Distributed intelligent calculation center coupled with land wind power generation and energy consumption control method thereof

Abstract

The invention discloses a distributed intelligent calculation center coupled with land wind power generation and an energy consumption control method thereof, and aims to solve the problem that land wind power generation resources are not matched with high-performance calculation requirements and realize effective utilization of power resources and optimal configuration of calculation resources. The distributed intelligent calculation center comprising the wind power generation system, the electric power allocation system, the calculation resource management system and the energy consumption monitoring control system is designed, and electric power supply is dynamically adjusted through the electric power allocation system according to the wind power generation power and the electricity demand of the intelligent calculation center. The computing resource management system reasonably allocates computing resources according to task load and computing requirements, and the energy consumption monitoring control system monitors energy consumption in real time and optimizes a strategy; the utilization rate of land wind power generation is improved, the energy consumption cost of an intelligent calculation center is reduced, and the overall efficiency of calculation resources is improved; in addition, diversified development of land economy is promoted, stable technical services and employment opportunities are provided for the local area, and deep fusion and application of new energy and information technologies are further promoted.

Description

Technical Field

[0001] The present invention relates to the field of intersectional technology of new energy and information technology, and in particular to a distributed intelligent computing center coupled with onshore wind power generation and an energy consumption control method thereof. Background Art

[0002] With the rapid development of information technology and the explosive growth of data volume, data centers, as core facilities for data processing and storage, have become increasingly important. However, the high energy consumption of data centers has always troubled the industry. Traditional data centers mostly rely on fossil energy for power supply, which not only consumes a lot of energy, but also has a serious impact on the environment. Therefore, how to reduce the energy consumption of data centers and improve energy efficiency has become a hot topic and difficulty in current research.

[0003] In terms of new energy utilization, wind power generation, as a clean and renewable energy source, has broad application prospects. Especially on land, due to high wind speed and abundant wind resources, wind power generation has received widespread attention and rapid development. However, wind power generation has problems of intermittency and volatility, and its output power is often difficult to control stably, which brings challenges to the widespread application of wind power generation.

[0004] In order to reduce the energy consumption of data centers and effectively utilize wind power generation, a variety of solutions combining new energy with data centers have emerged in recent years. For example, CN103019357A discloses a full solar data center for reducing the energy consumption of data centers; its technical solution is that it includes a server system and a power supply and distribution system, the power supply and distribution system includes a solar photovoltaic component, a diesel generator, a city power supply, a centralized UPS battery pack and a three-in-one controller, the three-in-one controller includes a charge and discharge protection controller and a rectifier charge controller, the solar photovoltaic component supplies power to the server system and charges the centralized UPS battery pack through the charge and discharge protection controller, the input end of the rectifier charge controller is connected to the diesel generator and the city power supply through a switching conversion switch, and its output end supplies power to the server system and charges the centralized UPS battery pack; the invention proposes to use solar power generation to power the data center, but the solution is greatly restricted by geographical location and weather conditions, and solar power generation also has intermittent problems.

[0005] In addition, there are also studies that attempt to combine wind power generation with energy storage systems to smooth out the fluctuations in wind power generation. For example, CN114812007B discloses an ultra-high temperature heat pump energy storage system coupled with wind power generation, including a high temperature heat pump energy storage system and a wind power generation system, which are connected by a clutch; the high temperature heat pump energy storage system includes an ultra-high temperature compressor, a molten salt heat exchanger, an expander, a regenerator, an air-cooled heat exchanger, a low temperature molten salt tank, and a high temperature molten salt tank; the wind power generation system includes a generator, a fan, a transformer, a gearbox, and a clutch; thereby, the present invention integrates the wind power generation system with the high temperature heat pump energy storage system, solves the disadvantage of unstable wind power generation, and realizes the controllable operation, negative power and over-power control of the wind power generation system; but such solutions are often costly, and the efficiency and life of the energy storage system are also key factors restricting its widespread application.

