CN209510394U - A real-time monitoring and early warning system for coal and gas outburst in mining face - Google Patents

Abstract

The utility model provides a real-time monitoring and early warning system for coal and gas outburst in mining working face, which relates to the technical field of mining engineering and includes a ground central station, a control host, a network switch, a monitoring substation, a vibration pick-up, a wind speed sensor, and a methane concentration sensor And the sound and light alarm, the sensor transmits the monitoring information to the monitoring substation, the monitoring substation transmits the monitoring data to the ground central station through the network switch, and the ground central station completes the early warning analysis through the real-time processing of the monitoring data, and the monitoring substation includes the module The conversion module, the data noise reduction module and the data screening module, and the monitoring substation are respectively connected with the vibration pickup, the wind speed sensor, the methane concentration sensor and the sound and light alarm through cables. The early warning system provided by the utility model solves the technical problem of difficulty in non-contact continuous real-time monitoring and early warning of coal and gas outbursts in the mining face, and also has the advantages of high accuracy and strong operability.

Description

A real-time monitoring and early warning system for coal and gas outburst in mining face

technical field

The utility model relates to the technical field of mining engineering, in particular to a real-time monitoring and early warning system for coal and gas outburst in mining working faces.

Background technique

Coal and gas outburst is an abnormal dynamic phenomenon in which broken coal and gas are suddenly thrown from the coal body to the mining space under the joint action of ground stress and gas. Scholars at home and abroad have formed a variety of academic views on the mechanism of coal and gas outburst, including "gas action theory", "ground stress action theory", "chemical essence theory" and "comprehensive action hypothesis". Gas outburst is the result of comprehensive effects of factors such as ground stress, gas pressure, and coal mechanical properties. The theory has been generally recognized because it fully considers the two main factors of the outburst, the force and the medium. Coal and gas outburst risk prediction is an important link in the comprehensive prevention and control system of coal and gas outburst. Most of the existing outburst prediction methods are static and discontinuous, and they cannot predict the risk of coal and gas outburst continuously in real time; Most of the methods are to predict the state of coal and gas outburst risk, without considering the development trend of the outburst risk, and it is difficult to reflect the breeding process of coal and gas outburst. In recent years, some scholars have proposed some coal and gas outburst early warning systems and methods, such as the Chinese patent document "Real-time Diagnosis Method for Coal and Gas Outburst" with the publication number CN101532397A, and the Chinese patent document "Coal and Gas Outburst" with the publication number CN101550841B. Gas outburst comprehensive early warning system and early warning method", but most of them indirectly or partially reflect the three main controlling factors affecting the outburst risk, including ground stress, gas pressure and coal mechanical properties, which cannot fully reflect the risk of coal and gas outburst ; Some scholars have proposed comprehensive utilization of acoustic emission (or microseismic) and gas dynamic gushing out to predict the size of coal and gas outburst danger, such as the Chinese patent document "a kind of coal and gas outburst real-time monitoring and early warning system" whose publication number is CN106194264A ", the Chinese patent document with the publication number CN101787897B "A system and method for real-time prediction of coal and gas outburst risks in mines", but the early warning model it uses does not really realize the coupling and correlation of the two kinds of information, and the previous systems have Without the positioning function, it is difficult to effectively eliminate the noise information generated by the mining operation in the mining face.

In addition, among the key areas of "public safety" determined in the "National Medium and Long-term Science and Technology Development Plan (2006-2020)", "major production accident warning and rescue" was included in the priority theme, and it was determined to "focus on the research and development of mine gas , water inrush, and dynamic disaster early warning and prevention and control technology”; the “Thirteenth Five-Year Plan for Coal Mine Safety” also clearly proposes to improve the level of informatization and disaster accident monitoring and early warning, and realize the prevention and control of gas, water, fire, and rock burst. Real-time monitoring and early warning of hidden dangers, which provides the basic conditions for the research and development of coal and gas real-time monitoring and early warning system.

