An early warning method for in-situ rock mass shear instability in deep resource exploitation

Abstract

This invention belongs to the field of mine safety and rock mass disaster early warning technology, and particularly relates to an early warning method for in-situ rock mass shear instability in deep resource mining. The method includes constructing a monitoring system; extracting signal features; calculating abrupt change features; and determining instability and providing graded early warning. This invention achieves precise monitoring and advanced early warning of deep rock mass shear instability disasters through real-time acoustic emission monitoring and nonlinear abrupt change feature identification. This technology collects real-time changes in acoustic emission energy rate and ringing count rate during the propagation of microcracks within deep rock masses. Combined with the swallowtail catastrophe theory, high-order nonlinear analysis is performed on the acoustic emission signals to accurately extract the characteristic quantity Δ of system stability abrupt changes, thereby effectively identifying the dynamic transition point of the rock mass from a stable to an unstable state. Compared with traditional acoustic emission early warning methods that rely on threshold settings, this invention significantly improves the accuracy and sensitivity of early warning.

Description

Technical Field

[0001] This invention belongs to the field of mine safety and rock mass disaster early warning technology, and particularly relates to an early warning method for in-situ rock mass shear instability in deep resource mining. Background Technology

[0002] Shear failure is one of the core problems in rock mechanics, and it is widely present in rock engineering applications such as hydraulic fracturing, tunnel support, landslides, and earthquakes. As mineral resource mining gradually moves deeper, rocks not only bear compressive loads from their own weight, but also huge shear loads caused by tectonic stress concentration. In complex geological environments at depth, shear failure is more common than tensile failure, and rock mass shear slip has been proven to be a key factor inducing major dynamic disasters such as rockbursts, coal mine water inrushes, and tunnel collapses. Due to the complexity of the shear behavior of deep rocks and its strong nonlinear characteristics, their instability process is difficult to predict, greatly increasing the technical difficulty of early warning systems for deep engineering disasters.

[0003] Currently, acoustic emission (AE) technology is one of the mainstream methods for monitoring rock mass crack propagation and stability. During the transition from stable crack growth to dynamic shear instability, acoustic emission signals exhibit significant nonlinear characteristics. Existing acoustic emission early warning methods mainly fall into two categories: The first is a prediction method based on comprehensive analysis of the nonlinear characteristics of acoustic emission signals. While theoretically feasible, this method is computationally expensive, time-consuming, and susceptible to model uncertainties, making it difficult to meet the real-time and response speed requirements of engineering sites. The second category is a threshold discrimination method based on a significant increase in acoustic emission parameters (such as acoustic emission energy, ringing count rate, and amplitude). However, this type of method heavily relies on the threshold setting and is easily influenced by subjective factors. In deep resource extraction, geological conditions and stress levels vary greatly in different areas. Setting the threshold too high may lead to missed detections, while setting it too low may lead to false alarms, making it difficult to accurately and objectively identify the key transition points from a stable to an unstable state in rock masses. Therefore, an early warning technology is needed that can overcome the drawbacks of threshold dependence and accurately identify nonlinear precursor signals of rock mass shear instability. Summary of the Invention

[0004] To address the technical problems of existing rock mass stability monitoring technologies, such as high computational costs for nonlinear characteristic analysis and reliance on threshold settings for acoustic emission parameter early warning, which are easily affected by subjective factors, this invention proposes an in-situ rock mass shear instability early warning method for deep resource mining.

[0005] This invention is achieved through the following technical solution:

[0006] An early warning method for in-situ rock mass shear instability during deep resource mining includes the following steps:

[0007] S1. Construct a monitoring system by deploying a multi-channel high-frequency acoustic emission sensor network in the deep rock mass area to be monitored, and construct an in-situ rock mass shear instability early warning system to collect acoustic emission signals inside the rock mass in real time.

[0008] S2. Extract signal features: Based on the real-time acquired acoustic emission signals, select a time window to segment the data and extract the acoustic emission energy rate and ringing count rate as basic feature data to characterize the rock mass damage evolution.

[0009] S3. Mutation feature calculation: Based on the swallowtail catastrophe theory, high-order nonlinear analysis is performed on the extracted acoustic emission energy rate and ringing count rate time series, and the time series is mapped to the control parameter space of the swallowtail catastrophe model to calculate the control variables and characteristic quantities Δ of the system.

