Methods and devices for early warning and precise energy release in dynamic pressure roadways during mine mining

Abstract

This invention discloses a method and device for early warning and precise energy release in dynamic pressure roadways during mine mining, belonging to the field of mine energy early warning technology. This invention collects acoustic emission monitoring data, pulse signal monitoring data, CT scan data, microseismic monitoring data, stress monitoring data, and drill cuttings volume monitoring data, normalizes them to obtain early warning indicators, and performs nonlinear processing and dynamic weighting to obtain a comprehensive early warning indicator. Early warning levels are divided according to the comprehensive early warning indicator to achieve graded early warning, and pressure relief schemes are preset for each level of early warning, forming an early warning-response closed loop. Compared with a single early warning system, this invention integrates multiple monitoring methods, comprehensively considering energy release (acoustic emission, microseismic), electromagnetic response (pulse), stress state (stress), structural integrity (CT), and surrounding rock damage (drill cuttings) multi-dimensional indicators, overcoming the limitations of single indicators and reducing the false alarm rate caused by the failure or interference of a single monitoring method.

Description

Technical Field

[0001] This invention relates to the field of mine energy early warning technology, and in particular to a method and device for early warning and precise energy release in mine mining dynamic pressure roadways. Background Technology

[0002] A mining roadway is a roadway that forms and serves the coal mining face during coal mining operations. A dynamic pressure roadway refers to a roadway that is affected by mining or dynamic effects during the coal mining process, where the surrounding rock is subjected to the superposition of original rock stress, fixed support pressure of the adjacent working face, and moving support pressure of the working face itself.

[0003] Typical dynamic hazards in coal mines (such as coal and gas outbursts and rock bursts) pose a significant challenge to coal mining safety. Traditional early warning systems for dynamic hazards in coal mines often rely on single or limited monitoring methods, which cannot fully reflect the overall stability of the system. They are susceptible to non-hazardous factors, resulting in frequent false alarms, disrupting production, and insufficient lead time to allow for adequate response time for pressure relief measures. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a method and device for early warning and precise energy release in dynamic pressure roadways during mine mining, thereby improving the accuracy, reliability, and practicality of early warning for dynamic disasters in coal mines.

[0005] The technical solution provided by this invention is as follows:

[0006] A method for early warning and precise energy release of dynamic pressure roadways in mine mining, the method comprising:

[0007] S1: Real-time acquisition of various monitoring data of the target monitoring area in the coal mine roadway to be monitored;

[0008] The various monitoring data include acoustic emission monitoring data, pulse signal monitoring data, CT scan data, microseismic monitoring data, stress monitoring data, and drill cuttings volume monitoring data.

[0009] S2: Normalize the various monitoring data separately to obtain multiple normalized early warning indicators;

[0010] Among them, the multiple normalized early warning indicators include acoustic emission energy rate indicator, pulse energy rate indicator, wave velocity anomaly coefficient indicator, microseismic energy release rate indicator, stress concentration coefficient indicator, and drill cuttings quantity indicator.

[0011] S3: Perform nonlinear processing and dynamic weighting on each normalized early warning indicator to obtain a comprehensive early warning indicator;

[0012] S4: Divide the warning level according to the comprehensive warning indicators, and set corresponding energy release and disaster relief measures according to the warning level.

[0013] Furthermore, normalization is performed using the following formula:

[0014]

[0015]

[0016]

[0017]

[0018]

[0019]

[0020] in, ~ These are, respectively, acoustic emission energy rate index, pulse energy rate index, wave velocity anomaly coefficient index, microseismic energy release rate index, stress concentration coefficient index, and drill cuttings quantity index; , , , , , These are, respectively, acoustic emission energy rate, pulse signal energy rate, absolute value of wave velocity anomaly coefficient, microseismic energy release rate, stress concentration factor, and drill cuttings index. , , , , These are the threshold values ​​that are set.

