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

InA the case of sudden power failure,imely provision of emergency supplies must be provided in a timely manneris necessary in the case of a sudden power outage to minimize the loss of lifeves and property caused by power failure andthe outage and to restore the living and working environment. Hou et al. [2] proposed a resource satisfaction transportation model for eleelectrical emergency rescue transport model based on node -integralted weights, and for resource satisfaction, createding an objective function with minimalthe least amount of material supply and time reduction. Gao et al. [14] created a leanness. A multi-point fault repair optimization model cowas created by Gao et al. [14], considering the distribution of first- aid materials, the cooperation of maintenancerepair teams, and the repair sequence of damagedorder of fixing broken equipment.

However, traditional electrical material rescue exists mainly primarily found in urban arealocations, making it more practical in favorable traffic and road conditions. The disadvantagerawbacks of tradihe conventional methods are more seriousvere in areas with poor road conditions, making drone transportation a more effective means to improvncrease transportation efficiency. The followingectiveness. Below are articles related to drone transportation.

Cheng et al. [15] further extended Dorling's research b By modeling UAVthe power consumption aof a drone as a nonlinear function of payload and trip time in thea multi-trip UAVaveling Drone routing problem model, taking into account considering a time window, Cheng et al. [15] further time windowextended Dorling’s study. Their model provideoffers logical and sub-gradient cuts to deal with handle nonlinear power functions, using the branch and -and-cut methods to to resolve the drone wiring problem. In the routing issue. In Gentili et al.’s [6] optimization problem of Gentili et al. [6]ssue, the value of emergency medical supplies is minimized baseddepending on their perishability. Given the Considering the drone’s battery life of drones when while transporting medical supplies, they assumedhypothesize that only each platform would only have one drone per platformthat could service one node at a time. The application of VRPD tofor heterogeneous UAVs hadrones is also been investigated in several of the studies. The application of VRPD tofor heterogeneous UAVs hadrones is also been investigated in several of the studies. For the routing problem of a heterogeneous fixed fleet UAVsdrone routing problem, Chowdhury et al. [16] proposuggested a mixed integer linear programming model to resolve the cost of post-disaster inspection,s because there areof several aspects. Chen et al. [17] dealt with the UAVdrone combafight trajectory planning problem of UAVfor drones with different capabilities in a multi-area system. They initially created an algorithm forto grouping regions into clusters, takdrawing inspiration from density-based clustering techniques. They then obtained an approximations of the ideal point-to-point path for the drone to perform theways for drones to carry out coverage tasks. In another different work by Chen et al. [18], they fcocusncentrated on the problemissue of coverage combafight trajectory planning for heterogeneous UAVdrones. They propose an ant csuggested a method based on the Ant Colony system-basedSystem (ACS) approach to getto obtain enough drone pathways and cover every region thoroughly and efficiently cover each areaectively. According to the literature, the biggest obstacle in UAV most significant barrier to drone distribution research is the constraint, which is one of the reasons why the traditiconventional vehicle routinge problem model is not suitable for UAV s are inapplicable to drone logistics.

Most previous studies have examined the is problem from the UAVsue from a drone-specific perspectiveangle, ignoring the coordinationed relationship between supply timeliness and UAVdrone scheduling, as well as the target requirements and UAVdemand and the drones’ safety considerations. BuildingThe construction of a drone scheduling models that focus focusing on delivering electrical materials is critical, asucial because models for the emergency transport of powered drones are still uncommon.

3. Algorithm for sSolving UAV task aTask Assignment

HIn drone task assignments, heuristic algorithms are more commonly used in UAV task assignmentfrequently utilized [19]. Based on this, Wu et al. [20] caperried out a quadraticform the secondary selection operation onf the genetic algorithm (GA) to improve the population diversity, and adopted an the secondary selection operation adopts the improved simulated annealing algorithm (SA) to solve the coopellaborative multi-task assignmenttasking allocation problem more effectively. Han et al. [21] proposuggested a fuzzy elite strategy genetic algorithm to deal withhandle complex problems. In order to improvicated issues. To increase its abilicapacity to escape the local optimal trap and accelerates and hasten convergence, Wang et al. [22] propose touggested combine sing Simulated aAnnealing (SA) and large nLarge Neighborhood sSearch (LNS) algorithms. Liu et al. [23] also proposed a collaborative optimization method combining GA and clustering methods to meesatisfy the task assignment of UAVs on forest fires. Li et al. [24] used u by drones. An upgraded cellular automataon (CA) and an optimal generativespanning tree technology tique were utilized by Li et al. [24] to build athe path network and find the best routes between eachvarious endpoints. Zhang et al. [25] dynamically partditionvided the particle swarm according tobased on the particle mass and changed the topology of the algorithm. In addition, dynamic problems in task processing problems are also common. In aAdditionally, the PSO algorithm has been usemployed in otheradditional combinations as a heuristic method: in their researchstudies, Geng et al. [26] proposed a quantified particle swarm optimization algorithm for the task assignmentllocation problem of UAV clusters;. Shao et al. [27] proposed a hybrid strategy based on discrete particle swarm optimization for the many-to-one task plannin g problem in the case of a constructinged a quantitativezed particle swarm optimization technology for many-to-one task planning problem. Chen et al. [28] enhanced the ique. The simulated annealing algorithm is enhanced by Chen et al. [28] using a Levi's distribution strategy, which is suitableand it works well for both dynamic and static task assignment models. Yang et al. [29] adoptused 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 s, provideing a new distributed algorithm for task assignments.