Method and system for detecting and collecting TBM (Tunnel Boring Machine) space estimation surface construction data

Abstract

The invention relates to the technical field of data detection, in particular to a TBM air deduction surface construction data detection and collection method and system, and the method comprises the steps: collecting a vibration signal in a TBM air deduction surface construction process; obtaining each IMF component and residual term of the vibration signal by using a signal decomposition algorithm; obtaining the periodic weight of each IMF component based on the low-frequency data of the IMF components and the extreme value distribution condition; calculating a frequency domain characteristic factor of each IMF component; calculating a spike pulse coefficient of each IMF component; calculating a rock vibration deviation coefficient of each IMF component; obtaining a time domain characteristic factor of each IMF component; and taking a sum value of the frequency domain characteristic factor and the time domain characteristic factor as a rock vibration discriminant value of each IMF component, obtaining a denoised rock vibration signal, and detecting and collecting TBM space estimation surface construction data. According to the invention, the detection accuracy of the TBM space estimation surface construction data is improved.

Description

Technical Field

[0001] The present application relates to the field of data detection technology, and in particular to a method and system for detecting and collecting TBM air-inferred surface construction data. Background Art

[0002] Tunnel Boring Machine (TBM) is a complete set of tunnel construction equipment that integrates mechanical, electrical, hydraulic, sensor and information technology. TBM realizes continuous tunneling through the coordinated work of its rock breaking mechanism, propulsion mechanism and rock slag loading mechanism. During the TBM excavation process, when encountering special geological changes, equipment maintenance or construction adjustments, dry pushing operations are required. During the dry pushing process, the TBM's propulsion mechanism will work, but the rock breaking mechanism is not in working state.

[0003] During the TBM push-pushing process, poor geological conditions will affect construction safety and advancement efficiency. Through real-time detection and collection of construction data, the geological structure and equipment status can be understood in a timely manner to ensure the safety and stability of construction. In the process of data detection, the seismic wave method is to arrange a vibrator on the face to send out a vibration signal and use a detector to receive the vibration signal. The seismic wave method can be used for detection during the push-pushing process. However, during the data detection process, the collected vibration signal will be interfered by many factors, such as construction vibration interference, strong electromagnetic fields, TBM equipment operation noise, etc., so that the data quality obtained is low and cannot meet the detection requirements, affecting the accurate assessment of construction conditions. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a TBM air inference surface construction data detection and collection method and system, the technical solutions adopted are as follows: In a first aspect, an embodiment of the present application provides a method for detecting and collecting TBM air inference surface construction data, the method comprising the following steps: Collect vibration signals during TBM air-to-air surface construction; Use signal decomposition algorithm to obtain the IMF components and residual terms of the vibration signal; Obtain the periodic weight of each IMF component based on the low-frequency data and extreme value distribution of the IMF component; Based on the periodic weight, the discrete degree of the energy intensity of the frequency response of the IMF component and the average frequency, the frequency domain characteristic factor of each IMF component is obtained; Obtain the peak pulse coefficient of each IMF component based on the distribution of elements near the extreme value in the IMF component; The rock vibration deviation coefficient of each IMF component is obtained based on the correlation between the elements preceding and following in time order in the IMF component; Based on the rock vibration deviation coefficient, peak pulse coefficient and the degree of dispersion of the extreme values ​​in the IMF components, the time domain characteristic factors of each IMF component are obtained; The sum of the frequency domain characteristic factor and the time domain characteristic factor is used as the rock vibration discrimination value of each IMF component. The denoised rock vibration signal is obtained by combining the residual term, and the TBM air-inferred surface construction data is detected and collected.

[0005] Furthermore, the method for obtaining the periodic weight is: Each IMF component of the vibration signal is used as the input of a low-pass filter to obtain low-frequency data of each IMF component; Use discrete Fourier transform to obtain the frequency response of low-frequency data of each IMF component; Use the peak detection algorithm to obtain the average period of each IMF component; For each IMF component of the vibration signal, the product of the maximum value of the energy intensity of the frequency response of the low-frequency data of the IMF component and the average period is calculated as the periodicity weight of each IMF component.