Since there is no early warning system in the existing technology that can comprehensively reflect the change trend of coal body structure in front of the mining face, the evolution of mining stress and the change characteristics of gas content, it does not have the ability to accurately locate microseismic events and effectively eliminate underground noise. Therefore, It is necessary to provide a continuous non-contact real-time monitoring and intelligent early warning system for outstanding dangers.

Utility model content

In order to solve the technical problem of difficult non-contact continuous real-time monitoring and early warning of coal and gas outburst in the working face, the present invention provides a real-time monitoring and early warning system for coal and gas outburst in the mining face. The specific technical scheme is as follows.

A real-time monitoring and early warning system for coal and gas outburst in mining face, including a ground central station, a control host, a network switch, a monitoring substation, a vibration pickup, a wind speed sensor, a methane concentration sensor, and an audible and visual alarm; the ground central station and control The host computer is connected through a network cable, the ground central station is connected with the network switch through optical fiber, the network switch is connected with the monitoring substation through optical fiber, and the monitoring substation is connected with the vibration pickup, wind speed sensor, methane concentration sensor and sound and light alarm respectively through cables; The above-mentioned vibration pickup, wind speed sensor and methane concentration sensor are set in the roadway, and the monitoring information is transmitted to the monitoring sub-station; the monitoring sub-station transmits the monitoring data to the ground central station through the network switch; the ground central station includes a data analysis module and an early warning module and storage module; the control host controls the work of the ground central station; the network switch transmits the early warning information sent by the early warning module to the monitoring substation; the monitoring substation includes an analog-to-digital conversion module, a data noise reduction module and a data screening module Monitoring data from vibrator, wind speed sensor and methane concentration sensor.

Preferably, the ground central station is provided with a GPS clock, and the GPS clock adjusts the monitoring data of each monitoring sub-station to be consistent; the ground central station is connected to a remote big data analysis service platform through a network, and the remote big data analysis The service platform is connected to the ground central stations of multiple mines through the network.

Preferably, the monitoring substation uses a main control MCU chip, a signal conditioner and an A/D converter, and the monitoring substation transmits the early warning signal to the sound and light alarm, and the sound and light alarm includes an alarm indicator light and a loudspeaker.

Further preferably, the network switch is connected to multiple monitoring substations.

Further preferably, the vibration pickup is installed at the tail of the bolt in the roadway through a fixing device.

It is further preferred that a group of vibration pickups, wind speed sensors and methane concentration sensors are arranged every 15m in the roadway, and a group of vibration pickups, wind speed sensors and methane concentration sensors are arranged at a position 5m away from the excavation face.

It is further preferred that the wind speed sensor and the methane concentration sensor are adjacently arranged in the same group, and the vibration pickups in the same group are arranged oppositely at the two sides of the roadway.

The beneficial effects of the utility model include:

(1) The real-time monitoring and early warning system for coal and gas outburst in the mining face provided by the utility model has realized the function of non-contact real-time monitoring of the danger of coal and gas outburst in the mining face. The coal and gas outburst fuzzy comprehensive early warning model adopted is mainly Taking microseismic events and the time series change characteristics of gas emission as judgment indicators, the coal and gas outburst possibility index I comprehensively reflects the change trend of the coal body structure in front of the mining face, the mining stress evolution and the gas content change characteristics. The critical value and correlation coefficient of outstanding criterion indicators can be continuously revised through the control center or remote big data analysis service platform, ensuring the accuracy and efficiency of early warning.

(2) The early warning system provided by the utility model realizes the real-time monitoring and automatic analysis and early warning functions of coal and gas outburst, and at the same time, it can also correct and control the outburst early warning results through the control center, thereby effectively avoiding the coal mine site engineering technology. In case of under-reporting due to poor professionalism of personnel and negligence, etc., the monitoring data can also be analyzed and interpreted through the control host or remote big data analysis service platform, and machine learning can be used to obtain the prominent feature expression of coal and gas, and then analyze and interpret the monitoring data. The early warning results are artificially intervened and corrected to effectively reduce false alarms of coal and gas outbursts.

