Large-scale isomorphic task allocation in crowdsourcing systems

Abstract

The invention discloses a large-scale isomorphic task distribution method in a crowdsourcing system, which comprises the following steps: (1) acquiring information of workers and tasks in the system;(2) inputting the relation model f between the worker's task execution rate and the load; (3) setting a batch allocation threshold theta and an allocation time interval T, and executing a task allocation process once every interval T; (4) during the task assignment interval T, the worker continuously performs the tasks on his task set; (5) after the assignment of all tasks in the system task set is completed, the load balancing adjustment is performed on the worker set every interval T. Compared with the prior art, the present invention has the following advantages: (1) considering the dynamiccharacteristics of the worker's task execution rate affected by the load, so as to optimize the system task completion time; (2) adopting the batch distribution method, the rate of workers' task execution can be kept at an optimal level.

Description

technical field [0001] The invention relates to a task allocation technology in a crowdsourcing system, in particular to a large-scale isomorphic task allocation method. Background technique [0002] With the advent of the Internet age, more and more network services have emerged, and the promotion of crowdsourcing systems provides a new model for task assignment and execution. In the crowdsourcing system, it mainly includes task publishers, crowdsourcing platforms, workers and tasks. The crowdsourcing platform assigns tasks issued by task publishers to workers for execution according to appropriate allocation standards. The task execution speed is fast, the cost is low, the accuracy is high, and some complex tasks that computers cannot complete can be completed. These characteristics make the crowdsourcing system receive more and more attention and promotion. In recent years, there have been more and more researches on crowdsourcing systems, among which task allocation met...

AI Technical Summary

Problems solved by technology

Relevant studies have found that in the real environment where social individuals perform tasks (such as crowdsourcing systems), the rate at which workers perform tasks will also be affected by the load that workers undertake and change dynamically, and there is an inverse relationship between the two that first increases and then decreases. U-shaped curve relationship, within a certain range, increasing the worker's load can increase the worker's speed of performing tasks, but beyond a certain range, increasing the worker's load will actually reduce the worker's speed

However, this point is often ignored in previous studies on task allocation, and the dynamic characteristics of the worker's speed affected by the load are not considered, which makes the existing task allocation methods have some limitations in the environment where social individuals perform tasks.

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