Coal mine roadway monitoring method and device and coal mine roadway detection system

Abstract

The application provides a coal mine roadway monitoring method and device and a coal mine roadway detection system. The method comprises: acquiring a thermal infrared image of a coal mine roadway; extracting infrared data in the thermal infrared image; acquiring an infrared data threshold of the infrared data; and determining whether the coal mine roadway is safe according to the size relationship between the infrared data and the infrared data threshold. In the scheme, the working face of the coal mine roadway is monitored by an infrared thermal imager. When the pressure of the working face suddenly changes or water inrush occurs, the infrared thermal imager can monitor the change of the infrared data in the thermal infrared image. Then, the infrared data threshold and the infrared data are determined to realize safety monitoring of the coal mine roadway. The automation degree of the scheme is high, manual monitoring is not required, the coal mine roadway can be monitored in real time, the monitoring efficiency is improved, and the coal rock mass stability of the coal mining working face and whether water inrush occurs can be accurately monitored.

Description

Technical Field

[0001] This application relates to the field of coal mine safety monitoring, and more specifically, to a method, apparatus, computer-readable storage medium, and detection system for coal mine roadways. Background Technology

[0002] Because some working faces and roadways in Hongliu Coal Mine have aquifers in their roofs, and roof water seepage can affect the mechanical properties of anchor bolts, anchor cables, and coal and rock mass, it is prone to coal pillar instability and roof water inrush. If there are problems such as inaccurate stability of the coal and rock mass in the mining face and inaccurate water inrush monitoring and early warning, it will cause disasters such as coal mine water inrush, resulting in loss of mine water resources, deterioration of the mining area's ecological environment, and danger to workers in the roadways. Therefore, it is necessary to monitor the stability of the coal and rock mass in the mining face of Hongliu Coal Mine and to monitor whether water inrush occurs.

[0003] Current methods typically employ rock movement monitoring, borehole surveying, analytical analysis, and numerical simulation. However, rock movement monitoring is challenging, borehole surveying is costly and cannot provide real-time tracking, and analytical and numerical simulation methods only simulate actual field conditions, leading to significant monitoring errors. Acoustic emission monitoring technology is commonly used, employing acoustic emission monitoring instruments. However, due to the harsh working environment, the performance of these instruments is unstable, and environmental noise can cause inaccurate and distorted interpretations of acoustic emission signals. Furthermore, current monitoring methods require real-time monitoring by the user. Therefore, the accuracy of current methods in monitoring the stability of coal and rock masses in coal mine working faces and detecting water inrushes is relatively low. Summary of the Invention

[0004] The main objective of this application is to provide a method, device, computer-readable storage medium, and detection system for monitoring coal mine roadways, in order to solve the problem of low accuracy in the prior art for monitoring the stability of coal and rock mass in coal mining faces and for monitoring whether water inrush occurs.

[0005] According to one aspect of the present invention, a method for monitoring coal mine roadways is provided, comprising: acquiring a thermal infrared image of the coal mine roadway, the thermal infrared image being monitored by an infrared thermal imager, the infrared thermal imager being installed on the side of the coal mine roadway away from the working face; extracting infrared data from the thermal infrared image, the infrared data including at least one of the following: the grayscale of the thermal infrared image, the entropy of the thermal infrared image, the entropy referring to the amount of data in a predetermined area of ​​the thermal infrared image; acquiring an infrared data threshold of the infrared data; and determining whether the coal mine roadway is safe based on the relationship between the infrared data and the infrared data threshold.

[0006] Optionally, the thermal infrared image has multiple frames. Extracting infrared data from the thermal infrared image includes: calculating the difference between the Nth frame of the thermal infrared image and the first frame of the thermal infrared image to obtain multiple pixel matrices, where each pixel matrix corresponds to one frame of the thermal infrared image, and the pixel matrix refers to the set of pixels in the thermal infrared image, where N≥2; performing median filtering on the multiple pixel matrices to obtain multiple initial thermal infrared images; performing denoising processing on the multiple initial thermal infrared images using a wavelet function to obtain multiple denoised thermal infrared images; and extracting the infrared data from the denoised thermal infrared images.

[0007] Optionally, extracting the infrared data from the denoised thermal infrared image includes: extracting the average gray value of the denoised thermal infrared image; extracting the maximum and minimum gray values ​​of the denoised thermal infrared image; extracting the gray variance of the denoised thermal infrared image; extracting the difference variance between the average gray value and the difference map, wherein the difference map refers to an image generated by the difference between the current frame of the denoised thermal infrared image and the previous frame of the denoised thermal infrared image; and extracting the one-dimensional entropy and relative entropy of the denoised thermal infrared image, wherein the one-dimensional entropy refers to the clustering feature of the gray distribution of the denoised thermal infrared image, and the relative entropy refers to the difference between the gray probability distribution of the current frame of the denoised thermal infrared image and the gray probability distribution of the first frame of the denoised thermal infrared image.

[0008] Optionally, determining whether the coal mine roadway is safe based on the relationship between the infrared data and the infrared data threshold includes: determining predetermined conditions, the predetermined conditions including at least one of the following: the average gray value is greater than or equal to the average gray value threshold, the maximum gray value is greater than or equal to the maximum gray value threshold, the minimum gray value is less than or equal to the minimum gray value threshold, the gray value variance is greater than or equal to the gray value variance threshold, the difference variance is greater than or equal to the difference variance threshold, the one-dimensional entropy is greater than or equal to the one-dimensional entropy threshold, and the relative entropy is greater than or equal to the relative entropy threshold; and determining whether the coal mine roadway is safe based on the infrared data, the infrared data threshold, and the predetermined conditions.

