Acoustic emission-thermal infrared combined diagnosis method and system for unloading damage of deep rock mass

Abstract

The invention discloses a deep rock mass unloading damage acoustic emission-thermal infrared combined diagnosis method and system. The method comprises the following steps: in a deep rock mass unloading process, synchronously acquiring acoustic emission signals and infrared thermal image data of a specific region of a rock mass; key acoustic emission parameters and key thermal infrared characteristics are extracted, and the relevance of the key acoustic emission parameters and the key thermal infrared characteristics in the unloading process is analyzed; according to the analysis result of the relevance of the key acoustic emission parameters and the key thermal infrared characteristics in the unloading process, the unloading damage state of the rock mass is recognized and judged by comparing a preset damage judgment threshold value or a combined criterion; and corresponding rock mass damage control measures are implemented based on the unloading damage state. According to the technical scheme, through joint monitoring analysis of acoustic emission and thermal infrared, the precision and early warning capability of deep rock mass unloading damage diagnosis are improved, a scientific basis is provided for timely and effective risk control measures, and engineering safety is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of geological engineering, and in particular to a method and system for combined acoustic emission and thermal infrared diagnosis of deep rock mass unloading damage. Background Art

[0002] With the growing global demand for mineral resources and the in-depth development of infrastructure such as transportation and water conservancy, a large number of engineering projects, such as deep mineral mining and long-distance tunnel construction, are gradually expanding into the deep Earth. Deep rock masses are often subject to high geostress environments. Engineering excavation (such as mine tunneling and tunneling) disrupts the rock mass's inherent stress equilibrium, leading to significant stress unloading in the rock mass near the excavation face and within the affected area. Compared to the mechanical behavior of rock masses under conventional loading conditions, the unloading process is more likely to induce the initiation, propagation, and even penetration of microcracks within the rock mass, forming macro-damage zones. Unloading damage not only reduces the bearing capacity and stability of the surrounding rock but, in severe cases, can also trigger catastrophic accidents such as rockbursts, large deformations, and landslides, posing a serious threat to engineering safety, equipment, and the lives and property of construction workers. Currently, the engineering community has adopted a variety of technical methods to monitor rock mass stability. Acoustic emission (AE) monitoring and infrared thermal imaging (IRT) monitoring are two promising non-destructive monitoring technologies. Acoustic emission technology passively receives transient elastic wave signals generated by microfracture events within the rock, and can reflect the intensity and frequency of microscopic damage activity within the rock mass in real time. It is considered an effective tool for studying rock failure processes. However, acoustic emission signals are easily interfered with by environmental noise such as on-site construction and mechanical operation, and their results usually reflect point or small-scale internal activities, making it difficult to intuitively display the spatial distribution characteristics of damage and the surface state of the rock mass. Infrared thermal imaging technology detects infrared radiation energy on the rock surface in a non-contact manner and converts it into a temperature distribution image. It can capture rock surface temperature anomalies caused by stress adjustment, crack expansion (tensile cooling, shear friction heating), or fluid migration. However, this technology mainly reflects the thermodynamic response of the surface or near-surface layer of the rock mass, and the detection depth is limited. In addition, the measurement results are easily affected by multiple factors such as ambient temperature, humidity, airflow, and target emissivity. While there have been attempts to apply acoustic emission and infrared thermal imaging technologies to rock mechanics research, these technologies are often used independently or simply superimposed, lacking in-depth exploration of the inherent correlation between the two types of information during the unloading damage process. Consequently, a systematic approach based on multi-source information fusion that can accurately identify damage states and guide control measures has not been developed. In particular, there are deficiencies in establishing reliable joint criteria and quantitative damage assessment, making it difficult to fully meet the practical needs of comprehensive, real-time, and accurate diagnosis and effective control of rock mass unloading damage processes in complex deep environments. Accurately and timely diagnosing and effectively controlling the damage evolution of deep rock masses during engineering unloading, and predicting and preventing potential instability risks, are key technical challenges that urgently need to be addressed in the field of deep engineering. Summary of the Invention

[0003] The present invention provides a combined acoustic emission and thermal infrared diagnosis method and system for deep rock mass unloading damage, which improves the accuracy and early warning capability of deep rock mass unloading damage diagnosis through combined acoustic emission and thermal infrared monitoring and analysis, provides a scientific basis for timely and effective risk control measures, and ensures engineering safety.

[0004] According to a first aspect of the present invention, a method for combined acoustic emission and thermal infrared diagnosis of deep rock mass unloading damage is provided. The method comprises:

[0005] During the unloading process of deep rock mass, the acoustic emission signal and infrared thermal image data of specific areas of the rock mass are obtained simultaneously;

[0006] extracting key acoustic emission parameters of the acoustic emission signal and key thermal infrared features of the infrared thermal image data, and analyzing the correlation between the key acoustic emission parameters and the key thermal infrared features during the unloading process;

[0007] According to the analysis results of the correlation between the key acoustic emission parameters and the key thermal infrared characteristics during the unloading process, and in comparison with the preset damage discrimination threshold or combined criterion, the unloading damage state of the rock mass is identified and judged;

[0008] Based on the unloading damage state, corresponding rock damage control measures are implemented.

[0009] In one embodiment, extracting key acoustic emission parameters of the acoustic emission signal includes:

[0010] Performing signal processing on the acquired acoustic emission signal to calculate at least one of the count rate, energy, amplitude, and frequency characteristics of the acoustic emission signal;

[0011] According to a priori rock mass properties or expected damage patterns, any one or more of the event count rate, energy, amplitude, and frequency characteristics of the acoustic emission signal are selected, and the calculated acoustic emission parameters are used as key acoustic emission parameters.

[0012] In one embodiment, extracting key thermal infrared features includes:

[0013] Performing image processing and analysis on the acquired infrared thermal image data to identify and quantify at least one of an abnormal temperature region, a temperature change rate over time, and a spatial temperature gradient of the deep rock mass;

[0014] Applying a multifractal analysis method to the infrared thermal image data to extract temperature field inhomogeneity parameters and frequency diversity parameters;

[0015] Any one or more of the temperature anomaly area, the temperature change rate over time, the spatial temperature gradient, the temperature field inhomogeneity parameter and the frequency diversity parameter is determined as the key thermal infrared feature.

[0016] In one embodiment, analyzing the temporal correlation between the key acoustic emission parameters and the key thermal infrared characteristics during the unloading process includes:

[0017] Identify the temporal sequence or synchronization between the active period of acoustic emission and the appearance of specific thermal infrared features or significant changes in thermal infrared features;

[0018] Combined with the changes in stress or strain during the unloading process, the response relationship between acoustic emission parameters and thermal infrared characteristics is analyzed to distinguish different damage mechanisms, such as tensile rupture or shear rupture.

