CN202383714U - Coal petrography interface identification system based on image - Google Patents

Abstract

The utility model discloses a coal petrography interface identification system based on an image, comprising a light source module, an imaging module, a processing module and an explosion-proof shell. The light source module is used to assistantly irradiate a coal petrography cut by a roller, the imaging module is used to collect the coal petrography image, and the processing module is composed of a control unit, an imaging unit, a storing unit, an image processing and identifying unit and a communication interface; the processing module is communicated with a coal cutter controller via the communication interface, the explosion-proof shell is used to satisfy underground explosion-proof requirements. The work process of the coal petrography interface identification system is divided into to two stages, separately being a classifier model establishing stage and an automatic identification stage. The coal petrography interface identification system provided by the utility model has the characteristics of simple structure, easy arrangement, strong adaptability, etc., can identify the coal petrography type cut by the roller real-timely and automatically, thereby providing reliable coal petrography interface information for the coal cutter roller automatic height adjustment.

Description

A kind of Coal-Rock Interface Recognition system based on image

Technical field

The utility model relates to a kind of Coal-Rock Interface Recognition system based on image, is used to discern the boundary position in coal-face coal seam and roof and floor rock stratum under the coal mine, belongs to the image recognition technology field.

Background technology

The adjusting of coal mining machine roller height mainly relies on manually-operated, and promptly the coalcutter driver judges that with the mode of listening noise coal mining machine roller is in coal cutting or is cutting rock, regulates the upright position of cylinder then through visual.Yet coal-face low visibility, noise are big; The coalcutter driver is difficult to accurately judge the cut state of coalcutter; Coal mining machine roller is understood cut unavoidably to top, floor rock, causes a series of problems thus: 1. the rock of a large amount of avalanches is sneaked in the raw coal, causes ature of coal to descend and the freight volume increase; 2. coalcutter driver's labour intensity and danger have been increased; 3. aggravate the pick wearing and tearing, shorten cutting-gear life; 4. the hard roof and floor of cut may produce spark, very easily causes gas explosion for high gassy, forms serious accident; 5. it is left inhomogeneous that cylinder position is regulated the improper roof and floor coal that also possibly cause, and reduces the rate of extraction, and the roof and floor surface irregularity can make hydraulic support and scraper conveyer pass difficulty simultaneously, reduces production efficiency.

Coal-Rock Interface Recognition is the gordian technique that realizes that coal mining machine roller is heightened automatically, promptly adopt a kind of method automatically identify coalcutter in the course of the work pick whether cut roof and floor, perhaps identify the thickness of left top ground coal.Coal-Rock Interface Recognition is the needs of mine safety High-efficient Production for reducing the getting working face operating personnel until realizing that the unmanned exploitation of workplace is significant, is again to alleviate labor strength and the needs that improve operating environment.

Each main producing coal state of the world all attaches great importance to the research of coal-rock interface identification method, has proposed the identification of kinds more than 20 sensor mechanisms such as natural gamma-ray detection method, radar detection method, stress pick method, infrared detecting method, active power monitoring method, shock detection method, sound detection method, dust detection method, memory cut method.At present, comparatively proven technique has nature gamma-ray detection method and memory cut method, is applied on the coalcutter.Nature gamma-ray detection method has adaptability preferably to the shale top board, for sandstone top board adaptability extreme difference then, in China the suitable nature gamma-ray detection method that adopts of mine about 20% is only arranged.Memory cut method is suitable for the mine that geologic condition is good, the coal seam is more smooth, and must adjust running parameter through coalcutter driver's manual operation, and there is certain limitation in effect.

One Chinese patent application numbers 201010160335.1; Open day 2010.9.22; A kind of development machine coal rock identification automatic cutting control method and system are disclosed; This method is calculated the Protodyakonov coeffic of ore deposit, current cut position rock according to the parameter of force-bearing situation, cutting motor and the angling cylinder of cutting head of roadheader, relatively judges that ore deposit rock type is coal or rock in the back with the standard Protodyakonov coeffic.

One Chinese patent application numbers 201010251520.1; Open day 2010.12.22; A kind of coal-rock interface identification method, recognition system and identification probe are disclosed; The high-pressure water shot that can not penetrate the particular level of rock stratum through penetrating the coal seam flows to the coal seam of top, tunnel and sprays, and real-time perception by the contrajet of coal seam or rock stratum reflection to the different effects power that nozzle forms, judge the distance of coal mining machine roller pick upper end and coal-rock interface in view of the above.

