A temporal and spatial interactive identification method for coal-rock dynamic hazards based on acoustic emission characteristic parameters

Abstract

The present invention belongs to the field of mine safety monitoring and early warning, and specifically relates to a method for temporal and spatial interactive identification of coal-rock dynamic disasters based on acoustic emission characteristic parameters. Acoustic emission prediction technology has the characteristics of real-time, dynamic, non-contact, and continuous prediction, and the acoustic emission signal contains precursor information of the occurrence of disasters. The prior art has applied acoustic emission prediction technology to the online monitoring of coal-rock dynamic disasters. Although the prior art has achieved continuous monitoring, it only performs identification in the time dimension, and does not take into account the correspondence between the mining and excavation distances of the mine production working face and the monitoring signal, that is, no analysis is performed in the spatial dimension, which directly affects the accuracy of the identification results. This scheme combines the time dimension and the spatial dimension to establish a coal-rock dynamic disaster identification method, improves the accuracy of identification, and provides a reliable means for accurate early warning of online monitoring of coal-rock dynamic disasters, which will play a positive role in promoting the prevention and control of coal mine coal-rock dynamic disasters.

Description

Technical Field

[0001] The invention relates to the field of mine safety monitoring and early warning, and in particular to a method for temporal and spatial interactive identification of coal-rock dynamic disasters based on acoustic emission characteristic parameters. Background Art

[0002] my country's coal mines have gradually entered the stage of deep mining. With the continuous improvement of the mechanization degree of mines, the scale of mining is getting larger and larger. The distance of each mining and excavation is getting larger and larger. The increase in mining or excavation speed has put forward new requirements for the prevention and control of coal-rock dynamic disasters in the mine production process. After deep mining, in addition to gas factors, coal-rock dynamic disasters dominated or participated by stress, geology and other factors are becoming more and more prominent. The prevention and control of coal-rock dynamic disasters with multi-factor coupling will be a technical problem that needs to be solved urgently after the mine enters deep mining. Therefore, effective online monitoring and early warning equipment and technology for coal-rock dynamic disasters are the key to solving this technical problem.

[0003] The traditional method of predicting coal-rock dynamic disasters is mainly to identify through construction drilling and measuring relevant prediction indicators. This method belongs to non-continuous point prediction, and the sensitivity of indicators varies under different coal seams and different mining depths, which will lead to deviations in the accuracy of identification. In recent years, the online monitoring technology of mine disasters, such as gas emission monitoring and stress monitoring, has achieved continuous monitoring, but the identification method is mainly based on monitoring data at a certain time interval, combined with relevant algorithms to identify in the time dimension, that is, no analysis is performed in the spatial dimension, which will directly affect the accuracy of the identification results.

[0004] Acoustic emission prediction technology has the characteristics of real-time, dynamic, non-contact and continuous prediction. The acoustic emission signal contains precursor information of disasters. It is very important to install acoustic emission monitoring equipment in the mine mining face in advance and extract effective signal characteristic parameters, and establish a coal-rock dynamic disaster identification method by combining the time dimension and the space dimension to realize online monitoring and accurate early warning of coal-rock dynamic disasters. Summary of the invention

[0005] In view of this, the purpose of the present invention is to provide a method for temporal and spatial interactive identification of coal-rock dynamic disasters based on acoustic emission characteristic parameters, so as to realize the advanced identification and prediction and early warning functions of coal-rock dynamic disasters during working face operations.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] The method for temporal and spatial interactive identification of coal-rock dynamic disasters based on acoustic emission characteristic parameters includes the following steps:

[0008] S1: Install the monitoring sensor ahead of the working face, and divide the working face into monitoring sections according to the installation position of the monitoring sensor; the working face includes the mining working face and the excavation working face; according to the mining or excavation distance, establish a mapping relationship between the monitoring section of the working face and the monitoring sensor;

[0009] S2: The length of a shift in a production mine is used as the identification cycle of coal-rock dynamic disasters, and the risk of coal-rock dynamic disasters for the next shift is predicted at the end of each shift;

