Rock burst active prevention and control method, system, electronic device and storage medium

Abstract

The present disclosure relates to the technical field of coal mining safety, and particularly relates to a rock burst active prevention and control method and system, an electronic device and a storage medium. The method comprises the following steps: obtaining a coal rock mass microstructure parameter, and predicting a rock burst parameter according to the coal rock mass microstructure parameter; determining a control parameter combination and a control target corresponding to the rock burst parameter according to a three-dimensional mapping relationship among a microstructure state, the control target and the control parameter combination; controlling the coal rock mass according to the control parameter combination to obtain a control result, and adjusting the control parameter combination until the control result meets the control target if the control result does not meet the control target. The present disclosure adopting the above scheme can realize active prevention and control of rock burst.

Description

Technical Field

[0001] This disclosure relates to the field of coal mine safety technology, and in particular to an active prevention and control method, system, electronic device and storage medium for rockburst. Background Technology

[0002] Rockburst is a sudden and violent destructive dynamic phenomenon caused by the instantaneous release of elastic deformation energy in the coal and rock mass surrounding the mine roadway or working face. It is often accompanied by instantaneous displacement and ejection of coal and rock mass, loud noises, and blast waves. Its occurrence mechanism is complex and influenced by numerous factors, seriously threatening the safe and efficient mining of deep coal mines. With the continuous increase in the depth and intensity of coal mining, the problem of rockburst is becoming increasingly prominent. Therefore, how to proactively prevent and control rockburst has become a key focus of attention. Summary of the Invention

[0003] This disclosure aims to at least partially address one of the technical problems in the related art.

[0004] Therefore, the first objective of this disclosure is to propose a proactive method for preventing and controlling rockbursts.

[0005] The second objective of this disclosure is to propose an active rockburst prevention and control system.

[0006] The third objective of this disclosure is to propose an electronic device.

[0007] The fourth objective of this disclosure is to provide a computer-readable storage medium.

[0008] The fifth objective of this disclosure is to provide a computer program product.

[0009] To achieve the above objectives, the first aspect of this disclosure proposes an active method for preventing and controlling rockbursts, comprising:

[0010] Obtain the microstructure parameters of the coal and rock mass, and predict the rockburst parameters based on the microstructure parameters of the coal and rock mass;

[0011] Based on the three-dimensional mapping relationship between the microstructure state, the control target, and the combination of control parameters, the combination of control parameters and the control target corresponding to the rockburst parameters are determined.

[0012] The coal and rock mass is regulated according to the combination of regulation parameters to obtain the regulation result. If the regulation result does not meet the regulation target, the combination of regulation parameters is adjusted until the regulation result meets the regulation target.

[0013] Optionally, the step of predicting rockburst parameters based on the coal and rock mass microstructure parameters includes:

[0014] Construct a quantitative relationship model between coal and rock mass microstructure parameters and rockburst parameters;

[0015] The coal and rock mass microstructure parameters are input into the quantitative relationship model to predict the rockburst parameters.

[0016] Optionally, the construction of a quantitative relationship model between coal and rock mass microstructure parameters and rockburst parameters includes:

[0017] Multi-field coupled in-situ computed tomography (CT) scanners were used to acquire information on the microstructure changes inside coal and rock masses under different environments, and the frequency of rockbursts in coal mines corresponding to the microstructure changes was monitored.

[0018] Digital image analysis is performed on the microstructure change information to obtain a quantitative characterization strategy for the microstructure parameters of coal and rock mass;

[0019] Data mining analysis was performed on the microstructure change information to obtain the correlation between the microstructure parameters and the mechanical parameters of the coal and rock mass;

[0020] Based on the quantitative characterization strategy, the correlation, and the frequency of rockburst occurrence in the coal mine, a quantitative relationship model between the microstructure parameters of coal and rock mass and the rockburst parameters is constructed.

[0021] Optionally, before determining the combination of control parameters and the control target corresponding to the rockburst parameter based on the three-dimensional mapping relationship between the microstructure state, the control target, and the combination of control parameters, the method further includes:

[0022] Based on historical data of microstructure parameters and corresponding control effect data, combined with the threshold of critical parameters for rockburst occurrence, a three-dimensional mapping relationship between microstructure state, control target, and parameter combination is constructed.

[0023] Optionally, determining the combination of control parameters and the control target corresponding to the rockburst parameter based on the three-dimensional mapping relationship between the microstructure state, the control target, and the combination of control parameters includes:

[0024] Determine the coal and rock type and microstructure state of the area corresponding to the rockburst parameters, wherein the rockburst parameters include the probability of rockburst occurrence, the location of occurrence, and the intensity of occurrence;

[0025] Based on the three-dimensional mapping relationship between the microstructure state, the control target, and the combination of control parameters, the combination of control parameters and the control target corresponding to the coal and rock type and the microstructure state are determined.

