Server energy consumption control method and system based on fuzzy control algorithm

Abstract

The invention relates to a server energy consumption control method and system based on a fuzzy control algorithm, and the method comprises the steps: deploying a data collection module, an AD converter and a fuzzy control module at a designated position in a server, and enabling the data collection module to collect the operation data and energy consumption data of a designated energy consumption part of the server; the digital signal is input into a fuzzy control module for fuzzy quantization processing, and the digital signal is converted into a corresponding fuzzy linguistic variable value; creating a fuzzy control rule, reasoning the digital signal input value after the input fuzzy quantization processing through the fuzzy control rule, and calculating a fuzzy output value; a fuzzy output value obtained through reasoning is converted to obtain an accurate output value, the accurate output value is converted into an action control parameter of a corresponding server energy consumption part, and the action control parameter is converted into an action control command; and transmitting the action control command to a corresponding server energy consumption part to execute a corresponding action. The energy consumption of the server is minimized, and the energy consumption cost is reduced while the carbon emission is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of server energy consumption control, and particularly relates to a server energy consumption control method and system based on a fuzzy control algorithm. Background Art

[0002] The development of electronic technology enables the control of power supplies to be achieved through electronic circuits, providing more precise power consumption control. The wide application of microcontrollers provides an intelligent control core for power consumption control. Sensing technologies, such as temperature sensors, power consumption sensors, etc., can monitor environmental parameters in real time and provide a basis for power consumption control. With the improvement of energy awareness, achieving energy conservation through power consumption control has become an important focus. In electronic devices, good power consumption control is crucial for performance and lifespan, and power consumption control can optimize the power consumption control effect. Different power consumptions meet users' requirements for air volume and noise. The concept of intelligent control realizes the automatic power consumption control of power supplies and dynamically adjusts according to the actual situation. By controlling power consumption, energy consumption can be reduced, and the impact on the environment can be minimized. Due to the requirements of system integration, power consumption control of power supplies needs to work in coordination with other system components. Reliability and stability: Ensure the long-term reliable operation of the power consumption control system.

[0003] The purpose of intelligent power consumption control strategies is to minimize power supply noise and energy consumption while meeting power consumption control requirements. The following are some common intelligent power consumption control strategies: Temperature-sensing power consumption control: Monitor the environmental or device temperature through a temperature sensor and automatically adjust the power consumption of the power supply according to temperature changes. The current working modes of server power supplies are generally load balancing or primary-backup modes; that is, the power supplies only work at the same power consumption, or let the specified power supply output while the rest of the power supplies are on standby. Especially in the case of the primary-backup mode being set, it will cause individual power supplies to be in high-temperature and high-load working conditions for a long time, which has a negative impact on the lifespan, temperature, and working noise of the power supplies. The current power supply working mode also has the problem that the power supply efficiency is not in the optimal output range. The output efficiencies of high-load and low-load power supplies are both relatively low, resulting in power waste and increased carbon emissions. The current speed control strategies of power supply fans are few and can be optimized. It is difficult to balance noise and temperature. In the process of server energy consumption control, not only the power supply and the fan need to be concerned, but more importantly, the main energy-consuming components of the server need to be concerned. By coordinating the main energy-consuming components, the overall energy consumption of the server can be controlled and reduced.

[0004] Therefore, how to provide a control strategy for energy consumption execution components to minimize the energy consumption of the server while ensuring the normal operation of the server, reduce carbon emissions, and lower the energy consumption cost is a technical problem that urgently needs to be solved at present. Summary of the Invention

[0005] The object of the present invention is to provide a server energy consumption control method and system based on a fuzzy control algorithm, so as to provide a control strategy for an energy consumption execution component, minimize the server energy consumption while ensuring the normal operation of the server, reduce carbon emissions, and reduce energy consumption costs.

