To address the challenges of target allocation efficiency and path planning safety in collaborative multi-rotor UAV strike missions within urban environments

this work proposes a two-stage decision-making framework that integrates game theory and intelligent optimization.A Stackelberg contract model is constructed in the task allocation phase to design a strike contract that incorporates risk-benefit considerations.The UAV dynamically selects the contract based on its state and realizes the balance of efficiency and fairness through reputation incentives. Improved multi-objective ivy algorithm is used in the path planning phase.The algorithm incorporates Levy flight to enhance the global search and establish a multi-objective optimization function of path length-energy consumption-safety.The two phases form a closed-loop optimization through path cost feedback. Simulation results show that the proposed method improves the total effectiveness of task assignment by 4% and reduces the length of generated paths by 13.4% compared to other methods. The proposed method provides an efficient and robust cooperative scheme for urban warfare with multi-UAV swarms.