CN115390130A - Coal mining high-energy microseismic event prediction method and device - Google Patents
Coal mining high-energy microseismic event prediction method and device Download PDFInfo
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Abstract
The invention relates to the technical field of coal mine engineering accident emergency early warning, in particular to a method and a device for predicting a coal mine mining high-energy microseismic event. The method comprises the following steps: acquiring energy and corresponding coordinates of a large-energy microseismic event which is predicted m days before a mining area; dividing the working face of the prediction mining area into n grids with equal area; accumulating and counting the energy of the large-energy microseismic events of the divided grids according to the coordinates to construct an energy matrix; calculating a system sample entropy of the energy matrix; and predicting the coal mining large-energy microseismic event according to the system sample entropy. By adopting the invention, the disaster precursor characteristic identification and early warning in coal mining can be realized, thereby preventing the accidents.
Description
Technical Field
The invention relates to the technical field of emergency early warning of coal mine engineering accidents, in particular to a method and a device for predicting a coal mining high-energy microseismic event.
Background
Coal mining depth in China is continuously increased, geological conditions, mining layout and the like are increasingly complex, coal rock dynamic disaster risks are increasingly increased, and the coal rock dynamic disaster risks become one of main disasters which restrict safe and efficient production of mines. How to establish an effective monitoring and early warning method is particularly important for reducing coal mining risks, preventing and controlling disasters and the like.
The coal rock body is broken, vibration waves are radiated outwards, and a high-energy microseismic event can be captured and recorded through a microseismic system. The large-energy micro-seismic event can be defined as sudden inelastic deformation in a certain volume of the coal rock body, the occurrence of the large-energy micro-seismic event is accompanied with the surrounding of a mining space, related disasters in coal mining can be caused, and the data of the large-energy micro-seismic event detected by the micro-seismic monitoring technology can provide early warning indexes for monitoring and early warning of coal rock dynamic disasters. Therefore, how to scientifically predict a high-energy micro-seismic event by using micro-seismic monitoring and evaluate the danger degree of disasters in coal mining is urgent work to be carried out in the aspect of predicting and early warning.
Disclosure of Invention
The invention provides a method and a device for predicting a coal mining high-energy microseismic event, which are used for predicting the coal mining high-energy microseismic event. The technical scheme is as follows:
in one aspect, a method for predicting a coal mining high-energy microseismic event is provided, and the method comprises the following steps:
acquiring the energy and corresponding coordinates of a large-energy microseismic event m days before a predicted mining area;
dividing the working face of the prediction mining area into n grids with equal area;
accumulating and counting the energy of the large-energy microseismic events of the divided grids according to the coordinates to construct an energy matrix;
calculating a system sample entropy of the energy matrix;
and predicting the coal mining large-energy microseismic event according to the system sample entropy.
Optionally, the accumulating and counting the energy of the large-energy microseismic event of the divided grid according to the coordinates, and the constructing the energy matrix specifically includes:
after the working face of the area to be mined is divided into n equal-area grids, the large-energy microseismic events corresponding to each small grid are counted through the coordinates, and the total energy value E of all the large-energy microseismic events in unit time in each small grid area is obtained through accumulation ij Wherein, i represents the ith grid of the division, and j represents the jth day; the data of the previous m days obtained by the microseismic monitoring method comprises the coordinate position (x, y) and the energy value E of the high-energy microseismic event, and the total energy value E in each grid ij Constructing an energy sequence E of 1 Xm in time sequence 1×m ；
E 1×m ＝[E 11 E 12 …E 1m-1 E 1m ]
When the working surface is divided into n grids with equal area, an energy matrix E consisting of n energy sequences is obtained n×m ；
Optionally, the calculating the system sample entropy of the energy matrix specifically includes:
after the energy matrix is obtained, dividing each sequence into sliding window sequences according to the window size of t, solving the system sample entropy of n sequences in the sliding window, and obtaining a system sample entropy sequence with m-t +1 data.
