Support risk identification method and device for fault zone marginal area

Abstract

The invention provides a support risk identification method and device for a fault zone marginal area, and relates to the technical field of coal mine safety. The method comprises the steps that in response to the fact that a coal mine roadway is tunneled to a fault zone marginal area, surrounding rock structure information of the fault zone marginal area is collected, a fault stress sudden change index of the fault zone marginal area and a deformation coefficient of a supporting structure are determined according to the surrounding rock structure information, and the supporting structure is determined according to the fault stress sudden change index and the deformation coefficient of the supporting structure. And determining the support risk level of the fault zone edge area. According to the method, the support risk of the edge area of the fault zone can be identified, and potential risk points can be predicted in advance, so that the safety of mine operation is guaranteed, and accidents are reduced.

Description

Technical Field

[0001] The present application relates to the field of coal mine safety technology, and in particular to a method and device for identifying support risks in a fault zone edge area. Background Art

[0002] Tunnel surrounding rock refers to the rock or soil structure surrounding the tunnel during the excavation of the underground tunnel. Its stress state directly affects the stability and safety of the tunnel. Since underground excavation will destroy the original stress balance of the surrounding rock, resulting in changes in the stress of the surrounding rock, if not analyzed and monitored in time, it may cause safety hazards such as landslides and deformation. Therefore, monitoring of the tunnel is particularly important. Summary of the invention

[0003] The purpose of this application is to solve one of the technical problems in the related art at least to some extent.

[0004] To this end, the first purpose of this application is to propose a method for identifying support risks in the edge area of the fault zone, which can identify the support risks in the edge area of the fault zone, help predict potential risk points in advance, and thus ensure the safety of mine operations and reduce the occurrence of accidents.

[0005] The second objective of the present application is to provide a support risk identification device for the edge area of a fault zone.

[0006] The third objective of the present application is to provide an electronic device.

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

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

[0009] To achieve the above-mentioned purpose, the first embodiment of the present application proposes a method for identifying support risks in the edge area of a fault zone, comprising:

[0010] In response to the coal mine tunnel being excavated to the edge area of the fault zone, the surrounding rock structure information of the edge area of the fault zone is collected;

[0011] Determining the fault stress mutation index and the deformation coefficient of the support structure in the edge area of the fault zone according to the surrounding rock structure information;

[0012] The support risk level of the edge area of the fault zone is determined according to the fault stress mutation index and the deformation coefficient of the support structure.

[0013] To achieve the above-mentioned purpose, the second embodiment of the present application proposes a support risk identification device for the edge area of a fault zone, comprising:

[0014] A collection module, configured to collect surrounding rock structure information of the edge area of the fault zone in response to the coal mine roadway being driven to the edge area of the fault zone;

[0015] A first determination module, configured to determine a fault stress mutation index of the edge area of the fault zone and a deformation coefficient of the support structure according to the surrounding rock structure information;

[0016] A second determination module, configured to determine a support risk level of the edge area of the fault zone according to the stress mutation index and the deformation coefficient of the support structure.

[0017] To achieve the above object, an embodiment of the third aspect of the present application provides 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, so that the processor can execute the support risk identification method for the edge area of the fault zone described in the embodiment of the first aspect above.

[0018] To achieve the above object, an embodiment of the fourth aspect of the present application provides a computer-readable storage medium, on which a computer program is stored, and the computer instructions are used to make the computer execute the support risk identification method for the edge area of the fault zone described in the embodiment of one of the above aspects.

[0019] To achieve the above object, an embodiment of the fifth aspect of the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the support risk identification method for the edge area of the fault zone described in the embodiment of one of the above aspects.

[0020] The support risk identification method and device for the edge area of the fault zone provided by the present application collect the surrounding rock structure information of the edge area of the fault zone, and based on the surrounding rock structure information, determine the fault stress mutation index of the edge area of the fault zone and the deformation coefficient of the support structure, and then determine the support risk level of the edge area of the fault zone. By collecting and monitoring the surrounding rock structure information of the edge area of the fault zone, the support risk of the edge area of the fault zone can be identified, which helps to predict potential risk points in advance, thereby ensuring the safety of mine operations and reducing the occurrence of accidents.

[0021] Some of the additional aspects and advantages of the present application will be given in the following description, some will become obvious from the following description, or will be understood through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above and / or additional aspects and advantages of the present application will become obvious and easy to understand from the following description of the embodiments in conjunction with the drawings, where:

[0023] Figure 1Schematic flowchart of a method for identifying support risks in the edge area of a fault zone provided by an embodiment of the present application;

[0024] Figure 2 Schematic flowchart of another method for identifying support risks in the edge area of a fault zone provided by an embodiment of the present application;

[0025] Figure 3 Schematic structural diagram of a device for identifying support risks in the edge area of a fault zone provided by an embodiment of the present application. Detailed implementation manners

[0026] The embodiments of the present application will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements with the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present application, and should not be construed as a limitation to the present application.

[0027] Based on the roadway surrounding rock stress analysis technology using big data, stress analysis can be carried out through a multi-step comprehensive process. First, various sensors installed in the roadway are used to monitor data such as the stress, displacement, and vibration of the surrounding rock in real time, and a database containing multi-dimensional information is established in combination with historical engineering data and geological information.

[0028] Furthermore, these data are preprocessed to ensure the accuracy of the analysis. Optionally, the preprocessing process may include but is not limited to processing such as cleaning outliers, filling missing values, and data normalization.

[0029] Furthermore, through machine learning and data mining technologies, in-depth analysis of the data is carried out, including identifying the stress change trend through time series analysis, identifying the surrounding rock behavior pattern through clustering analysis, and predicting the future stress state using a regression model. At the same time, the stress field of the roadway surrounding rock is simulated by means of numerical simulation technologies (such as finite element and discrete element methods), and the simulation results are compared with the real-time monitoring data, and then the model is corrected to improve the accuracy of the model. Finally, by integrating the analysis results into an intelligent early warning system, an alarm is triggered when the stress exceeds a preset threshold, thereby effectively ensuring the stability and safety of the roadway.

[0030] When a coal mine roadway is driven near a fault zone, due to the sudden change in the surrounding rock properties in the boundary area between the fault zone and the normal rock mass, the stress distribution in the boundary area shows significant differences, forming a complex non-linear stress concentration phenomenon, thus increasing the deformation risk of the support structure. Since the stress release near the fault zone is not uniform, local stress mutation points may generate abnormal stress concentration. In the related technologies, a global uniform stress distribution model is usually used to analyze the stress of the roadway surrounding rock based on big data, which is difficult to capture the subtle local stress abnormal small areas within the edge area of the fault zone. Therefore, it is impossible to accurately predict the potential risk of support deformation within the edge area of the fault zone. In the case of failing to identify the non-uniform stress concentration area, the stress assessment result near the fault zone is inaccurate, and then there is a problem that the support strength and layout are insufficient within this type of edge area. The support structure is prone to deformation or even failure due to overload during construction. This misjudgment ultimately increases the instability risk of the roadway in the fault zone area. Especially in the stress concentration area, local collapse or support damage may be triggered, seriously threatening construction safety.