[0006] In the above background, the present invention proposes a distributed intelligent computing center coupled with onshore wind power generation and an energy consumption control method thereof; the method aims to utilize the geographical and environmental advantages of onshore wind power generation, combine the intelligent computing center with the wind power generation system, and realize efficient utilization of energy and distributed processing of data; through real-time monitoring of wind power generation power, electricity demand and task volume of the intelligent computing center, dynamically adjust power allocation and computing resource allocation to further improve energy utilization efficiency and reduce energy consumption.

[0007] In summary, the existing technologies still have many problems in data center energy consumption control and new energy utilization, such as heavy reliance on fossil energy, difficulty in overcoming the intermittent and volatile nature of wind power generation, high cost and low efficiency of energy storage systems, etc. This invention proposes an innovative solution to these problems, aiming to promote the development of data centers in a green and sustainable direction. Summary of the invention

[0008] The technical problem to be solved by the present invention is to provide a distributed intelligent computing center coupled with onshore wind power generation and its energy consumption control method, so as to solve the problems of high energy consumption and insufficient utilization of new energy in data centers, especially the technical bottlenecks in the combined application of onshore wind power generation and data centers.

[0009] To solve the above technical problems, the technical solution adopted by the present invention is: a distributed intelligent computing center coupled with onshore wind power generation, including a wind power generation system set up onshore, a power dispatching system connected to the wind power generation system, which dynamically adjusts the power supply according to the real-time changes in wind power generation power and the power demand and task volume of the intelligent computing center; a computing resource management system connected to the power dispatching system, which reasonably allocates computing resources according to the task volume and computing requirements of the intelligent computing center, and an energy consumption monitoring and control system connected to the power dispatching system and the computing resource management system, which monitors the energy consumption of the entire intelligent computing center in real time, and further optimizes the power dispatching and computing resource allocation strategies according to the monitoring results.

[0010] In a preferred scheme, the power dispatching system includes a power monitoring unit, a power distribution unit and an energy storage unit. The power monitoring unit monitors the wind power generation power and the power demand of the intelligent computing center in real time. The power distribution unit dynamically adjusts the power supply according to the monitoring results. The energy storage unit stores electric energy when there is excess power and releases electric energy when there is insufficient power.

[0011] In a preferred solution, the computing resource management system includes a task scheduling unit and a resource allocation unit. The task scheduling unit allocates tasks to corresponding computing nodes according to task priorities and computing requirements, and the resource allocation unit dynamically adjusts the allocation of computing resources according to the load conditions of the computing nodes.

[0012] In a preferred scheme, the energy consumption monitoring and control system includes an energy consumption monitoring unit and a strategy optimization unit. The energy consumption monitoring unit monitors the energy consumption data of the intelligent computing center in real time, and the strategy optimization unit provides feedback and optimizes the power allocation and computing resource allocation strategies based on the monitoring results.

[0013] The preferred solution also includes an intelligent scheduling algorithm, which can automatically select the optimal computing nodes and computing resource allocation strategy according to the task type and computing requirements.

[0014] In a preferred solution, the energy consumption monitoring and control system also has a prediction function, which predicts future energy consumption trends based on historical energy consumption data and current task volume, and adjusts power allocation and computing resource allocation strategies in advance.

[0015] The energy consumption control method of a distributed intelligent computing center coupled with onshore wind power generation comprises the following steps: Step 1: Obtain wind power generation power and monitor the wind power generation power in real time through the power monitoring unit; Step 2: Calculate the power and computing resource requirements. According to the power demand and task volume of the intelligent computing center, calculate the required power and computing resources. Step 3: Dynamically adjust power supply and computing resource allocation, according to the real-time monitored wind power and the calculated power and computing resource requirements; Step 4: Monitor energy consumption in real time and optimize strategies. The energy consumption of the entire intelligent computing center is monitored in real time through the energy consumption monitoring and control system, and feedback and optimization of power allocation and computing resource allocation strategies are provided based on the monitoring results.

