A method for monitoring the danger of rock burst at a working face

Abstract

The present application relates to the technical field of coal mine rock burst monitoring, and particularly relates to a dangerous monitoring method for rock burst of a working face, comprising: performing grid division on the working face, obtaining average overburden weight, static mining depth, stress of each grid at each time and recording microseismic events; calculating the static total stress of a grid by using the average overburden weight of the working face, the static mining depth and the geometric shortest distance between the grid and the coal body boundary of each goaf; obtaining the microseismic events of each grid at each time in the local area of each grid; obtaining the dynamic-static coupling index of the grid at the time by using the static total stress of the grid, the stress increment of the grid at the time and the microseismic events of the grid at the time; calculating the spatial information entropy at the time according to the dynamic-static coupling index of each grid at the time and combining a Shannon information entropy calculation model, and then performing early warning based on the spatial information entropy change rate. The present application can effectively monitor the danger of rock burst of the working face.

Description

Technical Field

[0001] This invention relates to the field of coal mine rockburst monitoring technology, specifically to a method for monitoring the danger of rockburst at a working face. Background Technology

[0002] In the field of coal mine rockburst monitoring and early warning technology, current research generally relies on online microseismic and stress monitoring systems. This involves deploying microseismic detectors and borehole stress gauges in the working face and roadways to acquire key indicators such as the elastic energy released by surrounding rock fracturing and changes in internal coal stress in real time. The impact hazard is then determined based on a globally unified static empirical threshold. Specifically, existing methods typically set fixed microseismic energy thresholds (e.g., single event energy exceeding 10...). 5 J) and stress thresholds (e.g., coal stress exceeding 15 MPa). Once any one or both indicators exceed the set value, the corresponding area is determined to have a shock hazard and an early warning is issued. The core advantages of this type of method are its simple logic, low implementation cost, and high compatibility with existing standardized monitoring systems. In conventional first-mining faces with solid coal on both sides, no adjacent mining disturbances, and uniform and symmetrical initial stress distribution, since there is no complex historical residual stress, the coal stress increases linearly with mining. Using a globally unified threshold can effectively intercept dangerous areas, demonstrating good engineering applicability and operability.

[0003] While existing multi-source index threshold joint early warning methods based on online microseismic and stress monitoring are effective in conventional working faces with solid coal on both sides and uniformly symmetrical initial stress distribution, effectively intercepting dangerous areas due to the simple linear increase in coal stress with mining, this method suffers from a fundamental failure in isolated coal pillar working faces surrounded by goafs on three sides. The asymmetric lateral residual static pressure generated by the collapse of overburden and the suspended roof in the historical goafs on three sides places the coal body in an extremely distorted spatial heterogeneous stress field before mining, resulting in completely different bearing capacity reserves at different locations. Furthermore, in this scenario, the coal body exhibits a type II weak impact tendency, and the microseismic and stress precursor signals before critical instability are extremely weak. Conventional thresholds cannot detect this difference in critical points caused by spatial non-uniformity. In high-prestressed areas, even a small vibration far below the threshold can trigger a systemic impact, while in decompression areas, even large-energy events exceeding the threshold will not cause danger. Summary of the Invention

[0004] To address the aforementioned technical problems, the present invention aims to provide a method for monitoring the danger of rockburst at a working face, the specific technical solution of which is as follows:

[0005] One embodiment of the present invention provides a method for monitoring the danger of rockburst at a working face, the method comprising:

[0006] The working face is divided into grids, and the coordinates of the center point of each grid are used as the grid coordinates; the average unit weight of the overlying strata, the static mining depth, and the stress of each grid at each time are obtained, and microseismic events are recorded;

[0007] The static total stress of the grid is calculated using the average unit weight of the overlying strata of the working face, the static mining depth, and the geometric shortest distance between a grid and the coal body boundary of each goaf.

[0008] A circle is drawn with the center point of a grid as the center to represent the local region of the grid; microseismic events of the local region of a grid are extracted within a preset time period before a certain time as the microseismic events of the grid at that time; the static-dynamic coupling index of the grid at that time is obtained by using the total static stress of the grid, the stress increment of the grid at a certain time, and the microseismic events of the grid at that time.

[0009] The spatial information entropy at a given moment is calculated using the dynamic-static coupling index of each grid and the Shannon information entropy calculation model; early warning is issued based on the rate of change of spatial information entropy at each moment.

