CN113406296A - Coal petrography intelligent recognition system based on degree of depth learning - Google Patents
Coal petrography intelligent recognition system based on degree of depth learning Download PDFInfo
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Abstract
The invention discloses a coal rock intelligent identification system based on deep learning, which comprises: the acquisition module is used for acquiring signal data of the coal mining machine and coal rock image data; the processing module is connected with the acquisition module and is used for extracting the characteristics of the signal data and the coal rock image data of the coal mining machine and obtaining corresponding space coordinates; the identification module is connected with the processing module and used for identifying the data information after the characteristic extraction and obtaining coal rock boundary information according to the space coordinates; and the control module is connected with the identification module and used for controlling the rocker arm of the coal mining machine according to the coal rock boundary information. The invention has higher identification stability and identification rate, can provide reliable coal rock identification information for automation of the production process, effectively improves the accuracy of target detection, realizes accurate and rapid detection and identification of targets with different sizes in a mine, and has important significance for guaranteeing risk avoidance of underground personnel, collision avoidance of vehicles and intelligent safe mining.
Description
Technical Field
The invention belongs to the field of intelligent coal rock identification, and particularly relates to an intelligent coal rock identification system based on deep learning.
Background
In the coal mining field, improving the accuracy of identifying the interface of the coal seam and the rock stratum has important significance for accurately mastering the coal cutting strength, ensuring the coal quality, accurately calculating the recovery rate and saving the coal resources. At present, various coal rock identification methods, such as a natural gamma ray detection method, a radar detection method, a stress cutting tooth method, an infrared detection method, an active power monitoring method, a vibration detection method, a sound detection method, a dust detection method, a memory cutting method and the like, have the following problems: firstly, various sensors need to be additionally arranged on the existing equipment to acquire information, so that the device is complex in structure and high in cost. Secondly, the devices such as a roller of a coal mining machine, a heading machine and the like are complex in stress, violent in vibration, serious in abrasion, large in dust and difficult in sensor deployment in the production process, mechanical components, sensors and electric circuits are easily damaged, and the reliability of the device is poor. For different types of mechanical equipment, the optimal type of the sensor and the selection of the signal pickup point are greatly different, personalized customization is needed, and the adaptability of the system is poor.
In recent years, many scholars at home and abroad pay attention to theoretical research aiming at deep learning, and a Deep Convolutional Neural Network (DCNN) is considered as an important deep learning method suitable for target detection and classification tasks, has the characteristics of high identification precision, strong anti-interference capability, capability of obtaining target images at a long distance and the like, and becomes a research hotspot when applied to the fields of intelligent monitoring, moving target detection and identification, visual navigation and the like. Therefore, in order to overcome the defects of the existing mine target identification technology, the invention provides a coal rock intelligent identification system based on deep learning, and accurate detection, tracking and identification of mine targets are realized.
Disclosure of Invention
In order to achieve the purpose, the invention provides the following scheme: a coal petrography intelligent recognition system based on degree of depth learning includes:
the acquisition module is used for acquiring signal data of the coal mining machine and coal rock image data;
the processing module is connected with the acquisition module and is used for extracting the characteristics of the signal data and the coal rock image data of the coal mining machine and obtaining corresponding space coordinates;
the identification module is connected with the processing module and used for identifying the data information after the characteristic extraction and obtaining coal rock boundary information according to the space coordinates;
and the control module is connected with the identification module and used for controlling the rocker arm of the coal mining machine according to the coal rock boundary information.
Preferably, the acquisition module comprises a signal acquisition unit and an image acquisition unit;
the signal acquisition unit is used for acquiring a pressure signal of the heightening oil cylinder, a vibration state signal of the rocker arm, a current signal of the cutting motor, a torque signal of the roller shaft, a torsional vibration signal of the roller shaft and a hyperspectral signal;
the image acquisition unit is used for acquiring coal rock image information.
Preferably, the image acquisition unit at least comprises a visible light camera and an annular light source;
the visible light camera is used for acquiring the coal rock image information and sending the coal rock image information to the processing module;
the annular light source is arranged in front of the visible light camera and used for emitting annular light.