[0006] Further, the method for obtaining the average period is: A peak detection algorithm is used to obtain all the maximum points in each IMF component, and the ratio of the duration of the IMF component to the total number of all the maximum points in the IMF component is taken as the average period of each IMF component.

[0007] Furthermore, the method for obtaining the frequency domain characteristic factor is: Use discrete Fourier transform to obtain the frequency response of each IMF component of the vibration signal; The calculation formula of the frequency domain characteristic factor is: ; In the formula, is the frequency domain characteristic factor of the i-th IMF component; represents the variance of the energy intensity of all frequencies in the frequency response of the i-th IMF component; is the cyclical weight of the ith IMF component; represents the mean of all frequencies in the frequency response of the i-th IMF component.

[0008] Further, the method for obtaining the peak pulse coefficient is: Based on the distribution of the maximum value points in the IMF components, each rising interval and each falling interval in the IMF components are obtained; The calculation formula of the peak pulse coefficient is: ; In the formula, is the peak impulse coefficient of the i-th IMF component; is the range of all elements in the pth rising interval of the ith IMF component, represents the total number of elements in the pth rising interval of the i-th IMF component, represents the range of all elements in the xth descending interval of the ith IMF component, is the total number of elements in the xth descending interval of the ith IMF component, m is the total number of ascending intervals in the ith IMF component, and n is the total number of descending intervals in the ith IMF component.

[0009] Furthermore, the method for obtaining each ascending interval and each descending interval is: For each IMF component of the vibration signal and each maximum point in the IMF component, the interval consisting of the IMF component data of a preset length before the maximum point is used as the rising interval, and the interval consisting of the IMF component data of a preset length after the maximum point is used as the falling interval.

[0010] Further, the method for obtaining the rock vibration deviation coefficient is: For each IMF component of the vibration signal, a sequence consisting of the first preset number of elements in the IMF component in positive time order is used as the preceding sequence of each IMF component, and a sequence consisting of the other elements in the IMF component except the preceding sequence in positive time order is used as the succeeding sequence of each IMF component; The DTW distance between the preceding sequence and the succeeding sequence in each IMF component is calculated as the rock vibration deviation coefficient of each IMF component of the vibration signal.

[0011] Furthermore, the calculation formula of the time domain characteristic factor is: ; In the formula, is the time domain characteristic factor of the i-th IMF component; represents the average of the absolute values ​​of all elements in the i-th IMF component; represents the variance of all maxima in the i-th IMF component, represents the variance of all minima in the ith IMF component, is the rock vibration deviation coefficient of the i-th IMF component, is the peak impulse coefficient of the i-th IMF component.

[0012] Furthermore, the step of obtaining the denoised rock vibration signal and detecting and collecting TBM air-inferred surface construction data includes: The rock vibration discrimination values ​​of all IMF components of the vibration signal are used as the input of the Otsu threshold segmentation algorithm to obtain the optimal segmentation threshold, and the IMF components corresponding to the rock vibration discrimination values ​​greater than the optimal segmentation threshold are used as the IMF components of the rock vibration signal; The IMF component and residual term of the rock vibration signal are superimposed to obtain the denoised rock vibration signal.

[0013] In the second aspect, an embodiment of the present application also provides a TBM empty inference surface construction data detection and collection system, including a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, the steps of any one of the above methods are implemented.

[0014] This application has at least the following beneficial effects: The present application decomposes the vibration signal containing noise through the EMD algorithm to obtain several IMF components and a residual term, and then screens out the rock vibration signal from several IMF components by analyzing the frequency, energy concentration, periodicity strength, and front-back correlation, amplitude, stability, and waveform characteristics of the rock vibration signal, operating noise, mechanical vibration, and electromagnetic wave signal in the frequency domain, and removes the interference of noise, so that the rock vibration signal finally collected can more accurately reflect the rock characteristics, enhance the quality of the data, and improve the detection accuracy of TBM aerial inference surface construction data. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 A flowchart of a method for detecting and collecting TBM empty inference surface construction data provided by an embodiment of the present application; Figure 2 A flow chart for obtaining rock vibration discrimination values ​​provided in one embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of a TBM air inference surface construction data detection and collection method and system proposed in the present application, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0019] The following is a detailed description of a TBM air inference surface construction data detection and collection method and system provided by the present application in conjunction with the accompanying drawings.

