Standardized Framework for AI-Enabled Decision Support Systems for Sustainable Sports Facility Management
IoT Data Chain Interoperability, Resilience Assessment, and Cognitive Analysis
DOI:
https://doi.org/10.13052/jicts2245-800X.1432Keywords:
Sustainable sports facility management, Internet of Things data interoperability, graph neural network, cross-modal semantic alignment, AI-driven decision supportAbstract
In current sports facility management, the lack of unified IoT data standards and the semantic fragmentation of multi-source heterogeneous data make it difficult for AI decision support systems to form a consistent and effective data foundation. This paper proposes a graph neural network (GNN) model based on cross-modal semantic alignment. Taking multi-source sensor data as input, it maps heterogeneous data to a shared semantic embedding space through a unified encoding mechanism. A heterogeneous graph structure is constructed based on device topology and functional coupling. Under the guidance of a graph attention mechanism, weighted propagation and fusion of semantic information between nodes are completed. Simultaneously, contrastive learning constraints are introduced to compress the representation distance between semantically similar nodes, thereby achieving semantic consistency reconstruction of cross-system data chains. Finally, a unified feature representation is output to support upper-level decision analysis. Experimental results show that the system’s interoperability consistency score improves from 0.52 to 0.89, cumulative probability increases from 0.30 to 0.82 when the alignment error is 0.2, and semantic alignment accuracy reaches 92.4%, indicating that the proposed method achieves significant improvements in cross-system data semantic consistency reconstruction and feature representation capabilities. Research shows that this method provides a feasible path for data chain integration and intelligent decision-making in sustainable sports facility management.
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