Dynamic energy-saving control method and device

Abstract

The invention relates to the technical field of energy conservation. The dynamic energy-saving control method comprises the steps of obtaining real-time load information and environment information of all servers in a server cabinet, calculating energy efficiency scores of all the servers based on the real-time load information, confirming task allocation information through the energy efficiency scores, and according to the task allocation information, determining the energy efficiency of all the servers according to the task allocation information. Determining the workload distribution condition of each server, obtaining cooling demand information, inputting the cooling demand information into a cooling control model, obtaining cooling strategy information, determining the time period and condition of energy utilization based on the cooling strategy information and the environment information, generating an energy efficiency regulation and control strategy, and obtaining the energy efficiency regulation and control strategy according to the task distribution information and the cooling demand information. And generating energy-saving control information according to the cooling strategy information and the energy efficiency regulation and control strategy. The method has the effect of effectively reducing the energy consumption of the cabinet on the premise of ensuring the normal operation of each server in the server cabinet.

Description

Technical Field

[0001] The present invention relates to the technical field of energy saving, and in particular to a dynamic energy saving control method and device. Background Art

[0002] In the operation of modern data centers, the cooling and energy efficiency management of server cabinets are crucial. As computing density increases, the number of servers in a single cabinet continues to grow, generating a lot of heat. This poses a huge challenge to traditional cooling systems, not only increasing energy costs, but also affecting the overall operational efficiency of data centers. Therefore, how to reduce energy consumption through reasonable cooling control methods while ensuring the normal operation of each server has become a technical problem that needs to be solved. Summary of the invention

[0003] In order to effectively reduce the energy consumption of a server cabinet while ensuring the normal operation of each server in the server cabinet, the present application provides a dynamic energy-saving control method and device.

[0004] The above-mentioned invention objective of the present application is achieved through the following technical solutions: A dynamic energy-saving control method, the dynamic energy-saving control method comprising: Obtain real-time load information and environmental information of each server in the server cabinet; Based on the real-time load information, calculating the energy efficiency score of each server, and confirming the task allocation information through the energy efficiency score; Determine the workload distribution of each server according to the task allocation information to obtain cooling requirement information; Inputting the cooling demand information into a cooling control model to obtain cooling strategy information; Based on the cooling strategy information and the environmental information, determine the time period and conditions for energy utilization, and generate an energy efficiency control strategy; Energy-saving control information is generated according to the task allocation information, the cooling demand information, the cooling strategy information and the energy efficiency regulation strategy. The energy-saving control information is used to coordinate and control the load distribution of each server and the operating status of the cooling system.

[0005] By adopting the above technical solution, through the generation of cooling demand information and the application of cooling control model, the cooling strategy information is dynamically adjusted and cooling resources are reasonably allocated. At the same time, the efficient time period and conditions of energy utilization are determined in combination with environmental information, and energy efficiency control strategies are generated to achieve optimal energy management. Finally, through the generation of energy-saving control information, the coordinated control of server load distribution and cooling system is achieved, and the cooling energy consumption of server cabinets is effectively reduced while ensuring the normal operation of the server, thereby improving energy utilization efficiency.

[0006] In a preferred example, the present application may be further configured as follows: the acquisition of real-time load information and environment information of each server in the server cabinet includes: The real-time load information and the environmental information of each server are gradually collected through a preset inspection route in the server cabinet.

[0007] By adopting the above technical solution, the real-time load information and environmental information of each server in the server cabinet can be gradually collected according to the preset inspection route, ensuring that the information collection process is comprehensive and systematic. This inspection method avoids omissions and redundancies in information collection, making the monitoring of the server's load status and environmental status more accurate and real-time.

[0008] In a preferred example, the present application may be further configured as follows: calculating the energy efficiency score of each server based on the real-time load information, and confirming the task allocation information through the energy efficiency score, including: Obtaining historical average load information and real-time power consumption information of each server; Based on the historical average load information of each server, the real-time power consumption information and the real-time load information, the energy efficiency score is determined by the following formula: , where E i is the energy efficiency score of server i, W i represents the real-time load information of the server i, R i represents the response time of the server i, C i represents the real-time power consumption information of server i, H i represents the historical average load information of the server i, α and β are weight factors; The energy efficiency scores of the servers are sorted to obtain a sorting result, and the task allocation information is confirmed according to the sorting result. The task allocation information is used to preferentially allocate tasks to the server with the highest energy efficiency score, and put the server with the lowest energy efficiency score or not participating in the current task into a low power consumption mode.

[0009] By adopting the above technical solution, the energy efficiency score of each server can be calculated by comprehensively considering the historical average load information, real-time power consumption information and real-time load information of the server, so as to scientifically evaluate the energy efficiency level of the server. Based on the sorting of energy efficiency scores, servers with excellent energy efficiency performance can be accurately identified, and tasks can be assigned to these servers first, thereby improving overall computing efficiency. At the same time, putting servers with low energy efficiency scores or servers that are not currently participating in tasks in low power consumption mode can effectively reduce unnecessary energy consumption, further achieve the energy-saving goals of server cabinets, and improve resource utilization.

