CN109441547A - A kind of getting working face coal and gas prominent real-time system for monitoring and pre-warning and method - Google Patents
A kind of getting working face coal and gas prominent real-time system for monitoring and pre-warning and method Download PDFInfo
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- E—FIXED CONSTRUCTIONS
- E21—EARTH DRILLING; MINING
- E21F—SAFETY DEVICES, TRANSPORT, FILLING-UP, RESCUE, VENTILATION, OR DRAINING IN OR OF MINES OR TUNNELS
- E21F17/00—Methods or devices for use in mines or tunnels, not covered elsewhere
- E21F17/18—Special adaptations of signalling or alarm devices
Abstract
The present invention provides a kind of getting working face coal and gas prominent real-time system for monitoring and pre-warning and methods, it is related to mining engineering technical field, including ground central station, control host, the network switch, Monitor Sub-Station of Less, vibration pickup, air velocity transducer, sensing methane concentration device and combined aural and visual alarm, monitoring information is transmitted to Monitor Sub-Station of Less by sensor, monitoring data are transmitted to ground central station by the network switch by Monitor Sub-Station of Less, ground central station completes early warning analysis by being handled in real time monitoring data, specifically according to microseismic event variation characteristic index and the prominent risk of gas emission variation characteristic index comprehensive descision, coal and gas prominent fuzzy evaluation comprehensive pre-warning model is established based on this, judge danger classes.Early warning system and method provided by the invention solve the technical problem of the dangerous contactless continuous real-time monitoring of getting working face coal and gas prominent and early warning difficulty, in addition also have many advantages, such as accuracy height, strong operability.
Description
Technical field
It is especially a kind of for the real-time of getting working face coal and gas prominent the present invention relates to mining engineering technical field
The method of the system and overall merit coal and gas prominent danger of monitoring and warning.
Background technique
Coal and gas prominent be under the collective effect of crustal stress and gas, broken coal and gas from coal body suddenly to
The abnormal dynamic phenomenon that digging space is dished out.Domestic and foreign scholars about coal and gas prominent genesis mechanism form including " watt
This effect is said ", " crustal stress effect say ", a variety of academic viewpoints such as " chemical nature is said " and " comprehensive function hypothesis ", wherein integrating
Effect hypothesis thinks that coal and gas prominent is the knot acted on by combined factors such as the mechanical properties of crustal stress, gas pressure and coal
Fruit has obtained generally recognizing since the theory considers the principal element of 2 aspects of active force and medium of prominent generation comprehensively
It can.Important link of the coal and gas prominent danger forecasting as coal and gas prominent Synthetical control system, existing protrusion are pre-
Survey method mostly be it is static, discontinuous, still cannot real-time continuous ground forecasting coal and gas outburst risk；Common prediction
Method is mostly the state of forecasting coal and gas outburst risk, the development trend without considering outburst hazard, it is difficult to reflect
The preparation process of coal and gas prominent.In recent years, in recent years, some scholars propose some coal and gas prominent early warning systems
And method, such as the Chinese patent literature " coal and gas prominent real-time diagnosis method ", openly of Publication No. CN101532397A
Number Chinese patent literature " coal and gas prominent Integrated Early Warning System and method for early warning " for being CN101550841B, but between being mostly
It connects or three Dominated Factors of partial reflection influence outburst hazard includes the mechanical property of crustal stress, gas pressure and coal,
It cannot reflect coal and gas prominent risk size comprehensively；There are also some scholars to propose comprehensive utilization sound emission (or microseism)
Come forecasting coal and gas outburst risk size with dynamic gas emission, for example, Publication No. CN106194264A China specially
The Chinese patent text of sharp document " a kind of coal and gas prominent real-time monitoring and early warning system ", Publication No. CN101787897B
" a kind of system and method for real-time prediction mine coal and gas outburst risk " are offered, but its Early-warning Model used is not
There is the coupling association for really realizing two kinds of information, and previous system does not have positioning function, it is difficult to effectively rejecting mining work
The noise information that digging operation generates in face.
Due in the prior art there are no one kind can Coal Pore Structure variation tendency in front of concentrated expression getting working face, adopt
Dynamic stress develops and the method for early warning of gas bearing capacity variation characteristic, does not have microseismic event precise positioning, effectively rejecting underground is made an uproar
The ability of sound, it is therefore desirable to provide it is a kind of realize outburst danger continuously contactless real-time monitoring and the method for intelligent early-warning with
System.
Summary of the invention
For the technical problem for solving the dangerous contactless continuous real-time monitoring of working face coal and gas prominent and early warning difficulty,
This hair provides a kind of getting working face coal and gas prominent real-time system for monitoring and pre-warning and method, specific technical solution are as follows.
A kind of getting working face coal and gas prominent real-time system for monitoring and pre-warning, including ground central station, control host, net
Network interchanger, Monitor Sub-Station of Less, vibration pickup, air velocity transducer, sensing methane concentration device and combined aural and visual alarm；Ground central station and
It controls host to connect by cable, ground central station is connected with the network switch by optical fiber, the network switch and Monitor Sub-Station of Less
It is connected by optical fiber, Monitor Sub-Station of Less passes through cable and vibration pickup, air velocity transducer, sensing methane concentration device and combined aural and visual alarm
It is separately connected；The vibration pickup, air velocity transducer and sensing methane concentration device are arranged in tunnel, and monitoring information is transmitted to
Monitor Sub-Station of Less；Monitoring data are transmitted to ground central station by the network switch by Monitor Sub-Station of Less；Ground central station includes data
Analysis module, warning module and memory module；Control host control ground central station work；The network switch is by warning module
The warning information of sending is transmitted to Monitor Sub-Station of Less；Monitor Sub-Station of Less includes analog-to-digital conversion module, Noise reducing of data module and data screening
Module, Monitor Sub-Station of Less screen the monitoring data of vibration pickup, air velocity transducer and sensing methane concentration device.
