Rock burst parameter prediction method and system, electronic equipment and storage medium

Abstract

The invention relates to the technical field of mine rock mechanics and intelligent monitoring, in particular to a rock burst parameter prediction method and system, electronic equipment and a storage medium. The method comprises the following steps: constructing a microstructure parameter comprehensive evaluation index system, and determining a nonlinear mapping relationship between microstructure parameters and impact tendency indexes in the microstructure parameter comprehensive evaluation index system; on the basis of the microstructure parameter comprehensive evaluation index system, monitoring microstructure parameters of the to-be-measured coal and rock mass, and analyzing the change trend of the microstructure parameters to obtain an analysis result; and predicting the rock burst parameter of the to-be-measured coal rock mass according to the analysis result and the nonlinear mapping relation. By the adoption of the scheme, accurate prediction of the rock burst parameters can be achieved.

Description

Technical Field

[0001] The present disclosure relates to the field of mine rock mechanics and intelligent monitoring technology, and in particular to a rock burst parameter prediction method, system, electronic equipment, and storage medium. Background Art

[0002] Rock burst is a sudden, violent destructive phenomenon caused by the instantaneous release of elastic deformation energy in coal and rock masses surrounding coal mine tunnels or working faces. It is often accompanied by instantaneous displacement and ejection of coal and rock masses, loud noises, and air surges. Its complex mechanism and numerous influencing factors pose a serious threat to the safe and efficient mining of deep coal mines. With the continuous increase in mining depth and intensity, rock burst has become an increasingly prominent problem. Therefore, the accurate prediction of rock burst parameters and the effective prevention and control of these predictions have become a key concern. Summary of the Invention

[0003] The present disclosure aims to solve one of the technical problems in the related art at least to a certain extent.

[0004] To this end, the first purpose of the present disclosure is to propose a rock burst parameter prediction method to achieve accurate prediction of rock burst parameters.

[0005] The second objective of the present disclosure is to provide a rock burst parameter prediction system.

[0006] A third objective of the present disclosure is to provide an electronic device.

[0007] A fourth object of the present disclosure is to provide a computer-readable storage medium.

[0008] A fifth object of the present disclosure is to provide a computer program product.

[0009] To achieve the above objectives, the first embodiment of the present disclosure provides a method for predicting rock burst parameters, comprising:

[0010] Constructing a comprehensive evaluation index system for microstructural parameters and determining a nonlinear mapping relationship between microstructural parameters and impact proneness index in the comprehensive evaluation index system for microstructural parameters;

[0011] Based on the microstructural parameter comprehensive evaluation index system, the microstructural parameters of the coal and rock mass to be tested are monitored, and the variation trend of the microstructural parameters is analyzed to obtain analysis results;

[0012] The rock burst parameters of the coal rock mass to be measured are predicted based on the analysis results and the nonlinear mapping relationship.

[0013] Optionally, constructing a comprehensive evaluation index system for microstructure parameters includes:

[0014] Multi-field coupled in-situ electron computed tomography and nuclear magnetic resonance technology are used to obtain the microscopic parameters of coal and rock samples under different environments;

[0015] Using in-situ acoustic wave monitoring and digital image correlation method to dynamically track the microstructural changes of the coal and rock samples under different environments;

[0016] Based on fractal theory and damage mechanics, a comprehensive evaluation index system for microstructure parameters is constructed according to the mesoscopic parameters and the microstructure change information.

[0017] Optionally, determining the nonlinear mapping relationship between the microstructure parameters and the impact proneness index in the microstructure parameter comprehensive evaluation index system includes:

[0018] Through physical similarity simulation experiments, the impact tendency index of the coal rock sample under different microstructural parameters in the microstructural parameter comprehensive evaluation index system is obtained;

[0019] A support vector machine and a random forest algorithm are used to construct a nonlinear mapping relationship between the microstructure parameters and the impact tendency index.

[0020] Optionally, the constructing of a nonlinear mapping relationship between the microstructure parameter and the impact proneness index includes:

[0021] Determining the weight of each microstructure parameter in the microstructure parameter comprehensive evaluation index system;

[0022] Based on the weights, weighted processing is performed on the microstructure parameters to obtain weighted microstructure parameters;

[0023] A nonlinear mapping relationship between the weighted microstructure parameters and the impact tendency index is constructed.

