CN115965754A - Stratum interface interpolation modeling method for intelligent mining - Google Patents
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Abstract
The invention discloses a stratum interface interpolation modeling method facing intelligent mining, which comprises the following steps: step 1: the data measured by the working face are arranged into a set alpha; step 2: carrying out gridding processing on the set alpha to form a set beta 1 of grid points; and step 3: obtaining an insertion point coordinate in an insertion point set phi 1; and 4, step 4: setting an initial z coordinate of each point in a point set phi 1; and 5: setting the weight of each point in the point set phi 1 and the set beta 1; and 6: defining the potential eta of each point in the set phi 1 and the set beta 1 a (ii) a And 7: obtaining a union set of the point set phi 1 and the point set beta 1
Step 8, circularly executing step 7; and step 9: replacing the set beta 1 obtained in the step 8 with the set beta 1 obtained in the step 2 to obtain a new union set
Step 10: computing union
And judging whether the grid interval reaches the target grid interval. The invention can fix the size of the grid according to the realization of the working surface, can perform local dynamic update at the same time, effectively utilizes the data disclosed in real time and obtains a more accurate model.
Description
Technical Field
The invention belongs to the technical field of coal field geology, and relates to a stratum interface interpolation modeling method which is used for realizing the functions of stratum interface mesh subdivision and stratum interface dynamic update.
Background
The intellectualization and the little humanization of the coal mine are one of important ways for realizing the safety of the coal mine. The coal mine intellectualization undergoes four stages of visual remote intervention, automatic working face alignment, intelligent coal cutting based on a transparent working face and full-intelligent self-adaptive mining, and is currently in the key stage of the intelligent coal cutting technology of the transparent working face. The transparent working face intelligent coal cutting technology obtains the actual spreading condition of the working face through detection means such as three-dimensional earthquake, roadway measurement, drilling measurement and the like, establishes a working face model through different interpolation methods after analyzing the data, and then guides the mining work of a coal mining machine by using the working face model. The high-precision coal face coal layer model is the key of the high-precision coal face coal layer model, and the interpolation method is a necessary way for realizing the working face model.
The current common spatial interpolation methods include function interpolation, kriging interpolation and smooth discrete interpolation. Due to the principle of the smooth discrete interpolation algorithm, a grid with uniform size cannot be formed in the X direction and the Y direction, but the size of each knife of the working surface is basically fixed for the working surface. Therefore, the size of the grid needs to be matched with the size of each knife in the use process of the model. Due to the algorithm principle of the kriging interpolation algorithm, the updating of the model must be completely updated, the purpose of local updating is difficult to realize, and for the working face model, the model is usually large, the complete updating of the model puts higher requirements on the system, wastes resources, and the problem can be solved if local dynamic updating can be realized. The function interpolation comprises a distance weighted average interpolation method (IDW), a trend surface method, a spline function method and the like, the interpolation method predicts the data of unknown points through functions by fitting the data of the known points, and the accuracy of the model obtained through interpolation is low.
Disclosure of Invention
The invention aims to provide a method for modeling stratum interface interpolation, which aims to solve the problems that in the prior art, a grid with uniform size cannot be formed and local updating is difficult.
In order to achieve the purpose, the invention discloses a method for modeling stratum interface interpolation, which at least comprises the following steps:
a stratum interface interpolation modeling method facing intelligent mining specifically comprises the following steps:
step 1: data obtained by measuring the working surface are arranged to form a set alpha, wherein the set comprises three rows of data (x, y and z), x represents an abscissa, y represents an ordinate and z represents an elevation;
step 2: carrying out gridding processing on the set alpha to form a set beta 1 of grid points;
and 3, step 3: obtaining the coordinate of the insertion point in the insertion point set phi 1 according to the x and y coordinates of the set beta 1;
and 4, step 4: setting an initial z coordinate of each point in the point set phi 1;
specifically, assigning the average z coordinate of the set alpha to the initial z coordinate of each point in the point set phi 1;
and 5: setting the weight of each point in the point set phi 1 and the set beta 1;
step 6: defining the potential eta of each point in the set phi 1 and the set beta 1 a ；
And 7: let eta be a =0, calculate z for each grid point in the current set of points φ 1 and set β 1 according to the definition in step 6 a Value, resulting in a union of the set of points φ 1 and the set of points β 1
/>
Step 8, circularly executing the step 7, wherein the circulating times are not less than 10, and obtaining the union set of the current point set phi 1 and the point set beta 1
And step 9: replacing the current point set phi 1 and the point set beta 1 union obtained in the step 8 by the set beta 1 obtained in the step 2
β 1, resulting in a new union @>
Step 10: calculating the union obtained in step 9
Judging whether the grid interval reaches the target grid interval, if so, outputting and collecting the data>
As a final interpolation result; otherwise, the union is collected>
Returning to the step 3 as a set beta 1;
further, the gridding processing in the step 2 adopts function fitting calculation or kriging interpolation.