[0021] Furthermore,

[0022]

[0023]

[0024]

[0025]

[0026]

[0027] in, The sampling interval is... Let be the energy of the j1-th acoustic emission event, where j1 = 1, 2, ..., N1, and N1 is the sampling interval. The total number of acoustic emission events within the area. Sampling interval The number of acoustic emission events with an internal amplitude greater than twice the average amplitude;

[0028] Let be the energy of the j2th electromagnetic radiation pulse, where j2 = 1, 2, ..., N2, and N2 is the sampling interval. The total number of electromagnetic radiation pulses within;

[0029] The average wave velocity of the current scanned area. The historical average wave velocity;

[0030] Let be the energy of the j4th microseismic event, where j4 = 1, 2, ..., N4, and N4 is the sampling interval. The total number of microseismic events within the area, For the volume of the target monitoring area, The number of cluster events;

[0031] The current stress value at the monitoring point, This refers to the original rock stress;

[0032] S represents the measured amount of drill cuttings. This is normal drill cuttings volume. These are the maximum and minimum drill cuttings quantities, respectively.

[0033] Furthermore, the comprehensive early warning index is calculated using the following formula;

[0034]

[0035] in, For comprehensive early warning indicators, for The dynamic weights, i=1, 2, 3, 4, 5, 6, It is a nonlinear transformation function. This is the signal-to-noise ratio correction factor. The spatial consistency coefficient. For time evolution coefficients, This is the trend amplification factor. This represents the average time change rate of the comprehensive early warning indicators.

[0036] Furthermore,

[0037] in, Based on static weights, The coefficient of sensitivity to change. for At sampling interval Rate of change within;

[0038]

[0039] in, , These are nonlinear coefficients;

[0040]

[0041] in, for The current average signal-to-noise ratio of the monitoring method used. for The signal-to-noise ratio (SNR) threshold of the monitoring method used;

[0042]

[0043] in, The number of simultaneous anomalies among the acoustic emission energy rate index, pulse energy rate index, and microseismic energy release rate index. , This is the spatial coupling strength coefficient;

[0044]

[0045] in, , , and They are respectively At time t and The value at time n is a set positive integer.

[0046] Furthermore, S4 includes:

[0047] When Γ < 0.7, there is no warning;

[0048] When 0.7 ≤ Γ < 1.0, a Level 1 warning is issued, and large-diameter borehole drilling is performed to relieve pressure.

[0049] When 1.0 ≤ Γ < 1.5, a level-two warning is issued, and near-field blasting is carried out to relieve pressure.

[0050] When Γ≥1.5, a Level III warning is issued, and overburden blasting is carried out to relieve pressure.

[0051] A mine longwall mining roadway energy early warning and precise energy release device, the device comprising:

[0052] The data acquisition module is used to acquire various monitoring data of the target monitoring area of ​​the roadway to be monitored in the coal mine in real time;

[0053] The various monitoring data include acoustic emission monitoring data, pulse signal monitoring data, CT scan data, microseismic monitoring data, stress monitoring data, and drill cuttings volume monitoring data.

[0054] The normalization module is used to normalize various monitoring data separately to obtain multiple normalized early warning indicators;

[0055] Among them, the multiple normalized early warning indicators include acoustic emission energy rate indicator, pulse energy rate indicator, wave velocity anomaly coefficient indicator, microseismic energy release rate indicator, stress concentration coefficient indicator, and drill cuttings quantity indicator.

[0056] The comprehensive early warning index calculation module is used to perform nonlinear processing and dynamic weighting on various normalized early warning indicators to obtain a comprehensive early warning index.

[0057] The graded early warning module is used to classify early warning levels according to the comprehensive early warning indicators and set corresponding energy release and disaster relief measures according to the early warning levels.

[0058] Furthermore, normalization is performed using the following formula:

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065] in, ~ These are, respectively, acoustic emission energy rate index, pulse energy rate index, wave velocity anomaly coefficient index, microseismic energy release rate index, stress concentration coefficient index, and drill cuttings quantity index; , , , , , These are, respectively, acoustic emission energy rate, pulse signal energy rate, absolute value of wave velocity anomaly coefficient, microseismic energy release rate, stress concentration factor, and drill cuttings index. , , , , These are the threshold values ​​that are set.

