CN110533887B - Coal and gas outburst disaster discrete mode early warning method and device based on real-time monitoring data and storage medium - Google Patents

Coal and gas outburst disaster discrete mode early warning method and device based on real-time monitoring data and storage medium Download PDF

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CN110533887B
CN110533887B CN201910725777.7A CN201910725777A CN110533887B CN 110533887 B CN110533887 B CN 110533887B CN 201910725777 A CN201910725777 A CN 201910725777A CN 110533887 B CN110533887 B CN 110533887B
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early warning
coal
gas
real
interval
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CN110533887A (en
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卢新明
尹红
王永
涂辉
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Shandong Lionking Software Co ltd
Ping An Coal Mine Gas Control National Engineering Research Center Co Ltd
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Shandong Lionking Software Co ltd
Ping An Coal Mine Gas Control National Engineering Research Center Co Ltd
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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
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/02—Alarms for ensuring the safety of persons
    • G08B21/12—Alarms for ensuring the safety of persons responsive to undesired emission of substances, e.g. pollution alarms
    • G08B21/14—Toxic gas alarms
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/02—Alarms for ensuring the safety of persons
    • G08B21/12—Alarms for ensuring the safety of persons responsive to undesired emission of substances, e.g. pollution alarms
    • G08B21/16—Combustible gas alarms
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/18—Status alarms
    • G08B21/182—Level alarms, e.g. alarms responsive to variables exceeding a threshold
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

The invention provides a coal and gas outburst disaster discrete modal early warning method based on real-time monitoring data, a device and a storage medium, wherein a plurality of time intervals are pushed forwards on the basis of the current time, a coal mine gas monitoring system is utilized to calculate the variation trend and the oscillation variance of the relative gas emission quantity of each time interval of each mining working face in real time to form a discrete modal early warning sequence, and the early warning modal parameters and the trend early warning grading threshold are determined by a big data analysis method through the historical record of coal and gas outburst precursor information and by combining the occurrence mechanism, the hidden gas geology and mining process information of coal and gas outburst accidents, so that the online real-time dynamic grading early warning and alarm of the coal and gas outburst disasters are realized.

Description

Coal and gas outburst disaster discrete mode early warning method and device based on real-time monitoring data and storage medium
Technical Field
The invention relates to the field of safety production, in particular to a coal and gas outburst disaster discrete modal early warning method, a coal and gas outburst disaster discrete modal early warning device and a storage medium based on real-time monitoring data.
Background
For coal mines, coal and gas outburst disasters are one of major disasters, each time coal and gas outburst accidents occur, major casualties and economic losses are caused to the coal mines, gas explosion is further induced to cause larger losses to the coal mines sometimes, and the industrial competitiveness of high-gas mines is greatly influenced.
In the prior art, a sensing mode capable of comprehensively detecting the coal and gas outburst disaster precursor information is not perfect, most coal mines are only provided with a gas concentration sensor and a wind speed sensor, and how to effectively predict, forecast and early warn the gas outburst disaster by using the sensor data is a troublesome problem. Although a dynamic disaster modal early warning method based on real-time monitoring data is disclosed in the published patent document CN108506041A, which embodies the online embodiment of the precursor information of gas outburst disasters by a gas emission time sequence, a modal function needs to be constructed, which increases certain difficulty for realizing the method, and because the variation trend and oscillation condition of gas pressure and coal bed cracking (pore) gap along with mining disturbance and gas emission in the gas emission process are not considered, the online early warning and alarm of coal mine gas outburst disasters cannot be realized.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a coal and gas outburst disaster discrete mode early warning method based on real-time monitoring data, which realizes the on-line early warning and alarm of coal mine gas outburst disasters by using a discrete time sequence of relative gas emission quantity, and comprises the following steps:
firstly, calibrating a gas flow calculation method according to the section shape, section area and coal dropping quantity of a roadway at the installation position of a sensor, namely determining coefficients A and B in the following formula, and calculating an equivalent gas relative emission quantity sequence of a working surface in real time:
Figure GDA0002939181920000021
c-gas concentration, V-wind speed, S-roadway cross-sectional area, M-coal dropping, A and B are calibration calculation coefficients, W i Equivalent relative gas emission quantity;
step two, setting a gas emission quantity sequence { W of the working face i Setting the length D of the early warning calculation time interval according to the history records of the coal and gas outburst events and the abnormal gushing rate events t And the early warning time interval is Q ═ T-D t T), the time interval of the precursor information is Q 1 ＝[T-4D t ,T-3D t )，Q 2 ＝[T-3D t ,T-2D t )，Q 3 ＝[T-3D t ,T-2D t ) And assuming a relative gas emission quantity sequence { W } i At interval Q 1 、Q 2 、Q 3 The mean and the variance of the trend oscillation in Q are
Figure GDA0002939181920000022
Step three, utilizing
Figure GDA0002939181920000023
And sigma to calculate a gas emission quantity sequence W i Modality parameters N in each time interval:
Figure GDA0002939181920000024
step four, according to the mine gas geological conditions of the early warning working face, the occurrence condition of coal beds in the mining area and the production process, referring to the gas emission abnormal historical record and the precursor information record of coal and gas outburst accidents of the similar working face, and giving out an alarm threshold value (Nr, No, Ny, Nb) corresponding to the parameter N relative to the 4 levels;
step five, determining a relative gas emission quantity sequence { W) according to the working surface and the environment thereof and the precursor information record of the historical abnormal event i Preneutralizing each interval Q 1 、Q 2 、Q 3 Modal parameter N in sum Q 1 、N 2 、N 3 And N, calculating an early warning value Y in an early warning interval Q, and increasing rate coefficients a, b and c (a) according to set modal parameters>b>c>1) And the grade value interval of the early warning value Y
S r ＝[Y r ,1),S o ＝[Y o ,Y r ),S y ＝[Y y ,Y o ),S b ＝[Y b ,Y y ),S g ＝[0,Y b ) Respectively carrying out early warning prompt of each level;
step six, according to the modal parameter N of each interval 1 、N 2 、N 3 And N, calculating the early warning value Y in the early warning interval Q from high level to low level successively according to the following rules;
and step seven, according to the Y value, the system periodically sends the gas disaster early warning level and the early warning value Y of each working face to the terminal.
In some embodiments, step one further comprises: and a gas concentration sensor and a wind speed sensor are arranged in the mine working face return air tunnel to acquire gas concentration data and wind speed data in the mine working face return air tunnel.
In some embodiments, step two further comprises:
suppose that n sampled data are in a certain sampling interval, i.e. y 1 ,y 2 ,...,y n ，
Then in the sampling order of that interval;
1,2, n is an independent variable x, and a trend fitting straight line is obtained by taking the relative gas emission quantity W as a dependent variable y:
y＝kx+h
calculating the fitting value of each point:
Figure GDA0002939181920000031
calculating the interval mean value and the trend difference:
Figure GDA0002939181920000041
wherein the content of the first and second substances,
Figure GDA0002939181920000042
in some embodiments, step five further comprises:
classifying the early warning level into
Red, namely, super;
orange, namely the first grade;
yellow, namely, the color of the second grade;
blue, namely three-level;
early warning or green, namely, the alarm resolution level;
firstly, judging whether the red early warning level meets the following conditions:
condition 1, Red Warning
Red warning interval S r The method is divided into 10 grades of rapid rising, continuous rising, rapid rising, continuous rising, rapid rising beginning, continuous rising beginning and no falling, and the discriminant is as follows:
1) if N ≧ N r ,N≥aN 3 And N is 3 ≥aN 2 And N is 2 ≥aN 1 If Y is equal to Y r +0.9(1-Y r )