Preferably, the visible light camera at least comprises a CMOS image sensor, a communication module;
the CMOS image sensor is used for acquiring the coal rock image information;
the communication module is used for sending the coal rock image information to the processing module.
Preferably, the pressure signal of the heightening oil cylinder is acquired by a piezoresistive pressure sensor;
the rocker arm vibration state signal is acquired through a piezoelectric acceleration sensor;
the current signal of the cutting motor is obtained through a Hall current sensor;
a torque signal of the roller shaft is acquired through a strain type torque sensor;
the torsional vibration signal of the roller shaft is obtained through an incremental photoelectric encoder;
the hyperspectral signal is acquired by a hyperspectral meter.
Preferably, the processing module at least comprises a feature extraction unit, a static sounding sensing unit and a coordinate mapping unit.
Preferably, the characteristic extraction unit performs characteristic extraction on the rocker arm vibration state signal and the torsional vibration signal of the drum shaft based on a Daubechies wavelet function; performing feature extraction on the pressure signal of the heightening oil cylinder, the current signal of the cutting motor, the torque signal of the drum shaft and the hyperspectral signal based on a Parameter Server; extracting the characteristics of the coal rock image information based on wavelet transformation;
the static sounding sensing unit is used for gridding the coal wall and applying acting force to the coal wall so as to acquire the penetration resistance electric signal and the space coordinate of the corresponding grid;
the coordinate mapping unit is used for mapping a space coordinate system of the coal rock image information and a world coordinate system of the coal mining machine to obtain a calibration relation between the visible light camera and the coal mining machine.
Preferably, the spatial coordinates are four-dimensional coordinates (x, y, m, n), where x, y are horizontal and vertical coordinates of the coal rock image, and m, n represent the width and height of the coal rock.
And the identification module performs fusion analysis on the data information after the characteristic extraction based on the BP neural network to obtain coal rock interface information.
Preferably, the system further comprises a marking module for marking the production trajectory; the mining trajectory at least comprises time, depth, angle, mining volume and position coordinates.
The invention discloses the following technical effects:
the coal rock intelligent identification system based on deep learning provided by the invention has higher identification stability and identification rate, and can provide reliable coal rock identification information for production processes such as automatic mining, automatic coal discharge, automatic gangue selection and the like.
The method and the device perform feature extraction on the data information based on deep learning, effectively improve the accuracy of target detection, realize accurate and rapid detection and identification of different targets of the mine, and have important significance in guaranteeing risk avoidance of underground personnel, vehicle collision avoidance and intelligent safe mining.
The coal rock interface recognition method based on the high-lift oil cylinder pressure signal, the rocker arm vibration state signal, the current signal of the cutting motor, the torque signal of the roller shaft, the torsional vibration signal of the roller shaft and the hyperspectral signal greatly improves the accuracy and reliability of recognition; each signal is provided with three-dimensional attitude information, so that the accuracy of a data source is greatly improved, the identification error caused by the deviation of a sensor is avoided, and the accuracy and the reliability of an identification result are further ensured; and the whole process of mining can be visually displayed, and a reference basis is provided for later mining work.
The invention controls the rocker arm of the coal mining machine according to the coal rock boundary information, further adjusts the height of the coal mining machine according to the identification result, excavates the coal bed as much as possible, avoids hard rocks, prevents the cutter face of the coal mining machine from being damaged, prolongs the service life of coal mining equipment such as the coal mining machine and the like, and has important guiding significance for intelligent mining of a coal face.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a schematic diagram of the system of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
As shown in fig. 1, the invention provides a coal rock intelligent identification system based on deep learning, which comprises:
the acquisition module comprises a signal acquisition unit and is used for acquiring signal data of the coal mining machine; the image acquisition unit is used for acquiring coal rock image data; the method comprises the steps of acquiring coal mining machine signal data, wherein the coal mining machine signal data comprises acquiring a pressure signal of a heightening oil cylinder, a rocker arm vibration state signal, a current signal of a cutting motor, a torque signal of a roller shaft, a torsional vibration signal of the roller shaft and a hyperspectral signal;
acquiring a pressure signal of the heightening oil cylinder through a piezoresistive pressure sensor; the rocker arm vibration state signal is obtained through a piezoelectric acceleration sensor; the current signal of the cutting motor is obtained through a Hall current sensor; a torque signal of the roller shaft is acquired through a strain type torque sensor; the torsional vibration signal of the roller shaft is obtained through an incremental photoelectric encoder; the hyperspectral signal is acquired by a hyperspectral meter.
