Creating rational emergency distribution plans can significantly speed up the distribution system’s recovery [1,2]. Traditional emergency distribution relies heavily on the transportation network, which is susceptible to significant damage due to the mountainous regions of western China’s frequent natural catastrophes and complicated topographical conditions [3]. Drone airdrop material techniques can successfully address these issues.

How to perform fast drone flight trajectory assignment is one of the keys to solving the problem. The Vehicle Routing Problem of Drones (VRPD), a variation of the Vehicle Path Problem (VRP), has been researched by researchers. In the classic VRPD problem, Different needs and the actual situation can determine the objective function, such as the shortest transport delivery time of the material [4,5,6] and the time window constraints [6]. However, many of the current studies are poorly targeted, abstracted only to conventional material transport problems, ignoring the distinctions between various transport problems and the dynamics of the problem, which would be more pertinent if the target point’s characteristics were taken into account in the text.

Finding the best solution to the VRPD problem is a significant challenge. Heuristic algorithms are more efficient at solving the VRPD problem in emergency settings [7], such as the Particle Swarm Algorithm (PSO) [8], the Genetic Algorithm (GA) [9], and the Dynamical Artificial Bee Colony (DABC) [10] algorithm. Among these algorithms, the PSO has the benefits of simple computing and quick convergence and is appropriate for application in rapid-demand emergency rescue work allocation [11]. The fast decline in population variety as the number of iterations variegation rises makes PSO extremely vulnerable to local optima and highly reliant on parameter settings.

2. Electricity Emergency Distribution and Drone Distribution, Drone Fight Trajectory Issues

A timely provision of emergency supplies is necessary in the case of a sudden power outage to minimize the loss of lives and property caused by the outage and to restore the living and working environment. 在突然停电的情况下，必须及时提供应急物资，以尽量减少停电造成的生命和财产损失，恢复生活和工作环境。Hou et al. 等人[2] proposed an electrical emergency rescue transport model based on node-integrated weights for resource satisfaction, creating an objective function with the least amount of material supply and time leanness. A multi-point fault repair optimization model was created by 提出了一种基于节点积分权重的资源满足的电气应急救援运输模型，创建了一个材料供应量和时间精简量最小的目标函数。Gao et al. 等[14], considering the distribution of first aid materials, the cooperation of repair teams, and the order of fixing broken equipment.考虑了急救材料的分配、维修团队的配合以及损坏设备的修复顺序，创建了多点故障修复优化模型。 However, traditional electrical material rescue is primarily found in urban locations, making it more practical in favorable traffic and road conditions. The drawbacks of the conventional method are more severe in areas with poor road conditions, making drone transportation a more effective means to increase transportation effectiveness. Below are articles related to drone transport.然而，传统的电气材料救援主要存在于城市地区，使其在有利的交通和道路条件下更加实用。传统方法的弊端在路况较差的地区更为严重，使得无人机运输成为提高运输效率的更有效手段。以下是与无人机运输相关的文章。 By modeling the power consumption of a drone as a nonlinear function of payload and trip time in a multi-traveling Drone routing problem model, considering a time window, 通过将无人机的功耗建模为多行程无人机路由问题模型中有效载荷和跳闸时间的非线性函数，考虑时间窗口，Cheng et al. 等人[15] further extended 进一步扩展了Dorling’s study. Their model offers logical and sub-gradient cuts to handle nonlinear power functions, using the branch-and-cut method to resolve the drone routing issue. In 的研究。他们的模型提供逻辑和子梯度切割来处理非线性幂函数，使用分支和切割方法解决无人机布线问题。在Gentili et al.’s 等人[6] optimization issue, the value of emergency medical supplies is minimized depending on their perishability. Considering the drone’s battery life while transporting medical supplies, they hypothesize that each platform would only have one drone that could service one node at a time. The application of 的优化问题中，紧急医疗用品的价值根据其易腐烂性最小化。考虑到无人机在运输医疗用品时的电池寿命，他们假设每个平台一次只能有一架无人机为一个节点提供服务。VRPD for heterogeneous drones is also investigated in several of the studies. The application of VRPD for heterogeneous drones is also investigated in several of the studies. For a heterogeneous fixed fleet drone routing problem, 在异构无人机中的应用也在几项研究中进行了研究。VRPD在异构无人机中的应用也在几项研究中进行了研究。对于异构固定舰队无人机路由问题，Chowdhury et al. 等人[16] suggested a mixed integer linear programming model to resolve the cost of post-disaster inspections because of several aspects. 提出了一个混合整数线性规划模型来解决灾后检查的成本，因为有几个方面。Chen et al. 等人[17] dealt with the drone fight trajectory planning problem for drones with different capabilities in a multi-area system. They initially created an algorithm to group regions into clusters, drawing inspiration from density-based clustering techniques. They then obtained approximations of the ideal point-to-point pathways for drones to carry out coverage tasks. In a different work by 处理了多区域系统中不同能力的无人机的无人机战斗轨迹规划问题。他们最初创建了一种将区域分组为聚类的算法，从基于密度的聚类技术中汲取灵感。然后，他们获得了无人机执行覆盖任务的理想点对点路径的近似值。在Chen et al. 等人[18], they concentrated on the issue of coverage fight trajectory planning for heterogeneous drones. They suggested a method based on the 的另一份工作中，他们专注于异构无人机的覆盖作战轨迹规划问题。他们提出了一种基于蚁群系统（Ant Colony System (ACS) to obtain enough drone pathways and cover every region thoroughly and effectively. According to the literature, the most significant barrier to drone distribution research is the constraint, which is one of the reasons why conventional vehicle route problem models are inapplicable to drone logistics.CS）的方法，以获得足够的无人机路径，并彻底有效地覆盖每个区域。文献显示，无人机配送研究最大的障碍是约束，这也是传统车辆路线问题模型不适用于无人机物流的原因之一。 Most previous studies have examined the issue from a drone-specific angle, ignoring the coordinated relationship between supply timeliness and drone scheduling, as well as target demand and the drones’ safety considerations. The construction of a drone scheduling model focusing on delivering electrical materials is crucial because models for the emergency transport of power drones are still uncommon.以往的大多数研究都从无人机特有的角度考察了这个问题，忽略了供应及时性和无人机调度之间的协调关系，以及目标需求和无人机的安全考虑。构建专注于交付电气材料的无人机调度模型至关重要，因为用于紧急运输动力无人机的模型仍然不常见。