[0024] Optionally, the microstructure parameters include crack ratio, porosity, pore connectivity index, and surface roughness parameter; the analysis results include crack ratio change trend, porosity change trend, pore connectivity index change trend, and surface roughness parameter change trend; the rock burst parameters include rock burst occurrence probability; and predicting the rock burst parameters of the coal rock mass to be tested based on the analysis results and the nonlinear mapping relationship includes:

[0025] determining a probability of occurrence of a first rock burst according to the crack ratio variation trend and the porosity variation trend;

[0026] Determining, according to the nonlinear mapping relationship, a rock burst tendency index value corresponding to the parameter value of the pore connectivity index, and determining a probability of occurrence of a second rock burst according to a change trend of the pore connectivity index and the rock burst tendency index value;

[0027] determining a probability of occurrence of a third rock burst according to a variation trend of the surface roughness parameter;

[0028] The rock burst occurrence probability of the coal rock mass to be measured is determined according to the first rock burst occurrence probability, the second rock burst occurrence probability, and the third rock burst occurrence probability.

[0029] Optionally, the microstructure parameters include acoustic characteristic parameters and spatial characteristic parameters, the analysis results include acoustic characteristic parameter change trends and spatial characteristic parameter change trends, the rock burst parameters include rock burst occurrence locations, and predicting the rock burst parameters of the coal and rock mass to be tested based on the analysis results and the nonlinear mapping relationship includes:

[0030] The rock burst occurrence location of the coal rock mass to be measured is determined according to the variation trend of the acoustic characteristic parameters and the variation trend of the spatial characteristic parameters.

[0031] Optionally, the rock burst parameters include rock burst intensity, and predicting the rock burst parameters of the coal rock mass to be tested based on the analysis results and the nonlinear mapping relationship includes:

[0032] Determining a change trend of an impact tendency index based on the analysis results and the nonlinear mapping relationship;

[0033] The rock burst intensity of the coal rock mass to be tested is determined according to the change trend of the rock burst tendency index and the analysis result.

[0034] To achieve the above objectives, a second embodiment of the present disclosure provides a rock burst parameter prediction system, comprising:

[0035] a relationship determination unit, configured to construct a microstructure parameter comprehensive evaluation index system and determine a nonlinear mapping relationship between microstructure parameters and impact proneness index in the microstructure parameter comprehensive evaluation index system;

[0036] A parameter analysis unit is used to monitor the microstructural parameters of the coal and rock mass to be tested based on the microstructural parameter comprehensive evaluation index system, and to analyze the variation trend of the microstructural parameters to obtain analysis results;

[0037] A parameter prediction unit is used to predict the rock burst parameters of the coal rock mass to be tested based on the analysis results and the nonlinear mapping relationship.

[0038] To achieve the above-mentioned objectives, a third embodiment of the present disclosure provides an electronic device, including:

[0039] a memory for storing executable program code;

[0040] The processor is used to call and run the executable program code from the memory, so that the electronic device executes the method shown in any one of the first aspects above.

[0041] To achieve the above-mentioned purpose, the fourth aspect of the present disclosure provides a computer-readable storage medium, which stores a computer program. When the computer program is executed, it implements the method shown in any one of the above-mentioned first aspects.

[0042] To achieve the above-mentioned objectives, an embodiment of the fifth aspect of the present disclosure proposes a computer program product, including a computer program, which implements the method shown in any one of the above-mentioned first aspects when executed by a processor.

[0043] In summary, the method, system, electronic device, and storage medium provided herein construct a comprehensive microstructural parameter evaluation index system and determine a nonlinear mapping relationship between microstructural parameters and rock burst propensity index within the system. Based on the system, the microstructural parameters of the coal and rock mass to be tested are monitored and their changing trends are analyzed to obtain analysis results. Based on the analysis results and the nonlinear mapping relationship, rock burst parameters of the coal and rock mass to be tested are predicted. Consequently, accurate rock burst parameters can be predicted.

[0044] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The above and / or additional aspects and advantages of the present disclosure will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0046] Figure 1 A schematic flow chart of a rock burst parameter prediction method provided by an embodiment of the present disclosure;

[0047] Figure 2 A schematic diagram of the structure of a rock burst parameter prediction system provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0048] The following describes in detail embodiments of the present disclosure, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0049] It should be noted that rock burst is a highly destructive dynamic disaster in deep coal mining, and its occurrence is closely related to the evolution of the internal microstructure of the coal rock mass. Existing studies have shown that there is a significant correlation between microstructural parameters such as the extension of coal microcracks and porosity changes and the rock burst tendency index. However, current technologies have the following limitations: (1) The means of obtaining microstructural parameters are single (such as computed tomography (CT) or mercury intrusion), which makes it difficult to cover the full-scale pore structure (micropores, mesopores, and macropores), and lacks a comprehensive multi-scale and multi-dimensional characterization method; (2) The characterization dimension is limited, and there is a lack of coordinated analysis of multi-dimensional parameters such as pore connectivity and surface roughness; (3) The dynamic correlation is insufficient, and the relationship between microstructural parameters and rock burst has not yet formed a quantitative model of the spatiotemporal evolution of the relationship, resulting in poor prediction timeliness and low accuracy; (4) Existing prediction models are mostly based on empirical formulas and are not adaptable to complex geological conditions.

