Method and device for determining loss data of coal seam roadway, storage medium, processor and program product

Abstract

The invention discloses a method and a device for determining loss data of a coal seam roadway, a storage medium, a processor and a program product. The method comprises the steps that at least one characteristic parameter of a vibration event in a coal seam roadway is obtained, and the characteristic parameter is used for at least representing the vibration frequency of the vibration event; the characteristic parameters are predicted, the impact danger level of the coal seam roadway is obtained, and the impact danger level is in positive correlation with impact ground pressure acting on the coal seam roadway; in response to the situation that the impact danger level is larger than a level threshold value, the characteristic parameters are constructed, loss data are obtained, and the loss data are at least used for representing the expansion condition of cracks in the coal seam roadway under the impact ground pressure. The technical problem that the accuracy of determining the influence condition of the micro-seismic event on the coal seam roadway is low is solved.

Description

Technical Field

[0001] The present application relates to the field of mining technology, and in particular to a method, device, storage medium, processor and program product for determining loss data of a coal seam tunnel. Background Art

[0002] At present, for the prediction of rock burst and impact ground pressure in coal seam tunnels, how to integrate intelligent models to study dynamic disaster problems and form data-driven intelligent early warning methods to effectively identify impact danger areas and danger levels is crucial to realizing intelligent early warning of dangers in coal seam tunnels.

[0003] In the related art, the rock burst and rock burst prediction is usually completed by using the manual identification method. However, the above method has the technical problem of low accuracy in determining the impact of microseismic events on coal seam tunnels.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present application provide a method, device, storage medium, processor and program product for determining loss data of a coal seam tunnel, so as to at least solve the technical problem of low accuracy in determining the impact of microseismic events on coal seam tunnels.

[0006] According to one aspect of an embodiment of the present application, a method for determining loss data of a coal seam tunnel is provided, and the method may include: obtaining at least one characteristic parameter of a vibration event in a coal seam tunnel, wherein the characteristic parameter is used to at least characterize the vibration frequency of the vibration event; predicting the characteristic parameter to obtain the impact hazard level of the coal seam tunnel, wherein the impact hazard level is positively correlated with the impact ground pressure acting on the coal seam tunnel; in response to the impact hazard level being greater than a level threshold, constructing the characteristic parameter to obtain loss data, wherein the loss data is at least used to characterize the expansion of cracks in the coal seam tunnel under the impact ground pressure.

[0007] Optionally, the characteristic parameters are predicted to obtain the impact hazard level of the coal seam tunnel, including: using a prediction model to predict the characteristic parameters to obtain the impact hazard level, wherein the prediction model is trained based on historical characteristic parameters and historical impact hazard levels corresponding to the historical characteristic parameters.

[0008] Optionally, in response to the impact hazard level being greater than a level threshold, characteristic parameters are constructed to obtain a loss function, including: in response to the impact hazard level being greater than the level threshold, determining the occurrence time of the vibration event and the occurrence location of the vibration event; allocating the vibration event to a corresponding grid window according to the occurrence time and occurrence location to obtain a spatial area corresponding to the vibration event; determining a characteristic parameter set corresponding to the spatial area from multiple characteristic parameters; and constructing the characteristic parameter set to obtain loss data.

[0009] Optionally, vibration events are assigned to corresponding grid windows according to the time and location of occurrence to obtain a spatial area corresponding to the vibration event, including: determining the spatial scanning area of ​​the coal seam tunnel; constructing an enclosing grid corresponding to the coal seam tunnel based on the spatial scanning area; and assigning vibration events to grid windows in the enclosing grid according to the time and location of occurrence to obtain a spatial area.

[0010] Optionally, according to the time and location of occurrence, the vibration event is assigned to a grid window in the enclosing grid to obtain a spatial area, including: determining the size information of the enclosing grid; based on the size information, determining the coordinate information of the grid window, wherein the coordinate information is used to characterize the position of the grid window in the coal seam tunnel; according to the coordinate information, time and location of occurrence, the grid window corresponding to the vibration event is determined as the spatial area.

[0011] Optionally, constructing a characteristic parameter set to obtain loss data includes: determining a change trend of the crack based on the characteristic parameter set; and predicting the change trend to obtain loss data.

[0012] According to another aspect of an embodiment of the present application, a device for determining loss data of a coal seam tunnel is also provided, and the device may include: an acquisition unit, used to acquire at least one characteristic parameter of a vibration event in a coal seam tunnel, wherein the characteristic parameter is used to at least characterize the vibration frequency of the vibration event; a prediction unit, used to predict the characteristic parameter to obtain the impact hazard level of the coal seam tunnel, wherein the impact hazard level is positively correlated with the impact ground pressure acting on the coal seam tunnel; a construction unit, used to construct the characteristic parameter in response to the impact hazard level being greater than a level threshold, to obtain loss data, wherein the loss data is at least used to characterize the expansion of cracks in the coal seam tunnel under the impact ground pressure.

[0013] According to another aspect of an embodiment of the present application, a computer-readable storage medium is also provided, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the method for determining the loss data of the coal seam tunnel of the embodiment of the present application.

[0014] According to another aspect of an embodiment of the present application, a processor is further provided, the processor being used to run a program, wherein when the program is run by the processor, the method for determining loss data of a coal seam tunnel of an embodiment of the present application is executed.

[0015] According to another aspect of an embodiment of the present application, a computer program product is also provided, which includes computer instructions, wherein the computer instructions, when executed by a processor, implement the method for determining loss data of a coal seam tunnel of an embodiment of the present application.

