In the real-time and complex network environment， how to motivate workers to participate in tasks and obtain high-quality perception data is the focus of spatio-temporal crowdsourcing research. Based on this， a spatio-temporal crowdsourcing’s online incentive mechanism based on quality perception was proposed. Firstly， in order to adapt to the real-time characteristics of spatio-temporal crowdsourcing， a Phased Online selection of workers Algorithm （POA） was proposed. In this algorithm， the entire crowdsourcing activity cycle was divided into multiple stages under budget constraints， and workers were selected online in each stage. Secondly， in order to improve the accuracy and efficiency of quality prediction， an Improved Expected Maximum （IEM） algorithm was proposed. In this algorithm， the task results submitted by workers with high reliability were given priority in the process of algorithm iteration. Finally， the effectiveness of the proposed incentive mechanism in improving platform utility was verified by comparison experiments on real datasets. Experimental results show that in terms of efficiency， compared with the Improved Two-stage Auction （ITA） algorithm， the Multi-attribute and ITA （M-ITA） algorithm， Lyapunov-based Vickrey-Clarke-Groves （L-VCG） and other auction algorithms， the efficiency of POA has increased by 11.11% on average， and the amount of additional rewards for workers has increased by 12.12% on average， which can encourage workers to move to remote and unpopular areas； In terms of quality estimation， the IEM algorithm has an average improvement of 5.06% in accuracy and 14.2% in efficiency compared to other quality estimation algorithms.