移动众包平台的任务定价模型优化策略研究

Research on Optimization Strategy of Task Pricing Model for Mobile Crowdsourcing Platform

摘要:

移动众包平台是一项基于定位的众包运行模式，优化移动众包平台的任务定价策略有助于缓解任务完成度低的问题。采用BP神经网络建立基于定位的定价模型；分析任务完成度的影响因素，根据影响因素引入奖励函数、抢任务的概率分布函数以及PID负反馈网络对定价模型进行优化，提出定价调整方案。实验结果对比表明，优化后的任务定价模型有效地降低了任务发布方的成本支出，在提高会员的参与积极性的同时，保证整体任务的完成度。

Abstract:

Mobile crowdsourcing platform is a location-based crowdsourcing operation mode. Optimizing the task pricing strategy for mobile crowdsourcing platform can help alleviate the problem of low task accomplishment. This article establishes an initial pricing model based on location, by using back propagation neural networks. What’s more, based on the factors affecting the completion of tasks, the incentive function, the probability distribution function of task snatching and the proportion integration differentiation negative feedback network are introduced to adjust the pricing model. Eventually, a final pricing adjustment scheme is proposed. Experimental results contrast shows that the optimized task pricing model effectively reduces the task publisher's cost expenditure, while increasing members' engagement and, at the same time, guaranteeing the overall task accomplishment.