Design method of data center energy-saving system

Abstract

The invention relates to a design method of a data center energy-saving system, and the method comprises the following steps: monitoring the operation state of a data center: collecting the temperature, humidity and energy consumption data of the data center in real time, and transmitting the collected data to an intelligent control center; energy consumption analysis and prediction: the intelligent control center analyzes the energy consumption of the data center according to the collected data and predicts the future energy consumption trend; energy-saving strategy making: according to the energy consumption analysis and prediction result, the intelligent control center makes a corresponding energy-saving strategy, and the energy-saving strategy comprises server load adjustment, cooling system optimization and power management adjustment; energy-saving strategy execution: the intelligent control center sends the formulated energy-saving strategy to an execution system, and the execution system adjusts operation parameters of the data center according to the energy-saving strategy; and effect evaluation and optimization: after the energy-saving strategy is executed, the intelligent control center evaluates the energy-saving effect, the energy-saving strategy is optimized according to an evaluation result, and the data center executes the optimized energy-saving strategy.

Description

Technical Field

[0001] The present application relates to the technical field of data centers, and in particular to a design method for an energy-saving system of a data center. Background Art

[0002] The investment scale of my country's data center market continues to grow rapidly, and the scale of data centers continues to expand, which will inevitably lead to a continuous increase in data center energy consumption. However, the resource utilization levels of various data centers are uneven. Data centers generally have problems such as extensive heat dissipation design, insufficient heat dissipation capacity, uneven heat dissipation effect, and high overall energy waste. With the rapid development of information technology, data centers, as the core place for data processing and storage, play an increasingly important role. However, the high-density deployment of servers and network equipment inside the data center will generate a lot of heat when running at high speed. If the heat dissipation problem is not properly solved, it will not only affect the performance and stability of the equipment, but may even cause equipment failure, resulting in data loss or service interruption. Therefore, data center heat dissipation technology has become a key link in ensuring the stable operation of the data center and data security.

[0003] The significance of data center heat dissipation is to create a good operating environment for the equipment inside the data center to avoid performance degradation and failure risks caused by high temperature. Through effective heat dissipation measures, data centers can ensure that equipment operates in the best state, improve the efficiency of data processing and storage, and at the same time extend the service life of equipment and reduce maintenance costs. With the rapid development of information technology, the scale of data centers continues to expand, and its energy consumption problem is becoming increasingly prominent. Existing data center energy-saving technologies mainly focus on the optimization of hardware equipment and the improvement of cooling systems, but these methods often require large transformation costs and long implementation cycles, and the effect is limited. Summary of the invention

[0004] The present application provides a design method for a data center energy-saving system, which can reduce the transformation cost and transformation cycle while improving the energy-saving effect.

[0005] The design method of the data center energy saving system provided in this application includes the following steps:

[0006] 1) Data center operation status monitoring: collect data on temperature, humidity and energy consumption of the data center in real time through sensors, and transmit the collected data to the intelligent control center;

[0007] 2) Energy consumption analysis and prediction: The intelligent control center uses big data analysis technology to analyze the energy consumption of the data center based on the collected data and predict future energy consumption trends;

[0008] 3) Energy-saving strategy formulation: Based on the energy consumption analysis and prediction results, the intelligent control center formulates corresponding energy-saving strategies, which include adjusting server load, optimizing cooling system and adjusting power management;

[0009] 4) Energy-saving strategy execution: The intelligent control center sends the formulated energy-saving strategy to the execution system of the data center, and the execution system adjusts the operating parameters of the data center according to the energy-saving strategy;

[0010] 5) Effect evaluation and optimization: After the energy-saving strategy is executed, the intelligent control center evaluates the energy-saving effect and optimizes the energy-saving strategy according to the evaluation result. The data center executes the optimized energy-saving strategy.