[0010] Preferably, the calculation model for the static total stress is as follows:

[0011] ,

[0012] in, The coordinates of the center point are The static total stress of the mesh; It is the average unit weight of the overlying rock strata of the working face; is the static mining depth of the working face; i is the adjacent goaf number; The coordinates of the center point are The grid to the first The geometric shortest distance between the boundaries of the coal seams in each goaf area; It is the uniaxial compressive strength of the coal seam at the working face; is the multiple of the additional constraint stress between the grid with center point coordinates (x,y) and the coal body boundary of the i-th goaf; min is the minimum value function;

[0013] Specifically:

[0014] ,

[0015] in, The maximum lateral support pressure concentration factor; The radius of maximum influence of lateral support pressure; This is the peak offset parameter.

[0016] Preferably, the static-dynamic coupling index of a grid at a given moment is obtained using the total static stress of the grid, the stress increment of the grid at a given moment, and the microseismic events of the grid at that moment, including:

[0017] The first ratio is obtained by adding the total static stress of a grid to the stress increment of that grid at a certain moment and comparing it with the uniaxial compressive strength of the coal seam at the working face. The difference between the first ratio and the first preset value is mapped using the hyperbolic tangent function to obtain a mapped value. The mean of the mapped value and the first preset value is calculated to obtain the static pressure approximation degree of the grid at that moment. The sum of the elastic energy released by each microseismic event of the grid at that moment is divided by the microseismic energy benchmark value and added to the first preset value to obtain the summation result. The summation result is mapped using a logarithmic function with the natural constant as the base and added to the first preset value to obtain the microseismic dynamic amplification factor of the grid at that moment. The dynamic amplification factor of the grid at that moment is multiplied by the static pressure approximation degree to obtain the dynamic and static values ​​of the grid at that moment.

[0018] Preferably, the spatial information entropy at a given moment is calculated based on the dynamic-static coupling index of each grid at a given moment, combined with the Shannon information entropy calculation model, including:

[0019] The probability distribution of a grid at a given moment is obtained by dividing the dynamic-static coupling index of a grid at that moment by the sum of the dynamic-static coupling indices of all grids at that moment. The spatial information entropy of the working surface at that moment is obtained by combining the Shannon information entropy calculation model with the probability distribution of all grids at that moment.

[0020] Preferably, early warning is given based on the rate of change of spatial information entropy at each time point, including:

[0021] The judgment threshold is obtained based on the rate of change of spatial information entropy at normal times in history; when the rate of change of spatial information entropy at a certain time is less than the judgment threshold, an early warning is issued.

[0022] Preferably, the method for obtaining the rate of change of spatial information entropy at each time step is as follows:

[0023] Calculate the difference between the spatial information entropy at a given moment and the moment before that moment, and then compare it with the time interval between the two moments to obtain the rate of change of spatial information entropy at that moment.

[0024] Preferably, the judgment threshold is obtained based on the rate of change of spatial information entropy at various normal times in history, including:

[0025] Calculate the sample quantiles of the rate of change of spatial information entropy at each normal time in history, and take the rate of change of spatial information entropy corresponding to the preset percentile as the judgment threshold.

[0026] The embodiments of this invention have at least the following beneficial effects: This application divides the working face into grids, using the coordinates of the center point of each grid as the grid coordinates. Then, it calculates the average unit weight of the overlying strata, the static mining depth, and the stress of each grid at each time point, and records microseismic events. Next, it calculates the static total stress of the grid using the average unit weight of the overlying strata, the static mining depth, and the geometric shortest distance between a grid and the coal seam boundary of each goaf. By obtaining the static total stress of each grid, a static background stress map can be reconstructed to pinpoint the inherently high-stress target area. Then, it obtains the microseismic events in the local area of ​​each grid at each time point, and then combines the static stress of the grid with the microseismic events in the local area of ​​each grid at each time point. The dynamic-static coupling index of each grid at each time moment is obtained by using the total stress and the stress increment of each grid at each time moment. The dynamic-static coupling index is used to track the spatiotemporal migration path of the danger point in real time. Then, the spatial information entropy of each grid at a time moment is calculated by combining the dynamic-static coupling index of each grid at a time moment with the Shannon information entropy calculation model. The warning is given according to the change rate of spatial information entropy at each time moment. The structural mutation of critical instability of weakly impacted coal body is captured by using the sudden drop rate of spatial information entropy of the dynamic-static coupling index. Thus, accurate dynamic warning of rockburst danger is achieved without relying on the globally unified static threshold condition, which significantly improves the timeliness and specificity of rockburst warning in isolated working faces. Attached Figure Description

[0027] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 A flowchart illustrating a method for monitoring the risk of rockburst at a working face, provided in an embodiment of the present invention. Detailed Implementation

[0029] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a working face rockburst hazard monitoring method proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0031] The following description, in conjunction with the accompanying drawings, details the specific scheme of the method for monitoring the danger of rockburst at a working face provided by the present invention.

