Sound pressure combined feature recognition and diffusion filling monitoring system in deep hole grouting process

Abstract

The invention relates to a sound pressure combined feature recognition and diffusion filling monitoring system in a deep hole grouting process. According to the system, a sound wave excitation module emits an acoustic signal with a set frequency to excite slurry and a stratum to form a dynamic sound field, and a sound pressure sensing module collects sound pressure reflection data of different depths along multiple points of a grouting pipe; filtering, feature extraction and multi-modal fusion recognition are carried out through the signal processing and feature recognition module, the diffusion volume and compactness of slurry are inversed through the diffusion filling judgment module, parameters are dynamically corrected, and real-time display, abnormity early warning and data archiving are achieved through the visual monitoring and data management module. Therefore, the sound pressure combined feature identification and diffusion filling monitoring system in the deep hole grouting process can accurately identify the diffusion state in the grouting process and intelligently monitor the whole process. The system is characterized by comprising a sound wave excitation module, a sound pressure sensing module, a signal processing and feature recognition module, a diffusion filling judgment module and a visual monitoring and data management module.

Description

Technical Field

[0001] This invention relates to a deep-hole grouting process acoustic pressure joint feature identification and diffusion filling monitoring system, which is used for monitoring the grouting construction process in underground engineering. It belongs to the field of intelligent monitoring technology for grouting construction, and specifically relates to a system that uses an acoustic excitation module to emit acoustic signals of a set frequency to excite the grout and the stratum to form a dynamic sound field. An acoustic pressure sensing module collects acoustic pressure reflection data at multiple points along the grouting pipe at different depths. A signal processing and feature recognition module performs filtering, feature extraction, and multi-modal fusion identification. A diffusion filling determination module inverts the grout diffusion volume and density and dynamically corrects parameters. A visualization monitoring and data management module enables real-time display, anomaly warning, and data archiving. This system achieves accurate identification of the diffusion state during the grouting process and intelligent monitoring of the entire process. Background Technology

[0002] Deep hole grouting technology is an important process in foundation reinforcement and seepage prevention engineering. Its construction quality is closely related to the diffusion state of the grout within the hole, thus real-time monitoring of the grouting process is of great significance. Existing monitoring methods mainly include pressure monitoring, resistivity methods, and acoustic detection methods. Among them, pressure monitoring can only indirectly reflect the diffusion situation through single-point pressure changes at the borehole opening, making it difficult to obtain spatial distribution information at multiple depths. Resistivity methods are easily affected by the conductivity of the borehole wall and the distribution of moisture, resulting in poor stability of monitoring results. Although traditional acoustic detection methods can reflect grout changes, they mostly use single-point sensor deployment or single-channel measurement, which cannot achieve continuous acquisition of acoustic responses at different depths, making it difficult to accurately determine the diffusion boundary and saturation state. At the same time, in existing technologies, each monitoring point often acquires signals independently, lacking time synchronization and data fusion mechanisms, and failing to form a hierarchical collaborative monitoring array. In terms of signal analysis, mainstream methods usually only make judgments based on sound pressure amplitude or reflection delay, failing to combine multi-dimensional features such as spectrum, phase, and energy for fusion identification, making it difficult to achieve quantitative inversion of grout diffusion volume and density.

[0003] CN114878684A discloses a method for detecting the compactness of concrete filling in arch ribs based on piezoelectric ceramic ultrasound. The method involves pre-embedding piezoelectric ceramic transducers inside and on the surface of prestressed ducts to transmit and receive ultrasonic signals, respectively. Empirical Mode Decomposition (EMD) is used to decompose the ultrasonic signals, constructing a distribution matrix including all decomposed narrow-bandwidth signal components. A Long Short-Time Memory (LSTM) network model is established, and the distribution matrix composed of the narrow-bandwidth signal components is input into the model to evaluate the grouting quality of the prestressed duct. This method can identify the type and range of defects over long distances along the length of the steel-concrete arch rib. However, the signal analysis of the above-mentioned device still focuses on energy or amplitude characteristics and fails to establish a coupling relationship between multimodal sound pressure parameters and grout diffusion characteristics. This results in monitoring results that are sensitive to noise, have insufficient spatial resolution, and rely on manual experience for data interpretation, making it difficult to meet the needs of intelligent grouting monitoring under complex geological conditions. Summary of the Invention

[0004] To improve the above situation, the present invention provides a deep hole grouting process acoustic pressure joint feature identification and diffusion filling monitoring system. This system uses an acoustic excitation module to emit an acoustic signal of a set frequency to excite the grout and the formation to form a dynamic sound field. An acoustic pressure sensing module collects acoustic pressure reflection data at multiple points along the grouting pipe at different depths. A signal processing and feature identification module performs filtering, feature extraction, and multi-modal fusion identification. A diffusion filling determination module inverts the grout diffusion volume and density and dynamically corrects parameters. A visualization monitoring and data management module enables real-time display, anomaly warning, and data archiving. This system achieves accurate identification of the diffusion state during the grouting process and intelligent monitoring of the entire process.