[0010] In a preferred example, the present application may be further configured as follows: determining the workload distribution of each server according to the task allocation information to obtain cooling requirement information includes: Based on the task allocation information, analyzing the real-time load status of each server, generating load distribution information, so as to identify high-load areas and low-load areas in the server cabinet; The cooling requirement of the high-load area and the cooling adjustment requirement of the low-load area are determined according to the load distribution information, and the cooling requirement information is generated.

[0011] By adopting the above technical solution, the real-time load status of each server can be analyzed based on the task allocation information to generate load distribution information. This enables the system to accurately identify high-load areas and low-load areas in the server cabinet and realize regionalized load management. Furthermore, the cooling demand of the high-load area and the cooling adjustment demand of the low-load area are determined based on the load distribution information, and cooling demand information is generated to achieve accurate allocation of cooling resources. This method can not only effectively improve cooling efficiency, but also avoid overcooling of low-load areas, optimize energy utilization, and help save overall energy consumption.

[0012] In a preferred example, the present application may be further configured as follows: the cooling demand information is input into a cooling control model to obtain cooling strategy information, including: Inputting the cooling demand information into the cooling management model, analyzing the temperature distribution of different areas in the server cabinet based on the temperature status and cooling demand of each area, and generating priority cooling information and cooling restriction information; According to the priority cooling information, a directional cooling measure is initiated for the area whose temperature exceeds the preset threshold, so as to ensure that the use of cooling resources is preferentially satisfied with the needs of the area whose temperature exceeds the preset threshold; According to the cooling restriction information, a directional cooling measure is initiated in an area where the temperature is lower than a preset threshold, so as to reduce the cooling resource allocated to the area where the temperature is lower than the preset threshold.

[0013] By adopting the above technical solution, by preferentially starting directional cooling measures for areas where the temperature exceeds the preset threshold according to the priority cooling information, it is ensured that the demand for cooling resources in this area is met first, thereby effectively reducing the temperature of the high-temperature area and preventing the performance degradation or damage of the equipment due to excessive temperature. At the same time, by starting directional cooling measures in areas where the temperature is lower than the preset threshold according to the cooling restriction information, the cooling resources allocated to the low-temperature area are reduced, thereby saving energy and avoiding excessive consumption of cooling resources, and further optimizing the energy efficiency of the overall cooling system.

[0014] In a preferred example, the present application may be further configured as follows: determining the time period and conditions for energy utilization based on the cooling strategy information and the environmental information, and generating an energy efficiency control strategy, including: Analyze the cooling demand of each area based on the cooling strategy information, and identify the cooling efficiency during peak hours and off-peak hours in combination with the temperature and humidity in the environmental information; According to the cooling efficiency, the energy efficiency control strategy is formulated to increase the cooling load during the peak cooling demand period and reduce the cooling load during the low demand period.

[0015] By adopting the above technical solution, it is possible to conduct an in-depth analysis of the cooling demand of each area based on the cooling strategy information, and accurately identify the cooling efficiency during peak and off-peak hours by combining the temperature and humidity in the environmental information. This process generates energy efficiency optimization period information, which provides a basis for formulating the energy efficiency control strategy based on the cooling efficiency. During the peak cooling demand period, the system can increase the cooling load in a timely manner to ensure stable operation of the equipment, and during the low demand period, the cooling load will be reduced accordingly, thereby avoiding energy waste.

[0016] In a preferred example, the present application may be further configured as follows: analyzing the cooling demand of each area based on the cooling strategy information, combining the temperature and humidity in the environmental information, and identifying the cooling efficiency during peak hours and off-peak hours, including: Determine the change pattern of cooling efficiency within a daily operation cycle by analyzing the cooling demand of each area and the temperature and humidity in the environmental information; Based on the variation rule, the cooling efficiency is divided into the peak period and the non-peak period.

[0017] By adopting the above technical solutions, we can accurately analyze the changing rules of cooling efficiency during the daily operation cycle based on the cooling demand of each area and the ambient temperature and humidity information, so as to grasp the dynamic change characteristics of cooling efficiency. The system can increase the cooling load in time during the peak cooling demand period and reduce the cooling load during the low demand period, effectively optimize energy distribution, thereby improving the energy efficiency level of the overall cooling system and reducing operating costs.

[0018] The second object of the invention is achieved by the following technical solutions: A dynamic energy-saving control device, the dynamic energy-saving control device comprising: An information acquisition module is used to obtain real-time load information and environmental information of each server in the server cabinet; A task allocation module, used to calculate the energy efficiency score of each server based on the real-time load information, and confirm the task allocation information through the energy efficiency score; A cooling demand analysis module, used to determine the workload distribution of each server according to the task allocation information, and obtain cooling demand information; A cooling control module, used for inputting the cooling demand information into a cooling control model to obtain cooling strategy information; An energy efficiency control module, used to determine the time period and conditions for energy utilization based on the cooling strategy information and the environmental information, and generate an energy efficiency control strategy; The energy-saving coordination control module is used to generate energy-saving control information based on the task allocation information, the cooling demand information, the cooling strategy information and the energy efficiency regulation strategy. The energy-saving control information is used to coordinate and control the load distribution of each server and the operating status of the cooling system.