[0050] The present disclosure is described in detail below with reference to specific embodiments.

[0051] In the first embodiment, if Figure 1 As shown, Figure 1 This is a flow chart of a rockburst parameter prediction method provided by an embodiment of the present disclosure. This method can be implemented using a computer program and run on a system for rockburst parameter prediction. The computer program can be integrated into an application or run as a standalone tool application.

[0052] The rock burst parameter prediction method may be executed by an electronic device.

[0053] For example, the rock burst parameter prediction method includes the following steps:

[0054] S101, constructing a comprehensive evaluation index system for microstructure parameters, and determining a nonlinear mapping relationship between microstructure parameters and impact proneness index in the comprehensive evaluation index system for microstructure parameters;

[0055] According to some embodiments, the comprehensive evaluation index system of microstructural parameters includes a variety of microstructural parameters that affect rock burst parameters corresponding to coal and rock masses, and can be used for multi-scale and multi-dimensional comprehensive characterization of microstructural parameters.

[0056] In some embodiments, the impact propensity index refers to a key indicator related to the impact propensity of coal rock.

[0057] S102, based on the comprehensive evaluation index system of microstructural parameters, monitoring the microstructural parameters of the coal and rock mass to be tested, and analyzing the variation trend of the microstructural parameters to obtain analysis results;

[0058] According to some embodiments, the microstructure parameters include, but are not limited to, crack ratio, porosity, pore connectivity index, surface roughness parameters, acoustic characteristic parameters, spatial characteristic parameters, and the like.

[0059] S103, predicting rock burst parameters of the coal rock mass to be tested based on the analysis results and the nonlinear mapping relationship.

[0060] In some embodiments, rock burst parameters refer to parameters related to rock burst pressure. The rock burst parameters include but are not limited to rock burst probability P r , occurrence position P(x,y,z), occurrence intensity I and other parameters.

[0061] In summary, the method provided in this embodiment constructs a comprehensive microstructural parameter evaluation index system and determines a nonlinear mapping relationship between microstructural parameters and rock burst propensity index within the system. Based on this system, the microstructural parameters of the coal and rock mass to be tested are monitored and their changing trends are analyzed to obtain analysis results. Based on the analysis results and the nonlinear mapping relationship, rock burst parameters of the coal and rock mass to be tested are predicted. Consequently, accurate rock burst parameters can be predicted.

[0062] Another embodiment of the present disclosure provides a method for predicting rock burst parameters, which can be executed by an electronic device.

[0063] For example, the rock burst parameter prediction method may include the following steps:

[0064] S201, using multi-field coupled in-situ computed tomography and nuclear magnetic resonance technology to obtain the microscopic parameters of coal and rock samples under different environments;

[0065] According to some embodiments, different environments include but are not limited to stress, seepage, high and low temperature environments, and the like.

[0066] In some embodiments, microscopic parameters include but are not limited to crack ratio, crack fractal dimension, porosity, micropore fractal dimension D CO2 , mesopore fractal dimension D N2 , macropore fractal dimension D Hg , fracture connectivity, pore size distribution and other parameters.

[0067] Among them, for pore size distribution, through multimodal detection, low-temperature gas adsorption (N2 / CO2), high-pressure mercury injection and micron CT can be integrated to cover the full-scale pore size distribution from micropores (<2nm) to macropores (>50μm).

[0068] Taking a scenario as an example, a plug sample (20-50 mm in diameter) and a particle sample (≤ 200 mesh) can be prepared and then dried for CT scanning, mercury intrusion, and gas adsorption experiments. The CT images can be processed using ImageJ and MATLAB software to extract pore morphology parameters such as crack ratio, crack fractal dimension, porosity, and micropore fractal dimension D. CO2 , mesopore fractal dimension D N2 , macropore fractal dimension D Hg , crack connectivity, etc.; the specific surface area is calculated by the Brunauer-Emmett-Teller (BET) method, the pore size distribution is inverted based on the mercury injection curve, or the pore size distribution characteristics are obtained by inverting the Nuclear Magnetic Resonance Spectroscopy (NMR) relaxation time spectrum.

[0069] S202, using in-situ acoustic wave monitoring and digital image correlation method to dynamically track microstructural changes of coal and rock samples under different environments;

[0070] According to some embodiments, the microstructure change information includes but is not limited to information on the initiation, expansion, and penetration of microcracks.

