Energy consumption optimization method and system for computing power server

Abstract

The invention discloses an energy consumption optimization method and system for a computing power server, and relates to the technical field of server optimization, and the method comprises the steps: monitoring the operation data of the computing power server in real time, and recognizing the task and load conditions; constructing a load prediction model to predict the future load of the computing power server, and carrying out the energy consumption optimization of the computing power server according to a predicted load scheduling task; continuously optimizing the load prediction model based on a reinforcement learning algorithm; and displaying the load condition of the computing power server in real time, and storing the operation data of the computing power server to a database. The load prediction model is constructed by collecting the processing tasks and the load data of the computing power server to predict the future load, the accuracy of load prediction is improved, task processing scheduling is performed by predicting the future load to achieve load optimization of the computing power server, and load energy consumption adjustment of the processing tasks of the computing power server is effectively achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of server optimization, and in particular to an energy consumption optimization method and system for a computing server. Background Art

[0002] With the rapid development of information technology, computing servers are increasingly used in various industries, especially in cloud computing, big data analysis, artificial intelligence and other fields, which require a large amount of computing resources. This makes the energy efficiency of computing servers a key issue that needs to be solved urgently. Especially in the context of global energy shortages and increasingly serious environmental pollution, how to efficiently use computing resources and reduce energy waste has become an important topic in computer hardware design and data center management. At the same time, as the load of computing servers continues to increase, how to effectively adjust the load to reduce energy consumption and improve energy efficiency has become a core direction for optimizing data center operations.

[0003] At present, the energy efficiency optimization of computing servers usually adopts technical means such as dynamic voltage scaling (DVFS), load balancing and task scheduling. For example, load balancing technology reduces energy waste by reasonably distributing the computing load of the server to avoid overload and idleness; dynamic voltage and frequency scaling (DVFS) achieves power optimization by adjusting the voltage and frequency of the server. In recent years, with the continuous advancement of artificial intelligence technology, intelligent scheduling algorithms based on machine learning have gradually become an important means of optimizing the energy efficiency of computing servers. These technologies reduce unnecessary power consumption by analyzing historical load data, predicting future loads and scheduling tasks in advance.

[0004] However, the existing technology still has shortcomings. Traditional load prediction methods usually rely on linear regression or simple algorithms based on historical data. They have difficulty handling dynamic fluctuations in server load and sudden tasks, resulting in low prediction accuracy, affecting the effects of task scheduling and power optimization, and are unable to respond quickly to real-time changing load demands, which means that computing resources cannot be optimally utilized. Summary of the invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides an energy consumption optimization method and system for a computing server to solve the problem that the prior art is difficult to handle dynamic fluctuations and sudden tasks of server load and is unable to respond quickly according to real-time changing load demands.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides a method for optimizing energy consumption of a computing server, which comprises: Monitor computing server operation data in real time and identify tasks and load conditions; Build a load prediction model to predict the future load of the computing server, and optimize the energy consumption of the computing server according to the predicted load scheduling tasks; Continuously optimize the load forecasting model based on reinforcement learning algorithm; The load status of the computing power server is displayed in real time, and the running data of the computing power server is stored in the database.

[0008] As a preferred solution of the energy consumption optimization method of the computing power server described in the present invention, the real-time monitoring of the computing power server operation data and identification of tasks and load conditions refer to installing sensors on the computing nodes of the computing power server to monitor the operation data of the computing power server in real time, and identifying the tasks and load data on the computing power server through the operation data, wherein the load data includes the load required for each task and the comprehensive operation load.

[0009] As a preferred solution of the energy consumption optimization method of the computing power server described in the present invention, wherein: the construction of the load prediction model to predict the future load of the computing power server refers to standardizing the load data and constructing the LSTM model, setting the LSTM model input as the load data, and the output as the preliminary predicted load data, and using the mean square error MSE as the original loss function of the LSTM model; Simultaneously build a support vector machine regression model, set the input of the support vector machine regression model as the preliminary predicted load data, the output as the final predicted load data, and build the loss function of the support vector machine regression model ; The loss function of the support vector machine regression model is Combined with the original loss function of the LSTM model to form an optimized LSTM model loss function : in is the actual load data at time t, Predict load data for the LSTM model at time t, To predict the duration, is the regularization coefficient, is the i-th slack variable, n is the number of slack variables; Based on the LSTM model training data set, the LSTM model loss function is optimized Train the LSTM model and output preliminary predicted load data through the trained LSTM model; The loss function in the support vector machine regression model The regularization term of linear regression is introduced to form the loss function of the optimized support vector machine regression model : in is the jth linear regression coefficient, p is the number of linear regression coefficients, is the weight vector of the support vector machine regression model, is the regularization parameter; Based on the support vector machine regression model training data set, the loss function of the support vector machine regression model is optimized The support vector machine regression model is trained, and the final predicted load data is obtained based on the trained support vector machine regression model.