[0016] In the embodiment of the present application, at least one characteristic parameter of a vibration event in a coal seam tunnel is obtained, wherein the characteristic parameter is used to at least characterize the vibration frequency of the vibration event; the characteristic parameter is predicted to obtain the impact hazard level of the coal seam tunnel, wherein the impact hazard level is positively correlated with the rock burst acting on the coal seam tunnel; in response to the rock burst hazard level being greater than the level threshold, the characteristic parameter is constructed to obtain loss data, wherein the loss data is at least used to characterize the expansion of cracks in the coal seam tunnel under rock burst. That is, in the embodiment of the present application, at least one characteristic parameter of a vibration event in a coal seam tunnel is determined, and the rock burst hazard level of the coal seam tunnel is determined based on the characteristic parameter. If the rock burst hazard level is greater than the level threshold, the characteristic parameter can be constructed to obtain the expansion of cracks in the coal seam tunnel under rock burst, thereby solving the technical problem of low accuracy in determining the impact of microseismic events on coal seam tunnels, and achieving the technical effect of improving the accuracy of determining the impact of microseismic events on coal seam tunnels. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0018] Figure 1 is a flow chart of a method for determining loss data of a coal seam tunnel according to an embodiment of the present application;

[0019] Figure 2 is a flowchart of constructing a prediction model according to an embodiment of the present application;

[0020] Figure 3 is a schematic diagram of a spatial scan according to an embodiment of the present application;

[0021] Figure 4 is a schematic diagram of a device for determining loss data of a coal seam tunnel according to an embodiment of the present application;

[0022] Figure 5 is a structural block diagram of a computer terminal according to an embodiment of the present application;

[0023] Figure 6 It is a block diagram of an electronic device for a method for determining loss data of a coal seam tunnel according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] According to an embodiment of the present application, an embodiment of a method for determining loss data of a coal seam tunnel is provided. The steps shown in the flowchart of the accompanying drawing can be executed in a computer system such as a set of computer executable instructions, and, although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0027] In this embodiment, a method for determining loss data of coal seam tunnels is proposed. The method can determine at least one characteristic parameter of a vibration event in a coal seam tunnel, and determine the impact hazard level of the coal seam tunnel based on the characteristic parameter. If the impact hazard level is greater than the level threshold, the characteristic parameter can be constructed to obtain the expansion of cracks in the coal seam tunnel under impact ground pressure, thereby solving the technical problem of low accuracy in determining the impact of microseismic events on coal seam tunnels and achieving the technical effect of improving the accuracy of determining the impact of microseismic events on coal seam tunnels.

[0028] Figure 1 FIG. 1 is a flow chart of a method for determining loss data of a coal seam tunnel according to an embodiment of the present application. Figure 1 As shown, the method may include the following steps:

[0029] Step S102, obtaining at least one characteristic parameter of a vibration event in a coal seam tunnel, wherein the characteristic parameter is used to at least characterize a vibration frequency of the vibration event.

[0030] In the technical solution provided in the above step S102 of the present application, the above vibration event may be a microseismic event, which may refer to a small earthquake activity caused by stress release in rocks, coal seams or other geological bodies, and may be caused by underground engineering activities, such as mining, tunneling, hydraulic fracturing and other activities, which trigger changes in the internal stress state of the rock mass. The above coal seam roadway may be a driving roadway, which may be a passage mechanically or manually excavated in underground rocks or coal seams for the purposes of prospecting, mining, ventilation, transportation, drainage, power supply, pedestrians, etc. during mining or underground engineering construction, for example, it may be a steeply inclined and thick coal seam roadway. The above-mentioned characteristic parameters may include relevant indicators for rock burst prediction, and physical information indicators with strong sensitivity to rock burst pressure, which may be multi-source microseismic information, which may be used to at least characterize the vibration frequency of vibration events, which may be microseismic information indicators, and relevant indicators for rock burst prediction, which may include the commonly used b value, Ab value, mine earthquake activity S value, total fault area At value, missing earthquake value, cumulative number of microseismic events in the region N, microseismic event rate (per day) NR, cumulative microseismic energy E in the region, cumulative microseismic energy rate (per day) ER in the region, cumulative microseismic crack volume V, cumulative microseismic crack volume rate (per day) and other data for integrating rock burst pressure. It should be noted that this is only an example, and there is no specific restriction on the type of characteristic parameters.

[0031] Optionally, the above characteristic parameters can be relevant indicators for rockburst prediction. The above indicators are dynamic and diverse, and have significant correlation with each other, and are highly sensitive to the predicted impact hazard level. At least one characteristic parameter can be obtained, and by integrating multiple cumulative damage indicators and comprehensively evaluating the development trend of impact hazard, it can effectively make up for the shortcomings of limited data monitoring range and limited early warning capability of a single indicator, and can effectively improve the timeliness and reliability of early warning.

[0032] Step S104, predicting the characteristic parameters to obtain the impact hazard level of the coal seam tunnel, wherein the impact hazard level is positively correlated with the rock burst pressure acting on the coal seam tunnel.

[0033] In the technical solution provided in the above step S104 of the present application, the characteristic parameters can be predicted to determine the impact hazard level of the coal seam tunnel. The above impact hazard level can be a risk level, which can include a strong level, a medium level, a weak level and no level, etc., which is positively correlated with the rock burst pressure of the coal seam tunnel, that is, the greater the rock burst pressure, the higher the corresponding impact hazard level. It should be noted that this is only an example, and there is no specific restriction on the type of impact hazard level.

[0034] Optionally, the characteristic parameters are predicted to obtain the impact hazard level corresponding to the coal seam tunnel, so as to provide early warning of potential dangers in the coal seam tunnel.

[0035] Step S106, in response to the impact hazard level being greater than the level threshold, constructing characteristic parameters to obtain loss data, wherein the loss data is at least used to characterize the expansion of cracks in the coal seam tunnel under impact ground pressure.

[0036] In the technical solution provided in the above step S106 of the present application, it is determined whether the impact hazard level is greater than the level threshold. If the impact hazard level is greater than the level threshold, the characteristic parameters can be constructed to obtain loss parameters. Among them, the above loss parameters can be used to determine the damage to the coal body and the expansion of cracks, at least to characterize the expansion of cracks in the coal seam tunnel under rock burst pressure, to determine the cumulative damage of the coal seam (or coal body), and to evaluate the degree of crack expansion in the coal seam. It should be noted that this is only an example, and there is no specific restriction on the type of loss function.

[0037] Optionally, the rock burst can be a dynamic disaster phenomenon caused by the accumulation and concentrated release of stress inside the rock mass during underground mining or tunneling, and can occur in high-stress environments, such as deep mines, high-strength rock or coal mining areas. When the stress inside the rock mass exceeds the strength limit of the rock, the cracks inside the rock expand rapidly and suddenly release energy, rock burst will be generated. Rock bursts can manifest in various forms, including sudden rock rupture, explosions, displacement of rock strata, rock bursts, sudden ejection of rock debris, etc.

