CN116122910A - Ultrasonic multi-point real-time monitoring method for mine surrounding rock roof disasters - Google Patents
Ultrasonic multi-point real-time monitoring method for mine surrounding rock roof disasters Download PDFInfo
- Publication number
- CN116122910A CN116122910A CN202310171097.1A CN202310171097A CN116122910A CN 116122910 A CN116122910 A CN 116122910A CN 202310171097 A CN202310171097 A CN 202310171097A CN 116122910 A CN116122910 A CN 116122910A
- Authority
- CN
- China
- Prior art keywords
- mine
- image
- surrounding rock
- roof
- rock roof
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 239000011435 rock Substances 0.000 title claims abstract description 86
- 238000000034 method Methods 0.000 title claims abstract description 72
- 238000012544 monitoring process Methods 0.000 title claims abstract description 68
- 230000035699 permeability Effects 0.000 claims abstract description 44
- 239000002245 particle Substances 0.000 claims abstract description 17
- 230000008569 process Effects 0.000 claims abstract description 14
- 238000010801 machine learning Methods 0.000 claims abstract description 13
- 238000003384 imaging method Methods 0.000 claims abstract description 10
- 230000009467 reduction Effects 0.000 claims abstract description 8
- 238000012549 training Methods 0.000 claims abstract description 8
- 239000003245 coal Substances 0.000 claims description 54
- 238000012545 processing Methods 0.000 claims description 32
- 230000011218 segmentation Effects 0.000 claims description 15
- 230000000694 effects Effects 0.000 claims description 14
- 238000001514 detection method Methods 0.000 claims description 12
- 238000001914 filtration Methods 0.000 claims description 12
- 238000005065 mining Methods 0.000 claims description 12
- 230000006835 compression Effects 0.000 claims description 6
- 238000007906 compression Methods 0.000 claims description 6
- 238000010586 diagram Methods 0.000 claims description 6
- 238000009826 distribution Methods 0.000 claims description 6
- 238000000605 extraction Methods 0.000 claims description 6
- 238000003709 image segmentation Methods 0.000 claims description 6
- 230000014759 maintenance of location Effects 0.000 claims description 6
- 238000007781 pre-processing Methods 0.000 claims description 6
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 5
- 238000005516 engineering process Methods 0.000 claims description 4
- 230000002708 enhancing effect Effects 0.000 claims description 4
- 238000005520 cutting process Methods 0.000 claims description 3
- 238000009499 grossing Methods 0.000 claims description 3
- 238000010191 image analysis Methods 0.000 claims description 3
- 230000000877 morphologic effect Effects 0.000 claims description 3
- 238000011160 research Methods 0.000 claims description 3
- 238000012706 support-vector machine Methods 0.000 claims description 3
- 230000001629 suppression Effects 0.000 claims description 3
- 238000012937 correction Methods 0.000 abstract description 5
- 238000004519 manufacturing process Methods 0.000 description 7
- 230000008901 benefit Effects 0.000 description 4
- 238000013461 design Methods 0.000 description 3
- 238000003491 array Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000012827 research and development Methods 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Images
Classifications
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21F—SAFETY DEVICES, TRANSPORT, FILLING-UP, RESCUE, VENTILATION, OR DRAINING IN OR OF MINES OR TUNNELS
- E21F17/00—Methods or devices for use in mines or tunnels, not covered elsewhere
- E21F17/18—Special adaptations of signalling or alarm devices
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21F—SAFETY DEVICES, TRANSPORT, FILLING-UP, RESCUE, VENTILATION, OR DRAINING IN OR OF MINES OR TUNNELS
- E21F17/00—Methods or devices for use in mines or tunnels, not covered elsewhere
- E21F17/18—Special adaptations of signalling or alarm devices
- E21F17/185—Rock-pressure control devices with or without alarm devices; Alarm devices in case of roof subsidence
Landscapes
- Engineering & Computer Science (AREA)
- Mining & Mineral Resources (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Geochemistry & Mineralogy (AREA)
- Geology (AREA)
- Measurement Of Velocity Or Position Using Acoustic Or Ultrasonic Waves (AREA)
Abstract
The invention belongs to the technical field of disaster ultrasonic multipoint real-time monitoring, and discloses an ultrasonic multipoint real-time monitoring method for disaster of a mine surrounding rock roof. The method comprises the steps of carrying out prediction on permeability of a mine surrounding rock roof, carrying out correction on an electric imaging image by utilizing MATLAB, carrying out pretreatment processes such as image noise reduction and the like to realize image enhancement, forming a basic image which can be used for logging interpretation, extracting available information of the image by utilizing machine learning to prepare available data for permeability prediction, and accurately predicting the mine surrounding rock roof permeability according to particle size information and a permeability training regression model by utilizing MATLAB machine learning; meanwhile, the hardness of the mine surrounding rock roof can be accurately measured by the method for measuring the hardness of the mine surrounding rock roof, so that collapse caused by overlarge bearing weight is prevented.