[0010] S4. Instability detection: Real-time monitoring of the changing trend of characteristic quantity Δ. When a sudden increase or change in characteristic quantity Δ is detected, it is identified as a sudden point of change in the rock mass from a stable state to an unstable state, and the rock mass is judged to have entered the unstable transition stage.

[0011] S5. Graded early warning: The system automatically issues early instability warning signals based on the judgment results, and classifies the warning categories and outputs the warning results according to the order of response of acoustic emission energy rate and ringing count rate at the mutation point.

[0012] Furthermore, the construction of the monitoring system in S1 specifically involves:

[0013] Select the deep rock mass area that needs to be monitored, and deploy acoustic emission sensors on the rock mass surface or in the borehole to achieve three-dimensional coverage;

[0014] Use an acquisition system with 8 or more channels and set the sampling frequency to 2MHz or higher;

[0015] Establish a data acquisition, real-time storage and processing platform, and equip it with front-end amplification and filtering hardware modules to eliminate the impact of environmental noise.

[0016] Furthermore, the extraction of signal features in S2 specifically involves:

[0017] Within each selected time window, the total energy of all acoustic emission events is calculated and normalized to a unit time to obtain the energy rate change curve; at the same time, the number of ringing events is calculated and normalized to a unit time to form the ringing count rate curve; and the data is processed using moving average or wavelet noise reduction methods to eliminate outlier data points.

[0018] Furthermore, the analysis of acoustic emission energy rate and ringing count rate in S3 to obtain the characteristic quantity Δ specifically involves:

[0019] Polynomial Taylor expansion is used to characterize the high-order nonlinear evolution of the time series of acoustic emission energy rate and ringing count rate;

[0020] Key nonlinear parameters are obtained through nonlinear time transformation. and ;

[0021] Based on this, three-dimensional control parameters are constructed in the swallowtail catastrophe model. ;

[0022] Based on the swallowtail catastrophe theory formula, the characteristic quantity Δ of the system catastrophe is derived, and this characteristic quantity Δ is used to characterize the catastrophe intensity and critical proximity during the rock mass instability process.

[0023] Furthermore, in S3, key nonlinear parameters are calculated. , and control variables The specific formula is as follows:

[0024] acoustic emission time series Represented as a fifth-order Taylor series expansion polynomial The coefficient is , ;

[0025] Key nonlinear parameters are determined by matching higher-order terms. and :

[0026] ;

[0027] ;

[0028] Based on this, further calculations of control variables are performed. :

[0029] ;

[0030] ;

[0031] ;

[0032] in, It mainly characterizes the low- to mid-order nonlinear acceleration effect, reflecting the transition of fracture evolution from steady-state propagation to accelerated propagation; This reflects higher-order nonlinear coupling.

[0033] Furthermore, the bifurcation set expression derived in S3, i.e., the mutation feature Δ, is:

[0034] ;

[0035] Where Δ is a feature quantity, representing a comprehensive index of mutation sensitivity in a high-dimensional parameter space; control variables Characterizing the deterioration and hardening trend of rock mass stiffness; control variables Reflects the degree of damage accumulation and acoustic emission concentration; control variables It characterizes the intensity of external disturbances and the degree of initial internal defects.

[0036] Furthermore, the instability judgment criterion in S4 is as follows:

[0037] The characteristic quantity Δ is continuously monitored over time. When the characteristic quantity Δ changes slowly over a period of time and maintains the same sign, the system is determined to be in a relatively stable evolution stage. When abnormal and violent fluctuations in the characteristic quantity Δ are observed, the rock mass system is determined to have entered an unstable transition stage from a stable stage. The time of the abrupt change is recorded, and the corresponding rock mass region is marked.

[0038] Furthermore, the tiered early warning system in S5 specifically includes:

[0039] A Level 1 warning is issued when the ringing count rate is abnormally increased first when a sudden change in the characteristic quantity Δ is detected. This level corresponds to an early warning of in-situ rock mass shear instability under low stress conditions.

[0040] Level II warning: If a sudden change in the characteristic quantity Δ is detected and the acoustic emission energy rate rises sharply first, it is judged as Level II warning. This level corresponds to the early warning of in-situ rock mass shear instability under high stress conditions.

[0041] The system automatically saves all early warning events and environmental condition information to form a database.