[0009] Optionally, determining whether the coal mine roadway is safe based on the infrared data, the infrared data threshold, and the predetermined conditions includes: determining that the coal mine roadway is dangerous if the infrared data and the infrared data threshold meet the predetermined conditions; and determining that the coal mine roadway is safe if the infrared data and the infrared data threshold do not meet the predetermined conditions.

[0010] Optionally, the method further includes: acquiring a visible light cloud image of the coal mine roadway, wherein the visible light cloud image is detected by the infrared thermal imager; acquiring a fusion percentage, wherein the fusion percentage refers to the proportion of the visible light cloud image in the image obtained after fusion; fusing the visible light cloud image into the thermal infrared image according to the fusion percentage to obtain a target image, wherein image fusion refers to image data about the same target acquired by multiple channels being processed to extract image data from each channel and then synthesized to generate another image; and displaying the target image on a display device.

[0011] Optionally, the method further includes: acquiring the pressure distribution of the support pressure at the working face of the coal mine roadway; acquiring the monitoring distance of the infrared thermal imager; and determining the installation position of the infrared thermal imager based on the pressure distribution of the support pressure at the working face of the coal mine roadway and the monitoring distance of the infrared thermal imager.

[0012] According to another aspect of the present invention, a monitoring device for a coal mine roadway is also provided, comprising: a first acquisition unit for acquiring a thermal infrared image of the coal mine roadway, the thermal infrared image being detected by an infrared thermal imager, the infrared thermal imager being installed on the side of the coal mine roadway away from the working face; an extraction unit for extracting infrared data from the thermal infrared image, the infrared data including at least one of the following: the grayscale of the thermal infrared image, the entropy of the thermal infrared image, the entropy referring to the amount of data in a predetermined area of ​​the thermal infrared image; a second acquisition unit for acquiring an infrared data threshold of the infrared data; and a first determination unit for determining whether the coal mine roadway is safe based on the relationship between the infrared data and the infrared data threshold.

[0013] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored program, wherein the program executes any one of the methods described.

[0014] According to another aspect of the present invention, a monitoring system for coal mine roadways is also provided, comprising: an infrared thermal imager, a monitoring device for coal mine roadways, and a display device, wherein the monitoring device for coal mine roadways communicates with the infrared thermal imager and the display device respectively, and the monitoring device for coal mine roadways is used to execute any one of the methods described.

[0015] In this embodiment of the invention, a thermal infrared image of the coal mine roadway is first acquired. Then, infrared data is extracted from the thermal infrared image. Next, an infrared data threshold is obtained. Finally, the safety of the coal mine roadway is determined based on the relationship between the infrared data and the infrared data threshold. In this scheme, an infrared thermal imager monitors the working face of the coal mine roadway. When there is a sudden change in pressure at the working face or a water inrush, the infrared thermal imager detects changes in the infrared data in the thermal infrared image. The infrared data threshold and the infrared data are then compared to determine the safety of the coal mine roadway. This scheme has a high degree of automation, requires no manual monitoring, and can monitor the coal mine roadway in real time, improving monitoring efficiency. It can accurately monitor the stability of the coal and rock mass at the coal mining face and detect whether a water inrush has occurred. Attached Figure Description

[0016] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0017] Figure 1 A flowchart illustrating a method for monitoring coal mine roadways according to an embodiment of this application is shown.

[0018] Figure 2 A schematic diagram of the infrared thermal imager setup is shown.

[0019] Figure 3 A schematic diagram of the structure of a monitoring device for a coal mine roadway according to an embodiment of this application is shown;

[0020] Figure 4 A schematic diagram of the structure of a monitoring system for a coal mine roadway according to an embodiment of this application is shown.

[0021] The above figures include the following reference numerals:

[0022] 11. Infrared thermal imager; 12. Monitoring device for coal mine roadways; 13. Display equipment. Detailed Implementation

[0023] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0024] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] It should be understood that when an element (such as a layer, film, region, or substrate) is described as being "on" another element, the element may be directly on the other element, or there may be an intermediate element present. Furthermore, in the specification and claims, when an element is described as being "connected" to another element, the element may be "directly connected" to the other element, or "connected" to the other element via a third element.

[0027] As mentioned in the background section, the accuracy of existing technologies for monitoring the stability of coal and rock mass in coal mining faces and for detecting water inrush is low. To address these issues, this application provides a typical embodiment of a method, apparatus, computer-readable storage medium, and detection system for monitoring coal mine roadways.

[0028] According to an embodiment of this application, a method for monitoring coal mine roadways is provided.

[0029] Figure 1 This is a flowchart of a coal mine roadway monitoring method according to an embodiment of this application. For example... Figure 1 As shown, the method includes the following steps:

[0030] Step S101: Acquire a thermal infrared image of the coal mine roadway. The thermal infrared image is detected by an infrared thermal imager, which is installed on the side of the coal mine roadway away from the working face.

[0031] Specifically, an infrared camera is installed on an infrared thermal imager, and the thermal infrared image can be detected by the infrared camera on the infrared thermal imager.

[0032] Step S102: Extract infrared data from the thermal infrared image. The infrared data includes at least one of the following: the grayscale of the thermal infrared image and the entropy of the thermal infrared image. The entropy refers to the amount of data in a predetermined area of ​​the thermal infrared image.

[0033] Step S103: Obtain the infrared data threshold of the above infrared data;

[0034] Step S104: Determine whether the coal mine roadway is safe based on the relationship between the infrared data and the infrared data threshold.