[0038] In this embodiment, considering that the occurrence of rock burst in the tunneling tunnel has strong similarities with the occurrence mechanism and characteristics of time-lagged rock burst, this embodiment draws on each other in the monitoring and early warning concepts and methods. Optionally, based on the spatial scanning method, relevant indicators for rock burst prediction are introduced, for example, the commonly used b value and Ab value of rock burst, the S value of mine earthquake activity, the total fault area At value, and the missing earthquake value are integrated.

[0039] Furthermore, the impact danger area and level of steeply inclined and thick coal seam tunnel excavation can be modeled, and the physical information indicators with strong sensitivity to rock burst in coal seam tunnels can be sorted and analyzed. The microseismic data during the historical excavation of the mine can be used as source data to calculate multiple rock burst monitoring indicators, which can fully enhance the timeliness and reliability of early warning of impact danger areas. Among them, the introduced rock burst prediction related indicators can also include: the cumulative number of microseismic events in the region N, the microseismic event rate (per day) NR in the region, the cumulative microseismic energy E in the region, the cumulative microseismic energy rate (per day) ER in the region, the cumulative microseismic crack volume V, and the cumulative microseismic crack volume rate (per day) VR.

[0040] Optionally, the above evaluation method of the fracture volume V may be calculated using an octree algorithm, and the calculation process may include the following:

[0041] Set the target density threshold t, divide the microseismic event points to get n non-empty child nodes when the threshold is t, set the maximum tree depth to hm, and set the three-dimensional microseismic event set S within a certain grid range:

[0042] S={s i |s i =(x i ,y i ,z i ),i=1,2,L,|S·}

[0043] Where S is the three-dimensional seismic point set, and the fracture volume V based on the octree algorithm is calculated based on this point set. si is the event point set in the i-th non-empty node after division, |s i |≤t.

[0044] Create a global index set B for all non-empty nodes:

[0045] B = {b i |i=1,2,L,n}

[0046] Among them, b i A global index number that can be used to identify each node.

[0047] Furthermore, the spatial query function H(x) can be used to calculate the hierarchy of non-empty nodes based on the parent-child relationship between the index and the node:

[0048] h i =H(b i );

[0049] For each node, the edge length set L0 along the x, y, and z axes is:

[0050]

[0051] Among them, l0 j It can be the edge length of the i-th layer node along the j direction.

[0052] The result of each iteration of the algorithm is eight equal divisions, and for three dimensions, it is two equal divisions, from which the edge length of the i-th layer node along the j direction can be obtained:

[0053]

[0054] Therefore, the volume V of a single non-empty node in the i-th layer can be obtained i :

[0055]

[0056] By summing up the volumes of all non-empty nodes, we can get the cumulative crack volume V:

[0057]

[0058] Optionally, Table 1 is a table of calculation formulas and meanings of various indicators, and the calculation formulas and meanings of other selected indicators are shown in Table 1.

[0059] Table 1 Calculation formula and meaning of each indicator

[0060]

[0061] Optionally, characteristic parameters are obtained and predicted to obtain the impact hazard level of the coal seam tunnel. If the impact hazard level is greater than the level threshold, the characteristic parameters can be adjusted to determine the distribution and generation of cracks in the coal seam tunnel under the current rock burst pressure.

[0062] In this embodiment, at least one characteristic parameter of the vibration event is obtained, and based on the characteristic parameter, the impact risk level corresponding to the rock burst in the coal seam tunnel is determined. If the impact risk is equal to or greater than the level threshold, the loss data can be determined and an early warning can be issued to remind the user that the rock burst has a great impact on the tunnel. The early warning can be issued by SMS, voice, etc. It should be noted that this is only an example, and there is no specific limitation on the early warning method.

[0063] Through the above steps S102 and S106 of the present application, at least one characteristic parameter of the vibration event in the coal seam tunnel is obtained, wherein the characteristic parameter is used to at least characterize the vibration frequency of the vibration event; the characteristic parameter is predicted to obtain the impact hazard level of the coal seam tunnel, wherein the impact hazard level is positively correlated with the rock burst acting on the coal seam tunnel; in response to the rock burst hazard level being greater than the level threshold, the characteristic parameter is constructed to obtain loss data, wherein the loss data is at least used to characterize the expansion of cracks in the coal seam tunnel under rock burst. That is, in the embodiment of the present application, at least one characteristic parameter of the vibration event in the coal seam tunnel is determined, and the rock burst hazard level of the coal seam tunnel is determined based on the characteristic parameter. If the rock burst hazard level is greater than the level threshold, the characteristic parameter can be constructed to obtain the expansion of cracks in the coal seam tunnel under rock burst, thereby solving the technical problem of low accuracy in determining the impact of microseismic events on coal seam tunnels, and achieving the technical effect of improving the accuracy of determining the impact of microseismic events on coal seam tunnels.

[0064] The above method of this embodiment is further introduced below.

[0065] As an optional implementation, step S104 predicts the characteristic parameters to obtain the impact hazard level of the coal seam tunnel, including: using a prediction model to predict the characteristic parameters to obtain the impact hazard level, wherein the prediction model is trained based on historical characteristic parameters and historical impact hazard levels corresponding to the historical characteristic parameters.

[0066] In this embodiment, a prediction model can be obtained in advance based on historical characteristic parameters and historical impact hazard levels corresponding to the historical characteristic parameters. Using the prediction model, the characteristic parameters can be predicted to obtain the impact hazard level. The above prediction model can be a machine learning algorithm model built based on the distributed gradient boosting decision tree algorithm (XGBoost) framework, which can be used to determine the regional impact hazard under the steeply inclined and thick coal seam excavation tunnel.

[0067] Table 2 Classification of historical impact hazard levels

[0068]

[0069]

[0070] Optionally, based on historical characteristic parameters and historical impact hazard levels corresponding to the historical characteristic parameters, training data is constructed, that is, the various microseismic information indicators (that is, historical characteristic parameters) of the impact hazard area of ​​the steeply inclined and thick coal seam tunneling tunnel are integrated as the feature set, and the maximum energy occurring in each area is used as the impact hazard level. Optionally, Table 2 is a historical impact hazard level classification table. As shown in Table 2, the impact hazard levels corresponding to different microseismic energies (e) are different, thereby obtaining a data set for training. It should be noted that the historical impact hazard level classification table here is only for example, and there is no specific restriction on the size of the parameters in the historical impact hazard level classification table.

[0071] Optionally, historical characteristic parameters and historical impact hazard levels corresponding to the historical characteristic parameters are obtained to determine the microseismic energy corresponding to the historical characteristic parameters. The above data can be divided into training samples (accounting for 60% of the total samples) and test samples (accounting for 20% of the total samples). The prediction model can be constructed using the training samples and the test samples.