[0010] As a preferred solution of the energy consumption optimization method of the computing power server described in the present invention, wherein: the energy consumption optimization of the computing power server according to the predicted load scheduling task refers to obtaining the predicted load of each task from the final predicted load data to calculate the priority of each task : in is the processing time of the ith task, is the predicted load of the ith task, and is the weight coefficient; The tasks are sorted according to their priority, and the tasks are processed in sequence according to the priority. The predicted load of each task is optimized through sliding mode control: in To optimize the function, is the adjustment coefficient of the i-th task, and m is the number of tasks; Optimize to get the optimized predicted load for each task , recording the actual load required to process each task , the predicted load of each task is further optimized using the MPC optimization objective function: in is the actual load of the task at time k+j, O is the transposition, is the optimized predicted load at time k+j, and R is the weighting matrix; The load prediction of the secondary optimization task is obtained by optimizing the objective function of MPC optimization , calculate the load adjustment of the computing server when processing tasks through the coordination control formula : in To control the gain, is a Lipschitz continuous function; The calculated load adjustment of the computing server Real-time adjustment of the load on the computing server when processing tasks.

[0011] As a preferred solution of the energy consumption optimization method of the computing power server described in the present invention, the continuous optimization of the load prediction model based on the reinforcement learning algorithm refers to periodically statistics on the accuracy of the load prediction model. When the model accuracy is lower than the set threshold, the load prediction model is optimized and adjusted using the Q-learning reinforcement learning algorithm until the model accuracy is greater than or equal to the set threshold.

[0012] As a preferred solution of the energy consumption optimization method of the computing power server described in the present invention, the real-time display of the computing power server load status refers to visually displaying the adjusted computing power server load status, and simultaneously displaying the task processing status and the load required for the task.

[0013] As a preferred solution of the energy consumption optimization method of the computing power server described in the present invention, wherein: the storing of the computing power server operating data in the database refers to storing the collected computing power server operating data in the database, the database regularly performs integrity and security checks on the stored data, and uploads the stored data to the cloud for storage.

[0014] In a second aspect, the present invention provides an energy consumption optimization system for a computing server, comprising: Data collection module, used to detect computing server operation data and identify processing tasks and load data; The energy consumption optimization module is used to build a load prediction model to predict the task load of the computing server and adjust the load of the computing server according to the predicted load; Reinforcement learning module, used to continuously optimize the load forecasting model using reinforcement learning algorithm; The display storage module is used to display the load status of the computing server in real time and store the collected operating data in the database.

[0015] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the method for optimizing energy consumption of a computing power server as described in the first aspect of the present invention is implemented.

[0016] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the method for optimizing energy consumption of a computing power server as described in the first aspect of the present invention.

[0017] The beneficial effects of the present invention are as follows: the present invention collects computing power server processing tasks and load data to build a load prediction model to predict future loads, thereby improving the accuracy of load predictions, and optimizes the load of the computing power server by predicting future loads to perform task processing scheduling, thereby effectively realizing the load energy consumption adjustment of the computing power server processing tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0019] Figure 1 This is a flow chart of the energy consumption optimization method of the computing power server in Example 1.

[0020] Figure 2 This is a structural diagram of the energy consumption optimization system of the computing power server in Example 1. DETAILED DESCRIPTION

[0021] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0022] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0023] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0024] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a method for optimizing energy consumption of a computing server, comprising the following steps: S1. Real-time monitoring of computing server operation data, identification of tasks and load conditions; Specifically, real-time monitoring of the computing server's operating data and identification of tasks and load conditions refers to installing sensors on the computing nodes of the computing server to monitor the computing server's operating data in real time, including CPU usage, GPU usage, memory usage, storage usage, and temperature data, and identifying tasks and load data on the computing server through the operating data, wherein the load data includes the load required for each task and the comprehensive operating load.

[0025] Real-time monitoring of computing server operation data. By installing sensors, the collected CPU usage, GPU usage, memory usage, storage usage and temperature data provide comprehensive basic data for subsequent load prediction and task scheduling. This is a feature that traditional energy efficiency optimization methods do not have, because these methods usually rely on static rules or historical data and fail to reflect the real-time changes of the system in a timely manner. Through real-time monitoring, the system can dynamically adjust according to the actual operation of the server, improving the accuracy and real-time response capability of energy efficiency optimization. Identifying tasks and load conditions can accurately identify the resource requirements of the current task and the overall load of the system based on the data collected in real time. By real-time monitoring of the load requirements of the task, it can provide accurate load prediction, making task scheduling and power management more accurate.