[0011] In an optional solution, the following steps are included when performing the data center operation status monitoring:

[0012] 1.1) Sensor deployment: Deploy temperature, humidity, energy consumption and server sensors at key locations in the data center;

[0013] 1.2) Data collection: Each sensor collects data in real time and transmits it to the monitoring system of the data center. Data collection can be periodic or event-driven;

[0014] 1.3) Data preprocessing: Clean and format the collected data to ensure data accuracy and consistency, including removing outliers, filling missing data and data standardization;

[0015] 1.4) Data storage: Store the preprocessed data in the database;

[0016] 1.5) Real-time monitoring: The monitoring system displays the operating status of the data center in real time;

[0017] 1.6) Alarm mechanism: Set thresholds. When key indicators exceed the preset thresholds, the monitoring system automatically triggers an alarm.

[0018] In an optional solution, the energy consumption analysis and prediction includes the following steps:

[0019] 2.1) Data collection: Collecting total energy consumption data, business load data, and environmental data related to energy consumption of the data center;

[0020] 2.2) Data preprocessing: remove outliers and erroneous data, convert data of different dimensions to a unified dimension, and use interpolation to fill in missing data points;

[0021] 2.3) Data analysis: Analyze the trend of energy consumption over time, identify seasonal fluctuations, differences between weekdays and weekends, and analyze the correlation between different energy consumption factors;

[0022] 2.4) Establishment of energy consumption prediction model: Select appropriate prediction model according to data characteristics, select features that have a significant impact on energy consumption prediction, use historical data to train the model, and adjust model parameters to obtain the best prediction performance;

[0023] 2.5) Prediction execution: Use the trained model to make real-time predictions of future energy consumption and regularly conduct energy consumption forecasts to adapt to changes in data center business;

[0024] 2.6) Result evaluation and optimization: By comparing the predicted values ​​with the actual values, the accuracy and reliability of the model are evaluated. According to the evaluation results, the model parameters are adjusted or a more appropriate model is selected to improve the prediction accuracy.

[0025] In an optional solution, in step 2.1), the total energy consumption data of the data center includes power consumption, cooling system energy consumption and lighting system energy consumption, the business load data includes server utilization and network traffic, and the environmental data related to energy consumption includes temperature, humidity and external weather conditions;

[0026] In step 2.3), the correlation between different energy consumption factors includes the relationship between temperature and cooling system energy consumption, and abnormal energy consumption changes in the data.

[0027] In an optional solution, the energy saving strategy formulation includes the following steps:

[0028] 3.1) Data analysis and evaluation: Analyze energy consumption data, identify patterns and trends in energy consumption, including peak and off-peak hours, and determine key factors affecting data center energy consumption, including server utilization, temperature, and humidity;

[0029] 3.2) Energy saving target setting: According to the business needs and energy consumption status of the data center, set specific energy saving targets and set a timetable for achieving the energy saving targets, including short-term and long-term goals;

[0030] 3.3) Energy-saving measures formulation: adjust the operating status of the server and the operating parameters of the cooling system according to the server load; implement an energy management system to monitor and manage the energy use of the data center in real time;

[0031] 3.4) Implementation of energy-saving measures: Develop a detailed implementation plan for each energy-saving measure, including implementation steps, responsibility allocation, and time schedule;

[0032] 3.5) Effect monitoring and evaluation: After implementing energy-saving measures, continuously monitor the energy consumption of the data center to ensure that the energy-saving measures are implemented as planned, regularly evaluate the actual effects of energy-saving measures, compare them with the set energy-saving targets, and analyze whether the energy-saving effects meet expectations;

[0033] 3.6) Continuous improvement: Make necessary adjustments and optimizations to energy-saving measures based on the results of monitoring and evaluation.

[0034] In an optional solution, the energy-saving measures formulated in step 3.3) also include using energy-saving lamps, optimizing the control strategy of the lighting system, and controlling the lighting by using motion sensors or light sensors;

[0035] When adjusting the operating status of the server, it includes shutting down idle servers and optimizing virtualization resource allocation. When adjusting the operating parameters of the cooling system, it includes increasing the cooling water temperature and optimizing the cooling water flow to reduce cooling energy consumption.

