Multi-dimensional Resource Joint Optimization Algorithm for UAV Inspection of Collaborative Tasks of Perception and AI

LI Shiyang; ZHU Xiaorong

doi:10.11999/JEIT251284

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LI Shiyang, ZHU Xiaorong. Multi-dimensional Resource Joint Optimization Algorithm for UAV Inspection of Collaborative Tasks of Perception and AI[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251284

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LI Shiyang, ZHU Xiaorong. Multi-dimensional Resource Joint Optimization Algorithm for UAV Inspection of Collaborative Tasks of Perception and AI[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251284

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Multi-dimensional Resource Joint Optimization Algorithm for UAV Inspection of Collaborative Tasks of Perception and AI

doi: 10.11999/JEIT251284 cstr: 32379.14.JEIT251284

LI Shiyang,
ZHU Xiaorong^,

School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjin 210003, China

Funds: The Natural Science Foundation of China (No.92367102), The Postgraduate Research & Practice Innovation Program of Jiangsu Province (No.KYCX22_0944)

Accepted Date: 2026-03-03
Rev Recd Date: 2026-03-03

Available Online: 2026-03-15

Abstract

Abstract

Objective With the increasing demand for aerial activities, the operational capabilities of various aircraft are gradually expanding to all airspace and multiple industries. The application scope of UAVs has covered multiple altitude layers from low to high altitudes, including micro, medium and large models, and is widely used in public safety, transportation, emergency management, logistics distribution, geographic surveying and mapping and other fields, continuously promoting the innovation and transformation of production and lifestyle. Compared with the traditional manual inspection method, UAV inspection, as an emerging business, can obtain image information that is difficult for the human eye to capture, which not only significantly reduces labor costs, but also improves the accuracy and efficiency of inspection operations. However, UAV inspection also poses new challenges to the allocation of multidimensional resources and task scheduling planning. Taking power system inspection as an example, transmission lines are exposed to the outdoors for a long time and are prone to corrosion, aging and even damage, and need to rely on regular inspections to ensure operational safety. Methods The four-stage multi-dimensional resource inspection and scheduling collaborative optimization algorithm decomposes the original optimization problem into four sub-problems based on the inspection process. After mathematical analysis of each sub-problem, a corresponding solution method is proposed. For the node selection problem, a dual-aided MILP transformation method is used; for the UAV data acquisition problem, a data-driven boundary learning method is employed; for the UAV communication resource allocation problem, a bandwidth-power joint optimization algorithm based on SCA is used; and for the node computing power allocation problem, a lower-bound analytical allocation method is employed. Finally, the original problem is solved using an alternating optimization method for the sub-problems, forming the entire algorithm. Results and Discussions Simulation results show that the proposed algorithm improves the overall energy consumption of the UAV compared to the comparative algorithms. This paper conducts simulation training on visual positioning and fault detection services, investigating the relationship between compression ratio and data volume and both. Figures 2-5 show that the fault detection accuracy is optimal when using 60% data volume and 60% compression ratio. Visual positioning accuracy is optimal when using 80% data volume and 80% compression ratio. Figure 6 shows that the proposed algorithm outperforms the comparative algorithms in terms of accuracy for AI services. As shown in Figures 7 and 8, with changes in bandwidth, computing power, and other resources, the proposed algorithm consistently outperforms the comparative algorithms in terms of energy consumption, effectively reducing overall energy consumption. Conclusions This paper proposes a multi-dimensional resource joint optimization algorithm for intelligent UAV inspection, focusing on the collaborative optimization of perception and AI. It forms an optimization problem with minimizing UAV energy consumption as the objective, and bandwidth, power, computing power, node selection, data volume, and actual compression ratio as variables. This algorithm simultaneously minimizes UAV energy consumption for both fault detection and visual localization, two AI services. Simulation results show that the algorithm can reduce the total energy consumption of the UAV and improve the accuracy of model training. This research focuses on the application scenario of single-UAV inspection; future research can further explore more complex multi-UAV collaborative inspection scenarios and incorporate more services for comprehensive study.
- Unmanned Aerial Vehicle,
- Intelligent Inspection,
- Resource Scheduling,
- Joint Optimization

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References(22)

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