Distributed optical fiber vibration signal processing and interpretation method for grouting pipeline monitoring
By using distributed fiber optic acoustic sensing technology to process signals in grouting pipelines, the problems of large blind spots and difficult positioning in traditional monitoring methods are solved. This enables real-time, high-precision monitoring of grouting pipelines and early identification of blockage points, improving the signal-to-noise ratio and monitoring sensitivity, and supporting long-term, uninterrupted real-time monitoring.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- XIAN RES INST OF CHINA COAL TECH & ENG GRP CORP
- Filing Date
- 2026-02-09
- Publication Date
- 2026-06-16
AI Technical Summary
In existing technologies, grouting pipeline monitoring relies on point-type equipment, which has problems such as large blind spots and difficulty in positioning. Traditional monitoring methods cannot achieve real-time, high-precision status monitoring of grouting pipelines, especially the early identification and location of blockage events.
Distributed fiber optic acoustic wave sensing technology is used to acquire the raw DAS signal of the grouting pipeline, perform preprocessing, DC removal, anti-aliasing and noise attenuation processing, convert it into a strain rate signal, and perform gain compensation and data frequency division energy calculation. Combined with variance feature resampling, dynamic visualization and threshold alarm are realized.
It enables real-time, high-precision monitoring of grouting pipelines, allowing for early identification and precise location of blockages. It improves the signal-to-noise ratio and monitoring sensitivity, compresses data volume by several orders of magnitude, supports long-term, uninterrupted real-time monitoring, and provides intuitive pipeline status perception and accurate decision support.
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Figure CN122221082A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of geophysical exploration and signal processing, and specifically to a distributed fiber optic vibration signal processing and interpretation method for monitoring grouting pipelines. Background Technology
[0002] In underground engineering and mine backfilling, grouting technology is a key process for enhancing ground stability. This technology relies on high-pressure pipeline transport of grout, but problems such as grout coagulation and impurity deposition can easily lead to pipeline blockage, seriously affecting construction safety and progress. Therefore, real-time, high-precision status monitoring of grouting pipelines, especially the early identification and location of blockage events, is crucial. Traditional monitoring relies on point-based equipment such as pressure sensors, which suffers from large blind spots and difficulty in location. Distributed fiber optic acoustic sensing (DAS) technology, with its unique advantages of long distance and high spatiotemporal resolution, provides a new approach to solving this problem. It transforms the optical cable laid along the pipeline into a continuously distributed "stethoscope," capable of capturing in real time the acoustic vibration signals of the entire pipeline generated by grout flow and even precursors to blockage. However, current DAS signal processing for grout blockage scenarios is still in its early stages of exploration and cannot yet achieve the interpretation of distributed fiber optic vibration signals from grouting pipeline monitoring. Summary of the Invention
[0003] To overcome at least one deficiency in the prior art, this application provides a distributed optical fiber vibration signal processing and interpretation method for grouting pipeline monitoring.
[0004] Firstly, a distributed optical fiber vibration signal processing and interpretation method for grouting pipeline monitoring is provided, including: The raw DAS signal of the grouting pipeline is acquired and preprocessed to obtain the preprocessed DAS signal; the preprocessed DAS signal includes the raw DAS signal at each location of the grouting pipeline. The preprocessed DAS signal is subjected to DC removal, anti-aliasing, and noise attenuation to obtain a denoised DAS signal. The denoised DAS signal is converted from strain to strain rate to obtain the converted DAS signal. Gain compensation is applied to the converted DAS signal to obtain the gain-compensated DAS signal. The data frequency division energy is calculated for the DAS signal after gain compensation, and the variance feature is resampled based on the data frequency division energy to obtain the resampled feature value signal. The resampled feature value signal is dynamically visualized, and a threshold is set. Based on the relationship between the threshold and the feature value, the location of the abnormal pipeline is determined and an alarm is triggered.
[0005] In one embodiment, the preprocessed DAS signal is subjected to DC removal, anti-aliasing, and noise attenuation processing to obtain a denoised DAS signal, including: The preprocessed DAS signal includes the DAS signal of each channel. The average value of the DAS signal of each channel is subtracted to achieve DC removal. Each channel corresponds to a pipe position. A low-pass filter is used to process the DAS signal of each channel to achieve anti-aliasing processing; the cutoff frequency of the low-pass filter is greater than the effective frequency band. Principal component analysis was used to attenuate the noise in the DAS signals of each channel to obtain the denoised DAS signals.
[0006] In one embodiment, the denoised DAS signal is converted from strain to strain rate to obtain the converted DAS signal, using the following formula:
[0007] in, Let be the strain rate of the DAS signal at time t. The strain of the DAS signal at time t+1 The strain of the DAS signal at time t-1 The time sampling interval is denoted as .
[0008] In one embodiment, gain compensation is performed on the converted DAS signal, using a gain compensation coefficient of:
[0009] in, For fiber optic propagation distance The compensation coefficient at the meter. This is the optical cable attenuation coefficient.
