Flywheel energy storage system-based computing power load power demand data calculation method

Abstract

The invention relates to the technical field of electric power energy storage, and particularly discloses a computing power load electric power demand data calculation method based on a flywheel energy storage system, which comprises the following steps: acquiring real-time computing power load data of a data center server through data center monitoring equipment, and analyzing and calculating the computing power load data to obtain the computing power load of a processor; calculating to obtain a comprehensive calculation force load value by combining the calculation force load of the processor; a power demand model of the server is established based on the comprehensive calculation force load value and the environmental parameter comprehensive analysis result of the server, and a power demand value is obtained through analysis; based on the obtained power demand value, a demand change gradient is obtained through analysis, and power demand data of the flywheel energy storage system is obtained through comprehensive analysis; and optimizing and adjusting the power demand data of the flywheel energy storage system according to the dynamic change of the power demand value in combination with the environment temperature compensation function.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy storage, and particularly to a calculation method for power demand data of computing power load based on a flywheel energy storage system. Background Art

[0002] In the prior art, a single index is focused on to evaluate power demand, ignoring the impact of memory load, network bandwidth load, and environmental parameters on power demand. The single-dimensional analysis cannot comprehensively and accurately reflect the actual power demand situation of the data center, resulting in unreasonable power supply planning, problems such as insufficient supply or waste, lacking systematicness and scientificity in data processing, not effectively screening, cleaning, and standardizing the data. The noise and redundancy in the data may interfere with the analysis results, leading to deviations in the evaluation of power demand. The time series characteristics of the data are not fully utilized, unable to accurately grasp the changing trend of power demand, not fully considering the complex operating conditions of the server and the influence of environmental factors, unable to accurately simulate the actual change of power demand, resulting in a large error between the calculated power demand value and the actual demand, and unable to provide reliable guidance for power supply. There is a lack of refined strategies in the management of the flywheel energy storage system, not dynamically adjusting the charge and discharge strategies, unable to give full play to the advantages of the flywheel energy storage system, resulting in low energy utilization efficiency, not considering the influence of environmental factors on the performance of the energy storage system. When the server load or environmental conditions change, the power supply and the operating strategy of the energy storage system cannot be adjusted in time, resulting in insufficient or excessive power supply, increasing energy costs and the instability of system operation. Therefore, a calculation method for power demand data of computing power load based on a flywheel energy storage system is needed. Summary of the Invention

[0003] The purpose of the present invention is to provide a calculation method for power demand data of computing power load based on a flywheel energy storage system. To solve the above-mentioned problems in the prior art, the present invention is achieved through the following technical solutions: In a first aspect, the calculation method for power demand data of computing power load based on a flywheel energy storage system provided by an embodiment of the present invention specifically includes the following steps: Obtain the real-time computing power load data of the data center server through the data center monitoring device, calculate the computing power load of the processor, and obtain the comprehensive computing power load value through comprehensive calculation of the computing power load; Based on the comprehensive analysis result of the comprehensive computing power load value and the environmental parameters of the server, establish a power demand model of the server, and analyze to obtain the power demand value; Based on the obtained power demand value, establish a power demand change curve to analyze the demand change gradient, and comprehensively analyze to obtain the power demand data of the flywheel energy storage system; According to the dynamic changes of the power demand value and the ambient temperature compensation function, the power demand data of the flywheel energy storage system is optimized and compensated.