Further, in step 3, the calculation formula of the coordinates of the insertion point is as follows:
x n ＝(x n+1 +x n-1 )/2
y n ＝(y n+1 +y n-1 )/2
wherein x is n 、y n To be inserted intoCoordinate value of an insertion point in the insertion point set phi 1; x is the number of n-1 、y n-1 、x n+1 、y n+1 The coordinate values of two adjacent points in the set β 1.
Further, in step 5, the weight of each point is set to 1.
Further, in step 6, the potential η of each point in the set of points φ 1 and the set of points β 1 a Is defined as follows:
o (a) represents a set of four grid points, i is the serial number of the four points, i is the serial number of the upper, lower, left and right, which share a common boundary with the point a; lambda [ alpha ] i Is the weight of point i;
if the point a is a middle grid, the z values of four grid points a1, a2, a3 and a4 with common edges are involved in the calculation;
if the point a is a boundary grid, z values of three grid points a1, a2 and a3 which have common edges with the point a are involved in calculation;
if the point a is a corner grid, the z values of two grid points a1 and a2 having a common edge are involved in the calculation.
Further, in the step 8, the number of cycles is 10.
Compared with the prior art, the method has the following technical effects:
1. the size of the grid can be set according to the specific condition of the working face, and the size of the fixed grid can be realized. In the intelligent mining process, a cutting curve can be directly provided according to the cutting depth of the coal mining machine without data processing.
2. The local dynamic updating can be carried out, and the data disclosed in real time can be effectively utilized. And continuously revealing new data along with the mining of the working face, and dynamically updating the revealed data within a certain range to obtain a more accurate model.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are some implementation cases of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of the location of grid a, wherein (a) the middle grid; (b) edge grids; (c) a corner grid;
FIG. 2 is a diagram of the effects of a work surface generated by the method of the present invention;
fig. 3 is an effect diagram of the invention after dynamic update of the working surface.
Detailed Description
The stratum interface interpolation modeling method facing intelligent mining provided by the invention specifically comprises the following steps:
step 1: and (3) sorting the data obtained by the measurement of the working face to form a set alpha, wherein the set comprises three columns of data (x, y, z), and the three columns of data (x, y, z) are the coordinate data of the coal seam obtained by the measurement and the writing of the working face. Wherein x represents an abscissa, y represents a ordinate (x and y are commonly represented by longitude and latitude), and z represents elevation;
TABLE 1 Format of points in set alpha
| Serial number | x | y | z |
| 1 | x1 | y1 | z1 |
| 2 | x2 | y2 | z2 |
| 3 | x3 | y3 | z3 |
| ... | ... | ... | ... |
Step 2: carrying out gridding processing on the set alpha to form a set beta 1 of grid points;
specifically, there are many methods for converting the discrete data obtained in step 1 into equidistant grid data, which can implement the gridding of the discrete data, including function fitting calculation, kriging interpolation, and the like. Where the kriging interpolation can be performed using a PyKrige toolbox.
And step 3: obtaining the coordinate of the insertion point in the insertion point set phi 1 according to the x and y coordinates of the set beta 1;
x n ＝(x n+1 +x n-1 )/2
y n ＝(y n+1 +y n-1 )/2
wherein x is n 、y n A coordinate value of an insertion point in the insertion point set phi 1; x is the number of n-1 、y n-1 、x n+1 、y n+1 The coordinate values of two adjacent points in the set β 1.
And 4, step 4: setting an initial z coordinate of each point in a point set phi 1;
specifically, the average z-coordinate of the set α is assigned to the initial z-coordinate of each point in the set φ 1.
And 5: setting the weight size lambda of each point in the set phi 1 and the set beta 1 of points i ；
Specifically, the value is assigned according to the accuracy of the data. For example, if the measured data is more accurate, the weight of the measured data can be slightly larger, and if the error of the three-dimensional seismic interpretation is larger, the weight of the measured data can be reduced. The weight of each point is typically set to 1.