[0019] In one embodiment, the method for establishing the preset damage judgment threshold or joint judgment criterion includes:

[0020] Set independent warning thresholds for key acoustic emission parameters and key thermal infrared features respectively;

[0021] The comprehensive damage index CDI of the combined criterion is calculated as follows:

[0022] CDI=W ae *(AE param / AE threshold )+W irt *(IRT feature / IRT threshold )

[0023] Among them, AE param is the key acoustic emission parameter value of the current monitoring period, AE threshold is the warning threshold of critical acoustic emission, IRT feature is the key thermal infrared characteristic value of the current monitoring period, IRT threshold is the warning threshold of key thermal infrared features, W ae and W irt are the weight coefficients of acoustic emission parameters and thermal infrared characteristics, respectively, and W ae +W irt =1;

[0024] When the comprehensive damage index CDI reaches or exceeds the preset comprehensive damage judgment threshold D threshold When , it is determined that significant unloading damage occurs.

[0025] In one embodiment, the implementing corresponding rock damage control measures includes:

[0026] Adjust the excavation rate or change the excavation sequence;

[0027] Installing or strengthening support systems, including anchors, shotcrete, or steel supports;

[0028] Implementing stress relief techniques such as pre-splitting blasting or hydraulic fracturing;

[0029] Implement grouting reinforcement.

[0030] According to a second aspect of the present invention, there is provided a combined acoustic emission and thermal infrared diagnosis system for deep rock mass unloading damage, comprising:

[0031] Acquisition module, used to synchronously acquire acoustic emission signals and infrared thermal image data of specific areas of the rock mass during the unloading process of the deep rock mass;

[0032] an analysis module, configured to extract key acoustic emission parameters of the acoustic emission signal and key thermal infrared features of the infrared thermal image data, and analyze the correlation between the key acoustic emission parameters and the key thermal infrared features during the unloading process;

[0033] an identification module for identifying and judging the unloading damage state of the rock mass based on the analysis results of the correlation between the key acoustic emission parameters and the key thermal infrared characteristics during the unloading process and comparing with a preset damage discrimination threshold or a combined criterion;

[0034] The implementation module is used to implement corresponding rock damage control measures based on the unloading damage state.

[0035] In one embodiment, the acquisition module, the analysis module, the identification module and the implementation module are controlled to execute any one of the above-mentioned combined acoustic emission and thermal infrared diagnosis methods for deep rock mass unloading damage.

[0036] According to a third aspect of the present invention, there is provided an electronic device, the electronic device comprising: a communication interface, a processor, and a memory;

[0037] The memory is used to store program instructions, and when the program instructions are executed by the processor that is communicatively connected to the memory through the communication interface, any of the above-mentioned deep rock unloading damage joint acoustic emission-thermal infrared diagnosis methods is implemented.

[0038] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a computer (for example, a processor in the computer), any of the above-mentioned combined acoustic emission and thermal infrared diagnostic methods for deep rock unloading damage is implemented.

[0039] In summary, the present invention provides a method and system for combined acoustic emission and thermal infrared diagnosis of deep rock unloading damage, the method comprising: synchronously acquiring acoustic emission signals and infrared thermal image data of specific areas of the rock mass during the unloading process of the deep rock mass; extracting key acoustic emission parameters of the acoustic emission signals and key thermal infrared features of the infrared thermal image data, and analyzing the correlation between the key acoustic emission parameters and the key thermal infrared features during the unloading process; identifying and judging the unloading damage state of the rock mass based on the analysis results of the correlation between the key acoustic emission parameters and the key thermal infrared features during the unloading process, and comparing with a preset damage discrimination threshold or joint criterion; and implementing corresponding rock mass damage control measures based on the unloading damage state. The technical solution of the present application overcomes the limitations of one-sided information of a single monitoring method by synchronously acquiring and jointly analyzing two complementary information, acoustic emission (reflecting internal microfractures) and infrared thermal images (reflecting surface thermal response), and can more comprehensively and accurately grasp the internal mechanism and external manifestations of deep rock unloading damage. By analyzing the temporal correlation between acoustic emission parameters and thermal infrared signatures during the unloading process and establishing a damage identification model based on combined criteria (such as the Comprehensive Damage Index (CDI), this effectively eliminates some interfering information, improves the reliability of damage identification, and potentially enables earlier damage warnings. More reliable damage diagnosis results enable more timely and accurate assessment of the damage state and development trends of the rock mass, providing insights for selecting and implementing appropriate control measures (such as adjusting excavation and optimizing support).

[0040] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0041] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0043] Figure 1 A flowchart of a combined acoustic emission and thermal infrared diagnosis method for deep rock mass unloading damage provided by an embodiment of the present invention;

[0044] Figure 2A flowchart of step S12 of a method for combined acoustic emission and thermal infrared diagnosis of deep rock mass unloading damage provided by an embodiment of the present invention;

[0045] Figure 3 A flowchart of step S12 of another method for combined acoustic emission and thermal infrared diagnosis of deep rock mass unloading damage provided by an embodiment of the present invention;

[0046] Figure 4 A flowchart of step S13 of a method for combined acoustic emission and thermal infrared diagnosis of deep rock mass unloading damage provided by an embodiment of the present invention;

[0047] Figure 5 A flowchart of step S13 of another method for combined acoustic emission and thermal infrared diagnosis of deep rock mass unloading damage provided by an embodiment of the present invention;

[0048] Figure 6 A structural diagram of a deep rock mass unloading damage combined acoustic emission and thermal infrared diagnosis system provided by an embodiment of the present invention;

[0049] Figure 7 A structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is merely to provide a better understanding of the present application by illustrating the examples of the present application.

[0051] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, the elements defined by the phrase "comprising..." do not exclude the presence of other identical elements in the process, method, article, or device comprising the elements.

[0052] like Figure 1As shown, the present invention provides a method for combined acoustic emission and thermal infrared diagnosis of deep rock mass unloading damage, which includes:

[0053] In step S11, during the unloading process of the deep rock mass, acoustic emission signals and infrared thermal image data of a specific area of the rock mass are synchronously acquired;

[0054] In step S12, key acoustic emission parameters of the acoustic emission signal and key thermal infrared features of the infrared thermal image data are extracted, and the correlation between the key acoustic emission parameters and the key thermal infrared features during the unloading process is analyzed;

[0055] In step S13, the unloading damage state of the rock mass is identified and judged based on the analysis results of the correlation between the key acoustic emission parameters and the key thermal infrared characteristics during the unloading process and compared with a preset damage discrimination threshold or a combined criterion;

[0056] In step S14, corresponding rock damage control measures are implemented based on the unloading damage state.

