基于BP神经网络的大尺寸测距精度优化方法OA

A precision optimization method for large-scale distance measurement based on BP neural network is proposed.A training set is constructed through the simulation generation model of ranging data.The strong nonlinear modeling capability of the multi-environmental parameter error compensation model is utilized to solve the coupling problem of environmental parameters.In experiments conducted on a 1.2 km standard baseline field,the error characteristics of long-distance 1.2 km and short-distance 72 m measurements are systematically analyzed.The experimental results show that for long-distance measurement,the model compensation reduces the mean error from 7.1 mm to 0.6 mm significantly,and the temperature correlation coefficient is improved from-0.99 to-0.03.For short-distance measurement,the mean error is optimized from 0.8 mm to 0.3 mm,and the temperature correlation coefficient is improved from-0.80 to 0.04.Multi-distance experimental verification shows that the average errors after compensation are all better than 0.5 mm,the error is reduced by 91.4%at 1 176 m compared with the traditional method,and the distance correlation is effectively eliminated.This method provides a reliable technical solution for high-precision field measurement.