CN116733533B - Mine tunneling engineering space early warning method and device based on GIS platform - Google Patents
Mine tunneling engineering space early warning method and device based on GIS platform Download PDFInfo
- Publication number
- CN116733533B CN116733533B CN202310602458.3A CN202310602458A CN116733533B CN 116733533 B CN116733533 B CN 116733533B CN 202310602458 A CN202310602458 A CN 202310602458A CN 116733533 B CN116733533 B CN 116733533B
- Authority
- CN
- China
- Prior art keywords
- roadway
- early warning
- point
- data
- footage
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 230000005641 tunneling Effects 0.000 title claims abstract description 98
- 238000000034 method Methods 0.000 title claims abstract description 65
- 238000004519 manufacturing process Methods 0.000 claims description 76
- 238000005516 engineering process Methods 0.000 claims description 29
- 238000004364 calculation method Methods 0.000 claims description 28
- 230000008569 process Effects 0.000 claims description 20
- 230000004927 fusion Effects 0.000 claims description 10
- 238000012800 visualization Methods 0.000 claims description 8
- 238000010276 construction Methods 0.000 claims description 5
- 238000002372 labelling Methods 0.000 claims description 4
- 239000003086 colorant Substances 0.000 claims description 3
- 238000005065 mining Methods 0.000 claims description 3
- 238000004422 calculation algorithm Methods 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 230000008901 benefit Effects 0.000 description 3
- 239000003245 coal Substances 0.000 description 3
- 238000009412 basement excavation Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000003449 preventive effect Effects 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 230000001154 acute effect Effects 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 230000035515 penetration Effects 0.000 description 1
Classifications
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21F—SAFETY DEVICES, TRANSPORT, FILLING-UP, RESCUE, VENTILATION, OR DRAINING IN OR OF MINES OR TUNNELS
- E21F17/00—Methods or devices for use in mines or tunnels, not covered elsewhere
- E21F17/18—Special adaptations of signalling or alarm devices
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21D—SHAFTS; TUNNELS; GALLERIES; LARGE UNDERGROUND CHAMBERS
- E21D9/00—Tunnels or galleries, with or without linings; Methods or apparatus for making thereof; Layout of tunnels or galleries
- E21D9/003—Arrangement of measuring or indicating devices for use during driving of tunnels, e.g. for guiding machines
Landscapes
- Engineering & Computer Science (AREA)
- Mining & Mineral Resources (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Geochemistry & Mineralogy (AREA)
- Geology (AREA)
- Environmental & Geological Engineering (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention provides a mine tunneling engineering space early warning method and device based on a GIS platform, and relates to the technical field of mine tunneling engineering management.
Description
Technical Field
The invention relates to the technical field of mine tunneling engineering management, in particular to a mine tunneling engineering space early warning method and a mine tunneling engineering space early warning device based on a GIS platform.
Background
The mine tunneling engineering is a basic part of coal mine construction, is also an important link of mining connection, and is a heavy weight in coal mine production. At present, the domestic tunnel constructors basically take manual drawing as a main part, the existing business production data and GIS data form a data island, the data island cannot be displayed in a fusion way with the GIS, the tunnel propulsion position cannot be accurately calculated, various geological structures of the tunnel in the tunneling process cannot be accurately calculated, and a prediction and early warning model based on tunneling engineering cannot be formed.
Disclosure of Invention
In view of the problems, the invention provides a mine tunneling engineering space early warning method and a mine tunneling engineering space early warning device based on a GIS platform.
The embodiment of the invention provides a mine tunneling engineering space early warning method based on a GIS platform, which comprises the following steps:
step 1: marking a position corresponding to a hidden disaster causing factor on a GIS platform according to historical geological data of a mine, and setting a roadway name associated with the hidden disaster causing factor, wherein the hidden disaster causing factor comprises a plurality of types;
Step 2: carrying out early warning classification on the underground mining disaster-causing factors according to different types of underground disaster-causing factors, setting different early warning distances according to different early warning classifications, and establishing a prediction early warning management model in the tunnel tunneling process based on the fusion of a GIS technology and tunneling production data, wherein the early warning distances are dynamically configured according to the actual conditions of mines;
Step 3: calibrating roadway footage data based on the wire guide point data to correct roadway production footage, and improving early warning accuracy of the predictive early warning management model;
step 4: marking the position of a central line of the roadway based on the mine GIS graph, and automatically extending according to roadway production footage data to obtain roadway footage automatic extension data;
step 5: according to the roadway footage automatic extension data, automatically calculating the distance between a tunneling head and the hidden disaster causing factors, and combining the predictive early warning management model and the distance to generate early warning information;
Step 6: and displaying early warning information of the front part of the related roadway in the tunneling production process through a GIS space visualization technology, displaying the early warning information to a target terminal through a big data display technology, and pushing the early warning information to a target responsible person through a mobile terminal.
Optionally, marking the position of the central line of the roadway based on the mine GIS graph, automatically extending according to the production scale feeding data to obtain roadway scale feeding automatic extension data, including:
connecting a mine end production database, inquiring production footage data of different time ranges of a roadway from the database, wherein the production data comprises historical production data of the roadway and accumulating the production data in each time period;
Labeling the position of the central line of the roadway based on the mine GIS graph, acquiring a central coordinate of the roadway, and converting the central coordinate into a geodetic coordinate system according to a coordinate system where the central coordinate of the roadway is located, wherein the geodetic coordinate system is an aggregate array;
Calculating the angle a n of each distance difference value according to the tunnel center coordinates,
In the above formula, Δx, Δy represent the difference value of the x-axis coordinates and the difference value of the y-axis coordinates between two coordinate points, respectively;
Calculating the extension length of the footage of each section of the roadway from the starting point of the central line of the roadway according to the production footage data, and calculating to obtain the coordinates of the extension point;
and according to the roadway width and the trigonometric function formula, performing projection calculation on the coordinates of the initial point of the roadway center line and the extension point to obtain four projected coordinate points, and obtaining the roadway footage automatic extension data.
