This study tackles the challenge of optimizing aesthetics, energy use, and material sustainability for eco-friendly visual identity systems in dynamically operated green buildings. A “Metric-Simulation-Control-Evaluation” closed-loop framework was developed. It integrates multi-objective entropy weight assessment, light-thermal simulation, and a vector quantized-variational autoencoder-based color-footprint material library, supporting the generation and selection of sustainable visual solutions. In operation, an adaptive dimming system using the twin delayed deep deterministic policy gradient reinforcement learning algorithm enabled real-time coordinated control of lighting and shading. Experiments demonstrated annual electricity savings of 12,412 kWh and increased user satisfaction to 4.5 (on a five-point scale), confirming synergistic gains in energy conservation, environmental impact, and experience. This work provides a practical pathway for dynamic, intelligent ecological visual interaction in green buildings.
Immersive extended reality environments increasingly require adaptive simulations responsive to users’ affective and behavioral states. Existing systems often rely on rule-based interactions, limiting presence and flow. In this study, the author presents an artificial intelligence-driven ambient intelligence framework for context-aware extended reality that combines affective semantic–action mapping, reinforcement learning-based multisensory control, flow prediction, and adaptive narrative generation in a closed-loop architecture. A 60-participant experiment using heart rate variability, galvanic skin response, interaction logs, and experience measures showed significant improvements in presence and flow, with reduced cognitive workload and stress. Results demonstrated the promise of context-aware AI for enhancing immersive human–computer interaction.
This article presents a virtual reality landscape sculpture framework that applied ambient intelligence to transform conventional static landscape sculptures into context-aware interactive installations. Based on multi-source environmental sensors and semantic annotation, the system supported real-time three-way interaction between environment, sculptures, and users. It integrated the multisensory fusion rendering algorithm for synchronized visual–auditory–tactile feedback and the gesture eye synergy model for implicit natural interaction, with 42 ms response latency. Tests with 48 participants showed that the virtual reality–landscape sculpture framework achieved significantly higher immersion depth (0.82 versus 0.66), emotion valence (0.79 versus 0.63), and knowledge retention (0.68 versus 0.55) than mobile augmented reality, all at p < 0.01. The 30-day knowledge retention rate reached 42%, 2.3 times that of traditional static viewing. This study offers a reproducible technical approach for immersive public art design and sustainable digital conservation of cultural heritage.
This study investigates the design and application of a remote monitoring system for green logistics transportation devices based on image control and processing modules. By integrating GPS positioning, wireless communication, and image processing technologies, the system enables real-time monitoring of vehicle temperature, humidity, and location data, as well as anomaly alerts and remote scheduling. The study develops a framework for a green logistics transportation system that optimizes resource allocation and transport routes to reduce operational costs. Experimental results demonstrate that the system performs effectively in environmental monitoring and node conflict management, significantly enhancing the automation and environmental sustainability of logistics transportation. Furthermore, the integration of dynamic frame slot and fixed frame delay algorithms effectively reduces node conflicts and improves system stability. The findings provide a practical technological solution for promoting the intelligent and sustainable development of green logistics.
Smart city big data governance is encumbered by data siloing, poor quality, and inefficient sharing, restricting ambient computing integration. This study applies artificial intelligence technologies—federated learning, knowledge graphs, and deep learning—to data cleaning, cross-departmental model sharing, and decision making. It proposes a technology–institution–context integrated model. Empirically analyzing three Chinese pilot cities from 2020 to 2023 shows that the AI strategy significantly reduced data cleaning time, elevated anomaly detection accuracy to over 95%, improved data standardization from approximately 30% to over 80% (with City C reaching 90.5%), increased cross-departmental shared data by approximately 5.5 to over 6 times, cut governance costs by approximately 62%, and lowered decision errors to approximately 1/3 of that of traditional methods. Challenges persist: inconsistent cross-regional standards, a lack of unified AI norms, and weak algorithm interpretability. This study enriches ambient computing governance theory and provides a practical framework for urban managers.
