Automatic recognition method of ventilator wind pressure performance curve for mine ventilation network calculation

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 .