[0032] In this embodiment, the main application scenario of the present invention is: This application mainly monitors the risk of rockburst in isolated coal pillar working faces surrounded by goaf areas on three sides.

[0033] Please see Figure 1 The diagram illustrates a method flowchart for monitoring the risk of rockburst at a working face, provided by an embodiment of the present invention. The method includes the following steps:

[0034] Step S1: Divide the working face into grids and use the coordinates of the center point of each grid as the grid coordinates; obtain the average unit weight of the overlying strata, the static mining depth, and the stress of each grid at each time, and record the microseismic events.

[0035] First, based on the mine geological report, mining engineering plan, and existing monitoring system, two types of basic data are collected from the isolated coal pillar working face surrounded by three goaf areas: one is static geological and mining parameters, including the average unit weight of the overlying strata, the mining depth of the working face, and the geometric shortest distance from each grid point to the boundary of the three goaf areas; the other is dynamic monitoring data, including the spatiotemporal intensity parameters of microseismic events output in real time by the microseismic monitoring system and the dynamic stress readings of the online stress system.

[0036] Specifically, the working face first needs to be gridded to facilitate subsequent analysis. Specifically, the entire working face is gridded from the mining engineering plan according to the preset grid size, for example, the grid size is 10m*10m. The entire working face is divided into grids. Assuming the working face is 200m long and 100m wide, after division, there are 200 grid units. The coordinates (x, y) of the center point of each grid are the coordinates of each grid. The coordinate system is an independent rectangular coordinate system for the working face, with the lower left corner of the working face cut as the origin, the x-axis along the strike and the y-axis along the dip.

[0037] Furthermore, the average unit weight of the overlying strata of the working face and the static mining depth of the working face are obtained from the mine geological report. The static mining depth refers to the depth of the unmined working face. The geometric shortest distance from each grid to the coal body boundary of the goaf on the three sides of the working face is measured on site. When obtaining the geometric shortest distance, it is taken from the center point of the grid.

[0038] Simultaneously, it is also necessary to acquire microseismic events, export all microseismic events within the time window from the microseismic monitoring system, retain the time of occurrence, three-dimensional coordinates of the source, and released energy, and remove invalid events with positioning residuals greater than the set threshold or negative energy values; extract the stress values ​​of each measuring point (grid center point) at each time from the online stress system as the stress of each grid at each time, record the installation coordinates of the measuring points, and fill in the temporary data loss caused by transmission interruption through linear interpolation to form a continuous stress time series.

[0039] The processed microseismic events and mesh stresses are stored in a standard data structure (such as JSON or CSV) with attached data quality tags for direct use in subsequent steps.

[0040] Step S2: Calculate the static total stress of the grid by using the average unit weight of the overlying strata of the working face, the static mining depth, and the geometric shortest distance between a grid and the coal body boundary of each goaf.

[0041] Existing methods directly use microseismic energy and stress values ​​as independent indicators for assessing rockburst risk, and make judgments through a globally unified threshold. Although the calculations are simple, these quantities are essentially isolated descriptions of the coal body's response under different physical fields. This makes traditional methods, in extremely heterogeneous stress scenarios such as isolated coal pillars in three-sided goaf areas, essentially a threshold-triggered passive response, making it difficult to capture the faint precursors before the critical instability of coal bodies with weak rockburst tendency. To solve this problem, this proposal suggests a method for monitoring the risk of rockburst at the working face.

[0042] First, based on the geometric effect of the goaf boundary, the static heterogeneous background stress field of the working face before mining is reconstructed. The asymmetric lateral residual static pressure transmitted from the old goaf on the three sides is condensed into a spatially continuous stress map, the inherent high stress concentration target area is locked, and the static background prestress at each location is obtained, providing a structural basis for subsequent dynamic analysis.