[0006] To solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0007] A server energy consumption control method based on a fuzzy control algorithm includes the following steps:

[0008] S1: Deploy a data acquisition module, an AD converter, and a fuzzy control module at a specified position in the server, establish a connection between the data acquisition module and the AD converter and a connection between the AD converter and the fuzzy control module, and connect the fuzzy control module to a specified energy consumption component of the server;

[0009] S2: The data acquisition module collects the operation data and energy consumption data of the specified energy consumption component of the server, and the AD converter converts the analog signals of the collected operation data and energy consumption data into digital signals and inputs them into the fuzzy control module;

[0010] S3: The fuzzy control module performs fuzzy quantization processing on the digital signals and converts the digital signals into corresponding fuzzy language variable values;

[0011] S4: Create a fuzzy control rule, which is used to describe the relationship between the input value and the output value, and perform inference on the input digital signal value after fuzzy quantization processing through the fuzzy control rule to calculate the fuzzy output value;

[0012] S5: Convert the inferred fuzzy output value into an accurate output value, convert the accurate output value into the action control parameters of the corresponding server energy consumption component, and convert the action control parameters into action control commands;

[0013] S6: Transmit the action control command to the corresponding server energy consumption component, and the server energy consumption component performs corresponding actions based on the action control command.

[0014] Preferably, the specific process of the AD converter converting the analog signals of the collected operation data and energy consumption data into digital signals in step S2 is as follows:

[0015] S21: Discretize the analog signals of the operation data and energy consumption data at a preset time point, and sample the analog signals of the operation data and energy consumption data at a preset time interval;

[0016] S22: Hold the instant analog signal obtained by sampling for a specified time to obtain a stable analog signal, approximate the analog signals of the running data and energy consumption data with continuous amplitudes by a finite number of amplitude values, and convert the continuous amplitudes of the analog signals of the running data and energy consumption data into a finite number of discrete values with a certain interval;

[0017] S23: Encode the discrete values with a certain interval into binary digital codes according to a preset rule to obtain the digital signals of the running data and energy consumption data.

[0018] Preferably, the specific process of the fuzzy control module performing fuzzy quantization processing on the digital signal in step S3 is as follows:

[0019] S31: Create a fuzzy data set according to the digital signals of the row data and energy consumption data obtained in step S23;

[0020] S32: Determine the upper bound, lower bound and rules of the fuzzy data set, and divide the range determined by the upper bound, lower bound and rules into three equal - divided regions, and set different membership degrees for the three equal - divided regions;

[0021] S33: Specify the boundaries of the three equal - divided regions, divide the value range of the input variable corresponding to each region into at least three hundred equal parts, and construct the membership function of the entire fuzzy data set with the membership degrees of the input variables at the specified values.

[0022] Preferably, the specific process of converting the output value obtained by inference in step S5 into an accurate output value is as follows:

[0023] S51: Obtain all the fuzzy information of the output value of the fuzzy inference, and construct a triple representation of the fuzzy information according to all the fuzzy information;

[0024] S52: Calculate the centroid position of the fuzzy information of the output value through the triple representation, and output the centroid position, where the centroid position represents the center, position and size of the fuzzy data set.

[0025] Preferably, the specific process of converting the accurate output value into an action control command for the energy consumption component of the corresponding server is as follows:

[0026] S53: Decode the accurate output value to obtain the type, operand and execution steps of the corresponding operation of the accurate output value;

[0027] S54: Generate a corresponding control signal according to the type, operand and execution steps of the corresponding operation, where the control signal is a series of binary bits;

[0028] S55: Transmit the generated control signals to the hardware execution modules that need to perform corresponding operations through the internal bus system of the server, and the hardware execution modules perform corresponding actions according to the control signals.

[0029] In a second aspect, a server energy consumption control system based on a fuzzy control algorithm is provided for implementing the server energy consumption control method based on a fuzzy control algorithm, including a data acquisition module, an AD converter, and a fuzzy control module. The data acquisition module is connected to the AD converter, and the AD converter is connected to the fuzzy control module. The fuzzy control module includes a fuzzy quantization unit, a fuzzy rule creation unit, a defuzzification unit, and a control command generation unit.