Optionally, calculating the extension length of the footage of each period of the roadway from the starting point of the central line of the roadway according to the production footage data, and calculating to obtain the coordinates of the extension point, including:
Starting calculation from two adjacent points in the center of a roadway, judging the quadrant where the two adjacent points are located according to the coordinate difference value, calculating the coordinate of an extension point, setting a line segment formed by i=1, p i and p i+1 as d i, if l i<＝di, the extension point is in the range of the line segment, otherwise, if the difference value corresponding to (l i-di) is not in the line segment, moving one point backwards to calculate i=i+1, wherein the calculation mode is as follows:
If Δx i > =0 and Δyi＞＝0,xk＝xi+li*cos(ai),yk＝yi+li*sin(ai);
If Δx i > =0 and Δyi＜0,xk＝xi+li*cos(ai),yk＝yi+(-li)*sin(ai);
If Deltax i-1 is less than 0 and Δyi-1＞＝0,xk＝xi+(-li)*cos(ai),yk＝yi+li*sin(ai);
If Deltax i is less than 0 and Δyi＜0,xk＝xi+(-li)*cos(ai),yk＝yi+(-li)*sin(ai);
In the above formula, Δx i,Δyi represents the difference between the x-axis coordinates and the y-axis coordinates of p i(xi,yi) and p i+1(xi+1,yi+1), and l i represents the distance of roadway footage.
Optionally, according to the roadway width and the trigonometric function formula, performing projection calculation on respective coordinates of the roadway center line starting point and the extension point to obtain four projected coordinate points, including:
The coordinates of two projection points p b(xb,yb),pc(xc,yc corresponding to the starting point are calculated according to the starting point p i(xi,yi), the roadway width w and the trigonometric function formula as follows:
The extension point p a(xk,yk) acquires two projected point coordinates p d(xd,yd),pe(xe,ye corresponding to the extension point in the same manner according to the start point.
Optionally, the roadway propulsion corresponding to the roadway production footage data is configured with an automatic color marking function, and the method for realizing the function comprises the following steps:
according to the different time schedules, setting color sets corresponding to the different time schedules: c i{(t1,c1),(t2,c2),......(tn,cn) };
and drawing a progress chart of the roadway through a GIS front-end engine, and setting different time progress to be represented by rectangular color blocks with different colors.
Optionally, based on the integration of the GIS technology and the tunneling production data, a prediction early warning management model in the tunneling process is established, and the method comprises the following steps:
And establishing index classification of the prediction early-warning model for hidden disaster causing factors encountered in a roadway tunneling process based on the GIS spatial relationship and the tunneling production data in the GIS technology, and combining a mine tunneling business flow to obtain the early-warning prediction type and the early-warning rule of the prediction early-warning model.
Optionally, automatically calculating the distance between the heading head and the hidden disaster factor according to the roadway footage automatic extension data, including:
projecting two-dimensional space coordinates of various hidden disaster factors onto the central line of the roadway;
calculating whether the tunnel reaches an alarm range within a projection distance range according to the latest tunneling position of the tunnel;
And if the early warning range is reached, carrying out advanced early warning, and prompting that x meters are expected to reach a T structure.
Optionally, calculating whether the warning range is reached in the projection distance range according to the latest tunneling position of the roadway comprises:
Connecting the latest tunneling position point p k(xk,yk) of the tunnel with the last central line segment point p m(xm,ym) of the tunnel with a straight line;
According to p k(xk,yk)、pm(xm,ym), a two-point formula is applied as follows:
(y-yk)/(x-xk)＝(y-ym)/(x-xm)
The linear equation for the straight line is calculated as: (y m-yk)x+(xm-xk)y+(xmyk-xkym) =0, sorted as ax+by+c=0;
coordinate point p f(xf,yf) of the hidden disaster causing factor is as follows: bx-ay+ (-bx f+ayf) =0;
Obtaining a projection point p' f(xf,yf of the coordinate point of the hidden disaster causing factor on the straight line by solving the intersection point of the straight line and the normal corresponding to the normal vector, namely:
the projection points corresponding to the hidden disaster causing factors are provided with a plurality of points or a single point;
If the projection points corresponding to the hidden disaster causing factors have multiple points, comparing the distances of different projection points to determine the point position range of the maximum distance: p max(xmax,ymax) and p min(xmin,ymin), and early warning is carried out if the latest tunneling position reaches the point location range;
And if the projection point corresponding to the hidden disaster causing factor is a single point, the latest tunneling position reaches the projection point corresponding to the hidden disaster causing factor for early warning.
Optionally, calibrating roadway footage data based on the wire point data to correct roadway footage, and improving early warning accuracy of the predictive early warning management model, including:
Entering the wire guide point data through a system, wherein the wire guide point data comprises: wire point time, mileage, lead-forward distance;
according to the time t i of each wire point of the roadway, the distance S i of the wire point is calculated, and the length data of the time period is corrected by combining the distance S dq before the wire point, and the calculation formula is as follows:
Lt＝Si+Sdq
In the above formula, L t represents the actual roadway mileage of the time difference value of two wire points;
Inquiring date and time ranges of two conducting wire points: t i(t1,t2,t3,t4...tn), inquiring the footage of the time ：Li{(t1,s1),(t2,s2),(t3,s3)....(tn,sn)};
Calculating the difference between the wire point history and the roadway history in the time range according to the wire point mileage L t Calculating the mean value a=d/n by dividing the difference D by the number of footage sets n;
The set ：Li{(t1,s1-a),(t2,s2-a),(t3,s3-a)....(tn,sn-a)}, is recalculated as a correction scale for the time range for that roadway.