Step 6: defining the potential eta of each point in the set phi 1 and the set beta 1 a ；
In particular, the method comprises the following steps of,
where o (a) represents a set of four grid points, upper, lower, left, and right, having a common boundary with point a (i is the number of the four points, upper, lower, left, and right);
if the point a is a middle grid (as shown in fig. 1 (a)), the z values of four grid points a1, a2, a3 and a4 having common edges are involved in the calculation;
if the point a is a boundary grid (as shown in fig. 1 (b)), the z values of three grid points a1, a2 and a3 having common edges with the point a are involved in the calculation;
if the point a is a corner grid (as shown in fig. 1 (b)), the z values of the two grid points a1 and a2 having a common edge are involved in the calculation.
And 7: let eta be a =0, calculate z for each grid point in the current set of points φ 1 and set β 1 according to the definition in step 6 a Value, to obtain a union of the point set phi 1 and the point set beta 1
Specifically, let η a =0, will z a The settings are unknown, while the other values in the defined formula of step 6 are known, so that z can be found for each point in the set of points φ 1 and the set of points β 1 a The value is obtained.
Step 8, circularly executing the step 7, wherein the circulating times are not less than 10, and obtaining the union set of the current point set phi 1 and the point set beta 1
The step is a circular iteration process, and continuous iteration calculation is performed, so that the parameters of the obtained points are smoother, and the iteration times are determined according to actual conditions. If the stratum interface variation is more, namely the stratum fluctuation condition is larger, the iteration times need to be increased; if the stratum interface is stable and the fluctuation is small, the iteration times can be properly reduced; in order to make the stratum interface tend to be smooth, the number of iterations is recommended to be not less than 10.
And step 9: replacing the current point set phi 1 and the point set beta 1 union obtained in the step 8 by the set beta 1 obtained in the step 2
β 1, resulting in a new union @>
Step 10: calculating the union obtained in step 9
Judging whether the grid spacing reaches the target grid spacing, and if so, outputting and collecting the data in the device>
As a final interpolation result; otherwise the union will be +>
And returning to the step 3 as the set beta 1.
The calculation formula of the grid spacing is as follows:
Δx＝x n -x n-1
Δy＝y n -y n-1
wherein x is n 、y n Coordinate values for the insertion point; x is the number of n-1 、x n+1 、、y n-1 、y n+1 The (grid point coordinate values) obtained in step 3.
The target grid spacing is selected according to the length of each cut constructed on the working surface in the x and y directions. And when the calculated values in the x and y directions of the grid spacing are smaller than the value of the target grid spacing at the same time, the target grid spacing is considered to be reached.
Example (b):
1. summary of modeled coal seam, i.e., coal seam geological data
The length of a working face is 3500m, and the width of a cut hole of the working face is 271m. And taking the whole working face as a research object, and modeling the top plate interface of the working face. The coal thickness in the working face range is 1.3 m-3.43 m, the average coal thickness is about 2.72m, and the coal seam dip angle is larger in the middle of the working face.
The modeled data mainly includes: (1) and (3) mining the written data of the roadway (2) and cutting the written data.
The written data of the roadway are arranged to obtain the interface points of the coal seam floor, and part of the data are shown in table 2:
TABLE 2
2. Gridding process
This example uses PyKrige toolbox to achieve meshing of discrete data. The method comprises the following specific steps: inputting discrete points and establishing an interpolation model; and establishing a grid, and solving a z value on the grid to obtain a set beta 1 according to the established interpolation model.
3. And solving the x and y values of the new insertion point to obtain the coordinate of the insertion point in the insertion point set phi 1.
And setting the initial z value of each point in the point set phi 1 as the average z coordinate obtained by each iteration calculation.
4. Assigning an initial z coordinate of a point set phi 1;
5. assigning the weight values of all points in the point set phi 1 and the set beta 1 as 1;
6. defining the potential eta of each point in the set phi 1 and the set beta 1 a ；
7. Let eta be a =0, according to the definition of the step six, z of each grid point in the current point set phi 1 and the set beta 1 is calculated a Value, to obtain a union of the point set phi 1 and the point set beta 1
8. Circularly executing the step seven, wherein the iteration times are 10 times;
9. obtain a new union
The union set obtained in the step nine is calculated>
The target grid interval is determined to be (0.8m, 0.1m) at the moment, the three-nine steps are executed for 5 times in total, the calculated grid interval is smaller than the values of two directions of the target grid interval, the target requirement is reached at the moment, and the output and the collection are/are carried out>
The final model was obtained as shown in fig. 2.
