No abstract

No abstract

To better analyze and process power data to obtain effective information, a power big data analysis scheme based on Hadoop architecture is proposed. We analyze the cloud computing environment Hadoop distributed platform to obtain massive data of large-scale distributed power system. According to the characteristics of intelligent power consumption data, common data mining algorithm modules such as parallel classification algorithm and parallel real-time clustering algorithm are designed, and the implementation of clustering algorithm with different principles is further analyzed. Then, HBase is adopted to access data in a distributed way, and MapReduce is used to realize the visual management of power GIS data. The experimental results show that the parallel processing method of power big data based on Hadoop has high efficiency and good scalability, and the algorithm has good identification ability for massive data in the cluster mode.

No abstract

The fittings detection of transmission lines plays a vital role in ensuring the safe and stable operation of transmission lines. The fitting detection method based on deep learning only scales the original image to a smaller size. However, it ignores the high-definition resolution of the aerial image in the transmission line, resulting in the loss of rich features in the high-resolution aerial image. In order to solve this problem, we observe that aerial images of fittings are concentrated in a particular area of the aerial image. Therefore, we propose a cascading YOLOx model, including Dense Target Regions YOLOx (DTR-YOLOx), which can detect dense target areas, and Multi-Fitting YOLOx (MF-YOLOx), which can detect multiple categories of fittings. In addition, an algorithm based on the connected regions is proposed to automatically generate the dense target region for DTR-YOLOx training, reducing manual labeling costs. Furthermore, the EIOU loss function is introduced to improve the precision of the model's coordinate regression. Experiments show that the AP 0.50:0.95 value of our proposed model is 18.4% higher than that of the YOLOx model.