[0016] In a preferred solution, the power supply and computing resource allocation are dynamically adjusted in Step 3, specifically by adjusting the power supply through a power allocation unit and adjusting the computing resource allocation through a resource allocation unit.

[0017] In a preferred embodiment, the method further includes a step of predicting future energy consumption trends, predicting future energy consumption trends based on historical energy consumption data and current task volume, and adjusting power allocation and computing resource allocation strategies in advance.

[0018] In a preferred solution, the method further includes the step of automatically selecting the optimal computing node and computing resource allocation strategy using an intelligent scheduling algorithm.

[0019] The distributed intelligent computing center coupled with onshore wind power generation and the energy consumption control method thereof provided by the present invention have the following beneficial effects: 1. The present invention proposes an innovative solution to the problems of high energy consumption in data centers and insufficient utilization of new energy (especially onshore wind power generation). Through a distributed intelligent computing center coupled with onshore wind power generation and an energy consumption control method thereof, the limitation of existing data centers relying on fossil energy is overcome, and the utilization rate of new energy is improved; 2. The flexible scheduling and efficient resource utilization of the intelligent computing center of the present invention. The intelligent computing center can be flexibly scheduled according to the task volume, ensuring the efficient utilization of computing resources. This flexibility not only improves the speed and efficiency of data processing, but also avoids the waste of computing resources; 3. The power dispatching system of the present invention dynamically adjusts the power supply according to the wind power generation power and the power demand of the intelligent computing center, thus realizing the effective use of power; 4. The computing resource management system of the present invention reasonably allocates computing resources according to the task volume and computing requirements, thereby improving the overall efficiency of computing resources; 5. The energy consumption monitoring and control system of the present invention monitors energy consumption in real time and optimizes strategies, thereby reducing the energy consumption cost of the intelligent computing center; 6. The present invention effectively reduces the data center's dependence on fossil energy by utilizing abundant onshore wind energy and combining it with dynamic power allocation and computing resource allocation strategies. This not only reduces energy consumption costs, but also improves energy utilization efficiency, which is in line with the concept of green and sustainable development. 7. The present invention can dynamically adjust the power supply by real-time monitoring of wind power generation and the power demand of the intelligent computing center, thus overcoming the problem of intermittent and fluctuating wind power generation. This dynamic adjustment strategy ensures that the intelligent computing center can still operate stably when the wind power generation fluctuates, thereby improving the reliability and stability of the system. 8. The present invention ensures efficient execution of data processing tasks by reasonably allocating computing resources, while avoiding waste of computing resources and improving the stability and reliability of data processing; 9. The present invention adopts a dynamic power allocation strategy, which reduces the dependence on the energy storage system, reduces the cost of the energy storage system, and simplifies the structure and operation complexity of the energy storage system; 10. The experimental results show that compared with the existing technology, the present invention has significant advantages in reducing energy consumption, improving energy utilization efficiency, overcoming the intermittent and volatility of wind power generation, and improving data processing efficiency and stability. This further proves the effectiveness and practicality of the present invention, and provides new ideas and methods for the utilization of new energy and energy efficiency improvement in data centers. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The present invention will be further described below in conjunction with the accompanying drawings and implementation examples: Figure 1 It is a schematic diagram of the overall structure of the intelligent computing center of the present invention; Figure 2 It is a schematic flow chart of the energy consumption control method of the present invention; In the figure: wind power generation system 1, power dispatching system 2, computing resource management system 3, energy consumption monitoring and control system 4. DETAILED DESCRIPTION

[0021] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments: Example 1 like Figure 1 As shown, in order to solve the above technical problems, the technical solution adopted by the present invention is: a distributed intelligent computing center coupled with onshore wind power generation, including a wind power generation system 1 set up onshore, and a power dispatching system 2 connected to the wind power generation system, which dynamically adjusts the power supply according to the real-time changes in wind power generation power and the power demand and task volume of the intelligent computing center; a computing resource management system 3 connected to the power dispatching system 2, which reasonably allocates computing resources according to the task volume and computing requirements of the intelligent computing center, and an energy consumption monitoring and control system 4 connected to the power dispatching system 2 and the computing resource management system 3, which monitors the energy consumption of the entire intelligent computing center in real time, and further optimizes the power dispatching and computing resource allocation strategies according to the monitoring results.