[0072] In this embodiment, an XGBoost model can be established, trained using training samples, and the model parameters can be optimized through a grid search algorithm and a cross-validation (GridSearchCV) method to determine the optimal hyperparameter combination of the model. After determining the most optimal hyperparameter combination of the model, an intelligent identification model (i.e., a prediction model) of impact hazard areas and hazard levels based on the cumulative damage to the surrounding rock of the tunnel can be obtained. Furthermore, the constructed prediction model can be tested using a test sample, and the model performance can be scored using the harmonic mean (F1 value) of accuracy, precision, and recall to verify the excellence of the model used. Finally, the above prediction model can be used for actual on-site predictions.

[0073] Figure 2 is a flowchart of constructing a prediction model according to an embodiment of the present application, such as Figure 2 As shown, the prediction model can be constructed by following the steps below:

[0074] Step S202: collecting historical characteristic parameters and historical impact risk levels corresponding to the historical characteristic parameters.

[0075] In this embodiment, historical characteristic parameters and historical impact risk levels corresponding to the historical characteristic parameters may be collected. The historical characteristic parameters may refer to characteristic data of microseismic information indicators in the impact risk area. The historical impact risk level may be regional impact risk level label data.

[0076] Step S204, grouping the historical characteristic parameters and historical impact risk levels.

[0077] In this embodiment, data grouping may be performed on historical characteristic parameters and historical impact risk levels to obtain test samples and training samples.

[0078] Step S206: obtain a prediction model by training using the training samples.

[0079] In this embodiment, the XGBoost model can be trained using the training samples.

[0080] Optionally, the model parameters can be optimized by grid search algorithm and cross validation method to determine the optimal hyperparameter combination of the model. After determining the most optimal hyperparameter combination of the model, an intelligent identification model (i.e., prediction model) of impact hazard area and hazard level based on the accumulated surrounding rock damage of the tunnel can be obtained.

[0081] Step S208: Use the test samples to perform model evaluation on the prediction model.

[0082] In this embodiment, the constructed prediction model can be tested using the test sample, and the model performance can be scored using the harmonic mean of accuracy, precision and recall (F1 value) to verify the excellence of the model used. Based on the verification results, it can be determined whether the model parameters of the prediction model need to be adjusted to obtain a prediction model with high prediction accuracy.

[0083] In this embodiment, the impact hazard area of ​​the coal seam tunnel is modeled by the XGBoost model to determine the impact hazard level corresponding to the impact hazard area. The above method has high accuracy, can timely warn of potential dangers, and has good prediction potential for impact hazards under the geological conditions of steeply inclined and thick coal seams. It can effectively improve the limited monitoring range and poor model generalization ability of existing technologies, thereby achieving the purpose of automatic calculation of multiple indicators of microseismic rupture monitoring data and reducing parameter fluctuations and interference.

[0084] As an optional implementation, step S106, in response to the impact hazard level being greater than the level threshold, constructing characteristic parameters to obtain a loss function, including: in response to the impact hazard level being greater than the level threshold, determining the occurrence time of the vibration event and the occurrence location of the vibration event; according to the occurrence time and occurrence location, allocating the vibration event to the corresponding grid window to obtain the spatial area corresponding to the vibration event; determining the characteristic parameter set corresponding to the spatial area from multiple characteristic parameters; and constructing the characteristic parameter set to obtain loss data.

[0085] In this embodiment, if it is determined that the impact hazard level is greater than the level threshold, the time and place of occurrence of the vibration event can be determined. According to the time and place of occurrence, the vibration event can be assigned to the corresponding grid space to obtain the spatial area corresponding to the vibration event. The characteristic parameter set corresponding to the spatial area can be determined from multiple characteristic parameters; the characteristic parameter set is constructed to obtain loss data. Among them, the above-mentioned occurrence location can be used to determine the coordinate information of the vibration event, which can be the three-dimensional spatial position coordinates of the vibration event. It should be noted that this is only an example, and there is no specific restriction on the content of the above-mentioned occurrence location.

[0086] Optionally, if the impact hazard level is greater than the level threshold, the time and location of the vibration event (e.g., microseismic event) can be determined. The spatial scanning technology can be used to divide the grid window, and the vibration event can be assigned to the corresponding grid window according to the location (e.g., coordinates) and time of the vibration event to obtain the spatial area corresponding to the vibration event. The characteristic parameters corresponding to the vibration event can be assigned to the corresponding spatial area, thereby obtaining the characteristic data sets corresponding to different spatial areas, which can be microseismic data sets.

[0087] Optionally, the microseismic parameters in the microseismic data set can be used to represent the characteristics of the spatial area. For example, the microseismic parameter b value and A(b) value can be used to describe the precursor information of rock burst and can be used to reflect the abnormal characteristics before rock burst occurs. They are highly sensitive to the prediction of rock burst hazards.

[0088] In this embodiment, the event points (i.e., vibration events) are assigned to corresponding grid windows. The data points are screened one by one, and whether they are within the enclosing grid range is determined according to their coordinate positions. Then, the point information (including date, coordinates, and energy) is transferred to the corresponding grid (i.e., spatial region) to obtain the feature parameter set corresponding to the spatial region.

[0089] As an optional implementation, vibration events are assigned to corresponding grid windows according to the time and location of occurrence to obtain a spatial area corresponding to the vibration event, including: determining a spatial scanning area of ​​a coal seam tunnel; constructing an enclosing grid corresponding to the coal seam tunnel based on the spatial scanning area; and assigning vibration events to grid windows in the enclosing grid according to the time and location of occurrence to obtain a spatial area.

[0090] In this embodiment, the spatial scanning area of ​​the coal seam roadway can be determined; based on the spatial scanning area, an enclosing grid corresponding to the coal seam roadway is constructed; according to the time and location of occurrence, the vibration event is assigned to the grid window in the enclosing grid to obtain the spatial area. The above-mentioned spatial scanning area can be the spatial scanning range of the coal seam roadway, which can be used to determine the length and width of the working face rectangle. The scanning area can be used to construct an enclosing grid, which can accommodate at least one microseismic event point (that is, a vibration event).

