Rock burst prevention and control measure evaluation method, equipment and medium

Abstract

The invention provides a rock burst prevention and control measure evaluation method and device and a medium, and the method comprises the steps: obtaining prevention and control measure data which comprises a prevention and control measure implementation engineering drawing and various monitoring data; in the simulation software, a three-dimensional model conforming to an actual mine is established, geological parameters and control measure data of underground rock mass of the mine are input, dynamic numerical analysis is conducted through the simulation software, numerical simulation results are obtained, and the numerical simulation results at least comprise a stress concentration coefficient and the vertical distance between a stress concentration area and a roadway; acquiring micro-seismic monitoring data within a preset time period after implementation of the prevention and control measures; and judging the effect of the rock burst prevention and control measure according to the stress concentration coefficient, the vertical distance between the stress concentration area and the roadway and the micro-seismic monitoring data. The method solves the problem of limitation of evaluating rock burst prevention and control measures by a single index in the prior art.

Description

Technical Field

[0001] This invention relates to the field of coal, and more particularly to a method, equipment, and medium for evaluating rockburst prevention measures. Background Technology

[0002] Rockbursts are a common mining hazard, typically occurring in coal mines, metal mines, and other underground mines, especially during deep mining operations. Their occurrence mechanism is complex, usually closely related to stress accumulation in rock strata, the brittle characteristics of coal and rock masses, and changes in mining methods. Rockbursts not only pose a serious threat to mine production but can also lead to damage to mine equipment, casualties, and even catastrophic accidents. Therefore, rockburst prevention and control is a crucial task in ensuring safe mine operations.

[0003] Traditional prevention and control technologies mainly employ methods such as borehole decompression, blasting decompression, and water injection softening. However, these methods have significant limitations in practical applications, and their effectiveness remains unclear. The design of prevention and control measures relies heavily on engineering experience and lacks quantitative analysis based on the mechanical response of coal and rock masses, resulting in insufficient matching between the decompression range and the stress transfer depth. Existing technologies typically rely solely on the frequency and energy level of microseismic events for judgment, resulting in a single evaluation system that fails to comprehensively reflect the energy accumulation state within the coal body. Furthermore, the optimization of prevention and control measures lacks a dynamic closed-loop mechanism, making it impossible to achieve full-process linkage of "monitoring-simulation-optimization-verification". Summary of the Invention

[0004] Based on the above problems, this invention proposes a method, equipment, and medium for evaluating rockburst prevention and control measures. This invention overcomes the limitations of existing technologies that rely on single indicators to evaluate rockburst prevention and control measures. By combining monitoring data with simulation software to construct a three-dimensional geological model, it achieves precise quantitative analysis of stress field distribution and identifies rockburst hazard zones that still exist around the mining area after the implementation of stress relief measures. By establishing a closed-loop mechanism of "monitoring-simulation-optimization-verification," using the stress concentration zone within six times the equivalent radius of the roadway as the key threshold, stress relief measures are dynamically optimized, significantly improving the targeting and reliability of prevention and control measures. Compared with existing technologies, this method upgrades rockburst prevention and control from qualitative judgment to quantitative decision-making. It not only solves the problem of insufficient matching between stress relief range and stress transfer depth but also forms a technical closed loop through continuous monitoring and verification, making the prevention and control effect verifiable. This provides standardized technical support for safe mining in deep mines and has significant engineering application value.

[0005] This invention proposes a method for evaluating rockburst prevention measures, comprising:

[0006] Acquire data on prevention and control measures, including: engineering diagrams of the implementation of prevention and control measures and various monitoring data;

[0007] In the simulation software, a three-dimensional model that matches the actual mine is established, geological parameters of the underground rock mass and prevention and control measures data are input, and dynamic numerical analysis is performed using the simulation software to obtain numerical simulation results. The numerical simulation results include at least: stress concentration factor and vertical distance of stress concentration zone from roadway.

[0008] Obtain microseismic monitoring data within a preset time period after the implementation of prevention and control measures;

[0009] The effectiveness of rockburst prevention measures is judged based on the stress concentration factor, the vertical distance of the stress concentration zone from the roadway, and microseismic monitoring data.

[0010] In addition, the various monitoring data include: microseismic monitoring data, electromagnetic radiation data, and ground sound data.

