CN109441547B - Real-time monitoring and early warning system and method for coal and gas outburst of mining working face - Google Patents
Real-time monitoring and early warning system and method for coal and gas outburst of mining working face Download PDFInfo
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Abstract
The invention provides a real-time monitoring and early warning system and method for coal and gas outburst of a mining working face, which relate to the technical field of mining engineering and comprise a ground central station, a control host, a network switch, a monitoring substation, a vibration pickup, a wind speed sensor, a methane concentration sensor and an audible and visual alarm, wherein the sensor transmits monitoring information to the monitoring substation, the monitoring substation transmits monitoring data to the ground central station through the network switch, the ground central station completes early warning analysis by processing the monitoring data in real time, and specifically comprehensively judges the outburst risk according to a micro-vibration event change characteristic index and a gas emission change characteristic index, and establishes a comprehensive early warning model for coal and gas outburst fuzzy evaluation based on the basis of the comprehensive early warning model and judges the dangerous grade. The early warning system and the early warning method solve the technical problems of difficult continuous non-contact real-time monitoring and early warning of the coal and gas outburst danger of the mining working face, and have the advantages of high accuracy, strong operability and the like.
Description
Technical Field
The invention relates to the technical field of mining engineering, in particular to a real-time monitoring and pre-warning system for coal and gas outburst of a mining working face and a method for comprehensively evaluating coal and gas outburst risks.
Background
The coal and gas outburst is an abnormal dynamic phenomenon that broken coal and gas are suddenly thrown out to a mining space from the coal body under the combined action of ground stress and gas. The scholars at home and abroad form various academic views including a gas action theory, a ground stress action theory, a chemical essence theory, a comprehensive action hypothesis and the like on the occurrence mechanism of the coal and gas outburst, wherein the comprehensive action hypothesis considers that the coal and the gas outburst is the result of the comprehensive action of the ground stress, the gas pressure, the mechanical property of the coal and other factors, and the main factors of the outburst occurrence acting force and 2 aspects of medium are comprehensively considered by the theory, so that the comprehensive acceptance is obtained. The coal and gas outburst risk prediction is used as an important link of a comprehensive control system for coal and gas outburst, and the existing outburst prediction method is static and discontinuous, and cannot continuously predict the coal and gas outburst risk in real time; the conventional prediction method is to predict the state of the outburst risk of coal and gas, but does not consider the development trend of the outburst risk, and is difficult to reflect the inoculation process of the outburst risk of coal and gas. In recent years, some scholars propose some coal and gas outburst early warning systems and methods, for example, the Chinese patent literature "coal and gas outburst real-time diagnosis method" with publication number of CN101532397A and the Chinese patent literature "coal and gas outburst comprehensive early warning system and early warning method" with publication number of CN101550841B, but three main factors which indirectly or partially reflect the outburst risk influence include ground stress, gas pressure and mechanical properties of coal, and the size of the coal and gas outburst risk cannot be comprehensively reflected; still other scholars propose to comprehensively utilize acoustic emission (or microseism) and dynamic gas surge to predict the coal and gas outburst risk, for example, the Chinese patent literature with publication number of CN106194264A, namely a coal and gas outburst real-time monitoring and early warning system, the Chinese patent literature with publication number of CN101787897B, namely a system and method for predicting the mine coal and gas outburst risk in real time, but the early warning model adopted by the system does not really realize the coupling association of two kinds of information, and the conventional system does not have a positioning function, so that noise information generated by mining operation in a mining working face is difficult to effectively remove.
Because the prior art does not have an early warning method capable of comprehensively reflecting the structural change trend, mining stress evolution and gas content change characteristics of the coal body in front of the mining working face, and does not have the capability of accurately positioning a microseismic event and effectively eliminating underground noise, a method and a system for realizing continuous non-contact real-time monitoring and intelligent early warning of the outstanding danger are needed.
Disclosure of Invention
The invention provides a real-time monitoring and early warning system and method for coal and gas outburst of a mining working face, which aims to solve the technical problem that the real-time monitoring and early warning of the coal and gas outburst of the working face are difficult in a non-contact continuous real-time monitoring and early warning mode.
The real-time monitoring and early warning system for coal and gas outburst of the mining working face comprises a ground central station, a control host, a network exchange machine, a monitoring substation, a vibration pickup, a wind speed sensor, a methane concentration sensor and an audible and visual alarm; the ground central station is connected with the control host through a network cable, the ground central station is connected with the network switch through optical fibers, the network switch is connected with the monitoring substation through optical fibers, and the monitoring substation is respectively connected with the vibration pickup, the wind speed sensor, the methane concentration sensor and the audible and visual alarm through cables; the vibration pickup, the wind speed sensor and the methane concentration sensor are arranged in the roadway, and monitoring information is transmitted to the monitoring substation; the monitoring substation transmits monitoring data to the ground central station through the network switch; the ground central station comprises a data analysis module, an early warning module and a storage module; the control host controls the ground central station to work; the network switch transmits the early warning information sent by the early warning module to the monitoring substation; the monitoring substation comprises an analog-to-digital conversion module, a data noise reduction module and a data screening module, and monitors data of the monitoring substation screening vibration pickup, the wind speed sensor and the methane concentration sensor.
Preferably, the ground central station is provided with a GPS clock, and the GPS clock adjusts the time of monitoring data of each monitoring substation; the ground central station is connected with the remote big data analysis service platform through a network, the remote big data analysis service platform is connected with the ground central stations of the plurality of mines through a network, the remote big data analysis service platform extracts characteristic information of monitoring data through a machine learning algorithm, and the ground central station adjusts critical values of early warning indexes according to analysis results of the remote big data analysis service platform.