[0042] The beneficial effects of this invention are:

[0043] This invention achieves precise monitoring and early warning of shear instability disasters in deep rock masses through real-time acoustic emission monitoring and nonlinear catastrophe feature identification. The technology collects the changes in acoustic emission energy rate and ringing count rate during the propagation of microcracks inside deep rock masses in real time. Combined with the swallowtail catastrophe theory, it performs high-order nonlinear analysis on the acoustic emission signal to accurately extract the characteristic quantity Δ of the system stability catastrophe, thereby effectively identifying the dynamic transition point of the rock mass from a stable state to an unstable state.

[0044] Compared with traditional acoustic emission early warning methods that rely on threshold settings, this invention significantly improves the accuracy and sensitivity of early warning, effectively overcomes problems such as high model uncertainty and response lag, and greatly improves the early warning timeliness of shear instability disasters in deep and complex environments.

[0045] Another significant advantage of this invention lies in its dual-index early warning and classification mechanism based on acoustic emission energy rate and ringing count rate. Under low-stress conditions, changes in ringing count rate dominate, while under high-stress conditions, abrupt changes in energy rate dominate. A unified criterion is achieved through the characteristic quantity Δ, enabling dynamic adaptation to the rock mass instability evolution characteristics under different deep mining conditions, thus achieving precise and phased early warning and prevention. This technology has broad application prospects in deep mines, tunnel engineering, and high-stress underground spaces. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a schematic flowchart of an early warning method for in-situ rock mass shear instability in deep resource mining proposed in this invention.

[0048] Figure 2 This is a schematic diagram of the terminal equipment for an early warning method for in-situ rock mass shear instability in deep resource mining proposed in this invention.

[0049] Figure 3 This is a readable storage medium schematic diagram of an early warning method for in-situ rock mass shear instability in deep resource mining proposed in this invention.

[0050] In the diagram, 200 is the terminal device, 210 is the memory, 211 is the RAM, 212 is the cache, 213 is the ROM, 214 is the program / utility, 215 is the program module, 220 is the processor, 230 is the bus, 240 is the external device, 250 is the I / O interface, 260 is the network adapter, and 300 is the program product. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0052] Example 1

[0053] This embodiment provides an in-situ rock mass shear instability early warning technology for deep resource mining. This technology constructs an in-situ rock mass shear instability early warning system and utilizes real-time acoustic emission monitoring combined with the nonlinear dynamic characteristics of the swallowtail catastrophe theory to achieve accurate identification and early warning of the transition process of deep rock mass from a stable state to an unstable state. In the specific engineering implementation process, this method deeply integrates geological engineering, rock mechanics and nonlinear mathematical theory, forming a closed loop through continuous and closely related steps such as constructing a monitoring system, extracting signal features, calculating and modeling catastrophe features, instability discrimination and graded early warning release.

[0054] In the initial stage of implementing this invention, to obtain accurate damage evolution information within deep rock masses, a high-precision acoustic emission monitoring system needs to be constructed in the deep rock mass area to be monitored. Technicians first determine key areas of the deep rock mass requiring focused monitoring based on geological exploration data and on-site mining plans, such as high-stress concentration zones, fault fracture zones, or areas in front of the mining face. Within the selected monitoring area, technicians deploy a multi-channel high-frequency acoustic emission sensor network on the rock surface or in pre-drilled monitoring holes. To ensure the capture of three-dimensional spatial location information of micro-fractures within the rock mass and to avoid monitoring blind spots, the sensors should provide three-dimensional coverage, and a coupling agent should be used between the sensors and the rock wall to ensure good signal transmission. This embodiment uses an 8-channel or higher acoustic emission acquisition system. Considering the high frequency and weak energy of acoustic emission signals during the micro-crack initiation stage in deep rock masses, the system sampling frequency is generally set to above 2MHz to ensure the capture of microsecond-level micro-crack propagation events. At the same time, it is necessary to establish a supporting data acquisition, real-time storage and processing platform. This platform needs to be equipped with high-performance front-end amplifiers and bandpass filter hardware modules, for example, setting the filtering range to 1kHz to 400kHz, so as to eliminate the interference of complex environmental noises at the mine site, such as mechanical vibration and wind noise, on the effective signal at the hardware level, and ensure the signal-to-noise ratio of the subsequent analysis data.