[0036] In an optional solution, the following steps are included when executing the energy saving strategy:

[0037] 4.1) Strategy planning and preparation: Break down energy-saving strategies into specific implementation steps and operation guidelines, ensure that each step has clear implementation standards and responsible persons, and ensure that the resources required to implement energy-saving strategies are in place or can be obtained in a timely manner;

[0038] 4.2) System adjustment and optimization: adjust the hardware equipment of the data center according to the energy-saving strategy, and configure or update the software system of the data center, including the operating system, virtualization software, and monitoring software, to support the implementation of the energy-saving strategy;

[0039] 4.3) Implement monitoring and management: Implement a real-time monitoring system to monitor the key indicators of energy consumption, temperature and humidity of the data center in real time, and manage and control the operating status of the data center;

[0040] 4.4) Implementation of energy-saving measures: Implement specific energy-saving measures in accordance with the established energy-saving strategies, record key data and events during the implementation process, and generate energy-saving reports regularly;

[0041] 4.5) Effect evaluation and feedback: Regularly evaluate the implementation effect of energy-saving measures, compare with energy-saving targets, analyze whether the energy-saving effect meets expectations, and make necessary adjustments and optimizations to energy-saving strategies based on the evaluation results to ensure the effectiveness and sustainability of energy-saving measures;

[0042] 4.6) Continuous Improvement: Continuously monitor the energy consumption and operating status of the data center to ensure the continuous implementation of energy-saving measures.

[0043] In an optional solution, the effect evaluation and optimization includes the following steps:

[0044] 5.1) Data collection and analysis: Collect energy consumption data, environmental data, and business data before and after the implementation of energy-saving strategies, compare the data before and after the implementation of energy-saving strategies, and analyze the effects of energy-saving measures;

[0045] 5.2) Effect evaluation: evaluate whether the energy-saving measures have achieved the predetermined energy-saving goals;

[0046] 5.3) Problem identification: Identify performance problems that arise during the implementation of energy-saving measures;

[0047] 5.4) Optimization strategy formulation: formulate corresponding solutions for the identified problems, and continuously improve energy-saving strategies based on evaluation results and feedback to improve energy-saving effects and system performance;

[0048] 5.5) Implement optimization measures: Implement specific optimization measures according to the optimization strategy to ensure continuous improvement of energy-saving measures. During the implementation of optimization measures, continuously monitor the operating status of the data center and make necessary adjustments based on actual conditions;

[0049] 5.6) Effect re-evaluation: Regularly re-evaluate the implementation effect of energy-saving measures to ensure that energy-saving measures continue to be effective, track the long-term effects of energy-saving measures, and evaluate the long-term benefits and sustainability of energy-saving measures;

[0050] 5.7) Continuous monitoring and feedback: Continuously monitor the energy consumption and operating status of the data center to ensure the continuous implementation of energy-saving measures, establish an effective feedback mechanism, provide feedback and continuously optimize energy-saving strategies.

[0051] In an optional solution, the energy-saving measures in step 5.2) include: reducing the percentage of energy consumption and reducing carbon emissions; evaluating the cost of implementing energy-saving measures and the economic benefits of energy saving, including direct costs, indirect costs and long-term benefits; evaluating the impact of energy-saving measures on data center services to ensure that energy-saving measures do not affect business continuity and service quality;

[0052] In step 5.3), the performance issues identified include longer server response time, reduced system stability, and problems in identifying technical implementation, including equipment failure and software compatibility issues.

[0053] The beneficial effects of this application are:

[0054] The design method of the data center energy-saving system in this application can effectively reduce the energy consumption of the data center, improve energy utilization efficiency, and reduce the impact on the environment through real-time monitoring and intelligent control; in addition, it can also reduce low-energy operating costs, improve the operating efficiency and reliability of the data center, reduce the impact on the environment, and reduce carbon emissions.

[0055] It should be understood that the foregoing general description and the following detailed description are exemplary only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A schematic diagram of the overall process of the design method of the data center energy saving system provided in this application;

[0057] Figure 2 A flow chart of the data center operation status monitoring process provided for this application;

[0058] Figure 3 A schematic diagram of the energy consumption analysis and prediction process provided for this application;

[0059] Figure 4 A schematic diagram of the process of formulating the energy-saving strategy provided for this application;

[0060] Figure 5 A schematic diagram of the process flow when the energy-saving strategy provided for this application is executed;

[0061] Figure 6 A schematic diagram of the process flow for effect evaluation and optimization provided for this application.