The embodiment of the invention provides a mine tunneling engineering space early warning device based on a GIS platform, which comprises:
The marking position association name module is used for marking positions corresponding to hidden disaster causing factors on the GIS platform according to historical geological data of the mine, and setting roadway names associated with the hidden disaster causing factors, wherein the hidden disaster causing factors comprise a plurality of types;
The classification construction module is used for carrying out early warning classification on the hidden disaster causing factors according to different types, setting different early warning distances according to different early warning classifications, and establishing a prediction early warning management model in the tunneling process based on the fusion of a GIS technology and tunneling production data, wherein the early warning distances are dynamically configured according to the actual conditions of mines;
the roadway length-advance calibration module is used for calibrating roadway length-advance data based on the wire point data so as to correct roadway production length and improve the early warning accuracy of the prediction early warning management model;
The roadway automatic extension calculation module is used for marking the position of the center line of the roadway based on the mine GIS graph, and automatically extending according to roadway production footage data to obtain roadway footage automatic extension data;
The space early warning distance calculation module is used for automatically calculating the distance between the tunneling head and the hidden disaster-causing factor according to the roadway footage automatic extension data, and generating early warning information by combining the prediction early warning management model and the distance;
the display module is used for displaying early warning information of the front part of the related roadway in the tunneling production process through a GIS space visualization technology, displaying the early warning information to the target terminal through a big data display technology, and simultaneously pushing the early warning information to a target responsible person through a mobile terminal.
According to the mine tunneling engineering space early warning method based on the GIS platform, firstly, according to historical geological data of a mine, positions corresponding to hidden disaster factors are marked on the GIS platform, and roadway names associated with the hidden disaster factors are set; and then carrying out early warning classification on the tunnel according to different types of hidden disaster causing factors, setting different early warning distances according to different early warning classifications, and establishing a prediction early warning management model in the tunnel tunneling process based on the fusion of a GIS technology and tunneling production data.
Calibrating roadway footage data based on the wire guide point data to correct roadway production footage, and improving early warning accuracy of a predictive early warning management model; and marking the position of the central line of the roadway based on the mine GIS graph, and automatically extending according to the roadway production footage data to obtain roadway footage automatic extension data.
According to the roadway footage automatic extension data, automatically calculating the distance between the tunneling head and the hidden disaster factor, and combining a predictive early warning management model and the distance to generate early warning information; and finally, displaying early warning information of the front part of the relevant roadway in the tunneling production process through a GIS space visualization technology, displaying the early warning information to a target terminal through a big data display technology, and simultaneously pushing the early warning information to a target responsible person through a mobile terminal.
According to the invention, the manual drawing is not required to be taken as a main drawing, business production data and GIS data are fused and displayed, the roadway propulsion position is accurately calculated, the equidistance of various geological structures encountered by the roadway in the tunneling process is accurately calculated, and a predictive early warning model based on tunneling engineering is formed. The advanced early warning is realized, full guarantee is provided for the safe production of the roadway, preventive measures are prepared in advance, and the probability of the occurrence of production accidents of the roadway is greatly reduced. The method can provide effective support for safe production of the roadway and emergency command, and reduce the probability of accidents.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
FIG. 1 is a flow chart of a mine tunneling engineering space early warning method based on a GIS platform in an embodiment of the invention;
FIG. 2 is a flowchart outlining an early warning method according to an embodiment of the present invention;
Fig. 3 is a block diagram of a mine tunneling engineering space early warning device based on a GIS platform in an embodiment of the invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, a flowchart of a mine tunneling engineering space early warning method based on a GIS platform according to an embodiment of the present invention is shown, where the mine tunneling engineering space early warning method includes:
Step 101: marking the position corresponding to the hidden disaster causing factor on the GIS platform according to the historical geological data of the mine, and setting the roadway name associated with the hidden disaster causing factor, wherein the hidden disaster causing factor comprises a plurality of types.
Firstly, marking positions corresponding to hidden disaster causing factors on a GIS platform according to historical geological data of a mine, wherein the hidden disaster causing factors can comprise: geological structure, tunnel penetration, small coal pillar temporary cave chambers, over-air tunnels, goafs, water areas, excavation and necessary exploration, ultra-long distance air supply, ultra-long distance power supply, excavation and cutting edge and the like, the positions of the hidden disaster causing factors are marked on a GIS platform, and tunnel names associated with the hidden disaster causing factors are set. As can be seen from the foregoing, the hidden disaster causing factors include a plurality of types.
Step 102: the method comprises the steps of carrying out early warning classification on hidden disaster causing factors of different types, setting different early warning distances according to different early warning classifications, and establishing a prediction early warning management model in the tunneling process based on the fusion of a GIS technology and tunneling production data, wherein the early warning distances are dynamically configured according to the actual conditions of mines.
Because the hidden disaster causing factors comprise a plurality of types, all the hidden disaster causing factors need to be subjected to early warning classification according to different types of hidden disaster causing factors, and different early warning distances are set according to different early warning classifications, wherein the early warning distances can be dynamically configured according to the actual conditions of mines. And meanwhile, based on the fusion of the GIS technology and tunneling production data, a prediction early warning management model in the tunneling process is established.
The method can establish index classification of a prediction early-warning model for hidden disaster-causing factors encountered in a roadway driving process based on GIS space relation and driving production data in a GIS technology, and combines a mine driving business flow to obtain early-warning prediction types and early-warning rules of the prediction early-warning model. The pre-warning prediction classification table may reflect each type and the corresponding pre-warning distance, for example, as shown in the following table:
The table above shows the pre-warning prediction classification corresponding to 10 hidden disaster causing factors by way of example, and does not indicate that the space pre-warning method for mine tunneling engineering provided by the invention can only pre-warn and predict the 10 hidden disaster causing factors.