[0026] S2. Build a load prediction model to predict the future load of the computing server, and optimize the energy consumption of the computing server according to the predicted load scheduling tasks; Specifically, building a load prediction model to predict the future load of the computing server means standardizing the load data and building an LSTM model, including an input layer, a hidden layer, and an output layer. The LSTM model input is set as the load data, and the output is set as the preliminary predicted load data. The mean square error MSE is used as the original loss function of the LSTM model. Simultaneously build a support vector machine regression model, set the input of the support vector machine regression model as the preliminary predicted load data, the output as the final predicted load data, and build the loss function of the support vector machine regression model : in is the weight vector of the support vector machine regression model, is the regularization parameter, is the i-th slack variable, n is the number of slack variables; The loss function of the support vector machine regression model is Combined with the original loss function of the LSTM model to form an optimized LSTM model loss function , the loss function introduced here is Regularization term content: in is the actual load data at time t, Predict load data for the LSTM model at time t, To predict the duration, is the regularization coefficient, is the i-th slack variable, n is the number of slack variables; Based on the LSTM model training data set, the LSTM model loss function is optimized Train the LSTM model and output preliminary predicted load data through the trained LSTM model; The loss function in the support vector machine regression model The regularization term of linear regression is introduced to form the loss function of the optimized support vector machine regression model : in is the jth linear regression coefficient, p is the number of linear regression coefficients, is the weight vector of the support vector machine regression model, is the regularization parameter; Based on the support vector machine regression model training data set, the loss function of the support vector machine regression model is optimized The support vector machine regression model is trained, and the final predicted load data is obtained based on the trained support vector machine regression model.

[0027] By standardizing the load data, the dimensional differences between different tasks and load data are eliminated, so that the model can better capture the law of load changes. The standardized data helps the LSTM model to more accurately predict time series and reduce errors caused by data scale differences. The LSTM model itself can effectively handle long-term dependencies in time series data, which is especially applicable to load prediction of computing servers, because server loads usually have strong temporal characteristics. After the initial load prediction is output by LSTM, the SVR model is used for optimization, which can not only further improve the prediction accuracy, but also effectively handle the nonlinear characteristics in the load data. LSTM is suitable for processing time series data, while SVR can capture complex nonlinearities in the data. mode. The combination of the two makes the final load prediction more accurate and adapts to complex and dynamically changing server load conditions. By combining the loss function of the SVR model with the original loss function of the LSTM model and introducing the regularization term, not only the training process of the LSTM model is optimized, but also the model overfitting is prevented. The addition of the regularization term ensures that the model can balance the fitting accuracy and model complexity when facing complex load data, avoiding the problem of overfitting training data. The introduction of the regularization term of linear regression in the SVR model can effectively integrate linear and nonlinear features, thereby further improving the prediction accuracy. The introduction of the linear regression coefficient enables SVR to better balance the linear and nonlinear relationship in the load data, thereby optimizing the load prediction results.

[0028] Furthermore, optimizing the energy consumption of computing servers by scheduling tasks based on predicted load means obtaining the predicted load of each task from the final predicted load data and calculating the priority of each task. : in is the processing time of the ith task, obtained according to the final processing time limit of the task, is the predicted load of the ith task, and is the weight coefficient; The tasks are sorted according to their priority, and the tasks are processed in sequence according to the priority. The predicted load of each task is optimized through sliding mode control: in To optimize the function, is the adjustment coefficient of the i-th task, and m is the number of tasks; Optimize to get the optimized predicted load for each task , recording the actual load required to process each task , the predicted load of each task is further optimized using the MPC optimization objective function: in is the actual load of the task at time k+j, which can be obtained by linear fitting, O is the transposition, is the optimized predicted load at time k+j, and R are weighted matrices used to adjust and optimize the predicted load; The load prediction of the secondary optimization task is obtained by optimizing the objective function of MPC optimization , calculate the load adjustment of the computing server when processing tasks through the coordination control formula : in To control the gain, is a Lipschitz continuous function; The calculated load adjustment of the computing server Real-time adjustment of the load on the computing server when processing tasks.