(3) The coal and gas outburst real-time monitoring and early warning system provided by the utility model has the function of automatically eliminating noise, and the monitoring data is preprocessed by the monitoring substation, which can greatly improve the monitoring accuracy of coal and rock rupture information. By pre-writing the wave velocity value and the coordinate information of the vibration picker, the exact position of the coal rock mass rupture can be calculated according to the coal rock mass rupture shock wave and time received by each vibration picker, and then the excavation work can be accurately eliminated. Interference information generated by in-plane mining construction.

(4) The coal and gas outburst real-time monitoring and early warning system provided by the utility model and the high-precision network time service protocol are used by the GPS clock, which gets rid of the problem that the synchronous clock communication system needs to be separately arranged for the time service in the past. The data transmission rate is high. While realizing the transmission of data collection and control commands, it also realizes the real-time calibration of the clocks of each monitoring substation, so the utility model can ensure that the original data collected by each monitoring substation is kept accurate and consistent in time , effectively improving the monitoring accuracy of microseismic events.

Description of drawings

Figure 1 is a schematic structural diagram of the real-time monitoring and early warning system for coal and gas outburst in the mining face;

Fig. 2 is a structural schematic diagram of a monitoring substation;

Fig. 3 is a schematic diagram of the arrangement of measuring points in the excavation working face;

In the figure: 1-ground central station; 2-control host; 3-network switch; 4-monitoring substation; 41-main control MCU chip; 42-signal conditioner; 43-A/D converter; 5-vibration pickup 6-wind speed sensor; 7-methane concentration sensor; 8-sound and light alarm; 9-remote big data analysis service platform; 10-GPS clock.

Detailed ways

Referring to Fig. 1 to Fig. 3, the specific implementation of the coal and gas outburst real-time monitoring and early warning system in the mining face provided by the utility model is as follows.

Example 1

A real-time monitoring and early warning system for coal and gas outburst in mining working face. alarm8. Wherein, the ground central station 1 and the control host 2 are connected by a network cable, the ground central station 1 and the network switch 3 are connected by an optical fiber, the network switch 3 and the monitoring substation 4 are connected by an optical fiber, and the monitoring substation 4 is connected to the vibration pickup 5, The wind speed sensor 6, the methane concentration sensor 7 and the sound and light alarm 8 are connected respectively.

Vibrator 5, wind speed sensor 6 and methane concentration sensor 7 are set in the roadway to monitor the microseismic specimen, wind speed and gas concentration, and transmit the monitoring information to the monitoring substation 4, and the monitoring substation 4 transmits the monitoring data through the network switch Transmission to the ground central station 1. The ground central station 1 includes a data analysis module, an early warning module and a storage module. The data analysis module analyzes and processes the monitoring data. The early warning module determines the early warning level according to the data processing results. The storage module saves the monitoring data and can send the monitoring data to the remote end through the network Big data analysis service platform, the control host 2 controls the work of the ground central station, and can adjust the data processing parameters of the ground central station 1 and perform programming. The network switch 3 transmits the early warning information sent by the early warning module to the monitoring sub-station, and the monitoring sub-station 4. Transmitting the early warning information to the sound and light alarm, the sound and light alarm 8 sends out different alarms according to the early warning information, and the sound and light alarm 8 includes an alarm indicator light and a loudspeaker.

Monitoring substation 4 comprises analog-to-digital conversion module, data noise reduction module and data screening module, and monitoring substation uses main control MCU chip 41, signal conditioner 42 and A/D converter 43, and monitoring substation 4 will measure the monitoring of point The data is converted into a signal, and the monitoring substation 4 screens the monitoring data of the vibration pickup, the wind speed sensor and the methane concentration sensor. In addition, the monitoring substation can preprocess the monitoring data, which can greatly improve the monitoring accuracy of coal and rock fracture information. By pre-writing the wave velocity value and the coordinate information of the vibration picker, the exact position of the coal rock mass rupture can be calculated according to the coal rock mass rupture shock wave and time received by each vibration picker, and then the excavation work can be accurately eliminated. Interference information generated by in-plane mining construction.