[0030] The data acquisition module is used to acquire the operation data and energy consumption data of the specified energy consumption components of the server.

[0031] The AD converter is used to convert the analog signals of the acquired operation data and energy consumption data into digital signals and input them into the fuzzy control module.

[0032] The fuzzy quantization unit is used to perform fuzzy quantization processing on the digital signals and convert the digital signals into corresponding fuzzy linguistic variable values.

[0033] The fuzzy rule creation unit is used to create fuzzy control rules. The fuzzy control rules are used to describe the relationship between the input values and the output values, and infer the input digital signal values after fuzzy quantization processing through the fuzzy control rules to calculate the fuzzy output values.

[0034] The defuzzification unit is used to convert the inferred fuzzy output values into accurate output values.

[0035] The control command generation unit is used to convert the accurate output values into action control parameters for the corresponding server energy consumption components and convert the action control parameters into action control commands.

[0036] The beneficial effects of the present invention include:

[0037] The server energy consumption control method and system based on the fuzzy control algorithm provided by the present invention deploy a data acquisition module, an AD converter, and a fuzzy control module at specified positions in the server. The data acquisition module collects the operation data and energy consumption data of the specified energy consumption components in the server, converts them into digital signals, and then inputs them into the fuzzy control module for fuzzy quantization processing to convert the digital signals into corresponding fuzzy language variable values; create fuzzy control rules, and use the fuzzy control rules to infer the input digital signal input values after fuzzy quantization processing to calculate the fuzzy output values; convert the inferred fuzzy output values into accurate output values, and convert them into the action control parameters of the corresponding server energy consumption components, and convert the action control parameters into action control commands; transmit the action control commands to the corresponding server energy consumption components to execute corresponding actions. This enables the minimization of server energy consumption, reduces carbon emissions, and simultaneously reduces energy consumption costs.

[0038] First, by discretizing the analog signals of the operation data and energy consumption data at preset time points, sampling the analog signals of the operation data and energy consumption data at a preset time interval, and approximating the analog signals of the continuous amplitude operation data and energy consumption data with a finite number of amplitude values to obtain discrete values with a certain interval; the process of encoding the discrete values with a certain interval into binary digital codes according to a preset rule to obtain the digital signals of the operation data and energy consumption data greatly reduces the data processing volume and improves the data processing efficiency of fuzzy control.

[0039] Secondly, by creating a fuzzy data set for the digital signals of the row data and energy consumption data, determining the upper bound, lower bound, and rules of the fuzzy data set, and dividing the range determined by the upper bound, lower bound, and rules into three equal parts, setting different membership degrees for the three equal parts, specifying the boundaries of the three equal parts, dividing the variable value range of the input corresponding to each region into at least three hundred equal parts, and constructing the membership function of the entire fuzzy data set based on the membership degrees of the input variables under the specified values, the fuzzy relationship between the input value and the output value can be accurately described, and the control accuracy of subsequent fuzzy control can be improved.

[0040] Finally, by obtaining all the fuzzy information of the output value of the fuzzy inference, constructing a triple table of the fuzzy information according to all the fuzzy information, and calculating the centroid position of the fuzzy information of the output value for output. Decoding the accurate output value to obtain the type, operand, and execution steps of the corresponding operation of the accurate output value; generating corresponding control signals according to the type, operand, and execution steps of the corresponding operation, and transmitting the generated control signals to the hardware execution modules that need to execute the corresponding operations through the bus system inside the server to execute corresponding actions, which realizes the precise control of the energy consumption execution components and reduces the server energy consumption through the fuzzy control process while ensuring the normal operation of the server. Description of the Drawings

[0041] Figure 1 This is a schematic flow chart of the server energy consumption control method based on the fuzzy control algorithm of the present invention.

[0042] Figure 2 This is a schematic flow chart of the fuzzy quantization process for the digital signal of the present invention.

