Method and device for sensing energy consumption of green electricity data center based on offshore oil exploration

Abstract

The invention discloses a green power data center energy consumption sensing method and equipment based on offshore oil exploration, and the method comprises the steps: deploying green power grids on sea surface islands, and supplying power to a green power data center for offshore oil exploration; calculating the green power output of each green power grid and the offshore oil exploration task load of the green power data center, and determining the green power margin of each green power grid; whether the green power margin of the green power grid with the shortest distance to the green power data center meets the requirement of the green power data center for dynamically adjusting the server frequency is judged, and if yes, the green power grid with the shortest distance serves as the optimal green power grid; otherwise, adopting a minimum green power waste priority strategy to select an optimal green power grid; and dynamically adjusting the server frequency of the green power data center according to the green power margin of the optimal green power grid, and executing the offshore oil exploration task of the green power center. According to the energy consumption perception of the green power data center, the server frequency is dynamically adjusted to match the green power output, and the green power utilization rate is improved.

Description

Technical Field

[0001] This invention relates to the field of energy consumption sensing technology, specifically to a method and device for energy consumption sensing in a green data center based on offshore oil exploration. Background Technology

[0002] With increasing global emphasis on green development and energy conservation and emission reduction, the high energy consumption of traditional data centers is becoming increasingly prominent. For offshore oil exploration, an energy-intensive industry, building green data centers is a crucial way to achieve energy transition and reduce carbon emissions. Green data centers in offshore oil exploration primarily use renewable energy sources to generate green electricity, including photovoltaic power plants, wind power plants, and wave power plants, which can significantly reduce carbon emissions.

[0003] Green electricity is inherently volatile, while traditional green electricity data centers use static frequency servers that require stable power input. As a result, green electricity data centers need to rely on backup power sources to cope with insufficient green electricity. Since there is a millisecond delay in switching backup power sources, it may cause server restarts or data loss. In addition, under static frequency, servers cannot dynamically adjust their load according to the green electricity output, resulting in some green electricity being abandoned, leading to insufficient green electricity utilization. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention provides a method and device for energy consumption sensing of green electricity data centers based on offshore oil exploration. Based on the energy consumption sensing of the green electricity data center, the server frequency is dynamically adjusted to match the green electricity output, thereby improving the utilization rate of green electricity.

[0005] To achieve the above technical objectives, the present invention adopts the following technical solution: a green energy data center energy consumption sensing method based on offshore oil exploration, comprising the following steps: Step S1: Deploy green power grids on islands at sea to power the green data center for offshore oil exploration; Step S2: Calculate the green power output of each green grid and the offshore oil exploration task load of the green power data center to determine the green power margin of each green grid; Step S3: Determine whether the green power margin of the green grid with the shortest distance to the green power data center meets the requirements of the green power data center to dynamically adjust the server frequency. If it does, the green grid with the shortest distance is selected as the optimal green grid; otherwise, the minimum green power waste priority strategy is adopted to select the optimal green grid. Step S4: Dynamically adjust the server frequency of the green power data center according to the green power margin of the optimal green power grid, and execute the offshore oil exploration task of the green power center.

[0006] Furthermore, each green grid includes: photovoltaic power plants, wind power plants, and wave power plants, and the calculation process for the green electricity output of each green grid is as follows:

[0007] in, Indicates the first i A green power grid t Green electricity output at any time Indicates the first i A green power grid t The output of photovoltaic power plants at this moment Indicates the first i A green power grid t The current output of the wind power station Indicates the first i A green power grid t The power output of the wave power station at any given moment.

[0008] Furthermore, the first i A green power grid t Photovoltaic power plant output at any time Represented as:

[0009] in, Indicates the first i A green power grid t The number of parallel strings of the photovoltaic array connected at any given time. Indicates the first i A green power grid t The number of photovoltaic cells connected in series in the photovoltaic array at any given time. express t The voltage of a single photovoltaic array at a given time. express t The current of a single photovoltaic array at a given moment.

