Optimization of indoor environment design based on intelligent algorithms

In response to the problem of low positioning accuracy of existing indoor positioning algorithms, a logarithmic normal model was adopted based on the characteristics of the indoor environment. The Taylor formula was used to improve the traditional least squares algorithm, achieving more accurate indoor target positioning. In this algorithm, the mean Kalman filter (KF) is first used to model and process the indoor environment, eliminating the interference of random noise. Next, the estimated coordinates obtained by the least squares algorithm are expanded using the Taylor formula, and a cyclic iteration is constructed to gradually approximate the true target position. In addition, the design of an indoor environmental spatial layout optimization system based on Bayesian networks. The test results show that the improved algorithm has better improved the positioning accuracy, obtained more accurate estimates of coordinate positions, and ensured the stability of positioning deviations. The indoor environmental spatial layout optimization system based on Bayesian networks can not only reduce the energy consumption of indoor environmental spatial layout optimization, but also improve the accuracy of indoor environmental spatial layout optimization, thereby greatly improving the performance of the indoor environmental spatial layout optimization system.