[0057] In one embodiment, during the construction and operation of deep rock projects (such as underground caverns, deep tunnels, steep slopes, etc.), problems of rock damage induced by high ground stress and unloading are often faced. Once the deep rock mass is unloaded, stress redistribution will occur, which may lead to catastrophic risks such as microcrack expansion, rock collapse or more serious rock bursts. How to accurately monitor, timely diagnose and effectively control this unloading damage is one of the core problems in the field of geological engineering. Traditional monitoring methods may rely on a single physical quantity (such as acoustic emission or displacement monitoring), and it is often difficult to obtain multi-dimensional signs of damage inside the rock mass. To this end, by combining dual-channel monitoring of acoustic emission signals and infrared thermal imaging data, the ability to identify and warn of rock damage dynamics can be greatly improved through comprehensive diagnosis of elastic wave signals and heat release signs.

[0058] The technical solution in this embodiment synchronously acquires acoustic emission signals and infrared thermal images; extracts key acoustic emission parameters and thermal infrared features and analyzes their correlation; determines the rock damage state based on preset thresholds or criteria; and implements corresponding rock damage control measures based on the damage state. During the unloading process of deep rock mass, acoustic emission signals and infrared thermal image data of specific rock mass regions are synchronously acquired, that is, the emission acquisition system (AE) and the infrared thermal imager are matched in time and space. When deep rock mass is unloaded, microscopic to macroscopic fracture activity occurs, releasing elastic wave energy; at the same time, rock crushing also triggers local thermal effects (frictional heating, frictional sliding of crack surfaces, etc.), and infrared thermal images can capture the temperature rise phenomenon on the rock surface. An acoustic emission sensor array is deployed, and key rock mass sections are covered as much as possible within the unloading influence range; the infrared thermal imager is placed at an appropriate angle to avoid interference from environmental thermal noise and light sources; and a unified clock or trigger mechanism is used to achieve time stamp alignment between the acoustic emission and thermal infrared data frames. Key acoustic emission parameters of the acoustic emission signal and key thermal infrared features of the infrared thermal image data are extracted, and their correlation during the unloading process is analyzed. Key acoustic emission parameters typically include event count, ring count, energy value, amplitude, spectral characteristics, and positioning information. These parameters can reflect microcrack growth rate, rupture scale, and rupture propagation mode, respectively. Key thermal infrared features primarily involve the amplitude of temperature change, the area of hotspots, the time and spatial location of hotspots, and the gradient of the temperature field over time. In the temporal dimension, high-frequency acoustic emission events typically occur first, followed by the observation of localized hotspots in the infrared thermal image several seconds to tens of seconds later. In the spatial dimension, matching can also be achieved through localization (acoustic source location vs. surface hotspot location). A higher correlation often indicates that the thermal anomaly caused by the rupture and the acoustic anomaly are of the same origin, thus further confirming the specific stage and severity of the unloading damage. After obtaining this fused information, the next step is to set thresholds or joint criteria in engineering or research. The preset damage discrimination thresholds may include the AE event cumulative counting rate threshold, the maximum amplitude threshold, the infrared temperature rise threshold, the hotspot area threshold, etc. If a certain critical value is met or exceeded, it can be preliminarily determined that there is a certain degree of rock fracture. The abnormalities of acoustic emission and thermal infrared must be coupled in time and space to be classified as real and serious unloading damage. For example, it is set that only when the number of acoustic emission events per unit time rises sharply and an obvious hot spot appears at the corresponding position, will it be regarded as major damage. The above technical solution can effectively eliminate false alarms such as environmental noise and temperature fluctuations, and establish a mapping between physical quantities, monitoring data and the actual degree of damage in the project.

[0059] Adjusting the excavation rate or changing the excavation sequence means that if the monitoring shows that the rock mass is severely damaged, it means that it is sensitive to construction disturbances. The excavation speed can be slowed down, the excavation can be carried out in layers and sections, or even partially suspended to stabilize the surrounding rock. Conversely, if the damage level is within a safe range, the construction process can be moderately accelerated to improve efficiency. Installing or strengthening the support system refers to commonly used support methods including anchor rods, shotcrete, steel supports, etc. When monitoring shows that the damage in a certain place has intensified, the anchor rods can be increased, the shotcrete layer can be thickened, or steel arches can be added to ensure that the rock mass does not deform too much during the unloading process. Implementing stress release technology refers to methods such as pre-splitting blasting or hydraulic fracturing, which actively release some high stress before or during excavation to reduce the possibility of brittle fracture. Implementing grouting reinforcement means using slurry (such as cement slurry, high-water material, etc.) to fill cracks, enhance the integrity of the rock mass, and effectively suppress the rate and scale of crack propagation during the unloading process.

[0060] In the early stages of rock mass unloading and failure, microcracks are unlikely to cause a significant temperature rise on the surface, but acoustic emission (AE) can often immediately detect elastic wave signals. As cracks expand, the energy dissipated by the fracture is converted into significant heat, and infrared thermal imaging cameras can detect the temperature rise in or near the crack. Acoustic signals are fast but susceptible to noise, while thermal signals are more intuitive but exhibit a certain lag. The coupling of these two signals allows for mutual verification, making damage identification more reliable. To effectively correlate AE and infrared data, appropriate thresholds or criteria are needed to quantify the degree and level of damage. In real-world projects, these values or formulas are developed based on rock sample test results and field monitoring experience. For example, the number of AE events per minute exceeds a certain threshold and the thermal infrared temperature rise exceeds the background by 2°C; or the cumulative AE energy exceeds the limit within a certain period (e.g., several seconds), while the infrared hotspot area reaches a certain percentage. Once such a threshold is triggered, it indicates an increased risk and requires intervention. Such methods often do not rely on complex algorithms, are highly operational, and have good field applicability. For example, once it is determined that the rock mass has entered a highly fractured stage, the excavation rate can be immediately reduced, anchor rods or prestressed support can be added, grouting reinforcement can be implemented, etc. This can effectively prevent large-scale collapse or rock burst.