Step 103: and calibrating the roadway footage data based on the wire guide point data so as to correct roadway production footage and improve the early warning accuracy of the predictive early warning management model.
In order to improve the early warning accuracy of the predictive early warning management model, the roadway footage data needs to be calibrated based on the wire guide point data so as to correct roadway production footage.
A preferred correction method comprises the following steps:
Entering wire point data by a system, the wire point data comprising: wire point time, mileage, lead-forward distance; and then calculating a wire point distance S i according to the time t i of each wire point of the roadway, and correcting the footage data of the time by combining the lead distance S dq, wherein the calculation formula is as follows:
Lt＝Si+Sdq
In the above formula, L t represents the roadway actual mileage of the time difference between two conducting wire points.
Inquiring date and time ranges of two conducting wire points: t i(t1,t2,t3,t4...tn), inquiring the footage of the time ：Li{(t1,s1),(t2,s2),(t3,s3)....(tn,sn)}.
Calculating the difference between the wire point history and the roadway history in the time range according to the wire point mileage L t Calculating the mean value a=d/n by dividing the difference D by the number of footage sets n; and finally, recalculating the set ：Li{(t1,s1-a),(t2,s2-a),(t3,s3-a)....(tn,sn-a)}, to take the set as a correction scale of the time range of the roadway.
Step 104: and marking the position of the center line of the roadway based on the mine GIS graph, and automatically extending according to the roadway production footage data to obtain roadway footage automatic extension data.
The automatic extension data of the roadway footage is also needed to be obtained, the extension data is obtained by marking the position of the center line of the roadway based on the mine GIS graph and automatically extending according to the roadway production footage data. In one possible embodiment, the method for specifically obtaining roadway footage automatic extension data comprises the following steps:
1) Firstly connecting a mining-end production database, inquiring production footage data of different time ranges of a roadway from the database, wherein the production data comprises historical production data of the roadway and accumulating the production data in each time period.
2) And marking the position of the central line of the roadway based on the mine GIS graph, acquiring the central coordinate of the roadway, and converting the central coordinate of the roadway into a geodetic coordinate system according to a coordinate system where the central coordinate of the roadway is located, wherein the geodetic coordinate system is an aggregate array.
3) Calculating the angle a n of each distance difference value according to the acquired tunnel center coordinates,
In the above expression, Δx, Δy represent the difference in x-axis coordinates and the difference in y-axis coordinates between two coordinate points, respectively.
4) And calculating the extension length of the footage of each section of the roadway from the starting point of the central line of the roadway according to the existing production footage data, and calculating to obtain the coordinates of the extension points.
The method for specifically calculating the coordinates of the extension points comprises the following steps: starting calculation from two adjacent points in the center of a roadway, judging the quadrant where the two adjacent points are located according to the coordinate difference value, calculating the coordinate of an extension point, setting a line segment formed by i=1, p i and p i+1 as d i, if l i＜＝di, the extension point is in the range of the line segment, otherwise, if the difference value corresponding to (l i-di) is not in the line segment, moving one point backwards to calculate i=i+1, wherein the calculation mode is as follows:
If Δx i > =0 and Δyi＞＝0,xk＝xi+li*cos(ai),yk＝yi+li*sin(ai);
If Δx i > =0 and Δyi＜0,xk＝xi+li*cos(ai),yk＝yi+(-li)*sin(ai);
If Deltax i-1 is less than 0 and Δyi-1＞＝0,xk＝xi+(-li)*cos(ai),yk＝yi+li*sin(ai);
If Deltax i is less than 0 and Δyi＜0,xk＝xi+(-li)*cos(ai),yk＝yi+(-li)*sin(ai);
In the above formula, Δx i,Δyi represents the difference between the x-axis coordinates and the y-axis coordinates of p i(xi,yi) and p i+1(xi+1,yi+1), and l i represents the distance of roadway footage.
5) And performing projection calculation on the respective coordinates of the starting point and the extension point of the central line of the roadway according to the roadway width and the trigonometric function formula to obtain four projected coordinate points, and obtaining roadway footage automatic extension data.
The coordinates of the extension points are obtained through the previous calculation, then the respective coordinates of the starting point of the central line of the roadway and the extension points are subjected to projection calculation according to the roadway width and the trigonometric function formula, so that four projected coordinate points are obtained, namely, two projected coordinate points are obtained through calculation of the starting point of the central line of the roadway, and two projected coordinate points are obtained through calculation of the extension points. The four projection coordinate points represent roadway footage automatic extension data.
Taking the starting point as an example: the two projection points p b(xb,yb),pc(xc,yc) corresponding to the starting point are calculated according to the starting point p i(xi,yi), the roadway width w and the trigonometric function formula as follows:
Extension point p a(xk,yk) may acquire two projected point coordinates p d(xd,yd),pe(xe,ye of the corresponding extension point in the same manner as the two projected coordinate points of the starting point are obtained as described above.
In addition, the roadway pushing progress corresponding to the roadway production footage data can be further configured with an automatic color marking function, and the method for realizing the function comprises the following steps:
According to the different time schedules, setting color sets corresponding to the different time schedules: c i{(t1,c1),(t2,c2),......(tn,cn) }; and drawing a progress chart of the roadway through a GIS front-end engine, and setting different time progress to be represented by rectangular color blocks with different colors.
Step 105: according to the roadway footage automatic extension data, the distance between the tunneling head and the hidden disaster causing factors is automatically calculated, and the pre-warning information is generated by combining the pre-warning management model and the distance.
After the automatic extension data of the roadway footage are obtained, the distance between the tunneling head and the hidden disaster factor can be automatically calculated according to the automatic extension data of the roadway footage, and the distance between the current tunneling head position and the hidden disaster factor position can be obtained. And combining the predicted early warning management model and the calculated distance to generate early warning information.