Preferably, the monitoring substation uses a main control MCU chip, a signal conditioner and an A/D converter, and transmits an early warning signal to an audible and visual alarm, wherein the audible and visual alarm comprises an alarm indicator lamp and a loudspeaker.
The real-time monitoring and early warning method for the coal and gas outburst of the mining working face comprises the following steps of:
arranging a ground central station, a control host and a network switch, setting a monitoring substation in a roadway where a working face is located according to geological conditions and mining conditions of a mine, and connecting and installing a vibration pickup, a wind speed sensor, a methane concentration sensor and an audible and visual alarm;
debugging a ground central station, a control host, a network switch, a monitoring substation, a vibration pickup, a wind speed sensor, a methane concentration sensor and an audible and visual alarm after installation to ensure normal operation, connecting a ground center with a GPS clock and connecting the ground center with a remote big data analysis service platform;
setting parameters in a data analysis module of the ground central station through a control host, wherein the parameters comprise: the lengths of time m and n, the initial critical value e of the moving average of the microseismic events 1 Initial critical value e of deviation rate 2 And a discrete rate initial critical value e 3 Initial threshold value e of running average of gas emission 1 ' deviation fromRate initial critical value e 2 ' and the initial critical value of the discrete rate e 3 ' weight w of microseismic event change feature 1 And a weight w of a gas emission amount variation characteristic 2 ；
And fourthly, starting a real-time monitoring and early warning system for coal and gas outburst of the mining working face, transmitting monitoring data to a ground central station by a vibration pickup, a wind speed sensor and a methane concentration sensor, analyzing and processing the monitoring data by a data analysis module of the ground central station, storing the monitoring data by a storage module, transmitting the monitoring data to a remote big data analysis service platform, and transmitting early warning information to an audible and visual alarm by the early warning module through a network switch and a monitoring substation.
Preferably, the early warning information of the early warning module comprises no prominent danger, prominent threat and prominent danger, wherein the early warning module sends out the alarm indicator light of the audible and visual alarm when no prominent danger to display green, the early warning module sends out the alarm indicator light of the audible and visual alarm when prominent threat to display yellow, and the early warning module sends out the alarm indicator light of the audible and visual alarm when prominent danger to display red and starts the loudspeaker.
It is also preferable that the microseismic event change characteristic index I m A (n) is a sliding average according to the time series of microseismic events t Deviation rate Y (n) t And a discrete rate V (m) t To determine;
sliding average A (n) t The expression of (2) is:
wherein n is the length of time; a (n) t A is the moving average value of the microseismic events within the latest time length n, and a is the number of the microseismic events;
deviation rate Y (n) t The expression of (2) is:
wherein a is t The number of microseismic events at time t;
discrete rate V (m) t The expression of (2) is:
wherein mu is the sample mean value of the microseismic event time sequence; m is the time length;
a (n) is a sliding average according to the time series of microseismic events t Deviation rate Y (n) t And a discrete rate V (m) t To calculate the characteristic index I of the micro-seismic event change m Determining initial critical value e of moving average of microseismic events 1 Initial critical value e of deviation rate 2 And a discrete rate initial critical value e 3 The method comprises the steps of carrying out a first treatment on the surface of the Then, the sliding average value of the microseismic events is assigned as alpha, when the sliding average value of the microseismic events is larger than e 1 When the time is assigned to be 1, the sliding average value of the microseismic events is less than or equal to e 1 Assigning a value of 0; the deviation rate of the microseismic event is assigned as beta, and when the deviation rate of the microseismic event is larger than e 2 When the time is assigned to be 1, the deviation rate of the microseismic event is less than or equal to e 2 Assigning a value of 0; assigning the microseismic event discrete rate as gamma, and when the microseismic event discrete rate is larger than e 3 The time value is 1, and the dispersion rate of the microseismic events is less than or equal to e 3 Assigning a value of 0; comprehensively judging the value x=alpha+beta+gamma of the micro-seismic event change characteristic, wherein x= {0,1,2,3};
microseismic event change characteristic index I m The expression of (2) is:
it is also preferable that the gas emission variation characteristic index I g A (n) as a running average of a time series of gas emission' t Deviation rate Y (n)' t And a discrete rate V (m)' t To determine;
running average A (n)' t The expression of (2) is:
wherein n is the length of time; a (n)' t The gas emission quantity is a sliding average value of the gas emission quantity in the last n time periods, and c is the gas emission quantity;
deviation rate Y (n)' t The expression of (2) is:
wherein c t The gas emission quantity at the time t;
discrete ratio V (m)' t The expression of (2) is:
wherein μ' is a sample mean of the gas emission time series; m is the time length;
a (n) as a running average of a time series of gas emission' t Deviation rate Y (n)' t And a discrete rate V (m)' t Calculating a gas emission quantity characteristic index I g Respectively determining the initial critical value e of the moving average of the gas emission quantity 1 ' initial critical value e of deviation rate 2 ' and the initial critical value of the discrete rate e 3 'then, the running average of the gas emission amount is assigned as alpha', when the running average of the gas emission amount is larger than e 1 When' is assigned to be 1, the gas emission quantity sliding average value is less than or equal to e 1 ' time value is 0; the gas emission deviation rate is assigned as beta', when the gas emission deviation rate is larger than e 2 When' is assigned to be 1, the deviation rate of the gas emission quantity is less than or equal to e 2 ' time value is 0; the gas emission quantity discrete rate is assigned as gamma', and when the gas emission quantity discrete rate is larger than e 3 When' time is assigned to be 1, the gas emission quantity discrete rate is less than or equal to e 3 ' time value is 0; comprehensively judging the change characteristic assignment y=alpha ' +beta ' +gamma ', wherein y= {0,1,2,3};
