Method and device for predicting danger of coal mine rock burst

Abstract

The invention discloses a coal mine rock burst risk prediction method and device, relates to the technical field of coal mine risk prediction, and solves the technical problems that prediction errors exist and further prediction inaccuracy is caused when prediction is carried out through single release of impact energy. According to the invention, whether there is an anomaly is judged according to the sensor parameters of the monitoring area, the anomaly condition is analyzed, multi-aspect parameter acquisition is carried out on the monitoring area, systematization processing is carried out in combination with corresponding historical data when the acquired parameters are analyzed, and the monitoring accuracy is improved. Meanwhile, the obtained parameters are preprocessed, the accuracy of the obtained parameters is guaranteed, the impact danger index corresponding to the monitoring area is calculated according to the impact event probability, the impact event influence degree value and the impact range value of the monitoring area, judgment is conducted according to the impact danger index, analysis is conducted through multi-aspect data, and the monitoring accuracy is improved. The overall prediction error is reduced, and the prediction accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of coal mine hazard prediction, and in particular to a method and device for predicting the hazard of coal mine rock burst. Background Art

[0002] Rock burst is a dynamic phenomenon in mines, which is characterized by sudden, rapid and violent destruction of coal and rock masses in mine tunnels and around mining areas due to the release of deformation energy. There are generally no obvious macroscopic precursors before rock burst occurs.

[0003] According to Chinese patent application number CN202311605387.9, a method and device for predicting the danger of rock burst in coal mines are disclosed. The method includes: determining an energy cumulative value representing the rock burst of the coal mine working face every day based on a first average value of the rock burst energy released per meter of the coal mine working face and a second average value of the rock burst energy released per day by the coal mine working face; determining a prediction criterion for the rock burst hazard level of the coal mine working face, the rock burst hazard level prediction criterion including: determining whether there is a rock burst hazard based on the energy cumulative value, the rock burst hazard level depends on the size of the energy cumulative value and the number of days it lasts; predicting the rock burst hazard level of the coal mine working face based on the rock burst hazard level prediction criterion, thereby judging the rock burst hazard by the energy cumulative value and the number of days it lasts, and providing a warning criterion for the rock burst hazard level of the working face under different energy cumulative values ​​and duration conditions.

[0004] The above patent makes corresponding predictions by analyzing the impact energy released by the coal mine working face. However, since the prediction of coal mine rock burst involves data analysis in multiple aspects, there are errors in the single prediction based on impact energy, resulting in inaccurate prediction results. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a method and device for predicting the danger of rock burst in coal mines, which solves the problem that prediction by releasing impact energy alone may lead to errors in the prediction and further cause inaccurate prediction.

[0006] To achieve the above objectives, the present invention is implemented by the following technical scheme: A method for predicting the danger of rock burst in a coal mine comprises the following steps: Step 1: Determine the monitoring area and obtain sensor data of the monitoring area, process the sensor data to obtain data features, and determine whether the monitoring area is abnormal based on the data features to generate a judgment result, which includes a normal monitoring signal and a danger prediction signal; Step 2: Analyze the monitoring area based on the generated danger prediction signal, calculate the probability of rock burst events based on the relationship between rock burst pressure in the monitoring area and the mining depth and thickness of the coal mine, and use the Bayesian formula; Step 3: Calculate the severity of the impact event corresponding to the monitoring area by quantifying the casualties, equipment damage and production interruption in the historical parameters in the historical data, and calculate the impact event impact value based on the casualty quantification value, equipment damage mark value and production interruption value obtained by the quantitative calculation; Step 4: Calculate the corresponding impact range value by analyzing the impact range in the historical data of the monitoring area; Step 5: Comprehensively calculate the calculated impact event probability, impact event impact degree value and impact range value to obtain the impact risk index, and make a judgment based on the impact risk index to obtain the prediction result.

[0007] As a further solution of the present invention: the specific method of generating the judgment result in step 1 is: Firstly, the corresponding rock burst monitoring area is determined, and then the sensor data of the monitoring area is acquired with the time period t as the acquisition period to obtain n groups of sensor data, and the interference data is extracted from the acquired sensor data, and the remaining data is recorded as standard data, and the standard data is averaged, and the calculated average is used as the standard of the same type of data, and data features are generated at the same time; The obtained data features are compared with the normal standard range. When the data features exist within the normal standard range, it means that the data in the monitoring area of ​​the corresponding period is normal, and a continuous monitoring signal is generated. Conversely, when the data features do not exist within the normal standard range, it means that the data in the monitoring area of ​​the corresponding period is abnormal, and the corresponding monitoring area is marked as an abnormal area, and a danger prediction signal is generated.