[0011] Furthermore, the assessment of the effectiveness of rockburst prevention measures based on stress concentration factor, vertical distance of stress concentration zone from roadway, and microseismic monitoring data includes:

[0012] If the stress concentration factor is less than the first preset stress value, then the rockburst prevention measures are considered to be effective in terms of stress.

[0013] If the stress concentration factor is greater than or equal to the first preset stress value and less than the second preset stress value, then the rockburst prevention measures are considered to be effective in terms of stress.

[0014] If the stress concentration factor is greater than the second preset stress value, then the effectiveness of the rockburst prevention measures is judged to be poor in terms of stress dimension.

[0015] In addition, judging the effectiveness of rockburst prevention measures based on the vertical distance between the stress concentration zone and the roadway includes:

[0016] If the vertical distance between the stress concentration zone and the roadway is greater than the first preset distance value, then the effectiveness of the rockburst prevention measures is judged to be good in the dimension of vertical distance.

[0017] If the vertical distance between the stress concentration zone and the roadway is less than the first preset distance value but greater than the second preset distance value, then the effectiveness of the rockburst prevention measures is judged to be better in the dimension of vertical distance.

[0018] If the vertical distance between the stress concentration zone and the roadway is less than the second preset distance value, then the effectiveness of the rockburst prevention measures is judged to be poor in the dimension of vertical distance.

[0019] In addition, judging the effectiveness of rockburst prevention measures based on microseismic monitoring data includes:

[0020] If the microseismic monitoring data frequently reaches or exceeds the monitoring and early warning indicators within a preset time period, the effectiveness of rockburst prevention measures is judged to be poor in the microseismic monitoring dimension. The monitoring and early warning indicators are dynamic thresholds established based on historical data.

[0021] Furthermore, when comprehensively judging the effectiveness of rockburst prevention measures based on stress concentration coefficient, vertical distance of stress concentration zone from roadway, and microseismic monitoring data, the judgment results in the microseismic monitoring dimension have the highest weight, the judgment results in the vertical distance dimension have the second highest weight, and the judgment results in the stress dimension have the lowest weight.

[0022] Adjust rockburst prevention measures according to the weight of the judgment results.

[0023] In addition, the first preset stress value is 1, and the second preset stress value is 1.5.

[0024] In addition, the first preset distance value is 6r, the second preset distance value is 4r, r is the equivalent radius, and r is the radius of the outer circle of the tunnel.

[0025] The present invention also proposes an electronic device, comprising:

[0026] At least one processor; and,

[0027] A memory communicatively connected to at least one of the processors; wherein,

[0028] The memory stores instructions that can be executed by at least one of the processors to enable at least one of the processors to perform the rockburst prevention and control measures evaluation method as described in any of the preceding claims.

[0029] The present invention also proposes a storage medium storing computer instructions, which, when executed by a computer, are used to perform the evaluation method for rockburst prevention measures as described in any of the preceding claims.

[0030] This invention addresses the limitations of existing technologies that rely on single indicators to evaluate rockburst prevention measures. By combining monitoring data with simulation software to construct a three-dimensional geological model, it achieves precise quantitative analysis of stress field distribution and identifies rockburst hazard zones that still exist around the mining area after stress relief measures are implemented. Through a closed-loop mechanism of "monitoring-simulation-optimization-verification," using the stress concentration zone within six times the equivalent radius of the roadway as the key threshold, stress relief measures are dynamically optimized, significantly improving the targeting and reliability of prevention measures. Compared to existing technologies, this method upgrades rockburst prevention from qualitative judgment to quantitative decision-making. It not only solves the problem of insufficient matching between stress relief range and stress transfer depth but also forms a technical closed loop through continuous monitoring and verification, making the prevention effect verifiable. This provides standardized technical support for safe mining in deep mines and has significant engineering application value. Attached Figure Description

[0031] Figure 1 A flowchart illustrating an embodiment of the method for evaluating rockburst prevention measures provided by the present invention;

[0032] Figure 2 This is a layout diagram of rockburst prevention measures provided in one embodiment of the present invention;

[0033] Figure 3 A simulation diagram of the simulation software in the rockburst prevention and control measure evaluation method provided in one embodiment of the present invention;

[0034] Figure 4 This is an energy map of microseismic events obtained under the rockburst prevention and control measures in an evaluation method for rockburst prevention and control measures provided in an embodiment of the present invention;

[0035] Figure 5 This is a diagram illustrating the layout of coal-rock synergistic measures in an evaluation method for rockburst prevention and control provided in one embodiment of the present invention.