[0043] Figure 3 This is a schematic architecture diagram of the server energy consumption control system based on the fuzzy control algorithm of the present invention. Detailed Description of the Invention

[0044] The following is a further detailed description of the present invention in conjunction with the attached Figures 1 to 3 drawings:

[0045] Embodiment 1

[0046] Referring to the attached Figure 1 drawings, a server energy consumption control method based on the fuzzy control algorithm includes the following steps:

[0047] S1: Deploy a data acquisition module, an AD converter, and a fuzzy control module at a specified location in the server, establish a connection between the data acquisition module and the AD converter and a connection between the AD converter and the fuzzy control module, and connect the fuzzy control module to a specified energy consumption component of the server;

[0048] S2: The data acquisition module collects the operation data and energy consumption data of the specified energy consumption component of the server, and the AD converter converts the analog signals of the collected operation data and energy consumption data into digital signals and inputs them to the fuzzy control module;

[0049] S3: The fuzzy control module performs fuzzy quantization processing on the digital signals and converts the digital signals into corresponding fuzzy linguistic variable values;

[0050] S4: Create fuzzy control rules, which are used to describe the relationship between input values and output values, and perform inference on the input digital signal values after fuzzy quantization processing through the fuzzy control rules to calculate fuzzy output values;

[0051] S5: Convert the inferred fuzzy output values into accurate output values, convert the accurate output values into action control parameters for the corresponding server energy consumption components, and convert the action control parameters into action control commands;

[0052] S6: Transmit the action control commands to the corresponding server energy consumption components, and the server energy consumption components perform corresponding actions based on the action control commands.

[0053] In the process of controlling the energy consumption of the server of the present invention, the operation data and energy consumption data of the specified energy consumption components of the server are collected by the data acquisition module, converted into digital signals, and then input into the fuzzy control module for fuzzy quantization processing, and the digital signals are converted into corresponding fuzzy linguistic variable values; fuzzy control rules are created, and the input digital signal input values after fuzzy quantization processing are inferred through the fuzzy control rules to calculate the fuzzy output values; the fuzzy output values obtained by inference are converted to obtain accurate output values, and then converted into action control parameters for the corresponding server energy consumption components, and the action control parameters are converted into action control commands; the action control commands are transmitted to the corresponding server energy consumption components to execute corresponding actions, so as to reduce the overall energy consumption of the server.

[0054] Embodiment 2

[0055] On the basis of Embodiment 1, the specific process of the AD converter in step S2 for converting the analog signals of the collected operation data and energy consumption data into digital signals is as follows:

[0056] S21: Discretize the analog signals of the operation data and energy consumption data at preset time points, and sample the analog signals of the operation data and energy consumption data at a preset time interval.

[0057] S22: Hold the instantaneous analog signals obtained by sampling for a specified time to obtain stable analog signals, approximate the analog signals of the operation data and energy consumption data with continuous amplitudes through a finite number of amplitude values, and convert the continuous amplitudes of the analog signals of the operation data and energy consumption data into a finite number of discrete values with a certain interval.

[0058] S23: Encode the discrete values with a certain interval into binary digital codes according to a preset rule to obtain the digital signals of the operation data and energy consumption data.

[0059] In this embodiment, by discretizing the analog signals of the operation data and energy consumption data at preset time points, sampling the analog signals of the operation data and energy consumption data at a preset time interval, approximating the analog signals of the operation data and energy consumption data with continuous amplitudes through a finite number of amplitude values to obtain discrete values with a certain interval; and encoding the discrete values with a certain interval into binary digital codes according to a preset rule to obtain the digital signals of the operation data and energy consumption data, the data processing amount is greatly reduced, and the data processing efficiency of fuzzy control is improved.