[0043] Existing methods treat the coal face as a uniform stress field, directly using the measured stress points to represent the entire area. This ignores the distortion of the spatial background caused by the long-term asymmetric lateral residual static pressure transmitted from the overburden collapse and overhanging roof in the three-sided goaf. To address this issue, this application aims to reconstruct the static background stress distribution inside the coal body before mining, which was disturbed by the three-sided goaf, from the existing geological and mining conditions information of the working face. This provides a spatially continuous structural base that physically reflects the influence of non-uniform lateral pressure for subsequent dynamic analysis.

[0044] Specifically, based on the coal seam burial depth and average unit weight of the overlying strata, the shortest geometric distance from each grid to the coal body boundary of each adjacent goaf is measured from the mining engineering plan. Combined with the maximum influence radius of lateral support pressure and the standard maximum concentration factor, a geometric distance linear attenuation model is used to superimpose natural original rock stress, and the static background prestress value is calculated grid by grid. The asymmetric loading effect of the three goaf areas is automatically reflected through the natural superposition of distance attenuation terms in each direction. Finally, a spatially continuous static background prestress distribution map is output, clearly indicating the inherently high stress concentration core area formed by the superposition of lateral residual static pressure from the three goaf areas.

[0045] Therefore, the static total stress of the grid is calculated using the average unit weight of the overlying strata of the working face, the static mining depth, and the geometric shortest distance between a grid and the coal body boundary of each goaf.

[0046] The specific calculation model is as follows:

[0047] ,

[0048] in, The coordinates of the center point are The static total stress of the mesh; It is the average unit weight of the overlying rock strata of the working face; It is the static mining depth of the working face, that is, the burial depth of the coal seam in the working face before mining; It is the number of adjacent goaf areas ( ); The coordinates of the center point are The grid to the first The geometric shortest distance between the boundaries of the coal seams in each goaf area; It is the uniaxial compressive strength of the coal seam in the working face (the value determined by standard laboratory rock mechanics test, which is the physical and mechanical constant of the coal seam, and the value in the material is taken as 13.60MPa). It is used to prevent the stress from being abnormally high after the three sides are superimposed. The value is the uniaxial compressive strength, which means that even if the elastic calculated stress is high, the actual ultimate stress that can be withstood will not exceed this value. At the same time, it also indicates that when the calculated value reaches the cutoff upper limit, it means that plastic failure has occurred in this area, but it is still a high-risk area for rockburst.

[0049] It is the natural original rock vertical stress, obtained by multiplying the unit weight and the burial depth, representing the initial static pressure reference before mining and disturbance;

[0050] It is an asymmetric superposition of three goaf areas, with the coordinates of the center point being... The grid and the constraint additional stress multiple of the coal body boundary of the i-th goaf for:

[0051] ,

[0052] It is the maximum lateral support pressure concentration factor (rock mechanics standard factor, taken as 2-3, reflecting the amplification factor of the peak stress at the edge of the goaf). This is the maximum influence radius of the lateral support pressure (a standard parameter in mine stress science, generally 150-200m, which can be determined based on experience with mine stress distribution or industry standards); the actual distribution of lateral support pressure in the goaf exhibits nonlinear characteristics. Based on these actual nonlinear characteristics... This is the peak offset parameter (values ​​range from 18 to 35m). This is a linear simplification of the coal wall's state of being in an overall elevated state during both the stress reduction and stress increase zones; when At this point, it enters the boundary of gradual decay, and at this time, as... As the stress increases, it begins to decrease until it reaches the boundary of maximum influence. At that point, the minimum value is reached; all others are greater than that. The influence of distance is negligible, and the stress is taken as 0.

[0053] The final output is the total static stress of each mesh on the working surface. The resulting stress diagram serves as the basis for subsequent working surface analysis.

[0054] Step S3: Draw a circle with the center point of a grid as the center to represent the local region of the grid; extract the microseismic events of the local region of the grid within a preset time period before a certain time as the microseismic events of the grid at that time; obtain the dynamic-static coupling index of the grid at that time using the total static stress of the grid, the stress increment of the grid at a certain time, and the microseismic events of the grid at that time.