characteristic index I of variation of gas emission quantity g The expression of (2) is:
further preferred is a microseismic event change characterization index I m And a gas emission variation characteristic index I g The method is applied to a data analysis module to establish a comprehensive early warning model for fuzzy evaluation of coal and gas outburst and judge the risk level of coal and gas outburst;
step a, establishing a fuzzy evaluation influence factor set, specifically a microseismic event change characteristic u 1 And a gas emission amount variation characteristic u 2 The evaluation factor set is u= { U 1 ，u 2 }；
Step b, establishing a coal and gas outburst evaluation set, namely establishing a coal and gas outburst possible evaluation result set V= { coal and gas outburst does not occur }, wherein I represents that coal and gas outburst occurs, and II represents that coal and gas outburst does not occur, and V= { I, II };
step c, establishing a weight set, and establishing a weight set W= { W of each influence factor according to different importance of the influence factors in evaluation 1 ，w 2 -w is 1 +w 2 ＝1，
Step d, single-factor fuzzy evaluation, namely establishing a membership function relation between a fuzzy evaluation influence factor set U and a coal and gas outburst evaluation set V, wherein the characteristic U of the variation of the microseismic event 1 The membership functions with the saliency evaluation set V are:
variation characteristic u of gas emission quantity 2 The membership functions with the saliency evaluation set V are:
determining the membership degree of the influence factors by utilizing single factor evaluation judgment to obtain a single factor evaluation set:
R 1 ＝[I m (x)，1-I m (x)]
R 2 ＝[I g (y)，1-I g (y)]
step e, fuzzy comprehensive evaluation of coal and gas outburst, and establishing a comprehensive evaluation matrix:
R＝{R 1 ，R 2 } T
combining the weight set W of the influence factors and the comprehensive evaluation matrix R, and obtaining a fuzzy comprehensive evaluation set B by adopting a weighted average method according to a multiplication algorithm of a fuzzy matrix:
determining a coal and gas outburst probability index I and a microseismic event change characteristic index I m And a gas emission variation characteristic index I g The functional relation between them is I (x, y) =w 1 I m (x)+w 2 I g (y)。
Still further preferably, the remote big data analysis service platform receives the data uploaded by the ground central station, and determines parameters m, n and e by using a machine learning algorithm according to the actual conditions of the outburst of mine coal and gas 1 、 e 2 、e 3 、e 1 ’、e 2 ’、e 3 ’、w 1 And w 2 And fed back to the ground center.
The beneficial effects of the invention include:
(1) The coal and gas outburst real-time monitoring and early warning system and method for the mining working face realize the function of non-contact real-time monitoring of coal and gas outburst risk of the mining working face, the adopted coal and gas outburst fuzzy comprehensive early warning model mainly takes the time sequence change characteristics of a microseismic event and a gas emission amount as judgment indexes, the coal and gas outburst probability index I comprehensively reflects the change trend of a coal body structure in front of the mining working face, mining stress evolution and gas content change characteristics, the adopted outburst criterion indexes fully consider the characteristics of the coal and gas outburst dynamic evolution process, and the system can continuously correct the critical value and the correlation coefficient of the outburst criterion indexes through a control center or a far-end big data analysis service platform, so that early warning precision and early warning efficiency are ensured.
(2) The early warning system and the method provided by the invention realize the functions of real-time monitoring and automatic analysis and early warning of coal and gas outburst, and can also correct and control the outburst early warning result through the control center, thereby effectively avoiding the phenomena of missing report caused by poor professional and carelessness of coal mine field engineering technicians, and the like, and can also analyze and explain the monitoring data through the control host or the remote big data analysis service platform, acquire the characteristic expression of coal and gas outburst by utilizing machine learning, further perform artificial intervention and correction on the early warning result, and effectively reduce the false report phenomenon of coal and gas outburst.
(3) The coal and gas outburst real-time monitoring and early warning system and method provided by the invention have the function of automatically removing noise, and the monitoring substation is utilized to preprocess monitoring data, so that the monitoring precision of coal and rock fracture information can be greatly improved. By writing the wave speed value and the coordinate information of the vibration pickers in advance, the accurate position of the coal rock body breaking can be calculated according to the coal rock body breaking vibration wave and time received by each vibration pickup, and then the interference information generated by mining construction in the mining working face can be accurately removed.
(4) According to the coal and gas outburst real-time monitoring and early warning system and method provided by the invention, a GPS clock is used to adopt a high-precision network time service protocol, the problem that a synchronous clock communication system is required to be arranged independently in the past time service is solved, and because a local area network is used to build a network architecture, the system data transmission rate is high, the clock of each monitoring substation is calibrated in real time while the data acquisition and control command transmission is realized, so that the original data acquired by each monitoring substation can be ensured to be accurate and consistent in time, and the microseismic event monitoring precision is effectively improved.
Drawings
FIG. 1 is a schematic diagram of a real-time monitoring and early warning system for coal and gas outburst of a mining working face;
FIG. 2 is a schematic diagram of the structure of a monitoring substation;
FIG. 3 is a schematic diagram of a tunneling face station arrangement;
in the figure: 1-a ground central station; 2-controlling a host; 3-a network switch; 4-monitoring a substation; 41-a master control MCU chip; 42-a signal conditioner; a 43-A/D converter; 5-vibration pickup; 6-a wind speed sensor; 7-methane concentration sensor; 8-an audible and visual alarm; 9-a remote big data analysis service platform; 10-GPS clock.
