优化森林防火：基于无人机的监视系统的智能调度算法
Optimizing Forest Fire Prevention: Intelligent Scheduling Algorithms for Drone-Based Surveillance System
AI总结：
本研究针对森林防火中无人机监测任务调度问题，开发了多种智能调度算法以最小化任务总完成时间，实验表明算法$RID$表现最佳，成功率达90.3%。
摘要：
鉴于森林的重要性及其在维持生态平衡中的作用，这直接影响着地球、气候以及地球上的生命，本研究提出了使用无人机进行森林火灾监测的问题。森林监测过程持续进行，以跟踪森林监测区域内的任何变化。在火灾期间，无人机捕获的数据用于提高后续速度并增强对这些火灾的控制过程，以防止其蔓延。在此类问题中，时间因素决定了灭火过程的成功率，因为在正确的时间获取适当的数据可能是控制火灾、防止其蔓延、将其扑灭并限制其损失的决定性因素。因此，本研究提出了森林监测系统中无人机监测任务调度的问题。该问题通过开发几种算法来解决，旨在最小化执行所有无人机分配任务所需的总完成时间。系统性能通过使用三类不同实例的990个实例进行测量。所进行的实验结果表明了所提出算法的有效性及其高效行动以实现预期目标的能力。算法$RID$取得了最佳性能，百分比率高达90.3%，耗时0.088秒。
英文摘要：
Given the importance of forests and their role in maintaining the ecological balance, which directly affects the planet, the climate, and the life on this planet, this research presents the problem of forest fire monitoring using drones. The forest monitoring process is performed continuously to track any changes in the monitored region within the forest. During fires, drones' capture data is used to increase the follow-up speed and enhance the control process of these fires to prevent their spread. The time factor in such problems determines the success rate of the fire extinguishing process, as appropriate data at the right time may be the decisive factor in controlling fires, preventing their spread, extinguishing them, and limiting their losses. Therefore, this research presented the problem of monitoring task scheduling for drones in the forest monitoring system. This problem is solved by developing several algorithms with the aim of minimizing the total completion time required to carry out all the drones' assigned tasks. System performance is measured by using 990 instances of three different classes. The performed experimental results indicated the effectiveness of the proposed algorithms and their ability to act efficiently to achieve the desired goal. The algorithm $RID$ achieved the best performance with a percentage rate of up to 90.3% with a time of 0.088 seconds.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