[0008] As a further solution of the present invention: the specific method of calculating the probability of the impact event according to the Bayesian formula in step 2 is as follows: Obtain historical data, and analyze the relationship between rock burst and mining depth and coal mine thickness based on the historical data, and calculate the probability of rock burst events using the Bayesian formula. The specific calculation formula is: A represents rock burst, B represents mining depth, and C represents coal mine thickness, where P(A|B,C) is the possibility of rock burst A occurring when mining depth B and coal mine thickness C occur, P(A) represents the probability of rock burst occurring alone, P(B|A) represents the probability of mining thickness B occurring when rock burst A occurs, P(C|A,B) represents the probability of coal mine thickness C occurring when rock burst A and mining depth B occur, P(B) represents the probability of mining depth B occurring alone, and P(C|B) represents the probability of coal mine thickness occurring when mining depth B occurs. The calculated rock burst event probability is denoted as P.

[0009] As a further solution of the present invention: the specific method of quantitatively calculating the casualties, equipment damage and production interruption in the historical parameters in the historical data in step 3 is: According to the historical data, all rock burst times are obtained and recorded as C. At the same time, the historical parameters corresponding to any rock burst are obtained for analysis. The historical parameters obtained include casualties, equipment damage and production interruption, and the casualties, equipment damage and production interruption are quantitatively analyzed; S1: The method of quantitatively analyzing the indicators of equipment damage is as follows: the number of damaged equipment is obtained, recorded as a, and the degree of equipment damage is obtained and quantified. The equipment damage value is obtained by scoring different parts of the equipment and summing up the scores of all parts, recorded as Ka, and the equipment damage value corresponding to the number of rock bursts C is calculated, recorded as K1, and K1 is used as the standard value of equipment damage; S2: The specific method of quantitatively analyzing the casualties is as follows: obtaining the corresponding number of casualties, calculating the proportion of casualties as the casualty value, then calculating the total casualties corresponding to the number of impacts K in the historical data, and calculating the average casualties, and taking the average casualties as the casualty quantification value as G; S3: The specific method for quantitative analysis of production interruption indicators is as follows: obtain the number of interruption days and maintenance days, and calculate the ratio of maintenance days to interruption days as the maintenance ratio. At the same time, obtain the change of the maintenance ratio within the time period T. When the maintenance ratio within the time period T increases, all maintenance ratios are summed up and the average is calculated. The calculated average maintenance ratio is used as the production interruption value Q. When the maintenance ratio within the time period T decreases, the maintenance ratio with the largest value is selected as the production interruption value, recorded as Q.

[0010] As a further solution of the present invention: the specific method of calculating the impact degree value of the impact event according to the quantitative casualty value, equipment damage mark value and production interruption value obtained by quantitative calculation in step 3 is: Then, substitute the equipment damage standard value K1, the casualty quantitative value G and the production interruption value Q obtained according to the analysis into the formula The impact degree value Y of the impact event is calculated, where N is a preset parameter.

[0011] As a further solution of the present invention: the specific method of analyzing the influence range in the historical data of the monitoring area in step 4 to calculate the corresponding influence range value is: The influence range of the monitoring area in the historical data is obtained, and the influence range is quantified and recorded as M. The influence range here is expressed as the influence area corresponding to the impact ground pressure. At the same time, the area S of the monitoring interval is obtained, and the influence range ratio is calculated. Then, all influence range ratios in the historical data are obtained, and the sum is calculated to calculate the mean. The calculated mean is recorded as the influence range value and recorded as F.

[0012] As a further solution of the present invention: the specific method of obtaining the prediction result according to the impact risk index in step 5 is: Substitute the probability of the impact event P, the impact degree of the impact event Y and the impact range value F into the calculation formula: The impact risk index wt corresponding to the monitoring area is calculated, where is the preset proportional coefficient, and the calculated impact risk index wt is compared with the assessment range to obtain the corresponding prediction result.