[0026] Optionally, determining the combination of control parameters and the control target corresponding to the coal and rock type and the microstructure state based on the three-dimensional mapping relationship between the microstructure state, the control target, and the combination of control parameters includes:

[0027] The machine learning model is invoked to output the combination of control parameters and control targets corresponding to the coal and rock type and the microstructure state. The machine learning model is constructed based on the three-dimensional mapping relationship between the microstructure state, the control target, and the combination of control parameters.

[0028] Optionally, adjusting the combination of control parameters includes:

[0029] Determine the difference between the measured values ​​of the coal and rock mass microstructure parameters and the target values ​​of the coal and rock mass microstructure parameters indicated by the control target;

[0030] If the difference is greater than the difference threshold, then the parameter compensation strategy corresponding to the control target is determined, and the control parameter combination is adjusted according to the parameter compensation strategy.

[0031] To achieve the above objectives, a second aspect of this disclosure provides an active rockburst prevention and control system, comprising:

[0032] The parameter acquisition unit is used to acquire the microstructure parameters of the coal and rock mass and predict the rockburst parameters based on the microstructure parameters of the coal and rock mass.

[0033] The target determination unit is used to determine the combination of control parameters and the control target corresponding to the rockburst parameter based on the three-dimensional mapping relationship between the microstructure state, the control target, and the combination of control parameters.

[0034] The parameter control unit is used to control the coal and rock mass according to the control parameter combination, obtain the control result, and adjust the control parameter combination if the control result does not meet the control target until the control result meets the control target.

[0035] To achieve the above objectives, a third aspect of this disclosure provides an electronic device comprising:

[0036] Memory, used to store executable program code;

[0037] A processor for calling and running the executable program code from the memory, causing the electronic device to perform the method shown in any of the first aspects above.

[0038] To achieve the above objectives, a fourth aspect of this disclosure provides a computer-readable storage medium storing a computer program that, when executed, implements the method shown in any of the first aspects above.

[0039] To achieve the above objectives, a fifth aspect of this disclosure provides a computer program product including a computer program that, when executed by a processor, implements the method shown in any of the first aspects above.

[0040] In summary, the methods, systems, electronic devices, and storage media provided in this disclosure achieve proactive prevention and control of rockbursts by optimizing and controlling the microstructure parameters of coal and rock masses. This realizes a closed-loop process from "microstructure parameter monitoring - rockburst prediction - intelligent generation of control parameters - real-time feedback correction," ensuring that the control parameters are accurately matched with the real-time state of the coal and rock masses, and minimizing the risk of rockbursts.

[0041] Additional aspects and advantages of this disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this disclosure. Attached Figure Description

[0042] The above and / or additional aspects and advantages of this disclosure will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, in which:

[0043] Figure 1 This is a schematic flowchart of an active rockburst prevention and control method provided in an embodiment of this disclosure;

[0044] Figure 2 This is a schematic diagram of the structure of an active rockburst control system provided in an embodiment of this disclosure. Detailed Implementation

[0045] Embodiments of this disclosure are described in detail below. Examples of these embodiments are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this disclosure, and should not be construed as limiting this disclosure.

[0046] Traditional methods for controlling rockbursts have many drawbacks. On the one hand, they require a large number of workers, increasing mining costs; on the other hand, the number of workers and production scale in rockburst-prone coal seams are strictly controlled, significantly reducing coal mine production efficiency. Furthermore, deep rockbursts are often accompanied by other complex disasters, further increasing the difficulty of control. Currently, some rockburst control technologies exist, such as borehole decompression, controlling mining speed, and strengthening support. However, these technologies are mostly passive responses and still have the following problems: a lack of microscopic mechanism support, failing to consider the direct impact of coal and rock mass microstructure (pores, fractures, mineral distribution) on energy accumulation and release, resulting in insufficient targeted control measures; existing decompression technology parameters (such as borehole spacing and water injection volume) are mostly based on experience and cannot be dynamically adjusted according to real-time microstructure evolution; the correlation mechanism between macroscopic stress field and microscopic damage is not yet clear, making it difficult to achieve a "proactive intervention-precise prediction" closed loop. Therefore, existing technologies cannot fundamentally solve the rockburst problem, and developing a new, efficient, and proactive rockburst control technology is of significant practical importance.

[0047] The present disclosure will now be described in detail with reference to specific embodiments.

[0048] In the first embodiment, such as Figure 1 As shown, Figure 1 This is a flowchart illustrating an active rockburst control method provided in an embodiment of this disclosure. The method can be implemented using a computer program and can run on a system for active rockburst control. The computer program can be integrated into an application or run as a standalone utility application.

[0049] This active rockburst prevention and control method can be implemented by electronic equipment.