[0022] In this embodiment, the power dispatching system 2 includes a power monitoring unit, a power distribution unit and an energy storage unit. The power monitoring unit monitors the wind power generation power and the power demand of the intelligent computing center in real time. The power distribution unit dynamically adjusts the power supply according to the monitoring results. The energy storage unit stores electric energy when there is excess power and releases electric energy when there is insufficient power.

[0023] Furthermore, the computing resource management system 3 includes a task scheduling unit and a resource allocation unit. The task scheduling unit allocates tasks to corresponding computing nodes according to task priorities and computing requirements, and the resource allocation unit dynamically adjusts the allocation of computing resources according to the load conditions of the computing nodes.

[0024] Furthermore, the energy consumption monitoring and control system 4 includes an energy consumption monitoring unit and a strategy optimization unit. The energy consumption monitoring unit monitors the energy consumption data of the intelligent computing center in real time, and the strategy optimization unit provides feedback and optimizes the power allocation and computing resource allocation strategies according to the monitoring results.

[0025] Furthermore, it also includes an intelligent scheduling algorithm, which can automatically select the optimal computing nodes and computing resource allocation strategy according to the task type and computing requirements.

[0026] Furthermore, the energy consumption monitoring and control system 4 also has a prediction function, which predicts future energy consumption trends based on historical energy consumption data and current task volume, and adjusts power allocation and computing resource allocation strategies in advance.

[0027] Example 2 In another preferred embodiment, based on Example 1, Figure 2 As shown, the energy consumption control method for a distributed intelligent computing center coupled with onshore wind power generation includes the following steps: Step 1: Obtain wind power generation power and monitor the wind power generation power in real time through the power monitoring unit; Step 2: Calculate the power and computing resource requirements. According to the power demand and task volume of the intelligent computing center, calculate the required power and computing resources. Step 3: Dynamically adjust power supply and computing resource allocation, according to the real-time monitored wind power and the calculated power and computing resource requirements; Step 4: Real-time monitoring of energy consumption and optimization of strategies. The energy consumption of the entire intelligent computing center is monitored in real time through the energy consumption monitoring and control system (4), and feedback and optimization of power allocation and computing resource allocation strategies are provided based on the monitoring results.

[0028] In this embodiment, the power supply and computing resource allocation are dynamically adjusted in Step 3, specifically, the power supply is adjusted by the power allocation unit, and the computing resource allocation is adjusted by the resource allocation unit.

[0029] Furthermore, the method also includes the step of predicting future energy consumption trends, predicting future energy consumption trends based on historical energy consumption data and current task volume, and adjusting power allocation and computing resource allocation strategies in advance.

[0030] Furthermore, the method also includes the step of automatically selecting the optimal computing node and computing resource allocation strategy using an intelligent scheduling algorithm.

[0031] Example 3 In another preferred embodiment, on the basis of embodiments 1 and 2, the present invention adopts the following technical solution: Specifically, the present invention designs a distributed intelligent computing center coupled with onshore wind power generation, which includes a wind power generation system 1, a power dispatching system 2, a computing resource management system 3 and an energy consumption monitoring and control system 4. The wind power generation system 1 is used to convert the abundant wind energy on land into electrical energy; the power dispatching system 2 dynamically adjusts the power supply according to the real-time changes in wind power generation power, as well as the power demand and task volume of the intelligent computing center; the computing resource management system 3 reasonably allocates computing resources according to the task volume and computing requirements to ensure the efficiency and stability of data processing; the energy consumption monitoring and control system 4 monitors the energy consumption of the entire intelligent computing center in real time, and further optimizes the power dispatching and computing resource allocation strategies according to the monitoring results.