[0091] Optionally, the range of the spatial scan is determined, and the length and width of the working surface rectangle are set to accommodate all microseismic event points to form an enclosing grid. Further, the vibration events can be assigned to the grid windows of the enclosing grid according to the time and location of occurrence to obtain the spatial area.

[0092] As an optional implementation, the vibration event is assigned to a grid window in the enclosing grid according to the time and location of occurrence to obtain a spatial area, including: determining the size information of the enclosing grid; determining the coordinate information of the grid window based on the size information, wherein the coordinate information is used to characterize the position of the grid window in the coal seam tunnel; according to the coordinate information, time and location of occurrence, the grid window corresponding to the vibration event is determined as the spatial area.

[0093] In this embodiment, the size information may include the size of the surrounding grid and the sliding step. The grid window may be grids. The coordinate information may be the coordinate position of the grid window, which may be used to determine the position of the grid window.

[0094] For example, determine the size of the grid window and the sliding step length. It is predetermined that the rock burst in the tunneling roadway mainly occurs in the area about 100 meters behind the coal seam tunnel head, so the side length of each grid window can be set to 100 meters. In order to include the maximum number of data points (that is, vibration events) to construct a data set and reduce the impact of redundant data, the sliding step length can be set to 25 meters. According to the length and width of the enclosing grid, the number of grids and the coordinate position of each grid can be determined.

[0095] As an optional implementation, a characteristic parameter set is constructed to obtain loss data, including: determining a change trend of cracks based on the characteristic parameter set; and predicting the change trend to obtain loss data.

[0096] In this embodiment, by analyzing the microseismic parameters in the grid window, the dynamic evolution of rock burst in the spatial region of the coal seam tunnel can be tracked, and then the cumulative damage and crack propagation of the coal seam tunnel in the spatial region can be evaluated.

[0097] Optionally, Figure 3is a schematic diagram of a spatial scan according to an embodiment of the present application, such as Figure 3 As shown, the window can be slid along the sliding direction to complete the spatial scanning.

[0098] In this embodiment, the range of the spatial scan is determined, and the length and width of the working face rectangle can be set to form an enclosing grid. The size of the grid window and the sliding step can be determined. For example, the sliding step can be set to 25 meters. The event points can be assigned to the corresponding grid window, and the data points can be screened one by one. It is determined whether they are within the enclosing grid range based on their coordinate positions, and then the point information (including date, coordinates, and energy) is transferred to the corresponding grid. Furthermore, the magnitude of each event in each grid window can be determined. In order to exclude the influence of small energy events on the sample, points with a single event energy of less than 100 joules (J) can be deleted, and grids with a number of data points greater than 50 can be screened out to form a grid data point set. The grids in the screened grid data point set can be divided into impact hazard thresholds. When an event with energy of more than the fifth power occurs in the tunnel excavation, the possibility of impact hazard is very high. As shown in Table 1, the impact hazard level can be divided according to the energy size.

[0099] Optionally, through the spatial scanning method, the various characteristics of the impact danger zone of the steeply inclined and thick coal seam tunneling can be calculated, which can well reflect the spatiotemporal evolution law of the impact ground pressure of tunneling, thereby achieving a spatiotemporal coordinated impact ground pressure warning effect.

[0100] In an embodiment of the present application, at least one characteristic parameter of a vibration event in a coal seam tunnel is determined, and based on the characteristic parameter, the impact hazard level of the coal seam tunnel is determined. If the impact hazard level is greater than the level threshold, the characteristic parameter can be constructed to obtain the expansion of cracks in the coal seam tunnel under impact ground pressure, thereby solving the technical problem of low accuracy in determining the impact of microseismic events on coal seam tunnels and achieving the technical effect of improving the accuracy of determining the impact of microseismic events on coal seam tunnels.

[0101] The following is a detailed description of a time-lagged impact rock pressure warning method based on the cumulative spatiotemporal distribution characteristics of tunnel surrounding rock damage in conjunction with the accompanying drawings and specific implementation methods. It should be noted that the present application is not limited to the following specific embodiments, and all equivalent changes made on the basis of the technical solution of the present application fall within the protection scope of the present application.

[0102] At present, with the continuous deepening of research, for the prediction of rock burst and impact ground pressure in coal seam tunnels, especially for the safe and efficient excavation of steeply inclined and thick coal seam tunnels, how to integrate intelligent models to study dynamic disaster problems, form data-driven intelligent early warning methods, effectively extract the characteristics of disaster precursor data, and effectively identify impact danger areas and danger levels is crucial to realizing intelligent early warning.

[0103] In the existing technology, there are still the following problems in the prediction of rock burst and rock burst in coal seam tunnels during the process of rock burst and rock burst: (1) Due to the large delay in microseismic monitoring and analysis, there are data errors and discrete interference in the time series evolution characteristics of geophysical information transmitted by microseismic transmission, and the timeliness and reliability of rock burst risk assessment based solely on microseismic source parameters are not stable enough; (2) For the processing of effective microseismic rupture signals, manual processing is slow and subjective, which has large interference and fluctuations in parameter calculation, and the generalization ability of the model is poor; (3) There are certain spatial characteristics of rock burst in tunnel excavation, and existing methods rarely involve the identification of rock burst areas and spatiotemporal coordination research. Understanding the mechanical mechanism of rock burst is the key to successful early warning. How to improve the interpretability of microseismic parameters for rock burst early warning requires in-depth research.

[0104] In this embodiment, considering that the rock burst in the tunnel exhibits certain periodic characteristics in time and space, this phenomenon is similar to a time-lag rock burst, and therefore a new spatial early warning idea for rock burst is proposed: when the stress is concentrated to a certain extent, exceeding the crack strength of the rock, the crack begins to expand and release energy.

[0105] Optionally, relevant concepts such as event rate, energy rate, volume rate, etc. in the field of rockburst prediction are introduced to analyze the cumulative damage of the coal body and evaluate the degree of crack propagation in order to predict the impact risk in advance, and to realize the identification of impact hazard in spatial areas by optimizing the judgment criteria of microseismic monitoring results.

[0106] Optionally, the coal damage and crack expansion in the spatial region can be evaluated through relevant parameters such as event rate, energy rate and octree algorithm. Secondly, combined with the time-lag warning indicators, a multi-indicator evolution data set of the area where the impact hazard occurs is constructed, and a time-lag impact rock pressure warning model suitable for time-lag is established, so as to realize the intelligent early warning of impact rock pressure in the tunnel of steeply inclined and thick coal seams, and provide scientific guidance for the safe excavation of the tunnel.

