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The automatic perception and alarm method for coal mine rock burst and coal and gas outburst is an effective measure to timely detect accidents and emergency rescue, reduce casualties, avoid or reduce secondary accidents such as gas and coal dust explosions, and curb delayed, missed, and concealed reporting of accidents. It is difficult to perceive coal mine rock burst accidents, and there is currently no automatic detection and alarm method for coal mine rock burst accidents. Coal mine rock burst accidents are mainly discovered manually. At present, there are only automatic alarm methods for coal and gas outbursts based on methane, wind speed, and direction sensors. There are problems such as slow response speed and inability to detect significant increases in methane concentration before methane sensor damage. A method for image perception and alarm of coal mine rock burst and coal and gas outburst has been proposed. Based on the image features of coal mine rock burst and coal and gas outburst temperature, color, depth, burial, etc., the method recognizes coal mine rock burst and coal and gas outburst. Based on the changes in gas concentration in the roadway space and mining face, the method distinguishes between rock burst and coal and gas outburst. If the gas concentration rapidly increases over a large area, it is judged as coal and gas outburst, otherwise it is judged as rock burst. This method has the advantages of intuitiveness, fast response speed, non-contact, wide monitoring range, simplicity and reliability, and can intuitively record the real situation of coal mine rock burst and coal and gas outburst. When the coal mine rock burst and coal and gas outburst accidents are alarmed, the personnel on duty in the control room can immediately confirm the accident through video recording and carry out emergency rescue in a timely manner. A method has been proposed to reduce the impact of coal mine rock burst and coal and gas outbursts on image perception, including multi point arrangement of cameras, setting of cameras at higher positions, timely transmission of video data, and multi-point arrangement of methane sensors.

Early detection of mine electric sparks and alarm can prevent or reduce gas and coal dust explosions and mine fire accidents. There are no natural light sources such as sunlight, moonlight, and starlight underground. The main factor affecting the recognition of mine electric sparks is the mine light source. By adjusting the installation position and angle of the camera, the impact of fixed mine light sources on electric spark recognition can be avoided or reduced. But it cannot solve the impact of mobile mine light sources on electric spark recognition. The discharge cycle of electric sparks generated by different forms of circuits is different, but the discharge time of electric sparks is less than 4 ms. The minimum bright duration of the flash light source is 240 ms. Therefore, the features of the short emission time of electric sparks and longer exposure time of mine moving light sources to cameras can be utilized to eliminate the impact of mine moving light sources on camera exposure. The camera shoots at a high frame rate, and the electric spark image has a feature of 1 frame dark -1 frame bright -1 frame dark, that is, a 'dark light dark' frame feature. The 'bright' frames with sparks have a large sum of pixel grayscales in a single frame. The 'dark' frames without sparks have a small sum of pixel grayscales in a single frame. The illumination of a moving light source on the camera is variable, going through a process of no light, light, and no light. In the absence of electric sparks, the camera also shoots at a high frame rate. The images of both moving constant bright light sources and moving flashing light sources do not exhibit the 'dark bright dark' frame feature. Based on the unique 'dark bright dark' frame feature of electric spark images, a mine electric spark recognition and alarm method based on the sum of adjacent frame pixel grayscale is proposed. The method collects monitoring area video images in real time. According to the set frame rate, the method preprocesses the video image into frames and calculates the pixel grayscale of a single frame image separately. If the difference between the current frame image pixel grayscale and the previous frame image pixel grayscale is less than the pre-set threshold, the method continues to collect the video image. Otherwise, the method calculates the difference between the current frame image pixel grayscale and the subsequent frame image pixel grayscale. If the difference is less than the pre-set threshold, the method continues to collect video images. Otherwise, the method issues a mine electric spark alarm signal. After the mine electric spark alarm, if the emergency response is not activated manually, the mine electric spark alarm will continue. Otherwise, the method exits the current alarm state and continues to collect video images. This method can effectively eliminate the interference of moving constant light sources and flashing light sources.