[0035] The above method first acquires thermal infrared images of the coal mine roadway, then extracts infrared data from the thermal infrared images, obtains the infrared data threshold, and finally determines the safety of the coal mine roadway based on the relationship between the infrared data and the infrared data threshold. In this scheme, an infrared thermal imager monitors the working face of the coal mine roadway. When there is a sudden change in pressure or water inrush at the working face, the infrared thermal imager detects changes in the infrared data in the thermal infrared image. The infrared data threshold and the infrared data are then compared to determine the safety of the coal mine roadway. This scheme has a high degree of automation, requires no manual monitoring, and can monitor the coal mine roadway in real time, improving monitoring efficiency and accurately monitoring the stability of the coal and rock mass at the coal mining face and detecting any water inrush.

[0036] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0037] Specifically, the collected infrared data can be processed, denoised, and feature extracted. Since there are multiple infrared thermal imagers, processing the infrared data refers to classifying the infrared images monitored by different infrared thermal imagers. The infrared images monitored by one infrared thermal imager constitute one category, and the number of categories is consistent with the number of infrared thermal imagers. Denoising the infrared data can ensure that the accuracy of the denoised infrared data is high, and thus the extracted features (such as grayscale and entropy of the thermal infrared image) are more accurate. Among them, the entropy of the thermal infrared image refers to the average number of bits of the set of grayscale levels of the thermal infrared image, with the unit being bits / pixel. It can be used to describe the average amount of information in the thermal infrared image, such as the number of pixels in a certain predetermined area. The higher the entropy, the clearer the thermal infrared image.

[0038] In one embodiment of this application, the aforementioned thermal infrared image comprises multiple frames. Extracting infrared data from the aforementioned thermal infrared image includes: calculating the difference between the Nth frame of the aforementioned thermal infrared image and the first frame of the aforementioned thermal infrared image to obtain multiple pixel matrices, each pixel matrix corresponding to one frame of the aforementioned thermal infrared image, wherein the pixel matrix refers to the set of pixels of the aforementioned thermal infrared image, and N≥2; performing median filtering on the multiple pixel matrices to obtain multiple initial thermal infrared images; performing denoising processing on the multiple initial thermal infrared images using a wavelet function to obtain multiple denoised thermal infrared images; and extracting the aforementioned infrared data from the denoised thermal infrared images. In this embodiment, since the first frame of thermal infrared image is the thermal infrared image acquired at the beginning of the acquisition, and the temperature change will not be seen until the Nth frame of thermal infrared image, the first frame of thermal infrared image is actually used to determine the temperature change. The difference between the Nth frame of thermal infrared image and the first frame of thermal infrared image can be directly used as the temperature change. Then, median filtering and wavelet denoising processing can be performed to ensure that the extracted infrared data is more accurate. This can further improve the accuracy of monitoring the stability of coal and rock mass in coal mining face and the monitoring of whether water inrush occurs.

[0039] Specifically, the formula for calculating the difference between the Nth frame thermal infrared image and the first frame thermal infrared image to obtain multiple pixel matrices is as follows: in L represents the pixel matrix after subtracting the first frame of the thermal infrared image. p (x,y) R×C L represents the pixel matrix of the Nth frame of the thermal infrared image. 1 (x,y) R×C This represents the pixel matrix of the first frame of the thermal infrared image, where R represents the length of the thermal infrared image, C represents the width of the thermal infrared image, x represents the row number of the pixel, y represents the column number of the pixel, and p represents the frame number of the thermal infrared image.

[0040] Again Adaptive median filtering is performed within a 3x3 pixel area, followed by a single-layer wavelet decomposition using coif4 as the wavelet function, and then wavelet soft thresholding for noise reduction. The threshold calculation formula is as follows: Where, γ j Denotes the threshold at the j-th scale, j=1, M j σ represents the number of pixels in different scale decomposition layers of a thermal infrared image. j The standard deviation of the wavelet decomposition noise at each level is represented by the following: [Image of the denoised thermal infrared image].

[0041] In another embodiment of this application, extracting the infrared data from the denoised thermal infrared image includes: extracting the average gray value of the denoised thermal infrared image; extracting the maximum and minimum gray values ​​of the denoised thermal infrared image; extracting the gray variance of the denoised thermal infrared image; extracting the difference variance between the average gray value and the difference map, wherein the difference map refers to an image generated by the difference between the current frame denoised thermal infrared image and the previous frame denoised thermal infrared image; and extracting the one-dimensional entropy and relative entropy of the denoised thermal infrared image, wherein the one-dimensional entropy refers to the clustering feature of the gray distribution of the denoised thermal infrared image, and the relative entropy refers to the difference between the gray probability distribution of the current frame denoised thermal infrared image and the gray probability distribution of the first frame denoised thermal infrared image. In this embodiment, multiple infrared data are extracted from the denoised thermal infrared image, including average gray value, maximum gray value, minimum gray value, gray value variance, difference variance, one-dimensional entropy and relative entropy. Of course, it is not limited to the above types of data, but can also be other infrared data. In this way, the safety status of coal mine roadways can be determined more accurately through multiple infrared data.

[0042] Specifically, the formula for calculating the average grayscale value is: AGV(p) represents the average grayscale value, and the formula for calculating the maximum grayscale value is: The formulas for calculating the maximum and minimum grayscale values ​​are: The minimum grayscale value is represented by the formula for calculating the grayscale variance: GVOIT(p) represents the gray-level variance, and the formula for calculating the difference variance is: DVOIT(p) represents the difference variance. The pixel matrix representing the difference map, AGV Diff (p) represents the average gray value. The formula for calculating the pixel matrix of the difference image is: The formula for calculating the average gray value is: The grayscale value of pixel (x,y) in the denoised thermal infrared image of frame p is defined as g(x,y), and the proportion of pixels with this grayscale value in the denoised thermal infrared image of frame p is defined as P. g(x,y) Then, the formula for calculating the one-dimensional entropy of the denoised thermal infrared image of the p-th frame is: IIOIT p Let C represent the one-dimensional entropy of the denoised thermal infrared image of frame p. The denoised thermal infrared image of the first frame is taken as the reference image, denoted as IRI. The probability distribution of pixel gray level i in IRI is C = {C0, C1, ..., C...}. n The probability distribution of pixel grayscale values ​​in the denoised thermal infrared image of frame p during monitoring is as follows: The formula for calculating relative entropy is: Among them, ITRE p c represents relative entropy. i This represents the proportion of pixels with grayscale value i to the left and right pixels in the IRI. Let i represent the proportion of pixels with grayscale value i in the denoised thermal infrared image of frame p, where 0 ≤ i ≤ 255.