[0010] Furthermore, the first i A green power grid t Wind power output at current time Represented as:

[0011] in, Indicates the first i The rated power of wind power stations in a green power grid Indicates the first i A green power grid t The wind speed at that moment, Indicates the first i The wind speed at which a green power grid is integrated. Indicates the firsti The rated wind speed of a green power grid Indicates the first i The cut-out wind speed of a green power grid.

[0012] Furthermore, the first i A green power grid t Wave power plant output at any time Represented as:

[0013] in, Indicates the density of seawater. Represents gravitational acceleration. Indicates the first i A green power grid t The effective wave height at time t is Indicates the first i A green power grid t wavelength at time , Indicates the peak period, Indicates the first i A green power grid t The water depth at that moment, This represents the hyperbolic sine function.

[0014] Furthermore, the calculation process for the green electricity margin of the green power grid is as follows:

[0015] in, Indicates the first i A green power grid t Current green energy margin Indicates the first i A green power grid t Green electricity output at any time express t The current green data center is responsible for the offshore oil exploration mission load.

[0016] Furthermore, the specific process in step S3 for determining whether the green electricity margin of the green power grid shortest to the green electricity data center meets the requirements of the green electricity data center for dynamically adjusting the server frequency is as follows: If the green power margin of the green power grid that is closest to the green power data center is greater than 0, the green power grid meets the requirements of the green power data center to dynamically adjust the server frequency. If the green power margin of the green power grid that is closest to the green power data center is below 0, and the green power data center still maintains a server frequency above the server frequency threshold by dynamically adjusting the server frequency, then the green power grid meets the requirement of the green power data center to dynamically adjust the server frequency. If the green power margin of the green power grid closest to the green power data center is below 0, and the green power data center adjusts its server frequency to below the server frequency threshold, then the green power grid does not meet the requirement of the green power data center to dynamically adjust its server frequency.

[0017] Furthermore, the process of dynamically adjusting the server frequency of the green electricity data center is as follows:

[0018] in, This indicates the adjusted server frequency. Indicates the server's base frequency. This represents the adjustment coefficient. express t The optimal green power margin for a green grid at any given time. express t The current green data center is responsible for the offshore oil exploration mission load.

[0019] Furthermore, in step S3, a strategy prioritizing minimal green electricity waste is adopted, and the process of selecting the optimal green grid is as follows:

[0020] in, Indicators representing the evaluation criteria for green power grid use I This represents the set of all green power grids except for the one closest to the green power data center. Indicates the first i A green power grid t Current green energy margin express t The current green data center is responsible for the offshore oil exploration mission load.

[0021] Furthermore, the present invention also provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the energy consumption sensing method for green data centers based on offshore oil exploration.

[0022] Compared with the prior art, the present invention has the following beneficial effects: (1) The energy consumption sensing method of green data center based on offshore oil exploration in this invention uses a green power grid to power the green data center of offshore oil exploration, which can achieve zero carbon emissions and reduce dependence on fossil energy. (2) This invention selects the optimal green grid to supply power to the green data center based on the green power margin of the green grid, avoiding the risk of power outage. At the same time, in order to improve energy utilization efficiency and reduce transmission loss, when the green power margin of the green grid with the shortest distance to the green data center meets the requirements of the green data center to dynamically adjust the server frequency, the green grid with the shortest distance is used as the optimal green grid for power supply, which significantly reduces heat loss in transmission, reduces overall energy consumption, and improves power supply reliability. When the green power margin of the green grid with the shortest distance to the green data center does not meet the requirements, the minimum green power waste priority strategy is adopted to select the optimal green grid, promote green power consumption, reduce green power abandonment, and improve emission reduction efficiency. (3) The present invention dynamically adjusts the server frequency of the green power data center according to the green power margin of the selected green power grid, so as to accurately match the supply and demand relationship of green power, avoid power abandonment, and improve the utilization rate of green power. Attached Figure Description

[0023] Figure 1 This is a flowchart of the energy consumption sensing method for green electricity data centers based on offshore oil exploration according to the present invention; Figure 2 This is a schematic diagram illustrating the deployment of the green power grid in this invention. Detailed Implementation

[0024] The technical solution of the present invention will be further explained and described below with reference to the accompanying drawings.

