通过强化学习驱动的任务技术适应和服务互动来实现非标准服务任务的动态适应
Yixin Sun1, Jinming Gao1, Kai Han2
1Department of Computer Engineering, Pai Chai University, Deajeon, Republic of Korea.
Scientific reports
|February 13, 2026
概括
本研究介绍了微型企业实现数字化转型的新方法. 任务服务HCI方法及其强化学习驱动的任务技术适应优化系统提高了用户满意度和系统智能.
科学领域:
- 信息系统信息系统信息系统
- 人与计算机的交互
- 业务流程管理 业务流程管理
背景情况:
- 微型企业面临数字化转型的障碍,原因是各种各样的任务和缺乏标准的工作流程.
- 现有的解决方案往往无法满足这些企业独特的定制服务需求.
研究的目的:
- 为微型企业进行数字化转型提出一个轻量级的嵌入式平台方法 (Task-Service-HCI).
- 设计一个基于强化学习的任务技术适应优化 (RL-TTFO) 系统,用于自适应的工作流生成.
- 在现实环境中评估拟议的方法和系统的有效性.
主要方法:
- 开发了任务-服务-HCI (TSH) 方法,整合了任务技术合适性 (TTF),服务流程定制 (SPC) 和人机交互 (HCI).
- 设计了一种基于强化学习的任务技术合适优化 (RL-TTFO) 架构,用于语义任务解析和自适应工作流生成.
- 在专业组织行业部署了原型平台,收集了300名用户的行为和调查数据.
主要成果:
- 结构方程建模证实,任务技术匹配显著提高了用户满意度 (β=0.62,p<0.001).
- 配置参与调解了系统与感知用户控制之间的关系 (p<0.01).
- 与静态配置相比,RL-TTFO机制提高了35.4%的使用频率和27.1%的感知系统智能 (p<0.001).
结论:
- TSH方法和RL-TTFO系统为微型企业提供了可持续的,低障碍的数字化转型解决方案.
- 该方法有效地解决了异质任务和定制服务环境的挑战.
- 这种方法使资源有限的企业能够提高用户满意度和系统适应性.
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