Monitoring device, system and method for tailing pond flood prevention and drainage tunnel

Abstract

The invention discloses a monitoring device, system and method for a flood prevention and drainage tunnel of a tailings pond, and relates to the technical field of safety monitoring and disaster prevention and control of mine tailings ponds.The monitoring device comprises a sensor monitoring module, a data receiving module, a data processing module and a data processing module, the system comprises a data receiving module used for receiving resistivity and acoustic emission data of a flood prevention and drainage tunnel, a signal storage and emission module used for collecting the resistivity and acoustic emission data, and a computer which is connected with the data receiving module, is placed above a flood prevention and drainage tunnel portal, can store the data and transmits signals to a monitoring center through wireless emission. The data are stored in a computer memory of a monitoring center; according to the monitoring device, system and method for the tailing pond flood prevention and drainage tunnel, continuous monitoring of the change process of internal damage of tunnel concrete is achieved, and the problem that quantitative diagnosis cannot be achieved through an existing method is effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of safety monitoring and disaster prevention of mine tailing ponds, and specifically relates to a monitoring device, system and method for flood prevention and drainage tunnels of tailing ponds. Background Art

[0002] The safety monitoring and disaster prevention of tailing ponds have always been one of the important tasks in China to contain major accidents. Whether the tailing ponds operate safely and stably is related to the safety of people's lives and property and the sustainable and safe development of China's mining industry. The flood prevention and drainage structures of tailing ponds are related to the safety of the entire tailing pond. Once damaged or ineffective, it may lead to the loss of tailings and cause damage to the ecological environment in the downstream area, and in severe cases, it will cause casualties and huge economic losses. At present, the failure monitoring methods and means of flood prevention and drainage structures mainly include methods such as water level monitoring and video monitoring, and the monitoring contents mainly include monitoring the blockage of sundries in the flood prevention and drainage structures, the damage of intercepting ditches, the drainage capacity, etc.

[0003] The current methods have many problems: First, the failure modes of the flood prevention and drainage structures of tailing ponds are complex and diverse, and the existing methods cannot accurately quantify and evaluate their failure degrees; second, the existing methods cannot accurately predict the failure positions of the flood prevention and drainage structures of tailing ponds; third, the performance degradation evolution mechanism of the flood prevention and drainage structures of tailing ponds is complex, and the prevention and control technology system is not perfect, and it is impossible to accurately evaluate their different damage degrees, so as to take effective targeted measures. In the safe operation of tailing ponds, as the throat of the tailing pond, whether the flood prevention and drainage tunnel can operate safely and effectively is of great significance. Therefore, it is very necessary to develop a failure monitoring device, system and method for flood prevention and drainage tunnels with accurate quantification and high precision. For different damage degrees of the flood prevention and drainage tunnels of tailing ponds, establish a failure assessment method for the flood prevention and drainage tunnels of tailing ponds, and form a precise monitoring of the vulnerable failure areas of the flood prevention and drainage tunnels, a prediction of the failure area positions and a failure prevention and control technology system. Through the above analysis, the problems and defects of the existing technology are: the existing methods cannot accurately quantify and evaluate the damage and failure degrees of the flood prevention and drainage tunnels; nor can they accurately predict the failure positions of the flood prevention and drainage tunnels of tailing ponds; and they cannot accurately evaluate different failure degrees effectively and take effective targeted measures. Summary of the Invention

[0004] The purpose of the present invention is to provide a monitoring device, system and method for flood prevention and drainage tunnels of tailing ponds to solve the problems existing in the prior art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A monitoring device for a flood prevention and drainage tunnel of a tailing pond, comprising:

[0006] The sensor monitoring module is used to collect the resistivity and acoustic emission data by using the resistivity and acoustic emission units for collecting the flood prevention and drainage tunnel, and collect the resistivity and acoustic emission data;

[0007] The data receiving module is used to receive the resistivity and acoustic emission data units of the flood prevention and drainage tunnel and collect the resistivity and acoustic emission data;

[0008] The signal storage and transmission module is connected to the data receiving module and is placed above the entrance of the flood prevention and drainage tunnel. It can store data and transmit the signal to the computer in the monitoring center through wireless transmission and save it to the computer storage in the monitoring center;

[0009] The data analysis system module is set in the computer in the monitoring center and uses the software self-developed by MATLAB to analyze the variation laws of the resistivity and acoustic emission in the monitoring module and issue early warnings and alarms based on the variation laws of the two;

[0010] The monitoring and early warning system module is set in the data analysis system module and uses the software self-developed by MATLAB. When abnormal values appear, an alarm pop-up window appears on the computer software interface, and the information is synchronously sent to the specified mobile phone number.

[0011] Preferably, the sensor monitoring module includes:

[0012] The resistivity data acquisition unit is used to acquire the resistivity data obtained by the sensor monitoring module;

[0013] The acoustic emission data acquisition unit is used to acquire the acoustic emission data obtained by the sensor monitoring module.

[0014] Preferably, the data receiving module includes:

[0015] The resistivity data receiving unit is used to receive the resistivity data obtained by the sensor monitoring module;

[0016] The acoustic emission data receiving unit is used to receive the acoustic emission data obtained by the sensor monitoring module;

[0017] The temperature data receiving unit is used to receive the real-time temperature data in the flood prevention and drainage structure;

[0018] The data synchronization unit is used to synchronously receive the resistivity, acoustic emission, and temperature signal data.

[0019] Preferably, the signal storage and transmission module includes:

[0020] The signal storage module: used to store the resistivity data and acoustic emission data obtained by the data receiving module;

[0021] The signal transmission module: used to transmit the resistivity data and acoustic emission data obtained by the data receiving module;

[0022] Power supply module: used to supply power to the signal storage and transmission module, data receiving module, and sensor monitoring module.

[0023] Preferably, the data analysis system module includes:

[0024] Resistivity data analysis and processing unit: used to analyze and process the obtained resistivity data;

[0025] Acoustic emission data analysis and processing unit: used to analyze and process the obtained acoustic emission data;

[0026] Data coupling analysis software unit: used to couple and analyze the obtained resistivity and acoustic emission data;

[0027] Computer unit: used to store resistivity and acoustic emission data and corresponding analysis software, and carry the data analysis system module.

[0028] Preferably, the monitoring and early warning system module includes:

[0029] Monitoring and early warning system unit: used to set the thresholds for resistivity change and acoustic emission change. When the resistivity change or acoustic emission change reaches the set threshold, an alarm pop-up interface appears on the computer software interface;

[0030] Alarm system unit: used to send alarm information to a specified mobile phone number when it is higher than the early warning value;

[0031] Computer unit: used to store resistivity and acoustic emission data and corresponding analysis software, and carry the monitoring and early warning system module.

[0032] A monitoring system for the flood drainage tunnel of a tailings pond, the monitoring system includes the monitoring device for the flood drainage tunnel of the tailings pond described above.

[0033] A monitoring method for the flood drainage tunnel of a tailings pond, using the monitoring system for the flood drainage tunnel of the tailings pond described above, the method includes:

[0034] Step 1, drill 1 hole at the key bearing or easily failing positions of the flood drainage tunnel;

[0035] Step 2, wash the hole with a high-pressure water and air pipe to ensure that there are no other impurities in the hole;

[0036] Step 3, insert the conductive ring 1 into the hole, keep the ring perpendicular to the hole wall, 20 - 40 mm away from the bottom of the hole, and lead the wire out of the hole mouth;

[0037] Step 4, fill or bury the self-sensing composite material in the drill hole, and fill it to a depth of 20 - 40 mm from the hole mouth. Among them, the self-sensing composite material includes: cement, sand, fly ash, carbon fiber, foaming agent;

[0038] Step Five: Insert the conductive ring 2 into the hole, keep the ring perpendicular to the hole wall, 20 - 40 mm away from the hole opening, and lead the wire out of the hole opening;

[0039] Step Six: Insert the acoustic emission sensor into the self - sensing composite material, and lead the communication wire out of the hole opening;

[0040] Step Seven: The self - sensing composite material foams by itself. After the self - sensing composite material foams, expands and sets, and closely adheres to the surfaces of the acoustic emission and the tunnel concrete, scrape the material flat to be flush with the hole opening;

[0041] Step Eight: Connect the acoustic emission communication wire and the conductive ring wire together and connect them to the data receiving module;

[0042] Step Nine: Fix the data receiving module on the surface of the hole opening, and lead the communication wire out to the junction box;

[0043] Step Ten: Open another 3 - 7 drill holes at the key bearing or other positions prone to failure in the flood - control and drainage tunnel, and repeat the operations of Step Two to Step Nine to set up 4 - 8 monitoring points in total;

[0044] Step Eleven: Connect the communication wire to the signal storage and transmission module;

[0045] Step Twelve: Open the data analysis system module on the computer and debug it;

[0046] Step Thirteen: Through knocking and current testing, test whether there is a signal on the computer terminal;

[0047] Step Fourteen: Select 2 - 3 different positions between each point for knocking tests. When signals appear simultaneously at 4 - 8 monitoring points, the installation is effective; otherwise, reset the acquisition parameters until the signals appear normally;

[0048] Step Fifteen: Use the data analysis system module to analyze resistivity, acoustic emission, and temperature;

[0049] Step Sixteen: Set the alarm threshold on the monitoring and early - warning system module;

[0050] Step Seventeen: Set the alarm mobile phone number on the monitoring and early - warning system module;

[0051] In Step One, the diameter of the drill hole is 50 ± 2 mm, and the hole depth is generally 200 - 400 mm according to the thickness of the tunnel concrete;

[0052] In Step Three, the outer diameter of the conductive ring is 45 mm, the inner diameter is 40 mm, and the conductive ring is connected with 6 - square BV wire, which is convenient for installation and signal transmission.

[0053] It can be seen from the above technical solution that the present invention has the following beneficial effects:

[0054] The monitoring device, system and method for tailings pond flood control tunnels realize continuous monitoring of the internal damage changes (such as cracking, leakage, and deterioration) of tunnel concrete by deploying embedded resistivity and acoustic emission dual sensors and combining the response characteristics of self-sensing composite materials. Combined with the multi-source coupling analysis model developed by MATLAB, the comprehensive risk index D value is output, providing a quantifiable and traceable structural safety status indicator, effectively solving the problem that existing methods cannot diagnose quantitatively, accurately identifying the failure-prone sections of tailings pond flood control tunnels, and making early judgments on the abnormal evolution trend of the structure. The linkage analysis of the changes in D values at different points further supports the identification of regional structural failure trends, provides a scientific basis for the realization of "key inspection + structural reinforcement + disaster avoidance", and constructs a two-level early warning mechanism including single-point local risk warning and multi-point regional risk alarm, realizing a closed-loop risk management chain from early warning, key inspection to emergency response, filling the gap in the existing technology of separation of early warning and response and inability to link disposal. The proposed embedded self-sensing material + standardized sensor module structure, combined with independent power supply and wireless communication design, greatly improves the system's adaptability in complex geological environments such as remote, high humidity, and high burial, and is significantly better than the traditional power supply method that requires manual inspection and wiring, reducing construction difficulty and operation and maintenance costs, and establishing a full-chain intelligent monitoring and early warning platform consisting of material response-parameter collection-intelligent analysis-threshold judgment-information push, which has a high degree of practicality and engineering implementation value. The present invention will provide high-precision, highly intelligent core equipment and technical methods for disaster prevention and control of tailings pond flood control and drainage structures, and provide solid technical guarantees for curbing mass casualties and mass injuries. It has important social security value and economic significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a schematic diagram of the installation of the sensor monitoring module and the data receiving module of the present invention;

[0056] Figure 2 It is a plane schematic diagram of the acoustic emission sensor of the present invention;

[0057] Figure 3 For the invention of a cross-sectional view of a conductive ring;

[0058] Figure 4 It is a side view of the conductive ring 1 of the present invention;

[0059] Figure 5 It is a side view of the conductive ring 2 of the present invention;

[0060] Figure 6 This is a side view of the flood control and drainage tunnel of the present invention;

[0061] Figure 7This is the algorithm flowchart of the present invention.

