Safety monitoring method and inspection device for mining belt conveyor

Abstract

The invention discloses a mining belt conveyor safety monitoring method and an inspection device. The mining belt conveyor safety monitoring method comprises the following steps: S1, acquiring a belt picture and environment monitoring data of a belt conveyor; s2, carrying out image preprocessing on the belt image through OpenCV software to obtain an enhanced belt image; s3, carrying out edge detection and gradient value optimization processing on the enhanced belt picture through a self-adaptive Canny algorithm in OpenCV software; s4, setting a high threshold value and a low threshold value to perform edge extraction on the enhanced belt picture, and obtaining a judgment condition that the belt has cracks according to the degree of the cracks on the belt in the enhanced picture; and S5, performing noise reduction processing and physical quantity conversion on the environment monitoring data, and comparing the physical quantity with a safety threshold to complete safety monitoring of the mining belt conveyor. According to the method, duplex wireless communication between the inspection robot and the measurement and control computer can be achieved, and all-directional uninterrupted monitoring and safety early warning of the belt conveyor are achieved through real-time processing of data.

Description

Technical Field

[0001] The invention relates to the technical field of safe operation in underground coal mines, and in particular to a safety monitoring method and an inspection device for a mining belt conveyor. Background Art

[0002] Mining belt conveyors are widely used in coal transportation operations in coal mines. They have the advantages of large transportation capacity and high efficiency. They can efficiently transfer large amounts of coal or other mineral resources from mining sites to storage or processing sites, improving the overall transportation efficiency of the coal mine production chain. Mining belt conveyors are usually suitable for working in a temperature range of +40°C to -10°C. They have strict requirements on dust prevention, explosion prevention, corrosion prevention, and whether the belt is torn. Therefore, it is necessary to inspect its working environment and belt status. The current common inspection method is manual inspection, but manual inspection is not only unable to achieve real-time monitoring, but also highly subjective and labor-intensive. In order to effectively improve the production efficiency and safety of coal mines, reduce the probability of accidents, and reduce the labor intensity of workers, it is necessary to provide a method for uninterrupted monitoring of belt conveyors. Summary of the invention

[0003] In order to solve the problems existing in the above-mentioned prior art, the present invention provides a safety monitoring method and inspection device for a mining belt conveyor, and the specific technical scheme is as follows:

[0004] A mine belt conveyor safety monitoring method comprises the following steps:

[0005] S1, monitoring the belt state of the mining belt conveyor and the working environment of the mining belt conveyor through monitoring cameras, smoke sensors, dust concentration sensors, and gas detectors, and obtaining belt images and environmental monitoring data, wherein the environmental monitoring data includes smoke content, dust content, and gas content;

[0006] S2, performing grayscale transformation, image filtering, and image enhancement on the belt image through OpenCV software to obtain an enhanced belt image;

[0007] S3, using the adaptive Canny algorithm in OpenCV software, edge detection is performed on the enhanced belt image, and the belt crack detection in the enhanced belt image is completed by optimizing the gradient value of the 3×3 area of ​​the pixel points;

[0008] The method for optimizing the gradient value is as follows:

[0009] The gradient values ​​in the 3×3 area of ​​pixels of the enhanced belt image are calculated in six directions: 0°, 45°, 90°, 135°, 225°, and 315°. The gradient value calculation formula is as follows:

[0010]

[0011] In the formula,

[0012] G3(x, y) is the gradient value in the 3×3 area of ​​the pixel point of the enhanced belt image;

[0013] G m (x, y) is the representation of the image gradient value in 6 directions, that is, G m (x, y) = {G 0° (x, y); G 90° (x, y); G 45° (x, y); G 135° (x, y); G 225° (x, y); G 315° (x, y)};

[0014] m is the direction of gradient calculation;

[0015] x is the horizontal coordinate point position of the image pixel;

[0016] y is the vertical coordinate point position of the image pixel;

[0017] S4, setting a high threshold and a low threshold to extract the edge of the enhanced belt image, and obtaining the judgment condition of the existence of cracks on the belt according to the degree of cracks on the belt in the enhanced image;

[0018] S5, performs noise reduction processing on the environmental monitoring data, and converts the processed environmental monitoring data into physical quantities respectively, and then compares each physical quantity with its corresponding safety threshold to complete the monitoring of the smoke content, dust content, and gas content of the mining belt conveyor.

[0019] Further, in step S4, a high threshold and a low threshold are set to extract the edge of the enhanced belt image, including the following steps: setting the high threshold to N h , the lower threshold is N l , the maximum pixel value is N max When the gradient value G3(x, y) of a pixel point in the enhanced belt image is higher than the high threshold N h When the gradient value G3(x, y) of a pixel point in the enhanced belt image is lower than the low threshold N1, the point is considered a non-edge; when the gradient value G3(x, y) of a pixel point in the enhanced belt image is between the high and low thresholds N h 、N l In the middle, the point is considered a weak edge.

[0020] Furthermore, the method for determining the degree of cracks on the belt in the enhanced image is as follows: define three pixel intervals of the enhanced belt image as [0, N l ]、[N l+1, N h ]、[N h +1, N max ]; According to the three pixel intervals, the pixel values ​​of the enhanced belt image are divided into three pixel value parts, and the difference between the three pixel value parts is the degree of cracks on the belt in the enhanced image; the judgment condition for the presence of cracks in the belt is that the difference between the high and low thresholds is >28.5.

