Concerning the problem that in the research of spatial crowdsourcing task allocation, the benefits of multiple participants and the global optimization of continuous task allocation are not considered, which leads to the problem of poor allocation effect, an online task allocation algorithm was proposed for the global optimization of tripartite comprehensive benefit. Firstly, the distribution of crowdsourcing objects (crowdsourcing tasks and workers) in the next time stamp was predicted based on online random forest and gated recurrent unit network. Then, a bipartite graph model was constructed based on the situation of crowdsourcing objects in the current time stamp. Finally, the optimal matching algorithm of weighted bipartite graph was used to complete the task allocation. The experimental results show that the proposed algorithm realize the global optimization of continuous task allocation. Compared with greedy algorithm, this algorithm improves the success rate of task allocation by 25.7%, the average comprehensive benefit by 32.2% and the average opportunity cost of workers by 37.8%; compared with random threshold algorithm, the algorithm improves the success rate of task allocation by 27.4%, the average comprehensive benefit by 34.7% and the average opportunity cost of workers by 40.2%.

Focusing on the poor overall allocation effect due to the total utility of task allocation or task waiting time being considered respectively in the study of task allocation under spatial crowdsourcing environment, a dynamic threshold algorithm based on allocation time factor was proposed. Firstly, the allocation time factor of task was calculated based on the estimated waiting time and the already waiting time. Secondly, the task allocation order was obtained by comprehensively considering the return value of task and the allocation time factor. Thirdly, the dynamic adjustment item was added based on the initial value to set the threshold for each task. Finally, candidate matching set was set for each task according to the threshold condition, and the candidate matching pair with the largest matching coefficient was selected from the candidate matching set to join the result set, and the task allocation was completed. When the task allocation rate was 95.8%, compared with greedy algorithm, the proposed algorithm increased total allocation utility by 20.4%; compared with random threshold algorithm, it increased total allocation utility by 17.8% and decreased task average waiting time by 13.2%; compared with Two phase based Global Online Allocation-Greedy (TGOA-Greedy) algorithm, it increased total allocation utility by 13.9%. The experimental results show that proposed algorithm can shorten the average waiting time of task while improving the total utility of task allocation, to achieve the balance between the total allocation utility and the task waiting time.