Underground engineering surrounding rock supporting structure distributed coupling monitoring system based on multi-source sensing

Abstract

The invention relates to the technical field of geotechnical engineering monitoring, and discloses an underground engineering surrounding rock supporting structure distributed coupling monitoring system based on multi-source sensing, which comprises a plurality of intelligent anchor rod sensing units distributed and deployed along an underground engineering surrounding rock supporting structure, and the intelligent anchor rod sensing units form a sensing network through sound wave communication; and the piezomagnetic effect transduction module is used for measuring the axial stress of the intelligent anchor rod sensing unit. By utilizing the advantage of physical stability of acoustic elasticity measurement, periodic dynamic calibration is carried out on a piezomagnetic measurement result which is easily influenced by the environment to generate long-term drifting, the bimodal coupling measurement mode fundamentally solves the problem of measurement data misalignment caused by material aging or temperature and humidity change of a traditional single sensor, and the measurement accuracy is improved. High reliability and consistency of monitoring data in the whole life cycle can be guaranteed without manual intervention, and the accuracy basis of underground engineering safety assessment is remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geotechnical engineering monitoring, in particular to a distributed coupling monitoring system for surrounding rock support structure of underground engineering based on multi-source sensing. BACKGROUND

[0002] The long-term stability and operation safety of underground engineering, such as tunnels, mine roadways and underground storage, etc., highly depend on the synergistic effect of surrounding rock and support system. Therefore, long-term and effective state monitoring of surrounding rock support structure, especially the anchor rod system as the main support means, is a key technical link to ensure engineering safety, optimize support design and realize disaster warning.

[0003] In the prior art practice, such monitoring is usually achieved by deploying various sensors at key positions. For the anchor rod support system, monitoring the axial stress state is one of the most direct and effective means, and the commonly used technologies include installing vibrating string load cells, resistance strain load cells or fiber grating sensors on the anchor rod pad or integrating them in the rod body to obtain the load data of the anchor rod. These sensors transmit data to the monitoring center through wired or regular manual inspection to evaluate the health status of the support system.

[0004] However, the various anchor rod force sensors widely used in the prior art generally face a core technical bottleneck: the long-term reliability of the measurement data is difficult to guarantee. Due to the creep and aging of the sensor material itself, as well as the influence of complex temperature and humidity changes in the underground engineering environment, the sensor will inevitably produce measurement drift. This drift will accumulate over time, causing the monitoring data to gradually deviate from the true value, and in severe cases, it may lead to misjudgment of the structure's safety state, such as interpreting data anomalies caused by drift as a warning of structure instability, or masking real safety hazards, thus greatly reducing the value of long-term monitoring. SUMMARY

[0005] In view of the shortcomings of the prior art, the present application provides a distributed coupling monitoring system for surrounding rock support structure of underground engineering based on multi-source sensing, which solves the problem of low long-term reliability of monitoring data.

[0006] To achieve the above purpose, the present application realizes the following technical scheme: a distributed coupling monitoring system for surrounding rock support structure of underground engineering based on multi-source sensing, comprising: a plurality of intelligent anchor rod sensing units distributed along the surrounding rock support structure of underground engineering, the intelligent anchor rod sensing units being connected through acoustic communication to form a sensing network; Each of the intelligent anchor rod sensing units comprises: a piezomagnetic effect transduction module for measuring the axial stress of the intelligent anchor rod sensing unit itself; An acoustic waveguide transducer and energy harvesting module for actively exciting and receiving acoustic wave signals and collecting environmental vibration energy; and a micro energy management and intelligent computing unit connected to the piezomagnetic transducer and the acoustic waveguide transducer and energy harvesting module for processing measurement signals, performing acoustic wave communication, and managing the collected energy.

[0007] Preferably, the acoustic waveguide transducer and energy harvesting module is also used to measure the axial stress of the smart anchor sensing unit through the acoustoelastic effect; The micro energy management and intelligent computing unit is configured to: perform online calibration of the axial stress value measured by the piezomagnetic effect transducer module based on the axial stress value measured by the acoustic waveguide transducer and energy harvesting module through the acoustic elastic effect.

[0008] Preferably, the micro energy management and intelligent computing unit further includes an event pre-classification circuit; The event pre-classification circuit is configured to: extract features from the environmental vibration energy signals collected by the acoustic waveguide transducer and energy harvesting module to obtain an energy fingerprint; The micro energy management and intelligent computing unit is further configured to: classify vibration event sources according to the energy fingerprint, and selectively wake up the intelligent anchor sensing unit based on the classification results.