2) If N ≧ N r ,N≥bN 3 And N is 3 ≥bN 2 And N is 2 ≥bN 1 If Y is equal to Y r +0.8(1-Y r )
3) If N ≧ N r ,N≥cN 3 And N is 3 ≥cN 2 And N is 2 ≥cN 1 If Y is equal to Y r +0.7(1-Y r )
4) If N ≧ N r ,N≥aN 3 And N is 3 ≥aN 2 If Y is equal to Y r +0.6(1-Y r )
5) If N ≧ N r ,N≥bN 3 And N is 3 ≥bN 2 If Y is equal to Y r +0.5(1-Y r )
6) If N ≧ N r ,N≥cN 3 And N is 3 ≥cN 2 If Y is equal to Y r +0.4(1-Y r )
7) If N ≧ N r ,N≥aN 3 If Y is equal to Y r +0.3(1-Y r )
8) If N ≧ N r ,N≥bN 3 If Y is equal to Y r +0.2(1-Y r )
9) If N ≧ N r ,N≥cN 3 If Y is equal to Y r +0.1(1-Y r )
10) If N ≧ N r ,N≥N 3 If Y is equal to Y r
If the early warning level is not full of red, judging the early warning level of orange:
case 2, orange Pre-alarm
0) If N ≧ N o ,N 3 ≥N r If Y is equal to Y r
1) If N ≧ N o ,N≥aN 3 And N is 3 ≥aN 2 And N is 2 ≥aN 1 If Y is equal to Y o +0.9(Y r -Y o )
2) If N ≧ N o ,N≥bN 3 And N is 3 ≥bN 2 And N is 2 ≥bN 1 If Y is equal to Y o +0.8(Y r -Y o )
3) If N ≧ N o ,N≥cN 3 And N is 3 ≥cN 2 And N is 2 ≥cN 1 If Y is equal to Y o +0.7(Y r -Y o )
4) If N ≧ N o ,N≥aN 3 And N is 3 ≥aN 2 If Y is equal to Y o +0.6(Y r -Y o )
5) If N ≧ N o ,N≥bN 3 And N is 3 ≥bN 2 If Y is equal to Y o +0.5(Y r -Y o )
6) If N ≧ N o ,N≥cN 3 And N is 3 ≥cN 2 If Y is equal to Y o +0.4(Y r -Y o )
7) If N ≧ N o ,N≥aN 3 If Y is equal to Y o +0.3(Y r -Y o )
8) If N ≧ N o ,N≥bN 3 If Y is equal to Y o +0.2(Y r -Y o )
9) If N ≧ N o ,N≥cN 3 If Y is equal to Y o +0.1(Y r -Y o )
10) If N ≧ N o ,N≥N 3 If Y is equal to Y o
If the orange early warning level is not met, judging the yellow early warning level:
condition 3, yellow Pre-warning
0) If N ≧ N y ,N 3 ≥N o If Y is equal to Y o
1) -10) is calculated as orange, except that N is o By changing to N y Handle Y r By changing to Y o Handle Y o By changing to Y y Then the method is finished;
if the yellow early warning level is not satisfied, judging the blue early warning level:
condition 4, blue Warning
0) If N ≧ N b ,N 3 ≥N y If Y is equal to Y y
1) -10) is calculated in the same manner as orange, except that N is added o By changing to N b Handle Y r By changing to Y y Handle Y o By changing to Y b Then the method can be carried out;
if the blue color early warning level is not available, judging the green early warning level:
condition 5, Green Warning
0) If N is present<N b ,N 3 ≥N b If Y is equal to Y b
If N is present<N b Then get
Figure GDA0002939181920000061
Otherwise, also take Y ═ Y b And carrying out blue early warning.
In some embodiments, in step seven, the terminal includes: early warning database, PC end, removal end, digital indicating equipment and warning device.
The invention also provides equipment for realizing the coal and gas outburst disaster discrete modal early warning method based on real-time monitoring data, which comprises the following steps:
the memory is used for storing a computer program and a coal and gas outburst disaster discrete mode early warning method based on real-time monitoring data;
and the processor is used for executing the computer program and the coal and gas outburst disaster discrete modal early warning method based on the real-time monitoring data so as to realize the steps of the coal and gas outburst disaster discrete modal early warning method based on the real-time monitoring data.
The invention also provides a computer readable storage medium with a coal and gas outburst disaster discrete modal early warning method based on real-time monitoring data, wherein a computer program is stored on the computer readable storage medium and is executed by a processor to realize the steps of the coal and gas outburst disaster discrete modal early warning method based on real-time monitoring data.
According to the technical scheme, the invention has the following advantages:
the invention provides a discrete modal early warning method based on real-time monitoring data, which can realize the on-line early warning and alarm resolution of coal mine gas outburst disasters by directly utilizing a discrete time sequence of relative gas emission quantity, not only considers the variation trend and oscillation condition of gas pressure and coal bed cracking (hole) gaps along with mining disturbance and gas emission in the gas emission process, but also implies the environmental evolution rule of the gas outburst disasters, and provides a simple and easy method for the advanced perception and early warning and alarm resolution of the coal mine gas outburst disasters.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions of the present invention will be clearly and completely described below by using specific embodiments, and it is obvious that the embodiments described below are only a part of embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the scope of protection of this patent.