[0025] like Figure 1 This is a flowchart of the energy consumption sensing method for green data centers based on offshore oil exploration according to the present invention. The energy consumption sensing method for green data centers includes the following steps: Step S1: Deploy green power grids on the islands at sea to power the green data center for offshore oil exploration; such as Figure 2 Each green grid in this invention includes a photovoltaic power station, a wind power station, and a wave power station. The photovoltaic power station uses the photovoltaic effect of semiconductor materials to convert solar energy into electrical energy. The wind power station uses wind to drive the wind turbine blades to rotate, which in turn drives the generator to convert mechanical energy into electrical energy. The wave power station uses the up-and-down movement of ocean surface waves to convert wave energy into hydraulic energy, which then drives the generator to generate electricity. The above-mentioned green grid does not require the burning of fossil fuels, can achieve zero carbon emissions, and reduce dependence on fossil energy.

[0026] Step S2: Calculate the green power output of each green grid and the load of the offshore oil exploration mission of the green data center, and determine the green power margin of each green grid. The green power margin is the remaining green power supply capacity of the green grid after meeting the load of the green data center. Since the offshore oil exploration mission has extremely high requirements for the continuity of power supply, the green power margin can be used to determine whether the green grid meets the load requirements of the green data center, ensuring power supply stability and reducing risks.

[0027] The calculation process for the green power output of each green grid in this invention is as follows:

[0028] in, Indicates the first i A green power grid t Green electricity output at any time Indicates the first i A green power grid t The output of photovoltaic power plants at this moment Indicates the first i A green power grid t The current output of the wind power station Indicates the first i A green power grid t The power output of the wave power station at any given moment.

[0029] In this invention, the first i A green power grid t Photovoltaic power plant output at any time Represented as:

[0030] in, Indicates the first i A green power grid t The number of parallel strings of the photovoltaic array connected at any given time. Indicates the first i A green power grid t The number of photovoltaic cells connected in series in the photovoltaic array at any given time. express t The voltage of a single photovoltaic array at a given time. express t The current of a single photovoltaic array at a given moment.

[0031] In this invention, the first i A green power grid t Wind power output at current time Represented as:

[0032] in, Indicates the first i The rated power of wind power stations in a green power grid Indicates the first i A green power grid t The wind speed at that moment, Indicates the first i The wind speed at which a green power grid is integrated. Indicates the first iThe rated wind speed of a green power grid Indicates the first i The cut-out wind speed of a green power grid.

[0033] In this invention, the first i A green power grid t Wave power plant output at any time Represented as:

[0034] in, Indicates the density of seawater. Represents gravitational acceleration. Indicates the first i A green power grid t The effective wave height at time t is Indicates the first i A green power grid t wavelength at time , Indicates the peak period, Indicates the first i A green power grid t The water depth at that moment, This represents the hyperbolic sine function.

[0035] The calculation process for the green electricity margin of the green power grid in this invention is as follows:

[0036] in, Indicates the first i A green power grid t Current green energy margin Indicates the first i A green power grid t Green electricity output at any time express t The current green data center is responsible for the offshore oil exploration mission load.

[0037] Step S3: This invention selects the optimal green grid to power the green data center based on the green grid's green power margin, avoiding data loss due to power outages. By determining whether the green power margin of the green grid closest to the green data center meets the requirements for dynamically adjusting the server frequency of the green data center, if it does, the green grid with the shortest distance is selected as the optimal green grid, which can improve energy utilization efficiency, reduce transmission losses, significantly reduce heat loss during transmission, reduce overall energy consumption, and improve power supply reliability; otherwise, a strategy of prioritizing minimum green power waste is adopted to select the optimal green grid, which can promote green power consumption, reduce green power abandonment, and improve emission reduction efficiency.