The ground center station 1 can also be provided with a GPS clock 10, and the GPS clock 10 adjusts the time of the monitoring data of each monitoring sub-station to be consistent. The use of GPS clock 10 adopts the high-precision network time service IEEE1588 protocol, which gets rid of the problem that a separate synchronous clock communication system needs to be arranged separately for time service in the past. Due to the use of a local area network to build a network architecture, the system data transmission rate is high. While realizing the transmission of data collection and control commands, It also realizes real-time calibration of the clocks of each monitoring substation, so the utility model can ensure that the original data collected by each monitoring substation 4 is kept accurate and consistent in time, and effectively improves the monitoring accuracy of microseismic events.

The ground central station 1 is connected to the remote big data analysis service platform through the network. The remote big data analysis service platform is connected to the ground central stations of multiple mines through the network. The remote big data analysis service platform extracts the monitoring data through machine learning algorithms. For characteristic information, the ground central station 1 adjusts the critical value of early warning indicators according to the analysis results of the remote big data analysis service platform. The system can also continuously correct the critical values and correlation coefficients of prominent criterion indicators through the control center or remote big data analysis service platform, ensuring the accuracy and efficiency of early warning.

When the monitoring system is arranged underground, the network switch 3 is connected to multiple monitoring substations, and the vibration pickup is installed at the tail of the bolt in the roadway through a fixing device to ensure the stability of the vibration pickup. Set up a group of vibration pickups, wind speed sensors and methane concentration sensors every 15m in the roadway, and arrange a group of vibration pickups, wind speed sensors and methane concentration sensors at a position 5m away from the excavation working face, which can ensure the safety of the working face and roadway. Full coverage monitoring. The adjacent arrangement of the wind speed sensor and the methane concentration sensor in the same group ensures the consistency and accuracy of the monitoring data, and calculates the gas concentration and gas emission by using the wind speed and concentration. , to ensure the stability of microseismic monitoring.

In order to further explain the structural relationship of the system, the use of the system is further explained through a real-time monitoring and early warning method of coal and gas outburst in the mining face. The steps of the method include:

Step 1. Arrange the ground central station 1, the control host 2 and the network switch 3. According to the mine geological conditions and mining conditions, set up a monitoring substation in the roadway where the working face is located and connect and install the vibration pickup 5, the wind speed sensor 6, and the methane concentration. Sensor 7 and sound and light alarm 8.

Step 2. After installation, debug the ground central station 1, control host 2, network switch 3, monitoring substation 4, vibration pickup 5, wind speed sensor 6, methane concentration sensor 7 and sound and light alarm 8 to ensure normal operation. Connect the ground center and GPS clock, connect the ground center and the remote big data analysis service platform.

Step 3. Set the parameters in the data analysis module of the ground central station through the control host, including: the length of time m and n, the initial critical value e ₁ of the moving average of the microseismic event, the initial critical value e ₂ of the deviation rate and the initial discrete rate The critical value e ₃ , the sliding average initial critical value e ₁ ' of the gas emission, the initial critical value e ₂ ' of the deviation rate and the initial critical value e ₃ ' of the dispersion rate, the weight w ₁ of the change characteristics of the microseismic event and the gas emission Vary the weight w ₂ of the feature.

Step 4. Start the real-time monitoring and early warning system for coal and gas outburst in the mining face. The vibration pickup, wind speed sensor and methane concentration sensor transmit the monitoring data to the ground central station. The data analysis module of the ground central station analyzes and processes the monitoring data, and the storage module saves the monitoring data. The monitoring data is transmitted to the remote big data analysis service platform, and the early warning module transmits the early warning information to the sound and light alarm through the network switch and the monitoring substation.