[0061] In deep engineering scenarios, ambient temperatures are often high and ventilation is poor, making infrared detection susceptible to natural convection and humidity. Furthermore, acoustic emission (AE) acquisition is susceptible to external interference, such as mechanical vibration, hydraulic equipment noise, and blasting vibration. To ensure that synchronized data from the two truly reflects fractures within the rock mass, rather than ambient noise, engineers must carefully consider hardware deployment and acquisition strategies. For example, baseline measurements can be conducted during non-blasting periods to establish a noise background; appropriate filtering and signal discrimination thresholds can be configured to minimize interference from machine vibration and drilling noise. A common pattern is an initial pulsed increase in AE, or a sudden surge in energy. Subsequently, if the fracture is generating sufficient heat to be measured, the infrared image will reveal a hotspot or a localized temperature spike. This interval typically lasts from a few tenths of a second to several seconds or over ten seconds. A typical "acoustic-thermal" peak between the two signals indicates an actual fracture event. However, if the AE level is low but the temperature suddenly increases, it is necessary to determine whether external interference, such as lighting or mechanical heat exhaust, is responsible. Multi-sensor AE localization can identify the approximate fracture site; infrared images can also reveal the location of surface hotspots. Different lithologies and stress environments can lead to differences in the intensities of acoustic emission and thermal infrared signals. For example, during a rockburst in hard and brittle granite, there will be a significant peak in acoustic emission energy and a sharp increase in the thermal infrared hotspot. In contrast, for relatively soft slate, acoustic emission events may be numerous and dispersed, with a relatively gradual increase in thermal image temperature. Preset thresholds or combined criteria are not fixed values and require flexible adjustment through experimental calibration or pilot monitoring.

[0062] During deep rock excavation, the speed of face advancement and the sequence of advances directly affect the rate of stress redistribution in the rock mass. If monitoring reveals rapid damage accumulation, steps can be shortened, layer heights reduced, or temporary intermediate walls added to alleviate stress concentrations. If anchor bolts are combined with shotcrete to form primary support, if the risk of local collapse is detected, arches can be added and the shotcrete thickness increased. The density and length of anchor bolts should be appropriately increased, especially for layered or fractured rock masses. Pre-splitting blasting artificially controls crack propagation, creating localized "weak planes" and reducing the subsequent occurrence of random cracks. Hydraulic fracturing creates pre-determined fracture channels in the rock mass to release high stresses and mitigate the suddenness of damage. Grouting in fractured areas or loose surrounding rock can significantly improve surrounding rock integrity and reduce local delamination, spalling, and rock block fall during stress release. The type of grout and timing of grouting should be based on monitoring trends to ensure timely reinforcement before imminent danger.

[0063] The technical solution in this embodiment overcomes the limitations of one-sided information of a single monitoring method by synchronously acquiring and jointly analyzing two complementary information, acoustic emission (reflecting internal micro-fractures) and infrared thermal images (reflecting surface thermal response), and can more comprehensively and accurately grasp the internal mechanism and external manifestations of deep rock unloading damage. By analyzing the temporal correlation between acoustic emission parameters and thermal infrared characteristics during the unloading process, and establishing a damage identification model based on a joint criterion (such as the comprehensive damage index CDI), some interference information is effectively eliminated, the reliability of damage identification is improved, and earlier damage warnings may be achieved. Based on more reliable damage diagnosis results, the damage state and development trend of the rock mass can be judged more promptly and accurately, providing a scientific basis for selecting and implementing appropriate control measures (such as adjusting excavation and optimizing support), thereby effectively controlling risks and ensuring project safety.

[0064] In one embodiment, Figure 2 As shown, step S12 includes the following steps S21-S22:

[0065] In step S21, signal processing is performed on the acquired acoustic emission signal to calculate at least one of the count rate, energy, amplitude, and frequency characteristics of the acoustic emission signal;

[0066] In step S22, according to the prior rock mass characteristics or expected damage mode, any one or more of the event count rate, energy, amplitude, and frequency characteristics of the acoustic emission signal are selected, and the calculated acoustic emission parameters are used as key acoustic emission parameters.

[0067] In one embodiment, acoustic emission signals and infrared thermal image data are synchronously collected and processed to extract key parameters and analyze their correlations, thereby identifying the damage state of the rock mass and taking appropriate control measures. The acquired raw acoustic emission signals typically contain a large amount of noise and interference information, so signal processing is required. The purpose of signal processing is to remove noise and extract useful signal features. Common signal processing methods include filtering, denoising, and signal enhancement. Key parameters are calculated from the processed acoustic emission signals, including count rate, energy, amplitude, and frequency characteristics. The count rate refers to the number of acoustic emission events detected per unit time and reflects the activity of crack expansion. Energy refers to the total energy of the acoustic emission signal and is related to the energy released during crack expansion. Amplitude refers to the peak amplitude of the acoustic emission signal and is related to the intensity of crack expansion. Frequency characteristics refer to the frequency distribution of the acoustic emission signal and reflect the pattern and mechanism of crack expansion. Based on the physical and mechanical properties of the rock mass (such as rock type, crack density, rock mass strength, etc.), the acoustic emission parameters most relevant to the rock mass damage state are selected. For example, for rock masses with high fracture density, count rate may be a key parameter, while for high-strength rock masses, energy and amplitude may be more important. Based on engineering experience and expected damage modes (such as crack propagation, local instability, and water inrush), acoustic emission parameters that effectively reflect these damage modes are selected. For example, count rate and energy typically increase significantly during crack propagation, while local instability may cause sudden changes in amplitude.

[0068] The count rate reflects the frequency and activity of crack propagation within the rock mass. A high count rate typically indicates frequent crack propagation and a high degree of rock damage. For example, in mining and tunnel construction, a sudden increase in the count rate may indicate impending rock mass instability, necessitating prompt control measures. Energy reflects the total energy released during crack propagation. High energy typically indicates significant crack propagation or penetration, potentially leading to localized rock mass instability. For example, in underground cavern excavation, a significant increase in energy may indicate localized rock mass instability or the risk of water inrush, necessitating strengthened support or adjustments to the excavation sequence. Amplitude reflects the intensity of crack propagation. High amplitude typically indicates significant crack propagation or localized instability. For example, in deep mining, sudden changes in amplitude may indicate localized rock mass instability or the risk of water inrush, necessitating prompt grouting reinforcement or stress relief techniques. Frequency characteristics reflect the pattern and mechanism of crack propagation. Different frequency distributions may correspond to different crack propagation mechanisms (e.g., tension, shear, etc.). For example, in tunnel construction, changes in frequency characteristics can help identify patterns of crack propagation, thereby optimizing support design and construction parameters.