The coal and gas outburst disaster discrete modality early warning method process parameters and the sequence of steps based on real-time monitoring data described and/or illustrated in the present invention are given by way of example only and may be changed as required. For example, although the steps shown and/or described for the coal and gas outburst disaster discrete modality early warning method based on real-time monitoring data may be shown or discussed in a particular order, the steps need not be performed in the order shown or discussed. Various example methods described and/or illustrated herein may also omit one or more of the steps described or illustrated herein, or include additional steps in addition to those disclosed.
While various embodiments have been described and/or illustrated in the context of a fully functional computing system, one or more of these exemplary embodiments may be distributed as various forms of program product, regardless of the particular type of computer-readable media used to actually carry out the distribution. The disclosed embodiments of the present method may also be implemented using modules that perform certain tasks. These modules may include script files, batch files, or other executable files that may be stored on a computer-readable storage medium or in a computing system. In some embodiments, these modules may configure a computing system to perform one or more of the exemplary embodiments disclosed herein.
The invention aims to provide a discrete modal early warning method based on real-time monitoring data, which can realize on-line early warning and alarm of coal mine gas outburst disasters by directly utilizing a discrete time sequence of relative gas emission quantity.
The invention is realized by the following steps:
assuming that the sampling period of the monitoring system is t seconds, and every 10 samples is an early warning interval, that is, n is 10, the time length D of the early warning interval t Nt 10 t. In addition, the modal gain coefficient a is 1.2, b is 1.1, and c is 1.05. The patent is implemented as follows:
the method comprises the steps that firstly, aiming at the coal and gas outburst disaster early warning problem of the working faces of different mines, a gas concentration sensor and a wind speed sensor are installed in an air return roadway of the working face, a gas flow calculation method is calibrated according to the roadway section shape, the section area and the coal falling amount of a sensing installation position, namely coefficients A and B in the following formula are determined, and the equivalent gas relative emission amount sequence of the working face is calculated in real time:
Figure GDA0002939181920000091
c-gas concentration, V-wind speed, S-roadway cross-sectional area, M-coal dropping, A and B are calibration calculation coefficients, W i Equivalent gas relative emission amount (hereinafter referred to as gas emission amount).
Step two, setting a gas emission quantity sequence { W of the working face i Setting the length D of the early warning calculation time interval according to the history records of the coal and gas outburst events and the abnormal gushing rate events t And the early warning time interval is Q ═ T-D t T), the time interval of the precursor information is Q 1 ＝[T-4D t ,T-3D t )，Q 2 ＝[T-3D t ,T-2D t )，Q 3 ＝[T-3D t ,T-2D t ) And assuming the relative emission quantity sequence of gas { W } i At interval Q 1 、Q 2 、Q 3 The mean and the variance of the trend oscillation (variance with respect to the trend line in each interval) in Q are
Figure GDA0002939181920000092
The calculation method is as follows:
suppose that n sampled data are in a certain sampling interval, i.e. y 1 ,y 2 ,...,y n Then, in the sampling order of the interval
1,2, n is an independent variable x, and a trend fitting straight line is obtained by taking the relative gas emission quantity W as a dependent variable y:
y＝kx+h
calculating the fitting value of each point:
Figure GDA0002939181920000101
wherein:
Figure GDA0002939181920000102
Figure GDA0002939181920000103
Figure GDA0002939181920000104
Figure GDA0002939181920000105
calculating the interval mean value and the trend difference:
Figure GDA0002939181920000106
step three, utilizing
Figure GDA0002939181920000107
And sigma to calculate a gas emission quantity sequence W i Modality parameters N in each time interval:
Figure GDA0002939181920000108
may be taken as alpha 0.25
And step four, according to the mine gas geological conditions of the early warning working face, the occurrence condition of coal beds in the mining area and the production process, referring to the gas emission abnormal historical record and the precursor information record of coal and gas outburst accidents of the similar working face, and giving an alarm threshold value (Nr, No, Ny, Nb) corresponding to the parameter N relative to the (red, orange, yellow and blue) 4 level.