[0062] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application. DETAILED DESCRIPTION

[0063] In order to better understand the technical solution of the present application, the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0064] It should be clear that the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other technical solutions obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0065] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0066] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0067] like Figure 1 As shown, the embodiment of the present application provides a design method for a data center energy-saving system, and the design method for a data center energy-saving system mainly includes the following steps:

[0068] 1) Data center operation status monitoring: collect data on temperature, humidity and energy consumption of the data center in real time through sensors, and transmit the collected data to the intelligent control center;

[0069] 2) Energy consumption analysis and prediction: The intelligent control center uses big data analysis technology to analyze the energy consumption of the data center based on the collected data and predict future energy consumption trends;

[0070] 3) Energy-saving strategy formulation: Based on the energy consumption analysis and prediction results, the intelligent control center formulates corresponding energy-saving strategies, including but not limited to adjusting server load, optimizing cooling system and adjusting power management;

[0071] 4) Energy-saving strategy execution: The intelligent control center sends the formulated energy-saving strategy to the execution system of the data center, and the execution system adjusts the operating parameters of the data center according to the energy-saving strategy;

[0072] 5) Effect evaluation and optimization: After the energy-saving strategy is executed, the intelligent control center evaluates the energy-saving effect and optimizes the energy-saving strategy based on the evaluation results. The data center executes the optimized energy-saving strategy.

[0073] The design method of the data center energy-saving system in this embodiment can effectively reduce the energy consumption of the data center, improve energy utilization efficiency, and reduce the impact on the environment through real-time monitoring and intelligent control; in addition, it can also reduce low-energy consumption operating costs, improve the operating efficiency and reliability of the data center, reduce the impact on the environment, and reduce carbon emissions.

[0074] like Figure 2 As shown, in a specific embodiment, the following steps are included when monitoring the operation status of a data center:

[0075] 1.1) Sensor deployment: Deploy temperature, humidity, energy consumption, and server sensors at key locations in the data center. These sensors can be physical sensors and / or software agents.

[0076] 1.2) Data collection: Sensors collect data in real time and transmit it to the monitoring system in the data center through the network. Data collection can be periodic or event-driven;

[0077] 1.3) Data preprocessing: Clean and format the collected data to ensure data accuracy and consistency, including removing outliers, filling missing data and data standardization;

[0078] 1.4) Data storage: Store the preprocessed data in a database for subsequent analysis and query;

[0079] 1.5) Real-time monitoring: The monitoring system displays the operating status of the data center in real time, including real-time data of key indicators such as temperature, humidity and energy consumption;

[0080] 1.6) Alarm mechanism: Set thresholds. When key indicators exceed the preset thresholds, the monitoring system automatically triggers an alarm and notifies the operation and maintenance personnel to take corresponding measures.

[0081] like Figure 3 As shown, in a specific embodiment, the energy consumption analysis and prediction includes the following steps:

[0082] 2.1) Data collection: Collect the total energy consumption data of the data center, including power consumption, cooling system energy consumption and lighting system energy consumption; collect environmental data related to energy consumption, including temperature, humidity and external weather conditions; collect business load data of the data center, including server utilization and network traffic;

[0083] 2.2) Data preprocessing: remove outliers and erroneous data, convert data of different dimensions to a unified dimension for easy analysis, and use interpolation methods to fill in missing data points;

[0084] 2.3) Data analysis: Analyze the trend of energy consumption over time, identify seasonal fluctuations, weekday and weekend differences, analyze the correlation between different energy consumption factors, including the relationship between temperature and cooling system energy consumption, and identify abnormal patterns in energy consumption data, such as sudden increases or decreases in energy consumption;

[0085] 2.4) Establishment of energy consumption prediction model: Select appropriate prediction model according to data characteristics, select features that have significant impact on energy consumption prediction, such as temperature, humidity and business load, use historical data to train the model, and adjust model parameters to obtain the best prediction performance;