Description
Technical Field
The invention belongs to the technical field of disaster ultrasonic multi-point real-time monitoring, and particularly relates to an ultrasonic multi-point real-time monitoring method for a mine surrounding rock roof disaster.
Background
Mines are a generic term for roadways, chambers, equipment, floor structures and structures that form underground coal production systems. Inclined shafts, galleries, etc. in underground mine development are sometimes referred to as mines. The determination of the size of the well Tian Fanwei, the production capacity and the service life of each well is one of the key problems that must be solved in the design of the self-body of the well; mine throughput generally refers to the design throughput of a mine, expressed in tens of thousands of tons/a. Some production mines have original production capacity which needs to be changed for various reasons, so that the capacity of each production system of the mine needs to be rechecked, and the comprehensive production capacity after the verification is called verification production capacity; however, the existing mine surrounding rock roof disaster ultrasonic multi-point real-time monitoring method is inaccurate in predicting the permeability of the mine surrounding rock roof, so that a mine water seepage event is caused; meanwhile, the hardness of the mine surrounding rock roof is measured inaccurately, so that the bearing weight is overlarge and collapse is caused.
Through the above analysis, the problems and defects existing in the prior art are as follows:
(1) The existing mine surrounding rock roof disaster ultrasonic multi-point real-time monitoring method is inaccurate in predicting the permeability of the mine surrounding rock roof, and therefore mine water seepage events are caused.
(2) Inaccurate measurement of the hardness of the mine surrounding rock roof results in excessive bearing weight and collapse.
(3) The ultrasonic multi-point real-time monitoring video of the mine surrounding rock roof disaster is unclear, and the monitoring result is affected.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention provides an ultrasonic multi-point real-time monitoring method for mine surrounding rock roof disasters.
The invention discloses an ultrasonic multipoint real-time monitoring method for mine surrounding rock roof disasters, which comprises the following steps:
arranging a plurality of camera equipment on the periphery of a mine surrounding rock roof, and carrying out enhancement processing on a camera monitoring video;
the method for enhancing the camera monitoring video comprises the following steps:
acquiring the resolution of a camera monitoring video of a goaf of a coal face of a coal mine to be displayed; if the resolution is the first resolution, acquiring an algorithm for reducing noise of the goaf shooting monitoring video of the coal mine working face to be displayed as a goaf shooting monitoring video enhancement algorithm of the coal mine working face, wherein the algorithm for reducing noise is used for cutting a goaf shooting monitoring video frame of the coal mine working face to be displayed into a first part and a second part, and carrying out noise reduction treatment on the second part, wherein the first part is an edge part, and the second part is a part except the edge part;
if the resolution is the second resolution, acquiring an algorithm for carrying out contrast enhancement and saturation enhancement on the goaf shooting monitoring video of the coal mining face to be displayed as a goaf shooting monitoring video enhancement algorithm of the coal mining face; wherein the second resolution is greater than the first resolution, the first resolution is a low resolution, and the second resolution is a high resolution;
the goaf shooting monitoring video of the coal mining face to be displayed is enhanced through the goaf shooting monitoring video enhancement algorithm of the coal mining face;
the first resolution is the resolution corresponding to the standard definition and the video of the shot monitoring of the goaf of the coal face of the fluent coal mine; the second resolution is the resolution corresponding to the high-definition coal mine coal face goaf shooting monitoring video and the ultra-definition coal mine coal face goaf shooting monitoring video;
step two, detecting the mine surrounding rock roof structure through detection equipment; predicting permeability of a mine surrounding rock roof; measuring the hardness of a mine surrounding rock roof;
step three, ultrasonic detection equipment, a vibration sensor, a water level sensor and rainfall meter equipment are installed on a mine surrounding rock roof; and according to disaster information monitored by the equipment, timely issuing disaster warning through a wireless network.