The early warning information of the early warning module includes no outstanding danger, outstanding threat and outstanding danger. When the early warning module emits no outstanding danger, the alarm indicator light of the sound and light alarm is green, and the early warning module emits the alarm signal of the sound and light alarm when there is an outstanding threat. The alarm indicator light is yellow, and the alarm indicator light of the sound and light alarm is red and the loudspeaker is started when the early warning module emits a prominent danger. The sound and light alarm device will also emit an alarm sound, and the data analysis and processing module in the ground central station will automatically send an early warning message to the mine leader and safety person in charge through email, SMS, and APP.

In step three, the microseismic event change characteristic index I _m is determined according to the moving average A(n) _t , deviation rate Y(n) _t and dispersion rate V(m) _t of the microseismic event time series.

The expression of the moving average A(n) _t of microseismic events is:

Among them, n is the length of time; A(n) _t is the moving average of microseismic events within the latest time length n, and a is the number of microseismic events.

The expression of the deviation rate Y(n) _t of microseismic events is:

Among them, a _t is the number of microseismic events at time t.

The expression of the dispersion rate V(m) _t of microseismic events is:

Among them, μ is the sample mean of the time series of microseismic events; m is the time length.

According to the moving average A(n) _t , deviation rate Y(n) _t and dispersion rate V(m) _t of the time series of microseismic events, the characteristic index I _m of microseismic events is calculated, and the initial critical value e of the moving average of microseismic events is determined respectively _1. The initial critical value of the deviation rate e ₂ and the initial critical value of the dispersion rate e ₃ ; then the sliding average value of the microseismic event is assigned a value of α, when the sliding average value of the microseismic event is greater than e ₁ , it is assigned a value of 1, and the sliding average value of the microseismic event is less than or equal to When e ₁ is assigned a value of 0; the assignment of the deviation rate of microseismic events is β, when the deviation rate of microseismic events is greater than e ₂ , it is assigned a value of 1, and when the deviation rate of microseismic events is less than or equal to e ₂ , it is assigned a value of 0; the dispersion rate of microseismic events is assigned a value of γ, when Assign a value of 1 when the dispersion rate of microseismic events is greater than e ₃ , and assign a value of 0 when the dispersion rate of microseismic events is less than or equal to e ₃ ; comprehensively judge the change characteristics of microseismic events x = α + β + γ, x = {0, 1, 2, 3 }.

The expression of the microseismic event change characteristic index I _m is:

In step three, the gas emission change characteristic index I _g is calculated according to the moving average A(n)' _t of the gas emission time series, the deviation rate Y(n)' _t and the dispersion rate V(m)' _t Sure.

The expression of the moving average A(n)' _t of gas emission is:

Among them, n is the length of time; A(n)' _t is the moving average of the gas emission in the latest n time length, and c is the size of the gas emission.

The expression of gas emission deviation rate Y(n)' _t is:

Among them, c _t is the gas emission amount at time t.

The expression of the dispersion rate V(m)' _t of gas emission is:

Among them, μ' is the sample mean value of gas emission time series; m is the time length.

According to the moving average A(n)' _t , the deviation rate Y(n)' _t and the dispersion rate V(m)' _t of the gas emission time series, the gas emission change characteristic index I _g is calculated, and the gas emission rate is determined respectively. The sliding average initial critical value e ₁ ' of the gas emission, the initial critical value of the deviation rate e ₂ ', and the initial critical value of the dispersion rate e ₃ ', and then assign the sliding average value of the gas emission to α', when the gas emission When the sliding average value of gas emission is greater than e ₁ ', it is assigned a value of 1, and when the sliding average value of gas emission is less than or equal to e ₁ ', it is assigned a value of 0; the deviation rate of gas emission is assigned β', when the deviation rate of gas emission is greater than e ₂ ' is assigned a value of 1, and when the deviation rate of gas emission amount is less than or equal to e ₂ ', it is assigned a value of 0; the gas emission amount dispersion rate is assigned a value of γ', and when the gas emission amount dispersion rate is greater than e ₃ ', it is assigned a value of 1. When the dispersion rate of gas emission is less than or equal to e ₃ ', it is assigned a value of 0; for the comprehensive judgment of microseismic event change characteristics, it is assigned a value of y=α'+β'+γ', where y={0, 1, 2, 3}.

