Spatial crowdsourcing (SC) engages large worker pools for location-based tasks, attracting growing research interest. However, prior SC task allocation approaches exhibit limitations in computational efficiency, balanced matching, and participation incentives. To address these challenges, we propose a graph-based allocation framework optimized for massive heterogeneous spatial data. The framework first clusters similar tasks and workers separately to reduce allocation scale. Next, it constructs novel non-crossing graph structures to model balanced adjacencies between unevenly distributed tasks and workers. Based on the graphs, a bidirectional worker-task matching scheme is designed to produce allocations optimized for mutual interests. Extensive experiments on real-world datasets analyze the performance under various parameter settings.

翻译：空间众包（SC）通过大规模工人群体执行基于位置的任务，近年来吸引了日益增长的研究关注。然而，现有的SC任务分配方法在计算效率、均衡匹配和参与激励方面存在局限性。为应对这些挑战，我们提出了一种面向海量异构空间数据、基于图结构的优化分配框架。该框架首先分别对相似任务和工人进行聚类，以降低分配规模；其次，针对非均匀分布的任务与工人，构建新型无交叉图结构以建模均衡邻接关系；最后，基于该图结构设计双向工人-任务匹配方案，生成兼顾双方利益的优化分配结果。基于真实数据集的广泛实验分析了不同参数设置下的系统性能。