Step five, determining a gas relative surge quantity sequence { W) according to the working face and the environment thereof and the precursor information record of the historical abnormal event i Preneutralizing each interval Q 1 、Q 2 、Q 3 And modal parameter N in Q 1 、N 2 、N 3 And N, calculating an early warning value Y in an early warning interval Q, and increasing rate coefficients a, b and c (a) according to set modal parameters>b>c>1) And a level value interval S of the early warning value Y r ＝[Y r ,1),S o ＝[Y o ,Y r ),S y ＝[Y y ,Y o ),S b ＝[Y b ,Y y ),S g ＝[0,Y b ) Red (special grade), orange (first grade), yellow (second grade), blue (third grade) early warning or green (alarm solving) are respectively carried out.
Step six, according to the modal parameter N of each interval 1 、N 2 、N 3 And N, calculating the early warning value Y in the early warning interval Q from high level to low level successively according to the following rules:
firstly, judging whether the red early warning level meets the following conditions: condition 1, Red Warning
Red warning interval S r The method is divided into 10 grades of rapid rising, continuous rising, rapid rising, continuous rising, rapid rising beginning, continuous rising beginning and no falling, and the discriminant is as follows:
1) if N ≧ N r ,N≥aN 3 And N is 3 ≥aN 2 And N is 2 ≥aN 1 If Y is equal to Y r +0.9(1-Y r )
2) If N ≧ N r ,N≥bN 3 And N is 3 ≥bN 2 And N is 2 ≥bN 1 If Y is equal to Y r +0.8(1-Y r )
3) If N ≧ N r ,N≥cN 3 And N is 3 ≥cN 2 And N is 2 ≥cN 1 If Y is equal to Y r +0.7(1-Y r )
4) If N ≧ N r ,N≥aN 3 And N is 3 ≥aN 2 If Y is equal to Y r +0.6(1-Y r )
5) If N ≧ N r ,N≥bN 3 And N is 3 ≥bN 2 If Y is equal to Y r +0.5(1-Y r )
6) If N ≧ N r ,N≥cN 3 And N is 3 ≥cN 2 If Y is equal to Y r +0.4(1-Y r )
7) If N ≧ N r ,N≥aN 3 If Y is equal to Y r +0.3(1-Y r )
8) If N ≧ N r ,N≥bN 3 If Y is equal to Y r +0.2(1-Y r )
9) If N ≧ N r ,N≥cN 3 If Y is equal to Y r +0.1(1-Y r )
10) If N ≧ N r ,N≥N 3 If Y is equal to Y r
If the red early warning level is not satisfied, judging the orange early warning level:
case 2, orange Pre-alarm
0) If N ≧ N o ,N 3 ≥N r If Y is equal to Y r
1) If N ≧ N o ,N≥aN 3 And N is 3 ≥aN 2 And N is 2 ≥aN 1 If Y is equal to Y o +0.9(Y r -Y o )
2) If N ≧ N o ,N≥bN 3 And N is 3 ≥bN 2 And N is 2 ≥bN 1 If Y is equal to Y o +0.8(Y r -Y o )
3) If N ≧ N o ,N≥cN 3 And N is 3 ≥cN 2 And N is 2 ≥cN 1 If Y is equal to Y o +0.7(Y r -Y o )
4) If N ≧ N o ,N≥aN 3 And N is 3 ≥aN 2 If Y is equal to Y o +0.6(Y r -Y o )
5) If N ≧ N o ,N≥bN 3 And N is 3 ≥bN 2 If Y is equal to Y o +0.5(Y r -Y o )
6) If N ≧ N o ,N≥cN 3 And N is 3 ≥cN 2 If Y is equal to Y o +0.4(Y r -Y o )
7) If N ≧ N o ,N≥aN 3 If Y is equal to Y o +0.3(Y r -Y o )
8) If N ≧ N o ,N≥bN 3 If Y is equal to Y o +0.2(Y r -Y o )
9) If N ≧ N o ,N≥cN 3 If Y is equal to Y o +0.1(Y r -Y o )
10) If N ≧ N o ,N≥N 3 If Y is equal to Y o
If the early warning level is less than the orange early warning level, judging the yellow early warning level:
condition 3, yellow Pre-warning
0) If N ≧ N y ,N 3 ≥N o If Y is equal to Y o
1) -10) is calculated in the same manner as orange, except that N is added o By changing to N y Handle Y r By changing to Y o Handle Y o By changing to Y y And (4) finishing.