The image acquisition unit at least comprises a visible light camera and an annular light source; the visible light camera is used for collecting coal rock image information and sending the coal rock image information to the processing module; and the annular light source is arranged in front of the visible light camera and used for emitting annular light.
The visible light camera comprises a CMOS image sensor and a communication module; the CMOS image sensor is used for collecting coal rock image information; the communication module is used for sending the coal rock image information to the processing module. When the visible light camera shoots, n effective images can be obtained, therefore, before feature vector extraction is carried out on the n effective images, preprocessing can be carried out, effective images are named according to the number and the space position of the acquisition equipment, the sizes of the effective images are unified, corresponding feature vectors can be conveniently obtained, and coal and rock recognition is carried out according to the feature vectors.
And the processing module is connected with the acquisition module and at least comprises a feature extraction unit, a static sounding sensing unit and a coordinate mapping unit. The system is used for extracting the characteristics of the signal data and the image data and acquiring corresponding space coordinates; the image data processing module is used for extracting coal and rock image characteristic vectors through the existing image processing algorithm and performing cluster analysis on the coal and rock image characteristic vectors in the sample library.
The image processing module comprises two parts, an off-line processing module and an on-line processing module.
The off-line processing module is used for processing the original coal and rock images in the sample library (the coal and the rock in the sample library are separated), and preprocessing the original coal and rock images, such as unifying the image size and removing noise; and then, carrying out feature extraction and quantization on the preprocessed coal image to obtain a coal feature vector set c ═ { c ═ c1,c2,…,ck},ckRepresenting the characteristic vector of the coal image in the kth sample library, and performing characteristic extraction and quantification on the preprocessed rock image to obtain a rock characteristic vector set beta ═ { beta ═ beta [ beta ])1,β2,…，βm},βmAnd representing the feature vector of the rock image in the mth sample library, wherein the extracted features comprise texture features, angular point features, Gaussian features and the like. The feature vectors of the coal and rock images of the sample library are classified once, and theoretically, the Hamming distance or the Euclidean distance of the feature vectors of different coal images and different rock images is very close, and the feature vector distance of different rock images is also very close.
In this embodiment, the hamming distance or euclidean distance T of the feature vector between the coal feature vector set and the rock feature vector set is calculated respectively (the calculation of the hamming distance or euclidean distance is an existing mathematical formula, and is not described here again), for example, c in the coal feature vector set c is calculated1And the feature vector ({ beta ] of each rock image in the rock feature vector set beta1，β2，…，βm}) separately distance, then c2And { beta ]1，β2，…，βmGet the distance until ckAnd beta1～βmRespectively calculating the distances to obtain a distance set T ═ T11，T12，…，Tkm}，TkmAnd representing the distance between the characteristic vector of the coal image in the kth sample bank and the characteristic vector of the rock image in the mth sample bank.
The online processing module is used for processing the coal wall image acquired during the working of the coal mining machine in real time, changing the size of the image to be consistent with that of the offline processing module through image preprocessing, and then obtaining a coal wall characteristic vector set gamma through characteristic extraction and quantification in the same method as the offline processing modulentRepresenting the characteristic vector of the coal wall image acquired by the nth image acquisition touch sensor at the t time, and then performing cluster analysis on the coal wall characteristic vector set gamma, the coal characteristic vector set c in the sample library and the rock characteristic vector set beta, wherein the specific cluster analysis method comprises the following steps: calculating the distances (Hamming distance or Euclidean distance) of all the coal images in the set gamma and the set c and taking the minimum value a; then calculating the distances of all rock images in the set gamma and the set beta and taking the minimum value b; comparing the sizes of a and b, and classifying the image into a corresponding class according to which one is small. If a and b are both larger than T, the image is not classified as rock or coal, and the image is judged to be an invalid image; according to the actual situation, the T value is adjusted.
