Construction of efficient numerical calculation and analysis model for large-scale data sets

Aiming at the problems of low computational efficiency, poor scalability and insufficient model accuracy in large-scale data set processing, this paper proposes an efficient numerical calculation and analysis model for large-scale data sets. Based on the design idea of "divide and conquer, parallel processing and dynamic optimization", the model realizes efficient processing of large-scale data sets through key technologies such as data fragmentation and distributed storage, parallel computing framework design, dynamic optimization mechanism and precision guarantee mechanism. The experimental results show that the speed of this model is 27% faster than Spark MLlib, the speedup ratio is 8.2, and the error rate is only 0.85%, which is significantly better than the existing distributed model, and it shows good linear expansion ability when the data scale grows. This model provides an effective solution for efficient numerical calculation and analysis of large-scale data sets, and has a wide application prospect.