Because the positions of various hidden disaster causing factors are marked on the GIS map in advance according to the actual geological data, and the associated roadway names are set. On the basis, a better way of calculating the distance is as follows:
Firstly, projecting two-dimensional space coordinates of various hidden disaster causing factors onto a tunnel central line; and then calculating whether the position reaches an alarm range within a projection distance range according to the latest tunneling position of the tunnel, namely the position of the tunneling head.
Specific:
Firstly, connecting the latest tunneling position point p k(xk,yk) of the tunnel with the last central line segment point p m(xm,ym) of the tunnel; again according to p k(xk,yk)、pm(xm,ym), a two-point formula is applied as follows:
(y-yk)/(x-xk)＝(y-ym)/(x-xm)
the linear equation for solving the connection to obtain the straight line is: (y m-yk)x+(xm-xk)y+(xmyk-xkym) =0, sorted as ax+by+c=0.
Coordinate point p f(xf,yf of hidden disaster causing factor) is: bx-ay+ (-bx f+ayf) =0; and obtaining a projection point p' f(xf,yf of the coordinate point of the hidden disaster causing factor on the straight line by solving the intersection point of the straight line and the normal line corresponding to the normal vector, namely:
In actual calculation, the projection points corresponding to the hidden disaster causing factors may have a plurality of points or may be only a single point; if the projection points corresponding to the hidden disaster causing factors have multiple points, comparing the distances of different projection points to determine the point position range of the maximum distance: p max(xmax,ymax) and p min(xmin,ymin), and early warning is carried out if the latest tunneling position reaches the point position range. If the projection point corresponding to the hidden disaster causing factor is a single point, the latest tunneling position reaches the projection point corresponding to the hidden disaster causing factor and early warning is carried out.
In general, if the alarm range is not reached, no early warning is needed; if the pre-warning range is reached, advanced pre-warning is carried out, and the estimated x meters are prompted to reach the T structure. For example: prompting 50 meters to arrive at the goaf, prompting 20 meters to arrive at the ponding region, and the like.
In general, the hidden disaster causing factor of the early warning is in front of the roadway, namely an included angle formed by the coordinates of the position where the hidden disaster causing factor is located and the coordinates of the tunneling position of the roadway is an acute angle. Many types of disaster causing factors are hidden, such as: goaf, water accumulation area, etc. the corresponding coordinates of which form regular or irregular polygons, and whether the projection points forming the polygons are positioned right in front of the roadway is determined to be early-warning. Such pre-warning may also indicate how many meters, i.e. how many meters, are going into the area, such as: i.e. 30 meters into the goaf, i.e. 10 meters out of the goaf, etc.
Step 106: the early warning information of the front part of the relevant roadway in the tunneling production process is displayed through a GIS space visualization technology, the early warning information is displayed to a target terminal through a big data display technology, and meanwhile the early warning information is pushed to a target responsible person through a mobile terminal.
The early warning prediction of the hidden disaster causing factors can be realized through the steps 101 to 105. If the early warning information appears, the early warning information of the front part of the relevant roadway in the tunneling production process can be displayed through a GIS space visualization technology, the early warning information is displayed to the target terminal through a big data display technology, and meanwhile the early warning information is pushed to a target responsible person through the mobile terminal.
The above process can be summarized by using a pre-warning method overview flow chart shown in fig. 2, and the GIS-platform-based mine tunneling engineering space pre-warning method can use GIS space coordinate data, a footage automatic extension algorithm, wire point basic data, a pre-warning model system (i.e. a pre-warning management model) and a space distance pre-warning algorithm (i.e. a calculation algorithm for calculating the distance between a tunneling head and a hidden disaster causing factor).
Firstly, acquiring historical geological data of a mine from a production database, wherein a GIS space database is used for supporting various functions of a GIS platform. And marking hidden disaster factors such as geological structures and the like in a GIS map on the GIS platform, and setting early warning distances of different classifications.
And automatically calculating the current tunneling position based on the roadway footage pushing progress, calculating the distance between the tunneling head and the hidden disaster causing factor according to a space distance early warning algorithm, judging whether the early warning condition is met, and if so, carrying out GIS space early warning display.
In an embodiment of the present invention, based on the foregoing method for pre-warning a space of a mine tunneling project, a space pre-warning device of a mine tunneling project based on a GIS platform is further provided, and referring to fig. 3, the space pre-warning device of a mine tunneling project includes:
The marking position association name module 310 is configured to mark a position corresponding to a hidden disaster causing factor on a GIS platform according to historical geological data of a mine, and set a roadway name associated with the hidden disaster causing factor, where the hidden disaster causing factor includes multiple types;
The classification construction module 320 is configured to classify the tunnel according to different types of hidden disaster-causing factors, set different early warning distances according to different early warning classifications, and establish a prediction early warning management model in the tunnel tunneling process based on fusion of a GIS technology and tunneling production data, wherein the early warning distances are dynamically configured according to actual conditions of the mine;
The roadway footage calibration module 330 is configured to calibrate roadway footage data based on the wire guide point data, so as to correct roadway production footage and improve early warning accuracy of the predictive early warning management model;
the roadway automatic extension calculation module 340 is configured to mark a roadway center line position based on a mine GIS graph, and automatically extend according to roadway production footage data to obtain roadway footage automatic extension data;
the space early warning distance calculation module 350 is configured to automatically calculate the distance between the heading head and the hidden disaster factor according to the roadway footage automatic extension data, and combine the prediction early warning management model and the distance to generate early warning information;
The display module 360 is configured to display early warning information of a front side of a relevant roadway in a tunneling production process through a GIS space visualization technology, display the early warning information to a target terminal through a big data display technology, and push the early warning information to a target responsible person through a mobile terminal.