[0009] Preferably, the micro energy management and intelligent computing unit is configured as follows: When performing acoustic communication between any two of the smart anchor sensing units, the data to be transmitted is encoded into an acoustic pulse sequence; At the receiving end, the channel impulse response of the acoustic wave propagation path between the two smart anchor sensing units is estimated by performing calculations on the received acoustic pulse sequence and the known transmitted sequence. Channel state parameters characterizing the physical properties of the surrounding rock are extracted from the channel impulse response.

[0010] Preferably, it also includes gateway anchors installed in the network; The gateway anchor is configured to: aggregate the channel state parameters of multiple acoustic wave propagation paths, and reconstruct the physical property distribution image of the surrounding rock within the area covered by the multiple smart anchor sensing units by executing a tomographic inversion algorithm.

[0011] A distributed coupled monitoring method for underground engineering surrounding rock support structures based on multi-source sensing includes the following steps: S1. Measure the axial stress of the intelligent anchor rod sensing unit through the piezomagnetic effect transducer module; S2. Through the acoustic waveguide transducer and energy harvesting module, acoustic wave signals are actively excited and received between different smart anchor rod sensing units to perform data communication; S3. The axial stress measurement results are processed by the micro energy management and intelligent computing unit, and the data in the data communication is parsed.

[0012] Preferably, the process further includes the following step between step S1 and step S3: The axial stress of the smart anchor rod sensing unit is measured based on the acoustoelastic effect using the acoustic waveguide transducer and energy harvesting module to obtain a stress reference value; and the axial stress measured by the piezomagnetic effect transducer module in step S1 is calibrated online using the stress reference value.

[0013] Preferably, it also includes a system wake-up step: The acoustic waveguide transducer and energy harvesting module continuously collects environmental vibration energy and converts it into vibration signals. Extract the energy fingerprint of the vibration signal; The vibration event source is classified and identified based on the energy fingerprint; and when a preset high-value event is identified, the smart anchor sensing unit is activated to execute S1 and S2.

[0014] Preferably, steps S2 and S3 specifically include: At the transmitting end, the data to be transmitted is encoded into an acoustic pulse sequence and then transmitted through the acoustic waveguide transducer and energy harvesting module. At the receiving end, the channel impulse response of the sound wave propagation path is estimated by performing operations on the received signal and the known acoustic pulse sequence; and channel state parameters, including first-arrival time of flight, energy attenuation, and root mean square delay spread, are extracted from the channel impulse response.

[0015] Preferred options also include: The channel state parameters of multiple propagation paths in the sensor network are aggregated; and based on the aggregated channel state parameters, a multi-parameter joint inversion tomographic imaging algorithm is executed to reconstruct the distribution image of wave velocity, attenuation coefficient, or scattering coefficient of the surrounding rock medium.

[0016] Preferably, the aforementioned.

[0017] This invention provides a distributed coupled monitoring system for underground engineering surrounding rock support structures based on multi-source sensing. It has the following beneficial effects: 1. This invention utilizes the advantages of acoustoelasticity in physical stability to perform periodic dynamic calibration of piezomagnetic measurement results, which are susceptible to long-term drift due to environmental influences. This dual-mode coupled measurement method fundamentally solves the problem of measurement data inaccuracy caused by material aging or temperature and humidity changes in traditional single sensors. It ensures high reliability and consistency of monitoring data throughout its entire life cycle without manual intervention, significantly improving the accuracy of underground engineering safety assessments.

[0018] 2. This invention obtains the electrical energy required for operation from the continuous micro-vibrations of the surrounding rock, eliminating dependence on external power supply or limited battery life. More importantly, by analyzing the characteristics of the vibration signals, the system can autonomously identify key early warning events such as micro-fractures in the rock mass and perform focused monitoring, while keeping irrelevant interference noise dormant. This greatly optimizes energy distribution efficiency and ensures that the system can achieve ultra-long-term autonomous and stable operation in harsh, unattended environments.

