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<p>As China’s aging problem intensifies and urban-rural resource allocation remains uneven, the shortage of property-based elderly care service resources in counties within urban-rural integration zones has become increasingly prominent. Traditional early warning models struggle to dynamically capture the supply-demand fluctuation patterns of property-based elderly care resources. To optimize the “property + elderly care” service, this paper proposes an innovative dynamic early warning model based on the Long Short-Term Memory with Attention Mechanism (LSTM-AM), introducing “property response” and “service supply-demand spatio-temporal coupling degree” as dual core indicators and establishing a dynamic threshold-based graded early warning mechanism. This method shifts from reactive remediation to proactive adaptation, offering intelligent tools to support the coordinated development of property-based elderly care services in urban-rural integration areas. Results show that, taking Xuchang City, Henan Province as an example, by integrating multi-source time-series data such as county-level population aging rates, property-based elderly care service coverage rates, community facility maintenance data, and resident demand feedback, the model’s prediction error (MAE ≤ 0. 12, RMSE ≤ 0. 18) was reduced by 35% compared to traditional methods, and the warning accuracy (F1-score = 0. 89) was significantly superior to traditional models. The model can predict resource shortages 8–12 months in advance and generate facility allocation and personnel scheduling plans.</p>

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A dynamic early warning model based on LSTM-AM for property-based elderly care resource shortages in urban-rural integrated counties in China

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月12日

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  • Qingheng Sun

摘要

As China’s aging problem intensifies and urban-rural resource allocation remains uneven, the shortage of property-based elderly care service resources in counties within urban-rural integration zones has become increasingly prominent. Traditional early warning models struggle to dynamically capture the supply-demand fluctuation patterns of property-based elderly care resources. To optimize the “property + elderly care” service, this paper proposes an innovative dynamic early warning model based on the Long Short-Term Memory with Attention Mechanism (LSTM-AM), introducing “property response” and “service supply-demand spatio-temporal coupling degree” as dual core indicators and establishing a dynamic threshold-based graded early warning mechanism. This method shifts from reactive remediation to proactive adaptation, offering intelligent tools to support the coordinated development of property-based elderly care services in urban-rural integration areas. Results show that, taking Xuchang City, Henan Province as an example, by integrating multi-source time-series data such as county-level population aging rates, property-based elderly care service coverage rates, community facility maintenance data, and resident demand feedback, the model’s prediction error (MAE ≤ 0. 12, RMSE ≤ 0. 18) was reduced by 35% compared to traditional methods, and the warning accuracy (F1-score = 0. 89) was significantly superior to traditional models. The model can predict resource shortages 8–12 months in advance and generate facility allocation and personnel scheduling plans.