LU501938B1 - Method and system for intelligent analysis of big data on unmanned mining in mine - Google Patents
Method and system for intelligent analysis of big data on unmanned mining in mine Download PDFInfo
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Abstract
The present application provides a decision-making method and system of big data analytics for intelligent mining in transparent working face. The present application continuously modifies and updates the transparent geological model through the coal seams exposed by the cutting hole in the mining process and the new geological data generated in the production process, so as to obtain the accurate control decision information of the fully mechanized mining equipment, and the planned cutting model is modified in real time by using big data, so as to realize the intelligent and accurate mining of the coal mining face.
Description
BL-5491 ' LU501938
BACKGROUND Field of Invention The present application relates to the field of intelligent mining technology, in particular relates to a decision-making method and system of big data analytics for intelligent unmanned mining in mines. Background of the Invention Energy 1s the cornerstone of the legal array of human society and the basic condition for economic development and civilization progress. Coal 1s the main energy and important industrial raw material in China, and it is an important support for the healthy development of China's economy; coal accounts for 90% of China’s primary energy resources. China’s resource endowment characteristics of “poor oil, rich coal and less gas” determine that coal occupies the dominant position in China’s energy structure.
In February 2020, eight ministries and commissions of the state jointly issued the Guiding Opinions on Accelerating the Intelligent Development of Coal Mines, which required that the fully mechanized mining face should be operated by few people or no one by 2021. Therefore, it has become an urgent problem to solve the intelligent and accurate mining
BL-5491 LU501938 under the dynamic and complex environment of fully mechanized coal face and the irregular change of coal seam.
SUMMARY The purpose of the invention is to provide a decision-making method and system of big data analytics for intelligent unmanned mining in mines, in particular relates to intelligent mining of transparent working face, so as to solve or alleviate the problems existing in the above existing technologies. In order to achieve the above purpose, the invention provides the following technical scheme: the present application provides a decision-making method of big data analytics for intelligent mining in transparent working face, comprising: step S101: constructing a transparent geological model and a planned cutting template of the coal mining face, step S102: based on the “CT” slicing technology, the transparent geological model is sectioned to obtain the interface curve of the cutting roof and the interface curve of the cutting floor of the transparent geological model, and based on the inertial navigation technology and radar positioning technology, the cutting model is modified in real time according to the working condition monitoring data of the fully mechanized mining equipment; step S103: the fully
BL-5491 ’ LU501938 mechanized mining machine performs real-time automatic cutting of the coal mining face according to the modified planned cutting template.
The present application provides a decision-making system of big data analytics for intelligent mining in transparent working face, comprising: a model construction unit, which is configured to constructing a transparent geological model of the coal mining face and a planned cutting template; the model construction unit includes: geological model construction sub unit and planned cutting template construction sub unit, wherein the model correction unit includes: a first correction sub unit, a second correction sub unit and a third correction sub unit; Beneficial effect The present application through the technologies of roadway fine measurement, drilling detection and slot wave seismic exploration, the geological data are collected and analyzed, the transparent geological model with multiple steps on the transparent working face is established, and the overall design framework of intelligent mining big data analysis and decision-making of transparent working face is constructed; using the unification and correlation of various communication protocol data of different equipment of coal mining face; based on the mining technology, fully mechanized mining automation control technology, inertial navigation technology and radar ranging technology, the transparent geological model is constantly modified and updated through the coal
BL-5491 ° LU501938 seams exposed in the mining process and the newly generated geological data in the production process, so as to obtain the accurate control decision information of fully mechanized mining equipment, big data is used to correct the planning cutting model in real time to realize intelligent and precise mining of coal mining face.
BRIEF DESCRIPTION OF THE DRAWINGS Fig. 1 is a flow diagram of a decision-making method of big data analysis for intelligent mining in transparent working face; Fig. 2 is a flow diagram of constructing transparent geological model of coal mining face; Fig. 3 1s flow diagram of roof/floor interface curve of coal mining face; Fig. 4 is flow diagram of the process of planning and cutting template correction; Fig. 5 is structure diagram of a decision-making system of big data analysis for intelligent mining in transparent working face.
DETAILED DESCRIPTION OF THE EMBODIMENTS First of all, it should be noted that “CT” slicing technology is to section the digital model of the coal seam of the working face to be mined according to the cutting plan, then optimize the cutting path and parameters of the shearer according to the cutting plane of the digital model of the coal
BL-5491 ) LU501938 seam and the requirements of intelligent mining, so as to control the shearer to mine according to the planned cutting path.