[0005] The deep hole grouting process acoustic pressure joint feature identification and diffusion filling monitoring system of the present invention is implemented as follows: The deep hole grouting process acoustic pressure joint feature identification and diffusion filling monitoring system of the present invention includes an acoustic excitation module, an acoustic pressure sensing module, a signal processing and feature identification module, a diffusion filling determination module, and a visualization monitoring and data management module. The features include: the acoustic excitation module establishing signal interaction with the acoustic pressure sensing module; the acoustic pressure sensing module establishing signal interaction with the signal processing and feature recognition module; the signal processing and feature recognition module establishing signal interaction with the diffusion filling determination module; the diffusion filling determination module establishing signal interaction with the visualization monitoring and data management module; and the visualization monitoring and data management module establishing signal interaction with the ground control terminal. The acoustic excitation module is preferably located at the borehole opening or bottom of the deep borehole. Its position can be adjusted according to the borehole depth, acoustic field attenuation characteristics, and on-site construction conditions. This module is used to emit acoustic signals of a set frequency into the slurry and formation medium within the borehole, exciting the diffusion area to form a dynamic sound field for identification by the downstream monitoring array. The acoustic excitation module includes a signal generation unit, a power amplification unit, and a transducer transmitting unit. The signal generating unit is used to generate acoustic wave signals for controlling the frequency, supporting three working modes: pulse wave, sweep wave, and linear frequency modulated wave, with a frequency range of 2 kHz to 50 kHz, which can cover the acoustic response of the slurry at different diffusion stages. Preferably, when the temperature or density inside the hole changes significantly, the signal generating unit compensates for phase drift through a self-calibration circuit to maintain acoustic path consistency. The power amplification unit amplifies the signal amplitude and performs impedance matching to ensure that the transducer transmitter operates in its optimal output state. The transducer unit is made of piezoelectric ceramic material and covered with a high-damping rubber layer to reduce interface reflection and structural resonance interference. Its outer shell has waterproof and sealing properties, making it suitable for long-term operation in high-humidity environments. The sound pressure sensing module includes several deep-hole grouting sound pressure sensing devices arranged along the axial direction of the grouting pipe. Each of the deep hole grouting sound pressure sensing devices includes a fixed arc, a connecting block, a fixing strap, a sealing rubber, a fixing ring, a sealing ring, a sound pressure sensor probe, and a sound pressure signal transmission line. The fixing arc has an arc-shaped structure, and its two ends are in contact with the sides of the grouting pipe. The connecting block is fixed on the outer arc surface of the fixed arc. Preferably, the connecting block has a U-shaped structure, with both ends fixedly connected to the fixed arc, and the connecting block is located at the middle position in the arc direction. Preferably, each of the fixed arcs is provided with two connecting blocks, respectively located near the top and bottom ends of the fixed arc. Each fixing cable tie corresponds to a connecting block. One end of the fixing cable tie passes through the connecting block, surrounds the grouting pipe and the fixing arc, and is snapped together with the other end. The sealing rubber is fixedly connected to the inner arc surface of the fixed arc. Each of the fixed arcs corresponds to two sealing rubbers, which are respectively fixedly connected to the inner arc surfaces of the top and bottom ends of the fixed arc. When the fixed arc is in contact with the grouting pipe, the inner arc surface of the sealing rubber is in contact with the outer surface of the grouting pipe. A notch is provided in the middle of the fixed arc, and one end of the fixing ring is fixedly connected to the inner arc surface of the middle of the fixed arc. The fixing ring communicates with the notch of the fixed arc. The inner ring surface of the fixing ring and the side of the fixing arc notch are provided with sealing rings. One end of the sound pressure sensor probe is positioned between the grouting pipe and the fixed arc, the other end of the sound pressure sensor probe is clipped inside the sealing ring, and the other end is positioned outside the fixed arc. One end of the sound pressure signal transmission line is connected to a sound pressure sensor probe, and each sound pressure sensor probe corresponds to a sound pressure signal transmission line. The other end of the plurality of sound pressure signal transmission lines is electrically connected to a sound pressure data acquisition device. Preferably, the sound pressure signal transmission line and the sound pressure sensor probe adopt an integral injection molding encapsulation structure. Preferably, the sound pressure signal transmission line adopts a multi-layer sheath structure, with the inner insulation layer being cross-linked polyethylene or fluoropolymer material, a tin-plated copper wire braided shielding layer in the middle, and the outer sheath being an integrally extruded alkali-resistant and wear-resistant polyurethane or fluororubber material. The signal processing and feature recognition module is used to preprocess, extract features, and perform pattern recognition on the multi-point sound pressure signals output by the sound pressure sensing module in order to obtain key acoustic feature parameters related to slurry diffusion. The signal processing and feature recognition module includes a signal preprocessing submodule, a feature extraction submodule, a feature fusion and recognition submodule, and an intelligent analysis submodule. The signal preprocessing submodule performs filtering, denoising, and envelope extraction on the sound pressure signal. The filtering uses a bandpass filter to remove background noise and an adaptive Wiener algorithm to suppress random interference. Envelope extraction is used to reflect changes in the reflected wave energy. The feature extraction submodule calculates the power spectral density and energy decay rate of the sound pressure signal using Fast Fourier Transform (FFT), and extracts the instantaneous phase, envelope, and reflection delay of the signal using Hilbert Transform. Based on the reflection envelope curves and phase change patterns obtained at different depths, the diffusion trend of the slurry can be preliminarily determined. The feature fusion and recognition submodule forms a multi-dimensional matrix from sound pressure features at different depths, and uses a support vector machine (SVM) recognition model to perform pattern recognition on the grouting process, outputting indicators such as the location of the diffusion boundary, the arrival time of the front edge, and the signal stabilization time. Preferably, the feature fusion and recognition submodule may also employ a weighted time-difference superposition algorithm to enhance the time sensitivity of sound pressure features at different depths and improve the stability of diffusion front determination. Preferably, the feature fusion and recognition submodule can automatically adjust the classification threshold based on historical samples to adapt to different geological conditions. Based on the recognition results, the intelligent analysis submodule establishes a sound pressure characteristic-diffusion parameter mapping model, comprehensively analyzes the sound energy attenuation rate, phase drift, and reflection energy ratio, and calculates the grout diffusion velocity and direction. When the signal amplitude changes tend to stabilize and the energy gradient converges, the system automatically determines that the grouting of the corresponding section is approaching saturation. The diffusion filling determination module includes a diffusion boundary identification submodule, a volume inversion submodule, a density calculation submodule, and a dynamic correction submodule. The diffusion boundary identification submodule calculates the depth reached by the slurry front by using the acoustic pressure phase difference and energy ratio between adjacent monitoring layers; The volume inversion submodule inverts the slurry diffusion volume based on the sound wave propagation speed and energy attenuation coefficient. The density calculation submodule determines the porosity saturation through the sound pressure gradient distribution and generates a density index. The dynamic correction submodule corrects parameters such as sound velocity and density in real time, and uses multiple iterative fitting to eliminate the influence of formation inhomogeneity on the results, making the inversion curve more stable. The system can output diffusion profiles, contour maps, and diffusion rate curves. Preferably, the dynamic correction submodule employs a multi-iteration fitting algorithm to eliminate the influence of formation inhomogeneity on the results. The visualization monitoring and data management module is used to graphically display the analysis results output by the diffusion filling judgment module, manage historical records and data interaction, and provide construction personnel with an intuitive monitoring interface and data support. The visualization monitoring and data management module includes a real-time display submodule, an alarm and control submodule, a historical analysis submodule, and a data interface submodule. The real-time display submodule is used for real-time data display of the ground terminal, providing visualization of sound pressure waveforms, diffusion boundaries, density curves, and diffusion profiles. The alarm and control submodule analyzes the sound pressure change rate and abnormal waveforms. When a sudden change or abnormal attenuation of reflected energy occurs at a monitoring point, the system will issue an audible and visual alarm and send a pause or adjustment signal to the grouting control device via the communication interface to achieve process intervention. The historical analysis submodule archives, compares, and analyzes trends from multiple grouting data sessions, generating statistical reports and pattern models to provide a basis for subsequent optimization of process parameters. The data interface submodule provides a standardized data exchange interface, supporting integration with engineering quality management systems or geological information platforms. Preferably, the data interface submodule supports exporting monitoring data in CSV or JSON format. Beneficial effects