[0032] Furthermore, the energy consumption control method of the present invention includes the following steps: first, obtaining real-time wind power generation power through the wind power generation system 1; second, calculating the required electricity and computing resources according to the power demand and task volume of the intelligent computing center; then, dynamically adjusting the power supply and computing resource allocation through the power allocation system 2 and the computing resource management system 3; finally, monitoring the energy consumption in real time through the energy consumption monitoring and control system 4, and providing feedback and optimizing the power allocation and computing resource allocation strategies based on the monitoring results.

[0033] Example 4 In another preferred embodiment, based on embodiments 1, 2, and 3, Figure 1 and Figure 2 As shown, the following is a detailed description of a specific implementation method of the distributed intelligent computing center coupled with onshore wind power generation and its energy consumption control method of the present invention: 1. Intelligent computing center structure design The distributed intelligent computing center in a specific embodiment of the present invention has a structural design including a wind power generation system 1, a power dispatching system 2, a computing resource management system 3 and an energy consumption monitoring and control system 4: 1. Wind power generation system 1: This system is located on land and uses abundant wind energy resources to generate electricity; wind power generation system 1 includes wind turbines, transformers and power transmission lines; wind turbines convert wind energy into electrical energy, which is then boosted by transformers and transmitted to the intelligent computing center through power transmission lines.

[0034] 2. Power dispatching system 2: This system dynamically adjusts the power supply according to the real-time changes in wind power generation and the power demand and task volume of the intelligent computing center. The power dispatching system 2 includes a power monitoring unit, a power distribution unit and an energy storage unit. The power monitoring unit monitors the wind power generation and the power demand of the intelligent computing center in real time, the power distribution unit dynamically adjusts the power supply according to the monitoring results, and the energy storage unit stores electricity when there is excess electricity and releases electricity when there is insufficient electricity, so as to smooth out power fluctuations.

[0035] 3. Computing resource management system 3: This system reasonably allocates computing resources according to the task volume and computing requirements of the intelligent computing center; the computing resource management system includes a task scheduling unit and a resource allocation unit. The task scheduling unit allocates tasks to corresponding computing nodes according to task priority and computing requirements, and the resource allocation unit dynamically adjusts the allocation of computing resources according to the load of the computing nodes.

[0036] 4. Energy consumption monitoring and control system: The system monitors the energy consumption of the entire intelligent computing center in real time, and further optimizes the power allocation and computing resource allocation strategies based on the monitoring results; the energy consumption monitoring and control system 4 includes an energy consumption monitoring unit and a strategy optimization unit; the energy consumption monitoring unit monitors the energy consumption data of the intelligent computing center in real time, and the strategy optimization unit provides feedback and optimization on the power allocation and computing resource allocation strategies based on the monitoring results.

[0037] 2. Implementation steps of energy consumption control method The specific implementation steps of the energy consumption control method of the present invention are as follows: 1. Obtaining wind power generation power: The wind power generation power is monitored in real time through the power monitoring unit of the wind power generation system 1, and is used as the basis for power allocation; 2. Calculate power and computing resource requirements: Calculate the required power and computing resources based on the power demand and task volume of the intelligent computing center. This step is completed by the task scheduling unit and resource allocation unit of the computing resource management system 3. 3. Dynamically adjust power supply and computing resource allocation: According to the real-time monitored wind power and the calculated power and computing resource requirements, the power dispatching system 2 and the computing resource management system 3 dynamically adjust the power supply and computing resource allocation respectively; the power dispatching system 2 adjusts the power supply through the power allocation unit, and the computing resource management system 3 adjusts the allocation of computing resources through the resource allocation unit; 4. Real-time monitoring of energy consumption and optimization of strategies: The energy consumption monitoring and control system 4 monitors the energy consumption of the entire intelligent computing center in real time, and provides feedback and optimization of power allocation and computing resource allocation strategies based on the monitoring results. This step is completed by the monitoring results of the energy consumption monitoring unit and the optimization strategy of the strategy optimization unit.

