Aiming at the problem of autonomous coordination of networked UAV swarm and the advantages and disadvantages of market auction method, the idea of “computation-for-communication” and its corresponding distributed task scheduling method were proposed. By analyzing explicit and implicit conflicting tasks, a set of task-related agents was established. A local optimization method based on task suppression was proposed to resolve some task conflicts in advance, so as to reduce the number of algorithm iterations. An agent position inference method based on historical bidding information was designed to provide necessary information input for local optimization. Monte Carlo simulation experiments were carried out based on the networking simulation platform and the swarm rescue scenario. The results show that compared with the representative consensus-based bundle algorithm and performance impact algorithm in the market auction method, the proposed method can obtain fewer iterations, shorter convergence time and better scheduling performance.