[0036] Figure 6 A software simulation diagram of coal-rock synergistic measures in an evaluation method for rockburst prevention measures provided in an embodiment of the present invention;

[0037] Figure 7 for Figure 5 Microseismic event energy map after the implementation of coal-rock synergistic measures;

[0038] Figure 8 This is a schematic diagram of an electronic device provided according to an embodiment of the present invention. Detailed Implementation

[0039] The present invention will be further described in detail below with reference to specific embodiments and accompanying drawings. This description is intended only to illustrate specific embodiments of the invention and does not constitute any limitation on the invention. The scope of protection of the invention is defined by the claims.

[0040] Reference Figure 1-4 This invention proposes a method for evaluating rockburst prevention measures, comprising:

[0041] Step S001: Obtain prevention and control measures data, which includes: implementation engineering diagrams of prevention and control measures and various monitoring data;

[0042] Step S002: In the simulation software, establish a three-dimensional model that matches the actual mine, input the geological parameters and prevention and control measures data of the underground rock mass of the mine, and use the simulation software to perform dynamic numerical analysis to obtain numerical simulation results. The numerical simulation results include at least: stress concentration factor and vertical distance of stress concentration zone from roadway.

[0043] Step S003: Obtain microseismic monitoring data within a preset time period after the implementation of prevention and control measures;

[0044] Step S004: Determine the effectiveness of rockburst prevention measures based on the stress concentration factor, the vertical distance of the stress concentration zone from the roadway, and microseismic monitoring data.

[0045] Currently, the evaluation of rockburst prevention and control measures mainly relies on single indicators, utilizing monitoring data or methods such as drill cuttings analysis. Microseismic monitoring analyzes the location of the seismic source, energy release, and frequency changes to determine stress adjustment in the rockburst hazard area. If the prevention and control measures are effective, microseismic events should show a trend of decreasing energy and frequency. Stress monitoring uses borehole stress gauges to directly measure stress changes in the coal and rock mass. If the stress concentration decreases, it indicates that the prevention and control measures are effective. Drill cuttings analysis is a traditional local evaluation method that indirectly reflects the stress state of the coal body through borehole dust discharge and drill cuttings particle size. Existing technical evaluation systems are too simplistic, relying solely on the frequency of microseismic events or indicators, making it difficult to comprehensively reflect the energy accumulation state and stress field reconstruction effect within the coal body. Optimization design of prevention and control measures largely depends on engineering experience, lacking a dynamic closed-loop mechanism and quantitative analysis based on the mechanical response of the coal and rock mass. This leads to insufficient matching between the stress relief range and stress transfer depth, making it difficult to guarantee the actual effectiveness of optimized prevention and control schemes, and failing to achieve full-process linkage of "monitoring-simulation-optimization-verification".

[0046] To address the aforementioned problems, this invention proposes an evaluation method for rockburst prevention measures that combines numerical simulation and monitoring data.

[0047] In step S001, prevention and control measure data is acquired. This data includes: engineering drawings of the prevention and control measures implementation and various monitoring data. The engineering drawings provide specific data for each prevention and control measure. For example, a mine uses a horizontal segmented top coal caving mining method, with each segment height being 25m, including a coal cutting thickness of 3m and a coal caving height of 22m. The upper goaf is backfilled with loess. The rock burst prevention measure is a rock mass measure, which involves drilling three pressure relief boreholes in the rock mass in the B3 and B6 roadways. Various monitoring data include microseismic monitoring data, electromagnetic radiation data, and ground sound data. Acquiring this data prepares the software for simulating the prevention and control measures.

[0048] In step S002, a three-dimensional model consistent with the actual mine is established in the simulation software. Geological parameters and prevention and control measures of the underground rock mass of the mine are input, and dynamic numerical analysis is performed using the simulation software to obtain numerical simulation results. The numerical simulation results include at least: stress concentration factor and vertical distance of stress concentration zone from roadway.

[0049] Numerical simulations are performed using simulation software to simulate the layout and process of existing rockburst prevention measures. Optionally, the prevention measures can be numerically simulated using FLAC 3D software. FLAC 3D is a numerical analysis software for three-dimensional continuous medium modeling in geotechnical mechanics, developed by Itasca Consulting Group. It is mainly used to solve complex geotechnical engineering problems and is widely used in the fields of soil, rock, and concrete analysis.