[0013] The present invention provides a method and device for predicting the danger of rock burst in coal mines. Compared with the prior art, it has the following beneficial effects: The present invention determines whether there is an abnormality based on the sensor parameters of the monitoring area, analyzes the abnormal situation, obtains various parameters of the monitoring area, and systematically processes the obtained parameters in combination with corresponding historical data when analyzing the obtained parameters. At the same time, the obtained parameters are pre-processed to ensure the accuracy of the acquired parameters. The impact risk index corresponding to the monitoring area is calculated by analyzing the data and based on the impact event probability, impact event impact degree value and impact range value of the monitoring area, and judgment is made based on the impact risk index. The multi-faceted data is used for analysis to reduce the overall prediction error and improve the prediction accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a flow chart of the method of the present invention; Figure 2 It is a comprehensive index table for evaluating the level of rock burst danger status of the present invention. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] For example, see Figure 1 The present application provides a method for predicting the danger of rock burst in coal mines, which specifically includes the following steps: Step 1: Determine the monitoring area and obtain the sensor data of the monitoring area, process the sensor data to obtain data features, and judge whether the monitoring area is abnormal based on the data features to generate a judgment result. The judgment result includes normal monitoring signals and danger prediction signals, and the specific judgment method is as follows: First, the corresponding rock burst monitoring area is determined, and then the sensor data of the monitoring area is acquired with a time period t as the acquisition period, and the time period can be expressed as recording once every 30 seconds to obtain n groups of sensor data, and the specific sensor data includes acoustic emission (AE) and electromagnetic radiation (EMR) signals, which are collected by corresponding sensors. At the same time, interference data is extracted from the acquired sensor data, and the interference data here is the data row of classification C. The remaining data is recorded as standard data, and the standard data is averaged. Specifically, the average of the same type of data is calculated for the n groups of sensor data collected, and the calculated average is used as the standard for the same type of data, and data features are generated at the same time; Suppose that rock burst monitoring is carried out in a coal mine. An area with a high risk of rock burst is selected as the monitoring area, and multiple acoustic emission and electromagnetic radiation sensors are deployed. During a monitoring period (such as a week), data is recorded every 30 seconds, and thousands of data points are collected. Through data screening, abnormal data (data rows of classification C) caused by equipment failure or external interference are excluded, and the remaining data are processed by averaging to obtain the average activity level of acoustic emission and electromagnetic radiation during the period.

[0017] The obtained data features are compared with the normal standard range, and the normal standard range is a numerical range consisting of a minimum value and a maximum value, and the specific values ​​of the minimum value and the maximum value are obtained by analyzing past historical data. When the data features exist within the normal standard range, it means that the monitoring area data of the corresponding period is normal, and a continuous monitoring signal is generated. On the contrary, when the data features do not exist within the normal standard range, it means that the monitoring area data of the corresponding period is abnormal, and the corresponding monitoring area is marked as an abnormal area, and a danger prediction signal is generated.

[0018] Specifically, for the continuous monitoring signals obtained, real-time early warning analysis of the monitoring area is carried out by continuously monitoring the sensor data, while for the generated danger prediction signals, further analysis and danger prediction of the monitoring area are required.

[0019] Step 2: Analyze the monitoring area based on the generated danger prediction signal, calculate the probability of impact event based on the relationship between the impact rock pressure in the monitoring area and the mining depth and coal mine thickness, and the Bayesian formula. The specific calculation method is as follows: Obtain historical data, and analyze the relationship between rock burst and mining depth and coal mine thickness based on the historical data, and calculate the probability of rock burst events using the Bayesian formula. The specific calculation formula is: A represents rock burst, B represents mining depth, and C represents coal mine thickness, where P(A|B,C) is the possibility of rock burst A occurring when mining depth B and coal mine thickness C occur, P(A) represents the probability of rock burst occurring alone, P(B|A) represents the probability of mining thickness B occurring when rock burst A occurs, P(C|A,B) represents the probability of coal mine thickness C occurring when rock burst A and mining depth B occur, P(B) represents the probability of mining depth B occurring alone, and P(C|B) represents the probability of coal mine thickness occurring when mining depth B occurs. The calculated rock burst event probability is recorded as P; Step 3: Calculate the severity of the impact event corresponding to the monitoring area by quantifying the casualties, equipment damage and production interruption in the historical parameters in the historical data, and calculate the impact event impact value based on the quantitative casualty value, equipment damage mark value and production interruption value obtained by the quantitative calculation. The specific calculation method is as follows: According to the historical data, all rock burst times are obtained and recorded as C. At the same time, the historical parameters corresponding to any rock burst are obtained for analysis. The historical parameters obtained include casualties, equipment damage and production interruption. The historical parameters are quantitatively analyzed, and the specific analysis method is as follows: S1: The method of quantitatively analyzing the indicators of equipment damage is as follows: the number of damaged equipment is obtained, recorded as a, and the degree of equipment damage is obtained, and the degree of equipment damage is quantified. Different parts of the equipment are scored, and here the main working parts of the equipment are scored, such as the motor, transmission shaft and mechanical arm of the equipment. Professional operators score each part, and the scores of all parts are summed up to obtain the equipment damage value, recorded as Ka. At the same time, the equipment damage value corresponding to the number of rock bursts C is calculated and recorded as K1, and K1 is used as the standard value of equipment damage. The calculation method here is to sum up all equipment damage values ​​and calculate the mean to obtain the standard value of equipment damage; Combined with actual analysis, for example, suppose that after a rock burst event, a total of 5 devices were damaged. The motors, transmission shafts and mechanical arms of these devices were scored, and the scores were as follows: the motor of device 1 scored 3 points, the transmission shaft scored 2 points, and the mechanical arm scored 4 points, for a total of 9 points; the motor of device 2 scored 5 points, the transmission shaft scored 3 points, and the mechanical arm scored 2 points, for a total of 10 points; the motor of device 3 scored 2 points, the transmission shaft scored 4 points, and the mechanical arm scored 3 points, for a total of 9 points; the motor of device 4 scored 4 points, the transmission shaft scored 3 points, and the mechanical arm scored 5 points, for a total of 12 points; the motor of device 5 scored 3 points, the transmission shaft scored 4 points, and the mechanical arm scored 3 points, for a total of 10 points; Adding these scores together gives the total score: 9 + 10 + 9 + 12 + 10 = 499 + 10 + 9 + 12 + 10 = 49. Therefore, the equipment damage standard value K for this rock burst event is 49 ÷ 5 = 9.8.

