Abstract: When collecting spatial multidimensional data, collection devices are often discrete, and due to equipment failures, environmental factors, and other factors, there may be omissions or anomalies in spatial multidimensional data, a spatial multidimensional data calibration model based on the Lagrange interpolation algorithm is proposed. Firstly, Star shaped and snowflake shaped spatial multidimensional database structure is established to clarify the data distribution characteristics. Then, the initial data is preprocessed, and the consistent division of data dimension is realized through parameter initialization operation, so as to improve the data quality. Then the data classification is completed through the processes of information entropy ant colony clustering, optimization and merging. And the data with the same characteristics are gathered into the same cluster to reduce outliers. Finally, the Lagrange interpolation polynomial is established by using the basis function. And the normalization idea is introduced to ensure that the value floats in a certain range, avoid Runge phenomenon, and generate a new interpolation polynomial. The polynomial calculation result is the calibrated data value. The experimental results show that this method has good data preprocessing ability and can effectively reduce the calibration error.

Key words: lagrange interpolation algorithm, spatial multidimensional data, calibration model, information entropy ant colony algorithm, normalization thought