[0019] 3. This invention, through acoustic communication between intelligent anchor units, not only transmits data but also extracts channel state parameters characterizing the physical properties of the surrounding rock by analyzing the changes in the acoustic signals carrying the data as they propagate in the rock mass. Based on these parameters, tomographic imaging inversion is performed, thereby upgrading traditional point or line monitoring to volumetric imaging monitoring. This provides images of the internal structure distribution of the surrounding rock covering the entire monitoring area, enabling accurate identification and characterization of abnormal geological structures such as potential weak interlayers, fault zones, or water-rich areas. Attached Figure Description

[0020] Figure 1 This is a system architecture diagram of the present invention. Detailed Implementation

[0021] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Please see the appendix Figure 1 This invention provides a distributed coupled monitoring system for underground engineering rock support structures based on multi-source sensing, comprising multiple intelligent anchor bolt sensing units. These intelligent anchor bolt sensing units are distributed along the rock support structure in underground engineering projects. After installation, each intelligent anchor bolt sensing unit self-organizes into a sensing network through acoustic communication using the soil and rock mass as the medium.

[0023] Structure and function of intelligent anchor bolt sensing unit In the embodiments of the present invention, each intelligent anchor bolt sensing unit is structurally compatible with conventional mortar anchor bolts or resin anchor bolts. Its core lies in the integration of multiple functional modules, which enable it to perform support functions while possessing the capabilities of sensing, computing, communication and energy management.

[0024] 1. Piezomagnetic effect transducer module This module is used to realize long-term, passive monitoring of the axial stress of the intelligent anchor bolt sensing unit. Its structure includes a giant magnetostrictive material set inside or on the surface of the anchor bolt body, and an induction coil set around the material. The giant magnetostrictive material has the Villari effect, and its magnetic permeability changes regularly with the stress it is subjected to.

[0025] When the anchor bolt is subjected to axial stress due to the deformation of the surrounding rock At that time, the magnetic permeability of the giant magnetostrictive material The change occurs when the micro energy management and intelligent computing unit applies a high-frequency AC excitation signal to the induction coil and measures its inductance value. Inductance value With permeability Directly related, this relationship can be represented as:

[0026] in, It is a constant determined by the geometry of the induction coil.

[0027] Using a pre-calibrated stress-inductance relationship model, the measured inductance value can be obtained from... Calculate the current axial stress value :

[0028] 2. Acoustic waveguide transducer and energy harvesting module The core of this module is a piezoelectric transducer, integrated into the head or tail of the anchor bolt. This module has three core functions: Function 1: Active acoustic wave excitation. Driven by the micro energy management and intelligent computing unit, the piezoelectric transducer applies an alternating voltage, generating mechanical vibration based on the inverse piezoelectric effect, thereby exciting a beam of ultrasonic guided waves in the anchor rod.

[0029] Function 2: Acoustic signal reception. When external acoustic signals, including communication signals emitted by other smart anchor sensing units or reflected signals of ultrasonic guided waves, reach the piezoelectric transducer, it converts mechanical vibrations into measurable voltage signals based on the positive piezoelectric effect.

[0030] Function 3: Environmental energy harvesting. Environmental vibrations in underground engineering, including micro-vibrations and engineering disturbances, will cause slight vibrations in the anchor bolts. This module converts the energy of these continuous mechanical vibrations into electrical energy through the positive piezoelectric effect, providing power for the operation of the entire intelligent anchor bolt sensing unit.

[0031] 3. Micro Energy Management and Intelligent Computing Unit This unit is the control and computing core of the intelligent anchor bolt sensing unit. It includes an ultra-low power microcontroller, a power management circuit, and an energy storage element. Its specific functions include: Energy management: Rectifies, regulates, and stores the electrical energy collected by the acoustic waveguide transducer and energy harvesting module, and supplies power to each module according to the system's operating needs.

[0032] Sensing control and signal processing: Controls the measurement process of the piezomagnetic effect transducer module, drives the acoustic waveguide transducer and energy harvesting module to excite acoustic waves, and amplifies, filters and converts the received voltage signal into digital signal.

[0033] Data computation and storage: Performs computational tasks such as stress calculation and channel parameter extraction, and stores its own ID, measurement data and event logs.

[0034] Communication protocol execution: responsible for data encoding and decoding, as well as the timing control of acoustic wave communication transmission and reception.

[0035] Operating Mode Description of Intelligent Anchor Bolt Sensing Unit To further clarify the internal working mechanism of the intelligent anchor sensing unit in this invention, it is hereby stated that its various functional modules work collaboratively in a time-sharing manner under the unified scheduling of the micro energy management and intelligent computing unit, in order to avoid physical functional conflicts and achieve ultimate energy optimization, mainly switching between the following modes: Deep Sleep and Energy Harvesting Mode: This is the system's default basic mode. In this mode, only the energy harvesting circuit in the acoustic waveguide transducer and energy harvesting module and the event pre-classification circuit in the micro energy management and intelligent computing unit operate at extremely low power. The acoustic waveguide transducer and energy harvesting module continuously converts environmental vibration energy into electrical energy to charge the energy storage components.