[0019] By adopting the above technical solution, through the generation of cooling demand information and the application of cooling control model, the cooling strategy information is dynamically adjusted and cooling resources are reasonably allocated. At the same time, the efficient time period and conditions of energy utilization are determined in combination with environmental information, and energy efficiency control strategies are generated to achieve optimal energy management. Finally, through the generation of energy-saving control information, the coordinated control of server load distribution and cooling system is achieved, and the cooling energy consumption of server cabinets is effectively reduced while ensuring the normal operation of the server, thereby improving energy utilization efficiency.

[0020] The third objective of the present application is achieved through the following technical solutions: A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned dynamic energy-saving control method when executing the computer program.

[0021] The fourth objective of the present application is achieved through the following technical solutions: A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned dynamic energy-saving control method are implemented.

[0022] In summary, the present application includes at least one of the following beneficial technical effects: 1. Through the generation of cooling demand information and the application of cooling control models, the cooling strategy information is dynamically adjusted and cooling resources are reasonably allocated. At the same time, the efficient time periods and conditions for energy utilization are determined in combination with environmental information, and energy efficiency control strategies are generated to achieve optimal energy management. Finally, through the generation of energy-saving control information, the coordinated control of server load distribution and cooling systems is achieved. Under the premise of ensuring the normal operation of the server, the cooling energy consumption of the server cabinet is effectively reduced, thereby improving energy utilization efficiency; 2. By starting directional cooling measures in priority for areas where the temperature exceeds the preset threshold according to the priority cooling information, it is ensured that the demand for cooling resources in this area is met first, thereby effectively reducing the temperature in the high-temperature area and preventing the performance degradation or damage of the equipment due to excessive temperature. At the same time, by starting directional cooling measures in areas where the temperature is below the preset threshold according to the cooling restriction information, the cooling resources allocated to the low-temperature area are reduced, thereby saving energy and avoiding excessive consumption of cooling resources, further optimizing the energy efficiency of the overall cooling system; 3. Based on the cooling strategy information, it can conduct in-depth analysis of the cooling demand of each area, and combine the temperature and humidity in the environmental information to accurately identify the cooling efficiency during peak hours and off-peak hours. This process generates energy efficiency optimization period information, which provides a basis for formulating the energy efficiency control strategy based on the cooling efficiency. During the peak cooling demand period, the system can increase the cooling load in time to ensure stable operation of the equipment, and during the low demand period, the cooling load will be reduced accordingly, thereby avoiding energy waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is a flow chart of a dynamic energy-saving control method in one embodiment of the present application; Figure 2 This is a flowchart for implementing step S10 of the dynamic energy-saving control method in one embodiment of the present application; Figure 3 This is a flowchart for implementing step S20 of the dynamic energy-saving control method in one embodiment of the present application; Figure 4 This is a flowchart for implementing step S30 of the dynamic energy-saving control method in one embodiment of the present application; Figure 5 This is a flowchart for implementing step S40 of the dynamic energy-saving control method in one embodiment of the present application; Figure 6 This is a flowchart of the implementation of step S50 of the dynamic energy-saving control method in one embodiment of the present application; Figure 7 This is a flowchart of the implementation of step S501 of the dynamic energy-saving control method in one embodiment of the present application; Figure 8 It is a principle block diagram of a dynamic energy-saving control device in one embodiment of the present application; Fig. 9 It is a schematic diagram of a device in an embodiment of the present application. DETAILED DESCRIPTION

[0024] The present application is further described in detail below in conjunction with the accompanying drawings.

[0025] In one embodiment, if Figure 1As shown, the present application discloses a dynamic energy-saving control method, which specifically includes the following steps: S10: Acquire real-time load information and environment information of each server in the server cabinet.

[0026] Specifically, real-time load information and environmental information of each server can be obtained by deploying monitoring equipment in the server cabinet, such as configuring temperature and humidity sensors, power consumption monitors, and load data collectors. The monitoring equipment gradually collects real-time operation data of each server along a fixed inspection route inside the cabinet. Real-time load information includes the current CPU usage rate, memory occupancy rate and other resource load conditions of each server, while environmental information covers the temperature and humidity conditions inside the cabinet.

[0027] S20: Based on the real-time load information, the energy efficiency score of each server is calculated, and the task allocation information is confirmed through the energy efficiency score.