[0050] For example, this active rockburst prevention method includes the following steps:

[0051] S101, obtain the microstructure parameters of the coal and rock mass, and predict the rockburst parameters based on the microstructure parameters of the coal and rock mass;

[0052] According to some embodiments, coal and rock mass microstructure parameters refer to the microstructure parameters within the coal and rock mass. These coal and rock mass microstructure parameters include, but are not limited to, the coal and rock mass fracture ratio K. T Fractal dimension of fracture, porosity φ, fractal dimension of pore D f Fracture connectivity and mineral interface bonding strength σ c Parameters such as these.

[0053] In some embodiments, rockburst parameters refer to parameters related to rockburst low pressure. These rockburst parameters include, but are not limited to, the probability of rockburst occurrence P. r Parameters such as occurrence location P(x,y,z) and occurrence intensity I.

[0054] S102, Based on the three-dimensional mapping relationship between the microstructure state, the control target, and the combination of control parameters, determine the combination of control parameters and the control target corresponding to the rockburst parameters;

[0055] According to some embodiments, the microstructure state is used to indicate the state of the microstructure inside the coal and rock mass.

[0056] In some embodiments, the control target is used to indicate the target to which the microstructure inside the coal and rock mass needs to be controlled.

[0057] In some embodiments, the combination of control parameters includes at least one control parameter. A control parameter refers to a parameter among the microstructure parameters of the coal and rock mass that needs to be controlled.

[0058] S103, the coal and rock mass is regulated according to the combination of regulation parameters to obtain the regulation result. If the regulation result does not meet the regulation target, the combination of regulation parameters is adjusted until the regulation result meets the regulation target.

[0059] According to some implementation methods, if the control results meet the control objectives, it indicates that the active prevention and control of rockburst has been completed.

[0060] In summary, the method provided in this embodiment achieves proactive prevention and control of rockbursts by optimizing and controlling the microstructure parameters of the coal and rock mass. It realizes a closed loop of the entire process from "microstructure parameter monitoring - rockburst prediction - intelligent generation of control parameters - real-time feedback correction", which can ensure that the control parameters are accurately matched with the real-time state of the coal and rock mass, and can minimize the risk of rockbursts.

[0061] Another embodiment of this disclosure provides a method for active prevention and control of rockbursts. This method can be executed by electronic equipment.

[0062] For example, this active rockburst prevention method may include the following steps:

[0063] S201, obtain microstructure parameters of coal and rock mass;

[0064] According to some embodiments, in-situ sampling can be performed underground to conduct in-situ computed tomography (CT) scans to obtain microstructural parameters of coal and rock masses.

[0065] S202, construct a quantitative relationship model between coal and rock mass microstructure parameters and rockburst parameters, and input the coal and rock mass microstructure parameters into the quantitative relationship model to predict the rockburst parameters;

[0066] According to some embodiments, multi-field coupled in-situ computed tomography (CT) scanners can be used to acquire information on microstructural changes within coal and rock masses under different environments, and monitor the frequency of rockbursts in coal mines corresponding to these microstructural changes. Digital image analysis of the microstructural changes yields a quantitative characterization strategy for the microstructural parameters of the coal and rock mass. Data mining analysis of the microstructural changes reveals the correlation between the microstructural parameters and the mechanical parameters of the coal and rock mass. Based on the quantitative characterization strategy, the correlation, and the frequency of rockbursts in coal mines, a quantitative relationship model between the microstructural parameters and the rockburst parameters is constructed.

[0067] In some embodiments, different environments include, but are not limited to, stress, seepage, high and low temperature environments.

[0068] In some embodiments, microstructure change information includes, but is not limited to, information on the initiation, propagation, and connection of microcracks within the coal and rock mass.

[0069] In some embodiments, the mechanical parameters of coal and rock mass include, but are not limited to, the stress, strain, and other mechanical parameters of coal and rock mass.

[0070] In some embodiments, a three-dimensional monitoring network can be formed using various methods such as microseismic monitoring, ground sound monitoring, and ultrasonic monitoring to ensure comprehensive coverage of the monitoring area and obtain the frequency of rockbursts at the coal mine site. The monitoring sensors can be calibrated and maintained regularly to ensure the accuracy and reliability of the monitoring data.

[0071] It should be noted that digital image analysis technology can accurately establish quantitative characterization methods for microstructure parameters, improving the accuracy of determining quantitative characterization strategies. By utilizing data processing and analysis algorithms to deeply mine and analyze monitoring data, the correlation between coal and rock mass microstructure parameters and coal and rock mass mechanical parameters can be analyzed, accurately obtaining the correlation between these parameters.

[0072] According to some embodiments, the quantitative relationship model between the microstructure parameters of coal and rock mass and the rockburst parameters can be used to analyze the changing trend of the microstructure parameters of coal and rock mass, and predict the possibility, location and intensity of rockburst based on the analysis results.