[0060] See Figure 2 As shown, the specific process of the fuzzy control module in step S3 for performing fuzzy quantization processing on the digital signals is as follows:

[0061] S31: Create a fuzzy data set based on the digital signals of the row data and energy consumption data obtained in step S23;

[0062] S32: Determine the upper bound, lower bound, and rules of the fuzzy data set, and divide the range determined by the upper bound, lower bound, and rules into three equal - divided regions, and set different membership degrees for the three equal - divided regions;

[0063] S33: Specify the boundaries of the three equal - divided regions, divide the value range of the input variable corresponding to each region into at least three hundred equal parts, and construct the membership function of the entire fuzzy data set based on the membership degrees of the input variables at the specified values.

[0064] The process of creating a fuzzy data set based on the digital signals of the row data and energy consumption data, determining the upper bound, lower bound, and rules of the fuzzy data set, dividing the range determined by the upper bound, lower bound, and rules into three equal - divided regions, setting different membership degrees for the three equal - divided regions, specifying the boundaries of the three equal - divided regions, dividing the value range of the input variable corresponding to each region into at least three hundred equal parts, and constructing the membership function of the entire fuzzy data set based on the membership degrees of the input variables at the specified values can accurately describe the fuzzy relationship between the input value and the output value, and improve the control accuracy of subsequent fuzzy control.

[0065] Embodiment 3

[0066] Based on Embodiment 1 or Embodiment 2, the specific process of converting the output value obtained by inference in step S5 into an accurate output value is as follows:

[0067] S51: Obtain all the fuzzy information of the output value of the fuzzy inference, and construct a triple representation of the fuzzy information according to all the fuzzy information;

[0068] S52: Calculate the centroid position of the fuzzy information of the output value through the triple representation, and output the centroid position, where the centroid position represents the center, position, and size of the fuzzy data set.

[0069] In this embodiment, the specific process of converting the accurate output value into an action control command for the energy consumption component of the corresponding server is as follows:

[0070] S53: Decode the accurate output value to obtain the type, operand, and execution steps of the corresponding operation of the accurate output value;

[0071] S54: Generate a corresponding control signal according to the type, operand, and execution steps of the corresponding operation, and the control signal is a series of binary bits;

[0072] S55: Transmit the generated control signals to the hardware execution modules that need to perform corresponding operations respectively through the bus system inside the server, and the hardware execution modules perform corresponding actions according to the control signals.

[0073] By obtaining all the fuzzy information of the output value of fuzzy inference, constructing a triple table of fuzzy information based on all the fuzzy information, and calculating the centroid position output of the fuzzy information of the output value. Decode the precise output value to obtain the type, operands, and execution steps of the corresponding operation of the precise output value; generate corresponding control signals according to the type, operands, and execution steps of the corresponding operation, and transmit the generated control signals to the hardware execution modules that need to perform corresponding operations respectively through the bus system inside the server to perform corresponding actions, which realizes the precise control of the energy consumption execution components. Under the condition of ensuring the normal operation of the server, the energy consumption of the server is reduced through the fuzzy control process.

[0074] In the present invention, the control variables of fuzzy control include power energy consumption, power supply life, power supply fan speed, internal temperature of the server, and load balance.

[0075] A server energy consumption control system based on a fuzzy control algorithm is used to implement the server energy consumption control method based on the fuzzy control algorithm as described above. See Figure 3 As shown in the figure, it includes a data acquisition module, an AD converter, and a fuzzy control module. The data acquisition module is connected to the AD converter, and the AD converter is connected to the fuzzy control module. The fuzzy control module includes a fuzzy quantization unit, a fuzzy rule creation unit, a defuzzification unit, and a control command generation unit. The data acquisition module is used to collect the operation data and energy consumption data of the specified energy consumption components of the server; the AD converter is used to convert the analog signals of the collected operation data and energy consumption data into digital signals and input them to the fuzzy control module; the fuzzy quantization unit is used to perform fuzzy quantization processing on the digital signals and convert the digital signals into corresponding fuzzy language variable values; the fuzzy rule creation unit is used to create fuzzy control rules, and the fuzzy control rules are used to describe the relationship between the input value and the output value, and perform inference on the input digital signal values after fuzzy quantization processing through the fuzzy control rules to calculate the fuzzy output value; the defuzzification unit is used to convert the inferred fuzzy output value into a precise output value; the control command generation unit is used to convert the precise output value into the action control parameters of the corresponding server energy consumption components and convert the action control parameters into action control commands.

