## 1 Introduction  
Emergency rescue scenarios primarily target natural disasters such as fires, floods, earthquakes, and landslides, as well as public safety incidents such as urban counter-terrorism, hostage rescues, and explosive disposal. Typically, natural disasters present complex conditions including communication outages, power interruptions, road damage, and extreme weather, while safety incidents face challenges such as intelligence fog, rapidly changing situations, complex public opinion, and crowd gatherings. Therefore, efficient intelligence collection and upload, as well as command decision distribution, become critical to solving these problems. Drones, with their plug-and-play capability and high flexibility, can effectively expand the boundaries of intelligence collection and deploy airborne base stations to achieve the agile deployment of dedicated rescue networks and have thus become a focal point for researchers [1]. However, drones in rescue scenarios often encounter various environmental disturbances and challenges [2]. Not only do they need to serve as relay nodes to establish network links, but they also need to support the overall system in achieving computationally intensive tasks such as disaster data integration, intelligence situation analysis, and intelligent auxiliary decision-making. In addition, the payload carried by the drones during the execution of related tasks also contributes to the total energy consumption of the drones. To this end, we comprehensively consider the needs of latency-sensitive and compute-intensive tasks in emergency rescue scenarios, addressing issues such as limited reconnaissance boundaries, complex data fusion, and heterogeneous communication networks. We propose a layered network architecture that is structurally simple and elastically powerful. Using 5G networks [3] as the backbone, it provides a dedicated communication network with large bandwidth, low latency, and high reliability. Mobile Edge Computing (MEC) [4] servers are deployed at both fixed and mobile nodes to offer a localized computing platform for intelligence data processing. This significantly improves the efficiency of drone control and data transmission, reduces the difficulty of data fusion processing, and extends the boundaries of drone search and rescue operations, as well as intelligence reconnaissance. Furthermore, to tackle the contradiction between the limited local processing resources for computational tasks and the higher latency associated with remote processing, we use the Seagull Optimization Algorithm (SOA) [5] as a baseline and propose the Global Learning Seagull Algorithm for Gaussian Mapping (GLSOAG). Corresponding task offloading strategies are established to enable the system to effectively utilize computing resources, balance task delivery latency with system energy consumption, and adapt to emergency rescue operations under complex conditions. The main contributions of this paper are as follows:  
* Taking into account current issues in emergency rescue scenarios, such as limited reconnaissance boundaries, heterogeneous communication networks, complex data fusion, high task latency, and limited equipment endurance, a hierarchical network architecture based on 5G MEC is designed. Using the characteristics of 5G, including large bandwidth, low latency, and high reliability, MEC servers with the corresponding computing power are deployed as needed. This makes the system architecture feature flexible plug-and-play, network simplicity, strong applicability, and elastic computing power, effectively alleviating the aforementioned pain points. Not only improves efficiency, it also reduces the safety risks for rescue personnel. * The Global Learning Seagull Algorithm for Gaussian Mapping (GLSOAG) is proposed. Firstly, the population is initialized using Gaussian mapping.