The projection-augmented reality assembly assistance system, which supports natural multi-modal human–computer interaction, has the potential to improve interaction efficiency. However, the effectiveness of the interaction method is compromised by the interference caused by the dynamically changing industrial environment. To address this issue, this study proposed a multi-model interaction model based on fuzzy theory and developed an environment perception decision-making interaction system. This system continuously monitors real-time environment data, evaluates interaction performance, and visually recommends the optimal method to assist users in completing their tasks. A comparative experiment was conducted between the proposed system and a non-environment perception interaction system. The collected data, including interaction success rate, task time, system usability, and user experience, were analyzed. The results demonstrated that the proposed system effectively enhances the adaptability and comfort of computer systems in complex environments and improves communication efficiency between humans and machines.

Opportunistic beamforming (OBF) is a promising multiple-input–multiple-output (MIMO) downlink precoding technique, where multiuser diversity gain is achieved to deal with the performance loss caused by low-complexity channel estimation. Due to high-performance and low-complexity channel estimation, OBF is appropriate for Internet of Things (IoT) networks. However, the conventional OBF cannot achieve satisfactory performance in post-disaster communication, and the low signal-to-noise ratio (SNR) makes it hard to establish communication between the base station (BS) and users. Therefore, we propose an unmanned aerial vehicle (UAV)-assisted OBF IoT network (UON) to satisfy the Quality-of-Service (QoS) requirements of users. Then, an optimization problem is formulated to maximize the weighted energy and spectrum efficiencies, while the optimization problem is nonconvex and hard to solve. To obtain the solution, we divide the optimization problem into three suboptimal issues, and then a joint iterative algorithm is applied. According to numerical results, the proposed scheme achieves higher system performances compared with the conventional OBF scheme in both Rayleigh and Rician channels. Moreover, we discuss the objective functions with different weights of spectrum efficiency (SE) and energy efficiency (EE) to adapt the QoS requirements of different scenarios. It is indicated that the proposed scheme can achieve high performance with low computational complexity, regardless of the weighted energy and SE.