-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.

Soil organic carbon (SOC) plays a critical role in rice production, but its feedback to the fate of fertilizer nitrogen (N) is not clear. In this study, a pot experiment was conducted to investigate the responses of rice yield and the fate of fertilizer N to different SOC levels using ¹⁵N-labelled urea. The results showed that rice biomass, yield and the total N uptake increased significantly with increasing SOC content. Both rice N uptake from soil and urea increased significantly with increasing SOC content. The recovery rate and residual rate of fertilizer N improved significantly with increasing SOC content, leading to a reduced rate of not-specified fertilizer N. Therefore, it was concluded that high SOC could not only improve rice yield and fertilizer N recovery, but also could increase the retention of fertilizer N and decrease the not-specified N in the paddy soil.