[0050] The stress concentration factor is, for example, k = 1.72, and the vertical distance from the stress concentration zone to the roadway is, for example, d = 3r. r is the equivalent radius, which is the radius of the circumcircle of the roadway.

[0051] The stress concentration factor directly reflects the degree of stress risk in the surrounding rock. It quantifies the magnitude of stress increase around the tunnel, and its value is directly related to the likelihood of rock failure. When this factor exceeds the strength limit of the surrounding rock, it can easily lead to instability problems such as tunnel deformation, spalling, and roof collapse. For example, when the stress concentration factor reaches 3 in a circular tunnel under a uniaxial stress field, the compressive strength of the surrounding rock mass needs close attention; when the stress concentration factor at the corner of a rectangular tunnel reaches 5-7, it is often a critical area for support design. Therefore, this factor can intuitively determine whether the surrounding rock is in a dangerous stress state and is a core indicator for assessing tunnel stability.

[0052] The vertical distance between the stress concentration zone and the roadway reflects the scope of risk impact and support requirements. This distance determines the spatial location of the stress concentration zone and directly relates to the depth and scope of the support design. If the stress concentration zone is close to the roadway surface (e.g., within 1-2m), it indicates that the shallow surrounding rock is under severe stress, requiring strengthened surface support to resist the concentrated stress. If the distance is greater (e.g., beyond 3-5m), it indicates that the stress is shifting to deeper layers, and the self-supporting capacity of the surrounding rock can be utilized through deep anchoring or other methods. Simultaneously, this distance also reflects the impact range of roadway excavation on the surrounding rock mass, providing a basis for reserving safety space and avoiding secondary disturbances.

[0053] In summary, both parameters comprehensively characterize the stress risk features of the surrounding rock of the roadway from the two dimensions of "stress intensity" and "spatial location," respectively. They are key parameters for judging roadway stability and guiding support design and engineering decisions. For the reasons mentioned above, the stress concentration factor and the vertical distance of the stress concentration zone from the roadway are selected as the basis for judgment.

[0054] In step S003, microseismic monitoring data is acquired within a preset time period after the implementation of prevention and control measures. The preset time period is, for example, one month. By acquiring microseismic monitoring data within a certain period, it is possible to know the number of times the energy of microseismic events in the coal mine exceeds the monitoring and early warning indicators within one month. Based on this frequency, it is judged whether the prevention and control measures are reasonable.

[0055] In step S004, the effectiveness of rockburst prevention measures is judged based on the stress concentration factor, the vertical distance of the stress concentration zone from the roadway, and microseismic monitoring data.

[0056] Since these three factors are important for rockburst prevention and control measures, the effectiveness of these measures is evaluated based on data from these three factors.

[0057] This invention comprehensively evaluates existing prevention and control measures, combines theoretical analysis and numerical models to optimize prevention and control, and verifies the optimization effect through monitoring and early warning indicators, thereby improving the scientific nature and effectiveness of rockburst prevention and control measures. This method provides a reliable technical means for safe production in mines and can be widely applied to rockburst prevention and control and related research in mines, providing important support for industry safety.

[0058] This invention addresses the limitations of existing technologies that rely on single indicators to evaluate rockburst prevention measures. By combining monitoring data with simulation software to construct a three-dimensional geological model, it achieves precise quantitative analysis of stress field distribution and identifies rockburst hazard zones that still exist around the mining area after stress relief measures are implemented. Through a closed-loop mechanism of "monitoring-simulation-optimization-verification," using the stress concentration zone within six times the equivalent radius of the roadway as the key threshold, stress relief measures are dynamically optimized, significantly improving the targeting and reliability of prevention measures. Compared to existing technologies, this method upgrades rockburst prevention from qualitative judgment to quantitative decision-making. It not only solves the problem of insufficient matching between stress relief range and stress transfer depth but also forms a technical closed loop through continuous monitoring and verification, making the prevention effect verifiable. This provides standardized technical support for safe mining in deep mines and has significant engineering application value.

[0059] In one embodiment, the various monitoring data include: microseismic monitoring data, electromagnetic radiation data, and ground sound data. These data provide data support for simulation software to model the deployment of rockburst prevention measures.

