Large-Scale Isomorphic Task Allocation Method in Crowdsourcing System

Abstract

The invention discloses a large-scale isomorphic task assignment method in a crowdsourcing system, which includes the following steps: (1) acquiring information about workers and tasks in the system; (2) inputting a relationship model f between worker task execution rate and load; ( 3) Set the batch allocation threshold θ and the allocation time interval T, and execute the task allocation process once at each interval T; (4) Within the task allocation time interval T, workers continue to execute the tasks on their task set; (5) After all the tasks in the system task set are allocated, every time interval T, a load balancing adjustment is performed on the worker set. Compared with the prior art, the present invention has the following advantages: (1) considers the dynamic characteristics of the worker's task execution rate affected by the load, so as to optimize the system task completion time; (2) adopts the batch distribution method, which can maintain Worker task execution speed is at an optimal level.

Description

technical field [0001] The present 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, among which 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 the task publishers to workers for execution according to appropriate distribution standards. The task execution rate is fast, the cost is low, the accuracy rate is high, and some complex tasks that cannot be completed by computers can be completed. These characteristics make crowdsourcing systems receive more and more attention and promotion. In recent years, there have been more and more studies on crowdsourcing systems, amon...

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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