[0066] Furthermore,

[0067]

[0068]

[0069]

[0070]

[0071]

[0072] in, The sampling interval is... Let be the energy of the j1-th acoustic emission event, where j1 = 1, 2, ..., N1, and N1 is the sampling interval. The total number of acoustic emission events within the area. Sampling interval The number of acoustic emission events with an internal amplitude greater than twice the average amplitude;

[0073] Let be the energy of the j2th electromagnetic radiation pulse, where j2 = 1, 2, ..., N2, and N2 is the sampling interval. The total number of electromagnetic radiation pulses within;

[0074] The average wave velocity of the current scanned area. The historical average wave velocity;

[0075] Let be the energy of the j4th microseismic event, where j4 = 1, 2, ..., N4, and N4 is the sampling interval. The total number of microseismic events within the area, For the volume of the target monitoring area, The number of cluster events;

[0076] The current stress value at the monitoring point, This refers to the original rock stress;

[0077] S represents the measured amount of drill cuttings. This is normal drill cuttings volume. These are the maximum and minimum drill cuttings quantities, respectively.

[0078] Furthermore, the comprehensive early warning index is calculated using the following formula;

[0079]

[0080] in, For comprehensive early warning indicators, for The dynamic weights, i=1, 2, 3, 4, 5, 6, It is a nonlinear transformation function. This is the signal-to-noise ratio correction factor. The spatial consistency coefficient. For time evolution coefficients, This is the trend amplification factor. This represents the average time change rate of the comprehensive early warning indicators.

[0081] Furthermore,

[0082] in, Based on static weights, The coefficient of sensitivity to change. for At sampling interval Rate of change within;

[0083]

[0084] in, , These are nonlinear coefficients;

[0085]

[0086] in, for The current average signal-to-noise ratio of the monitoring method used. for The signal-to-noise ratio (SNR) threshold of the monitoring method used;

[0087]

[0088] in, The number of simultaneous anomalies among the acoustic emission energy rate index, pulse energy rate index, and microseismic energy release rate index. , This is the spatial coupling strength coefficient;

[0089]

[0090] in, , , and They are respectively At time t and The value at time n is a set positive integer.

[0091] Furthermore, in the tiered early warning module, when Γ < 0.7, there is no early warning;

[0092] When 0.7 ≤ Γ < 1.0, a Level 1 warning is issued, and large-diameter borehole drilling is performed to relieve pressure.

[0093] When 1.0 ≤ Γ < 1.5, a level-two warning is issued, and near-field blasting is carried out to relieve pressure.

[0094] When Γ≥1.5, a Level III warning is issued, and overburden blasting is carried out to relieve pressure.

[0095] The present invention has the following beneficial effects:

[0096] Compared to a single early warning system, this invention integrates multiple monitoring methods, comprehensively considering multi-dimensional indicators such as energy release (acoustic emission, microvibration), electromagnetic response (pulse), stress state (stress), structural integrity (CT), and surrounding rock damage (drill cuttings). This overcomes the limitations of single indicators and reduces the false alarm rate caused by malfunctions or interference with a single monitoring method. It significantly improves the accuracy, reliability, and practicality of early warning systems for coal mine dynamic disasters. Attached Figure Description

[0097] Figure 1 The flowchart shows the method for early warning and precise energy release of dynamic pressure roadways in mine mining according to the present invention.

[0098] Figure 2 This is a schematic diagram of the energy early warning and precise energy release device for dynamic pressure roadways in mine mining according to the present invention. Detailed Implementation

[0099] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0100] Example 1:

[0101] This invention provides a method for early warning and precise energy release in dynamic pressure roadways during mine mining, applicable to coal mine roadways, especially those in deep, thick alluvial mines. For example... Figure 1 As shown, the method includes:

[0102] S1: Real-time acquisition of various monitoring data of the target monitoring area in the coal mine roadway to be monitored.