[0055] After completing the physical construction and signal acquisition of the system, the system will enter the data processing and feature extraction stage. Since the original acoustic emission waveform data is massive and contains a large amount of redundant information, direct processing is inefficient. Therefore, it is necessary to extract key parameters characterizing the evolution of rock mass damage. In implementation, the system selects a suitable time window based on real-time monitoring data, such as a sliding window set according to the mining advance speed, to segment the continuous acoustic emission data. Within each set time window, the system performs dual-parameter extraction: on the one hand, it calculates the total energy of all acoustic emission events within the window and normalizes it to a unit time, thus obtaining the acoustic emission energy rate, i.e., the curve of the rate of change of energy over time. This parameter reflects the intensity and rate of energy release within the rock mass. On the other hand, it calculates the number of ringing events within the window, i.e., the number of oscillations exceeding a threshold value, and normalizes it to a unit time, forming the ringing count rate, i.e., the curve of the rate of change of ringing count over time. This parameter reflects the frequency of micro-fractures occurring within the rock mass. To ensure the stability of subsequent nonlinear calculations, mathematical methods such as moving average or wavelet denoising are used to preprocess the two curve data, removing outlier data points caused by accidental electromagnetic interference, and ensuring that the extracted parameter sequence has good continuity and reliability.

[0056] After obtaining the time series of acoustic emission energy rate and ringing count rate, the core of this invention, the mutation feature calculation and modeling stage, begins. The purpose of this stage is to map the acoustic emission data from the physical space into the mathematical control space of the swallowtail catastrophe model to identify system stability mutations. Unlike traditional threshold judgments, this method is based on swallowtail catastrophe theory, a catastrophe model used to describe complex nonlinear systems with three control factors and one state variable. To construct this model, high-order nonlinear features need to be extracted from the time series of acoustic emission energy rate and ringing count rate.

[0057] Specifically, for acoustic emission time series data, this embodiment employs a Taylor series expansion up to the first five terms to capture its nonlinear instability characteristics to the greatest extent possible. The acoustic emission time series is denoted as... It can be expressed as a fifth-degree polynomial:

[0058] ;

[0059] In this formula, the comprehensive variation As the source of state variables in the swallowtail catastrophe model, it can simultaneously reflect the energy release intensity of crack propagation and the frequency of event occurrence within a single time series; variables Represents acoustic emission time; coefficient to These are the polynomial coefficients obtained through the least squares method or other fitting algorithms. These coefficients describe the contributions of time terms of different orders to the acoustic emission evolution process. Their specific physical meaning is... Characterizing the initial acoustic emission activity level; coefficient Reflects the linear growth or decay trend of acoustic emission over time; coefficient Higher-order terms capture the acceleration, deceleration, and inflection point characteristics of acoustic emissions in the later stages of loading, which is particularly crucial for identifying the approaching instability phase.

[0060] After establishing the above time series model, a nonlinear transformation needs to be introduced to map the actual physical process to the standard form of the swallowtail catastrophe model. The standard potential function form of the swallowtail catastrophe model is:

[0061] ;

[0062] In this potential function, It is a control variable. It is the system's state variable, representing the damage evolution level characterized by the combined acoustic emission energy rate and ringing count rate. The equilibrium surface equation of the system is obtained by taking the first derivative of the potential function:

[0063] ;

[0064] This equation gives the set of possible equilibrium states of the system under different control parameter values. In order to determine the critical conditions for a sudden change in the system, it is necessary to further differentiate the equilibrium surface equation to obtain the equation for the set of sudden change points:

[0065] ;

[0066] By simultaneously solving the equilibrium surface equations and the mutation point set equations, the state variables are eliminated. This allows us to obtain the set of divergence points. The expression:

[0067] ;

[0068] In the analysis of acoustic emission instability characteristics in rocks, the swallowtail catastrophe model can finely characterize the multi-path evolution process of a system transitioning from stability to instability in a three-dimensional control space, including the set of bifurcation points. The control space is divided into five regions with different topological properties, each corresponding to a typical acoustic emission evolution path. When the control parameter points representing the system's loading state slowly cross the boundaries of these regions over time, the equilibrium state undergoes a qualitative change, manifested as a sudden increase in acoustic emission energy and a dramatic jump in ring count, indicating that the rock mass transitions from a slow damage stage to a rapid instability stage. Therefore, the bifurcation conditions corresponding to the region boundaries can be regarded as the critical criteria for the direct shear nonlinear instability behavior of the rock mass.

