Data center energy-saving system

Abstract

The invention discloses a data center energy-saving system, which relates to the technical field of data centers and comprises a refrigeration optimization module, an energy management module, a renewable energy module, a server virtualization module, a monitoring maintenance module, a utilization rate adjustment module and a cold and hot channel management module. The refrigeration optimization module is used for executing the following steps that a cold and hot channel is formed through physical isolation, invalid circulation of cooling capacity in the data center is reduced, low-temperature resources of the external natural environment are utilized, and an external cold source is introduced through an air cooling or water cooling system. Through mutual cooperation of the refrigeration optimization module, the energy management module and the renewable energy module, through optimization and energy saving of refrigeration in an original data center room and energy distribution control, energy consumption is reduced, and meanwhile, through cooperation with power supply of renewable energy provided by the renewable energy module to the data center, energy consumption is reduced. And the energy-saving effect of the data center is further improved.

Description

Technical Field

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

[0002] With the deepening of informatization, more and more users need a large amount of data processing and information exchange. Accordingly, more and more companies have begun to build data centers to provide places for the convergence of business systems and data resources. A large number of computer equipment are installed in data centers. The trend of information processing development is that the computer processing speed is getting faster and faster, the storage capacity is getting larger and larger, and the volume is getting smaller and smaller. In other words, the heat dissipation per unit area is getting larger and larger. According to statistics, the heat dissipation of a server cabinet generally reaches several kilowatts to tens of kilowatts per hour. The following problems exist in the existing technology:

[0003] As the scale of data centers expands, air conditioning systems need to be configured to solve the heat dissipation problem and ensure a good temperature and humidity environment in the data center. This has led to an increase in the power consumption of data centers. The huge electricity bills have exceeded the investment in infrastructure and have become the first cost of data centers. Summary of the invention

[0004] The present invention provides a data center energy-saving system to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] A data center energy-saving system comprises a refrigeration optimization module, an energy management module, a renewable energy module, a server virtualization module, a monitoring and maintenance module, a utilization rate adjustment module, and a cold and hot channel management module.

[0007] The cooling optimization module is used to perform the following steps: forming hot and cold channels through physical isolation, reducing the ineffective circulation of cold energy inside the data center, and utilizing the low-temperature resources of the external natural environment to introduce an external cold source through an air cooling or water cooling system.

[0008] The energy management module includes an intelligent power distribution module and a dynamic energy management module.

[0009] The renewable energy module includes a solar photovoltaic system, a wind energy system, and a geothermal energy system.

[0010] The server virtualization module is used to perform the following steps: integrating multiple physical servers into multiple virtual servers through virtualization technology, and pooling and dynamically allocating storage resources.

[0011] The monitoring and maintenance module uses AI and big data technologies to intelligently manage the data center infrastructure, including an intelligent monitoring module and a predictive maintenance module.

[0012] The utilization adjustment module is an important component of the test and development environment and the energy consumption of the data center.

[0013] The hot and cold aisle management module is used to perform the following steps: strictly distinguish between hot and cold aisles, ensure that hot and cold aisles do not interfere with each other through physical isolation or airflow organization, regularly clean and maintain hot and cold aisles, handle obstacles and dust in hot and cold aisles, and ensure unimpeded airflow.

[0014] A further improvement of the technical solution of the present invention is that the intelligent power distribution module is used to intelligently distribute and dispatch electricity, and the dynamic energy management module is used to monitor the energy consumption of various parts of the data center in real time, automatically adjust energy use according to load changes, and reduce unnecessary energy consumption.

[0015] A further improvement of the technical solution of the present invention is that the solar photovoltaic system can convert solar energy into electrical energy for use in the data center through solar photovoltaic panels, the wind energy system can power the data center through wind power generation, and the geothermal energy system uses the underground constant temperature layer as a cold and heat source to provide cooling or heating for the data center through ground source heat pump technology.