[0021] Furthermore, the high threshold N is calculated by the following equation: h and low threshold N l To solve:

[0022] Equation 1:

[0023] Equation 2:

[0024] Where σ is the inter-class variance of the three pixel intervals of the enhanced belt image; N h is the high threshold; N1 is the low threshold.

[0025] Furthermore, the inter-class variance σ of the three pixel intervals is calculated by the following formula:

[0026]

[0027] Where σ is the inter-class variance of the three pixel intervals; P1, P2, and P3 are the probabilities of the three pixel intervals; and u1, u2, and u3 are the grayscale means of the three pixel value parts.

[0028] Furthermore, the probability of the three pixel intervals is calculated by the following formula:

[0029]

[0030] Where P1, P2, and P3 are the probabilities of three pixel intervals respectively; N h is the high threshold; N l is the low threshold; N max is the maximum pixel value; N k is the number of pixel values ​​equal to k; N sum is the total number of pixels in the enhanced image.

[0031] Furthermore, the grayscale mean of the three pixel value parts is calculated by the following formula:

[0032]

[0033] Where u1, u2, and u3 are the grayscale means of the three parts respectively; N h is the high threshold; N l is the low threshold; Nmax is the maximum pixel value.

[0034] Furthermore, in step S5, the environmental monitoring data is subjected to noise reduction processing, including the following steps:

[0035] S501, define the environmental monitoring data as a one-dimensional signal y(t), and process the one-dimensional signal y(t) through wavelet decomposition to obtain a decomposed signal W j,k = <y(t),ψ j,k (t)>, where the wavelet packet is

[0036] Where W j,k is the decomposition signal of the one-dimensional signal; j,k is the wavelet packet; j is the scale parameter of the wavelet packet; k is the position parameter of the wavelet packet;

[0037] S502, by comparing the threshold formula 1 and the threshold formula 2, the threshold selection of wavelet denoising is determined:

[0038] Threshold formula 1:

[0039]

[0040] Threshold formula 2:

[0041]

[0042] Wavelet denoising threshold selection formula 3:

[0043]

[0044] Wavelet denoising threshold selection formula 4:

[0045]

[0046] The calculation formula of the sensor signal risk curve is:

[0047]

[0048] Where N is the number of sampling points; j is the signal sequence; y(j) is the jth data size of the one-dimensional signal; k is the data sequence of the signal risk curve, that is, the horizontal coordinate of the signal; λ is the wavelet denoising threshold; k min is the sequence corresponding to the minimum risk point in the sensor signal risk curve;

[0049] When threshold formula 1 < threshold formula 2, the wavelet denoising threshold is selected from formula 3;

[0050] When threshold formula 1>threshold formula 2, the wavelet denoising threshold selects the minimum value between formula 3 and formula 4.

[0051] Furthermore, the output noise reduction coefficient pass While reconstructing the signal, accurate sensing signals are obtained after noise reduction;

[0052] The noise reduction coefficient is calculated by comparing threshold formula 1 and threshold formula 2 The output formula is as follows:

[0053]

[0054] When the coefficient value is greater than or equal to λ, take sign(ω)(|ω|-λ), otherwise the coefficient is 0;

[0055] In the formula, represents the wavelet coefficient after threshold processing; ω is the original wavelet coefficient; λ is the wavelet denoising threshold.

[0056] Furthermore, in step S5, an acquisition card is used to collect various physical quantities in the environmental monitoring data, and the data range of the sensor is set to 0 to 65535. The steps for converting the physical quantities of the environmental monitoring data after noise reduction are as follows:

[0057] a. In the conversion of physical quantity signal of smoke sensor, suppose the range of selected smoke sensor is [S ymin , S ymax ], assuming that the data collected by the acquisition card for the smoke sensor is S yc , the actual concentration of smoke is S y , then the conversion relationship between the monitoring data collected by the acquisition card and the actual analog quantity is

[0058] S y =S yc ×(S ymax -S ymin ) / 65535;

[0059] b. In the conversion of physical quantity signals of gas detectors, the range of the selected gas detector is [S wmin , S wmax ], assuming that the data collected by the acquisition card for the gas detector is S wc , the actual concentration of gas is S w , then the conversion relationship between the monitoring data collected by the acquisition card and the actual analog quantity is

[0060] S w =S wc ×(S wmax -S wmin ) / 65535;

[0061] c. Assume that the data collected by the acquisition card for the dust concentration sensor is S fc, the range is [S fmin , S fmax ], the actual concentration of dust is S f , then the conversion relationship between the monitoring data collected by the acquisition card and the actual analog quantity is

[0062] S f =S fc ×(S fmax -S fmin ) / 65535.

[0063] Furthermore, the safety detection method is also provided with an alarm activation mechanism, and the triggering mechanism of the alarm activation mechanism is as follows:

[0064] a. Compare the smoke content based on the real-time monitoring data of the smoke sensor. y When <5%obs / m, the smoke is within the normal range. y When the smoke exceeds the limit by >5% obs / m, the alarm is activated;

[0065] b. According to the real-time monitoring data of the dust concentration sensor, the dust content is compared. When S f <3.0mg / m 3 When the dust concentration is normal, when S f ≥3.0 mg / m 3 , enable the alarm;

[0066] c. Compare the gas content based on the real-time monitoring data of the gas detector. w <0.75%, the gas concentration is within the normal range. w When the gas concentration is less than 1%, the yellow light for gas concentration detection should be turned on and the cause should be found out. w When it is >1%, the alarm is activated and measures are taken to handle it.