[0028] Specifically, the system first comprehensively evaluates the operating status of each server based on the real-time load information obtained and calculates its energy efficiency score. The energy efficiency score measures the energy utilization efficiency of the server under the current load level and reflects the power consumption efficiency of the server when processing tasks. To calculate the energy efficiency score, the system compares the real-time load information with the real-time power consumption of the server and generates a specific score through a formulaic calculation method, such as considering the response time of each server, the historical average load and other factors. This score represents the relative energy efficiency performance of each server under the current load. Then, the system sorts these scores to identify servers with higher energy efficiency scores. This score sorting helps the system determine the list of servers that are most suitable for the current task allocation, and prioritizes the tasks to the servers with the highest scores to maximize processing efficiency. At the same time, for servers with low scores or temporarily not participating in task processing, the system will adjust their operating status accordingly and put them in low power mode to reduce overall energy consumption.

[0029] S30: Determine the workload distribution of each server according to the task allocation information and obtain cooling requirement information.

[0030] Specifically, the workload distribution of each server is determined based on the task allocation information, which can be analyzed by analyzing the load requirements of the assigned tasks, the current load status of each server, and the historical operating status. The task allocation information indicates that a specific computing task is assigned to a specific server, so the current load status of each server is first determined based on the number and type of tasks received by each server. Then, the load status of all servers is divided into regions, and the high-load and low-load areas are analyzed to generate a workload distribution information. Through this distribution information, the cooling requirements of each area inside the cabinet are identified, for example, high-load areas correspond to higher cooling requirements, while low-load areas only require basic cooling.

[0031] S40: Input cooling demand information into a cooling control model to obtain cooling strategy information.

[0032] Specifically, after the cooling demand information is input into the cooling control model, the model analyzes the temperature distribution and cooling demand of different areas to obtain targeted cooling strategies. Based on the distribution characteristics of the temperature and load inside the cabinet, the cooling control model uses the input cooling demand information to identify areas that require priority cooling and areas with limited cooling. The model will formulate priority cooling and cooling restriction strategies based on the temperature threshold of each area. For example, high-temperature areas have a high cooling priority and require concentrated cooling resources; while areas with moderate temperatures can reduce the investment in cooling resources. The cooling strategy information includes specific cooling resource allocation plans and cooling priorities.

[0033] S50: Based on the cooling strategy information and environmental information, determine the time period and conditions for energy utilization, and generate an energy efficiency control strategy.

[0034] Specifically, based on the cooling strategy information and environmental information, the cooling efficiency changes of the cabinet in different time periods can be determined by analyzing the cooling demand of each area, the temperature and humidity inside the cabinet and other environmental factors. The cooling strategy information provides the cooling resource priority of each area, while the environmental information reflects the current external temperature, humidity and other factors that affect the cooling demand. Based on this information, the system will identify the peak and off-peak periods of cooling demand. For example, stronger cooling may be required during high temperatures during the day, while cooling power can be reduced at night or in low temperature environments. By identifying peak and off-peak periods, energy efficiency control strategies can be formulated based on cooling efficiency, such as increasing the cooling load during peak cooling demand periods and reducing the cooling load during low demand periods to ensure that the operation of the cooling system meets the cooling needs of the cabinet.

[0035] S60: Generate energy-saving control information according to the task allocation information, cooling demand information, cooling strategy information and energy efficiency regulation strategy. The energy-saving control information is used to coordinate and control the load distribution of each server and the operating status of the cooling system.

[0036] Specifically, first, the current load situation is confirmed based on the task allocation information, and the resource allocation of the cooling system is determined in combination with the cooling demand and cooling strategy. Next, the energy efficiency control strategy sets the corresponding cooling power requirements for different time periods, providing sufficient cooling resources during high-load and high-demand periods, and saving cooling power during low-load or low-demand periods. Energy-saving control information uniformly allocates server load and cooling resources to ensure that the cooling system prioritizes the needs of high-load areas, while minimizing the use of cooling resources in low-load areas, ultimately achieving energy efficiency optimization and energy conservation for the entire cabinet.

[0037] In one embodiment, if Figure 2 As shown, in step S10, real-time load information and environmental information of each server in the server cabinet is obtained, including: S101: Real-time load information and environmental information of each server are gradually collected through a preset inspection route in the server cabinet.

[0038] Specifically, the sensors in the inspection device read the real-time load information of each server, such as CPU usage, memory occupancy, and current power consumption. In addition, the environmental information sensor monitors the temperature, humidity, and air circulation in the cabinet. The collected data is transmitted to the system in real time via the wireless network for centralized analysis and processing. The path and residence time of the inspection device will be optimized according to the thermal distribution characteristics of the cabinet to ensure that temperature fluctuations and load changes in key areas can be fed back in a timely manner, thereby ensuring data accuracy and inspection efficiency.

[0039] In one embodiment, if Figure 3 As shown, in step S20, the energy efficiency score of each server is calculated based on the real-time load information, and the task allocation information is confirmed by the energy efficiency score, including: S201: Obtain historical average load information and real-time power consumption information of each server.

