CN111980736A - Danger degree prediction and roof fall early warning method for mine anchor bolt supporting roadway - Google Patents

Danger degree prediction and roof fall early warning method for mine anchor bolt supporting roadway Download PDF

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CN111980736A
CN111980736A CN202010893806.3A CN202010893806A CN111980736A CN 111980736 A CN111980736 A CN 111980736A CN 202010893806 A CN202010893806 A CN 202010893806A CN 111980736 A CN111980736 A CN 111980736A
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史明辉
乔宏哲
陶国正
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Dragon Totem Technology Hefei Co ltd
Yunnan Dacheng Safety Technology Service Co ltd
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Changzhou Vocational Institute of Mechatronic Technology
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    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
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    • E21D21/00—Anchoring-bolts for roof, floor in galleries or longwall working, or shaft-lining protection
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Abstract

The invention discloses a method for predicting the risk degree of a mine anchor bolt supporting roadway and early warning roof collapse, which comprises the following steps: s1, processing the pressure and displacement data of the corresponding anchor rods in the cloud server, calculating the pressure ratio and the displacement ratio of the corresponding anchor rods, dividing the anchor rods with the same support type into the same group, and sequencing the anchor rods in the support structure according to the key degree; s2, respectively calculating the optimal coefficients of the pressure ratio and the displacement ratio to obtain the optimal objective functions of the pressure ratio coefficient and the displacement ratio coefficient; s3, quantifying the pressure ratio and the displacement ratio of the corresponding anchor rods; and S4, solving the classification hyperplane and the normal vector thereof by using a soft space SVM. The invention provides a danger degree prediction and roof fall early warning method for a mine anchor bolt supporting roadway.

Description

Danger degree prediction and roof fall early warning method for mine anchor bolt supporting roadway
Technical Field
The invention relates to a danger degree prediction and roof fall early warning method for a mine anchor bolt supporting roadway.
Background
At present, coal is the most important disposable energy in China, and the coal industry is the main basic industry of national economy and plays an important role in the national economic development. However, in the coal mining process, due to the fact that the production environment is severe, the production process is complex, and meanwhile, due to the influence of factors such as complex ground stress and mining dynamic pressure, various disasters such as working face caving, roof caving, frame pressing, roadway bottom bulging, deformation of two sides, coal rock outburst and the like are caused, and the safety and high-efficiency mining of coal and the safety of personnel and equipment are seriously influenced. In the anchor bolt supporting engineering, the stress state of the surrounding rock of the roadway is improved by the constraint action of the anchor bolt on the separation layer and expansion of the surrounding rock. The supporting body and the surrounding rock act together to form a complete and stable bearing ring along the roadway, so that the anchor bolt supporting effect is fully exerted, and the effects of actively reinforcing the surrounding rock and maintaining the roadway are achieved. The coal mine safety production requires safe and reliable roadway support, guarantees the stability of roadway surrounding rocks, avoids roof collapse accidents, and is the key point for avoiding coal mine casualty accidents.
However, the problems of data recording lag, low measurement accuracy, large error caused by the fact that the measurement is easily influenced by the environment and the like exist in the conventional mine pressure monitoring. Most of the monitoring data are still observed manually, the data acquisition amount is small and discontinuous, the real-time transmission and analysis of the monitoring data cannot be realized, and accurate and timely early warning cannot be carried out.
Part of mines adopt mine pressure monitoring systems based on the Internet of things, but the mine pressure monitoring systems mainly stay at the preliminary analysis stage of data acquisition, storage and data at present, and do not achieve the level of deeper analysis and excavation of the data. Once the roof fall accident arrives, the system often has the characteristics of strong abruptness and high speed, and due to insufficient data utilization, the system often cannot achieve early protection of the accident.