[0031] To solve the above problems, an embodiment of the present application provides a method and device for identifying the support risk in the edge area of the fault zone. The method and device for identifying the support risk in the edge area of the fault zone will be explained below in conjunction with the accompanying drawings.

[0032] Figure 1 The flow chart of a method for identifying the support risk in the edge area of the fault zone provided by an embodiment of the present application is as Figure 1 shown. The support risk identification method may include but is not limited to the following steps:

[0033] S101, in response to the coal mine roadway being driven to the edge area of the fault zone, collect the surrounding rock structure information of the edge area of the fault zone.

[0034] In some embodiments, the process of driving the coal mine roadway can be monitored in real time to determine whether the coal mine roadway has reached the edge area of the fault zone.

[0035] In some embodiments, in response to the coal mine roadway being driven to the edge area of the fault zone, that is, when the coal mine roadway is driven near the fault zone, the surrounding rock structure information of the edge area of the fault zone can be collected through various types of sensors.

[0036] In some embodiments, stress sensors such as fiber optic grating sensors (FOGS) or piezoelectric sensors can be deployed, and these sensors can monitor the stress change of the surrounding rock in real time.

[0037] In some embodiments, displacement sensors (such as laser scanners or displacement gauges) can be deployed to obtain information on the displacement or deformation of surrounding rock, and in particular, can detect minute displacement changes that may occur in the edge area of the fault zone.

[0038] In some embodiments, a ground penetrating radar system and acoustic detection equipment can be used to detect structural changes inside the surrounding rock, such as crack propagation, pore changes, or ore seam characteristics.

[0039] It can be understood that the above sensors can upload the collected information to the data center in real time through wireless transmission or a data collector for subsequent analysis and processing.

[0040] S102. According to the surrounding rock structure information, determine the fault stress mutation index in the edge area of the fault zone and the deformation coefficient of the support structure.

[0041] In some embodiments, after obtaining the surrounding rock structure information, the surrounding rock structure information in the edge area of the fault zone can be analyzed to determine the fault stress mutation index in the edge area of the fault zone and the deformation coefficient of the support structure in the edge area of the fault zone.

[0042] In some embodiments, the surrounding rock structure information in the edge area of the fault zone can be preprocessed. Further, according to the preprocessed surrounding rock structure information, determine the surrounding rock stress information and the response information of the support structure in the edge area of the fault zone, and according to the surrounding rock stress information, determine the fault stress mutation index in the edge area of the fault zone, and according to the response information of the support structure and the attribute information of the support structure, determine the deformation coefficient of the support structure.

[0043] In some embodiments, preprocessing the collected surrounding rock structure information can improve the data quality and ensure the accuracy of the analysis results. Since the data collected by the sensors may contain noise, missing values, and outliers, noise removal, missing value filling, and outlier elimination are performed through preprocessing.

[0044] In some embodiments, the steps of preprocessing may include but are not limited to the following steps:

[0045] First, remove noise and smooth the data through a filtering algorithm to reduce the influence of factors such as electromagnetic interference;

[0046] Second, for data with missing values, use an interpolation method (such as linear interpolation or interpolation based on neighboring data) to fill in the missing values of the data to ensure the integrity of the data;

[0047] Third, use a standardization method (such as Z-score standardization) to convert data from different sources into a unified scale to eliminate the dimension difference and avoid some variables occupying inappropriate weights in subsequent analysis;

[0048] Finally, an outlier detection algorithm (such as outlier detection based on mean and standard deviation) is adopted to identify and remove abnormal data to prevent these outliers from having an adverse impact on the analysis results.

[0049] It can be understood that through the above preprocessing steps, the surrounding rock structure information can be made clearer, more consistent and meet the requirements of subsequent analysis, thus ensuring the accuracy of subsequent parameter and model evaluation.

[0050] S103. Determine the support risk level of the fault zone edge area according to the fault stress mutation index and the deformation coefficient of the support structure.

[0051] In some embodiments, the magnitude of the fault stress mutation index (Earthquake Stress Index, ESI) directly reflects the severity of stress changes in the fault zone edge area and is closely related to the support risk level. The larger the ESI value, the more significant the stress mutation at the fault zone edge and the higher the stress concentration. At this time, the surrounding rock is prone to instability, causing the support structure to bear greater deformation pressure, thereby increasing the risk of support failure or collapse. Optionally, for a higher ESI value, the support risk level is evaluated as high risk and stronger support design is required; for a medium ESI value, the support risk level is evaluated as medium risk and appropriate reinforcement may be needed; while a lower ESI value indicates that the stress distribution at the fault zone edge is relatively stable, and the support risk level is evaluated as low risk, and conventional support measures can be adopted. By quantifying the magnitude of ESI, the support requirements in the fault zone edge area can be effectively evaluated to ensure the safety and economy of the support design.

[0052] In some embodiments, the magnitude of the deformation coefficient of the support structure (Shape Deformation Coefficient, SDC) directly reflects the deformation ability of the support structure under stress and is closely related to the support risk level of the fault zone edge area. The larger the SDC value, the greater the deformation degree of the support structure when stressed, and the relatively lower the stiffness and anti-deformation ability of the material. This means that the support structure is more likely to lose stability due to deformation, thereby increasing the support risk in the fault zone edge area. Optionally, for a higher SDC value, the support risk level is evaluated as high risk and stronger support design is required, such as increasing the support thickness or selecting higher-strength materials; for a medium SDC value, the support risk level is evaluated as medium risk and appropriate reinforcement may be needed; while a lower SDC value indicates that the support structure remains stable under the current stress, and the support risk level is evaluated as low risk, and conventional support measures can be adopted. By the magnitude of the SDC value, the reliability of the support structure in the fault zone edge area can be effectively evaluated to ensure the safety of the project.

[0053] In some embodiments, after obtaining the fault stress mutation index and the deformation coefficient of the support structure, the support risk level of the fault zone edge area can be determined according to the fault stress mutation index and the deformation coefficient of the support structure.

[0054] In some embodiments, a fault support risk assessment model can be pre-constructed based on the fault stress mutation index and the deformation coefficient of the support structure collected historically.

[0055] In some embodiments, the fault stress mutation index and the deformation coefficient of the support structure determined in step S102 are input into the fault support risk assessment model, and the fault support risk assessment coefficient of the fault zone edge area is output through the fault support risk assessment model. Further, according to the fault support risk assessment coefficient, the support risk level of the fault zone edge area is evaluated. For example, the support risk level can include but is not limited to low risk, medium risk, and high risk.