[0036] Active Probe Mode: The system enters this mode when preset wake-up conditions are met (e.g., the preset measurement cycle is reached, an external query command is received, or the system is awakened by a high-value event identified by the event pre-classification circuit). In this mode, the function of the acoustic waveguide transducer and energy harvesting module is switched from energy harvesting to actively exciting and receiving acoustic signals to perform stress calibration measurements or data communication. The piezomagnetic transducer module is also awakened to perform stress measurements. This mode runs for a very short time; after completion, the system immediately returns to deep sleep and energy harvesting mode.

[0037] Example 1 This embodiment provides a basic version of a distributed coupled monitoring system for underground engineering surrounding rock support structures based on multi-source sensing.

[0038] In this embodiment, the system also includes multiple smart anchor sensing units, each of which includes a piezomagnetic effect transducer module, an acoustic waveguide transducer and energy harvesting module, and a micro energy management and intelligent computing unit.

[0039] The core functionality of this basic version system is: Long-term monitoring of the axial stress of the anchor bolt is achieved through a piezomagnetic transducer module.

[0040] Basic data communication between units is achieved through acoustic waveguide transducers and energy harvesting modules. For example, ground inspection personnel can send a query command to a designated smart anchor sensing unit using a handheld acoustic excitation device. Upon receiving the command, the unit encodes the stored stress data and transmits it back via acoustic signals.

[0041] The system continuously collects environmental vibration energy through an acoustic waveguide transducer and energy harvesting module, providing power for the aforementioned monitoring and communication functions and enabling the system to be self-powered.

[0042] In this embodiment, the online calibration function based on the acoustoelastic effect in the second embodiment may be omitted, as may the selective wake-up function based on energy fingerprint and the tomographic imaging function based on channel state parameters. This simplified embodiment itself constitutes a complete technical solution that can solve the problems of the prior art, namely, providing a self-powered intelligent anchor monitoring system that can read data through wireless sound waves.

[0043] Example 2 Dual-modal stress measurement and online calibration In a preferred embodiment, this system combines piezomagnetic and acoustic measurement modes to achieve online self-calibration of stress monitoring, thereby improving the reliability of long-term monitoring.

[0044] This process is based on the acoustoelastic effect, which states that the propagation speed of elastic waves in a solid material changes with the stress it is subjected to.

[0045] The specific implementation steps are as follows: The micro energy management and intelligent computing unit drives the acoustic waveguide transducer and energy harvesting module to generate an ultrasonic wave after a preset calibration cycle or after a specific event is triggered.

[0046] The guided wave propagates along the anchor rod and is reflected at its end or received by neighboring units. The guided wave is measured over a known length. Flight time along the propagation path The current group velocity is calculated. .

[0047] stress value The relationship with the group velocity change is as follows:

[0048] in, The wave group velocity of the anchor bolt under zero stress or initial conditions is a reference value obtained through initial calibration. It is the acoustic elastic coefficient of the material.

[0049] Because the acoustoelastic effect possesses physical stability and repeatability, the stress value calculated using this method... It can be used as a reliable dynamic benchmark.

[0050] The micro energy management and intelligent computing unit will set this benchmark value The inductance value measured by the piezomagnetic effect transducer at the same time To establish a correlation, the aforementioned stress-inductance relationship model is... Perform dynamic updates or corrections.

[0051] This process enables periodic, in-situ self-calibration of stress monitoring.

[0052] Event recognition and selective wake-up based on energy fingerprinting In a preferred embodiment, in order to optimize energy usage efficiency, the system employs an intelligent wake-up mechanism based on energy fingerprints.

[0053] The acoustic waveguide transducer and energy harvesting module converts collected environmental vibrations into continuous voltage signals. An event pre-classification circuit, as part of a micro-energy management and intelligent computing unit, performs real-time analysis of this voltage signal to extract its energy fingerprint, which is a multi-dimensional feature vector. The physical characteristics used to characterize this vibration event are:

[0054] in, It is the peak frequency of the signal. It is the bandwidth of the signal. It is the duration of the signal. It is a parameter that characterizes the shape of the signal energy envelope.

