Intelligent landscape design and land planning based on neural network and wireless sensor network

Design and Evaluation of Regenerated Landscapes of Factory Sites Based on Evaluation Factors

Research in the field of industrial heritage regeneration suffers from high subjectivity and low reliability in design and evaluation. The study takes seven regeneration projects of the Kailuan family of industrial heritage as examples, designs four optimisation and improvement strategies and constructs an evaluation system containing four intermediate layers and 23 indicator layers based on the evaluation factor method to realise the evaluation of design solutions. The average evaluation value of the four intermediate layers was approximately 0.65, and the average evaluation value of the 23 evaluation indicators was approximately 0.68. The evaluation values of the four intermediate layers for the seven Kailuan projects showed that the evaluation of the park and the evaluation of the participants’ perceptions were roughly higher than the standard values, while the evaluation of the buildings and the evaluation of the environmental image had lower evaluation values. The correlations between the park evaluation and the other three intermediate level evaluation indicators were all over 0.500, and all had positive correlations, while the correlations between any two of the remaining indicators were weak and not statistically significant. The regenerative landscape design and evaluation of the Kailuan system of factory heritage enriches the current landscape design evaluation system and provides corresponding optimisation strategies for landscape optimisation design.

Construction of a Landscape Design and Greenery Maintenance Scheduling System Based on Multimodal Intelligent Computing and Deep Neural Networks

Digitalization brings challenges and new opportunities to the development of landscape gardening, “smart gardening,” which is a product of landscape gardening in response to the development of the digital era. Based on the multimodal intelligent computing method and deep neural network machine learning algorithm, this paper adopts “digital landscape design logic” to analyze and research smart gardens and digital design. The digital landscape design process and methods are discussed based on design logic, design basis, environment analysis, and results presentation, and the greenery maintenance scheduling system is constructed. The paper focuses on the digital implementation of the environmental analysis of the site and uses Rhino software and Grasshopper visual programming language to build parametric logic, establish parametric analysis models, and conduct a comprehensive analysis of the current environment. The main theme of the whole paper is a logical approach to digital landscape design for smart gardens, using digital technology tools from the perspective of smart garden thinking, combining quantitative analysis and qualitative design, and intervening in digital landscape garden planning and design to explore the application of digital technology and tools.

Design and Implementation of a Multidimensional Visualization Reconstruction System for Old Urban Spaces Based on Neural Networks

This article presents an in-depth study and analysis of the construction of a convolutional neural network model and multidimensional visualization system of old urban space and proposes the design of a multifaceted visualization reconstruction system of old urban space based on a neural network. It also quantitatively analyzes the essential spatial attribute characteristics of urban shadow areas as nodes of the overall urban dynamic network in three dimensions—spatial connection strength, spatial connection distance, and spatial connection direction—summarizes the characteristics of urban old spatial structure from the perspective of a dynamic network, and then proposes the model of urban old spatial design from the perspective of an active network. The shallow depth of the network structure is used to reduce the parameters in the learning process of reconfigurable convolutional neural networks using data sets so that the model learns more general features. For the situation where the number of data sets is small, data augmentation is used to expand the size of the data sets and improve the recognition accuracy of the reconfigurable convolutional neural network. A real-time update method of multifaceted data visualization for big data scenarios is proposed and implemented to reduce the network load and network latency caused by charts of multidimensional data changes, reduce the data error rate, and maintain the system stability in the old urban space concurrency scenario.