[0069] To perform specific numerical calculations, polynomial coefficients must be established. to Control variables in the swallowtail mutation model The quantitative relationship between them. Therefore, a nonlinear transformation is introduced to transform the real-time variable... Mapping to new evolutionary parameters Space obtained And match higher-order terms to determine the number of key nonlinear parameters. and Based on the polynomial coefficients, the key nonlinear parameters The calculation formula is:

[0070] ;

[0071] Key nonlinear parameters The calculation formula is:

[0072] ;

[0073] In the physical sense of acoustic emission, parameters It mainly characterizes low- to mid-order nonlinear acceleration effects, reflecting the transition of fracture evolution from steady-state propagation to accelerated propagation; parameters This reflects higher-order nonlinear coupling more effectively and is more sensitive to complex evolutionary patterns such as sudden increases, decreases, and re-enhancements in the system before instability. Based on this, the calculated... and And polynomial coefficients, to further calculate control variables , which serves as the core parameter for describing the complex dynamic behavior of a system.

[0074] The first control variable The calculation formula is:

[0075] ;

[0076] Second control variable The calculation formula is:

[0077] ;

[0078] Third control variable The calculation formula is:

[0079] ;

[0080] The three control variables mentioned above have clear physical meanings, among which As the first control variable, it comprehensively characterizes the trend of rock mass stiffness deterioration and hardening, and is sensitive to loading mode, confining pressure conditions and large-scale stress level; As the second control variable, it reflects the degree of damage accumulation and acoustic emission concentration, and is sensitive to microcrack clustering and propagation and energy burst behavior; As the third control variable, it comprehensively characterizes the intensity of external disturbances and the degree of internal initial defects, and has a significant impact on the level of acoustic emission activity and the crack initiation threshold.

[0081] Obtaining control variables After obtaining the numerical value, in order to quantify how close the system is to an unstable state, this embodiment further derives a specific and computable expression for the bifurcation set, namely the mutation characteristic Δ.

[0082] Based on the derivation, the formula for calculating Δ is:

[0083] ;

[0084] Among them, Δ is a characteristic quantity, which can be regarded as a comprehensive index of the sensitivity to sudden changes in a high-dimensional parameter space. The numerical change of this characteristic quantity directly corresponds to the stability state of the rock mass system.

[0085] Next, the system enters the instability detection step, continuously calculating and monitoring the change of the abrupt change characteristic value Δ over time to observe whether abnormal and violent fluctuations occur. When Δ changes slowly over a relatively long period of time while maintaining the same sign, it indicates that the system is still in a relatively stable evolution stage, corresponding to a gradual change in acoustic emission energy rate and ringing count rate. At this time, although the microcracks inside the rock mass are expanding, the overall structure has not yet reached the instability critical point.

[0086] When a sharp change in the Δ value is detected just before the failure point, manifested as a dramatic increase or a jump in magnitude, it usually indicates that the rock mass damage structure has approached or crossed the instability threshold. This signifies that the rock mass system has transitioned from a stable phase to an unstable transition phase, and this moment is identified as the abrupt change point. The system records the time of the abrupt change point and marks the corresponding rock mass region, as this foreshadows catastrophic events such as macroscopic fracture penetration and overall shear slippage within a short period. Therefore, the characteristic quantity Δ constructed based on the swallowtail catastrophe model can serve as an important criterion for identifying precursors of nonlinear instability in rock mass acoustic emission and for achieving disaster prediction and early warning.

[0087] Finally, when the system detects a sudden change in the characteristic quantity Δ, it will trigger a graded early warning mechanism. The system will automatically generate an early instability warning signal. In order to improve the pertinence and guidance of the warning, this embodiment divides different warning levels according to the change patterns of acoustic emission energy rate and ringing count rate.

[0088] The system analyzes whether the ringing count rate or the acoustic emission energy rate shows an abnormal response first at the Δ mutation moment. If the monitoring data shows that the ringing count rate rises abnormally first, i.e., the first warning is issued, it is judged as a Level I ringing count rate warning. The Level I ringing count rate warning is based on the mutation of the ringing count rate and is applicable to the early warning of in-situ rock mass shear instability under low stress conditions. This situation often corresponds to the stage in which cracks are densely initiated inside the rock mass but energy has not yet accumulated and been released on a large scale.

[0089] If the monitoring data shows that the acoustic emission energy rate rises sharply first, that is, the first warning, it is judged as a Level II acoustic emission energy rate warning. The Level II acoustic emission energy rate warning is based on the sudden change of acoustic emission energy rate. It is applicable to the early warning of in-situ rock mass shear instability under high stress conditions. This situation often corresponds to the rock mass being subjected to extremely high stress, the energy accumulation has reached the limit, and once released, it will cause serious damage.