[0004] In a second aspect, the computing power load power demand data calculation system based on the flywheel energy storage system provided by the embodiment of the present invention specifically includes the following modules: Data collection module: Use the monitoring tools provided by the data center server operating system to obtain CPU usage, memory usage, network bandwidth usage, and process running status information, capture user state, kernel state, and idle time ratios from the kernel state file, and collect server environmental parameters, including the server's ambient temperature and the cooling power of the server's cooling system; Computing load analysis module: Calculate the CPU computing load, memory computing load and network bandwidth load based on the collected data, integrate the obtained CPU computing load, memory computing load and network bandwidth load, build a time window matrix, set m sampling points, sample the computing load data at different time points, and capture the changing characteristics of the computing load data in the time series; standardize the sampled data, unify the data of different magnitudes and distribution ranges to the same scale through the standardized formula, eliminate the impact of different dimensions and value ranges of the data, and calculate the information entropy of the sampled data through the formula based on the standardized sampled data, and calculate the weight of the computing load data to obtain the comprehensive computing load value; Power demand analysis module: The power demand value is obtained based on the comprehensive computing load value and the environmental parameters of the server, and a power demand change curve of the power demand value over time is established. Through this curve, the change trend of the power demand value over time can be intuitively presented. Based on the power demand change curve, the power demand of the server under a specific working state in the power demand curve is analyzed; the demand change gradient is calculated based on the power demand change curve to reflect the rate of change of the power demand value over time; Power demand data adjustment module: The flywheel energy storage system is adjusted based on the power demand value. Taking into account the impact of ambient temperature on the discharge power of the flywheel energy storage system, the discharge power of the flywheel energy storage system is compensated. According to the dynamic changes in the power demand value, the power demand data of the flywheel energy storage system is optimized and adjusted in real time. The changes in power demand are continuously monitored and analyzed, and the charge and discharge strategies and power of the flywheel energy storage system are adjusted, so that the entire system can adapt to the changing power demand of the data center in real time.

[0005] Beneficial effects of the present invention: 1. Incorporate the environmental parameters of the server into consideration through computing power load data. By constructing a time window matrix, setting sampling points, performing data standardization, and calculating information entropy, establish a power demand model for the server based on the comprehensive computing power load value and environmental parameters, reflecting the power demand of the server under different operating states and environmental conditions, and providing an effective basis for accurately calculating the power demand value; calculate the power demand value in real time according to the changes in the server operating state and environmental parameters, effectively mine the obtained data, and improve the quality and usability of the data; 2. By constructing a power demand change curve, clearly and intuitively analyze the data in the power demand curve, calculate the demand change gradient based on the power demand change curve, and set the charge and discharge conditions of the flywheel energy storage system according to the load fluctuation type and power demand value; improve the energy utilization efficiency of the flywheel energy storage system in the power supply of the data center. By setting an environmental temperature compensation function to perform power compensation on the discharge power, optimize and adjust the power demand data of the flywheel energy storage system according to the dynamic changes in the power demand value, enabling the entire system to adapt to the continuously changing power demand of the data center in real time. Description of the Drawings

[0006] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for description in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0007] Figure 1 is the flowchart of the steps of the computing power load power demand data calculation method based on the flywheel energy storage system provided in Embodiment 1 of the present invention; Figure 2 is the structural schematic diagram of the computing power load power demand data calculation system based on the flywheel energy storage system provided in Embodiment 2 of the present invention. Detailed Embodiments