[0055] The above steps obtained the static total stress of each grid on the working face. However, the static background prestress only reflects the initial state before mining and cannot characterize the real-time transfer of stress field and the dynamic evolution of energy release during mining disturbance. To address this, the microseismic energy release and online stress increment are projected in real time to each static background prestress location. The current load state is reflected by the superimposed stress increment of the static pressure base, and the microseismic energy activity factor is used as a dynamic amplification term. The two are coupled to construct a dynamic-static coupling index, realizing the dimensionless fusion of stress load level and microseismic activity in a unified spatiotemporal field, thereby tracking the spatiotemporal migration path of danger points in real time.

[0056] The stress map identifies the inherent high stress concentration target area inside the coal body before mining. However, this base map is a static field that does not change over time and cannot reflect the dynamic evolution process of stress field transfer, local loading and unloading, and energy release from surrounding rock fracture caused by mining disturbance during the tunneling and mining process.

[0057] To address this issue, real-time microseismic monitoring data and online stress increment data are used as dynamic disturbance sources, projected onto a static stress base map time-by-time. The current load state is reflected by the superimposed stress increment on the static pressure base, and the microseismic energy activity factor is used as a dynamic amplification term. These two factors are coupled to construct a spatiotemporally unified dynamic-static coupling index, DSCI. This index integrates the previously fragmented analysis of microseismic energy and stress values ​​into a single dimensionless index, achieving a dynamic quantitative characterization of the "relative degree of approaching the failure limit" at various locations within the coal seam, thereby enabling real-time tracking of the spatiotemporal migration path of hazardous points under mining disturbances.

[0058] Therefore, for each grid point, it is necessary to first delineate its local area and analyze the microseismic events in its local area. Thus, a circle is drawn with the center point of a grid as the center to represent the local area of ​​that grid, and the radius of the circle is set as R, which is the spatial search radius of the microseismic influence. In this application, the standard setting value is 50m, which is determined based on the microseismic energy attenuation law and the scale of the working surface. Furthermore, for a given moment, the microseismic events of a local area of ​​a grid within a preset time period before that moment are obtained as the microseismic events of that grid at that moment. Preferably, the length of the preset time period in this application is one hour, which can be adjusted by the implementer according to the actual situation, and the last moment of the preset time period is that moment.

[0059] Therefore, the static-dynamic coupling index of a grid at a given moment can be obtained by using the total static stress of the grid, the stress increment of the grid at a given moment, and the microseismic event of the grid at that moment.

[0060] Specifically, the static total stress of a grid is added to the stress increment of that grid at a certain moment, and then divided by the uniaxial compressive strength of the coal seam at the working face to obtain a first ratio. The difference between the first ratio and a first preset value is mapped using a hyperbolic tangent function to obtain a mapped value. The mean of the mapped value and the first preset value is calculated to obtain the static pressure approximation degree of the grid at that moment. The sum of the elastic energy released by each microseismic event of the grid at that moment is divided by the microseismic energy benchmark value and added to the first preset value to obtain a summation result. The summation result is mapped using a logarithmic function with the natural constant as the base and added to the first preset value to obtain the microseismic dynamic amplification factor of the grid at that moment. The microseismic dynamic amplification factor of the grid at that moment is multiplied by the static pressure approximation degree to obtain the dynamic-static coupling index of the grid at that moment.

[0061] The specific calculation model is as follows:

[0062] ,

[0063] in, The coordinates of the center point are The static total stress of the mesh; The coordinates of the center point mapped to the center point at time t via spatial interpolation are: The stress increment of the grid, which is the stress increment of the grid at time t, is the measured vertical stress minus the equivalent static vertical stress, reflecting the change in vertical stress caused by mining disturbance, and is taken from the mine stress online monitoring system (such as KJ743). It is the uniaxial compressive strength of the coal seam at the working face; The elastic energy released by the j-th microseismic event in each microseismic event at time t in this grid (taken from the microseismic monitoring system (such as KJ648), and output in real time by the source location and energy calculation module); It is the microseismic energy benchmark value (taken as the microseismic early warning energy threshold value specified in the mine rockfall prevention code or industry standard, such as 10). 4 J); tanh is the hyperbolic tangent function used for normalization; the first preset value is 1;

[0064] This is the static pressure approximation, which characterizes the degree to which the current load statically approaches the coal body failure limit. The closer the ratio is to 1, the closer the point is to the static failure criticality. The numerator is the actual total static load of the grid at time t, which is obtained by adding the change caused by mining disturbance to the static base stress. The denominator is the normalized compressive strength. Finally, the static pressure approximation is obtained by nonnegation processing through hyperbolic tangent bounded mapping. The first ratio, For mapping values;