[0010] S3: Detect the sensitivity between the internal fracture of coal and rock mass and the acoustic emission characteristic parameters, and select the acoustic emission characteristic parameter index with the highest feedback on the internal fracture sensitivity of coal and rock mass as the acoustic emission identification parameter;

[0011] S4: Extracting effective values ​​of acoustic emission identification parameters from acoustic emission signals of the working face collected by monitoring sensors, calculating and storing the average value of acoustic emission identification parameters every hour and the cumulative value of each shift; obtaining and storing the mining distance or excavation distance of each shift;

[0012] S5: Disaster identification in the time dimension is performed based on the effective values ​​of the acoustic emission identification parameters extracted and stored in step S4: at the end of each shift, the fitting slope of multiple data used for identification is calculated, and the slope change rate Δk is extracted. n is the identification index, denoted by Δk n =(k n -k n-1 ) / k n-1 , where n is the number of shifts, and satisfies n≥4; when Δk n When the first preset threshold is exceeded, the result of the time dimension judgment is dangerous, otherwise it is safe;

[0013] S6: Disaster identification in the spatial dimension is performed according to the acoustic emission identification parameter values ​​extracted and stored in step S4 and the mining distance or excavation distance of the shift: the identification index Sv is extracted as the cumulative amount of the acoustic emission identification parameters generated when the mining distance or excavation distance is 1m, Sv=LA / d, where LA is the cumulative value of the acoustic emission identification parameters of the shift, d is the mining distance or excavation distance of the shift, and d≥0 is satisfied; when Sv exceeds the second preset threshold, the identification result in the spatial dimension is dangerous, otherwise it is safe;

[0014] S7: If the identification results in the time dimension and the identification results in the space dimension are both safe, then the identification result of the coal-rock dynamic disaster of the current shift is safe; otherwise, the identification result of the coal-rock dynamic disaster of the current shift is dangerous, and an early warning signal is output.

[0015] Furthermore, in the step S1, at least two monitoring sensors are installed on the advance mining working face or the excavation working face.

[0016] Furthermore, in step S2, the duration of one shift is 8 hours.

[0017] Furthermore, the acoustic emission characteristic parameter indicators include ring count, impact count, energy, number of events and maximum amplitude.

[0018] Furthermore, in the step S4, the effective value of the acoustic emission identification parameter is specifically: the acoustic emission signal collected by the monitoring sensor after removing the interference signal.

[0019] Further, in step S5, the calculation process of the data fitting slope k is as follows:

[0020] S51: At the end of the shift, retrieve the average value of the acoustic emission identification parameters of this shift and the past two shifts every 1 hour, recorded as A i (i=1,2,3…,24), where i is the number of 1h average value data, A 24 It is the average value calculated within the last hour of the shift;

[0021] S52: Accumulate the average data of each hour and record SA i =∑A i (i=1,2,3…,24);

[0022] S53: Calculate the data fitting slope k,

[0023] Further, the calculation method of the first preset threshold and the second preset threshold is: using acoustic emission characteristic parameters of multiple normal shift identification cycles as samples, and calculating the Δk of the samples according to the method of S5 n The average value is used as the first preset threshold, and the Sv average value of the samples calculated according to the method of S6 is used as the second preset threshold, and the number of samples is not less than 10.

[0024] Furthermore, in step S6, when d=0, the result of the spatial dimension judgment is safe.

[0025] The beneficial effects of the present invention are:

[0026] The method of time-space interactive identification of coal-rock dynamic disasters based on acoustic emission characteristic parameters of this technical solution realizes the effective capture of disaster precursor information and disaster identification under large data volume conditions, combines the identification of the two dimensions of time and space, and performs time-space interactive dual identification. Compared with the existing technology that only identifies from the time dimension, it improves the identification efficiency and accuracy. The accuracy rate of this solution for identifying coal-rock dynamic disasters as safe is 100%, and the accuracy rate for identifying dangerous results is nearly 85%. It realizes accurate early warning of online monitoring of coal-rock dynamic disasters, can guarantee the safety of workers in underground excavation and mining to a greater extent, and will play a positive role in promoting the prevention and control of coal-rock dynamic disasters in coal mines.