[0008] To enable those skilled in the art to better understand the solution of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0009] Embodiment 1 As Figure 1 shown, the computing power load power demand data calculation method based on the flywheel energy storage system provided in the embodiment of the present invention specifically includes the following steps: Step 1: Obtain the real-time computing power load data of the data center servers through the monitoring devices in the data center. Construct a comprehensive computing power load value based on the computing power load data, and comprehensively analyze the comprehensive computing power load value and the environmental parameters of the servers to establish a power demand model for the servers and analyze to obtain the power demand value. It should be noted that the computing power load data includes: CPU computing power load, memory computing power load, and network bandwidth load, and the environmental parameters include: the environmental temperature of the servers and the cooling power of the server cooling systems. In some embodiments, obtain the CPU usage rate through the monitoring tools built into the data center server operating system , the memory usage rate , the network bandwidth usage rate and the process running status information; It should be noted that the process running status information is a data set reflecting the current status of the processes running in the computer, covering the basic attributes of the processes, resource occupancy, and execution status; Capture the proportion of user mode, kernel mode, and idle time through the kernel status file, and calculate the average multi-core utilization rate of the CPU through normalization , through the composite weight formula: Obtain the CPU computing power load , where represents the number of context switches per second, represents the benchmark value of the preset context switch times of the CPU, represents the number of processes waiting in the run queue, represents the number of physical cores of the CPU, , and are both preset weight coefficients, takes a value of 0.6, takes a value of 0.25, takes a value of 0.15; Based on the obtained memory usage rate through the weight formula: Obtain the memory computing power load , where represents the usage amount of the memory swap partition, represents the amount of physical memory, represents the cache hit rate, , and are both preset weight coefficients, takes a value of 0.7, The value is 0.2, the value is 0.1; It should be noted that the amount of swap partition used represents the size of the space occupied when the operating system stores the data that is not temporarily used in the physical memory into the swap partition when the physical memory of the system is insufficient; the cache hit rate represents the proportion of the requested data that can be directly found in the cache system; Based on the obtained network bandwidth utilization rate Through the formula: Calculate the network bandwidth load , where is the received bandwidth utilization rate, is the transmitted bandwidth utilization rate and , represents the network received bandwidth, represents the network transmitted bandwidth, represents the nominal bandwidth of the network card, represents the packet error rate and , represents the preset correction coefficient, the value is 0.3; Based on the obtained computing power load data, construct a time window matrix , set m sampling points, and the sampling data groups respectively correspond to the sampling data of CPU computing power load, memory computing power load and network bandwidth load. Standardize the obtained sampling data, and obtain the standardized sampling data through the standardization formula where, is the sampling eigenvalue of the computing power load data in the i-th row and j-th column of the time window matrix, is the maximum value of the sampling eigenvalues in the time window matrix, is the minimum value of the sampling eigenvalues in the time window matrix; Based on the obtained standardized sampling data, through the formula: Obtain the sampling data information entropy , where j is the data index of the sampling data group, i is the sampling data point index, and m represents the number of sampling points; Calculate the weights of each computing power load data through the redundancy of the sampling data information entropy = , , corresponding to the weights of CPU computing power load, memory computing power load and network bandwidth load respectively, n is the number of sampling data points, is the redundancy of each group of sampling data and , is the number of discretization intervals, is the information entropy of the sampling data, through the formula: obtain the comprehensive computing power load value , where, is the CPU computing power load value, is the memory computing power load value, is the network bandwidth load value, represents the sampling time point, , and are respectively the CPU computing power load, memory computing power load and network bandwidth load at time point t, , and are respectively the CPU computing power load, memory computing power load and network bandwidth load at time point t-1, represents the sampling interval, represents the rate-of-change compensation coefficient, with a value of 0.2; Based on the obtained comprehensive computing power load value, establish the power demand model of the server by integrating environmental parameters; It should be noted that the power demand model of the server is established by real-time monitoring of the computing power load and environmental parameters. The model can accurately quantify the energy consumption characteristics of the server cluster. Exemplarily, when the GPU utilization rate reaches 90% during the AI training task, combined with the condition of the computer room temperature of 28 °C, the model can dynamically adjust the power supply strategy: migrate non-real-time tasks to low-temperature periods to execute, and use natural cooling to reduce the power consumption of the air conditioner; at the same time, trigger the flywheel energy storage system to compensate for the instantaneous power gap and reduce the dependence on the high-price peak power of the power grid; Specifically, obtain the no-load power consumption of the server and the environmental temperature of the server , and based on the comprehensive computing power load value, obtain the basic power consumption of the server through the formula , where, is the linear coefficient of the computing power load and power consumption, and the power consumption increment corresponding to the unit computing power load is obtained by fitting historical data; Based on the obtained environmental temperature of the server , through the formula: Establish the power demand model of the server and obtain the power demand value , where, represents the temperature coefficient, with a value of 0.02 / , It represents the heat dissipation efficiency coefficient, The value is 0.05, It represents the maximum cooling power of the cooling system, It represents the ambient reference temperature; It should be noted that the power consumption of the server when it is idle It represents the power consumption of the server when it is idle at the ambient reference temperature Under this condition, the coupling relationship between the environmental parameters and the power demand directly affects the stability of the equipment. Predict the corrosion risk of electrical components, automatically reduce the GPU main frequency to control the temperature rise, and migrate high-priority tasks to the stable area server. By analyzing the load-fault correlation in historical data, the model generates maintenance warnings in advance. Considering multiple key factors such as the power consumption of the server when it is idle, the comprehensive computing power load value, and the environmental temperature to calculate the basic power consumption and power demand value is more comprehensive than the method of simply estimating by considering a single factor, which can more accurately reflect the power consumption situation of the server during actual operation, making the calculation result closer to the true value, and providing more accurate data support for power supply and energy consumption management; in the formula for calculating the power demand value, the temperature coefficient and the heat dissipation efficiency coefficient are clearly introduced to quantitatively evaluate the impact of the environmental temperature on the power demand and accurately measure the corresponding change in the power demand of the server when the environmental temperature changes, avoiding the calculation deviation of the power demand caused by temperature; The technical solution of the present invention is as follows: By incorporating the environmental parameters of the server into consideration through the computing power load data, a power demand model of the server is established based on the comprehensive computing power load value and the environmental parameters by constructing a time window matrix, setting sampling points, performing data standardization, and calculating information entropy, which can reflect the power demand situation of the server under different operating states and environmental conditions and provide an effective basis for accurately calculating the power demand value; Calculate the power demand value in real time according to the changes in the operating state of the server and the environmental parameters, effectively mine the obtained data, and improve the quality and usability of the data.

