Visual-inertial odometry(VIO) is an efficient localization method in GPS-denied environments. However, due to sensor noise and accumulated errors, drift inevitably occurs, affecting the accuracy of localization. To address this issue, this paper proposes a monocular visual-inertial-UWB tightly coupled localization method that integrates distance gradient information. By constructing a residual model of the distance and its gradient information from the ultra-wideband(UWB) ranging sensor, the information from VIO and UWB is fused using factor graph optimization. The introduction of distance gradient information helps to correct velocity, thereby further suppressing drift in the navigation system and improving localization accuracy through tight multi-sensor fusion. The proposed method is experimentally validated on the EuRoc public dataset and an actual UAV platform, covering various flight conditions. The results show that the proposed method is highly feasible and demonstrates superior navigation and localization performance, achieving a 13.7% improvement in accuracy compared to similar methods using the same measurement information.