[0020] S2: The specific method of quantitatively analyzing the casualties is as follows: obtaining the corresponding number of casualties, calculating the proportion of casualties as the casualty value, then calculating the total casualties corresponding to the number of impacts K in the historical data, and calculating the average casualties, and taking the average casualties as the casualty quantification value as G; Combined with actual analysis, for example, assume that in the past 10 rock burst events, the number of casualties was 3, 2, 5, 1, 0, 4, 3, 2, 1, and 0. If the total number of people present in each event is 50, then the corresponding casualty values ​​are 0.06, 0.04, 0.1, 0.02, 0, 0.08, 0.06, 0.04, 0.02, and 0. Calculate the sum of these casualty values ​​and divide by 10, and the average casualty value G is 0.044.

[0021] S3: The specific method for quantitative analysis of production interruption indicators is as follows: obtain the number of interruption days and maintenance days, and calculate the ratio of maintenance days to interruption days as the maintenance ratio. At the same time, obtain the change of the maintenance ratio within the time period T. When the maintenance ratio within the time period T increases, all maintenance ratios are summed up and the average is calculated. The calculated average maintenance ratio is used as the production interruption value Q. When the maintenance ratio within the time period T decreases, the maintenance ratio with the largest value is selected as the production interruption value, recorded as Q.

[0022] For example, suppose that in a coal mine, the monthly production interruptions and maintenance in the past 5 months are as follows: Month 3: 6 days of interruption and 2 days of maintenance, with maintenance accounting for about 0.33 (2 / 6); Month 4: 4 days of interruption and 1 day of maintenance, with maintenance accounting for approximately 0.25 (1 / 4); Month 5: 8 days of interruption, 3 days of maintenance, maintenance ratio is about 0.38 (3 / 8); From the third month onwards, the maintenance ratio starts to decrease, so the maximum maintenance ratio of 0.38 is selected as the production interruption value Q.

[0023] S4: Then substitute the equipment damage standard value K1, the casualty quantitative value G and the production interruption value Q obtained according to the analysis into the formula The impact degree value Y of the impact event is calculated, where N is a preset parameter, and in this application, N=3.12.

[0024] Step 4: Calculate the corresponding impact range value by analyzing the impact range in the historical data of the monitoring area, and the specific calculation method is as follows: The influence range of the monitoring area in the historical data is obtained, and the influence range is quantified and recorded as M. The influence range here is expressed as the influence area corresponding to the impact ground pressure. At the same time, the area S of the monitoring interval is obtained, and the influence range ratio is calculated. Then, all influence range ratios in the historical data are obtained, and the sum is calculated to calculate the mean. The calculated mean is recorded as the influence range value and recorded as F.