[0038] Through the above-mentioned embodiments, the present invention successfully realizes the energy consumption control of the distributed intelligent computing center coupled with onshore wind power generation; this embodiment not only reduces the energy consumption of the data center and improves the energy utilization efficiency, but also overcomes the problem of intermittent and volatile wind power generation, and improves the efficiency and stability of data processing.

[0039] Example 5 In another preferred embodiment, based on embodiments 1, 2, 3, and 4, in order to further demonstrate the diversity and practicality of the present invention, another specific embodiment is provided below.

[0040] In this embodiment, the computing resource management system 3 of the intelligent computing center also includes an intelligent scheduling algorithm, which can automatically select the optimal computing node and computing resource allocation strategy according to the task type and computing requirements; in addition, the energy consumption monitoring and control system 4 also has a prediction function, which can predict future energy consumption trends based on historical energy consumption data and current task volume, and adjust power allocation and computing resource allocation strategies in advance to further optimize energy consumption; This embodiment not only inherits all the advantages of Embodiment 4, but also further improves the efficiency of data processing and the accuracy of energy consumption control by introducing intelligent scheduling algorithms and prediction functions.

[0041] Compared with the closest prior art, the present invention has the following significant differences: Dynamic power allocation and computing resource allocation: The present invention realizes dynamic allocation and distribution of power and computing resources by real-time monitoring of wind power generation power and the power demand and task volume of the intelligent computing center, thereby overcoming the problems of low energy utilization efficiency and unstable data processing in the prior art.

[0042] Optimal utilization of energy storage units: The present invention introduces energy storage units into the power dispatching system. The energy storage units store electric energy when there is excess power and release electric energy when there is insufficient power, thereby effectively smoothing out power fluctuations and improving energy utilization efficiency.

[0043] Intelligent scheduling algorithm and prediction function: In Example 5, the present invention further introduces an intelligent scheduling algorithm and prediction function. Through the algorithm and prediction, automatic selection of computing resources and precise control of energy consumption are realized, further improving the efficiency of data processing and the accuracy of energy consumption control.

[0044] In the preferred scheme, the power dispatching system 2 includes a power monitoring unit, a power distribution unit and an energy storage unit. The power monitoring unit monitors the wind power generation power and the power demand of the intelligent computing center in real time. The power distribution unit dynamically adjusts the power supply according to the monitoring results. The energy storage unit stores electric energy when there is excess power and releases electric energy when there is insufficient power. The above settings ensure the stability and efficiency of the power supply of the intelligent computing center, effectively avoid operational failures caused by power fluctuations, and at the same time improve the utilization rate of wind energy, realizing green and sustainable energy management.

[0045] In the preferred scheme, the computing resource management system 3 includes a task scheduling unit and a resource allocation unit. The task scheduling unit allocates tasks to corresponding computing nodes according to task priority and computing requirements, and the resource allocation unit dynamically adjusts the allocation of computing resources according to the load of the computing nodes. The above settings ensure that high-priority tasks can quickly obtain the required resources while avoiding overloading of computing nodes, thereby improving the resource utilization and task execution efficiency of the entire system and realizing flexible management and efficient utilization of computing resources.

[0046] In the preferred scheme, the energy consumption monitoring and control system 4 includes an energy consumption monitoring unit and a strategy optimization unit. The energy consumption monitoring unit monitors the energy consumption data of the intelligent computing center in real time, and the strategy optimization unit provides feedback and optimization on the power allocation and computing resource allocation strategies based on the monitoring results. The above settings are intended to achieve dynamic management and intelligent regulation of energy consumption and reduce unnecessary energy waste. At the same time, the system can also predict future energy consumption trends based on historical energy consumption data, providing a scientific basis for the long-term energy-saving planning of the intelligent computing center.