[0043] In another embodiment of this application, determining whether a coal mine roadway is safe based on the relationship between the infrared data and the infrared data thresholds includes: determining predetermined conditions, which include at least one of the following: the average gray value is greater than or equal to an average gray value threshold, the maximum gray value is greater than or equal to a maximum gray value threshold, the minimum gray value is less than or equal to a minimum gray value threshold, the gray value variance is greater than or equal to a gray value variance threshold, the difference variance is greater than or equal to a difference variance threshold, the one-dimensional entropy is greater than or equal to a one-dimensional entropy threshold, and the relative entropy is greater than or equal to a relative entropy threshold; and determining whether the coal mine roadway is safe based on the infrared data, the infrared data thresholds, and the predetermined conditions. In this embodiment, the predetermined conditions include the relationship between the magnitudes of infrared data thresholds in multiple infrared data domains, so that the safety of the coal mine roadway can be further accurately determined based on the combination of different combinations of the magnitudes of infrared data thresholds in different infrared data domains.

[0044] For example, the predetermined conditions can be conditions where the average gray value is greater than or equal to the average gray value threshold, the maximum gray value is greater than or equal to the maximum gray value threshold, or the average gray value is greater than or equal to the average gray value threshold, the maximum gray value is greater than or equal to the maximum gray value threshold, and the minimum gray value is less than or equal to the minimum gray value threshold. They can also be conditions where the one-dimensional entropy is greater than or equal to the one-dimensional entropy threshold, or the relative entropy is greater than or equal to the relative entropy threshold. They are not limited to the above situations and can be any other feasible combination.

[0045] In another embodiment of this application, determining whether a coal mine roadway is safe based on the aforementioned infrared data, the aforementioned infrared data threshold, and the aforementioned predetermined conditions includes: determining that the coal mine roadway is dangerous if the aforementioned infrared data and the aforementioned infrared data threshold meet the aforementioned predetermined conditions; and determining that the coal mine roadway is safe if the aforementioned infrared data and the aforementioned infrared data threshold do not meet the aforementioned predetermined conditions. In this embodiment, the safety of a coal mine roadway can be further accurately determined based on whether the infrared data and the infrared data threshold meet the predetermined conditions.

[0046] In one specific embodiment of this application, the method further includes: acquiring a visible light cloud image of the coal mine roadway, wherein the visible light cloud image is detected by the infrared thermal imager; acquiring a fusion percentage, wherein the fusion percentage refers to the proportion of the visible light cloud image in the image obtained after fusion; fusing the visible light cloud image into the thermal infrared image according to the fusion percentage to obtain a target image, wherein image fusion refers to image data about the same target collected by multiple channels being processed to extract image data from each channel and then synthesized to generate another image; and displaying the target image on a display device. In this embodiment, the target image can be displayed on a display device, which makes it convenient for staff to view the target image and understand the situation of the coal mine roadway.

[0047] Specifically, an infrared thermal imager is equipped with a regular camera, and the thermal infrared image can be detected by the regular camera on the infrared thermal imager.

[0048] The thermal infrared images and visible light cloud images detected by the infrared thermal imager can be transmitted via fiber optic cable. The transmitted data may also include the infrared thermal imager's serial number and the location of the monitored area. The infrared thermal imager's serial number is paired with the infrared thermal imager. The location of the monitored area includes the name of the coal mine roadway and the distance between the infrared thermal imager and the working face. When the fusion percentage is 0%, the target image is a thermal infrared image. When the fusion percentage is 100%, the target image is a visible light cloud image.

[0049] In practical applications, time-series curves of infrared data can also be generated and displayed on display devices. The displayed curves can be determined manually. When there are abnormal changes in the monitored area, the display device can display abnormal information, as well as information from the thermal imager. The location and time of the abnormality can be displayed in the time-series curves and target images.

[0050] To promptly alert staff to unstable coal and rock masses and / or water inrushes in coal mine roadways, an alarm signal can be generated when a hazard is detected in the roadway. This alarm signal is then sent to an infrared thermal imager, which controls the flashing of indicator lights based on the alarm signal.

[0051] In order to accurately determine the installation location of the infrared thermal imager and further accurately monitor the infrared data of the coal mine roadway, in another specific embodiment of this application, the above method further includes: obtaining the pressure distribution of the support pressure of the working face of the coal mine roadway; obtaining the monitoring distance of the infrared thermal imager; and determining the installation location of the infrared thermal imager based on the pressure distribution of the support pressure of the working face of the coal mine roadway and the monitoring distance of the infrared thermal imager.