[0056] The support risk identification method for the fault zone edge area provided by the embodiments of the present application accurately collects and monitors the surrounding rock structure information of the fault zone edge area through multiple types of sensors. Further, based on the surrounding rock structure information, the fault stress mutation index and the deformation coefficient of the support structure in the fault zone edge area can be determined, so as to determine the support risk level of the fault zone edge area. By collecting and monitoring the surrounding rock structure information of the fault zone edge area, the support risk of the fault zone edge area can be identified, which helps to predict potential risk points in advance, thereby ensuring the safety of mine operations and reducing the occurrence of accidents.

[0057] Figure 2 It is a schematic flow chart of a support risk identification method for a fault zone edge area provided by the embodiments of the present application, as Figure 2 shown. The support risk identification method can include but is not limited to the following steps:

[0058] S201, in response to the coal mine roadway being driven to the fault zone edge area, collect the surrounding rock structure information of the fault zone edge area.

[0059] In some embodiments, when the coal mine roadway is driven to the fault zone edge area, the surrounding rock structure information of the fault zone edge area can be collected through multiple types of sensors. The specific process can refer to the relevant content recorded in the above embodiments and will not be elaborated here.

[0060] In some embodiments, after obtaining the surrounding rock structure information, a stress monitoring framework for the fault zone edge area can be established through data analysis and processing algorithms.

[0061] Optionally, after collecting data such as stress and displacement, the data can be cleaned and preprocessed to remove noise and correct sensor errors. Further, using a simulation method based on Finite Element Analysis (FEA), stress distribution simulation and extrapolation are performed according to the actually collected surrounding rock parameters (such as rock layer thickness, density, friction coefficient, etc.). By setting the boundary conditions and initial conditions of the edge area of the fault zone, a corresponding stress field model is established to accurately depict the stress distribution of the edge area of the fault zone.

[0062] Optionally, these analysis results are presented as easy-to-understand graphics or heat maps through visualization techniques (such as 3D modeling software), providing real-time stress monitoring and change trends to support real-time decision-making and emergency response.

[0063] In the embodiments of the present application, by collecting surrounding rock structure information in real time through a variety of high-precision sensors and combining stress simulation to establish a monitoring framework, the stress changes of the surrounding rock in the edge area of the fault zone can be accurately monitored to ensure safety during the construction process. When the coal mine roadway is driven to the edge area of the fault zone, due to the sudden change of the surrounding rock properties and the uneven stress release, local stress concentration may occur, and it is very difficult to capture this local stress concentration situation through a global uniform model. Through accurate stress data collection and dynamic simulation, abnormal stress concentration areas in the surrounding rock can be identified in a timely manner, providing real-time and reliable basis for support design and construction process. This helps to predict potential risk points in advance, avoid the failure or instability of the support structure caused by insufficient support design, thereby ensuring the safety of mine operations and reducing the occurrence of accidents.

[0064] S202. Preprocess the surrounding rock structure information of the edge area of the fault zone to obtain preprocessed surrounding rock structure information.

[0065] For the specific introduction of step S202, reference can be made to the relevant content recorded in step S102 above, which will not be elaborated here.

[0066] S203. Determine the surrounding rock stress information and the response information of the support structure in the edge area of the fault zone according to the preprocessed surrounding rock structure information.

[0067] In some embodiments, the surrounding rock stress information in the edge area of the fault zone may include, but is not limited to: stress values, widths, and surrounding rock elastic moduli at different times within a set period in the edge area of the fault zone.

[0068] Optionally, the stress values at different times within a set period in the edge area of the fault zone can be marked as σ m , σ m can represent the stress value of the edge area of the fault zone at the m-th moment within the set period.

[0069] Optionally, the widths of the fault zone edge region at different times within the set period can be marked as L m , L m can represent the width of the fault zone edge region at the m-th moment within the set period.

[0070] Optionally, the elastic modulus of the surrounding rock of the fault zone edge region at different times within the set period can be marked as E m , E m can represent the elastic modulus of the surrounding rock of the fault zone edge region at the m-th moment within the set period.

[0071] where m = 1, 2, 3, …, g, and g is a positive integer.

[0072] In some embodiments, the stress information of the surrounding rock in the fault zone edge region can be processed by a software system for the data collected by sensors. First, a high-precision sensor network including stress sensors, displacement sensors, etc. is deployed in the fault zone edge region to collect the stress values of the surrounding rock, the width of the fault zone, and the elastic modulus of the surrounding rock. Optionally, through the data acquisition module in the software system, the data streams of these sensors are received and processed in real time.

[0073] Optionally, the stress values of the surrounding rock are directly measured by stress sensors installed in the fault zone edge region. These sensors sense the stress state of the surrounding rock in real time and transmit the data to the software system. The software system can classify and store the stress data collected at different times according to the time stamps to form a stress value sequence over a period of time. Further, the software system will call a data parsing algorithm to extract the stress values, widths, and elastic moduli of the surrounding rock at each time point from the preprocessed data and store these values in the corresponding database for subsequent analysis.

[0074] In some embodiments, the width and the elastic modulus of the surrounding rock of the fault zone edge region can also be processed and stored by the software system. The width of the fault zone edge region is usually measured by a laser rangefinder or a 3D laser scanner. The software system can process and filter the received width to generate an accurate width sequence of the fault zone edge region. The elastic modulus of the surrounding rock can be obtained through prior laboratory tests or on-site rock sample tests and input into the software system as a reference value. If the elastic modulus may change over time, the software system can collect data through sensors such as acoustic waves or vibration frequencies, and then deduce the change trend of the elastic modulus. The acquisition and update of these data are all realized through the software system, so as to ensure the dynamics and accuracy of the relevant information of the fault zone edge region.

[0075] In some embodiments, the response information of the support structure within the edge region of the fault zone may include, but is not limited to, the stress values of the support structure at different times within a set period.

[0076] Optionally, the stress values of the support structure at different times within a set period may be marked as ZHσ n ; ZHσ n may represent the stress value borne by the support structure at the nth moment within a set period. Wherein, n = 1, 2, 3, …, k, and k is a positive integer.

[0077] Optionally, the response information of the support structure in the edge region of the fault zone can be determined by the software system analyzing the real-time monitoring data and the material information in the database. First, stress sensors (such as strain gauges) are installed at key positions of the support structure, and the stress values of the support structure at different time points are collected in real time through the sensors. The monitoring module of the software system will automatically receive and organize these stress data and file them in chronological order. At the same time, the software system will retrieve the elastic modulus data of the support structure material and the geometric parameters (cross-sectional area A and length ZHL) of the support structure from the material property database, and associate these data with the real-time stress data to form the complete response information of the support structure. Through this data integration, the software system can extract the stress values and other physical properties of the support structure at different times for subsequent analysis.