Optionally, the dividing each sequence into sliding window sequences according to the window size of t, determining the system sample entropies of n sequences in the sliding window, and obtaining a system sample entropy sequence with m-t +1 data specifically includes:
energy matrix E n×m Adding a sliding window with the size of t to each sequence to obtain m-t +1 sliding window sequences;
from each time series, a sub-record k of length d < l is selected, starting from each qth data point, i.e. from t = k × q +1=0 × q +1,1 × q +1,2 × q +1, \ 8230, as long as k × q + d < = l, so as to obtain one sub-record;
a sub-records are selected from each time series, and a group of n x a template vectors are constructed from the system, namely
Solving the Euclidean distance between every two sequences;
wherein the content of the first and second substances,
and σ β Are respectively a time sequence
And x β (t) standard deviation, γ defines a similarity criterion and is a non-zero constant;
a sub-record of length d + p is chosen to similarly construct another set Θ (d + p, q, a), in order to reduce the freedom of this parameter and save computation time, taking p = q, then
The entropy of the system samples is determined according to a formula, i.e.
Where A is the number of proximity vector pairs from the set Θ (d + p, q, a), B is the number of proximity vector pairs from the set Θ (d, q, a), and l eff (n)＝a*p+d；
Calculating the system sample entropies of all sliding window sequences according to the method to obtain a system sample entropy sequence S 1×(m-t+1) ；
S 1×(m-t+1) ＝[s 11 s 12 …s 1m-t s 1m-t+1 ]。
Optionally, the predicting the coal mining large energy microseismic event according to the system sample entropy specifically includes:
calculating the average value of the system sample entropy;
observation system sample entropy sequence S 1×(m-t+1) If s is 1i ＞s 1i-1 And s 1i ＞s 1i+1 Then s 1i Is a officeA partial extremum, determining all local extrema;
and taking the average value as a threshold, and judging whether a large-energy microseismic event in coal mining occurs or not by comparing a local extreme value of the system sample entropy with the threshold: if the local extreme value
The large-energy micro-seismic event will occur, and corresponding prevention and treatment measures need to be taken; if the local extreme value
The large energy micro-seismic event can not occur, and the device is temporarily in a safe state; and respectively judging all the determined local extreme values to obtain the time points at which the large-energy microseismic events will occur, thereby achieving the early warning effect.
In another aspect, there is provided a coal mining high energy microseismic event prediction device, comprising:
the acquisition module is used for acquiring the energy and corresponding coordinates of the large-energy microseismic events which are predicted m days before the mining area;
the dividing module is used for dividing the working face of the prediction mining area into n equal-area grids;
the construction module is used for accumulating and counting the energy of the large-energy microseismic events of the divided grids according to the coordinates to construct an energy matrix;
the computing module is used for computing the system sample entropy of the energy matrix;
and the prediction module is used for predicting the coal mining high-energy microseismic event according to the system sample entropy.
Optionally, the building module is specifically configured to:
after the working face of the area to be mined is divided into n equal-area grids, the large-energy microseismic events corresponding to each small grid are counted through the coordinates, and the total energy value E of all the large-energy microseismic events in unit time in each small grid area is obtained through accumulation ij Wherein i represents the ith grid of the division, and j represents the jth day; by microThe data of the first m days acquired by the earthquake monitoring method comprises the coordinate position (x, y) and the energy value E of the high-energy microseismic event, and the total energy value E in each grid ij Constructing an energy sequence E of 1 Xm in time sequence 1×m ；
E 1×m ＝[E 11 E 12 …E 1m-1 E 1m ]
When the working surface is divided into n grids with equal area, an energy matrix E consisting of n energy sequences is obtained n×m ；
Optionally, the calculation module is specifically configured to: after the energy matrix is obtained, dividing each sequence into sliding window sequences according to the window size of t, solving the system sample entropy of n sequences in the sliding window, and obtaining a system sample entropy sequence with m-t +1 data.
Optionally, the calculation module is specifically configured to:
energy matrix E n×m Adding a sliding window with the size of t to each sequence to obtain m-t +1 sliding window sequences;
from each time series, a sub-record k of length d < l is selected, starting from each q-th data point, i.e. from t = k × q +1=0 × q +1,1 × q +1,2 × q +1, \ 8230, as long as k × q + d < = l, so as to obtain one sub-record;
a sub-records are selected from each time series, and a group of n x a template vectors are constructed from the system, namely
Solving the Euclidean distance between every two sequences;
wherein the content of the first and second substances,
and σ β Are respectively a time sequence
And x β (t) standard deviation, γ defines a similarity criterion and is a non-zero constant;
a sub-record of length d + p is chosen to similarly construct another set Θ (d + p, q, a), in order to reduce the freedom of this parameter and save computation time, taking p = q, then
The entropy of the system samples is determined according to a formula, i.e.