[0010] Embodiment 2 As Figure 1 shown, the method for calculating the power demand data of the computing power load based on the flywheel energy storage system provided by the embodiment of the present invention specifically includes the following steps: Step 2: Based on the obtained power demand value, establish a power demand change curve to analyze the demand change gradient, comprehensively analyze to obtain the power demand data of the flywheel energy storage system, and optimize and adjust the power demand data of the flywheel energy storage system according to the dynamic change of the power demand value; It should be noted that the power demand data includes: the discharge power and the charging power of the flywheel energy storage system; In some embodiments, obtain the power demand value of the server, and establish a power demand change curve of the power demand value with respect to time with time as the X-axis and the power demand value as the Y-axis; It should be noted that by visually observing the fluctuations of the server power demand over time through the curve, one can clearly understand the type of change and the increase or decrease of the power demand value, and easily identify the outliers or mutation points of the power demand. When the curve shows obvious peaks or troughs that do not conform to the normal trend, one can quickly detect possible problems such as server failures and service anomalies, which is convenient for timely troubleshooting and handling. Exemplarily, if the curve shows periodic peaks and troughs, it means that there are periodic busy and idle periods in the service; Based on the obtained power demand change curve, analyze the peak value generated by the sudden increase in instantaneous power consumption when the CPU is fully loaded in the power demand curve and analyze the average power demand value at high load during the continuous high-load period when processing batch tasks and analyze the power demand value in the trough section during low load at night ; It should be noted that by analyzing the peak value of the sudden increase in instantaneous power consumption when the CPU is fully loaded, one can understand the power consumption of the server hardware under extreme load, thereby evaluating the performance boundary of the hardware, which helps to determine whether the server can withstand sudden high-load tasks and provides a basis for hardware upgrade or expansion. Analyzing the average power demand value at high load during the continuous high-load period when processing batch tasks can understand the demand characteristics of batch tasks for power resources, optimize the task scheduling strategy, and improve the overall performance and stability of the system. Analyzing the power demand value in the trough section during low load at night can discover whether there is power waste or resource idleness in the system under low load conditions, providing a direction for further performance optimization; Calculate the demand change gradient based on the power demand change curve where, represents the difference in power demand values between adjacent sampling time points, represents the sampling time interval, and identify sudden load fluctuations and progressive load fluctuations; It should be noted that the sampling time is set by those skilled in the art of the present invention based on historical experience and can be adjusted according to actual situations; If the absolute value of the demand change gradient is greater than or equal to the preset demand change gradient threshold , it indicates that the load fluctuation is a sudden load fluctuation; If the absolute value of the demand change gradient is less than or equal to the preset demand change gradient threshold , it indicates that the load fluctuation is a gradual load fluctuation; If the absolute value of the demand change gradient is greater than the preset demand change gradient threshold and less than the preset demand change gradient threshold , it indicates that the load fluctuation is a mixed load fluctuation; It should be noted that the preset demand change gradient threshold is set by those skilled in the technical field of the present invention according to historical experience and can be adjusted according to actual situations; Based on the identified load fluctuation type and the obtained power demand value, analyze and obtain the power demand data of the flywheel energy storage system; Specifically, if the demand change gradient is greater than or equal to the preset demand change gradient threshold and the power demand value is greater than or equal to the preset power demand threshold , then the flywheel energy storage system triggers the discharge condition, and the discharge power is calculated by the formula: Obtain the discharge power of the flywheel energy storage system , where represents the rated maximum power of the flywheel, represents the current energy storage state of the flywheel energy storage system and ; If the power demand value is less than or equal to the preset power demand threshold and there is grid redundancy, then the flywheel energy storage system triggers the charging condition, and the charging power is calculated by the formula: Obtain the charging power of the flywheel energy storage system , where represents the charging efficiency of the flywheel energy storage system; If the power demand value is greater than the preset power demand threshold and less than the preset power demand threshold , then the flywheel energy storage system does not discharge to the server and charges based on the rated charging power of the flywheel energy storage system until the energy storage state reaches 100%; It should be noted that by calculating the demand change gradient of the power demand change curve and comparing it with the preset demand change gradient threshold, the load fluctuations can be accurately classified into sudden, progressive, and mixed types. Precise identification of the fluctuation type helps the operation and maintenance personnel to deeply understand the change characteristics of the server load and adopt different coping strategies for different types of fluctuations; Based on the obtained discharge power of the flywheel energy storage system , perform power compensation on the discharge power of the flywheel energy storage system through the ambient temperature. Calculate the ambient temperature compensation function by the formula: Perform power compensation on the discharge power of the flywheel energy storage system, where represents the ambient temperature of the server, represents the preset temperature compensation coefficient, takes a value of 0.6, represents a preset compensation reference temperature and ; Based on the obtained ambient temperature compensation function, through the formula: obtain the compensated and optimized compensated discharge power , where represents the power consumption of the server when idle, represents the power demand value of the server, represents a preset power demand threshold, represents the rated maximum power of the flywheel, represents the current energy storage state of the flywheel energy storage system and , represents the ambient temperature compensation function; It should be noted that the ambient temperature will affect the performance of the flywheel energy storage system. By introducing the ambient temperature compensation function, the discharge power is adjusted in real time according to the actual ambient temperature, so that the flywheel energy storage system can maintain a relatively stable discharge power output under different temperature conditions, enhance the system's adaptability to the environment, reduce the system performance fluctuations caused by temperature changes, ensure reliable power supply for the server under different temperature environments, consider the compensation of the discharge power due to the ambient temperature, make the flywheel energy storage system discharge reasonably at different temperatures, avoid abnormal discharge power caused by too high or too low temperature, thereby improving the overall energy storage and discharge efficiency of the flywheel energy storage system, making full use of the energy storage capacity of the flywheel energy storage system, realizing the optimal allocation of resources, combining the power consumption of the server when idle and the preset power demand threshold to calculate the compensated discharge power, and making the discharge power of the flywheel energy storage system better match the actual power demand of the server; The technical solution of the embodiment of the present invention is: by constructing a power demand change curve, clearly and intuitively analyzing the data in the power demand curve, calculating the demand change gradient based on the power demand change curve, and thereby identifying sudden load fluctuations, progressive load fluctuations and mixed load fluctuations, setting the charge and discharge conditions of the flywheel energy storage system according to the load fluctuation type and the power demand value; enabling the flywheel energy storage system to improve the energy utilization efficiency in the power supply of the data center, considering the influence of the ambient temperature, and performing power compensation on the discharge power by setting the ambient temperature compensation function, optimizing and adjusting the power demand data of the flywheel energy storage system according to the dynamic change of the power demand value, so that the entire system can adapt to the changing power demand situation of the data center in real time, accurately analyzing the power demand and reasonably managing the flywheel energy storage system, and enhancing the stability of the power supply system of the data center.