The expression of gas emission change characteristic index I _g is:

The microseismic event change characteristic index I _m and the gas emission change characteristic index I _g are applied to the data analysis module to establish a comprehensive early warning model for coal and gas outburst fuzzy evaluation, and to judge the risk level of coal and gas outburst.

Step a. Establish a set of fuzzy evaluation influencing factors, specifically microseismic event change characteristics u ₁ and gas emission change characteristics u ₂ , and the evaluation factor set is U={u ₁ , u ₂ }.

Step b. Establish a coal and gas outburst evaluation set, specifically to establish a coal and gas outburst possible evaluation result set V={coal and gas outburst occurs, coal and gas outburst does not occur}, use I to represent coal and gas outburst, and use II means no coal and gas outburst, then V={I, II}.

Step c. Establish a weight set, and according to the importance of the influencing factors in the evaluation, establish a weight set W={w ₁ , w ₂ } for each influencing factor, where w ₁ +w ₂ =1.

Step d. Single-factor fuzzy evaluation, establishing the membership function relationship between the fuzzy evaluation influencing factor set U and the coal and gas outburst evaluation set V, wherein the membership function of the microseismic event change characteristics u ₁ and the outburst evaluation set V is:

The membership function of gas emission change feature u ₂ and outstanding evaluation set V is:

Using the single-factor evaluation judgment, determine the degree of membership of the influencing factors, and obtain the single-factor evaluation set:

R ₁ =[I _m (x), 1-I _m (x)]

R ₂ =[I _g (y), 1-I _g (y)]

Step e. Coal and gas outburst fuzzy comprehensive evaluation, establish a comprehensive evaluation matrix:

R = {R ₁ , R ₂ } ^T

Combining the weight set W of the influencing factors and the comprehensive evaluation matrix R, the weighted average method is adopted, and the fuzzy comprehensive evaluation set B is obtained according to the multiplication algorithm of the fuzzy matrix:

Determine the functional relationship between the coal and gas outburst possibility index I, microseismic event change characteristic index I _m and gas emission change characteristic index I _g as I(x, y)=w ₁ I _m (x)+w ₂ I _g (y).

In addition, the remote big data analysis service platform receives the data uploaded by the ground central station. According to the actual situation of mine coal and gas outburst, the remote big data analysis service platform uses machine learning algorithms to determine the parameters m, n, e ₁ , e ₂ , e ₃ , e ₁ ', e ₂ ', e ₃ ', w ₁ and w ₂ , and fed back to the center of the ground. The remote big data analysis service platform is connected to several ground central stations distributed in different mines through the network, and can receive the data uploaded by each ground central station in real time or regularly, and combine the dynamic phenomena or outstanding disasters that occur in the actual production process of each mine The platform can timely adjust the critical value of early warning indicators of the data analysis and processing module in the ground central station through the built-in machine learning algorithm.

Example 2

This embodiment is based on embodiment 1, taking the 1203 excavation face of a certain mine as an example, to further describe in detail a real-time monitoring and early warning system for coal and gas outburst in the excavation face.