[0060] In one embodiment, judging the effectiveness of rockburst prevention measures based on stress concentration factor, vertical distance of stress concentration zone from roadway, and microseismic monitoring data includes:

[0061] If the stress concentration factor is less than the first preset stress value, then the rockburst prevention measures are considered to be effective in terms of stress.

[0062] If the stress concentration factor is greater than or equal to the first preset stress value and less than the second preset stress value, then the rockburst prevention measures are considered to be effective in terms of stress.

[0063] If the stress concentration factor is greater than the second preset stress value, then the effectiveness of the rockburst prevention measures is judged to be poor in terms of stress dimension.

[0064] Optionally, the first preset stress value is 1, and the second preset stress value is 1.5.

[0065] When the stress concentration factor k ≤ 1, it indicates that the prevention and control measures are effective; when the stress concentration factor k ≤ 1.5, it indicates that the prevention and control measures are relatively effective; and when the stress concentration factor k > 1.5, it indicates that the prevention and control measures are ineffective.

[0066] By setting thresholds, the effectiveness of prevention and control can be rated.

[0067] In one embodiment, determining the effectiveness of rockburst prevention measures based on the vertical distance of the stress concentration zone from the roadway includes:

[0068] If the vertical distance between the stress concentration zone and the roadway is greater than the first preset distance value, then the effectiveness of the rockburst prevention measures is judged to be good in the dimension of vertical distance.

[0069] If the vertical distance between the stress concentration zone and the roadway is less than the first preset distance value but greater than the second preset distance value, then the effectiveness of the rockburst prevention measures is judged to be better in the dimension of vertical distance.

[0070] If the vertical distance between the stress concentration zone and the roadway is less than the second preset distance value, then the effectiveness of the rockburst prevention measures is judged to be poor in the dimension of vertical distance.

[0071] Optionally, the first preset distance is 6r, and the second preset distance is 4r. r is the equivalent radius, which refers to the radius of the circumcircle of the roadway. When the vertical distance d from the stress concentration zone to the roadway is greater than 6r, it indicates that the prevention and control measures are effective. When the vertical distance 4r from the stress concentration zone to the roadway is less than d and less than 6r, it indicates that the prevention and control measures are relatively effective. When the vertical distance d from the stress concentration zone to the roadway is less than 4r, it indicates that the prevention and control measures are ineffective.

[0072] By setting thresholds, the effectiveness of prevention and control measures can be graded.

[0073] In one embodiment, determining the effectiveness of rockburst prevention measures based on microseismic monitoring data includes:

[0074] If microseismic monitoring data frequently reaches or exceeds monitoring and early warning indicators within a preset time period, the effectiveness of rockburst prevention measures is judged to be poor from the perspective of microseismic monitoring. The monitoring and early warning indicators are dynamic thresholds established based on historical data. For example, the monitoring and early warning indicator could be 1×10⁻⁶. 4 J. Microseismic monitoring data is crucial for assessing the effectiveness of rockburst prevention measures, therefore, it is necessary to monitor it.

[0075] In one embodiment, when judging the effectiveness of rockburst prevention measures based on stress concentration factor, vertical distance of stress concentration zone from roadway and microseismic monitoring data, the judgment result in the microseismic monitoring dimension has the highest weight, the judgment result in the vertical distance dimension has the second highest weight, and the judgment result in the stress dimension has the lowest weight.

[0076] Adjust rockburst prevention measures according to the weight of the judgment results.

[0077] Since microseismic monitoring data represents actual values ​​obtained from monitoring instruments in coal mines, it has the highest weight. If the energy of microseismic events in a mine exceeds the monitoring and early warning indicators multiple times within a month, the prevention and control measures can be directly considered ineffective, or given a weight of 0.7. If, based on the simulation results provided by the simulation software, the vertical distance d from the stress concentration zone to the roadway is ≤4r, the existing prevention and control measures are considered ineffective, and the weight of the vertical distance from the stress concentration zone to the roadway is 0.2. When the stress concentration coefficient k > 1.5 based on the simulation results provided by the simulation software, the prevention and control measures are considered ineffective, and a stress concentration zone still exists below the roadway; the weight of the stress concentration coefficient can be 0.1.

[0078] By combining simulated numerical results with real monitoring data to evaluate prevention and control measures, the judgment becomes more accurate.