In order to improve the detection accuracy of coal mine gas and coal dust explosion, recognition method of coal mine gas and coal dust explosion based on the sound spectrogram and SVM was proposed: installing mining pickups in key monitoring areas of underground coal mines, monitoring of working sounds of underground coal mine equipment and environmental sound in real-time. The collected sound was extracted from the sound spectrogram composed of MFCC, and the energy, entropy, contrast, and correlation of 0°, 45°, 90°, and 135° were obtained by calculating the gray co-generation matrix of the sound spectrogram. Calculated their mean and standard deviation as the texture features of the sound spectrogram image, which constituted the feature quantity of the sound, which brought to SVM to establish the sound recognition model of coal mine gas and coal dust explosion. For the sound to be tested, the texture features were also extracted and input to the trained recognition model for sound recognition and classification, which has passed the test verification. Firstly, the MFCC feature values of different sounds, such as mining equipment operation, gas and coal dust explosion were extracted, and the distribution of MFCC feature values of different sounds was analyzed; the sound spectrograms of different sounds were extracted, and the mean and standard deviation of the sound spectrograms of different sounds were analyzed by analyzing the feature parameters of energy, entropy, contrast, and correlation, and the grayscale co-generation matrix feature parameters of the sound spectrograms constitute the feature quantities that can effectively characterize the sound signals. Secondly, the sound to be measured was input into the recognition model established to complete the recognition and classification. The results show that the recognition rate of the proposed method reaches 95%, and the overall recognition performance is higher than others models. Finally, the experimental results of Bayesian parameter optimization show that the recall rate and recognition rate of the optimized SVM recognition model increased by 10% and 3% respectively. It was better than the recognition model before optimization, which can meet the needs of coal mine gas and coal dust explosion sensing and alarming.

The burial image features of a large amount of black coal rock thrown out during rock burst and coal and gas outburst are analyzed. The features include changes in the area of the monitoring area's color and corresponding graphics, changes in the number of colors and corresponding graphics, changes in the shape of colors and corresponding graphics, and anomalies in burial positions. When a disaster occurs, it will result in a reduction in the area of non-black areas within the monitoring area, with a significant reduction in speed and acceleration. Based on the above feature, a perception alarm method for rock burst and coal and gas outburst is proposed based on the features of color and its corresponding graphic area changes. When a disaster occurs, it will result in a reduction in the number of non-black areas within the monitoring area, with a significant reduction in speed and acceleration. Based on the above feature, a perception alarm method for rock burst and coal and gas outburst is proposed based on the features of colour and its correspoding graphic number changes. When a disaster occurs, it will cause a decrease in the circularity, rectangularity, and area-to-perimeter ratio of non-black graphics within the monitoring area, with a significant reduction in speed and acceleration. Based on the above feature, a perception alarm method for rock burst and coal and gas outburst is proposed based on the features of color and its corresponding graphic shape changes. When a disaster occurs, it can lead to underground coal mine personnel, hydraulic support at the top and near the top being buried by coal and rock. Based on the above feature, a perception alarm method for rock burst and coal and gas outburst is proposed based on the features of abnormal burial position. The above perception alarm method for rock burst and coal and gas outburst based on burial image features has the advantages of fast response speed, non-contact, wide monitoring range, low cost, and convenient use and maintenance.

In order to solve the problem that most of the current mine external fire monitoring methods do not have fire source positioning function, a binocular vision-based perception and positioning method of mine external fire is proposed. Firstly, the method sets up mine visible binocular cameras or near-infrared binocular cameras at multiple points in roadways, chambers and working faces with cables, tapes and electromechanical equipment to capture images of the monitoring area. Then, the adopted images are preprocessed and binarized images are obtained by threshold segmentation. The roundness, rectangularity and number of sharp corners in the image are calculated, and the images are identified by flames according to the roundness, rectangularity and number of sharp corners. If there is flame in the image detection area, a fire alarm signal will be issued and the temperature, smoke, carbon dioxide, carbon monoxide, oxygen and infrared sensor information are taken into consideration to improve the accuracy of alarms. Finally, the method uses mine visible binocular cameras or near-infrared binocular cameras to measure the distance of the fire source, locates the fire source based on the camera position, and outputs the fire source location information to control the fire extinguishing device near the fire source. Far-infrared binocular cameras can also be used for fire perception and fire source positioning, but the cost is high. The binocular vision-based perception and positioning method of mine external fire can sense and locate the fire source. This method has the advantages of wide monitoring range, low cost, fast response and visualization, and solves the problem of locating the fire source of mine external fire.

