ID 原文 译文
52517 然后基于加权自然连通度来分析链路失效对网络抗毁性的影响，定量计算链路拓扑重要度，实现对骨干光通信网链路重要度的识别; By analyzing the impact of link failure on network invulnerability with the weighted natural connectivity, the link topology importance is quantitatively calculated, which can realize the identification of the link importance in the backbone optical communication networks.
52518 最后，对算法流程进行说明， Finally, the algorithm in this paper is expounded.
52519 并以具体电力通信骨干光网络模型为例， Taking a model of specific power communication optical backbone network as an example,
52520 通过仿真对比分析算法的有效性和准确性， the effectiveness and accuracy of the proposed algorithm are verified by comparison with other algorithms.
52521 仿真结果表明，加权自然连通度在评估链路重要度方面比自然连通度更全面合理。 In addition, it is demonstrated that the weighted natural connectivity is more comprehensive and reasonable in evaluating link importance than natural connectivity.
52522 军用光缆网是重要的国防基础通信设施， The military optical cable network is an important communication infrastructure for army.
52523 传统的人工徒步巡检是查找光缆线路隐患的主要措施， The traditional artificial walking patrol and inspection is the main method to find the hidden dangers of the optical cable lines.
52524 但其耗时长，人力物力消耗大，易受敷设方式和地形环境变化影响。 However, it takes a long time, and consumes a lot of manpower and material resources. It is also vulnerable to the changes in laying methods and terrain environment.
52525 而采用无人机进行光缆线路巡检，时效性强，安全性高且经济性好，是未来的重点发展方向。 However, the use of unmanned aerial vehicle for the patrol and inspection of optical cable lines, is the main development direction in the future due to the characteristics of fast, safe and economical.
52526 由于工程车辆施工挖掘是造成光缆线路障碍的最主要原因，为此，文章提出将深度学习更快的基于区域的卷积神经网络 (Faster R-CNN) 目标检测方法应用到无人机航拍巡检图像的工程车辆检测中。 Because the factor of excavation in construction is the most important reason for the obstacle of the optical cable lines, it is proposed to apply the deep learning Faster Region-based Convolutional Neural Network (Faster R-CNN) target detection method to detect the image of engineering vehicle obtained by unmanned aerial vehicle.