Detailed Description
Referring to fig. 1 to 3, the embodiment of the system and the method for monitoring and early warning the coal and gas outburst of the mining working face in real time are as follows.
Example 1
The real-time monitoring and early warning system for coal and gas outburst of the mining working face specifically comprises a ground central station 1, a control host 2, a network switch 3, a monitoring substation 4, a vibration pickup 5, a wind speed sensor 6, a methane concentration sensor 7 and an audible and visual alarm 8. The ground central station 1 is connected with the control host 2 through a network cable, the ground central station 1 is connected with the network switch 3 through optical fibers, the network switch 3 is connected with the monitoring substation 4 through optical fibers, and the monitoring substation 4 is respectively connected with the vibration pickup 5, the wind speed sensor 6, the methane concentration sensor 7 and the audible and visual alarm 8 through cables.
The vibration pickup 5, the wind speed sensor 6 and the methane concentration sensor 7 are arranged in a roadway and used for monitoring a microseismic test piece, wind speed and gas concentration, monitoring information is transmitted to the monitoring substation 4, and the monitoring substation 4 transmits monitoring data to the ground central station 1 through a network switch. The ground center station 1 comprises a data analysis module, an early warning module and a storage module, wherein the data analysis module analyzes and processes monitoring data, the early warning module determines early warning grades according to data processing results, the storage module stores the monitoring data and can send the monitoring data to a remote big data analysis service platform through a network, the control host 2 controls the ground center station to work and can adjust data processing parameters of the ground center station 1 and program the data processing parameters, the network switch 3 transmits early warning information sent by the early warning module to a monitoring substation, the monitoring substation 4 transmits the early warning information to an audible-visual alarm, the audible-visual alarm 8 sends different alarms according to the early warning information, and the audible-visual alarm 8 comprises an alarm indicator lamp and a loudspeaker.
The monitoring substation 4 comprises an analog-to-digital conversion module, a data noise reduction module and a data screening module, the monitoring substation uses a main control MCU chip 41, a signal conditioner 42 and an A/D converter 43, the monitoring substation 4 converts monitoring data of measuring points into signals, and in addition, the monitoring substation 4 screens monitoring data of a vibration pickup, a wind speed sensor and a methane concentration sensor. In addition, the monitoring substation can preprocess the monitoring data, and can greatly improve the monitoring precision of the coal rock fracture information. By writing the wave speed value and the coordinate information of the vibration pickers in advance, the accurate position of the coal rock body breaking can be calculated according to the coal rock body breaking vibration wave and time received by each vibration pickup, and then the interference information generated by mining construction in the mining working face can be accurately removed.
The ground center station 1 may also be provided with a GPS clock 10, the GPS clock 10 adjusting the time coincidence of the monitoring data of each monitoring substation. The GPS clock 10 is used for adopting a high-precision network time service protocol, the problem that a synchronous clock communication system is required to be independently arranged in the past time service is solved, and because a network architecture is built by using a local area network, the data transmission rate of the system is high, the clock of each monitoring substation is calibrated in real time while the data acquisition and control command transmission is realized, so that the invention can ensure that the original data acquired by each monitoring substation 4 is accurately and consistently kept in time, and the microseismic event monitoring precision is effectively improved.
The ground central station 1 is connected with a remote big data analysis service platform through a network, the remote big data analysis service platform is connected with the ground central stations of a plurality of mines through the network, the remote big data analysis service platform extracts characteristic information of monitoring data through a machine learning algorithm, and the ground central station 1 adjusts critical values of early warning indexes according to analysis results of the remote big data analysis service platform. The system can also continuously correct the critical value and the related coefficient of the outstanding criterion index through a control center or a remote big data analysis service platform, thereby ensuring the early warning precision and the early warning efficiency.
The real-time monitoring and early warning method for the coal and gas outburst of the mining working face comprises the following steps of:
step one, arranging a ground central station 1, a control host 2 and a network switch 3, setting a monitoring substation in a roadway where a working face is located according to geological conditions and mining conditions of a mine, and connecting and installing a vibration pickup 5, a wind speed sensor 6, a methane concentration sensor 7 and an audible and visual alarm 8.
And secondly, debugging the ground central station 1, the control host 2, the network switch 3, the monitoring substation 4, the vibration pickup 5, the wind speed sensor 6, the methane concentration sensor 7 and the audible and visual alarm 8 after installation to ensure normal work, connecting the ground center with a GPS clock and connecting the ground center with a remote big data analysis service platform.
Setting parameters in a data analysis module of the ground central station through a control host, wherein the parameters comprise: the lengths of time m and n, the initial critical value e of the moving average of the microseismic events 1 Initial critical value e of deviation rate 2 And a discrete rate initial critical value e 3 Initial threshold value e of running average of gas emission 1 ' initial critical value e of deviation rate 2 ' and the initial critical value of the discrete rate e 3 ' weight w of microseismic event change feature 1 And a weight w of a gas emission amount variation characteristic 2 。
And fourthly, starting a real-time monitoring and early warning system for coal and gas outburst of the mining working face, transmitting monitoring data to a ground central station by a vibration pickup, a wind speed sensor and a methane concentration sensor, analyzing and processing the monitoring data by a data analysis module of the ground central station, storing the monitoring data by a storage module, transmitting the monitoring data to a remote big data analysis service platform, and transmitting early warning information to an audible and visual alarm by the early warning module through a network switch and a monitoring substation.
The early warning information of the early warning module comprises no outstanding danger, outstanding threat and outstanding danger, wherein the early warning module sends out the alarm indicator light of the audible and visual alarm when no outstanding danger to display green, the early warning module sends out the alarm indicator light of the audible and visual alarm when outstanding threat to display yellow, and the early warning module sends out the alarm indicator light of the audible and visual alarm when outstanding danger to display red and starts a loudspeaker. The audible and visual alarm device can send out alarm sound, and the data analysis processing module in the ground central station can automatically send out early warning information to mine leaders, safety responsible persons and the like through mail, short messages, APP and the like.