[0073] In some embodiments, the acquired data can be transmitted to a data processing center in real time, and the data can be preprocessed using relevant data processing software, including data filtering, noise reduction, and outlier removal. Then, relevant data mining and analysis algorithms are used to perform in-depth analysis on the preprocessed data, extract the variation characteristics of coal and rock mass microstructure parameters, and input them into the established quantitative relationship model to predict the probability, location, and intensity of rockburst.

[0074] S203. Based on historical data of microstructure parameters and corresponding control effect data of microstructure parameters, combined with the critical parameter threshold for rockburst occurrence, a three-dimensional mapping relationship between microstructure state, control target, and parameter combination is constructed.

[0075] According to some embodiments, the control effect data include, but are not limited to, fracture penetration after fracturing and σ after nano-grouting. c Data such as improvement rate.

[0076] In some embodiments, historical data of microstructure parameters and corresponding control effect data can be imported into a database to construct a mapping database between microstructure parameters and control parameters. Then, based on this mapping database, and combined with the critical parameter threshold for rockburst occurrence, a three-dimensional mapping relationship between microstructure state, control target, and parameter combination can be constructed.

[0077] According to some embodiments, the critical parameter threshold for rockburst occurrence refers to the critical parameter threshold at which a rockburst occurs. This critical parameter threshold can be determined, for example, through laboratory coal and rock sample tests. For example, K... T When the value is greater than 15, the risk level of rockburst increases sharply, and K can be adjusted accordingly. T A value of 15 is used as the threshold for the critical parameter of rockburst occurrence.

[0078] In some embodiments, the risk level of rockburst occurrence refers to the rockburst tendency classification of the coal and rock mass. This risk level can be determined by rockburst parameters, including the probability of occurrence P. r The location of occurrence P(x,y,z) and the intensity of occurrence I are determined.

[0079] In some embodiments, laboratory coal and rock sample tests can be used to determine the combination of control parameters corresponding to different impact risk levels. For example, for low-risk areas, the combination of control parameters and the target value of the coal and rock mass microstructure parameters corresponding to each control parameter can be φ = 8% to 12%, σ c >5MPa (to avoid excessive weakening leading to roadway instability); for medium-risk areas, the combination of control parameters and the target value of the coal and rock mass microstructure parameters corresponding to each control parameter can be φ = 15%~20% (to increase energy dissipation channels), σ c Reduce by 20% (to control the elastic modulus); for high-risk areas, the combination of control parameters and the target value of the coal and rock mass microstructure parameters corresponding to each control parameter can be forced to φ>25% (to form a network of interconnected fractures), σ c Reduced by more than 40% (used to significantly reduce the tendency to shock).

[0080] S204, determine the coal and rock type and microstructure state of the area corresponding to the rockburst parameters;

[0081] According to some embodiments, the predicted rockburst parameters, such as the probability of rockburst occurrence P, can be input. r The occurrence location P(x,y,z) and occurrence intensity I are matched with the coal and rock type and microstructure state of the corresponding region.

[0082] In some embodiments, the coal and rock types include, but are not limited to, anthracite, lignite, etc.

[0083] In some embodiments, when matching the microstructure state of a corresponding region, it can be obtained by matching the microstructure parameters of the coal and rock mass corresponding to the rockburst parameters.

[0084] S205, based on the three-dimensional mapping relationship between microstructure state, control target, and control parameter combination, determine the control parameter combination and control target corresponding to coal and rock type and microstructure state;

[0085] According to some embodiments, machine learning models can be invoked to output combinations of control parameters and control targets corresponding to coal and rock types and microstructure states.

[0086] In some embodiments, the machine learning model is constructed based on a three-dimensional mapping relationship between the microstructure state, the control objective, and the combination of control parameters. This machine learning model can be, for example, a random forest regression model.

[0087] S206, the coal and rock mass is regulated according to the combination of regulation parameters to obtain the regulation result, and if the regulation result does not meet the regulation target, the combination of regulation parameters is adjusted until the regulation result meets the regulation target.

[0088] In some embodiments, the control results can be obtained, for example, through on-site monitoring feedback. These control results include data such as changes in the frequency of microseismic events during fracturing and measured values ​​of coal and rock mass microstructure parameters.

[0089] For example, a machine learning model (such as random forest regression) is invoked, and the output combination of control parameters, along with the target values ​​for the microstructure parameters of the coal and rock mass corresponding to each control parameter, are: nano-grouting flow rate Q = 50–80 L / min and fracturing pressure P = 8–12 MPa. The control target is, for example, based on the "impact energy index W". ET Reduced to a safe threshold The following is the core objective, and the optimization function is constructed based on the combination of control parameters and the target values ​​of the coal and rock mass microstructure parameters corresponding to each control parameter:

[0090]

[0091] Where, φ 目标 These are the target values ​​for the microstructure parameters of the coal and rock mass: Δφ and Δσ. c It is a microstructure parameter control quantity, coupled with physical quantities such as nano-grouting pressure and fracturing fluid flow rate.