[0090] While issuing an early warning signal, the system automatically saves all early warning event data, control variable values ​​during the calculation process, change curves of abrupt change characteristics, and environmental conditions at the time, forming a historical database. This data is not only used for recording the current early warning but also for subsequent offline analysis and optimization of the early warning model parameters, continuously improving the system's adaptability to specific mine geological environments and its early warning accuracy. Through the organic combination of the above steps, this embodiment realizes an early warning technology solution for rock mass shear instability that does not rely on human experience thresholds, can adapt to deep and complex environments, and has a solid theoretical foundation in nonlinear mechanics.

[0091] Example 2

[0092] refer to Figure 2 Based on Example 1, this example proposes a terminal device for an early warning method of in-situ rock mass shear instability in deep resource mining. The terminal device 200 includes at least one memory 210, at least one processor 220, and a bus 230 connecting different platform systems.

[0093] The memory 210 may include a readable medium in the form of volatile memory, such as RAM 211 and / or cache memory 212, and may further include ROM 213.

[0094] The memory 210 also stores a computer program that can be executed by the processor 220, causing the processor 220 to perform any of the above-described methods for early warning of in-situ rock mass shear instability in deep resource mining as described in the embodiments of this application. The specific implementation method and the technical effects achieved are consistent with those described in the embodiments of the above applications, and some details will not be repeated here. The memory 210 may also include a program / utility 214 having a set (at least one) of program modules 215. Such program modules include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment.

[0095] Accordingly, processor 220 can execute the aforementioned computer program, as well as executable program / utility 214.

[0096] Bus 230 can represent one or more of several types of bus structures, including a memory bus or memory controller, peripheral bus, graphics acceleration port, processor, or a local bus using any of the various bus structures.

[0097] Terminal device 200 can also communicate with one or more external devices 240, such as keyboards, pointing devices, Bluetooth devices, etc., and with one or more devices capable of interacting with it, and / or with any device that enables it to communicate with one or more other computing devices (e.g., routers, modems, etc.). This communication can be performed via I / O interface 250. Furthermore, terminal device 200 can communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via network adapter 260. Network adapter 260 can communicate with other modules of terminal device 200 via bus 230. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with terminal device 200, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0098] Example 3

[0099] This embodiment proposes a readable storage medium for an early warning method of in-situ rock mass shear instability in deep resource mining. The computer-readable storage medium stores instructions that, when executed by a processor, implement any of the above-mentioned early warning methods of in-situ rock mass shear instability in deep resource mining. The specific implementation method and the technical effects achieved are consistent with those described in the above-mentioned application embodiments, and some details will not be repeated.

[0100] Figure 3 The present embodiment illustrates a program product 300 for implementing the above-described applications. This product may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product 300 of the present invention is not limited thereto. In this embodiment, the readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device. The program product 300 may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0101] Computer-readable storage media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof. Program code for performing operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on a user computing device, partially on a user device, as a standalone software package, partially on a user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing devices can be connected to user computing devices via any type of network, including local area networks (LANs) or wide area networks (WANs), or they can be connected to external computing devices (e.g., via the Internet using an Internet service provider).

[0102] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for early warning of in-situ rock mass shear instability in deep resource mining, characterized in that, Includes the following steps: S1. Construct a monitoring system by deploying a multi-channel high-frequency acoustic emission sensor network in the deep rock mass area to be monitored, and construct an in-situ rock mass shear instability early warning system to collect acoustic emission signals inside the rock mass in real time. S2. Extract signal features: Based on the real-time acquired acoustic emission signals, select a time window to segment the data and extract the acoustic emission energy rate and ringing count rate as basic feature data to characterize the rock mass damage evolution. S3. Mutation feature calculation: Based on the swallowtail catastrophe theory, high-order nonlinear analysis is performed on the extracted acoustic emission energy rate and ringing count rate time series, and the time series is mapped to the control parameter space of the swallowtail catastrophe model to calculate the control variables and characteristic quantities Δ of the system. S4. Instability detection: Real-time monitoring of the changing trend of characteristic quantity Δ. When a sudden increase or change in characteristic quantity Δ is detected, it is identified as a sudden point of change in the rock mass from a stable state to an unstable state, and the rock mass is judged to have entered the unstable transition stage. S5. Graded early warning: The system automatically issues early instability warning signals based on the judgment results, and classifies the warning categories and outputs the warning results according to the order of response of acoustic emission energy rate and ringing count rate at the mutation point.