[0025] Step 5: Comprehensively calculate the calculated impact event probability, impact event impact degree value and impact range value to obtain the impact risk index, and make a judgment based on the impact risk index to obtain the prediction result. The specific judgment method is as follows: Substitute the probability of the impact event P, the impact degree of the impact event Y and the impact range value F into the calculation formula: The impact risk index wt corresponding to the monitoring area is calculated, where is the preset proportionality coefficient, and the calculated impact risk index wt is compared with the assessment range to obtain the corresponding prediction result, and the assessment range is as shown in the attached Figure 2 As shown, the impact risk index wt is compared with the parameters corresponding to the assessment range to determine the interval corresponding to the impact range index and further determine the prediction result.

[0026] Specifically, a periodic analysis is performed on the acquired impact risk index to obtain the impact risk index wt corresponding to the monitoring area within the time period t1, and the change of the impact risk index wt within the adjacent time period is judged. When the impact risk index wt increases, the real-time impact risk index is compared with the warning value and a corresponding warning result is generated. When the impact index wt decreases, a normal monitoring result is generated.

[0027] Embodiment 2, a device for predicting the danger of rock burst in a coal mine, the device specifically comprises: an information collection module, a data processing module and a danger prediction module; An information acquisition module is used to transmit the acquired sensor data of the monitoring area to the data processing module; The data processing module is used to judge the monitoring area according to the acquired sensor data to generate normal monitoring signals and danger prediction signals. For the danger prediction signal, the historical data is analyzed, and the probability of impact events, the impact event impact degree value and the impact range value of the monitoring area are calculated. At the same time, the impact event probability, the impact event impact degree value and the impact range value are comprehensively calculated to obtain the impact risk index of the monitoring area. The method for calculating the impact risk index of the monitoring area is the same as the calculation method in steps 2, 3 and 4 in Example 1.

[0028] The danger prediction module is used to perform danger prediction on the monitoring area according to the acquired impact danger index to obtain a prediction result, and the method of obtaining the prediction result is the same as the processing method of step five in the first embodiment.

[0029] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0030] The above embodiments are only used to illustrate the technical method 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 method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for predicting the danger of rock burst in coal mines, characterized in that: The following steps are involved: Step 1: Determine the monitoring area and obtain sensor data of the monitoring area, process the sensor data to obtain data features, and determine whether the monitoring area is abnormal based on the data features to generate a judgment result, which includes a normal monitoring signal and a danger prediction signal; Step 2: Analyze the monitoring area based on the generated danger prediction signal, calculate the probability of rock burst events based on the relationship between rock burst pressure in the monitoring area and the mining depth and thickness of the coal mine, and use the Bayesian formula; Step 3: Calculate the severity of the impact event corresponding to the monitoring area by quantifying the casualties, equipment damage and production interruption in the historical parameters in the historical data, and calculate the impact event impact value based on the casualty quantification value, equipment damage mark value and production interruption value obtained by the quantitative calculation; Step 4: Calculate the corresponding impact range value by analyzing the impact range in the historical data of the monitoring area; Step 5: Comprehensively calculate the calculated impact event probability, impact event impact degree value and impact range value to obtain the impact risk index, and make a judgment based on the impact risk index to obtain the prediction result.

2. The method for predicting the danger of rock burst in coal mines according to claim 1, characterized in that: The specific method of generating the judgment result in step 1 is: Firstly, the corresponding rock burst monitoring area is determined, and then the sensor data of the monitoring area is acquired with the time period t as the acquisition period to obtain n groups of sensor data, and the interference data is extracted from the acquired sensor data, and the remaining data is recorded as standard data, and the standard data is averaged, and the calculated average is used as the standard of the same type of data, and data features are generated at the same time; The obtained data features are compared with the normal standard range. When the data features exist within the normal standard range, it means that the data in the monitoring area of ​​the corresponding period is normal, and a continuous monitoring signal is generated. Conversely, when the data features do not exist within the normal standard range, it means that the data in the monitoring area of ​​the corresponding period is abnormal, and the corresponding monitoring area is marked as an abnormal area, and a danger prediction signal is generated.

3. The method for predicting the danger of rock burst in coal mines according to claim 1, characterized in that: The specific method of calculating the probability of the impact event according to the Bayesian formula in step 2 is as follows: Obtain historical data, and analyze the relationship between rock burst and mining depth and coal mine thickness based on the historical data, and calculate the probability of rock burst events using the Bayesian formula. The specific calculation formula is: A represents rock burst, B represents mining depth, and C represents coal mine thickness, where P(A|B,C) is the possibility of rock burst A occurring when mining depth B and coal mine thickness C occur, P(A) represents the probability of rock burst occurring alone, P(B|A) represents the probability of mining thickness B occurring when rock burst A occurs, P(C|A,B) represents the probability of coal mine thickness C occurring when rock burst A and mining depth B occur, P(B) represents the probability of mining depth B occurring alone, and P(C|B) represents the probability of coal mine thickness occurring when mining depth B occurs. The calculated rock burst event probability is denoted as P.

