<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Under the background of population aging, the rational use of resources for senior care services has more difficulty, leading to increasingly prominent conflicts between supply and demand. This paper uses the Gini coefficient, cohort effect, and other methods to build a matching supply and demand model for innovative senior care services based on the multi-source data of senior care services. The probabilistic linguistic term set is used for evaluation and a calculation method is proposed to match satisfaction. The model’s feasibility is demonstrated by analyzing arithmetic examples. Five types of senior care demand groups can be obtained through clustering analysis, with three kinds of intelligent senior care, namely, competent institutions, smart communities, and smart homes, in which the Gini coefficient value of community senior care facilities in X city is 0.4203, and there is an imbalance between supply and demand. The method proposed in this paper identifies five types of elderly groups, which can be matched with different innovative senior care services for elderly groups with distinct characteristics.</p>
</abstract>

Under the background of population aging, the rational use of resources for senior care services has more difficulty, leading to increasingly prominent conflicts between supply and demand. This paper uses the Gini coefficient, cohort effect, and other methods to build a matching supply and demand model for innovative senior care services based on the multi-source data of senior care services. The probabilistic linguistic term set is used for evaluation and a calculation method is proposed to match satisfaction. The model’s feasibility is demonstrated by analyzing arithmetic examples. Five types of senior care demand groups can be obtained through clustering analysis, with three kinds of intelligent senior care, namely, competent institutions, smart communities, and smart homes, in which the Gini coefficient value of community senior care facilities in X city is 0.4203, and there is an imbalance between supply and demand. The method proposed in this paper identifies five types of elderly groups, which can be matched with different innovative senior care services for elderly groups with distinct characteristics.

Exploration of Smart Elderly Service Supply and Demand Matching for Multi-source Data

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Under the background of population aging, the rational use of resources for senior care services has more difficulty, leading to increasingly prominent...

Exploration of Smart Elderly Service Supply and Demand Matching for Multi-source Data

Publié en ligne: 03 mai 2024

Pages: -

Reçu: 08 avr. 2024

Accepté: 28 avr. 2024

DOI: https://doi.org/10.2478/amns-2024-0956

Mots clés
Multi-source data, Gini coefficient, Cohort effect, Supply and demand matching model, Competent elderly care

© 2024 Miaoying Fang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Exploration of Smart Elderly Service Supply and Demand Matching for Multi-source Data

Publié en ligne: 03 mai 2024

Pages: -

Reçu: 08 avr. 2024

Accepté: 28 avr. 2024

DOI: https://doi.org/10.2478/amns-2024-0956

Mots clésMulti-source data, Gini coefficient, Cohort effect, Supply and demand matching model, Competent elderly care

© 2024 Miaoying Fang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Multi-source data, Gini coefficient, Cohort effect, Supply and demand matching model, Competent elderly care