[0027] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0029] Figure 1 A flow chart of the coal-rock dynamic disaster identification method implemented for the invention;

[0030] Figure 2 This is a schematic diagram of the sensor layout and monitoring range division of the mining face;

[0031] Figure 3 Schematic diagram of sensor layout and monitoring range division on the excavation working face. DETAILED DESCRIPTION

[0032] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0033] Among them, the drawings are only used for illustrative explanations, and they only represent schematic diagrams rather than actual pictures, and should not be understood as limitations on the present invention. In order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0034] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "front", "rear", etc. indicate the orientation or position relationship, they are based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0035] See also Figure 1 to Figure 3 The method for temporal and spatial interactive identification of coal-rock dynamic disasters based on acoustic emission characteristic parameters includes the following steps:

[0036] S1: Install the monitoring sensor ahead of the working face. The monitoring sensor can use an acoustic emission signal generator. The working face includes the mining working face and the excavation working face. At least two acoustic emission signal generators are installed ahead of the mining working face or the excavation working face, and the working face is divided into monitoring sections according to the installation positions of the acoustic emission signal generators. According to the mining progress or excavation, a mapping relationship between the monitoring section of the working face and the acoustic emission signal generator is established; that is, according to the mining progress or excavation progress, the acoustic emission signal generator corresponding to the acoustic emission signal for coal-rock dynamic disaster identification at different positions of the working face is established.

[0037] S2: The length of a shift in a production mine is used as the identification cycle of coal-rock dynamic disasters, and the danger of coal-rock dynamic disasters in the next shift is predicted at the end of each shift. The production mine performs three shifts of night, morning and midday every day, that is, the cumulative time of each shift is 8 hours. For example: the morning shift is 8:00-16:00, the midday shift is 16:00-24:00, and the night shift is 0:00-8:00. If the acoustic emission signal of the night shift is given an early warning at the end of the midday shift, the night shift operation can be guided according to the early warning information; if it is predicted that the night shift will be dangerous, the early warning information can be notified in time, the operation can be suspended, and the operation can be carried out after the danger is over to avoid safety accidents; if the prediction result is safe, the operation can be carried out at the beginning of the night shift, and the night shift will be continuously monitored to avoid danger caused by the operation of the night shift.

[0038] S3: Detect the sensitivity between the internal fracture of the coal rock mass and the acoustic emission characteristic parameters, and select the acoustic emission characteristic parameter index with the highest sensitivity to the internal fracture of the coal rock mass as the acoustic emission identification parameter. There are multiple indicators of acoustic emission characteristic parameters, and the acoustic emission characteristic parameter indicators usually include ring count, impact count, energy, number of events, maximum amplitude, duration, signal root mean square value, average signal level, voltage threshold, rise time, peak count, average frequency, back-calculated frequency, initial frequency, signal strength, absolute frequency, local power spectrum, centroid frequency, peak frequency and arrival time, etc. The acoustic emission characteristic parameter indicators mainly referenced in this embodiment include ring count, impact count, energy, number of events and maximum amplitude. First, detect the sensitivity between the internal fracture of the coal rock mass and the acoustic emission characteristic parameters fed back by different parameter indicators of the coal rock formation on site, and select the indicator with the highest sensitivity as the characteristic parameter of the comprehensive early warning of acoustic emission, which can reflect the stress change of the coal rock formation to the greatest extent and more accurately predict the occurrence of disasters in the coal rock formation.