[0076] In summary, the server energy consumption control method based on the fuzzy control algorithm provided by the present invention deploys a data acquisition module, an AD converter, and a fuzzy control module at specified positions in the server. The data acquisition module collects the operation data and energy consumption data of specified energy-consuming components in the server, converts them into digital signals, and then inputs them into the fuzzy control module for fuzzy quantization processing to convert the digital signals into corresponding fuzzy language variable values; creates fuzzy control rules, and infers the input digital signal input values after fuzzy quantization processing through the fuzzy control rules to calculate the fuzzy output values; converts the inferred fuzzy output values into accurate output values, converts them into action control parameters for the corresponding server energy-consuming components, and converts the action control parameters into action control commands; transmits the action control commands to the corresponding server energy-consuming components to perform corresponding actions. This enables the minimization of server energy consumption, reduces carbon emissions while lowering energy consumption costs. By discretizing the analog signals of the operation data and energy consumption data at preset time points, sampling the analog signals of the operation data and energy consumption data at a preset time interval, and approximating the analog signals of the operation data and energy consumption data with continuous amplitudes through a finite number of amplitude values to obtain discrete values with a certain interval; the process of encoding the discrete values with a certain interval into binary digital codes according to a preset rule to obtain the digital signals of the operation data and energy consumption data greatly reduces the data processing volume and improves the data processing efficiency of fuzzy control.

[0077] By creating a fuzzy data set for the digital signals of the row data and energy consumption data, determining the upper bound, lower bound, and rules of the fuzzy data set, and dividing the range determined by the upper bound, lower bound, and rules into three equal parts, setting different membership degrees for the three equal parts, specifying the boundaries of the three equal parts, dividing the variable value range of the input corresponding to each region into at least three hundred equal parts, and constructing the membership function of the entire fuzzy data set with the membership degrees of the input variables under the specified values, the fuzzy relationship between the input value and the output value can be accurately described, and the control accuracy of subsequent fuzzy control can be improved. By obtaining all the fuzzy information of the output value of the fuzzy inference, constructing a triple table of the fuzzy information according to all the fuzzy information, and calculating the centroid position of the fuzzy information of the output value for output. Decoding the accurate output value to obtain the type, operand, and execution steps of the corresponding operation of the accurate output value; generating corresponding control signals according to the type, operand, and execution steps of the corresponding operation, and transmitting the generated control signals to the hardware execution modules that need to perform the corresponding operations through the bus system inside the server to perform corresponding actions, which realizes the precise control of the energy consumption execution components. Under the condition of ensuring the normal operation of the server, the server energy consumption is reduced through the fuzzy control process.

Claims

1. A server energy consumption control method based on a fuzzy control algorithm, characterized in that, Including the following steps: S1: Deploy a data acquisition module, an AD converter, and a fuzzy control module at a specified location in the server, establish connections between the data acquisition module and the AD converter and between the AD converter and the fuzzy control module, and connect the fuzzy control module to a specified energy-consuming component of the server; S2: The data acquisition module acquires the operation data and energy consumption data of the specified energy-consuming component of the server, and the AD converter converts the analog signals of the acquired operation data and energy consumption data into digital signals and inputs them to the fuzzy control module; S3: The fuzzy control module performs fuzzy quantization processing on the digital signals and converts the digital signals into corresponding fuzzy language variable values; S4: Create fuzzy control rules, which are used to describe the relationship between input values and output values, and perform inference on the input digital signal values after fuzzy quantization processing through the fuzzy control rules to calculate fuzzy output values; S5: Convert the fuzzy output value obtained by inference into an accurate output value, convert the accurate output value into action control parameters for the corresponding energy-consuming component of the server, and convert the action control parameters into action control commands; S6: Transmit the action control commands to the corresponding energy-consuming components of the server, and the energy-consuming components of the server execute corresponding actions based on the action control commands.