If the yellow early warning level is not satisfied, judging the blue early warning level:
condition 4, blue Warning
0) If N ≧ N b ,N 3 ≥N y If Y is equal to Y y
1) -10) is calculated in the same manner as orange, except that N is added o By changing to N b Handle Y r By changing to Y y Handle Y o By changing to Y b And (4) finishing.
If the blue color early warning level is not available, judging the green early warning level:
condition 5, Green Warning
0) If N is present<N b ,N 3 ≥N b If Y is equal to Y b
If N is present<N b Then get
Figure GDA0002939181920000131
Otherwise, also take Y ═ Y b And carrying out blue early warning.
And step seven, according to the Y value, the system periodically sends the gas disaster early warning level and the early warning value Y of each working face to a related early warning database, a PC (personal computer) terminal, a mobile terminal and digital indicating and warning equipment.
The system periodically sends the gas disaster early warning level and the early warning value Y of each working face to the terminal. Terminals herein include, but are not limited to, laptop computers, tablet computers, desktop computers, servers, cellular phones, Personal Digital Assistants (PDAs), multimedia players, embedded systems, wearable devices (e.g., smart watches, smart glasses, etc.), variations and combinations of one or more of the above, or any other suitable computing system.
The present invention may be server-based for management and control of system information, including but not limited to storage servers, database servers, application servers, and/or web servers configured to run certain software applications and/or provide various storage, database, and/or web services.
The communication networks to which the present invention relates include, but are not limited to: an intranet, a Wide Area Network (WAN), a Local Area Network (LAN), a Personal Area Network (PAN), the internet, Power Line Communications (PLC), a cellular network (e.g., a global system for mobile communications (GSM) network), portions of one or more of the above, variations or combinations of one or more of the above, or any other suitable network. So that the sensing data and the early warning data can be effectively transmitted.
The invention also provides equipment for realizing the coal and gas outburst disaster discrete modal early warning method based on the real-time monitoring data, which comprises the following steps:
the memory is used for storing a computer program and a coal and gas outburst disaster discrete mode early warning method based on real-time monitoring data; and the processor is used for executing the computer program and the coal and gas outburst disaster discrete modal early warning method based on the real-time monitoring data so as to realize the steps of the coal and gas outburst disaster discrete modal early warning method based on the real-time monitoring data.
And providing a computer readable storage medium having a coal and gas outburst disaster discrete modal early warning method based on real-time monitoring data, the computer readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the steps of the coal and gas outburst disaster discrete modal early warning method based on real-time monitoring data.
The coal and gas outburst disaster discrete modal early warning method based on real-time monitoring data is implemented in hardware, software, firmware or any combination of the hardware, the software and the firmware. Various features are described as modules, units or components that may be implemented together in an integrated logic device or separately as discrete but interoperable logic devices or other hardware devices. In some cases, various features of an electronic circuit may be implemented as one or more integrated circuit devices, such as an integrated circuit chip or chipset.
The coal and gas outburst disaster discrete modal early warning method based on real-time monitoring data can be used as a processor or an integrated circuit device, such as an integrated circuit chip or a chip set. Alternatively or additionally, if implemented in software or firmware, the techniques may implement a data storage medium readable at least in part by a computer, comprising instructions that when executed cause a processor to perform one or more of the above-described methods. For example, a computer-readable data storage medium may store instructions such as are executed by a processor.
