As the demand for smart city services grows, unmanned aerial vehicle (UAV) swarm have achieved tremendous success in industries such as traffic management, logistics transportation, and road inspection. Despite their promising potential, a critical gap exists in the domain of drone swarm mission planning-a lack of a universal task planning method that can effectively address the complexities of diverse mission scenarios. To address this challenge, this paper introduces a novel scenario model-driven task planning method for UAV swarm. This method leverages scenario models as input, enabling the parsing of scenario tasks, UAV swarm resources, and scenario constraints. It subsequently facilitates multi-constraint task allocation through auction mechanisms and path planning via reinforcement learning. Through simulation experiments conducted in scenarios such as highway inspection and campus logistics, we validate the efficacy and versatility of the proposed method across different contexts.