Fig. 1 is a flow diagram of a decision-making method of big data analysis for intelligent mining in transparent working face provided by the present application; as shown in Fig. 1, the decision-making method of big data analysis for intelligent mining in transparent working face comprising: Step S101: constructing a transparent geological model and a planned cutting template of the coal mining face; according to the geological data obtained from roadway fine measurement, slot wave seismic exploration and underground drilling, constructing the step model of the coal mining face based on the implicit iterative interpolation algorithm, and obtaining the transparent geological model with multiple steps of coal mining face; wherein the geological data at least includes: geological structure data, coal thickness floor data, coal seam fluctuation state data, coal seam concealed structure data, coal thickness distribution data, the position data of the borehole through the coal seam roof and floor; wherein the geological structure data, coal thickness floor data and coal seam fluctuation state data can be obtained through roadway fine measurement; the hidden structure data of coal seam can be obtained through slot wave seismic exploration system; through underground drilling, the coal thickness distribution data and the position data of drilling through the coal seam roof and floor can be obtained. In
BL-5491 LU501938 addition, based on the implicit iterative interpolation algorithm, the transparent geological model can be smoother, more consistent with the actual situation of the bottom layer, and the model accuracy 1s higher.
When constructing the transparent geological model, it is necessary to analyze the geological conditions, including coal seam fluctuation shape, fault development, existence of collapse column, distribution of scouring zone, coal seam bifurcation and other coal seam structural information.
Wherein the analysis of coal seam fluctuation shape is mainly through drawing the contour map of roof and floor and the contour map of coal thickness, on the one hand, the contour map of roof and floor is constrained by as many geological data as possible (roadway fine measurement and three-dimensional seismic exploration); on the other hand, by analogy with the contour line of the panel roof and floor, the constraint information of the roof and floor is added to basically determine the coal seam fluctuation shape.
The fault development is judged by means of slot wave seismic exploration and pit penetration and other geophysical exploration methods, combined with the contour line of coal seam roof and floor.
The fault is reconfirmed by observing both sides of the roadway, cutting hole fault evidence and peeping at gas drainage boreholes.
Finally, combined with the regional tectonic geological conditions (tectonic stress distribution, paleoenvironment and exposed faults in the panel), the fault analysis is carried out.
The existence of collapse column is detected by geophysical
BL-5491 LU501938 methods such as three-dimensional seismic exploration, slot wave seismic exploration and pit penetration, and confirmed by means of drilling peeping, supplementary drilling and lithology analysis. For the distribution of the scour zone, it is necessary to determine the nature of scouring zone (syngenesis) first, the shape of the scour zone is usually lenticular or gourd string, the trend surface analysis is carried out to predict the scope of the scour zone according to the shape of the scour zone. Combined with lithology (silt content) and other information, the two wings of scour zone can be judged. Coal seam bifurcation: according to the realistic situation, defining the coal seam bifurcation and judging the nature of coal seam bifurcation.
Fig. 2 is a flow diagram of constructing transparent geological model of coal mining face provided by the present application; as shown in Fig. 2, constructing a transparent geological model of the coal mining face, comprising: Step S111, according to the data of air inlet and return roadway, ground drilling data and realistic data of cutting hole, constructing the first- class transparent geological model of coal mining face; the data of air inlet and return roadway and the realistic data of cutting hole are included in the coal seam fluctuation state data and coal thickness distribution data obtained through the fine measurement of roadway; the ground drilling data is included in the position data of drilling through the
BL-5491 ; LU501938 coal seam roof and floor obtained through underground drilling, which mainly refers to a large number of drilling projects implemented before coal mining in the working face, which are used for gas drainage, structural detection and water detection and drainage.
Step S121, based on the first-class transparent geological model, the second-class transparent geological model of coal mining face is constructed according to the data of air inlet and return roadway, ground drilling data, realistic data of cutting hole, drilling measurement data and slot wave seismic exploration data; wherein the borehole measurement data at least includes: coal thickness distribution data and the position data of the borehole through the coal seam roof and floor; the slot wave seismic exploration data at least includes: coal seam concealed structure data; based on the first-class transparent geological model, the first-class transparent geological model is dynamically updated through borehole measurement data and slot wave seismic exploration data to improve the model accuracy, so that the model accuracy of the second-class transparent geological model is within 150 mm.
The borehole measurement data is included in the position data of the borehole through the coal seam roof and floor obtained by underground drilling, which mainly refers to the use of detection instruments and equipment to carry out the borehole trajectory and stratum marks of different lithology, to obtain the lithology analysis histogram of the
BL-5491 LU501938 borehole, and carry out statistical rectification of the borehole through layer points, so as to provide constraint condition for analyzing the spatial distribution form of coal seam and the thickness distribution of coal seam and constructing three-dimensional geological model.