[0107] Optionally, this method can track the dynamic evolution of rock burst pressure in the spatial area of ​​the tunneling tunnel by analyzing the microseismic parameters within the grid window, and then evaluate the cumulative damage and crack propagation of the coal body in the spatial area.

[0108] In this embodiment, historical tunnel excavation data of steeply inclined and extremely thick coal seam tunnel excavation can be used as source data. The historical microseismic data (i.e., historical characteristic data) is representative, and the data in the area where large energy events or rock bursts occurred during the historical period can be well used for model training and learning, so that a prediction model with high prediction accuracy can be constructed for predicting the rock burst hazard level.

[0109] In this embodiment, in view of the extremely complex geological conditions of coal rock occurrence environment such as steeply inclined and thick coal seams, useful information is efficiently extracted from the massive data of rock burst pressure of steeply inclined and thick coal seams, which can effectively enhance the generalization ability and prediction accuracy of the model.

[0110] In this embodiment, an intelligent prediction method for the spatiotemporal characteristic indicators of the cumulative damage to the surrounding rock of the tunnel is constructed. Through spatial scanning to identify effective precursor information of microseismic events (i.e., characteristic parameters), the cumulative degree of fracture of coal and rock and the accumulation of energy are captured, and the prediction threshold is fully moved forward to timely judge the abnormal area, which can effectively make up for the deficiency of expert knowledge indicators. The comprehensive regional intelligent identification model (i.e., prediction model) can be used to significantly enhance the accuracy of rock burst prediction, and can provide reference and reference ideas for mines under the same geological conditions. The rock mechanics theory and intelligent algorithms are cross-integrated to improve the reliability of early warning. The proposed prediction method for the impact danger area of ​​the accumulated spatiotemporal coordinated damage to the surrounding rock of the tunnel based on machine learning can provide a certain reference value for the anti-bumping early warning of the tunnel in steeply inclined and thick coal seams.

[0111] According to an embodiment of the present application, a device for determining loss data of a coal seam tunnel is also provided. It should be noted that the device for determining loss data of a coal seam tunnel of this embodiment can be used to execute the method for determining loss data of a coal seam tunnel of embodiment 1 of the present application.

[0112] Figure 4 Schematic diagram of a device for determining loss data of a coal seam tunnel according to an embodiment of the present application. Figure 4 As shown, the device 40 for determining the loss data of the coal seam tunnel may include: an acquisition unit 402 , a prediction unit 404 and a construction unit 406 .

[0113] The acquisition unit 402 is used to acquire at least one characteristic parameter of a vibration event in a coal seam tunnel, wherein the characteristic parameter is used to at least characterize a vibration frequency of the vibration event.

[0114] The prediction unit 404 is used to predict the characteristic parameters to obtain the impact hazard level of the coal seam tunnel, wherein the impact hazard level is positively correlated with the impact ground pressure acting on the coal seam tunnel.

[0115] The construction unit 406 is used to construct characteristic parameters in response to the impact hazard level being greater than the level threshold to obtain loss data, wherein the loss data is at least used to characterize the expansion of cracks in the coal seam tunnel under impact ground pressure.

[0116] The device for determining loss data of a coal seam tunnel of this embodiment obtains at least one characteristic parameter of a vibration event in a coal seam tunnel through an acquisition unit, wherein the characteristic parameter is used to at least characterize the vibration frequency of the vibration event; predicts the characteristic parameter through a prediction unit to obtain the impact hazard level of the coal seam tunnel, wherein the impact hazard level is positively correlated with the impact ground pressure acting on the coal seam tunnel; and constructs the characteristic parameter through a construction unit in response to the impact hazard level being greater than a level threshold to obtain loss data, wherein the loss data is at least used to characterize the expansion of cracks in the coal seam tunnel under the impact ground pressure, thereby solving the technical problem of low accuracy in determining the impact of microseismic events on coal seam tunnels and achieving the technical effect of improving the accuracy of determining the impact of microseismic events on coal seam tunnels.

[0117] The embodiment of the present application may provide a computer terminal, which may be any computer terminal device in a computer terminal group. Optionally, in this embodiment, the computer terminal may also be replaced by a terminal device such as a mobile terminal.

[0118] Optionally, in this embodiment, the computer terminal may be located in at least one network device among a plurality of network devices of the computer network.

[0119] In this embodiment, the above-mentioned computer terminal can execute the program code of the following steps in the method for determining the loss data of the coal seam tunnel: obtaining at least one characteristic parameter of the vibration event in the coal seam tunnel, wherein the characteristic parameter is used to at least characterize the vibration frequency of the vibration event; predicting the characteristic parameter to obtain the impact hazard level of the coal seam tunnel, wherein the impact hazard level is positively correlated with the impact ground pressure acting on the coal seam tunnel; in response to the impact hazard level being greater than the level threshold, constructing the characteristic parameter to obtain loss data, wherein the loss data is at least used to characterize the expansion of cracks in the coal seam tunnel under the impact ground pressure.

[0120] Optionally, Figure 5 is a structural block diagram of a computer terminal according to an embodiment of the present application, such as Figure 5 As shown, the computer terminal 508 may include: one or more (only one is shown in the figure) processors 502 , a memory 504 and a transmission device 506 .

[0121] Among them, the memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the method and device for determining the loss data of the coal seam tunnel in the embodiment of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, the above-mentioned method for determining the loss data of the coal seam tunnel is realized. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the computer terminal 508 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0122] The processor can call the information and application programs stored in the memory through the transmission device to perform the following steps: obtain at least one characteristic parameter of the vibration event in the coal seam tunnel, wherein the characteristic parameter is used to at least characterize the vibration frequency of the vibration event; predict the characteristic parameter to obtain the impact hazard level of the coal seam tunnel, wherein the impact hazard level is positively correlated with the impact ground pressure acting on the coal seam tunnel; in response to the impact hazard level being greater than the level threshold, construct the characteristic parameter to obtain loss data, wherein the loss data is at least used to characterize the expansion of cracks in the coal seam tunnel under the impact ground pressure.

[0123] It can be understood by those skilled in the art that Figure 5 The structure shown is for illustration only, and the computer terminal 508 may also be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a PDA, a mobile Internet device (MID for short), a PAD, or other terminal devices. Figure 5 The structure of the computer terminal 508 is not limited. For example, the computer terminal 508 may also include Figure 5 More or fewer components (such as network interfaces, display devices, etc.) shown in, or having Figure 5 Different configurations are shown.