[0071] In some embodiments, multiple acoustic wave monitoring sensors can be arranged to obtain acoustic signals inside the coal rock mass through in-situ acoustic wave monitoring. When microcracks change, the acoustic wave propagation speed and amplitude change, and the wave velocity distribution can be inverted using acoustic wave tomography technology to obtain information on microstructural changes.

[0072] S203, based on fractal theory and damage mechanics, a comprehensive evaluation index system for microstructural parameters is constructed according to mesoscopic parameters and microstructural change information;

[0073] According to some embodiments, based on the CT image segmentation algorithm, spatial characteristic parameters such as pore coordination number, throat tortuosity fractal dimension, and throat radius median can also be extracted and incorporated into the comprehensive evaluation index system of microstructure parameters.

[0074] In some embodiments, the spatial characteristic parameters can be used to analyze spatial distribution and reconstruct three-dimensional structures.

[0075] According to some embodiments, a pore connectivity index L can be introduced by using the fractal dimension d of the throat tortuosity and the median throat radius Dt, where L = d / Dt. Incorporating the pore connectivity index L into the comprehensive evaluation index system of microstructural parameters can quantify connectivity and implement a hierarchical evaluation of pore network connectivity.

[0076] In some embodiments, the pore fractal dimension D can be obtained by combining the Frenkel-Halsey-Hill (FHH) model, the VS (Vulcan Software) model, and the capillary bundle theory to establish a weighted comprehensive formula for the fractal dimensions of micropores, mesopores, and macropores. f , where D f =C1D CO2 +C2D N2 +C3D Hg , C1, C2, C3 are the pore volume coefficients of each scale, which can be obtained by pore volume ratio or machine learning optimization. f By putting it into the comprehensive evaluation index system of microstructure parameters, the fusion calculation of fractal dimension can be realized.

[0077] According to some embodiments, an atomic force microscope (AFM) can be used to scan no less than two-thirds of the sample area to obtain surface topography data, calculate the fractal dimension and root mean square roughness, and obtain a surface roughness parameter. This surface roughness parameter can be incorporated into a comprehensive evaluation index system for microstructural parameters to reveal the complexity of the pore surface.

[0078] In some embodiments, when in-situ acoustic wave monitoring is used to dynamically track microstructure change information, the acquired acoustic characteristic parameters can also be included in the comprehensive evaluation index system of microstructure parameters.

[0079] S204, obtaining the impact tendency index of the coal rock sample under different microstructural parameters in the microstructural parameter comprehensive evaluation index system through physical similarity simulation experiments;

[0080] According to some embodiments, the impact proneness index includes but is not limited to indicators such as the impact energy index (WET) and the elastic energy index (WPI).

[0081] For example, through physical similarity simulation experiments, key indicators such as the impact energy index (WET) and elastic energy index (WPI) of coal rocks with pore connectivity index L varying between 0.1-0.8 and surface roughness parameters varying between levels 3-8 can be obtained.

[0082] It should be noted that the accuracy of obtaining the impact tendency index can be improved by obtaining the impact tendency index of coal rock samples under different microstructural parameters in a physical similarity simulation experiment in a manner driven by experimental data.

[0083] S205, using support vector machine and random forest algorithm to construct nonlinear mapping relationship between microstructure parameters and impact proneness index;

[0084] For example, by using support vector machines and random forest algorithms, we can establish a correlation curve between the L values of different coal rock samples and the impact tendency of coal rock, and establish a correlation mechanism between surface roughness parameters and the impact tendency of coal rock.

[0085] According to some embodiments, key microstructural parameters and their weights can be set, and an attention mechanism can be introduced to dynamically focus on the key parameters. Specifically, the weight of each microstructural parameter in the comprehensive microstructural parameter evaluation index system can be determined; based on the weights, the microstructural parameters can be weighted to obtain weighted microstructural parameters; and a nonlinear mapping relationship between the weighted microstructural parameters and the impact propensity index can be established.

[0086] S206, based on the comprehensive evaluation index system of microstructural parameters, monitoring the microstructural parameters of the coal and rock mass to be tested, and analyzing the variation trend of the microstructural parameters to obtain analysis results;

[0087] According to some embodiments, a multi-source data real-time acquisition network can be constructed by integrating downhole stress sensors, microseismic monitors and borehole peek equipment, and the microstructural parameters of the coal and rock mass to be measured can be monitored based on the multi-source data real-time acquisition network.