Step S131, based on the second-class transparent geological model, the third-class transparent geological model of coal face is constructed according to the data of air inlet and return roadway, ground drilling data, borehole realistic data, borehole measurement data, updated realistic data and slot wave seismic exploration data, wherein the updated realistic data are the re-realistic data of coal seam exposed by borehole in the mining process and the new geological data generated in the production process. Through updating realistic data, the second-class transparent geological model is constructed in steps and dynamically updated to improve the accuracy of the model, with the increasing of updated realistic data, the accuracy of the transparent geological model is also constantly improved. Wherein the updated realistic data specifically includes the realistic data of cutting hole, borehole measurement data, slot wave seismic exploration data and other geological data generated during production. Based on the “CT” slicing technology, the planned cutting template is generated according to the transparent geological model. That is, according to the cutting plan, the coal seam digital model (transparent geological model) of the working face to be mined 1s sectioned, and the cutting path
BL-5491 LU501938 of the shearer 1s obtained according to the cutting plane of the coal seam digital model.
Specifically, based on the “CT” slicing technology, the transparent geological model is gridded, and the coal seam floor at the stopping point of the air inlet roadway is selected as the reference zero point for relative coordinate transmission, in the relative coordinate system, the planned cutting template of the coal mining face is established according to the coal seam floor, propulsive degree, pitch angle, mining height, mining inclination, mining speed and mining direction. the planned cutting template at least includes: the planning cutting model of shearer, the planning control model of hydraulic support and the planning model of scraper conveyor; the planned cutting model of shearer includes: the basic state information of shearer and the relationship between related equipment of shearer; wherein the basic state information of shearer includes: shearer operation state, shearer attitude sensor, actual displacement of shearer encoder, mining height and undercover value accuracy, shearer video information; the associated equipment relationship of shearer includes: the associated relationship between shearer and transparent geological model “CT” slice, hydraulic support and scraper conveyor; the planning and control model of hydraulic support includes: support information and related equipment relationship of support; wherein the support information includes: support status, support attitude sensor,
BL-5491 LU501938 support travel sensor and support video information; the related equipment relationship of support includes: the related relationship between hydraulic support and scraper conveyor and shearer; the planning model of scraper conveyor includes: the basic state information of conveyor and the relationship between related equipment of conveyor, wherein the basic state information of the conveyor includes: flatness measurement data, conveyor pitch angle, conveyor load, conveyor motor operation data and conveyor video information; the associated equipment relationship of the conveyor includes: the associated relationship between the conveyor and the hydraulic support, the associated relationship between the conveyor and the shearer; the relationship between the conveyor and the hydraulic support includes: the moving position of the conveyor and the hydraulic support and the upward and downward range of the conveyor relative to the support.
Step 102, based on the “CT” slicing technology, the transparent geological model is sectioned to obtain the interface curve of the cutting roof and the interface curve of the cutting floor of the transparent geological model, and based on the inertial navigation technology and radar positioning technology, the cutting model is modified in real time according to the working condition monitoring data of the fully mechanized mining equipment,
BL-5491 LU501938
In some optional embodiments, based on the “CT” slicing technology, the transparent geological model is sectioned to obtain the interface curve of the cutting roof and the cutting floor of the transparent geological model, combined with the mining technology, the planned cutting template is modified in real time;
Fig. 3 1s a flow diagram of roof/floor interface curve of coal mining face provided by the present application; as shown in Fig. 3, based on the “CT” slicing technology, the transparent geological model is sectioned to obtain the interface curve of the cutting roof and the cutting floor of the transparent geological model comprising:
S112A, geological model gridding: based on “CT” slicing technology, the transparent geological model is divided into two-dimensional plane grids;
In the embodiment of the present application, in the process of geological model gridding, setting the grid step length in the strike and dip direction of the coal mining face, gridding the coal seam digital model in two directions, and projecting the grid to a two-dimensional horizontal plane.
Specifically, setting the grid step length in the length and width direction of the working face, and dividing the coal seam on the two-
dimensional plane to obtain the two-dimensional plane grid, roof grid and floor grid.
BL-5491 LU501938 S122A, discretizing the cutting path: projecting the planned cutting path onto the grid plane, discretizing the projection curve into a limited number of line segments, and determining the plane coordinates of the intersection points of each line segment and the grid line, so as to obtain the projection point sequence of the planned cutting path on the two- dimensional plane; By projecting the planned cutting route of the shearer onto the grid plane projection diagram, and roughly dividing the planned cutting route into limited straight line segments, that is, projecting the planned cutting route onto the grid plane, and discretizing the projection curve into n line segments; for the i-th line segment, the coordinates of the two end points are (Xi, yi) and CXi+1» Yirı) , respectively, and the linear equation between the two points is: y = k;x + b; Wherein x € [min (x; Xi+1) , max (x; » x;+1)]> k; = pm b; = Xi ee i=1, 2, 3, ---n, nis a positive integer.
