变电站现场表计巡检数据

变电站现场表计巡检功能旨在代替人工巡检工作，能够弥补人工巡检的不足。主要应用于：通过变电站轮式巡检机器人采集表计图像，结合传统图像算法(霍夫变换等)与深度学习算法模型(YOLO系列)综合读取表计读数，判断表计设备状态是否正常，实现变电站内的表计设备状态预警与报警，大大提升了巡检工作效率与可靠性，为变电站稳定安全运行提供良好保障。通过实时采集变电站内各类表计图像，利用深度学习算法识别表计读数和状态，实现电力设备的自动化巡检与异常预警。系统支持圆形表计的高精度识别，并能对设备运行状态进行趋势分析。
（1）数据采集：设置相机倍率参数，并设置云台垂直角度、云台水平角度，转动云台到预设位置后，控制相机进行采集拍照；
（2）数据处理：（a）获取采集的图像后，先进行巡检图像与模版样本图像进行匹配算法校正；（b）根据算法校正计算出的偏差角度进行调整云台及相机倍率的设置；（c）结合YOLOv8模型检测表计后，针对指针式表计进行霍夫变化提取表盘特征，并通过动态阈值调整算法精准提取指针；（d）根据提取指针后的角度偏差值，计算其对应的表计读数；
（3）将判断分析后的表计设备读数值上传至界面进行结果展示。设备状态为正常时，不预警，显示为0。设备状态为阈值告警时，进行预警，显示为1。从而实现表计设备的预警与报警，便于现场人员快速判别设备的状态是否异常；
（4）同时可根据识别出的数据按天、月、年等时间维度进行趋势分析，形成安全报表数据。

The substation on-site meter inspection function aims to replace manual inspection work, offsetting the limitations of manual inspection. It is mainly applied as follows: collecting meter images via wheeled inspection robots in substations, comprehensively reading meter readings by combining traditional image processing algorithms (such as "Hough Transform") and deep learning algorithm models (such as the YOLO series), judging whether the meter devices are in normal state, realizing early warning and alarm for meter devices in substations, greatly improving the efficiency and reliability of inspection work, and providing a solid guarantee for the stable and safe operation of substations. By collecting various meter images in substations in real time, using deep learning algorithms to identify meter readings and states, it realizes automated inspection and abnormal early warning for power equipment. The system supports high-precision recognition of circular meters, and can perform trend analysis on device operating states.
(1) Data Collection: Set the camera magnification parameters, as well as the pan-tilt's vertical and horizontal angles. After rotating the pan-tilt to the preset position, control the camera to capture images.
(2) Data Processing:
(a) After obtaining the captured images, first perform matching algorithm correction between the inspection images and template sample images;
(b) Adjust the settings of the pan-tilt and camera magnification based on the deviation angle calculated by the algorithm correction;
(c) After detecting the meter using the YOLOv8 model, extract the dial features of the pointer-type meter through "Hough Transform", and accurately extract the pointer via dynamic threshold adjustment algorithm;
(d) Calculate the corresponding meter reading based on the angle deviation value after extracting the pointer;
(3) Upload the analyzed meter device reading values to the interface for result display. When the device state is normal, no early warning is triggered and the display value is 0. When the device state reaches the threshold alarm, an early warning is triggered and the display value is 1. This realizes the early warning and alarm for meter devices, facilitating on-site personnel to quickly judge whether the device state is abnormal;
(4) Meanwhile, trend analysis can be performed based on the identified data according to time dimensions such as day, month and year, forming safety report data.

搜集汇总

背景与挑战

背景概述

该数据集包含501条变电站现场表计巡检数据，每日更新，格式为xlsx。数据通过轮式巡检机器人采集，结合霍夫变换和YOLO系列算法识别表计读数和状态，实现自动化巡检与异常预警，提升变电站巡检效率和可靠性。