[0079] Reference Figure 5-6 Optionally, the optimization method for rockburst prevention measures includes: based on whether the vertical distance between the stress concentration zone and the roadway is within 6 times the equivalent radius, implementing supplementary prevention measures in the corresponding area at the bottom of the roadway to obtain an optimized coal-rock synergistic measures scheme, performing a numerical model on the optimized measures scheme, and judging the implementation effect of the optimized prevention measures.

[0080] In one embodiment, the first preset stress value is 1, and the second preset stress value is 1.5.

[0081] Based on 3D simulations and engineering data, a first preset stress value of 1 and a second preset stress value of 1.5 are set. By setting thresholds, the effectiveness of prevention and control measures can be evaluated in a graded manner.

[0082] In one embodiment, the first preset distance value is 6r, and the second preset distance value is 4r, where r is the equivalent radius, and r is the radius of the circumcircle of the roadway. Based on 3D simulation and engineering data experience, the first preset distance value is set to 6r, and the second preset distance value is set to 4r. By setting thresholds, the effectiveness of prevention and control measures can be evaluated in a graded manner.

[0083] When optimizing the prevention and control measures, it is necessary to determine whether the vertical distance between the stress concentration area and the roadway is within 6 times the equivalent radius. If the vertical distance between the stress concentration area and the roadway is within 6 times the equivalent radius, additional prevention and control measures need to be taken for the stress concentration area. The purpose is to eliminate the stress concentration area within 6 times the equivalent radius of the roadway. This optimization plan can reduce the threat of rockburst to the roadway and working face during mining.

[0084] The optimized measures were modeled numerically to assess their effectiveness. Specifically, the optimized measures were integrated into a three-dimensional geological model to construct a refined numerical model containing the optimized measures. The model focused on whether stress concentration zones within a vertical distance of 6 times the equivalent radius from the roadway were completely eliminated. The reduction in peak and range of horizontal and vertical stress before and after optimization was compared and analyzed to evaluate its effectiveness.

[0085] To test the effectiveness of the optimized prevention and control measures in a real-world environment, the optimized measures were implemented in mine roadways. After implementation, continuous monitoring of the target area was conducted for 30 days, and the monitoring data was collected by the system. If the monitoring data did not reach or exceed the monitoring and early warning indicators, the optimized measures were considered effective.

[0086] Reference Figure 5-7 In one embodiment, an example is given of an evaluation method for rockburst prevention measures, and the process of optimizing prevention measures based on the evaluation, re-simulating and verifying the results, and finally implementing the prevention measures:

[0087] The first step is to obtain detailed data on rockburst prevention measures. A certain mine uses a horizontal segmented top-coal caving mining method, with each segment 25m high, including a 3m cutting thickness and a 22m caving height. The upper goaf is backfilled with loess, representing a high-mining-to-caving ratio top-coal caving mining method. Furthermore, it employs an upward mining and downward tunneling development method, typically with mining and tunneling activities occurring simultaneously. Rockburst prevention measures include rock mass measures, such as drilling three pressure relief boreholes in the rock mass in roadways B3 and B6. Figure 2 As shown.

[0088] Step 2: Use software to perform numerical simulation to simulate existing rockburst prevention measures; perform numerical simulation of the prevention measures in FLAC3D software to determine the stress concentration factor k = 1.72 and the vertical distance d = 3r from the stress concentration zone to the roadway.

[0089] Step 3: A comprehensive evaluation of the effectiveness of existing prevention and control measures is conducted by combining numerical simulation results with actual rockburst monitoring data. The microseismic data from a certain working face of the mine one month after implementing existing prevention and control measures are as follows: Figure 3As shown, the monitoring and early warning index for this mine is 1×10⁴ J. Analysis of the microseismic events in the mine within one month revealed that the energy exceeded the monitoring and early warning index multiple times, indicating that the prevention and control measures were not implemented effectively. When the stress concentration coefficient k > 1.5, the existing prevention and control measures are deemed ineffective. A stress concentration zone still exists below the roadway, and the vertical distance d from the stress concentration zone to the roadway is ≤ 4r, further indicating that the existing prevention and control measures are ineffective.

[0090] Step 4: Optimize prevention and control measures through theoretical analysis; based on whether the vertical distance between the stress concentration zone and the roadway is within 6 times the equivalent radius, implement supplementary prevention and control measures in the corresponding area at the bottom of the roadway, thus obtaining an optimized coal-rock synergistic measure scheme, such as... Figure 5 As shown.