[0040] Specifically, in order to obtain the historical average load information and real-time power consumption information of each server, it is necessary to deploy data monitoring equipment on the server side. The data monitoring equipment will regularly record the load of the server, such as the average usage of key resources such as CPU, memory, and network, and store these data in the historical record database for easy access and analysis at any time. At the same time, real-time power consumption information is collected by power monitoring equipment installed in the cabinet or server to record instantaneous power consumption or average power consumption within the calculation cycle. The system collects data according to the preset sampling frequency (such as every 5 seconds or every minute) to ensure that the current and historical load conditions can be accurately reflected, and the historical average value is calculated by hour, day or longer period according to demand.

[0041] S202: Based on the historical average load information, real-time power consumption information and real-time load information of each server, the energy efficiency score is determined by the following formula: , where E i is the energy efficiency score of server i, W i represents the real-time load information of server i, R i represents the response time of server i, C i represents the real-time power consumption information of server i, H i represents the historical average load information of server i, and α and β are weight factors.

[0042] Specifically, when calculating the score, the system automatically retrieves real-time parameters such as current load and power consumption, and performs calculations based on the historical average load. i and R i Indicates the server's operating load and response speed. Higher load or response delay will reduce the energy efficiency score, while servers with lower power consumption will receive higher scores. The final score is used to evaluate the server's energy efficiency and help identify load distribution priorities.

[0043] S203: Sort the energy efficiency scores of the servers to obtain sorting results, and confirm task allocation information based on the sorting results. The task allocation information is used to preferentially allocate tasks to servers with the highest energy efficiency scores, and put servers with the lowest energy efficiency scores or not participating in the current task into low power consumption mode.

[0044] Specifically, after calculating the energy efficiency score of each server, these scores are sorted to determine the priority of task allocation. The sorting algorithm can use quick sort or merge sort to ensure the speed and efficiency of data processing. The system arranges the servers from high to low according to the score, and marks the servers with the highest scores as the priority objects for task allocation. These servers have better energy efficiency performance and take on heavier computing tasks first. For servers with lower scores, the system automatically switches them to low power mode to reduce energy consumption, or sets them to standby mode and reactivates them when the subsequent load demand increases. In this way, through score sorting and task allocation, the system can reasonably distribute the load.

[0045] In one embodiment, if Figure 4 As shown, in step S30, the workload distribution of each server is determined according to the task allocation information to obtain cooling requirement information, including: S301: Based on the task allocation information, the real-time load status of each server is analyzed to generate load distribution information to identify high-load areas and low-load areas in the server cabinet.

[0046] Specifically, based on the task allocation information, the real-time load data of each server is collected to extract the usage of resources such as CPU, memory, and network bandwidth. Then, these load data are summarized according to the cabinet layout and server location to calculate the average load level of each physical area. The system compares the load of each server through the data analysis module to obtain a load heat map in the cabinet. In this heat map, areas with higher loads are marked as high-load areas, while areas with lower loads are marked as low-load areas. Through this real-time distribution map, you can clearly understand the current resource usage in each area.

[0047] S302: Determine the cooling requirement of the high-load area and the cooling adjustment requirement of the low-load area according to the load distribution information, and generate cooling requirement information.

[0048] Specifically, after determining the load distribution information of each area, the cooling demand information can be further analyzed and generated. For high-load areas, due to the continuous high occupancy of server resources, more heat is generated, so cooling measures need to be enhanced to ensure that it operates within a safe temperature range. By calculating the heat dissipation rate of the high-load area and the surrounding temperature rise rate, the cooling power or cooling air volume required for the area is obtained. In the low-load area, since the load is relatively low and the heat dissipation demand is reduced, the cooling power can be appropriately reduced to save energy.

[0049] In one embodiment, if Figure 5 As shown, in step S40, the cooling demand information is input into the cooling control model to obtain cooling strategy information, including: S401: Input cooling demand information into a cooling management model, analyze the temperature distribution of different areas in the server cabinet based on the temperature status and cooling demand of each area, and generate priority cooling information and cooling restriction information.

[0050] Specifically, after the cooling demand information is input into the cooling management model, the model will first calculate based on the current temperature status of each area and the specific cooling demand. The model analyzes the temperature distribution of different areas inside the server cabinet and identifies areas with higher temperatures and areas with lower temperatures. By comparing the temperature change trend and cooling demand data, the system derives the priority cooling level of each area and generates priority cooling information, indicating which areas require higher cooling priority under current conditions. At the same time, the model also generates cooling restriction information to determine which areas can reduce the supply of cooling resources due to lower temperatures or lighter loads.

[0051] S402: Based on the priority cooling information, a directional cooling measure is initiated for the area whose temperature exceeds the preset threshold, so as to ensure that the demand of the area whose temperature exceeds the preset threshold is met first in the use of cooling resources.

