基于动态方差卡尔曼滤波的高洪期河流流量监测研究OA

To address the problems of low accuracy and weak anti-interference ability of traditional river flow monitoring methods during the high flood period,high-definition cameras were used to non-contact collect river surface images.The Sobel algorithm was applied to extract water level lines,and the optical flow method was employed to analyze the spatiotemporal motion characteristics of water surface pixels.Optical flow vectors were converted into actual flow velocities to achieve local velocity estimation.A state-space model was constructed with flow rate and water level as state variables,and regional flow velocity and water level as observations,incorporating a dynamic noise covariance online update mechanism.River flow was estimated by integrating multi-source observation data with iterative Kalman filtering operations.Experimental results showed that when the proposed method was applied to river flow monitoring during high flood period,the drift accumulation rate remained stable below 1.5％,enabling full-process optimization from water level extraction and local velocity estimation to global flow inversion.The application of the dynamic variance Kalman filtering algorithm effectively enhances the accuracy and stability of flow monitoring,providing a new and effective approach for river flow monitoring during high flood period.