[0006] 1. By deploying a multi-point acoustic pressure sensing array along the axial direction of the grouting pipe, acoustic pressure reflection signals can be collected synchronously at different depths, enabling layered dynamic monitoring of the grout diffusion process within the hole, which significantly improves the spatial resolution and real-time performance of the grouting process.

[0007] Second, it can accurately invert the distribution of grout diffusion boundaries, volume and density, realize intelligent identification and parameter quantification of grout diffusion state in complex strata, and improve the accuracy and reliability of grouting quality evaluation. Attached Figure Description

[0008] Figure 1 This is a three-dimensional structural diagram of the acoustic pressure sensing module of a deep hole grouting process acoustic pressure joint feature identification and diffusion filling monitoring system according to the present invention; Figure 2 This is a three-dimensional structural diagram of the acoustic pressure sensing module of a deep hole grouting process acoustic pressure joint feature identification and diffusion filling monitoring system according to the present invention; Figure 3 This is a schematic diagram of the structure of the acoustic pressure sensing device in the deep hole grouting process acoustic pressure joint feature identification and diffusion filling monitoring system of the present invention. Attached Figure

[0009] The components are: grouting pipe (1), fixing strap (2), fixing arc (3), sound pressure sensor probe (4), connecting block (5), sound pressure signal transmission line (6), sealing rubber (7), fixing ring (8), sealing ring (9). Detailed Implementation Example 1

