Data-Driven Demand Quantification and AI-Assisted Design of Community Low-Carbon Recycling Terminals

Dept. of Art and Design, Beijing University of Chemical Technology, Beijing, China

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Abstract

Insufficient user demand quantification and weak integration of intelligent functions remain common problems in the design of community recycling terminals. To address these issues, this paper presents a data-driven and AI-assisted design framework for community low-carbon recycling terminals. Field surveys and questionnaires were used to collect user demand data, and a coupled K-means and Kano-AHP model was adopted to support user segmentation, requirement classification, and design priority ranking. On this basis, a low-carbon recycling terminal and a multi-terminal service platform were developed, including edge-side visual recognition, servo-driven compaction, photovoltaic-assisted power supply, fill-level alerts, intelligent collection scheduling, and carbon-footprint visualization. The proposed framework forms a closed service loop covering disposal, recognition, collection, operation management, and carbon accounting. Methodologically, the integrated K-means clustering and Kano-AHP analysis process provides a standardized, replicable quantitative workflow for user-centric public facility design, eliminating the subjectivity of traditional experience-based design decision-making. This study also provides an application-oriented design case for intelligent public facilities and offers a practical reference for the low-carbon transformation of community recycling services.