2. The server energy consumption control method based on the fuzzy control algorithm according to claim 1, wherein The specific process of the AD converter converting the analog signals of the acquired operation data and energy consumption data into digital signals in step S2 is as follows: S21: Discretize the analog signals of the operation data and energy consumption data at preset time points and sample the analog signals of the operation data and energy consumption data at a preset time interval; S22: Hold the instantaneous analog signals obtained by sampling for a specified time to obtain stable analog signals, approximate the continuous amplitude of the analog signals of the operation data and energy consumption data with a finite number of amplitude values, and convert the continuous amplitude of the analog signals of the operation data and energy consumption data into a finite number of discrete values with a certain interval; S23: Encode the discrete values with a certain interval into binary digital codes according to a preset rule to obtain the digital signals of the operation data and energy consumption data.

3. The server energy consumption control method based on the fuzzy control algorithm according to claim 2, wherein, The specific process of the fuzzy control module performing fuzzy quantization processing on the digital signals in step S3 is as follows: S31: Create a fuzzy data set according to the digital signals of the row data and energy consumption data obtained in step S23; S32: Determine the upper bound, lower bound, and rules of the fuzzy data set, and divide the range determined by the upper bound, lower bound, and rules into three equal parts, and set different membership degrees for the three equal parts; S33: Specify the boundaries of the three equal parts, divide the variable value range of the input corresponding to each region into at least three hundred equal parts, and construct a membership function of the entire fuzzy data set with the membership degrees of the input variables under the specified values.

4. A server energy consumption control method based on a fuzzy control algorithm according to claim 1, characterized in that, The specific process of converting the output value obtained by inference into an accurate output value in step S5 is as follows: S51: Obtain all the fuzzy information of the output value of the fuzzy inference, and construct a triple representation of the fuzzy information based on all the fuzzy information; S52: Calculate the centroid position of the fuzzy information of the output value through the triple representation, and output the centroid position, where the centroid position represents the center, position, and size of the fuzzy data set.

5. A server energy consumption control method based on a fuzzy control algorithm according to claim 1, characterized in that, The specific process of converting the precise output value into an action control command for the corresponding server energy consumption component is as follows: S53: Decode the precise output value to obtain the type, operand, and execution steps of the corresponding operation of the precise output value; S54: Generate corresponding control signals according to the type, operand, and execution steps of the corresponding operation, where the control signals are a series of binary bits; S55: Transmit the generated control signals to the hardware execution modules that need to perform the corresponding operations respectively through the bus system inside the server, and the hardware execution modules perform corresponding actions according to the control signals.

6. A server energy consumption control system based on a fuzzy control algorithm, which is used to implement a server energy consumption control method based on a fuzzy control algorithm described in any one of claims 1-5, and is characterized in that, It includes a data acquisition module, an AD converter, and a fuzzy control module. The data acquisition module is connected to the AD converter, and the AD converter is connected to the fuzzy control module. The fuzzy control module includes a fuzzy quantization unit, a fuzzy rule creation unit, a defuzzification unit, and a control command generation unit; The data acquisition module is used to acquire the operation data and energy consumption data of the specified energy consumption component of the server; The AD converter is used to convert the analog signals of the acquired operation data and energy consumption data into digital signals and input them into the fuzzy control module; The fuzzy quantization unit is used to perform fuzzy quantization processing on the digital signals and convert the digital signals into corresponding fuzzy linguistic variable values; The fuzzy rule creation unit is used to create fuzzy control rules, which are used to describe the relationship between the input value and the output value, and perform inference on the input digital signal values after fuzzy quantization processing through the fuzzy control rules to calculate the fuzzy output value; The defuzzification unit is used to convert the inferred fuzzy output value into a precise output value; The control command generation unit is used to convert the precise output value into an action control parameter for the corresponding server energy consumption component and convert the action control parameter into an action control command.

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