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

In the case of sudden power failure, emergency supplies must be provided in a timely manner to minimize the loss of life and property caused by power failure and 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 a resource satisfaction transportation model for electrical emergency rescue based on node integral weights, and created an objective function with minimal material supply and time reduction. 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] created a multi-point fault repair optimization model considering the distribution of first-aid materials, the cooperation of maintenance teams and the repair sequence of damaged equipment.

However, traditional electrical material rescue exists mainly in urban areas, making it more practical in favorable traffic and road conditions. The disadvantages of traditional methods are more serious in areas with poor road conditions, making drone transportation a more effective means to improve transportation efficiency. The following are articles related to drone transportation.

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。 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。 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 research by modeling UAV power consumption as a nonlinear function of payload and trip time in the multi-trip UAV routing problem model, taking into account the time window. Their model provides logical and sub-gradient cuts to deal with nonlinear power functions, using branch and cut methods to solve the drone wiring problem. In the optimization problem of 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. 等人[6], the value of emergency medical supplies is minimized based on their perishability. Given the battery life of drones when transporting medical supplies, they assumed that only one drone per platform 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 to heterogeneous UAVs has also been investigated in several studies. The application of VRPD to heterogeneous UAVs has also been investigated in several studies. For the routing problem of heterogeneous fixed fleet UAVs, 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] proposed a mixed integer linear programming model to solve the cost of post-disaster inspection, because there are 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 UAV combat trajectory planning problem of UAVs with different capabilities in a multi-area system. They initially created an algorithm for grouping regions into clusters, taking inspiration from density-based clustering techniques. They then obtained an approximation of the ideal point-to-point path for the drone to perform the coverage task. In another 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 focused on the problem of coverage combat trajectory planning for heterogeneous U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（AVs. They propose an ant colony system-based (ACS) approach to get enough drone paths and thoroughly and efficiently cover each area. According to the literature, the biggest obstacle in UAV distribution research is constraint, which is one of the reasons why the traditional vehicle routing problem model is not suitable for UAV logistics.

Most previous studies have examined this problem from the UAV-specific perspective, ignoring the coordination relationship between supply timeliness and UAV scheduling, as well as the target requirements and UAV safety considerations. Building drone scheduling models that focus on delivering electrical materials is critical, as models for emergency transport of powered drones are still uncommon.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。 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

Heuristic algorithms are more commonly used in UAV task assignment 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] carried out a quadratic selection operation on the genetic algorithm (对遗传算法（GA) to improve the population diversity, and adopted an improved simulated annealing algorithm (SA) to solve the cooperative multi-task assignment 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] proposed a fuzzy elite strategy genetic algorithm to deal with complex problems. In order to improve its ability to escape the local optimal trap and accelerate 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] propose to combine simulated annealing (建议将模拟退火（SA) and large neighborhood search (LNS) algorithms. Liu et al. ）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 meet the task assignment of UAVs on forest fires. 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] used upgraded cellular automata (利用升级的元胞自动机（CA) and optimal generative tree technology to build a path network and find the best route between each endpoint. Zhang et al. ）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等人[25] dynamically partitioned the particle swarm according to the particle mass and changed the topology of the algorithm. In addition, dynamic problems in task processing problems are also common. In addition, 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 used in other combinations as a heuristic method: in their research, 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] propose a quantified particle swarm optimization algorithm for the task assignment 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 in the case of constructing quantitative particle swarm optimization technology for many-to-one task planning problem. 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] enhanced the simulated annealing algorithm using 使用Levi's distribution strategy, which is suitable 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] adopted a distributed rational clustering algorithm for UAV clusters based on sensor networks and mobile information to improve the completion rate of UAV task assignment and provide a new distributed algorithm for task assignment.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。