Calculating the intersection of the linear equation y = k;x + b; 1s between the interval x € [min (x; Xi+1) ; max(x;> Xi41)] and the grid line. By performing the above steps for all line segments, the approximate projection point sequence of the planned cutting route on the two-dimensional plane can be obtained.
BL-5491 LU501938 Step S132A: calculating the plane coordinates of each discrete point: searching the grid point closest to the two-dimensional plane of the projection point in the roof grid and floor grid respectively, and taking the top and floor elevations of the point as the sequence of coal seam vertices and floor points at the projection point; Projecting the curved surfaces of the coal seam roof / floor are onto the two-dimensional horizontal plane respectively, for each straight line segment, according to the linear equation y = k;x + b; between the starting point and the ending point of the cutting sequence, calculating the plane coordinates of the intersection of the straight line segment and the grid line in step S122 A and the roof/floor elevation corresponding to this point. That 1s, for each projection point, the grid point closest to the two- dimensional plane of the projection point is searched in the roof grid and floor grid respectively, and the roof and floor elevation of the point is taken asthe elevation value of the coal seam roof and floor at the projection point, so as to obtain the roof point and floor point.
For the j-th projection point (xj, y;) , setting the neighborhood parameter r, and searching all points where the plane coordinates of the roof grid and floor grid points fall in the neighborhood area {x; + r < x < Xi +71, yj +r < Y < Yj41 +r] respectively; the elevations z;; and zy; of the roof and floor corresponding to the j-th projection point (x;
BL-5491 LU501938 y;) can be determined by the nearest distance method and the distance weighting method.
Step S142A, roof/floor interface curve: connecting the obtained roof/floor control points in sequence according to the direction of the straight line, so as to obtain the roof/floor interface curve of the coal mining face.
Based on the “CT” slicing technology, the transparent geological model is sectioned to obtain the interface curve of the cutting roof and the cutting floor of the transparent geological model: projecting roof/floor interface curve of the coal seam onto a two-dimensional horizontal plane respectively, for each straight line segment, according to the linear equation y = k;x + b;between the starting point and the ending point of the cutting sequence, calculating the plane coordinates of the intersection of the straight line segment and the corresponding roof/floor elevation of this point, and the obtained roof/floor control points are connected in sequence according to the direction of the straight line segment to obtain the roof / floor interface curve.
Based on the “CT” slicing technology, the transparent geological model is sectioned, and the accuracy of the model will have certain difference according to the different data, that is, the models of different steps are built with different data, wherein the data updating of the model is to re-describe the coal seam exposed by the cutting hole in the mining
BL-5491 LU501938 process and the new geological data generated in the production process. With the increasing of data, the accuracy of the model is constantly improved.
In some optional embodiments, the planned cutting template is modified in real time based on inertial navigation technology, radar positioning technology and big data analysis and decision-making technology, combined with mining technology, specifically, the execution results are obtained based on inertial navigation technology and radar positioning technology, according to the comparison difference between the execution results and the cutting template, and based on the big data analysis and decision-making technology, deviation analysis is carried out on the data accuracy of transparent geological model “CT” slice, angle conversion correction accuracy, working condition navigation position accuracy, mechanical characteristic error determination accuracy and manual intervention learning correction accuracy, and the planned cutting template is modified in real time according to the deviation analysis results. Fig. 4 is flow diagram of the process of planning and cutting template correction provided by the present application; as shown in Fig. 4, based on inertial navigation technology, radar positioning technology and big data analysis and decision-making technology, combined with mining technology, the planned cutting template is modified in real time, comprising:
BL-5491 LU501938 Step S112B, collecting the three-dimensional attitude data of the shearer in real time based on the inertial navigation technology; In the embodiment of the present application, an inertial navigation system is installed in the shearer to collect the attitude information (pitch angle, roll angle and heading angle) of the shearer in real time, combined with the odometer data, the accurate positioning of the fully mechanized mining equipment in the transparent geological model is realized, by recording the inertial navigation running track, technical support is provided for the automatic alignment control of the working face. At the same time, the displacement changes in the X, Y and Z directions of inertial navigation can reflect the current three-dimensional position and attitude of the shearer in real time, through the change of position and attitude, the mining height, slope and other data in the planning cutting model can be further modified and updated to provide the basis for accurate control.