[0069] By extracting multiple acoustic emission parameters, the multifaceted characteristics of rock damage are comprehensively reflected, improving the accuracy and reliability of damage identification. Real-time monitoring of changes in acoustic emission parameters enables dynamic monitoring of rock damage status, providing a basis for timely control measures. Key parameters are selected based on rock mass characteristics and damage patterns to ensure that monitoring results are highly correlated with the actual damage status. During deep mining, acoustic emission parameters are monitored in real time to identify the risks of crack expansion and local instability, and to optimize excavation parameters and support design. During tunnel excavation, acoustic emission parameters are monitored to identify the risks of crack expansion and water inrush, and to adjust the excavation sequence and support system. During underground cavern construction, acoustic emission parameters are monitored to identify the state of rock damage, and grouting reinforcement or stress release techniques are implemented to ensure construction safety.

[0070] The technical solution in this embodiment achieves accurate identification and dynamic monitoring of rock mass unloading damage by extracting key parameters from acoustic emission signals and selecting the most relevant parameters based on rock mass characteristics and damage patterns. This multi-parameter fusion approach overcomes the limitations of single-parameter monitoring and improves the accuracy and reliability of damage identification.

[0071] In one embodiment, Figure 3 As shown, step S12 further includes the following steps S31-S33:

[0072] In step S31, image processing and analysis are performed on the acquired infrared thermal image data to identify and quantify at least one of the temperature anomaly area, the temperature change rate over time, and the spatial temperature gradient of the deep rock mass;

[0073] In step S32, a multifractal analysis method is applied to the infrared thermal image data to extract temperature field non-uniformity parameters and frequency diversity parameters;

[0074] In step S33, any one or more of the temperature anomaly area, the temperature change rate over time, the spatial temperature gradient, the temperature field inhomogeneity parameter and the frequency diversity parameter is determined as a key thermal infrared feature.

[0075] In one embodiment, acoustic emission signals and infrared thermal image data are collected and processed synchronously, key parameters are extracted and their correlation is analyzed, and then the damage state of the rock mass is identified and corresponding control measures are taken.

[0076] Image processing techniques are used to identify and quantify areas of abnormal temperature on the rock mass surface. These areas typically correspond to areas of crack expansion or frictional heating within the rock mass and are important indicators of rock mass damage. The rate of temperature change over time in infrared thermal image data is analyzed to reflect the dynamic characteristics of rock mass temperature changes during unloading. A significant increase in the rate of temperature change may indicate rapid crack expansion or localized instability. The spatial gradient of rock mass surface temperature is calculated to reflect the spatial inhomogeneity of temperature distribution. Areas with large spatial temperature gradients typically correspond to areas with dense cracks or concentrated stress. Multifractal analysis is applied to extract temperature field inhomogeneity parameters. Multifractal analysis can quantify the complexity and inhomogeneity of the temperature field, providing a more detailed characterization for identifying rock mass damage. Multifractal analysis is used to extract the frequency diversity parameter of the temperature field. The frequency diversity parameter reflects the distribution of different frequency components in the temperature field and helps identify the patterns and mechanisms of crack expansion. Based on the physical and mechanical properties of the rock mass and the expected damage pattern, the most relevant features are selected as key thermal infrared features from the following categories: temperature anomaly areas, temperature change rates over time, spatial temperature gradients, temperature field inhomogeneity parameters, and frequency diversity parameters. The selected key thermal infrared features are fused to form a comprehensive thermal infrared feature set, which is used for correlation analysis with acoustic emission parameters to improve the accuracy and reliability of rock damage identification.

[0077] Abnormal temperature areas reflect areas of crack propagation or frictional heating within the rock mass and are a direct indicator of rock mass damage. In mining and tunnel construction, the appearance of abnormal temperature areas may indicate impending rock mass instability, necessitating timely control measures. The rate of temperature change over time reflects the dynamic characteristics of rock mass temperature changes during unloading and is related to the rate of crack propagation. In underground cavern excavation, a significant increase in the rate of temperature change may indicate localized rock mass instability or the risk of water inrush, necessitating strengthened support or adjustments to the excavation sequence. Spatial temperature gradients reflect the spatial inhomogeneity of temperature distribution and are associated with areas of dense cracks or stress concentration. In deep mining, areas with large spatial temperature gradients may correspond to areas of dense cracks or stress concentration, necessitating grouting reinforcement or stress relief techniques. Temperature field inhomogeneity parameters quantify the complexity and heterogeneity of the temperature field, reflecting the complexity of crack propagation and stress distribution. In tunnel construction, variations in temperature field inhomogeneity parameters can help identify crack propagation patterns and optimize support design and construction parameters. The frequency diversity parameter reflects the distribution of different frequency components in the temperature field and is related to the pattern and mechanism of crack growth. In mining, changes in the frequency diversity parameter can help identify crack growth patterns and provide a basis for taking targeted control measures.

[0078] By extracting a variety of thermal infrared features, the multifaceted characteristics of rock damage are comprehensively reflected, improving the accuracy and reliability of damage identification. Real-time monitoring of changes in thermal infrared features enables dynamic monitoring of rock damage status, providing a basis for timely control measures. Key features are selected based on rock properties and damage patterns to ensure that monitoring results are highly correlated with the actual damage status. During deep mining, thermal infrared features are monitored in real time to identify the risks of crack expansion and local instability, and to optimize excavation parameters and support design. During tunnel excavation, thermal infrared features are monitored to identify the risks of crack expansion and water inrush, and to adjust the excavation sequence and support system. During underground cavern construction, thermal infrared features are monitored to identify the state of rock damage, and grouting reinforcement or stress release techniques are implemented to ensure construction safety.

[0079] The technical solution in this embodiment extracts key features of infrared thermal image data and selects the most relevant features according to rock mass characteristics and damage patterns, thereby achieving accurate identification and dynamic monitoring of the unloading damage state of the rock mass.

[0080] In one embodiment, Figure 4 As shown, step S13 includes the following steps S41-S42:

[0081] In step S41, identifying the temporal sequence or synchronization between the active period of acoustic emission and the appearance of a specific thermal infrared feature or a significant change in the thermal infrared feature;

[0082] In step S42, the response relationship between the acoustic emission parameters and the thermal infrared characteristics is analyzed in combination with the changes in stress or strain during the unloading process to distinguish different damage mechanisms, such as tensile rupture or shear rupture.

