Research on Distributed Cooperative Storage Methods for Massive Data in the Context of Intelligent Sensing in Converter Station

In the context of converter stations, given the massive amount of heterogeneous data generated and the urgent need for reliability in supporting users' electricity demands, how can we improve data storage efficiency and data retrieval efficiency in a big data environment? Traditional distributed storage solutions have limited performance. Although they can meet the storage needs of data centers, they fail to effectively balance high-efficiency task allocation and intelligent storage perception (such as the rational selection of node storage solutions). The purpose of this invention is to provide a distributed collaborative storage solution for massive data, applied in the intelligent perception scenarios of converter stations. By implementing data perception of heterogeneous data, and after achieving core functions such as data fusion and integration, multimodal data processing, real-time perception, and decision-making, it realizes data segmentation and data replication during the pre-computation phase. In the current distributed environment, it dynamically perceives the computational power of node states, thereby enabling the most efficient distributed storage of data. This dynamic adaptability helps optimize task allocation, ensuring the best possible performance for handling large-scale heterogeneous data across various storage nodes. The solution ultimately aims to bridge the gap between traditional storage mechanisms and the complex demands of modern intelligent systems in converter stations, focusing on both high-efficiency storage management and intelligent, real-time data processing capabilities.