In step three, the microseismic event change characterization index I m A (n) is a sliding average according to the time series of microseismic events t Deviation rate Y (n) t And a discrete rate V (m) t To determine.
Microseismic event sliding average A (n) t The expression of (2) is:
wherein n is the length of time; a (n) t The moving average of microseismic events over the last time length n, a being the number of microseismic events.
Microseismic event rate of departure Y (n) t The expression of (2) is:
wherein a is t The number of microseismic events at time t.
Microseismic event discrete rate V (m) t The expression of (2) is:
wherein mu is the sample mean value of the microseismic event time sequence; m is the length of time.
A (n) is a sliding average according to the time series of microseismic events t Deviation rate Y (n) t And a discrete rate V (m) t To calculate the characteristic index I of the micro-seismic event change m Determining initial critical value e of moving average of microseismic events 1 Initial critical value e of deviation rate 2 And a discrete rate initial critical value e 3 The method comprises the steps of carrying out a first treatment on the surface of the Then, the sliding average value of the microseismic events is assigned as alpha, when the sliding average value of the microseismic events is larger than e 1 When the time is assigned to be 1, the sliding average value of the microseismic events is less than or equal to e 1 Assigning a value of 0; the deviation rate of the microseismic event is assigned as beta, and when the deviation rate of the microseismic event is larger than e 2 When the time is assigned to be 1, the deviation rate of the microseismic event is less than or equal to e 2 Assigning a value of 0; assigning the microseismic event discrete rate as gamma, and when the microseismic event discrete rate is larger than e 3 The time value is 1, and the dispersion rate of the microseismic events is less than or equal to e 3 Assigning a value of 0; and (3) comprehensively judging the value x=alpha+beta+gamma of the micro-seismic event change characteristic, wherein x= {0,1,2,3}.
Microseismic event change characteristic index I m The expression of (2) is:
in the third step, the gas emission variation characteristic index I g A (n) as a running average of a time series of gas emission' t Deviation rate Y (n)' t And a discrete rate V (m)' t To determine.
Gas emission amount sliding average value A (n)' t The expression of (2) is:
wherein n is the length of time; a (n)' t The gas emission amount is a sliding average value of the gas emission amount in the last n time periods, and c is the gas emission amount.
Deviation rate Y (n) of gas emission quantity' t The expression of (2) is:
wherein c t The gas emission amount at time t.
Gas emission rate V (m) 'of' t The expression of (2) is:
wherein μ' is a sample mean of the gas emission time series; m is the length of time.
A (n) as a running average of a time series of gas emission' t Deviation rate Y (n)' t And a discrete rate V (m)' t Calculating a gas emission quantity characteristic index I g Respectively determining the initial critical value e of the moving average of the gas emission quantity 1 ' initial critical value e of deviation rate 2 ' and the initial critical value of the discrete rate e 3 'then, the running average of the gas emission amount is assigned as alpha', when the running average of the gas emission amount is larger than e 1 When' is assigned to be 1, the gas emission quantity sliding average value is less than or equal to e 1 ' time value is 0; the gas emission deviation rate is assigned as beta', when the gas emission deviation rate is larger than e 2 When' is assigned to be 1, the deviation rate of the gas emission quantity is less than or equal to e 2 ' time value is 0; the gas emission quantity discrete rate is assigned as gamma', and when the gas emission quantity discrete rate is larger than e 3 When' time is assigned to be 1, the gas emission quantity discrete rate is less than or equal to e 3 ' time value is 0; and comprehensively judging the microseismic event change feature assignment y=alpha ' +beta ' +gamma ', wherein y= {0,1,2,3}.
Characteristic index I of variation of gas emission quantity g The expression of (2) is:
change characteristic index I of microseismic event m And a gas emission variation characteristic index I g The method is applied to a data analysis module to establish a comprehensive early warning model for fuzzy evaluation of coal and gas outburst and judge the risk of coal and gas outburstGrade.
Step a, establishing a fuzzy evaluation influence factor set, specifically a microseismic event change characteristic u 1 And a gas emission amount variation characteristic u 2 The evaluation factor set is u= { U 1 ，u 2 }。
And b, establishing a coal and gas outburst evaluation set, namely establishing a coal and gas outburst possible evaluation result set V= { coal and gas outburst does not occur }, wherein I represents that coal and gas outburst occurs, and II represents that coal and gas outburst does not occur, and V= { I and II }.