[0010] The deep hole grouting process acoustic pressure joint feature identification and diffusion filling monitoring system of the present invention is implemented as follows: The deep hole grouting process acoustic pressure joint feature identification and diffusion filling monitoring system of the present invention includes an acoustic excitation module, an acoustic pressure sensing module, a signal processing and feature identification module, a diffusion filling determination module, and a visualization monitoring and data management module. The features include: the acoustic excitation module establishing signal interaction with the acoustic pressure sensing module; the acoustic pressure sensing module establishing signal interaction with the signal processing and feature recognition module; the signal processing and feature recognition module establishing signal interaction with the diffusion filling determination module; the diffusion filling determination module establishing signal interaction with the visualization monitoring and data management module; and the visualization monitoring and data management module establishing signal interaction with the ground control terminal. The acoustic excitation module is preferably located at the borehole opening or bottom of the deep borehole. Its position can be adjusted according to the borehole depth, acoustic field attenuation characteristics, and on-site construction conditions. This module is used to emit acoustic signals of a set frequency into the slurry and formation medium within the borehole, exciting the diffusion area to form a dynamic sound field for identification by the downstream monitoring array. The acoustic excitation module includes a signal generation unit, a power amplification unit, and a transducer transmitting unit. The signal generating unit is used to generate acoustic wave signals for controlling the frequency, supporting three working modes: pulse wave, sweep wave, and linear frequency modulated wave, with a frequency range of 2 kHz to 50 kHz, which can cover the acoustic response of the slurry at different diffusion stages. Preferably, when the temperature or density inside the hole changes significantly, the signal generating unit compensates for phase drift through a self-calibration circuit to maintain acoustic path consistency. The power amplification unit amplifies the signal amplitude and performs impedance matching to ensure that the transducer transmitter operates in its optimal output state. The transducer unit is made of piezoelectric ceramic material and covered with a high-damping rubber layer to reduce interface reflection and structural resonance interference. Its outer shell has waterproof and sealing properties, making it suitable for long-term operation in high-humidity environments. The sound pressure sensing module includes several deep-hole grouting sound pressure sensing devices arranged along the axial direction of the grouting pipe (1). Each of the deep hole grouting sound pressure sensing devices includes a fixed arc (3), a connecting block (5), a fixing strap (2), a sealing rubber (7), a fixing ring (8), a sealing ring (9), a sound pressure sensor probe (4), and a sound pressure signal transmission line (6). The fixed arc (3) has an arc-shaped structure, and the two ends of the fixed arc (3) are in contact with the side of the grouting pipe (1). The connecting block (5) is fixed on the outer arc surface of the fixed arc (3). Preferably, the connecting block has a U-shaped structure, and both ends are fixedly connected to the fixed arc (3). The connecting block (5) is located in the middle position in the arc direction. Preferably, each of the fixed arcs (3) is provided with two connecting blocks (5), which are respectively located near the top and bottom of the fixed arc (3). The fixing straps (2) correspond one-to-one with the connecting blocks (5). One end of the fixing strap (2) passes through the connecting block (5), surrounds the grouting pipe (1) and the fixing arc (3), and is snapped to the other end. The sealing rubber (7) is fixedly connected to the inner arc surface of the fixed arc (3). Each of the fixed arcs (3) corresponds to two sealing rubbers (7). The two sealing rubbers (7) are fixedly connected to the inner arc surfaces of the top and bottom ends of the fixed arcs (3), respectively. When the fixed arc (3) is in contact with the grouting pipe (1), the inner arc surface of the sealing rubber (7) is in contact with the outer surface of the grouting pipe (1). The fixed arc (3) has a notch in the middle, and one end of the fixed ring (8) is fixedly connected to the inner arc surface of the fixed arc (3). The fixed ring (8) communicates with the notch of the fixed arc (3). The inner ring surface of the fixing ring (8) and the side of the notch of the fixing arc (3) are provided with sealing rings (9). One end of the sound pressure sensor probe (4) is placed between the grouting pipe (1) and the fixed arc (3), and the sound pressure sensor probe (4) is clipped inside the sealing ring (9) and the other end is placed outside the fixed arc (3). One end of the sound pressure signal transmission line (6) is connected to the sound pressure sensor probe (4), and the sound pressure sensor probe (4) corresponds one-to-one with the sound pressure signal transmission line (6). The other end of the plurality of sound pressure signal transmission lines (6) is electrically connected to a sound pressure data acquisition device. Preferably, the sound pressure signal transmission line (6) and the sound pressure sensor probe (4) adopt an integral injection molding encapsulation structure. Preferably, the sound pressure signal transmission line (6) adopts a multi-layer sheath structure, with the inner insulation layer being cross-linked polyethylene or fluorinated polymer material, a tin-plated copper wire braided shielding layer in the middle, and the outer sheath being an integrally extruded alkali-resistant and wear-resistant polyurethane or fluororubber material. The signal processing and feature recognition module is used to preprocess, extract features, and perform pattern recognition on the multi-point sound pressure signals output