面向可信数据空间的强化学习驱动众包感知数据聚合方法
By using a reinforcement learning-driven crowdsourced perception data aggregation system, a decentralized and trusted environment is built using blockchain and smart contracts. By combining deep reinforcement learning algorithms and user device edge nodes, the system solves the problems of data security and system scalability, achieves high efficiency and real-time performance in large-scale data processing, and improves the efficiency of task allocation and resource scheduling.
CN120897182BActive Publication Date: 2026-07-17YUNNAN UNIV +1
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- YUNNAN UNIV
- Filing Date
- 2025-07-21
- Publication Date
- 2026-07-17
Smart Images
Figure CN120897182B_ABST
Abstract
本发明公开了面向可信数据空间的强化学习驱动众包感知数据聚合方法，属于移动众包感知技术领域，包括S1、构建强化学习驱动众包感知数据聚合系统，S2、系统初始化，S3、任务处理及聚合准备，S4、基于DRL的计算卸载决策，S5、数据聚合与验证和S6和奖惩执行与状态更新；解决现有方案在实现数据高效聚合时忽视数据安全性和完整性，难以应对数据篡改、伪造及边缘服务器不可信的问题；改善传统架构在数据量爆炸式增长和任务复杂性提升时，系统延迟、能耗及可扩展性方面的缺陷，以满足大规模数据处理需求；挖掘区块链技术与边缘计算的融合潜力，提升任务分配和资源调度效率。
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