[0062] In the figure: 1. Communication wire; 2. Data receiving module; 3. Acoustic emission receiving end; 4. Low end of current excitation; 5. High end of current excitation; 6. Low end of voltage sampling; 7. High end of voltage sampling; 8. 6-square BV wire; 9. Acoustic emission sensor; 10. Conductive ring 2; 11. Concrete; 12. Conductive ring 1; 13. Self-sensing composite material; 14. 6-square BV wire; 15. Sensor monitoring module. Specific implementation mode

[0063] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0064] As Figures 1-6 shown, the present invention provides a technical solution: A monitoring device for the flood prevention and drainage tunnel of a tailings pond, including a sensor monitoring module, which is used to collect the resistivity and acoustic emission unit of the flood prevention and drainage tunnel, and collect the data of resistivity and acoustic emission; a data receiving module, which is used to receive the resistivity and acoustic emission data unit of the flood prevention and drainage tunnel, and collect the data of resistivity and acoustic emission; a signal storage and transmission module, which is connected to the data receiving module and placed above the flood prevention and drainage tunnel entrance, can store data, and transmit the signal to the computer in the monitoring center through wireless transmission, and save it to the computer storage in the monitoring center; a data analysis system module, which is set in the computer in the monitoring center, and uses the software self-written by MATLAB to analyze the change rules of resistivity and acoustic emission in the monitoring module, and make a forecast and alarm according to the change rules of the two; a monitoring and early warning system module, which is set in the data analysis system module, and uses the software self-written by MATLAB. When an abnormal value appears, an alarm pop-up window appears on the computer software interface, and the information is synchronously sent to the specified mobile phone number.

[0065] This device combines the principles of resistivity detection and acoustic emission monitoring. Resistivity monitoring reflects the water content and its change trend of the tunnel structural material or the surrounding rock and soil medium. As the water content changes or the structure deteriorates, its resistivity characteristics will also change significantly; acoustic emission monitoring captures potential damage signals by sensing the high-frequency signals released during the development of material microcracks. After the data is received, it is uploaded to the monitoring system through wireless transmission. The analysis system combines the time-series changes of the two physical quantities for multi-source fusion analysis, extracts key characteristic values with the algorithm developed by MATLAB, and realizes the functions of prediction and early warning. When the monitoring value reaches the set threshold, the system triggers an alarm to achieve early identification and timely intervention in the failure of the flood prevention and drainage tunnel of the tailings pond.

[0066] By combining two complementary non-destructive testing methods of resistivity and acoustic emission, the present invention can effectively improve the comprehensiveness and accuracy of the structural safety monitoring of the flood drainage tunnel in the tailings pond, and enhance the sensitivity of the monitoring system to micro-cracks and moisture content changes. At the same time, combined with the self-developed MATLAB software for intelligent data analysis, the signal processing efficiency and warning reliability are improved, which helps to predict failure signs in advance. The system has the advantages of simple structure, convenient installation and high automation, significantly reducing the frequency of manual inspections and the safety risks of personnel, and enhancing the safety management level of the tailings pond.

[0067] The sensor monitoring module includes a resistivity data acquisition unit for acquiring the resistivity data obtained by the sensor monitoring module; an acoustic emission data acquisition unit for acquiring the acoustic emission data obtained by the sensor monitoring module.

[0068] This embodiment introduces a dedicated resistivity data acquisition unit and an acoustic emission data acquisition unit in the sensor monitoring module to achieve independent acquisition and separation processing of resistivity and acoustic emission signals. The resistivity data acquisition unit uses the four-electrode measurement method to obtain the bulk resistivity parameters of materials or media through electrode pairs arranged around or on the inner wall of the tunnel; the acoustic emission data acquisition unit captures the transient high-frequency signals emitted during the crack propagation process by installing highly sensitive piezoelectric sensors. Each acquisition unit is equipped with an independent analog-to-digital converter and signal conditioning circuit to ensure signal integrity and anti-interference ability, thereby improving data acquisition accuracy. By subdividing the sensor monitoring module into two independent acquisition units, this embodiment strengthens the pertinence and system modularity in the data acquisition stage, facilitating independent maintenance and fault location. At the same time, this design improves the system's data processing efficiency and sampling accuracy, avoiding signal aliasing or misjudgment caused by multi-signal interference. The layout method, sampling frequency and signal conditioning strategy of each acquisition unit can be flexibly adjusted according to specific monitoring requirements, improving the adaptability and expandability of the system.

[0069] The data receiving module includes a resistivity data receiving unit for receiving the resistivity data obtained by the sensor monitoring module; an acoustic emission data receiving unit for receiving the acoustic emission data obtained by the sensor monitoring module; a temperature data receiving unit for receiving the real-time temperature data in the flood drainage structure; and a data synchronization unit for synchronously receiving the resistivity, acoustic emission, and temperature signal data.

[0070] The data receiving module is designed at the front-end signal aggregation layer. Functionally, it separately receives and processes data from different types of sensors, and completes timing alignment with the help of a data synchronization unit. Since the resistivity and acoustic emission signals have different sampling frequencies and data structures, independent buffer channels are used to achieve asynchronous access. The temperature signal is generally sampled periodically at a lower frequency and is mainly used for environmental correction of the monitoring data. The data synchronization unit synchronously encodes multi-source signals through methods such as timestamp marking and frame structure design to ensure that the data uploaded to the signal storage and transmission module is consistent and correlatable in the time dimension, thereby providing an accurate data basis for subsequent unified analysis. By setting up independent receiving units for resistivity, acoustic emission, and temperature, and cooperating with the data synchronization mechanism, this implementation significantly improves the system's concurrent processing ability for various monitoring data, ensuring that different physical quantities are compared and analyzed within the same timing framework. The introduction of temperature data helps to eliminate the interference of temperature changes on the resistivity and acoustic emission characteristics of materials, improving the environmental adaptability and credibility of the monitoring data. At the same time, this structure has good modular design characteristics, facilitating system expansion and maintenance, and effectively improving the configurability and on-site adaptability of the monitoring system.

[0071] The signal storage and transmission module includes a signal storage module: used to store the resistivity data and acoustic emission data obtained by the data receiving module; a signal transmission module: used to transmit the resistivity data and acoustic emission data obtained by the data receiving module; a power supply module: used to supply power to the signal storage and transmission module, the data receiving module, and the sensor monitoring module.

[0072] This module undertakes the core tasks of caching and transmitting front-end monitoring data. The resistivity and acoustic emission data obtained by the data receiving module are first input into the signal storage module for local storage. To ensure data integrity and prevent data loss caused by sudden power outages, the signal storage module adopts a circular buffer mechanism and a power-off protection mechanism. When the communication status is normal, the signal transmission module pushes the data to the monitoring center periodically or according to the trigger mechanism, supporting multiple transmission protocols such as FTP, MQTT, or HTTP to achieve data synchronization with the background server. The power supply module intelligently allocates power supply resources according to the system power consumption requirements and has functions such as power detection, fault warning, and remote control to ensure the continuous and stable operation of the system. By integrating the signal storage and transmission functions into an integrated module and equipping it with an independent power supply module, not only the operational independence and deployment flexibility of the system are improved, but also the data reliability and stability of the system are effectively enhanced. In case of emergencies such as extreme weather or communication interruption, it can ensure that the monitoring data is not lost and the power supply is not cut off, realizing long-term autonomous operation. In addition, the system supports flexible transmission methods and protocol adaptation capabilities, can be conveniently connected to various industrial networks or cloud platforms, realizes remote access and centralized management of data, and improves the response efficiency and management ability of the monitoring center.

[0073] The data analysis system module includes a resistivity data analysis and processing unit: used for analyzing and processing the obtained resistivity data; an acoustic emission data analysis and processing unit: used for analyzing and processing the obtained acoustic emission data; a data coupling analysis software unit: used for coupling and analyzing the obtained resistivity and acoustic emission data; a computer unit: used for storing the resistivity and acoustic emission data and the corresponding analysis software, and carrying the data analysis system module.

[0074] As the intelligent core of the entire monitoring system, the data analysis system module's function is based on the theory of multi-physical quantity data fusion analysis. The resistivity data analysis and processing unit first eliminates outliers and performs trend modeling on the original resistivity data, and identifies signs of infiltration or material deterioration through gradient analysis; the acoustic emission data analysis and processing unit performs event clustering analysis and AE energy statistics on high-frequency acoustic signals to determine whether there is crack propagation or structural deformation. The coupling analysis software unit inputs the above two types of results into the same evaluation model, and constructs a set of comprehensive early warning index systems through methods such as time series matching, statistical resonance analysis, or fuzzy logic algorithms. All analysis work is completed by the computer unit, and it supports the configuration of analysis parameters, visual output, result archiving, and remote access. This implementation method separates and jointly analyzes the resistivity and acoustic emission signals by setting dedicated processing units, greatly improving the system's fault detection sensitivity and abnormal identification accuracy. Especially through the data coupling analysis software unit, deep fusion between different types of signals is achieved, making the judgment basis more comprehensive and effectively overcoming the limitations of single-signal analysis. The computer unit not only undertakes the data analysis function, but also has good storage management capabilities and user interaction interfaces, facilitating engineering personnel to quickly obtain diagnostic results and forming an efficient auxiliary decision-making mechanism.

[0075] The monitoring and early warning system module includes a monitoring and early warning system unit: used for setting the thresholds for resistivity change and acoustic emission change. When the resistivity change or acoustic emission change reaches the set threshold, an alarm pop-up interface appears on the computer software interface; an alarm system unit: used for sending alarm information to a specified mobile phone number when it is higher than the early warning value; a computer unit: used for storing the resistivity and acoustic emission data and the corresponding analysis software, and carrying the monitoring and early warning system module.

[0076] This module operates based on a real-time data comparison and automatic response mechanism. The monitoring and early warning system unit periodically receives analysis results through linkage with the data analysis system module, and calls the set threshold model in real time for discrimination. The change in resistivity may reflect a sudden change in the moisture content of the medium, while the acoustic emission parameters can indicate stress concentration or crack propagation in the material. Once the safety threshold range is exceeded, the system immediately pops up a prominent alarm window on the graphical user interface (GUI), indicating the over-limit time, amplitude, and the corresponding monitoring point number of the value. The alarm system unit formats the alarm information and sends it to the specified mobile phone number through the built-in SMS gateway or Internet communication module, enabling remote reception and quick response by the on-duty personnel. The computer unit is responsible for managing system logs and historical data, and supports the functions of querying alarm records and configuring parameters. By setting a clear threshold discrimination mechanism and a dual-channel alarm feedback mechanism, this implementation realizes the real-time early warning and remote notification of the risk information of the flood prevention and drainage tunnel in the tailings pond, effectively improving the response speed and information closed-loop ability of the monitoring system. The alarm pop-up window on the GUI improves the visual identification efficiency of on-site operators, while the SMS push function ensures the monitoring continuity and safety guarantee in the unattended or night state, enhancing the practicality and intelligent level of the system.

[0077] A monitoring system for the flood prevention and drainage tunnel of a tailings pond is also provided, and the monitoring system includes the monitoring device for the flood prevention and drainage tunnel of the tailings pond described above.

[0078] The overall system is built on a technical framework of multi-source information collection, multi-level processing, and linkage response. The sensor monitoring module is deployed at key positions of the flood prevention and drainage tunnel to collect raw data such as resistivity, acoustic emission, and temperature; the data receiving module classifies and synchronously integrates various signals; the signal storage and transmission module is responsible for relay processing and remote upload; the data analysis system module realizes data fusion processing and risk identification through an algorithm platform; the monitoring and early warning system module makes state discrimination based on the set threshold and triggers the alarm mechanism, and finally completes graphical display, data recording, and remote information push through the computer system. The system can operate automatically all day long and has data redundancy and communication fault tolerance functions, suitable for continuous online monitoring tasks in high-risk geological disaster areas.

[0079] Such as Figure 7As shown in the figure, a monitoring method for the flood prevention and drainage tunnel of a tailings pond is also provided. The method uses the monitoring system for the flood prevention and drainage tunnel of a tailings pond described in claim 7, and is characterized in that the method includes step one: opening 1 borehole at the key bearing or easily failing positions of the flood prevention and drainage tunnel; step two: washing the borehole with a high-pressure water and air pipe to ensure that there are no other impurities in the borehole; step three: inserting conductive ring 1 into the borehole, keeping the ring perpendicular to the borehole wall, 20 - 40 mm away from the bottom of the borehole, and leading the wire out of the borehole; step four: filling or embedding the self-sensing composite material in the borehole, with the depth filled to 20 - 40 mm from the borehole opening. Among them, the self-sensing composite material includes: cement, sand, fly ash, carbon fiber, foaming agent; step five: inserting conductive ring 2 into the borehole, keeping the ring perpendicular to the borehole wall, 20 - 40 mm away from the borehole opening, and leading the wire out of the borehole; step six: inserting the acoustic emission sensor into the self-sensing composite material, and leading the communication wire out of the borehole; step seven: the self-sensing composite material foams by itself. After the self-sensing composite material foams, expands and sets, and closely adheres to the surface of the acoustic emission and the tunnel concrete, scrape the material flat to be flush with the borehole opening; step eight: connect the acoustic emission communication wire and the conductive ring wire together and connect them to the data receiving module; step nine: fix the data receiving module on the surface of the borehole opening, and lead the communication wire to the junction box; step ten: open 3 - 7 more boreholes at the remaining key bearing or easily failing positions of the flood prevention and drainage tunnel, and repeat the operations of step two to step nine, with a total of 4 - 8 monitoring points set; step eleven: connect the communication wire to the signal storage and transmission module; step twelve: open and debug the data analysis system module on the computer; step thirteen: through knocking and current testing, test whether there is a signal on the computer terminal. Step fourteen: Select 2 - 3 different positions between each point for knocking tests. When signals appear simultaneously at 4 - 8 monitoring points, the installation is effective; otherwise, reset the acquisition parameters until the signal appears normally. Step fifteen: Use the data analysis system module to analyze the resistivity, acoustic emission, and temperature; step sixteen: Set the alarm threshold on the monitoring and early warning system module; step seventeen: Set the alarm mobile phone number on the monitoring and early warning system module.

[0080] The method of the present invention establishes multiple intelligent monitoring points in the flood prevention and drainage tunnel. Each point constitutes an embedded monitoring unit based on self-sensing composite materials, integrating acoustic emission sensing and resistivity sensing capabilities, and is uniformly connected to the central data processing system. Through unified sampling, synchronous analysis, and joint warning, precise detection of the microstructural changes of the tunnel and rapid identification of abnormal events are realized. The multi-point linkage response mechanism enhances the reliability of signal recognition and ensures that the monitoring system can effectively cover the target area. This monitoring method constructs highly integrated and multi-parameter monitoring nodes in-situ, and uses self-sensing materials with both structural embedding and signal transmission capabilities to improve the installation convenience of the system and the signal acquisition efficiency. The whole-process standardized installation process ensures scientific layout of monitoring points, unified acquisition parameters, and stable operation of the system. At the same time, through multi-point collaborative judgment and test verification, the monitoring accuracy and false alarm suppression ability of the device are effectively improved, and it is applicable to the structural monitoring of flood prevention and drainage tunnels under large-scale and complex geological conditions.

[0081] In step one, the drilling diameter is 50 ± 2 mm, and the hole depth is generally 200 - 400 mm according to the thickness of the tunnel concrete. This parameterized drilling design helps to form a basic structure of the monitoring unit with good consistency. The limited value of the drilling diameter ensures that the sensing element and the self-sensing composite material can fully fit the hole wall, realizing effective stress conduction and signal coupling; while the reasonable setting of the drilling depth ensures that the sensor can accurately capture the deep structural changes of the tunnel, avoiding only responding to surface disturbances, and improving the representativeness and accuracy of the monitoring. In addition, the standardization of the hole diameter and depth helps to improve the installation standardization level, providing a unified basis for subsequent module layout, test operation, and signal comparison.

[0082] In step three, the outer diameter of the conductive ring is 45 mm, the inner diameter is 40 mm, and the conductive ring is connected with 6-square BV wire, which is convenient for installation and signal transmission. By using a conductive ring with an outer diameter of 45 mm and an inner diameter of 40 mm in terms of structural size, it is ensured that it can be stably embedded inside the drill hole and form an effective fitting surface with the hole wall, enhancing the current injection efficiency and the voltage acquisition stability. The conductive ring connecting wire is selected as 6-square BV wire, and its relatively large cross-sectional area helps to reduce the resistance loss in signal transmission and improve the anti-interference ability, which is especially suitable for monitoring applications in long-distance or high-impedance scenarios. Through this structural design, it can be ensured that the resistivity signal still maintains high fidelity and time synchronization in a complex environment.

[0083] In Step 4, the self-sensing composite material includes: cement, sand, fly ash, carbon fiber, and foaming agent, which are fully mixed in a ratio of 0.25:1:0.03:0.005:0.02. Under this mix ratio, the compressive strength and elastic modulus of the self-sensing material are both lower than those of the concrete material in the tunnel. By filling the above self-sensing composite material in the borehole, an intelligent interface material with electrical conductivity and acoustic sensing capabilities can be formed. The carbon fibers distributed in the material form an electrical conduction network, which can respond to changes in the stress state in real time and be reflected in the change of resistivity. At the same time, the elastic modulus of this material is relatively low, and acoustic emission events are more likely to occur during crack initiation, which is beneficial to the excitation and transmission of acoustic signals. The overall structure ensures that the sensor is placed in a flexible response layer to achieve precise capture of subtle changes in the structure.

[0084] In Step 6, the frequency range of the acoustic emission sensor is 20 kHz - 500 kHz, with a built-in preamplifier and a signal-to-noise ratio > 60 dB. The external dimensions of the acoustic emission sensor are: length 80 mm, diameter 28 mm, and the acoustic emission sensor is 30 - 60 mm away from the hole opening. The acoustic emission sensor is used to monitor the transient high-frequency elastic wave signals generated inside the tunnel concrete structure under stress. The acoustic wave is converted into an electrical signal through its piezoelectric element and preliminarily amplified by the built-in amplifier. The buried depth of the sensor is controlled between 30 - 60 mm, which not only ensures its effective coupling with the sound source but also avoids the influence of mechanical contact or gas disturbance in the hole opening area on its signal acquisition. Through the high-frequency bandwidth and high signal-to-noise ratio design, it is ensured that weak acoustic wave signals in the initial expansion stage of microcracks can be captured.

[0085] In Step 7, foaming starts 20 minutes after the self-sensing composite material is filled. The delayed foaming behavior of the self-sensing composite material 20 minutes after filling is beneficial to the precise layout and stable positioning of the sensor during on-site construction. During this time, construction personnel can adjust the depth, attitude, and wire direction of the sensor. After it is fixed, the material begins to expand and wrap the sensor, forming a complete and stable monitoring inlay. During the material expansion process, the foaming agent releases gas to form a large number of micropores in the cement matrix, making the material have both mechanical buffering ability and good signal conduction performance. This slow-release foaming characteristic improves the controllability of the installation operation and the consistency of the monitoring system.

[0086] In Step 8, the acoustic emission communication wire is connected to the acoustic emission receiving end of the data receiving module. The wire of conductive ring 1 is connected to the low end of current excitation and the low end of voltage sampling of the data receiving module. The wire of conductive ring 2 is connected to the high end of voltage sampling and the high end of current excitation of the data receiving module. The internal data synchronization unit of the data receiving module makes the time error less than 1 ms. Through reasonable wiring and split-end connection, parallel acquisition of acoustic emission and resistivity signals is realized. Among them, the resistivity signal is acquired through a four-electrode method measurement structure. Its two end electrodes (conductive ring 1 and conductive ring 2) not only undertake current excitation but also provide upper and lower sampling points respectively, and obtain the accurate voltage change value through the differential method, and calculate the equivalent resistivity in combination with the excitation current; while the acoustic emission signal is connected through an independent channel to collect the high-frequency acoustic wave data caused by the propagation of microcracks. The synchronization unit controls through hardware clock phase-locking and unifies the time stamp of the data frame to ensure that all acquired signals have a unified time reference. The time error is controlled within 1 ms, which can support the joint recognition of transient events and the frequency-domain-time-domain multi-dimensional data fusion analysis, and is an important basic condition for realizing accurate early warning and time sequence correlation.

[0087] In Step 9, the communication cable of the data monitoring module is towed to the vault of the tunnel and connected to the junction box, and the main line is led out through the junction box to the tunnel entrance. By centrally introducing the communication cables of all monitoring points into the junction box at the vault of the tunnel, centralized management of physical wiring and signal path integration are realized, which is convenient for signal unified transfer and system maintenance. The main line cable, as a multi-point signal aggregation channel, guides all monitoring data to the signal processing equipment at the tunnel entrance position, forming a "point - surface - aggregation" data transmission structure, improving the signal integration efficiency and system response ability. This layout can also reduce the exposed length of the cable, reduce the probability of water vapor erosion or human damage, and improve the service life of the system.

[0088] In Step 10, the drilling positions are distributed according to a grid or key areas. Grid-based point distribution can realize uniform sampling of the overall operation state of the tunnel and tracking of the change trend, while point distribution in key areas focuses on centralized perception of high-risk parts, improving the effectiveness of monitoring and the resource utilization efficiency. By pre-drawing the point distribution drawings and determining the drilling coordinates based on the structure diagram, stress simulation analysis results or historical damage records, the implementation can be quickly located during construction. Finally, a multi-point layout monitoring system with reasonable spatial coverage, strong data continuity and high response accuracy is established.

[0089] In Step 11, the signal storage and transmission module is placed at the tunnel entrance. It has a built-in power module and a solar panel outside, which can continuously supply power to the signal storage and transmission module, data receiving module, and sensor monitoring module. This power supply system uses solar energy as the main energy source and constructs a closed-loop green energy supply path through the energy chain of photovoltaic modules - charge controllers - batteries. The power consumption requirements of each sub-module of the monitoring system are calculated and protected by current limiting, and the system voltage, current, and power status can be monitored and uploaded in real time by the power management module to ensure stable energy consumption and efficient system operation. The power module can also implement load priority management to ensure the continuous operation of data collection and communication functions first when the power is insufficient, ensuring that the monitoring data is not interrupted.

[0090] In Step 12, the acoustic emission acquisition mode is debugged to the continuous acquisition mode, with a trigger threshold of 35 dB (to avoid missing small cracks), a sampling rate of 1 MHz, and the acquisition times of acoustic emission, resistivity, and temperature are all set to once per hour. In the continuous acquisition mode, the acoustic emission channel receives acoustic signal data in real time and identifies valid events based on the set trigger threshold of 35 dB. The system automatically filters out crack signals with physical significance and stores their waveform characteristics, energy values, duration, and other parameters. The resistivity acquisition channel periodically starts current excitation and voltage sampling to obtain the equivalent resistance change trend; the temperature signal reads the current ambient temperature periodically through a thermistor or temperature sensor for analyzing the environmental sensitivity of resistivity changes and material properties. All acquired signals are time-aligned, relying on the timestamp synchronization mechanism of the data receiving module to ensure the data correlation and joint analysis basis between different physical quantity samplings. The data is uniformly processed by the data analysis system module and then uploaded to the early warning module to support the intelligent evaluation and alarm determination of the tunnel health status.

[0091] In Step 15, the data analysis system module is used to analyze resistivity, acoustic emission, and temperature. The specific steps are as follows:

[0092] S1. Preprocess the collected data, including the following steps:

[0093] S11. Preprocess the resistivity signal, including the following steps:

[0094] S111. Use a moving average filter (window length 60 seconds) to perform point-by-point filtering on the real-time collected resistivity signal ρ raw using the following formula:

[0095]

[0096] where ρ raw is the received data, ρ filter is the filtered data, N = 60 is the number of window points, to eliminate high-frequency instantaneous interference;

[0097] S112, The data collected at 0:00 every morning is used as the acquisition reference value ρ base , Calculate the relative change rate of the real-time signal Perform dynamic baseline correction to eliminate baseline drift caused by environmental gradual change;

[0098] S113, Considering the temperature sensitivity of the resistivity of the self-sensing material, perform correction according to temperature compensation, and use the following formula for correction:

[0099] ρ corr = ρ filter ×[1 + α(T - T0)]

[0100] Among them, ρ corr is the corrected resistivity, T is the real-time acquisition temperature, T0 is the initial temperature of the tunnel, and α is the temperature coefficient of the self-sensing material (the measured α = 0.01 / °C).

[0101] S12, The acoustic emission signal is first subjected to adaptive threshold noise reduction, and the trigger threshold V is dynamically adjusted based on the statistical characteristics of the background noise th , Calculate the mean μ and standard deviation σ of the noise signal in the previous 5 minutes in real time, and the threshold is set to V th = μ + k σ , where k is the adjustment coefficient (the corresponding actual voltage threshold range is 30 - 50 dB, determined through calibration tests); secondly, extract the characteristic parameters such as event count, energy, amplitude, rise time, and main frequency of the acoustic emission according to the built-in Matlab program.

[0102] S2, Perform joint positioning according to the acoustic emission and resistivity signals, including the following steps:

[0103] S21, Use acoustic emission for three-dimensional time difference positioning, including the following steps:

[0104] S211, Let the spatial coordinates of the i-th sensor be S i (x i , y i , z i ), the coordinates of the damage event source are P(x, y, z), and the acoustic wave propagation speed is v (v takes 4000 m / s in concrete);

[0105] S212, For any two sensors S i and S j , the time difference of arrival of the signal is Δt ij = t i - t j , the following equation can be established:

[0106]

[0107] S213. Three-dimensional space positioning requires at least 4 sensors and generates 3 independent equations. The form of the equation set is as follows (taking 4 sensors as an example):

[0108]

[0109] S214. Use the Newton iteration method to solve the non-linear equation set, with the geometric center of the sensor array as the iteration starting point: Using the geometric center as the initial value can ensure that the iteration starting point is close to the potential event source area and improve the convergence speed. Let u = (x, y, z), and the equation set can be expressed as F(u) = 0, where:

[0110]

[0111] Jacobian matrix The elements of are:

[0112]

[0113] where di = ||u - S i| | is the distance from the event source to sensor i;

[0114] At the m-th iteration, first calculate the current residual F(u m ) and the Jacobian matrix J(u m ), then solve the linear equation set J(u m )·Δu m = -F(u m ), and finally update the solution u m+1 = u m + Δu m . It should be noted that it stops when .

[0115] S22. Use the resistivity signal for spatial verification, including the following steps:

[0116] S221. With the acoustic emission positioning point P(x, y, z) as the center, construct a spherical verification area with a radius of R (taking 0.5 - 1 m according to engineering experience), and extract the data ρ k , y k , z k ) of all resistivity monitoring points within the sphere; corr (k);

[0117] S222. Within the radius R of the acoustic emission positioning point, calculate the resistivity change rate of each monitoring point within the sphere: If |δ k | ≥ 10%, it is determined as an effective failure event (changes caused by cracks leading to the destruction of the conductive path or water seepage); if |δ kIf |δ| < 5%, it is marked as a suspected interference event and requires manual review; if at least one monitoring point satisfies |δ| ≥ 8% and lasts for ≥ 5 seconds, it is determined as an effective damage event. k If |δ| ≥ 8% and lasts for ≥ 5 seconds, it is determined as an effective damage event.

[0118] S3. Conduct a coupling analysis of acoustic emission and resistivity signals and establish a three-dimensional damage index model, including the following steps:

[0119] S31. Assign differential weights to the monitoring points around the positioning point through a distance attenuation model to reflect the spatial correlation of damage signals (the damage characteristics are more significant in the area closer to the event source). Define the Euclidean distance from monitoring point k to the coordinates P(x, y, z) of the damage source: d k =(x k -x) 2 +(y k -y) 2 +(z k -z) 2 . Adopt a Gaussian attenuation function or a hyperbolic attenuation function. Here, the hyperbolic model with simple calculation is selected: Normalize the weights of all monitoring points to ensure that the sum of the weights is 1: where N is the total number of monitoring points within the spherical verification area;

[0120] S32. Combine the spatial distribution characteristics of resistivity signals and the instantaneous energy characteristics of acoustic emission signals to construct a multi-source fusion damage index model:

[0121]

[0122] where δ k is the resistivity change rate, N AE is the current acoustic emission event count, N max is the maximum acoustic emission count during the historical monitoring period, E AE is the current acoustic emission event energy, N max is the maximum acoustic emission energy during the historical monitoring period, A AE is the current acoustic emission event amplitude, A max is the maximum acoustic emission amplitude during the historical monitoring period, RT AE is the current acoustic emission event rise time, RT max is the maximum acoustic emission rise time during the historical monitoring period, f AE is the current acoustic emission event frequency, f max is the maximum acoustic emission frequency during the historical monitoring period;

[0123] S33. Divide into three-level criteria according to the damage index D and generate a visual pop-up interface:

[0124] Green area: D < 0.3: Normal state;

[0125] Yellow area: 0.3 ≤ D < 0.6: Slight damage (notify inspection patrol).

[0126] Red area: D ≥ 0.6: Severe damage (trigger structural reinforcement warning, notify inspection patrol).

[0127] S4. Dynamic error correction, including the following steps:

[0128] S41. Adaptive correction of sound velocity

[0129] S411. If the deviation between multiple positioning results and the on-site verified position exceeds the threshold, start sound velocity correction, using the following formula:

[0130]

[0131] where (x real , y real , zreal) is the actual damage position, (x calc , y calc , z calc ) is the acoustic emission positioning result, and (x ref , y ref , z ref ) is the reference point (such as the center of the sensor array);

[0132] S412. Dynamically update the sound velocity: v new = v old ·r. The corrected sound velocity v new is used as the input parameter for subsequent positioning, forming a closed-loop calibration to gradually approach the true wave velocity (the defect of the traditional fixed value of 4000 m / s is compensated for when the wave velocity in concrete is affected by aggregate distribution and moisture content and varies with time);

[0133] S42. Adaptive adjustment of weights. Based on historical valid damage event data, dynamically adjust the weight coefficients α and β of resistivity and acoustic emission through a regression model;

[0134] S421. Feature extraction: Statistically calculate the proportion of significant points of resistivity change in historical valid events

[0135]

[0136] S422. Weight update: α(t) = α0 + γ·p, β(t) = 1 - α(t), where α0 is the initial weight (such as 0.5) and γ is the adjustment factor (determined through cross-validation, range 0.1 - 0.5).

[0137] S5. Repeat steps S2 - S3.

[0138] Step 16 sets the alarm threshold. Among them, the first-level alarm threshold is D1=0.6, that is, D≥0.6 for a single monitoring point. At this time, it indicates that the tunnel is at local risk and starts manual inspection and review; the second-level alarm threshold is D2=0.3, that is, D≥0.3 for three adjacent monitoring points. At this time, it indicates that the tunnel has regional risks, triggers structural reinforcement warning, and starts manual inspection and review. The dual-level alarm threshold mechanism can respond to the two typical failure trends of "isolated point cracks" and "group degradation" respectively. The D value, as a comprehensive risk indicator quantified after the fusion of multi-source signals, reflects the correlation between the local material change trend and the spatial structure. When a single point exceeds 0.6, it may indicate point cracks, holes or initial water seepage areas; when multiple points continuously rise to more than 0.3, it indicates regional stress concentration, material degradation or lining loose evolution. By setting the alarm level and binding the response action, a closed-loop early warning management from prompting, verification to action is achieved.

[0139] Step 17: When D≥0.3, send a message to the specified mobile phone number. After analyzing the acoustic emission, resistivity and temperature data of each monitoring point and calculating the comprehensive D value, the system compares it with the set threshold in real time. Once D≥0.3, whether it is a single-point trigger or a multi-point joint trigger, the system immediately retrieves the preset alarm template and the receiving number list through the data link, generates the SMS content, and the built-in communication module automatically completes the information transmission. The entire process is automatically executed by the software control module to ensure real-time and reliability.

[0140] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A monitoring device for a flood prevention and drainage tunnel of a tailings pond, characterized in that, Including: A sensor monitoring module, which is used to collect resistivity and acoustic emission data by using a resistivity and acoustic emission unit for flood prevention and drainage tunnels. A data receiving module, which is used to receive the resistivity and acoustic emission data units of the flood prevention and drainage tunnels and collect the resistivity and acoustic emission data. A signal storage and transmission module, connected to the data receiving module, placed above the entrance of the flood prevention and drainage tunnel, capable of storing data, and transmitting signals through wireless transmission to the computer in the monitoring center and saving them to the computer storage in the monitoring center. A data analysis system module, set in the computer of the monitoring center, using self-developed software in MATLAB, which is used to analyze the variation laws of resistivity and acoustic emission in the monitoring module and issue early warnings and alarms based on the variation laws of the two. A monitoring and early warning system module, set in the data analysis system module, using self-developed software in MATLAB. When abnormal values appear, an alarm pop-up window appears on the computer software interface, and information is synchronously sent to a specified mobile phone number.

2. The monitoring device for the flood prevention and drainage tunnel of the tailings pond according to claim 1, wherein: The sensor monitoring module includes: A resistivity data acquisition unit, which is used to acquire the resistivity data obtained by the sensor monitoring module. An acoustic emission data acquisition unit, which is used to acquire the acoustic emission data obtained by the sensor monitoring module.

3. The monitoring device for the flood prevention and drainage tunnel of the tailings pond according to claim 1, wherein: The data receiving module includes: A resistivity data receiving unit, which is used to receive the resistivity data obtained by the sensor monitoring module. An acoustic emission data receiving unit, which is used to receive the acoustic emission data obtained by the sensor monitoring module. A temperature data receiving unit, which is used to receive the real-time temperature data in the flood prevention and drainage structure. A data synchronization unit, which is used to synchronously receive resistivity, acoustic emission, and temperature signal data.

4. The monitoring device for the flood prevention and drainage tunnel of the tailings pond according to claim 1, characterized in that: The signal storage and transmission module includes: A signal storage module: which is used to store the resistivity data and acoustic emission data obtained by the data receiving module. A signal transmission module: which is used to transmit the resistivity data and acoustic emission data obtained by the data receiving module. A power supply module: which is used to provide power to the signal storage and transmission module, the data receiving module, and the sensor monitoring module.

5. The monitoring device for the flood prevention and drainage tunnel of the tailings pond according to claim 1, characterized in that: The data analysis system module includes: A resistivity data analysis and processing unit: which is used to analyze and process the obtained resistivity data. An acoustic emission data analysis and processing unit: which is used to analyze and process the obtained acoustic emission data. A data coupling analysis software unit: which is used to couple and analyze the obtained resistivity and acoustic emission data. A computer unit: which is used to store resistivity and acoustic emission data and the corresponding analysis software, and carry the data analysis system module.

6. The monitoring device for the flood prevention and drainage tunnel of the tailings pond according to claim 1, wherein: The monitoring and early warning system module includes: A monitoring and early warning system unit: which is used to set the thresholds for resistivity change and acoustic emission change. When the resistivity change or acoustic emission change reaches the set threshold, an alarm pop-up window interface appears on the computer software interface. An alarm system unit: which is used to send alarm information to a specified mobile phone number when it is higher than the early warning value. A computer unit: which is used to store resistivity and acoustic emission data and the corresponding analysis software, and carry the monitoring and early warning system module.

7. A monitoring system for a flood prevention and drainage tunnel of a tailings pond, characterized in that, The monitoring system includes the monitoring device for the flood prevention and drainage tunnel of the tailings pond according to any one of claims 1-6.

8. A monitoring method for the flood prevention and drainage tunnel of a tailings pond, which uses the monitoring system for the flood prevention and drainage tunnel of a tailings pond described in claim 7, is characterized in that, The method includes: Step 1, drill 1 borehole at the key load-bearing or easily failing positions of the flood prevention and drainage tunnel. Step 2: Use a high-pressure water and air hose to wash the hole to ensure that there are no other impurities in the hole; Step 3: Insert the conductive ring 1 into the hole, keep the ring perpendicular to the hole wall, 20 - 40 mm away from the bottom of the hole, and lead the wire out of the hole mouth; Step 4: Fill or embed the self-sensing composite material in the drill hole, and fill it to a depth of 20 - 40 mm from the hole mouth. Among them, the self-sensing composite material includes: cement, sand, fly ash, carbon fiber, and foaming agent; Step 5: Insert the conductive ring 2 into the hole, keep the ring perpendicular to the hole wall, 20 - 40 mm away from the hole mouth, and lead the wire out of the hole mouth; Step 6: Insert the acoustic emission sensor into the self-sensing composite material, and lead the communication wire out of the hole mouth; Step 7: Let the self-sensing composite material foam by itself. After the self-sensing composite material foams, expands, and sets, and is closely attached to the surface of the acoustic emission and the tunnel concrete, scrape the material flat to be flush with the hole mouth; Step 8: Connect the acoustic emission communication wire and the conductive ring wire and connect them to the data receiving module together; Step 9: Fix the data receiving module on the surface of the hole mouth, and lead the communication wire to the junction box; Step 10: Drill 3 - 7 more holes at the key load-bearing or easily failing positions of the flood drainage tunnel, and repeat the operations of Step 2 to Step 9 to set up 4 - 8 monitoring points in total; Step 11: Connect the communication wire to the signal storage and transmission module; Step 12: Open the data analysis system module on the computer and debug it; Step 13: Through knocking and current testing, test whether there is a signal on the computer terminal; Step 14: Select 2 - 3 different positions between each point to conduct knocking tests respectively. When signals appear simultaneously at 4 - 8 monitoring points, the installation is effective. Otherwise, re-set the acquisition parameters until the signal appears normally; Step 15: Use the data analysis system module to analyze the resistivity, acoustic emission, and temperature; Step 16: Set the alarm threshold on the monitoring and early warning system module; Step 17: Set the alarm mobile phone number on the monitoring and early warning system module.

9. A failure monitoring method for a flood prevention and drainage tunnel of a tailings pond according to claim 8, characterized in that: In Step 1, the drill hole diameter is 50 ± 2 mm, and the hole depth is generally 200 - 400 mm according to the thickness of the tunnel concrete.

10. A failure monitoring method for a flood prevention and drainage tunnel of a tailings pond according to claim 8, characterized in that: In Step 3, the outer diameter of the conductive ring is 45 mm, the inner diameter is 40 mm, and the conductive ring is connected with 6-square BV wire, which is convenient for installation and signal transmission.

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