The above 6 modules, the labeling position association name module 310 is used for implementing the method described in the above step 101, the classification construction module 320 is used for implementing the method described in the above step 102, the roadway footage calibration module 330 is used for implementing the method described in the above step 103, the roadway automatic extension calculation module 340 is used for implementing the method described in the above step 104, the spatial pre-warning distance calculation module 350 is used for implementing the method described in the above step 105, and the display module 360 is used for implementing the method described in the above step 106. The foregoing has been explained and illustrated in detail, and will not be repeated.
In summary, the invention does not need to take manual drawing as a main part, fuses and displays service production data and GIS data, accurately calculates the driving position of the roadway, accurately calculates the equidistance of various geological structures encountered by the roadway in the driving process, and forms a prediction and early warning model based on driving engineering. The advanced early warning is realized, full guarantee is provided for the safe production of the roadway, preventive measures are prepared in advance, and the probability of the occurrence of production accidents of the roadway is greatly reduced. The method can provide effective support for safe production of the roadway and emergency command, and reduce the probability of accidents.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or terminal device that comprises the element.
The embodiments of the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those having ordinary skill in the art without departing from the spirit of the present invention and the scope of the claims, which are to be protected by the present invention.
Claims (9)
1. The mine tunneling engineering space early warning method based on the GIS platform is characterized by comprising the following steps of:
step 1: marking a position corresponding to a hidden disaster causing factor on a GIS platform according to historical geological data of a mine, and setting a roadway name associated with the hidden disaster causing factor, wherein the hidden disaster causing factor comprises a plurality of types;
Step 2: carrying out early warning classification on the underground mining disaster-causing factors according to different types of underground disaster-causing factors, setting different early warning distances according to different early warning classifications, and establishing a prediction early warning management model in the tunnel tunneling process based on the fusion of a GIS technology and tunneling production data, wherein the early warning distances are dynamically configured according to the actual conditions of mines;
Step 3: calibrating roadway footage data based on the wire guide point data to correct roadway production footage, and improving early warning accuracy of the predictive early warning management model;
Step 4: connecting a mine end production database, inquiring production footage data of different time ranges of a roadway from the database, wherein the production footage data comprises historical production data of the roadway and accumulating the production footage data in each time period;
Labeling the position of the central line of the roadway based on a mine GIS graph, acquiring a central coordinate of the roadway, and converting the central coordinate of the roadway into a geodetic coordinate system according to a coordinate system where the central coordinate of the roadway is located, wherein the geodetic coordinate system is an aggregate array;
According to the tunnel center coordinates, calculating the angle a i of the distance difference between any two tunnel center coordinates,
In the above formula, Δx i represents the difference of the x-axis coordinates between the two tunnel center coordinate points, and Δy i represents the difference of the y-axis coordinates between the two tunnel center coordinate points;
Calculating the extension length of the footage of each section of the roadway from the starting point of the central line of the roadway according to the production footage data, and calculating to obtain the coordinates of the extension point;
According to the roadway width and the trigonometric function formula, performing projection calculation on the coordinates of the starting point of the central line of the roadway and the extending point to obtain four projected coordinate points, and obtaining roadway footage automatic extension data;
step 5: according to the roadway footage automatic extension data, automatically calculating the distance between a tunneling head and the hidden disaster-causing factors, and combining the predictive early warning management model and the distance to generate early warning information;
Step 6: and displaying early warning information of the front part of the related roadway in the tunneling production process through a GIS space visualization technology, displaying the early warning information to a target terminal through a big data display technology, and pushing the early warning information to a target responsible person through a mobile terminal.
2. The space pre-warning method for mine tunneling engineering according to claim 1, wherein calculating the extension length of the footage of each period of the roadway from the center line start point of the roadway according to the production footage data, and calculating the extension point coordinates comprises:
Starting calculation from two adjacent points in the center of a roadway, judging the quadrant where the two adjacent points are located according to the coordinate difference value, calculating the coordinate of an extension point, setting a line segment formed by i=1, p i and p i+1 as d i, if l i＜＝di, the extension point is in the range of the line segment, otherwise, if the difference value corresponding to (l i-di) is not in the line segment, moving one point backwards to calculate i=i+1, wherein the calculation mode is as follows:
If Δx i > =0 and Δyi＞＝0,xk＝xi+li*cos(ai),yk＝yi+li*sin(ai);
If Δx i > =0 and Δyi＜0,xk＝xi+li*cos(ai),yk＝yi+(-li)*sin(ai);
If Deltax i-1 is less than 0 and Δyi-1＞＝0,xk＝xi+(-li)*cos(ai),yk＝yi+li*sin(ai);
If Deltax i is less than 0 and Δyi＜0,xk＝xi+(-li)*cos(ai),yk＝yi+(-li)*sin(ai);
In the above formula, Δx i,Δyi represents the difference between the x-axis coordinates and the y-axis coordinates of p i(xi,yi) and p i+1(xi+1,yi+1), and l i represents the distance of roadway footage.
3. The mine tunneling engineering space early warning method according to claim 2, wherein the projection calculation is performed on the respective coordinates of the starting point and the extension point of the central line of the roadway according to the roadway width and the trigonometric function formula to obtain four projected coordinate points, and the method comprises the following steps:
The coordinates of two projection points p b(xb,yb),pc(xc,yc corresponding to the starting point are calculated according to the starting point p i(xi,yi), the roadway width w and the trigonometric function formula as follows:
The extension point p a(xk,yk) acquires two projected point coordinates p d(xd,yd),pe(xe,ye corresponding to the extension point in the same manner according to the start point).
4. The space pre-warning method for mine tunneling engineering according to claim 1, wherein the roadway propulsion corresponding to the production footage data is configured with an automatic color marking function, and the method for realizing the function comprises the following steps:
according to the different time schedules, setting color sets corresponding to the different time schedules: c i{(t1,c1),(t2,c2),......(tn,cn) };
and drawing a progress chart of the roadway through a GIS front-end engine, and setting different time progress to be represented by rectangular color blocks with different colors.