[0065] It is the dynamic amplification factor of microseismic events, where This is a relative energy ratio. The numerator is the total energy released by microseismic events in the area surrounding the grid within a preset time period, and the denominator is the energy threshold for microseismic early warning, used for normalization. This unifies energy events of different magnitudes to a uniform scale, making microseismic activity comparable across different times and spaces. Logarithmic smoothing and suppression (which can remove noise) are then used to prevent a single large event from excessively dominating the exponent, while ensuring that the logarithmic term is zero when there are no microseismic events, resulting in the logarithmically compressed microseismic activity. Finally, add 1 to make it a dynamic amplification factor for microseismic events. The more active the microseismic events, the larger the factor becomes. When there are no microseismic events, the factor is 1, indicating that no additional amplification is generated. ln represents a logarithmic function with the natural constant as the base.

[0066] In the two items above, the static pressure approximation degree represents the real-time foundation hazard level, while the microseismic dynamic amplification factor is used to dynamically amplify the foundation hazard. The product comprehensively reflects the relative degree to which this point approaches the coal body failure limit under the current load and disturbance level. The larger the value, the more dangerous it is. Finally, for a given moment, the output is the DSCI field composed of the dynamic-static coupling index DSCI(x,y,t) of each grid on the working face.

[0067] Step S4: Calculate the spatial information entropy at a given moment based on the dynamic-static coupling index of each grid and the Shannon information entropy calculation model; issue an early warning based on the rate of change of spatial information entropy at each moment.

[0068] To address the bottleneck that the absolute value of the dynamic-static coupling index in weakly rock-prone coal bodies often does not exceed the limit before critical instability, making it impossible to rely on traditional thresholds for judgment, this paper introduces the spatial information entropy of the dynamic-static coupling index and its rate of change as a criterion. As the system approaches the critical state, the originally dispersed and disordered energy distribution rapidly self-organizes and concentrates into a few core danger points, resulting in a sharp drop in spatial information entropy. The assessment is performed by monitoring whether this entropy change rate exceeds the critical negative threshold, ultimately outputting a dynamic judgment result for rockburst hazard that can accurately capture the moment of critical abrupt change.

[0069] While the output DSCI field can dynamically quantify the relative degree to which each location in the coal body approaches its failure limit and track the spatiotemporal migration path of hazard points in real time, the coal body in this scenario exhibits a unique danger: critical instability. Before this critical instability, micro-tremors and stress precursors are extremely weak, and the absolute value of the DSCI may never exceed the macroscopic safety threshold. Therefore, it fails to detect the critical instability state characteristic of coal bodies with a weak impact tendency (Type II), resulting in severely insufficient early warning sensitivity.

[0070] To address this issue, the entire DSCI field of the working face is considered as a spatially distributed system, and the ordering evolution of its internal structure is analyzed. Physically, as the system approaches critical instability, the originally dispersed and disordered energy and stress distribution rapidly self-organizes and concentrates in a few core hazardous areas, and the system state tends to change from highly disordered to highly ordered. This transformation is manifested in information theory as a sharp drop in spatial information entropy. The working face is discretized into a regular grid, and a probability distribution is constructed based on the proportion of the DSCI value of each grid to the total of the entire working face. The spatial information entropy of the DSCI field is calculated, and then the rate of entropy change within the sliding time window is calculated. When the rate of entropy change exceeds the preset critical abrupt negative threshold, it is determined that nonlinear self-organized lock-up has occurred within the system, triggering a red warning for rockburst instability, and the pressure relief and treatment target area is accurately located based on the grid where the probability peak is located. Since the information entropy is constructed based on a normalized probability distribution, it has strong robustness.

[0071] Because Class II coal bodies with a weak shock tendency may face the risk of critical instability, this risk spatially manifests as a gradual shift from random distribution to a more concentrated, directional, and coordinated pattern. However, individual points may not approach the failure limit, meaning their DSCI values ​​may not be high. Therefore, a holistic analysis is necessary. First, the DSCI is converted into a probability distribution for overall analysis.

[0072] Therefore, the spatial information entropy at a given moment is calculated using the dynamic-static coupling index of each grid at a given moment, combined with the Shannon information entropy calculation model.

[0073] Specifically, the probability distribution of a grid at a given moment is obtained by dividing the dynamic-static coupling index of a grid at that moment by the sum of the dynamic-static coupling indices of all grids at that moment; the spatial information entropy of the working surface at that moment is obtained by using the Shannon information entropy calculation model in combination with the probability distribution of all grids at that moment.