Preferably, ground central station is provided with GPS clock, GPS clock adjust the monitoring data of each Monitor Sub-Station of Less when
Between it is consistent；Ground central station is connected by network with distal end big data analysis service platform, distal end big data analysis service platform
By network connection, distal end big data analysis service platform is mentioned ground central station with multiple mines by machine learning algorithm
The characteristic information of monitoring data is taken, ground central station adjusts early warning according to the analysis result of distal end big data analysis service platform
The critical value of index.
Preferably, Monitor Sub-Station of Less uses main control MCU chip, signal conditioner and A/D converter, and Monitor Sub-Station of Less is by early warning
Signal is transmitted to combined aural and visual alarm, and combined aural and visual alarm includes alarm lamp and loudspeaker.
A kind of getting working face coal and gas prominent Monitoring and forecasting system in real-time method utilizes a kind of above-mentioned getting working face coal
With Gas Outburst real-time system for monitoring and pre-warning, step includes:
Step 1 arranges ground central station, control host and the network switch, according to geological condition of coal mine and digging condition
Setting is being arranged Monitor Sub-Station of Less and is connecting installation vibration pickup, air velocity transducer, sensing methane concentration in tunnel where working face
Device and combined aural and visual alarm；
Ground central station, control host, the network switch, Monitor Sub-Station of Less, vibration pickup, wind speed are passed after step 2 installation
Sensor, sensing methane concentration device and combined aural and visual alarm are debugged, it is ensured that and it works normally, connects ground center and GPS clock,
Connect ground center and distal end big data analysis service platform；
The parameter in data analysis module that step 3 passes through control host setting ground central station, comprising: the length of time
Spend m and n, the sliding average initial criticality value e of microseismic event1, bias ratio initial criticality value e2With dispersion ratio initial criticality value e3,
The sliding average initial criticality value e of gas emission1', bias ratio initial criticality value e2' and dispersion ratio initial criticality value e3', it is micro-
Shake the weight w of event change feature1With the weight w of gas emission variation characteristic2；
Step 4 start getting working face coal and gas prominent real-time system for monitoring and pre-warning, vibration pickup, air velocity transducer and
Monitoring data are transmitted to ground central station, the data analysis module analysis processing prison of ground central station by sensing methane concentration device
Measured data, memory module save monitoring data and monitoring data are transmitted to distal end big data analysis service platform, warning module
Warning information is transmitted to combined aural and visual alarm by the network switch and Monitor Sub-Station of Less.
Preferably, the warning information of warning module includes no outburst danger, has prominent threat and have outburst danger, wherein
The alarm lamp that warning module issues combined aural and visual alarm when without outburst danger shows green, and warning module sending has prominent prestige
The alarm lamp of combined aural and visual alarm shows yellow when the side of body, and warning module issues the alarm of combined aural and visual alarm when having outburst danger
Indicator light is displayed in red and starts loudspeaker.
It is also preferred that microseismic event variation characteristic index ImAccording to the sliding average A of microseismic event time series
(n)t, bias ratio Y (n)tWith dispersion ratio V (m)tTo determine；
Sliding average A (n)tExpression formula are as follows:
Wherein, n is the length of time；A(n)tFor the microseismic event sliding average in nearest time span n, a is microseism
Event number；
Bias ratio Y (n)tExpression formula are as follows:
Wherein, atFor the microseismic event number of t moment；
Dispersion ratio V (m)tExpression formula are as follows:
Wherein, μ is the sample average of microseismic event time series；M is time span；
According to the sliding average A (n) of microseismic event time seriest, bias ratio Y (n)tWith dispersion ratio V (m)tTo calculate
Microseismic event variation characteristic index Im, microseismic event sliding average initial criticality value e is determined respectively1, bias ratio initial criticality value e2
With dispersion ratio initial criticality value e3；Then α is assigned a value of to microseismic event sliding average, when microseismic event sliding average is greater than
e1When be assigned a value of 1, microseismic event sliding average is less than or equal to e1When be assigned a value of 0；Microseismic event bias ratio is assigned a value of β, when micro-
Shake event bias ratio is greater than e2When be assigned a value of 1, microseismic event bias ratio is less than or equal to e2When be assigned a value of 0；Microseismic event dispersion ratio
It is assigned a value of γ, when microseismic event dispersion ratio is greater than e3When be assigned a value of 1, microseismic event dispersion ratio is less than or equal to e3When be assigned a value of 0；It is comprehensive
Conjunction judges microseismic event variation characteristic assignment x=alpha+beta+γ, x={ 0,1,2,3 }；
Microseismic event variation characteristic index ImExpression formula are as follows:
It is also preferred that gas emission variation characteristic index IgAccording to the sliding average of gas emission time series
A(n)’ t, bias ratio Y (n) 'tWith dispersion ratio V (m) 'tTo determine；
Sliding average A (n) 'tExpression formula are as follows:
Wherein, n is the length of time；A(n)'tFor the gas emission sliding average in nearest n time span, c is
Gas emission size；
Bias ratio Y (n) 'tExpression formula are as follows:
Wherein, ctFor the gas emission of t moment；
Dispersion ratio V (m) 'tExpression formula are as follows:
Wherein, μ ' is the sample average of gas emission time series；M is time span；
According to the sliding average A (n) ' of gas emission time seriest, bias ratio Y (n) 'tWith dispersion ratio V (m) 'tCome
Calculate gas emission variation characteristic index Ig, the sliding average initial criticality value e of gas emission is determined respectively1', bias ratio
Initial criticality value e2' and dispersion ratio initial criticality value e3', α ' then is assigned a value of to the sliding average of gas emission, when watt
This outburst amount sliding average is greater than e1' when be assigned a value of 1, gas emission sliding average is less than or equal to e1' when be assigned a value of 0；
Gas emission bias ratio is assigned a value of β ', when gas emission bias ratio is greater than e2' when be assigned a value of 1, gas emission bias ratio