5. The mine tunneling engineering space early warning method according to claim 1, wherein the method for establishing the predictive early warning management model in the tunneling process based on the fusion of the GIS technology and tunneling production data comprises the following steps:
And establishing index classification of the prediction early warning management model for hidden disaster causing factors encountered in a roadway tunneling process based on the GIS spatial relationship and the tunneling production data in the GIS technology, and combining a mine tunneling business flow to obtain an early warning prediction type and an early warning rule of the prediction early warning management model.
6. The mine tunneling engineering space early warning method according to claim 1, wherein automatically calculating the distance between a tunneling head and the hidden disaster causing factor according to the roadway footage automatic extension data comprises the following steps:
projecting two-dimensional space coordinates of various hidden disaster factors onto the central line of the roadway;
calculating whether the tunnel reaches an early warning range within a projection distance range according to the latest tunneling position of the tunnel;
And if the early warning range is reached, carrying out advanced early warning, and prompting that x meters are expected to reach a T structure.
7. The mine tunneling engineering space early warning method according to claim 6, wherein calculating whether the warning range is reached in the projection distance range according to the latest tunneling position of the roadway comprises:
Connecting the latest tunneling position point p k(xk,yk) of the tunnel with the last central line segment point p m(xm,ym) of the tunnel with a straight line;
According to p k(xk,yk)、pm(xm,ym), a two-point formula is applied as follows:
(y-yk)/(x-xk)＝(y-ym)/(x-xm)
The linear equation for the straight line is calculated as: (y m-yk)x+(xm-xk)y+(xmyk-xkym) =0, sorted as ax+by+c=0;
coordinate point p f(xf,yf) of the hidden disaster causing factor is as follows: bx-ay+ (-bx f+ayf) =0;
Obtaining a projection point p' f(xf,yf of the coordinate point of the hidden disaster causing factor on the straight line by solving the intersection point of the straight line and the normal corresponding to the normal vector, namely:
where a represents a coefficient of x in the linear equation general formula ax+by+c=0, b represents a coefficient of y in the linear equation general formula ax+by+c=0, and c represents a constant of the linear equation general formula ax+by+c=0;
the projection points corresponding to the hidden disaster causing factors are provided with a plurality of points or a single point;
if the projection points corresponding to the hidden disaster causing factors have a plurality of points, comparing the distances of different projection points to determine the point position range of the maximum distance: p max(xmax,ymax) and p min(xmin,ymin), and early warning is carried out if the latest tunneling position reaches the point location range;
And if the projection point corresponding to the hidden disaster causing factor is a single point, the latest tunneling position reaches the projection point corresponding to the hidden disaster causing factor for early warning.
8. The space pre-warning method for mine tunneling engineering according to claim 1, wherein calibrating roadway footage data based on wire guide point data to correct roadway footage and improve pre-warning accuracy of the predictive pre-warning management model comprises:
Entering the wire guide point data through a system, wherein the wire guide point data comprises: wire point time, mileage, lead-forward distance;
according to the time t i of each wire point of the roadway, the distance S i of the wire point is calculated, and the length data of the time period is corrected by combining the distance S dq before the wire point, and the calculation formula is as follows:
Lt＝Si+Sdq
In the above formula, L t represents the actual mileage of a roadway with the time difference of two conducting wire points, and the time is the difference of the date and time of the two conducting wire points;
Inquiring date and time ranges of two conducting wire points: t i(t1,t2,t3,t4...tn), inquiring the footage of the time ：Li{(t1,s1),(t2,s2),(t3,s3)....(tn,sn)};
Calculating the difference between the wire point history and the roadway history in the time range according to the wire point mileage L t Calculating the mean value a=d/n by dividing the difference D by the number of footage sets n;
The set ：Li{(t1,s1-a),(t2,s2-a),(t3,s3-a)....(tn,sn-a)}, is recalculated as a correction scale for the time range for that roadway.
9. The utility model provides a mine tunneling engineering space early warning device based on GIS platform which characterized in that, mine tunneling engineering space early warning device includes:
The marking position association name module is used for marking positions corresponding to hidden disaster causing factors on the GIS platform according to historical geological data of the mine, and setting roadway names associated with the hidden disaster causing factors, wherein the hidden disaster causing factors comprise a plurality of types;
The classification construction module is used for carrying out early warning classification on the hidden disaster causing factors according to different types, setting different early warning distances according to different early warning classifications, and establishing a prediction early warning management model in the tunneling process based on the fusion of a GIS technology and tunneling production data, wherein the early warning distances are dynamically configured according to the actual conditions of mines;
the roadway length-advance calibration module is used for calibrating roadway length-advance data based on the wire point data so as to correct roadway production length and improve the early warning accuracy of the prediction early warning management model;
The automatic roadway extension calculation module is used for connecting an ore end production database, inquiring production footage data of different roadway time ranges from the database, wherein the production footage data comprises historical production data of the roadway and accumulating the production footage data in each time period;
Labeling the position of the central line of the roadway based on a mine GIS graph, acquiring a central coordinate of the roadway, and converting the central coordinate of the roadway into a geodetic coordinate system according to a coordinate system where the central coordinate of the roadway is located, wherein the geodetic coordinate system is an aggregate array;
According to the tunnel center coordinates, calculating the angle a i of the distance difference between any two tunnel center coordinates,