[0103] This invention first analyzes the displacement of the surrounding rock by comprehensively considering the engineering geological data and mining conditions of the mining area, then determines the roadway to be monitored, and finally collects multi-source data to obtain various monitoring data.

[0104] The monitoring data includes various types of data such as acoustic emission monitoring data, pulse signal monitoring data, CT scan data, microseismic monitoring data, stress monitoring data, and drill cuttings volume monitoring data.

[0105] One specific method for data collection is as follows:

[0106] 1. Deploy an acoustic emission monitoring system. Drill a monitoring borehole every 50 meters in the target monitoring area, with each borehole being 3-8 meters deep. High-sensitivity three-component accelerometers are used to collect acoustic emission monitoring data. Four intrinsically safe mining data acquisition substations are set up in chambers within the roadways. Each substation is responsible for receiving signals from 6-8 nearby accelerometers, performing preliminary amplification and A / D conversion, and transmitting the data wirelessly to the surface monitoring center, achieving high-speed, stable, and interference-resistant data transmission.

[0107] 2. Install a microseismic monitoring system in the selected roadways. Use GZC10 intrinsically safe three-component seismic sensors to collect microseismic monitoring data, ensuring at least eight sensors are installed. Determine the deployment scheme based on the D-value optimization criterion. Transmit the microseismic data monitored by the sensors wirelessly to the KJ959-F intrinsically safe microseismic monitoring substation. This substation connects to the surface base station, transmitting the monitoring data to the surface microseismic online monitoring system.

[0108] 3. Install borehole stress sensors on the roof and sides of the tunnel, with a monitoring section every 30 meters. The monitoring data is transmitted wirelessly to the monitoring substation, which is connected to the surface monitoring system and transmits the stress monitoring data to the online monitoring system.

[0109] 4. Drill cuttings monitoring equipment, pulse signal monitoring equipment, and CT scanning equipment were deployed in the test section of the tunnel. The corresponding monitoring data were acquired through the installed equipment and transmitted to the ground monitoring center via wireless network.

[0110] S2: Normalize the various monitoring data separately to obtain multiple normalized early warning indicators.

[0111] Among them, several normalized early warning indicators include acoustic emission energy rate, pulse energy rate, wave velocity anomaly coefficient, microseismic energy release rate, stress concentration coefficient, and drill cuttings volume.

[0112] In one example, normalization is performed using the following formula:

[0113]

[0114]

[0115]

[0116]

[0117]

[0118]

[0119] in, ~ These are, respectively, acoustic emission energy rate index, pulse energy rate index, wave velocity anomaly coefficient index, microseismic energy release rate index, stress concentration coefficient index, and drill cuttings quantity index; , , , , , These are, respectively, acoustic emission energy rate, pulse signal energy rate, absolute value of wave velocity anomaly coefficient, microseismic energy release rate, stress concentration factor, and drill cuttings index. , , , , These are the thresholds set for the corresponding indicators.

[0120] Acoustic emission energy rate The total energy of acoustic emission events per unit time is calculated using the following formula:

[0121]

[0122] in, The sampling interval is... Let be the energy of the j1-th acoustic emission event, where j1 = 1, 2, ..., N1, and N1 is the sampling interval. The total number of acoustic emission events within the area. Sampling interval The number of acoustic emission events with an internal amplitude greater than twice the average amplitude.

[0123] Calculated from the voltage signal of the accelerometer, ,t0 and Let be the start time and duration of the j1th acoustic emission event, respectively. Let t be the output voltage of the accelerometer at time t.

[0124] Pulse signal energy rate The total energy of electromagnetic radiation pulses per unit time is calculated using the following formula:

[0125]

[0126] Let be the energy of the j2th electromagnetic radiation pulse, where j2 = 1, 2, ..., N2, and N2 is the sampling interval. The total number of electromagnetic radiation pulses within. Calculated from the sensor output voltage of the pulse signal monitoring device. ,t1 and are the start time and duration of the j2th electromagnetic radiation pulse event, respectively. Let t be the sensor output voltage of the pulse signal monitoring device at time t, and R be the input impedance value of the measurement circuit of the pulse signal monitoring device.