Disclosure of Invention
The invention aims to solve the technical problem of overcoming the defects of the prior art and provides a method for predicting the danger degree and early warning the roof fall of a mine anchor bolt supporting roadway.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a danger degree prediction and roof fall early warning method for a mine anchor bolt supporting roadway comprises the following steps:
s1, processing the pressure and displacement data of the corresponding anchor rods in the cloud server, calculating the pressure ratio and the displacement ratio of the corresponding anchor rods, dividing the anchor rods with the same support type into the same group, and sequencing the anchor rods in the support structure according to the key degree;
s2, respectively calculating the optimal coefficients of the pressure ratio and the displacement ratio to obtain the optimal objective functions of the pressure ratio coefficient and the displacement ratio coefficient;
s3, quantifying the pressure ratio and the displacement ratio of the corresponding anchor rods;
s4, solving a classification hyperplane and a normal vector thereof by using a soft space SVM;
s5, solving the mean value and the standard deviation of the data obtained after the accident data are projected on the normal vector axis, solving the mean value and the standard deviation of the data obtained after the normal working data are projected on the normal vector axis, then solving the danger degree of the anchor bolt supporting roadway through the conditional entropy, and predicting the danger degree of the anchor bolt supporting roadway;
and S6, generating a roof collapse early warning strategy, and starting early warning when the danger degree of the anchor bolt supporting roadway is greater than an early warning threshold value.
Further, the step S1 includes:
let the pressure ratio of the ith recording data of the jth anchor rod be:
Figure BDA0002657802690000021
let the displacement ratio of the ith recording data of the jth anchor rod be:
Figure BDA0002657802690000022
wherein, i is the ith sample data, i is 1, 2.
j is the jth anchor rod, j is 1,2, a.
PjiRecording the pressure value of data for the ith anchor rod;
Pj0the pressure rating of the jth anchor rod;
Ljirecording the displacement value of the data for the ith anchor rod;
Lj0the allowable maximum displacement value of the jth anchor rod.
Further, the step S2 includes:
the optimized objective function of the pressure ratio coefficient and the displacement ratio coefficient is as follows:
Figure BDA0002657802690000023
wherein,
C1is the pressure ratio coefficient;
C2is a displacement ratio coefficient;
yifor class marking of ith data, yiA value of-1 indicates the occurrence of an accident:
Figure BDA0002657802690000031
c, i.e. (C1, C2) using a gradient descent method:
Figure BDA0002657802690000032
can be obtained when L (C) is minimum, i.e.
Figure BDA0002657802690000033
When approaching 0, iterating the obtained C*Is the optimal solution;
where α represents the step size.
Further, the step S3 includes:
quantizing the pressure ratio and the displacement ratio of the corresponding anchor rods, wherein the quantization interval is 0.1 to obtain
Figure BDA0002657802690000034
Further, the step S4 includes:
s41, establishing a data vector x ═ x(1),x(2),…,x(j),…,x(M))；
S42, establishing coefficient vector w ═ w (w)(1),w(2),…,w(j),…,w(M)) Wherein w is(j)For the corresponding feature x(j)The corresponding coefficient;
S43，xifor the ith training data vector, yiIs xiClass label of (1); y isiFor-time to indicate an accident, yiWhen the value is +1, the work is normal, and N is the number of training data;
s44, using a soft space SVM to solve a classification hyperplane with the maximum geometric space, and representing the problem as a constraint optimization problem:
Figure BDA0002657802690000035
S.tyi(w.xi+b)≥1-ξi
ξi≥0i＝1，2，...N；
wherein F is a penalty coefficient; xi is a relaxation variable; xiiRelaxation variables for the ith training data; b is an offset;
obtaining the optimal solution w of the optimal classification hyperplane and coefficient vector*，w*Namely, the normal vector of the optimal classification hyperplane:
Figure BDA0002657802690000036
wherein,
Figure BDA0002657802690000037
is the ith element of the solution to the dual problem in the lagrange multiplier vector.