Where A is the number of proximity vector pairs from the set Θ (d + p, q, a), B is the number of proximity vector pairs from the set Θ (d, q, a), and l eff (n)＝a*p+d；
Calculating the system sample entropies of all sliding window sequences according to the method to obtain a system sample entropy sequence S 1×(m-t+1) ；
S 1×(m-t+1) ＝[s 11 s 12 …s 1m-t s 1m-t+1 ]。
Optionally, the prediction module is specifically configured to:
calculating the average value of the system sample entropy;
observation system sample entropy sequence S 1×(m-t+1) If s is 1i ＞s 1i-1 And s 1i ＞s 1i+1 Then s 1i Determining all local extrema for the local extremum;
and taking the average value as a threshold, and judging whether a large-energy microseismic event in coal mining occurs or not by comparing a local extreme value of the system sample entropy with the threshold: if the local extreme value
The large-energy micro-seismic event will occur, and corresponding prevention and treatment measures need to be taken; if the local extreme value
The large energy micro-seismic event can not occur, and the device is temporarily in a safe state; and respectively judging all the determined local extreme values to obtain the time points at which the large-energy microseismic events will occur, thereby achieving the early warning effect.
In another aspect, an electronic device is provided, and the electronic device includes a processor and a memory, where at least one instruction is stored in the memory, and the at least one instruction is loaded and executed by the processor to implement the coal mining large energy microseismic event prediction method.
In another aspect, a computer-readable storage medium is provided, having at least one instruction stored therein, the at least one instruction being loaded and executed by a processor to implement the coal mining large energy microseismic event prediction method described above.
The technical scheme provided by the invention has the beneficial effects that at least:
1. through tests, the method can enable the prediction accuracy of the high-energy microseismic event to be higher, is simple and easy to operate, and can solve the problem that the high-energy microseismic event is difficult to predict and is complex. The large-energy microseismic event in coal mining can be predicted by combining the system sample entropy, and disaster precursor feature identification and early warning in coal mining can be realized, so that the accidents are prevented.
2. The large-energy micro-seismic event data acquired through micro-seismic monitoring can provide effective early warning indexes for early warning of coal rock dynamic disasters.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of a method for predicting a coal mining high-energy microseismic event according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating the detailed steps of a method for predicting a coal mining high-energy microseismic event according to an embodiment of the present invention;
FIG. 3 is a schematic representation of a working face of a swing coal mine 141 according to an embodiment of the present invention;
FIG. 4 is a graph comparing the entropy sequence of system samples with the occurrence of large energy microseismic events provided by embodiments of the present invention;
FIG. 5 is a block diagram of a coal mining high energy microseismic event prediction device provided by an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, an embodiment of the present invention provides a method for predicting a coal mining high-energy microseismic event, where the method includes:
s1, acquiring energy and corresponding coordinates of a large-energy microseismic event which is predicted m days before a mining area;
s2, dividing the working face of the prediction mining area into n equal-area grids;
s3, accumulating and counting the energy of the large-energy microseismic events of the divided grids according to the coordinates to construct an energy matrix;
s4, calculating the system sample entropy of the energy matrix;
and S5, predicting the coal mining large-energy microseismic event according to the system sample entropy.
Optionally, the accumulating and counting the energy of the large-energy microseismic event of the divided grid according to the coordinates, and the constructing the energy matrix specifically includes:
after the working face of the area to be mined is divided into n equal-area grids, the large-energy microseismic events corresponding to each small grid are counted through the coordinates, and the total energy value E of all the large-energy microseismic events in unit time in each small grid area is obtained through accumulation ij Wherein i represents the ith grid of the division, and j represents the jth day; the data of the previous m days obtained by the microseismic monitoring method comprises the coordinate position (x, y) and the energy value E of the high-energy microseismic event, and the total energy value E in each grid ij Constructing an energy sequence E of 1 Xm in time sequence 1×m ；
E 1×m ＝[E 11 E 12 …E 1m-1 E 1m ]
When the working surface is divided into n grids with equal area, an energy matrix E consisting of n energy sequences is obtained n×m ；
Optionally, the calculating the system sample entropy of the energy matrix specifically includes:
after the energy matrix is obtained, dividing each sequence into sliding window sequences according to the window size of t, solving the system sample entropy of n sequences in the sliding window, and obtaining a system sample entropy sequence with m-t +1 data.