[0127] Absolute value of wave velocity anomaly coefficient The percentage change in wave velocity relative to a reference value is calculated using the following formula:

[0128]

[0129] in, The average wave velocity of the current scanned area. This represents the historical average wave velocity. , Let be the wave velocity of the j3rd pixel, where j3 = 1, 2, ..., N3, and N3 is the total number of pixels.

[0130] Microseismic energy release rate The total microseismic energy per unit time and unit volume is calculated using the following formula:

[0131]

[0132] Let be the energy of the j4th microseismic event, where j4 = 1, 2, ..., N4, and N4 is the sampling interval. The total number of microseismic events within the area, For the volume of the target monitoring area, The number of events in the cluster. t2 and These represent the start time and duration of the j4th microseismic event, respectively. Let t be the ground velocity at time t, which is obtained by the seismic sensor.

[0133] The stress concentration factor K is the ratio of the current stress at the monitoring point to the original rock stress, and the calculation formula is as follows:

[0134]

[0135] The current stress value at the monitoring point is obtained by a stress sensor. This refers to the stress in the original rock.

[0136] Drill cuttings index The ratio of measured drill cuttings to normal drill cuttings is calculated using the following formula:

[0137]

[0138] S represents the measured amount of drill cuttings, indicating the weight of coal dust per meter of standard borehole. This is normal drill cuttings volume. These are the maximum and minimum drill cuttings quantities, respectively.

[0139] S3: Perform nonlinear processing and dynamic weighting on each normalized early warning indicator to obtain a comprehensive early warning indicator.

[0140] In this step, six normalized early warning indicators are input into the comprehensive early warning model for processing. The comprehensive early warning model dynamically weights and nonlinearly transforms the six normalized early warning indicators, and corrects the spatiotemporal correlation, signal-to-noise ratio and evolution trend of the comprehensive monitoring data to generate a unified comprehensive early warning indicator Γ.

[0141] As an example, the comprehensive early warning index can be calculated using the following formula;

[0142]

[0143] in, For comprehensive early warning indicators, for The dynamic weights, i=1, 2, 3, 4, 5, 6, It is a nonlinear transformation function. This is the signal-to-noise ratio correction factor. The spatial consistency coefficient. For time evolution coefficients, This is the trend amplification factor, with a value ranging from 2 to 3. This represents the average time change rate of the comprehensive early warning indicators.

[0144]

[0145] in, Based on static weights, For example, the rate of change sensitivity coefficient (e.g., , , =0.5; , , =0.2), for At sampling interval Rate of change within, .

[0146]

[0147] in, , For nonlinear coefficients, especially for highly sensitive indicators (such as the drill cuttings index K), s Take the larger value.

[0148]

[0149] in, for The current average signal-to-noise ratio of the monitoring method used (i.e., the current average signal-to-noise ratio of the i-th type of monitoring method). for The signal-to-noise ratio (SNR) threshold of the monitoring method used (i.e., the SNR threshold of the i-th type of monitoring method).

[0150]

[0151] in, This refers to the number of dynamic indicators (i.e., acoustic emission energy rate, pulse energy rate, and microseismic energy release rate) that simultaneously exhibit anomalies. The total number of dynamic indicators, i.e. , The spatial coupling strength coefficient is 0.5 to 1.0.

[0152]

[0153] in, , , and They are respectively At time t and The value at time n is a set positive integer.

[0154] S4: Classify the warning level according to the comprehensive warning indicators, and set corresponding energy release and disaster relief measures according to the warning level.

[0155] This step is used to classify early warning levels and establish mitigation measures. Specifically, based on the value of the comprehensive early warning index Γ, early warning levels are dynamically classified, and corresponding mitigation measures are set for each level. In detail:

[0156] When Γ < 0.7, there is no warning and production proceeds normally.

[0157] When 0.7≤Γ<1.0, a Level 1 warning is issued, indicating that the accumulated energy is close to or slightly exceeds the energy threshold, and large-diameter drilling is performed to relieve pressure.