Further, the step S5 includes:
s51, obtaining data obtained by projecting the data on a normal vector, and obtaining the mean value and the labeling difference of two data types of accidents and normal work:
Figure BDA0002657802690000038
Figure BDA0002657802690000039
Figure BDA0002657802690000041
Figure BDA0002657802690000042
Figure BDA0002657802690000043
wherein N isAThe number of samples in the category y is 1; n is a radical ofBThe number of samples in the category y-1; mu.sBFor data of accidents at w*Mean value of the data obtained after vector axis projection; mu.sANormal data for operation at w*Mean value of the data obtained after vector axis projection;Anormal data for operation at w*Standard deviation of data obtained after vector axis projection;Bfor data of accidents at w*Vector quantityStandard deviation of the data obtained after the axial projection;
s52, predicting the anchor bolt support roadway risk:
let xAFor the sum of sample data for all y ═ 1, ZAIs xAAt w*Projection of the vector axis;
let current data be xc，Zc＝w*xc；
Then the current bolting roadway risk degree V is:
when wxc≤μA+AWhen the risk degree V is 0;
when wxc≥μB-BThe risk V is 1;
when mu isA+A≤wxc≤μB-BThe risk V is:
Figure BDA0002657802690000044
wherein,
H(ZC|ZA) To give at a given zAUnder the condition of ZCThe conditional entropy of (a);
H(μB|ZA) To give at a given zAUnder the condition ofBThe conditional entropy of (1).
Further, the step S6 includes:
s61, when the risk degree V is larger than gamma, starting early warning;
wherein gamma is an early warning threshold value, and the range of the early warning threshold value gamma is 0.3-0.4;
s62, predicting the roof fall occurrence time:
T＝(1-V)T0；
wherein,
t is the time which starts to time from the current data recording time and predicts the time of roof fall;
T0and estimating the working time for the reference when the current anchor rod supporting structure works normally.
By adopting the technical scheme, the invention has the following beneficial effects:
1. the invention avoids the defects of strong subjectivity and great randomness of danger judgment and early warning manually according to monitoring data, and reduces the labor cost of the mine.
2. The invention can predict the risk degree and the roof fall occurrence time of the current anchor rod supporting system, and is beneficial to protecting accidents in advance.
Drawings
FIG. 1 is a schematic block diagram of the architecture of a mine pressure monitoring system of the present invention;
fig. 2 is a flowchart of a method for predicting the risk of a mine bolting roadway and warning roof collapse according to the invention.
Detailed Description
In order that the present invention may be more readily and clearly understood, a more particular description of the invention briefly described above will be rendered by reference to specific embodiments that are illustrated in the appended drawings.
Fig. 1 is a structure of a mine pressure monitoring system, and an acquisition node comprises a pressure node and a displacement node, is a basic unit forming the monitoring system, is located at the bottommost layer of the whole system, is installed on an underground anchoring device, is responsible for acquiring pressure and displacement data, transmits the data to a cloud server through a ZigBee coordinator, and trains an anchor bolt supporting roadway danger degree model according to the pressure and displacement data in the cloud server.
As shown in fig. 2, a method for predicting the risk of a mine bolting roadway and warning roof collapse includes:
s1, processing the pressure and displacement data of the corresponding anchor rods in the cloud server, calculating the pressure ratio and the displacement ratio of the corresponding anchor rods, dividing the anchor rods with the same support type into the same group, and sequencing the anchor rods in the support structure according to the key degree;
s2, respectively calculating the optimal coefficients of the pressure ratio and the displacement ratio to obtain the optimal objective functions of the pressure ratio coefficient and the displacement ratio coefficient;
s3, quantifying the pressure ratio and the displacement ratio of the corresponding anchor rods;
s4, solving a classification hyperplane and a normal vector thereof by using a soft space SVM;
s5, solving the mean value and the standard deviation of the data obtained after the accident data are projected on the normal vector axis, solving the mean value and the standard deviation of the data obtained after the normal working data are projected on the normal vector axis, then solving the danger degree of the anchor bolt supporting roadway through the conditional entropy, and predicting the danger degree of the anchor bolt supporting roadway;
and S6, generating a roof collapse early warning strategy, and starting early warning when the danger degree of the anchor bolt supporting roadway is greater than an early warning threshold value.
