Research on Big Data Query Optimization Method of Power System Substation Equipment Condition Monitoring

In order to solve the problem of long query time caused by high cost of non-row key data query, an optimization method for big data query of power system substation equipment condition monitoring is designed. According to the architecture of cloud computing platform, the distributed storage database of monitoring data is established to provide storage layer support for big data analysis. According to the association rules of multivariate time series, the attribute support is calculated, the key parameters of online monitoring data are extracted, and the parallel association algorithm of multi-source data is designed to transform multi-source data into local data structure. Based on the coprocessor, the secondary index is established to optimize the query of the row key data and non-row key data. The experimental results show that compared with the existing query methods, the big data query optimization method proposed in this paper has higher real-time performance, effectively reduces the query time of substation equipment status, and is suitable for power system substation equipment status monitoring.