CN113673119A - Dynamic and static coupling evaluation method for coal mine rock burst danger based on Bayes method - Google Patents
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Abstract
The invention provides a Bayesian method-based dynamic and static coupling evaluation method for coal mine rock burst danger, which comprises the following steps of: s1, obtaining dynamic index parameter data; s2, obtaining static index parameter data; s3, carrying out normalization processing on the dynamic and static index parameter data; s4, carrying out abnormal index conversion on each microseismic index obtained by monitoring the microseismic sensor; s5, carrying out comprehensive risk calculation on the drilling stress index value obtained by the drilling stress sensor; s6, performing fusion calculation on each dynamic and static index through a Bayes probability combination model; and S7, carrying out rock burst grade division on the combined model calculation result to realize intelligent grading early warning. According to the method and the device, data indexes of each monitoring system are effectively fused, dynamic and static indexes are comprehensively considered, dynamic weight calculation of each index based on a time sequence is realized, the safety early warning capability of rock burst is improved, and the problems that an existing multi-index safety early warning threshold is difficult to determine and the data fusion degree of each monitoring system is low are solved.
Description
Technical Field
The invention relates to the technical field of coal mine underground rock burst dynamic disaster safety monitoring and early warning, in particular to a Bayesian method-based dynamic and static coupling evaluation method for coal mine rock burst danger.
Background
Along with the increase of the coal mining depth, the stress of a deep coal seam is increased, and the possibility of disasters is higher, so that the underground monitoring and early warning device is particularly important for underground common dynamic disasters. In a deep mining area, rock burst is taken as a typical common coal rock dynamic disaster, and has important engineering value for underground monitoring and early warning.
The research work of monitoring and early warning of rock burst has achieved good results. In particular, monitoring systems for micro-seismic, earth sound, electromagnetic radiation, drilling stress and the like are widely popularized and applied, and have good application effects in many mine enterprises. At present, the monitoring and early warning of the rock burst are mainly comprehensively analyzed in two aspects of dynamic state and static state, the static state is analyzed according to two major indexes of geological conditions, comprehensive indexes of mining conditions and possibility indexes, and the dynamic indexes are used for researching the rock burst through data acquired by monitoring systems of microseismic events, energy, stress and the like. The inventor of the invention finds that the existing rock burst safety monitoring and early warning system has the following problems through research:
(1) monitoring data mining inadequacy
In the process of preventing and controlling rock burst, at present, China accumulates a large amount of geological data and monitoring data, the data fusion degree of each monitoring system is low, and how to use a Bayesian method to excavate beneficial data capable of safely early warning impact disasters through big data is lack of a reliable and effective calculation method at present.
(2) Determination of safety precaution threshold
When the constant analysis of each index of rock burst is carried out, most of safety early warning threshold values influencing the rock burst danger are obtained according to the actual situation on site, are biased to engineering experience and have certain uniqueness, and the safety early warning threshold values of each index are difficult to determine and random and cannot be applied to all projects.
(3) Linkage property lacking dynamic and static indexes
For a large number of monitoring indexes, one or two independent indexes are usually adopted as an early warning threshold value at present, and more than constant and more than monitoring data indexes can not be represented uniformly, which is obviously insufficient.
Disclosure of Invention
The invention provides a dynamic and static coupling evaluation method for coal mine rock burst danger based on a Bayesian method, aiming at the technical problems that the existing rock burst safety monitoring and early warning system is low in data fusion degree of each monitoring system, each index safety early warning threshold value is difficult to determine and random, and dynamic indexes and static indexes lack linkage.