Less than or equal to e2' when be assigned a value of 0；Gas emission dispersion ratio is assigned a value of γ ', when gas emission dispersion ratio is greater than e3' when assign
Value is 1, and gas emission dispersion ratio is less than or equal to e3' when be assigned a value of 0；Comprehensive descision microseismic event variation characteristic assignment y=α '
+ β '+γ ', wherein { 0,1,2,3 } y=；
Gas emission variation characteristic index IgExpression formula are as follows:
It may further be preferable that microseismic event variation characteristic index ImWith gas emission variation characteristic index IgIt is applied to
Data analysis module establishes coal and gas prominent fuzzy evaluation comprehensive pre-warning model, judges coal and gas prominent danger classes；
Step a. establishes fuzzy evaluation influence factor set, specially microseismic event variation characteristic u1With Gas quantitative change
Change feature u2, factor of evaluation collection is combined into U={ u1, u2}；
Step b. establishes coal and gas prominent evaluation set, specially establishes the possible evaluation result collection of coal and gas prominent
V={ coal and gas prominent occurring, coal and gas prominent does not occur } is closed, is represented with I and coal and gas prominent occurs, represented with II
Coal and gas prominent does not occur, then V={ I, II }；
Step c. establishes weight set, different according to importance of the influence factor in evaluation, establishes each influence factor
Weight set W={ w1, w2, wherein w1+w2=1,
Step d. single factor test fuzzy evaluation establishes fuzzy evaluation influence factor set U and coal and gas prominent evaluation set V
Between membership function relationship, wherein microseismic event variation characteristic u1With the membership function of prominent evaluation set V are as follows:
Gas emission variation characteristic u2With the membership function of prominent evaluation set V are as follows:
Judged using single factor evaluation, determine the subjection degree of influence factor, obtains single factor evaluation collection:
R1=[Im(x), 1-Im(x)]
R2=[Ig(y), 1-Ig(y)]
Step e. coal and gas prominent fuzzy overall evaluation, establishes synthetic evaluation matrix:
R={ R1, R2}T
In conjunction with the weight set W and synthetic evaluation matrix R of influence factor, using average weighted method, according to fuzzy square
The multiplying rule of battle array, obtains fuzzy overall evaluation set B:
Determine coal and gas prominent Possibility index I and microseismic event variation characteristic index ImChange with gas emission special
Levy index IgBetween functional relation be I (x, y)=w1Im(x)+w2Ig(y)。
It is still further preferred that distal end big data analysis service platform receives the data that ground central station is uploaded, root
According to the actual conditions of mine coal and gas prominent, distal end big data analysis service platform determines parameter using machine learning algorithm
m、n、e1、 e2、e3、e1’、e2’、e3’、w1And w2, and feed back to ground center.
The beneficial effect comprise that
(1) getting working face coal and gas prominent real-time system for monitoring and pre-warning provided by the invention and method, realize non-
The function of contact real-time monitoring getting working face coal and gas prominent risk, the coal and gas prominent fuzzy synthesis of use
Early-warning Model is mainly using microseismic event and gas emission time series variation feature as Judging index, coal and gas prominent
Coal Pore Structure variation tendency, mining induced stress develop in front of Possibility index I concentrated expression getting working face and gas bearing capacity becomes
Change feature, used prominent criterion index has fully considered that coal and gas prominent Dynamic Evolution feature, system can also lead to
It crosses control centre or distal end big data analysis service platform constantly corrects prominent criterion index critical value and related coefficient, it is ensured that
Early warning precision and efficiency.
(2) early warning system and method provided by the invention realize coal and gas prominent real-time monitoring and automatically analyze pre-
While alert function, prominent early warning result can also be modified and be controlled by control centre, to effectively prevent
Colliery scene engineers and technicians are professional poor, and failing to report phenomenon caused by negligence etc., can also pass through control and lead
Machine or distal end big data analysis service platform carry out analysis explanation to monitoring data, obtain coal and gas prominent using machine learning
Feature representation, and then artificial intervention and amendment are carried out to early warning result, the wrong report for effectively reducing coal and gas prominent is existing
As.
(3) coal and gas prominent real-time system for monitoring and pre-warning provided by the invention and method have the function of automatic rejection noise
Can, monitoring data are pre-processed using Monitor Sub-Station of Less, the monitoring accuracy for coal petrography rupture information can be greatly improved.It crosses
Be previously written the coordinate information of value of wave speed, vibration pickup, can according to coal and rock that each vibration pickup receives rupture shock wave and when
Between, the accurate location that coal and rock ruptures is calculated, and then accurately reject in getting working face caused by digging construction
Interference information.
(4) coal and gas prominent real-time system for monitoring and pre-warning provided by the invention and method, using GPS clock using high-precision
Time calibration in network agreement is spent, the problem of previous time service needs individually arrangement synchronised clock communication system is got rid of, due to using local
Net builds the network architecture, and system data transmission rate is high, while realizing transmission data acquisition and control order, also realizes
The clock of each Monitor Sub-Station of Less of real time calibration, therefore the present invention can guarantee that the collected initial data of each Monitor Sub-Station of Less exists
It keeps precisely consistent on time, effectively increases microseismic event monitoring accuracy.
Detailed description of the invention
Fig. 1 is getting working face coal and gas prominent real-time system for monitoring and pre-warning structural schematic diagram；
Fig. 2 is the structural schematic diagram of Monitor Sub-Station of Less；
Fig. 3 is driving face point layout schematic diagram；
In figure: 1- ground central station；2- controls host；The 3- network switch；4- Monitor Sub-Station of Less；41- main control MCU chip；
42- signal conditioner；43-A/D converter；5- vibration pickup；6- air velocity transducer；7- sensing methane concentration device；8- sound-light alarm
Device；The distal end 9- big data analysis service platform；10-GPS clock.
