distribution and avoid heat loss caused by too large a temperature gradient. Identifying the energy consumption of each cabinet during the preset time period provides a basis for the reasonable allocation of cooling resources. Through energy consumption sorting, cabinets with high energy consumption can be identified, providing key targets for adjusting the cooling strategy. Determining the minimum energy consumption helps to reduce unnecessary energy consumption while ensuring the cooling effect. Setting the cooling threshold ensures that the equipment operates within a safe temperature range and avoids failures caused by overheating. Calculating the deviation difference helps to evaluate the gap between the cooling effect and the cooling requirements and provides a reference for adjusting the cooling strategy. Analyzing the pipeline constraint conditions helps to consider the physical limitations of the equipment when allocating surplus cooling and ensures the stable operation of the cooling equipment.

[0103] In some embodiments, analyze the geometric parameters to determine the duct width, duct height, and duct length; according to the duct width and duct height, determine the air intake volume of the duct per unit time under different air output volumes; determine the current air output volume according to the cabinet operation data; determine the current air flow state according to the air intake volume, air output volume, and wind speed data; determine the convective heat transfer coefficient and air flow velocity of each computer room unit according to the air flow state.

[0104] The duct width can be the dimension of the duct in the horizontal direction, with data in meters (m).

[0105] The duct height can be the dimension of the duct in the vertical direction, with data in meters (m).

[0106] The duct length can be the distance from the starting point to the ending point of the duct, with data in meters (m).

[0107] The air intake volume can be the volume of air passing through the duct per unit time, usually in cubic meters per second (m³ / s).

[0108] The air flow in the duct per unit time can be the air flow situation in the duct within a certain time period.

[0109] The current moment can be a specific time point used to describe the real-time power, temperature and other real-time states of the equipment.

[0110] The air output volume can be the volume of air discharged from the end of the duct per unit time, usually in cubic meters per second (m³ / s).

[0111] The air flow state can be the flow characteristics of air in the duct, such as air velocity, direction, pressure distribution, etc.

[0112] Specifically, according to the collected data, determine the width, height and length of the duct. Calculate the cross-sectional area of the duct based on the width and height of the duct. Analyze the operation of the duct under different air output volumes according to the cooling requirements. Determine the air intake volume of the duct per unit time under different air output volumes based on the cross-sectional area of the duct and the air velocity in the duct. Collect the operation data such as power consumption and heat dissipation requirements of each cabinet in real time. Determine the air output volume at the current moment according to the heat dissipation requirements of the cabinet and the performance of the cooling equipment. Collect the wind speed data in the duct through a wind speed sensor. Analyze the air flow state such as wind speed, air flow direction and air flow distribution at the current moment based on the air intake volume, air output volume and wind speed data. Calculate the convective heat transfer coefficient of each computer room unit using the convective heat transfer formula based on the air flow state and the geometric parameters of the duct. Determine the air velocity of each computer room unit based on the wind speed data.

[0113] Through this solution, by analyzing the geometric parameters of the air duct, the physical characteristics of the air duct can be analyzed. According to the geometric dimensions of the air duct, the air intake of the air duct under different air output volumes can be calculated, which helps to evaluate the air flow capacity and pressure loss of the cooling equipment. Obtaining the current air output volume in real time helps to analyze the actual operating state of the cooling equipment and provides a basis for dynamically adjusting the cooling strategy. By analyzing the air intake volume, air output volume, and air velocity data, the flow state of the air in the computer room can be determined, providing data support for the convective heat transfer analysis. Calculating the convective heat transfer coefficient helps to evaluate the heat exchange efficiency between the air and the equipment and provides a reference for optimizing the design of the cooling equipment. Determining the air velocity can evaluate the cooling effect of the cooling equipment and provide a basis for adjusting the air duct design or the parameters of the cooling equipment.

[0114] In some embodiments, according to the air flow state and distribution density, the change in the heat source intensity of each computer room unit is determined.

[0115] Specifically, the operating data such as the power consumption, temperature, and fan speed of each cabinet are collected in real time. According to the collected cabinet distribution data, the cabinet distribution density within each computer room unit is calculated, that is, the number of cabinets per unit area. The operating data such as the power consumption, temperature, and fan speed of each cabinet are collected in real time. According to the power consumption data of the cabinets, the heat source intensity of each cabinet is calculated, that is, the heat generated by each cabinet per unit time. Analyze the heat source intensity of all the cabinets in each computer room unit to determine the maximum value, minimum value, and average value of the heat source intensity changes within the unit.

[0116] Through this solution, it is ensured that the distribution of the cabinets in the computer room can be identified. By calculating the distribution density, the high heat load areas can be identified, providing a basis for the optimal allocation of cooling resources. Real-time monitoring of the operating state of the cabinets helps to predict and evaluate the heat generation of the equipment. Calculating the heat source intensity helps to analyze the heat output of each cabinet and provides data support for adjusting the cooling strategy. By analyzing the change in the heat source intensity, the heat distribution in the computer room at different time points can be predicted, providing a basis for the dynamic adjustment of the cooling equipment.

