Monitoring and Early Warning of Transmission Line External Breakage Based on Satellite-Ground Coordination

This article first proposes a high-precision spatio-temporal registration method between satellite remote sensing images and ground sensors. Then, using satellite remote sensing images, an intelligent identification model for typical external damage hidden dangers of transmission lines based on satellite remote sensing is established to realize intelligent identification of transmission line construction work areas and mining affected areas. Aiming at the results of intelligent identification of construction work areas and mining-affected areas, the proposed YOLOv4-based external damage identification algorithm for transmission lines is used to detect external damage hidden dangers. Through the method in this paper, it is possible to realize a regular general survey of hidden dangers of external damage (construction work area, mining affected area) with full coverage of transmission channels, and carry out targeted 24-hour monitoring on the ground. The test results show that the satellite-ground coordinated transmission line external damage monitoring and early warning in this paper. The method timely and accurately realizes the monitoring and early warning of the external breakage of the transmission line.

A method to estimate the damage of Diabrotica speciosa larvae on potato tubers

ABSTRACT The marketable value of potato tubers is affected by damage caused by Diabrotica speciosa, whose larvae create holes on the tubers’ skin and internal feeding tunnels. The estimation of potato tuber damage is usually performed by assessing the external damage to the detriment of feeding tunnels caused by larvae. Thus, we propose a method to estimate the damage caused by D. speciosa larvae, considering the external and internal aspects of the tubers separately. For that, potato plants cv. Agata were grown under different infestations of larvae, measuring the area occupied by the holes and the volume of internal feeding tunnels, relating these data to the total area and volume of the tuber. The methodology used allowed us to characterize an increase in damage in tubers with the highest infestation of larvae. The correlation between internal and external damage caused by D. speciosa larvae was not significant, indicating that external damage alone is not an adequate parameter for the diagnosis of overall tuber quality. However, the method proposed here provides information regarding the volume of pulp consumed by the larvae, the extent of the galleries formed, and the relative volume of damage concerning the total tuber. Moreover, the method proposed here contemplates a more precise analysis of the external area damaged by herbivory relative to the total area of the tuber, which is not commonly considered in studies of underground plant structures.

Research on the application of target detection based on deep learning technology in power grid operation inspection

-External damage to power facilities caused by crane, excavator and other construction operations increases year by year, which will seriously threaten the safe operation of power system. It is an important measure to ensure the safe and reliable operation of power system to implement intelligent monitoring and early warning of power external breakdown through video and other non-contact observation means. The video data of power mainly comes from the fixed monitoring of helicopters, uavs and transformation poles and towers, which is characterized by large amount of data, complex scenes and serious environmental interference. The traditional target detection method usually selects the candidate area first, and then makes judgment based on the characteristics of human construction. The detection speed is slow and the accuracy is low, which makes it impossible to monitor the video data in real time, so as to make timely and accurate early warning and intervention fbr external damage. The target detection method based on deep learning optimizes or even eliminates the selection of candidate regions, which greatly speeds up the detection speed. By learning a lot of target samples through the deep neural network, the characteristics of high robustness are gradually fitted to make the target judgment more accurate. There are three key problems in introducing the target detection method based on deep learning into the power video detection: Firstly, the target detection method based on deep learning has a large amount of calculation and many parameters. In order to realize in-place operation on terminals with limited computing and storage capacity, it is necessary to find a practical method to simplify the network and reduce the amount of operational data in the detection process, which is the key to realize in-place operation and terminal operation of deep neural network. Secondly, for specific application scenarios, the effect of different target detection algorithms varies greatly, and there is a strong particularity of power video. Finding an effective target detection method is the key to improve the detection speed and accuracy. Finally, with the continuous development of deep learning, the structure of deep neural network changes with each passing day, and each has its own characteristics, which network structure is used as the feature extraction layer of target detection algorithm is the focus of research.