[0016] A further improvement of the technical solution of the present invention is that the AI ​​technology uses the Al algorithm to perform real-time monitoring and predictive analysis of the data center, accurately controls the cooling demand and cooling capacity of the air-conditioning system, and optimizes the overall energy consumption distribution of the data center.

[0017] A further improvement of the technical solution of the present invention is that the big data technology can monitor the operating status of the data center in a comprehensive and real-time manner, collect a large amount of equipment status, environmental parameters and energy consumption data, analyze the patterns and trends in the data, and help managers accurately grasp the energy consumption of the data center.

[0018] A further improvement of the technical solution of the present invention is that the big data technology can achieve rapid fault location and recovery, automatic upgrade and optimized configuration of equipment through automated operation and maintenance tools and systems.

[0019] A further improvement of the technical solution of the present invention is that the intelligent monitoring module is used to monitor the data center environment, equipment status and energy consumption data in real time, and to promptly discover and solve problems; the predictive maintenance module is used to predict equipment failure risks through data analysis, and to perform maintenance and replacement in advance.

[0020] A further improvement of the technical solution of the present invention is that the utilization adjustment module includes the use of simulators and sandboxes to reduce the use of experimental equipment, perform software testing and development through simulators and sandboxes, optimize the test process, and rationally plan the test plan.

[0021] A further improvement of the technical solution of the present invention is that the energy-saving system adopts a high-efficiency, low-loss non-gold alloy transformer to reduce eddy current loss. The circuit breaker configured for the high-voltage equipment is a vacuum circuit breaker with low power consumption. The reactive loss of the low-voltage electrical equipment is centrally compensated in the low-voltage distribution room.

[0022] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art:

[0023] 1. The present invention provides a data center energy-saving system, which optimizes and saves energy by optimizing the refrigeration in the original data center room and controls energy distribution through the mutual cooperation among the refrigeration optimization module, the energy management module and the renewable energy module, thereby reducing energy consumption. At the same time, the renewable energy provided by the renewable energy module is used to power the data center, thereby further improving the energy-saving effect of the data center.

[0024] 2. The present invention provides a data center energy-saving system, which helps the data center to achieve refined management and maximum utilization of energy and achieve energy-saving effects through the AI ​​and big data technologies possessed by the server virtualization module and the monitoring and maintenance module.

[0025] 3. The present invention provides a data center energy-saving system, which monitors the environment, equipment status and energy consumption data of the data center through the mutual cooperation between the intelligent monitoring module and the utilization adjustment module, timely detects energy consumption problems, optimizes the test process, and reduces test energy efficiency consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0027] The present invention is further described in detail below in conjunction with embodiments:

[0028] Example 1

[0029] like Figure 1 As shown, the present invention provides a data center energy-saving system, including a refrigeration optimization module, an energy management module, a renewable energy module, a server virtualization module, a monitoring and maintenance module, a utilization adjustment module, and a cold and hot channel management module;

[0030] The cooling optimization module is used to perform the following steps: forming hot and cold channels through physical isolation, reducing the ineffective circulation of cooling capacity inside the data center, and utilizing the low-temperature resources of the external natural environment to introduce external cooling sources through air cooling or water cooling systems, thereby improving cooling efficiency and reducing cooling energy consumption;

[0031] The energy management module includes intelligent power distribution module and dynamic energy management module;

[0032] Renewable energy modules include solar photovoltaic systems, wind energy systems, and geothermal energy systems;

[0033] The server virtualization module is used to perform the following steps: integrate multiple physical servers into multiple virtual servers through virtualization technology, and pool and dynamically allocate storage resources;

[0034] The monitoring and maintenance module uses AI and big data technologies to intelligently manage data center infrastructure, including intelligent monitoring modules and predictive maintenance modules;

[0035] The utilization adjustment module is an important part of the energy consumption of the data center in the test and development environment;

[0036] The hot and cold aisle management module is used to perform the following steps: strictly distinguish between hot and cold aisles, ensure that hot and cold aisles do not interfere with each other through physical isolation or airflow organization, regularly clean and maintain hot and cold aisles, handle obstacles and dust in hot and cold aisles, and ensure unimpeded airflow.