[0067] A mining belt conveyor inspection device is used to obtain the above-mentioned belt images and environmental monitoring data, including an inspection robot, a measurement and control computer, a rotating mechanism, and a fixing mechanism; the inspection robot is used to collect belt images and environmental monitoring data, and uses an ESP8266WLAN module with the measurement and control computer to perform duplex wireless communication through a serial port; the fixing mechanism is detachably and fixedly connected to the rotating mechanism, and can rotate along the rotating mechanism above the belt conveyor; the inspection robot is fixedly connected to the fixing mechanism and moves synchronously with the fixing mechanism.

[0068] Furthermore, the inspection robot includes a shell, a monitoring camera, a smoke sensor, a dust concentration sensor, a methane gas detector, and a single-chip microcomputer; the monitoring camera is fixedly arranged on the bottom end surface of the shell for taking pictures of the belt; the smoke sensor, the dust concentration sensor, and the methane gas detector are distributed on the outer side of the shell for collecting and obtaining environmental monitoring data; the single-chip microcomputer is fixedly arranged inside the shell, and is connected to the smoke sensor, the dust concentration sensor, and the methane gas detector through a serial communication interface to collect and transmit environmental monitoring data.

[0069] Furthermore, the fixing mechanism includes a cable gripper and a connecting rod, the two ends of the connecting rod are respectively fixedly connected to the cable gripper and the inspection robot shell, and the cable gripper is detachably fixedly mounted on the steel cable; the rotating mechanism includes a driving wheel, a driven wheel, a motor, and a steel cable; the driving wheel and the driven wheel are vertically fixed to the top plate of the tunnel by anchor rods, and the driving wheel and the driven wheel are connected by a steel cable. The driving wheel, under the cooperation of the power drive and the driven wheel, drives the steel cable to perform a circular rotation motion, thereby driving the inspection robot to perform a synchronous circular rotation motion with the steel cable.

[0070] Furthermore, the inspection robot is installed 60 cm away from the upper end of the belt conveyor; the sampling frequency of the monitoring camera is set to 15fps; and the maximum walking speed of the inspection robot is set to 0.8m / s.

[0071] Based on the above technical solution, the present invention has the following excellent effects:

[0072] 1. By setting up an inspection robot, the inspection robot can rotate around the mining belt conveyor at a set and adjustable speed. During the rotation process, it can collect belt images and smoke, dust, and gas concentration data in real time. The ESP8266WLAN module is used to achieve full-duplex wireless communication between the inspection robot and the measurement and control computer. The real-time data processing by the measurement and control robot can realize all-round and uninterrupted monitoring and safety warning of the mining belt conveyor.

[0073] 2. By optimizing the gradient values ​​of the 3×3 area of ​​pixels in the enhanced belt image, adding gradients in the directions of 45°, 135°, 225° and 315°, gradient calculations in six directions of 0°, 45°, 90°, 135°, 225° and 315° are formed, and an accurate calculation formula for the gradient value is given, which can more comprehensively detect the edges of belt cracks in the enhanced belt image.

[0074] 3. Extract the edge of the enhanced belt image by setting high and low thresholds, and set the judgment condition for the existence of cracks in the belt. h -N lIs it greater than 28.5? If it is, an alarm will be issued and the information will be fed back to the ground for manual confirmation and judgment of whether the belt is torn, which can timely and accurately complete the status judgment of whether the belt is torn.

[0075] 4. Perform noise reduction on the environmental monitoring data, then convert the processed environmental monitoring data into physical quantities, and finally compare each physical quantity with the safety threshold, so as to realize the smoke, dust and gas monitoring of the mining belt conveyor. While reconstructing the signal, accurate sensing signals are obtained after noise reduction.

[0076] 5. According to the measurement values ​​of smoke sensors, dust concentration sensors, and gas detectors, combined with the provisions of the "Coal Mine Safety Regulations", the working environment of the belt conveyor is judged in real time. Once the limit is exceeded, the safety alarm is immediately activated. In actual use, the sensors can be adjusted according to the inspection requirements. For example, CO, CO2, temperature and other parameter sensors can also be integrated into the inspection items, making this method widely practical. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 : Schematic diagram of edge extraction algorithm;

[0078] Figure 2 : Schematic diagram of the sensor noise reduction process;

[0079] Figure 3 : Layout diagram of belt conveyor inspection robot;

[0080] Figure 4 : Schematic diagram of the top view of the belt conveyor inspection robot layout;

[0081] Figure 5 : Schematic diagram of the inspection robot structure;

[0082] Figure 6 : Schematic diagram of data transmission of inspection robot.

[0083] Explanation of symbols

[0084] 1-housing, 101-surveillance camera, 102-smoke sensor, 103-dust concentration sensor, 104-gas detector, 105-WLAN antenna;

[0085] 201-driving wheel, 202-driven wheel, 203-steel cable;

[0086] 301-cable gripper, 302-connecting rod. DETAILED DESCRIPTION

[0087] It should be noted that:

[0088] 1. Certain words are used in the specification and claims to refer to specific components or structures. Those skilled in the art should understand that technicians may use different terms to refer to the same component or structure. This specification and claims do not use the difference in terms as a way to distinguish components or structures, but use the difference in the functions of components or structures as the criterion for distinction.