10. Using union of outputs
The local dynamic update is performed on the working face data, and the update result is shown in fig. 3. The fig. 3 incision is partially dynamically updated relative to the fig. 2 incision.
From the above, the advantages of the invention are:
1. the size of the grid can be set according to the specific situation of the working surface. In the intelligent mining process, a cutting curve can be directly provided according to the cutting depth of the coal mining machine without data processing.
2. The local dynamic updating can be carried out, and the data disclosed in real time can be effectively utilized. And continuously revealing new data along with the mining of the working face, and dynamically updating the revealed data within a certain range to obtain a more accurate model.
It should be apparent that the foregoing description and illustrations are by way of example only, and are not intended to limit the present disclosure, application or uses. While the embodiments have been described in the embodiments and depicted in the drawings, the present invention is not limited to the particular examples illustrated by the drawings and described in the embodiments as the best mode presently contemplated for carrying out the teachings of the present invention, and the scope of the present invention is intended to include any embodiments falling within the foregoing description and the appended claims.
Claims (6)
1. A stratum boundary interpolation modeling method for intelligent mining is characterized by comprising the following steps:
step 1: data obtained by measuring the working surface are arranged to form a set alpha, wherein the set comprises three rows of data (x, y and z), x represents an abscissa, y represents an ordinate and z represents an elevation;
step 2: carrying out gridding processing on the set alpha to form a set beta 1 of grid points;
and step 3: obtaining the coordinate of the insertion point in the insertion point set phi 1 according to the x and y coordinates of the set beta 1;
and 4, step 4: setting an initial z coordinate of each point in the point set phi 1;
specifically, assigning the average z coordinate of the set alpha to the initial z coordinate of each point in the point set phi 1;
and 5: setting the weight of each point in the point set phi 1 and the set beta 1;
and 6: defining the potential eta of each point in the set phi 1 and the set beta 1 a ；
And 7: let eta be a =0, calculate z for each grid point in the current set of points φ 1 and set β 1 according to the definition in step 6 a Value, resulting in a union of the set of points φ 1 and the set of points β 1
Step 8, circularly executing the step 7, wherein the circulating times are not less than 10, and obtaining the union set of the current point set phi 1 and the point set beta 1
And step 9: replacing the current point set phi 1 and the point set beta 1 union obtained in the step 8 by the set beta 1 obtained in the step 2
β 1, resulting in a new union @>
Step 10: calculating the union obtained in step 9
Judging whether the grid spacing reaches the target grid spacing, and if so, outputting and collecting the data in the device>
As a final interpolation result; otherwise, the union is collected>
And returning to the step 3 as the set beta 1.
2. An intelligent mining-oriented stratigraphic interface interpolation modeling method as claimed in claim 1, wherein the gridding process in step 2 employs function fitting calculation or kriging interpolation.
3. An intelligent mining-oriented stratigraphic interface interpolation modeling method as recited in claim 1, wherein in the step 3, the calculation formula of the coordinates of the insertion point is as follows:
x n ＝(x n+1 +x n-1 )/2
y n ＝(y n+1 +y n-1 )/2
wherein x is n 、y n A coordinate value of an insertion point in the insertion point set phi 1; x is the number of n-1 、y n-1 、x n+1 、y n+1 The coordinate values of two adjacent points in the set β 1.
4. An intelligent mining oriented stratigraphic interface interpolation modeling method as recited in claim 1, characterized in that in step 5, the weight of each point is set to 1.
5. The method for modeling interpolation of stratigraphic interface for intelligent mining as recited in claim 1, wherein in step 6, the potential η of each point in the set of points Φ 1 and the set β 1 a Is defined as:
o (a) represents a set of four grid points, i is the serial number of the four points, i is the serial number of the upper, lower, left and right, which share a common boundary with the point a; lambda [ alpha ] i Is the weight of point i;
if the point a is a middle grid, the z values of four grid points a1, a2, a3 and a4 with common edges are involved in the calculation;
if the point a is a boundary grid, the z values of three grid points a1, a2 and a3 which have common edges with the point a are involved in the calculation;
if the point a is a corner grid, the z values of two grid points a1 and a2 having a common edge with the point a are involved in the calculation.
6. An intelligent mining oriented stratigraphic interface interpolation modeling method as recited in claim 1 in which in step 8, the number of cycles is 10.
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