[0052] Specifically, there are supports in front of the working face in coal mine roadways, and these supports exert pressure of varying intensity. The infrared thermal imager needs to be positioned within a reasonable pressure range to allow for a longer period of operation before relocation. If positioned outside this range, the imager will need to be moved after a period of mining. Generally, it can be positioned within twice the stress range of the support pressure, approximately 30-70 meters, to determine the monitoring distance of the infrared thermal imager. Figure 2 As shown, the installation interval and the number of infrared thermal imagers can also be determined by the focal length of the infrared thermal imagers. Infrared thermal imagers are generally installed on the waistline position on the side furthest from the work surface. The formula for calculating the installation position of the infrared thermal imager is: S n Let L represent the installation position of the nth infrared camera on the rock face of the coal mine roadway, and L represent the monitoring distance. The installation position of the first infrared thermal imager can be... The installation location of the second infrared thermal imager can be

[0053] As the working face advances, the location and monitored area of ​​the infrared thermal imager are updated. After moving the imager, the above steps can be repeated until mining is completed. The relocation is done manually, and the relocation time is determined by the progress of the working face. If the working face advances at a rate of k / day, and the length of the advanced working face reaches the length K of the reserved mining area, then relocation is carried out. The length of the reserved mining area is the advance length of 7-14 days, i.e., 7k ≤ K ≤ 14k. With each mining operation, all equipment can be reused and relocated.

[0054] This application also provides a monitoring device for coal mine roadways. It should be noted that the monitoring device for coal mine roadways in this application can be used to execute the monitoring method for coal mine roadways provided in this application. The monitoring device for coal mine roadways provided in this application will be described below.

[0055] Figure 3 This is a schematic diagram of a monitoring device for coal mine roadways according to an embodiment of this application. Figure 3 As shown, the device includes:

[0056] The first acquisition unit 100 is used to acquire thermal infrared images of coal mine roadways. The thermal infrared images are detected by an infrared thermal imager, which is installed on the side of the coal mine roadway away from the working face.

[0057] Specifically, an infrared camera is installed on an infrared thermal imager, and the thermal infrared image can be detected by the infrared camera on the infrared thermal imager.

[0058] Extraction unit 200 is used to extract infrared data from the thermal infrared image, wherein the infrared data includes at least one of the following: grayscale of the thermal infrared image, entropy of the thermal infrared image, wherein the entropy refers to the amount of data in a predetermined area of ​​the thermal infrared image;

[0059] The second acquisition unit 300 is used to acquire the infrared data threshold of the above infrared data;

[0060] The first determining unit 400 is used to determine whether the coal mine roadway is safe based on the relationship between the infrared data and the infrared data threshold.

[0061] In the aforementioned device, the first acquisition unit acquires thermal infrared images of the coal mine roadway, the extraction unit extracts infrared data from the thermal infrared images, the second acquisition unit acquires the infrared data threshold, and the first determination unit determines whether the coal mine roadway is safe based on the relationship between the infrared data and the infrared data threshold. In this scheme, an infrared thermal imager monitors the working face of the coal mine roadway. In the event of a sudden pressure change or water inrush at the working face, the infrared thermal imager detects changes in the infrared data in the thermal infrared image. A judgment is then made based on the infrared data threshold and the infrared data to achieve safety monitoring of the coal mine roadway. This scheme has a high degree of automation, requires no manual monitoring, and can monitor the coal mine roadway in real time, improving monitoring efficiency. It can accurately monitor the stability of the coal and rock mass at the coal mining face and detect whether water inrush has occurred.

[0062] In one embodiment of this application, the aforementioned thermal infrared image comprises multiple frames. The extraction unit includes a first processing module, a second processing module, a third processing module, and an extraction module. The first processing module is used to calculate the difference between the Nth frame of the aforementioned thermal infrared image and the first frame of the aforementioned thermal infrared image to obtain multiple pixel matrices. Each pixel matrix corresponds to one frame of the aforementioned thermal infrared image. The pixel matrix refers to the set of pixels of the aforementioned thermal infrared image, where N≥2. The second processing module is used to perform median filtering on the multiple pixel matrices to obtain multiple initial thermal infrared images. The third processing module is used to perform denoising processing on the multiple initial thermal infrared images using a wavelet function to obtain multiple denoised thermal infrared images. The extraction module is used to extract the aforementioned infrared data from the denoised thermal infrared images. In this embodiment, since the first frame of thermal infrared image is the thermal infrared image acquired at the beginning of the acquisition, and the temperature change will not be seen until the Nth frame of thermal infrared image, the first frame of thermal infrared image is actually used to determine the temperature change. The difference between the Nth frame of thermal infrared image and the first frame of thermal infrared image can be directly used as the temperature change. Then, median filtering and wavelet denoising processing can be performed to ensure that the extracted infrared data is more accurate. This can further improve the accuracy of monitoring the stability of coal and rock mass in coal mining face and the monitoring of whether water inrush occurs.

[0063] In another embodiment of this application, the extraction module includes a first extraction submodule, a second extraction submodule, a third extraction submodule, a fourth extraction submodule, and a fifth extraction submodule. The first extraction submodule is used to extract the average gray value of the denoised thermal infrared image; the second extraction submodule is used to extract the maximum and minimum gray values ​​of the denoised thermal infrared image; the third extraction submodule is used to extract the gray value variance of the denoised thermal infrared image; the fourth extraction submodule is used to extract the difference variance between the average gray value and the difference map, where the difference map refers to the image generated by the difference between the current frame denoised thermal infrared image and the previous frame denoised thermal infrared image; the fifth extraction submodule is used to extract the one-dimensional entropy and relative entropy of the denoised thermal infrared image, where the one-dimensional entropy refers to the clustering characteristics of the gray value distribution of the denoised thermal infrared image, and the relative entropy refers to the difference between the gray value probability distribution of the current frame denoised thermal infrared image and the gray value probability distribution of the first frame denoised thermal infrared image. In this embodiment, multiple infrared data are extracted from the denoised thermal infrared image, including average gray value, maximum gray value, minimum gray value, gray value variance, difference variance, one-dimensional entropy and relative entropy. Of course, it is not limited to the above types of data, but can also be other infrared data. In this way, the safety status of coal mine roadways can be determined more accurately through multiple infrared data.

