Design and modelling of personalised multifunctional furniture based on smart networks and cloud rendering platforms

This study proposes an integrated design and modeling method for personalized multifunctional furniture based on intelligent networks and a cloud-rendering platform. A multisource requirement perception model is developed to transform users’ functional preferences, anthropometric dimensions, spatial constraints, aesthetic preferences, and budgets into structured design features. Parametric modeling, functional-module configuration, motion-constraint analysis, and cloud-based collaborative verification are then integrated to support the automatic generation and interactive modification of furniture schemes. A dynamic testing platform is established to evaluate the correspondence between physical furniture operations and cloud-rendered model states. The results show that the median dynamic errors of the m-sequence and OPRM schemes are approximately − 5.3% and − 5.5%, respectively. The OPRM error distribution ranges approximately from − 6.2% to − 4.8%, indicating greater adaptability to complex movements but higher variability. OPRM reduces the testing and model-calibration time from approximately 200 min to 50 min, corresponding to a reduction of about 75%. Its error fluctuations are mainly concentrated within the first one or two measurement cycles and subsequently converge toward a stable state. The proposed method consequently supports rapid personalized scheme generation, dynamic model correction, and reliable cloud-based visualization of multifunctional furniture.