[0052] Specifically, by inputting priority cooling information into the cooling system, the area with a temperature exceeding a preset threshold is identified and located to determine whether the temperature of the area exceeds the response threshold of the cooling system. Then, the cooling system starts the directional cooling device corresponding to the area or opens the corresponding cooling channel to reduce the temperature of the area more quickly and ensure that its cooling needs are met first. S403: Initiate directional cooling measures in areas where the temperature is lower than a preset threshold value according to the cooling restriction information, so as to reduce cooling resources allocated to areas where the temperature is lower than the preset threshold value.

[0053] Specifically, by importing cooling limit information into the cooling system, areas with temperatures below a preset threshold are monitored and identified. After receiving the information, the cooling system determines whether the current temperature of the area meets the conditions for reducing the allocation of cooling resources. Once it is confirmed that the temperature of the area is low, the system will appropriately shut down some cooling equipment or reduce the cooling power to reduce the use of cooling resources, thereby avoiding unnecessary energy consumption.

[0054] In one embodiment, if Figure 6 As shown, in step S50, based on the cooling strategy information and the environmental information, the time period and conditions for energy utilization are determined, and an energy efficiency control strategy is generated, including: S501: Analyze the cooling demand of each area based on the cooling strategy information, and identify the cooling efficiency during peak hours and off-peak hours in combination with the temperature and humidity in the environmental information.

[0055] Specifically, after obtaining the cooling strategy information, the system first analyzes the cooling demand of each area based on this information, and combines it with the real-time temperature and humidity data in the environmental information to evaluate the efficiency of the current cooling measures. The system uses an algorithm to statistically analyze the changes in cooling demand in different time periods, and by comparing and analyzing the load peak and the efficiency of cooling resource use, it identifies the non-peak hours with higher cooling efficiency and the peak hours with significant cooling demand.

[0056] S502: Formulate an energy efficiency control strategy based on the cooling efficiency to increase the cooling load during the peak cooling demand period and reduce the cooling load during the low demand period.

[0057] Specifically, under the guidance of the generated energy efficiency optimization period information, the system has formulated a specific energy efficiency control strategy. This strategy automatically increases the cooling load during the peak cooling demand period to ensure that the cooling needs of the server are met, such as by increasing the fan speed, increasing the coolant flow, or adjusting the airflow distribution to ensure the cooling effect during the peak period. Conversely, during low demand periods, the system reduces the overall cooling load by reducing the supply of cooling resources, such as slowing down the flow rate of the coolant or reducing the fan speed.

[0058] In one embodiment, if Figure 7 As shown, in step S501, the cooling demand of each area is analyzed based on the cooling strategy information, and the cooling efficiency during peak hours and off-peak hours is identified in combination with the temperature and humidity in the environmental information, including: S5011: By analyzing the cooling demand of each area and the temperature and humidity in the environmental information, determine the change pattern of cooling efficiency during the daily operation cycle.

[0059] Specifically, the system first conducts a detailed analysis of the cooling demand information of each area, combined with the real-time temperature and humidity data in the environmental information, in order to identify the changing pattern of cooling efficiency during the daily operation cycle. To this end, the system collects and summarizes the cooling demand and environmental data at different times of the day, and uses data fitting and statistical analysis methods to observe the relationship between temperature and humidity changes and cooling efficiency. By establishing a data association model, the system can identify the cooling efficiency fluctuation pattern of a specific period of time, for example, identifying the difference in cooling efficiency between day and night or the decline in efficiency due to increased humidity. Ultimately, this process reveals the dynamic changes in daily cooling efficiency over time and environmental conditions.

[0060] S5012: Based on the changing pattern, the cooling efficiency is divided into peak period and non-peak period.

[0061] Specifically, based on the cooling efficiency variation pattern obtained through analysis, the system further divides the cooling efficiency into peak hours and off-peak hours in order to reasonably adjust the allocation of cooling resources. The system marks the time periods with higher cooling efficiency as off-peak hours, which are usually when the equipment load is relatively low and the environmental conditions have a smaller cooling burden. The periods with lower cooling efficiency or higher cooling demand are identified as peak hours.

[0062] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0063] In one embodiment, a dynamic energy-saving control device is provided, and the dynamic energy-saving control device corresponds to the dynamic energy-saving control method in the above embodiment. Figure 8 As shown, the dynamic energy-saving control device includes an information acquisition module, a task allocation module, a cooling demand analysis module, a cooling control module, an energy efficiency regulation module and an energy-saving coordination control module. The detailed description of each functional module is as follows: An information acquisition module is used to obtain real-time load information and environmental information of each server in the server cabinet; The task allocation module is used to calculate the energy efficiency score of each server based on the real-time load information and confirm the task allocation information through the energy efficiency score; A cooling demand analysis module is used to determine the workload distribution of each server based on the task allocation information and obtain cooling demand information; A cooling control module is used to input cooling demand information into a cooling control model to obtain cooling strategy information; An energy efficiency control module is used to determine the time period and conditions of energy utilization based on cooling strategy information and environmental information, and generate an energy efficiency control strategy; The energy-saving coordination control module is used to generate energy-saving control information based on task allocation information, cooling demand information, cooling strategy information and energy efficiency regulation strategy. The energy-saving control information is used to coordinate and control the load distribution of each server and the operating status of the cooling system.