[0078] The acquisition methods of these quantitative data are different, and automatic collection and integration can be achieved through the software system. The stress data of the support structure are directly collected by stress sensors, which are installed at the main stress-bearing parts of the support structure and can capture the stress conditions of the support structure in the edge region of the fault zone in real time. The elastic modulus (ZHE) of the support structure material is usually obtained through laboratory tests and stored in the material database for ready access. The cross-sectional area (A) and length (ZHL) of the support structure are provided by engineering design data or actual measurement results, and these data have been input into the database before the support installation. The software system automatically integrates these data from different sources and provides them to the calculation module when needed to ensure the accuracy and real-time nature of the data, thus effectively supporting the subsequent calculation of the deformation coefficient.

[0079] S204. Determine the fault stress mutation index according to the surrounding rock stress information.

[0080] In some embodiments, according to the surrounding rock stress information, determine the stress value σ m , width L m and the elastic modulus E of the surrounding rock m at different times within a set period in the edge region of the fault zone. Further, from the stress values σ m at different times within a set period, determine the first maximum stress value σmax and the first minimum stress value σ min . According to the first maximum stress value σ max and the first minimum stress value σ min , the width L at different times within the set period m and the surrounding rock elastic modulus E m , determine the fault stress mutation index.

[0081] Optionally, using the stress values in the edge area of the fault zone at different times within the set period, construct a stress set G, then G = {σ1, σ2, σ3,..., σ g}, and calibrate the maximum and minimum values in the stress set G as the first maximum stress value σ max and the first minimum stress value σ min .

[0082] Optionally, calculate the fault stress mutation index ESI using the following formula (1):

[0083]

[0084] where ESI represents the fault stress mutation index; g represents the number of moments included in the set period; L m represents the width of the edge area of the fault zone at the m-th moment; E m represents the surrounding rock elastic modulus of the edge area of the fault zone at the m-th moment, σ max represents the first maximum stress value; σ min represents the first maximum stress value; g and m are positive integers.

[0085] The above formula (1) is used to calculate the fault stress mutation index ESI to quantify the stress mutation degree in the edge area of the fault zone over a period of time, so as to help identify the stress concentration risk in the edge area of the fault zone. In the above formula (1), (σ max -σ min ) represents the maximum stress difference in the edge area of the fault zone within a specific time period, reflecting the intensity of stress mutation; L m is used to quantify the spatial range where stress mutation occurs. The larger the width, the wider the range affected by stress concentration; E m represents the elastic response ability of the rock mass to stress at different time points. The higher the elastic modulus, the more stable the rock mass.

[0086] Furthermore, by taking the average value (dividing by the number of time periods g) of the data for multiple time periods, the average stress mutation situation over a period of time can be obtained.

[0087] The calculation steps of the above formula (1) include calculating the stress difference, multiplying by the fault zone width, dividing by the elastic modulus, and finally averaging the data at all time points to more accurately reflect the stress mutation trend in the edge area of the fault zone. The determination method of this application can balance short-term mutations and long-term trends, making the results more stable and representative.

[0088] S205. Determine the deformation coefficient of the support structure according to the response information of the support structure and the attribute information of the support structure.

[0089] In some embodiments, the attribute information of the support structure may include, but is not limited to: the cross-sectional area, length, and material elastic modulus of the support bracket.

[0090] Optionally, the cross-sectional area of the support bracket can be marked as A; the length of the support bracket can be marked as ZHL; the material elastic modulus of the support bracket can be marked as ZHE.

[0091] In some embodiments, according to the response information of the support structure, determine the stress values ZHσ of the support structure at different moments within a set period. n , from the stress values ZHσ of the support structure at different moments within a set period. n Determine the second maximum stress value ZHσ. max . Further, according to the second maximum stress value ZHσ. max And the attribute information of the support structure (the cross-sectional area A, length ZHL, and material elastic modulus ZHE of the support bracket), determine the deformation coefficient of the support structure.

[0092] Optionally, use the stress values borne by the support structure at different moments within a set period to construct a stress set H, then H = {ZHσ1, ZHσ2, ZHσ3,..., ZHσ. k}}. Mark the maximum value within the stress set H as the second maximum stress value ZHσ. max .

[0093] Optionally, calculate the deformation coefficient SDC of the support structure using the following formula (2):

[0094]

[0095] where SDC represents the deformation coefficient of the support structure; ZHσ. max represents the second maximum stress value; ZHL represents the length of the support structure; ZHE represents the material elastic modulus of the support structure; A represents the cross-sectional area of the support structure.

[0096] The above formula (2) is used to calculate the deformation coefficient SDC of the support structure to evaluate the deformation response of the support structure under the action of force. ZHσ in formula (2). maxIt represents the maximum stress value borne by the support structure, reflecting the extreme stress conditions that the support structure may experience in the stress concentration area of the fault zone; ZHL represents the length of the support structure, quantifying the deformation trend of the support structure in its length direction. The denominator part ZHE*A is the material elastic modulus of the support structure, representing the material stiffness or anti-deformation ability, and A is the cross-sectional area of the support structure, used to represent the stress-bearing cross-section of the structure. Formula (2) can combine stress (ZHσ max )、geometric characteristics (ZHL and A) and material characteristics (ZHE) to obtain the deformation response of the support structure under unit stress. Through the calculation of the above formula (2), SDC can quantify the deformation tendency of the support structure under extreme stress conditions. The larger the SDC value, the higher the deformation risk of the support structure, and additional support measures need to be taken to ensure the structural stability.

[0097] S206. Determine the support risk level in the edge area of the fault zone according to the fault stress mutation index and the deformation coefficient of the support structure.

[0098] In some embodiments, according to the fault stress mutation index and the deformation coefficient of the support structure, combined with a pre-trained fault support risk assessment model, determine the fault support risk assessment coefficient (FaultSupport riskassessment coefficient, FSRAC) in the edge area of the fault zone. That is, input the ESI and SDC obtained in steps S204 and S205 into the pre-trained fault support risk assessment model for processing to predict the FSRAC in the edge area of the fault zone.

[0099] Furthermore, after determining the fault support risk assessment coefficient, the support risk level in the edge area of the fault zone can be evaluated to obtain the support risk level in the edge area of the fault zone. Optionally, the support risk level can be divided into low risk, medium risk, high risk, etc.