[0011] Embodiment 3 Such as Figure 2As shown, the computing power load power demand data calculation system based on the flywheel energy storage system provided by the embodiment of the present invention specifically includes the following modules: Data collection module: Use the monitoring tools provided by the data center server operating system to obtain CPU usage, memory usage, network bandwidth usage, and process running status information, capture user state, kernel state, and idle time ratios from the kernel state file, and collect server environmental parameters, including the server's ambient temperature and the cooling power of the server's cooling system; Computing load analysis module: Calculate the CPU computing load, memory computing load and network bandwidth load based on the collected data, integrate the obtained CPU computing load, memory computing load and network bandwidth load, build a time window matrix, set m sampling points, sample the computing load data at different time points, and capture the changing characteristics of the computing load data in the time series; standardize the sampled data, unify the data of different magnitudes and distribution ranges to the same scale through the standardized formula, eliminate the impact of different dimensions and value ranges of the data, and calculate the information entropy of the sampled data through the formula based on the standardized sampled data, and calculate the weight of the computing load data to obtain the comprehensive computing load value; Power demand analysis module: The power demand value is obtained based on the comprehensive computing load value and the environmental parameters of the server, and a power demand change curve of the power demand value over time is established. Through this curve, the change trend of the power demand value over time can be intuitively presented. Based on the power demand change curve, the power demand of the server under a specific working state in the power demand curve is analyzed; the demand change gradient is calculated based on the power demand change curve to reflect the rate of change of the power demand value over time; Power demand data adjustment module: The flywheel energy storage system is adjusted based on the power demand value. Taking into account the impact of ambient temperature on the discharge power of the flywheel energy storage system, the discharge power of the flywheel energy storage system is compensated. According to the dynamic changes in the power demand value, the power demand data of the flywheel energy storage system is optimized and adjusted in real time. The changes in power demand are continuously monitored and analyzed, and the charge and discharge strategies and power of the flywheel energy storage system are adjusted, so that the entire system can adapt to the changing power demand of the data center in real time.

