Abstract:The extraction of measurement model is the key issue to detecting the geometric deformation of mining ventilator blades. In order to improve the accuracy of measurement model for the blade, a method of data optimization based on least squares principle for the 2D laser measurement is proposed. Firstly, the raw measurement data of the 2D laser sensor is processed based on Kalman filter algorithm in real-time, the accuracy of coordinate measurement is improved and the authentic data for subsequent model construction is provided. Then, three measurement models are solved respectively using methods of cubic polynomial, cubic B-spline, high-order Bessel, and the significance level of each model is evaluated based on the least squares principle, the measurement model suitable for the geometric characteristics of the fan blades is determined. Finally, the experimental research is implemented, and the results show that the best fitting effect can be achieved by establishing the measurement model of blade using cubic B-spline function, with the smallest significance evaluation factor (less than 0.05). The accuracy of the blade measurement model is effectively improved through data optimization, which provides a solid foundation for subsequent geometric evaluation of fan blades.