In concept design, effective decision making and management of schemes can shorten the design cycle and improve product quality. The decision maker (DM)’s confidence is one of the critical factors affecting the conceptual design evaluation. Although many studies use quantitative linguistic evaluation for design scheme decision-making, which improves product conceptual design decision-making efficiency and effectiveness, few studies consider the confidence level of a decision. A conceptual design evaluation method based on Z-numbers is proposed to solve this problem, considering the customer requirements and the DM’s confidence. Firstly, the evaluation criteria are determined by analyzing customer requirements; then, the fuzzy analytic hierarchy process in the Z-numbers environment (Z-AHP) is used to determine the criteria weight; Finally, the fuzzy technique for order preference by similarity to ideal solution method in the Z-numbers environment (Z-TOPSIS) is used to evaluate the design schemes to obtain the optimal scheme. The proposed method is applied to the selection of the design scheme of the waste containers in the kitchen. The results show that considering the DM’s self-confidence can achieve a more reasonable and practical evaluation of the conceptual design scheme, and it is easier to obtain the best scheme.

In order to improve the efficiency of information acquisition and task selection in Virtual Reality (VR) systems, enhance the interactive experience, and reduce cognitive load for users, it is crucial to effectively organize and leverage user cognitive psychology and design elements during the VR scene design phase. This paper focuses on analyzing the low cognitive load requirements of users and the need for a satisfactory user perceptual experience based on the cognitive resource theory. We propose a method for optimizing the design of VR system scenario tasks under low cognitive load requirements. By utilizing human-computer hybrid intelligent assistance for predicting user cognitive load and incorporating intelligent optimization genetic algorithms into the optimization of VR system design elements, we aim to minimize cognitive load as the objective function based on the principle of low cognitive load. Important knowledge granularity nodes are used as fitness functions in the optimization process of VR system design resource elements. An application study is conducted, combining the multi-channel cognition in a smart city VR system task information interface, to optimize the system resource features. The study validates and compares the solutions obtained through traditional design processes and the solutions optimized by the method proposed in this paper, using virtual reality eye-tracking experiments for the same design task requirements in VR systems. The results demonstrate that users experience lower cognitive load and better task operation experience when interacting with the optimized solutions proposed in this paper. Therefore, the optimization method studied in this paper can serve as a reference for the construction of virtual reality systems.