Step c, establishing a weight set, and establishing a weight set W= { W of each influence factor according to different importance of the influence factors in evaluation 1 ，w 2 -w is 1 +w 2 ＝1。
Step d, single-factor fuzzy evaluation, namely establishing a membership function relation between a fuzzy evaluation influence factor set U and a coal and gas outburst evaluation set V, wherein the characteristic U of the variation of the microseismic event 1 The membership functions with the saliency evaluation set V are:
variation characteristic u of gas emission quantity 2 The membership functions with the saliency evaluation set V are:
determining the membership degree of the influence factors by utilizing single factor evaluation judgment to obtain a single factor evaluation set:
R 1 ＝[I m (x)，1-I m (x)]
R 2 ＝[I g (y)，1-I g (y)]
step e, fuzzy comprehensive evaluation of coal and gas outburst, and establishing a comprehensive evaluation matrix:
R＝{R 1 ，R 2 } T
combining the weight set W of the influence factors and the comprehensive evaluation matrix R, and obtaining a fuzzy comprehensive evaluation set B by adopting a weighted average method according to a multiplication algorithm of a fuzzy matrix:
determining a coal and gas outburst probability index I and a microseismic event change characteristic index I m And a gas emission variation characteristic index I g The functional relation between them is I (x, y) =w 1 I m (x)+w 2 I g (y)。
In addition, the remote big data analysis service platform receives data uploaded by the ground central station, and determines parameters m, n and e by using a machine learning algorithm according to the actual conditions of mine coal and gas outburst 1 、e 2 、e 3 、 e 1 ’、e 2 ’、e 3 ’、w 1 And w 2 And fed back to the ground center. The remote big data analysis service platform is connected with a plurality of ground central stations distributed in different mines through a network, can receive data uploaded by each ground central station in real time or periodically, and can timely adjust the early warning index critical value of the data analysis processing module in the ground central station through a built-in machine learning algorithm in combination with dynamic phenomena or outstanding disaster conditions occurring in the actual production process of each mine.
Example 2
The embodiment takes a mining working face 1203 as an example on the basis of embodiment 1, and further describes a real-time monitoring and early warning system and method for coal and gas outburst of the mining working face.
The real-time monitoring and early warning system for coal and gas outburst of the mining working face comprises a ground central station 1, a control host 2, a network exchange 3, a monitoring substation 4, a vibration pickup 5, a wind speed sensor 6, a methane concentration sensor 7 and an audible and visual alarm 8. The ground central station 1 is connected with the control host 2 through a network cable, the ground central station 1 is connected with the network switch 3 through optical fibers, the network switch 3 is connected with the monitoring substation 4 through optical fibers, and the monitoring substation 4 is respectively connected with the vibration pickup 5, the wind speed sensor 6, the methane concentration sensor 7 and the audible and visual alarm 8 through cables. The vibration pickup 5, the wind speed sensor 6 and the methane concentration sensor 7 are arranged in a roadway, monitoring information is transmitted to the monitoring substation 4, and the monitoring substation 4 transmits monitoring data to the ground central station 1 through a network switch. The ground central station comprises a data analysis module, an early warning module and a storage module, the control host 2 controls the ground central station 1 to work, and the network switch 3 transmits early warning information sent by the early warning module to the monitoring substation. The monitoring substation 4 comprises an analog-to-digital conversion module, a data noise reduction module and a data screening module, and the monitoring substation 4 screens monitoring data of the vibration pickup 5, the wind speed sensor 6 and the methane concentration sensor 7. The ground central station 1 is provided with a GPS clock 10, the GPS clock 10 adopts the IEEE1588 protocol of high-precision network time service, and the GPS clock adjusts the time consistency of the monitoring data of each monitoring substation. The ground central station is connected with the remote big data analysis service platform 9 through a network, the remote big data analysis service platform 9 is connected with the ground central stations 1 of the plurality of mines through a network, the remote big data analysis service platform 9 extracts characteristic information of monitoring data through a machine learning algorithm, and the ground central station adjusts critical values of early warning indexes according to analysis results of the remote big data analysis service platform. The monitoring substation uses a main control MCU3 chip, a signal conditioner and an A/D converter, wherein the main control MCU3 (41) performs signal triggering required criterion operation, the signal conditioning circuit performs signal amplification and eliminates signal superposition, and the A/D converter converts multiple paths of analog vibration signals into digital signals. The monitoring substation 4 transmits the early warning signal to an audible and visual alarm, and the audible and visual alarm 8 comprises an alarm indicator lamp and a loudspeaker.
The method for monitoring and early warning the coal and gas outburst of the mining working face in real time by using the system comprises the following steps:
step one, arranging a ground central station 1, a control host 2 and a network switch 3, setting a monitoring substation in a roadway where a working face is located according to geological conditions and mining conditions of a mine, and connecting and installing a vibration pickup 5, a wind speed sensor 6, a methane concentration sensor 7 and an audible and visual alarm 8. According to the construction condition of 13102 working face return air gate heading face, 6 vibration pickup devices, 2 wind speed sensors and methane concentration sensors, 1 sound and light alarm device, 1 monitoring substation and 1 network exchanger are used. The vibration pickup is directly connected with the tail part of the anchor rod in the roadway through a special installation device, and is subjected to debugging after field installation is finished, so that each working module can work normally, and related parameters of each early warning index are set and determined simultaneously
And step two, debugging the ground central station 1, the control host 2, the network switch 3, the monitoring substation 4, the vibration pickup 5, the wind speed sensor 6, the methane concentration sensor 7 and the audible and visual alarm 9 after installation to ensure normal work, and connecting the ground central station 1 with the GPS clock 10, wherein the ground central station 1 and the far-end big data analysis service platform 9.
Setting parameters in a data analysis module of the ground central station through a control host, wherein the parameters comprise: the lengths of time m and n, the initial critical value e of the moving average of the microseismic events 1 Initial critical value e of deviation rate 2 And a discrete rate initial critical value e 3 Initial threshold value e of running average of gas emission 1 ' initial critical value e of deviation rate 2 ' and the initial critical value of the discrete rate e 3 ' weight w of microseismic event change feature 1 And a weight w of a gas emission amount variation characteristic 2 . In addition, the remote big data analysis service platform receives the data uploaded by the ground central station, and can determine parameters m, n and e by using a machine learning algorithm according to the actual conditions of the outburst of mine coal and gas 1 、e 2 、e 3 、e 1 ’、e 2 ’、e 3 ’、w 1 And w 2 And fed back to the ground center.