[0083] In one embodiment, during the unloading process of a rock mass, the active period of the acoustic emission signal typically corresponds to a period of rapid crack expansion. By analyzing the temporal relationship between the occurrence or significant changes in the acoustic emission period and thermal infrared characteristics (such as temperature anomaly areas and temperature change rates), the sequence or synchronization of crack expansion and thermal characteristic changes can be identified. For example, the acoustic emission active period may precede a significant change in the thermal infrared characteristics, indicating that crack expansion precedes frictional heating; or the two may change simultaneously, indicating that crack expansion and frictional heating occur simultaneously. In mining, identifying the sequential relationship between the acoustic emission active period and the changes in the thermal infrared characteristics can provide early warning of the risk of rock mass instability and provide a basis for timely control measures. During the unloading process of a rock mass, changes in stress or strain directly affect the acoustic emission parameters and thermal infrared characteristics. For example, when stress is concentrated, the number and energy of acoustic emission events typically increase, and the temperature anomaly areas and temperature change rates also change significantly. By analyzing the relationship between the response of acoustic emission parameters and thermal infrared characteristics to stress or strain changes, different damage mechanisms, such as tensile or shear failure, can be distinguished. Tensile fracture is often accompanied by high acoustic emission energy and a large temperature anomaly, while shear fracture may manifest as a high number of acoustic emission events and a rapid temperature change rate. In tunnel construction, analyzing the relationship between acoustic emission parameters and thermal infrared signatures can identify crack propagation patterns, thereby optimizing support design and construction parameters to ensure construction safety.

[0084] The active period of acoustic emission reflects the stage of rapid expansion of internal cracks in the rock mass and is an important indicator of rock damage. During underground cavern excavation, the occurrence of an active period of acoustic emission may indicate local instability of the rock mass or the risk of water inrush, requiring strengthened support or adjustment of the excavation sequence. Changes in thermal infrared characteristics (such as abnormal temperature areas, temperature change rate, etc.) reflect the expansion of internal cracks in the rock mass or the generation of frictional heat and are a direct indication of rock damage. In deep mining, significant changes in thermal infrared characteristics may indicate local instability of the rock mass or the risk of water inrush, requiring grouting reinforcement or stress release technology.

[0085] By combining acoustic emission signals and infrared thermal image data, the multifaceted characteristics of rock damage are comprehensively reflected, improving the accuracy and reliability of damage identification. Real-time monitoring of changes in acoustic emission parameters and thermal infrared characteristics enables dynamic monitoring of rock damage status, providing a basis for timely control measures. Key features are selected based on rock mass properties and damage patterns to ensure that monitoring results are highly correlated with the actual damage status. During deep mining, acoustic emission parameters and thermal infrared characteristics are monitored in real time to identify the risks of crack expansion and local instability, and to optimize excavation parameters and support design. During tunnel excavation, acoustic emission parameters and thermal infrared characteristics are monitored to identify the risks of crack expansion and water inrush, and to adjust the excavation sequence and support system. During underground cavern construction, acoustic emission parameters and thermal infrared characteristics are monitored to identify the state of rock damage, and grouting reinforcement or stress release techniques are implemented to ensure construction safety.

[0086] The technical solution in this embodiment accurately identifies and dynamically monitors the unloading damage state of rock masses by analyzing the temporal correlation between acoustic emission parameters and thermal infrared signatures, combined with changes in stress or strain. This multi-source information fusion approach overcomes the limitations of single monitoring methods and improves the accuracy and reliability of damage identification.

[0087] In one embodiment, Figure 5 As shown, step S13 further includes the following steps S51-S52:

[0088] In step S51, independent warning thresholds are set for key acoustic emission parameters and key thermal infrared features respectively;

[0089] In step S52, when the comprehensive damage index CD1 reaches or exceeds the preset comprehensive damage judgment threshold D threshold When , it is determined that significant unloading damage occurs.

[0090] In one embodiment, a warning threshold for the acoustic emission event count rate is set based on the physical and mechanical properties of the rock mass and the expected damage pattern. When the count rate exceeds the threshold, it indicates active crack expansion and a high level of rock damage. A warning threshold for the acoustic emission energy is set. When the energy exceeds the threshold, it indicates that a large amount of energy is released during crack expansion, potentially leading to local instability in the rock mass. A warning threshold for the acoustic emission amplitude is set. When the amplitude exceeds the threshold, it indicates that the crack expansion intensity is large, potentially indicating local instability in the rock mass. A warning threshold for the acoustic emission frequency signature is set. Different frequency distributions may correspond to different crack expansion mechanisms. When the frequency signature exceeds the threshold, changes in the crack expansion pattern can be identified. A warning threshold for the temperature anomaly area is set. When the area or temperature value of the temperature anomaly area exceeds the threshold, it indicates significant crack expansion or frictional heating within the rock mass, which is a key indicator of rock damage. A warning threshold for the temperature change rate over time is set. When the temperature change rate exceeds the threshold, it indicates that the rock mass temperature changes dramatically during unloading, potentially indicating rapid crack expansion or local instability. Set the warning threshold of the spatial temperature gradient. When the spatial temperature gradient exceeds the threshold, it indicates that the spatial temperature distribution is significantly non-uniform, corresponding to areas with dense cracks or stress concentration. Set the warning threshold of the temperature field non-uniformity parameter. When the parameter exceeds the threshold, it indicates that the temperature field is highly complex and non-uniform, reflecting the complexity of crack expansion and stress distribution. Set the warning threshold of the frequency diversity parameter. When the parameter exceeds the threshold, it indicates that the distribution of different frequency components in the temperature field is complex, which is related to the pattern and mechanism of crack expansion. The comprehensive damage index CDI of the combined criterion is calculated as follows:

[0091] CDI=W ae *(AE param / AE threshold )+W irt *(IRT feature / IRT threshold )

[0092] Among them, AE param is the key acoustic emission parameter value of the current monitoring period, AE threshold is the warning threshold of critical acoustic emission, IRT feature is the key thermal infrared characteristic value of the current monitoring period, IRT threshold is the warning threshold of key thermal infrared features, W ae and W irt are the weight coefficients of acoustic emission parameters and thermal infrared characteristics, respectively, and W ae +W irt=1. The comprehensive damage index (CDI) is constructed by weighted summation of key acoustic emission parameters and key thermal infrared features. The weight coefficient is determined according to the sensitivity and importance of each parameter to rock damage, ensuring that the CDI can fully reflect the damage status of the rock mass. According to the changes in acoustic emission parameters and thermal infrared features during the unloading process, the weight coefficient of the CDI is dynamically adjusted to adapt to the changes in damage characteristics at different stages, thereby improving the accuracy and reliability of damage identification. Based on a large amount of experimental data and engineering experience, the comprehensive damage judgment threshold D is set. threshold When CDI reaches or exceeds D threshold When D threshold When designing a fire alarm, a certain safety margin should be considered to ensure timely warning before damage occurs, providing sufficient time for taking control measures.

