基于深度学习的配电柜压板状态自动核对方法

doi:10.3969/j.issn.1000-5641.2026.04.006

摘要/Abstract

摘要：

在变电站内, 压板是配电柜上的核心部件, 若发生故障或人为无意操作, 易导致压板误投、误退, 因此, 对配电柜压板的状态核对是变电站人工巡检工作的重要部分之一. 提出了一种新的基于深度学习的配电柜压板状态自动核对方法: 首先, 从Excel格式的盘面图内提取模板压板框数据, 其中每一面配电柜均对应一份盘面图, 盘面图为该配电柜内各个部件的电子化记录, 包含了各个压板的正确投退状态、压板名称等信息, 即模板压板框数据; 然后, 使用YOLOv5s算法对拍摄图像进行压板目标检测, 得到预测的压板检测框数据, 使用PP-OCRv4的CTC(Connectionist Temporal Classification)概率结果作为预测压板名称和模板压板名称的文本相似度度量; 最后, 基于压板空间位置关系, 从行和列两个维度分别计算预测压板框和模板压板框的匹配概率分值, 结合修正策略取行列概率最大化结果, 从而实现拍摄图像中的预测压板框和盘面图记录的模板压板框一一对应, 进而核对压板的投退状态, 若状态不一致, 则报警通知工作人员. 在变电站实地场景拍摄的图像数据集中, 共有2685个压板, 所提出的压板核对方法能够100%地将预测压板框和模板压板框进行匹配和状态核对, 具有强鲁棒性, 能够用于实际的变电站配电柜的压板核对工作, 提高了人工的工作效率.

关键词: 压板状态核对, 智能巡检, 目标检测, 文本检测识别

Abstract:

In a substation, the platen is a core component on the distribution cabinet. In case of failure or unintentional human operation, the state verification of the platen of the distribution cabinet is important in the manual inspection of the substation as platen is prone to misinvestment and misreturn. Therefore, a new deep learning-based automatic checking method is proposed for the status of switchgear platen. First, the template platen frame data are extracted from the Excel-formatted panel diagram, in which every switchgear cabinet corresponds to a panel diagram. The panel diagram is an electronic record of the components in the switchgear cabinet, which contains the correct casting and retiring status of each platen, the name of the platen, and other information, i.e., the template platen frame data. Then the YOLOv5s algorithm is used to detect the platen target on the captured image to obtain the predicted platen detection frame data. The connectionist temporal classification (CTC) probability of paddle-to-paddle optical character recognition (PP-OCRv4) is used as the text similarity measure between the predicted and stencil platen names. Finally, based on the relationship between the spatial location of the platen, the matching probability scores of predicted and stencil platen frames are calculated from the row-column dimensions and combined with a correction strategy. Based on maximizing the row and column probability, a one-to-one correspondence is realized between the predicted platen frame in the captured image and stencil platen frame recorded in the disk diagram. Subsequently, the cast-in and cast-out status of the platen is checked, and the staff is notified with an alarm if the status is inconsistent. In the image dataset captured in the field scene of the substation, there were a total of 2685 platens, and the proposed platen checking method successfully matched the predicted platen frames with the stencil platen frames with 100% accuracy. Thus, the method is robust and can be used in the actual platen checking of the substation switchboard cabinets to improve manpower efficiency.

Key words: platen status verification, intelligent patrol inspection, object detection, text detection and recognition

中图分类号:

TP391

陈宁, 林荣胜, 袁成, 尚瑨, 白帆, 黄定江. 基于深度学习的配电柜压板状态自动核对方法[J]. 华东师范大学学报（自然科学版）, 2026, 2026(4): 51-62.

Ning CHEN, Rongsheng LIN, Cheng YUAN, Jin SHANG, Fan BAI, Dingjiang HUANG. Deep learning-based method for automatic verification of platen status in distribution cabinet[J]. J* E* C* N* U* N* S*, 2026, 2026(4): 51-62.

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精确率	召回率	$ {{\mathrm{mAP}}}_{0.5} $	$ {{\mathrm{mAP}}}_{0.5:0.95} $
0.99919	0.99964	0.99478	0.84441

算法	准确率	重复率
编辑距离	0.748	0.154
余弦文本相似度	0.718	0.177
Jaccard文本相似度	0.726	0.155
CTC文本相似度	0.872	0.073

算法	准确率	重复率
CTC文本相似度	0.872	0.073
CTC文本相似度 + 路径概率最大	0.929	0.064
CTC文本相似度 + 路径概率最大 + 匹配修正	1.000	0.000

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