[0012] An embodiment of the present invention is described in detail above, but the contents described are only preferred embodiments of the present invention and cannot be considered to limit the scope of implementation of the present invention; the above formulas are all dimensionless and numerical calculations, and the formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions and historical experience, and can be adjusted according to actual conditions; all equal changes and improvements made according to the scope of application of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A calculation method for power demand data of computing power load based on a flywheel energy storage system, characterized in that, Including the following steps: Calculate the computing power load of the processor based on real-time computing power load data, and obtain the comprehensive computing power load value through comprehensive calculation of the computing power load; Establish a power demand model for the server based on the comprehensive analysis result of the comprehensive computing power load value and the environmental parameters of the server, and analyze to obtain the power demand value; Based on the power demand value, establish a power demand change curve, and analyze to obtain the demand change gradient and the power demand data of the flywheel energy storage system; Optimize and compensate and adjust the power demand data of the flywheel energy storage system according to the dynamic change of the power demand value in combination with the environmental temperature compensation function.

2. The calculation method of the computing power load power demand data based on the flywheel energy storage system according to claim 1, wherein The process of obtaining the computing power load of the processor is as follows: Calculate the average utilization rate of multiple CPU cores, memory usage rate, and network bandwidth usage rate through normalization, and through the composite weight formula: Obtain the CPU computing power load and the memory computing power load and the network broadband load , where is the CPU usage rate, represents the number of context switches per second, represents the baseline value of the preset number of context switches of the CPU, represents the number of processes waiting in the run queue, represents the number of physical cores of the CPU, is the memory usage rate, , and are all preset weight coefficients, represents the usage amount of the memory swap partition, represents the amount of physical memory, represents the cache hit rate, , and are all preset weight coefficients, represents the network receive bandwidth, represents the network transmit bandwidth, represents the nominal bandwidth of the network card, represents the packet error rate and , represents the preset correction coefficient.

