优化教育资源分配使用灰色模型预测学龄人口的优化教育资源分配
Fei Pang1, Yingxu Li2, Guo Miao3
1School of Education Science, Hanshan Normal University, Chaozhou, 52041, China.
Scientific reports
|October 8, 2025
概括
这项研究改进了使用增强的灰色模型 (GM) (1,1) 与缓冲和局部回归的学龄人口预测. 新模型为教育资源规划提供了更高的准确性.
科学领域:
- 人口统计学 人口统计学
- 教育规划教育规划
- 数据科学数据科学数据科学
背景情况:
- 准确预测学龄人口对于有效的教育资源分配至关重要.
- 传统的预测模型与政策和环境因素影响的数据波动性作斗争.
- 灰色系统理论为模拟不确定的系统提供了一个框架,但需要对复杂的人口动态进行增强.
研究的目的:
- 增强灰色模型 (GM) (1,1) 以实现更准确的学龄人口预测.
- 开发一个集成的预测框架,以捕捉社会经济转变中的人口动态.
- 为优化教育资源分配提供数据驱动策略的信息.
主要方法:
- 使用缓冲运算符优化学龄人口数据,以尽量减少政策和环境干扰.
- 集成局部加权线性回归来改进灰色数计算,改善适合不稳定的数据.
- 开发了一个增强的GM(1,1) 框架,将灰色系统理论与局部回归技术结合起来.
主要成果:
- 与传统的GM(1,1) 模型相比,增强的GM(1,1) 模型显示出明显改善的预测准确性.
- 剩余量大幅减少,例如,2018年从-14.462降至0.399,这表明优越的趋势适应.
- 该模型有效地捕捉了中国社会经济转变 (2013-2020年) 期间不断变化的学龄人口趋势.
结论:
- 增强的GM(1,1) 模型为学龄人口预测提供了更精确的工具.
- 研究结果支持制定科学基础的教育资源分配策略.
- 该研究提出了一种新的方法论方法,对人口预测和教育规划具有理论和实际意义.
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