[0064] Optionally, the information acquisition module includes: The server cabinet inspection submodule is used to gradually collect the real-time load information and environmental information of each server through a preset inspection route in the server cabinet.

[0065] Optionally, the task allocation module includes: The historical load and power consumption acquisition submodule is used to obtain the historical average load information and real-time power consumption information of each server; The energy efficiency score calculation submodule is used to determine the energy efficiency score based on the historical average load information, real-time power consumption information and real-time load information of each server through the following formula: , where E i is the energy efficiency score of server i, W i represents the real-time load information of server i, R i represents the response time of server i, C i represents the real-time power consumption information of server i, H i represents the historical average load information of server i, α and β are weight factors; The task allocation and power consumption management submodule is used to sort the energy efficiency scores of each server, obtain the sorting results, and confirm the task allocation information based on the sorting results. The task allocation information is used to prioritize tasks to the servers with the highest energy efficiency scores, and put the servers with the lowest energy efficiency scores or not participating in the current tasks into low power consumption mode.

[0066] Optional cooling demand analysis module includes: A load distribution analysis submodule, for analyzing the real-time load status of each server based on the task allocation information, and generating load distribution information to identify high-load areas and low-load areas within the server cabinet; The cooling demand adjustment submodule is used to determine the cooling demand of the high-load area and the cooling adjustment demand of the low-load area according to the load distribution information, and generate cooling demand information.

[0067] Optionally, the cooling control module includes: The cooling demand analysis submodule is used to input the cooling demand information into the cooling management model, analyze the temperature distribution of different areas in the server cabinet based on the temperature status and cooling demand of each area, and generate priority cooling information and cooling restriction information; The high-temperature area directional cooling submodule is used to initiate directional cooling measures for areas where the temperature exceeds the preset threshold according to the priority cooling information, ensuring that the use of cooling resources gives priority to meeting the needs of areas where the temperature exceeds the preset threshold; The low-temperature area cooling resource adjustment submodule is used to start directional cooling measures in areas where the temperature is lower than a preset threshold according to the cooling restriction information, so as to reduce the cooling resources allocated to the areas where the temperature is lower than the preset threshold.

[0068] Optional energy efficiency control module includes: The cooling demand and efficiency analysis submodule is used to analyze the cooling demand of each area based on the cooling strategy information, and identify the cooling efficiency during peak and off-peak hours in combination with the temperature and humidity in the environmental information; The energy efficiency control strategy formulation submodule is used to formulate an energy efficiency control strategy based on the cooling efficiency, so as to increase the cooling load during the peak cooling demand period and reduce the cooling load during the low demand period.

[0069] Optional cooling demand and efficiency analysis submodules include: The cooling efficiency change analysis submodule is used to determine the change pattern of cooling efficiency during the daily operation cycle by analyzing the cooling demand of each area and the temperature and humidity in the environmental information; The peak and off-peak time division submodule is used to divide the cooling efficiency during peak time and off-peak time based on the changing rules.

[0070] For the specific definition of the dynamic energy-saving control device, please refer to the definition of the dynamic energy-saving control method above, which will not be repeated here. Each module in the above-mentioned dynamic energy-saving control device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0071] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Fig. 9As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used for a historical record database. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a dynamic energy-saving control method is implemented.

[0072] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: Obtain real-time load information and environmental information of each server in the server cabinet; Based on real-time load information, calculate the energy efficiency score of each server and confirm the task allocation information through the energy efficiency score; According to the task allocation information, determine the workload distribution of each server and obtain the cooling demand information; Input cooling demand information into a cooling control model to obtain cooling strategy information; Based on cooling strategy information and environmental information, determine the time period and conditions for energy utilization and generate energy efficiency control strategies; Energy-saving control information is generated based on task allocation information, cooling demand information, cooling strategy information and energy efficiency regulation strategy. The energy-saving control information is used to coordinate and control the load distribution of each server and the operating status of the cooling system.

[0073] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: Obtain real-time load information and environmental information of each server in the server cabinet; Based on real-time load information, calculate the energy efficiency score of each server and confirm the task allocation information through the energy efficiency score; According to the task allocation information, determine the workload distribution of each server and obtain the cooling demand information; Input cooling demand information into a cooling control model to obtain cooling strategy information; Based on cooling strategy information and environmental information, determine the time period and conditions for energy utilization and generate energy efficiency control strategies; Energy-saving control information is generated based on task allocation information, cooling demand information, cooling strategy information and energy efficiency regulation strategy. The energy-saving control information is used to coordinate and control the load distribution of each server and the operating status of the cooling system.