[0117] In some embodiments, according to the temperature transition region, the high load area and the low load area are determined; according to the pipeline constraint conditions, the tolerable capacity of the high load area per unit time is determined; according to the minimum energy consumption, deviation difference, and tolerable capacity, the release speed of the remaining cooling capacity in the low load area and the released cooling capacity per unit time are determined.

[0118] The high load area can be the area in the computer room where the equipment has a large power consumption and generates a large amount of heat.

[0119] The low load area can be the area in the computer room where the equipment has a small power consumption and generates a small amount of heat.

[0120] The unit time can be a fixed time period used to describe the consumption rate of resources.

[0121] The affordability can be the maximum heat or maximum cooling demand that the high-load area can safely handle per unit time.

[0122] The remaining cooling capacity can be the underutilized cooling capacity in the low-load area, that is, the cooling resources remaining in the low-load area after the cooling equipment meets the cooling demand in the high-load area.

[0123] The release rate can be the rate at which the remaining cooling capacity is released from the low-load area to the high-load area.

[0124] The released cooling capacity can be the amount of cooling released from the low-load area to the high-load area per unit time.

[0125] Specifically, analyze the temperature distribution inside the computer room to identify the temperature transition areas. According to the location and characteristics of the temperature transition areas, divide the computer room into high-load areas and low-load areas. Determine the constraint conditions such as the maximum flow rate, pressure limit, and pipe diameter of the pipeline based on the computer room scan data or pipeline design drawings. Analyze the cooling requirements such as the equipment power and heat dissipation requirements in each area according to the equipment operation status and temperature requirements. Determine the affordability of the high-load area per unit time using the principles of fluid mechanics and thermodynamics based on the pipeline constraint conditions and the cooling requirements of the high-load area. Analyze the energy consumption data to determine the most energy-efficient cooling configuration under the condition of meeting the cooling demand. Calculate the deviation difference between the cooling threshold and the actual cooling temperature in each cooling area. Analyze the cooling requirements of the low-load area to determine the remaining cooling capacity in the area. Determine the release rate of the remaining cooling capacity based on the remaining cooling capacity in the low-load area. Determine the released cooling capacity of the low-load area per unit time based on the release rate of the remaining cooling capacity.

[0126] Through this solution, identifying the areas with large temperature changes in the computer room helps to analyze and predict the heat distribution and flow patterns. Distinguishing the areas with different cooling requirements provides a basis for targeted cooling strategies. Identifying the limitations of pipeline equipment ensures that the design and operation of cooling equipment are within a safe range. Evaluating the maximum cooling demand of the high-load area provides a design basis for cooling equipment. Identifying the most energy-efficient cooling configuration helps to reduce the operating costs of the data center. Analyzing the gap between the actual cooling effect and the target cooling threshold provides a reference for adjusting the cooling strategy. Identifying the cooling potential of the low-load area provides data for optimizing the allocation of cooling resources. Planning the effective utilization of the remaining cooling capacity improves the overall efficiency of cooling equipment. Quantifying the cooling capacity of the low-load area provides specific indicators for the operation and management of cooling equipment.

[0127] Figure 3The following is a schematic structural diagram of an energy efficiency and security management system for information technology (IT) equipment in a data center according to an embodiment of the present application. As Figure 3 shown, the energy efficiency and security management system 300 for IT equipment in the data center of this embodiment includes: a region determination module 301, a region analysis module 302, a temperature field establishment module 303, a demand determination module 304, and a solution generation module 305.

[0128] The region determination module 301 is configured to obtain the layout of the data center and determine a plurality of cooling regions according to the layout of the data center. The region analysis module 302 is configured to analyze the plurality of cooling regions and determine the current cooling influencing factors for each cooling region. The temperature field establishment module 303 is configured to establish a dynamic temperature field according to the current cooling influencing factors. The demand determination module 304 is configured to obtain and analyze historical temperature data and determine the cooling demand for each cooling region during the peak load period. The solution generation module 305 is configured to generate a surplus cooling distribution solution according to the cooling demand, the current cooling influencing factors, and the dynamic temperature field, and perform dynamic scheduling according to the surplus cooling distribution solution.

[0129] Optionally, when the region determination module 301 determines a plurality of cooling regions according to the layout of the data center, it is configured to: Obtain the data center scan data and determine the air duct structure according to the data center scan data. Analyze the data center scan data and determine the three-dimensional coordinate set of the cabinets. Determine the distribution density of the cabinets according to the three-dimensional coordinate set. Determine the spatial heat conduction situation according to the air duct structure and the distribution density. Determine a plurality of cooling regions according to the spatial heat conduction situation.

