基于深度学习与图像识别的太阳能储能设备问题检测

doi:10.19799/j.cnki.2095-4239.2025.0726

储能科学与技术 ›› 2025, Vol. 14 ›› Issue (9): 3596-3598.doi: 10.19799/j.cnki.2095-4239.2025.0726

• 储能测试与评价 • 上一篇

基于深度学习与图像识别的太阳能储能设备问题检测

钟绍辉()

长沙工业学院，湖南长沙 410200

收稿日期:2025-08-08 修回日期:2025-08-13 出版日期:2025-09-28 发布日期:2025-09-05
作者简介:钟绍辉（1979—），男，硕士，副教授，主要研究方向为图像水印，图像处理，新能源设施视频分析，新能源系统安全认证，E-mail：zshtech_1234@sina.com。
基金资助:
湖南省自然科学基金(2019JJ7075)

Problem detection of solar energy storage equipment based on deep learning and image recognition

Shaohui ZHONG()

School of Cyberspace Security, Changsha Institute of Technology, Changsha 410200, Hunan, China

Received:2025-08-08 Revised:2025-08-13 Online:2025-09-28 Published:2025-09-05

摘要/Abstract

摘要：

随着太阳能储能设备的广泛应用，对其运行状态的实时监测和问题检测变得至关重要。本文结合深度学习与图像识别技术的相关技术以及理论基础提出了一种太阳能储能设备问题检测方法。通过采集太阳能储能设备的图像数据，利用深度学习算法对图像进行处理和分析，实现对设备潜在问题的准确识别和定位。该方法具有较高的检测准确率和效率，能够为太阳能储能设备的维护和管理提供有力支持。

关键词: 太阳能储能设备, 深度学习, 图像识别, 问题检测

Abstract:

With the widespread application of solar energy storage equipment, real-time monitoring of its operating status and problem detection have become crucial. This paper presents a method for detecting issues in solar energy storage equipment, which combines the relevant technologies and theoretical foundations of deep learning and image recognition. By collecting image data from the solar energy storage equipment, deep learning algorithms are utilized to process and analyze the images, achieving accurate identification and localization of potential problems. This method has a high detection accuracy and efficiency, providing strong support for the maintenance and management of solar energy storage equipment.

Key words: solar energy storage equipment, deep learning, image recognition, problem detection

中图分类号:

TP 391

钟绍辉. 基于深度学习与图像识别的太阳能储能设备问题检测[J]. 储能科学与技术, 2025, 14(9): 3596-3598.

Shaohui ZHONG. Problem detection of solar energy storage equipment based on deep learning and image recognition[J]. Energy Storage Science and Technology, 2025, 14(9): 3596-3598.

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基于深度学习与图像识别的太阳能储能设备问题检测

Problem detection of solar energy storage equipment based on deep learning and image recognition

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