[0088] In some embodiments, the analysis results include but are not limited to crack ratio change trends, porosity change trends, pore connectivity index change trends, surface roughness parameter change trends, acoustic characteristic parameter change trends, spatial characteristic parameter change trends, and other change trends.

[0089] S207, predicting rock burst parameters of the coal rock mass to be tested based on the analysis results and the nonlinear mapping relationship;

[0090] According to some embodiments, if the fracture rate increases rapidly within a short period of time, for example, from 5% to over 15% within a few days, and the porosity increases simultaneously, this indicates that the integrity of the coal rock mass is compromised, and the likelihood of rock burst is significantly increased. This is because the expansion of fractures and pores provides channels for energy release, making it easier for accumulated energy to exceed the bearing capacity.

[0091] Additionally, the pore connectivity index (L) reflects the degree of connectivity within the pore network. An increase in the L value from 0.3 to 0.6 indicates enhanced pore connectivity, facilitating energy transfer. Combining the L value with the coal rock burst susceptibility curve, a high L value indicates an increased likelihood of rock burst.

[0092] Secondly, surface roughness parameters are correlated with impact susceptibility. Using AFM to monitor pore surface roughness, an increase in fractal dimension from 2.5 to 3.2 and root mean square roughness from 5 nm to 8 nm indicates a more complex pore surface, affecting stress distribution and making stress concentration more likely, increasing the likelihood of rock bursts.

[0093] That is to say, the first probability of rock burst can be determined based on the trend of changes in the fracture ratio and the porosity; the impact tendency index value corresponding to the parameter value of the pore connectivity index can be determined based on the nonlinear mapping relationship, and the second probability of rock burst can be determined based on the trend of changes in the pore connectivity index and the impact tendency index value; the third probability of rock burst can be determined based on the trend of changes in the surface roughness parameter; the probability of rock burst of the coal rock mass to be tested can be determined based on the first probability of rock burst, the second probability of rock burst and the third probability of rock burst.

[0094] Among them, the first probability of rock burst corresponding to different crack rate change trends and different porosity change trends, the second probability of rock burst corresponding to different pore connectivity index change trends and different impact tendency index values, and the third probability of rock burst corresponding to different surface roughness parameter change trends can all be obtained through experiments.

[0095] Among them, experiments can also be conducted to obtain the correlation between the first rock burst probability, the second rock burst probability, and the third rock burst probability and the rock burst probability of the coal rock mass to be tested, so as to determine the rock burst probability of the coal rock mass to be tested based on the correlation.

[0096] According to some embodiments, for acoustic characteristic parameters, if the wave velocity in a certain area is significantly reduced and the change gradient is abnormal, and digital image correlation (DIC) is used to observe concentrated microcrack expansion in the area, this area is very likely to be the location where rock burst occurs.

[0097] Furthermore, regarding spatial characteristic parameters, regions with high pore coordination numbers and large throat tortuosity fractal dimensions have weak mechanical properties and are prone to deformation and failure. If these regions exhibit abnormal changes in microstructural parameters, such as a rapid increase in the fracture ratio or an abnormally high pore connectivity index, they may be the locations of rock bursts.

[0098] In other words, the rock burst location of the coal rock mass to be measured can be determined based on the changing trends of the acoustic characteristic parameters and the spatial characteristic parameters.

[0099] Among them, experiments can be conducted to obtain the correlation between the changing trend of acoustic characteristic parameters and the changing trend of spatial characteristic parameters and the location of rock burst, so as to determine the location of rock burst in the coal rock mass to be tested based on the correlation.

[0100] According to some embodiments, when microstructural parameters change, the current impact propensity index can be calculated based on a nonlinear mapping relationship. For example, if the impact energy index (WET) increases from 1.5 to above 3.0, or the elastic energy index (WPI) increases from 2.0 to 4.0, it indicates that the coal rock mass has accumulated a large amount of energy and the intensity of rock burst is likely to be high.

[0101] In addition, the rate of change of microstructural parameters such as fracture ratio, porosity, and pore connectivity index can be comprehensively considered to assess energy accumulation and release. If the fracture ratio and porosity increase rapidly, the pore connectivity index increases significantly, energy release channels form rapidly, and the release rate accelerates. Combined with the impact tendency index, if the energy accumulation is large and the release rate is fast, the intensity of the rock burst will be greater. For example, if the fracture ratio increases by 1% per day, the pore connectivity index L increases by 0.1 per week, and the impact energy index WET reaches 4.0 within a certain period of time, it can be predicted that a high-intensity rock burst will occur.

[0102] That is to say, the changing trend of the impact tendency index can be determined based on the analysis results and the nonlinear mapping relationship; and the rock burst intensity of the coal rock mass to be tested can be determined based on the changing trend of the impact tendency index and the analysis results.