[0092] In this case, if the impact energy index W ET Greater than the safety threshold The combination of control parameters can then be dynamically adjusted using the Particle Swarm Optimization (PSO) algorithm until the impact energy index W is reached. ET Reduced to a safe threshold the following.

[0093] Taking one scenario as an example, the injection parameters for the nano-grouting method include the injection pressure P. 注 Traffic Q 注 Nanofluid concentration C 纳米 (SiO2-Al2O3 composite ratio).

[0094] Among them, based on the porosity threshold, if the current φ < 8% (crack closure risk zone), it indicates that P needs to be increased. 注 =10~15MPa (used to overcome micropore throats), C 纳米 =3%~5% (for enhancing interfacial adhesion); if φ>15% (high penetration zone), reduce P injection =5~8MPa (to avoid overfilling), C 纳米 =2% to 3%.

[0095] Among them, σ can be measured by pull-out test based on the mineral interface strength. c Target value, reverse calculation of C 纳米 σ c =a·C 纳米 +b. Where the fitting coefficients a = 1.2 and b = 3.5, can be obtained based on anthracite sample data. For example, σ c When the target value increases by 50%, C 纳米 =4.2%.

[0096] Among these, based on spatial differentiation, for predicted high-risk impact locations, such as P(x,y,z) near faults, a "partitioned progressive injection" approach can be adopted: first, with Q... 注 =100L / min to quickly establish the channel, then with Q 注 =30L / min low-speed infiltration ensures uniform distribution of nanofluid in microcracks.

[0097] Taking a scenario as an example, for directional hydraulic fracturing parameters, the corresponding control parameter combination includes the fracturing pressure threshold P. 裂 Crack spacing d 裂 、Fracturing fluid viscosity μ.

[0098] Among them, based on crack propagation matching, the fractal dimension D of the original crack in the CT scan can be used. f , set d 裂 =5×(2-D) f )m, where, when D f When d = 1.8, 裂 =1m, used to promote the connection between artificial cracks and original cracks;

[0099] P 裂 =σ3+0.8×(σ1-σ3), where σ1 and σ3 are the measured values ​​of in-situ ground stress, which can ensure crack initiation along the direction of minimum principal stress.

[0100] Among them, based on the energy dissipation target, if the predicted impact intensity I > 5, the fracture ratio K after fracturing needs to be increased. T To increase this to over 25%, the required total fracturing fluid volume V=K can be calculated using numerical simulations (such as RFPA software). T ×V coal body×α, where α=1.2 is a correction coefficient, and the flow rate is distributed according to Q=V / t, where t is the fracturing time, which is controlled within 30~60min to avoid stress concentration.

[0101] Among them, based on the energy E of microseismic events 微震 E can be monitored in real time during fracturing. 微震 If E 微震 >10 4 J / time indicates excessive crack propagation; reduce P. 裂 =2MPa; if E 微震 <10 3 J / time, increase P 裂 =3MPa, until the microseismic energy distribution is uniform (target 10) 3 ~10 4 J / time).

[0102] It should be noted that by combining directional hydraulic fracturing technology with grouting technology, the microstructural parameters of coal and rock masses can be synergistically optimized and controlled. This innovative control method can more effectively alter the mechanical properties and energy storage and release characteristics of coal and rock masses, thereby improving the effectiveness of rockburst control.

[0103] According to some embodiments, when adjusting the combination of control parameters, parameter adjustment can be performed in a data-driven manner. For example, the difference between the measured value of the coal and rock mass microstructure parameters and the target value of the coal and rock mass microstructure parameters indicated by the control target can be determined; if the difference is greater than the difference threshold, a parameter compensation strategy corresponding to the control target is determined, and the combination of control parameters is adjusted according to the parameter compensation strategy.

[0104] For example, regarding the rate of change of φ after grouting and the rate of change of D after fracturing f Increment, if |φ 实测 -φ 目标 If |>2%, the parameter compensation mechanism is triggered: nano-grouting replenishment volume Q 补 =0.5×(φ) 目标 -φ 实测 )×V 煤体 If the fracture connectivity after fracturing does not meet the target (e.g., target value is 60%, measured value is 50%), initiate secondary fracturing and adjust d. 裂 The spacing is 80% of the original spacing, and P is increased. 裂 Up to 15%.