[0124] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a computer-readable storage medium, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0125] According to an embodiment of the present application, a computer-readable storage medium is also provided, which includes a stored program, wherein the program executes the method for determining the loss data of the coal seam tunnel in the above embodiment.

[0126] Optionally, in this embodiment, the computer-readable storage medium may be located in any one of the computer terminals in a computer terminal group in a computer network, or in any one of the mobile terminals in a mobile terminal group.

[0127] Optionally, in this embodiment, the computer-readable storage medium is configured to store program code for executing the following steps: obtaining at least one characteristic parameter of a vibration event in a coal seam tunnel, wherein the characteristic parameter is used to at least characterize the vibration frequency of the vibration event; predicting the characteristic parameter to obtain the impact hazard level of the coal seam tunnel, wherein the impact hazard level is positively correlated with the impact ground pressure acting on the coal seam tunnel; in response to the impact hazard level being greater than a level threshold, constructing the characteristic parameter to obtain loss data, wherein the loss data is at least used to characterize the expansion of cracks in the coal seam tunnel under the impact ground pressure.

[0128] Optionally, the computer-readable storage medium may also execute program code of the following steps: predicting characteristic parameters using a prediction model to obtain an impact risk level, wherein the prediction model is trained based on historical characteristic parameters and historical impact risk levels corresponding to the historical characteristic parameters.

[0129] Optionally, the computer-readable storage medium may also execute program codes for the following steps: in response to an impact hazard level being greater than a level threshold, determining the time of occurrence of a vibration event and the location of occurrence of the vibration event; allocating the vibration event to a corresponding grid window according to the time of occurrence and the location of occurrence to obtain a spatial region corresponding to the vibration event; determining a feature parameter set corresponding to the spatial region from a plurality of feature parameters; and constructing the feature parameter set to obtain loss data.

[0130] Optionally, the computer-readable storage medium may also execute program codes of the following steps: determining a spatial scanning area of ​​a coal seam tunnel; constructing an enclosing grid corresponding to the coal seam tunnel based on the spatial scanning area; and assigning vibration events to grid windows in the enclosing grid according to the time and location of occurrence to obtain a spatial area.

[0131] Optionally, the above-mentioned computer-readable storage medium can also execute the program code of the following steps: determine the size information of the enclosing grid; based on the size information, determine the coordinate information of the grid window, wherein the coordinate information is used to characterize the position of the grid window in the coal seam tunnel; according to the coordinate information, occurrence time, and occurrence location, determine the grid window corresponding to the vibration event as a spatial area.

[0132] Optionally, the computer-readable storage medium may also execute program codes for the following steps: determining a change trend of the crack based on a characteristic parameter set; and predicting the change trend to obtain loss data.

[0133] In this embodiment, at least one characteristic parameter of a vibration event in a coal seam tunnel is determined, and based on the characteristic parameter, the impact hazard level of the coal seam tunnel is determined. If the impact hazard level is greater than the level threshold, the characteristic parameter can be constructed to obtain the expansion of cracks in the coal seam tunnel under impact ground pressure, thereby solving the technical problem of low accuracy in determining the impact of microseismic events on coal seam tunnels and achieving the technical effect of improving the accuracy of determining the impact of microseismic events on coal seam tunnels.

[0134] According to an embodiment of the present application, a processor is also provided, which is used to run a program, wherein when the program is run by the processor, the method for determining the loss data of the coal seam tunnel in the above embodiment is executed.

[0135] Optionally, in this embodiment, the computer terminal may be located in at least one network device among a plurality of network devices of the computer network.

[0136] In this embodiment, the above-mentioned computer terminal can execute the program code of the following steps in the method for determining the loss data of the coal seam tunnel: obtaining at least one characteristic parameter of the vibration event in the coal seam tunnel, wherein the characteristic parameter is used to at least characterize the vibration frequency of the vibration event; predicting the characteristic parameter to obtain the impact hazard level of the coal seam tunnel, wherein the impact hazard level is positively correlated with the impact ground pressure acting on the coal seam tunnel; in response to the impact hazard level being greater than the level threshold, constructing the characteristic parameter to obtain loss data, wherein the loss data is at least used to characterize the expansion of cracks in the coal seam tunnel under the impact ground pressure.

[0137] Among them, the memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the method and device for determining the loss data of the coal seam tunnel in the embodiment of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, realizing the above-mentioned method for determining the loss data of the coal seam tunnel. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0138] The processor can call the information and application programs stored in the memory through the transmission device to perform the following steps: obtain at least one characteristic parameter of the vibration event in the coal seam tunnel, wherein the characteristic parameter is used to at least characterize the vibration frequency of the vibration event; predict the characteristic parameter to obtain the impact hazard level of the coal seam tunnel, wherein the impact hazard level is positively correlated with the impact ground pressure acting on the coal seam tunnel; in response to the impact hazard level being greater than the level threshold, construct the characteristic parameter to obtain loss data, wherein the loss data is at least used to characterize the expansion of cracks in the coal seam tunnel under the impact ground pressure.

[0139] Optionally, the processor may also execute program code of the following steps: predicting characteristic parameters using a prediction model to obtain an impact risk level, wherein the prediction model is trained based on historical characteristic parameters and historical impact risk levels corresponding to the historical characteristic parameters.

[0140] Optionally, the processor may also execute program codes of the following steps: in response to an impact hazard level being greater than a level threshold, determining the time of occurrence of a vibration event and the location of occurrence of the vibration event; allocating the vibration event to a corresponding grid window according to the time of occurrence and the location of occurrence to obtain a spatial region corresponding to the vibration event; determining a feature parameter set corresponding to the spatial region from a plurality of feature parameters; and constructing the feature parameter set to obtain loss data.

[0141] Optionally, the processor may also execute the program code of the following steps: determining the spatial scanning area of ​​the coal seam tunnel; constructing an enclosing grid corresponding to the coal seam tunnel based on the spatial scanning area; and assigning vibration events to grid windows in the enclosing grid according to the time and location of occurrence to obtain a spatial area.

[0142] Optionally, the processor may also execute the program code of the following steps: determining the size information of the enclosing grid; determining the coordinate information of the grid window based on the size information, wherein the coordinate information is used to characterize the position of the grid window in the coal seam tunnel; determining the grid window corresponding to the vibration event as a spatial area according to the coordinate information, occurrence time, and occurrence location.