Optionally, the dividing each sequence into sliding window sequences according to the window size of t, determining the system sample entropies of n sequences in the sliding window, and obtaining a system sample entropy sequence with m-t +1 data specifically includes:
energy matrix E n×m Adding a sliding window with the size of t to each sequence to obtain m-t +1 sliding window sequences;
from each time series, a sub-record k of length d < l is selected, starting from each qth data point, i.e. from t = k × q +1=0 × q +1,1 × q +1,2 × q +1, \ 8230, as long as k × q + d < = l, so as to obtain one sub-record;
a sub-records are selected from each time series, and a group of n x a template vectors are constructed from the system, namely
Calculating the Euclidean distance between every two sequences;
wherein the content of the first and second substances,
and σ β Are respectively a time sequence
And x β (t) standard deviation, γ defines a similarity criterion and is a non-zero constant;
a sub-record of length d + p is chosen to similarly construct another set Θ (d + p, q, a), in order to reduce the freedom of this parameter and save computation time, taking p = q, then
The entropy of the system samples is determined according to a formula, i.e.
Where A is the number of proximity vector pairs from the set Θ (d + p, q, a), B is the number of proximity vector pairs from the set Θ (d, q, a), and l eff (n)＝a*p+d；
Calculating the system sample entropies of all sliding window sequences according to the method to obtain a system sample entropy sequence S 1×(m-t+1) ；
S 1×(m-t+1) ＝[s 11 s 12 …s 1m-t s 1m-t+1 ]。
Optionally, the predicting the coal mining large energy microseismic event according to the system sample entropy specifically includes:
calculating the average value of the system sample entropy;
observation system sample entropy sequence S 1×(m-t+1) If s is 1i ＞s 1i-1 And s 1i ＞s 1i+1 Then s 1i Determining all local extrema for the local extrema;
and taking the average value as a threshold, and judging whether a large-energy microseismic event in coal mining occurs or not by comparing a local extreme value of the system sample entropy with the threshold: if the local extremum is
To illustrate that a large amount of energy will appear at this pointIn the event of slight shock, corresponding prevention and treatment measures need to be taken; if the local extreme value
The large energy micro-seismic event can not occur, and the device is temporarily in a safe state; and respectively judging all the determined local extreme values to obtain the time points at which the large-energy microseismic events will occur, thereby achieving the early warning effect.
The method for predicting the coal mining high-energy microseismic event according to the embodiment of the invention is described in detail with reference to fig. 2:
s21, acquiring and recording the energy and the coordinates of each microseismic event m days before the forecast mining area by using a microseismic monitoring means;
s22, dividing the working surface of the prediction mining area into n equal-area grids;
s23, accumulating and counting the divided grid daily microseismic event energy, constructing time sequence data of each grid microseismic event energy by taking day as unit time to obtain n sequences, and constructing an energy matrix by the n sequences;
the unit time is days, and the unit time may also be hours, and the embodiment of the present invention is not limited to a specific unit time, and all of the embodiments of the present invention are within the protection scope of the embodiment of the present invention.
S24, dividing each sequence of the energy matrix into sliding window sequences according to t as a window size, solving the system sample entropy of n sequences in the sliding window, and obtaining a system sample entropy sequence with m-t +1 data;
and S25, calculating the average value of the system sample entropy sequence and using the average value as a threshold, and predicting the large-energy event according to whether the local extreme value of the system sample entropy is larger than the threshold.
One specific example is illustrated below:
as shown in fig. 3, the energy and coordinate data of a large-energy microseismic event of a whole year and a day of a working face of a certain coal mine 141 are obtained in advance through a microseismic system;
dividing the 141 working surface into 30 equal-area grids of 5 × 6;
sitting from a high energy microseismic eventAccumulating daily high-energy microseismic events in each divided grid through Phyton, constructing time series data of the high-energy microseismic events of each grid by taking days as unit time, obtaining 30 time series by including 365 groups of data in each time series, and constructing an energy matrix E of 30 × 365 30×365 ；
Dividing each sequence of the energy matrix into sliding window sequences with a window size of t = 30;
calculating the system sample entropies of 30 sequences in a sliding window, and obtaining a system sample entropy sequence with 336 data;
S 1×336 ＝[s 11 s 12 …s 1335 s 1336 ]
calculating the average value of the entropy sequence of the system sample as a threshold value;
observation System sample entropy sequence S 1×(m-t+1) If s is 1i ＞s 1i-1 And s 1i ＞s 1i+1 Then s 1i Determining all local extrema for the local extremum;
according to the result, a comparison curve graph of the system sample entropy sequence and the occurred high-energy microseismic events is drawn as shown in fig. 4, a horizontal dotted line in the graph is a threshold value, when the obtained local extreme value is greater than the threshold value, the high-energy microseismic events occur, and the time of the high-energy microseismic event of a working face of a certain coal mine 141 is correctly predicted through the system sample entropy, so that the effectiveness and the accuracy of the method are verified.
