Unmanned Aerial Vehicle (UAV) swarms are pivotal in both civilian and military domains, adept at handling complex and dynamic multi-mission environments. At mission beginning, UAV swarms often far from mission areas, necessitating efficient path planning algorithms. Existing research primarily focuses on enhancing algorithmic capabilities to reduce swarm energy consumption, optimize flight ranges and so on, overlooking the multi-source disturbances encountered during missions. First, this paper establishes UAV models based on their diverse functionalities and performance, which includes analyzing the communication interactions between UAV nodes and establishes an effective operation network model tailored to mission requirements. Subsequently, the paper analyzes various disturbances encountered during missions, categorized into environmental and mission disturbances such as random failures, deliberate attacks, spatial failures, and mission change. To enhance swarm performance in the face of these disturbances, three dynamic reconfiguration strategies are proposed. Importantly, the paper introduces a mission reliability model based on the number of effective operation loop, Finally, the paper examines a UAV swarm composed of 100 diverse functionalities UAVs with two clusters, demonstrating that path planned using Rapidly-exploring Random Tree (RRT) algorithms exhibit superior mission reliability, validating their efficacy in path planning.