Task batch distribution method taking regard of remuneration discount in crowdsourcing system

Abstract

The invention discloses a task batch assignment method considering reward discounts in a crowdsourcing system, which mainly includes the following steps: (1) establishment of a reward discount model for task batch assignment; (2) difference calculation among tasks and central task selection; ( 3) Batch assignment process of crowdsourcing tasks. Compared with the prior art, the present invention has the following advantages: 1) By assigning large-scale crowdsourcing tasks in batches and discounting the rewards for batch tasks, compared with the traditional single task allocation method, it is possible to reduce the number of task issuers. 2) Compared with the mode of assigning tasks individually, the method of assigning tasks in batches can improve the rate of assignment of crowdsourcing tasks.

Description

technical field [0001] The invention relates to the field of crowdsourcing systems, in particular to a method for assigning batches of tasks in consideration of remuneration discounts in the crowdsourcing system. Background technique [0002] In recent years, the crowdsourcing model has developed rapidly. Crowdsourcing refers to a mode of publishing tasks in an open way and using the masses to complete tasks. In a crowdsourcing environment, the system needs to assign tasks to workers who can fulfill the requirements. For professional tasks, the completion of the task requires workers to have some skills and needs to be completed within the specified time frame. At present, the task allocation methods in the existing crowdsourcing systems are the allocation of a single task, that is, for each task, it is assigned to the workers independently. However, with the widespread application of crowdsourcing, this traditional method is gradually unsuitable for large-scale crowdsour...

AI Technical Summary

Problems solved by technology

However, with the widespread application of crowdsourcing, this traditional method is gradually unsuitable for large-scale crowdsourcing task scenarios, because the assignment of each task needs to consider which worker it should be assigned to, which requires a lot of computing costs

In addition, this allocation method considers worker selection separately, which may leave many workers with only a small number of tasks, and the skills of workers are not fully utilized.

Furthermore, in the distribution mode of a single task, the task issuer must bear the total cost of all tasks

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