[0091] Step 5: Develop a numerical model of the optimized prevention and control measures using software to assess their effectiveness. This includes numerically simulating the coal-rock synergistic measures, such as... Figure 6 The stress concentration zone within 6 times the equivalent radius at a vertical distance from the roadway has been eliminated.

[0092] Step 6: Implement the optimized rockburst prevention measures in the coal mine and verify their effectiveness. Collect monitoring data on the optimized prevention measures to test their effectiveness. It was found that after implementing the optimized measures, the energy of microseismic events no longer exceeded the monitoring and early warning indicators. Figure 7 Verification complete.

[0093] Reference Figure 8 The present invention also proposes a hardware structure diagram of an electronic device, comprising:

[0094] At least one processor 301; and,

[0095] A memory 302 communicatively connected to at least one of the processors 301; wherein,

[0096] The memory 302 stores instructions that can be executed by at least one processor, which enables the at least one processor to perform the aforementioned evaluation method for rockburst prevention measures.

[0097] Figure 8 Take processor 301 as an example.

[0098] The electronic device is preferably a coal controller. The electronic device may also include an input device 303 and a display device 304.

[0099] The processor 301, memory 302, input device 303 and display device 304 can be connected by a bus or other means. The figure shows an example of connection by bus.

[0100] The memory 302, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as the program instructions / modules corresponding to the rockburst prevention and control measure evaluation method in the embodiments of this application, for example, Figure 1 The method flow is shown. The processor 301 executes various functional applications and data processing by running non-volatile software programs, instructions, and modules stored in the memory 302, thereby realizing the rockburst prevention and control measure evaluation method in the above embodiments.

[0101] The memory 302 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the rockburst prevention and mitigation measures evaluation method. Furthermore, the memory 302 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 302 may optionally include memory remotely located relative to the processor 301, and these remote memories can be connected via a network to the apparatus performing the rockburst prevention and mitigation measures evaluation method. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0102] The input device 303 can receive user clicks and generate signal inputs related to user settings and function control of the evaluation method for rockburst prevention measures. The display device 304 may include display screens or other display devices.

[0103] One or more modules are stored in the memory 302, and when run by one or more processors 301, the rockburst prevention and control measure evaluation method in any of the above method embodiments is executed.

[0104] This invention addresses the limitations of existing technologies that rely on single indicators to evaluate rockburst prevention measures. By combining monitoring data with simulation software to construct a three-dimensional geological model, it achieves precise quantitative analysis of stress field distribution and identifies rockburst hazard zones that still exist around the mining area after stress relief measures are implemented. Through a closed-loop mechanism of "monitoring-simulation-optimization-verification," using the stress concentration zone within six times the equivalent radius of the roadway as the key threshold, stress relief measures are dynamically optimized, significantly improving the targeting and reliability of prevention measures. Compared to existing technologies, this method upgrades rockburst prevention from qualitative judgment to quantitative decision-making. It not only solves the problem of insufficient matching between stress relief range and stress transfer depth but also forms a technical closed loop through continuous monitoring and verification, making the prevention effect verifiable. This provides standardized technical support for safe mining in deep mines and has significant engineering application value.

[0105] One embodiment of the present invention provides a storage medium that stores computer instructions, which, when executed by a computer, are used to perform all the steps of the rockburst prevention and control measure evaluation method as described above.

[0106] In the context of this disclosure, a storage medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. The storage medium can be a machine-readable signal medium or a machine-readable storage medium. Optionally, the storage medium can be a non-transitory computer-readable storage medium, such as a ROM, random access memory (RAM), compact disc ROM (CD-ROM), magnetic tape, floppy disk, and optical data storage device.

[0107] This invention addresses the limitations of existing technologies that rely on single indicators to evaluate rockburst prevention measures. By combining monitoring data with simulation software to construct a three-dimensional geological model, it achieves precise quantitative analysis of stress field distribution and identifies rockburst hazard zones that still exist around the mining area after stress relief measures are implemented. Through a closed-loop mechanism of "monitoring-simulation-optimization-verification," using the stress concentration zone within six times the equivalent radius of the roadway as the key threshold, stress relief measures are dynamically optimized, significantly improving the targeting and reliability of prevention measures. Compared to existing technologies, this method upgrades rockburst prevention from qualitative judgment to quantitative decision-making. It not only solves the problem of insufficient matching between stress relief range and stress transfer depth but also forms a technical closed loop through continuous monitoring and verification, making the prevention effect verifiable. This provides standardized technical support for safe mining in deep mines and has significant engineering application value.