A computer readable medium storing a computer program and a discrete modal early warning method for coal and gas outburst disasters based on real-time monitoring data may include packaging materials. The computer-readable medium of data may include computer storage media such as Random Access Memory (RAM), Read Only Memory (ROM), non-volatile random access memory (NVRAM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory, magnetic or optical data storage media, and the like. In some embodiments, an article of manufacture may comprise one or more computer-readable storage media.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (6)

1. A coal and gas outburst disaster discrete mode early warning method based on real-time monitoring data is characterized by comprising the following steps:
firstly, calibrating a gas flow calculation method according to the section shape, section area and coal dropping quantity of a roadway at the installation position of a sensor, namely determining coefficients A and B in the following formula, and calculating the equivalent relative gas emission quantity of a working surface in real time:
Figure FDA0003710734200000011
c-gas concentration, V-wind speed, S-tunnel section area, M-coal dropping amount, A and B are calibration calculation coefficients, W i Equivalent relative gas emission quantity;
step two, setting a gas emission quantity sequence { W of the working face i And setting the length D of the early warning calculation time interval according to the historical records of the coal and gas outburst event and the abnormal gushing amount event t The early warning interval is Q ═ T-D t T), the time interval of the precursor information is Q 1 ＝[T-4D t ,T-3D t )，Q 2 ＝[T-3D t ,T-2D t )，Q 3 ＝[T-2D t ，T-D t ) And assume a gas emission quantity sequence { W } i At interval Q 1 、Q 2 、Q 3 The mean and the variance of the trend oscillation in Q are
Figure FDA0003710734200000012
Step three, utilizing
Figure FDA0003710734200000013
And σ n Calculating a gas emission quantity sequence (W) i Modality parameters in each time interval:
Figure FDA0003710734200000014
step four, according to the mine gas geological condition of the early warning working face, the occurrence condition of coal seams of the mining area and the production process, referring to a gas emission abnormal historical record and a precursor information record of coal and gas outburst accidents of the similar working face, and giving an alarm threshold value (Nr, No, Ny, Nb) corresponding to the modal parameter N relative to 4 levels;
step five, determining a gas emission quantity sequence { W) according to the working face and the environment thereof and the precursor information record of the historical abnormal event i And intervals Q 1 、Q 2 、Q 3 Modal parameter N in sum Q 1 、N 2 、N 3 And N n Calculating an early warning value Y in an early warning interval Q, and increasing rate coefficients a, b and c (a) according to set modal parameters>b>c>1) And the grade value interval of the early warning value Y
S r ＝[Y r ,1),S o ＝[Y o ,Y r ),S y ＝[Y y ,Y o ),S b ＝[Y b ,Y y ),S g ＝[0,Y b ) Respectively carrying out early warning prompts of each level;
step six, according to modal parameters N of each interval 1 、N 2 、N 3 And N n Calculating an early warning value Y in an early warning interval Q from a high level to a low level according to a preset rule;
and seventhly, the system periodically sends the gas disaster early warning level and the early warning value Y of each working face to the terminal.
2. The discrete modal early warning method for coal and gas outburst disasters based on real-time monitoring data according to claim 1,
the first step further comprises the following steps: and a gas concentration sensor and a wind speed sensor are arranged in the mine working face return air tunnel to acquire gas concentration data and wind speed data in the mine working face return air tunnel.
3. The discrete modal early warning method for coal and gas outburst disasters according to claim 1,
the second step further comprises:
suppose that n sampled data exist in a certain sampling interval, namely y 1 ,y 2 ,...,y n Then, a trend fitting straight line is obtained by using the sampling sequence i of the interval as 1, 2.
y＝kx+h
Calculating the fitting value of each point:
Figure FDA0003710734200000031
calculating the interval mean value and the trend difference:
Figure FDA0003710734200000032
wherein the content of the first and second substances,
Figure FDA0003710734200000033
4. the discrete modal early warning method for coal and gas outburst disasters according to claim 1,
in the seventh step, the terminal includes: early warning database, PC end, removal end, digital indicating equipment and warning device.
5. The utility model provides a realize equipment of coal and gas outburst calamity discrete mode early warning method based on real-time supervision data which characterized in that includes:
the memory is used for storing a computer program and a coal and gas outburst disaster discrete mode early warning method based on real-time monitoring data;
a processor for executing the computer program and the coal and gas outburst disaster discrete modal early warning method based on the real-time monitoring data to realize the steps of the coal and gas outburst disaster discrete modal early warning method based on the real-time monitoring data according to any one of claims 1 to 4.
6. A computer-readable storage medium having a coal and gas outburst disaster discrete modal early warning method based on real-time monitoring data, wherein the computer-readable storage medium has a computer program stored thereon, and the computer program is executed by a processor to implement the steps of the coal and gas outburst disaster discrete modal early warning method based on real-time monitoring data according to any one of claims 1 to 4.
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