There is following problem in present Coal-Rock Interface Recognition system: 1. signal deriving means complex structure, cost is high.In stress pick method, need carry out machine rebuilding to coal mining machine roller, install dynamometry pick and force cell additional, in the vibration-testing method, need on coalcutter, install sensors such as acceleration, torsional oscillation and moment of torsion additional, apparatus structure is complicated, and improvement cost is high.2. be difficult for disposing poor reliability.Coal mining machine roller stressed complicacy, cylinder vibration in the cut process is violent, serious wear, dust are big, and sensor is disposed relatively difficulty, causes mechanical component, sensor and electric wiring to be damaged easily, and device reliability is poor.3. bad adaptability.For dissimilar cylinders, pick, there are bigger difference in the best type of sensor and the selection of picking up signal point, need carry out personalized customization, the bad adaptability of system.

Summary of the invention

In order to overcome the deficiency that existing Coal-Rock Interface Recognition system exists; The utility model discloses a kind of Coal-Rock Interface Recognition system based on image; The coal lithotypes that can in real time, automatically identify cylinder cut place are coal or rock, for coal mining machine roller is heightened the reliable coal-rock interface information that provides automatically.

The described Coal-Rock Interface Recognition of the utility model system comprises light source module, image-forming module, processing module and explosion-resistant enclosure; Said light source module is used for the coal petrography that auxiliary irradiation cylinder cut is crossed; Said image-forming module is used to gather the image of coal petrography; Processing module is made up of control module, image-generating unit, storage unit, Flame Image Process and recognition unit and communication interface; Processing module is communicated by letter with the coalcutter controller through communication interface; Said explosion-resistant enclosure is used for satisfying the downhole anti-explosion requirement.

The high brightness annular white-light illuminating lamp that said light source module is made up of many groups led array, the quantity that led array is lighted is controlled by processing module, for gathering the coal petrography image suitable illumination is provided.Said image-forming module is a charge-coupled device (CCD) camera, has automatic focusing and regulates exposure function automatically, and it is gathered action and is triggered by processing module.The form of inlaying in the said explosion-resistant enclosure is high printing opacity.

The course of work of Coal-Rock Interface Recognition system is divided into two stages: sorter model establishment stage and automatic cognitive phase; At the sorter model establishment stage; Recognition system is gathered the coloured image of several coals and rock respectively under the supervision of coalcutter controller and control; Extract sample characteristics vector based on characteristics of image; Obtain the known sample collection of coal and rock, set up the coal petrography sorter model with the known sample collection as training sample set then; At automatic cognitive phase, recognition system is gathered the coloured image of the coal petrography that the cylinder cut crosses in real time, extracts the sample characteristics vector, is input to its type of coal petrography sorter model identification.

Characteristic parameter based on gray level co-occurrence matrixes has 20 kinds; Be the basis with a large amount of coals, rock image pattern data; Adopt PCA (Principal Component Analysis; PCA) isolated four kinds for distinguishing coal petrography contribution biggest characteristic value: significantly cluster, contrast, energy and entropy constitute the sample characteristics vector, have reduced the data dimension effectively.

The leaching process of said sample characteristics vector may further comprise the steps:

(1) converts the coloured image of gathering to gray level image;

(2) gray level of gray level image is carried out thick quantification treatment;

(3) extract gray level image at 0 °, 45 °, 90 °, the gray level co-occurrence matrixes of 135 ° of four directions;

(4) the equal value matrix of four gray level co-occurrence matrixes of calculating;

(5) the equal value matrix that step (4) is obtained carries out normalization to be handled;

(6) remarkable cluster, contrast, energy and the entropy of calculating gray level co-occurrence matrixes are formed the proper vector of a four-dimensional vector as sample.

The Fisher linear discriminant analysis is adopted in the foundation of said coal petrography sorter model, and detailed process may further comprise the steps:

(1) calculates the mean vector of coal, rock known sample collection respectively;

(2) calculate the within class scatter matrix of coal, rock known sample collection respectively;

(3) total within class scatter matrix of calculating coal, two types of sample sets of rock;

(4) maximum value of calculation criterion function is separated w ^*

(5) calculate boundary threshold value y ₀

The beneficial effect of the utility model is, simple in structure, be easy to arrange, adaptability is strong, discrimination is high, can in real time, automatically identify the coal lithotypes at cylinder cut place, for coal mining machine roller is heightened the reliable coal-rock interface information that provides automatically.