[0143] Optionally, the processor may also execute program codes of the following steps: determining a change trend of the crack based on a characteristic parameter set; and predicting the change trend to obtain loss data.

[0144] By adopting the embodiment of the present application, at least one characteristic parameter of the vibration event in the coal seam tunnel is determined, and the impact hazard level of the coal seam tunnel is determined based on the characteristic parameter. If the impact hazard level is greater than the level threshold, the characteristic parameter can be constructed to obtain the expansion of cracks in the coal seam tunnel under impact ground pressure, thereby solving the technical problem of low accuracy in determining the impact of microseismic events on coal seam tunnels and achieving the technical effect of improving the accuracy of determining the impact of microseismic events on coal seam tunnels.

[0145] According to an embodiment of the present application, a computer program product is also provided, which includes computer instructions, wherein when the computer instructions are executed by a processor, the method for determining the loss data of the coal seam tunnel in the above embodiment is implemented.

[0146] An embodiment of the present application may provide an electronic device, which may include a memory and a processor.

[0147] Figure 6 It is a block diagram of an electronic device for determining a method for loss data of a coal seam tunnel according to an embodiment of the present application. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0148] like Figure 6 As shown, the device 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 to a random access memory (RAM) 603. In RAM 603, various programs and data required for the operation of the device 600 can also be stored. The computing unit 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0149] A number of components in the device 600 are connected to the I / O interface 605, including: an input unit 606, such as a keyboard, a mouse, etc.; an output unit 607, such as various types of displays, speakers, etc.; a storage unit 608, such as a disk, an optical disk, etc.; and a communication unit 609, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0150] The computing unit 601 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as a verification method for data. For example, in some embodiments, the verification method for data may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 600 via the ROM 602 and / or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the verification method for data described above may be executed. Alternatively, in other embodiments, the computing unit 601 may be configured to execute the data verification method in any other appropriate manner (for example, by means of firmware).

[0151] According to an embodiment of the present application, a method for determining loss data of a coal seam tunnel is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0152] Various embodiments of the systems and techniques described above herein may be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that may be executed and / or interpreted on a programmable system including at least one programmable processor that may be a dedicated or general purpose programmable processor that may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0153] The program code for implementing the method of the present application can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0154] In the context of the present application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0155] To provide interaction with a user, the systems and techniques described herein may be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or an LCD (liquid crystal display, monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a path ball) through which the user can provide input to the computer. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0156] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a Local Area Network (LAN), a Wide Area Network (WAN), and the Internet.

[0157] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0158] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0159] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0160] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0161] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed over multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0162] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0163] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of each embodiment method of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk, etc., which can store program code.

[0164] The above are only preferred implementations of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for determining loss data of a coal seam tunnel, characterized in that: include: Acquire at least one characteristic parameter of a vibration event in a coal seam tunnel, wherein the characteristic parameter is used to at least characterize a vibration frequency of the vibration event; Predicting the characteristic parameters to obtain the impact hazard level of the coal seam tunnel, wherein the impact hazard level is positively correlated with the rock burst pressure acting on the coal seam tunnel; In response to the impact hazard level being greater than a level threshold, the characteristic parameters are constructed to obtain loss data, wherein the loss data is at least used to characterize the expansion of cracks in the coal seam tunnel under the impact ground pressure.

2. The method according to claim 1, characterized in that The predicting of the characteristic parameters to obtain the impact hazard level of the coal seam tunnel includes: The characteristic parameters are predicted using a prediction model to obtain the impact risk level, wherein the prediction model is trained based on historical characteristic parameters and historical impact risk levels corresponding to the historical characteristic parameters.

3. The method according to claim 1, characterized in that In response to the impact risk level being greater than a level threshold, constructing the characteristic parameters to obtain a loss function includes: In response to the shock risk level being greater than the level threshold, determining the time of occurrence of the vibration event and the location of occurrence of the vibration event; According to the occurrence time and the occurrence location, the vibration event is allocated to a corresponding grid window to obtain a spatial area corresponding to the vibration event; Determining a characteristic parameter set corresponding to the spatial region from the plurality of characteristic parameters; The characteristic parameter set is constructed to obtain the loss data.

4. The method according to claim 3, characterized in that: The step of allocating the vibration event to a corresponding grid window according to the occurrence time and the occurrence location to obtain a spatial area corresponding to the vibration event includes: Determine the spatial scanning area of ​​the coal seam tunnel; Based on the spatial scanning area, construct an enclosing grid corresponding to the coal seam roadway; According to the occurrence time and the occurrence location, the vibration event is allocated to a grid window in the surrounding grid to obtain the spatial area.

5. The method according to claim 4, characterized in that The step of allocating the vibration event to a grid window in the surrounding grid according to the occurrence time and the occurrence location to obtain the spatial region comprises: Determining size information of the enclosing grid; Based on the size information, determining coordinate information of the grid window, wherein the coordinate information is used to characterize the position of the grid window in the coal seam tunnel; According to the coordinate information, the occurrence time, and the occurrence location, the grid window corresponding to the vibration event is determined as the spatial area.

6. The method according to claim 3, characterized in that The step of constructing the characteristic parameter set to obtain the loss data includes: Based on the characteristic parameter set, determining a change trend of the crack; The change trend is predicted to obtain the loss data.

7. A device for determining loss data of a coal seam tunnel, characterized in that: include: An acquisition unit, used for acquiring at least one characteristic parameter of a vibration event in a coal seam tunnel, wherein the characteristic parameter is used for at least characterizing a vibration frequency of the vibration event; A prediction unit, used for predicting the characteristic parameters to obtain the impact hazard level of the coal seam tunnel, wherein the impact hazard level is positively correlated with the rock burst pressure acting on the coal seam tunnel; A construction unit is used to construct the characteristic parameters in response to the impact hazard level being greater than the level threshold to obtain loss data, wherein the loss data is at least used to characterize the expansion of cracks in the coal seam tunnel under the impact ground pressure.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 6.

9. A processor, characterized in that: The processor is used to run a program, wherein the program executes the method according to any one of claims 1 to 6 when being run by the processor.

10. A computer program product, characterized in that The method comprises computer instructions, which implement the method according to any one of claims 1 to 6 when executed by a processor.

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