In order to solve the technical problems, the invention adopts the following technical scheme:
the dynamic and static coupling evaluation method for the coal mine rock burst danger based on the Bayesian method comprises the following steps:
s1, transmitting and storing the data received by the underground micro-seismic sensor and the borehole stress sensor to a monitoring system through a data transmission device to obtain dynamic index parameter data with time series characteristics;
s2, performing comprehensive index evaluation calculation from two aspects of geological factors and mining technologies, performing probability index calculation from stress and coal seam impact tendency, and obtaining regional static index parameter data based on the comprehensive index evaluation calculation and the probability index calculation;
s3, preliminarily carrying out normalization processing on each dynamic index parameter data and each static index parameter data by introducing an abnormal conversion function, and obtaining multi-parameter abnormal indexes uniformly constrained in a dimensionless quantity in a 0-1 closed interval;
s4, converting the abnormal indexes of the microseismic indexes obtained by monitoring the microseismic sensors at different moments according to the following formula:
in the formula, λijThe membership degree of the corresponding index in the statistical time window t is in a value range of 0-1;
s5, carrying out comprehensive risk calculation on drilling stress index values obtained by monitoring the drilling stress sensor at different moments according to the following formula to obtain the risk level at any moment:
PI＝k1I1+k2I2+k3I3
in the formula I1The risk index is the magnitude of the stress value of the measuring point; i is2The stress amplitude value is a dangerous index of the stress amplitude value, namely a value obtained by calculating the difference between the stress value of the current measuring point and the stress value of the initial setting point; i is3A risk indicator which is a stress acceleration value; k is a radical of1、k2、k3Are respectively I1、I2、I3The weight ratio of (a);
s6, analyzing each dynamic and static index after the comprehensive risk calculation by using a Bayesian probability model, and comprehensively evaluating the risk by using a combined model; setting each dynamic and static index as independent evaluation model, and assuming the weight of the kth evaluation modelIs PkIf n independent evaluation models coexist, the posterior probability of the combined model is:
wherein O (C) represents the posterior probability of event C; p (C) represents the prior probability of event C, typically set to 1/n;
no event occurs; here, the weight of each evaluation model is determined as a probability according to the result obtained in steps S2, S3, S5;
s7, obtaining a dimensionless quantity P belonging to a 0-1 closed interval through calculation of the combined model, carrying out grade division on the dimensionless quantity P according to a preset rock burst risk grade division standard to obtain rock burst grades at all times, and realizing intelligent grading early warning according to the rock burst grades.
Compared with the prior art, the dynamic and static coupling evaluation method for the coal mine rock burst danger based on the Bayes method comprises the steps of firstly collecting regional dynamic index parameter data and regional static index parameter data, then converting each dynamic and static index by introducing an abnormal conversion function to obtain a plurality of constant abnormal indexes, constraining each dynamic and static index in a dimensionless quantity of a 0-1 closed interval, then performing fusion calculation on each dynamic and static index by a mathematical probability statistical method to obtain the comprehensive danger degree of each index weight and rock burst, and performing safety early warning according to a preset rock burst danger grade division standard. The method is based on a Bayesian method, establishes multi-index-fused rock burst disaster safety monitoring and early warning, effectively fuses data indexes of each monitoring system, comprehensively considers dynamic indexes and static indexes, realizes dynamic weight calculation of each index based on time series, reduces uncertainty, randomness and fuzziness in the calculation process, and improves the rock burst safety early warning capability, so that the problem of poor unified effect of fusion of each index and a module of the existing online monitoring system is effectively solved, and the technical problem that the existing multi-index safety early warning threshold is difficult to determine is solved.
Further, the microseismic index obtained by monitoring the microseismic sensor in the step S4 includes a microseismic intensity factor, a microseismic equivalent energy level parameter, a microseismic timing factor, a microseismic b value, and a microseismic a (b) value.
Further, the parameter λ in the step S4ijThe calculation method of (2) is as follows:
for the forward anomaly indicators:
λij＝(Sij-Smin)/(Smax-Smin)
for negative anomaly indicators:
λij＝(Smax-Sij)/(Smax-Smin)
in the formula, SijIs the value of the microseismic index at a certain moment in the time window, SmaxThe maximum value S of each microseismic index is taken at each moment in the time windowminAnd taking the minimum value of the values of each microseismic index at each moment in the time window.
Further, the preset criterions for classifying the dangerousness of rock burst in step S7 specifically classify the possibility of the dimensionless quantity P into four grades, i.e., none, weak, medium and strong, the criterions for classifying the none, weak, medium and strong grades are respectively 0 ≦ P <0.25, 0.25 ≦ P <0.5, 0.5 ≦ P <0.75 and 0.75 ≦ P <1, and the four grades are respectively pre-warned with green, yellow, orange and red.
Drawings
FIG. 1 is a schematic flow chart of a dynamic and static coupling evaluation method for coal mine rock burst hazard based on a Bayesian method provided by the invention.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further explained below by combining the specific drawings.
Referring to fig. 1, the dynamic and static coupling evaluation method for coal mine rock burst hazard based on the bayesian method provided by the invention comprises the following steps:
s1, transmitting and storing the data received by the underground micro-seismic sensor and the borehole stress sensor to a monitoring system through a data transmission device to obtain dynamic index parameter data with time series characteristics; the data of the database is read in real time by interpreting and coding the types of the monitoring system database and then compiling an interface, and the data of multiple monitoring systems are fused and analyzed uniformly.
S2, performing comprehensive index evaluation calculation from two aspects of geological factors and mining technologies, performing probability index calculation from stress and coal seam impact tendency, and obtaining regional static index parameter data based on the comprehensive index evaluation calculation and the probability index calculation, namely discussing static indexes with dangerousness from the comprehensive index evaluation calculation and the probability index calculation, wherein the specific calculation processes of the comprehensive index evaluation calculation and the probability index calculation are the prior art well known to those skilled in the art, and are not repeated herein.