[0093] When the acoustic emission parameters (such as count rate, energy, amplitude, frequency characteristics) exceed the corresponding warning threshold, the acoustic emission warning is triggered, indicating that the rock mass is in a damaged state in the corresponding aspect. When the thermal infrared characteristics (such as temperature anomaly area, temperature change rate, spatial temperature gradient, temperature field inhomogeneity parameter, frequency diversity parameter) exceed the corresponding warning threshold, the thermal infrared warning is triggered, also indicating that the rock mass is in a damaged state in the corresponding aspect. When the comprehensive damage index CDI reaches or exceeds the comprehensive damage judgment threshold D threshold When a warning signal is detected, a combined criterion warning is triggered, indicating that significant unloading damage has occurred and requiring immediate control measures. By combining acoustic emission signals and infrared thermal image data, the multifaceted characteristics of rock damage are comprehensively reflected, improving the accuracy and reliability of damage identification. Real-time monitoring of changes in acoustic emission parameters and thermal infrared signatures enables dynamic monitoring and early warning of rock damage status, providing a basis for timely control measures. Independent warning thresholds and combined criteria are set based on rock mass characteristics and damage patterns to ensure that monitoring results are highly correlated with the actual damage status. During deep mining, real-time monitoring of acoustic emission parameters and thermal infrared signatures is used to identify the risks of crack expansion and local instability, allowing for optimization of excavation parameters and support design. During tunnel excavation, acoustic emission parameters and thermal infrared signatures are monitored to identify the risks of crack expansion and water inrush, allowing for adjustments to the excavation sequence and support system. During underground cavern construction, acoustic emission parameters and thermal infrared signatures are monitored to identify rock damage status, allowing for grouting reinforcement or stress relief techniques to ensure construction safety.

[0094] The technical solution in this embodiment achieves accurate identification and dynamic monitoring of the unloading damage state of the rock mass by setting independent warning thresholds for key acoustic emission parameters and key thermal infrared characteristics and constructing a comprehensive damage index CDI based on joint criteria.

[0095] In one embodiment, Figure 6This is a block diagram of a deep rock mass unloading damage combined acoustic emission and thermal infrared diagnosis system according to an exemplary embodiment. Figure 6 As shown, the deep rock unloading damage acoustic emission-thermal infrared combined diagnosis system includes an acquisition module 61, an analysis module 62, an identification module 63 and an implementation module 64.

[0096] The acquisition module 61 is used to synchronously acquire acoustic emission signals and infrared thermal image data of a specific area of the rock mass during the unloading process of the deep rock mass;

[0097] The analysis module 62 is used to extract key acoustic emission parameters of the acoustic emission signal and key thermal infrared features of the infrared thermal image data, and analyze the correlation between the key acoustic emission parameters and the key thermal infrared features during the unloading process;

[0098] The identification module 63 is used to identify and determine the unloading damage state of the rock mass based on the analysis results of the correlation between the key acoustic emission parameters and the key thermal infrared characteristics during the unloading process and in comparison with a preset damage judgment threshold or a combined criterion;

[0099] The implementation module 64 is used to implement corresponding rock damage control measures based on the unloading damage state.

[0100] The acquisition module 61, the analysis module 62, the identification module 63 and the implementation module 64 included in the block diagram of the deep rock unloading damage acoustic emission-thermal infrared joint diagnosis system are controlled to execute the deep rock unloading damage acoustic emission-thermal infrared joint diagnosis method described in any of the above embodiments.

[0101] like Figure 7 As shown, the present invention provides an electronic device 700, which includes: a communication interface, a processor 701, and a memory 702;

[0102] In which, the memory 702 is used to store program instructions. When the program instructions are executed by the processor 701 that is communicatively connected to the memory 702 through the communication interface, during the unloading process of the deep rock mass, the acoustic emission signals and infrared thermal image data of a specific area of the rock mass are synchronously acquired; key acoustic emission parameters of the acoustic emission signals and key thermal infrared features of the infrared thermal image data are extracted, and the correlation between the key acoustic emission parameters and the key thermal infrared features during the unloading process is analyzed; based on the analysis results of the correlation between the key acoustic emission parameters and the key thermal infrared features during the unloading process, and in comparison with a preset damage discrimination threshold or joint criterion, the unloading damage state of the rock mass is identified and judged; and based on the unloading damage state, corresponding rock damage control measures are implemented.

[0103] The present invention provides a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, during the unloading process of a deep rock mass, acoustic emission signals and infrared thermal image data of a specific area of the rock mass are synchronously acquired; key acoustic emission parameters of the acoustic emission signals and key thermal infrared features of the infrared thermal image data are extracted, and the correlation between the key acoustic emission parameters and the key thermal infrared features during the unloading process is analyzed; based on the analysis result of the correlation between the key acoustic emission parameters and the key thermal infrared features during the unloading process and in comparison with a preset damage discrimination threshold or a joint criterion, the unloading damage state of the rock mass is identified and judged; and based on the unloading damage state, corresponding rock mass damage control measures are implemented.

[0104] It should be understood that the specific features, operations and details described herein above with respect to the method of the present invention may also be similarly applied to the apparatus and system of the present invention, or vice versa. In addition, each step of the method of the present invention described above may be performed by the corresponding components or units of the apparatus or system of the present invention.

[0105] It should be understood that the various modules / units of the apparatus of the present invention may be implemented in whole or in part by software, hardware, firmware, or a combination thereof. Each module / unit may be embedded in a processor of a computer device in the form of hardware or firmware or may be independent of the processor, or may be stored in a memory of a computer device in the form of software for the processor to call to execute the operations of each module / unit. Each module / unit may be implemented as an independent component or module, or two or more modules / units may be implemented as a single component or module.

[0106] In one embodiment, a computer device is provided, comprising a memory and a processor. The memory stores computer instructions executable by the processor, which, when executed by the processor, instruct the processor to perform the steps of the method according to an embodiment of the present invention. The computer device can be broadly defined as a server, a terminal, or any other electronic device with the necessary computing and / or processing capabilities. In one embodiment, the computer device can include a processor, memory, a network interface, a communication interface, etc. connected via a system bus. The processor of the computer device can be used to provide the necessary computing, processing, and / or control capabilities. The memory of the computer device can include a non-volatile storage medium and an internal memory. An operating system, a computer program, etc. can be stored in or on the non-volatile storage medium. The internal memory can provide an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface and communication interface of the computer device can be used to connect to and communicate with external devices via a network. When the computer program is executed by the processor, the steps of the method according to the present invention are performed.