In another aspect, as shown in fig. 5, there is provided a coal mining high energy microseismic event prediction device, the device comprising:
an obtaining module 510, configured to obtain energy and corresponding coordinates of a large-energy microseismic event predicted m days before a mining area;
a dividing module 520, configured to divide the working surface of the predicted mining area into n equal-area grids;
a constructing module 530, configured to perform cumulative statistics on the energy of the large-energy microseismic event of the divided grid according to the coordinates, and construct an energy matrix;
a calculating module 540, configured to calculate system sample entropy of the energy matrix;
and the prediction module 550 is configured to predict the coal mining high-energy microseismic event according to the system sample entropy.
Optionally, the building module is specifically configured to:
after the working face of the area to be mined is divided into n equal-area grids, the large-energy microseismic events corresponding to each small grid are counted through the coordinates, and the total energy value E of all the large-energy microseismic events in unit time in each small grid area is obtained through accumulation ij Wherein i represents the ith grid of the division, and j represents the jth day; the data of the first m days acquired by the microseismic monitoring method comprises the coordinate position (x, y) and the energy value E of the high-energy microseismic event, and the total energy value E in each grid ij Constructing an energy sequence E of 1 Xm in time sequence 1×m ；
E 1×m ＝[E 11 E 12 …E 1m-1 E 1m ]
When the working surface is divided into n grids with equal area, an energy matrix E consisting of n energy sequences is obtained n×m ；
Optionally, the calculation module is specifically configured to: after the energy matrix is obtained, dividing each sequence into sliding window sequences according to the window size of t, solving the system sample entropy of n sequences in the sliding window, and obtaining a system sample entropy sequence with m-t +1 data.
Optionally, the calculation module is specifically configured to:
energy matrix E n×m Adding a sliding window with the size of t to each sequence to obtain m-t +1 sliding window sequences;
from each time series, a sub-record k of length d < l is selected, starting from each qth data point, i.e. from t = k × q +1=0 × q +1,1 × q +1,2 × q +1, \ 8230, as long as k × q + d < = l, so as to obtain one sub-record;
a sub-records are selected from each time series, and a group of n x a template vectors are constructed from the system, namely
Calculating the Euclidean distance between every two sequences;
wherein the content of the first and second substances,
and σ β Are respectively a time sequence
And x β (t) standard deviation, γ defines a similarity criterion and is a non-zero constant;
a sub-record of length d + p is chosen to similarly construct another set Θ (d + p, q, a), in order to reduce the freedom of this parameter and save computation time, taking p = q, then
The entropy of the system samples is determined according to a formula, i.e.
Where A is the number of proximity vector pairs from the set Θ (d + p, q, a), B is the number of proximity vector pairs from the set Θ (d, q, a), and l eff (n)＝a*p+d；
Calculating the system sample entropies of all sliding window sequences according to the method to obtain a system sample entropy sequence S 1×(m-t+1) ；
S 1×(m-t+1) ＝[s 11 s 12 …s 1m-t s 1m-t+1 ]。
Optionally, the prediction module is specifically configured to:
calculating the average value of the system sample entropy;
observation system sample entropy sequence S 1×(m-t+1) If s is 1i ＞s 1i-1 And s 1i ＞s 1i+1 Then s 1i Determining all local extrema for the local extrema;
and taking the average value as a threshold, and judging whether a large-energy microseismic event in coal mining occurs or not by comparing a local extreme value of the system sample entropy with the threshold: if the local extreme value
The large-energy micro-seismic event occurs at this point, and corresponding prevention and treatment measures need to be taken; if the local extreme value
The large energy micro-seismic event can not occur, and the device is temporarily in a safe state; and respectively judging all the determined local extreme values to obtain the time points at which the large-energy microseismic events will occur, thereby achieving the early warning effect.