[0108] The above description is merely the principle and preferred embodiment of the present invention. It should be noted that, for those skilled in the art, several other modifications can be made based on the principle of the present invention, and these modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for evaluating rockburst prevention measures, characterized in that, include: Acquire data on prevention and control measures, including: engineering diagrams of the implementation of prevention and control measures and various monitoring data; In the simulation software, a three-dimensional model that matches the actual mine is established, geological parameters of the underground rock mass and prevention and control measures data are input, and dynamic numerical analysis is performed using the simulation software to obtain numerical simulation results. The numerical simulation results include at least: stress concentration factor and vertical distance of stress concentration zone from roadway. Obtain microseismic monitoring data within a preset time period after the implementation of prevention and control measures; The effectiveness of rockburst prevention measures is judged based on the stress concentration factor, the vertical distance of the stress concentration zone from the roadway, and microseismic monitoring data.

2. The evaluation method for rockburst prevention measures according to claim 1, characterized in that, The various monitoring data include: microseismic monitoring data, electromagnetic radiation data, and ground sound data.

3. The evaluation method for rockburst prevention measures according to claim 1, characterized in that, The assessment of the effectiveness of rockburst prevention measures based on stress concentration factor, vertical distance of stress concentration zone from roadway, and microseismic monitoring data includes: If the stress concentration factor is less than the first preset stress value, then the rockburst prevention measures are considered to be effective in terms of stress. If the stress concentration factor is greater than or equal to the first preset stress value and less than the second preset stress value, then the rockburst prevention measures are considered to be effective in terms of stress. If the stress concentration factor is greater than the second preset stress value, then the effectiveness of the rockburst prevention measures is judged to be poor in terms of stress dimension.

4. The evaluation method for rockburst prevention measures according to claim 3, characterized in that, Judging the effectiveness of rockburst prevention measures based on the vertical distance between the stress concentration zone and the roadway includes: If the vertical distance between the stress concentration zone and the roadway is greater than the first preset distance value, then the effectiveness of the rockburst prevention measures is judged to be good in the dimension of vertical distance. If the vertical distance between the stress concentration zone and the roadway is less than the first preset distance value but greater than the second preset distance value, then the effectiveness of the rockburst prevention measures is judged to be better in the dimension of vertical distance. If the vertical distance between the stress concentration zone and the roadway is less than the second preset distance value, then the effectiveness of the rockburst prevention measures is judged to be poor in the dimension of vertical distance.

5. The evaluation method for rockburst prevention measures according to claim 4, characterized in that, Judging the effectiveness of rockburst prevention measures based on microseismic monitoring data includes: If the microseismic monitoring data frequently reaches or exceeds the monitoring and early warning indicators within a preset time period, the effectiveness of rockburst prevention measures is judged to be poor in the microseismic monitoring dimension. The monitoring and early warning indicators are dynamic thresholds established based on historical data.

6. The evaluation method for rockburst prevention measures according to claim 5, characterized in that, When comprehensively judging the effectiveness of rockburst prevention measures based on stress concentration factor, vertical distance of stress concentration zone from roadway and microseismic monitoring data, the judgment result in the microseismic monitoring dimension has the highest weight, the judgment result in the vertical distance dimension has the second highest weight, and the judgment result in the stress dimension has the lowest weight. Adjust rockburst prevention measures according to the weight of the judgment results.

7. The evaluation method for rockburst prevention measures according to claim 5, characterized in that, The first preset stress value is 1, and the second preset stress value is 1.

5.

8. The evaluation method for rockburst prevention measures according to claim 5, characterized in that, The first preset distance value is 6r, and the second preset distance value is 4r, where r is the equivalent radius, and r is the radius of the circumcircle of the tunnel.

9. An electronic device, characterized in that, include: at least one processor; as well as, A memory communicatively connected to at least one of the processors; wherein, The memory stores instructions that can be executed by at least one of the processors, which, when executed by at least one of the processors, enable the at least one of the processors to perform the rockburst prevention and control measure evaluation method as described in any one of claims 1 to 8.

10. A storage medium, characterized in that, The storage medium stores computer instructions, which, when executed by the computer, are used to perform the evaluation method for rockburst prevention measures as described in any one of claims 1 to 8.

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