Specific embodiment
In conjunction with shown in Fig. 1 to Fig. 3, a kind of getting working face coal and gas prominent Monitoring and forecasting system in real-time system provided by the invention
System and method specific embodiment are as follows.
Embodiment 1
A kind of getting working face coal and gas prominent real-time system for monitoring and pre-warning specific structure includes ground central station 1, control
Host 2, the network switch 3, Monitor Sub-Station of Less 4, vibration pickup 5, air velocity transducer 6, sensing methane concentration device 7 and combined aural and visual alarm processed
8.Wherein, ground central station 1 is connected with control host 2 by cable, and ground central station 1 and the network switch 3 are connected by optical fiber
It connects, the network switch 3 is connected with Monitor Sub-Station of Less 4 by optical fiber, and Monitor Sub-Station of Less 4 passes through cable and vibration pickup 5, air velocity transducer
6, sensing methane concentration device 7 and combined aural and visual alarm 8 are separately connected.
Vibration pickup 5, air velocity transducer 6 and sensing methane concentration device 7 are arranged in tunnel, for monitoring microseism test specimen, wind
Speed and gas density, are transmitted to Monitor Sub-Station of Less 4 for monitoring information, Monitor Sub-Station of Less 4 is transmitted monitoring data by the network switch
To ground central station 1.Ground central station 1 includes data analysis module, warning module and memory module, data analysis module point
Analysis processing monitoring data, warning module determine warning grade according to data processed result, and memory module saves monitoring data simultaneously
Monitoring data can be sent to distal end big data analysis service platform by network, control host 2 controls ground central station work
Make, and the data processing parameters of adjustable ground central station 1, and be programmed, the network switch 3 issues warning module
Warning information be transmitted to Monitor Sub-Station of Less, warning information is sent to combined aural and visual alarm, 8 basis of combined aural and visual alarm by Monitor Sub-Station of Less 4
Warning information issues different alarms, and combined aural and visual alarm 8 includes alarm lamp and loudspeaker.
Monitor Sub-Station of Less 4 includes analog-to-digital conversion module, Noise reducing of data module and data screening module, and Monitor Sub-Station of Less uses master control
The monitoring data of measuring point are converted to signal by MCU chip 41, signal conditioner 42 and A/D converter 43, Monitor Sub-Station of Less 4, in addition
Monitor Sub-Station of Less 4 screens the monitoring data of vibration pickup, air velocity transducer and sensing methane concentration device.In addition Monitor Sub-Station of Less can be to prison
Measured data is pre-processed, and the monitoring accuracy for coal petrography rupture information can be greatly improved.It crosses and is previously written value of wave speed, vibration pickup
Coordinate information, can according to coal and rock that each vibration pickup receives rupture shock wave and time, calculate coal and rock occur it is broken
The accurate location split, and then accurately reject interference information caused by digging construction in getting working face.
Ground central station 1 is also provided with GPS clock 10, and GPS clock 10 adjusts the monitoring data of each Monitor Sub-Station of Less
Time consistency.High-accuracy network time service agreement is used using GPS clock 10, previous time service is got rid of and needs individually arrangement synchronization
The problem of clock communication system, due to using local area network to build the network architecture, system data transmission rate is high, transmits realizing
While data acquisition and control order, the clock of each Monitor Sub-Station of Less of real time calibration is also achieved, therefore the present invention can guarantee
The collected initial data of each Monitor Sub-Station of Less 4 keeps precisely consistent in time, effectively increases microseismic event monitoring essence
Degree.
Ground central station 1 is connected by network with distal end big data analysis service platform, and big data analysis service in distal end is flat
The ground central station of platform and multiple mines passes through machine learning algorithm by network connection, distal end big data analysis service platform
The characteristic information of monitoring data is extracted, ground central station 1 adjusts pre- according to the analysis result of distal end big data analysis service platform
The critical value of alert index.System can also constantly correct prominent criterion by control centre or distal end big data analysis service platform
Index critical value and related coefficient, it is ensured that early warning precision and efficiency.
A kind of getting working face coal and gas prominent Monitoring and forecasting system in real-time method utilizes a kind of above-mentioned getting working face coal
With Gas Outburst real-time system for monitoring and pre-warning, step includes:
Step 1 arranges ground central station 1, control host 2 and the network switch 3, according to geological condition of coal mine and digging
Condition setting, it is dense Monitor Sub-Station of Less is arranged where working face in tunnel and connects installation vibration pickup 5, air velocity transducer 6, methane
Spend sensor 7 and combined aural and visual alarm 8.
To ground central station 1, control host 2, the network switch 3, Monitor Sub-Station of Less 4, vibration pickup 5, wind after step 2 installation
Fast sensor 6, sensing methane concentration device 7 and combined aural and visual alarm 8 are debugged, it is ensured that work normally, connection ground center and
GPS clock connects ground center and distal end big data analysis service platform.
The parameter in data analysis module that step 3 passes through control host setting ground central station, comprising: the length of time
Spend m and n, the sliding average initial criticality value e of microseismic event1, bias ratio initial criticality value e2With dispersion ratio initial criticality value e3,
The sliding average initial criticality value e of gas emission1', bias ratio initial criticality value e2' and dispersion ratio initial criticality value e3', it is micro-
Shake the weight w of event change feature1With the weight w of gas emission variation characteristic2。
Step 4 start getting working face coal and gas prominent real-time system for monitoring and pre-warning, vibration pickup, air velocity transducer and
Monitoring data are transmitted to ground central station, the data analysis module analysis processing prison of ground central station by sensing methane concentration device
Measured data, memory module save monitoring data and monitoring data are transmitted to distal end big data analysis service platform, warning module
Warning information is transmitted to combined aural and visual alarm by the network switch and Monitor Sub-Station of Less.