[0100] In some embodiments, a fault support risk assessment model can be constructed based on historical fault stress mutation indices and deformation coefficients of the support structure. The specific construction process can include the following steps:

[0101] Optionally, collect a number of fault stress mutation indices, deformation coefficients of the support structure, and the corresponding support risk assessment coefficient FSRAC within a historical period of time, and calibrate ESI x 、SDC x and FSRAC x . It can be understood that x represents the numbering of a number of fault stress mutation indices, deformation coefficients of the support structure, and the corresponding fault support risk assessment coefficients generated within a historical period of time. Among them, x = 1, 2, 3,... d, and d is a positive integer.

[0102] Further, the above data collected over a historical period is formed into a historical dataset (ESI x , SDC x and FSRAC x ). Optionally, after obtaining the above historical dataset, the collected historical dataset can be automatically stored in the data through a software system, and the collected historical dataset can be managed.

[0103] Optionally, whenever new values of the fault stress mutation index and the deformation coefficient of the support structure are calculated, the software system will automatically store these data together with the timestamp and location information in the database, and calculate the current fault support risk assessment coefficient according to the pre-trained fault support risk assessment model, and synchronously store the fault support risk assessment coefficient and its corresponding ESI and SDC in the database.

[0104] For the convenience of subsequent analysis, the system will integrate the data at each time point into a complete data record, including the fault stress mutation index, the deformation coefficient of the support structure, the fault support risk assessment coefficient, and the associated time and location information.

[0105] In some embodiments, the database can be set with an automatic archiving function to regularly organize and back up these data according to time or region, ensuring that historical data over a period of time can be conveniently retrieved when needed for model training or trend analysis. Through this automated data management method of the software, the real-time and integrity of data collection can be ensured, avoiding omissions or errors in manual collection, and improving the accuracy of analysis.

[0106] It can be understood that the value of d is limited to a positive integer greater than or equal to 3 to ensure that there are enough equations when calculating the regression coefficients to accurately solve the values of each regression coefficient. Your idea is very correct because there is only one target equation, and the three regression coefficients in the regression model need to be determined by a system of equations. Therefore, at least three independent data points are required to generate three equations to form a solvable system of equations.

[0107] In addition, restricting the value of d to be not less than 3 can also ensure that the model has higher stability in practical applications because using multiple data points can reduce the influence of accidental errors and improve the fitting degree and accuracy of the regression model to the real situation. Therefore, calculating the regression coefficients through at least three historical sample data not only meets the mathematical requirements for solving the system of equations but also enhances the robustness of the model.

[0108] In some embodiments, a multiple regression model can be selected as the fault support risk assessment model and trained through the historical dataset to determine the values of the regression coefficients, according to the formula:

[0109] FSRAC x = β0 + β1 * ESI x + β2 * SDC x (3)

[0110] Wherein, β0, β1, and β2 are the regression coefficients of the fault support risk assessment model.

[0111] The multiple regression model is a statistical analysis method used to study the influence relationship of multiple independent variables on a dependent variable, and quantifies the contribution of each independent variable to the dependent variable by establishing an equation. In the fault support risk assessment, selecting the multiple regression model can comprehensively consider the influence of ESI and SDC on FSRAC, thereby making the identification of support risk more accurate. By using the historical dataset for training, the values of the regression coefficients can be accurately calculated, enabling the multiple regression model to accurately predict the support risk in the edge area of the fault zone.

[0112] It should be noted that the regression coefficient β0 is the intercept, indicating the baseline risk level when both ESI x and SDC x are zero; β1 is the regression coefficient of ESI x quantifying the influence degree of the sudden change of fault stress on the support risk; β2 is the regression coefficient of SDC x reflecting the influence of the deformation of the support structure on the support risk. The determination of these regression coefficients enables the multiple regression model to accurately predict the support risk and guide the support design decision-making in the project.

[0113] Optionally, to ensure that the multiple regression model can accurately reflect the influence of ESI and SDC on FSRAC, the regression coefficients β0, β1, and β2 can be optimized or adjusted by minimizing the error between the predicted value and the actual value, and finally the values of the regression coefficients β0, β1, and β2 are determined.

[0114] In some embodiments, the regression coefficients can be optimized by the least squares method, that is, by adjusting the regression coefficients β0, β1, and β2, so that the sum of the squared errors between the predicted value of the multiple regression model and FSRAC in the historical dataset reaches the minimum. Through the optimization of the least squares method, the fitting degree of the multiple regression model to the data can be improved, ensuring that the regression coefficients reflect the true contribution of each independent variable to the risk assessment, thereby making the multiple regression model have higher prediction accuracy in practical applications.

[0115] Furthermore, after determining the final β0, β1, and β2, the final fault support risk assessment model can be obtained. Input the real-time determined ESI and SDC into the fault support risk assessment model, and the fault support risk assessment model will output the FSRAC in the edge area of the fault zone in real time.

[0116] In some embodiments, after obtaining the real-time FSRAC of the fault zone edge area, the real-time FSRAC of the fault zone edge area is compared with a preset FSRAC threshold interval to determine the support risk level of the fault zone edge area.

[0117] Optionally, the FSRAC threshold interval may include a first threshold and a second threshold, wherein the first threshold is less than the second threshold. Optionally, the first threshold may be marked as FSRAC min , the second threshold can be labeled FSRAC max The first threshold and the second threshold can generate the FSRAC threshold interval as [FSRAC max ,FSRAC min ], further, the real-time FSRAC of the fault zone edge area is compared with the FSRAC threshold interval [FSRAC min ,FSRAC max ] are compared, and the support risk level of the fault zone edge area is evaluated based on the comparison results.

[0118] Optionally, if FSRAC < FSRAC min , it can be determined that the support risk level of the edge area of the fault zone is low risk; the low risk state means that the stress environment in the edge area of the fault zone is relatively stable, and the deformation risk of the support structure under the current stress is very low. In this case, the surrounding rock and support structure are subjected to small forces, and conventional support measures can be sufficient to meet safety requirements without additional reinforcement or adjustment. In the low risk state, the project can proceed as planned, and the construction cost and support material requirements are low, which helps to improve construction efficiency.

[0119] Optionally, if FSRAC min ≤FSRAC≤FSRAC max , it can be determined that the support risk level of the edge area of the fault zone is medium risk. The medium risk status means that the support structure in the edge area of the fault zone is under high stress and may be deformed to a certain extent, but it is still within the controllable range. In this case, although the support structure can remain stable temporarily, it should be closely monitored and appropriate reinforcement measures can be taken if necessary to prevent further increase in stress and cause instability. The medium risk status indicates that the support design needs to be appropriately adjusted to cope with possible stress concentration or deformation requirements, so as to ensure the continued safety of the project.