Fig. 6 is a schematic structural diagram of an electronic device 600 according to an embodiment of the present invention, where the electronic device 600 may generate relatively large differences due to different configurations or performances, and may include one or more processors (CPUs) 601 and one or more memories 602, where the memory 602 stores at least one instruction, and the at least one instruction is loaded and executed by the processor 601 to implement the steps of the coal mining high-energy microseismic event prediction method.
In an exemplary embodiment, a computer readable storage medium, such as a memory including instructions executable by a processor in a terminal, is also provided to perform the coal mining large energy microseismic event prediction method described above. For example, the computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention, which is intended to cover any modifications, equivalents, improvements, etc. within the spirit and scope of the present invention.
Claims (10)
1. A method for predicting a coal mining high-energy microseismic event, which is characterized by comprising the following steps:
acquiring energy and corresponding coordinates of a large-energy microseismic event which is predicted m days before a mining area;
dividing the working face of the prediction mining area into n grids with equal area;
accumulating and counting the energy of the large-energy microseismic events of the divided grids according to the coordinates to construct an energy matrix;
calculating a system sample entropy of the energy matrix;
and predicting the coal mining large-energy microseismic event according to the system sample entropy.
2. The method according to claim 1, wherein the step of performing cumulative statistics on the energies of the macroenergy microseismic events of the divided grids according to the coordinates to construct an energy matrix specifically comprises:
after the working face of the area to be mined is divided into n equal-area grids, the large-energy microseismic events corresponding to each small grid are counted through the coordinates, and the total energy value E of all the large-energy microseismic events in unit time in each small grid area is obtained through accumulation ij Wherein i represents the ith grid of the division, and j represents the jth day;
the data of the previous m days obtained by the microseismic monitoring method comprises the coordinate position (x, y) and the energy value E of the high-energy microseismic event, and the total energy value E in each grid ij Constructing an energy sequence E of 1 Xm in time sequence 1×m ；
E 1×m ＝[E 11 E 12 …E 1m-1 E 1m ]
When the working surface is divided into n grids with equal area, an energy matrix E consisting of n energy sequences is obtained n×m ；
3. The method according to claim 2, wherein the calculating the system sample entropy of the energy matrix comprises in particular:
after the energy matrix is obtained, dividing each sequence into sliding window sequences according to the window size of t, solving the system sample entropy of n sequences in the sliding window, and obtaining a system sample entropy sequence with m-t +1 data.
4. The method of claim 3, wherein the dividing each sequence into sliding window sequences according to t as a window size, determining the systematic sample entropy of n sequences in the sliding window, and obtaining a systematic sample entropy sequence with m-t +1 data specifically comprises:
energy matrix E n×m Adding a sliding window with the size of t to each sequence to obtain m-t +1 sliding window sequences;
from each time series, a sub-record k of length d < l is selected, starting from each qth data point, i.e. from t = k × q +1=0 × q +1,1 × q +1,2 × q +1, \ 8230, as long as k × q + d < = l, so as to obtain one sub-record;
a sub-records are selected from each time series, and a group of n x a template vectors are constructed from the system, namely
Calculating the Euclidean distance between every two sequences;
wherein the content of the first and second substances,
and σ β Are respectively a time sequence
And x β (t) standard deviation, γ defines a similarity criterion and is a non-zero constant;
a sub-record of length d + p is chosen to similarly construct another set Θ (d + p, q, a), in order to reduce the freedom of this parameter and save computation time, taking p = q, then
The entropy of the system samples is determined according to a formula, i.e.
Where A is the number of proximity vector pairs from the set Θ (d + p, q, a), B is the number of proximity vector pairs from the set Θ (d, q, a), and l eff (n)＝a*p+d；
Calculating the system sample entropies of all sliding window sequences according to the method to obtain a system sample entropy sequence S 1×(m-t+1) ；
S 1×(m-t+1) ＝[s 11 s 12 …s 1m-t s 1m-t+1 ]。
5. The method of claim 1, wherein predicting the coal mining high energy microseismic event based on the system sample entropy specifically comprises:
calculating the average value of the system sample entropy;
observation system sample entropy sequence S 1×(m-t+1) If s is 1i ＞s 1i-1 And s 1i ＞s 1i+1 Then s 1i Determining all local extrema for the local extremum;
and taking the average value as a threshold, and judging whether a large-energy microseismic event in coal mining occurs or not by comparing a local extreme value of the system sample entropy with the threshold: if the local extremum is
The large-energy micro-seismic event will occur, and corresponding prevention and treatment measures need to be taken; if the local extreme value
The large energy micro-seismic event can not occur, and the device is temporarily in a safe state; and respectively judging all the determined local extreme values to obtain the time points at which the large-energy microseismic events will occur, thereby achieving the early warning effect.