[0089] 2. Unless otherwise clearly specified and limited, the terms "installation", "setting", "connection", etc. should be understood in a broad sense. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0090] 3. Unless otherwise defined, technical or scientific terms used in the present disclosure should have the common meanings understood by persons having ordinary skills in the field to which the present disclosure belongs.

[0091] Before describing the security monitoring method recorded in this embodiment, the adaptive Canny algorithm is briefly described as follows:

[0092] The adaptive Canny algorithm is an improved edge detection algorithm that optimizes the traditional Canny algorithm through adaptive threshold and filtering technology. Its basic principles and steps are as follows:

[0093] a. Adaptive median filtering: dynamically adjust the window size to cope with different noise levels. When strong noise is detected, the window size is automatically increased; otherwise, the window size remains unchanged.

[0094] b. Calculate the gradient value and gradient direction: Use the Sobel operator to calculate the gradient value and gradient direction of the image;

[0095] c. Non-maximum suppression: Non-maximum suppression is performed in the gradient direction to eliminate non-edge pixels;

[0096] d. Double threshold processing: Through adaptive threshold processing, high and low thresholds are set to detect and connect edges. If the pixel value exceeds the high threshold, it is considered an edge; if the pixel value is between the high and low thresholds, it needs to be connected to a pixel higher than the high threshold to be considered an edge.

[0097] The following is combined with Figure 1 and attached Figure 2 The present invention is described in detail by way of an embodiment, and the present invention records a method for safety monitoring of a mining belt conveyor.

[0098] The following steps are involved:

[0099] S1, use monitoring cameras, smoke sensors, dust concentration sensors, gas detectors and other equipment to continuously monitor the belt status and working environment of the mine belt conveyor, obtain belt images and environmental monitoring data, and the environmental monitoring data includes smoke content, dust content, and gas content.

[0100] S2, in the measurement and control computer, the image preprocessing of the belt image is completed by using OpenCV software to obtain an enhanced belt image. The image preprocessing includes but is not limited to grayscale transformation, image filtering, and image enhancement.

[0101] S3, using the adaptive Canny algorithm in OpenCV software, performs edge detection on the enhanced belt image, thereby accurately completing the belt crack detection in the enhanced belt image.

[0102] To improve the accuracy and robustness of edge detection, Figure 6 As shown, the gradient value optimization process is performed on the 3×3 pixel area of ​​the enhanced belt image. The specific method is as follows:

[0103] Based on the original gradient algorithm of the adaptive Canny algorithm, gradients in the directions of 45°, 135°, 225° and 315° are added to the 3×3 area of ​​pixels of the enhanced belt image, forming gradient calculations in six directions of 0°, 45°, 90°, 135°, 225° and 315°, thereby more comprehensively detecting the edges of belt cracks in the enhanced belt image.

[0104] The formula for calculating the gradient value of the 3×3 pixel area of ​​the enhanced belt image is as follows:

[0105]

[0106] In the formula,

[0107] G3(x, y) is the gradient value in the 3×3 area of ​​the pixel point of the enhanced belt image;

[0108] G m (x, y) is the representation of the image gradient value in 6 directions, that is, G m (x, y) = {G 0° (x, y); G 90° (x, y); G 45° (x, y); G 135° (x, y); G 225° (x, y); G 315° (x, y)};

[0109] m is the direction of gradient calculation;

[0110] x is the horizontal coordinate point position of the image pixel;

[0111] y is the vertical coordinate position of the image pixel.

[0112] S4, after determining the gradient value of the enhanced belt image, set high and low thresholds to extract the edge of the enhanced belt image, and set the judgment condition of whether the belt has cracks. The specific method is as follows:

[0113] S401, set the high threshold to N h , the lower threshold is Nl, the maximum pixel value is N max

[0114] When the gradient value G3(x, y) of a pixel point in the enhanced belt image is higher than the high threshold N h When , the point is regarded as a strong edge;

[0115] When the gradient value G3(x, y) of a pixel point in the enhanced belt image is lower than the low threshold N l , the point is considered as a non-edge;

[0116] When the gradient value G3(x, y) of a pixel point in the enhanced belt image is between the high and low thresholds N h 、N l In the middle, the point is considered a weak edge.

[0117] S402, define three pixel intervals of the enhanced belt image as [0, N l ]、[N l +1, N h ]、[N h +1, N max ], according to the three pixel intervals, the pixel values ​​of the enhanced belt image are divided into three pixel value parts, and the difference value between the three pixel value parts is the degree of cracks on the belt in the enhanced image.

[0118] S403, when there is an obvious edge detection effect, it means that cracks appear on the belt at this time. Therefore, it can be judged whether there are cracks on the belt according to the difference between the high and low thresholds. In this embodiment, the judgment condition for whether there are cracks on the belt is set as follows: N h -N l >28.5.

[0119] In this step S4:

[0120] 1. Use the following equation to calculate the high threshold N h and low threshold N l To solve:

[0121] Equation 1:

[0122] Equation 2:

[0123] In the formula,

[0124] σ is the inter-class variance of three pixel intervals of the enhanced belt image;

[0125] N h is the high threshold;

[0126] N l is the low threshold.

[0127] The above equation shows that when the between-class variance σ takes the maximum value, the selected N h 、N l Most suitable, at this time the edge detection of the belt is best.