[0105] For example, the predicted rockburst parameter is the predicted rockburst probability P of a certain working face. T =85%, the location is concentrated in the area of ​​x=100~150m, the intensity level is 6, and CT scan shows that the area has K T =12% ​​(below the critical value), φ=7% (severe crack closure), σ c =8MPa (strength too high). Analysis indicates that K needs to be... T Increased to 30%, φ increased to 18%, σ c The strength was reduced to 5 MPa (this can be achieved through the synergistic effect of chemical modifiers; a suitable chemical modifier formulation can be developed, and specialized injection equipment can be used to inject the chemical modifier into the coal and rock mass, such as through borehole injection or coal seam water injection, ensuring that the modifier can uniformly penetrate into the micropores and microcracks of the coal and rock mass. After injection, the physical and mechanical properties of the coal and rock mass are tested to ensure that the strength meets the requirements). Next, the in-situ stresses σ1 and σ3 are measured, and P is calculated. 裂 d 裂 Original and compared with the fractal dimension D of the original fracture f and the total amount of fracturing fluid V. Based on the above solution, P... 裂 As the nano-grouting pressure P 注 C 纳米 =4% (target σ) c (Reduced by 37.5%), the injection time was 4 hours divided into two stages: high speed for the first hour and low speed for the next 3 hours. The average microseismic energy during the fracturing process was monitored using a microseismic monitoring system, and was 8 × 10⁻⁶. 3J / time (meets standards), φ measured after grouting is 17.5% (error <3%), no correction is needed; if there are no impact events in the area within the next 72 hours, the parameter combination is deemed effective.

[0106] It should be noted that the control results obtained after each control of the coal and rock mass based on the combination of control parameters, along with the corresponding combination of control parameters, can be placed into a mapping database between microstructure parameters and control parameters. After each control cycle (monitoring-execution-feedback) is completed, the actual parameters and the rockburst suppression effect (e.g., zero occurrence of rockburst events within the next 24 hours is considered effective) are input into a deep learning model (e.g., an LSTM network) to dynamically update the parameter inversion algorithm, so that the control accuracy gradually improves with the mining progress. For example, the parameter matching error can be reduced to below 10% after 5 cycles.

[0107] In summary, the method provided in this embodiment achieves, for the first time, precise monitoring and analysis of multi-scale microstructural parameters of coal and rock masses from micro to macro levels. This enables a more comprehensive and in-depth understanding of the physical and mechanical changes within the coal and rock mass, providing a more accurate basis for proactive prevention and control of rockbursts. Secondly, by precisely optimizing the microstructural parameters of the coal and rock mass, the mechanical properties and energy storage and release characteristics of the mass are altered, achieving proactive prevention and control of rockbursts and improving the safety and efficiency of coal mining. Furthermore, by utilizing big data and artificial intelligence technologies to achieve proactive prevention and control decision support, intelligent generation and dynamic adjustment of rockburst prevention and control schemes are realized. Based on real-time monitoring data and complex and ever-changing mining conditions, optimal prevention and control strategies can be formulated quickly and accurately, greatly improving the scientific rigor and timeliness of rockburst prevention and control.

[0108] To achieve the above embodiments, this disclosure also proposes an active rockburst prevention and control system.

[0109] For example, Figure 2 This is a schematic diagram of the structure of an active rockburst prevention and control system provided in an embodiment of this disclosure. Figure 2 As shown, the active rockburst prevention and control system 200 includes:

[0110] The parameter acquisition unit 201 is used to acquire the microstructure parameters of the coal and rock mass and predict the rockburst parameters based on the microstructure parameters of the coal and rock mass.

[0111] The target determination unit 202 is used to determine the control parameter combination and control target corresponding to the rockburst parameter based on the three-dimensional mapping relationship between the microstructure state, the control target, and the control parameter combination.

[0112] The parameter control unit 203 is used to control the coal and rock mass according to the combination of control parameters, obtain the control result, and adjust the combination of control parameters if the control result does not meet the control target until the control result meets the control target.

[0113] Optionally, when the parameter acquisition unit 201 is used to predict rockburst parameters based on the microstructure parameters of the coal and rock mass, it is specifically used for:

[0114] Construct a quantitative relationship model between coal and rock mass microstructure parameters and rockburst parameters;

[0115] The parameters of rock and rock mass microstructure are input into a quantitative relationship model to predict rockburst parameters.

[0116] Optionally, when the parameter acquisition unit 201 is used to construct a quantitative relationship model between coal and rock mass microstructure parameters and rockburst parameters, it is specifically used for:

[0117] Multi-field coupled in-situ computed tomography was used to acquire information on the microstructure changes inside coal and rock bodies under different environments, and the frequency of rockbursts in coal mines corresponding to the microstructure changes was monitored.

[0118] Digital image analysis of microstructure changes yields a quantitative characterization strategy for coal and rock mass microstructure parameters.

[0119] Data mining analysis was performed on the microstructure changes to obtain the correlation between the microstructure parameters and the mechanical parameters of the coal and rock mass;

[0120] Based on quantitative characterization strategies, correlations, and the frequency of rockbursts in coal mines, a quantitative relationship model between coal and rock mass microstructure parameters and rockburst parameters is constructed.