[0037] Example 2

[0038] like Figure 1 As shown, based on Example 1, the present invention provides a technical solution: preferably, the intelligent power distribution module is used to intelligently distribute and dispatch electricity, the dynamic energy management module is used to monitor the energy consumption of various parts of the data center in real time, automatically adjust energy use according to load changes, and reduce unnecessary energy consumption. The solar photovoltaic system can convert solar energy into electrical energy for use in the data center through solar photovoltaic panels, the wind energy system can power the data center through wind power generation, and the geothermal energy system uses the underground constant temperature layer as a cold and heat source to provide cooling or heating for the data center through ground source heat pump technology.

[0039] In this embodiment, the intelligent power distribution module can ensure the stability and efficiency of the power supply, and can set energy efficiency goals: such as the PUE (power usage efficiency) indicator, regularly evaluate and optimize to achieve higher energy efficiency levels. The solar photovoltaic system, wind energy system, and geothermal energy system can all reduce the energy usage pressure of the data center.

[0040] Example 3

[0041] like Figure 1As shown, on the basis of Example 1, the present invention provides a technical solution: preferably, AI technology uses an Al algorithm to perform real-time monitoring and predictive analysis on the data center, accurately controls the cooling demand and cooling capacity of the air-conditioning system, and optimizes the overall energy consumption distribution of the data center. Big data technology can comprehensively and real-time monitor the operating status of the data center, collect a large amount of equipment status, environmental parameters and energy consumption data, analyze the rules and trends in the data, and help managers accurately grasp the energy consumption of the data center, ensure that timely measures are taken when problems arise, and reduce energy waste. Big data technology can achieve rapid fault location and recovery, automatic upgrade and optimal configuration of equipment through automated operation and maintenance tools and systems.

[0042] In this embodiment, the AI ​​technology and big data technology can realize automated operation and maintenance and energy efficiency optimization. Big data technology can also automatically adjust the energy use strategy of the data center according to the real-time load conditions, automatically shut down some non-essential equipment or adjust it to low power mode when the load is low, and automatically allocate more resources to meet demand during peak loads. By continuously optimizing energy use strategies, big data technology can help data centers achieve refined energy management and maximize energy utilization, thereby achieving energy-saving effects.

[0043] Example 4

[0044] like Figure 1 As shown, on the basis of Example 1, the present invention provides a technical solution: preferably, the intelligent monitoring module is used to monitor the data center environment, equipment status and energy consumption data in real time, and to discover and solve problems in time. The predictive maintenance module is used to predict the risk of equipment failure through data analysis, and to perform maintenance and replacement in advance. The utilization adjustment module includes the use of simulators and sandboxes to reduce the use of experimental equipment, and to perform software testing and development through simulators and sandboxes, and optimize the test process, reasonably plan the test plan, avoid repeated tests and invalid tests, which is an important part of the energy consumption of the data center. The energy-saving system adopts a high-efficiency, low-loss non-gold alloy transformer to reduce eddy current losses. The circuit breaker configured for the high-voltage equipment is a vacuum circuit breaker with low power consumption. The reactive loss of the low-voltage electrical equipment is centrally compensated in the low-distribution room, thereby greatly reducing the energy consumption of the air-conditioning equipment in the data center.

[0045] The following is a detailed description of the working principle of the data center energy saving system.

[0046] like Figure 1As shown, the cooling effect in the data center room is improved through the cooling optimization module, and the energy management module includes the intelligent power distribution module and the dynamic energy management module to monitor the energy consumption of the data center, so as to make adjustments and reasonably control the energy output. The renewable energy module includes the solar photovoltaic system, wind energy system, and geothermal energy system to control the power supply pressure of the data center, and use AI and big data technology to intelligently manage the infrastructure of the data center, so as to help the data center achieve refined energy management and maximize the utilization of energy, achieve energy saving effects, and reasonably plan the test plan through the utilization adjustment module to avoid repeated and invalid tests and reduce the energy required for testing.