[0029] By calculating the priority of each task and sorting them, the present invention ensures that the computing server gives priority to tasks that need to be executed urgently when scheduling tasks. The calculation of task priority not only considers the urgency of the task, but also introduces predicted load data, which makes task scheduling more scientific and reasonable. Through optimized priority sorting, the system can automatically allocate more resources to high-priority tasks under high load to avoid delays in task execution or waste of resources. Sliding mode control, as a nonlinear control method, can effectively cope with the uncertainty in load prediction and the dynamic changes of the system. By using sliding mode control to optimize the predicted load of each task, the system's response speed to load fluctuations can be improved, and the waste of resources caused by load fluctuations can be reduced. At the same time, sliding mode control can provide system stability and avoid excessive load adjustment. The MPC method further optimizes the predicted load of the task by predicting future load changes. MPC dynamically adjusts the distribution of task loads by optimizing the objective function and combining real-time data to ensure that task scheduling not only meets real-time requirements but also takes into account future load changes, thereby effectively improving resource utilization and energy efficiency. Through secondary optimization, the present invention can make the load distribution of each task more accurate and avoid resource waste caused by rough estimation. Through coordinated control, the system can comprehensively consider the relationship between task scheduling and power management, and further optimize the load of the computing server. Coordinated control can dynamically adjust the server load and task scheduling strategy through a feedback mechanism, so that the system can respond to load changes in real time and maintain the optimal balance between task execution and resource utilization.

[0030] S3, continuously optimize the load forecasting model based on reinforcement learning algorithm; Specifically, the load forecasting model is continuously optimized based on the reinforcement learning algorithm, and the accuracy of the load forecasting model is statistically analyzed periodically. When the model accuracy is lower than the set threshold, the load forecasting model is optimized and adjusted using the Q-learning reinforcement learning algorithm until the model accuracy is greater than or equal to the set threshold.

[0031] By regularly calculating the accuracy of the model, the present invention can evaluate the performance of the load forecasting model in real time to ensure that the model always maintains efficient forecasting capabilities. Unlike the fixed optimization cycle or manual adjustment of the model in the prior art, the present invention can timely discover and correct potential deviations of the model by regularly monitoring the accuracy, which enables the model to quickly adapt and continue to maintain forecasting accuracy when facing long-term operation or changes in load patterns. When the accuracy of the load forecasting model is lower than the set threshold, the Q-learning reinforcement learning algorithm is used to optimize and adjust the model until its accuracy reaches the set threshold. The innovation of this strategy is that Q-learning self-adjusts through interaction with the environment (i.e., feedback on load data) and does not rely on human intervention. Work intervention can continuously explore the optimal model parameters to optimize the prediction accuracy. Q-learning can adaptively adjust the prediction model according to the load characteristics of the current task, and effectively cope with complex and dynamic computing load changes. The core of the Q-learning optimization process is to adjust the model through the reward and penalty mechanism to continuously improve the performance of the load prediction model. Specifically, Q-learning adjusts the model parameters according to the error feedback signal between the predicted load and the actual load, thereby improving the load prediction accuracy. This process makes the load prediction not only a static calculation based on historical data, but also a dynamic optimization based on the real-time feedback of the current task, so that the task scheduling and energy efficiency optimization are more accurate.

[0032] S4. Display the load status of the computing server in real time and store the running data of the computing server in the database; Specifically, displaying the load status of the computing power server in real time means visually displaying the adjusted load status of the computing power server, and simultaneously displaying the task processing status and the load required for the task.

[0033] Furthermore, storing the computing power server operation data in a database means storing the collected computing power server operation data in a database, the database regularly performs integrity and security checks on the stored data, and uploads the stored data to the cloud for storage.

[0034] This embodiment also provides an energy consumption optimization system for a computing power server, including: Data collection module, used to detect computing server operation data and identify processing tasks and load data; The energy consumption optimization module is used to build a load prediction model to predict the task load of the computing server and adjust the load of the computing server according to the predicted load; Reinforcement learning module, used to continuously optimize the load forecasting model using reinforcement learning algorithm; The display storage module is used to display the load status of the computing server in real time and store the collected operating data in the database.

[0035] This embodiment also provides a computer device, which is suitable for the energy consumption optimization method of a computing power server, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the energy consumption optimization method of a computing power server proposed in the above embodiment.

[0036] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device 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 and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0037] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for optimizing the energy consumption of a computing power server proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0038] In summary, the present invention collects computing power server processing tasks and load data to build a load prediction model to predict future loads, thereby improving the accuracy of load prediction, and optimizes the load of the computing power server by predicting future loads to perform task processing scheduling, thereby effectively realizing the load energy consumption adjustment of the computing power server processing tasks.