[0038] Specifically, the process for determining whether the green electricity margin of the green power grid shortest to the green electricity data center meets the requirements for dynamically adjusting the server frequency of the green electricity data center is as follows: If the green power margin of the green power grid that is closest to the green power data center is greater than 0, the green power grid meets the requirements of the green power data center to dynamically adjust the server frequency. If the green power margin of the green power grid that is closest to the green power data center is below 0, and the green power data center still maintains a server frequency above the server frequency threshold by dynamically adjusting the server frequency, then the green power grid meets the requirements for the green power data center to dynamically adjust the server frequency. If the green power margin of the green power grid closest to the green power data center is below 0, and the green power data center dynamically adjusts the server frequency below the server frequency threshold, causing a sharp drop in server performance during low-frequency operation, making it difficult to sustain offshore oil exploration missions, then the green power grid does not meet the requirements for the green power data center to dynamically adjust the server frequency.

[0039] This invention employs a strategy prioritizing minimal green energy waste, and the process for selecting the optimal green power grid is as follows:

[0040] in, Indicators representing the evaluation criteria for green power grid use I This represents the set of all green power grids except for the one closest to the green power data center. Indicates the first i A green power grid t Current green energy margin express t The current green data center is responsible for the offshore oil exploration mission load.

[0041] Step S4: Dynamically adjust the server frequency of the green power data center according to the green power margin of the optimal green power grid, and execute the offshore oil exploration tasks of the green power center. This can accurately match the supply and demand of green power, avoid power curtailment, and improve the utilization rate of green power.

[0042] The process of dynamically adjusting the server frequency of the green electricity data center in this invention is as follows:

[0043] in, This indicates the adjusted server frequency. Indicates the server's base frequency. This represents the adjustment coefficient. express t The optimal green power margin for a green grid at any given time. express tThe current green data center is responsible for the offshore oil exploration mission load.

[0044] when When, adjustment coefficient A positive value increases the adjusted server frequency, increases power consumption, and accelerates the completion of non-real-time computing tasks in the offshore oil exploration mission of the green electricity data center, converting excess green electricity into effective computing resources; when When, adjustment coefficient A negative value reduces the adjusted server frequency, decreases power consumption, and provides limited support for critical tasks in offshore oil exploration at the green energy data center, thereby achieving a match between green energy supply and demand and avoiding power curtailment.

[0045] This invention relates to a green energy data center energy consumption sensing method based on offshore oil exploration. By sensing energy consumption, the method determines the offshore oil exploration task load of the green energy data center, calculates the green energy margin of each green power grid, and uses different strategies to select a green power grid that meets the needs of dynamically adjusting the server frequency of the green energy data center to supply power. Based on the green energy margin of the green power grid, the method dynamically adjusts the server frequency of the green energy data center, which greatly improves the green energy utilization rate and ensures the stable operation of the green energy data center.

[0046] In one technical solution of the present invention, an electronic device is also provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the energy consumption sensing method for green data centers based on offshore oil exploration of the present invention.

[0047] In the embodiments disclosed in this application, a computer storage medium may be a tangible medium that may contain or store programs for use by or in conjunction with an instruction execution system, apparatus, or device. The computer storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of computer storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0048] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0049] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should be considered within the scope of protection of the present invention.

Claims

1. A method for sensing energy consumption in a green data center based on offshore oil exploration, characterized in that, Includes the following steps: Step S1: Deploy green power grids on islands at sea to power the green data center for offshore oil exploration; Step S2: Calculate the green power output of each green grid and the offshore oil exploration task load of the green power data center to determine the green power margin of each green grid; Step S3: Determine whether the green power margin of the green grid with the shortest distance to the green power data center meets the requirements of the green power data center to dynamically adjust the server frequency. If it does, the green grid with the shortest distance is selected as the optimal green grid; otherwise, the minimum green power waste priority strategy is adopted to select the optimal green grid. Step S4: Dynamically adjust the server frequency of the green power data center according to the green power margin of the optimal green power grid, and execute the offshore oil exploration task of the green power center.