In the above formula, Δx i represents the difference of the x-axis coordinates between the two tunnel center coordinate points, and Δy i represents the difference of the y-axis coordinates between the two tunnel center coordinate points;
Calculating the extension length of the footage of each section of the roadway from the starting point of the central line of the roadway according to the production footage data, and calculating to obtain the coordinates of the extension point;
According to the roadway width and the trigonometric function formula, performing projection calculation on the coordinates of the starting point of the central line of the roadway and the extending point to obtain four projected coordinate points, and obtaining roadway footage automatic extension data;
The space early warning distance calculation module is used for automatically calculating the distance between the tunneling head and the hidden disaster-causing factor according to the roadway footage automatic extension data, and generating early warning information by combining the prediction early warning management model and the distance;
the display module is used for displaying early warning information of the front part of the related roadway in the tunneling production process through a GIS space visualization technology, displaying the early warning information to the target terminal through a big data display technology, and simultaneously pushing the early warning information to a target responsible person through a mobile terminal.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310602458.3A CN116733533B (en) | 2023-05-25 | 2023-05-25 | Mine tunneling engineering space early warning method and device based on GIS platform |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310602458.3A CN116733533B (en) | 2023-05-25 | 2023-05-25 | Mine tunneling engineering space early warning method and device based on GIS platform |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116733533A CN116733533A (en) | 2023-09-12 |
| CN116733533B true CN116733533B (en) | 2024-04-30 |
Family
ID=87907102
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310602458.3A Active CN116733533B (en) | 2023-05-25 | 2023-05-25 | Mine tunneling engineering space early warning method and device based on GIS platform |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116733533B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117514358A (en) * | 2023-12-19 | 2024-02-06 | 中铁成都规划设计院有限责任公司 | Real-time early warning method and system for poor geology in tunnel construction process |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1715615A (en) * | 2005-08-05 | 2006-01-04 | 郑州煤炭工业(集团)有限责任公司 | Gas preventing and control navigation system for coal mine production mine |
| CN110187390A (en) * | 2019-06-17 | 2019-08-30 | 中国矿业大学(北京) | A kind of parallel survey line stereoscopic seismic observation of coal mine roadway and imaging method |
| CA3102005A1 (en) * | 2019-12-12 | 2021-06-12 | IFP Energies Nouvelles | Method for updating a stratigraphic model of a sedimentary basin by means of measurements |
| CN113722866A (en) * | 2021-07-16 | 2021-11-30 | 陕煤集团神木张家峁矿业有限公司 | Method and device for updating tunneling roadway space topological network model |
| CN114611180A (en) * | 2022-05-12 | 2022-06-10 | 北京龙软科技股份有限公司 | Method for building intelligent mine management and control platform based on geological survey support system |
-
2023
- 2023-05-25 CN CN202310602458.3A patent/CN116733533B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1715615A (en) * | 2005-08-05 | 2006-01-04 | 郑州煤炭工业(集团)有限责任公司 | Gas preventing and control navigation system for coal mine production mine |
| CN110187390A (en) * | 2019-06-17 | 2019-08-30 | 中国矿业大学(北京) | A kind of parallel survey line stereoscopic seismic observation of coal mine roadway and imaging method |
| CA3102005A1 (en) * | 2019-12-12 | 2021-06-12 | IFP Energies Nouvelles | Method for updating a stratigraphic model of a sedimentary basin by means of measurements |
| CN113722866A (en) * | 2021-07-16 | 2021-11-30 | 陕煤集团神木张家峁矿业有限公司 | Method and device for updating tunneling roadway space topological network model |
| CN114611180A (en) * | 2022-05-12 | 2022-06-10 | 北京龙软科技股份有限公司 | Method for building intelligent mine management and control platform based on geological survey support system |
Non-Patent Citations (2)
| Title |
|---|
| 煤矿采空区综合勘探信息管理系统的建设及应用;林培军;《内蒙古煤炭经济》(第第1期期);第4-5、96页 * |
| 隐蔽灾害治理示范工程建设的可行性分析;李亚斌;于强;;山西焦煤科技(第02期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116733533A (en) | 2023-09-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116733533B (en) | Mine tunneling engineering space early warning method and device based on GIS platform | |
| US9383212B2 (en) | Bezier curves for advanced driver assistance system applications | |
| EP2287565B1 (en) | Method of transmitting position information of digital map and apparatus utilized for the method | |
| CN110751308B (en) | Method for planning land space and determining region boundary | |
| US11205079B2 (en) | Determining position data | |
| CN108351216A (en) | estimation device, control method, program and storage medium | |
| KR19980070628A (en) | Navigation system and operation program therefor Storage medium for storing the program | |
| CN107218923A (en) | Surrounding enviroment history settles methods of risk assessment along subway based on PS InSAR technologies | |
| CN102620721A (en) | Fine digital terrain model based road surveying method | |
| CN112818776B (en) | Railway existing line cross section measurement method based on airborne LiDAR point cloud | |
| US20130163865A1 (en) | Guidance device, guidance method, and guidance program | |
| CN111504334B (en) | Road updating method and device of electronic map, computer equipment and storage medium | |
| JP5217462B2 (en) | Road information management device | |
| Krumm et al. | Risk-Aware Planning: Methods and Case Study on Safe Driving Route | |
| KR100576175B1 (en) | Map data production system for providing map data including road grade data | |
| KR20160053201A (en) | Method for matching map of high-precision with navigation link | |
| CN109937342B (en) | Method, device and system for locating moving object | |
| JP4526804B2 (en) | Object position guidance system | |
| CN111414442A (en) | Navigation mark position checking method based on navigation channel topographic map and water level data | |
| US20200402400A1 (en) | History information storage apparatus, route calculation method, and influence range distribution system | |
| CN113720310A (en) | Roadway photographic geology compiling and recording method and system | |
| Mustafin et al. | Earth surface monitoring on undermined territories | |
| Kamel et al. | FTF: a quick surveying approach for constructing high resolution digital surface model for road elements | |
| JP7058817B1 (en) | Distance marker distance determination device, distance marker distance determination method, and distance marker distance determination program | |
| Ganić et al. | Measuring methods for cross sections of underground mine chambers |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |