Omnibearing intelligent monitoring system, method and device for underground anchoring pull-out test

Abstract

The invention belongs to the technical field of underground engineering, and discloses an omnibearing intelligent monitoring system, method and device for an underground anchoring pull-out test. According to the method, electromagnetic signals generated in the stress process of the anchor rod are captured in real time through an electromagnetic sensor, and internal stress changes and energy distribution are reflected; the deformation and stress states of the anchor rod in the dynamic environment are evaluated through a strain gauge and accelerometer combined data combination method, the deformation condition of the anchor rod is monitored through the strain gauge, and the stress-strain relation is recorded; an accelerometer is used for detecting the dynamic response of surrounding rock mass so as to evaluate the overall stability of the rock mass; a coupling medium is injected, the computer terminal stores the collected signals locally or in a cloud end and analyzes the signals, stress and deformation parameters in an anchor rod pull-out test are extracted, and an anchor rod stress cloud picture is generated in real time. According to the method, the safety and reliability of underground engineering are remarkably improved, and a new technical support is provided for evaluation of anchoring quality.

Description

Technical Field

[0001] The present invention belongs to the field of underground engineering technology, and in particular relates to an all-round intelligent monitoring system, method and device for underground anchor pull-out tests. Background Art

[0002] In underground engineering, the safety of anchoring is crucial, especially in high-risk underground environments. The reliability of the anchoring system is directly related to the stability and safety of the project. Traditional anchor pullout testing methods typically rely on mechanical sensors and manual observation, focusing primarily on monitoring the ultimate force at the anchor end. While this monitoring method can provide certain force data, it has many limitations and is in urgent need of improvement.

[0003] The limitations of traditional pull-out tests are primarily reflected in the fact that their detection methods focus solely on the ultimate mechanical performance of the anchor end of the anchor rod, failing to fully reflect the stress and energy fields of the anchor rod and the surrounding rock during the entire pull-out process, as well as the complex state of interaction between the anchor rod and the surrounding rock mass. This means that traditional methods only provide ultimate stress data at the anchor end, but fail to reveal the true state of the anchor rod during the anchoring process, resulting in an incomplete and inaccurate assessment of anchoring quality. Furthermore, traditional monitoring methods are susceptible to interference from external environmental factors such as downhole noise and vibration, which can affect the stability and accuracy of the monitoring signal, thereby reducing the reliability of the data. Therefore, new monitoring technologies are urgently needed to address the shortcomings of traditional methods and improve the detection accuracy and process control capabilities of anchoring quality.

[0004] The invention patent discloses an anchor system based on fiber Bragg grating anchoring monitoring (publication number CN202410456669.5, publication date 20240702), which relates to the field of mine tunnel construction technology. Although the system can improve the anchoring effect when dealing with high stress conditions, the optical fiber may still be damaged when subjected to excessive tension, thereby affecting the accuracy of monitoring. In addition, the system mainly monitors the external condition of the anchor rod, and has limited understanding of the internal structure and surrounding rock mass, which may lead to incomplete information. At the same time, the installation and commissioning of the Bragg grating requires high technical precision, which may increase the difficulty and cost of construction.

[0005] Utility model patent CN202323169878.1 discloses a device for monitoring and alarming the anchor bolt anchoring status in deep tunnels (publication number CN202323169878.1, publication date 20241101), which relates to the field of anchor bolt anchoring status monitoring technology. This device for monitoring and alarming the anchor bolt anchoring status in deep tunnels has several shortcomings. While the device can monitor in specific tunnel environments, various environmental factors, such as temperature, humidity, and corrosive gases, may affect the accuracy and reliability of monitoring. On-site response may be delayed. If the stress corrosion monitoring device fails to provide timely feedback on the corrosion status or stress changes of the anchor bolt, it may fail to issue an alarm in the event of anchor bolt fracture, increasing safety risks. Furthermore, the device primarily relies on stress and corrosion level as monitoring indicators, lacking a comprehensive assessment of changes in the state of the anchor bolt and the surrounding rock, resulting in incomplete information. Furthermore, the embedded device is complex to maintain in harsh environments, requiring professional inspection and repair in the event of a malfunction. These shortcomings may affect monitoring effectiveness and overall safety.

[0006] Through the above analysis, the problems and defects of the existing technology are: the traditional anchor pull-out test method mainly obtains the stress conditions at the top of the anchor, lacks comprehensive monitoring of the rock mass inside the anchor and its surroundings, resulting in insufficient information, poor real-time performance and complex data processing. Summary of the Invention

[0007] To overcome the problems existing in related technologies, the disclosed embodiments of the present invention provide a comprehensive intelligent monitoring system and method for downhole anchor pullout tests. Specifically, they relate to an intelligent monitoring system for downhole anchor pullout tests based on CT electromagnetic technology. To address the limitations of these traditional monitoring methods, the present invention proposes a comprehensive intelligent monitoring system for downhole anchor pullout tests based on electromagnetic technology. This system is designed to overcome the shortcomings of traditional methods and provide a more comprehensive, efficient, and accurate monitoring solution.

[0008] The technical solution is as follows: a comprehensive intelligent monitoring method for underground anchor pull-out test, comprising the following steps:

[0009] S1, drilling multiple sensor holes evenly around the anchor rod, and installing sensor monitoring equipment in the holes drilled for sensor arrangement; the sensor monitoring equipment includes an electromagnetic sensor, a strain gauge, and an accelerometer;

[0010] S2 uses electromagnetic sensors to capture the electromagnetic signals generated by the anchor in real time during the stress process, reflecting the internal stress changes and energy distribution; uses a combination of strain gauges and accelerometers to evaluate the deformation and stress state of the anchor in a dynamic environment. The strain gauges are used to monitor the deformation of the anchor and record the stress-strain relationship; the accelerometers are used to detect the dynamic response of the surrounding rock mass to evaluate the overall stability of the rock mass;

[0011] S3, based on the principle of water coupling and the control of water injection volume, the coupling medium is injected into the hole where the sensor is arranged, and the anchor pull-out test is carried out. The monitoring signals of the electromagnetic sensor, strain gauge and accelerometer are transmitted to the data acquisition system and computer terminal;

[0012] S4, the computer terminal stores the collected signals locally or in the cloud, analyzes them, extracts the stress and deformation parameters in the anchor pull-out test, and generates the anchor force cloud map in real time.

[0013] In step S1, four sensor arrangement holes of the same length as the anchor are evenly opened around the anchor, and the diameter of the sensor arrangement holes is designed according to the size of the sensor monitoring equipment;

[0014] The electromagnetic sensor, strain gauge and accelerometer are mounted on a telescopic rod, which rests in a hole drilled for sensor arrangement.

[0015] In step S2, electromagnetic sensors are used to capture in real time the electromagnetic signals generated by the anchor rod during the stress process, reflecting the internal stress changes and energy distribution, including:

[0016] (I01) Monitoring the electromagnetic characteristics of anchor bolt deformation-affecting materials through an improved stress-electromagnetic signal relationship model;

[0017] (I02) Perform frequency analysis and feature extraction of electromagnetic signals through improved spectrum analysis methods.

[0018] In step (I01), the improved stress-electromagnetic signal relationship model is:

[0019]

[0020] Where B is the magnetic field intensity, μ0 is the vacuum magnetic permeability, F is the external force acting on the anchor, A is the cross-sectional area of the anchor, and r is the distance between the electromagnetic sensor and the anchor.

[0021] In step (I02), the improved spectrum analysis method is combined with fast Fourier transform (FFT) to perform real-time analysis on the electromagnetic signal and extract useful frequency features. The formula is:

[0022]

[0023] Where S(f) represents the spectrum of the signal, x(t) represents the time domain electromagnetic signal, f represents the frequency, and t represents the time.

[0024] In step S2, the deformation of the anchor rod is monitored using a strain gauge, and the stress-strain relationship is recorded, including:

[0025] (II01) The strain gauge is used to measure the deformation of the anchor rod surface to reflect the stress change of the anchor rod under the action of external force. The deformation of the anchor rod is related to the stress through the following formula:

[0026] σ=E·ε

[0027] Where σ represents stress, E represents the elastic modulus of the material, and ε represents strain;

[0028] The stress of the anchor rod is calculated through the above relationship, and the relationship between the change in resistance ΔR output by the strain gauge and the strain is converted through the sensitivity coefficient of the strain gauge;

[0029] (II02) Using an improved temperature compensation method to eliminate the influence of temperature changes, and using the compensation of transverse strain to correct the actual deformation of the anchor rod; wherein the improved temperature compensation method includes: the resistance change of the strain gauge is not only related to the strain, but also closely related to the temperature change, and the improved temperature compensation formula is:

[0030] ε corr =ε measured -α·ΔT

[0031] Where, ε corr is the corrected strain, ε measured is the directly measured strain, α is the temperature coefficient of the strain gauge, and ΔT is the temperature change;

[0032] The compensation of the lateral effect corrects the actual deformation of the anchor rod. When the strain gauge is installed on the surface of the anchor rod, the lateral strain comes from the lateral deformation of the anchor rod or the effect of external load, and is corrected by the following formula:

[0033] ε long =ε measured -k·ε transverse

[0034] Where, ε long is the modified longitudinal strain, ε transverse is the transverse strain, k is the correction coefficient; by compensating for the transverse strain, the actual deformation of the anchor is reflected;

[0035] (II03) A nonlinear material model is used to correct the stress-strain relationship; the nonlinear material model includes a hyperbola model, and the stress-strain relationship is fitted using the hyperbola model as follows:

[0036]

[0037] Where ε0 is the starting point of strain softening and n is the nonlinear index of the material.

[0038] In step S2, the accelerometer is used to detect the dynamic response of the surrounding rock mass to evaluate the overall stability of the rock mass, including:

[0039] (III01) Obtaining dynamic response information of rock mass through dynamic equations;

[0040] The dynamic equations are modeled using a simplified single degree of freedom system SDOF:

[0041] m·a(t)+c·v(t)+k·u(t)=F(t);

[0042] Where m represents the mass of the rock mass, a(t) represents acceleration, c represents the damping coefficient, v(t) represents velocity, k represents the stiffness coefficient, u(t) represents displacement, and F(t) represents external dynamic action;

[0043] The acceleration data a(t) obtained by the accelerometer is further integrated to obtain the velocity v(t) and displacement u(t) of the rock mass:

[0044] v(t)=∫a(t)dt;

[0045] u(t)=∫v(t)dt=∫∫a(t)dt dt;

[0046] (III02) Obtaining dynamic response information of rock mass through improved frequency domain analysis and modal analysis to further analyze the vibration response of rock mass at different frequencies and predict the response of rock mass under different dynamic loads;

[0047] The improved frequency domain analysis includes: converting the acceleration signal from the time domain to the frequency domain through Fourier transform to identify the natural frequency and dynamic characteristics of the rock mass; the formula for frequency domain analysis is:

[0048] A(f)=∫a(t)e -j2πft dt

[0049] Where A(f) is the acceleration response in the frequency domain, f is the frequency, and a(t) is the acceleration in the time domain;

[0050] The modal analysis includes: modal analysis to identify the natural vibration modes and frequencies of the rock mass, and to predict the response of the rock mass under different dynamic loads.

[0051] In step S2, the deformation and stress state of the anchor in a dynamic environment are evaluated using a data combination method of a strain gauge and an accelerometer, including:

[0052] The joint correction is performed by the following model:

[0053] σ(t)=E·ε(t)+β·∫a(t)dt

[0054] Where σ(t) is the stress in the time series, ε(t) is the strain, a(t) is the acceleration, and β is the dynamic correction coefficient.

[0055] Another object of the present invention is to provide a comprehensive intelligent monitoring system for downhole anchor pull-out tests, which implements the comprehensive intelligent monitoring method for downhole anchor pull-out tests. The system comprises:

[0056] A sensor monitoring device installation system is used to evenly drill multiple sensor holes around the anchor rod and install sensor monitoring devices in the holes drilled for sensor arrangement; the sensor monitoring devices include electromagnetic sensors, strain gauges, and accelerometers;

[0057] The anchor deformation and stress state monitoring system uses electromagnetic sensors to capture the electromagnetic signals generated by the anchor in real time during the stress process, reflecting the internal stress changes and energy distribution. It uses a combination of strain gauges and accelerometers to evaluate the deformation and stress state of the anchor in a dynamic environment. The strain gauges are used to monitor the deformation of the anchor and record the stress-strain relationship. The accelerometers are used to detect the dynamic response of the surrounding rock mass to assess the overall stability of the rock mass.