And fourthly, starting a real-time monitoring and early warning system for coal and gas outburst of the mining working face, transmitting monitoring data to a ground central station by a vibration pickup, a wind speed sensor and a methane concentration sensor, analyzing and processing the monitoring data by a data analysis module of the ground central station, storing the monitoring data by a storage module, transmitting the monitoring data to a remote big data analysis service platform, and transmitting early warning information to an audible and visual alarm by the early warning module through a network switch and a monitoring substation. The early warning information of the early warning module comprises no outstanding danger, outstanding threat and outstanding danger, wherein the early warning module sends out the alarm indicator light of the audible and visual alarm when no outstanding danger to display green, the early warning module sends out the alarm indicator light of the audible and visual alarm when outstanding threat to display yellow, and the early warning module sends out the alarm indicator light of the audible and visual alarm when outstanding danger to display red and starts a loudspeaker. The audible and visual alarm device can also send out alarm sounds, and the data analysis processing module in the ground central station can also automatically send out early warning messages to mine leaders, safety responsible persons and the like in the modes of mail, short messages, APP and the like.
It should be understood that the above description is not intended to limit the invention to the particular embodiments disclosed, but to limit the invention to the particular embodiments disclosed, and that the invention is not limited to the particular embodiments disclosed, but is intended to cover modifications, adaptations, additions and alternatives falling within the spirit and scope of the invention.
Claims (3)
1. A real-time monitoring and early warning method for coal and gas outburst of a mining working face is characterized in that a real-time monitoring and early warning system for coal and gas outburst of the mining working face is utilized, and the real-time monitoring and early warning system comprises a ground central station, a control host, a network switch, monitoring substations, vibration pickups, a wind speed sensor, a methane concentration sensor and an audible and visual alarm; the ground central station is connected with the control host through a network cable, the ground central station is connected with the network switch through optical fibers, the network switch is connected with the monitoring substation through optical fibers, and the monitoring substation is respectively connected with the vibration pickup, the wind speed sensor, the methane concentration sensor and the audible-visual alarm through cables; the vibration pickup, the wind speed sensor and the methane concentration sensor are arranged in the roadway, and monitoring information is transmitted to the monitoring substation; the monitoring substation transmits monitoring data to the ground central station through a network switch; the ground central station comprises a data analysis module, an early warning module and a storage module; the control host controls the ground central station to work; the network switch transmits the early warning information sent by the early warning module to the monitoring substation; the monitoring substation comprises an analog-to-digital conversion module, a data noise reduction module and a data screening module, and monitors monitoring data of a screening vibration pickup, a wind speed sensor and a methane concentration sensor of the monitoring substation; the ground central station is provided with a GPS clock, and the GPS clock adjusts the time consistency of the monitoring data of each monitoring substation; the ground central station is connected with the remote big data analysis service platforms through a network, the remote big data analysis service platforms are connected with the ground central stations of the plurality of mines through the network, the remote big data analysis service platforms extract characteristic information of monitoring data through a machine learning algorithm, and the ground central stations adjust critical values of early warning indexes according to analysis results of the remote big data analysis service platforms; the monitoring substation uses a main control MCU chip, a signal conditioner and an A/D converter, and transmits an early warning signal to an audible and visual alarm, wherein the audible and visual alarm comprises an alarm indicator lamp and a loudspeaker;
the method comprises the following steps:
arranging a ground central station, a control host and a network switch, setting a monitoring substation in a roadway where a working face is located according to geological conditions and mining conditions of a mine, and connecting and installing a vibration pickup, a wind speed sensor, a methane concentration sensor and an audible and visual alarm;
debugging a ground central station, a control host, a network switch, a monitoring substation, a vibration pickup, a wind speed sensor, a methane concentration sensor and an audible and visual alarm after installation to ensure normal operation, connecting the ground central station with a GPS clock and connecting the ground central station with a remote big data analysis service platform;
setting parameters in a data analysis module of the ground central station through a control host, wherein the parameters comprise: the lengths of time m and n, the initial critical value e of the moving average of the microseismic events 1 Initial critical value e of deviation rate 2 And a discrete rate initial faceBoundary value e 3 Initial critical value e of moving average of gas emission 1 ' initial critical value e of deviation rate 2 ' and the initial critical value of the discrete rate e 3 ' weight w of microseismic event change feature 1 And a weight w of a gas emission amount variation characteristic 2 ；
Step four, starting a real-time monitoring and early warning system for coal and gas outburst of the mining working face, transmitting monitoring data to a ground central station by a vibration pickup, a wind speed sensor and a methane concentration sensor, analyzing and processing the monitoring data by a data analysis module of the ground central station, storing the monitoring data by a storage module and transmitting the monitoring data to a remote big data analysis service platform, and transmitting early warning information to an audible and visual alarm by the early warning module through a network switch and a monitoring substation;
the early warning information of the early warning module comprises no outstanding danger, outstanding threat and outstanding danger, wherein the early warning module sends out the alarm indicator light of the audible and visual alarm when no outstanding danger to display green, the early warning module sends out the alarm indicator light of the audible and visual alarm when outstanding threat to display yellow, and the early warning module sends out the alarm indicator light of the audible and visual alarm when outstanding danger to display red and starts a loudspeaker;
