基于改进分批估计算法的水闸安全监测数据处理方法OA

To address the challenges in sluice safety monitoring,where multi-sensor data from the same type of monitoring item are susceptible to environmental disturbances,equipment failures,and variations in instrument accuracy,resulting in insufficient data accuracy,poor stability,frequent outliers,and difficulties in achieving reliable data fusion,this paper proposes a sluice safety monitoring data processing method based on an improved batch estimation algorithm.Firstly,gross errors are eliminated through data preprocessing.Then,batch estimation is carried out by grouping according to the characteristics of monitoring areas.Finally,adaptive weights are calculated in real time based on the variance of each group to realize the optimal fusion of multi-sensor data.Field monitoring data collected from a sluice in a plain area were used to validate the proposed method,and its performance was compared with those of the conventional averaging method and the traditional batch estimation algorithm.The results showed that the time-varying variance of the proposed method was significantly reduced compared with traditional methods.In the scenario with outliers,the global variance decreased from 3.719 to 0.129,and the root mean square error declined from 0.762 to 0.481,with simultaneous improvement in accuracy and stability.Extreme outliers caused by equipment faults were effectively suppressed.The research indicates that this method can effectively improve the accuracy and stability of data processing,and provide reliable data support for the safety state evaluation of sluices.