2. The energy consumption sensing method for a green electricity data center based on offshore oil exploration as described in claim 1, characterized in that, Each green grid includes: photovoltaic power plants, wind power plants, and wave power plants, and the calculation process for the green electricity output of each green grid is as follows: in, Indicates the first i A green power grid t Green electricity output at any time Indicates the first i A green power grid t The output of photovoltaic power plants at this moment Indicates the first i A green power grid t The current output of the wind power station Indicates the first i A green power grid t The power output of the wave power station at any given moment.

3. The energy consumption sensing method for a green electricity data center based on offshore oil exploration according to claim 2, characterized in that, The first i A green power grid t Photovoltaic power plant output at any time Represented as: in, Indicates the first i A green power grid t The number of parallel strings of the photovoltaic array connected at any given time. Indicates the first i A green power grid t The number of photovoltaic cells connected in series in the photovoltaic array at any given time. express t The voltage of a single photovoltaic array at a given time. express t The current of a single photovoltaic array at a given moment.

4. The energy consumption sensing method for a green electricity data center based on offshore oil exploration according to claim 2, characterized in that, The first i A green power grid t Wind power output at current time Represented as: in, Indicates the first i The rated power of wind power stations in a green power grid Indicates the first i A green power grid t The wind speed at that moment, Indicates the first i The wind speed at which a green power grid is integrated. Indicates the first i The rated wind speed of a green power grid Indicates the first i The cut-out wind speed of a green power grid.

5. The energy consumption sensing method for a green electricity data center based on offshore oil exploration according to claim 2, characterized in that, The first i A green power grid t Wave power plant output at any time Represented as: in, Indicates the density of seawater. Represents gravitational acceleration. Indicates the first i A green power grid t The effective wave height at time t is Indicates the first i A green power grid t wavelength at time , Indicates the peak period, Indicates the first i A green power grid t The water depth at that moment, This represents the hyperbolic sine function.

6. The energy consumption sensing method for a green electricity data center based on offshore oil exploration according to claim 1, characterized in that, The calculation process for the green electricity margin of the green power grid is as follows: in, Indicates the first i A green power grid t Current green energy margin Indicates the first i A green power grid t Green electricity output at any time express t The current green data center is responsible for the offshore oil exploration mission load.

7. The energy consumption sensing method for a green electricity data center based on offshore oil exploration according to claim 1, characterized in that, The specific process for determining whether the green electricity margin of the green power grid with the shortest distance to the green electricity data center in step S3 meets the requirements of the green electricity data center for dynamically adjusting the server frequency is as follows: If the green power margin of the green power grid that is closest to the green power data center is greater than 0, the green power grid meets the requirements of the green power data center to dynamically adjust the server frequency. If the green power margin of the green power grid that is closest to the green power data center is below 0, and the green power data center still maintains a server frequency above the server frequency threshold by dynamically adjusting the server frequency, then the green power grid meets the requirement of the green power data center to dynamically adjust the server frequency. If the green power margin of the green power grid closest to the green power data center is below 0, and the green power data center adjusts its server frequency to below the server frequency threshold, then the green power grid does not meet the requirement of the green power data center to dynamically adjust its server frequency.

8. The energy consumption sensing method for a green electricity data center based on offshore oil exploration according to claim 1, characterized in that, The process of dynamically adjusting the server frequency of the green electricity data center is as follows: in, This indicates the adjusted server frequency. Indicates the server's base frequency. This represents the adjustment coefficient. express t The optimal green power margin for a green grid at any given time. express t The current green data center is responsible for the offshore oil exploration mission load.

9. The energy consumption sensing method for a green electricity data center based on offshore oil exploration according to claim 1, characterized in that, Step S3 employs a strategy prioritizing minimal green electricity waste, and the process of selecting the optimal green grid is as follows: in, Indicators representing the evaluation criteria for green power grid use I This represents the set of all green power grids except for the one closest to the green power data center. Indicates the first i A green power grid t Current green energy margin express t The current green data center is responsible for the offshore oil exploration mission load.

10. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the energy consumption sensing method for green data centers based on offshore oil exploration as described in any one of claims 1-9.

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