[0120] Optionally, FSRAC>FSRAC max, it is possible to determine that the support risk level in the edge area of the fault zone is a high-level risk; the high-risk state indicates that the support structure in the edge area of the fault zone is in a high-stress environment, with significant deformation risks and a relatively high possibility of support instability or damage. The high-risk state means that the support structure may not be sufficient to handle the current stress concentration, and immediate emergency reinforcement measures need to be taken, such as increasing the support thickness, replacing high-strength materials, or using special support techniques to ensure safety. If not addressed in a timely manner, the high-risk state may lead to the failure of the support structure, which in turn may trigger safety accidents such as collapses, posing a serious threat to the project and personnel.

[0121] In some embodiments, statistical methods can be used on historical datasets through data analysis software to pre-determine the FSRAC threshold range. Optionally, the software system collects and collates the FSRAC in a large number of known fault support projects and analyzes it based on the actual support conditions corresponding to the FSRAC in the known fault support projects, that is, analyzes the actual support performance in the low-risk state, medium-risk state, or high-risk state. Optionally, statistical analysis methods (such as quantile analysis or clustering analysis) are used to determine the distribution characteristics of the FSRAC and find the FSRAC threshold ranges corresponding to different risk levels. Optionally, the FSRAC in the historical dataset can be divided by risk level, and the upper limit value of the low-risk state can be selected as the FSRAC min , and the lower limit value of the high-risk state can be selected as the FSRAC max .

[0122] Optionally, through regression analysis or machine learning models, the rationality of the FSRAC threshold range is further verified to ensure that the FSRAC threshold range can accurately reflect the risk level of the support structure in different stress environments. Through statistical analysis and verification based on the historical dataset, the software system can automatically determine a reasonable FSRAC threshold range, thereby improving the accuracy of risk level identification.

[0123] In some embodiments, according to the support risk level corresponding to the edge area of the fault zone, support emergency tasks can be matched with the support risk level and the support emergency tasks can be executed for the edge area of the fault zone. That is to say, according to the support risk level corresponding to the edge area of the fault zone, it is possible to determine the corresponding support measures and suggestions that need to be taken for the edge area of the fault zone, and further generate support emergency tasks for the edge area of the fault zone based on the corresponding support measures and suggestions.

[0124] In some embodiments, for the case where the support risk level is a low-level risk, the generated support emergency tasks may include, but are not limited to: maintaining the existing support design, suggesting construction according to the conventional support plan, without additional reinforcement, and performing regular conventional monitoring.

[0125] In some embodiments, for the case where the support risk level is low risk, the existing support design and routine monitoring can be maintained through the monitoring and management functions of the software system. For example, the software system can set a regular monitoring frequency, collect stress and deformation data of the support structure regularly, and automatically archive these data into a database. By setting a threshold alarm function, basic monitoring is maintained within the low-risk range, avoiding overly frequent data collection and saving resources. The above countermeasures can ensure that the support structure can work properly in a stable stress environment without additional reinforcement. Moreover, problems or abnormalities can be detected in a timely manner through periodic inspections, thereby ensuring safety.

[0126] In some embodiments, for the case where the support risk level is medium risk, the generated support emergency tasks may include, but are not limited to: increasing the density or thickness of the support structure to enhance the support bearing capacity, while it is recommended to increase the monitoring frequency and closely observe the deformation of the support structure, and take further reinforcement measures if necessary.

[0127] In some embodiments, for the case where the support risk level is medium risk, the software system can adjust the density or thickness of the support through the intelligent management function. Optionally, it can include generating support reinforcement suggestions according to the change trend of real-time monitoring data. Further, the monitoring frequency can be increased to conduct more intensive data collection and analysis on the stress and deformation of the support structure. Through the change rate of the monitoring data, reinforcement suggestions are automatically generated, such as adding support structures or thickening support materials, and the engineering personnel are reminded to execute through the task management function. In the embodiment, through more refined monitoring and appropriate reinforcement, the cumulative deformation of the support structure with medium risk can be prevented after long-term stress, thereby improving the support stability and reliability.

[0128] In some embodiments, for the case where the support risk level is high risk, the generated support emergency tasks may include, but are not limited to: immediately implementing emergency reinforcement measures, such as increasing the support thickness, selecting high-strength support materials, and adopting special support technologies, strengthening the stability of the support structure, and conducting high-frequency monitoring to ensure the safety of the support structure and prevent support failure.

[0129] In some embodiments, for the case where the support risk level is high risk, an emergency reinforcement program can be automatically started, which is achieved through a high-frequency monitoring mode and support strengthening.

[0130] Optionally, the data acquisition frequency can be increased to frequently and real-time track key parameters such as the deformation and stress of the support structure. Further, emergency reinforcement suggestions can be generated, such as immediately increasing the support thickness, using high-strength materials, and applying special support techniques (such as bolt support or shotcrete). Through high-frequency monitoring, sudden deformations caused by stress concentration can be captured in a timely manner, and engineering personnel can be notified to take rapid reinforcement measures. This embodiment method can ensure the timely reinforcement and stability of the support structure under high stress conditions, thereby preventing support failure or accidents under high-level risks and ensuring the safety of the project and personnel.

[0131] In some embodiments, the surrounding rock structure information in the edge area of the fault zone can be continuously monitored, and based on the real-time monitoring data, the support risk assessment coefficient in the edge area of the fault zone can be updated in real time. Further, in response to the support risk assessment coefficient in the fault zone meeting the abnormal identification conditions, the risk assessment strategy can be optimized; and / or, the support emergency task can be adjusted. That is, by continuously monitoring the surrounding rock structure information in the edge area of the fault zone, according to the real-time surrounding rock structure information, the risk assessment strategy and / or the support emergency plan can be adjusted and optimized in real time.

[0132] In some embodiments, the surrounding rock structure information in the edge area of the fault zone can be continuously monitored through the real-time monitoring module and sensor network of the software system. Optionally, high-precision stress sensors, displacement sensors, and ground-penetrating radars and other devices are arranged in the edge area of the fault zone. Through the monitoring of the software system, information such as the stress, deformation, and rock mass stability of the surrounding rock is collected in real time. These sensors will automatically transmit the data to the platform where the software system is located to form a real-time data stream. The software system then stores and visually displays the data in chronological order, facilitating engineering personnel to grasp the on-site situation at any time, realizing continuous monitoring of the surrounding rock state, and avoiding the risk of support failure caused by sudden stress changes or surrounding rock deformations.

[0133] In some embodiments, the risk assessment strategy and the support emergency plan can be adjusted in real time through the automatic analysis of the software system. Optionally, the software system will analyze the monitoring data regularly through preset algorithms and thresholds, generate a real-time support risk assessment coefficient for the fault zone, and compare it with the preset risk level threshold. When the FSRAC shows significant fluctuations or exceeds the safe range, the risk assessment strategy is automatically adjusted, and a new support emergency task (such as support suggestions) is generated.