Further, the step S1 includes:
let the pressure ratio of the ith recording data of the jth anchor rod be:
Figure BDA0002657802690000051
let the displacement ratio of the ith recording data of the jth anchor rod be:
Figure BDA0002657802690000061
wherein, i is the ith sample data, i is 1, 2.
j is the jth anchor rod, j is 1,2, a.
PjiRecording the pressure value of data for the ith anchor rod;
Pj0the pressure rating of the jth anchor rod;
Ljirecording the displacement value of the data for the ith anchor rod;
Lj0the allowable maximum displacement value of the jth anchor rod.
Further, the step S2 includes:
the optimized objective function of the pressure ratio coefficient and the displacement ratio coefficient is as follows:
Figure BDA0002657802690000062
wherein,
C1is the pressure ratio coefficient;
C2is a displacement ratio coefficient;
yifor class marking of ith data, yiA value of-1 indicates the occurrence of an accident:
Figure BDA0002657802690000063
c, i.e. (C1, C2) using a gradient descent method:
Figure BDA0002657802690000064
can be obtained when L (C) is minimum, i.e.
Figure BDA0002657802690000065
When approaching 0, iterating the obtained C*Is the optimal solution;
where α represents the step size.
Further, the step S3 includes:
quantizing the pressure ratio and the displacement ratio of the corresponding anchor rods, wherein the quantization interval is 0.1 to obtain
Figure BDA0002657802690000066
Further, the step S4 includes:
s41, establishing a data vector x ═ x(1),x(2),…,x(j),…,x(M))；
S42, establishing coefficient vector w ═ w (w)(1),w(2),…,w(j),…,w(M)) Wherein w is(j)For the corresponding feature x(j)The corresponding coefficient;
S43，xifor the ith training data vector, yiIs xiClass label of (1); y isiFor-time to indicate an accident, yiWhen the value is +1, the work is normal, and N is the number of training data;
s44, using a soft space SVM to solve a classification hyperplane with the maximum geometric space, and representing the problem as a constraint optimization problem:
Figure BDA0002657802690000071
S.tyi(w.xi+b)≥1-ξi
ξi≥0i＝1，2，...N；
wherein F is a penalty coefficient; xi is a relaxation variable; xiiRelaxation variables for the ith training data; b is an offset; by converting the original problem into the dual problem, the optimal solution w of the dual problem is solved by using the KKT condition, and the optimal solution w of the optimal classification hyperplane and the coefficient vector can be obtained*，w*Namely, the normal vector of the optimal classification hyperplane:
Figure BDA0002657802690000072
wherein,
Figure BDA0002657802690000073
is the ith element of the solution to the dual problem in the lagrange multiplier vector.
Further, the step S5 includes:
s51, obtaining data obtained by projecting the data on a normal vector, and obtaining the mean value and the labeling difference of two data types of accidents and normal work:
Figure BDA0002657802690000074
Figure BDA0002657802690000075
Figure BDA0002657802690000076
Figure BDA0002657802690000077
Figure BDA0002657802690000078
wherein N isAThe number of samples in the category y is 1; n is a radical ofBThe number of samples in the category y-1; mu.sBFor data of accidents at w*Mean value of the data obtained after vector axis projection; mu.sANormal data for operation at w*Mean value of the data obtained after vector axis projection;Anormal data for operation at w*Standard deviation of data obtained after vector axis projection;Bfor data of accidents at w*Standard deviation of data obtained after vector axis projection;
s52, predicting the anchor bolt support roadway risk:
let xAFor the sum of sample data for all y ═ 1, ZAIs xAAt w*Projection of the vector axis;
let current data be xc，Zc＝w*xc；
Then the current bolting roadway risk degree V is:
when wxc≤μA+AWhen the risk degree V is 0;
when wxc≥μB-BThe risk V is 1;
when mu isA+A≤wxc≤μB-BThe risk V is:
Figure BDA0002657802690000081
wherein,
H(ZC|ZA) To give at a given zAUnder the condition of ZCThe conditional entropy of (a);
H(μB|ZA) To give at a given zAUnder the condition ofBThe conditional entropy of (a);
finally, the risk degree V is a number ranging from 0 to 1, the closer the risk degree V is to 0, the smaller the risk degree is, the closer the risk degree V is to 1, the larger the risk degree is, and a user can decide preventive maintenance and early protection according to the magnitude of the risk degree V.