Further, the method for predicting the permeability of the mine surrounding rock roof is as follows:
(1) Acquiring micro resistivity scanning imaging of a mine surrounding rock roof to obtain a corresponding FMI image for extracting particle size information so as to predict corresponding permeability; performing enhancement processing on the FMI image;
(2) Graying the image;
the original color image is in an RGB color mode, the color of each pixel is determined by R, G, B components, each component has 255 variation values, and the three components are processed respectively during processing. On the basis of the RGB value of the color image, the gray value of the corresponding pixel point is calculated by using a formula (1), wherein the formula (1) is as follows: gray= 0.2989 ×R+0.5870 ×G+0.1140 ×B, in practice, RGB cannot reflect morphological features of an image, and further increases workload, so that image graying processing is often used as a preprocessing step of image processing, which can greatly reduce the calculated amount and preserve features such as original chromaticity and brightness of the image;
(3) Intermediate filtering;
FMI images are often contaminated with noise during downhole imaging, affecting the quality of the overall image. In the digital image signal, noise is represented as extreme values which are larger or smaller, and the extreme values act on the true gray values of the image pixels through addition and subtraction, so that bright and dark point interference is caused to the image, the image quality is greatly reduced, the later work of image restoration, segmentation and the like is influenced, and therefore filtering noise suppression is necessary. By adopting median filtering, pixel points affected by noise are exactly eliminated compared with mean filtering, and compared with a linear filter, the method can overcome the blurring of image details and obtain better effects of smoothing and protecting edges;
(4) Threshold segmentation;
image segmentation is a precondition and basis for image analysis and image understanding, and is an image preprocessing process necessary before feature extraction. The quality of the image segmentation effect can greatly influence the subsequent research, and the method can be divided into a threshold value, an edge, a graph theory, an energy functional and the like from the segmentation point of view. The invention adopts a threshold technology, and has the key points of simple realization, small calculated amount and stable performance, and can well reduce the time of image processing;
(5) Extracting features;
when the particle size distribution is calculated, global threshold is adopted, the sizes of structural elements are compared by adopting 80 structural elements with equal distance, gray level difference values between the sizes of adjacent structural elements are recorded, and the sum of changed pixel points is divided by the sizes of the structural elements, so that the size frequency distribution of particles in an original image is obtained.
(6) Regression prediction;
the method comprises the steps of carrying out feature extraction on particle size information of an X well and a Y well according to an existing X well threshold segmentation diagram n (not less than 70) and a Y well threshold segmentation diagram m (not less than 30), obtaining X81 groups of data sets according to a group of permeability data of the existing X well, training three groups of data of the Y well by using a Gaussian process regression model, a regression tree model and a support vector machine model in a Regressionlearner of machine learning in MATLAB, respectively using the three models to predict the permeability of the Y well, fitting the Y well with the known permeability, comparing which model effect is optimal, and finally obtaining a result for predicting the permeability.
Further, the method for measuring the hardness of the mine surrounding rock roof comprises the following steps:
1) Configuring a detection device parameter, receiving force information by the detection device, the force information representing one or more forces applied to the mine roof sample by at least one roller during roller crushing of the mine roof sample;
2) Determining size information indicative of a size of the mine surrounding rock roof sample; at least one hardness parameter indicative of a hardness of the mine surrounding rock roof sample is determined based at least on the force information and the size information.
Further, the determining the size information includes: a processing time is determined based at least on the force information, during which the one or more forces are applied to the mine bedrock roof sample during the roller crushing of the mine bedrock roof sample.
Further, the determining the processing time includes: the processing time is determined as a time period during which the one or more forces applied to the mine surrounding rock roof sample remain above a threshold force.
Further, the determining the size information includes: determining a dimension parameter representative of the dimension of the mine surrounding rock roof sample based at least in part on the processing time, and wherein determining the at least one hardness parameter comprises: the at least one stiffness parameter is determined based at least in part on the force information and the dimensional parameter.
Further, the measuring further includes: receiving nip information indicative of a nip size provided by the at least one roller during the roller crushing, and wherein determining the at least one hardness parameter comprises: determining the at least one hardness parameter based at least in part on the nip information;
determining the size information includes: the dimensional information is determined based at least in part on the nip information.
Further, the determining the at least one hardness parameter comprises: determining a maximum nip size during roller crushing of the mine surrounding rock roof sample from the nip information, and determining the at least one hardness parameter based at least in part on the maximum nip size.
Further, the determining the at least one hardness parameter comprises: determining a compression distance of the mine surrounding rock roof sample during roller crushing, and multiplying a crushing force of the one or more forces represented by the force information by the compression distance to determine a crushing energy.
Further, the receiving the force information includes: the method includes receiving an indication of one or more sensed roller retention forces holding the at least one roller against at least one gap limiter during the roller crushing, and determining the one or more forces based at least in part on the one or more sensed roller retention forces.