Step S122B, based on the radar positioning technology, measuring the distance between the head and tail of the scraper conveyor and the side of the inlet and return air roadway in real time, so as to obtain the upward movement and downward movement of the scraper conveyor on the coal mining face; By installing laser radar on the head and tail of the scraper conveyor, monitoring the distance between the head and tail of the conveyor and the side of the air inlet and return roadway in real time, so as to obtain the
BL-5491 LU501938 upward movement and downward movement of the scraper conveyor on the working face, so as to provide decision-making basis for accurate control.
At the same time, installing laser reflectors at equal distances between the two sides of the air inlet and return roadway, and monitoring the advancing distance of the working face in real time, so as to realize the accurate positioning in the transparent geological model and lay the foundation for the accurate control of the next coal.
Step 132B, based on the big data analysis technology, the cutting model is modified in real time according to the roof / floor interface curve of the coal mining face, the upward movement and downward movement of the scraper conveyor of the coal mining face and the three-dimensional attitude data of the shearer.
On the basis of transparent geological model and real-time monitoring data of working face, combined with radar ranging data and inertial navigation three-dimensional attitude monitoring data, and the key data such as cutting curve of shearer, automatic tractor-trailer of hydraulic support, push-slip stroke of scraper conveyor and control parameters of fully mechanized mining equipment (cutting track, cutting drum height adjustment, cutting drum undercover, support pushing, pull frame pushing) are modified and updated through the decision-making data obtained through big data analysis, so as to achieve the purpose of accurate control
BL-5491 LU501938 and continuous pushing of the fully mechanized mining equipment in the dynamic production process.
Based on this, through inertial navigation technology, radar positioning technology and sensors, the position of the shearer in the working face can be accurately located, and the attitude information of the shearer in the working process can be detected; the redundant system of inclination sensor and angular displacement sensor is used to accurately monitor the mining height, so that the mining height error during mining 1s less than 10 mm; using big data analysis and decision-making technology, the shearer can be remotely controlled through the precision control center and the ground big data analysis and decision-making platform, and parametric programming can be realized, the planned cutting path of the shearer can be modified according to the instructions of the big data precision control center, the speed can be adjusted according to the speed requirements of the big data precision control platform, and the mining height can be adjusted according to the mining height requirements of the big data precision control platform.
Wherein the correction of the cutting curve of the shearer is as follows: correcting the mining height, slope, advancing direction, cutting direction and undercover in the planned cutting template.
When adjusting the mining height according to the mining height requirements of the big data precise control platform, the modified planned
BL-5491 LU501938 cutting template 1s sent to the fully mechanized mining machinery of the coal mining face through industrial Ethernet, so that the fully mechanized mining machinery can operate according to the modified planned cutting template and adaptively adjust the drum cutting height. That 1s, constructing the refined roof and floor digital elevation model in advance by using the transparent geological model, monitoring the position and attitude of the shearer by using the shearer real-time data sensing system, calculating the current cutting boundary point of the drum, and the superposition analysis is carried out with the roof and floor digital elevation model to determine the height adjustment value of the drum.
When adjusting the speed according to the speed requirements of the big data accurate control platform, according to the transparent geological model and the big data intelligent analysis and decision-making system, the cutting curve obtained by planning, combined with the mining process ofthe shearer, presetting the shearer operating speed and turning back point position of different process sections, and setting the early deceleration mechanism through the program to control the speed reduction and reversing of the shearer at the turn back position. Due to human interference or other factors, the shearer exits the planned cutting mode, after entering the planned cutting mode again, the shearer program automatically adjusts the speed to the set speed of the process section
BL-5491 LU501938 through speed comparison, so as to realize the automatic adjustment of the planned cutting speed of the shearer.
When correcting the planned cutting path of the shearer according to the instructions of the big data accurate control center, through the established geological data model of the coal mining face, combined with a variety of sensors of the working face, big data decision analysis is carried out to form the planned cutting curve, which is sent to the shearer control system, and the shearer control system will automatically cut according to the planned cutting curve.
First of all, before the planned cutting of the shearer, it is necessary to confirm whether the communication between the shearer and the precision control center is normal, and whether the communication between the shearer and the inertial navigation system is normal. Secondly, after the communication between the shearer and the precision control center and between the shearer and the inertial navigation system is confirmed to be normal, the precision control center issues the planned cutting curve to the shearer. Then, quadrant setting is required after the planned cutting curve is issued, after the quadrant setting is completed, it enters the planned cutting mode, and the shearer operates in the planned direction.
