Research on Crowdsourcing-oriented Global Complex Task Assignment Based on Artificial Intelligence

In the traditional crowdsourcing platform, every time a complex task is published, a new team needs to be formed from the system to meet the skill requirements of the task. However, this one-sided consideration of the assignment of tasks not only fails to enable workers to perform the appropriate tasks to the best of their ability, but also the number of successful tasks. This becomes even more difficult when a large number of complex tasks are distributed across the globe. The goal of this study is to focus on tasks and the assignment of global workers: to maximize the number of tasks successfully assigned, and to maximize the effort of the workers to complete the appropriate tasks. Then the task assignment process is abstracted into a weighted bipartite graph matching model, which is solved by an improved KM algorithm. Finally, experiments are carried out on real data sets, and the results show that, compared with the previous methods, the method proposed in this paper has achieved good results in increasing the number of successful assignments, improving work efficiency and reducing cost.