[0058] The coupling medium injection system is used to inject coupling medium into the holes drilled for sensor arrangement according to the principle of water coupling and the control of water injection volume, conduct anchor pull-out tests, and transmit the monitoring signals of electromagnetic sensors, strain gauges and accelerometers to the data acquisition system and computer terminals;

[0059] The anchor rod force cloud map generation system is used to store the collected signals locally or in the cloud through a computer terminal, analyze them, extract the stress and deformation parameters in the anchor rod pull-out test, and generate the anchor rod force cloud map in real time.

[0060] Another object of the present invention is to provide an all-round intelligent monitoring device for downhole anchor pull-out test, which implements the all-round intelligent monitoring method for downhole anchor pull-out test, and the device comprises: surrounding rock;

[0061] An anchoring borehole is opened in the center of the surrounding rock. The anchor rod passes through the anchoring borehole and is accommodated in the surrounding rock. The end of the anchor rod is fixed to the inner rear end of the surrounding rock through the anchoring end. The middle part of the anchor rod is provided with a free end. The front part of the anchor rod is fixed to a tray. The tray is equipped with a sleeve for accommodating the anchor rod and a pull rod. The pull rod is connected to the front part of the anchor rod. The rear end of the sleeve is fixed with an oil cylinder connected to the pull rod. A displacement meter for detecting the movement distance of the oil cylinder is fixed to the oil cylinder through a nut. The displacement meter is connected to the computer terminal via a signal line.

[0062] The oil cylinder is connected to the high-pressure pump via a solenoid valve, and the solenoid valve is connected to the computer terminal via a signal line;

[0063] A plurality of sensor arrangement holes of the same length as the anchor rod are evenly opened around the anchor rod, and evenly distributed sensor monitoring devices are placed inside the holes of the sensor arrangement holes;

[0064] The hole is filled with a coupling medium; a blocking device is installed at the front end of the sensor arrangement drill hole, the sensor monitoring device is connected to the data acquisition system through a signal line passing through the blocking device, and the data acquisition system is connected to the computer terminal through a signal line;

[0065] Sensor monitoring equipment includes electromagnetic sensors, strain gauges, and accelerometers;

[0066] The electromagnetic sensor, strain gauge and accelerometer are mounted on a telescopic rod, and the telescopic rod rests in a hole drilled for sensor arrangement;

[0067] Electromagnetic sensors capture the electromagnetic signals generated by the anchor rod during the stress process in real time, reflecting the internal stress changes and energy distribution; strain gauges monitor the deformation of the anchor rod and record the stress-strain relationship; accelerometers are used to detect the dynamic response of the surrounding rock mass to evaluate the overall stability.

[0068] Combining all of the above technical solutions, the present invention achieves the following beneficial effects: It improves the monitoring accuracy and safety of anchor structures. By placing a fluid coupling medium around the anchor rod, the transmission quality of the electromagnetic signal is enhanced, effectively overcoming environmental noise interference and ensuring the reliability of the monitoring data. The system architecture comprises high-strength anchor rods, evenly distributed holes, a sensor network equipped with CT electromagnetic sensors and other monitoring equipment, and a data acquisition and processing system. The system collects signals using the CT electromagnetic sensors and analyzes signal characteristics (such as phase, frequency, and amplitude). Changes in the electromagnetic signal can effectively reflect the material's damage state and stress distribution, providing dynamic feedback for monitoring. CT electromagnetic technology, with its high resolution and sensitivity to subtle changes, enables comprehensive monitoring of the health of the anchor structure. Advanced signal processing algorithms are used for signal denoising and feature extraction, enhancing the accuracy of monitoring results. The introduction of the fluid coupling medium improves signal propagation efficiency, reduces signal attenuation, and further enhances monitoring reliability. By combining CT electromagnetic technology with fluid coupling, the system significantly improves the safety and reliability of underground projects and provides new technical support for anchor quality assessment. This innovative monitoring method will provide a more robust foundation for subsequent engineering applications.

[0069] Through multi-sensor collaborative monitoring using electromagnetic sensors, strain gauges, and accelerometers, the present invention can comprehensively capture the multi-parameter status of the anchor rod and the surrounding rock mass, providing more accurate and comprehensive monitoring data. Real-time capture and spectrum analysis of electromagnetic signals can reveal stress changes and energy distribution within the anchor rod, compensating for the shortcomings of traditional methods that only focus on surface deformation. The assessment of deformation and stress state in a dynamic environment, combined with the dynamic correction coefficient, can more accurately reflect the true state of the anchor rod in a complex dynamic environment. The dynamic response and stability assessment of the rock mass can comprehensively evaluate the dynamic characteristics and stability of the rock mass through dynamic equations, frequency domain analysis, and modal analysis.

[0070] By monitoring the stress and deformation of anchor rods and the dynamic response of the surrounding rock mass in real time, this invention can promptly detect potential instability or failures, thereby improving the safety and stability of the project. Dynamic monitoring and analysis can provide a scientific basis for project decision-making and reduce the risk of accidents caused by unstable anchoring. Through data collection and analysis, the anchoring construction process can be optimized, improving construction efficiency and quality. The real-time generated anchor rod stress cloud map can intuitively display the stress state of the anchor rod, providing visual decision-making support for construction personnel.

[0071] Through long-term monitoring and data analysis, this invention can predict long-term changes in the state of anchor bolts and rock masses, proactively identify potential problems and initiate maintenance, thereby extending the service life of these bolts and rock masses. Cloud data storage and remote access facilitate long-term data management and analysis, improving maintenance efficiency. Improved spectral analysis, modal analysis, and nonlinear material models enable more precise analysis of monitoring data, providing more reliable decision support. Historical data comparison and trend forecasting capabilities help identify long-term trends and potential risks.

[0072] The comprehensive intelligent monitoring method presented in this paper can significantly improve the accuracy and comprehensiveness of anchor pullout tests, filling a technical gap in traditional monitoring methods and demonstrating strong market competitiveness. This method is particularly suitable for high-risk underground engineering environments, such as mines, tunnels, and underground construction, and holds broad market potential. Through cloud data storage and remote access capabilities, it can provide data-driven services such as long-term data management, analysis, and forecasting, creating added value. Data analysis and decision support capabilities can provide users with a higher level of service, enhancing customer experience and satisfaction.

[0073] The technical solution of the present invention has high scalability and can be applied to monitoring and analysis in other fields, such as bridges, buildings, and mechanical equipment. The core technologies of this method (such as multi-sensor collaborative monitoring, spectrum analysis, modal analysis, etc.) have cross-domain application potential and can be expanded to more engineering fields. The technical solution proposed by the present invention achieves all-round monitoring of the anchor rod and the surrounding rock mass by arranging multiple sensors (including electromagnetic sensors, strain gauges, and accelerometers, etc.) around the anchor rod. This multi-dimensional monitoring method makes up for the limitation of traditional technology that is limited to the force applied to the top of the anchor rod, and can capture the stress changes, deformation state, and potential damage inside the anchor rod in real time. At the same time, by monitoring the dynamic response of the rock mass through accelerometers, the stability of the rock mass can be evaluated, thereby greatly improving the accuracy and reliability of the anchor quality assessment. Most existing technologies at home and abroad can only monitor the stress-bearing end or local area of the anchor rod, and cannot fully reflect the stress field, energy field, and the complex interaction between the anchor rod and the surrounding rock mass during the entire pulling process. The present invention fills the technical gap in this regard.

[0074] This invention incorporates advanced algorithms such as Kalman filtering, moving averages, and short-time Fourier transforms (STFTs) for data processing and signal denoising. These techniques effectively suppress noise, smooth signals, and extract time-frequency features during the processing and analysis of anchor pullout test data, thereby improving data accuracy and reliability. Furthermore, the invention combines stress-magnetic field relationship models with nonlinear analysis methods (such as the Lyapunov exponent) to efficiently identify potential damage, significantly enhancing the robustness and accuracy of the monitoring system. Intelligent data processing methods are not yet widely used in domestic and international technologies.

[0075] The present invention innovatively combines a variety of monitoring technologies such as electromagnetic sensors, strain gauges and accelerometers. The electromagnetic sensors are used to capture the electromagnetic signals of the anchor rod during the stress process in real time, reflecting the internal stress changes and potential damage, and providing multi-dimensional data support for the anchor structure. This system has strong signal penetration capabilities and excellent anti-interference performance, and can work stably in complex underground environments. In particular, for traditional monitoring methods that are interfered with by external factors such as underground noise and vibration, the present invention can effectively solve the problems of signal stability and accuracy. Compared with traditional technologies, the present invention can better cope with interference in complex environments, ensure the high resolution and reliability of monitoring data, and fill the gap in the application of electromagnetic technology in the field of underground anchor monitoring at home and abroad.

[0076] This invention proposes a technique for injecting a water coupling medium, further enhancing the quality of monitoring signals and sensor accuracy. During anchor pullout tests, the application of the coupling medium effectively reduces the impact of environmental factors and improves sensor signal stability and transmission. This approach, combined with water coupling medium injection, fills a gap in signal enhancement and stable transmission, particularly in the high-risk and challenging construction environment of underground projects.

[0077] This invention incorporates a retractable rod and an adjustable sensor mechanism, allowing the sensor to be dynamically adjusted along the anchor rod as needed, ensuring full monitoring of the anchor's state changes during the extraction process. This design overcomes the limitations of traditional fixed sensor layouts, adapting to dynamic changes in actual working conditions and ensuring monitoring coverage and accuracy. Existing technologies, both domestically and internationally, often fail to address this aspect, particularly when the position of monitoring points needs to be adjusted at any time and flexibly respond to changes in the construction environment.

[0078] The technical solution of the present invention effectively solves a number of technical difficulties existing in traditional anchor pull-out tests, which have always been key issues that need to be urgently resolved in the field of underground engineering, especially in high-risk underground operations. The following are specific technical difficulties and the solutions provided by the present invention: Traditional anchor pull-out test methods are usually limited to monitoring the stress conditions at the anchoring end of the anchor rod, and fail to fully monitor the state changes of the anchor rod during the entire pulling process, especially the complex interaction between the anchor rod and the surrounding rock mass. Therefore, this leads to an incomplete assessment of the anchoring quality and an inability to truly reflect the overall stability of the anchor rod and the rock mass. The present invention achieves all-round and three-dimensional monitoring of the anchor rod and its surrounding environment by arranging a variety of sensors (such as electromagnetic sensors, strain gauges, accelerometers, etc.) throughout the anchor rod and the surrounding rock mass. This not only improves the monitoring accuracy, but also can capture data such as the stress, deformation, and potential damage of the anchor rod in a timely manner, thereby solving the problem of limitations of traditional methods.

[0079] Traditional monitoring systems use fixed sensor positions, preventing real-time adjustments based on anchor deformation or dynamic changes in stress. During pullout tests, the length and stress state of the anchor change dynamically with the pullout force. To address this, the present invention incorporates an adjustable mechanism on a retractable rod, allowing the sensor to slide along the rod and adjust to the desired position in real time, ensuring the integrity and accuracy of the monitoring range.

[0080] Traditional monitoring methods are susceptible to interference from external factors such as noise and vibration in complex underground environments, leading to unstable monitoring signals and reduced data accuracy. To address this issue, the present invention combines electromagnetic sensors with a water coupling medium, providing strong anti-interference capabilities, enabling stable signal acquisition and effectively reflecting subtle changes in the anchor bolt's stress response. This technological innovation significantly improves the monitoring system's reliability in complex environments, ensuring data stability and high resolution.

[0081] Traditional monitoring methods often rely on manual observation and simple data processing, making it difficult to cope with large-scale, highly complex monitoring tasks. By introducing advanced signal processing algorithms such as Kalman filtering, moving average method, and short-time Fourier transform, the present invention can effectively denoise, smooth signals, and extract key features, ensuring the efficiency and accuracy of data processing. At the same time, combined with nonlinear analysis methods and stress-magnetic field relationship models, the present invention can efficiently identify potential damage and provide early warning, thereby providing a more reliable basis for decision-making. This intelligent processing method fills the gaps in data analysis accuracy and efficiency of traditional monitoring methods.

