With the improvement of carbon asset trading mechanism in China

trading has become increasingly active. The rapid increase of carbon asset types and data volume from trading has brought challenges to data aggregation and management of carbon asset operation departments. In order to improve the management efficiency

this paper studies an anomaly detection method for multi-source carbon assets aggregation data based on improved isolation forest algorithm. Types of multi-source carbon assets and the characteristics of aggregation data are discussed. Mutual information-based segmentation of aggregation data is proposed to construct the isolation tree

aiming to reduce data dimension of isolation tree and improve the stability of calculation result

thereby improving the computational efficiency of the algorithm. Experimental results indicate that the proposed improved method has higher efficiency in anomaly detection for carbon assets aggregation data.