In combination with the above technical solution and the technical problems to be solved, please analyze the following aspects to provide the following advantages and positive effects:
first, aiming at the technical problems in the prior art and the difficulty in solving the problems, the technical problems solved by the technical proposal of the invention are analyzed in detail and deeply by tightly combining the technical proposal to be protected, the results and data in the research and development process, and the like, and some technical effects brought after the problems are solved have creative technical effects. The specific description is as follows:
the method comprises the steps of carrying out prediction on permeability of a mine surrounding rock roof, carrying out correction on an electric imaging image by utilizing MATLAB, carrying out pretreatment processes such as image noise reduction and the like to realize image enhancement, forming a basic image which can be used for logging interpretation, extracting available information of the image by utilizing machine learning to prepare available data for permeability prediction, and accurately predicting the mine surrounding rock roof permeability according to particle size information and a permeability training regression model by utilizing MATLAB machine learning; meanwhile, the hardness of the mine surrounding rock roof can be accurately measured by the method for measuring the hardness of the mine surrounding rock roof, so that collapse caused by overlarge bearing weight is prevented.
According to the method for enhancing the video of the monitoring of the goaf of the coal mine, different video enhancement algorithms of the video of the monitoring of the goaf of the coal mine are obtained according to different resolutions of the video of the monitoring of the goaf of the coal mine, so that the video enhancement of the video of the goaf of the coal mine is enhanced according to different modes of the video of the monitoring of the goaf of the coal mine, the enhancement processing of the video of the goaf of the coal mine is more in line with the characteristics of the video of the goaf of the coal mine, a better video enhancement effect of the video of the goaf of the coal mine is obtained, and the accuracy of monitoring results is provided.
Secondly, the technical scheme is regarded as a whole or from the perspective of products, and the technical scheme to be protected has the following technical effects and advantages:
the method comprises the steps of carrying out prediction on permeability of a mine surrounding rock roof, carrying out correction on an electric imaging image by utilizing MATLAB, carrying out pretreatment processes such as image noise reduction and the like to realize image enhancement, forming a basic image which can be used for logging interpretation, extracting available information of the image by utilizing machine learning to prepare available data for permeability prediction, and accurately predicting the mine surrounding rock roof permeability according to particle size information and a permeability training regression model by utilizing MATLAB machine learning; meanwhile, the hardness of the mine surrounding rock roof can be accurately measured by the method for measuring the hardness of the mine surrounding rock roof, so that collapse caused by overlarge bearing weight is prevented.
Drawings
Fig. 1 is a flowchart of an ultrasonic multi-point real-time monitoring method for mine surrounding rock roof disasters provided by an embodiment of the invention.
Fig. 2 is a flowchart of a method for predicting permeability of a mine surrounding rock roof according to an embodiment of the present invention.
Fig. 3 is a flowchart of a method for measuring hardness of a mine surrounding rock roof according to an embodiment of the invention.
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
1. The embodiments are explained. In order to fully understand how the invention may be embodied by those skilled in the art, this section is an illustrative embodiment in which the claims are presented for purposes of illustration.
As shown in fig. 1, the invention provides an ultrasonic multi-point real-time monitoring method for mine surrounding rock roof disasters, which comprises the following steps:
s101, arranging a plurality of camera equipment on the periphery of a mine surrounding rock roof, and carrying out enhancement processing on a camera monitoring video;
the method for enhancing the camera monitoring video comprises the following steps:
acquiring the resolution of a camera monitoring video of a goaf of a coal face of a coal mine to be displayed; if the resolution is the first resolution, acquiring an algorithm for reducing noise of the goaf shooting monitoring video of the coal mine working face to be displayed as a goaf shooting monitoring video enhancement algorithm of the coal mine working face, wherein the algorithm for reducing noise is used for cutting a goaf shooting monitoring video frame of the coal mine working face to be displayed into a first part and a second part, and carrying out noise reduction treatment on the second part, wherein the first part is an edge part, and the second part is a part except the edge part;
if the resolution is the second resolution, acquiring an algorithm for carrying out contrast enhancement and saturation enhancement on the goaf shooting monitoring video of the coal mining face to be displayed as a goaf shooting monitoring video enhancement algorithm of the coal mining face; wherein the second resolution is greater than the first resolution, the first resolution is a low resolution, and the second resolution is a high resolution;
the goaf shooting monitoring video of the coal mining face to be displayed is enhanced through the goaf shooting monitoring video enhancement algorithm of the coal mining face;
the first resolution is the resolution corresponding to the standard definition and the video of the shot monitoring of the goaf of the coal face of the fluent coal mine; the second resolution is the resolution corresponding to the high-definition coal mine coal face goaf shooting monitoring video and the ultra-definition coal mine coal face goaf shooting monitoring video;
s102, detecting a mine surrounding rock roof structure through detection equipment; predicting permeability of a mine surrounding rock roof; measuring the hardness of a mine surrounding rock roof;
s103, ultrasonic detection equipment, a vibration sensor, a water level sensor and rainfall meter equipment are installed on a mine surrounding rock roof; and according to disaster information monitored by the equipment, timely issuing disaster warning through a wireless network.