by the sound pressure sensing module in order to obtain key acoustic feature parameters related to slurry diffusion. The signal processing and feature recognition module includes a signal preprocessing submodule, a feature extraction submodule, a feature fusion and recognition submodule, and an intelligent analysis submodule. The signal preprocessing submodule performs filtering, denoising, and envelope extraction on the sound pressure signal. The filtering uses a bandpass filter to remove background noise and an adaptive Wiener algorithm to suppress random interference. Envelope extraction is used to reflect changes in the reflected wave energy. The feature extraction submodule calculates the power spectral density and energy decay rate of the sound pressure signal using Fast Fourier Transform (FFT), and extracts the instantaneous phase, envelope, and reflection delay of the signal using Hilbert Transform. Based on the reflection envelope curves and phase change patterns obtained at different depths, the diffusion trend of the slurry can be preliminarily determined. The feature fusion and recognition submodule forms a multi-dimensional matrix from sound pressure features at different depths, and uses a support vector machine (SVM) recognition model to perform pattern recognition on the grouting process, outputting indicators such as the location of the diffusion boundary, the arrival time of the front edge, and the signal stabilization time. Preferably, the feature fusion and recognition submodule may also employ a weighted time-difference superposition algorithm to enhance the time sensitivity of sound pressure features at different depths and improve the stability of diffusion front determination. Preferably, the feature fusion and recognition submodule can automatically adjust the classification threshold based on historical samples to adapt to different geological conditions. Based on the recognition results, the intelligent analysis submodule establishes a sound pressure characteristic-diffusion parameter mapping model, comprehensively analyzes the sound energy attenuation rate, phase drift, and reflection energy ratio, and calculates the grout diffusion velocity and direction. When the signal amplitude changes tend to stabilize and the energy gradient converges, the system automatically determines that the grouting of the corresponding section is approaching saturation. The diffusion filling determination module includes a diffusion boundary identification submodule, a volume inversion submodule, a density calculation submodule, and a dynamic correction submodule. The diffusion boundary identification submodule calculates the depth reached by the slurry front by using the acoustic pressure phase difference and energy ratio between adjacent monitoring layers; The volume inversion submodule inverts the slurry diffusion volume based on the sound wave propagation speed and energy attenuation coefficient. The density calculation submodule determines the porosity saturation through the sound pressure gradient distribution and generates a density index. The dynamic correction submodule corrects parameters such as sound velocity and density in real time, and uses multiple iterative fitting to eliminate the influence of formation inhomogeneity on the results, making the inversion curve more stable. The system can output diffusion profiles, contour maps, and diffusion rate curves. Preferably, the dynamic correction submodule employs a multi-iteration fitting algorithm to eliminate the influence of formation inhomogeneity on the results. The visualization monitoring and data management module is used to graphically display the analysis results output by the diffusion filling judgment module, manage historical records and data interaction, and provide construction personnel with an intuitive monitoring interface and data support. The visualization monitoring and data management module includes a real-time display submodule, an alarm and control submodule, a historical analysis submodule, and a data interface submodule. The real-time display submodule is used for real-time data display of the ground terminal, providing visualization of sound pressure waveforms, diffusion boundaries, density curves, and diffusion profiles. The alarm and control submodule analyzes the sound pressure change rate and abnormal waveforms. When a sudden change or abnormal attenuation of reflected energy occurs at a monitoring point, the system will issue an audible and visual alarm and send a pause or adjustment signal to the grouting control device via the communication interface to achieve process intervention. The historical analysis submodule archives, compares, and analyzes trends from multiple grouting data sessions, generating statistical reports and pattern models to provide a basis for subsequent optimization of process parameters. The data interface submodule provides a standardized data exchange interface, supporting integration with engineering quality management systems or geological information platforms. Preferably, the data interface submodule supports exporting monitoring data in CSV or JSON format. In use, firstly, according to the design requirements, insert the pre-installed grouting pipe (1) into the wall of the deep hole, and then arrange several sound pressure sensing devices at equal intervals along its axis. The fixing arc (3) of each device is attached to the outer wall of the grouting pipe (1), and is fastened to the fixing strap (2) by the connecting block (5) to make it firmly fixed at the specified height position. Then, insert the sound pressure sensor probe (4) into the sealing ring (9) in the middle of the fixing arc (3), with the probe end face facing the stratum. The sound pressure signal transmission line (6) is led out from the gap between the sealing rubber (7) and the grouting pipe and led up layer by layer. The flexible sealing rubber (7) can tightly wrap the cable to prevent grout from leaking into the fixing arc (3). After installation, all transmission lines are gathered at the borehole opening and electrically connected to the ground sound pressure data acquisition equipment. At the same time, a sound wave excitation