[0086] 2.5) Prediction execution: Use the trained model to make real-time predictions of future energy consumption and regularly conduct energy consumption forecasts to adapt to changes in data center business;

[0087] 2.6) Result evaluation and optimization: By comparing the predicted value with the actual value, the accuracy and reliability of the model are evaluated, and the model parameters are adjusted or a more appropriate model is selected according to the evaluation results to improve the prediction accuracy;

[0088] 2.7) Energy-saving strategy formulation: Based on the energy consumption forecast results, formulate corresponding energy-saving measures, including adjusting the cooling system settings, optimizing the server load, implementing energy-saving measures, and monitoring their effects to ensure the achievement of energy-saving goals;

[0089] 2.8) Continuous improvement: Feedback the actual effects of energy-saving measures into the prediction model, and continuously optimize the prediction model and energy-saving strategy.

[0090] like Figure 4 As shown, in a specific embodiment, the energy saving strategy formulation includes the following steps:

[0091] 3.1) Data analysis and evaluation: Collect data center energy consumption data, including but not limited to power consumption, cooling system energy consumption and server load, analyze energy consumption data, identify energy consumption patterns and trends, including peak and off-peak hours, and determine key factors affecting data center energy consumption, including server utilization, temperature and humidity;

[0092] 3.2) Energy saving target setting: According to the business needs and energy consumption status of the data center, set specific energy saving targets and set a reasonable timetable for achieving the energy saving targets, including short-term and long-term goals;

[0093] 3.3) Energy-saving measures: Adjust the operating status of the server according to the server load, including shutting down idle servers and optimizing virtualized resource allocation; adjust the operating parameters of the cooling system, including increasing the cooling water temperature and optimizing the cooling water flow to reduce cooling energy consumption; use energy-saving lamps and optimize the control strategy of the lighting system, and control the lighting by using motion sensors or light sensors; implement an energy management system to monitor and manage the energy use of the data center in real time;

[0094] 3.4) Implementation of energy-saving measures: Develop a detailed implementation plan for each energy-saving measure, including implementation steps, responsibility allocation, and time schedule; train data center operation and maintenance personnel on energy-saving measures, and ensure that all relevant personnel understand the importance of energy-saving measures;

[0095] 3.5) Effect monitoring and evaluation: After implementing energy-saving measures, continuously monitor the energy consumption of the data center to ensure that the energy-saving measures are implemented as planned, regularly evaluate the actual effects of energy-saving measures, compare them with the set energy-saving targets, and analyze whether the energy-saving effects meet expectations;

[0096] 3.6) Continuous improvement: Make necessary adjustments and optimizations to energy-saving measures based on the results of monitoring and evaluation, pay attention to the latest energy-saving technologies and products, and continuously introduce new technologies to improve the energy efficiency of data centers;

[0097] 3.7) Documentation and reporting: Record the implementation process of energy-saving measures, problems encountered and solutions in detail, prepare energy-saving reports regularly, summarize the implementation status and effects of energy-saving measures, and provide reference for future energy-saving work.

[0098] like Figure 5 As shown, in a specific embodiment, the energy saving strategy execution includes the following steps:

[0099] 4.1) Strategic planning and preparation: breaking down energy-saving strategies into specific implementation steps and operational guidelines, ensuring that each step has clear implementation standards and responsible persons, and ensuring that the resources required to implement energy-saving strategies are in place or can be obtained in a timely manner;

[0100] 4.2) Personnel training and communication: Provide necessary training for personnel who implement energy-saving strategies to ensure that they understand the implementation details and operational requirements of energy-saving measures, and communicate with the data center's operation and maintenance team, management and other relevant departments to ensure that energy-saving strategies are fully understood and supported;

[0101] 4.3) System adjustment and optimization: adjust the hardware equipment of the data center according to the energy-saving strategy, and configure or update the software system of the data center, including the operating system, virtualization software, and monitoring software, to support the implementation of the energy-saving strategy;

[0102] 4.4) Implement monitoring and management, implement a real-time monitoring system, monitor the key indicators of energy consumption, temperature and humidity of the data center in real time, and manage and control the operation status of the data center through the data center management system or automatic control system;