In order to solve the problem that visible light images are greatly interfered by mine lights, car lights, roadway lights, dust and red object in mines, the mine external fire sensing method based on near-infrared and visible light images is proposed. The near-infrared images and visible light images are fused to bring into play the advantages of obvious early flame characteristics in near-infrared images and obvious mid-term flame characteristics in visible light images so as to improve the recognition rate of mine external fire. This paper proposes a mine external fire sensing method based on multiple information fusion of near-infrared image, visible light image, temperature sensor, gas sensor and smoke sensor. The method fuses near-infrared image, visible image and sensor information. The sensor does not need no blind area coverage. Only a certain number of gas sensors, temperature sensors and smoke sensors need to be set up, which has the advantages of being timely, reliable and low-cost.The fire alarms are divided into four categories, including the image, temperature, smoke and gas fire alarms. The image fire alarm information includes near-infrared image and visible light image information. When the fire is detected by near-infrared image or visible light image, the image fire alarm signal is issued.When the temperature exceeds the limit, the temperature alarm signal is issued. When the smoke exceeds the limit, the smoke alarm signal is issued. The gas fire alarm information includes CO, CO2, O2, etc. When CO, CO2 or O2 exceeds the limit, the gas alarm signal is issued. The fire alarms are divided into four levels, including blue, yellow, orange and red, with red being the highest level.When one of the four alarm signals of image, temperature, smoke and gas issued, the blue alarm signal is issued. When two of the four alarm signals of image, temperature, smoke and gas issued, the yellow alarm signal is issued. When three of the four alarm signals of image, temperature, smoke and gas issued,the orange alarm signal is issued. When all four alarm signals of image, temperature, smoke and gas issued, the red alarm signal is issued.

The characteristics of mine flood video images were analyzed. The mine flood identification and trend prediction methods based on video images were proposed, including flood video dynamic identification, region segmentation, area estimation and trend prediction. The results were verified by experiments. The main conclusions are as follows: ① Both threshold pixel grayscale statistical method and pixel grayscale statistical method can monitor and identify floods. The threshold pixel grayscale statistical method not only can suppresses noise below the grayscale threshold and improve the accuracy of recognition, but also can reduce the pixel grayscale statistics, enhance contrast of a particular pixel grayscale range. ② Both the threshold segmentation method and the video differential segmentation method can segment the image of the flood area, the former is better overall and the latter is more detailed.③ The area of the water inrush area can be estimated and the trend can be forecast based on the segmented flood area image.

Based on analysis of principle and characteristics of mine flood perception methods such as water quality monitoring method, water inflow monitoring method, water temperature monitoring method, temperature monitoring method, humidity monitoring method, water level monitoring method, resistivity monitoring method, stress monitoring method, microseismic monitoring method and hydrological drilling method, mine flood perception method based on image monitoring and image-based big data mine flood perception and water source determination method were proposed. Conclusions were got as following: ① The water quality monitoring method can not only perceive flood, but also determine water source that causes flood, which has high perception accuracy of mine flood caused by surface water and goaf water. ② Time-based water inflow monitoring method has high accuracy, but complex deployment and poor real-time performance. The water inflow monitoring method based on flow velocity has advantages of simple operation and good real-time performance, but large measurement error. ③ The water temperature monitoring method can not only perceive mine flood, but also determine water source that causes flood, but it is not applicable to the flood perception that difference between mine flood water source temperature and normal mine water inflow temperature is small. ④ The temperature monitoring method has advantages of simplicity and convenience, but it is affected by mine fire such as coal spontaneous combustion, explosion of gas and coal dust, surface air temperature, mine ventilation quantity, underground equipments starting and stopping, and number of underground workers. The temperature monitoring method is not applicable to the flood perception that difference between mine flood water source temperature and normal mine water inflow temperature is small. ⑤ The humidity monitoring method has advantages of simplicity and convenience, but it is affected by humidity and temperature of surface air, mine ventilation quantity, and mine fire such as coal spontaneous combustion. ⑥ Change of surface water and groundwater source can be timely grasped by the water level monitoring method, but location of goaf water needs to be proved. ⑦ The resistivity monitoring method has advantages of fast response and high sensitivity, but accuracy is greatly affected by mining environment, and electrode arrangement is difficult. ⑧ The stress monitoring method and the microseismic monitoring method have advantages of good real-time performance, but due to impact of coal and gas outburst and rock burst pressure, the methods need to be used together with other mine flood perception methods. ⑨ The hydrological drilling method has advantage of large amount of information, but it needs to be used together with other mine flood perception methods. ⑩ The mine flood perception method based on image monitoring has advantages of non-contact, real-time and rapid, wide monitoring range, simple deployment and installation, low cost and convenient use and maintenance. Image-based big data mine flood perception and water source determination method simultaneously monitors mine water, water conducted channel and water source, which can not only perceive mine flood, but also determine water source that causes flood, and has advantage of high reliability.