3. The calculation method of the computing power load power demand data based on the flywheel energy storage system according to claim 1, characterized in that, The process of obtaining the comprehensive computing power load value is as follows: Calculate the weights of each computing power load data by the redundancy of the sampled data information entropy = , corresponding to the weights of the CPU computing power load, memory computing power load, and network bandwidth load respectively, where j is the data index of the sampled data group, i is the sampled data point index, n is the number of sampled data points, is the redundancy of each group of sampled data and , is the number of discretization intervals, is the sampled data information entropy, through the formula: Obtain the comprehensive computing power load value , where is the CPU computing power load value, is the memory computing power load value, is the network bandwidth load value, represents the sampling time point, , and are the CPU computing power load, memory computing power load, and network bandwidth load at time point t respectively, , and are the CPU computing power load, memory computing power load, and network bandwidth load at time point t - 1 respectively, represents the sampling interval, represents the change rate compensation coefficient.

4. The calculation method of the computing power load power demand data based on the flywheel energy storage system according to claim 3, characterized in that The process of obtaining the information entropy of the sampled data information is as follows: Based on the obtained computing power load data, construct a time window matrix , set m sampling points, standardize the obtained sampling data, and obtain the standardized sampling data through the standardization formula where is the sampling eigenvalue of the computing power load data in the i-th row and j-th column of the time window matrix, is the maximum value of the sampling eigenvalues in the time window matrix, is the minimum value of the sampling eigenvalues in the time window matrix; ​ Based on the obtained standardized sampled data, through the formula: Obtain the information entropy of the sampled data , where j is the data index of the sampled data group, i is the sampled data point index, and m represents the number of sampling points.

5. The calculation method of the computing power load power demand data based on the flywheel energy storage system according to claim 1, wherein The process of obtaining the power demand value is as follows: Obtain the no-load power consumption of the server and the ambient temperature of the server , based on the comprehensive computing power load value through the formula obtain the basic power consumption of the server , where is the linear coefficient of computing power load and power consumption; Based on the obtained ambient temperature of the server , through the formula: Establish a power demand model for the server and obtain the power demand value , where represents the temperature coefficient represents the heat dissipation efficiency coefficient represents the maximum cooling power of the cooling system represents the environmental reference temperature 6. The calculation method of the computing power load power demand data based on the flywheel energy storage system according to claim 1, characterized in that The process of obtaining the demand change gradient is as follows: Calculating the demand change gradient based on the power demand change curve , where represents the difference in power demand values between adjacent sampling time points, represents the sampling time interval, and identify sudden load fluctuations and progressive load fluctuations.

7. The calculation method of the computing power load power demand data based on the flywheel energy storage system according to claim 1, wherein The process of obtaining the power demand data is as follows: Based on the identified load fluctuation type and the obtained power demand value, analyze to obtain the discharge power and charge power of the flywheel energy storage system; When the demand change gradient is greater than or equal to a preset demand change gradient threshold and the power demand value is greater than or equal to a preset power demand threshold the discharge condition is triggered, and the discharge power of the flywheel energy storage system is calculated through the discharge power formula.

8. The calculation method of the computing power load power demand data based on the flywheel energy storage system according to claim 7, characterized in that, The process of obtaining the charge power is as follows: When the power demand value is less than or equal to a preset power demand threshold and the power grid is redundant, the charging condition is triggered, and the charging power of the flywheel energy storage system is calculated through the charging power formula.

9. The calculation method of the computing power load power demand data based on the flywheel energy storage system according to claim 1, characterized in that, The process of obtaining the environmental temperature compensation function is as follows: Based on the obtained discharge power of the flywheel energy storage system, power compensation for the discharge power of the flywheel energy storage system is performed through the ambient temperature. Calculate the ambient temperature compensation function through the formula Perform power compensation on the discharge power of the flywheel energy storage system, where represents the ambient temperature of the server, is the preset temperature compensation coefficient, takes a value of 0.6, is the preset compensation reference temperature and .

10. The calculation method of the computing power load power demand data based on the flywheel energy storage system according to claim 1, wherein The optimization compensation process is as follows: Based on the obtained environmental temperature compensation function, through the formula obtain the compensated and optimized compensated discharge power, where represents the power consumption of the server when it is idle, represents the power demand value of the server, represents a preset power demand threshold, represents the rated maximum power of the flywheel, represents the current energy storage state of the flywheel energy storage system and , represents the environmental temperature compensation function.

Images