[0082] Traditional technologies have poor adaptability to complex environments, especially those characterized by high stress and frequent vibration underground. By combining electromagnetic sensors with accelerometers and enhancing signal stability through a water-coupled medium injection system, this new technology can effectively address complex stress environments underground, ensuring that monitoring signals are unaffected by environmental factors.

[0083] The present invention breaks through the limitations of traditional monitoring methods. Traditional anchor pull-out test technology usually relies on mechanical sensors and manual observation, and mainly focuses on monitoring the ultimate force data of the anchor end of the anchor rod. Although this method can provide certain information, due to the limitations of its monitoring range, it cannot fully reflect the complex stress field, energy field of the anchor rod and the surrounding rock during the entire pulling process, as well as the interaction between the anchor rod and the rock mass. In response to the prejudices existing in the existing technology: In the past technical concepts, many technicians believed that it was sufficient to evaluate the anchoring quality by monitoring the mechanical performance of the anchor end of the anchor rod, ignoring the complex interaction process between the inside of the anchor rod and the rock mass. The present invention realizes all-round and multi-dimensional monitoring of the anchor rod and the surrounding rock mass by arranging a variety of sensors (such as electromagnetic sensors, strain gauges, accelerometers, etc.), so as to fully grasp the stress and deformation state during the anchoring process. This technical solution breaks the limitation of the traditional focus on the anchor end, and can provide more accurate and comprehensive data support for the anchoring quality.

[0084] The present invention is a breakthrough in the monitoring bias in dynamic environments. Traditional monitoring methods are often affected by external environmental interference (such as underground noise, vibration, etc.), resulting in instability and unreliability of data. Many technical experts believe that these interferences are inevitable and even give up further solutions to these factors. In response to the bias existing in the existing technology: many people believe that the noise and vibration in the underground environment cannot be effectively eliminated, so they can only adapt to these interferences by reducing the monitoring range or simplifying the monitoring system. The present invention innovatively introduces a combination of multiple sensors such as electromagnetic sensors, accelerometers and strain gauges, and enhances the anti-interference ability through water-coupled medium technology to ensure signal stability and high resolution. In this way, the system can stably monitor the status of the anchor and the surrounding rock mass, and can effectively obtain accurate monitoring data even in complex underground environments. This move breaks through the powerlessness of traditional monitoring technology in the face of noise and interference.

[0085] The present invention breaks through the reliance on fixed sensor positions. Traditional monitoring systems mostly rely on sensors at fixed positions, which lack flexibility and cannot adapt to changes in anchor rod length during the pulling process, resulting in omissions or inaccuracies in monitoring data. Past technical concepts believed that once the sensor was deployed, it should remain fixed without considering how to cope with dynamic changes. In response to the prejudices that exist in the existing technology: Many engineering and technical personnel believe that the installation of sensors and fixed monitoring positions are the only effective way, ignoring the deformation and displacement that may occur in anchor rods during the pulling process. The present invention adopts a retractable rod design, so that the sensor can adjust its position at any time. The adjustable design allows the monitoring equipment to dynamically adjust its position according to changes in the anchor rod pulling process, ensuring that the entire pulling process is within the monitoring range. This design completely changes the traditional technology's reliance on fixed position sensors and overcomes the shortcomings of traditional technology in dealing with changes in the pulling process.

[0086] The present invention represents an innovative breakthrough in data processing in complex environments. Traditional data processing methods often rely on manual judgment and simple data processing techniques, making it difficult to effectively process the large amounts of data generated in complex environments. In particular, in dynamically changing environments, traditional methods may overlook potential risk signals. In response to the biases that exist in existing technologies: Many engineering and technical personnel may believe that in complex underground environments, it is sufficient to process data through traditional manual analysis and simple algorithms, and that high-precision and efficient data processing requirements cannot be met to meet monitoring needs in dynamic environments. The present invention introduces advanced algorithms such as Kalman filtering, moving average method, and short-time Fourier transform to denoise, smooth, and extract time-frequency features from data, and combines them with nonlinear analysis methods to identify potential damage. This intelligent data processing method greatly improves the accuracy and efficiency of data analysis, and provides powerful decision-making support for the monitoring system.

[0087] The present invention is a breakthrough in the integration of comprehensive technologies. In the past, technical prejudices were mainly focused on the separate use of different technologies (for example, only using strain gauges or accelerometers, etc.), and it was believed that technology integration may bring complexity or unsolvable problems, especially for underground engineering monitoring with higher complexity. In response to the prejudices existing in the existing technology: many experts believe that combining multiple technologies (such as electromagnetic sensors, strain gauges, accelerometers, etc.) for multi-dimensional monitoring may be difficult to implement, and may even cause conflicts and difficulties in signal processing. The technical solution of the present invention successfully combines multiple sensor technologies and integrates them through intelligent data processing technology. It not only overcomes the limitations of traditional single technologies, but also greatly enhances the reliability and robustness of the monitoring system, ensuring its effective application in complex underground environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure;

[0089] Figure 1 This is a schematic diagram of the sensor arrangement drilling layout provided by an embodiment of the present invention;

[0090] Figure 2 Schematic diagram of an all-round intelligent monitoring device for downhole anchor pull-out tests provided by an embodiment of the present invention;

[0091] Figure 3 This is an internal view of the all-round intelligent monitoring device for downhole anchor pull-out tests provided by an embodiment of the present invention;

[0092] Figure 4 This is a schematic diagram of the connection of sensor monitoring devices provided by an embodiment of the present invention;

[0093] In the figure: 1. Surrounding rock; 2. Anchor end; 3. Free end; 4. Sensor monitoring equipment; 5. Tray; 6. Pull rod; 7. Cylinder; 8. Nut; 9. Displacement meter; 10. Anchor rod; 11. Sleeve; 12. High-pressure pump; 13. Sensor arrangement borehole; 14. Anchor borehole; 15. Coupling medium; 16. Data acquisition system; 17. Computer terminal; 18. Blocking device. DETAILED DESCRIPTION

[0094] To make the above-mentioned objects, features, and advantages of the present invention more readily apparent, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings. The following description sets forth numerous specific details to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways than those described herein, and those skilled in the art may make similar modifications without departing from the scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0095] The innovation of this invention lies in the fact that it combines multiple sensors (electromagnetic sensors, strain gauges, accelerometers, etc.) with a retractable rod to achieve real-time, multi-dimensional monitoring of the anchor rod and surrounding rock mass, overcoming the limitation of traditional monitoring methods that focus only on the anchor end of the anchor rod. The system achieves accurate assessment of anchoring quality through advanced data processing technologies such as improved stress-electromagnetic signal relationship models, spectrum analysis, and Kalman filtering algorithms, and can capture stress changes, deformation, and potential damage within the anchor rod and rock mass in real time. At the same time, intelligent data processing and strong anti-interference capabilities ensure efficient and stable monitoring in complex underground environments, providing important technical support for improving the safety and reliability of anchoring projects.

[0096] Example 1. The all-round intelligent monitoring system for downhole anchor pull-out tests provided by the embodiment of the present invention solves many limitations of traditional monitoring methods in anchor quality assessment. First, traditional methods usually rely on extreme stress monitoring, which cannot fully reflect the stress state of the anchor rod at different depths and directions, resulting in inaccurate assessment of anchor quality. Secondly, data collection often relies on manual labor, and feedback delays make it difficult for engineers to detect potential problems in a timely manner, increasing safety risks. In addition, traditional monitoring is easily affected by noise in complex environments, affecting the reliability of the data. At the same time, the monitoring range is narrow and lacks comprehensive data, which cannot meet the needs of complex projects.

[0097] By incorporating multi-point real-time monitoring and advanced sensing technology, the present invention comprehensively captures the stress and internal state of the anchor, as well as the changes in the surrounding rock during the pullout test. This provides accurate and reliable data support, significantly improving the scientific nature of anchor quality assessment and engineering safety. The core feature of this system is the drilling of four sensor holes of equal length around the anchor aperture. Electromagnetic sensors and other types of sensors are placed within each hole to enable comprehensive monitoring of the anchor during the pullout test. Through the multi-point sensor arrangement, the system can capture the stress state of the anchor at different depths and directions in real time, monitoring its energy changes and internal state. Furthermore, the electromagnetic sensing technology employed in this invention exhibits strong anti-interference capabilities, enabling stable operation in complex underground environments. While traditional monitoring methods are often affected by external environmental noise and vibration, the design of this system effectively reduces the impact of these interferences on the monitoring results, thereby ensuring data accuracy and reliability. This makes the system highly adaptable to various complex working conditions and suitable for a wide range of underground engineering projects.

[0098] For example, Figures 1-4 As shown, the all-round intelligent monitoring device for downhole anchoring pull-out test provided by the embodiment of the present invention includes: surrounding rock 1, an anchoring borehole 14 is opened in the center of the surrounding rock 1, the anchor rod 10 passes through the anchoring borehole 14 and is accommodated in the surrounding rock 1, the end of the anchor rod 10 is fixed to the inner rear end of the surrounding rock 1 through the anchoring end 2, and the middle part of the anchor rod 10 is provided with a free end 3; the front part of the anchor rod 10 is fixed to the tray 5, the tray 5 is equipped with a sleeve 11 for accommodating the anchor rod 10 and the pull rod 6, the pull rod 6 is connected to the front part of the anchor rod 10, the rear end of the sleeve 11 is fixed with an oil cylinder 7 connected to the pull rod 6, the oil cylinder 7 is fixed with a displacement meter 9 for detecting the moving distance of the oil cylinder 7 through a nut 8, and the displacement meter 9 is connected to the computer terminal 17 through a signal line;

[0099] The oil cylinder 7 is connected to the high-pressure pump 12 via a solenoid valve, and the solenoid valve is connected to the computer terminal 17 via a signal line;

[0100] A plurality of sensor arrangement holes 13 of the same length as the anchor rod 10 are evenly provided around the anchor rod 10, and evenly distributed sensor monitoring devices 4 are placed inside the holes of the sensor arrangement holes 13;

[0101] The hole is filled with a coupling medium 15; a blocking device 18 is installed at the front end of the sensor arrangement borehole 13, the sensor monitoring device 4 is connected to the data acquisition system 16 through a signal line passing through the blocking device 18, and the data acquisition system 16 is connected to the computer terminal 17 through a signal line.

[0102] For example, the anchor rod 1 is made of a high-strength material and has sufficient tensile strength and corrosion resistance.

[0103] Four sensor arrangement holes 13 with the same length as the anchor rod are opened around the anchor rod 10. These holes are evenly distributed along the periphery of the anchor rod to ensure that the stress condition of the anchor rod can be monitored from different directions.

[0104] Sensor monitoring equipment 4, a variety of monitoring equipment such as highly sensitive electromagnetic sensors, strain gauges and accelerometers are installed inside the hole. The electromagnetic sensors, strain gauges and accelerometers are evenly installed on the telescopic rod ( Figure 4 (not shown) on;

[0105] Electromagnetic sensors can capture the tiny electromagnetic signals generated by the anchor rod during the stress process in real time, reflecting the changes in internal stress; strain gauges monitor the deformation of the anchor rod and record the stress-strain relationship; accelerometers are used to detect the dynamic response of the surrounding rock mass to evaluate the overall stability.

[0106] Water is injected into sensor borehole 13 as a coupling medium to enhance signal transmission from sensor monitoring device 3. The use of water effectively enhances acoustic signal propagation and reduces interference from ambient noise, thereby ensuring the accuracy and reliability of monitoring data. Furthermore, the properties of the water can be adjusted to specific monitoring requirements, enhancing the system's adaptability.

[0107] The data acquisition system 16 and computer terminal 17 include a signal processing unit connected to all sensor monitoring devices 3, responsible for real-time acquisition, processing, and analysis of sensor data. The system utilizes advanced signal processing algorithms to generate real-time stress cloud maps of the anchor bolt 1 and other relevant visualization data, helping engineers intuitively understand the state of the anchor structure. The computer terminal 17 (processing system) also features storage, query, and remote access capabilities, facilitating subsequent analysis and decision support.

[0108] In Example 2, an all-round intelligent monitoring method for downhole anchor pull-out test provided by an embodiment of the present invention includes:

[0109] S1, drilling multiple sensor holes evenly around the anchor rod, and installing sensor monitoring equipment in the holes drilled for sensor arrangement; the sensor monitoring equipment includes an electromagnetic sensor, a strain gauge, and an accelerometer;

[0110] S2 uses electromagnetic sensors to capture the electromagnetic signals generated by the anchor in real time during the stress process, reflecting the internal stress changes and energy distribution; uses a combination of strain gauges and accelerometers to evaluate the deformation and stress state of the anchor in a dynamic environment. The strain gauges are used to monitor the deformation of the anchor and record the stress-strain relationship; the accelerometers are used to detect the dynamic response of the surrounding rock mass to evaluate the overall stability of the rock mass;

[0111] S3, based on the principle of water coupling and the control of water injection volume, the coupling medium is injected into the hole where the sensor is arranged, and the anchor pull-out test is carried out. The monitoring signals of the electromagnetic sensor, strain gauge and accelerometer are transmitted to the data acquisition system and computer terminal;

[0112] S4, the computer terminal stores the collected signals locally or in the cloud, analyzes them, extracts the stress and deformation parameters in the anchor pull-out test, and generates the anchor force cloud map in real time.

[0113] In step S1, four sensor arrangement holes of the same length as the anchor are evenly opened around the anchor, and the diameter of the sensor arrangement holes is designed according to the size of the sensor monitoring equipment;

[0114] The electromagnetic sensor, strain gauge and accelerometer are mounted on a telescopic rod, which rests in a hole drilled for sensor arrangement.

[0115] The present invention provides an all-round intelligent monitoring system for underground anchor pull-out tests, comprising:

[0116] A sensor monitoring device installation system is used to evenly drill multiple sensor holes around the anchor rod and install sensor monitoring devices in the holes drilled for sensor arrangement; the sensor monitoring devices include electromagnetic sensors, strain gauges, and accelerometers;

[0117] The anchor deformation and stress state monitoring system uses electromagnetic sensors to capture the electromagnetic signals generated by the anchor in real time during the stress process, reflecting the internal stress changes and energy distribution. It uses a combination of strain gauges and accelerometers to evaluate the deformation and stress state of the anchor in a dynamic environment. The strain gauges are used to monitor the deformation of the anchor and record the stress-strain relationship. The accelerometers are used to detect the dynamic response of the surrounding rock mass to assess the overall stability of the rock mass.

[0118] The coupling medium injection system is used to inject coupling medium into the holes drilled for sensor arrangement according to the principle of water coupling and the control of water injection volume, conduct anchor pull-out tests, and transmit the monitoring signals of electromagnetic sensors, strain gauges and accelerometers to the data acquisition system and computer terminals;

[0119] The anchor rod force cloud map generation system is used to store the collected signals locally or in the cloud through a computer terminal, analyze them, extract the stress and deformation parameters in the anchor rod pull-out test, and generate the anchor rod force cloud map in real time.

[0120] Example 3, as another embodiment of the present invention, the embodiment of the present invention provides an all-round intelligent monitoring method for downhole anchor pull-out test, comprising the following steps:

[0121] Step 1: Drilling and opening of sensor layout.

[0122] like Figure 1 As shown, special equipment is used to evenly open four sensor arrangement holes with the same length as the anchor rod around the anchor rod. The hole diameter of the sensor arrangement hole 13 is designed according to the size of the sensor monitoring device 4 to facilitate the installation of the sensor monitoring device 4.

[0123] Step 2: The sensor monitoring device 4 is installed in the hole.

[0124] like Figure 2 As shown in the three-dimensional structural diagram, the sensor layout includes various monitoring devices, including highly sensitive electromagnetic sensors, strain gauges, and accelerometers, installed within borehole 13. The electromagnetic sensors capture the electromagnetic signals generated by the anchor bolt during stress in real time, reflecting internal stress changes and energy distribution. The strain gauges monitor anchor bolt deformation and record the stress-strain relationship. The accelerometers detect the dynamic response of the surrounding rock mass to assess overall stability. These monitoring devices work together to provide multi-dimensional data support.

[0125] like Figure 4 As shown, electromagnetic sensors, strain gauges, and accelerometers are evenly arranged on the telescopic rod. The number of electromagnetic sensors, strain gauges, and accelerometers and the length of the telescopic rod are adjusted according to the number of anchor rods 10 to ensure that the state changes of the anchor rods 10 during the pull-out test can be fully monitored. Finally, the measured data is transmitted to a data receiver.

[0126] For example, since the anchor rod 10 may deform during the pullout test, the length of the anchor rod 10 will change as the pulling force increases or decreases. Therefore, the design of the anchor rod 10 needs to allow the sensor to adjust its position at any time to monitor the state changes of the anchor rod in real time during the pullout process.

[0127] Telescopic Rod Design: The telescopic rod should be designed to be retractable or adjustable. For example, a rod with adjustable sections could be used, or sliding devices could be installed at specific locations on the rod, allowing electromagnetic sensors, strain gauges, and accelerometers to slide along the rod. This allows the various monitoring devices, such as electromagnetic sensors, strain gauges, and accelerometers, to be adjusted to appropriate positions as needed during the pulling process, ensuring full monitoring coverage throughout the entire process.

[0128] Adjustment Mechanism for Sensor Monitoring Device 4: Sensor Monitoring Device 4 is typically fixed to a rod-mounted sliding mechanism, which can be electrically or manually adjustable. For example, an electrically driven mechanism allows the sensor monitoring device 4 to freely move axially along the telescopic rod. Alternatively, a mechanical mechanism can be used to fix or adjust the position of the sensor monitoring device 4. This dynamic adjustment ensures that the sensor monitoring device 4 always covers the area most in need of monitoring.

[0129] An important aspect of the present invention is that four sensor holes 13 of equal length are drilled around the sensor hole 13, and electromagnetic sensors and other types of sensors are placed in each sensor hole 13, in order to achieve comprehensive monitoring of the state of the anchor rod 10 during the pullout test.

[0130] Step 3: Use electromagnetic sensors to capture the tiny electromagnetic signals generated by the anchor rod during the stress process in real time to reflect the internal stress changes; use the combined data method of strain gauges and accelerometers to evaluate the deformation and stress state of the anchor rod in a dynamic environment, where the strain gauge is used to monitor the deformation of the anchor rod and record the stress-strain relationship; the accelerometer is used to detect the dynamic response of the surrounding rock mass to evaluate the overall stability of the rock mass.

[0131] For example, electromagnetic sensors are used to capture electromagnetic signals generated during the anchoring material extraction process. These signals can effectively reflect internal stress changes and potential damage to the anchor rod 10. During installation, the electromagnetic sensor, strain gauge, and accelerometer are fixed to the wall of the sensor arrangement borehole 13, ensuring close contact with the surrounding medium to maximize the sensitivity of signal acquisition.

[0132] For example, in order to ensure the efficient operation of the system, wired transmission technology is adopted. The computer terminal 17 stores and processes the real-time data collected by the data acquisition system 16, denoises and smoothes the signal through Kalman filtering and moving average method, and then uses short-time Fourier transform (STFT) to extract time-frequency features, and then combines the stress-magnetic field relationship model to analyze stress changes. Damage identification is carried out through nonlinear analysis methods such as mutation feature analysis and Lyapunov exponent. Evaluation indicators include model accuracy, feature importance, residual analysis and goodness of fit (R 2 value) to ensure the algorithm's effectiveness and robustness. Electromagnetic sensors offer advantages in interference resistance and can provide stable data in complex environments, thereby improving monitoring accuracy and reliability. This system not only improves the safety of pull-out tests but also provides a scientific basis for subsequent engineering decisions, achieving an efficient and intelligent monitoring solution.

[0133] For example, in geotechnical engineering, particularly in support systems for underground projects like tunnels and mines, monitoring and health assessment of anchor bolts 10 are crucial for ensuring structural safety and reliability. By utilizing monitoring tools such as electromagnetic sensors, strain gauges, and accelerometers, various changes in the anchor bolts 10 during stress loading can be tracked in real time, providing data support for project maintenance and optimization. Combined with improved calculation formulas or methods, these technologies can more precisely describe their operating principles and practical applications.

[0134] The specific analysis is as follows:

[0135] (I) The electromagnetic sensor captures the electromagnetic signal generated by the anchor rod 10 during the stress process in real time, reflecting the internal stress changes and energy distribution, specifically including:

[0136] (I01) Monitoring the electromagnetic characteristic information of the material affected by the deformation of the anchor rod 10 through an improved stress-electromagnetic signal relationship model.

[0137] When the anchor rod 10 is subjected to external forces (such as tension, pressure, or bending), the deformation (such as stress and strain) of the anchor rod 10 will affect the electromagnetic properties of the material. This process can be described by an improved stress-electromagnetic signal relationship model. For example:

[0138]

[0139] Where B is the magnetic field intensity, μ0 is the vacuum magnetic permeability, F is the external force acting on the anchor, A is the cross-sectional area of the anchor, and r is the distance between the electromagnetic sensor and the anchor.

[0140] As you can understand, this formula is based on a simple relationship between the change in magnetic field under the action of an external force and is suitable for analyzing the effect of force on magnetic field distribution. In practical applications, it can be optimized based on the magnetic characteristics of different materials and the geometric opening of the anchor.

[0141] (I02) Perform frequency analysis and feature extraction of electromagnetic signals through improved spectrum analysis methods.

[0142] Electromagnetic signals typically have specific frequency characteristics, and different states during the stress process will cause changes in the signal frequency. For example, when the anchor rod 10 is subjected to stress, the frequency response may show specific harmonic components. Spectral analysis can help determine the type and degree of stress.

[0143] The improved spectrum analysis method can be combined with fast Fourier transform (FFT) to perform real-time analysis of electromagnetic signals and extract useful frequency features. The formula is:

[0144]

[0145] Where S(f) represents the signal's spectrum, x(t) represents the time-domain electromagnetic signal, f represents frequency, and t represents time. This frequency analysis can further improve signal extraction and classification algorithms, accurately determining the different states of anchor bolt stress.

[0146] In another exemplary embodiment, the deformation of the anchor rod is monitored using a strain gauge, and the stress-strain relationship is recorded, including:

[0147] (II01) A strain gauge is used to measure the tiny deformation (strain) on the surface of the anchor rod 10 to reflect the stress changes of the anchor rod under the action of external forces. The basic principle is based on the characteristic that the resistance of the strain gauge changes with strain. Generally, the deformation of the anchor rod 10 can be related to the stress using the following formula:

[0148] σ=E·ε

[0149] Where σ represents stress, E represents the elastic modulus of the material, and ε represents strain;

[0150] The strain gauge records the strain signal, and the stress in the anchor can be calculated using the above relationship. Since the strain gauge outputs the change in resistance (ΔR), the relationship between this and strain can be converted using the strain gauge's sensitivity coefficient.

[0151] (II02) Use an improved temperature compensation method to eliminate the influence of temperature changes, and use the compensation of lateral strain to correct the actual deformation of the anchor rod; because the output of the strain gauge may be affected by temperature changes and lateral strain, appropriate compensation and correction are required.

[0152] Among them, the improved temperature compensation method includes: the resistance change of the strain gauge is not only related to the strain, but also closely related to the temperature change. In order to eliminate the influence of temperature change, an improved temperature compensation formula can be used:

[0153] ε corr =ε measured -α·ΔT

[0154] Where, ε corr is the corrected strain, ε measured is the directly measured strain, α is the temperature coefficient of the strain gauge, and ΔT is the temperature change. Temperature compensation can improve the accuracy of the strain gauge under changing environmental conditions.

[0155] The compensation of the lateral effect corrects the actual deformation of the anchor rod, including: when the strain gauge is installed on the surface of the anchor rod, it may be affected by the lateral strain. The lateral strain mainly comes from the lateral deformation of the anchor rod or the effect of external load, and can be corrected by the following formula:

[0156] ε long =εmeasured -k·ε transverse

[0157] Where, ε long is the modified longitudinal strain, ε transverse is the transverse strain, and k is the correction coefficient. By compensating for the transverse strain, the actual deformation of the anchor can be reflected more accurately.

[0158] (II03) Use nonlinear material model to correct stress-strain relationship.

