基于Mamba多目标识别网络的光伏运维机器人引导控制系统OA

[Objective]In the context of the global vigorous promotion of clean energy transformation,photovoltaic power generation,as a green and sustainable energy source,is becoming increasingly important in the energy structure.However,photovoltaic panels are exposed to the outdoors for a long time and are affected by natural factors such as strong winds,sand,rainfall,and bird activities.These factors often lead to problems such as panel damage and stains,which seriously reduce the photovoltaic power generation efficiency and system stability.The traditional manual operation and maintenance method not only consumes a large amount of human,material,and time resources but also has difficulty in ensuring the timeliness and effectiveness of operation and maintenance in complex terrains and harsh weather conditions.Therefore,it is urgent to develop highly efficient and intelligent photovoltaic panel operation and maintenance technologies.[Methods]An innovative photovoltaic panel operation and maintenance solution was proposed in this paper.It integrated a high-efficiency object recognition network based on Mamba module and a path-planning strategy using an improved particle swarm optimization algorithm to promote the intelligent operation of photovoltaic operation and maintenance robot.In the object recognition stage,the Mamba module was applied to construct an object detection network.The unique architecture of Mamba enabled it to accurately capture the subtle damage textures and stain marks on photovoltaic panels and quickly identify abnormal panels.The multi-scale detection strategy was introduced to extract and fuse image features at different scales,effectively solving the problems of easy loss of small-object features and information loss caused by occlusion between panels.It significantly improved the detection accuracy and speed,meeting the real-time requirements of photovoltaic operation and maintenance.In terms of path planning,the traditional particle swarm optimization algorithm was optimized and improved,and an adaptive inertia weight update strategy was introduced.This strategy adjusted the particle search behavior in real time dynamically according to the detection and positioning results of the object recognition network,enabling the particles to quickly converge to the global optimal solution.It planned the shortest and most effective maintenance path for the operation and maintenance robot,avoiding invalid and repeated paths and greatly improving the operation and maintenance efficiency.[Results]The results of simulation experiments and practical project tests show that this method has achieved remarkable results in terms of detection accuracy and path planning.In terms of detection accuracy,the average detection accuracy for various types of damaged and stained panels is significantly higher than that of traditional detection algorithms.Regarding the path planning effect,compared with traditional algorithms,the proposed method greatly enhances the working efficiency of photovoltaic operation and maintenance robots,providing reliable technical support and practical examples for the intelligent and efficient operation and maintenance of photovoltaic panels.[Conclusions]The proposed method performs outstandingly in terms of detection accuracy and speed.It effectively improves the working efficiency of photovoltaic operation and maintenance robots,provides a practical and innovative solution for the operation and maintenance of photovoltaic panels,and thus has high application value and broad promotion prospects.