The characteristic extraction unit is used for extracting the characteristics of the vibration state signal of the rocker arm and the torsional vibration signal of the roller shaft based on the Daubechies wavelet function; performing feature extraction on a pressure signal of a heightening oil cylinder, a current signal of a cutting motor, a torque signal of a roller shaft and a hyperspectral signal based on a Parameter Server; extracting the characteristics of the coal rock image information based on wavelet transformation;
the static sounding sensing unit is used for meshing the coal wall and applying acting force to the coal wall so as to acquire an electric signal of penetration resistance and a space coordinate of a corresponding grid; static sounding sensing unit needs to contact with the coal wall, thereby discerns the coal petrography through exerting the effort and producing the penetration resistance to the coal wall, and in the motion process, static sounding sensing unit deepens the coal wall certain distance (for example 2.7cm), produces the penetration resistance, and then gathers the penetration resistance electric signal and transmits to the controller and carry out the coal petrography discernment.
And the coordinate mapping unit is used for mapping the space coordinate system of the coal rock image information and the world coordinate system of the coal mining machine to obtain the calibration relation between the visible light camera and the coal mining machine.
The space coordinate is a four-dimensional coordinate (x, y, m, n), wherein x, y are horizontal and vertical coordinates of the coal rock image, and m, n represent the width and height of the coal rock.
And the identification module is connected with the processing module and used for identifying the data information after the characteristic extraction and obtaining the coal rock boundary information according to the space coordinate. When the data information after the characteristic extraction is identified, firstly, respectively performing wavelet transformation on image information and signal information to obtain corresponding wavelet domain low-frequency components and high-frequency components, and processing the wavelet domain low-frequency components and the wavelet domain high-frequency components by using a preset threshold function to eliminate background noise; in order to improve the real-time performance of coal rock identification and ensure the synchronization of the coal rock identification speed and the operation of a coal mining machine, wavelet transformation needs to be realized in a high-speed FPGA; and then, performing fusion analysis on the data information after feature extraction based on the BP neural network to obtain coal-rock interface information, specifically, dividing the data after feature extraction into a training set, a verification set and a test set according to the ratio of 7:2:1, training the initial BP neural network through the training set, verifying the initial BP neural network through the verification set to obtain a target BP neural network, and performing fusion analysis by taking the test set as input based on the target BP neural network to identify and obtain the coal-rock interface information.
And the coal bed and the rock stratum can be assisted to be distinguished by calculating the coal rock hardness coefficient of the coal rock stratum. Wherein f is less than or equal to 4 for coal and soft rock, f is less than or equal to 4-8 for medium hard rock, and f is less than or equal to 8-20 for hard rock (hardest rock); coal with f less than or equal to 1.5 is called soft coal, coal with f 1.5-3.0 is called medium hard coal, and coal with f less than or equal to 3.0 is called hard coal.
And the control module is connected with the identification module and used for controlling the rocker arm of the coal mining machine according to the coal rock boundary information.
The system also comprises a marking module, a data processing module and a data processing module, wherein the marking module is used for marking the mining track; the production trajectory includes at least time, depth, angle, production volume, location coordinates.
The above-described embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solutions of the present invention can be made by those skilled in the art without departing from the spirit of the present invention, and the technical solutions of the present invention are within the scope of the present invention defined by the claims.
Claims (10)
1. The utility model provides a coal petrography intelligent recognition system based on deep learning which characterized in that includes:
the acquisition module is used for acquiring signal data of the coal mining machine and coal rock image data;
the processing module is connected with the acquisition module and is used for extracting the characteristics of the signal data and the coal rock image data of the coal mining machine and obtaining corresponding space coordinates;
the identification module is connected with the processing module and used for identifying the data information after the characteristic extraction and obtaining coal rock boundary information according to the space coordinates;
and the control module is connected with the identification module and used for controlling the rocker arm of the coal mining machine according to the coal rock boundary information.