[0134] For example, when the evaluation result shows an increase in risk, it can be automatically recommended to increase the support density or use high-strength support materials. Optionally, the generated support emergency suggestions can be sent to engineering personnel through the notification component. This automated analysis and adjustment ensure that the support plan can flexibly respond to the dynamic changes in the surrounding rock state and improve the timely responsiveness of the support structure.

[0135] In the embodiments of the present application, precise acquisition and monitoring of the surrounding rock structure information in the edge area of the fault zone are carried out through multiple types of sensors. Further, based on the surrounding rock structure information, the fault stress mutation index and the deformation coefficient of the support structure in the edge area of the fault zone can be determined, so as to determine the support risk level in the edge area of the fault zone. By collecting and monitoring the surrounding rock structure information in the edge area of the fault zone, the support risk in the edge area of the fault zone can be identified, which helps to predict potential risk points in advance, thereby ensuring the safety of mine operations and reducing the occurrence of accidents.

[0136] Further, by continuously monitoring the surrounding rock structure information, the adjustment or optimization of the risk assessment strategy and the support emergency plan can be realized, which can ensure the support safety in the edge area of the fault zone.

[0137] In the case where the surrounding rock stress and structure state in the edge area of the fault zone may change suddenly due to tunneling or other environmental factors, resulting in the risk of the support structure facing overload, deformation or instability, by continuously monitoring the surrounding rock structure information, the risk can be monitored in real time, and potential support problems can be detected early. Further, the support plan can be automatically adjusted based on the risk situation, so as to be able to take corresponding support measures quickly, ensure the support safety in the edge area of the fault zone, thereby ensuring the safety and continuity of the project, and avoiding unnecessary shutdowns or support reconstruction costs.

[0138] It should be noted that the formulas involved in the embodiments of the present application are all dimensionless and take their numerical values for calculation. The formula is obtained by collecting a large amount of data and performing software simulation to obtain a formula closest to the actual situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0139] Figure 3 It is a schematic structural diagram of a support risk identification device for the edge area of a fault zone provided by the embodiments of the present application. As Figure 3 shown, the support risk identification device 300 for the edge area of the fault zone includes: a collection module 301, a first determination module 302, and a second determination module 303.

[0140] The collection module 301 is configured to collect the surrounding rock structure information of the edge area of the fault zone in response to the coal mine roadway being driven to the edge area of the fault zone;

[0141] The first determination module 302 is configured to determine the fault stress mutation index and the deformation coefficient of the support structure in the edge area of the fault zone according to the surrounding rock structure information;

[0142] The second determination module 303 is configured to determine the support risk level in the edge area of the fault zone according to the stress mutation index and the deformation coefficient of the support structure.

[0143] In some embodiments, the first determination module 302 is further configured to:

[0144] Preprocess the surrounding rock structure information of the fault zone edge area to obtain preprocessed surrounding rock structure information;

[0145] Determine the surrounding rock stress information and the response information of the support structure in the fault zone edge area according to the preprocessed surrounding rock structure information;

[0146] Determine the fault stress mutation index according to the surrounding rock stress information;

[0147] Determine the deformation coefficient of the support structure according to the response information of the support structure and the attribute information of the support structure.

[0148] In some embodiments, the first determination module 302 is further configured to:

[0149] Determine the stress values, widths, and surrounding rock elastic moduli at different times within a set period in the fault zone edge area according to the surrounding rock stress information;

[0150] Determine a first maximum stress value and a first minimum stress value from the stress values at different times within the set period;

[0151] Determine the fault stress mutation index according to the first maximum stress value, the first minimum stress value, the widths at different times within the set period, and the surrounding rock elastic modulus.

[0152] In some embodiments, the first determination module 302 is further configured to determine the stress mutation index using the following formula:

[0153]

[0154] where ESI represents the fault stress mutation index; g represents the number of times included in the set period; L m represents the width of the fault zone edge area at the m-th time; E m represents the surrounding rock elastic modulus of the fault zone edge area at the m-th time; σ max represents the first maximum stress value; σ min represents the first maximum stress value; g and m are positive integers.

[0155] In some embodiments, the first determination module 302 is further configured to:

[0156] Determine the stress values of the support structure at different times within a set period according to the response information of the support structure;

[0157] Determine a second maximum stress value from the stress values of the support structure at different times within the set period;

[0158] Determine the deformation coefficient of the support structure according to the second maximum stress value and the attribute information.

[0159] In some embodiments, the first determination module 302 is further configured to determine the deformation coefficient of the support structure by using the following formula:

[0160]

[0161] where SDC represents the deformation coefficient of the support structure; ZHσ max represents the second maximum stress value; ZHL represents the length of the support structure; ZHE represents the elastic modulus of the material of the support structure; A represents the cross-sectional area of the support structure.

[0162] In some embodiments, the second determination module 303 is further configured to:

[0163] Determine the fault support risk assessment coefficient of the fault zone edge area according to the stress mutation index and the deformation coefficient of the support structure, in combination with a pre-trained fault support risk assessment model;

[0164] Compare the fault support risk assessment coefficient with a threshold interval to determine the support risk level, where the threshold interval includes a first threshold and a second threshold, and the first threshold is less than the second threshold.

[0165] In some embodiments, the second determination module 303 is further configured to:

[0166] After determining the support risk level of the fault zone edge area, generate a support emergency task matching the support risk level, and execute the support emergency task on the fault zone edge area.

[0167] In some embodiments, the second determination module 303 is further configured to:

[0168] After executing the support emergency task on the fault zone edge area, continuously monitor the surrounding rock structure information of the fault zone edge area;

[0169] According to the real-time monitoring data, update the fault support risk assessment coefficient in real time;

[0170] In response to the fault support risk assessment coefficient satisfying the abnormal identification condition, optimize the risk assessment strategy; and / or, adjust the support emergency task.

[0171] The support risk identification device for the edge area of the fault zone provided by the embodiments of the present application precisely collects and monitors the surrounding rock structure information of the edge area of the fault zone through various types of sensors. Further, based on the surrounding rock structure information, the fault stress mutation index and the deformation coefficient of the support structure in the edge area of the fault zone can be determined, so as to determine the support risk level in the edge area of the fault zone. By collecting and monitoring the surrounding rock structure information of the edge area of the fault zone, the support risk in the edge area of the fault zone can be identified, which helps to predict potential risk points in advance, thereby ensuring the safety of mine operations and reducing the occurrence of accidents.

[0172] Further, by continuously monitoring the surrounding rock structure information, the adjustment or optimization of the risk assessment strategy and the support emergency plan can be realized, which can ensure the support safety in the edge area of the fault zone. The surrounding rock stress and structural state in the edge area of the fault zone may change suddenly due to tunneling or other environmental factors, and the support structure may face risks such as overload, deformation or instability. Through the real-time monitoring and automatic adjustment of the software system, not only can potential support problems be detected early, but also corresponding support measures can be taken quickly to ensure the safety and continuity of the project and avoid unnecessary shutdowns or support reconstruction costs.