[0064] In another embodiment of this application, the first determining unit includes a first determining module and a second determining module. The first determining module is used to determine predetermined conditions, which include at least one of the following: the average gray value is greater than or equal to an average gray value threshold, the maximum gray value is greater than or equal to a maximum gray value threshold, the minimum gray value is less than or equal to a minimum gray value threshold, the gray value variance is greater than or equal to a gray value variance threshold, the difference variance is greater than or equal to a difference variance threshold, the one-dimensional entropy is greater than or equal to a one-dimensional entropy threshold, and the relative entropy is greater than or equal to a relative entropy threshold. The second determining module is used to determine whether the coal mine roadway is safe based on the infrared data, the infrared data thresholds, and the predetermined conditions. In this embodiment, the predetermined conditions include the magnitude relationships of multiple infrared data domain infrared data thresholds, so that the safety of the coal mine roadway can be further accurately determined based on the combination of different infrared data domain infrared data threshold magnitude relationships.

[0065] In another embodiment of this application, the second determining module includes a first determining submodule and a second determining submodule. The first determining submodule is used to determine that the coal mine roadway is dangerous if the infrared data and the infrared data threshold meet the predetermined conditions. The second determining submodule is used to determine that the coal mine roadway is safe if the infrared data and the infrared data threshold do not meet the predetermined conditions. In this embodiment, the safety of the coal mine roadway can be further accurately determined based on whether the infrared data and the infrared data threshold meet the predetermined conditions.

[0066] In one specific embodiment of this application, the above-mentioned device further includes a third acquisition unit, a fourth acquisition unit, a fusion unit, and a display unit. The third acquisition unit is used to acquire a visible light cloud image of the coal mine roadway, which is detected by the infrared thermal imager. The fourth acquisition unit is used to acquire a fusion percentage, which refers to the proportion of the visible light cloud image in the image obtained after fusion. The fusion unit is used to fuse the visible light cloud image into the thermal infrared image according to the fusion percentage to obtain a target image. Image fusion refers to the process of image data about the same target collected by multiple channels, extracting the image data from each channel, and then combining them to generate another image. The display unit is used to display the target image on a display device. In this embodiment, the target image can be displayed on the display device, which makes it convenient for staff to view the target image and understand the situation of the coal mine roadway.

[0067] Specifically, an infrared thermal imager is equipped with a regular camera, and the thermal infrared image can be detected by the regular camera on the infrared thermal imager.

[0068] In order to accurately determine the installation location of the infrared thermal imager and further accurately monitor the infrared data of the coal mine roadway, in another specific embodiment of this application, the above-mentioned device further includes a fifth acquisition unit, a sixth acquisition unit, and a second determination unit. The fifth acquisition unit is used to acquire the pressure distribution of the support pressure of the working face of the coal mine roadway; the sixth acquisition unit is used to acquire the monitoring distance of the infrared thermal imager; and the second determination unit is used to determine the installation location of the infrared thermal imager based on the pressure distribution of the support pressure of the working face of the coal mine roadway and the monitoring distance of the infrared thermal imager.

[0069] The aforementioned coal mine roadway monitoring device includes a processor and a memory. The first acquisition unit, extraction unit, second acquisition unit, and first determination unit are all stored in the memory as program units, and the processor executes the aforementioned program units stored in the memory to achieve the corresponding functions.

[0070] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and by adjusting kernel parameters, the stability of the coal and rock mass at the coal mine working face can be accurately monitored, as well as the occurrence of water inrush.

[0071] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0072] This invention provides a computer-readable storage medium storing a program that, when executed by a processor, implements the aforementioned coal mine roadway monitoring method.

[0073] This invention provides a processor for running a program, wherein the program executes the coal mine roadway monitoring method.

[0074] This application also provides a monitoring system for coal mine roadways, such as... Figure 4 As shown, the device includes an infrared thermal imager 11, a coal mine roadway monitoring device 12, and a display device 13. The coal mine roadway monitoring device 12 communicates with the infrared thermal imager 11 and the display device 13, respectively. The coal mine roadway monitoring device 12 is used to perform any of the methods described above.

[0075] The aforementioned system, incorporating any of the methods described above, first acquires a thermal infrared image of the coal mine roadway, then extracts infrared data from the thermal infrared image, subsequently obtains an infrared data threshold, and finally determines the safety of the coal mine roadway based on the relationship between the infrared data and the infrared data threshold. In this scheme, an infrared thermal imager monitors the working face of the coal mine roadway. In the event of a sudden pressure change or water inrush at the working face, the infrared thermal imager detects changes in the infrared data in the thermal infrared image. A judgment is then made based on the infrared data threshold and the infrared data to achieve safety monitoring of the coal mine roadway. This scheme has a high degree of automation, requires no manual monitoring, can monitor the coal mine roadway in real time, improves monitoring efficiency, and can accurately monitor the stability of the coal and rock mass at the coal mining face and detect whether water inrush has occurred.

[0076] This invention provides a device including a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs at least the following steps:

[0077] Step S101: Acquire a thermal infrared image of the coal mine roadway. The thermal infrared image is detected by an infrared thermal imager, which is installed on the side of the coal mine roadway away from the working face.

[0078] Step S102: Extract infrared data from the thermal infrared image. The infrared data includes at least one of the following: the grayscale of the thermal infrared image and the entropy of the thermal infrared image. The entropy refers to the amount of data in a predetermined area of ​​the thermal infrared image.

