College of Safety Science and Engineering, Xi'an University of Science and Technology

从通风机性能曲线图像中采样并识别风压性能曲线，进而拟合出风压性能函数是矿井通风网络解算的关键技术。目前常用人工方式识别风压性能曲线，效率低且准确性不高。提出一种基于图像处理技术的通风机风压性能曲线自动识别方法。采用双边滤波、图像锐化和二值化技术对原始通风机风压性能曲线图像进行预处理，以提高图像质量。分别基于腐蚀算法、轮廓检测算法提取通风机性能曲线图像中的网格线和坐标文字，采用逻辑运算、中值滤波、轮廓检测和K3M算法提取风压性能曲线。以逐行像素识别方式识别风压性能曲线的像素坐标，采用模板匹配算法识别坐标数字，进而完成像素坐标到物理坐标的转换，实现风压性能曲线识别。将通风机风压性能曲线自动识别方法集成至通风网络解算软件，对通风机风压性能曲线进行识别试验，结果表明，该方法对单条风压性能曲线的采样速度为24 Samples/s，识别的风压性能曲线与原始曲线的重合度高，风压拟合值与原始值的最大误差仅为0.88%，较人工识别方法大大提高了通风网络解算效率和准确性。

Sampling and recognizing the wind pressure performance curve from the wind performance curve image, and then fitting the wind pressure performance function, is a key technology for solving the mine ventilation network. Currently, manual methods are commonly used to recognize wind pressure performance curves, which have low efficiency and poor accuracy. This study proposes an automatic recognition method for the wind pressure performance curve of ventilator based on image processing technology. The method uses bilateral filtering, image sharpening, and binarization techniques to preprocess the original ventilator wind pressure performance curve image, in order to improve image quality. The method extracts grid lines and coordinate text from the performance curve image of the ventilator based on corrosion algorithm and contour detection algorithm. The method uses logical operation, median filtering, contour detection, and K3M algorithm to extract the wind pressure performance curve. The pixel coordinates of the wind pressure performance curve are recognized using a row by row pixel recognition method. The method uses template matching algorithm to recognize coordinate numbers, and then complete the conversion from pixel coordinates to physical coordinates, achieving wind pressure performance curve recognition. The automatic recognition method for the wind pressure performance curve of the ventilator is integrated into the ventilation network calculation software. The recognition experiment is conducted on the wind pressure performance curve of the ventilator. The results show that the sampling speed of the method for a wind pressure performance curve is 24 Samples/s. The recognized wind pressure performance curve has a high overlap with the original curve. The maximum error between the wind pressure fitting value and the original value is only 0.88%. Compared to manual recognition methods, the method greatly improves the efficiency and accuracy of the ventilation network calculation .

国家自然科学基金资助项目（51974232）。

吴奉亮，寇露. 用于矿井通风网络解算的通风机风压性能曲线自动识别方法[J]. 工矿自动化，2024，50（4）：103-111.

WU Fengliang, KOU Lu. Automatic recognition method of ventilator wind pressure performance curve for mine ventilation network calculation[J]. Journal of Mine Automation，2024，50（4）：103-111.