[0074] The specific calculation model for the probability distribution is as follows:

[0075] ,

[0076] in, This represents the probability distribution of the k-th grid at time t. Let represent the static-dynamic coupling index of the k-th grid at time t; the numerator is the DSCI of the k-th grid, and the denominator is the sum of the DSCIs of all grids. This indicates the proportion of hazardous energy concentrated in the k-th grid across the entire working face, if all grids... If they are roughly the same, the energy is evenly distributed; if a few grids are... If the grid is significantly larger than other grids, it indicates a high concentration of energy; N is the number of grids.

[0077] Shannon's information entropy calculation model is , Let t be the spatial information entropy. When energy is completely uniformly distributed, the entropy reaches its maximum value, the system is most chaotic, and there is no obvious dangerous core. In the most extreme case, only one grid has energy, and the rest have zero. The energy is extremely concentrated, and the entropy reaches its minimum value. The system is most ordered, and all energy is locked at one point. This is the structural characteristic before critical instability. The process of the entropy value decreasing from high to low is the process of the system starting to become unstable.

[0078] Entropy itself is affected by factors such as the number of grids and the scale of the working surface, making direct comparison between different working surfaces difficult. However, the rate of change of entropy is a more general indicator. The rate of change of spatial information entropy at a given moment is obtained by calculating the difference between the spatial information entropy at a given moment and the previous moment, and then dividing it by the time interval between the two moments.

[0079] If the rate of change of spatial information entropy is greater than 0, the system tends to be more uniform and disordered, which is safe. If the rate of change of spatial information entropy is equal to 0, the system tends to be in a steady state. If the rate of change of spatial information entropy is less than 0, the system tends to be more concentrated and ordered, which is dangerous. The larger the absolute value, the faster the current change. Instability is manifested as a rapid decrease in entropy, that is, the rate of change of spatial information entropy is less than 0 and the absolute value of the rate of change increases.

[0080] Furthermore, a judgment threshold is obtained based on the rate of change of spatial information entropy at various normal historical moments. Specifically, the sample quantiles of the rate of change of spatial information entropy at various normal historical moments are calculated, and the rate of change of spatial information entropy corresponding to a preset percentile is taken as the judgment threshold. In this embodiment, the preset percentile is the 5th percentile (i.e., approximately 95% of normal fluctuations are higher than this value). When the rate of decrease of entropy exceeds this threshold, it is determined to be a critical sudden change. When the rate of change of spatial information entropy at a certain moment is less than the judgment threshold, an early warning is issued, and it is determined that there is a critical risk of rockburst instability at the working face. If it is greater than or equal to the judgment threshold, the system determines it to be in a normal state and continues to monitor. The various normal historical moments are the moments within a continuous period during the initial stage of mining at the working face, when there are no records of rockburst events.

[0081] After the warning is triggered, the probability distribution of each grid at that moment is obtained, and the coordinates of the grid with the largest probability distribution are extracted. This location is the core danger area with highly self-organized energy concentration at that moment, and is directly used as the priority pressure relief and treatment target area.

[0082] Activate corresponding response measures. After the warning is triggered, immediately stop mining operations in the target area and adjacent areas, and implement active pressure relief measures such as coal seam drilling and deep hole blasting to reduce the elasticity of coal accumulation in the locked target area.

[0083] After the depressurization measures are implemented, the subsequent change rate of spatial information entropy is continuously monitored. If the change rate of spatial information entropy returns to the normal fluctuation range, it indicates that the depressurization is effective and the critical state is lifted. If the change rate of spatial information entropy remains negative and continues to be below the threshold, it is necessary to supplement the depressurization or expand the depressurization range until the system returns to a safe state.

[0084] This application obtained the entropy change rate of the entire working face and the location of the peak grid of the probability distribution corresponding to the triggering of the early warning. The spatial information entropy change rate can distinguish between normal state fluctuations and critical instability precursors from the system structure level. Using it as the core criterion for early warning decision-making, it can replace traditional static indicators such as microseismic energy threshold or stress threshold, and realize graded early warning and adaptive pressure relief control of rockburst hazard.