[0103] 4.5) Implementation of energy-saving measures: Implement specific energy-saving measures in accordance with the established energy-saving strategies, such as adjusting server loads, optimizing cooling systems, and implementing energy efficiency management; record key data and events during the implementation process; generate energy-saving reports regularly; and report energy-saving effects to management and relevant departments;

[0104] 4.6) Effect evaluation and feedback: regularly evaluate the implementation effect of energy-saving measures, compare them with energy-saving targets, analyze whether the energy-saving effect meets expectations, and make necessary adjustments and optimizations to energy-saving strategies based on the evaluation results to ensure the effectiveness and sustainability of energy-saving measures;

[0105] 4.7) Continuous improvement, continuous monitoring of the energy consumption and operating status of the data center, ensuring the continuous implementation of energy-saving measures, paying attention to the latest energy-saving technologies and products, and continuously introducing new technologies to improve the energy efficiency of the data center;

[0106] 4.8) Document and knowledge management: record the implementation process of energy-saving strategies, problems encountered and solutions, and form documents for future reference and improvement. Share the implementation experience of energy-saving strategies and knowledge of best practices to enhance the energy-saving awareness and capabilities of the entire organization.

[0107] like Figure 6 As shown, in a specific embodiment, the effect evaluation and optimization includes the following steps:

[0108] 5.1) Data collection and analysis: Collect energy consumption data, environmental data, business data, etc. before and after the implementation of energy-saving strategies, ensure the integrity and accuracy of the data, compare the data before and after the implementation of energy-saving strategies, and analyze the effects of energy-saving measures, including the percentage of energy consumption reduction, cost savings, and environmental impact;

[0109] 5.2) Effect evaluation: evaluate whether the energy-saving measures have achieved the predetermined energy-saving goals, such as the percentage of energy consumption reduction and carbon emissions reduction; evaluate the cost of implementing energy-saving measures and the economic benefits brought by energy saving, including direct costs, indirect costs and long-term benefits; evaluate the impact of energy-saving measures on data center business, and ensure that energy-saving measures will not affect business continuity and service quality;

[0110] 5.3) Problem identification: identifying performance problems that occur during the implementation of energy-saving measures, such as longer server response time and reduced system stability, and identifying problems in technical implementation, such as equipment failures and software compatibility issues;

[0111] 5.4) Optimize strategy formulation, formulate corresponding solutions for identified problems, such as adjusting server configuration, upgrading software versions, and continuously improve energy-saving strategies based on evaluation results and feedback to improve energy-saving effects and system performance;

[0112] 5.5) Implement optimization measures. Implement specific optimization measures according to the optimization strategy to ensure continuous improvement of energy-saving measures. During the implementation of optimization measures, continuously monitor the operating status of the data center and make necessary adjustments based on actual conditions;

[0113] 5.6) Effect re-evaluation: Regularly re-evaluate the implementation effect of energy-saving measures to ensure that energy-saving measures are continuously effective, track the long-term effects of energy-saving measures, and evaluate the long-term benefits and sustainability of energy-saving measures;

[0114] 5.7) Documentation and knowledge sharing: record key decisions, implementation steps and results in the optimization process, and form documents for future reference and improvement. Share the implementation experience and best practices of energy-saving strategies to enhance the energy-saving awareness and capabilities of the entire organization.

[0115] 5.8) Continuous monitoring and feedback: Continuously monitor the energy consumption and operating status of the data center to ensure the continuous implementation of energy-saving measures, establish an effective feedback mechanism, collect feedback from users and operation and maintenance personnel, and continuously optimize energy-saving strategies.

