监测国家计划的数字模型:哈萨克斯坦的经验
Mafura Uandykova1, Tolkyn Mirkassimova1, Gulnar Mukhamejanova1
1School of Digital Technologies, Narxoz University, Almaty, Kazakhstan.
Frontiers in artificial intelligence
|December 5, 2025
概括
本研究介绍了国家计划监测 (DMNPM) 的数字模型,以提高公共管理的有效性. 该模型使用集成数据和机器学习来更好地监督计划和跟踪社会经济结果.
科学领域:
- 公共管理 公共管理
- 信息系统信息系统信息系统
- 社会经济分析 社会经济分析
背景情况:
- 传统的程序监控面临着数据碎片化和缺乏动态跟踪等挑战.
- 现有的方法往往无法充分评估社会经济结果.
- 数字化转型需要在公共管理中提供先进的监控解决方案.
研究的目的:
- 为国家计划监测 (DMNPM) 提出一个原创的数字模型.
- 通过数字化转型提高国家计划的有效性,透明度和绩效.
- 为了解决传统监测方法的局限性.
主要方法:
- 开发一个概念数字模型,整合多种数据源 (egov.kz,智能桥梁,开放数据).
- 整合机器学习用于预测分析,风险预测和因果关系评估.
- 在监测框架内实施双向反机制.
主要成果:
- "数字哈萨克斯坦"",Nurly Zhol"",Zhaily Mektep"和"Auyldyq Densaulyq Saqtau"的案例研究证明了DMNPM的适用性.
- 一个准实验性的试点项目显示,监测效率和报告效率有了显著的改善.
- 该模型为那些正在发展数字基础设施的国家提供了实用解决方案.
结论:
- 国家计划监测 (DMNPM) 数字模型为公共计划监督提供了一种全面和先进的方法.
- 通过利用数字化转型和机器学习,DMNPM提高了程序的有效性,透明度和性能.
- 这项研究有助于数字治理理论和监测方法,为国家计划管理提供了可扩展的解决方案.
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