6. A coal mining high energy microseismic event prediction device, the device comprising:
the acquisition module is used for acquiring the energy and corresponding coordinates of the large-energy microseismic events which are predicted m days before the mining area;
the dividing module is used for dividing the working face of the prediction mining area into n equal-area grids;
the construction module is used for accumulating and counting the energy of the large-energy microseismic events of the divided grids according to the coordinates to construct an energy matrix;
the computing module is used for computing the system sample entropy of the energy matrix;
and the prediction module is used for predicting the coal mining large energy microseismic event according to the system sample entropy.
7. The apparatus according to claim 6, wherein the building block is specifically configured to:
after the working face of the area to be mined is divided into n equal-area grids, the large-energy microseismic events corresponding to each small grid are counted through the coordinates, and the total energy value E of all the large-energy microseismic events in unit time in each small grid area is obtained through accumulation ij Wherein, i represents the ith grid of the division, and j represents the jth day;
the data of the first m days acquired by the microseismic monitoring method comprises the coordinate position (x, y) and the energy value E of the high-energy microseismic event, and the total energy value E in each grid ij Constructing an energy sequence E of 1 Xm in time sequence 1×m ；
E 1×m ＝[E 11 E 12 …E 1m-1 E 1m ]
When the working surface is divided into n grids with equal area, an energy matrix E consisting of n energy sequences is obtained n×m ；
8. The apparatus of claim 7, wherein the computing module is specifically configured to: after the energy matrix is obtained, dividing each sequence into sliding window sequences according to the window size of t, solving the system sample entropy of n sequences in the sliding window, and obtaining a system sample entropy sequence with m-t +1 data.
9. The apparatus of claim 8, wherein the computing module is specifically configured to:
energy matrix E n×m Adding a sliding window with the size of t to each sequence to obtain m-t +1 sliding window sequences;
from each time series, a sub-record k of length d < l is selected, starting from each qth data point, i.e. from t = k × q +1=0 × q +1,1 × q +1,2 × q +1, \ 8230, as long as k × q + d < = l, so as to obtain one sub-record;
a sub-records are selected from each time series, and a group of n x a template vectors are constructed from the system, namely
Calculating the Euclidean distance between every two sequences;
wherein the content of the first and second substances,
and σ β Are respectively a time series
And x β (t) standard ofPoor, γ defines a similarity criterion and is a non-zero constant;
a sub-record of length d + p is chosen to similarly construct another set Θ (d + p, q, a), in order to reduce the freedom of this parameter and save computation time, taking p = q, then
The entropy of the system samples is determined according to a formula, i.e.
Where A is the number of proximity vector pairs from the set Θ (d + p, q, a), B is the number of proximity vector pairs from the set Θ (d, q, a), and l eff (n) = a × p + d; calculating the system sample entropies of all sliding window sequences according to the method to obtain a system sample entropy sequence S 1×(m-t+1) ；
S 1×(m-t+1) ＝[s 11 s 12 …s 1m-t s 1m-t+1 ]。
10. The apparatus of claim 9, wherein the prediction module is specifically configured to:
calculating the average value of the system sample entropy;
observation system sample entropy sequence S 1×(m-t+1) If s is 1i ＞s 1i-1 And s 1i ＞s 1i+1 Then s 1i Determining all local extrema for the local extremum;
and taking the average value as a threshold, and judging whether a large-energy microseismic event in coal mining occurs or not by comparing a local extreme value of the system sample entropy with the threshold: if the local extreme value
The large-energy micro-seismic event will occur, and corresponding prevention and treatment measures need to be taken; if the local extreme value
The large energy micro-seismic event can not occur, and the device is temporarily in a safe state; and respectively judging all the determined local extreme values to obtain the time points at which the large-energy microseismic events will occur, thereby achieving the early warning effect.
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