[0039] S4: extracting effective values ​​of acoustic emission identification parameters from the acoustic emission signals of the working face collected by the acoustic emission signal generator, i.e., the acoustic emission identification parameters after noise removal, calculating and storing the average value of the acoustic emission identification parameters every 1 hour and the cumulative value of each shift; obtaining and storing the mining distance or excavation distance of each shift;

[0040] S5: Disaster identification in the time dimension is performed based on the effective values ​​of the acoustic emission identification parameters extracted and stored in step S4: at the end of each shift, the fitting slope k of the multiple data used for identification is calculated, and the slope change rate Δk is extracted n is the identification index, denoted by Δk n =(k n -k n-1 ) / k n-1 , and satisfy n≥4; when Δk n When the first preset threshold is exceeded, the result of the time dimension judgment is dangerous, otherwise it is safe;

[0041] The calculation process of the data fitting slope k is as follows:

[0042] S51: At the end of the shift, retrieve the average value of the acoustic emission identification parameters of this shift and the past two shifts every 1 hour, recorded as A i (i=1,2,3…,24), where i is the number of 1h average value data, A 24 It is the average value calculated within the last hour of the shift;

[0043] S52: Accumulate the average data of each hour and record SA i =∑A i (i=1,2,3…,24);

[0044] S53: Calculate the data fitting slope k,

[0045] S6: Disaster identification in the spatial dimension is performed according to the acoustic emission identification parameter values ​​extracted and stored in step S4 and the mining (or excavation) distance of the shift: the identification index Sv is extracted as the cumulative amount of acoustic emission identification parameters generated when each mining distance or excavation distance is 1m, Sv=LA / d, where LA is the cumulative value of the acoustic emission identification parameters of the shift, and d is the mining distance or excavation distance of the shift; when d>0 and Sv exceeds the second preset threshold value, the identification result in the spatial dimension is dangerous, otherwise it is safe; when d=0, the mining distance or excavation distance has not increased, no external force is currently applied to the coal rock structure, and no coal rock dynamic disaster will be caused by external force, and the identification result in the spatial dimension is safe.

[0046] S7: If the judgment results in the time dimension and the judgment results in the space dimension are both safe, then the judgment result of the coal-rock dynamic disaster for the current shift is safe; otherwise, the judgment result of the coal-rock dynamic disaster for the current shift is dangerous, and an early warning signal is output to the alarm device, an alarm is triggered, and the mining personnel are notified to evacuate.

[0047] The calculation method of the first preset threshold and the second preset threshold is: using acoustic emission characteristic parameters of multiple normal shift identification cycles as samples, and the number of samples is not less than 10. In this embodiment, the number of samples is 10, and the Δk of the 10 samples is calculated according to the method of S5. n , and calculate these 10 Δk n The average value of is taken as the first preset threshold; similarly, the Sv of the 10 samples is calculated according to the method of S6, and the average value of the 10 Sv is taken as the second preset threshold.

[0048] This method improves the identification method of the time dimension and adds the identification method of the space dimension, so that the identification of the two dimensions of time and space can be combined to perform time-space interactive dual identification. Compared with the current identification only from the time dimension, the identification efficiency is improved and the identification accuracy is improved, thereby providing a reliable means for accurate early warning of online monitoring of coal rock dynamic disasters, and further playing a positive role in promoting the prevention and control of coal mine coal rock dynamic disasters.