[0121] Optionally, before determining the combination of control parameters and the control target corresponding to the rockburst parameters based on the three-dimensional mapping relationship between the microstructure state, the control target, and the combination of control parameters, the target determination unit 202 is further used for:

[0122] Based on historical data of microstructure parameters and corresponding control effect data, combined with the critical parameter threshold for rockburst occurrence, a three-dimensional mapping relationship between microstructure state, control target, and parameter combination is constructed.

[0123] Optionally, when the target determination unit 202 determines the combination of control parameters and the control target corresponding to the rockburst parameters based on the three-dimensional mapping relationship between the microstructure state, the control target, and the combination of control parameters, it is specifically used for:

[0124] Determine the coal and rock type and microstructure state of the area corresponding to the rockburst parameters, where rockburst parameters include the probability of rockburst occurrence, the location of occurrence, and the intensity of occurrence;

[0125] Based on the three-dimensional mapping relationship between microstructure state, control target, and control parameter combination, the control parameter combination and control target corresponding to coal and rock type and microstructure state are determined.

[0126] Optionally, when the target determination unit 202 determines the combination of control parameters and the control target corresponding to the coal and rock type and microstructure state based on the three-dimensional mapping relationship between the microstructure state, the control target, and the combination of control parameters, it is specifically used for:

[0127] The machine learning model is invoked to output the combination of control parameters and control targets corresponding to coal and rock types and microstructure states. The machine learning model is constructed based on the three-dimensional mapping relationship between microstructure state, control target, and combination of control parameters.

[0128] Optionally, when the parameter control unit 203 is used to adjust the combination of control parameters, it is specifically used for:

[0129] Determine the difference between the measured values ​​of the microstructure parameters of the coal and rock mass and the target values ​​of the microstructure parameters of the coal and rock mass indicated by the control target;

[0130] If the difference is greater than the difference threshold, the parameter compensation strategy corresponding to the control target is determined, and the combination of control parameters is adjusted according to the parameter compensation strategy.

[0131] It should be noted that the foregoing explanation of the active rockburst control method embodiment also applies to the active rockburst control system of this embodiment, and will not be repeated here.

[0132] In summary, the system provided in this disclosure achieves proactive prevention and control of rockbursts by optimizing and controlling the microstructure parameters of the coal and rock mass. It realizes a closed loop of the entire process from "microstructure parameter monitoring - rockburst prediction - intelligent generation of control parameters - real-time feedback correction", which can ensure that the control parameters are accurately matched with the real-time state of the coal and rock mass, and can minimize the risk of rockbursts.

[0133] To implement the above embodiments, this disclosure also proposes an electronic device, including: a processor and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided in the foregoing embodiments.

[0134] To implement the above embodiments, this disclosure also proposes a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the methods provided in the foregoing embodiments.

[0135] To implement the above embodiments, this disclosure also proposes a computer program product, including a computer program that, when executed by a processor, implements the methods provided in the foregoing embodiments.

[0136] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in this disclosure all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0137] It should be noted that personal information collected from users should be used for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. Furthermore, such collection / sharing should only be conducted after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization that includes authorization of relevant user information before the user uses the function. In addition, any necessary steps must be taken to protect and safeguard access to such personal information data and ensure that others with access to personal information data comply with their privacy policies and procedures.

[0138] This disclosure is intended to provide implementation schemes for users to selectively prevent the use or access to their personal information data. Specifically, this disclosure is intended to provide hardware and / or software to prevent or block access to such personal information data. Once personal information data is no longer needed, risks can be minimized by restricting data collection and deleting data. Furthermore, where applicable, such personal information is de-identified to protect user privacy.

[0139] In the foregoing descriptions of the embodiments, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0140] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this disclosure, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0141] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of this disclosure includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of this disclosure pertain.

[0142] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disks (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) or flash memory, fiber optic devices, and compact disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0143] It should be understood that various parts of this disclosure can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0144] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware, and the program can be stored in a computer-readable storage medium. When executed, the program includes one or a combination of the steps of the method embodiments.

[0145] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0146] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present disclosure have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present disclosure.