In some optional embodiments, according to the working condition monitoring data of fully mechanized mining equipment, the planning cutting template is modified in real time by using big data machine learning,
BL-5491 LU501938 data aggregation, interpolation, compensation and unbounded flow algorithms.
By planning the cutting process, the mining efficiency and safety index system are established, according to the evaluation results of the mining efficiency and safety index system, the parameter combination of the planned cutting process is trained and the planned cutting model 1s modified.
Through the sensor collecting working face data and iterative training of historical data, the sensor monitoring data can be filtered, compensated and updated in real time; by comparing the difference between the planning cutting model and the execution results, real-time feedback is sent to the big data intelligent analysis and decision center, the reasons for the deviation of the transparent geological model “CT” slice data accuracy, angle conversion correction accuracy, working condition navigation position accuracy, mechanical characteristic setting accuracy, and manual intervention learning correction accuracy are analyzed by using the execution effect evaluation system and data mining technology, the planned cutting model is modified in time, and the correction is issued again for verification until the deviation disappears.
Through the study and analysis of shearer working condition monitoring data and expected planning data, data correction and update are realized by using mathematical algorithms (data aggregation, interpolation,
BL-5491 LU501938 compensation and unbounded flow algorithm). Wherein the working condition monitoring data set is: Yi = (1, Yu 21, he, h2} wherein x, is the advancing direction, y, is the cutting direction, 7, isthe mining height direction, h, is the mining height and h22 is the undercover.
Planning data is: Yo = {X2,Y2, 23, hz, hy} wherein x, is the advancing direction, y, is the cutting direction, z, is the mining height direction, hzis the mining height and h, is the undercover.
Expected mining height value is: hs = hy — hs; + by wherein b, is the data loss compensation value; The expected undercover value is: he = hy — h, +b, Wherein b, is the data loss compensation value; Evaluating the planning cutting template, the model evaluation strategy includes training set and test set, loss function and empirical risk; training error and test error.
The loss function is used to measure the error of model prediction, and model F(h) is selected as the decision function,
BL-5491 LU501938 F(h)=axh+b Wherein a is the adjustment multiple of empirical parameters, and b is the compensation value; for a given input parameter h, F(h) is the prediction result and Y is the real result; the deviation between F(h) and Y uses a loss function to measure the degree of prediction deviation L(Y, F (h)); L(Y,F(h))=|Y — F(h)| Inertial navigation technology is applied to measure the straightness of scraper conveyor and the displacement change of shearer in three-axis (X,Y, Z) direction in real time, so as to provide technical basis for automatic alignment and accurate positioning of working face; laser radar ranging technology is applied to monitor the distance between the head and tail of the conveyor and the side of the inlet and return air roadway in real time, and obtaining the amount of upward movement and downward movement of the scraper conveyor in the working face, so as to provide decision-making basis for accurate control; laser reflectors are installed at equal distance between the two sides of the air inlet and return roadway, and monitoring the advancing distance of the working face in real time, so as to achieve accurate positioning in the transparent geological model.
Through the technologies of roadway fine measurement, drilling detection and slot wave seismic exploration, the geological data are collected and analyzed, the transparent geological model with multiple
BL-5491 LU501938 steps on the transparent working face is established, and the overall design framework of intelligent mining big data analysis and decision-making of transparent working face is constructed; based on the mining technology, fully mechanized mining automation control technology, inertial navigation technology and radar ranging technology, the transparent geological model is constantly modified and updated through the coal seams exposed in the mining process and the newly generated geological data in the production process, so as to obtain the accurate control decision information of fully mechanized mining equipment, big data is used to correct the planning cutting model in real time to realize intelligent and precise mining of coal face.