[0158] When 1.0≤Γ<1.5, a level two warning is issued, indicating that the energy accumulation exceeds the critical state, and a near-field blasting decompression is carried out.

[0159] When Γ≥1.5, a Level III warning is issued, indicating that the accumulated energy has seriously exceeded the safety limit. Personnel are organized to evacuate and overburden blasting is carried out to relieve pressure.

[0160] This invention first collects six types of monitoring data, including acoustic emission monitoring data, pulse signal monitoring data, CT scan data, microseismic monitoring data, stress monitoring data, and drill cuttings volume monitoring data. These data are then normalized to obtain early warning indicators. After nonlinear processing, these indicators are corrected using dynamic weights, signal-to-noise ratio correction coefficients, spatial consistency coefficients, and time change rates to obtain a comprehensive early warning indicator. Finally, early warning levels are divided based on the comprehensive early warning indicator to achieve graded early warning. Pressure relief schemes are preset for each level of early warning to form an early warning-rescue closed loop.

[0161] Compared to a single early warning system, this invention integrates multiple monitoring methods, combining energy release (acoustic emission, micro-vibration), electromagnetic response (pulse), stress state (stress), structural integrity (CT), and surrounding rock damage (drill cuttings) into a multi-dimensional index. This overcomes the limitations of a single index and reduces the false alarm rate caused by the failure or interference of a single monitoring method.

[0162] The comprehensive early warning index incorporates a time evolution coefficient and dynamic weights. When a sensitive indicator shows an accelerating trend, the dynamic weights shift towards it, amplifying its impact through the time evolution coefficient. This allows the comprehensive index to capture early warning signs of system instability more quickly, significantly extending the warning lead time. Through a signal-to-noise ratio correction coefficient, the system automatically assesses the data quality of each channel, conservatively outputting data when overall signal quality deteriorates to avoid false alarms. The spatial consistency coefficient requires anomalies to be spatially consistent, eliminating scattered and isolated interference signals and providing good anti-interference capabilities. Therefore, this invention significantly improves the accuracy, reliability, and practicality of early warning systems for coal mine dynamic disasters.

[0163] Example 2:

[0164] This invention provides, in embodiment 7, an energy early warning and precise energy release device for dynamic pressure roadways in mine mining, such as... Figure 2 As shown, the device includes:

[0165] Data acquisition module 1 is used to acquire various monitoring data of the target monitoring area of ​​the roadway to be monitored in the coal mine in real time.

[0166] The monitoring data includes various types of data such as acoustic emission monitoring data, pulse signal monitoring data, CT scan data, microseismic monitoring data, stress monitoring data, and drill cuttings volume monitoring data.

[0167] Normalization module 2 is used to normalize various monitoring data to obtain multiple normalized early warning indicators.

[0168] Among them, several normalized early warning indicators include acoustic emission energy rate, pulse energy rate, wave velocity anomaly coefficient, microseismic energy release rate, stress concentration coefficient, and drill cuttings volume.

[0169] In one example, normalization can be performed using the following formula:

[0170]

[0171]

[0172]

[0173]

[0174]

[0175]

[0176] in, ~ These are, respectively, acoustic emission energy rate index, pulse energy rate index, wave velocity anomaly coefficient index, microseismic energy release rate index, stress concentration coefficient index, and drill cuttings quantity index; , , , , , These are, respectively, acoustic emission energy rate, pulse signal energy rate, absolute value of wave velocity anomaly coefficient, microseismic energy release rate, stress concentration factor, and drill cuttings index. , , , , These are the threshold values ​​that are set.

[0177]

[0178]

[0179]

[0180]

[0181]

[0182]

[0183] in, The sampling interval is... Let be the energy of the j1-th acoustic emission event, where j1 = 1, 2, ..., N1, and N1 is the sampling interval. The total number of acoustic emission events within the area. Sampling interval The number of acoustic emission events with an internal amplitude greater than twice the average amplitude.