2. The coal rock intelligent recognition system based on deep learning of claim 1,
the acquisition module comprises a signal acquisition unit and an image acquisition unit;
the signal acquisition unit is used for acquiring a pressure signal of the heightening oil cylinder, a vibration state signal of the rocker arm, a current signal of the cutting motor, a torque signal of the roller shaft, a torsional vibration signal of the roller shaft and a hyperspectral signal;
the image acquisition unit is used for acquiring coal rock image information.
3. The coal rock intelligent recognition system based on deep learning of claim 2,
the image acquisition unit at least comprises a visible light camera and an annular light source;
the visible light camera is used for acquiring the coal rock image information and sending the coal rock image information to the processing module;
the annular light source is arranged in front of the visible light camera and used for emitting annular light.
4. The coal rock intelligent recognition system based on deep learning of claim 3,
the visible light camera at least comprises a CMOS image sensor and a communication module;
the CMOS image sensor is used for acquiring the coal rock image information;
the communication module is used for sending the coal rock image information to the processing module.
5. The coal rock intelligent recognition system based on deep learning of claim 2,
the pressure signal of the heightening oil cylinder is acquired by a piezoresistive pressure sensor;
the rocker arm vibration state signal is acquired through a piezoelectric acceleration sensor;
the current signal of the cutting motor is obtained through a Hall current sensor;
a torque signal of the roller shaft is acquired through a strain type torque sensor;
the torsional vibration signal of the roller shaft is obtained through an incremental photoelectric encoder;
the hyperspectral signal is acquired by a hyperspectral meter.
6. The coal rock intelligent recognition system based on deep learning of claim 1,
the processing module at least comprises a feature extraction unit, a static sounding sensing unit and a coordinate mapping unit.
7. The coal rock intelligent recognition system based on deep learning of claim 6,
the characteristic extraction unit is used for extracting the characteristics of the rocker arm vibration state signal and the torsional vibration signal of the roller shaft based on a Daubechies wavelet function; performing feature extraction on the pressure signal of the heightening oil cylinder, the current signal of the cutting motor, the torque signal of the drum shaft and the hyperspectral signal based on a Parameter Server; extracting the characteristics of the coal rock image information based on wavelet transformation;
the static sounding sensing unit is used for gridding the coal wall and applying acting force to the coal wall so as to acquire the penetration resistance electric signal and the space coordinate of the corresponding grid;
the coordinate mapping unit is used for mapping a space coordinate system of the coal rock image information and a world coordinate system of the coal mining machine to obtain a calibration relation between the visible light camera and the coal mining machine.
8. The system for intelligent coal petrography recognition based on deep learning of claim 7,
the space coordinates are four-dimensional coordinates (x, y, m and n), wherein x and y are horizontal and vertical coordinates of the coal rock image, and m and n represent the width and height of the coal rock.
9. The coal rock intelligent recognition system based on deep learning of claim 1,
and the identification module performs fusion analysis on the data information after the characteristic extraction based on the BP neural network to obtain coal rock interface information.
10. The coal rock intelligent recognition system based on deep learning of claim 1,
the mining track marking device further comprises a marking module, wherein the marking module is used for marking the mining track; the mining trajectory at least comprises time, depth, angle, mining volume and position coordinates.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113863926A (en) * | 2021-10-29 | 2021-12-31 | 北京华电力拓能源科技有限公司 | AI (automatic identification) method for cutting state of drum for coal mining machine |
| CN116310843A (en) * | 2023-05-16 | 2023-06-23 | 三一重型装备有限公司 | Coal rock identification method, device, readable storage medium and heading machine |
| CN116297544A (en) * | 2023-03-16 | 2023-06-23 | 南京京烁雷达科技有限公司 | Method and device for extracting target object of coal rock identification ground penetrating radar |
| CN116883852A (en) * | 2023-08-29 | 2023-10-13 | 北京建工环境修复股份有限公司 | Core data acquisition method and system based on hyperspectrum |
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