Further, the step S6 includes:
s61, when the risk degree V is larger than gamma, starting early warning;
wherein gamma is an early warning threshold value, the range of the early warning threshold value gamma is 0.3-0.4, and a user can independently select the threshold value setting in the range;
the value range of the early warning threshold gamma is reasonable, and a compromise is well made between false alarm avoidance and false alarm avoidance.
S62, predicting the roof fall occurrence time:
T＝(1-V)T0；
wherein,
t is the time which starts to time from the current data recording time and predicts the time of roof fall;
T0the working time is estimated for the reference when the current anchor rod supporting structure works normally, and the working time can be estimated and set by experts or experienced personnel according to actual conditions.
The above embodiments are described in further detail to solve the technical problems, technical solutions and advantages of the present invention, and it should be understood that the above embodiments are only examples of the present invention and are not intended to limit the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (7)

1. The method for predicting the risk degree of a mine anchor bolt supporting roadway and warning roof collapse is characterized by comprising the following steps of:
s1, processing the pressure and displacement data of the corresponding anchor rods in the cloud server, calculating the pressure ratio and the displacement ratio of the corresponding anchor rods, dividing the anchor rods with the same support type into the same group, and sequencing the anchor rods in the support structure according to the key degree;
s2, respectively calculating the optimal coefficients of the pressure ratio and the displacement ratio to obtain the optimal objective functions of the pressure ratio coefficient and the displacement ratio coefficient;
s3, quantifying the pressure ratio and the displacement ratio of the corresponding anchor rods;
s4, solving a classification hyperplane and a normal vector thereof by using a soft space SVM;
s5, solving the mean value and the standard deviation of the data obtained after the accident data are projected on the normal vector axis, solving the mean value and the standard deviation of the data obtained after the normal working data are projected on the normal vector axis, then solving the danger degree of the anchor bolt supporting roadway through the conditional entropy, and predicting the danger degree of the anchor bolt supporting roadway;
and S6, generating a roof collapse early warning strategy, and starting early warning when the danger degree of the anchor bolt supporting roadway is greater than an early warning threshold value.
2. The method for predicting the risk degree of the mine bolting roadway and warning the roof fall according to claim 1, wherein the step S1 includes:
let the pressure ratio of the ith recording data of the jth anchor rod be:
Figure FDA0002657802680000011
let the displacement ratio of the ith recording data of the jth anchor rod be:
Figure FDA0002657802680000012
wherein, i is the ith sample data, i is 1, 2.
j is the jth anchor rod, j is 1,2, a.
PjiRecording the pressure value of data for the ith anchor rod;
Pj0the pressure rating of the jth anchor rod;
Ljirecording the displacement value of the data for the ith anchor rod;
Lj0the allowable maximum displacement value of the jth anchor rod.
3. The method for predicting the risk degree of the mine bolting roadway and warning the roof fall according to claim 2, wherein the step S2 includes:
the optimized objective function of the pressure ratio coefficient and the displacement ratio coefficient is as follows:
Figure FDA0002657802680000021
wherein,
C1is the pressure ratio coefficient;
C2is a displacement ratio coefficient;
yifor class marking of ith data, yiA value of-1 indicates the occurrence of an accident:
Figure FDA0002657802680000022
c, i.e. (C1, C2) using a gradient descent method:
Figure FDA0002657802680000023
can be obtained when L (C) is minimum, i.e.