As shown in fig. 2, the method for predicting the permeability of the mine surrounding rock roof provided by the invention is as follows:
s201, acquiring a mine surrounding rock roof micro-resistivity scanning image to obtain a corresponding FMI image for extracting particle size information so as to predict corresponding permeability; performing enhancement processing on the FMI image;
s202, image graying processing;
the original color image is in an RGB color mode, the color of each pixel is determined by R, G, B components, each component has 255 variation values, and the three components are processed respectively during processing. On the basis of the RGB value of the color image, the gray value of the corresponding pixel point is calculated by using a formula (1), wherein the formula (1) is as follows: gray= 0.2989 ×R+0.5870 ×G+0.1140 ×B, in practice, RGB cannot reflect morphological features of an image, and further increases workload, so that image graying processing is often used as a preprocessing step of image processing, which can greatly reduce the calculated amount and preserve features such as original chromaticity and brightness of the image;
s203, intermediate filtering;
FMI images are often contaminated with noise during downhole imaging, affecting the quality of the overall image. In the digital image signal, noise is represented as extreme values which are larger or smaller, and the extreme values act on the true gray values of the image pixels through addition and subtraction, so that bright and dark point interference is caused to the image, the image quality is greatly reduced, the later work of image restoration, segmentation and the like is influenced, and therefore filtering noise suppression is necessary. By adopting median filtering, pixel points affected by noise are exactly eliminated compared with mean filtering, and compared with a linear filter, the method can overcome the blurring of image details and obtain better effects of smoothing and protecting edges;
s204, threshold segmentation;
image segmentation is a precondition and basis for image analysis and image understanding, and is an image preprocessing process necessary before feature extraction. The quality of the image segmentation effect can greatly influence the subsequent research, and the method can be divided into a threshold value, an edge, a graph theory, an energy functional and the like from the segmentation point of view. The invention adopts a threshold technology, and has the key points of simple realization, small calculated amount and stable performance, and can well reduce the time of image processing;
s205, extracting features;
when the particle size distribution is calculated, global threshold is adopted, the sizes of structural elements are compared by adopting 80 structural elements with equal distance, gray level difference values between the sizes of adjacent structural elements are recorded, and the sum of changed pixel points is divided by the sizes of the structural elements, so that the size frequency distribution of particles in an original image is obtained.
S206, regression prediction;
the method comprises the steps of carrying out feature extraction on particle size information of an X well and a Y well according to an existing X well threshold segmentation diagram n (not less than 70) and a Y well threshold segmentation diagram m (not less than 30), obtaining X81 groups of data sets according to a group of permeability data of the existing X well, training three groups of data of the Y well by using a Gaussian process regression model, a regression tree model and a support vector machine model in a Regressionlearner of machine learning in MATLAB, respectively using the three models to predict the permeability of the Y well, fitting the Y well with the known permeability, comparing which model effect is optimal, and finally obtaining a result for predicting the permeability.
As shown in fig. 3, the method for measuring the hardness of the mine surrounding rock roof provided by the invention comprises the following steps:
s301 configuring a detection device parameter, receiving force information by the detection device, the force information representing one or more forces applied to the mine roof sample by at least one roller during roller crushing of the mine roof sample;
s302, determining size information representing the size of the mine surrounding rock roof sample; at least one hardness parameter indicative of a hardness of the mine surrounding rock roof sample is determined based at least on the force information and the size information.
The method for determining the size information comprises the following steps: a processing time is determined based at least on the force information, during which the one or more forces are applied to the mine bedrock roof sample during the roller crushing of the mine bedrock roof sample.
The method for determining the processing time comprises the following steps: the processing time is determined as a time period during which the one or more forces applied to the mine surrounding rock roof sample remain above a threshold force.
The method for determining the size information comprises the following steps: determining a dimension parameter representative of the dimension of the mine surrounding rock roof sample based at least in part on the processing time, and wherein determining the at least one hardness parameter comprises: the at least one stiffness parameter is determined based at least in part on the force information and the dimensional parameter.
The measurement provided by the invention further comprises: receiving nip information indicative of a nip size provided by the at least one roller during the roller crushing, and wherein determining the at least one hardness parameter comprises: determining the at least one hardness parameter based at least in part on the nip information;
determining the size information includes: the dimensional information is determined based at least in part on the nip information.
The determining of the at least one hardness parameter provided by the invention comprises: determining a maximum nip size during roller crushing of the mine surrounding rock roof sample from the nip information, and determining the at least one hardness parameter based at least in part on the maximum nip size.
