Granularity Nested Multi-Objective Offloading Strategy for Transmission Line Inspection

TAN Ling; WANG Hai-feng; SONG Jing; YAO Yong-lei; XU Hai

doi:10.12263/DZXB.20250268

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Granularity Nested Multi-Objective Offloading Strategy for Transmission Line Inspection

PAPERS | 更新时间：2026-02-05

- Granularity Nested Multi-Objective Offloading Strategy for Transmission Line Inspection
- ACTA ELECTRONICA SINICA Vol. 53, Issue 10, Pages: 3514-3528(2025)
- 作者机构：
  
  南京信息工程大学计算机学院，江苏南京 210044
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62271264)
- DOI：10.12263/DZXB.20250268
  CLC： TN929.5;
- Received：09 April 2025，
  
  Accepted：11 October 2025，
  
  Published：25 October 2025
- 稿件说明：
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谈玲, 汪海峰, 宋静, 等. 基于粒度嵌套的多目标输电线路巡检卸载策略[J]. 电子学报, 2025, 53(10): 3514-3528.

TAN Ling, WANG Hai-feng, SONG Jing, et al. Granularity Nested Multi-Objective Offloading Strategy for Transmission Line Inspection[J]. Acta Electronica Sinica, 2025, 53(10): 3514-3528.
谈玲, 汪海峰, 宋静, 等. 基于粒度嵌套的多目标输电线路巡检卸载策略[J]. 电子学报, 2025, 53(10): 3514-3528. DOI：10.12263/DZXB.20250268

TAN Ling, WANG Hai-feng, SONG Jing, et al. Granularity Nested Multi-Objective Offloading Strategy for Transmission Line Inspection[J]. Acta Electronica Sinica, 2025, 53(10): 3514-3528. DOI：10.12263/DZXB.20250268

摘要

在电网巡检中，输电线路巡检机器人（Transmission Line Inspection Robot，TLIR）承担全覆盖式巡检任务，其长距离作业与高频数据采集对任务处理的实时性和能效提出严苛要求.移动边缘计算（Mobile Edge Computing，MEC）通过在网络边缘部署计算与卸载能力，能够有效支撑输电线路巡检的实时数据处理.传统巡检策略将路径规划和任务卸载视为两个独立过程进行分阶段优化，忽略了变量间的动态关联与时序联动，难以实现系统性能的全局最优.针对MEC辅助密集输电线路巡检中存在的决策时序差异、任务低时延需求与系统节能难以兼顾的问题，本文提出一种基于粒度嵌套的多目标输电线路巡检卸载策略，通过构建多单一窗口嵌套于复合窗口的粒度嵌套结构，实现路径规划、任务卸载和资源分配的联合优化.在该粒度嵌套结构中，复合窗口主要控制TLIR的巡检路径规划，单一窗口则依据通信状态和资源变化动态决策任务卸载与资源分配，以应对多个优化任务间的控制周期差异，确保系统时延和能耗最小化.为实现TLIR的全覆盖式巡检，本文引入欧拉图策略，通过研究巡检场景的拓扑特性，构建覆盖所有电力线的最短欧拉回路，并采用李雅普诺夫优化技术将数据积压和能耗管理的长期随机优化问题逐步转化为时隙级的确定性问题.针对优化变量间的复杂耦合与决策时序差异特性，本文进一步提出一种粒度嵌套感知的多目标自适应卸载算法（a Nested-Granularity-Aware Multi-Objective Adaptive Offloading algorithm，NGA-MOAO），将原NP-hard问题分解为两个子问题，并设计基于单一窗口激励反馈的跨窗口联合优化策略，通过动态调整单一窗口的任务卸载与资源分配来生成激励信号，进而在信号中叠加全覆盖硬性约束的惩罚项以引导复合窗口中的路径规划，最终实现多变量间的协同优化.仿真结果表明，NGA-MOAO算法在不同杆塔数量、加权系数占比和任务量激增下，时延和能耗均优于各对比方案且波动更小.在确保全覆盖巡检的前提下，NGA-MOAO算法相较于对比方案在巡检成本、能耗和时延上分别减少了11.75%、15.11%和8.32%以上，资源利用率提高9.47%以上，适用于复杂输电线路环境中的全覆盖式巡检.

Abstract

In power grid inspection

the transmission line inspection robot (TLIR) undertakes full-coverage inspection tasks; its long-distance operation and high-frequency data acquisition impose stringent requirements on the real-time performance and energy efficiency of task processing. Mobile edge computing (MEC) deploys computing and offloading capabilities at the network edge and can effectively support the real-time data processing of transmission line inspection. Traditional inspection strategies treat path planning and task offloading as two independent processes optimized in separate stages

overlooking the dynamic associations and temporal couplings among variables and making it difficult to achieve a global optimum in system performance. To address the inconsistent decision timing in MEC-assisted dense transmission line inspection and the difficulty of balancing low-latency task demands with system energy saving

this paper proposes a granularity-nested multi-objective offloading strategy for transmission line inspection

which achieves joint optimization of path planning

task offloading

and resource allocation by constructing a granularity-nested structure in which multiple single windows are embedded within a composite window. In this granularity-nested structure

the composite window primarily controls TLIR path planning

while the single windows dynamically decide task offloading and resource allocation according to variations in communication conditions and available resources

thereby coping with the different control periods among multiple optimization tasks and ensuring the minimization of system delay and energy consumption. To realize full-coverage inspection by the TLIR

an Eulerian-graph-based strategy is introduced which

by analyzing the topological characteristics of the inspection scenario

constructs the shortest Eulerian circuit covering all transmission lines

and Lyapunov optimization is employed to gradually transform the long-term stochastic optimization of task backlog and energy management into a deterministic problem at the time-slot level. In view of the complex coupling among optimization variables and the inconsistency of decision timing

this paper further proposes a nested-granularity-aware multi-objective adaptive offloading algorithm (NGA-MOAO)

which decomposes the original NP-hard problem into two subproblems and designs a cross-window joint optimization strategy based on single-window incentive feedback

in which the single windows generate incentive signals by dynamically adjusting task offloading and resource allocation

and a penalty term encoding the hard full-coverage constraint is superimposed on these signals to guide path planning in the composite window

ultimately achieving collaborative optimization among multiple variables. Simulation results show that

under different numbers of towers

weight-coefficient proportions

and task surges

the delay and energy consumption of NGA-MOAO are both superior to those of the comparison schemes and exhibit smaller fluctuations. Under the premise of full-coverage inspection

compared with the baselines

the NGA-MOAO algorithm reduces inspection cost

energy consumption

and delay by at least 11.75%

15.11% and 8.32%

respectively

and increases resource utilization by at least 9.47%

making it applicable to full-coverage inspection in complex transmission line environments.

关键词

Keywords

references

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