[0079] Step S103: Obtain the infrared data threshold of the above infrared data;

[0080] Step S104: Determine whether the coal mine roadway is safe based on the relationship between the infrared data and the infrared data threshold.

[0081] The devices mentioned in this article can be servers, PCs, tablets, mobile phones, etc.

[0082] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program having at least the following method steps:

[0083] Step S101: Acquire a thermal infrared image of the coal mine roadway. The thermal infrared image is detected by an infrared thermal imager, which is installed on the side of the coal mine roadway away from the working face.

[0084] Step S102: Extract infrared data from the thermal infrared image. The infrared data includes at least one of the following: the grayscale of the thermal infrared image and the entropy of the thermal infrared image. The entropy refers to the amount of data in a predetermined area of ​​the thermal infrared image.

[0085] Step S103: Obtain the infrared data threshold of the above infrared data;

[0086] Step S104: Determine whether the coal mine roadway is safe based on the relationship between the infrared data and the infrared data threshold.

[0087] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0088] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units described above can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0089] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0090] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0091] If the aforementioned integrated units are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0092] As can be seen from the above description, the embodiments of this application achieve the following technical effects:

[0093] 1) The coal mine roadway monitoring method of this application first acquires a thermal infrared image of the coal mine roadway, then extracts infrared data from the thermal infrared image, then obtains the infrared data threshold, and finally determines whether the coal mine roadway is safe based on the relationship between the infrared data and the infrared data threshold. In this scheme, the working face of the coal mine roadway is monitored by an infrared thermal imager. In the event of a sudden change in pressure or water inrush at the working face, the infrared thermal imager will detect the change in infrared data in the thermal infrared image. The judgment is then made based on the infrared data threshold and the infrared data to achieve safety monitoring of the coal mine roadway. This scheme has a high degree of automation, requires no manual monitoring, can monitor the coal mine roadway in real time, improves monitoring efficiency, and can accurately monitor the stability of the coal and rock mass at the coal mining face and detect whether water inrush has occurred.

[0094] 2) The coal mine roadway monitoring device of this application comprises a first acquisition unit acquiring a thermal infrared image of the coal mine roadway, an extraction unit extracting infrared data from the thermal infrared image, a second acquisition unit acquiring an infrared data threshold, and a first determination unit determining whether the coal mine roadway is safe based on the relationship between the infrared data and the infrared data threshold. In this scheme, an infrared thermal imager monitors the working face of the coal mine roadway. In the event of a sudden pressure change or water inrush at the working face, the infrared thermal imager detects changes in the infrared data in the thermal infrared image. The determination is then made based on the infrared data threshold and the infrared data to achieve safety monitoring of the coal mine roadway. This scheme has a high degree of automation, requires no manual monitoring, and can monitor the coal mine roadway in real time, improving monitoring efficiency and accurately monitoring the stability of the coal and rock mass at the coal mining face and detecting any water inrush.

[0095] 3) The coal mine roadway monitoring system of this application includes any of the above-mentioned methods. This method first acquires a thermal infrared image of the coal mine roadway, then extracts infrared data from the thermal infrared image, then obtains the infrared data threshold, and finally determines whether the coal mine roadway is safe based on the relationship between the infrared data and the infrared data threshold. In this scheme, the working face of the coal mine roadway is monitored by an infrared thermal imager. In the event of a sudden change in pressure or water inrush at the working face, the infrared thermal imager will detect changes in the infrared data in the thermal infrared image. The system then makes a judgment based on the infrared data threshold and the infrared data to achieve safety monitoring of the coal mine roadway. This scheme has a high degree of automation, requires no manual monitoring, can monitor the coal mine roadway in real time, improves monitoring efficiency, and can accurately monitor the stability of the coal and rock mass at the coal mining face and detect whether water inrush has occurred.

[0096] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for monitoring coal mine roadways, characterized in that, include: Acquire thermal infrared images of coal mine roadways, the thermal infrared images being monitored by an infrared thermal imager installed on the side of the coal mine roadway away from the working face; Infrared data is extracted from the thermal infrared image, and the infrared data includes: the grayscale of the thermal infrared image and the entropy of the thermal infrared image, wherein the entropy refers to the amount of data in a predetermined area of ​​the thermal infrared image; Obtain the infrared data threshold of the infrared data; Based on the relationship between the infrared data and the infrared data threshold, determine whether the coal mine roadway is safe; The thermal infrared image consists of multiple frames. Extracting infrared data from the thermal infrared image includes: calculating the difference between the Nth frame of the thermal infrared image and the first frame of the thermal infrared image to obtain multiple pixel matrices. Each pixel matrix corresponds to one frame of the thermal infrared image. The pixel matrix refers to the set of pixels in the thermal infrared image, where N≥2. The formula for the multiple pixel matrices is: ,in This represents the pixel matrix after subtracting the first frame of the thermal infrared image. This represents the pixel matrix of the Nth frame of the thermal infrared image. This represents the pixel matrix of the first frame of the thermal infrared image. Indicates the length of the thermal infrared image. Indicates the width of the thermal infrared image. The row number represents the pixel. Indicates the column number of the pixel. The number of frames in the thermal infrared image is indicated; median filtering is performed on multiple pixel matrices to obtain multiple initial thermal infrared images; wavelet functions are used to denoise the multiple initial thermal infrared images to obtain multiple denoised thermal infrared images; the infrared data is extracted from the denoised thermal infrared images. Extracting the infrared data from the denoised thermal infrared image includes: extracting the average gray value of the denoised thermal infrared image; extracting the maximum and minimum gray values ​​of the denoised thermal infrared image; extracting the gray value variance of the denoised thermal infrared image; extracting the difference variance between the average gray value and the difference map, where the difference map is an image generated by the difference between the current frame of the denoised thermal infrared image and the previous frame of the denoised thermal infrared image; and extracting the one-dimensional entropy and relative entropy of the denoised thermal infrared image, where the one-dimensional entropy refers to the clustering characteristics of the gray value distribution of the denoised thermal infrared image, and the relative entropy refers to the difference between the gray value probability distribution of the current frame of the denoised thermal infrared image and the gray value probability distribution of the first frame of the denoised thermal infrared image.