[0103] Among them, the impact tendency index change trend and the correlation between the analysis results and the impact rock pressure intensity can be obtained by conducting experiments, so as to determine the impact rock pressure intensity of the coal rock mass to be tested based on the correlation.

[0104] According to some embodiments, a 3D geological model can be constructed, combined with mining progress dynamics, to delineate rock burst risk zones based on real-time predicted rock burst parameters. Visual warning maps and graded warning maps can then be generated based on the prediction results. For example, low-risk areas can be colored green, medium-risk areas can be colored yellow, and high-risk areas can be colored red.

[0105] In some embodiments, a prediction model can be constructed according to the method provided in this embodiment. Specifically, a data set containing at least 2,000 samples (60% laboratory data and 40% field data) can be constructed based on laboratory physical simulation data and field measured data; K-fold cross-validation is used to optimize model hyperparameters to ensure the generalization ability of the model; then, the model prediction accuracy is verified using engineering cases, so that the model prediction accuracy reaches more than 92%; secondly, the dynamic mapping model can be continuously optimized using the latest monitoring data through an incremental learning mechanism to improve the adaptability of the dynamic mapping model.

[0106] In addition, when deploying and using the prediction model, edge computing nodes can be deployed in underground substations to realize real-time data preprocessing and preliminary analysis; ground servers can also be used to receive data through 5G networks, run prediction models, and combine with geographic information systems (Geographic Information System or Geo-Information system, GIS) to output impact risk heat maps to support real-time viewing on mobile terminals; early warning results can also be pushed to the working face through mine intrinsically safe terminals, with a response time of less than 10 seconds.

[0107] In summary, the method provided in this embodiment breaks through the limitations of single parameter characterization by combining CT / NMR microscopic parameters with field monitoring data, proposes a coal rock microstructure parameter characterization framework that integrates multimodal detection and intelligent algorithms, and constructs a dynamic mapping model of multi-dimensional parameters and impact tendency, thereby realizing accurate prediction of rock burst.

[0108] In order to implement the above embodiments, the present disclosure also proposes a rock burst parameter prediction system.

[0109] For example, Figure 2 This is a schematic diagram of the structure of a rock burst parameter prediction system provided by an embodiment of the present disclosure. Figure 2 As shown, the rock burst parameter prediction system 200 includes:

[0110] The relationship determination unit 201 is used to construct a microstructure parameter comprehensive evaluation index system and determine a nonlinear mapping relationship between the microstructure parameters and the impact tendency index in the microstructure parameter comprehensive evaluation index system;

[0111] The parameter analysis unit 202 is used to monitor the microstructural parameters of the coal and rock mass to be tested based on the comprehensive evaluation index system of microstructural parameters, and analyze the changing trend of the microstructural parameters to obtain analysis results;

[0112] The parameter prediction unit 203 is used to predict the rock burst parameters of the coal rock mass to be tested based on the analysis results and the nonlinear mapping relationship.

[0113] Optionally, when the relationship determination unit 201 is used to construct a comprehensive evaluation index system for microstructure parameters, it is specifically used to:

[0114] Multi-field coupled in-situ electron computed tomography and nuclear magnetic resonance technology are used to obtain the microscopic parameters of coal and rock samples under different environments;

[0115] Use in-situ acoustic wave monitoring and digital image correlation to dynamically track microstructural changes in coal and rock samples under different environments;

[0116] Based on fractal theory and damage mechanics, a comprehensive evaluation index system for microstructural parameters is constructed according to mesoscopic parameters and microstructural change information.

[0117] Optionally, when the relationship determination unit 201 is used to determine the nonlinear mapping relationship between the microstructure parameters and the impact proneness index in the microstructure parameter comprehensive evaluation index system, it is specifically used to:

[0118] Through physical similarity simulation experiments, the impact tendency index of coal rock samples under different microstructural parameters in the comprehensive evaluation index system of microstructural parameters is obtained;

[0119] Support vector machine and random forest algorithms are used to construct a nonlinear mapping relationship between microstructure parameters and impact proneness index.

[0120] Optionally, when the relationship determination unit 201 is used to construct a nonlinear mapping relationship between the microstructure parameter and the impact proneness index, it is specifically used to:

[0121] Determine the weight of each microstructure parameter in the comprehensive evaluation index system of microstructure parameters;

[0122] Based on the weights, the microstructure parameters are weighted to obtain weighted microstructure parameters;

[0123] A nonlinear mapping relationship between weighted microstructural parameters and impact proneness index is constructed.