3. Algorithm for Solving UAV Task Assignment解决无人机任务分配的算法

In drone task assignments, heuristic algorithms are more frequently utilized 在无人机任务分配中，启发式算法更常用[19]. Based on this, 。基于此，Wu et al. 等[20] perform the secondary selection operation of the genetic algorithm (对遗传算法（GA) to improve the population diversity, and the secondary selection operation adopts the improved simulated annealing algorithm (SA) to solve the collaborative multitasking allocation problem more effectively. Han et al. ）进行二次选择运算以提高种群多样性，二次选择运算采用改进的模拟退火算法（SA）更有效地解决协同多任务分配问题。Han等人[21] suggested a fuzzy elite strategy genetic algorithm to handle complicated issues. To increase its capacity to escape local optimal traps and hasten convergence, 提出了一种模糊精英策略遗传算法来处理复杂的问题。为了提高其逃脱局部最优陷阱并加速收敛的能力，Wang et al. 等人[22] suggested combining 建议将模拟退火（Simulated Annealing (SA) and Large Neighborhood Search (LNS) algorithms. Liu et al. A）和大邻域搜索（LNS）算法结合起来。Liu等人[23] also proposed a collaborative optimization method combining 还提出了一种结合GA and clustering methods to satisfy the task assignment of forest fires by drones. An upgraded cellular automaton (CA) and an optimal spanning tree technique were utilized by Li et al. 和聚类方法的协同优化方法，以满足无人机对森林火灾的任务分配。Li等人[24] to build the path network and find the best routes between various endpoints. 利用升级的元胞自动机（CA）和最优生成树技术来构建路径网络并找到各个端点之间的最佳路由。Zhang et al. 等人[25] dynamically divided the particle swarm based on the particle mass and changed the topology of the algorithm. In addition, dynamic problems in tasking problems are common. Additionally, the 根据粒子质量动态划分粒子群，并改变了算法的拓扑结构。此外，任务处理问题中的动态问题也很常见。此外，PSO algorithm has been employed in additional combinations as a heuristic method: in their studies, Geng et al. 算法已被用于其他组合作为启发式方法：在他们的研究中，Geng等人[26] proposed a quantified particle swarm optimization algorithm for the task allocation problem of UAV clusters提出了一种量化的粒子群优化算法，用于无人机集群的任务分配问题;. Shao et al. Shao等人[27] proposed a hybrid strategy based on discrete particle swarm optimization for the many-to-one task planning problem in the case of a constructed a quantized particle swarm optimization technique. The simulated annealing algorithm is enhanced by 在构建量化粒子群优化技术的情况下，提出了一种基于离散粒子群优化的混合策略，用于多对一任务规划问题。Chen et al. 等人[28] using a 使用Levi distribution strategy, and it works well for both dynamic and static task assignment models. Yang et al. 分布策略增强了模拟退火算法，它适用于动态和静态任务分配模型。Yang等[29] use a distributed rational clustering algorithm for UAV clusters based on sensor networks and mobile information to improve the completion rate of UAV task assignments, providing a new distributed algorithm for task assignments.采用基于传感器网络和移动信息的无人机集群分布式理性聚类算法，提高无人机任务分配完成率，为任务分配提供了一种新的分布式算法。