Ontology-Based Optimization Method for Multi-Source Heterogeneous Data Organization in Large Power Grid Planning and Operation Platforms

The construction of a large power grid planning and operation platform requires the integration and organization of multi-source heterogeneous data. However, the various types of power grid business have differing levels of importance and high demands for timely response, which current data organization methods struggle to meet. This paper proposes an ontology-based optimization method for multi-source heterogeneous data organization in large power grid planning and operation platforms. A mathematical optimization model is constructed, taking into account both business retrieval efficiency and data redundancy. Additionally, the genetic algorithm is improved based on the specific characteristics of this optimization problem. The method proposed in this paper is then applied to integrate data from multiple power software applications. Experimental results show that this method effectively balances data retrieval efficiency and storage space utilization, achieving automated organization optimization of multi-source heterogeneous data for power grid planning and operation.