A real-time monitoring and early warning system for coal and gas outburst in a mining face, including a ground central station 1, a control host 2, a network switch 3, a monitoring substation 4, a vibration pickup 5, a wind speed sensor 6, a methane concentration sensor 7, and an audible and visual alarm 8. The ground central station 1 and the control host 2 are connected through a network cable, the ground central station 1 and the network switch 3 are connected through an optical fiber, the network switch 3 and the monitoring substation 4 are connected through an optical fiber, and the monitoring substation 4 is connected to the vibration pickup 5 and the wind speed sensor through a cable 6. The methane concentration sensor 7 and the sound and light alarm 8 are connected respectively. The vibration pickup 5, the wind speed sensor 6 and the methane concentration sensor 7 are arranged in the roadway, and the monitoring information is transmitted to the monitoring substation 4, and the monitoring substation 4 transmits the monitoring data to the ground central station 1 through the network switch. The ground central station includes a data analysis module, an early warning module and a storage module. The control host 2 controls the work of the ground central station 1, and the network switch 3 transmits the early warning information sent by the early warning module to the monitoring substation. The monitoring substation 4 includes an analog-to-digital conversion module, a data noise reduction module and a data screening module, and the monitoring substation 4 filters the monitoring data of the vibration pickup 5 , the wind speed sensor 6 and the methane concentration sensor 7 . Ground central station 1 is provided with GPS clock 10, and GPS clock 10 adopts high-precision network time service IEEE1588 agreement, and GPS clock adjusts the time of the monitoring data of each monitoring sub-station to be consistent. The ground central station is connected to the remote big data analysis service platform 9 through the network, the remote big data analysis service platform 9 is connected to the ground central station 1 of multiple mines through the network, and the remote big data analysis service platform 9 extracts Based on the characteristic information of the monitoring data, the ground central station adjusts the critical value of the early warning indicators according to the analysis results of the remote big data analysis service platform. The monitoring substation uses the main control MCU3 chip, signal conditioner and A/D converter, wherein the main control MCU3 (41) performs the calculation of the criteria required for signal triggering, the signal conditioning circuit performs signal amplification and eliminates signal superposition, A/D conversion The converter converts multiple analog vibration signals into digital signals. The monitoring substation 4 transmits the early warning signal to the sound and light alarm, and the sound and light alarm 8 includes an alarm indicator light and a loudspeaker.

A method for real-time monitoring and early warning of coal and gas outburst in mining face by using the above system, the steps include:

Step 1. Arrange the ground central station 1, the control host 2 and the network switch 3. According to the mine geological conditions and mining conditions, set up a monitoring substation in the roadway where the working face is located and connect and install the vibration pickup 5, the wind speed sensor 6, and the methane concentration. Sensor 7 and sound and light alarm 8. According to the construction situation of the 1203 working face return wind tunnel excavation face, it is determined to use a total of 6 vibration pickups, 2 wind speed sensors and 2 methane concentration sensors, 1 sound and light alarm device, 1 monitoring substation and 1 network switch . The vibration pickup is directly connected to the end of the bolt in the roadway through a special installation device. After the on-site installation is completed, it is debugged to ensure that all working modules can work normally, and at the same time set and determine the relevant parameters of each early warning indicator.

Step 2. After installation, debug the ground central station 1, control host 2, network switch 3, monitoring substation 4, vibration pickup 5, wind speed sensor 6, methane concentration sensor 7 and sound and light alarm 9 to ensure normal operation. Connect ground central station 1 and GPS clock 10, ground central station 1 and remote big data analysis service platform 9.

Step 3. Set the parameters in the data analysis module of the ground central station through the control host, including the length of time m and n, the weight w ₁ of the microseismic event change feature, and the weight w ₂ of the gas emission change feature. In addition, the remote big data analysis service platform receives the data uploaded by the ground central station. According to the actual situation of mine coal and gas outburst, the remote big data analysis service platform uses machine learning algorithms to determine the parameters m, n, e ₁ , e ₂ , e ₃ , e ₁ ', e ₂ ', e ₃ ', w ₁ and w ₂ , and fed back to the center of the ground.