Wherein the warning information of warning module includes no outburst danger, has prominent threat and have outburst danger, wherein early warning
The alarm lamp that module issues combined aural and visual alarm when without outburst danger shows green, when warning module sending has prominent threaten
The alarm lamp of combined aural and visual alarm shows yellow, and warning module issues the police instruction of combined aural and visual alarm when having outburst danger
Lamp is displayed in red and starts loudspeaker.Acoustic-optic alarm can also issue alarm, the Data Analysis Services in ground central station
Module can also be led automatically by modes such as mail, short message and APP to mine and the sending early warning informations such as safety director.
In step 3, microseismic event variation characteristic index ImAccording to the sliding average A of microseismic event time series
(n)t, bias ratio Y (n)tWith dispersion ratio V (m)tTo determine.
Microseismic event sliding average A (n)tExpression formula are as follows:
Wherein, n is the length of time；A(n)tFor the microseismic event sliding average in nearest time span n, a is microseism
Event number.
Microseismic event bias ratio Y (n)tExpression formula are as follows:
Wherein, atFor the microseismic event number of t moment.
Microseismic event dispersion ratio V (m)tExpression formula are as follows:
Wherein, μ is the sample average of microseismic event time series；M is time span.
According to the sliding average A (n) of microseismic event time seriest, bias ratio Y (n)tWith dispersion ratio V (m)tTo calculate
Microseismic event variation characteristic index Im, microseismic event sliding average initial criticality value e is determined respectively1, bias ratio initial criticality value e2
With dispersion ratio initial criticality value e3；Then α is assigned a value of to microseismic event sliding average, when microseismic event sliding average is greater than
e1When be assigned a value of 1, microseismic event sliding average is less than or equal to e1When be assigned a value of 0；Microseismic event bias ratio is assigned a value of β, when micro-
Shake event bias ratio is greater than e2When be assigned a value of 1, microseismic event bias ratio is less than or equal to e2When be assigned a value of 0；Microseismic event dispersion ratio
It is assigned a value of γ, when microseismic event dispersion ratio is greater than e3When be assigned a value of 1, microseismic event dispersion ratio is less than or equal to e3When be assigned a value of 0；It is comprehensive
Conjunction judges microseismic event variation characteristic assignment x=alpha+beta+γ, x={ 0,1,2,3 }.
Microseismic event variation characteristic index ImExpression formula are as follows:
In step 3, gas emission variation characteristic index IgAccording to the sliding average of gas emission time series
A(n)’ t, bias ratio Y (n) 'tWith dispersion ratio V (m) 'tTo determine.
Gas emission sliding average A (n) 'tExpression formula are as follows:
Wherein, n is the length of time；A(n)'tFor the gas emission sliding average in nearest n time span, c is
Gas emission size.
Gas emission bias ratio Y (n) 'tExpression formula are as follows:
Wherein, ctFor the gas emission of t moment.
Gas emission dispersion ratio V (m) 'tExpression formula are as follows:
Wherein, μ ' is the sample average of gas emission time series；M is time span.
According to the sliding average A (n) ' of gas emission time seriest, bias ratio Y (n) 'tWith dispersion ratio V (m) 'tCome
Calculate gas emission variation characteristic index Ig, the sliding average initial criticality value e of gas emission is determined respectively1', bias ratio
Initial criticality value e2' and dispersion ratio initial criticality value e3', α ' then is assigned a value of to the sliding average of gas emission, when watt
This outburst amount sliding average is greater than e1' when be assigned a value of 1, gas emission sliding average is less than or equal to e1' when be assigned a value of 0；
Gas emission bias ratio is assigned a value of β ', when gas emission bias ratio is greater than e2' when be assigned a value of 1, gas emission bias ratio
Less than or equal to e2' when be assigned a value of 0；Gas emission dispersion ratio is assigned a value of γ ', when gas emission dispersion ratio is greater than e3' when assign
Value is 1, and gas emission dispersion ratio is less than or equal to e3' when be assigned a value of 0；Comprehensive descision microseismic event variation characteristic assignment y=α '
+ β '+γ ', wherein { 0,1,2,3 } y=.
Gas emission variation characteristic index IgExpression formula are as follows:
By microseismic event variation characteristic index ImWith gas emission variation characteristic index IgIt is built applied to data analysis module
Vertical coal and gas prominent fuzzy evaluation comprehensive pre-warning model, judges coal and gas prominent danger classes.
Step a. establishes fuzzy evaluation influence factor set, specially microseismic event variation characteristic u1With Gas quantitative change
Change feature u2, factor of evaluation collection is combined into U={ u1, u2}。
Step b. establishes coal and gas prominent evaluation set, specially establishes the possible evaluation result collection of coal and gas prominent
V={ coal and gas prominent occurring, coal and gas prominent does not occur } is closed, is represented with I and coal and gas prominent occurs, represented with II
Coal and gas prominent does not occur, then V={ I, II }.
Step c. establishes weight set, different according to importance of the influence factor in evaluation, establishes each influence factor
Weight set W={ w1, w2, wherein w1+w2=1.
Step d. single factor test fuzzy evaluation establishes fuzzy evaluation influence factor set U and coal and gas prominent evaluation set V
Between membership function relationship, wherein microseismic event variation characteristic u1With the membership function of prominent evaluation set V are as follows:
Gas emission variation characteristic u2With the membership function of prominent evaluation set V are as follows:
Judged using single factor evaluation, determine the subjection degree of influence factor, obtains single factor evaluation collection:
R1=[Im(x), 1-Im(x)]
R2=[Ig(y), 1-Ig(y)]
Step e. coal and gas prominent fuzzy overall evaluation, establishes synthetic evaluation matrix:
R={ R1, R2}T
In conjunction with the weight set W and synthetic evaluation matrix R of influence factor, using average weighted method, according to fuzzy square
The multiplying rule of battle array, obtains fuzzy overall evaluation set B:
Determine coal and gas prominent Possibility index I and microseismic event variation characteristic index ImChange with gas emission special
Levy index IgBetween functional relation be I (x, y)=w1Im(x)+w2Ig(y)。
In addition, distal end big data analysis service platform receives the data that are uploaded of ground central station, according to mine coal with watt
This actual conditions outstanding, distal end big data analysis service platform determine parameter m, n, e using machine learning algorithm1、e2、e3、
e1’、e2’、e3’、w1And w2, and feed back to ground center.If distal end big data analysis service platform mine different from being distributed in
Dry ground central station can in real time or periodically receive the data that each ground central station is uploaded, in conjunction with each mine by network connection
The dynamic phenomenon or prominent disaster scenarios it, the platform occurred in well actual production process can pass through built-in machine learning algorithm
The warning index critical value of Data Analysis Services module in ground central station is adjusted in time.