The determining of the at least one hardness parameter provided by the invention comprises: determining a compression distance of the mine surrounding rock roof sample during roller crushing, and multiplying a crushing force of the one or more forces represented by the force information by the compression distance to determine a crushing energy.
The receiving the force information provided by the invention comprises the following steps: the method includes receiving an indication of one or more sensed roller retention forces holding the at least one roller against at least one gap limiter during the roller crushing, and determining the one or more forces based at least in part on the one or more sensed roller retention forces.
2. Application example. In order to prove the inventive and technical value of the technical solution of the present invention, this section is an application example on specific products or related technologies of the claim technical solution.
The method comprises the steps of carrying out prediction on permeability of a mine surrounding rock roof, carrying out correction on an electric imaging image by utilizing MATLAB, carrying out pretreatment processes such as image noise reduction and the like to realize image enhancement, forming a basic image which can be used for logging interpretation, extracting available information of the image by utilizing machine learning to prepare available data for permeability prediction, and accurately predicting the mine surrounding rock roof permeability according to particle size information and a permeability training regression model by utilizing MATLAB machine learning; meanwhile, the hardness of the mine surrounding rock roof can be accurately measured by the method for measuring the hardness of the mine surrounding rock roof, so that collapse caused by overlarge bearing weight is prevented.
It should be noted that the embodiments of the present invention can be realized in hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or special purpose design hardware. Those of ordinary skill in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such as provided on a carrier medium such as a magnetic disk, CD or DVD-ROM, a programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The device of the present invention and its modules may be implemented by hardware circuitry, such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., as well as software executed by various types of processors, or by a combination of the above hardware circuitry and software, such as firmware.
3. Evidence of the effect of the examples. The embodiment of the invention has a great advantage in the research and development or use process, and has the following description in combination with data, charts and the like of the test process.
The method comprises the steps of carrying out prediction on permeability of a mine surrounding rock roof, carrying out correction on an electric imaging image by utilizing MATLAB, carrying out pretreatment processes such as image noise reduction and the like to realize image enhancement, forming a basic image which can be used for logging interpretation, extracting available information of the image by utilizing machine learning to prepare available data for permeability prediction, and accurately predicting the mine surrounding rock roof permeability according to particle size information and a permeability training regression model by utilizing MATLAB machine learning; meanwhile, the hardness of the mine surrounding rock roof can be accurately measured by the method for measuring the hardness of the mine surrounding rock roof, so that collapse caused by overlarge bearing weight is prevented.
The foregoing is merely illustrative of specific embodiments of the present invention, and the scope of the invention is not limited thereto, but any modifications, equivalents, improvements and alternatives falling within the spirit and principles of the present invention will be apparent to those skilled in the art within the scope of the present invention.
Claims (10)
1. The ultrasonic multipoint real-time monitoring method for the disaster of the mine surrounding rock roof is characterized by comprising the following steps of:
arranging a plurality of camera equipment on the periphery of a mine surrounding rock roof, and carrying out enhancement processing on a camera monitoring video;
the method for enhancing the camera monitoring video comprises the following steps:
acquiring the resolution of a camera monitoring video of a goaf of a coal face of a coal mine to be displayed; if the resolution is the first resolution, acquiring an algorithm for reducing noise of the goaf shooting monitoring video of the coal mine working face to be displayed as a goaf shooting monitoring video enhancement algorithm of the coal mine working face, wherein the algorithm for reducing noise is used for cutting a goaf shooting monitoring video frame of the coal mine working face to be displayed into a first part and a second part, and carrying out noise reduction treatment on the second part, wherein the first part is an edge part, and the second part is a part except the edge part;
if the resolution is the second resolution, acquiring an algorithm for carrying out contrast enhancement and saturation enhancement on the goaf shooting monitoring video of the coal mining face to be displayed as a goaf shooting monitoring video enhancement algorithm of the coal mining face; wherein the second resolution is greater than the first resolution, the first resolution is a low resolution, and the second resolution is a high resolution;
the goaf shooting monitoring video of the coal mining face to be displayed is enhanced through the goaf shooting monitoring video enhancement algorithm of the coal mining face;
the first resolution is the resolution corresponding to the standard definition and the video of the shot monitoring of the goaf of the coal face of the fluent coal mine; the second resolution is the resolution corresponding to the high-definition coal mine coal face goaf shooting monitoring video and the ultra-definition coal mine coal face goaf shooting monitoring video;
step two, detecting the mine surrounding rock roof structure through detection equipment; predicting permeability of a mine surrounding rock roof; measuring the hardness of a mine surrounding rock roof;
step three, ultrasonic detection equipment, a vibration sensor, a water level sensor and rainfall meter equipment are installed on a mine surrounding rock roof; and according to disaster information monitored by the equipment, timely issuing disaster warning through a wireless network.