module is arranged on the side of the borehole opening or at the bottom of the borehole, and the sound field is calibrated according to the geological characteristics. After the system is started, the sound wave excitation module emits a sound wave signal of a set frequency, which is amplified and transmitted into the slurry and surrounding rock medium in the borehole after power amplification and transduction, forming a dynamic sound field. Sound pressure sensor probes (4) at different depths synchronously receive sound pressure reflection and attenuation signals. The signals are transmitted to the signal processing and feature recognition module via transmission line (6). This module filters, denoises and extracts features from the signals, and obtains the power spectrum through fast Fourier transform and Hilbert transform. Parameters such as density, phase difference, and reflection delay are used to form a multi-dimensional acoustic matrix through a feature fusion and recognition submodule. A weighted time-difference superposition algorithm or a support vector machine (SVM) model is then used to identify the diffusion mode during grouting. The intelligent analysis submodule further calculates the grout diffusion velocity, direction, and saturation level. When the sound pressure amplitude stabilizes and the energy gradient converges, the system automatically determines that the grouting in that layer is saturated. Subsequently, the diffusion filling judgment module identifies the grout diffusion boundary based on the sound pressure phase difference and energy ratio between adjacent monitoring layers. It then combines the sound wave propagation velocity and energy attenuation coefficient to invert the diffusion volume and density index. The dynamic correction submodule eliminates ground defects through multiple iterative fitting. To mitigate layer non-uniformity errors, the system outputs stable diffusion profiles and isosurface maps. Finally, the visualization monitoring and data management module displays the sound pressure waveform, diffusion boundary, and density curve in real time on the ground terminal. When abnormal reflection energy or signal abrupt change is detected, the system automatically issues audible and visual alarms and can send adjustment commands to the grouting control device to achieve process intervention. All monitoring data is archived and compared by the historical analysis submodule to generate statistical reports and trend charts, which can be exported in CSV or JSON format via the data interface submodule and linked with the engineering quality management system or geological information platform to achieve dynamic monitoring, diffusion determination, and intelligent control of the entire deep hole grouting process. The acoustic excitation module is preferably arranged at the orifice side or bottom end of the deep hole. The position can be adjusted according to the hole depth, sound field attenuation characteristics and on-site construction conditions. It can achieve accurate transmission of acoustic signals and optimization of sound field distribution under different hole depths and formation conditions, and ensure that the excitation acoustic energy is effectively transmitted into the slurry and formation medium. The transducer unit is made of piezoelectric ceramic material, covered with a high-damping rubber layer, and the outer shell is designed to be waterproof and sealed, which can significantly reduce interface reflection and resonance interference in sound wave propagation, and improve the stability and environmental adaptability of the transmitted signal. The design of the acoustic pressure sensing module, which is equipped with several deep-hole grouting acoustic pressure sensing devices arranged along the axial direction of the grouting pipe, enables the layered synchronous acquisition of acoustic pressure signals at different depths, forming a longitudinal multi-layer monitoring array, thereby accurately reflecting the spatial dynamic process of grout diffusion. The feature fusion and recognition submodule forms a multi-dimensional matrix of sound pressure features at different depths and uses a support vector machine (SVM) recognition model to perform pattern recognition on the grouting process. It outputs indicators such as the location of the diffusion boundary, the arrival time of the front edge, and the moment when the signal stabilizes. This enables multi-modal intelligent analysis of the sound pressure signal and accurate identification of the grout diffusion boundary and the arrival time of the front edge. The dynamic correction submodule employs a multi-iteration fitting algorithm to eliminate the influence of formation inhomogeneity on the results. It can achieve real-time correction of sound velocity and density parameters under complex geological conditions, making the diffusion inversion results more stable and reliable. The visualization monitoring and data management module is designed to graphically display the analysis results output by the diffusion filling judgment module, and to manage historical records and data interaction. It can realize real-time display of monitoring data, anomaly warning and data archiving and sharing, and provide intuitive support for dynamic control of the grouting process and subsequent quality assessment. The system can achieve the goal of accurately identifying the diffusion state of the grouting process and intelligently monitoring the entire process by emitting acoustic signals of a set frequency through the acoustic excitation module, stimulating the grout to form a dynamic sound field with the formation, collecting sound pressure reflection data at different depths at multiple points along the grouting pipe through the sound pressure sensing module, filtering, feature extraction and multimodal fusion recognition through the signal processing and feature recognition module, inverting the grout diffusion volume and density through the diffusion filling judgment module and dynamically correcting the parameters, and realizing real-time display, anomaly warning and data archiving through the visualization monitoring and data management module.

