• Investigates the heterogeneous multi-task allocation (HMTA) problem for hybrid spatial crowdsourcing scenarios and provides a formal problem definition.
• Proposes a role division approach embedded with an individual sorting model (RD-ISM) based on a batch-based mode (BBM) to prioritize tasks and workers.
• Designs a role division model based on an attraction–repulsion mechanism to effectively match heterogeneous tasks and workers.
• Verifies the effectiveness and scalability of the approach using real and synthetic datasets, with comparative analysis against other algorithms.