[0128] 2. The inter-class variance σ of the three pixel intervals is calculated by the following formula:

[0129]

[0130] In the formula,

[0131] σ is the inter-class variance of the three pixel intervals;

[0132] P1, P2, and P3 are the probabilities of three pixel intervals;

[0133] u1, u2, and u3 are the grayscale means of the three pixel value parts respectively.

[0134] 3. The probability of three pixel intervals is calculated by the following formula:

[0135]

[0136]

[0137] In the formula,

[0138] P1, P2, and P3 are the probabilities of three pixel intervals;

[0139] N h is the high threshold;

[0140] N l Low threshold

[0141] N max is the maximum pixel value;

[0142] N k is the number of pixel values ​​equal to k;

[0143] N sum is the total number of pixels in the enhanced image.

[0144] 4. The grayscale mean of the three pixel value parts is calculated by the following formula:

[0145]

[0146] In the formula,

[0147] u1, u2, and u3 are the grayscale means of the three parts respectively;

[0148] N h is the high threshold;

[0149] N l Low threshold

[0150] N max is the maximum pixel value.

[0151] According to the above calculation results, we can know that by judging N h -N l Is it greater than 28.5? If it is, an alarm will be issued and the information will be fed back to the ground for manual confirmation and judgment of whether the belt is torn, which can timely and accurately complete the status judgment of whether the belt is torn.

[0152] S5, the environmental monitoring data collected by the smoke sensor, dust concentration sensor, and gas detector are typical one-dimensional signals. To improve the detection accuracy, the environmental monitoring data is first subjected to noise reduction processing, and then the processed environmental monitoring data is converted into physical quantities respectively, and finally each physical quantity is compared with the safety threshold, thereby realizing the monitoring of the smoke content, dust content, and gas content of the mining belt conveyor. The specific steps are as follows:

[0153] S501, the environmental monitoring data is regarded as a one-dimensional signal y(t), and the one-dimensional signal y(t) is processed by wavelet decomposition to obtain a decomposed signal W j,k = <y(t),ψ j,k (t)>, where the wavelet packet is

[0154] In the formula,

[0155] W j,k is the decomposition signal of the one-dimensional signal;

[0156] Ψ j,k is a wavelet packet;

[0157] j is the scale parameter of the wavelet packet;

[0158] k is the location parameter of the wavelet packet;

[0159] S502, by comparing the threshold formula 1 and the threshold formula 2, the threshold selection of wavelet denoising is determined:

[0160] Threshold formula 1:

[0161]

[0162] Threshold formula 2:

[0163]

[0164] Wavelet denoising threshold selection formula 3:

[0165]

[0166] Wavelet denoising threshold selection formula 4:

[0167]

[0168] The calculation formula of the sensor signal risk curve is:

[0169]

[0170] In the formula,

[0171] N is the number of sampling points;

[0172] j is the signal sequence;

[0173] y(j) is the jth data size of the one-dimensional signal;

[0174] k is the data sequence of the signal risk curve (i.e., the horizontal coordinate of the signal);

[0175] λ is the wavelet denoising threshold;

[0176] k min It is the sequence corresponding to the minimum risk point in the sensor signal risk curve.

[0177] When threshold formula 1 < threshold formula 2, the wavelet denoising threshold is selected from formula 3;

[0178] When threshold formula 1>threshold formula 2, the wavelet denoising threshold selects the minimum value between formula 3 and formula 4.

[0179] S503: Calculate the noise reduction coefficient by comparing threshold formula 1 with threshold formula 2 The output formula is as follows:

[0180]

[0181] When the coefficient value is greater than or equal to λ, take sign(ω)(|ω|-λ), otherwise the coefficient is 0.

[0182] In the formula,

[0183] represents the wavelet coefficient after threshold processing;

[0184] ω is the original wavelet coefficient;

[0185] λ is the wavelet denoising threshold.

[0186] S504, passed While reconstructing the signal, accurate sensing signals are obtained after noise reduction.

[0187] S6, using a 16-bit analog acquisition card to collect the physical quantities in the environmental monitoring data, setting the sensor data range to 0 to 65535, the steps for converting the physical quantities of the sensor signal environmental monitoring data after noise reduction are as follows:

[0188] S601, in the conversion of the physical quantity signal of the smoke sensor, the range of the selected smoke sensor is [S ymin , S ymax ], assuming that the data collected by the acquisition card for the smoke sensor is S yc , the actual concentration of smoke is S y , then the conversion relationship between the data collected by the acquisition card and the actual analog quantity is

[0189] S y =S yc ×(S ymax -S ymin ) / 65535

[0190] S602, in the conversion of the physical quantity signal of the gas detector, the range of the selected gas detector is [S wmin , S wmax ], assuming that the data collected by the acquisition card for the gas detector is S wc , the actual concentration of gas is S w , then the conversion relationship between the data collected by the acquisition card and the actual analog quantity is

[0191] S w =S wc ×(S wmax -S wmin ) / 65535

[0192] S603, assuming that the data collected by the acquisition card for the dust concentration sensor is S fc , the range is [S fmin , S fmax ], the actual concentration of dust is S f , then the conversion relationship between the data collected by the acquisition card and the actual analog quantity is

[0193] S f =S fc ×(S fmax -Sfmin ) / 65535

[0194] S7, based on the measurement values ​​of smoke sensors, dust concentration sensors, and methane gas detectors, combined with the provisions of the "Coal Mine Safety Regulations", makes real-time judgments on the working environment of the belt conveyor, and immediately activates a safety alarm once the limit is exceeded.