[0130] Optionally, when the region analysis module 302 analyzes the plurality of cooling regions and determines the current cooling influencing factors for each cooling region, it is configured to: For each cooling region, obtain the cabinet operation data of each cold zone region. Analyze the cabinet operation data and determine the power time series data of each cabinet. Analyze the power time series data and determine the real-time energy consumption density of each cooling region. Use the real-time energy consumption density as the current cooling influencing factor for each cooling region.

[0131] Optionally, when the temperature field establishment module 303 establishes a dynamic temperature field according to the current cooling influencing factors, it is configured to: Obtain the initial temperature distribution data in the computer room and the wind speed data at the air outlet of the air conditioner; Construct a three-dimensional unsteady heat conduction model according to the distribution density, real-time energy consumption density and the initial temperature distribution data; Use the finite volume method to discretize the three-dimensional unsteady heat conduction model in space to generate a grid node temperature prediction model; Establish a dynamic temperature field according to the power time series data and the grid node temperature prediction model.

[0132] Optionally, when the area determination module 301 determines the space heat conduction condition according to the air duct structure and the distribution density, it is used for: Divide the computer room into grids according to the computer room scan data to obtain a number of computer room units; Analyze the air duct structure based on the computer room scan data to determine the air duct space coordinate set; Determine the geometric parameters of the air duct structure according to the air duct space coordinate set; Determine the convective heat transfer coefficient and air velocity of each computer room unit according to the geometric parameters; Determine the change in heat source intensity of each computer room unit according to the distribution density; Generate a heat flux density matrix for each computer room unit according to the convective heat transfer coefficient, the change in heat source intensity and the air velocity, and obtain the space heat conduction condition.

[0133] Optionally, when the solution generation module 305 generates a surplus cooling distribution plan according to the cooling demand, the current cooling influencing factors and the dynamic temperature field, it is used for: Determine the temperature transition region between any adjacent cooling regions according to the dynamic temperature field; Determine the total energy consumption of each cabinet within a preset time period according to the power time series data; Sort the total energy consumption of each cabinet within a preset time period to obtain a sorting result, and determine the minimum energy consumption within the preset time period according to the sorting result; Determine the cooling threshold of each cold area according to the cooling demand; Obtain the cooling temperature of each cooling area, and calculate the deviation difference between the cooling threshold and the cooling temperature of each cooling area; Determine the pipeline constraint conditions according to the computer room scan data; Generate a surplus cooling distribution plan based on the cooling demand, the minimum energy consumption, the deviation difference, the temperature transition region and the pipeline constraint conditions.

[0134] Optionally, when determining the convective heat transfer coefficient and air velocity of each computer room unit according to the geometric parameters, the area determination module 302 is configured to: Analyze the geometric parameters to determine the duct width, duct height, and duct length; Determine the air inflow rate of the duct per unit time under different air output rates according to the duct width and the duct height; Determine the air output rate at the current moment according to the cabinet operation data; Determine the air flow state at the current moment according to the air inflow rate, the air output rate, and the wind speed data; Determine the convective heat transfer coefficient and air velocity of each computer room unit according to the air flow state.

[0135] Optionally, when determining the change in heat source intensity of each computer room unit according to the distribution density, the area determination module 302 is configured to: Determine the change in heat source intensity of each computer room unit according to the air flow state and the distribution density.

[0136] Optionally, when generating a surplus cooling distribution plan based on the cooling demand according to the minimum energy consumption, the deviation difference, the temperature transition region, and the pipeline constraint conditions, the plan generation module 305 is configured to: Determine the high-load area and the low-load area according to the temperature transition region; Determine the tolerable capacity of the high-load area per unit time according to the pipeline constraint conditions; Determine the release speed of the remaining cooling capacity and the released cooling capacity per unit time in the low-load area according to the minimum energy consumption, the deviation difference, and the tolerable capacity.

[0137] The system of this embodiment can be used to execute the method of any of the above embodiments, and its implementation principle and technical effects are similar, which will not be elaborated here.

Claims

1. An energy efficiency and security management method for information-based computer room equipment, characterized in that, Including: Obtain the computer room layout, and determine several cooling areas according to the computer room layout; Analyze the several cooling areas, and determine the current cooling influencing factors for each cooling area; Establish a dynamic temperature field according to the current cooling influencing factors; Obtain and analyze historical temperature data, and determine the cooling requirements of each cooling area during the peak load period; Generate a surplus cooling allocation plan according to the cooling requirements, the current cooling influencing factors, and the dynamic temperature field, and perform dynamic scheduling according to the surplus cooling allocation plan.