[0085] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0086] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. The above descriptions are merely preferred embodiments of the present invention and are not intended to limit the invention. Any modifications, equivalent substitutions, or improvements made within the principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for monitoring the risk of rockburst at a working face, characterized in that, The method includes: The working face is divided into grids, and the coordinates of the center point of each grid are used as the grid coordinates; the average unit weight of the overlying strata, the static mining depth, and the stress of each grid at each time are obtained, and microseismic events are recorded; The static total stress of the grid is calculated using the average unit weight of the overlying strata of the working face, the static mining depth, and the geometric shortest distance between a grid and the coal body boundary of each goaf. A circle is drawn with the center point of a grid as the center to represent the local region of the grid; microseismic events of the local region of a grid are extracted within a preset time period before a certain time as the microseismic events of the grid at that time; the static-dynamic coupling index of the grid at that time is obtained by using the total static stress of the grid, the stress increment of the grid at a certain time, and the microseismic events of the grid at that time. The spatial information entropy at a given moment is calculated using the dynamic-static coupling index of each grid and the Shannon information entropy calculation model; early warning is issued based on the rate of change of spatial information entropy at each moment.

2. The method for monitoring the risk of rockburst at a working face according to claim 1, characterized in that, The specific calculation model for the static total stress is as follows: ， in, The coordinates of the center point are The static total stress of the mesh; It is the average unit weight of the overlying rock strata of the working face; is the static mining depth of the working face; i is the adjacent goaf number; The coordinates of the center point are The grid to the first The geometric shortest distance between the boundaries of the coal seams in each goaf area; It is the uniaxial compressive strength of the coal seam at the working face; is the multiple of the additional constraint stress between the grid with center point coordinates (x,y) and the coal body boundary of the i-th goaf; min is the minimum value function; Specifically: ， in, The maximum lateral support pressure concentration factor; The radius of maximum influence of lateral support pressure; This is the peak offset parameter.

3. The method for monitoring the risk of rockburst at a working face according to claim 1, characterized in that, The method of obtaining the static-dynamic coupling index of a grid at a given moment using the total static stress of the grid, the stress increment of the grid at a given moment, and the microseismic events of the grid at that moment includes: The first ratio is obtained by adding the total static stress of a grid to the stress increment of that grid at a certain moment and comparing it with the uniaxial compressive strength of the coal seam at the working face. The difference between the first ratio and the first preset value is mapped using the hyperbolic tangent function to obtain a mapped value. The mean of the mapped value and the first preset value is calculated to obtain the static pressure approximation degree of the grid at that moment. The sum of the elastic energy released by each microseismic event of the grid at that moment is divided by the microseismic energy benchmark value and added to the first preset value to obtain the summation result. The summation result is mapped using a logarithmic function with the natural constant as the base and added to the first preset value to obtain the microseismic dynamic amplification factor of the grid at that moment. The dynamic amplification factor of the grid at that moment is multiplied by the static pressure approximation degree to obtain the dynamic and static values ​​of the grid at that moment.

4. The method for monitoring the risk of rockburst at a working face according to claim 1, characterized in that, The calculation of spatial information entropy at a given moment based on the dynamic-static coupling index of each grid and the Shannon information entropy calculation model includes: The probability distribution of a grid at a given moment is obtained by dividing the dynamic-static coupling index of a grid at that moment by the sum of the dynamic-static coupling indices of all grids at that moment. The spatial information entropy of the working surface at that moment is obtained by combining the Shannon information entropy calculation model with the probability distribution of all grids at that moment.

5. The method for monitoring the risk of rockburst at a working face according to claim 1, characterized in that, The method of issuing early warnings based on the rate of change of spatial information entropy at each moment includes: The judgment threshold is obtained based on the rate of change of spatial information entropy at normal times in history; when the rate of change of spatial information entropy at a certain time is less than the judgment threshold, an early warning is issued.

6. The method for monitoring the risk of rockburst at a working face according to claim 5, characterized in that, The method for obtaining the rate of change of spatial information entropy at each moment is as follows: Calculate the difference between the spatial information entropy at a given moment and the moment before that moment, and then compare it with the time interval between the two moments to obtain the rate of change of spatial information entropy at that moment.

7. The method for monitoring the danger of rockburst at a working face according to claim 5, characterized in that, The threshold for determining the judgment based on the rate of change of spatial information entropy at various historically normal moments includes: Calculate the sample quantiles of the rate of change of spatial information entropy at each normal time in history, and take the rate of change of spatial information entropy corresponding to the preset percentile as the judgment threshold.

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