[0047] The preferred solution also includes an intelligent scheduling algorithm, which can automatically select the optimal computing nodes and computing resource allocation strategy according to the task type and computing requirements. The above settings can significantly improve task execution efficiency and reduce resource waste. At the same time, the algorithm also supports dynamic adjustment and can flexibly respond to various emergencies according to actual conditions, ensuring that the system always runs in the best state.

[0048] In the preferred scheme, the energy consumption monitoring and control system 4 also has a prediction function, which predicts future energy consumption trends based on historical energy consumption data and current task volume, and adjusts power allocation and computing resource allocation strategies in advance; the above settings enable the system to efficiently respond to energy consumption changes, optimize energy utilization efficiency, and reduce unnecessary energy waste, thereby providing strong support for the green operation of data centers or buildings.

[0049] In the preferred scheme, the power supply and computing resource allocation are dynamically adjusted in Step 3, specifically, the power supply is adjusted by the power distribution unit, and the computing resource allocation is adjusted by the resource allocation unit; the above settings can dynamically optimize energy and computing power according to the actual operating load, ensure that the system reduces energy waste while operating efficiently, improve the overall energy efficiency ratio, and enhance the stability and response speed of the system.

[0050] In the preferred scheme, the method also includes the step of predicting future energy consumption trends. Based on historical energy consumption data and current task volume, future energy consumption trends are predicted, and power allocation and computing resource allocation strategies are adjusted in advance. The above settings can significantly improve the energy efficiency of the system and reduce unnecessary energy waste. At the same time, it ensures that the system can operate stably during peak task periods, providing users with a more reliable and efficient service experience.

[0051] In the preferred scheme, the method also includes the step of automatically selecting the optimal computing node and computing resource allocation strategy using an intelligent scheduling algorithm; the above settings can significantly improve the execution efficiency of computing tasks, and dynamically adjust resource allocation by real-time monitoring of resource usage, ensuring that the system stability and response speed can be maintained in high concurrency scenarios.

[0052] In summary, the distributed intelligent computing center coupled with onshore wind power generation and its energy consumption control method proposed in the present invention have successfully solved the problems of high energy consumption and insufficient utilization of new energy in data centers, especially the technical bottlenecks in the combined application of onshore wind power generation and data centers; the present invention innovatively combines the distributed coupling layout of the intelligent computing center and the onshore wind power generation system for the first time, making full use of the natural environmental advantages of the wind farm (such as the temperature and wind speed in high-altitude areas) for natural heat dissipation, effectively reducing the heat dissipation cost and overall energy consumption of the intelligent computing center; the scheme dynamically adjusts the power supply according to the real-time changes in wind power generation power and the power demand and task volume of the intelligent computing center through the power allocation system 2, thereby realizing flexible power allocation. At the same time, the computing resource management system reasonably allocates computing resources according to the task volume and computing requirements, ensuring flexible configuration and efficient utilization of resources; in addition, the present invention also creatively introduces an intelligent scheduling algorithm, which can It can automatically select the optimal computing node and computing resource allocation strategy according to the task type and computing requirements, further improving the resource utilization efficiency; the energy consumption monitoring and control system 4 has a powerful prediction function, which can accurately predict the future energy consumption trend according to the historical energy consumption data and the current task volume, and adjust the power allocation and computing resource allocation strategy in advance, thereby realizing further optimization of energy consumption; it is worth mentioning that the present invention integrates the data center and the wind power plant, sharing the management system, maintenance path and space. This measure not only significantly improves the management efficiency, but also greatly reduces the investment cost, and opens up a new path for the coupling application of onshore wind power generation and data centers; in terms of environmental benefits, the invention significantly reduces carbon emissions by maximizing the use of renewable energy, setting a new benchmark for the construction of green data centers. At the same time, its innovative model also provides valuable experience and demonstration effect for the integrated development of other renewable energy sources and data centers.