2. The method according to claim 1, characterized in that, Determining the safety of a coal mine roadway based on the relationship between the infrared data and the infrared data threshold includes: Determine predetermined conditions, which include at least one of the following: the average gray value is greater than or equal to an average gray value threshold, the maximum gray value is greater than or equal to a maximum gray value threshold, the minimum gray value is less than or equal to a minimum gray value threshold, the gray value variance is greater than or equal to a gray value variance threshold, the difference variance is greater than or equal to a difference variance threshold, the one-dimensional entropy is greater than or equal to a one-dimensional entropy threshold, and the relative entropy is greater than or equal to a relative entropy threshold. The safety of the coal mine roadway is determined based on the infrared data, the infrared data threshold, and the predetermined conditions.

3. The method according to claim 2, characterized in that, Determining whether the coal mine roadway is safe based on the infrared data, the infrared data threshold, and the predetermined conditions includes: If the infrared data and the infrared data threshold meet the predetermined conditions, it is determined that there is a danger in the coal mine roadway; If the infrared data and the infrared data threshold do not meet the predetermined conditions, the safety of the coal mine roadway is determined.

4. The method according to claim 1, characterized in that, The method further includes: Obtain a visible light cloud image of the coal mine roadway, wherein the visible light cloud image is detected by the infrared thermal imager; Obtain the fusion percentage, which refers to the proportion of the visible light cloud image in the image obtained after fusion; According to the fusion percentage, the visible light cloud image is fused into the thermal infrared image to obtain the target image. Image fusion refers to the process of image data about the same target collected by multiple channels, extracting the image data from each channel, and then combining them to generate another image. The target image is displayed on a display device.

5. The method according to claim 1, characterized in that, The method further includes: Obtain the pressure distribution of the supporting pressure at the working face of the coal mine roadway; Obtain the monitoring distance of the infrared thermal imager; The installation location of the infrared thermal imager is determined based on the pressure distribution of the support pressure at the working face of the coal mine roadway and the monitoring distance of the infrared thermal imager.

6. A monitoring device for coal mine roadways, characterized in that, include: The first acquisition unit is used to acquire thermal infrared images of coal mine roadways. The thermal infrared images are detected by an infrared thermal imager, which is installed on the side of the coal mine roadway away from the working face. An extraction unit is used to extract infrared data from the thermal infrared image. The infrared data includes: the grayscale of the thermal infrared image and the entropy of the thermal infrared image. The entropy refers to the amount of data in a predetermined area of ​​the thermal infrared image. The second acquisition unit is used to acquire the infrared data threshold of the infrared data; The first determining unit is used to determine whether the coal mine roadway is safe based on the relationship between the infrared data and the infrared data threshold. The thermal infrared image consists of multiple frames. The extraction unit includes a first processing module, a second processing module, a third processing module, and an extraction module. The first processing module calculates the difference between the Nth frame of the thermal infrared image and the first frame of the thermal infrared image to obtain multiple pixel matrices. Each pixel matrix corresponds to one frame of the thermal infrared image. The pixel matrix refers to the set of pixels in the thermal infrared image, where N≥2. The formula for the multiple pixel matrices is: ,in This represents the pixel matrix after subtracting the first frame of the thermal infrared image. This represents the pixel matrix of the Nth frame of the thermal infrared image. This represents the pixel matrix of the first frame of the thermal infrared image. Indicates the length of the thermal infrared image. Indicates the width of the thermal infrared image. The row number represents the pixel. Indicates the column number of the pixel. The first module represents the frame number of the thermal infrared image; the second module performs median filtering on the multiple pixel matrices to obtain multiple initial thermal infrared images; the third module uses a wavelet function to denoise the multiple initial thermal infrared images to obtain multiple denoised thermal infrared images; and the extraction module extracts the infrared data from the denoised thermal infrared images. The extraction module includes a first extraction submodule, a second extraction submodule, a third extraction submodule, a fourth extraction submodule, and a fifth extraction submodule. The first extraction submodule is used to extract the average gray value of the denoised thermal infrared image; the second extraction submodule is used to extract the maximum and minimum gray values ​​of the denoised thermal infrared image; the third extraction submodule is used to extract the gray value variance of the denoised thermal infrared image; the fourth extraction submodule is used to extract the difference variance between the average gray value and the difference map, where the difference map refers to the image generated by the difference between the current frame's denoised thermal infrared image and the previous frame's denoised thermal infrared image; the fifth extraction submodule is used to extract the one-dimensional entropy and relative entropy of the denoised thermal infrared image, where the one-dimensional entropy refers to the clustering characteristics of the gray value distribution of the denoised thermal infrared image, and the relative entropy refers to the difference between the gray value probability distribution of the current frame's denoised thermal infrared image and the gray value probability distribution of the first frame's denoised thermal infrared image.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program performs the method according to any one of claims 1 to 5.

8. A monitoring system for coal mine roadways, characterized in that, include: An infrared thermal imager, a coal mine roadway monitoring device, and a display device are provided, wherein the coal mine roadway monitoring device communicates with the infrared thermal imager and the display device, respectively, and the coal mine roadway monitoring device is used to perform the method described in any one of claims 1 to 5.

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