[0184] Let be the energy of the j2th electromagnetic radiation pulse, where j2 = 1, 2, ..., N2, and N2 is the sampling interval. The total number of electromagnetic radiation pulses within.

[0185] The average wave velocity of the current scanned area. This represents the historical average wave velocity.

[0186] Let be the energy of the j4th microseismic event, where j4 = 1, 2, ..., N4, and N4 is the sampling interval. The total number of microseismic events within the area, For the volume of the target monitoring area, The number of events in the cluster.

[0187] The current stress value at the monitoring point, This refers to the stress in the original rock.

[0188] S represents the measured amount of drill cuttings. This is normal drill cuttings volume. These are the maximum and minimum drill cuttings quantities, respectively.

[0189] The comprehensive early warning index calculation module 3 is used to perform nonlinear processing and dynamic weighting on each normalized early warning index to obtain a comprehensive early warning index.

[0190] The comprehensive early warning index can be calculated using the following formula;

[0191]

[0192] in, For comprehensive early warning indicators, for The dynamic weights, i=1, 2, 3, 4, 5, 6, It is a nonlinear transformation function. This is the signal-to-noise ratio correction factor. The spatial consistency coefficient. For time evolution coefficients, This is the trend amplification factor. This represents the average time change rate of the comprehensive early warning indicators.

[0193]

[0194] in, Based on static weights, The coefficient of sensitivity to change. for At sampling interval Rate of change within.

[0195]

[0196] in, , These are nonlinear coefficients.

[0197]

[0198] in, for The current average signal-to-noise ratio of the monitoring method used. for The signal-to-noise ratio (SNR) threshold of the monitoring method used.

[0199]

[0200] in, The number of simultaneous anomalies among the acoustic emission energy rate index, pulse energy rate index, and microseismic energy release rate index. , This represents the spatial coupling strength coefficient.

[0201]

[0202] in, , , and They are respectively At time t and The value at time n is a set positive integer.

[0203] The graded early warning module 4 is used to classify early warning levels based on comprehensive early warning indicators and set corresponding energy release and disaster relief measures according to the early warning level.

[0204] Specifically, when Γ < 0.7, there is no warning.

[0205] When 0.7≤Γ<1.0, a Level 1 warning is issued, and large-diameter boreholes are drilled to relieve pressure.

[0206] When 1.0≤Γ<1.5, a level II warning is issued, and near-field blasting is carried out to relieve pressure.

[0207] When Γ≥1.5, a Level III warning is issued, and overburden blasting is carried out to relieve pressure.

[0208] The apparatus provided in this embodiment of the invention has the same implementation principle and technical effects as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the apparatus embodiment can be referred to the corresponding content in the aforementioned method embodiment 1. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the apparatus and units described above can all be referred to the corresponding processes in the above method embodiments, and will not be repeated here.

[0209] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the scope of the technology disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention.

Claims

1. A method for early warning and precise energy release of dynamic pressure roadways in mine mining, characterized in that, The method includes: S1: Real-time acquisition of various monitoring data of the target monitoring area in the coal mine roadway to be monitored; The various monitoring data include acoustic emission monitoring data, pulse signal monitoring data, CT scan data, microseismic monitoring data, stress monitoring data, and drill cuttings volume monitoring data. S2: Normalize the various monitoring data separately to obtain multiple normalized early warning indicators; Among them, the multiple normalized early warning indicators include acoustic emission energy rate indicator, pulse energy rate indicator, wave velocity anomaly coefficient indicator, microseismic energy release rate indicator, stress concentration coefficient indicator, and drill cuttings quantity indicator. S3: Perform nonlinear processing and dynamic weighting on each normalized early warning indicator to obtain a comprehensive early warning indicator; S4: Divide the warning level according to the comprehensive warning indicators, and set corresponding energy release and disaster relief measures according to the warning level.