[0055] The micro energy management and intelligent computing unit internally stores an event fingerprint database. This database pre-loads typical energy fingerprints corresponding to different event sources. When a new energy fingerprint... After being extracted, the computing unit compares it with fingerprints in the database using a classification algorithm to identify the source of the event.

[0056] Based on the identification results, the system executes selective wake-up logic: if the event is identified as a preset high-value signal such as micro-fracture of rock mass, the system will be fully woken up and perform a complete measurement and data communication; if the event is identified as a known high-energy interference such as blasting, the system will only record the timestamp and return to sleep; for other background noise, the system will only perform energy harvesting.

[0057] Communication and Tomography Integration In a preferred embodiment, the system integrates the data communication process with the detection process of the surrounding rock medium.

[0058] When one smart anchor sensor unit sends data to another, the micro energy management and intelligent computing unit first encodes the data packet into an acoustic pulse sequence with excellent autocorrelation characteristics. .

[0059] The sequence was then emitted by the driven acoustic waveguide transducer and energy harvesting module and propagated in the surrounding rock medium.

[0060] The signal received by the receiver It is a sending sequence Channel impulse response with propagation path The result of convolution is superimposed with ambient noise. :

[0061] The receiving end's computing unit processes the received signal. With locally stored transmission sequence template By performing mathematical operations such as cross-correlation, the channel impulse response can be estimated. .

[0062] Channel impulse response It records the physical changes of sound waves during propagation.

[0063] A set of channel state parameters can be extracted from this, including: the flight time of the first wave. Used to calculate wave velocity; signal energy attenuation, used to calculate the effective attenuation coefficient of the medium. ; and root mean square delay spread It is used to characterize the multipath effect of signals and the scattering properties of media.

[0064] The gateway anchor in the system is responsible for aggregating the channel state parameters of all paths in the network to form an observation dataset.

[0065] To clarify the implementation method of this gateway anchor, the present invention provides the following two embodiments: In one embodiment, the gateway anchor is identical in hardware configuration to other smart anchor sensing units; its gateway role is designated through software configuration, enabling it to undertake additional data aggregation and computation tasks. This approach offers flexible deployment and uniform cost.

[0066] In another preferred embodiment, to cope with the computational pressure brought by large-scale sensor networks or complex inversion algorithms, the gateway anchor is a hardware-enhanced unit. In addition to having all the functions of a regular smart anchor sensing unit, it can also be configured with a more powerful microprocessor, a larger capacity memory, and a wireless communication module for remote data interaction with the ground monitoring center, such as a LoRa or 4G / 5G communication module.

[0067] Subsequently, the gateway anchor performs a tomographic inversion algorithm.

[0068] This tomographic inversion algorithm aims to solve for a model of the surrounding rock physical properties that can best interpret all observation data through an iterative optimization process. Specifically, the algorithm aims to find a set of model parameters that can describe the distribution of physical properties (such as wave velocity, attenuation coefficient, etc.) inside the surrounding rock, so as to minimize the difference between the theoretical data calculated based on the model and the channel state parameters actually observed by each smart anchor sensing unit.

[0069] Meanwhile, in order to ensure the stability and physical rationality of the solution, the algorithm also imposes constraints on the model parameters themselves during the optimization process, such as requiring the model parameters to have a certain degree of smoothness or continuity in space.

[0070] Through the above iterative optimization process, a set of model parameters that best balances data fit and model rationality is finally obtained. The spatial distribution of these parameters reconstructs the distribution image of the physical properties within the surrounding rock area covered by the anchor network.

[0071] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A distributed coupled monitoring system for underground engineering surrounding rock support structures based on multi-source sensing, characterized in that, include: Multiple intelligent anchor bolt sensing units are distributed along the surrounding rock support structure of underground engineering, and the intelligent anchor bolt sensing units form a sensing network through acoustic communication. Each of the aforementioned smart anchor sensing units includes: A piezomagnetic transducer module used to measure the axial stress of the intelligent anchor rod sensing unit itself; An acoustic waveguide transducer and energy harvesting module for actively exciting and receiving acoustic wave signals and collecting environmental vibration energy; and a micro energy management and intelligent computing unit connected to the piezomagnetic transducer and the acoustic waveguide transducer and energy harvesting module for processing measurement signals, performing acoustic wave communication, and managing the collected energy.