Claims

1. A method for active prevention and control of rockburst, characterized in that, include: The microstructure parameters of the coal and rock mass are obtained, and the rockburst parameters are predicted based on the microstructure parameters of the coal and rock mass. The microstructure parameters of the coal and rock mass are the microstructure parameters inside the coal and rock mass, including the coal and rock mass fracture rate, fracture fractal dimension, porosity, pore fractal dimension, fracture connectivity, and mineral interface bonding strength. Based on the three-dimensional mapping relationship between the microstructure state, the control target, and the combination of control parameters, the combination of control parameters and the control target corresponding to the rockburst parameters are determined. The coal and rock mass is regulated according to the combination of regulation parameters to obtain the regulation result. If the regulation result does not meet the regulation target, the combination of regulation parameters is adjusted until the regulation result meets the regulation target. The prediction of rockburst parameters based on the microstructure parameters of the coal and rock mass includes: Multi-field coupled in-situ computed tomography (CT) scanners were used to acquire information on the microstructure changes inside coal and rock masses under different environments, and the frequency of rockbursts in coal mines corresponding to the microstructure changes was monitored. Digital image analysis is performed on the microstructure change information to obtain a quantitative characterization strategy for the microstructure parameters of coal and rock mass; Data mining analysis was performed on the microstructure change information to obtain the correlation between the microstructure parameters and the mechanical parameters of the coal and rock mass; Based on the quantitative characterization strategy, the correlation, and the frequency of rockburst occurrence in the coal mine, a quantitative relationship model between the microstructure parameters of coal and rock mass and the rockburst parameters is constructed. The coal and rock mass microstructure parameters are input into the quantitative relationship model to predict the rockburst parameters.

2. The method according to claim 1, characterized in that, Before determining the combination of control parameters and the control target corresponding to the rockburst parameter based on the three-dimensional mapping relationship between the microstructure state, the control target, and the combination of control parameters, the method further includes: Based on historical data of microstructure parameters and corresponding control effect data, combined with the critical parameter threshold for rockburst occurrence, a three-dimensional mapping relationship between microstructure state, control target, and parameter combination is constructed.

3. The method according to claim 1, characterized in that, The process of determining the control parameter combination and control target corresponding to the rockburst parameter based on the three-dimensional mapping relationship between the microstructure state, the control target, and the control parameter combination includes: Determine the coal and rock type and microstructure state of the area corresponding to the rockburst parameters, wherein the rockburst parameters include the probability of rockburst occurrence, the location of occurrence, and the intensity of occurrence; Based on the three-dimensional mapping relationship between the microstructure state, the control target, and the combination of control parameters, the combination of control parameters and the control target corresponding to the coal and rock type and the microstructure state are determined.

4. The method according to claim 3, characterized in that, The step of determining the combination of control parameters and the control target corresponding to the coal and rock type and the microstructure state based on the three-dimensional mapping relationship between the microstructure state, the control target, and the combination of control parameters includes: The machine learning model is invoked to output the combination of control parameters and control targets corresponding to the coal and rock type and the microstructure state. The machine learning model is constructed based on the three-dimensional mapping relationship between the microstructure state, the control target, and the combination of control parameters.

5. The method according to claim 1, characterized in that, The adjustment of the combination of control parameters includes: Determine the difference between the measured values ​​of the coal and rock mass microstructure parameters and the target values ​​of the coal and rock mass microstructure parameters indicated by the control target; If the difference is greater than the difference threshold, then the parameter compensation strategy corresponding to the control target is determined, and the control parameter combination is adjusted according to the parameter compensation strategy.

6. An active rockburst prevention and control system, characterized in that, include: The parameter acquisition unit is used to acquire the microstructure parameters of the coal and rock mass and predict the rockburst parameters based on the microstructure parameters of the coal and rock mass. The microstructure parameters of the coal and rock mass are the microstructure parameters inside the coal and rock mass, including the coal and rock mass fracture rate, fracture fractal dimension, porosity, pore fractal dimension, fracture connectivity, and mineral interface bonding strength. The target determination unit is used to determine the combination of control parameters and the control target corresponding to the rockburst parameter based on the three-dimensional mapping relationship between the microstructure state, the control target, and the combination of control parameters. The parameter control unit is used to control the coal and rock mass according to the control parameter combination, obtain the control result, and adjust the control parameter combination if the control result does not meet the control target until the control result meets the control target. The parameter acquisition unit is used to acquire information on the microstructure changes inside the coal and rock mass under different environments using a multi-field coupled in-situ computed tomography scanner, and to monitor the frequency of rockbursts in the coal mine corresponding to the microstructure changes. Digital image analysis is performed on the microstructure change information to obtain a quantitative characterization strategy for the microstructure parameters of coal and rock mass; Data mining analysis was performed on the microstructure change information to obtain the correlation between the microstructure parameters and the mechanical parameters of the coal and rock mass; Based on the quantitative characterization strategy, the correlation, and the frequency of rockburst occurrence in the coal mine, a quantitative relationship model between the microstructure parameters of coal and rock mass and the rockburst parameters is constructed. The coal and rock mass microstructure parameters are input into the quantitative relationship model to predict the rockburst parameters.

7. An electronic device, characterized in that, The electronic device includes: Memory, used to store executable program code; A processor for calling and running the executable program code from the memory, causing the electronic device to perform the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1 to 5.

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