Fig. 5 is structure diagram of a decision-making system of big data analysis for intelligent mining in transparent working face provided by the present application; as shown in Fig. 5, the decision-making system of big data analysis for intelligent mining in transparent working face comprising: a model construction unit 501, which is configured to constructing a transparent geological model of the coal mining face and a planned cutting template; the model construction unit 501 includes: geological model construction sub unit and planned cutting template construction sub unit, the geological model construction sub unit, which is configured to construct the step model of the coal mining face based on the implicit iterative interpolation algorithm according to the geological data obtained
BL-5491 LU501938 from roadway fine measurement, slot wave seismic exploration and underground drilling, so as to obtain the transparent geological model with multiple steps of the coal mining face based on the implicit iterative interpolation algorithm; wherein the geological data at least includes: geological structure data, coal thickness floor data, coal seam fluctuation state data, coal seam concealed structure data, coal thickness distribution data, drilling through coal seam roof and floor position data; planned cutting template construction sub unit, which is configured to generate the planned cutting template according to the transparent geological model based on the “CT” slice technology;
a model correction unit 502, which is configured to section the transparent geological model based on the “CT” slicing technology, so as to obtain the interface curve of the cutting roof and the interface curve of the cutting floor of the transparent geological model, and based on the
1nertial navigation technology and radar positioning technology, the cutting model is modified in real time according to the working condition monitoring data of the fully mechanized mining equipment; wherein the model correction unit 502 includes: a first correction sub unit, a second correction sub unit and a third correction sub unit; the first correction sub unit, which is configured to section the transparent geological model based on the “CT” slicing technology, so as to obtain the interface curve of the cutting roof and the interface curve of the transparent geological model,
BL-5491 LU501938 combined with the mining technology, the planned cutting template is modified in real time; the second correction sub unit, which is configured to correct the planned cutting template in real time based on inertial navigation technology, radar positioning technology and big data analysis and decision-making technology, combined with mining technology; the third correction sub unit, which is configured to correct the planned cutting template in real time by using big data machine learning, data aggregation,
interpolation, compensation and unbounded flow algorithm according to the working condition monitoring data of fully mechanized mining equipment;
a model issuing unit 503, which is configured to issue the modified planned cutting template to the fully mechanized mining machinery, so that the fully mechanized mining machinery can automatically cut the coal face in real time according to the modified planned cutting template.
Claims (5)
- BL-5491 LU501938CLAIMS I. A decision-making method of big data analytics for intelligent mining in transparent working face, characterized by comprising: step S101: constructing a transparent geological model and a planned cutting template of the coal mining face, comprising: construction of transparent geological model: according to the geological data obtained from roadway fine measurement, slot wave seismic exploration and underground drilling, constructing the step model of coal mining face based on implicit iterative interpolation algorithm, and obtaining the transparent geological model with multiple steps of coal mining face; wherein the geological data at least includes: geological structure data, coal thickness floor data, coal seam fluctuation state data, coal seam concealed structure data, coal thickness distribution data, the position data of the borehole through the coal seam roof and floor; construction of planned cutting template: based on “CT” slicing technology, the planned cutting template is generated according to the transparent geological model; step S102: based on the “CT” slicing technology, the transparent geological model is sectioned to obtain the interface curve of the cutting roof and the interface curve of the cutting floor of the transparent geological model, and based on the inertial navigation technology and radar positioning technology, the cutting model is modified in real timeBL-5491 LU501938 according to the working condition monitoring data of the fully mechanized mining equipment, comprising: based on the “CT” slicing technology, the transparent geological model 1s sectioned to obtain the interface curve of the cutting roof and the cutting floor of the transparent geological model, combined with the mining technology, the planned cutting template is modified in real time; based on inertial navigation technology, radar positioning technology and big data analysis and decision-making technology, combined with mining technology, the planned cutting template is modified in real time; according to the working condition monitoring data of fully mechanized mining equipment, the planned cutting template is modified in real time by using big data machine learning, data aggregation, interpolation, compensation and unbounded flow algorithm; step S103: the fully mechanized mining machine performs real-time automatic cutting of the coal mining face according to the modified planned cutting template.
- 2. The decision-making method of big data analytics for intelligent mining of transparent working face according to claim 1, characterized in that in step S101, constructing the transparent geological model of coal mining working face comprising:BL-5491 LU501938 according to the data of air inlet and return roadway, ground drilling data and realistic data of cutting hole, constructing the first-class transparent geological model of coal mining face; based on the first-class transparent geological model, the second-class transparent geological model of coal mining face is constructed according to the data of air inlet and return roadway, ground drilling data, realistic data of cutting hole, drilling measurement data and slot wave seismic exploration data; wherein the borehole measurement data at least includes: coal thickness distribution data and the position data of the borehole through the coal seam roof and floor; slot wave seismic exploration data at least includes: coal seam concealed structure data; based on the second-class transparent geological model, the third- class transparent geological model of coal face is constructed according to the data of air inlet and return roadway, ground drilling data, borehole realistic data, borehole measurement data, updated realistic data and slot wave seismic exploration data, wherein the updated realistic data are the re-realistic data of coal seam exposed by borehole in the mining process and the new geological data generated in the production process.
- 3. The decision-making method of big data analytics for intelligent mining of transparent working face according to claim 1, characterized in that in step S101,BL-5491 LU501938 based on the “CT” slicing technology, the transparent geological model is gridded, and the coal seam floor at the stopping point of the air inlet roadway is selected as the reference zero point for relative coordinate transmission, in the relative coordinate system, the planned cutting template of the coal mining face is established according to the coal seam floor, propulsive degree, pitch angle, mining height, mining inclination, mining speed and mining direction.