Embodiment 2
The present embodiment is on the basis of embodiment 1, by taking certain 1203 driving face of mine as an example, to a kind of getting working face
Coal and gas prominent real-time system for monitoring and pre-warning and method are described in further detail.
A kind of getting working face coal and gas prominent real-time system for monitoring and pre-warning include ground central station 1, control host 2,
The network switch 3, Monitor Sub-Station of Less 4, vibration pickup 5, air velocity transducer 6, sensing methane concentration device 7 and combined aural and visual alarm 8.Ground
Central station 1 is connected with control host 2 by cable, and ground central station 1 is connected with the network switch 3 by optical fiber, network exchange
Machine 3 is connected with Monitor Sub-Station of Less 4 by optical fiber, and Monitor Sub-Station of Less 4 is passed by cable and vibration pickup 5, air velocity transducer 6, methane concentration
Sensor 7 and combined aural and visual alarm 8 are separately connected.Vibration pickup 5, air velocity transducer 6 and sensing methane concentration device 7 are arranged in tunnel,
Monitoring information is transmitted to Monitor Sub-Station of Less 4, monitoring data are transmitted to ground central station by the network switch by Monitor Sub-Station of Less 4
1.Ground central station includes data analysis module, warning module and memory module, and control host 2 controls 1 work of ground central station
Make, the warning information that warning module issues is transmitted to Monitor Sub-Station of Less by the network switch 3.Monitor Sub-Station of Less 4 includes analog-to-digital conversion mould
Block, Noise reducing of data module and data screening module, Monitor Sub-Station of Less 4 screen vibration pickup 5, air velocity transducer 6 and sensing methane concentration
The monitoring data of device 7.Ground central station 1 is provided with GPS clock 10, and GPS clock 10 uses high-accuracy network time service IEEE1588
Agreement, GPS clock adjust the time consistency of the monitoring data of each Monitor Sub-Station of Less.Ground central station passes through the big number of network and distal end
It is connected according to Analysis Service platform 9, the ground central station 1 of distal end big data analysis service platform 9 and multiple mines is connected by network
It connects, distal end big data analysis service platform 9 extracts the characteristic information of monitoring data, ground central station by machine learning algorithm
The critical value of warning index is adjusted according to the analysis result of distal end big data analysis service platform.Monitor Sub-Station of Less uses master control
MCU3 chip, signal conditioner and A/D converter, wherein main control MCU 3 (41) carries out criterion operation needed for signal triggers, signal
Conditioning circuit carries out signal amplification and eliminates Signal averaging, and multi-channel analog vibration signal is converted to digital letter by A/D converter
Number.Pre-warning signal is transmitted to combined aural and visual alarm by Monitor Sub-Station of Less 4, and combined aural and visual alarm 8 includes alarm lamp and loudspeaker.
The method for carrying out getting working face coal and gas prominent Monitoring and forecasting system in real-time using above system, step include:
Step 1 arranges ground central station 1, control host 2 and the network switch 3, according to geological condition of coal mine and digging
Condition setting, it is dense Monitor Sub-Station of Less is arranged where working face in tunnel and connects installation vibration pickup 5, air velocity transducer 6, methane
Spend sensor 7 and combined aural and visual alarm 8.According to the condition of construction of 13102 working face air return way development ends, determines and use pick-up
Device totally 6, air velocity transducer and sensing methane concentration device is 2 each, acoustic-optic alarm 1, Monitor Sub-Station of Less 1 and network are handed over
It changes planes 1.Vibration pickup is directly connected with the anchor pole tail portion in tunnel by special mounting device, after the completion of in-site installation
It is debugged, it is ensured that each operational module can work normally, while being arranged and determining each warning index relevant parameter
To ground central station 1, control host 2, the network switch 3, Monitor Sub-Station of Less 4, vibration pickup 5, wind after step 2 installation
Fast sensor 6, sensing methane concentration device 7 and combined aural and visual alarm 9 are debugged, it is ensured that are worked normally, connected ground central station 1
With GPS clock 10, ground central station 1 and distal end big data analysis service platform 9.
The parameter in data analysis module that step 3 passes through control host setting ground central station, comprising: the length of time
Spend m and n, the sliding average initial criticality value e of microseismic event1, bias ratio initial criticality value e2With dispersion ratio initial criticality value e3,
The sliding average initial criticality value e of gas emission1', bias ratio initial criticality value e2' and dispersion ratio initial criticality value e3', it is micro-
Shake the weight w of event change feature1With the weight w of gas emission variation characteristic2.In addition, big data analysis service in distal end is flat
Platform receives the data that ground central station is uploaded, according to the actual conditions of mine coal and gas prominent, distal end big data analysis clothes
Business platform can use machine learning algorithm and determine parameter m, n, e1、e2、e3、e1’、e2’、e3’、w1And w2, and feed back to ground
Center.