[0039] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for optimizing energy consumption of a computing server, characterized in that: include, Monitor computing server operation data in real time and identify tasks and load conditions; Build a load prediction model to predict the future load of the computing server, and optimize the energy consumption of the computing server according to the predicted load scheduling tasks; Continuously optimize the load forecasting model based on reinforcement learning algorithm; The load status of the computing power server is displayed in real time, and the running data of the computing power server is stored in the database.

2. The method for optimizing energy consumption of a computing server according to claim 1, characterized in that: The real-time monitoring of the computing server's operating data and identification of tasks and load conditions refers to installing sensors on the computing nodes of the computing server to monitor the computing server's operating data in real time, and identifying tasks and load data on the computing server through the operating data, wherein the load data includes the load required for each task and the comprehensive operating load.

3. The method for optimizing energy consumption of a computing server according to claim 2, characterized in that: The construction of the load prediction model to predict the future load of the computing power server refers to standardizing the load data and constructing the LSTM model, setting the LSTM model input as the load data, the output as the preliminary predicted load data, and using the mean square error MSE as the original loss function of the LSTM model; Simultaneously build a support vector machine regression model, set the input of the support vector machine regression model as the preliminary predicted load data, the output as the final predicted load data, and build the loss function of the support vector machine regression model ; The loss function of the support vector machine regression model is Combined with the original loss function of the LSTM model to form an optimized LSTM model loss function : in is the actual load data at time t, Predict load data for the LSTM model at time t, To predict the duration, is the regularization coefficient, is the i-th slack variable, n is the number of slack variables; Based on the LSTM model training data set, the LSTM model loss function is optimized Train the LSTM model and output preliminary predicted load data through the trained LSTM model; The loss function in the support vector machine regression model The regularization term of linear regression is introduced to form the loss function of the optimized support vector machine regression model : in is the jth linear regression coefficient, p is the number of linear regression coefficients, is the weight vector of the support vector machine regression model, is the regularization parameter; Based on the support vector machine regression model training data set, the loss function of the support vector machine regression model is optimized The support vector machine regression model is trained, and the final predicted load data is obtained based on the trained support vector machine regression model.

4. The method for optimizing energy consumption of a computing server according to claim 3, characterized in that: The optimization of computing server energy consumption according to the predicted load scheduling task refers to obtaining the predicted load of each task from the final predicted load data and calculating the priority of each task. : in is the processing time of the ith task, is the predicted load of the ith task, and is the weight coefficient; The tasks are sorted according to their priority, and the tasks are processed in sequence according to the priority. The predicted load of each task is optimized through sliding mode control: in To optimize the function, is the adjustment coefficient of the i-th task, and m is the number of tasks; Optimize to get the optimized predicted load for each task , recording the actual load required to process each task , the predicted load of each task is further optimized using the MPC optimization objective function: in is the actual load of the task at time k+j, O is the transposition, is the optimized predicted load at time k+j, and R is the weighting matrix; The load prediction of the secondary optimization task is obtained by optimizing the objective function of MPC optimization , calculate the load adjustment of the computing server when processing tasks through the coordination control formula : in To control the gain, is a Lipschitz continuous function; The calculated load adjustment of the computing server Real-time adjustment of the load on the computing server when processing tasks.

5. The method for optimizing energy consumption of a computing server according to claim 4, characterized in that: The continuous optimization of the load prediction model based on the reinforcement learning algorithm refers to periodically collecting statistics on the accuracy of the load prediction model. When the model accuracy is lower than a set threshold, the load prediction model is optimized and adjusted using the Q-learning reinforcement learning algorithm until the model accuracy is greater than or equal to the set threshold.

6. The method for optimizing energy consumption of a computing server according to claim 5, characterized in that: The real-time display of the computing power server load situation refers to visually displaying the adjusted computing power server load situation, and synchronously displaying the task processing situation and the load required for the task.

7. The method for optimizing energy consumption of a computing server according to claim 6, characterized in that: Storing the computing power server operation data in a database refers to storing the collected computing power server operation data in a database, the database regularly performs integrity and security checks on the stored data, and uploads the stored data to the cloud for storage.

8. A system for optimizing energy consumption of a computing server, based on the method for optimizing energy consumption of a computing server according to any one of claims 1 to 7, characterized in that: include, Data collection module, used to detect computing server operation data and identify processing tasks and load data; The energy consumption optimization module is used to build a load prediction model to predict the task load of the computing server and adjust the load of the computing server according to the predicted load; Reinforcement learning module, used to continuously optimize the load forecasting model using reinforcement learning algorithm; The display storage module is used to display the load status of the computing server in real time and store the collected operating data in the database.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the energy consumption optimization method of the computing power server described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the energy consumption optimization method of the computing power server described in any one of claims 1 to 7 are implemented.

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