- 4. The decision-making method of big data analytics for intelligent mining of transparent working face according to claim 3, characterized in that in step S101, the planned cutting template at least includes: the planning cutting model of shearer, the planning control model of hydraulic support and the planning model of scraper conveyor; the planned cutting model of shearer includes: the basic state information of shearer and the relationship between related equipment of shearer; wherein the basic state information of shearer includes: shearer operation state, shearer attitude sensor, actual displacement of shearer encoder, mining height and undercover value accuracy, shearer video information; the associated equipment relationship of shearer includes: the associated relationship between shearer and transparent geological model “CT” slice, hydraulic support and scraper conveyor;BL-5491 LU501938 the planning and control model of hydraulic support includes: support information and related equipment relationship of support; wherein the support information includes: support status, support attitude sensor, support travel sensor and support video information; the related equipment relationship of support includes: the related relationship between hydraulic support and scraper conveyor and shearer; the planning model of scraper conveyor includes: the basic state information of conveyor and the relationship between related equipment of conveyor, wherein the basic state information of the conveyor includes: flatness measurement data, conveyor pitch angle, conveyor load, conveyor motor operation data and conveyor video information; the associated equipment relationship of the conveyor includes: the associated relationship between the conveyor and the hydraulic support, the associated relationship between the conveyor and the shearer; the relationship between the conveyor and the hydraulic support includes: the moving position of the conveyor and the hydraulic support and the upward and downward range of the conveyor relative to the support.
- 5. The decision-making system of big data analytics for intelligent mining of transparent working face according to claim 3, characterized by comprising:BL-5491 LU501938 a model construction unit, which is configured to constructing a transparent geological model of the coal mining face and a planned cutting template; the model construction unit includes: geological model construction sub unit and planned cutting template construction sub unit, a geological model construction sub unit, which is configured to construct the step model of the coal mining face based on the implicit iterative interpolation algorithm according to the geological data obtained from roadway fine measurement, slot wave seismic exploration and underground drilling, so as to obtain the transparent geological model with multiple steps of the coal mining face based on the implicit iterative interpolation algorithm; wherein the geological data at least includes: geological structure data, coal thick floor data, coal seam fluctuation state data, coal seam concealed structure data, coal thickness distribution data, drilling through coal seam roof and floor position data;a planned cutting template construction sub unit, which isconfigured to generate the planned cutting template according to the transparent geological model based on the “CT” slice technology;a model correction unit, which is configured to section the transparent geological model based on the “CT” slicing technology, so as to obtain the interface curve of the cutting roof and the interface curve of the cutting floor of the transparent geological model, and based on the inertial navigation technology and radar positioning technology, the cutting modelBL-5491 LU501938 1s modified in real time according to the working condition monitoring data of the fully mechanized mining equipment; wherein the model correction unit includes: a first correction sub unit, a second correction sub unit and a third correction sub unit;the first correction sub unit, which is configured to section the transparent geological model based on the “CT” slicing technology, so as to obtain the interface curve of the cutting roof and the interface curve of the transparent geological model, combined with the mining technology, the planned cutting template is modified in real time;the second correction sub unit, which is configured to correct the planned cutting template in real time based on inertial navigation technology, radar positioning technology and big data analysis and decision-making technology, combined with mining technology;the third correction sub unit, which is configured to correct the planned cutting template in real time by using big data machine learning, data aggregation, interpolation, compensation and unbounded flow algorithm according to the working condition monitoring data of fully mechanized mining equipment;the model issuing unit, which is configured to issue the modified planned cutting template to the fully mechanized mining machinery, so that the fully mechanized mining machinery can automatically cut the coal face in real time according to the modified planned cutting template.
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| CN112433245B (en) * | 2020-11-04 | 2024-03-22 | 陕西彬长孟村矿业有限公司 | Coal mine fault structure prediction and forecast method |
| CN114033365A (en) * | 2021-09-29 | 2022-02-11 | 武汉长盛煤安科技有限公司 | Coal mining machine, early warning method of coal mining machine and electronic equipment |
| CN113962008A (en) * | 2021-11-09 | 2022-01-21 | 华北科技学院(中国煤矿安全技术培训中心) | Method for generating three-dimensional mine roadway model and constructing transparent working surface |
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| CN114663627B (en) * | 2022-03-28 | 2023-05-16 | 中国矿业大学 | Mine digital model building method based on three-dimensional point cloud database |
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| CN114997482A (en) * | 2022-05-24 | 2022-09-02 | 徐州大屯工程咨询有限公司 | Coal mine underground working face digital transparent geological technical method |
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