[0195] S701, compare the smoke content according to the real-time monitoring data of the smoke sensor. y <5% obs / m, smoke is within the normal range. y When the smoke exceeds the limit by >5% obs / m, the alarm will be activated immediately.

[0196] S702, based on the real-time monitoring data of the dust concentration sensor, compare the dust content. f <3.0mg / m 3 When the dust concentration is normal, when S f ≥3.0 mg / m 3 , the alarm is activated immediately.

[0197] S703, based on the real-time monitoring data of the gas detector, the gas content is compared. w <0.75%, the gas concentration is within the normal range. w <1%, the yellow gas concentration detection light should be turned on and the cause should be found out immediately. w When it is >1%, the alarm will be activated immediately, the work of underground workers will be stopped, and measures will be taken immediately to deal with the problem.

[0198] This embodiment lists the inspection methods of smoke, dust concentration, and gas. In actual use, the sensors can be adjusted according to the inspection requirements. For example, parameter sensors such as CO, CO2, and temperature can also be integrated into the inspection items.

[0199] As attached Figure 3 To Attachment Figure 6 As shown, the present invention records a mining belt conveyor inspection device, which is used to realize the collection of belt images and environmental monitoring data in the above monitoring method, including an inspection robot, a measurement and control computer, a rotating mechanism, and a fixing mechanism.

[0200] The inspection robot comprises a housing 1, a monitoring camera 101, a smoke sensor 102, a dust concentration sensor 103, a gas detector 104, and a single chip microcomputer.

[0201] The housing 1 is in a square shape as a whole, and a mounting groove is provided on its bottom end surface for fixing and mounting the monitoring camera 101 .

[0202] The monitoring camera 101 is used to collect the running status of the belt laid on the belt conveyor. The monitoring camera 101 preferably adopts a spherical camera, and is set to rotate 355° with a left and right rotation speed upper limit of 12° / second. By reserving a 5° rotation space for the camera and setting an upper limit rotation speed, the spherical camera can smoothly complete the shooting operation while ensuring the safe operation of the camera and avoiding overload of the stroke.

[0203] The smoke sensor 102, the dust concentration sensor 103, and the gas detector 104 are distributed on the outer side of the housing 1;

[0204] The smoke sensor 102 is used to monitor and obtain the smoke concentration in the working environment;

[0205] The dust concentration sensor 103 is used to monitor and obtain the dust concentration in the working environment;

[0206] The methane gas detector 104 is used to monitor and obtain the methane concentration in the working environment.

[0207] The single-chip microcomputer is fixedly arranged inside the housing 1, and a lithium battery is arranged inside the housing 1. The lithium battery is electrically connected to the single-chip microcomputer and is used to continuously supply power to the single-chip microcomputer at a stable voltage.

[0208] The single chip microcomputer is connected to the smoke sensor 102, dust concentration sensor 103, and gas detector 104 through serial communication interfaces such as I2C, SPI, and UART, and preferably connected by aviation plug connectors. The single chip microcomputer uses the 8-bit single chip microcomputer C8051F020 of Cygnal Company of the United States to collect data from the above sensors.

[0209] The single chip microcomputer and the measurement and control computer use ESP8266WLAN module, and realize duplex wireless communication between the two through the serial port. The monitoring personnel can transmit the original path of the control parameters to the inspection robot on the measurement and control computer to realize remote control of the inspection robot.

[0210] The inspection robot is also provided with a WLAN antenna 105 for enhancing signal strength. The WLAN antenna 105 is arranged outside the housing 1 and is electrically connected to the single chip microcomputer.

[0211] The rotating mechanism includes a driving wheel 201 , a driven wheel 202 , a motor, and a steel cable 203 .

[0212] The driving wheel 201 and the driven wheel 202 are vertically fixed on the tunnel roof through anchor rods, and the driving wheel 201 and the driven wheel 202 are connected through a steel cable 203.

[0213] A motor is installed at the anchor rod of the driving wheel, and the motor provides driving force for the driving wheel 201. With the cooperation of the driven wheel 202, the steel cable 203 performs a cyclic rotation motion.

[0214] The fixing mechanism is a cable gripper 301 and a connecting rod 302. The two ends of the connecting rod 302 are respectively fixedly connected to the cable gripper 301 and the inspection robot housing 1, and the cable gripper 301 is detachably fixedly mounted on the steel cable 203. The cable gripper 301 drives the inspection robot to perform synchronous cyclic rotational motion with the steel cable 203 through the connecting rod 302.

[0215] In this embodiment,

[0216] 1. The monitoring camera 101, smoke sensor 102, dust concentration sensor 103, and gas detector 104 work synchronously. The belt image and environmental monitoring data are converted into TCP data packets through the single-chip microcomputer, and the TCP data packets are transmitted to the measurement and control computer through the ESP8266WLAN module.

[0217] 2. To obtain better monitoring effect:

[0218] a. Based on multiple adjustments to the installation position of the surveillance camera and the comprehensive shooting width and clarity of the surveillance camera, the inspection robot was installed 60 cm from the upper end of the belt conveyor;

[0219] b. Set the sampling frequency of the surveillance camera to 15fps;

[0220] c. Set the maximum walking speed of the inspection robot to 0.8m / s.