2. The ultrasonic multipoint real-time monitoring method for mine surrounding rock roof disasters of claim 1, wherein the method for predicting the mine surrounding rock roof permeability is as follows:
(1) Acquiring micro resistivity scanning imaging of a mine surrounding rock roof to obtain a corresponding FMI image for extracting particle size information so as to predict corresponding permeability; performing enhancement processing on the FMI image;
(2) Graying the image;
the original color image is in an RGB color mode, the color of each pixel is determined by R, G, B components, each component has 255 variation values, and the three components are processed respectively during processing. On the basis of the RGB value of the color image, the gray value of the corresponding pixel point is calculated by using a formula (1), wherein the formula (1) is as follows: gray= 0.2989 ×R+0.5870 ×G+0.1140 ×B, in practice, RGB cannot reflect morphological features of an image, and further increases workload, so that image graying processing is often used as a preprocessing step of image processing, which can greatly reduce the calculated amount and preserve features such as original chromaticity and brightness of the image;
(3) Intermediate filtering;
FMI images are often contaminated with noise during downhole imaging, affecting the quality of the overall image. In the digital image signal, noise is represented as extreme values which are larger or smaller, and the extreme values act on the true gray values of the image pixels through addition and subtraction, so that bright and dark point interference is caused to the image, the image quality is greatly reduced, the later work of image restoration, segmentation and the like is influenced, and therefore filtering noise suppression is necessary. By adopting median filtering, pixel points affected by noise are exactly eliminated compared with mean filtering, and compared with a linear filter, the method can overcome the blurring of image details and obtain better effects of smoothing and protecting edges;
(4) Threshold segmentation;
image segmentation is a precondition and basis for image analysis and image understanding, and is an image preprocessing process necessary before feature extraction. The quality of the image segmentation effect can greatly influence the subsequent research, and the method can be divided into a threshold value, an edge, a graph theory, an energy functional and the like from the segmentation point of view. The invention adopts a threshold technology, and has the key points of simple realization, small calculated amount and stable performance, and can well reduce the time of image processing;
(5) Extracting features;
when the particle size distribution is calculated, global threshold is adopted, the sizes of structural elements are compared by adopting 80 structural elements with equal distance, gray level difference values between the sizes of adjacent structural elements are recorded, and the sum of changed pixel points is divided by the sizes of the structural elements, so that the size frequency distribution of particles in an original image is obtained.
(6) Regression prediction;
the method comprises the steps of carrying out feature extraction on particle size information of an X well and a Y well according to an existing X well threshold segmentation diagram n (not less than 70) and a Y well threshold segmentation diagram m (not less than 30), obtaining X81 groups of data sets according to a group of permeability data of the existing X well, training three groups of data of the Y well by using a Gaussian process regression model, a regression tree model and a support vector machine model in Regression learner of machine learning in MATLAB, respectively using the three models to predict the permeability of the Y well, fitting the Y well with the known permeability, comparing which model has the best effect, and finally obtaining a result for predicting the permeability.
3. The ultrasonic multipoint real-time monitoring method for mine surrounding rock roof disasters of claim 1, wherein the method for measuring mine surrounding rock roof hardness comprises the following steps:
1) Configuring a detection device parameter, receiving force information by the detection device, the force information representing one or more forces applied to the mine roof sample by at least one roller during roller crushing of the mine roof sample;
2) Determining size information indicative of a size of the mine surrounding rock roof sample; at least one hardness parameter indicative of a hardness of the mine surrounding rock roof sample is determined based at least on the force information and the size information.
4. The ultrasonic multipoint real-time monitoring method for mine surrounding rock roof disasters of claim 3, wherein the determining the size information comprises: a processing time is determined based at least on the force information, during which the one or more forces are applied to the mine bedrock roof sample during the roller crushing of the mine bedrock roof sample.
5. The ultrasonic multipoint real-time monitoring method for mine surrounding rock roof disasters of claim 3, wherein the determining the processing time comprises: the processing time is determined as a time period during which the one or more forces applied to the mine surrounding rock roof sample remain above a threshold force.
6. The ultrasonic multipoint real-time monitoring method for mine surrounding rock roof disasters of claim 3, wherein the determining the size information comprises: determining a dimension parameter representative of the dimension of the mine surrounding rock roof sample based at least in part on the processing time, and wherein determining the at least one hardness parameter comprises: the at least one stiffness parameter is determined based at least in part on the force information and the dimensional parameter.
7. The ultrasonic multipoint real-time monitoring method for mine surrounding rock roof disasters of claim 3, wherein the measuring further comprises: receiving nip information indicative of a nip size provided by the at least one roller during the roller crushing, and wherein determining the at least one hardness parameter comprises: determining the at least one hardness parameter based at least in part on the nip information;
determining the size information includes: the dimensional information is determined based at least in part on the nip information.