[0047] The above generally describes the present invention in detail, but it is obvious to a person skilled in the art that some modifications or improvements can be made to the present invention. Therefore, modifications or improvements that do not depart from the spirit of the present invention are within the scope of protection of the present invention.

Claims

1. A data center energy saving system, characterized in that: It includes cooling optimization module, energy management module, renewable energy module, server virtualization module, monitoring and maintenance module, utilization adjustment module, and hot and cold channel management module; The cooling optimization module is used to perform the following steps: forming hot and cold channels through physical isolation, reducing the ineffective circulation of cooling capacity inside the data center, and utilizing the low-temperature resources of the external natural environment to introduce an external cooling source through an air cooling or water cooling system; The energy management module includes an intelligent power distribution module and a dynamic energy management module; The renewable energy module includes a solar photovoltaic system, a wind energy system, and a geothermal energy system; The server virtualization module is used to perform the following steps: integrating multiple physical servers into multiple virtual servers through virtualization technology, and pooling and dynamically allocating storage resources; The monitoring and maintenance module uses AI and big data technologies to intelligently manage the data center infrastructure, including an intelligent monitoring module and a predictive maintenance module; The utilization adjustment module is an important component of the data center energy consumption in the test and development environment; The hot and cold aisle management module is used to perform the following steps: strictly distinguish between hot and cold aisles, ensure that hot and cold aisles do not interfere with each other through physical isolation or airflow organization, regularly clean and maintain hot and cold aisles, handle obstacles and dust in hot and cold aisles, and ensure unimpeded airflow.

2. A data center energy saving system according to claim 1, characterized in that: The intelligent power distribution module is used to intelligently distribute and dispatch electricity, and the dynamic energy management module is used to monitor the energy consumption of various parts of the data center in real time, automatically adjust energy use according to load changes, and reduce unnecessary energy consumption.

3. The data center energy saving system according to claim 1, characterized in that: The solar photovoltaic system can convert solar energy into electrical energy for use in the data center through solar photovoltaic panels, the wind energy system can power the data center through wind power generation, and the geothermal energy system uses the underground constant temperature layer as a cold and heat source to provide cooling or heating for the data center through ground source heat pump technology.

4. The data center energy saving system according to claim 1, characterized in that: The AI ​​technology uses Al algorithms to perform real-time monitoring and predictive analysis of the data center, accurately controls the cooling demand and cooling capacity of the air-conditioning system, and optimizes the overall energy consumption distribution of the data center.

5. The data center energy saving system according to claim 1, characterized in that: The big data technology can monitor the operating status of the data center in a comprehensive and real-time manner. By collecting a large amount of equipment status, environmental parameters and energy consumption data, and analyzing the patterns and trends in the data, it can help managers accurately understand the energy consumption of the data center.

6. A data center energy saving system according to claim 5, characterized in that: The big data technology can achieve rapid fault location and recovery, automatic equipment upgrade and optimized configuration through automated operation and maintenance tools and systems.

7. The data center energy saving system according to claim 1, characterized in that: The intelligent monitoring module is used to monitor the data center environment, equipment status and energy consumption data in real time, and to promptly discover and solve problems. The predictive maintenance module is used to predict equipment failure risks through data analysis and to perform maintenance and replacement in advance.

8. The data center energy saving system according to claim 1, characterized in that: The utilization adjustment module includes using simulators and sandboxes to reduce the use of experimental equipment, performing software testing and development through simulators and sandboxes, optimizing the test process, and rationally planning the test plan.

9. The data center energy saving system according to claim 1, characterized in that: The energy-saving system adopts a high-efficiency, low-loss non-gold alloy transformer to reduce eddy current loss. The circuit breaker configured for the high-voltage equipment is a vacuum circuit breaker with low power consumption. The reactive loss of the low-voltage electrical equipment is centrally compensated in the low-voltage distribution room.

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