[0020] See also Figure 1 , which shows a flow chart of the steps of a TBM air inference surface construction data detection and collection method provided by an embodiment of the present application, the method comprising the following steps: Step S1, collecting vibration signals during TBM air inference surface construction.

[0021] When a fracture zone appears in the rock in front of the tunnel face, the rock in the fracture zone usually has more cracks, which will change the wave velocity propagation characteristics of the rock; when water flow appears in front of the tunnel face, the wave velocity propagation characteristics of the water flow and the rock are also significantly different. Therefore, the wave velocity propagation characteristics of the vibration signal in the rock in front of the tunnel face can be used to detect whether a fracture zone or water flow appears in the rock.

[0022] A vibrator is arranged in the borehole of the tunnel face side wall to transmit vibration signals to the surrounding rock in front; a detector is arranged in the borehole of the tunnel side wall at a certain distance from the vibrator to receive the vibration signal. The distance between the vibrator and the detector should be determined according to the propagation speed and attenuation characteristics of the vibration signal in the rock. Usually, the distance between the detector and the vibrator is set in the range of 40m to 80m. In this embodiment, the distance between the detector and the vibrator is set to 50m. The implementer can select other values ​​within the range according to the actual situation.

[0023] Step S2, using a signal decomposition algorithm to obtain each IMF component and residual term of the vibration signal; obtaining the periodic weight of each IMF component based on the low-frequency data and extreme value distribution of the IMF component; obtaining the frequency domain characteristic factor of each IMF component based on the periodic weight, the discrete degree of the energy intensity of the frequency response of the IMF component and the average frequency; obtaining the peak pulse coefficient of each IMF component based on the distribution of elements near the extreme value in the IMF component.

[0024] Since the geophone can not only receive vibration signals, but also sound waves and electromagnetic waves, the operation of the TBM equipment in the construction tunnel will generate noise and vibration, and the electrical equipment inside the TBM will also generate electromagnetic waves. These factors will interfere with the acquisition of vibration signals by the geophone, making the collected vibration signals unable to accurately reflect the rock characteristics. Therefore, it is necessary to clean the collected data.

[0025] During the operation of TBM equipment, operating noise, mechanical vibration and electromagnetic waves are generated, and these signals are always present. The vibration signal collected by the detector is used as the input of the empirical mode decomposition (EMD) algorithm, the number of intrinsic mode functions (IMFs) is set to s, the termination threshold value range is 0.2-0.3, and the IMF components of the vibration signal and a residual term are output. In this embodiment, the value of s is 20, and the implementer can select other values ​​according to the actual situation.

[0026] The vibration signal is decomposed into various IMF components by using the EMD algorithm, wherein each IMF component contains local characteristic information of the vibration signal at different time scales. The Empirical Mode Decomposition (EMD) algorithm is a well-known technology and will not be described in detail in this embodiment.

[0027] Furthermore, it is necessary to distinguish each IMF component and determine which IMF components may belong to rock vibration signals. Operation noise mainly comes from the mechanical operation of TBM equipment, such as hydraulic system operation, ventilation equipment, etc. This noise usually manifests as random fluctuations, there is no obvious correlation between the waveforms before and after, and the frequency is usually concentrated in the high frequency band (above 100Hz), and the energy distribution is relatively dispersed; mechanical vibration mainly comes from the impact of the cutterhead, the load transfer of the propulsion system, and the vibration of the engine. This signal is mainly transmitted near the TBM equipment, so the amplitude of mechanical vibration is relatively smaller than that of rock vibration signals, and the frequency is mainly concentrated in the low frequency band (below 20Hz), and the energy distribution in the frequency domain is relatively concentrated; electromagnetic wave signals mainly come from the electrical system and cables of TBM, usually manifested as spike pulses or high-frequency oscillations, and the frequency is usually concentrated in the high frequency band (above 100Hz).

[0028] The rock vibration signal is the signal emitted by the vibrator to the rock. It has a relatively large amplitude and is usually manifested as a low-frequency (below 20 Hz) and steady fluctuation. The waveforms before and after are usually relatively consistent, with strong periodicity. The energy distribution in the frequency domain is relatively concentrated and the intensity is relatively large.

[0029] Each IMF component is used as the input of a low-pass filter to obtain low-frequency data of each IMF component. In this embodiment, the cutoff frequency of the low-pass filter is set to 20 Hz. The principle of the low-pass filter is a well-known technology, and the specific process will not be repeated. The low-frequency data of each IMF component is used as the input of a discrete Fourier transform (DFT) to obtain the frequency response of the low-frequency data of each IMF component. DFT is a well-known technology, and the specific process will not be repeated. Among them, the frequency response includes the energy intensity and frequency of the low-frequency data, which are well-known and will not be repeated in this embodiment.

[0030] Further, a peak detection algorithm is used to obtain all the maximum points in each IMF component, and the ratio of the duration of the IMF component to the total number of all the maximum points in the IMF component is used as the average period of each IMF component. The peak detection algorithm is a well-known technology and will not be described in detail in this embodiment.

[0031] Based on the above analysis, in order to reflect the strength of the periodicity of the low-frequency data of each IMF component, the periodic weight of each IMF component is obtained based on the distribution of the maximum values ​​of each IMF component and the energy intensity of the low-frequency data of the IMF component. The calculation formula is: ; In the formula, is the cyclical weight of the ith IMF component; is the average period of the ith IMF component, Represents the maximum value of the energy intensity in the frequency response of the low-frequency data of the i-th IMF component.

[0032] It should be noted that for each IMF component of the vibration signal, when the average period of the IMF component is longer and the maximum value of the energy intensity of the frequency response of the low-frequency data of the IMF is larger, it is more consistent with the characteristics of the rock vibration signal. At this time, the periodic weight obtained is The larger the value of is, the stronger the periodicity of the low-frequency data of the ith IMF component is. On the contrary, when the characteristics of the IMF component are less consistent with the characteristics of the rock vibration signal, the periodicity weight obtained is The smaller the value of .

[0033] Furthermore, each IMF component of the vibration signal is input into a discrete Fourier transform algorithm to obtain a frequency response of each IMF component of the vibration signal.

[0034] Furthermore, in order to reflect the similarity between the frequency domain characteristics of each IMF component and the frequency domain characteristics of the rock vibration signal, the frequency domain characteristic factor of each IMF component is obtained based on the discrete degree of the frequency response of the low-frequency data of the IMF component, the periodic weight and the average frequency of the frequency response of the IMF component. The calculation formula is: ; In the formula, is the frequency domain characteristic factor of the i-th IMF component; represents the variance of the energy intensity of all frequencies in the frequency response of the i-th IMF component; is the cyclical weight of the ith IMF component; represents the mean of all frequencies in the frequency response of the i-th IMF component.

[0035] It should be noted that for each IMF component, when the energy distribution of the frequency response of the low-frequency data of the IMF component is relatively concentrated, the energy will be concentrated on certain frequency components, resulting in the situation that the energy of some frequency components is very large and the energy of some frequency components is very small. At this time, the variance of the energy intensity of all frequencies in the frequency response of the low-frequency data of the IMF component obtained is larger; the stronger the periodicity of the IMF component, the more the frequency domain characteristics of the IMF component conform to the characteristics of the rock vibration signal, and the larger the value of the periodicity weight obtained at this time; when there are more low-frequency components in the frequency domain of the IMF energy, the more it conforms to the frequency domain characteristics of the rock vibration signal, The smaller the value of , the larger the value of the frequency domain characteristic factor of the IMF component obtained at this time; conversely, the smaller the value of the frequency domain characteristic factor of the IMF component obtained.

[0036] Further, for each IMF component of the vibration signal, an extreme point detection algorithm is used to obtain each maximum value and each minimum value in the IMF component. For each maximum point in the IMF component, the interval composed of the IMF component data of the preset length before the maximum point is used as the rising interval, and the interval composed of the IMF component data of the preset length after the maximum point is used as the descending interval; wherein the extreme point detection algorithm is a well-known technology and is not described in detail in this embodiment. In this embodiment, the value of the preset length is 3, and the implementer can select other values ​​according to actual conditions.

[0037] Furthermore, based on the distribution of elements in the rising interval and falling interval of the IMF component, the peak pulse coefficient of each IMF component of the vibration signal is obtained, and the calculation formula is: ; In the formula, is the peak impulse coefficient of the i-th IMF component; is the range of all elements in the pth rising interval of the ith IMF component, represents the total number of elements in the pth rising interval of the i-th IMF component, represents the range of all elements in the xth descending interval of the ith IMF component, is the total number of elements in the xth descending interval of the ith IMF component, m is the total number of ascending intervals in the ith IMF component, and n is the total number of descending intervals in the ith IMF component.

[0038] It should be noted that The larger the value of is, the steeper the pth rising interval of the ith IMF component is, and the more likely it is that the rising interval is the rising edge of the spike pulse. The larger the value of is, the steeper the xth falling interval of the ith IMF component is, and the more likely it is that the falling interval is the falling edge of the spike pulse.

[0039] Step S3, based on the correlation between the elements in front and behind in time order in the IMF component, obtain the rock vibration deviation coefficient of each IMF component; based on the rock vibration deviation coefficient, the peak pulse coefficient and the discrete degree of the extreme values ​​in the IMF component, obtain the time domain characteristic factor of each IMF component; and use the sum of the frequency domain characteristic factor and the time domain characteristic factor as the rock vibration discrimination value of each IMF component.

[0040] Further, for each IMF component of the vibration signal, the sequence of the first preset number of elements in the IMF component in time order is used as the first term sequence of each IMF component, and the sequence of the other elements in the IMF component except the first term sequence in time order is used as the second term sequence of each IMF component. The value of the preset number in this embodiment is , where j is the total number of elements in the IMF component, is a rounding function, and the implementer can select other values ​​according to the actual situation; the DTW distance between the preceding sequence and the succeeding sequence in each IMF component is calculated as the rock vibration deviation coefficient of each IMF component of the vibration signal.

[0041] Specifically, when the value of the rock vibration deviation coefficient is larger, the correlation between the waveform of the first half and the waveform of the second half of the IMF component of the vibration signal is smaller, and the less it conforms to the characteristics of the rock vibration signal; conversely, when the value of the rock vibration deviation coefficient is smaller, the characteristics of the IMF component of the vibration signal are more consistent with the characteristics of the rock vibration signal.

[0042] Furthermore, in order to reflect the similarity between the time domain characteristics of the IMF components of the vibration signal and the time domain characteristics of the rock vibration signal, the time domain characteristic factors of each IMF component of the vibration signal are obtained based on the peak pulse coefficient, the rock vibration deviation coefficient and the average and discrete degree of the elements in the IMF component. The calculation formula is: ; In the formula, is the time domain characteristic factor of the i-th IMF component; represents the average of the absolute values ​​of all elements in the i-th IMF component; represents the variance of all maxima in the i-th IMF component, represents the variance of all minima in the ith IMF component, is the rock vibration deviation coefficient of the i-th IMF component, is the peak impulse coefficient of the i-th IMF component.

[0043] It should be noted that The larger the value of is, the larger the amplitude of the i-th IMF component is, and the i-th IMF component is more likely to be a rock vibration signal; The smaller the value is, the smoother the waveform of the i-th IMF component is, and the i-th IMF component is more likely to be a rock vibration signal; the smaller the value of the rock vibration deviation coefficient is, the stronger the correlation between the waveform before and after the i-th IMF component is, and the i-th IMF component is more likely to be a rock vibration signal; The smaller the value of is, the less the waveform of the i-th IMF component is a spike pulse, and the i-th IMF component is more likely to be a rock vibration signal.

[0044] Furthermore, according to the frequency domain characteristic factor and time domain characteristic factor of the i-th IMF component, a rock vibration discrimination value is constructed to characterize the possibility that the i-th IMF component belongs to the rock vibration signal. The calculation formula is: ; In the formula, is the rock vibration discrimination value of the i-th IMF component; is the frequency domain characteristic factor of the i-th IMF component; is the time domain characteristic factor of the i-th IMF component. Among them, the flow chart for obtaining the rock vibration discrimination value is as follows: Figure 2 shown.

[0045] It should be noted that when the IMF component of the vibration signal is more likely to be a rock vibration signal, the IMF component of the vibration signal is more consistent with the characteristics of the rock vibration signal in both the time domain and the frequency domain. At this time, the larger the values ​​of the frequency domain characteristic factor and the time domain characteristic factor obtained, the larger the rock vibration discrimination value; conversely, the smaller the rock vibration discrimination value obtained.

[0046] The rock vibration discrimination value of each IMF component is calculated, and all rock vibration discrimination values ​​are used as the input of the Otsu threshold segmentation algorithm to obtain the optimal segmentation threshold. The IMF component corresponding to the rock vibration discrimination value greater than the optimal segmentation threshold is used as the IMF component of the rock vibration signal.

[0047] So far, the IMF components belonging to the rock vibration signal have been obtained.

[0048] Step S4, combining the residual term to obtain the denoised rock vibration signal, and detecting and collecting the TBM air inference surface construction data.

[0049] Furthermore, the IMF component and the residual term belonging to the rock vibration signal are superimposed to obtain the denoised rock vibration signal, obtain a more accurate rock vibration signal, and complete the detection and collection of TBM air inference surface construction data.

[0050] Based on the same inventive concept as the above-mentioned method, an embodiment of the present application also provides a TBM empty inference surface construction data detection and collection system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above-mentioned TBM empty inference surface construction data detection and collection methods are implemented.

[0051] It should be noted that the above sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0052] The various embodiments in the present application are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0053] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for detecting and collecting TBM air inference surface construction data, characterized in that: The method comprises the following steps: Collect vibration signals during TBM air-to-air surface construction; Use signal decomposition algorithm to obtain the IMF components and residual terms of the vibration signal; Obtain the periodic weight of each IMF component based on the low-frequency data and extreme value distribution of the IMF component; Based on the periodic weight, the discrete degree of the energy intensity of the frequency response of the IMF component and the average frequency, the frequency domain characteristic factor of each IMF component is obtained; Obtain the peak pulse coefficient of each IMF component based on the distribution of elements near the extreme value in the IMF component; The rock vibration deviation coefficient of each IMF component is obtained based on the correlation between the elements preceding and following in time order in the IMF component; Based on the rock vibration deviation coefficient, peak pulse coefficient and the degree of dispersion of the extreme values ​​in the IMF components, the time domain characteristic factors of each IMF component are obtained; The sum of the frequency domain characteristic factor and the time domain characteristic factor is used as the rock vibration discrimination value of each IMF component. The denoised rock vibration signal is obtained by combining the residual term, and the TBM air-inferred surface construction data is detected and collected.

2. A TBM air inference surface construction data detection and collection method as claimed in claim 1, characterized in that: The method for obtaining the periodic weight is: Each IMF component of the vibration signal is used as the input of a low-pass filter to obtain low-frequency data of each IMF component; Use discrete Fourier transform to obtain the frequency response of low-frequency data of each IMF component; Use the peak detection algorithm to obtain the average period of each IMF component; For each IMF component of the vibration signal, the product of the maximum value of the energy intensity of the frequency response of the low-frequency data of the IMF component and the average period is calculated as the periodicity weight of each IMF component.

3. A TBM air inference surface construction data detection and collection method as claimed in claim 2, characterized in that: The method for obtaining the average period is: A peak detection algorithm is used to obtain all the maximum points in each IMF component, and the ratio of the duration of the IMF component to the total number of all the maximum points in the IMF component is taken as the average period of each IMF component.

4. A TBM air inference surface construction data detection and collection method as claimed in claim 1, characterized in that: The method for obtaining the frequency domain characteristic factor is: Use discrete Fourier transform to obtain the frequency response of each IMF component of the vibration signal; The calculation formula of the frequency domain characteristic factor is: ; In the formula, is the frequency domain characteristic factor of the i-th IMF component; represents the variance of the energy intensity of all frequencies in the frequency response of the i-th IMF component; is the cyclical weight of the ith IMF component; represents the mean of all frequencies in the frequency response of the i-th IMF component.

5. A TBM air inference surface construction data detection and collection method as claimed in claim 1, characterized in that: The method for obtaining the peak pulse coefficient is: Based on the distribution of the maximum value points in the IMF components, each rising interval and each falling interval in the IMF components are obtained; The calculation formula of the peak pulse coefficient is: ; In the formula, is the peak impulse coefficient of the i-th IMF component; is the range of all elements in the pth rising interval of the ith IMF component, represents the total number of elements in the pth rising interval of the i-th IMF component, represents the range of all elements in the xth descending interval of the ith IMF component, is the total number of elements in the xth descending interval of the ith IMF component, m is the total number of ascending intervals in the ith IMF component, and n is the total number of descending intervals in the ith IMF component.

6. A TBM air inference surface construction data detection and collection method as claimed in claim 5, characterized in that: The method for obtaining each ascending interval and each descending interval is: For each IMF component of the vibration signal and each maximum point in the IMF component, the interval consisting of the IMF component data of a preset length before the maximum point is used as the rising interval, and the interval consisting of the IMF component data of a preset length after the maximum point is used as the falling interval.

7. A method for detecting and collecting TBM air inference surface construction data according to claim 1, characterized in that: The method for obtaining the rock vibration deviation coefficient is: For each IMF component of the vibration signal, a sequence consisting of the first preset number of elements in the IMF component in positive time order is used as the preceding sequence of each IMF component, and a sequence consisting of the other elements in the IMF component except the preceding sequence in positive time order is used as the succeeding sequence of each IMF component; The DTW distance between the preceding sequence and the succeeding sequence in each IMF component is calculated as the rock vibration deviation coefficient of each IMF component of the vibration signal.

8. A method for detecting and collecting TBM air inference surface construction data according to claim 1, characterized in that: The calculation formula of the time domain characteristic factor is: ; In the formula, is the time domain characteristic factor of the i-th IMF component; represents the average of the absolute values ​​of all elements in the i-th IMF component; represents the variance of all maxima in the i-th IMF component, represents the variance of all minima in the ith IMF component, is the rock vibration deviation coefficient of the i-th IMF component, is the peak impulse coefficient of the i-th IMF component.

9. A method for detecting and collecting TBM air inference surface construction data according to claim 1, characterized in that: The method of obtaining the denoised rock vibration signal and detecting and collecting TBM air-inferred surface construction data includes: The rock vibration discrimination values ​​of all IMF components of the vibration signal are used as the input of the Otsu threshold segmentation algorithm to obtain the optimal segmentation threshold, and the IMF components corresponding to the rock vibration discrimination values ​​greater than the optimal segmentation threshold are used as the IMF components of the rock vibration signal; The IMF component and residual term of the rock vibration signal are superimposed to obtain the denoised rock vibration signal.

10. A TBM empty inference surface construction data detection and collection system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of a TBM empty inference surface construction data detection and collection method as described in any one of claims 1-9 are implemented.

Images