Figure FDA0002657802680000024
When approaching 0, iterating the obtained C*Is the optimal solution;
where α represents the step size.
4. The method for predicting the risk degree of the mine bolting roadway and warning the roof fall according to claim 3, wherein the step S3 includes:
quantizing the pressure ratio and the displacement ratio of the corresponding anchor rods, wherein the quantization interval is 0.1 to obtain
Figure FDA0002657802680000025
5. The method for predicting the risk degree of the mine bolting roadway and warning the roof fall according to claim 4, wherein the step S4 includes:
s41, establishing a data vector x ═ x(1),x(2),…,x(j),…,x(M))；
S42, establishing coefficient vector w ═ w (w)(1),w(2),…,w(j),…,w(M)) Wherein w is(j)For the corresponding feature x(j)The corresponding coefficient;
S43，xifor the ith training data vector, yiIs xiClass label of (1); y isiFor-time to indicate an accident, yiWhen the value is +1, the work is normal, and N is the number of training data;
s44, using a soft space SVM to solve the classification hyperplane with the maximum geometric space:
Figure FDA0002657802680000026
S.t yi(w.xi+b)≥1-ξi
ξi≥0 i＝1，2，...N；
wherein F is a penalty coefficient; xi is a relaxation variable; xiiRelaxation variables for the ith training data; b is an offset;
obtaining the optimal solution of the optimal classification hyperplane and coefficient vectorw*，w*Namely, the normal vector of the optimal classification hyperplane:
Figure FDA0002657802680000031
wherein,
Figure FDA0002657802680000032
is the ith element of the solution to the dual problem in the lagrange multiplier vector.
6. The method for predicting the risk degree of the mine bolting roadway and warning the roof fall according to claim 5, wherein the step S5 includes:
s51, obtaining data obtained by projecting the data on a normal vector, and obtaining the mean value and the labeling difference of two data types of accidents and normal work:
Figure FDA0002657802680000033
Figure FDA0002657802680000034
Figure FDA0002657802680000035
Figure FDA0002657802680000036
Figure FDA0002657802680000037
wherein N isAIs as in class y-1The number of the books; n is a radical ofBThe number of samples in the category y-1; mu.sBFor data of accidents at w*Mean value of the data obtained after vector axis projection; mu.sANormal data for operation at w*Mean value of the data obtained after vector axis projection;Anormal data for operation at w*Standard deviation of data obtained after vector axis projection;Bfor data of accidents at w*Standard deviation of data obtained after vector axis projection;
s52, predicting the anchor bolt support roadway risk:
let xAFor the sum of sample data for all y ═ 1, ZAIs xAAt w*Projection of the vector axis;
let current data be xc，Zc＝w*xc；
Then the current bolting roadway risk degree V is:
when wxc≤μA+AWhen the risk degree V is 0;
when wxc≥μB-BThe risk V is 1;
when mu isA+A≤wxc≤μB-BThe risk V is:
Figure FDA0002657802680000038
wherein,
H(ZC|ZA) To give at a given zAUnder the condition of ZCThe conditional entropy of (a);
H(μB|ZA) To give at a given zAUnder the condition ofBThe conditional entropy of (1).
7. The method for predicting the risk degree of the mine bolting roadway and warning the roof fall according to claim 6, wherein the step S6 includes:
s61, when the risk degree V is larger than gamma, starting early warning;
wherein gamma is an early warning threshold value, and the range of the early warning threshold value gamma is 0.3-0.4;
s62, predicting the roof fall occurrence time:
T＝(1-V)T0；
wherein,
t is the time which starts to time from the current data recording time and predicts the time of roof fall;
T0and estimating the working time for the reference when the current anchor rod supporting structure works normally.
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CN114904195A (en) * 2022-05-13 2022-08-16 常州机电职业技术学院 Fire early warning fire extinguishing system based on large-space warehouse fire early warning model

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