4. The method for predicting the danger of rock burst in coal mines according to claim 1, characterized in that: The specific method of quantitatively calculating the casualties, equipment damage and production interruption in the historical parameters in the historical data in step 3 is as follows: According to the historical data, all rock burst times are obtained and recorded as C. At the same time, the historical parameters corresponding to any rock burst are obtained for analysis. The historical parameters obtained include casualties, equipment damage and production interruption, and the casualties, equipment damage and production interruption are quantitatively analyzed; S1: The method of quantitatively analyzing the indicators of equipment damage is as follows: the number of damaged equipment is obtained, recorded as a, and the degree of equipment damage is obtained and quantified. The equipment damage value is obtained by scoring different parts of the equipment and summing up the scores of all parts, recorded as Ka, and the equipment damage value corresponding to the number of rock bursts C is calculated, recorded as K1, and K1 is used as the standard value of equipment damage; S2: The specific method of quantitatively analyzing the casualties is as follows: obtaining the corresponding number of casualties, calculating the proportion of casualties as the casualty value, then calculating the total casualties corresponding to the number of impacts K in the historical data, and calculating the average casualties, and taking the average casualties as the casualty quantification value as G; S3: The specific method for quantitative analysis of production interruption indicators is as follows: obtain the number of interruption days and maintenance days, and calculate the ratio of maintenance days to interruption days as the maintenance ratio. At the same time, obtain the change of the maintenance ratio within the time period T. When the maintenance ratio within the time period T increases, all maintenance ratios are summed up and the average is calculated. The calculated average maintenance ratio is used as the production interruption value Q. When the maintenance ratio within the time period T decreases, the maintenance ratio with the largest value is selected as the production interruption value, recorded as Q.

5. The method for predicting the danger of rock burst in coal mines according to claim 1, characterized in that: The specific method of calculating the impact degree value of the impact event according to the quantitative casualty value, equipment damage mark value and production interruption value obtained by quantitative calculation in step 3 is: Then, substitute the equipment damage standard value K1, the casualty quantitative value G and the production interruption value Q obtained according to the analysis into the formula The impact degree value Y of the impact event is calculated, where N is a preset parameter.

6. The method for predicting the danger of rock burst in coal mines according to claim 1, characterized in that: The specific method of analyzing the influence range in the historical data of the monitoring area in step 4 to calculate the corresponding influence range value is: The influence range of the monitoring area in the historical data is obtained, and the influence range is quantified and recorded as M. The influence range here is expressed as the influence area corresponding to the impact ground pressure. At the same time, the area S of the monitoring interval is obtained, and the influence range ratio is calculated. Then, all influence range ratios in the historical data are obtained, and the sum is calculated to calculate the mean. The calculated mean is recorded as the influence range value and recorded as F.

7. The method for predicting the danger of rock burst in coal mines according to claim 1, characterized in that: The specific method of obtaining the prediction result by judging according to the impact risk index in step 5 is: Substitute the probability of the impact event P, the impact degree of the impact event Y and the impact range value F into the calculation formula: The impact risk index wt corresponding to the monitoring area is calculated, where is the preset proportional coefficient, and the calculated impact risk index wt is compared with the assessment range to obtain the corresponding prediction result.

8. A device for predicting the danger of rock burst in a coal mine is used to execute the method for predicting the danger of rock burst in a coal mine according to any one of claims 1 to 7, characterized in that: include: Information collection module, data processing module and danger prediction module; An information acquisition module is used to transmit the acquired sensor data of the monitoring area to the data processing module; A data processing module is used to judge the monitoring area according to the acquired sensor data to generate a normal monitoring signal and a danger prediction signal, and for the danger prediction signal, by analyzing the historical data, and calculating the probability of the impact event, the impact degree value and the impact range value of the monitoring area, and at the same time, comprehensively calculating the probability of the impact event, the impact degree value and the impact range value of the impact event to obtain the impact danger index of the monitoring area, and transmitting the impact danger index to the danger prediction module; The danger prediction module is used to predict the danger of the monitoring area according to the acquired impact danger index to obtain the prediction result.

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