S3, performing normalization processing on each dynamic index parameter data and each static index parameter data preliminarily by introducing an abnormal conversion function, and obtaining multi-parameter abnormal indexes uniformly constrained within a dimensionless quantity in a 0-1 closed interval.
S4, monitoring the microseismic sensors to obtain each microseismic index: the index values of the microseismic intensity factor, the microseismic equivalent energy level parameter, the microseismic time sequence factor, the microseismic b value and the microseismic A (b) value at different moments are converted into abnormal indexes according to the following formula:
in the formula, λijThe membership degree of the corresponding index in the statistical time window t is in a value range of 0-1; parameter lambdaijThe calculation method of (2) is as follows:
for the forward anomaly indicators:
λij＝(Sij-Smin)/(Smax-Smin)
for negative anomaly indicators:
λij＝(Smax-Sij)/(Smax-Smin)
in the formula, SijIs the value of the microseismic index at a certain moment in the time window, SmaxThe maximum value S of each microseismic index is taken at each moment in the time windowminAnd taking the minimum value of the values of each microseismic index at each moment in the time window.
S5, carrying out comprehensive risk calculation on drilling stress index values obtained by monitoring the drilling stress sensor at different moments according to the following formula to obtain the risk level at any moment:
PI＝k1I1+k2I2+k3I3
in the formula I1The risk index is the magnitude of the stress value of the measuring point; i is2The stress amplitude value is a dangerous index of the stress amplitude value, namely a value obtained by calculating the difference between the stress value of the current measuring point and the stress value of the initial setting point; i is3A risk indicator which is a stress acceleration value; k is a radical of1、k2、k3Are respectively I1、I2、I3The weight ratio of (2).
S6, after dimensionless quantities of dynamic and static indexes of the rock burst are calculated, analyzing the dynamic and static indexes after comprehensive risk calculation by using a Bayes probability model, and comprehensively evaluating risks by using a combined model; setting each dynamic and static index as independent evaluation model, and assuming that the weight of the kth evaluation model is PkIf n independent evaluation models coexist, the posterior probability of the combined model is:
wherein O (C) represents the posterior probability of event C; p (C) represents the prior probability of event C, typically set to 1/n;
no event occurs; here, the weight of each evaluation model is determined as a probability according to the result obtained in steps S2, S3, S5;
s7, obtaining a dimensionless quantity P belonging to a 0-1 closed interval through calculation of the combined model, carrying out grade division on the dimensionless quantity P according to a preset rock burst risk grade division standard to obtain rock burst grades at all times, and realizing intelligent grading early warning according to the rock burst grades.
Compared with the prior art, the dynamic and static coupling evaluation method for the coal mine rock burst danger based on the Bayes method comprises the steps of firstly collecting regional dynamic index parameter data and regional static index parameter data, then converting each dynamic and static index by introducing an abnormal conversion function to obtain a plurality of constant abnormal indexes, constraining each dynamic and static index in a dimensionless quantity of a 0-1 closed interval, then performing fusion calculation on each dynamic and static index by a mathematical probability statistical method to obtain the comprehensive danger degree of each index weight and rock burst, and performing safety early warning according to a preset rock burst danger grade division standard. The method is based on a Bayesian method, establishes multi-index-fused rock burst disaster safety monitoring and early warning, effectively fuses data indexes of each monitoring system, comprehensively considers dynamic indexes and static indexes, realizes dynamic weight calculation of each index based on time series, reduces uncertainty, randomness and fuzziness in the calculation process, and improves the rock burst safety early warning capability, so that the problem of poor unified effect of fusion of each index and a module of the existing online monitoring system is effectively solved, and the technical problem that the existing multi-index safety early warning threshold is difficult to determine is solved.
As a specific example, the preset criterions for the risk rating of rock burst at step S7 specifically classify the possibility of the dimensionless quantity P into four grades, i.e., none, weak, medium and strong, and the criterions for classification at each of the grades of none, weak, medium and strong are 0 ≦ P <0.25, 0.25 ≦ P <0.5, 0.5 ≦ P <0.75, 0.75 ≦ P <1, as shown in table 1 below:
TABLE 1 grade of rock burst hazard
The four grades of none, weak, medium and strong adopt green, yellow, orange and red respectively to carry out early warning, thereby realizing intelligent grading safety early warning.
Finally, the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all of them should be covered in the claims of the present invention.