Description of drawings

Below in conjunction with accompanying drawing and embodiment the utility model is done further to describe in detail.

Fig. 1 is the principle of work block diagram of the said Coal-Rock Interface Recognition of the utility model system;

Fig. 2 is the structured flowchart of Coal-Rock Interface Recognition system handles module;

Fig. 3 is that the image pattern proper vector is extracted process flow diagram;

Fig. 4 is that the coal petrography sorter model is set up process flow diagram;

Fig. 5 is a coal petrography sorter model recognition decision process flow diagram.

Among the figure, 1. rock stratum, 2. coal seam, 3. coal mining machine roller; 4. coal petrography cut face, 5. image-forming module, 6. light source module, 7. processing module; 8. explosion-resistant enclosure, 9. form, 10. coalcutter controller, 21. control modules; 22. Flame Image Process and recognition unit, 23. collecting units, 24. storage unit, 25. communication interfaces.

Embodiment

At first the principle of work based on the Coal-Rock Interface Recognition system of image is described.With reference to Fig. 1; At coal-face; The rib that coal mining machine roller 3 cuts are made up of rock stratum 1 and coal seam 2, the Coal-Rock Interface Recognition system gathers the coal petrography that the cylinder cut crosses in real time and cuts the image in cross section 4, according to the difference of image texture characteristic discern current cut to as if coal seam or rock stratum.The Coal-Rock Interface Recognition system is by light source module 6, image-forming module 5, and processing module 7 is formed with explosion-resistant enclosure 8.The high brightness annular white-light illuminating lamp that said light source module 6 is made up of many groups led array, the quantity that led array is lighted provides suitable illumination by processing module 7 controls for gathering the coal petrography image.Said image-forming module 5 is charge-coupled device (CCD) cameras, is used to gather the image of coal petrography cut face 4, has automatic focusing and regulates exposure function automatically, and it is gathered action and is triggered by processing module 7; Said processing module 7 is responsible for light source adjusting, IMAQ, Flame Image Process, sorter model foundation and Classification and Identification task.Said explosion-resistant enclosure 8 is in order to satisfy the requirement of explosion proof under the coal mine, and the form of wherein inlaying 9 is high printing opacities.Processing module is communicated by letter with coalcutter controller 10 through communication interface, receives the steering order of coalcutter.The course of work of Coal-Rock Interface Recognition system is divided into two stages: sorter model establishment stage and automatic cognitive phase.At the sorter model establishment stage; Recognition system is gathered the coloured image of several coals and rock respectively under the supervision of coalcutter controller and control; Extract sample characteristics vector based on characteristics of image; Obtain the known sample collection of coal and rock, set up the coal petrography sorter model with the known sample collection as training sample set then; At automatic cognitive phase, recognition system is gathered the coloured image of the coal petrography that the cylinder cut crosses in real time, extracts the sample characteristics vector, its type of input coal petrography sorter model identification.

Fig. 2 is the structured flowchart of processing module, and processing module is made up of control module, image-generating unit, storage unit, Flame Image Process and recognition unit and communication interface.Control module 21 is control cores of processing module, communicates by letter with coalcutter controller 10 through communication interface 25, starts or closes the Coal-Rock Interface Recognition task according to the instruction of coalcutter controller 10.The view data that Flame Image Process and recognition unit 22 control collecting units 23 collection cameras 5 are gathered also stores storage unit 24 into, and Flame Image Process and recognition unit 22 carry out Flame Image Process and identification from storage unit 24 reading images again.The image of gathering can be reached on the coalcutter controller 10 by communication interface 25 and show.The collection action of camera 5 is triggered by control module 21, and LED lights quantity by control module 21 controls in the light source 6.At the sorter model establishment stage, the operating personnel checks the workplace image scene that recognition system is passed back by the monitor of coalcutter controller 10, adjusts roller height, gathers the coloured image of several coals and rock respectively, sets up the coal petrography sorter model then.At automatic cognitive phase; Recognition system is gathered the coloured image of the coal petrography that the cylinder cut crosses in real time; Be input to the coal petrography sorter model and identify coal lithotypes, recognition result reaches the coalcutter controller via communication interface, and the coalcutter controller is regulated roller height in view of the above.

