In recent years, rural communities in China have been actively exploring a novel approach to elderly care that integrates treatment with health preservation. This study aims to contribute to the sustainable development of rural elderly care by introducing smart technology, specifically fall posture monitoring, into public services and facilities within rural communities. The focus of this study is on addressing a critical issue in elderly medical care: the timely feedback and treatment of falls. To begin, we conduct a comprehensive review of the current status and challenges associated with the application of fall posture monitoring technology. Additionally, we examine the environmental factors that contribute to the risk of falls in public spaces for the elderly. These assessments serve as the technical and environmental foundation for developing the proposed service framework. Our research is conducted from two primary perspectives: the supply of service resources that combine treatment with health preservation and the identification of risk factors associated with outdoor public spaces in the community where falls are likely to occur. Data for this study are collected through behavior mapping and field interviews. In conclusion, we present a constructive logic for the development of a public service field that effectively combines treatment with health preservation. This logic encompasses the integration of technology applications, resource coordination, and improvements to the physical environment. The findings from this study provide a scientific basis for the construction of public service fields in “smart villages” and serve as practical references for similar villages striving to adopt this innovative model. By leveraging the insights gained from this research, it is expected that rural communities will be better equipped to address the challenges of elderly care and facilitate the widespread adoption of this integrated care model.

An analysis of the practice path and methodology system of Artificial Intelligence Generated Content (AIGC) technology has been conducted in the field of inheritance and innovation of boundary paintings from Song Dynasty. This paper aims to provide valuable reference and guidance for the application of AI technology in Song Dynasty boundary painting (Song painting) in the interior decoration design, so as to promote the effective integration of traditional aesthetics and modern design concepts. Firstly, the natural processing language model is used to generate the index layer suitable for the indoor soft decoration style of Song paintings, and the Analytic Hierarchy Process weight classification is used to select the cue words of the generated image. Secondly, Midjourney is used to generate Song Dynasty style images for keywords. Finally, Stable Diffusion control model is used to transfer the style of Song painting elements to interior decoration design. AIGC technology can effectively generate images with the style of Song painting elements, and play a unique role in style transfer and pattern design. It provides an innovative path for the integration of traditional art and modern design, and provides a wealth of possibilities for the modern application of Song painting. AIGC technology has significant potential in the inheritance and innovation of Song painting, which can bring new ideas and methods for interior decoration design, and contribute to the wide application and innovative development of Song painting art in the field of modern design.

To address the challenge of efficiently and cost-effectively generating images of intangible cultural heritage (ICH) furniture that can adapt to diverse modern spatial contexts for visual communication, this paper proposes and constructs an Generative Artificial Intelligence (AIGC) virtual studio system based on ComfyUI. The system is designed for ICH furniture designers, cultural communicators, and digital preservation practitioners, aiming to overcome the bottlenecks of scene switching encountered in traditional photography and 3D modeling. First, furniture images and user scene descriptions are collected, and a dual lexicon consisting of AI prompts and user prompts is constructed. The analytic hierarchy process (AHP) is then applied to weight and filter prompt combinations, forming a quantifiable and integrated prompt system. Second, a visual workflow incorporating ControlNet and IPAdapter nodes is built in ComfyUI to enable the transfer of ICH furniture images to various preset spatial scenes. Finally, a Likert-scale comparison is conducted between the experimental group (using AHP-weighted prompts) and the control group (using unweighted prompts). The results show that the experimental group achieves significant improvements in image realism, style consistency, and cultural communication effectiveness. The images generated by this system can be directly used for digital display, e-commerce product pages, design proposals, and cultural archives of ICH furniture. The method is applicable to the context-aware AIGC generation of traditional furniture and home products, provided that a certain amount of image data and a ComfyUI environment are available. This study provides a reusable technical pathway for the modern visual presentation of ICH furniture and offers methodological support and empirical evidence for the integration of AIGC into environmental design.

Addressing the challenges associated with partial renovation of existing residential interiors, including ordinary homeowners’ incomplete expression of design needs, limited access to professional design support, and the inability of conventional AIGC tools to accurately interpret complex design requirements, this study proposes an AI Agent-assisted design method for partial interior renovation. An AI Agent workflow was developed to establish an integrated process encompassing requirement acquisition, design semantic mapping, and design-scheme generation. The workflow automatically transforms natural-language requirements into structured design information and improves the quality and consistency of generated designs through multimodal information analysis and prompt-weight optimization. Experimental results show that the AI Agent-assisted method achieves improvements in requirement alignment, spatial structure preservation, spatial aesthetics, and generation stability. SUS analysis indicates that this method can provide relatively accessible design decision support for non-professional users. Overall, this study demonstrates that AI Agent-assisted human–AI collaborative design has the potential to support homeowners in independently designing and refining partial residential interior renovation schemes. Although this study did not directly measure indicators such as material consumption, embodied carbon, or service-life extension, the proposed workflow may also offer a potential decision-support pathway for more efficient and incremental renovation of existing homes.