To effectively organize design elements in virtual reality (VR) scene design and provide evaluation methods for the design process, we built a user image space cognitive model. This involved perceptual engineering methods and optimization of the VR interface. First, we studied the coupling of user cognition and design features in the VR system via the Kansei Engineering (KE) method. The quantitative theory I and KE model regression analysis were used to analyze the design elements of the VR system’s human–computer interaction interface. Combined with the complex network method, we summarized the relationship between design features and analyzed the important design features that affect users’ perceptual imagery. Then, based on the characteristics of machine learning, we used a convolutional neural network (CNN) to predict and analyze the user’s perceptual imagery in the VR system, to provide assistance for the design optimization of the VR system design. Finally, we verified the validity and feasibility of the solution by combining it with the human–machine interface design of the VR system. We conducted a feasibility analysis of the KE model, in which the similarity between the multivariate regression analysis of the VR intention space and the experimental test was approximately 97% and the error was very small; thus, the VR intention space model was well correlated. The Mean Square Error (MSE) of the convolutional neural network (CNN) prediction model was calculated with a measured value of 0.0074, and the MSE value was less than 0.01. The results show that this method can improve the effectiveness and feasibility of the design scheme. Designers use important design feature elements to assist in VR system optimization design and use CNN machine learning methods to predict user image values in VR systems and improve the design efficiency. Facing the same design task requirements in VR system interfaces, the traditional design scheme was compared with the scheme optimized by this method. The results showed that the design scheme optimized by this method better fits the user’s perceptual imagery index, and thus the user’s task operation experience was better.

中文翻译：

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基于感性工程图像空间研究的虚拟现实可视化界面优化设计

为了有效地组织虚拟现实 (VR) 场景设计中的设计元素并为设计过程提供评估方法，我们构建了用户图像空间认知模型。这涉及感知工程方法和 VR 界面的优化。首先，我们通过感性工程 (KE) 方法研究了 VR 系统中用户认知和设计特征的耦合。采用定量理论Ⅰ和KE模型回归分析对VR系统人机交互界面的设计要素进行分析。结合复杂网络方法，总结了设计特征之间的关系，分析了影响用户感知意象的重要设计特征。然后，根据机器学习的特点，我们使用卷积神经网络 (CNN) 来预测和分析用户在 VR 系统中的感知图像，为 VR 系统设计的设计优化提供帮助。最后，结合VR系统的人机界面设计，验证了该解决方案的有效性和可行性。我们对KE模型进行了可行性分析，其中VR意图空间的多元回归分析与实验测试的相似度约为97%，误差非常小；因此，VR 意图空间模型具有良好的相关性。计算卷积神经网络（CNN）预测模型的均方误差（MSE），实测值为0.0074，MSE值小于0.01。结果表明，该方法可以提高设计方案的有效性和可行性。设计师利用重要的设计特征元素辅助VR系统优化设计，利用CNN机器学习方法预测VR系统中的用户图像值，提高设计效率。面对相同的VR系统界面设计任务需求，将传统设计方案与通过该方法优化后的方案进行对比。结果表明，该方法优化后的设计方案更贴合用户的感知意象指标，从而用户的任务操作体验更好。设计师利用重要的设计特征元素辅助VR系统优化设计，利用CNN机器学习方法预测VR系统中的用户图像值，提高设计效率。面对相同的VR系统界面设计任务需求，将传统设计方案与通过该方法优化后的方案进行对比。结果表明，该方法优化后的设计方案更符合用户的感知意象指标，从而用户的任务操作体验更好。设计师利用重要的设计特征元素辅助VR系统优化设计，利用CNN机器学习方法预测VR系统中的用户图像值，提高设计效率。面对相同的VR系统界面设计任务需求，将传统设计方案与通过该方法优化后的方案进行对比。结果表明，该方法优化后的设计方案更贴合用户的感知意象指标，从而用户的任务操作体验更好。