Claims (4)
1. The method for evaluating the dynamic and static coupling of the coal mine rock burst dangerousness based on the Bayesian method is characterized by comprising the following steps of:
s1, transmitting and storing the data received by the underground micro-seismic sensor and the borehole stress sensor to a monitoring system through a data transmission device to obtain dynamic index parameter data with time series characteristics;
s2, performing comprehensive index evaluation calculation from two aspects of geological factors and mining technologies, performing probability index calculation from stress and coal seam impact tendency, and obtaining regional static index parameter data based on the comprehensive index evaluation calculation and the probability index calculation;
s3, preliminarily carrying out normalization processing on each dynamic index parameter data and each static index parameter data by introducing an abnormal conversion function, and obtaining multi-parameter abnormal indexes uniformly constrained in a dimensionless quantity in a 0-1 closed interval;
s4, converting the abnormal indexes of the microseismic indexes obtained by monitoring the microseismic sensors at different moments according to the following formula:
in the formula, λijThe membership degree of the corresponding index in the statistical time window t is in a value range of 0-1;
s5, carrying out comprehensive risk calculation on drilling stress index values obtained by monitoring the drilling stress sensor at different moments according to the following formula to obtain the risk level at any moment:
PI＝k1I1+k2I2+k3I3
in the formula I1The risk index is the magnitude of the stress value of the measuring point; i is2The stress amplitude value is a dangerous index of the stress amplitude value, namely a value obtained by calculating the difference between the stress value of the current measuring point and the stress value of the initial setting point; i is3A risk indicator which is a stress acceleration value; k is a radical of1、k2、k3Are respectively I1、I2、I3The weight ratio of (a);
s6, analyzing each dynamic and static index after the comprehensive risk calculation by using a Bayesian probability model, and comprehensively evaluating the risk by using a combined model; setting each dynamic and static index as independent evaluation model, and assuming that the weight of the kth evaluation model is PkIf n independent evaluation models coexist, the posterior probability of the combined model is:
wherein O (C) represents the posterior probability of event C; p (C) represents the prior probability of event C, typically set to 1/n;
no event occurs; here, the weight of each evaluation model is determined as a probability according to the result obtained in steps S2, S3, S5;
s7, obtaining a dimensionless quantity P belonging to a 0-1 closed interval through calculation of the combined model, carrying out grade division on the dimensionless quantity P according to a preset rock burst risk grade division standard to obtain rock burst grades at all times, and realizing intelligent grading early warning according to the rock burst grades.
2. The Bayesian method-based coal mine rock burst hazard dynamic-static coupling evaluation method as recited in claim 1, wherein microseismic indexes obtained by monitoring of the microseismic sensor in step S4 include a microseismic intensity factor, a microseismic equivalent energy level parameter, a microseismic time sequence factor, a microseismic b value and a microseismic A (b) value.
3. The Bayesian method-based coal mine rock burst hazard dynamic-static coupling evaluation method according to claim 1, wherein in the step S4, parameter λ isijThe calculation method of (2) is as follows:
for the forward anomaly indicators:
λij＝(Sij-Smin)/(Smax-Smin)
for negative anomaly indicators:
λij＝(Smax-Sij)/(Smax-Smin)
in the formula, SijIs the value of the microseismic index at a certain moment in the time window, SmaxThe maximum value S of each microseismic index is taken at each moment in the time windowminAnd taking the minimum value of the values of each microseismic index at each moment in the time window.
4. The Bayesian method-based coal mine rock burst risk dynamic-static coupling evaluation method according to claim 1, wherein in step S7, preset rock burst risk grade division criteria specifically divide the possibility of a dimensionless quantity P into four grades of none, weak, medium and strong, the division criteria values of the none, weak, medium and strong grades are respectively 0. ltoreq.P <0.25, 0.25. ltoreq.P <0.5, 0.5. ltoreq.P <0.75, 0.75. ltoreq.P <1, and the four grades of none, weak, medium and strong adopt green, yellow, orange and red for early warning respectively.
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| CN114352355A (en) * | 2022-03-15 | 2022-04-15 | 中国矿业大学(北京) | Deep mine rock burst control method |
| CN114595581A (en) * | 2022-03-11 | 2022-06-07 | 重庆地质矿产研究院 | Regional geological disaster danger probability model based on influence factor dynamic weight |
| CN115511379A (en) * | 2022-10-28 | 2022-12-23 | 北京科技大学 | Dynamic dividing method and device for rock burst dangerous area |
| CN115860582A (en) * | 2023-02-28 | 2023-03-28 | 山东科技大学 | Intelligent impact risk early warning method based on self-adaptive lifting algorithm |
| CN118154046A (en) * | 2024-05-10 | 2024-06-07 | 太原向明智控科技有限公司 | Top plate pressure grade dividing method |
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