[0124] Optionally, the microstructure parameters include crack ratio, porosity, pore connectivity index, and surface roughness parameter; the analysis results include crack ratio change trend, porosity change trend, pore connectivity index change trend, and surface roughness parameter change trend; the rock burst parameter includes rock burst occurrence probability; the parameter prediction unit 203 is used to predict the rock burst parameter of the coal rock mass to be tested based on the analysis results and the nonlinear mapping relationship, specifically for:

[0125] Determine the probability of the first rock burst according to the crack ratio and porosity variation trends;

[0126] According to the nonlinear mapping relationship, the impact tendency index value corresponding to the parameter value of the pore connectivity index is determined, and the probability of the second rock burst is determined according to the change trend of the pore connectivity index and the impact tendency index value;

[0127] Determine the probability of the third rock burst according to the variation trend of surface roughness parameters;

[0128] The rock burst occurrence probability of the coal rock mass to be measured is determined based on the first rock burst occurrence probability, the second rock burst occurrence probability and the third rock burst occurrence probability.

[0129] Optionally, the microstructure parameters include acoustic characteristic parameters and spatial characteristic parameters, the analysis results include acoustic characteristic parameter change trends and spatial characteristic parameter change trends, the rock burst parameters include rock burst occurrence locations, and the parameter prediction unit 203 is used to predict the rock burst parameters of the coal and rock mass to be tested based on the analysis results and the nonlinear mapping relationship, specifically for:

[0130] According to the changing trends of acoustic characteristic parameters and spatial characteristic parameters, the rock burst occurrence location of the coal rock mass to be measured is determined.

[0131] Optionally, the rock burst parameters include rock burst intensity. The parameter prediction unit 203 is configured to predict the rock burst parameters of the coal and rock mass to be tested based on the analysis results and the nonlinear mapping relationship, specifically for:

[0132] According to the analysis results and nonlinear mapping relationship, the change trend of the impact tendency index is determined;

[0133] According to the changing trend of the impact tendency index and the analysis results, the rock burst intensity of the coal rock mass to be tested is determined.

[0134] It should be noted that the aforementioned explanation of the embodiment of the rock burst parameter prediction method is also applicable to the rock burst parameter prediction system of this embodiment, and will not be repeated here.

[0135] In summary, the system provided by the embodiments of the present disclosure constructs a comprehensive microstructural parameter evaluation index system and determines a nonlinear mapping relationship between microstructural parameters and rock burst propensity index within the system. Based on this system, the microstructural parameters of the coal and rock mass to be tested are monitored and their changing trends are analyzed to obtain analysis results. Based on the analysis results and the nonlinear mapping relationship, rock burst parameters of the coal and rock mass to be tested are predicted. Consequently, accurate rock burst parameters can be predicted.

[0136] In order to implement the above embodiments, the present 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 by the above embodiments.

[0137] In order to implement the above embodiments, the present disclosure further proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the methods provided by the above embodiments.

[0138] In order to implement the above embodiments, the present disclosure further provides a computer program product, including a computer program, which implements the methods provided in the above embodiments when executed by a processor.

[0139] The collection, storage, use, processing, transmission, provision and disclosure of user personal information involved in this disclosure are in compliance with relevant laws and regulations and do not violate public order and good morals.

[0140] It is important to note that personal information collected from users should be used for legitimate and reasonable purposes and should not be shared or sold beyond these legitimate uses. Furthermore, such collection / sharing should be conducted only 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 the relevant user information before using the feature. Furthermore, any necessary steps must be taken to safeguard and secure access to such personal information and ensure that others with access to personal information comply with its privacy policy and procedures.

[0141] This disclosure contemplates providing implementations that allow users to selectively block the use or access of personal information data. Specifically, this disclosure contemplates providing hardware and / or software to prevent or block access to such personal information data. Risks can be minimized by limiting data collection and deleting data once it is no longer needed. Furthermore, where applicable, such personal information can be de-identified to protect user privacy.

[0142] In the descriptions of the aforementioned embodiments, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are mutually inconsistent.

[0143] 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 being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. Throughout the present disclosure, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0144] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present disclosure includes additional implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present disclosure belong.

[0145] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the 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 (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM) or flash memory, a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0146] It should be understood that various parts of the present disclosure can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement the present invention: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0147] Those skilled in the art will appreciate that all or part of the steps in the method for implementing the above-mentioned embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0148] In addition, the functional units in the various embodiments of the present disclosure may be integrated into a single processing module, each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.

[0149] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present disclosure have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure. A person of ordinary skill in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present disclosure.