[0074] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0075] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0076] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A dynamic energy-saving control method, characterized in that: The dynamic energy-saving control method comprises: Obtain real-time load information and environmental information of each server in the server cabinet; Based on the real-time load information, calculating the energy efficiency score of each server, and confirming the task allocation information through the energy efficiency score; Determine the workload distribution of each server according to the task allocation information to obtain cooling requirement information; Inputting the cooling demand information into a cooling control model to obtain cooling strategy information; Based on the cooling strategy information and the environmental information, determine the time period and conditions for energy utilization, and generate an energy efficiency control strategy; Energy-saving control information is generated according to the task allocation information, the cooling demand information, the cooling strategy information and the energy efficiency regulation strategy. The energy-saving control information is used to coordinate and control the load distribution of each server and the operating status of the cooling system.

2. The dynamic energy-saving control method according to claim 1, characterized in that: The obtaining of real-time load information and environment information of each server in the server cabinet includes: The real-time load information and the environmental information of each server are gradually collected through a preset inspection route in the server cabinet.

3. The dynamic energy-saving control method according to claim 2, characterized in that: The step of calculating the energy efficiency score of each server based on the real-time load information and confirming the task allocation information by the energy efficiency score includes: Obtaining historical average load information and real-time power consumption information of each server; Based on the historical average load information of each server, the real-time power consumption information and the real-time load information, the energy efficiency score is determined by the following formula: , where E i is the energy efficiency score of server i, W i represents the real-time load information of the server i, R i represents the response time of the server i, C i represents the real-time power consumption information of server i, H i represents the historical average load information of the server i, α and β are weight factors; The energy efficiency scores of the servers are sorted to obtain a sorting result, and the task allocation information is confirmed according to the sorting result. The task allocation information is used to preferentially allocate tasks to the server with the highest energy efficiency score, and put the server with the lowest energy efficiency score or not participating in the current task into a low power consumption mode.

4. The dynamic energy-saving control method according to claim 1, characterized in that: Determining the workload distribution of each server according to the task allocation information to obtain cooling requirement information includes: Based on the task allocation information, analyzing the real-time load status of each server, generating load distribution information, so as to identify high-load areas and low-load areas in the server cabinet; The cooling requirement of the high-load area and the cooling adjustment requirement of the low-load area are determined according to the load distribution information, and the cooling requirement information is generated.

5. The dynamic energy-saving control method according to claim 1, characterized in that: The step of inputting the cooling demand information into a cooling control model to obtain cooling strategy information includes: Inputting the cooling demand information into the cooling management model, analyzing the temperature distribution of different areas in the server cabinet based on the temperature status and cooling demand of each area, and generating priority cooling information and cooling restriction information; According to the priority cooling information, a directional cooling measure is initiated for the area whose temperature exceeds the preset threshold, so as to ensure that the use of cooling resources is preferentially satisfied with the needs of the area whose temperature exceeds the preset threshold; According to the cooling restriction information, a directional cooling measure is initiated in an area where the temperature is lower than a preset threshold, so as to reduce the cooling resource allocated to the area where the temperature is lower than the preset threshold.

6. The dynamic energy-saving control method according to claim 1, characterized in that: The step of determining the time period and conditions for energy utilization based on the cooling strategy information and the environmental information and generating an energy efficiency control strategy includes: Analyze the cooling demand of each area based on the cooling strategy information, and identify the cooling efficiency during peak hours and off-peak hours in combination with the temperature and humidity in the environmental information; According to the cooling efficiency, the energy efficiency control strategy is formulated to increase the cooling load during the peak cooling demand period and reduce the cooling load during the low demand period.

7. The dynamic energy-saving control method according to claim 1, characterized in that: The analyzing the cooling demand of each area based on the cooling strategy information, combining the temperature and humidity in the environmental information, and identifying the cooling efficiency during peak hours and off-peak hours, includes: Determine the change pattern of cooling efficiency within a daily operation cycle by analyzing the cooling demand of each area and the temperature and humidity in the environmental information; Based on the variation rule, the cooling efficiency is divided into the peak period and the non-peak period.

8. A dynamic energy-saving control device, characterized in that: The dynamic energy-saving control device comprises: An information acquisition module is used to obtain real-time load information and environmental information of each server in the server cabinet; A task allocation module, used to calculate the energy efficiency score of each server based on the real-time load information, and confirm the task allocation information through the energy efficiency score; A cooling demand analysis module, used to determine the workload distribution of each server according to the task allocation information, and obtain cooling demand information; A cooling control module, used for inputting the cooling demand information into a cooling control model to obtain cooling strategy information; An energy efficiency control module, used to determine the time period and conditions for energy utilization based on the cooling strategy information and the environmental information, and generate an energy efficiency control strategy; The energy-saving coordination control module is used to generate energy-saving control information based on the task allocation information, the cooling demand information, the cooling strategy information and the energy efficiency regulation strategy. The energy-saving control information is used to coordinate and control the load distribution of each server and the operating status of the cooling system.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the dynamic energy-saving control method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the dynamic energy-saving control method according to any one of claims 1 to 7 are implemented.

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