[0221] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A mine belt conveyor safety monitoring method, characterized in that: The following steps are involved: S1, monitoring the belt state of the mining belt conveyor and the working environment of the mining belt conveyor through monitoring cameras, smoke sensors, dust concentration sensors, and gas detectors, and obtaining belt images and environmental monitoring data, wherein the environmental monitoring data includes smoke content, dust content, and gas content; S2, performing grayscale transformation, image filtering, and image enhancement on the belt image through OpenCV software to obtain an enhanced belt image; S3, using the adaptive Canny algorithm in OpenCV software, edge detection is performed on the enhanced belt image, and the belt crack detection in the enhanced belt image is completed by optimizing the gradient value of the 3×3 area of ​​the pixel points; the gradient value optimization method is: the gradient value of the 3×3 area of ​​the pixel points of the enhanced belt image is calculated in six directions of 0°, 45°, 90°, 135°, 225°, and 315° respectively; S4, setting a high threshold and a low threshold to extract the edge of the enhanced belt image, and obtaining the judgment condition of the existence of cracks on the belt according to the degree of cracks on the belt in the enhanced image; S5, performs noise reduction processing on the environmental monitoring data, and converts the processed environmental monitoring data into physical quantities respectively, and then compares each physical quantity with its corresponding safety threshold to complete the monitoring of the smoke content, dust content, and gas content of the mining belt conveyor.

2. A mining belt conveyor safety monitoring method according to claim 1, characterized in that: In step S3, the gradient value calculation formula is as follows: In the formula, G3(x, y) is the gradient value in the 3×3 area of ​​the pixel point of the enhanced belt image; G m (x, y) is the representation of the image gradient value in 6 directions, that is, G m (x, y) = {G 0° (x, y); G 90° (x, y); G 45° (x, y); G 135° (x, y); G 225° (x, y); G 315° (x, y)}; m is the direction of gradient calculation; x is the horizontal coordinate point position of the image pixel; y is the vertical coordinate point position of the image pixel; In step S4, a high threshold and a low threshold are set to extract the edge of the enhanced belt image, including the following steps: setting the high threshold to N h , the lower threshold is N l , the maximum pixel value is N max ; When the gradient value G3(x, y) of a pixel point in the enhanced belt image is higher than the high threshold N h When the gradient value G3(x, y) of a pixel point in the enhanced belt image is lower than the low threshold N l When the gradient value G3(x,y) of a pixel point in the enhanced belt image is between the high and low thresholds N h 、N l In the middle, the point is considered a weak edge.

3. A mining belt conveyor safety monitoring method according to claim 2, characterized in that: The method for determining the degree of cracks on the belt in the enhanced picture is: Define the three pixel intervals of the enhanced belt image as [0, N l ]、[N l +1,N h ]、[N h +1,N max ]; According to the three pixel intervals, the pixel values ​​of the enhanced belt image are divided into three pixel value parts, and the difference values ​​between the three pixel value parts are the degree of cracks on the belt in the enhanced image; The condition for judging whether the belt has cracks is that the difference between the upper and lower thresholds is >28.

5.

4. A mining belt conveyor safety monitoring method according to claim 2, characterized in that: The high threshold N is calculated by the following equation h and low threshold N l To solve: Equation 1: Equation 2: In the formula, σ is the inter-class variance of three pixel intervals of the enhanced belt image; N h is the high threshold; N l is the low threshold.

5. A mining belt conveyor safety monitoring method according to claim 4, characterized in that: The inter-class variance σ of the three pixel intervals is calculated by the following formula: In the formula, σ is the inter-class variance of the three pixel intervals; P1, P2, and P3 are the probabilities of three pixel intervals; u1, u2, and u3 are the grayscale means of the three pixel value parts respectively.

6. A mining belt conveyor safety monitoring method according to claim 5, characterized in that: The probability of three pixel intervals is calculated by the following formula: In the formula, P1, P2, and P3 are the probabilities of three pixel intervals; N h is the high threshold; N l Low threshold N max is the maximum pixel value; N k is the number of pixel values ​​equal to k; N sum is the total number of pixels in the enhanced image.

7. A mining belt conveyor safety monitoring method according to claim 5, characterized in that: The grayscale mean of the three pixel value parts is calculated by the following formula: In the formula, u1, u2, and u3 are the grayscale means of the three parts respectively; N h is the high threshold; N l Low threshold N max is the maximum pixel value.

8. A mining belt conveyor safety monitoring method according to claim 1, characterized in that: In step S5, the environmental monitoring data is subjected to noise reduction processing, including the following steps: S501, define the environmental monitoring data as a one-dimensional signal y(t), and process the one-dimensional signal y(t) through wavelet decomposition to obtain a decomposed signal W j,k = <y(t),ψ j,k (t)>, where the wavelet packet is In the formula, W j,k is the decomposition signal of the one-dimensional signal; Ψ j,k is a wavelet packet; j is the scale parameter of the wavelet packet; k is the location parameter of the wavelet packet; S502, by comparing the threshold formula 1 and the threshold formula 2, the threshold selection of wavelet denoising is determined: Threshold formula 1: Threshold formula 2: Wavelet denoising threshold selection formula 3: Wavelet denoising threshold selection formula 4: The calculation formula of the sensor signal risk curve is: In the formula, N is the number of sampling points; j is the signal sequence; y(j) is the jth data size of the one-dimensional signal; k is the data sequence of the signal risk curve (i.e., the horizontal coordinate of the signal); λ is the wavelet denoising threshold; k min is the sequence corresponding to the minimum risk point in the sensor signal risk curve; When threshold formula 1 < threshold formula 2, the wavelet denoising threshold is selected from formula 3; When threshold formula 1>threshold formula 2, the wavelet denoising threshold selects the minimum value between formula 3 and formula 4.