[0011] It should be noted that, unless otherwise explicitly specified and limited, the terms "placed," "connected," and "linked" should be interpreted broadly. For example, they can refer to fixed connections such as folded edges, rivets, pins, adhesives, and welds; detachable connections such as threaded connections, snap-fit ​​connections, and hinges; integral connections; electrical connections; direct connections; or indirect connections via an intermediate medium; or internal connections between two components. Those skilled in the art can understand the specific meaning of these terms in this invention based on the specific circumstances.

Claims

1. A system for combined acoustic pressure feature identification and diffusion filling monitoring in deep hole grouting process, comprising an acoustic excitation module, an acoustic pressure sensing module, a signal processing and feature identification module, a diffusion filling determination module, and a visualization monitoring and data management module, characterized in that: The acoustic excitation module is used to emit acoustic signals of a set frequency into the slurry and formation medium inside the borehole, exciting the diffusion area to form a dynamic sound field for downstream monitoring array identification. The signal processing and feature recognition module is used to preprocess, extract features and recognize patterns of the multi-point sound pressure signals output by the sound pressure sensing module to obtain key acoustic feature parameters related to slurry diffusion. The visualization monitoring and data management module is used to graphically display, record history and manage data interaction of the analysis results output by the diffusion filling judgment module, providing construction personnel with an intuitive monitoring interface and data support.

2. The sound pressure combined feature identification and diffusion filling monitoring system for deep hole grouting process according to claim 1, characterized in that... The acoustic excitation module includes a signal generation unit, a power amplification unit, and a transducer transmitting unit. The signal generation unit generates an acoustic signal with a control frequency, supporting three working modes: pulse wave, sweep wave, and linear frequency modulated wave, with a frequency range of 2 kHz to 50 kHz. This covers the acoustic response of the slurry at different diffusion stages. When the temperature or density inside the hole changes significantly, the signal generation unit compensates for phase drift through a self-calibration circuit to maintain acoustic path consistency. The power amplification unit amplifies the signal amplitude and performs impedance matching to ensure that the transducer transmitting unit operates in the optimal output state. The transducer transmitting unit is made of piezoelectric ceramic material and covered with a high-damping rubber layer to reduce interface reflection and structural resonance interference. Its outer shell has waterproof sealing performance and is suitable for long-term operation in high-humidity environments. The acoustic excitation module is preferably arranged at the orifice side or bottom end of the deep hole, and its position can be adjusted according to the hole depth, sound field attenuation characteristics, and on-site construction conditions.

3. The sound pressure combined feature identification and diffusion filling monitoring system for deep hole grouting process according to claim 1, characterized in that... The sound pressure sensing module includes several deep-hole grouting sound pressure sensing devices arranged along the axial direction of the grouting pipe. Each deep-hole grouting sound pressure sensing device includes a fixed arc, a connecting block, a fixing strap, a sealing rubber, a fixing ring, a sealing ring, a sound pressure sensor probe, and a sound pressure signal transmission line. The connecting block is fixed on the outer arc surface of the fixed arc. One end of the fixing strap passes through the connecting block, surrounds the grouting pipe and the fixed arc, and is snapped to the other end. The sealing rubber is fixedly connected to the inner arc surface of the fixed arc. Each fixed arc corresponds to two sealing rubbers, and the two sealing rubbers are respectively fixed to the inner arc surfaces of the top and bottom ends of the fixed arc. The fixed connection includes a notch in the middle of the fixed arc, one end of a fixed ring fixedly connected to the inner arc surface of the middle of the fixed arc, the fixed ring communicating with the notch of the fixed arc, a sealing ring provided on the inner ring surface of the fixed ring and the side of the notch of the fixed arc, one end of the sound pressure sensor probe being placed between the grouting pipe and the fixed arc, the sound pressure sensor probe being clipped into the sealing ring and the other end being placed on the outside of the fixed arc, one end of the sound pressure signal transmission line being connected to the sound pressure sensor probe, the sound pressure sensor probe and the sound pressure signal transmission line corresponding one-to-one, and the other end of the plurality of sound pressure signal transmission lines being electrically connected to a sound pressure data acquisition device.

4. The sound pressure combined feature identification and diffusion filling monitoring system for deep hole grouting process according to claim 1, characterized in that... The signal processing and feature recognition module includes a signal preprocessing submodule, a feature extraction submodule, a feature fusion and recognition submodule, and an intelligent analysis submodule. The signal preprocessing submodule performs filtering, denoising, and envelope extraction on the sound pressure signal. The filtering uses a bandpass filter to remove background noise and an adaptive Wiener algorithm to suppress random interference. Envelope extraction reflects changes in the reflected wave energy. The feature extraction submodule calculates the power spectral density and energy decay rate of the sound pressure signal using Fast Fourier Transform (FFT) and extracts the instantaneous phase, envelope, and reflection delay of the signal using Hilbert Transform. This is based on reflections obtained at different depths. The envelope curve and phase change pattern can preliminarily determine the grout diffusion trend. The feature fusion and recognition submodule forms a multi-dimensional matrix of sound pressure features at different depths and uses a support vector machine (SVM) recognition model to perform pattern recognition on the grouting process, outputting indicators such as the diffusion boundary position, the arrival time of the front edge, and the signal stabilization time. The intelligent analysis submodule establishes a sound pressure feature-diffusion parameter mapping model based on the recognition results, comprehensively analyzes the sound energy attenuation rate, phase drift, and reflection energy ratio, and calculates the grout diffusion speed and direction. When the signal amplitude change tends to stabilize and the energy gradient converges, the system automatically determines that the grouting of the corresponding layer tends to be saturated.

