Office Furniture Partition Space Design Based on Intelligent Domain Perception and Digital Twins

Abstract

With the increasing focus on sustainable development in society, intelligent domain perception and digital twin technology can be used to evaluate and optimize the design of office furniture. This study analyzed sensor data through machine-learning and data-mining techniques to identify patterns and trends in the office environment. Simultaneously, a digital twin model of office furniture partition space was established to simulate the usage of furniture partition space throughout its full lifecycle. When 50% of nodes fail, the minimum transmission energy mode was significantly better than the maximum greedy forwarding mode in terms of cumulative throughput. The distributed, event-based, unsupervised clustering algorithm successfully reduced communication energy consumption, and the lightweight gradient boosting machine algorithm achieved the best design optimization rate, with an improvement of 0.53%. The ratio of value-added time to non-value-added time increased by 56.3%. The study aimed to provide innovative ideas for the development of intelligent office environments, promote the design of office furniture toward intelligence and sustainability, and improve the adaptability and efficiency of the work environment.