2. The method for early warning of in-situ rock mass shear instability in deep resource mining according to claim 1, characterized in that, The monitoring system constructed in S1 is specifically as follows: Select the deep rock mass area that needs to be monitored, and deploy acoustic emission sensors on the rock mass surface or in the borehole to achieve three-dimensional coverage; Use an acquisition system with 8 or more channels and set the sampling frequency to 2MHz or higher; Establish a data acquisition, real-time storage and processing platform, and equip it with front-end amplification and filtering hardware modules to eliminate the impact of environmental noise.

3. The method for early warning of in-situ rock mass shear instability in deep resource mining according to claim 1, characterized in that, The specific steps for extracting signal features in S2 are as follows: Within each selected time window, the total energy of all acoustic emission events is calculated and normalized to a unit time to obtain the energy rate change curve; at the same time, the number of ringing events of acoustic emission events is calculated and normalized to a unit time to form the ringing count rate curve. The data is processed using moving average or wavelet denoising methods to eliminate outlier data points.

4. The method for early warning of in-situ rock mass shear instability in deep resource mining according to claim 1, characterized in that, In S3, the acoustic emission energy rate and ringing count rate are analyzed, and the characteristic quantity Δ is obtained as follows: Polynomial Taylor expansion is used to characterize the high-order nonlinear evolution of the time series of acoustic emission energy rate and ringing count rate; Key nonlinear parameters are obtained through nonlinear time transformation. and ; Based on this, three-dimensional control parameters are constructed in the swallowtail catastrophe model. ; Based on the swallowtail catastrophe theory formula, the characteristic quantity Δ of the system catastrophe is derived, and this characteristic quantity Δ is used to characterize the catastrophe intensity and critical proximity during the rock mass instability process.

5. A method for early warning of in-situ rock mass shear instability in deep resource mining according to claim 4, characterized in that, The key nonlinear parameters are calculated in S3. , and control variables The specific formula is as follows: acoustic emission time series Represented as a fifth-order Taylor series expansion polynomial The coefficient is , ; Key nonlinear parameters are determined by matching higher-order terms. and : ； ； Based on this, further calculations of control variables are performed. : ； ； ； in, It mainly characterizes the low- to mid-order nonlinear acceleration effect, reflecting the transition of fracture evolution from steady-state propagation to accelerated propagation; This reflects higher-order nonlinear coupling.

6. The method for early warning of in-situ rock mass shear instability in deep resource mining according to claim 5, characterized in that, The bifurcation set expression derived in S3, i.e., the expression for the mutation feature quantity Δ bifurcation set, is as follows: ； Where Δ is a feature quantity, representing a comprehensive index of mutation sensitivity in a high-dimensional parameter space; control variables Characterizing the deterioration and hardening trend of rock mass stiffness; control variables Reflects the degree of damage accumulation and acoustic emission concentration; control variables It characterizes the intensity of external disturbances and the degree of initial internal defects.

7. The method for early warning of in-situ rock mass shear instability in deep resource mining according to claim 1, characterized in that, The instability criterion in S4 is as follows: The characteristic quantity Δ is continuously monitored over time. When the characteristic quantity Δ changes slowly over a period of time and maintains the same sign, the system is determined to be in a relatively stable evolution stage. When abnormal and violent fluctuations in the characteristic quantity Δ are observed, the rock mass system is determined to have entered an unstable transition stage from a stable stage. The time of the abrupt change is recorded, and the corresponding rock mass region is marked.

8. The method for early warning of in-situ rock mass shear instability in deep resource mining according to claim 1, characterized in that, The tiered early warning system in S5 specifically includes: A Level 1 warning is issued when the ringing count rate is abnormally increased first when a sudden change in the characteristic quantity Δ is detected. This level corresponds to an early warning of in-situ rock mass shear instability under low stress conditions. Level II warning: If a sudden change in the characteristic quantity Δ is detected and the acoustic emission energy rate rises sharply first, it is judged as Level II warning. This level corresponds to the early warning of in-situ rock mass shear instability under high stress conditions. The system automatically saves all early warning events and environmental condition information to form a database.

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