[0159] Typically, the stress-strain relationship of an anchor is linear, but under high stress or large deformation conditions, it may exhibit nonlinear behavior. To more accurately describe the stress-strain relationship, a nonlinear material model (such as a hyperbolic model or an exponential model) can be used for correction.

[0160] Among them, a hyperbolic model is used to fit the stress-strain relationship:

[0161]

[0162] Where σ is stress, ε is strain, E is the elastic modulus, ε0 is the strain softening starting point, and n is the material's nonlinear exponent. This correction method can better adapt to nonlinear changes in stress and strain in actual working conditions.

[0163] Among them, the exponential model can use the existing model:

[0164] r i =E(r i )+m i +e i

[0165] Where, E(r i ) is the expected return of the user at the beginning of the holding period, m i The impact of unexpected macro events on the application object’s earnings during the user’s holding period, e i The impact of unexpected user-specific events on the benefits of the application object during the holding period of the application object; m i and e i All have zero expected value, they are all the impact of unexpected events, and by definition their average value must be zero. If the non-predicted component of the macro factor is F, the sensitivity of the application object i to the macro event is β i , then the macro component of the benefit of application i is m i =β i F, then the above formula becomes:

[0166] r i =E(r i )+β i F+ei

[0167] Index models generate graded maps by combining index values calculated from multiple grids or maps. The key to building index models lies in the GIS user's scoring and weighting of observed variable values. This allows for a simplified distinction between macro- and specific factors.

[0168] As another example, accelerometers are used to monitor the dynamic response of surrounding rock masses to assess their overall stability. Accelerometers measure the rock mass's acceleration response to record its vibration characteristics when subjected to external dynamic loads (such as earthquakes and blasting). Accelerometers typically use the inertial principle, sensing changes in rock mass acceleration during motion and converting them into electrical signals. These acceleration signals can be used to further analyze the rock mass's dynamic response.

[0169] (III01) Obtain the dynamic response information of the rock mass through the dynamic equation.

[0170] The dynamic response of the rock mass can be described by the dynamic equations, using a simplified single degree of freedom (SDOF) system for modeling:

[0171] m·a(t)+c·v(t)+k·u(t)=F(t);

[0172] Where m represents the mass of the rock mass, a(t) represents acceleration, c represents the damping coefficient, v(t) represents velocity, k represents the stiffness coefficient, u(t) represents displacement, and F(t) represents external dynamic action (such as seismic waves, blasting shock, etc.);

[0173] The acceleration data a(t) obtained by the accelerometer can be further integrated to obtain the velocity v(t) and displacement u(t) of the rock mass:

[0174] v(t)=∫a(t)dt;

[0175] u(t)=∫v(t)dt=∫∫a(t)dt dt;

[0176] (III02) The dynamic response information of the rock mass is obtained through improved frequency domain analysis and modal analysis to further analyze the vibration response of the rock mass at different frequencies and predict the response of the rock mass under different dynamic loads; in order to more effectively analyze the dynamic response of the rock mass, frequency domain analysis and modal analysis techniques are used.

[0177] Among them, the improved frequency domain analysis includes: Through Fourier transform, the acceleration signal can be converted from the time domain to the frequency domain to identify the natural frequency and dynamic characteristics of the rock mass. Frequency domain analysis helps to understand the vibration response of the rock mass at different frequencies, especially its performance under dynamic loads such as earthquakes and blasting. The formula for frequency domain analysis is:

[0178] A(f)=∫a(t)e -j2πft dt

[0179] Where A(f) is the acceleration response in the frequency domain, f is the frequency, and a(t) is the acceleration in the time domain.

[0180] Modal analysis includes: Modal analysis can identify the natural vibration modes and frequencies of rock masses, helping to predict their response under different dynamic loads. In rock mass design and stability assessment, modal analysis can reveal possible resonance phenomena and prevent rock mass failure.

[0181] Another exemplary method of using a combination of strain gauges and accelerometers to evaluate the deformation and stress state of an anchor in a dynamic environment includes:

[0182] Strain gauge and accelerometer data can complement each other, improving understanding of the rock mass and anchor system through combined analysis. For example, combining the dynamic response data provided by accelerometers with the static or quasi-static deformation data provided by strain gauges can better assess anchor deformation and rock mass stability. During analysis, combined corrections can be made using the following models:

[0183] σ(t)=E·ε(t)+β·∫a(t)dt

[0184] Where σ(t) is the time series stress, ε(t) is the strain, a(t) is the acceleration, and β is the dynamic correction factor. This combined analysis method can more accurately assess the deformation and stress state of anchor bolts in dynamic environments, thereby helping to develop more reasonable anchor bolt design solutions.

[0185] Step 4: Inject water coupling medium according to the principle of water coupling and control of water injection volume.

[0186] Water is injected into sensor borehole 13 as a coupling medium 15 to enhance the sensitivity of sensor monitoring device 4, allowing for more efficient transmission of electromagnetic sensor, strain gauge, and accelerometer signals. Water reduces the air's impedance to signal transmission, thereby improving signal transmission efficiency.

[0187] The principles of water coupling include:

[0188] Coupling efficiency: The amount of water injected should be sufficient to fully couple the contact surface between the sensor and the hole wall to ensure that the acoustic wave signal can be effectively transmitted. Generally, the amount of water injected needs to cover the surface of the sensor and be in close contact with it.

[0189] Avoid excessive water: Although a certain amount of water is required to improve coupling, excessive water may cause the pore fluid to overflow, which not only affects the performance of electromagnetic sensors, strain gauges, and accelerometers, but may also cause wetting of the surrounding rock mass and potential stability problems. Therefore, the water injection rate should be controlled within an appropriate range.

[0190] The control of water injection volume includes:

[0191] Partial filling: Generally speaking, the hole does not need to be completely filled with water. The water level should be kept close to the surface of the electromagnetic sensor, strain gauge, and accelerometer. This can prevent liquid overflow and ensure good coupling.

[0192] Monitoring and adjustment: During the water injection process, the water level and the state of the liquid in the hole can be monitored to make appropriate adjustments to ensure the best coupling effect and avoid liquid overflow.

[0193] exist Figure 3 The innovative mechanism of the invention can be clearly seen in the internal view.

[0194] Step 5: Preparation for the drawing test.

[0195] Connect the top of the anchor rod 10 to the pull-out test equipment and prepare for the pull-out test. Before starting the pull-out test, ensure that all sensors are working properly and the data acquisition system is ready for real-time monitoring. The pull-out test equipment includes: pull rod 6, cylinder 7, nut 8, displacement meter 9, anchor rod 10, sleeve 11, and high-pressure pump 12. Specific experimental preparations include:

[0196] Step 5.1: Pull out the test equipment connection.

[0197] Equipment selection and preparation:

[0198] Equipment selection: Select pull-out test equipment suitable for underground environments, such as a hydraulic puller or an electric puller, and ensure that it can provide sufficient pull-out force (at least higher than the design load-bearing capacity of the anchor).

[0199] Equipment inspection: Before connecting, check the various functions of the pull-out test equipment, including the power supply, control panel, operating arm, etc., to ensure that it can operate stably during the test.

[0200] Connecting anchor rod 10:

[0201] Connection method: Use a dedicated connection device (such as an anchor flange, clamp, etc.) to securely connect the pull rod 6 of the pull test equipment to the top of the anchor rod 10. Ensure the stability of the connection point to avoid loosening or slipping when the pull force is applied.

[0202] Position adjustment: Make sure that the pulling device and the anchor rod 10 are in a vertical position to ensure that the applied pulling force can be directly transmitted to the anchor rod 10.

[0203] Step 5.2: sensor monitoring device 4 connects and transmits data.

[0204] Installation of sensor monitoring equipment 4: Electromagnetic sensors, strain gauges, accelerometers, and other monitoring equipment are installed in sensor boreholes 13 around the anchor bolt 10. The electromagnetic sensors are used to capture the electromagnetic signals generated by the anchor bolt during stress loading in real time, reflecting internal stress changes and potential damage. The strain gauges monitor the anchor bolt's deformation and record the stress-strain relationship. Accelerometers are used to detect the dynamic response of the surrounding rock mass to assess its overall stability. To ensure the secure mounting of the electromagnetic sensors, strain gauges, and accelerometers, appropriate fixtures (retractable rods) are used to prevent displacement due to vibration or other factors.

[0205] Data Transmission and Storage: Signal cables connect the output of each electromagnetic sensor, strain gauge, and accelerometer to a high-performance data acquisition system 16, ensuring a stable connection and compliance with electrical safety standards. Depending on the type of electromagnetic sensor, strain gauge, and accelerometer, use appropriate data transmission interfaces (such as BNC, RJ45, etc.) to ensure accurate signal transmission. Configure the software of the data acquisition system 16 and set the appropriate sampling frequency and data recording format to ensure the accuracy and validity of real-time monitoring data. Transmit data to a computer terminal 17 (computer or central monitoring system) via a wired or wireless network. The collected data can be stored locally or in the cloud for subsequent analysis and report generation.

[0206] Step 5.3: System ecology construction.

[0207] Overall Architecture: A complete monitoring system is constructed, including pullout testing equipment, a sensor monitoring device network, a data acquisition system 16, and analysis software on a computer terminal 17. The pullout testing equipment is responsible for applying force and recording relevant data. The sensor monitoring device network, consisting of electromagnetic sensors, strain gauges, and accelerometers, monitors the anchor bolt's status in real time. The data acquisition system 16 centrally processes the output signals from the sensor monitoring devices 4, ensuring accurate and timely information transmission. The analysis software on the computer terminal 17 is responsible for subsequent data processing and result presentation. Standard communication protocols are used to establish an effective communication and collaboration mechanism between these components, ensuring seamless information integration.

[0208] Data synchronization: Ensure high-precision data synchronization between the data acquisition system 16 and the tensile testing equipment. Timestamp technology is used to correlate the time of force application with the corresponding monitoring data, ensuring that stress changes and deformation during the force application process are accurately reflected during data analysis. This synchronization mechanism can be achieved by using a shared clock or high-precision timer to ensure data timeliness and consistency.

[0209] Step 5.4, data analysis and reporting.

[0210] Analysis tools: Use Python to process and analyze the collected data. In addition, the analysis software of the computer terminal 17 should be able to conduct comparative analysis of historical data and identify long-term trends and potential risks.

[0211] Cloud Data Storage: Monitoring data is regularly uploaded to cloud storage for long-term preservation and remote access. Secure cloud storage solutions (such as AWS and Azure) are used to ensure data security and privacy. Users can access data through the online platform for in-depth analysis and research, facilitating long-term tracking and technical support of experimental processes.

[0212] Step 5.6, test preparation confirmation.

[0213] After all equipment and systems are set up, a comprehensive inspection is conducted to ensure that all equipment is functioning properly and that all sensor connections are stable and providing good signals. A final test is conducted on the data acquisition system to confirm that it can receive and record real-time data from the electromagnetic sensors, strain gauges, and accelerometers. The safety of the work area is ensured, and all operators are aware of the emergency plan and can respond promptly to any emergencies during the test.

[0214] After confirming that all preparations are correct, notify relevant personnel to prepare to start the pulling test.

[0215] Step 6: Perform a pull-out test.

[0216] In the underground anchor pull-out test based on electromagnetic technology, the system slowly applies a pull-out force to the anchor rod 10 to ensure the stability and safety of the test. First, check the normal operation of the electromagnetic sensor, strain gauge, and accelerometer, and tightly install the electromagnetic sensor, strain gauge, and accelerometer on the surface of the anchor rod 10 to ensure accurate positioning. Then, set the data acquisition frequency and calibrate the sensor. When the pull-out force is applied, the monitoring system records the sensor data in real time to observe whether the signal is abnormal. The data acquisition system continuously records and transmits real-time data to the processing system for analysis to identify potential instability or failure. After the test is completed, the data is sorted and a detailed report is generated, including the changes in the electromagnetic signal and the evaluation of the structural response. Finally, the pull-out force is gradually unloaded, and a post-inspection is carried out to verify the integrity of the anchor structure. Through real-time monitoring and data analysis, the system effectively improves the analysis accuracy of the anchor structure performance and ensures the safety and stability of the project.

[0217] Step 7: Data analysis.

[0218] During the pull-out test, the computer terminal 17 analyzes the collected signals, extracts key parameters such as stress and deformation, and generates a real-time anchor force cloud map. The cloud map clearly shows the stress and deformation of each part of the anchor. The data processing process of the computer terminal 17 includes:

[0219] Step 1: Signal preprocessing.

[0220] Signal preprocessing mainly includes two parts: denoising and signal smoothing. In this process, Kalman filtering is used for denoising and moving average method is used for smoothing.

[0221] Step 7.1, denoising (Kalman filtering).

[0222] The Kalman filter is a recursive filtering algorithm that effectively removes noise from signals by estimating the system state through a state-space model. Its core idea is to gradually estimate a more accurate signal state based on the system dynamics and the noise characteristics of the measurement.

[0223] Step 7.1.1, define the state space model.

[0224] Assume that the true value (state) of the signal can be represented by a state space model:

[0225] x k =A·x k-1 +B·u k +w k

[0226] Where x k is the current state (signal estimation value), A is the state transfer matrix, B is the control matrix, u kis the control input, w k is the process noise.

[0227] The observation model is:

[0228] z k =H·x k +v k

[0229] Where z k is the observation value (original signal with noise), H is the observation matrix, v k is the observation noise.

[0230] Step 7.1.2, initialize the state estimate and error covariance matrix.

[0231] Assume that the state estimate at the initial moment is x0 and the error covariance matrix P0, which can be set based on experience or prior data. Generally, the initial state can be set to zero and the error covariance matrix can be set to the identity matrix or a larger value.

[0232] Step 7.1.3, recursive calculation, in the process of Kalman filtering, perform recursive calculation according to the following steps:

[0233] (1) Prediction steps:

[0234] Predict current status:

[0235] Forecast error covariance:

[0236] (2) Update steps:

[0237] Calculate the Kalman gain:

[0238] Update the state estimate:

[0239] Update the error covariance matrix:

[0240] Step 7.1.4, output the denoised signal.

[0241] The denoised signal is the estimated value It is updated at every moment based on new observation data, thus effectively removing noise from the signal.

[0242] Python (example).

[0243] Step 7.1.4.1, input parameters:

[0244] z: measurement data (observation signal), providing a scalar measurement value in each iteration.

[0245] A: State transfer matrix, used to infer the current state from the previous state.

[0246] H: Observation matrix, mapping the state to the measurement space.

[0247] Q: Process noise covariance matrix, describing the uncertainty of state estimation.

[0248] R: Measurement noise covariance matrix, describing the noise uncertainty of the measurement.

[0249] x_init: Initial state estimate.

[0250] P_init: covariance matrix of the initial state estimate.

[0251] Step 7.1.4.2, filtering step:

[0252] Prediction step: Use the state transition matrix A to predict the current state and calculate the prediction error covariance P_minus.

[0253] Update step: Update the state estimate using the Kalman gain K and update the error covariance matrix.

[0254] Step 7.1.4.3, return result: return the filtered signal x_filtered, which is the denoised data.

[0255] Step 7.2, smoothing (moving average method).

[0256] Smoothing helps remove short-term fluctuations in the signal, making it smoother and easier to analyze long-term trends. A common smoothing technique is the moving average method, which calculates the average value within a certain window around the data point.

[0257] step:

[0258] Step 7.2.1, determine the window size.

[0259] The window size (i.e., the length of the sliding window) determines the effectiveness of the smoothing process. Generally, 5 or 10 data points are selected as the window size, and it is adjusted according to the fluctuation of the actual data.

[0260] Step 7.2.2, write code to implement moving average

[0261] The moving average calculation formula is as follows:

[0262]

[0263] Among them, N is the window size, x k is the current data point, MA(x k) is the smoothed value.

[0264] You can choose a simple moving average (each data point has the same weight) or a weighted moving average (different weights give different influence to data points).

[0265] Step 7.2.3, output the smoothed signal.

[0266] Outputs a smoothed signal, usually used to eliminate short-term fluctuations in the signal and obtain a more stable signal.

[0267] Step 7.2.3.1, np.ones(window_size) / window_size:

[0268] Generates an array of size window_size, where each element has a value of 1 / window_size. This array serves as the mean window, and has the same effect as ones(1,window_size) / window_size in MATLAB.

[0269] Step 7.2.3.2, np.convolve(data,window,mode='valid'):

[0270] The np.convolve function performs a convolution operation, similar to the conv function in MATLAB. mode = 'valid' indicates that only valid convolution results, i.e., those completely covered by the kernel, are returned. This is equivalent to the 'valid' parameter in MATLAB.

[0271] Step 7.2.3.3, np.random.randn(100):

[0272] Generates an array of 100 random numbers drawn from a standard normal distribution, similar to randn(1,100) in MATLAB.

[0273] Step 2: Parameter extraction.

[0274] 2.1) Feature extraction algorithm.

[0275] Select a time-frequency analysis method:

[0276] Short-time Fourier transform (STFT);

[0277] Set window size and overlap ratio;

[0278] Window size: Depending on the signal characteristics and frequency resolution requirements, a window size of 512, 1024, or 2048 points is typically selected. A larger window size results in higher frequency resolution but lower time resolution.

[0279] Overlap ratio: Generally, an overlap of 50% to 75% is selected to effectively balance time resolution and frequency resolution.

[0280] For example, if the window size is 1024, you can set the overlap to 512 or 768 points.

[0281] Perform STFT calculation on the denoised and smoothed signal

[0282] First, the signal is preprocessed, including denoising (for example, using wavelet transform or moving average) and smoothing (for example, using Savitzky-Golay filtering).

[0283] Use STFT to calculate the time-frequency representation of the signal:

[0284]

[0285] Where w[n] is a window function (such as a Hanning window or a Hamming window).

[0286] Extract characteristic energy features such as energy, frequency, and phase: Calculate the energy of each frequency point, usually expressed as the sum of squares:

[0287] E(t,f)=|X(t,f)| 2

[0288] Frequency characteristics: The instantaneous frequency can be extracted by finding the peak in the energy spectrum at each moment.

[0289] Phase characteristics: Extract phase information through phase spectrum:

[0290] φ(t,f)=arg(X(t,f))

[0291] 2.1) Stress and deformation calculation;

[0292] Formula selection:

[0293] Select the appropriate calculation formula based on the data of the force sensor and displacement sensor.

[0294] Common formula:

[0295] Stress calculation: Where F is the applied force and A is the cross-sectional area.

[0296] Deformation calculation: Where L is the original length and E is the elastic modulus.

[0297] Collect force and displacement sensor data.

[0298] Write code to calculate stress and deformation values.

[0299] During the anchor bolt's stress process, as external forces (such as tension, pressure, or bending force) act, the anchor bolt's deformation (such as stress and strain) will affect the electromagnetic properties of the material. This process can be described by an improved stress-electromagnetic signal relationship model. For example:

[0300]

[0301] Where: B is the magnetic field intensity, μ0 is the vacuum magnetic permeability, F is the external force acting on the anchor, A is the cross-sectional area of the anchor, and r is the distance between the sensor and the anchor.

[0302] This formula, based on a simple relationship between the change in magnetic field under the action of an external force, is suitable for analyzing the effect of force on magnetic field distribution. In practical applications, it can be optimized based on the magnetic characteristics of different materials and the geometric opening of the anchor.

[0303] Step 3. Model the relationship between stress and electromagnetic signal;

[0304] 3.1 Theoretical modeling;

[0305] The relationship between stress and magnetic field must first be established through a physical model. Assuming that the magnetic response of the material is related to the applied stress, the following relationship can usually be used:

[0306] Stress-strain relationship (elasticity model):

[0307] σ=E·ε;

[0308] Where σ is stress (unit: Pa or MPa), E is the elastic modulus (unit: Pa or MPa), and ε is strain (unitless). Strain ε is the degree of deformation of the object and is calculated as:

[0309]

[0310] Where ΔL is the length change and L0 is the original length. Relationship between stress and magnetic field (magnetic model):

[0311] Relationship between stress and magnetic field (magnetic model): Stress also affects the magnetic response of the material. According to the characteristics of certain magnetic materials, there is usually a linear relationship between magnetic field strength (H) and stress (σ):

[0312] H=α·σ

[0313] Where: H is the magnetic field intensity (unit: A / m), α is the stress-magnetic coupling constant, which describes the degree to which stress affects the magnetic field intensity (unit: A·m / MPa), and σ is the stress (unit: MPa);

[0314] 3.2 Data fitting.

[0315] The main purpose of data fitting is to find the mathematical relationship between stress and magnetic field intensity based on experimental data. Here we use linear regression method, which can help us find the best fit line between stress and magnetic field intensity.

[0316] Step 3.2.1: Import necessary libraries;

[0317] Python

[0318] import numpy as np

[0319] import matplotlib.pyplot as plt

[0320] from sklearn.linear_model import LinearRegression

[0321] Step 3.2.2: Prepare experimental data;

[0322] Convert the stress and magnetic field data into a form suitable for model training. Assume there are 5 data points:

[0323] Python

[0324] #Assume experimental data

[0325] stress=np.array([10,20,30,40,50])#stress (MPa)

[0326] magnetic_field = np.array([2.1,4.2,6.3,8.4,10.5]) # Magnetic field strength (A / m)

[0327] Step 3.2.3: Create and train a regression model;

[0328] Use LinearRegression to fit a linear model:

[0329] Python

[0330] #Build regression model

[0331] model = LinearRegression()

[0332] #Reshape the stress data to adapt to the input format of the linear regression model

[0333] model.fit(stress.reshape(-1,1),magnetic_field);

[0334] The fit method fits an optimal linear model based on the input stress data and magnetic field data.

[0335] Step 3.2.4: Make predictions;

[0336] Use the trained model to make predictions, that is, predict the magnetic field strength under a given stress based on the fitted relationship:

[0337] Python

[0338] #Use the model to predict magnetic field strength

[0339] predicted_magnetic_field=model.predict(stress.reshape(-1,1));

[0340] Step 3.2.5: Visualize the fitting results;

[0341] In order to understand the fitting results more intuitively, we can plot the data points and the fitting line:

[0342] Python

[0343] #Visualize data and fitting results

[0344] plt.scatter(stress,magnetic_field,color='blue',label='ExperimentalData')

[0345] plt.plot(stress,predicted_magnetic_field,color='red',label='FittedLine')

[0346] plt.xlabel('Stress(MPa)')

[0347] plt.ylabel('Magnetic Field(A / m)')

[0348] plt.legend()

[0349] plt.show();

[0350] Step 3.2.6: Output the fitted coefficients;

[0351] Obtain the slope and intercept of the linear regression model through model.coef_ and model.intercept_, and output the specific equation of the stress-magnetic field relationship:

[0352] Python

[0353] # Output the fitted coefficients

[0354] print(f"Stress-magnetic field relationship: magnetic field = {model.coef_[0]:.2f}*stress+{model.intercept_:.2f}")

[0355] 3.4 Evaluation Model;

[0356] When evaluating a model, we usually focus on the following aspects:

[0357] Goodness of fit (R 2 ): Measures the model's ability to explain the data variance. This can be calculated using model.score.

[0358] Python

[0359] r_squared=model.score(stress.reshape(-1,1),magnetic_field)

[0360] print(f"Goodness of fit R 2 :{r_squared:.4f}")

[0361] The value range is [0,1], and the closer it is to 1, the better the fit.

[0362] Residual analysis: Examines the differences between predicted and actual values to see if there are any systematic deviations.

[0363] Python

[0364] residuals=magnetic_field-predicted_magnetic_field

[0365] plt.scatter(stress,residuals)

[0366] plt.axhline(y=0,color='r',linestyle='--')

[0367] plt.xlabel('Stress(MPa)')

[0368] plt.ylabel('Residuals(A / m)')

[0369] plt.title('Residual Plot')

[0370] plt.show()

[0371] 3.5 Summary;

[0372] From the experimental data, linear regression is used to establish the mathematical relationship between stress and magnetic field.

[0373] The output fitting equation can describe the influence of stress on the magnetic field.

[0374] By fitting results and R 2 value, to evaluate the accuracy of the model.

[0375] Use residual analysis to check the fit and ensure that there are no obvious systematic errors.

[0376] Step 4. Damage identification and location;

[0377] 4.1. Using signal characteristics to identify impairments;

[0378] Damage can be identified by analyzing the signal's mutation and nonlinear characteristics. The following is a step-by-step implementation process.

[0379] Step 4.1.1: Mutational characterization analysis;

[0380] Mutation signatures can be detected by analyzing changes in statistical quantities such as the signal's amplitude, mean, and variance. When damage occurs, the statistical characteristics of the signal will change significantly. To identify these changes, we need to calculate the signal's mean and variance and identify the mutation point.

[0381] Step 4.1.2: Nonlinear characteristic analysis;

[0382] The nonlinear characteristics of signals are often analyzed using nonlinear dynamics methods such as the Lyapunov exponent. The Lyapunov exponent can reveal nonlinear behavior in a signal and determine whether it exhibits chaotic characteristics. Under conditions of damage or abnormality, the dynamic behavior of a signal can become even more complex.

[0383] Step 4.1.2.1, generate a signal with mutation (generate_signal function);

[0384] Function: Generates a signal with a sudden change. The first half is a sine wave, the second half is added with noise, and a sudden change (amplitude change) is added at a specific location (damage_point).

[0385] parameter:

[0386] Length, the length of the signal.

[0387] noise_level, the noise level of the signal (standard deviation).

[0388] damage_point, the time point when damage occurs, that is, the location of signal mutation.

[0389] process:

[0390] Generate a sine wave signal with a frequency of 0.1 Hz.

[0391] Starting from damage_point, a random noise is added to the signal and the amplitude is abruptly changed (increased by a constant value of 5).

[0392] Returns: The time series and the generated signal.

[0393]

[0394] Step 4.1.2.2, mutation analysis (analyze_mutation function);

[0395] Function: Detect the mutation point of the signal by calculating the changes in the local mean and variance. The mutation point usually means that the change of the signal has changed significantly.

[0396] parameter:

[0397] Signal, input signal.

[0398] process:

[0399] A sliding window (window_size=50) is used to segment the signal and calculate the mean and variance within each window.

[0400] The changes (differences) in the mean and variance between adjacent windows are calculated, and if a change is greater than twice the standard deviation, a mutation is considered to have occurred.

[0401] Returns: The index (position) where the mean and variance are mutated.

[0402]

[0403]

[0404] Step 4.1.2.3, nonlinear characteristic analysis (lyapunov_exponent function);

[0405] Function: Analyze the nonlinear characteristics of a signal by calculating the Lyapunov exponent. The Lyapunov exponent is an important indicator for measuring whether a system's dynamics exhibits chaotic behavior.

[0406] parameter:

[0407] Signal, input signal.

[0408] M, the dimension of delay embedding, is usually used to construct the phase space (embedding dimension).

[0409] Tau, the delay time, determines the time interval of the embedded timing.

[0410] process:

[0411] Construct the phase space and generate a multidimensional space by delay embedding, where each dimension is the value of the signal at different time points.

[0412] Compute the distances between all points in phase space and calculate the Lyapunov exponents based on the distances.

[0413] The larger the Lyapunov exponent, the stronger the nonlinear characteristics of the system, and the more likely chaotic behavior may exist.

[0414] Returns: Lyapunov exponent value.

[0415]

[0416] Step 4.1.2.4, generate signals and perform mutation analysis;

[0417] Generate a signal using the generate_signal function and analyze the signal's mean and variance mutations using the analyze_mutation function.

[0418] Then, use lyapunov_exponent to calculate the Lyapunov exponent of the signal and analyze the nonlinear characteristics of the signal.

[0419] Python

[0420] time,signal=generate_signal()

[0421] mean_mutation,var_mutation=analyze_mutation(signal)

[0422] Step 4.1.2.5, drawing;

[0423] Use matplotlib to plot the generated signal and mark the detected mutation points (mean mutation points and variance mutation points).

[0424] Displays the Lyapunov exponent, which is used to reflect the nonlinear characteristics of the signal.

[0425] Python

[0426] plt.figure(figsize=(10,6))

[0427] #Draw the signal and mean mutation points

[0428] plt.subplot(2,1,1)

[0429] plt.plot(time,signal,label="Signal with damage")

[0430] plt.axvline(x=time[mean_mutation[0]],color='r',linestyle='--',label="Mean Mutation Point")

[0431] plt.title("Signal with Mutation(Amplitude Change)")

[0432] plt.xlabel("Time")

[0433] plt.ylabel("Signal Amplitude")

[0434] plt.legend()

[0435] #Calculate the Lyapunov exponent

[0436] lyapunov_val=lyapunov_exponent(signal)

[0437] print(f"Lyapunov Exponent:{lyapunov_val:.4f}")

[0438] #Draw the signal and variance mutation points

[0439] plt.subplot(2,1,2)

[0440] plt.plot(time,signal,label="Signal with damage")

[0441] plt.axvline(x=time[var_mutation[0]],color='g',linestyle='--',label="Variance Mutation Point")

[0442] plt.title("Signal with Variance Mutation")

[0443] plt.xlabel("Time")

[0444] plt.ylabel("Signal Amplitude")

[0445] plt.legend()

[0446] plt.tight_layout()

[0447] plt.show().

[0448] As can be seen from the above examples, the data processing and analysis algorithms in this invention build on widely known signal processing, physical modeling, and statistical analysis techniques. By combining these techniques for specific damage identification and magnetic field modeling tasks, a systematic signal processing framework is provided. The innovation in this application scenario lies in how these common methods are combined with specific tasks to better meet practical engineering needs.

[0449] The present invention has the following advantages by combining specific tasks:

[0450] Mutation analysis methods: Mutation analysis uses statistics (mean and variance differences) to detect mutations in signals and is applied to signal processing tasks. Damage points are identified by different features (such as changes in mean and variance).

[0451] Use of Lyapunov exponent: The Lyapunov exponent is used to measure the nonlinear characteristics of a signal. Combining this method with the nonlinear changes of a signal, especially when a sudden change occurs, has practical application significance in damage identification and signal analysis.

[0452] Step 8: Data processing after the experiment.

[0453] After the pull-out test, the data processing system organizes and analyzes the test data to produce a complete test report. The report should include information such as the anchor force cloud diagram, sensor data, and test process records.

[0454] The comprehensive monitoring system for underground anchor pullout tests, provided by this invention, utilizes an innovative combination of electromagnetic technology and a water-coupling medium to provide new insights and methods for assessing anchor quality. The implementation of this system can significantly improve the safety and reliability of anchoring projects, providing important technical support for research and application in related fields.

[0455] It can be seen from the above embodiments that the present invention has a multi-dimensional comprehensive monitoring capability: the system realizes all-round monitoring of the anchor rod and the surrounding rock mass by arranging a variety of sensors (such as acoustic emission sensors, strain gauges, and accelerometers) around the anchor rod, overcoming the limitation of traditional methods that can only monitor the force on the top of the anchor rod, and can capture the stress changes and potential damage inside the anchor rod in real time, thereby improving the accuracy of anchoring quality assessment.

[0456] Intelligent Data Processing: Equipped with advanced data acquisition and processing capabilities, this system incorporates advanced algorithms such as Kalman filtering, moving averages, and short-time Fourier transforms (STFTs) for signal denoising, smoothing, and time-frequency feature extraction. Furthermore, it incorporates stress-magnetic field relationship models and nonlinear analysis methods (such as the Lyapunov exponent) to efficiently identify potential damage to the anchor body, improving the robustness and accuracy of the monitoring system. This intelligent processing enhances decision-making efficiency and accuracy, ensuring safety during construction.

[0457] Application of electromagnetic technology and anti-interference capabilities: This invention innovatively combines multiple sensor technologies, including electromagnetic sensors, strain gauges, and accelerometers, to simultaneously monitor stress, deformation, and dynamic response around the anchor rod. Electromagnetic sensors capture the electromagnetic signals generated during the anchor rod's stress process in real time, effectively reflecting internal stress changes and potential damage, and providing multi-dimensional, all-round data support for the anchor structure. With superior signal penetration and strong anti-interference capabilities, it can stably obtain status information on the anchor rod and surrounding rock mass in complex environments. Compared with other technologies, it can more effectively capture subtle changes, ensuring high resolution and reliability of monitoring data.

[0458] To further illustrate the effects of the embodiments of the present invention, the following experiments were conducted.

[0459] Experimental design scheme for all-round intelligent monitoring system of underground anchor pull-out test

[0460] 1. Experimental Purpose: This experiment aims to verify the effectiveness of a comprehensive intelligent monitoring system for underground anchor pullout tests based on electromagnetic technology and evaluate its real-time monitoring capabilities for anchor stress, deformation, and the dynamic response of the surrounding rock mass. Furthermore, the system's adaptability and data accuracy in complex underground environments will be analyzed to ensure its reliability and stability in practical engineering applications.

[0461] 2. Experimental site and equipment

[0462] 2.1 Test Site

[0463] Select mine tunnels or laboratory simulated tunnels for experiments.

[0464] Tunnel surrounding rock types: hard rock, medium strength rock, soft rock and other different geological conditions.

[0465] Areas with different anchor density and anchoring methods were selected for testing.

[0466] 2.2 Main experimental equipment

[0467] Device Name Specifications and Features electromagnetic sensors Real-time monitoring of stress changes and energy distribution inside anchor bolts strain gauges Recording the stress-strain relationship of the anchor accelerometer Detect the dynamic response of the surrounding rock mass Data acquisition system Computer terminals to store and process monitoring data Pull-out testing machine Apply pull-out force to test the anchor rod under different stress conditions Simulating underground environment equipment (laboratory) Adjust temperature, humidity, and corrosive gases to simulate the real underground environment High-precision displacement sensor Monitoring the axial deformation of anchor bolts Rock vibration testing system Recording the response of a rock mass to external loads

[0468] 3. Experimental Methods

[0469] 3.1 Experimental variables: independent variables (experimental control factors), anchor type (conventional anchor, fiber Bragg grating anchor, electromagnetic induction anchor), pull-out rate (slow, medium, fast), rock type (hard rock, medium-strength rock, soft rock), sensor arrangement (different numbers and spacing), dependent variables (test indicators), stress-strain relationship curve, rock mass dynamic response (acceleration, vibration frequency), anchor deformation data, real-time performance and accuracy of the data acquisition system, correlation analysis of different sensor data, and the impact of the external environment on the monitoring effect.

[0470] 3.2 Experimental steps

[0471] Phase 1: Preliminary Preparation: Select a suitable test area within a mine tunnel or a laboratory simulated tunnel. Arrange different types of anchor bolts (conventional anchor bolts and electromagnetic induction anchor bolts) based on the experimental variables. Drill multiple sensor mounting holes around the anchor bolts and evenly distribute the sensors: electromagnetic sensors, strain gauges, and accelerometers are installed at the top, middle, and bottom of the anchor bolts. Accelerometers are placed at different depths in the surrounding rock to monitor the dynamic response of the rock mass. Connect the sensors to the data acquisition system and test the stability of data transmission.

[0472] Phase II: Pullout Test: The anchor bolts are subjected to stress tests at various pullout rates (slow, medium, and fast). Data from various sensors during the pullout process is recorded, including: Electromagnetic sensors: monitor changes in internal stress and electromagnetic signals; Strain gauges: generate stress-strain curves and record anchor bolt deformation; Accelerometers: detect rock mass vibration and analyze rock mass stability. Data processing: Sensor data is uploaded to the terminal, generating a real-time anchor bolt stress contour map.

[0473] Phase 3: Environmental Adaptability Testing: Temperature Testing: Analyze data stability under different temperature conditions (-20°C to 50°C). Humidity Testing: Monitor signal stability in a high humidity environment (90% RH). Corrosive Gas Effects: Test sensor durability and data accuracy in an environment containing corrosive gases such as hydrogen sulfide and carbon dioxide.

[0474] Phase 4: Data processing and analysis:

[0475] Signal Analysis: Fourier Transform (FFT) analysis of acceleration data extracts the natural frequency of the rock mass. Stress-strain relationships are calculated to determine the anchor's load characteristics. Changes in electromagnetic signals are evaluated to analyze internal energy distribution. System Real-Time and Stability Assessment: Latency calculations are performed for data acquisition, storage, and processing to ensure the system meets real-time monitoring requirements. Measurement accuracy is assessed by calculating the error range through multiple tests.

[0476] 4. Expected Results

[0477] Visualization of anchor bolt stress state: Accurately assess anchor bolt deformation and failure mechanisms through stress-strain curves. Electromagnetic signal analysis reflects the internal stress distribution and changes in anchor bolts. Rock mass dynamic response analysis: Identify the dynamic characteristics of the rock mass at different pullout stages and assess surrounding rock stability. System data accuracy verification: Ensure the reliability of monitoring data through multiple verifications of sensor data. Analyze the system's adaptability and error range through comparisons of different experimental environments. Optimize sensor layout: Adjust sensor layout based on experimental data to improve monitoring efficiency. Determine the optimal sensor combination to improve system stability and applicability.

[0478] 5.Possible problems and improvement measures

[0479] Possible problems Improvement plan The underground environment interference causes large signal noise Use signal filtering algorithm to improve data quality The sensor is not fixed firmly, resulting in measurement errors Use more reliable fixing methods, such as special adhesives or mechanical fixings High temperature and high humidity environment affect sensor performance Use high temperature resistant and waterproof sensor with protective cover Data collection delay affects real-time performance Optimize data transmission protocols to improve system response speed Impact of Downhole Vibration on Monitoring Data Distinguishing external interference signals through modal analysis

[0480] 6. Conclusion: This experimental plan, through a systematic testing approach, evaluated the performance and reliability of a comprehensive intelligent monitoring system for downhole anchor pullout tests. The experimental data will be used to optimize system design, improve monitoring accuracy and real-time performance, and provide a scientific basis for safety assessments of downhole anchoring projects.

[0481] The above description is only a preferred specific implementation method of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with this technical field within the technical scope disclosed by the present invention and within the spirit and principles of the present invention should be covered by the scope of protection of the present invention.

Claims

1. A comprehensive intelligent monitoring method for underground anchor pull-out test, characterized in that: The method comprises the following steps: S1, drilling multiple sensor holes evenly around the anchor rod, and installing sensor monitoring equipment in the holes drilled for sensor arrangement; the sensor monitoring equipment includes an electromagnetic sensor, a strain gauge, and an accelerometer; S2 uses electromagnetic sensors to capture the electromagnetic signals generated by the anchor in real time during the stress process, reflecting the internal stress changes and energy distribution; uses a combination of strain gauges and accelerometers to evaluate the deformation and stress state of the anchor in a dynamic environment. The strain gauges are used to monitor the deformation of the anchor and record the stress-strain relationship; the accelerometers are used to detect the dynamic response of the surrounding rock mass to evaluate the overall stability of the rock mass; S3, based on the principle of water coupling and the control of water injection volume, the coupling medium is injected into the hole where the sensor is arranged, and the anchor pull-out test is carried out. The monitoring signals of the electromagnetic sensor, strain gauge and accelerometer are transmitted to the data acquisition system and computer terminal; S4, the computer terminal stores the collected signals locally or in the cloud, analyzes them, extracts the stress and deformation parameters in the anchor pull-out test, and generates the anchor force cloud map in real time.

2. The all-round intelligent monitoring method for downhole anchor pull-out test according to claim 1 is characterized in that: In step S1, four sensor arrangement holes of the same length as the anchor are evenly opened around the anchor, and the diameter of the sensor arrangement holes is designed according to the size of the sensor monitoring equipment; The electromagnetic sensor, strain gauge and accelerometer are mounted on a telescopic rod, which rests in a hole drilled for sensor arrangement.

3. The all-round intelligent monitoring method for downhole anchor pull-out test according to claim 1 is characterized in that: In step S2, electromagnetic sensors are used to capture in real time the electromagnetic signals generated by the anchor rod during the stress process, reflecting the internal stress changes and energy distribution, including: (I01) Monitoring the electromagnetic characteristics of anchor bolt deformation-affecting materials through an improved stress-electromagnetic signal relationship model; (I02) Perform frequency analysis and feature extraction of electromagnetic signals through improved spectrum analysis methods.

4. The all-round intelligent monitoring method for downhole anchor pull-out test according to claim 3 is characterized in that: In step (I01), the improved stress-electromagnetic signal relationship model is: Where B is the magnetic field intensity, μ0 is the vacuum magnetic permeability, F is the external force acting on the anchor, A is the cross-sectional area of the anchor, and r is the distance between the electromagnetic sensor and the anchor.

5. The all-round intelligent monitoring method for downhole anchor pull-out test according to claim 3 is characterized in that: In step (I02), the improved spectrum analysis method is combined with fast Fourier transform (FFT) to perform real-time analysis on the electromagnetic signal and extract useful frequency features. The formula is: Where S(f) represents the spectrum of the signal, x(t) represents the time domain electromagnetic signal, f represents the frequency, and t represents the time.

6. The all-round intelligent monitoring method for downhole anchor pull-out test according to claim 1, characterized in that: In step S2, the deformation of the anchor rod is monitored using a strain gauge, and the stress-strain relationship is recorded, including: (II01) The strain gauge is used to measure the deformation of the anchor rod surface to reflect the stress change of the anchor rod under the action of external force. The deformation of the anchor rod is related to the stress through the following formula: σ=E·ε Where σ represents stress, E represents the elastic modulus of the material, and ε represents strain; The stress of the anchor rod is calculated through the above relationship, and the relationship between the change in resistance ΔR output by the strain gauge and the strain is converted through the sensitivity coefficient of the strain gauge; (II02) Using an improved temperature compensation method to eliminate the influence of temperature changes, and using the compensation of transverse strain to correct the actual deformation of the anchor rod; wherein the improved temperature compensation method includes: the resistance change of the strain gauge is not only related to the strain, but also closely related to the temperature change, and the improved temperature compensation formula is: E corr =e measured -a·ΔT Where, ε corr is the corrected strain, ε measured is the directly measured strain, α is the temperature coefficient of the strain gauge, and ΔT is the temperature change; The compensation of the lateral effect corrects the actual deformation of the anchor rod. When the strain gauge is installed on the surface of the anchor rod, the lateral strain comes from the lateral deformation of the anchor rod or the effect of external load, and is corrected by the following formula: e long =e measured -k·e transverse Where, ε long is the modified longitudinal strain, ε transverse is the transverse strain, k is the correction coefficient; by compensating for the transverse strain, the actual deformation of the anchor is reflected; (II03) A nonlinear material model is used to correct the stress-strain relationship; the nonlinear material model includes a hyperbola model, and the stress-strain relationship is fitted using the hyperbola model as follows: Where ε0 is the starting point of strain softening and n is the nonlinear index of the material.

7. The all-round intelligent monitoring method for downhole anchor pull-out test according to claim 1, characterized in that: In step S2, the accelerometer is used to detect the dynamic response of the surrounding rock mass to evaluate the overall stability of the rock mass, including: (III01) Obtaining dynamic response information of rock mass through dynamic equations; The dynamic equations are modeled using a simplified single degree of freedom system SDOF: m·a(t)+c·v(t)+k·u(t)=F(t); Where m represents the mass of the rock mass, a(t) represents acceleration, c represents the damping coefficient, v(t) represents velocity, k represents the stiffness coefficient, u(t) represents displacement, and F(t) represents external dynamic action; The acceleration data a(t) obtained by the accelerometer is further integrated to obtain the velocity v(t) and displacement u(t) of the rock mass: v(t)=∫a(t)dt; u(t)=∫v(t)dt=∫∫a(t)dt dt; (III02) Obtaining dynamic response information of rock mass through improved frequency domain analysis and modal analysis to further analyze the vibration response of rock mass at different frequencies and predict the response of rock mass under different dynamic loads; The improved frequency domain analysis includes: converting the acceleration signal from the time domain to the frequency domain through Fourier transform to identify the natural frequency and dynamic characteristics of the rock mass; the formula for frequency domain analysis is: A(f)=∫a(t)e -j2πft dt Where A(f) is the acceleration response in the frequency domain, f is the frequency, and a(t) is the acceleration in the time domain; The modal analysis includes: modal analysis to identify the natural vibration modes and frequencies of the rock mass, and to predict the response of the rock mass under different dynamic loads.

8. The all-round intelligent monitoring method for downhole anchor pull-out test according to claim 1, characterized in that: In step S2, the deformation and stress state of the anchor in a dynamic environment are evaluated using a data combination method of a strain gauge and an accelerometer, including: The joint correction is performed by the following model: σ(t)=E·ε(t)+β·∫a(t)dt Where σ(t) is the stress in the time series, ε(t) is the strain, a(t) is the acceleration, and β is the dynamic correction coefficient.

9. An all-round intelligent monitoring system for underground anchor pull-out test, characterized in that: The system implements the all-round intelligent monitoring method for downhole anchor pull-out test according to any one of claims 1 to 8, and the system comprises: A sensor monitoring device installation system is used to evenly drill multiple sensor holes around the anchor rod and install sensor monitoring devices in the holes drilled for sensor arrangement; the sensor monitoring devices include electromagnetic sensors, strain gauges, and accelerometers; The anchor deformation and stress state monitoring system uses electromagnetic sensors to capture the electromagnetic signals generated by the anchor in real time during the stress process, reflecting the internal stress changes and energy distribution. It uses a combination of strain gauges and accelerometers to evaluate the deformation and stress state of the anchor in a dynamic environment. The strain gauges are used to monitor the deformation of the anchor and record the stress-strain relationship. The accelerometers are used to detect the dynamic response of the surrounding rock mass to assess the overall stability of the rock mass. The coupling medium injection system is used to inject coupling medium into the holes drilled for sensor arrangement according to the principle of water coupling and the control of water injection volume, conduct anchor pull-out tests, and transmit the monitoring signals of electromagnetic sensors, strain gauges and accelerometers to the data acquisition system and computer terminals; The anchor rod force cloud map generation system is used to store the collected signals locally or in the cloud through a computer terminal, analyze them, extract the stress and deformation parameters in the anchor rod pull-out test, and generate the anchor rod force cloud map in real time.

10. An all-round intelligent monitoring device for underground anchor pull-out test, characterized in that: The device implements the all-round intelligent monitoring method for downhole anchor pull-out test according to any one of claims 1 to 8, and the device comprises: surrounding rock (1); An anchoring borehole (14) is provided at the center of the surrounding rock (1), and an anchor rod (10) is received inside the surrounding rock (1) through the anchoring borehole (14). The end of the anchor rod (10) is fixed to the inner rear end of the surrounding rock (1) through the anchoring end (2), and a free end (3) is provided in the middle of the anchor rod (10); the front of the anchor rod (10) is fixed on a tray (5), and a sleeve (11) for receiving the anchor rod (10) and a pull rod (6) is installed on the tray (5), and the pull rod (6) is connected to the front of the anchor rod (10), and an oil cylinder (7) connected to the pull rod (6) is fixed to the rear end of the sleeve (11), and a displacement meter (9) for detecting the moving distance of the oil cylinder (7) is fixed to the oil cylinder (7) through a nut (8), and the displacement meter (9) is connected to the computer terminal (17) through a signal line; The oil cylinder (7) is connected to the high-pressure pump (12) via a solenoid valve, and the solenoid valve is connected to the computer terminal (17) via a signal line; A plurality of sensor arrangement boreholes (13) having the same length as the anchor rod (10) are uniformly provided around the anchor rod (10), and uniformly arranged sensor monitoring devices (4) are placed inside the holes of the sensor arrangement boreholes (13); The hole is filled with a coupling medium (15); a blocking device (18) is installed at the front end of the sensor arrangement borehole (13); the sensor monitoring device (4) is connected to a data acquisition system (16) via a signal line passing through the blocking device (18); and the data acquisition system (16) is connected to a computer terminal (17) via a signal line. The sensor monitoring device (4) includes an electromagnetic sensor, a strain gauge and an accelerometer; The electromagnetic sensor, strain gauge and accelerometer are mounted on a telescopic rod, and the telescopic rod is placed in a hole of a sensor arrangement borehole (13); Electromagnetic sensors capture the electromagnetic signals generated by the anchor rod during the stress process in real time, reflecting the internal stress changes and energy distribution; strain gauges monitor the deformation of the anchor rod and record the stress-strain relationship; accelerometers are used to detect the dynamic response of the surrounding rock mass to evaluate the overall stability.

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