Fig. 3 is the extraction flow process of image pattern proper vector, and concrete steps comprise:

(1) converts the coloured image of gathering to gray level image (301).If the color digital image of being gathered is f, its R, G, the B component is respectively f _R, f _G, f _B, the data mode of f is a three-dimensional array, f _R, f _G, f _BData mode be two-dimensional array, (value of array element is that (x y) locates gray values of pixel points to image, and gray-scale value is got the integer in the interval [0,255], 0 corresponding black wherein, 255 corresponding whites for x, the y) coordinate of pixel in the correspondence image in the position of array element.f _R, f _G, f _BWith the relation of f be:

f _R＝f(：，：，1)；f _G＝f(：，：，2)；f _B＝f(：，：，3)；

The computing formula that coloured image f is converted into gray level image F is:

F(x，y)＝0.299f _R(x，y)+0.587f _G(x，y)+0.114f _B(x，y)

The data mode of gray level image F is a two-dimensional array, and (x, y) among the presentation video F (x, the gray-scale value of y) locating, gray-scale value get the integer in the interval [0,255] to F, 0 corresponding black wherein, 255 corresponding whites.

(2) gray level of gray level image F is carried out thick quantification treatment (302).If the gray level of piece image is 256, the size of gray level co-occurrence matrixes is 256 * 256, and calculated amount is big, for saving computing time, generally gray level is slightly quantized, as 256 grades of gray level images being transformed into 16 grades gray level image.Though the image through after the thick quantification treatment has distortion, and is little to the influence of textural characteristics.If the gray level of gray level image F is N, become the L level after the thick quantification treatment, tonal range is [0, L-1].The actual minimum gradation value of gray level 0 correspondence image F wherein, the actual maximum gradation value of L-1 correspondence image F, other gray level is evenly divided.

(3) extract gray level image F at 0 °, 45 °, 90 °, the gray level co-occurrence matrixes of 135 ° of four directions (303).Gray level co-occurrence matrixes P _{(δ, θ)}The computing formula of each element is:

p _(δ，θ)(i，j)＝#{[(x ₁，y ₁)，(x ₂，y ₂)]∈F|F(x ₁，y ₁)＝i&F(x ₂，y ₂)＝j}

The number that the # remarked pixel is right, i, j ∈ [0, L-1], x ₂=x ₁+ δ cos θ, y ₂=y ₁+ δ sin θ, the δ value is 1, and the θ value is respectively 0 °, and 45 °, 90 °, 135 °, four gray level co-occurrence matrixes that obtain are respectively P _{(1,0 °)}, P _{(1,45 °)}, P _{(1,90 °)}, P _{(1,135 °)}

(4) the equal value matrix (304) of four gray level co-occurrence matrixes of calculating.At coal-face, the texture of coal seam and rock stratum image does not have tangible directivity, therefore obtains the gray level co-occurrence matrixes of the equal value matrix of four matrixes as image, and computing formula is:

(5) gray level co-occurrence matrixes P is carried out normalization and handle (305).

Gray level co-occurrence matrixes homogenization method be original matrix each divided by matrix all with, homogenization matrix P _NEach p _N(i, j) computing formula is:

$p_N(i,j)=\frac{p(i,j)}{\underset{i=0}{\overset{L-1}{\mathrm{\Σ}}}\underset{j=0}{\overset{L-1}{\mathrm{\Σ}}}p(i,j)}$

Wherein (i is gray level co-occurrence matrixes P in that (i j) locates the value of element j) to p.

(6) calculate gray level co-occurrence matrixes P _NCharacteristic parameter: significantly cluster, contrast, energy and entropy, forms the sample characteristics vectorial (306) of a four-dimensional vector as this image, the computing formula of selected characteristic parameter is following:

(a) remarkable cluster

${\mathrm{<figure-callout\; id="1"\; label="f"\; filenames="HSA00000619866800011.png"\; state="\{\{state\}\}">f</figure-callout>}}_1=\underset{i=0}{\overset{L-1}{\mathrm{\&Sigma;}}}\underset{j=0}{\overset{L-1}{\mathrm{\&Sigma;}}}{(i+j-{\mathrm{\&mu;}}_x-{\mathrm{\&mu;}}_y)}^4{p}_N(i,j)$

(b) contrast

${\mathrm{<figure-callout\; id="2"\; label="f"\; filenames="HSA00000619866800011.png"\; state="\{\{state\}\}">f</figure-callout>}}_2=\underset{j=0}{\overset{L-1}{\mathrm{\&Sigma;}}}\underset{j=0}{\overset{L-1}{\mathrm{\&Sigma;}}}\left[{(i-j)}^2{p}_N(i,j)\right]$