Step 4 start getting working face coal and gas prominent real-time system for monitoring and pre-warning, vibration pickup, air velocity transducer and
Monitoring data are transmitted to ground central station, the data analysis module analysis processing prison of ground central station by sensing methane concentration device
Measured data, memory module save monitoring data and monitoring data are transmitted to distal end big data analysis service platform, warning module
Warning information is transmitted to combined aural and visual alarm by the network switch and Monitor Sub-Station of Less.The wherein warning information packet of warning module
No outburst danger is included, has prominent threat and has outburst danger, wherein warning module issues combined aural and visual alarm when without outburst danger
Alarm lamp display green, the alarm lamp that warning module issues combined aural and visual alarm when having prominent threaten show yellow, in advance
The alarm lamp that alert module issues combined aural and visual alarm when having outburst danger is displayed in red and starts loudspeaker.Sound-light alarm dress
Alarm can also be issued by setting, and the Data Analysis Services module in ground central station can also be automatically by mail, short message and APP etc.
Mode is led to mine and the sending early warning informations such as safety director.
Certainly, the above description is not a limitation of the present invention, and the present invention is also not limited to the example above, this technology neck
The variations, modifications, additions or substitutions that the technical staff in domain is made within the essential scope of the present invention, also should belong to the present invention
Protection scope.
Claims (9)
1. a kind of getting working face coal and gas prominent real-time system for monitoring and pre-warning, which is characterized in that
It is passed including ground central station, control host, the network switch, Monitor Sub-Station of Less, vibration pickup, air velocity transducer, methane concentration
Sensor and combined aural and visual alarm；The ground central station is connected with control host by cable, and the ground central station and network are handed over
Change planes and connected by optical fiber, the network switch is connected with Monitor Sub-Station of Less by optical fiber, the Monitor Sub-Station of Less by cable with
Vibration pickup, air velocity transducer, sensing methane concentration device and combined aural and visual alarm are separately connected；
The vibration pickup, air velocity transducer and sensing methane concentration device are arranged in tunnel, and monitoring information is transmitted to monitoring point
It stands；Monitoring data are transmitted to ground central station by the network switch by the Monitor Sub-Station of Less；The ground central station includes number
According to analysis module, warning module and memory module；The control host control ground central station work；The network switch will
The warning information that warning module issues is transmitted to Monitor Sub-Station of Less；
The Monitor Sub-Station of Less includes analog-to-digital conversion module, Noise reducing of data module and data screening module, and Monitor Sub-Station of Less screens pick-up
The monitoring data of device, air velocity transducer and sensing methane concentration device.
2. a kind of getting working face coal and gas prominent real-time system for monitoring and pre-warning according to claim 1, feature exist
In the ground central station is provided with GPS clock, and GPS clock adjusts the time consistency of the monitoring data of each Monitor Sub-Station of Less；It is described
Ground central station connect with distal end big data analysis service platform by network, the distal end big data analysis service platform with it is more
The ground central station of a mine is extracted by machine learning algorithm and is monitored by network connection, distal end big data analysis service platform
The characteristic information of data, the ground central station adjust warning index according to the analysis result of distal end big data analysis service platform
Critical value.
3. a kind of getting working face coal and gas prominent real-time system for monitoring and pre-warning according to claim 1, feature exist
In the Monitor Sub-Station of Less uses main control MCU chip, signal conditioner and A/D converter, and the Monitor Sub-Station of Less passes pre-warning signal
Combined aural and visual alarm is transported to, the combined aural and visual alarm includes alarm lamp and loudspeaker.
4. a kind of getting working face coal and gas prominent Monitoring and forecasting system in real-time method, which is characterized in that utilize claims 1 to 33
A kind of described in any item getting working face coal and gas prominent real-time system for monitoring and pre-warning, step include:
Step 1 arranges ground central station, control host and the network switch, is set according to geological condition of coal mine and digging condition
Set, where working face in tunnel be arranged Monitor Sub-Station of Less and connect installation vibration pickup, air velocity transducer, sensing methane concentration device and
Combined aural and visual alarm；
Step 2 installation after to ground central station, control host, the network switch, Monitor Sub-Station of Less, vibration pickup, air velocity transducer,
Sensing methane concentration device and combined aural and visual alarm are debugged, it is ensured that are worked normally, connected ground center and GPS clock, connection ground
Face center and distal end big data analysis service platform；
The parameter in data analysis module that step 3 passes through control host setting ground central station, comprising: the length m of time
And n, the sliding average initial criticality value e of microseismic event1, bias ratio initial criticality value e2With dispersion ratio initial criticality value e3, gas
The sliding average initial criticality value e of outburst amount1', bias ratio initial criticality value e2' and dispersion ratio initial criticality value e3', microseism thing
The weight w of part variation characteristic1With the weight w of gas emission variation characteristic2；
Step 4 starts getting working face coal and gas prominent real-time system for monitoring and pre-warning, vibration pickup, air velocity transducer and methane
Monitoring data are transmitted to ground central station, the data analysis module analysis processing monitoring number of ground central station by concentration sensor
According to memory module saves monitoring data and monitoring data are transmitted to distal end big data analysis service platform, the warning module
Warning information is transmitted to combined aural and visual alarm by the network switch and Monitor Sub-Station of Less.
5. a kind of getting working face coal and gas prominent Monitoring and forecasting system in real-time method according to claim 4, feature exist
In the warning information of the warning module includes no outburst danger, has prominent threat and have outburst danger, and wherein warning module is sent out
The alarm lamp of combined aural and visual alarm shows green, sound-light alarm when warning module sending has prominent threaten when out without outburst danger
The alarm lamp of device shows yellow, and the alarm lamp that warning module issues combined aural and visual alarm when having outburst danger is displayed in red
And start loudspeaker.
