Abstract: During the power flow planning, many data with a large dimension (>100) need to be frequently modified. Searching the parameters from a huge amount of data is very time-consuming, seriously reducing the efficiency of the planning and user experience. Hi4H is proposed as a hierarchical structure to build an index for each dimension separately and query over multidimensional data by a set intersection, avoiding the curse of dimensionality. Hi4H was compare with baseline indexes such as GridFile, R-Tree and Join-Index from the perspectives of both the index-building and data-querying performance. The experiments show that Hi4H is not sensitive to dimensions, and have a short construction time, which can meet the practical needs of trend inference.