[0116] The above are only preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A design method for a data center energy-saving system, characterized in that: The following steps are involved: 1) Data center operation status monitoring: collect data on temperature, humidity and energy consumption of the data center in real time through sensors, and transmit the collected data to the intelligent control center; 2) Energy consumption analysis and prediction: The intelligent control center uses big data analysis technology to analyze the energy consumption of the data center based on the collected data and predict future energy consumption trends; 3) Energy-saving strategy formulation: Based on the energy consumption analysis and prediction results, the intelligent control center formulates corresponding energy-saving strategies, which include adjusting server load, optimizing cooling system and adjusting power management; 4) Energy-saving strategy execution: the intelligent control center sends the formulated energy-saving strategy to the execution system of the data center, and the execution system adjusts the operating parameters of the data center according to the energy-saving strategy; 5) Effect evaluation and optimization: After the energy-saving strategy is executed, the intelligent control center evaluates the energy-saving effect and optimizes the energy-saving strategy according to the evaluation result. The data center executes the optimized energy-saving strategy.

2. The design method of a data center energy saving system according to claim 1, characterized in that: The following steps are included when monitoring the operation status of the data center: 1.1) Sensor deployment: Deploy temperature, humidity, energy consumption and server sensors at key locations in the data center; 1.2) Data collection: Each sensor collects data in real time and transmits it to the monitoring system of the data center. Data collection can be periodic or event-driven; 1.3) Data preprocessing: Clean and format the collected data to ensure data accuracy and consistency, including removing outliers, filling missing data and data standardization; 1.4) Data storage: Store the preprocessed data in the database; 1.5) Real-time monitoring: The monitoring system displays the operating status of the data center in real time; 1.6) Alarm mechanism: Set thresholds. When key indicators exceed the preset thresholds, the monitoring system automatically triggers an alarm.

3. The design method of a data center energy saving system according to claim 1 or 2, characterized in that: The energy consumption analysis and prediction includes the following steps: 2.1) Data collection: Collecting total energy consumption data, business load data, and environmental data related to energy consumption of the data center; 2.2) Data preprocessing: remove outliers and erroneous data, convert data of different dimensions to a unified dimension, and use interpolation to fill in missing data points; 2.3) Data analysis: Analyze the trend of energy consumption over time, identify seasonal fluctuations, differences between weekdays and weekends, and analyze the correlation between different energy consumption factors; 2.4) Establishment of energy consumption prediction model: Select appropriate prediction model according to data characteristics, select features that have a significant impact on energy consumption prediction, use historical data to train the model, and adjust model parameters to obtain the best prediction performance; 2.5) Prediction execution: Use the trained model to make real-time predictions of future energy consumption and regularly conduct energy consumption forecasts to adapt to changes in data center business; 2.6) Result evaluation and optimization: By comparing the predicted values ​​with the actual values, the accuracy and reliability of the model are evaluated. According to the evaluation results, the model parameters are adjusted or a more appropriate model is selected to improve the prediction accuracy.

4. The design method of a data center energy saving system according to claim 3, characterized in that: In step 2.1), the total energy consumption data of the data center includes power consumption, cooling system energy consumption and lighting system energy consumption, the business load data includes server utilization and network traffic, and the environmental data related to energy consumption includes temperature, humidity and external weather conditions; In step 2.3), the correlation between different energy consumption factors includes the relationship between temperature and cooling system energy consumption, and abnormal energy consumption changes in the data.

5. The design method of a data center energy-saving system according to claim 1, 2 or 4, characterized in that: The energy saving strategy formulation includes the following steps: 3.1) Data analysis and evaluation: Analyze energy consumption data, identify patterns and trends in energy consumption, including peak and off-peak hours, and determine key factors affecting data center energy consumption, including server utilization, temperature, and humidity; 3.2) Energy saving target setting: According to the business needs and energy consumption status of the data center, set specific energy saving targets and set a timetable for achieving the energy saving targets, including short-term and long-term goals; 3.3) Energy-saving measures formulation: adjust the operating status of the server and the operating parameters of the cooling system according to the server load; implement an energy management system to monitor and manage the energy use of the data center in real time; 3.4) Implementation of energy-saving measures: Develop a detailed implementation plan for each energy-saving measure, including implementation steps, responsibility allocation, and time schedule; 3.5) Effect monitoring and evaluation: After implementing energy-saving measures, continuously monitor the energy consumption of the data center to ensure that the energy-saving measures are implemented as planned, regularly evaluate the actual effects of energy-saving measures, compare them with the set energy-saving targets, and analyze whether the energy-saving effects meet expectations; 3.6) Continuous improvement: Make necessary adjustments and optimizations to energy-saving measures based on the results of monitoring and evaluation.

