Research on Table Data Extraction and Processing Methods Based on Precision Requirements

While investing a large amount of manpower and financial resources to obtain the results we need through experiments, it will inevitably generate a massive amount of experimental data. It is of great significance to deeply explore and utilize these data. In response to this, mathematical models were established for two-point interpolation, three-point interpolation, and least squares fitting. The key point in the interpolation and fitting process is exploring how to select data points, processing univariate and bivariate table data, ensuring accuracy, and verifying through simulation calculations. Research has shown that, using three-point interpolation and data extraction methods, the interpolation interval is about five times than two-point interpolation, and efficiency is significantly improved, under the condition of guaranteed accuracy. No matter how complex the table data is, and how high the engineering requirements for fitting accuracy are, it can be achieved by adjusting the fitting interval and data point interval, and fitting polynomial order according to the given method.