Fast grouping fusion method of dual carbon monitoring data based on DBSCAN clustering algorithm

Due to the serious redundant interference of dual-carbon monitoring data resources, threshold setting in the fusion process is inaccurate. To realize the sharing and utilization of monitoring data resources, a fast group fusion method of dual-carbon monitoring data based on DBSCAN (Ensity - Based Spatial Clustering of Applications with Noise) clustering algorithm is proposed. After collecting dual-carbon monitoring data through the wireless sensor network, DBSCAN clustering algorithm calculates the neighborhood distance threshold of each data object. Sensor nodes compare the dual-carbon monitoring data and the abnormal data, determine the threshold value, assess whether abnormal data exists and is valid through integrated support, rejecting if invalid. Least squares method realizes fusion of normal data monitored by sensor nodes in the group. Cluster head node determines weight of the normal dual-carbon data monitored by each sensor according to different confidence levels. Variance estimation learning algorithm achieves weighted fusion of data from sensors within the group. The experimental results show that the clustering algorithm has a good clustering effect on the dual-carbon monitoring data and can realize the rapid group fusion. In the complex environment with frequent invalid abnormal data, the mean square error of the fusion results is small.