6. The design method of a data center energy saving system according to claim 5, characterized in that: Energy-saving measures developed in step 3.3) also include adopting energy-saving lamps, optimizing the control strategy of the lighting system, and controlling lighting by using motion sensors or light sensors; When adjusting the operating status of the server, it includes shutting down idle servers and optimizing virtualization resource allocation. When adjusting the operating parameters of the cooling system, it includes increasing the cooling water temperature and optimizing the cooling water flow to reduce cooling energy consumption.

7. The design method of a data center energy-saving system according to claim 1 or 2 or 4 or 6, characterized in that: The energy saving strategy includes the following steps: 4.1) Strategy planning and preparation: Break down energy-saving strategies into specific implementation steps and operation guidelines, ensure that each step has clear implementation standards and responsible persons, and ensure that the resources required to implement energy-saving strategies are in place or can be obtained in a timely manner; 4.2) System adjustment and optimization: adjust the hardware equipment of the data center according to the energy-saving strategy, and configure or update the software system of the data center, including the operating system, virtualization software, and monitoring software, to support the implementation of the energy-saving strategy; 4.3) Implement monitoring and management: Implement a real-time monitoring system to monitor the key indicators of energy consumption, temperature and humidity of the data center in real time, and manage and control the operating status of the data center; 4.4) Implementation of energy-saving measures: Implement specific energy-saving measures in accordance with the established energy-saving strategies, record key data and events during the implementation process, and generate energy-saving reports regularly; 4.5) Effect evaluation and feedback: Regularly evaluate the implementation effect of energy-saving measures, compare with energy-saving targets, analyze whether the energy-saving effect meets expectations, and make necessary adjustments and optimizations to energy-saving strategies based on the evaluation results to ensure the effectiveness and sustainability of energy-saving measures; 4.6) Continuous Improvement: Continuously monitor the energy consumption and operating status of the data center to ensure the continuous implementation of energy-saving measures.

8. The design method of a data center energy-saving system according to claim 1, 2, 4 or 6, characterized in that: The following steps are included when performing the effect evaluation and optimization: 5.1) Data collection and analysis: Collect energy consumption data, environmental data, and business data before and after the implementation of energy-saving strategies, compare the data before and after the implementation of energy-saving strategies, and analyze the effects of energy-saving measures; 5.2) Effect evaluation: evaluate whether the energy-saving measures have achieved the predetermined energy-saving goals; 5.3) Problem identification: Identify performance problems that arise during the implementation of energy-saving measures; 5.4) Optimization strategy formulation: formulate corresponding solutions for the identified problems, and continuously improve energy-saving strategies based on evaluation results and feedback to improve energy-saving effects and system performance; 5.5) Implement optimization measures: Implement specific optimization measures according to the optimization strategy to ensure continuous improvement of energy-saving measures. During the implementation of optimization measures, continuously monitor the operating status of the data center and make necessary adjustments based on actual conditions; 5.6) Effect re-evaluation: Regularly re-evaluate the implementation effect of energy-saving measures to ensure that energy-saving measures continue to be effective, track the long-term effects of energy-saving measures, and evaluate the long-term benefits and sustainability of energy-saving measures; 5.7) Continuous monitoring and feedback: Continuously monitor the energy consumption and operating status of the data center to ensure the continuous implementation of energy-saving measures, establish an effective feedback mechanism, provide feedback and continuously optimize energy-saving strategies.

9. The design method of a data center energy saving system according to claim 8, characterized in that: The energy-saving measures implemented in step 5.2) include: reducing the percentage of energy consumption and reducing carbon emissions; evaluating the cost of implementing energy-saving measures and the economic benefits of energy saving, including direct costs, indirect costs and long-term benefits; evaluating the impact of energy-saving measures on data center business, and ensuring that energy-saving measures will not affect business continuity and service quality; In step 5.3), the performance issues identified include longer server response time, reduced system stability, and problems in identifying technical implementation, including equipment failure and software compatibility issues.

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