Claims

1. A distributed intelligent computing center coupled with onshore wind power generation, characterized by: The invention comprises a wind power generation system (1) connected to the land, a power dispatching system (2) connected to the wind power generation system, which dynamically adjusts the power supply according to the real-time changes in the wind power generation power and the power demand and task volume of the intelligent computing center; a computing resource management system (3) connected to the power dispatching system (2), which reasonably allocates computing resources according to the task volume and computing demand of the intelligent computing center; and an energy consumption monitoring and control system (4) connected to the power dispatching system (2) and the computing resource management system (3), which monitors the energy consumption of the entire intelligent computing center in real time, and further optimizes the power dispatching and computing resource allocation strategies according to the monitoring results.

2. The distributed intelligent computing center coupled with onshore wind power generation according to claim 1, characterized in that: The power dispatching system (2) comprises a power monitoring unit, a power distribution unit and an energy storage unit. The power monitoring unit monitors the wind power generation power and the power demand of the intelligent computing center in real time. The power distribution unit dynamically adjusts the power supply according to the monitoring results. The energy storage unit stores electric energy when there is excess power and releases electric energy when there is insufficient power.

3. The distributed intelligent computing center coupled with onshore wind power generation according to claim 1, characterized in that: The computing resource management system (3) comprises a task scheduling unit and a resource allocation unit. The task scheduling unit allocates tasks to corresponding computing nodes according to task priorities and computing requirements, and the resource allocation unit dynamically adjusts the allocation of computing resources according to the load conditions of the computing nodes.

4. The distributed intelligent computing center coupled with onshore wind power generation according to claim 1, characterized in that: The energy consumption monitoring and control system (4) comprises an energy consumption monitoring unit and a strategy optimization unit. The energy consumption monitoring unit monitors the energy consumption data of the intelligent computing center in real time. The strategy optimization unit provides feedback and optimizes the power allocation and computing resource allocation strategies based on the monitoring results.

5. The distributed intelligent computing center coupled with onshore wind power generation according to claim 1, characterized in that: It also includes an intelligent scheduling algorithm that can automatically select the optimal computing nodes and computing resource allocation strategy based on task type and computing requirements.

6. The distributed intelligent computing center coupled with onshore wind power generation according to claim 1, characterized in that: The energy consumption monitoring and control system (4) also has a prediction function, which predicts future energy consumption trends based on historical energy consumption data and current task volume, and adjusts power allocation and computing resource allocation strategies in advance.

7. An energy consumption control method for a distributed intelligent computing center coupled with onshore wind power generation, characterized in that: The method comprises the following steps: Step 1: Obtain wind power generation power and monitor the wind power generation power in real time through the power monitoring unit; Step 2: Calculate the power and computing resource requirements. According to the power demand and task volume of the intelligent computing center, calculate the required power and computing resources. Step 3: Dynamically adjust power supply and computing resource allocation, according to the real-time monitored wind power and the calculated power and computing resource requirements; Step 4: Real-time monitoring of energy consumption and optimization of strategies. The energy consumption of the entire intelligent computing center is monitored in real time through the energy consumption monitoring and control system (4), and feedback and optimization of power allocation and computing resource allocation strategies are provided based on the monitoring results.

8. The energy consumption control method of the distributed intelligent computing center coupled with onshore wind power generation according to claim 7 is characterized in that: In the Step 3, the power supply and computing resource allocation are adjusted dynamically, specifically, the power supply is adjusted by the power allocation unit, and the computing resource allocation is adjusted by the resource allocation unit.

9. The energy consumption control method of the distributed intelligent computing center coupled with onshore wind power generation according to claim 7 is characterized in that: The method also includes the step of predicting future energy consumption trends, predicting future energy consumption trends based on historical energy consumption data and current task volume, and adjusting power allocation and computing resource allocation strategies in advance.

10. The energy consumption control method of the distributed intelligent computing center coupled with onshore wind power generation according to claim 7, characterized in that: The method also includes the step of automatically selecting the optimal computing node and computing resource allocation strategy using an intelligent scheduling algorithm.

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