The bike sharing system （BSS） has become a significant component of implementing urban intelligent transportation systems. This paper proposes a spatio-temporal distribution dynamic perception-based campus bike-sharing resource scheduling system. To address the issue of sudden inventory changes at shared bicycle stations leading to inventory shortages， the system first models the dynamic changes at bicycle stations using the vector autoregressive moving average （VARMA） model， achieving predictions of future inventory shortage events at stations. Secondly， to resolve the contradiction between bicycle scheduling utility and cost in crowdsourced resource scheduling scenarios， it introduces a task assignment method based on a binary optimal matching model and specifically optimizes the Hungarian algorithm for efficient decision-making in task assignment. Simulation results show that the proposed method can effectively improve the system utility of bike-sharing scheduling， reduce the service quality loss caused by inventory shortages at bike stations， and effectively balance the spatio-temporal distribution of bicycles.