Research on a Linear Attention-Based Multi-Source Risk Early-Warning Model for the Pinglu Canal Cultural and Tourism Corridor

Riverine navigation-oriented linear cultural and tourism corridors often face challenges such as the unidirectional diffusion of upstream–downstream risks, inconsistent spatiotemporal standards for monitoring data across multiple departments, and difficulties in tracing the logic underlying the assessment of composite disasters. Existing long-series forecasting networks and basic retrieval-enhanced generation frameworks are often ill-suited to the unique chain-like risk evolution patterns inherent in linear waterways. Taking the Pinglu Canal Cultural and Tourism Integration Belt as a case study, this paper proposes a knowledge-driven, multi-source data fusion intelligent early-warning system named KE-MFEWF. The paper introduces an MSTF-Net spatiotemporal feature extraction network incorporating asymmetric corridor attention mechanisms, which automatically adjusts feature weights based on the differing patterns of risk propagation along the upstream and downstream sections of the waterway; it also develops a large-scale model simulation module leveraging graph-based and multi-vector database collaborative retrieval techniques, and establishes a dynamic early-warning determination criterion by integrating two types of correction coefficients—those accounting for seasonal variations and holiday passenger flow. To compensate for the lack of long-term observational samples for the canal under construction, this study employs a combined dataset obtained from real-world measurements, numerical simulations, and cross-basin data migration; the model’s generalization performance was tested on the navigable tourism section of the Xijiang River. When compared with mainstream benchmark models—including Informer, TimesNet, and single-channel RAG—the proposed method achieves an improvement of 6.8% in the AUC metric for comprehensive composite risk prediction; it can issue risk warnings up to 45 min in advance, while reducing the rates of false alarms and missed alerts by 7.2% and 6.5%, respectively; after undergoing INT lightweight compression, the model’s storage footprint is reduced by 65%, and the inference time for a single batch of time-series data on edge devices is less than 0.3 s, making it suitable for real-time on-site deployment. The comprehensive algorithmic framework proposed in this paper can serve as a reusable technical solution for the digital security management and control of similar inland waterway linear cultural tourism corridors in China.