8. The mine surrounding roof disaster ultrasonic multi-point real-time monitoring method as set forth in claim 7, wherein said determining said at least one hardness parameter comprises: determining a maximum nip size during roller crushing of the mine surrounding rock roof sample from the nip information, and determining the at least one hardness parameter based at least in part on the maximum nip size.
9. The mine surrounding roof disaster ultrasonic multi-point real-time monitoring method as recited in claim 3, wherein said determining said at least one hardness parameter comprises: determining a compression distance of the mine surrounding rock roof sample during roller crushing, and multiplying a crushing force of the one or more forces represented by the force information by the compression distance to determine a crushing energy.
10. The mine surrounding roof disaster ultrasonic multi-point real-time monitoring method as set forth in claim 3, wherein said receiving said force information comprises: the method includes receiving an indication of one or more sensed roller retention forces holding the at least one roller against at least one gap limiter during the roller crushing, and determining the one or more forces based at least in part on the one or more sensed roller retention forces.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310171097.1A CN116122910A (en) | 2023-02-27 | 2023-02-27 | Ultrasonic multi-point real-time monitoring method for mine surrounding rock roof disasters |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310171097.1A CN116122910A (en) | 2023-02-27 | 2023-02-27 | Ultrasonic multi-point real-time monitoring method for mine surrounding rock roof disasters |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116122910A true CN116122910A (en) | 2023-05-16 |
Family
ID=86311696
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310171097.1A Pending CN116122910A (en) | 2023-02-27 | 2023-02-27 | Ultrasonic multi-point real-time monitoring method for mine surrounding rock roof disasters |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116122910A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117404072A (en) * | 2023-12-15 | 2024-01-16 | 山东新云鹏电气有限公司 | Drilling site management system based on artificial intelligence |
-
2023
- 2023-02-27 CN CN202310171097.1A patent/CN116122910A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117404072A (en) * | 2023-12-15 | 2024-01-16 | 山东新云鹏电气有限公司 | Drilling site management system based on artificial intelligence |
| CN117404072B (en) * | 2023-12-15 | 2024-02-23 | 山东新云鹏电气有限公司 | Drilling site management system based on artificial intelligence |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Lei et al. | A novel tunnel-lining crack recognition system based on digital image technology | |
| CN109359594B (en) | Mine belt abnormity detection method and device based on video processing | |
| Kim et al. | Vision-based nonintrusive context documentation for earthmoving productivity simulation | |
| Yang et al. | Systematic image quality assessment for sewer inspection | |
| CN102279191B (en) | Detection method and apparatus for defects in periodic texture images | |
| CN116122910A (en) | Ultrasonic multi-point real-time monitoring method for mine surrounding rock roof disasters | |
| Qiao et al. | Deep learning-based pixel-level rock fragment recognition during tunnel excavation using instance segmentation model | |
| Huang et al. | Edge detection of ore and rock on the surface of explosion pile based on improved Canny operator | |
| CN111161292B (en) | Ore scale measurement method and application system | |
| CN109934170B (en) | Mine resource statistical method based on computer vision | |
| US11107237B2 (en) | Image foreground detection apparatus and method and electronic device | |
| JPWO2018061976A1 (en) | Image processing device | |
| CN107133963A (en) | Image processing method and device, the method and device of slag piece distributional analysis | |
| CN108629792A (en) | Laser eyepiece detection method and device based on background modeling Yu background difference | |
| Zuo et al. | Classifying cracks at sub-class level in closed circuit television sewer inspection videos | |
| CN107194943A (en) | Image partition method and device, image partition method and device for slag piece | |
| CN104754327A (en) | Method for detecting and eliminating defective pixels of high spectral image | |
| CN114140384A (en) | Transverse vibration image recognition algorithm for hoisting steel wire rope based on contour fitting and centroid tracking | |
| JPWO2018025336A1 (en) | Deterioration detection device, deterioration detection method, and program | |
| Jiang et al. | Application of canny operator threshold adaptive segmentation algorithm combined with digital image processing in tunnel face crevice extraction | |
| Guo et al. | Surface defect detection of civil structures using images: Review from data perspective | |
| Dawood et al. | Data fusion of multiple machine intelligent systems for the condition assessment of subway structures | |
| Menafoglio et al. | Object oriented spatial analysis of natural concentration levels of chemical species in regional-scale aquifers | |
| CN113899349A (en) | Sea wave parameter detection method, equipment and storage medium | |
| Yuanbin et al. | An improved VIBE based on Gaussian pyramid |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |