A method for identifying the hardness of a tunnel face based on the high-frequency energy ratio of rock breaking vibration of a TBM

Abstract

The application discloses a TBM rock breaking vibration high-frequency energy ratio-based tunnel face hardness and softness identification method, and belongs to the field of tunnel and underground engineering advanced detection technology. A plurality of vibration velocity sensors are arranged in a ring array on a shield segment. Radial velocity signals pointing to a rock breaking source of a tunnel face are directly obtained through data collection of the vibration velocity sensors or are solved through the collected data. The vibration velocity signals are used to effectively filter out the low-frequency modal interference of the segment by using the sensitivity of energy transmission and the directivity of high-frequency components to lithological brittleness. According to the rock breaking dynamic response characteristics, a high-frequency energy ratio evaluation index and a variation coefficient CV are constructed, and a polar coordinate lithological characteristic map is generated. Through analysis of the area characteristics and geometric distortion characteristics of the polar coordinate lithological characteristic map, near-field visual accurate identification of the hardness of the homogeneous stratum of the tunnel face and the occurrence of the composite stratum is realized.

Description

Technical Field

[0001] This invention belongs to the field of advanced detection technology for tunnels and underground engineering, specifically a method for identifying the hardness and softness of the tunnel face based on the high-frequency energy ratio of TBM rock-breaking vibration. Background Technology

[0002] TBMs have become the mainstream equipment for the construction of urban subways and deep-buried long tunnels. During the tunneling process, real-time perception of the geological conditions at the tunnel face is crucial for optimizing tunneling parameters and ensuring construction safety. If abrupt changes in lithology or complex strata cannot be identified in time, it can easily lead to engineering accidents such as machine jamming, abnormal wear of the cutterhead, or even instability of the tunnel boring machine.

[0003] Currently, TBM construction sites primarily rely on manual experience or vibration signal-based monitoring systems to infer geological conditions. Existing vibration monitoring methods typically use time-domain statistical indicators to characterize geological intensity. However, in actual, complex mixed-stratum tunneling, this identification method based on "total energy" has significant limitations and blind spots:

[0004] "Pseudo-high-energy" interference from soft rock: When tunneling through soft strata such as mudstone, the large plastic deformation characteristics of the rock mass often excite low-frequency vibrations with large amplitudes. This low-frequency, large-amplitude signal leads to an extremely high calculated total energy index (RMS) value, which is then misjudged by the system as "hard rock," seriously misleading construction personnel.

[0005] Low identification rate of complex strata: Traditional monitoring methods focus on the numerical changes of a single measuring point and lack the ability to perceive the spatial distribution in the circumferential direction of the tunnel face, making it difficult to identify dangerous complex strata structures that cause TBM wear and attitude deviation.

[0006] Therefore, how to eliminate low-frequency interference from soft rock plastic deformation from complex rock-breaking vibration signals, accurately extract effective features characterizing rock brittle fracture, and realize the visual reconstruction of lithological distribution at the tunnel face is a technical problem that urgently needs to be solved in the field of TBM intelligent tunneling. Summary of the Invention

[0007] To address the problems existing in the prior art, this invention provides a method for identifying the softness and hardness of the tunnel face based on the high-frequency energy ratio of TBM rock-breaking vibration. By eliminating low-frequency interference from the plastic deformation of soft rock in the rock-breaking vibration signal, the method accurately extracts effective features characterizing the brittle fracture of the rock and realizes the visual reconstruction of the lithological distribution of the tunnel face, thereby accurately determining the geological structure type of the tunnel face.

[0008] To achieve the above objectives, the technical solution adopted by this invention is: a method for identifying the hardness and softness of the tunnel face based on the high-frequency energy ratio of TBM rock-breaking vibration, comprising the following steps:

[0009] Step 1: Construct a near-field circumferential velocity monitoring array: Deploy multiple measuring points on the TBM segments to construct a near-field circumferential array observation system for real-time acquisition of vibration velocity signals from different measuring points during the tunneling process.

[0010] Step 2: Acquisition and calculation of radial rock breaking response: Acquire the components of vibration velocity signals at each measuring point in the cutting direction and the components perpendicular to the top and bottom plates; based on the position coordinates of each measuring point in the tunnel cross-section coordinate system, use coordinate transformation to eliminate circumferential mode interference of the tunnel segments, and calculate the radial vibration velocity pointing towards the rock breaking source at the tunnel face.

[0011] Step 3: Signal preprocessing: The radial vibration velocity is preprocessed to obtain a clean signal.

[0012] Step 4: Time window truncation and spectral analysis: Set a sliding time window to truncate the clean signal, and perform Discrete Fourier Transform (DFT) on the signal segment within the current time window to obtain the frequency domain velocity power spectral density of each measurement point.

[0013] Step 5: High-frequency energy ratio extraction: Based on the characteristics of high-frequency velocity response excited by brittle rock fracture, a characteristic cutoff frequency is set; the ratio of the energy above the characteristic cutoff frequency in the frequency domain velocity power spectral density at each measuring point to the total energy across the entire frequency band is calculated and defined as the high-frequency energy ratio. .

[0014] Step Six: Polar Coordinate Lithological Feature Map Imaging: Using the installation angle of each vibration sensor as the axis of the feature map, and the calculated... The value is the axial magnitude, and the construction is performed. Polar coordinate lithological characteristic map.

[0015] Step 7: Identification and updating of geological structure at the tunnel face: Calculate the geometric shape and coefficient of variation of the feature map, analyze and determine the geological structure type (homogeneous / composite) of the tunnel face, and update the feature map according to the set step length as the TBM advances, so as to realize the dynamic real-time prediction of geological conditions.

[0016] Furthermore, step one specifically involves: on the inner arc surface of the first ring-shaped segment behind the TBM tail, multiple measuring points are arranged at equal intervals along the circumference of the inner arc surface. Each measuring point is equipped with a vibration velocity sensor to construct a near-field circumferential array observation system. The sensitive axis of each vibration velocity sensor is perpendicular to the center of the circle pointed to by the inner arc surface, which is used to collect vibration velocity signals in real time during the tunneling process.

[0017] Furthermore, the preprocessing in step three specifically involves: detrending the radial vibration velocity by fitting the trend term in the signal using the least squares method and subtracting it from the original signal to obtain the processed pure signal, thereby eliminating the masking effect of DC components and extremely low-frequency drift on subsequent spectral analysis.

[0018] Furthermore, the detrending process specifically includes:

[0019] Assume the original radial velocity sequence at a certain measuring point is as follows: The trend term is constructed using the polynomial least squares method. The specific formula is as follows:

[0020]

[0021] in, To determine the order of the fitted polynomial, For polynomial coefficients, This represents the time series value of the current measuring point. Time series values of Power of 1.

[0022] By minimizing the objective function of the sum of squared residuals Solving for the optimal coefficients The objective function is defined as:

[0023]

[0024] in The total number of data points obtained at a certain measurement point. This represents the trend.

[0025] Obtain the best trend item Then, calculate the pure signal after detrending. :

[0026]

[0027] This pure signal This is used for subsequent analysis and processing to eliminate false high-energy peaks near 0Hz in the original spectrum.

[0028] Furthermore, in step five, the high-frequency energy ratio The calculation formula is:

[0029]

[0030] In the formula, For the first High-frequency energy ratio at each measurement point; The frequency domain velocity power spectral density; The characteristic cutoff frequency; The specific values ​​are as follows: select a frequency point higher than the fundamental frequency and its harmonics of the TBM cutterhead's mechanical rotation, and higher than the low-order modal frequency of the segment structure. Simultaneously, this frequency point must be lower than the lower limit of the main frequency band induced by brittle fracture in hard rock to ensure... The proportion of high-frequency energy used to characterize rock fragmentation.

[0031] Furthermore, the formula for calculating the coefficient of variation in step seven is as follows:

[0032] Build Each measuring point coefficient of variation of the value :

[0033]

[0034] in For all measuring points The mean of the values; For the first The high-frequency energy ratio of each measuring point; N is the number of measuring points.

[0035] Furthermore, a threshold for geological heterogeneity discrimination is set. , It is a dimensionless constant determined based on statistical laws; when using the coefficient of variation With threshold Before comparing and determining the geological structure type of the working face, an energy availability benchmark should be established first. ;like This indicates that the rock-breaking vibration energy is extremely low, and the strata respond weakly to high-frequency signals, thus directly classifying it as homogeneous soft rock; if This indicates that the signal is valid, and then proceeds to the subsequent coefficient of variation. With threshold The comparison and judgment can further improve the accuracy of identifying the geological structure type of the working face.

[0036] Furthermore, the specific process for analyzing and determining the geological structure type of the working face in step seven is as follows:

[0037] when At that time, it is determined that the data dispersion is low and the working face is a homogeneous layer: where, if the mean... A large value and a full polygonal shape on the radar image indicate hard rock; if the mean value is... The rock is small and appears to be contracted on the radar image, indicating it is soft rock.

[0038] when At that time, it was determined that the data dispersion was high, and the working face was a composite stratum of soft and hard rock. Then, the orientation of the distribution of soft and hard rocks in the composite stratum was determined:

[0039] If the radar chart shows an asymmetrical shape with a concave top and an expanded bottom, i.e., a domed measuring point... The value is significantly smaller than that of the measuring point at the bottom of the arch. If the value is positive, it indicates that soft rock and hard rock are distributed vertically in the composite strata, with the soft rock above the hard rock.

[0040] If the radar chart shows an asymmetrical shape with an expanded top and a concave bottom, i.e., a domed measuring point... The value is significantly greater than that of the measuring point at the bottom of the arch. If the value is positive, it indicates that hard rock and soft rock are distributed vertically in the composite strata, with the hard rock above the soft rock.

[0041] If the radar image shows an asymmetrical shape, it is determined that the hard rock and soft rock are distributed on the left and right sides of the composite strata, and the area with a lower high-frequency energy ratio corresponds to soft rock.

[0042] Furthermore, in step four, the sliding time window length is set to 2-5 seconds to ensure frequency resolution and capture local cutting features; in step seven, the step size is set to 1-2 seconds, and this step size is used in conjunction with the sliding time window to obtain high overlap data, thereby achieving near real-time rolling forecast of the geological conditions of the tunnel face.

[0043] Furthermore, the vibration velocity sensor is rigidly bonded to the inner arc surface of the first ring-shaped tube segment, and the first ring-shaped tube segment is used as a waveguide medium to receive the radial compression wave transmitted from the cutterhead breaking the rock and through the shield shell to the first ring-shaped tube segment.

[0044] Compared with the prior art, the present invention has the following advantages:

[0045] 1. Strong anti-interference capability: This invention establishes a high-frequency energy ratio through an innovative method. Indicators and detrending processing, based on the physical mechanism of high-frequency signals excited by brittle fracture in hard rock and low-frequency signals dominated by plastic deformation in soft rock, high-frequency energy ratio The index uses full-band energy as the denominator for normalization, effectively suppressing the masking effect of large-amplitude low-frequency background noise in soft rock tunneling, significantly improving the distinction between soft and hard rock types, and filtering out low-frequency "false signals" generated by TBM mechanical vibration and soft rock plastic rheology from a mechanistic perspective. It solves the shortcomings of the existing time-domain total energy (RMS value) index, which misclassifies "soft rock with large vibrations" as "hard rock". It utilizes the sensitivity of vibration velocity signals to energy transfer and the directionality of high-frequency components to lithological brittleness to effectively filter out low-frequency modal interference of tunnel segments, providing a reliable geological basis for the optimization of shield tunneling parameters and effectively ensuring the accuracy of the determination of the geological structure type of the tunnel face.

[0046] 2. High accuracy in judgment: This invention forms a circumferential array of measurement points and combines it with polar coordinate imaging technology to construct... Polar coordinate lithological feature maps are generated; the geometric shape of these maps is used to visually characterize the lithological distribution of the tunnel face. Subsequent data analysis allows for a clear understanding of the hardness distribution of the tunnel face, and this is combined with the aforementioned high-frequency energy ratios. The indicators and their coefficients of variation, by calculating the dispersion (CV value) of the circumferential data and the direction of the centroid offset, accurately identify the most risky composite strata in tunnel construction, and can accurately determine the orientation of the soft and hard distribution in the composite strata, thereby ensuring the safety of subsequent tunneling.

[0047] 3. Good engineering applicability: When implementing this invention, only vibration velocity sensors distributed in a ring array need to be installed on the inner arc surface of the tunnel segment. There is no need to modify the internal structure of the cutterhead. The tunnel segment is used as a waveguide medium, which makes installation and maintenance convenient. Moreover, the signal processing algorithm has a small computational load and is suitable for real-time monitoring during excavation and exploration. Attached Figure Description

[0048] Figure 1 This is a schematic diagram showing the location of the vibration sensor in an embodiment of the present invention.

[0049] Figure 2 This is a schematic diagram of the arrangement of the near-field circumferential array observation system composed of various vibration sensors in an embodiment of the present invention.

[0050] Figure 3 This is a flowchart illustrating the identification process in an embodiment of the present invention.

[0051] Figure 4 This is the case of homogeneous soft rock (mudstone) in the embodiments of the present invention. Polar coordinate lithological characteristic map.

[0052] Figure 5 This is the case of homogeneous hard rock (limestone) in the embodiments of the present invention. Polar coordinate lithological characteristic map.

[0053] Figure 6 The composite formation in the embodiments of the present invention Polar coordinate lithological characteristic map. Detailed Implementation

[0054] The present invention will be further described below.

[0055] like Figure 3 As shown, the present invention includes the following steps:

[0056] Step 1: Construct a near-field circumferential velocity monitoring array: On the inner arc surface of the first ring-shaped segment behind the TBM tail, arrange five measuring points, P1 to P5, at equal intervals along the circumference of the inner arc surface; For example... Figure 1 and Figure 2 As shown, vibration velocity sensors A1 to A5 are installed at each measuring point to construct a near-field circumferential array observation system. The sensitive axis of each vibration velocity sensor is perpendicular to the center of the circle pointed to by the inner arc surface. This system is used to collect vibration velocity signals during the tunneling process in real time and feed them back to the host in the control room via an extension line.

[0057] Step 2: Acquisition and Calculation of Radial Rock Breaking Response: Acquire the components of the vibration velocity signal at each measuring point in the cut direction and the component perpendicular to the top and bottom plates; based on the position coordinates of each measuring point in the tunnel cross-section coordinate system, use coordinate transformation to eliminate circumferential modal interference of the tunnel segments, and calculate the radial vibration velocity pointing towards the rock breaking source at the tunnel face. The specific calculation formula is as follows:

[0058]

[0059] in, For the first One measurement point; and The first The vibration velocity components along the cutting direction (horizontal) and perpendicular to the top and bottom plates, measured at each measuring point; ) is the first The coordinates of each measuring point in the Cartesian coordinate system of the tunnel cross section; For the first The distance from each measuring point to the center of the tunnel.

[0060] Step 3: Signal Preprocessing: The radial vibration velocity is detrended by fitting the trend term in the signal using the least squares method and subtracting it from the original signal to obtain a clean signal. This process eliminates the masking effect of DC components and extremely low-frequency drift on subsequent spectral analysis. Specifically:

[0061] Assume the original radial velocity sequence at a certain measuring point is as follows: The trend term is constructed using the polynomial least squares method. The specific formula is as follows:

[0062]

[0063] in, The order of the fitting polynomial (usually taken as ) To eliminate linear drift). For polynomial coefficients, This represents the time series value of the current measuring point. Time series values of Power of 1.

[0064] By minimizing the objective function of the sum of squared residuals Solving for the optimal coefficients The objective function is defined as:

[0065]

[0066] in The total number of data points obtained at a certain measurement point. This represents the trend.

[0067] Obtain the best trend item Then, calculate the pure signal after detrending. :

[0068]

[0069] This pure signal This is used for subsequent analysis and processing to eliminate false high-energy peaks near 0Hz in the original spectrum.

[0070] Step 4, Time Window Extraction and Spectral Analysis: Set the length of the sliding time window to 2-5 seconds to ensure frequency resolution and capture local cutting features; extract the clean signal and perform Discrete Fourier Transform (DFT) on the signal segment within the current time window to obtain the frequency domain velocity power spectral density of each measurement point.

[0071] Step 5: High-frequency energy ratio extraction: Based on the characteristics of high-frequency velocity response excited by brittle rock fracture, a characteristic cutoff frequency is set; the ratio of the energy above the characteristic cutoff frequency in the frequency domain velocity power spectral density at each measuring point to the total energy across the entire frequency band is calculated and defined as the high-frequency energy ratio. The specific calculation formula is as follows:

[0072]

[0073] In the formula, For the first High-frequency energy ratio at each measurement point; The frequency domain velocity power spectral density; The characteristic cutoff frequency is used to distinguish between low-frequency mechanical vibration / formation plastic response and high-frequency rock brittle fracture response; The specific values ​​are as follows: select a frequency point higher than the fundamental frequency and its harmonics of the TBM cutterhead's mechanical rotation, and higher than the low-order modal frequency of the segment structure. Simultaneously, this frequency point must be lower than the lower limit of the main frequency band induced by brittle fracture in hard rock to ensure... The proportion of high-frequency energy used to characterize rock fragmentation.

[0074] Step Six: Polar Coordinate Lithological Feature Map Imaging: Using the installation angle of each vibration sensor as the axis of the feature map, and the calculated... The value is the axial magnitude, and the construction is performed. Polar coordinate lithological characteristic map.

[0075] Step 7: Identification and Update of Geological Structure Attitude at the Working Face: Calculate the geometric morphology and coefficient of variation of the feature map, and analyze and determine the geological structure type (homogeneous / composite) of the working face. Specifically:

[0076] Build Each measuring point coefficient of variation of the value :

[0077]

[0078] in For all measuring points The mean of the values; For the first The high-frequency energy ratio of each measuring point; N is the number of measuring points; a threshold for geological heterogeneity is set. , In this embodiment, the dimensionless constant is determined based on statistical laws. The preferred value is 0.3.

[0079] To better and more accurately identify lithology, an energy availability benchmark is first established before determining data dispersion. In this embodiment, the value is set to 0.25.

[0080] like This indicates that the rock-breaking vibration energy is extremely low, and the formation's response to high-frequency signals is weak; therefore, it can be ignored. The magnitude of the value (due to small fluctuations at low signal-to-noise ratios) (The calculated value is inflated), and it is judged to be homogeneous soft rock. Figure 4 As shown.

[0081] like This indicates that the signal is valid, so further analysis is based on the coefficient of variation. With threshold The relationship is determined.

[0082] when At that time, the data dispersion was low, and the working face was a homogeneous layer: among which, such as Figure 5 As shown, if the mean A large value and a full polygonal shape on the radar image indicate hard rock; if the mean value is... The rock is small and appears to be contracted on the radar image, indicating it is soft rock.

[0083] when At that time, it was determined that the data dispersion was high, and the working face was a composite stratum of soft and hard rock. Then, the orientation of the distribution of soft and hard rocks in the composite stratum was determined:

[0084] If the radar chart shows an asymmetrical shape with a concave top and an expanded bottom, such as Figure 6 As shown, the measuring point at the top of the arch. The value is significantly smaller than that of the measuring point at the bottom of the arch. If the value is positive, it indicates that soft rock and hard rock are distributed vertically in the composite strata, with the soft rock above the hard rock.

[0085] If the radar chart shows an asymmetrical shape with an expanded top and a concave bottom, i.e., a domed measuring point... The value is significantly greater than that of the measuring point at the bottom of the arch. If the value is positive, it indicates that hard rock and soft rock are distributed vertically in the composite strata, with the hard rock above the soft rock.

[0086] If the radar image shows an asymmetrical shape, it is determined that the hard rock and soft rock are distributed on the left and right sides of the composite strata, and the area with a lower high-frequency energy ratio corresponds to soft rock.

[0087] Subsequently, as the TBM advances, the feature map is updated every 1-2 seconds according to the set step size. This step size is used in conjunction with the sliding time window to obtain high overlap data, thereby achieving near real-time rolling prediction of the geological conditions at the tunnel face.

[0088] As an improvement of the present invention, the vibration velocity sensor is rigidly bonded to the inner arc surface of the first ring-shaped tube segment, and the first ring-shaped tube segment is used as a waveguide medium to receive the radial compression wave transmitted from the cutterhead breaking the rock and through the shield shell to the first ring-shaped tube segment.

[0089] The above process removes low-frequency interference from soft rock plastic deformation from rock-breaking vibration signals, then accurately extracts effective features characterizing brittle rock fracture, and achieves a visual reconstruction of the lithological distribution at the tunnel face, thereby accurately determining the geological structure type of the tunnel face and ultimately improving the safety of subsequent tunneling.

Claims

1. A method for identifying the hardness and softness of a tunnel face based on the high-frequency energy ratio of TBM rock-breaking vibration, characterized in that, Includes the following steps: Step 1: Construct a near-field circumferential velocity monitoring array: Deploy multiple measuring points on the segments of the TBM to construct a near-field circumferential array observation system for real-time acquisition of vibration velocity signals during the tunneling process; Step 2: Acquisition and calculation of radial rock breaking response: Acquire the components of vibration velocity signals at each measuring point in the cutting direction and the components perpendicular to the top and bottom plates; Based on the position coordinates of each measuring point in the tunnel cross-section coordinate system, use coordinate transformation to eliminate circumferential mode interference of the tunnel segments, and calculate the radial vibration velocity pointing towards the rock breaking source at the tunnel face. Step 3: Signal preprocessing: The radial vibration velocity is preprocessed to obtain a clean signal. Step 4, Time Window Extraction and Spectral Analysis: Set a sliding time window to extract the clean signal, and perform a Discrete Fourier Transform on the signal segment within the current time window to obtain the frequency domain velocity power spectral density at each measurement point. Step 5: High-frequency energy ratio extraction: Based on the characteristics of high-frequency velocity response excited by brittle rock fracture, a characteristic cutoff frequency is set; the ratio of the energy above the characteristic cutoff frequency in the frequency domain velocity power spectral density at each measuring point to the total energy across the entire frequency band is calculated and defined as the high-frequency energy ratio. The specific calculation formula is as follows: In the formula, For the first High-frequency energy ratio at each measurement point; The frequency domain velocity power spectral density; The characteristic cutoff frequency; The specific value is as follows: select a frequency point that is higher than the fundamental frequency and its harmonics of the mechanical rotation of the TBM cutterhead, and higher than the low-order modal frequency of the segment structure. At the same time, this frequency point must be lower than the lower limit of the main frequency band induced by brittle fracture of hard rock. Step Six: Polar Coordinate Lithological Feature Map Imaging: Using the installation angle of each vibration sensor as the axis of the feature map, and the calculated... The value is the axial magnitude, and the construction is performed. Polar coordinate lithological characteristic map; Step 7, Identification and Update of Geological Structure Attitude at the Working Face: Calculate the geometric morphology and coefficient of variation of the feature map, analyze and determine the geological structure type of the working face, and update the feature map according to the set step size as the TBM advances, realizing dynamic real-time prediction of geological conditions; the specific calculation process of the coefficient of variation is as follows: Construct Each measuring point coefficient of variation of the value : in For all measuring points The mean of the values; For the first The high-frequency energy ratio of each measuring point; N is the number of measuring points.

2. The method for identifying the hardness and softness of the tunnel face based on the high-frequency energy ratio of TBM rock-breaking vibration according to claim 1, characterized in that, Step one specifically involves: on the inner arc surface of the first ring-shaped segment behind the TBM tail, multiple measuring points are arranged at equal intervals along the circumference of the inner arc surface. Each measuring point is equipped with a vibration velocity sensor to construct a near-field circumferential array observation system. The sensitive axis of each vibration velocity sensor is perpendicular to the center of the circle pointed to by the inner arc surface, which is used to collect vibration velocity signals in real time during the tunneling process.

3. The method for identifying the hardness and softness of the tunnel face based on the high-frequency energy ratio of TBM rock-breaking vibration according to claim 1, characterized in that, The preprocessing in step three specifically involves: detrending the radial vibration velocity by fitting the trend term in the signal using the least squares method and subtracting it from the original signal to obtain the processed pure signal.

4. The method for identifying the hardness and softness of the tunnel face based on the high-frequency energy ratio of TBM rock-breaking vibration according to claim 3, characterized in that, The detrending process specifically involves: Assume the original radial velocity sequence at a certain measuring point is as follows: The trend term is constructed using the polynomial least squares method. The specific formula is as follows: in, To determine the order of the fitted polynomial, These are the polynomial coefficients; This represents the time series value of the current measuring point. Time series values of Power of; By minimizing the objective function of the sum of squared residuals Solving for the optimal coefficients The objective function is defined as: in The total number of data points obtained at a certain measurement point. This is a trend item; Obtain the best trend item Then, calculate the pure signal after detrending. : This pure signal For subsequent analysis and processing.

5. The method for identifying the hardness and softness of the tunnel face based on the high-frequency energy ratio of TBM rock-breaking vibration according to claim 1, characterized in that, Setting a threshold for geological heterogeneity discrimination When using the coefficient of variation With threshold Before comparing and determining the geological structure type of the working face, an energy availability benchmark should be established first. If the mean This indicates that the rock-breaking vibration energy is extremely low, and the strata respond weakly to high-frequency signals, thus directly classifying it as homogeneous soft rock; if This indicates that the signal is valid, and then proceeds to the subsequent coefficient of variation. With threshold The comparison and judgment.

6. The method for identifying the hardness and softness of the tunnel face based on the high-frequency energy ratio of TBM rock-breaking vibration according to claim 5, characterized in that, The specific process for analyzing and determining the geological structure type of the working face in step seven is as follows: when At that time, it is determined that the data dispersion is low and the working face is a homogeneous layer: where, if the mean... A large value and a full polygonal shape on the radar image indicate hard rock; if the mean value is... The rock is relatively small and appears to be contracted on the radar image, indicating it is soft rock. when At that time, it was determined that the data dispersion was high, and the working face was a composite stratum of soft and hard rock. Then, the orientation of the distribution of soft and hard rocks in the composite stratum was determined: If the radar image shows an asymmetrical shape with a concave top and an expanded bottom, it is determined that soft rock and hard rock are distributed vertically in the composite strata, with the soft rock above the hard rock. If the radar image shows an asymmetrical shape with an expanded top and a concave bottom, it is determined that hard rock and soft rock are distributed vertically in the composite strata, with the hard rock above the soft rock. If the radar image shows an asymmetrical shape, it is determined that the hard rock and soft rock are distributed on the left and right sides of the composite strata, and the area with a lower high-frequency energy ratio corresponds to soft rock.

7. The method for identifying the hardness and softness of the tunnel face based on the high-frequency energy ratio of TBM rock-breaking vibration according to claim 1, characterized in that, In step four, the sliding time window length is set to 2-5 seconds; in step seven, the step size is set to 1-2 seconds.

8. The method for identifying the hardness and softness of the tunnel face based on the high-frequency energy ratio of TBM rock-breaking vibration according to claim 2, characterized in that, The vibration velocity sensor is rigidly bonded to the inner arc surface of the first ring-shaped tube segment, and the first ring-shaped tube segment is used as a waveguide medium to receive the radial compression wave transmitted from the cutterhead breaking the rock and through the shield shell to the first ring-shaped tube segment.

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