Step 4. Start the real-time monitoring and early warning system for coal and gas outburst in the mining face. The vibration pickup, wind speed sensor and methane concentration sensor transmit the monitoring data to the ground central station. The data analysis module of the ground central station analyzes and processes the monitoring data, and the storage module saves the monitoring data. The monitoring data is transmitted to the remote big data analysis service platform, and the early warning module transmits the early warning information to the sound and light alarm through the network switch and the monitoring substation. The early warning information of the early warning module includes no outstanding danger, outstanding threat and outstanding danger. When the early warning module emits no outstanding danger, the alarm indicator light of the sound and light alarm is green, and the early warning module emits the alarm signal of the sound and light alarm when there is an outstanding threat. The alarm indicator light is yellow, and the alarm indicator light of the sound and light alarm is red and the loudspeaker is started when the early warning module emits a prominent danger. The sound and light alarm device will also emit an alarm sound, and the data analysis and processing module in the ground central station will automatically send an early warning message to the mine leader and safety person in charge through email, SMS, and APP.

Of course, the above description is not a limitation of the present utility model, and the present utility model is not limited to the above-mentioned examples, and changes, modifications, additions or replacements made by those skilled in the art within the essential scope of the present utility model are also acceptable. Should belong to the protection scope of the utility model.

Claims (7)

1. A real-time monitoring and early warning system for coal and gas outburst in mining face, characterized in that, It includes a ground central station, a control host, a network switch, a monitoring substation, a vibration pick-up, a wind speed sensor, a methane concentration sensor, and an audible and visual alarm; the ground central station and the control host are connected by a network cable, and the ground central station and the network The switch is connected through an optical fiber, and the network switch and the monitoring substation are connected through an optical fiber, and the monitoring substation is respectively connected with the vibration pickup, the wind speed sensor, the methane concentration sensor and the sound and light alarm through cables; The vibration pickup, wind speed sensor and methane concentration sensor are arranged in the roadway, and the monitoring information is transmitted to the monitoring substation; the monitoring substation transmits the monitoring data to the ground central station through the network switch; the ground central station includes data An analysis module, an early warning module and a storage module; the control host controls the work of the ground central station; the network switch transmits the early warning information sent by the early warning module to the monitoring substation; The monitoring substation includes an analog-to-digital conversion module, a data noise reduction module and a data screening module, and the monitoring substation screens the monitoring data of the vibration pickup, the wind speed sensor and the methane concentration sensor. 2. A kind of mining face coal and gas outburst real-time monitoring and early warning system according to claim 1, it is characterized in that, described ground central station is provided with GPS clock, and GPS clock adjusts the time of the monitoring data of each monitoring sub-station to be consistent The ground central station is connected to the remote big data analysis service platform through the network, and the remote big data analysis service platform is connected to the ground central stations of multiple mines through the network. 3. The coal and gas outburst real-time monitoring and early warning system of a mining face according to claim 1, wherein said monitoring substation uses a main control MCU chip, a signal conditioner and an A/D converter, said The monitoring substation transmits the early warning signal to the sound and light alarm, and the sound and light alarm includes an alarm indicator light and a loudspeaker. 4. A real-time monitoring and early warning system for coal and gas outburst in mining face according to claim 2 or 3, characterized in that the network switch is connected to a plurality of monitoring sub-stations. 5. A real-time monitoring and early warning system for coal and gas outburst in mining face according to claim 2 or 3, characterized in that the vibration pickup is installed at the tail of the bolt in the roadway through a fixing device. 6. A real-time monitoring and early warning system for coal and gas outburst in mining face according to claim 2 or 3, characterized in that, the vibration pickup, wind speed sensor and methane concentration sensor are arranged in a group every 15m in the roadway , a group of vibration pickups, wind speed sensors and methane concentration sensors are arranged at a distance of 5m from the excavation face. 7. A real-time monitoring and early warning system for coal and gas outburst in mining face according to claim 6, characterized in that, the wind speed sensor and the methane concentration sensor are adjacently arranged in the same group, and the vibration pickers are in the same group In the roadway, the two gangs are arranged opposite to each other.