the microseismic event change characteristic index I m A (n) is a sliding average according to the time series of microseismic events t Deviation rate Y (n) t And a discrete rate V (m) t To determine;
the sliding average value A (n) t The expression of (2) is:
wherein n is the length of time; a (n) t A is the moving average value of the microseismic events within the latest time length n, and a is the number of the microseismic events;
the deviation rate Y (n) t The expression of (2) is:
wherein a is t The number of microseismic events at time t;
the discrete rate V (m) t The expression of (2) is:
wherein mu is the sample mean value of the microseismic event time sequence; m is the time length;
a (n) is a sliding average according to the time series of microseismic events t Deviation rate Y (n) t And a discrete rate V (m) t To calculate the characteristic index I of the micro-seismic event change m Determining initial critical value e of moving average of microseismic events 1 Initial critical value e of deviation rate 2 And a discrete rate initial critical value e 3 The method comprises the steps of carrying out a first treatment on the surface of the Then, the sliding average value of the microseismic events is assigned as alpha, when the sliding average value of the microseismic events is larger than e 1 When the time is assigned to be 1, the sliding average value of the microseismic events is less than or equal to e 1 Assigning a value of 0; the deviation rate of the microseismic event is assigned as beta, and when the deviation rate of the microseismic event is larger than e 2 When the time is assigned to be 1, the deviation rate of the microseismic event is less than or equal to e 2 Assigning a value of 0; assigning the microseismic event discrete rate as gamma, and when the microseismic event discrete rate is larger than e 3 The time value is 1, and the dispersion rate of the microseismic events is less than or equal to e 3 Assigning a value of 0; comprehensively judging the microseismic event change feature assignment x=alpha+beta+gamma, and x= {0,1,2,3};
microseismic event change characteristic index I m The expression of (2) is:
the gas emission quantity characteristic index I g A (n) as a running average of a time series of gas emission' t Deviation rate Y (n)' t And a discrete rate V (m)' t To determine;
the sliding average value A (n)' t The expression of (2) is:
wherein n is the length of time; a (n)' t The gas emission quantity is a sliding average value of the gas emission quantity within the last n time periods, and c is the gas emission quantity;
the deviation rate Y (n)' t The expression of (2) is:
wherein c t The gas emission quantity at the time t;
the discrete rate V (m)' t The expression of (2) is:
wherein μ' is a sample mean of the gas emission time series; m is the time length;
a (n) as a running average of a time series of gas emission' t Deviation rate Y (n)' t And a discrete rate V (m)' t Calculating a characteristic index I of the gas emission quantity g Respectively determining the initial critical value e of the moving average of the gas emission quantity 1 ' initial critical value e of deviation rate 2 ' and the initial critical value of the discrete rate e 3 Subsequently, the gas emission amount is assigned a' to the moving average value, and when the gas emission amount is greater than e 1 When' is assigned to be 1, the gas emission quantity sliding average value is less than or equal to e 1 ' time value is 0; the gas emission deviation rate is assigned as beta', and when the gas emission deviation rate is larger than e 2 When' is assigned to be 1, the deviation rate of the gas emission quantity is less than or equal to e 2 ' time value is 0; the gas emission quantity discrete rate is assigned as gamma', and when the gas emission quantity discrete rate is larger than e 3 When' is assigned to be 1, the gas emission discrete rate is less than or equal to e 3 ' time value is 0; comprehensively judging the change characteristic assignment y=alpha ' +beta ' +gamma ' of the microseismic event, whereiny＝{0，1，2，3}；
Characteristic index I of variation of gas emission quantity g The expression of (2) is:
2. the real-time monitoring and early warning method for coal and gas outburst of mining working face according to claim 1, wherein the microseismic event change characteristic index I m And a gas emission variation characteristic index I g The method is applied to a data analysis module to establish a comprehensive early warning model for fuzzy evaluation of coal and gas outburst and judge the risk level of coal and gas outburst;
step a, establishing a fuzzy evaluation influence factor set, specifically a microseismic event change characteristic u 1 And a gas emission amount variation characteristic u 2 The evaluation factor set is u= { U 1 ，u 2 }；
Step b, establishing a coal and gas outburst evaluation set, namely establishing a coal and gas outburst possible evaluation result set V= { coal and gas outburst does not occur }, wherein I represents that coal and gas outburst occurs, and II represents that coal and gas outburst does not occur, and V= { I, II };
step c, establishing a weight set, and establishing a weight set W= { W of each influence factor according to different importance of the influence factors in evaluation 1 ，w 2 -w is 1 +w 2 ＝1，
Step d, single-factor fuzzy evaluation, namely establishing a membership function relation between a fuzzy evaluation influence factor set U and a coal and gas outburst evaluation set V, wherein the characteristic U of the variation of the microseismic event 1 The membership functions with the saliency evaluation set V are:
variation characteristic u of gas emission quantity 2 The membership functions with the saliency evaluation set V are:
determining the membership degree of the influence factors by utilizing single factor evaluation judgment to obtain a single factor evaluation set:
R 1 ＝[I m (x)，1-I m (x)]
R 2 ＝[I g (y)，1-I g (y)]
step e, fuzzy comprehensive evaluation of coal and gas outburst, and establishing a comprehensive evaluation matrix:
R＝{R 1 ，R 2 } T
combining the weight set W of the influence factors and the comprehensive evaluation matrix R, and obtaining a fuzzy comprehensive evaluation set B by adopting a weighted average method according to a multiplication algorithm of a fuzzy matrix:
determining a coal and gas outburst probability index I and a microseismic event change characteristic index I m And a gas emission variation characteristic index I g The functional relation between them is I (x, y) =w 1 I m (x)+w 2 I g (y)。
3. The real-time monitoring and early warning method for coal and gas outburst of mining working face according to claim 2, wherein the remote big data analysis service platform receives data uploaded by a ground central station and is used for analyzing the service platform according to the actual conditions of coal and gas outburst of a mineDetermining parameters m, n, e using machine learning algorithm 1 、e 2 、e 3 、e 1 ’、e 2 ’、e 3 ’、w 1 And w 2 And fed back to the ground central station.
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