[0049] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A method for temporal and spatial interactive identification of coal and rock dynamic disasters based on acoustic emission characteristic parameters, characterized in that: The identification method includes the following steps: S1: Install the monitoring sensor ahead of the working face, and divide the working face into monitoring sections according to the installation position of the monitoring sensor; the working face includes the mining working face and the excavation working face; according to the mining or excavation distance, establish a mapping relationship between the monitoring section of the working face and the monitoring sensor; S2: The length of a shift in a production mine is used as the identification cycle of coal-rock dynamic disasters, and the risk of coal-rock dynamic disasters for the next shift is predicted at the end of each shift; S3: Detect the sensitivity between the internal fracture of coal and rock mass and the acoustic emission characteristic parameters, and select the acoustic emission characteristic parameter index with the highest feedback on the internal fracture sensitivity of coal and rock mass as the acoustic emission identification parameter; S4: Extracting effective values ​​of acoustic emission identification parameters from acoustic emission signals of the working face collected by monitoring sensors, calculating and storing the average value of acoustic emission identification parameters every hour and the cumulative value of each shift; obtaining and storing the mining distance or excavation distance of each shift; S5: Disaster identification in the time dimension is performed based on the effective values ​​of the acoustic emission identification parameters extracted and stored in step S4: at the end of each shift, the data fitting slope k of the multiple data used for identification is calculated, and the slope change rate Δk is extracted n is the identification index, denoted by Δk n =(k n -k n-1 ) / k n-1 , where n is the number of shifts, and satisfies n≥4; when Δk n When the first preset threshold is exceeded, the result of the time dimension judgment is dangerous, otherwise it is safe; S6: Disaster identification in the spatial dimension is performed according to the acoustic emission identification parameter values ​​extracted and stored in step S4 and the mining distance or excavation distance of the shift: the identification index Sv is extracted as the cumulative amount of the acoustic emission identification parameters generated when the mining distance or excavation distance is 1m, Sv=LA / d, where LA is the cumulative value of the acoustic emission identification parameters of the shift, d is the mining distance or excavation distance of the shift, and d≥0 is satisfied; when Sv exceeds the second preset threshold, the identification result in the spatial dimension is dangerous, otherwise it is safe; S7: If the identification results in the time dimension and the identification results in the space dimension are both safe, then the identification result of the coal-rock dynamic disaster of the current shift is safe; otherwise, the identification result of the coal-rock dynamic disaster of the current shift is dangerous, and an early warning signal is output.

2. The method for temporal and spatial interactive identification of coal-rock dynamic disasters based on acoustic emission characteristic parameters according to claim 1 is characterized in that: In the step S1, at least two monitoring sensors are installed ahead of the mining working face or the excavation working face.

3. The method for temporal and spatial interactive identification of coal-rock dynamic disasters based on acoustic emission characteristic parameters according to claim 1 is characterized in that: In step S2, the duration of one shift is 8 hours.

4. The method for temporal and spatial interactive identification of coal-rock dynamic disasters based on acoustic emission characteristic parameters according to claim 1 is characterized in that: In the step S3, the acoustic emission characteristic parameter indicators include ring count, impact count, energy, number of events and maximum amplitude.

5. The method for temporal and spatial interactive identification of coal-rock dynamic disasters based on acoustic emission characteristic parameters according to claim 1 is characterized in that: In the step S4, the effective value of the acoustic emission identification parameter is specifically: the acoustic emission signal collected by the monitoring sensor after removing the interference signal.

6. The method for temporal and spatial interactive identification of coal-rock dynamic disasters based on acoustic emission characteristic parameters according to claim 1 is characterized in that: In step S5, the calculation process of the data fitting slope k is as follows: S51: At the end of the shift, retrieve the average value of the acoustic emission identification parameters of this shift and the past two shifts every 1 hour, recorded as A i , i=1,2,3…,24, where i represents the i-th 1h time period, A 24 It is the average value calculated within the last hour of the shift; S52: Accumulate the average data of each hour and record SA i =∑A i ; S53: Calculate the data fitting slope k, 7. The method for temporal and spatial interactive identification of coal-rock dynamic disasters based on acoustic emission characteristic parameters according to claim 1 is characterized in that: The calculation method of the first preset threshold and the second preset threshold is: using the acoustic emission characteristic parameters of multiple normal shift identification cycles as samples, and calculating the Δk of the samples according to the method of S5 n The average value is used as the first preset threshold, and the Sv average value of the samples calculated according to the method of S6 is used as the second preset threshold, and the number of samples is not less than 10.

8. The method for temporal and spatial interactive identification of coal-rock dynamic disasters based on acoustic emission characteristic parameters according to claim 1 is characterized in that: In step S6, when d=0, the result of the spatial dimension judgment is safe.

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