6. a kind of getting working face coal and gas prominent Monitoring and forecasting system in real-time method according to claim 4, feature exist
In the microseismic event variation characteristic index ImAccording to the sliding average A (n) of microseismic event time seriest, bias ratio Y (n)t
With dispersion ratio V (m)tTo determine；
The sliding average A (n)tExpression formula are as follows:
Wherein, n is the length of time；A(n)tFor the microseismic event sliding average in nearest time span n, a is microseismic event
Number；
The bias ratio Y (n)tExpression formula are as follows:
Wherein, atFor the microseismic event number of t moment；
The dispersion ratio V (m)tExpression formula are as follows:
Wherein, μ is the sample average of microseismic event time series；M is time span；
According to the sliding average A (n) of microseismic event time seriest, bias ratio Y (n)tWith dispersion ratio V (m)tTo calculate microseism thing
Part variation characteristic index Im, microseismic event sliding average initial criticality value e is determined respectively1, bias ratio initial criticality value e2With it is discrete
Rate initial criticality value e3；Then α is assigned a value of to microseismic event sliding average, when microseismic event sliding average is greater than e1When assign
Value is 1, and microseismic event sliding average is less than or equal to e1When be assigned a value of 0；Microseismic event bias ratio is assigned a value of β, works as microseismic event
Bias ratio is greater than e2When be assigned a value of 1, microseismic event bias ratio is less than or equal to e2When be assigned a value of 0；Microseismic event dispersion ratio is assigned a value of
γ, when microseismic event dispersion ratio is greater than e3When be assigned a value of 1, microseismic event dispersion ratio is less than or equal to e3When be assigned a value of 0；Comprehensive descision
Microseismic event variation characteristic assignment x=alpha+beta+γ, x={ 0,1,2,3 }；
Microseismic event variation characteristic index ImExpression formula are as follows:
7. a kind of getting working face coal and gas prominent Monitoring and forecasting system in real-time method according to claim 4, feature exist
In the gas emission variation characteristic index IgAccording to the sliding average A (n) ' of gas emission time seriest, bias ratio
Y(n)’tWith dispersion ratio V (m) 'tTo determine；
The sliding average A (n) 'tExpression formula are as follows:
Wherein, n is the length of time；A(n)'tFor the gas emission sliding average in nearest n time span, c gushes for gas
Output size；
The bias ratio Y (n) 'tExpression formula are as follows:
Wherein, ctFor the gas emission of t moment；
The dispersion ratio V (m) 'tExpression formula are as follows:
Wherein, μ ' is the sample average of gas emission time series；M is time span；
According to the sliding average A (n) ' of gas emission time seriest, bias ratio Y (n) 'tWith dispersion ratio V (m) 'tTo calculate
Gas emission variation characteristic index Ig, the sliding average initial criticality value e of gas emission is determined respectively1', bias ratio it is initial
Critical value e2' and dispersion ratio initial criticality value e3', α ' then is assigned a value of to the sliding average of gas emission, works as Gas
It measures sliding average and is greater than e1' when be assigned a value of 1, gas emission sliding average is less than or equal to e1' when be assigned a value of 0；Gas
Amount bias ratio is assigned a value of β ', when gas emission bias ratio is greater than e2' when be assigned a value of 1, gas emission bias ratio is less than or equal to
e2' when be assigned a value of 0；Gas emission dispersion ratio is assigned a value of γ ', when gas emission dispersion ratio is greater than e3' when be assigned a value of 1, gas
Outburst amount dispersion ratio is less than or equal to e3' when be assigned a value of 0；Comprehensive descision microseismic event variation characteristic assignment y=α '+β '+γ ',
Middle y={ 0,1,2,3 }；
Gas emission variation characteristic index IgExpression formula are as follows:
8. a kind of getting working face coal and gas prominent Monitoring and forecasting system in real-time method according to claim 6 or 7, feature
It is, the microseismic event variation characteristic index ImWith gas emission variation characteristic index IgIt is built applied to data analysis module
Vertical coal and gas prominent fuzzy evaluation comprehensive pre-warning model, judges coal and gas prominent danger classes；
Step a. establishes fuzzy evaluation influence factor set, specially microseismic event variation characteristic u1Change with gas emission special
Levy u2, factor of evaluation collection is combined into U={ u1,, u2}；
Step b. establishes coal and gas prominent evaluation set, specially establishes the possible evaluation result set V of coal and gas prominent
={ coal and gas prominent occurring, coal and gas prominent does not occur }, is represented with I and coal and gas prominent occurs, and is not sent out with II representative
Coal and gas prominent is given birth to, then V={ I, II }；
Step c. establishes weight set, different according to importance of the influence factor in evaluation, establishes the power of each influence factor
Set W={ w again1, w2, wherein w1+w2=1,
Step d. single factor test fuzzy evaluation is established between fuzzy evaluation influence factor set U and coal and gas prominent evaluation set V
Membership function relationship, wherein microseismic event variation characteristic u1With the membership function of prominent evaluation set V are as follows:
Gas emission variation characteristic u2With the membership function of prominent evaluation set V are as follows:
Judged using single factor evaluation, determine the subjection degree of influence factor, obtains single factor evaluation collection:
R1=[Im(x), 1-Im(x)]
R2=[Ig(y), 1-Ig(y)]
Step e. coal and gas prominent fuzzy overall evaluation, establishes synthetic evaluation matrix:
R={ R1, R2}T
In conjunction with the weight set W and synthetic evaluation matrix R of influence factor, using average weighted method, according to fuzzy matrix
Multiplying rule obtains fuzzy overall evaluation set B:
Determine coal and gas prominent Possibility index I and microseismic event variation characteristic index ImRefer to gas emission variation characteristic
Number IgBetween functional relation be I (x, y)=w1Im(x)+w2Ig(y)。
9. a kind of getting working face coal and gas prominent Monitoring and forecasting system in real-time method according to claim 8, feature exist
In the distal end big data analysis service platform receives the data that ground central station is uploaded, according to mine coal and gas prominent
Actual conditions, distal end big data analysis service platform determines parameter m, n, e using machine learning algorithm1、e2、e3、e1’、e2’、
e3’、w1And w2, and feed back to ground center.
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