Claims

1. A method for predicting rock burst parameters, characterized in that: include: Constructing a comprehensive evaluation index system for microstructural parameters and determining a nonlinear mapping relationship between microstructural parameters and impact proneness index in the comprehensive evaluation index system for microstructural parameters; Based on the microstructural parameter comprehensive evaluation index system, the microstructural parameters of the coal and rock mass to be tested are monitored, and the variation trend of the microstructural parameters is analyzed to obtain analysis results; The rock burst parameters of the coal rock mass to be measured are predicted based on the analysis results and the nonlinear mapping relationship.

2. The method according to claim 1, characterized in that The construction of a comprehensive evaluation index system for microstructure parameters includes: Multi-field coupled in-situ electron computed tomography and nuclear magnetic resonance technology are used to obtain the microscopic parameters of coal and rock samples under different environments; Using in-situ acoustic wave monitoring and digital image correlation method to dynamically track the microstructural changes of the coal and rock samples under different environments; Based on fractal theory and damage mechanics, a comprehensive evaluation index system for microstructure parameters is constructed according to the mesoscopic parameters and the microstructure change information.

3. The method according to claim 1, characterized in that Determining the nonlinear mapping relationship between the microstructure parameters and the impact tendency index in the microstructure parameter comprehensive evaluation index system includes: Through physical similarity simulation experiments, the impact tendency index of the coal rock sample under different microstructural parameters in the microstructural parameter comprehensive evaluation index system is obtained; A support vector machine and a random forest algorithm are used to construct a nonlinear mapping relationship between the microstructure parameters and the impact tendency index.

4. The method according to claim 3, characterized in that The constructing of a nonlinear mapping relationship between the microstructure parameter and the impact tendency index includes: Determining the weight of each microstructure parameter in the microstructure parameter comprehensive evaluation index system; Based on the weights, weighted processing is performed on the microstructure parameters to obtain weighted microstructure parameters; A nonlinear mapping relationship between the weighted microstructure parameters and the impact tendency index is constructed.

5. The method according to claim 1, wherein The microstructure parameters include crack ratio, porosity, pore connectivity index, and surface roughness parameter; the analysis results include crack ratio change trend, porosity change trend, pore connectivity index change trend, and surface roughness parameter change trend; the rock burst parameters include rock burst occurrence probability; and predicting the rock burst parameters of the coal rock mass to be tested based on the analysis results and the nonlinear mapping relationship includes: determining a probability of occurrence of a first rock burst according to the crack ratio variation trend and the porosity variation trend; Determining, according to the nonlinear mapping relationship, a rock burst tendency index value corresponding to the parameter value of the pore connectivity index, and determining a probability of occurrence of a second rock burst according to a change trend of the pore connectivity index and the rock burst tendency index value; determining a probability of occurrence of a third rock burst according to a variation trend of the surface roughness parameter; The rock burst occurrence probability of the coal rock mass to be measured is determined according to the first rock burst occurrence probability, the second rock burst occurrence probability, and the third rock burst occurrence probability.

6. The method according to claim 1, characterized in that The microstructure parameters include acoustic characteristic parameters and spatial characteristic parameters, the analysis results include acoustic characteristic parameter change trends and spatial characteristic parameter change trends, the rock burst parameters include rock burst occurrence locations, and predicting the rock burst parameters of the coal rock mass to be tested based on the analysis results and the nonlinear mapping relationship includes: The rock burst occurrence location of the coal rock mass to be measured is determined according to the variation trend of the acoustic characteristic parameters and the variation trend of the spatial characteristic parameters.

7. The method according to claim 1, characterized in that The rock burst parameters include rock burst intensity. The rock burst parameters of the coal rock mass to be measured are predicted based on the analysis results and the nonlinear mapping relationship, including: Determining a change trend of an impact tendency index based on the analysis results and the nonlinear mapping relationship; The rock burst intensity of the coal rock mass to be tested is determined according to the change trend of the rock burst tendency index and the analysis result.

8. A rock burst parameter prediction system, characterized in that: include: a relationship determination unit, configured to construct a microstructure parameter comprehensive evaluation index system and determine a nonlinear mapping relationship between microstructure parameters and impact proneness index in the microstructure parameter comprehensive evaluation index system; A parameter analysis unit is used to monitor the microstructural parameters of the coal and rock mass to be tested based on the microstructural parameter comprehensive evaluation index system, and to analyze the variation trend of the microstructural parameters to obtain analysis results; A parameter prediction unit is used to predict the rock burst parameters of the coal rock mass to be tested based on the analysis results and the nonlinear mapping relationship.

9. An electronic device, characterized in that: The electronic device comprises: a memory for storing executable program code; A processor is configured to call and run the executable program code from the memory, so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

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