Positioning and navigation functions are often required in grid design and operation and maintenance, general grid model building, and power plant operation. GDOP (Geometric Dilution of Position Precision) is often used to evaluate positioning accuracy. The smaller the GDOP value, the higher the positioning accuracy, and vice versa. Through the calculation and analysis of GDOP, it can help the positioning and navigation functions in the power digital twin application to more accurately locate equipment or monitoring points. This is of great significance to the operation and maintenance of the power system. In order to improve the calculation of DOP value using CPU/GPU collaborative acceleration in this paper, this paper uses GPU to optimize the core operator in GDOP calculation, and proposes a CPU/GPU collaborative computing solution. The experimental results show that the GDOP value calculation time is reduced from the original 3753 seconds to 232 seconds, and the performance is improved by 16.18x.
Image matching is an important step in the process of power inspection image stitching and 3D reconstruction, which directly affects the effect of power line image stitching and 3D reconstruction, and is widely used in power line inspection. Traditional power inspection image matching applications are mostly implemented on X86 architecture, but ARM architecture has gradually entered the server field in recent years, showing high efficiency and energy-saving advantages, so it is necessary to transplant power inspection image matching applications to the server platform of ARM architecture. Since the application of image matching is very computation-based, in the process of porting the algorithm to the ARM platform, this paper adopts the Gaussian pyramid algorithm to accelerate the application of image matching through the search method from coarse to fine.
Since 2020, the national power situation has been very tense. With the national "carbon peak" and "carbon neutral" goals proposed, and the gradual establishment of a green, low-carbon and environmentally friendly economic system, if the future power load of the supercomputing center can be accurately predicted the situation, and according to the power dispatch and regulation, can achieve the purpose of energy saving and emission reduction. Power load forecasting using statistical and artificial intelligence technologies still has a lot of issues today. In order to increase the accuracy and stability of power load adjustment, this research suggests a novel Transformer approach based on the attention mechanism. It establishes a power load analysis and prediction model. This paper can provide the supercomputing center with the load demand situation in the future, and it has important application value for the regional power management department to formulate development plans and effectively manage the load, and is of great significance for responding to the national energy conservation and emission reduction policy.
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