2. The method according to claim 1, wherein The step of determining several cooling areas according to the computer room layout includes: Obtain computer room scan data, and determine the air duct structure according to the computer room scan data; Analyze the computer room scan data, and determine the three-dimensional coordinate set of the cabinets; Determine the distribution density of the cabinets according to the three-dimensional coordinate set; Determine the spatial heat conduction situation according to the air duct structure and the distribution density; Determine several cooling areas according to the spatial heat conduction situation.

3. The method according to claim 2, characterized in that, The step of analyzing the several cooling areas and determining the current cooling influencing factors for each cooling area includes: For each cooling area, obtain the cabinet operation data of each cold area; Analyze the cabinet operation data, and determine the power time series data of each cabinet; Analyze the power time series data, and determine the real-time energy consumption density of each cooling area; Take the real-time energy consumption density as the current cooling influencing factor for each cooling area.

4. The method according to claim 3, wherein The step of establishing a dynamic temperature field according to the current cooling influencing factors includes: Obtain the initial temperature distribution data in the computer room and the wind speed data at the air outlet of the air conditioner; Construct a three-dimensional unsteady heat conduction model according to the distribution density, the real-time energy consumption density, and the initial temperature distribution data; Use the finite volume method to perform spatial discretization on the three-dimensional unsteady heat conduction model to generate a grid node temperature prediction model; Establish a dynamic temperature field according to the power time series data and the grid node temperature prediction model.

5. The method according to claim 4, wherein The step of determining the spatial heat conduction situation according to the air duct structure and the distribution density includes: Divide the computer room into grids according to the computer room scan data to obtain several computer room units; Based on the computer room scan data, analyze the air duct structure to determine the air duct space coordinate set; Determine the geometric parameters of the air duct structure according to the air duct space coordinate set; Determine the convective heat transfer coefficient and air velocity of each computer room unit according to the geometric parameters; Determine the change in heat source intensity of each computer room unit according to the distribution density; Generate a heat flux density matrix for each computer room unit according to the convective heat transfer coefficient, the change in heat source intensity, and the air velocity, to obtain the spatial heat conduction situation.

6. The method according to claim 3, wherein The step of generating a surplus cooling allocation plan according to the cooling requirements, the current cooling influencing factors, and the dynamic temperature field includes: Determine the temperature transition area between any adjacent cooling areas according to the dynamic temperature field; Determine the total energy consumption of each cabinet within a preset time period according to the power time series data; Sort the total energy consumption of each cabinet within the preset time period to obtain a sorting result, and determine the minimum energy consumption within the preset time period according to the sorting result; Determine the cooling threshold for each cold zone according to the cooling requirement; Obtain the cooling temperature of each cooling zone, and calculate the deviation difference between the cooling threshold and the cooling temperature of each cooling zone; Determine the pipeline constraint conditions according to the computer room scan data; Based on the cooling requirement, generate a surplus cooling distribution plan according to the minimum energy consumption, the deviation difference, the temperature transition zone and the pipeline constraint conditions.

7. The method according to claim 5, wherein The determining the convective heat transfer coefficient and air velocity of each computer room unit according to the geometric parameters includes: Analyze the geometric parameters to determine the air duct width, air duct height and air duct length; According to the air duct width and the air duct height, determine the air intake of the air duct per unit time under different air output volumes; Determine the air output volume at the current moment according to the cabinet operation data; Determine the air flow state at the current moment according to the air intake, the air output volume and the wind speed data; Determine the convective heat transfer coefficient and air velocity of each computer room unit according to the air flow state.

8. The method according to claim 7, wherein The determining the change in heat source intensity of each computer room unit according to the distribution density includes: Determine the change in heat source intensity of each computer room unit according to the air flow state and the distribution density.

9. The method according to claim 6, wherein The generating a surplus cooling distribution plan based on the cooling requirement, according to the minimum energy consumption, the deviation difference, the temperature transition zone and the pipeline constraint conditions includes: Determine the high-load area and the low-load area according to the temperature transition zone; Determine the tolerable capacity of the high-load area per unit time according to the pipeline constraint conditions; According to the minimum energy consumption, the deviation difference and the tolerable capacity, determine the release speed of the remaining cooling capacity and the released cooling capacity per unit time in the low-load area.

10. An energy efficiency and security management system for information-based computer room equipment, characterized in that, Including: A region determination module, configured to obtain the computer room layout, and determine several cooling zones according to the computer room layout; A region analysis module, configured to analyze the several cooling zones and determine the current cooling influencing factors of each cooling zone; A temperature field establishment module, configured to establish a dynamic temperature field according to the current cooling influencing factors; A demand determination module, configured to obtain and analyze historical temperature data, and determine the cooling requirement of each cooling zone during the peak load period; A plan generation module, configured to generate a surplus cooling distribution plan according to the cooling requirement, the current cooling influencing factors and the dynamic temperature field, and perform dynamic scheduling according to the surplus cooling distribution plan.

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