2. The method for early warning and precise energy release in dynamic pressure roadways during mine mining according to claim 1, characterized in that, Normalization is performed using the following formula: in, ~ These are, respectively, acoustic emission energy rate index, pulse energy rate index, wave velocity anomaly coefficient index, microseismic energy release rate index, stress concentration coefficient index, and drill cuttings quantity index; , , , , , These are, respectively, acoustic emission energy rate, pulse signal energy rate, absolute value of wave velocity anomaly coefficient, microseismic energy release rate, stress concentration factor, and drill cuttings index. , , , , These are the threshold values ​​that are set.

3. The method for early warning and precise energy release in dynamic pressure roadways during mine mining according to claim 2, characterized in that: in, The sampling interval is... Let be the energy of the j1-th acoustic emission event, where j1 = 1, 2, ..., N1, and N1 is the sampling interval. The total number of acoustic emission events within the area. Sampling interval The number of acoustic emission events with an internal amplitude greater than twice the average amplitude; Let be the energy of the j2th electromagnetic radiation pulse, where j2 = 1, 2, ..., N2, and N2 is the sampling interval. The total number of electromagnetic radiation pulses within; The average wave velocity of the current scanned area. The historical average wave velocity; Let be the energy of the j4th microseismic event, where j4 = 1, 2, ..., N4, and N4 is the sampling interval. The total number of microseismic events within the area, For the volume of the target monitoring area, The number of cluster events; The current stress value at the monitoring point, This refers to the original rock stress; S represents the measured amount of drill cuttings. This is normal drill cuttings volume. These are the maximum and minimum drill cuttings quantities, respectively.

4. The method for early warning and precise energy release in dynamic pressure roadways during mine mining according to claim 3, characterized in that, The comprehensive early warning index is calculated using the following formula; in, For comprehensive early warning indicators, for The dynamic weights, i=1, 2, 3, 4, 5, 6, It is a nonlinear transformation function. This is the signal-to-noise ratio correction factor. The spatial consistency coefficient. For time evolution coefficients, This is the trend amplification factor. This represents the average time change rate of the comprehensive early warning indicators.

5. The method for early warning and precise energy release in dynamic pressure roadways during mine mining according to claim 4, characterized in that: in, Based on static weights, The coefficient of sensitivity to change. for At sampling interval Rate of change within; in, , These are nonlinear coefficients; in, for The current average signal-to-noise ratio of the monitoring method used. for The signal-to-noise ratio (SNR) threshold of the monitoring method used; in, The number of simultaneous anomalies among the acoustic emission energy rate index, pulse energy rate index, and microseismic energy release rate index. , This is the spatial coupling strength coefficient; in, , , and They are respectively At time t and The value at time n is a set positive integer.

6. The method for early warning and precise energy release in dynamic pressure roadways during mine mining according to claim 5, characterized in that, S4 includes: When Γ < 0.7, there is no warning; When 0.7 ≤ Γ < 1.0, a Level 1 warning is issued, and large-diameter borehole drilling is performed to relieve pressure. When 1.0 ≤ Γ < 1.5, a level-two warning is issued, and near-field blasting is carried out to relieve pressure. When Γ≥1.5, a Level III warning is issued, and overburden blasting is carried out to relieve pressure.

7. A device for early warning and precise energy release in dynamic pressure roadways during mine mining, characterized in that, The device includes: The data acquisition module is used to acquire various monitoring data of the target monitoring area of ​​the roadway to be monitored in the coal mine in real time; The various monitoring data include acoustic emission monitoring data, pulse signal monitoring data, CT scan data, microseismic monitoring data, stress monitoring data, and drill cuttings volume monitoring data. The normalization module is used to normalize various monitoring data separately to obtain multiple normalized early warning indicators; Among them, the multiple normalized early warning indicators include acoustic emission energy rate indicator, pulse energy rate indicator, wave velocity anomaly coefficient indicator, microseismic energy release rate indicator, stress concentration coefficient indicator, and drill cuttings quantity indicator. The comprehensive early warning index calculation module is used to perform nonlinear processing and dynamic weighting on various normalized early warning indicators to obtain a comprehensive early warning index. The graded early warning module is used to classify early warning levels according to the comprehensive early warning indicators and set corresponding energy release and disaster relief measures according to the early warning levels.

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