[0107] The present invention can be implemented as a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, causes the steps of the method of an embodiment of the present invention to be performed. In one embodiment, the computer program is distributed on a plurality of computer devices or processors coupled to a network so that the computer program is stored, accessed, and executed in a distributed manner by one or more computer devices or processors. A single method step / operation, or two or more method steps / operations, can be performed by a single computer device or processor or by two or more computer devices or processors. One or more method steps / operations can be performed by one or more computer devices or processors, and one or more other method steps / operations can be performed by one or more other computer devices or processors. One or more computer devices or processors can perform a single method step / operation, or perform two or more method steps / operations.

[0108] It will be understood by those skilled in the art that the method steps of the present invention can be performed by instructing relevant hardware such as a computer device or a processor through a computer program, and the computer program can be stored in a non-transitory computer-readable storage medium, which causes the steps of the present invention to be performed when the computer program is executed. Depending on the circumstances, any reference to memory, storage, database or other media herein may include non-volatile and / or volatile memory. Examples of non-volatile memory include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid-state disk, etc. Examples of volatile memory include random access memory (RAM), external cache memory, etc.

[0109] The various technical features described above can be combined arbitrarily. Although not all possible combinations of these technical features are described, any combination of these technical features should be considered to be covered by this specification as long as such combination does not conflict.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A combined acoustic emission and thermal infrared diagnostic method for deep rock mass unloading damage, characterized in that: include: During the unloading process of deep rock mass, the acoustic emission signal and infrared thermal image data of specific areas of the rock mass are obtained simultaneously; extracting key acoustic emission parameters of the acoustic emission signal and key thermal infrared features of the infrared thermal image data, and analyzing the correlation between the key acoustic emission parameters and the key thermal infrared features during the unloading process; According to the analysis results of the correlation between the key acoustic emission parameters and the key thermal infrared characteristics during the unloading process, and in comparison with the preset damage discrimination threshold or combined criterion, the unloading damage state of the rock mass is identified and judged; Based on the unloading damage state, corresponding rock damage control measures are implemented.

2. The deep rock mass unloading damage combined acoustic emission and thermal infrared diagnosis method according to claim 1, characterized in that: The extracting key acoustic emission parameters of the acoustic emission signal includes: Performing signal processing on the acquired acoustic emission signal to calculate at least one of the count rate, energy, amplitude, and frequency characteristics of the acoustic emission signal; According to a priori rock mass properties or expected damage patterns, any one or more of the event count rate, energy, amplitude, and frequency characteristics of the acoustic emission signal are selected, and the calculated acoustic emission parameters are used as key acoustic emission parameters.

3. The deep rock mass unloading damage combined acoustic emission and thermal infrared diagnosis method according to claim 1 is characterized in that: The extraction of key thermal infrared features includes: Performing image processing and analysis on the acquired infrared thermal image data to identify and quantify at least one of an abnormal temperature region, a temperature change rate over time, and a spatial temperature gradient of the deep rock mass; Applying a multifractal analysis method to the infrared thermal image data to extract temperature field inhomogeneity parameters and frequency diversity parameters; Any one or more of the temperature anomaly area, the temperature change rate over time, the spatial temperature gradient, the temperature field inhomogeneity parameter and the frequency diversity parameter is determined as the key thermal infrared feature.

4. The deep rock mass unloading damage combined acoustic emission and thermal infrared diagnosis method according to claim 1 is characterized in that: The analyzing the temporal correlation between the key acoustic emission parameters and the key thermal infrared characteristics during the unloading process includes: Identify the temporal sequence or synchronization between the active period of acoustic emission and the appearance of specific thermal infrared features or significant changes in thermal infrared features; Combined with the changes in stress or strain during the unloading process, the response relationship between acoustic emission parameters and thermal infrared characteristics is analyzed to distinguish different damage mechanisms, such as tensile rupture or shear rupture.

5. The combined acoustic emission and thermal infrared diagnosis method for deep rock mass unloading damage according to claim 2 or 3, characterized in that: The method for establishing the preset damage judgment threshold or combined judgment criterion includes: Set independent warning thresholds for key acoustic emission parameters and key thermal infrared features respectively; The comprehensive damage index CDI of the combined criterion is calculated as follows: CDI=W ae *(AE param / AE threshold )+W irt *(IRT feature / IRT threshold ) Among them, AE param is the key acoustic emission parameter value of the current monitoring period, AE threshold is the warning threshold of critical acoustic emission, IRT feature is the key thermal infrared characteristic value of the current monitoring period, IRT threshold is the warning threshold of key thermal infrared features, W ae and W irt are the weight coefficients of acoustic emission parameters and thermal infrared characteristics, respectively, and W ae +W irt =1; When the comprehensive damage index CDI reaches or exceeds the preset comprehensive damage judgment threshold D threshold When , it is determined that significant unloading damage occurs.

6. The combined acoustic emission and thermal infrared diagnosis method for deep rock mass unloading damage according to claim 1, characterized in that: The implementation of corresponding rock damage control measures includes: Adjust the excavation rate or change the excavation sequence; Installing or strengthening support systems, including anchors, shotcrete, or steel supports; Implementing stress relief techniques such as pre-splitting blasting or hydraulic fracturing; Implement grouting reinforcement.

7. A deep rock mass unloading damage combined acoustic emission and thermal infrared diagnosis system, characterized in that: include: Acquisition module, used to synchronously acquire acoustic emission signals and infrared thermal image data of specific areas of the rock mass during the unloading process of the deep rock mass; an analysis module, configured to extract key acoustic emission parameters of the acoustic emission signal and key thermal infrared features of the infrared thermal image data, and analyze the correlation between the key acoustic emission parameters and the key thermal infrared features during the unloading process; an identification module for identifying and judging the unloading damage state of the rock mass based on the analysis results of the correlation between the key acoustic emission parameters and the key thermal infrared characteristics during the unloading process and comparing with a preset damage discrimination threshold or a combined criterion; The implementation module is used to implement corresponding rock damage control measures based on the unloading damage state.

8. The deep rock mass unloading damage combined acoustic emission and thermal infrared diagnosis system according to claim 7, characterized in that: The acquisition module, the analysis module, the identification module and the implementation module are controlled to execute the deep rock mass unloading damage joint acoustic emission-thermal infrared diagnosis method according to any one of claims 1 to 6.

9. An electronic device, characterized in that: include: Communication interface, processor, memory; Wherein, the memory is used to store program instructions, and when the program instructions are executed by the processor that is communicatively connected to the memory through the communication interface, the electronic device implements the deep rock unloading damage joint acoustic emission-thermal infrared diagnosis method as described in any one of claims 1 to 6.

10. A computer-readable storage medium having program instructions stored thereon, characterized in that: When the program instructions are executed by a computer, the computer implements the deep rock mass unloading damage acoustic emission-thermal infrared combined diagnosis method as described in any one of claims 1 to 6.

Images