[0173] To implement the above embodiments, the present application 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 foregoing embodiments.

[0174] To implement the above embodiments, the present application also proposes a computer-readable storage medium storing computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the method provided by the foregoing embodiments.

[0175] To implement the above embodiments, the present application also proposes a computer program product including a computer program, and when the computer program is executed by a processor, it implements the method provided by the foregoing embodiments.

[0176] The collection, storage, use, processing, transmission, provision and application of the user's personal information involved in the present application all comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0177] It should be noted that personal information from users should be collected for legal and reasonable purposes and should not be shared or sold outside of such legal uses. In addition, such collection / sharing should be carried out after obtaining the informed consent of the users, including but not limited to notifying the users to read the user agreement / user notice before using the function and signing an agreement / authorization including authorizing the relevant user information. In addition, any necessary steps should be taken to defend and safeguard access to such personal information data and to ensure that others with access to the personal information data comply with their privacy policies and procedures.

[0178] This application is expected to provide an implementation scheme for users to selectively block the use or access of personal information data. That is, this application is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, the risk can be minimized by restricting data collection and deleting the data. In addition, when applicable, personal identifiers are removed from such personal information to protect the privacy of the users.

[0179] In the description of the foregoing embodiments, the descriptions referring to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0180] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of this application, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0181] Any process or method description shown in the flowchart or described in other ways herein can be understood as representing a module, segment, or part of code including one or more executable instructions for implementing a customized logical function or process, and the scope of the preferred implementation of this application includes additional implementations, where the functions can be executed in a manner not shown or discussed, including in a substantially simultaneous manner according to the involved functions or in a reverse order, which should be understood by those skilled in the art to which the embodiments of this application pertain.

[0182] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definable sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in conjunction with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "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 (non-exhaustive list) of computer-readable media include the following: electrical connection parts with one or more wirings (electronic devices), portable computer disk cartridges (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber devices, and portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then storing it in a computer memory.

[0183] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGA), field programmable gate arrays (FPGA), etc.

[0184] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the method of implementing the above embodiments can be completed by a program instructing relevant hardware, 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 embodiments.

[0185] In addition, each functional unit in various embodiments of the present application may be integrated into a processing module, may exist physically alone for each unit, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0186] The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A method for identifying the support risk in the edge area of a fault zone, characterized in that, The method includes: In response to the coal mine roadway driving to the edge area of the fault zone, collecting the surrounding rock structure information of the edge area of the fault zone; According to the surrounding rock structure information, determining the fault stress mutation index and the deformation coefficient of the support structure in the edge area of the fault zone; According to the fault stress mutation index and the deformation coefficient of the support structure, determining the support risk level in the edge area of the fault zone.

2. The method according to claim 1, wherein The step of determining the fault stress mutation index and the deformation coefficient of the support structure in the edge area of the fault zone according to the surrounding rock structure information includes: Preprocessing the surrounding rock structure information of the edge area of the fault zone to obtain preprocessed surrounding rock structure information; According to the preprocessed surrounding rock structure information, determining the surrounding rock stress information and the response information of the support structure in the edge area of the fault zone; According to the surrounding rock stress information, determining the fault stress mutation index; According to the response information of the support structure and the attribute information of the support structure, determining the deformation coefficient of the support structure.

3. The method according to claim 2, wherein The step of determining the fault stress mutation index according to the surrounding rock stress information includes: According to the surrounding rock stress information, determining the stress values, widths and surrounding rock elastic moduli at different times within a set period in the edge area of the fault zone; Determining the first maximum stress value and the first minimum stress value from the stress values at different times within the set period; According to the first maximum stress value, the first minimum stress value, the widths and the surrounding rock elastic moduli at different times within the set period, determining the fault stress mutation index.

4. The method according to claim 3, wherein The stress mutation index is determined by using the following formula: Among them, ESI represents the fault stress mutation index; g represents the number of moments included in the set period; L m represents the width of the edge area of the fault zone at the m-th moment; E m represents the elastic modulus of the surrounding rock of the edge area of the fault zone at the m-th moment; σ max represents the first maximum stress value; σ min represents the first maximum stress value; g and m are positive integers.

5. The method according to claim 2, wherein The step of determining the deformation coefficient of the support structure according to the response information of the support structure and the attribute information of the support structure includes: According to the response information of the support structure, determining the stress values of the support structure at different times within a set period; Determining the second maximum stress value from the stress values of the support structure at different times within the set period; According to the second maximum stress value and the attribute information, determining the deformation coefficient of the support structure.

6. The method according to claim 5, wherein The deformation coefficient of the support structure is determined by using the following formula: Among them, SDC represents the deformation coefficient of the support structure; ZHσ max represents the second maximum stress value; ZHL represents the length of the support structure; ZHE represents the elastic modulus of the material of the support structure; A represents the cross-sectional area of the support structure.

7. The method according to any one of claims 1-6, characterized in that, The step of determining the support risk level in the edge area of the fault zone according to the fault stress mutation index and the deformation coefficient of the support structure includes: According to the stress mutation index and the deformation coefficient of the support structure, combining with a pre-trained fault support risk assessment model, determining the fault support risk assessment coefficient in the edge area of the fault zone; Comparing the fault support risk assessment coefficient with a threshold interval to determine the support risk level, where the threshold interval includes a first threshold and a second threshold, and the first threshold is less than the second threshold.

8. The method according to claim 7, wherein After determining the support risk level in the edge area of the fault zone according to the stress mutation index and the deformation coefficient of the support structure, it further includes: Generating a support emergency task matching the support risk level, and executing the support emergency task on the edge area of the fault zone.

9. The method according to claim 8, wherein After generating the support emergency task matching the support risk level and executing the support emergency task on the edge area of the fault zone, it further includes: Continuously monitor the surrounding rock structure information of the edge area of the fault zone; According to the real-time monitoring data, update the fault support risk assessment coefficient in real time; In response to the fault support risk assessment coefficient meeting the abnormal identification condition, optimize the risk assessment strategy; and / or adjust the support emergency task.

10. A support risk identification device for the edge area of a fault zone, characterized in that, The device includes: A collection module, configured to collect the surrounding rock structure information of the edge area of the fault zone in response to the coal mine roadway advancing to the edge area of the fault zone; A first determination module, configured to determine the fault stress mutation index of the edge area of the fault zone and the deformation coefficient of the support structure according to the surrounding rock structure information; A second determination module, configured to determine the support risk level of the edge area of the fault zone according to the stress mutation index and the deformation coefficient of the support structure.

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