To solve the problems of traditional multi-robot path planning algorithms dealing with a single form of task and large non-essential loss, this article proposes a multi-group many-to-one task processing mode of cooperative dynamic priority safe interval path planning algorithm (Co-DPSIPP). Firstly, the algorithm utilizes the simulated annealing and diffusion search to determine the task handover point of each group of robots with the objective of minimizing the total path length. Then, the improved safe interval path planning algorithm is used to carry out the segmented path planning for all the robots. Furthermore, to deal with the problem that some irrational task handover points may cause regional congestion and lead to solution failure, a cluster prioritization and intermediate point dynamic adjustment planning strategy is designed. Finally, the test results on four benchmark maps show that, compared with the cooperative conflict-based search algorithm (Co-CBS), the proposed algorithm can improve the solution success rate by 73% on average, and reduce the running time and total path length by 56% and 5% on average, respectively. The experimental results show that the proposed algorithm provides a more flexible and scalable solution for the collaborative path planning problem of multi-robot in multi-group many-to-one task scenarios.