Modularized Information Fusion Design of Urban Garden Landscape in Big Data Background

Abstract

Traditional information fusion model has the problem of low efficiency in urban landscape design. In addition, using the current method to design urban commercial landscape public facilities, there are problems of large regional space occupation and unsatisfactory design effect. This paper designs a new modular information fusion model for urban landscape design process in view of genetic back propagation. On the basis of preprocessing sensor images, a digital elevation model is created using an ordered numerical sequence. Then, the stereo orthophoto image pair is obtained through the artificial parallax assistance mechanism, and the 3D garden landscape is generated by combining with the ant colony algorithm. The positive feedback mechanism of the ant colony algorithm is used to make the processing process converge continuously, and the optimal 3D garden landscape is finally generated by obtaining stereo orthophoto pairs through the artificial parallax-assisted mechanism. At the same time, the strong robustness and fault tolerance of neural network and parallel processing mechanism are utilized for fast information fusion. The scale and resources of garden design are described by the process dimension and the context dimension, and a modular garden landscape with distinct main body is built. Finally, the initial weight is optimized in the genetic real number coding algorithm, and the appropriate learning factor is selected to train the neural network so as to make the information fusion task. Experimental results show that the above model fusion process has good stability and low energy consumption for information fusion, which can promote the efficient construction of garden landscapes.