5. The sound pressure combined feature identification and diffusion filling monitoring system for deep hole grouting process according to claim 1, characterized in that... The diffusion filling determination module includes a diffusion boundary identification submodule, a volume inversion submodule, a compaction calculation submodule, and a dynamic correction submodule. The diffusion boundary identification submodule uses the acoustic pressure phase difference and energy ratio between adjacent monitoring layers to calculate the depth reached by the slurry front. The volume inversion submodule inverts the slurry diffusion volume based on the sound wave propagation speed and energy attenuation coefficient. The density calculation submodule obtains the porosity saturation through the sound pressure gradient distribution and generates a density index. The dynamic correction submodule corrects parameters such as sound velocity and density in real time and uses multiple iterative fitting to eliminate the influence of formation inhomogeneity on the results, making the inversion curve more stable. The system can output diffusion profiles, isosurface maps and diffusion rate curves. The dynamic correction submodule uses a multiple iterative fitting algorithm to eliminate the influence of formation inhomogeneity on the results.

6. The sound pressure combined feature identification and diffusion filling monitoring system for deep hole grouting process according to claim 1, characterized in that... The visualization monitoring and data management module includes a real-time display submodule, an alarm and control submodule, a historical analysis submodule, and a data interface submodule. The real-time display submodule is used for real-time data display of the ground terminal, providing visualization of sound pressure waveforms, diffusion boundaries, density curves, and diffusion profiles. The alarm and control submodule analyzes the sound pressure change rate and abnormal waveforms. When a sudden change or abnormal attenuation of reflected energy occurs at a monitoring point, the system will issue an audible and visual alarm and send a pause or adjustment signal to the grouting control device through the communication interface to achieve process intervention. The historical analysis submodule archives, compares, and analyzes trends of multiple grouting data to form statistical reports and pattern models, providing a basis for subsequent optimization of process parameters. The data interface submodule provides a standardized data exchange interface, supporting linkage with engineering quality management systems or geological information platforms. The data interface submodule supports exporting monitoring data in CSV or JSON format.

7. The sound pressure combined feature identification and diffusion filling monitoring system for deep hole grouting process according to claim 3, characterized in that... The fixed arc has an arc-shaped structure, and the two ends of the fixed arc are in contact with the side of the grouting pipe. The connecting block has a U-shaped structure and its two ends are fixedly connected to the fixed arc. The connecting block is located in the middle position in the arc direction. Each fixed arc is provided with two connecting blocks, which are respectively set near the top and bottom of the fixed arc.

8. The sound pressure combined feature identification and diffusion filling monitoring system for deep hole grouting process according to claim 3, characterized in that... The fixing straps correspond one-to-one with the connecting blocks. When the fixing arc is in contact with the grouting pipe, the inner arc surface of the sealing rubber is in contact with the outer surface of the grouting pipe. The sound pressure signal transmission line and the sound pressure sensor probe adopt an integral injection molding encapsulation structure. The sound pressure signal transmission line adopts a multi-layer sheath structure. The inner insulation layer is made of cross-linked polyethylene or fluorinated polymer material, and a tin-plated copper wire braided shielding layer is provided in the middle. The outer sheath is made of alkali-resistant and wear-resistant polyurethane or fluororubber material that is integrally extruded.

9. The sound pressure combined feature identification and diffusion filling monitoring system for deep hole grouting process according to claim 4, characterized in that... The feature fusion and recognition submodule can also employ a weighted time difference superposition algorithm to enhance the time sensitivity of sound pressure features at different depths and improve the stability of diffusion front determination. The feature fusion and recognition submodule can automatically adjust the classification threshold based on historical samples to adapt to different geological conditions.

10. The sound pressure combined feature identification and diffusion filling monitoring system for deep hole grouting process according to claim 1, characterized in that... The acoustic excitation module establishes signal interaction with the acoustic pressure sensing module, the acoustic pressure sensing module establishes signal interaction with the signal processing and feature recognition module, the signal processing and feature recognition module establishes signal interaction with the diffusion filling determination module, the diffusion filling determination module establishes signal interaction with the visualization monitoring and data management module, and the visualization monitoring and data management module establishes signal interaction with the ground control terminal.

Images