9. A mining belt conveyor safety monitoring method according to claim 8, characterized in that: Output Noise Reduction Coefficient pass While reconstructing the signal, accurate sensing signals are obtained after noise reduction; The noise reduction coefficient is calculated by comparing threshold formula 1 and threshold formula 2 The output formula is as follows: When the coefficient value is greater than or equal to λ, take sign(ω)(|ω|-λ), otherwise the coefficient is 0; In the formula, represents the wavelet coefficient after threshold processing; ω is the original wavelet coefficient; λ is the wavelet denoising threshold.

10. A mining belt conveyor safety monitoring method according to claim 1, characterized in that: In step S5, an acquisition card is used to collect the physical quantities in the environmental monitoring data, and the data range of the sensor is set to 0 to 65535. The steps for converting the physical quantities of the environmental monitoring data after noise reduction are as follows: a. In the conversion of physical quantity signal of smoke sensor, suppose the range of selected smoke sensor is [S ymin , S ymax ], assuming that the data collected by the acquisition card for the smoke sensor is S yc , the actual concentration of smoke is S y , then the conversion relationship between the monitoring data collected by the acquisition card and the actual analog quantity is S y =S yc ×(S ymax -S ymin ) / 65535; b. In the conversion of physical quantity signals of gas detectors, the range of the selected gas detector is [S wmin , S wmax ], assuming that the data collected by the acquisition card for the gas detector is S wc , the actual concentration of gas is S w , then the conversion relationship between the monitoring data collected by the acquisition card and the actual analog quantity is S w =S wc ×(S wmax -S wmin ) / 65535; c. Assume that the data collected by the acquisition card for the dust concentration sensor is S fc , the range is [S fmin , S fmax ], the actual concentration of dust is S f , then the conversion relationship between the monitoring data collected by the acquisition card and the actual analog quantity is S f =S fc ×(S fmax -S fmin ) / 65535。 11. A mining belt conveyor safety monitoring method according to claim 10, characterized in that: The safety detection method is also provided with an alarm activation mechanism, and the triggering mechanism of the alarm activation mechanism is as follows: a. Compare the smoke content based on the real-time monitoring data of the smoke sensor. y When <5%obs / m, the smoke is within the normal range. y When the smoke exceeds the limit by >5% obs / m, the alarm is activated; b. According to the real-time monitoring data of the dust concentration sensor, the dust content is compared. When S f <3.0mg / m 3 When the dust concentration is normal, when S f ≥3.0 mg / m 3 , enable the alarm; c. Compare the gas content based on the real-time monitoring data of the gas detector. w When 0.75%≤S w When the gas concentration is less than 1%, the yellow light for gas concentration detection should be turned on and the cause should be found out. w When it is >1%, the alarm is activated and measures are taken to handle it.

12. A mining belt conveyor inspection device, used to obtain the belt image and environmental monitoring data as described in any one of claims 1 to 11, characterized in that: include Inspection robot, measurement and control computer, rotating mechanism, fixed mechanism; The inspection robot is used to collect belt images and environmental monitoring data, and uses an ESP8266WLAN module with the measurement and control computer to perform duplex wireless communication through a serial port; The fixing mechanism is detachably fixedly connected to the rotating mechanism and can rotate along the rotating mechanism above the belt conveyor; The inspection robot is fixedly connected to the fixing mechanism and moves synchronously with the fixing mechanism.

13. A mining belt conveyor safety monitoring method according to claim 12, characterized in that: The inspection robot includes a shell, a monitoring camera, a smoke sensor, a dust concentration sensor, a gas detector, and a single-chip microcomputer; The monitoring camera is fixedly arranged on the bottom end surface of the housing and is used to take pictures of the belt; The smoke sensor, dust concentration sensor and gas detector are distributed on the outer side of the shell to collect and obtain environmental monitoring data; The single chip microcomputer is fixedly arranged inside the shell, and is connected with the smoke sensor, the dust concentration sensor, and the methane gas detector through a serial communication interface to collect and transmit environmental monitoring data.

14. A mining belt conveyor safety monitoring method according to claim 12, characterized in that: The fixing mechanism comprises a cable gripper and a connecting rod, the two ends of the connecting rod are respectively fixedly connected to the cable gripper and the inspection robot housing, and the cable gripper is detachably fixedly mounted on the steel cable; The rotating mechanism includes a driving wheel, a driven wheel, a motor, and a steel cable; The driving wheel and the driven wheel are vertically fixed on the tunnel roof by anchor rods, and the driving wheel and the driven wheel are connected by steel cables. The driving wheel, under the cooperation of power drive and driven wheel, drives the steel cable to perform circular rotation motion, thereby driving the inspection robot to perform synchronous circular rotation motion with the steel cable.

15. A mining belt conveyor safety monitoring method according to claim 14, characterized in that: The inspection robot is installed 60 cm away from the upper end of the belt conveyor; the sampling frequency of the monitoring camera is set to 15fps; and the maximum walking speed of the inspection robot is set to 0.8m / s.

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