传感器网络任务与资源智能匹配方法

doi:10.12305/j.issn.1001-506X.2026.08.18

摘要/Abstract

摘要：

面向传感器网络的复杂任务异构性、复杂资源动态性以及任务与资源匹配的灵活性等需求，提出基于时序知识图谱的任务资源智能匹配方法。首先，构建任务知识图谱存储任务信息，传感器能力时序知识图谱表征网络节点能力，提高任务与网络知识的利用率。其次，提出任务资源匹配框架，为任务资源匹配提供标准化的描述方法。然后，设计任务资源匹配规则，从时间、空间和信息3个维度关联分析，优化网络任务与资源匹配。仿真结果表明，所提方法在能量消耗和资源利用率上均优于传统算法，证明了其在传感器网络任务与资源匹配的可行性和有效性。

关键词: 传感器网络, 时序知识图谱, 任务与资源匹配, 知识图谱

Abstract:

To address the requirements of complex task heterogeneity, dynamic complexity of resource, and the flexibility of task and resource matching in sensor networks, an intelligent task and resource matching method based on temporal knowledge graph is proposed. Firstly, a task knowledge graph and a sensor capability temporal knowledge graph are constructed to store task information and characterize network node capabilities, the utilization rate of task and network knowledge is improved. Secondly, a task resource matching framework is designed to provide standardized descriptions for task resource matching. Subsequently, task resource matching rules are designed to conduct a correlation analysis from three dimensions: time, space, and information, optimizing network task and resource matching. Simulation results show that the proposed method outperforms traditional algorithms in terms of energy consumption and resource utilizatbion rates, proving its feasibility and effectiveness in sensor network task and resource matching.

Key words: sensor network, temporal knowledge graph (TKG), task and resource matching, knowledge graph

中图分类号:

TN 915.04

李紫璇, 杨春刚, 李彤, 王瑶, 吴涵, 李杰. 传感器网络任务与资源智能匹配方法[J]. 系统工程与电子技术, 2026, 48(8): 2713-2724.

Zixuan LI, Chungang YANG, Tong LI, Yao WANG, Han WU, Jie LI. Intelligent task and resource matching method for sensor network[J]. Systems Engineering and Electronics, 2026, 48(8): 2713-2724.

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编号	描述	所需能力	匹配成员列表
T₁	海上侦察	侦察、空域	S₁（电光传感器）、S₃（无人机平台）、 S₈（雷达传感器）
T₂	夜间监视	侦察、低光成像、空域	S₄（红外成像传感器）、 S₄（无人机平台）
T₃	高分辨率侦察	高分辨	S₁₆（高分辨率成像传感器）、 S₅（无人机平台）
T₄	侦察信息回传	低丢包率	S₁₃（雷达传感器-可通信传输）

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