(c) energy

${\mathrm{<figure-callout\; id="3"\; label="f"\; filenames="HSA00000619866800011.png"\; state="\{\{state\}\}">f</figure-callout>}}_3=\underset{i=0}{\overset{L-1}{\mathrm{\&Sigma;}}}\underset{j=0}{\overset{L-1}{\mathrm{\&Sigma;}}}{p}_N{(i,j)}^2$

(d) entropy

${\mathrm{<figure-callout\; id="4"\; label="f"\; filenames="HSA00000619866800011.png"\; state="\{\{state\}\}">f</figure-callout>}}_4=-\underset{i=0}{\overset{L-1}{\mathrm{\&Sigma;}}}\underset{j=0}{\overset{L-1}{\mathrm{\&Sigma;}}}{p}_N(i,j)\ln \left(p_N(i,j)\right)$

Where?

, respectively GLCM row, column, the average deviation.

Fig. 4 is the process flow diagram of setting up of coal petrography sorter model.At the sorter model establishment stage, system gathers N respectively ₁Width of cloth coal image and N ₂Width of cloth rock image obtains N through feature extraction ₁Individual coal sample proper vector and N ₂Individual rock sample eigen vector.If the coal training sample set is X ₁, sample number is N ₁, the rock training sample set is X ₂, sample number is N ₂The establishment step of coal petrography discriminator device model comprises:

(a) input coal training sample set X ₁(401) and rock training sample set X ₂(402), ask the mean vector m of coal training sample set ₁(403) and the mean vector m of rock training sample set ₂(404), computing formula is:

$m_i=\frac{1}{N_i}\underset{x\&Element;X_i}{\mathrm{\&Sigma;}}x,i=\mathrm{1,2}$

(b) ask coal sample within class scatter matrix S ₁(405) and this within class scatter matrix of rock sample S ₂(406), computing formula is:

$S_i=\underset{x\&Element;X_i}{\mathrm{\&Sigma;}}(x-m_i){(x-m_i)}^T,i=\mathrm{1,2}$

(c) ask total within class scatter matrix S _w(407), computing formula is:

S _w＝S ₁+S ₂

(d) ask Fisher criterion function maximum value to separate w ^*(408), computing formula is:

w ^*＝S _w ^-1(m ₁-m ₂)

(e) confirm boundary threshold value y ₀(409), computing formula is:

$y_0=\frac{{\left(w^{*}\right)}^{\mathrm{<figure-callout\; id="1"\; label="T\; m"\; filenames="HSA00000619866800011.png"\; state="\{\{state\}\}">T</figure-callout>}}{m}_{}{}_1+{\left(w^{*}\right)}^{\mathrm{<figure-callout\; id="2"\; label="T\; m"\; filenames="HSA00000619866800011.png"\; state="\{\{state\}\}">T</figure-callout>}}{m}_{}{}_2}{2}$

Fig. 5 is a coal petrography sorter model recognition decision process flow diagram.System gathers the image (501) of coal petrography cut face in real time, extracts a four-dimensional proper vector x (502) of image, is entered into coal petrography discriminator device, calculates y=(w ^*) ^TX (503).Y and boundary threshold value y0 are compared (504), if y＞y0, then discerning coal lithotypes is coal (505); If y＜y0, then discerning coal lithotypes is rock (507); If y=y0, then refusal identification (506).

Claims (4)

1. the Coal-Rock Interface Recognition system based on image is characterized in that said system comprises light source module, image-forming module, processing module and explosion-resistant enclosure; Said light source module is used for the coal petrography that auxiliary irradiation cylinder cut is crossed; Said image-forming module is used to gather the image of coal petrography; Processing module is made up of control module, image-generating unit, storage unit, Flame Image Process and recognition unit and communication interface; Processing module is communicated by letter with the coalcutter controller through communication interface; Said explosion-resistant enclosure is used for satisfying the downhole anti-explosion requirement.

2. system according to claim 1 is characterized in that, the high brightness annular white-light illuminating lamp that said light source module is made up of many groups led array, and the quantity that led array is lighted is controlled by processing module, for gathering the coal petrography image suitable illumination is provided.

3. system according to claim 1 is characterized in that, said image-forming module is a charge-coupled device (CCD) camera, has automatic focusing and regulates exposure function automatically, and it is gathered action and is triggered by processing module.

4. system according to claim 1 is characterized in that, the form of inlaying in the said explosion-resistant enclosure is high printing opacity.

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