2. The distributed coupled monitoring system for underground engineering surrounding rock support structure based on multi-source sensing according to claim 1, characterized in that, The acoustic waveguide transducer and energy harvesting module is also used to measure the axial stress of the smart anchor sensing unit through the acoustoelastic effect. The micro energy management and intelligent computing unit is configured to: perform online calibration of the axial stress value measured by the piezomagnetic effect transducer module based on the axial stress value measured by the acoustic waveguide transducer and energy harvesting module through the acoustic elastic effect.

3. The distributed coupled monitoring system for underground engineering surrounding rock support structure based on multi-source sensing according to claim 1, characterized in that, The micro energy management and intelligent computing unit also includes an event pre-classification circuit; The event pre-classification circuit is configured to: extract features from the environmental vibration energy signals collected by the acoustic waveguide transducer and energy harvesting module to obtain an energy fingerprint; The micro energy management and intelligent computing unit is further configured to: classify vibration event sources according to the energy fingerprint, and selectively wake up the intelligent anchor sensing unit based on the classification results.

4. The distributed coupled monitoring system for underground engineering surrounding rock support structure based on multi-source sensing according to claim 1, characterized in that, The micro energy management and intelligent computing unit is configured as follows: When performing acoustic communication between any two of the smart anchor sensing units, the data to be transmitted is encoded into an acoustic pulse sequence; At the receiving end, the channel impulse response of the acoustic wave propagation path between the two smart anchor sensing units is estimated by performing calculations on the received acoustic pulse sequence and the known transmitted sequence. Channel state parameters characterizing the physical properties of the surrounding rock are extracted from the channel impulse response.

5. The distributed coupled monitoring system for underground engineering surrounding rock support structure based on multi-source sensing according to claim 4, characterized in that, This also includes gateway anchors installed in the network; The gateway anchor is configured to: aggregate the channel state parameters of multiple acoustic wave propagation paths, and reconstruct the physical property distribution image of the surrounding rock within the area covered by the multiple smart anchor sensing units by executing a tomographic inversion algorithm.

6. A distributed coupled monitoring method for underground engineering surrounding rock support structures based on multi-source sensing, applied to the distributed coupled monitoring system for underground engineering surrounding rock support structures based on multi-source sensing as described in claim 1, characterized in that, Includes the following steps: S1. Measure the axial stress of the intelligent anchor rod sensing unit through the piezomagnetic effect transducer module; S2. Through the acoustic waveguide transducer and energy harvesting module, acoustic wave signals are actively excited and received between different smart anchor rod sensing units to perform data communication; S3. The axial stress measurement results are processed by the micro energy management and intelligent computing unit, and the data in the data communication is parsed.

7. The distributed coupled monitoring method for underground engineering surrounding rock support structure based on multi-source sensing according to claim 6, characterized in that, Between step S1 and step S3, the following is also included: The axial stress of the smart anchor rod sensing unit is measured based on the acoustoelastic effect using the acoustic waveguide transducer and energy harvesting module to obtain a stress reference value; and the axial stress measured by the piezomagnetic effect transducer module in step S1 is calibrated online using the stress reference value.

8. The distributed coupled monitoring method for underground engineering surrounding rock support structure based on multi-source sensing according to claim 6, characterized in that, It also includes the system wake-up process: The acoustic waveguide transducer and energy harvesting module continuously collects environmental vibration energy and converts it into vibration signals. Extract the energy fingerprint of the vibration signal; The sources of vibration events are classified and identified based on the energy fingerprint. And when a preset high-value event is detected, the smart anchor sensing unit is activated to execute S1 and S2.

9. The distributed coupled monitoring method for underground engineering surrounding rock support structure based on multi-source sensing according to claim 6, characterized in that, Steps S2 and S3 specifically include: At the transmitting end, the data to be transmitted is encoded into an acoustic pulse sequence and then transmitted through the acoustic waveguide transducer and energy harvesting module. At the receiving end, the channel impulse response of the sound wave propagation path is estimated by performing operations on the received signal and the known acoustic pulse sequence; and channel state parameters, including first-arrival time of flight, energy attenuation, and root mean square delay spread, are extracted from the channel impulse response.

10. The distributed coupled monitoring method for underground engineering surrounding rock support structure based on multi-source sensing according to claim 9, characterized in that, Also includes: The channel state parameters of multiple propagation paths in the sensor network are aggregated; Based on the converged channel state parameters, a multi-parameter joint inversion tomographic imaging algorithm is executed to reconstruct the distribution image of wave velocity, attenuation coefficient, or scattering coefficient of the surrounding rock medium.

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