[0010] In one embodiment, the data frequency division energy of the gain-compensated DAS signal is calculated using the following formula:
[0011]
[0012] in, Represents time frame Data frequency division energy in the dominant frequency band This represents the lower limit of the dominant frequency band. This is the upper limit of the dominant frequency band. The result is the short-time Fourier transform of the DAS signal. The power spectral density of the DAS signal. For time frames, This refers to a frequency point.
[0013] In one embodiment, the DAS signal is resampled based on the data frequency division energy to obtain the resampled eigenvalue signal, using the following formula:
[0014] in, For the first The variance of each resampled signal. This is the length of the resampling window. ) represents a time frame Data frequency division energy in the dominant frequency band The sampling point number is used for resampling. This represents the number of sampling points recorded in the original DAS signal.
[0015] Secondly, a distributed fiber optic vibration signal processing and interpretation device for monitoring grouting pipelines is provided, comprising: The data preprocessing module is used to acquire the raw DAS signal of the grouting pipeline and preprocess the raw DAS signal to obtain the preprocessed DAS signal; the preprocessed DAS signal includes the raw DAS signal at each location of the grouting pipeline. The noise reduction module is used to perform DC removal, anti-aliasing, and noise attenuation on the preprocessed DAS signal to obtain a noise-reduced DAS signal. The conversion module is used to convert the denoised DAS signal from strain to strain rate to obtain the converted DAS signal. The gain compensation module is used to compensate the gain of the converted DAS signal to obtain the gain-compensated DAS signal. The resampling module is used to calculate the data frequency division energy of the DAS signal after gain compensation, and to perform variance feature resampling on the DAS signal based on the data frequency division energy to obtain the resampled feature value signal. The anomaly detection module is used to dynamically visualize the resampled feature value signal, set a threshold, and determine the location of the abnormal pipeline based on the relationship between the threshold and the feature value, and trigger an alarm.
[0016] Compared with the prior art, this application has the following beneficial effects: 1. Real-time acquisition and format conversion of DAS signals are achieved, converting DAS signals into strain rate signals that are sensitive to grouting pipeline monitoring, laying the foundation for DAS signal processing and interpretation for grouting pipeline monitoring.
[0017] 2. Real-time DC removal, common-mode noise attenuation, and random noise suppression are applied to the DAS signal to effectively overcome strong background noise interference in the DAS signal and improve the signal-to-noise ratio of grouting-related vibration signals.
[0018] 3. By frequency division processing, the dominant frequency band of the DAS vibration signal in the monitoring pipeline is extracted. The amplitude energy of the DAS signal is extracted within the dominant frequency band. Based on this, DAS signal downsampling processing based on variance characteristics is performed to realize the dynamic processing of DAS signal from data recording to real-time feature extraction. Attached Figure Description
[0019] This application can be better understood by referring to the description given below in conjunction with the accompanying drawings, which, together with the detailed description below, are incorporated in and form part of this specification. In the drawings: Figure 1 A flowchart of a distributed optical fiber vibration signal processing and interpretation method for grouting pipeline monitoring is shown. Figure 2 The raw DAS signal from the grouting pipeline monitoring is shown; Figure 3 The results of DAS signal preprocessing are shown; Figure 4 The signal after DAS signal noise suppression is shown; Figure 5 The results of FBE in the dominant frequency band after the DAS signal is resampled based on variance features are shown. Detailed Implementation
[0020] Exemplary embodiments of the present application will be described below with reference to the accompanying drawings. For clarity and brevity, not all features of the actual embodiments are described in the specification. However, it should be understood that many embodiment-specific decisions can be made in the development of any such actual embodiment to achieve the developer’s specific objectives, and these decisions may vary as the embodiments differ.
[0021] It should also be noted that, in order to avoid obscuring this application with unnecessary details, only the device structure closely related to the solution of this application is shown in the accompanying drawings, while other details that are not closely related to this application are omitted.
[0022] It should be understood that this application is not limited to the described embodiments by virtue of the following description with reference to the accompanying drawings. In this document, embodiments may be combined with each other, features may be substituted or borrowed between different embodiments, and one or more features may be omitted in one embodiment, where feasible.
[0023] This application provides a distributed optical fiber vibration signal processing and interpretation method for monitoring grouting pipelines. Figure 1 A flowchart illustrating a distributed fiber optic vibration signal processing and interpretation method for grouting pipeline monitoring is shown. (See [link]). Figure 1The method mainly includes the following steps: Step S1: Obtain the original DAS signal of the grouting pipe and preprocess the original DAS signal to obtain the preprocessed DAS signal; the preprocessed DAS signal includes the original DAS signal at each location of the grouting pipe.
[0024] The system reads and decodes the raw DAS signal in real time, performs channel screening based on the valid channel information of pipeline monitoring, removes bad channels, and performs interpolation to obtain the complete DAS signal for the monitoring space. Furthermore, it performs fiber optic calibration to accurately map the channel number to the actual pipeline location, and stores the calibrated pipeline information in the DAS signal header.
[0025] A common method for fiber optic calibration is to determine the fiber's position using physical points or markers of known length or location. Then, linear interpolation is performed between these marker points.
[0026] In the above formula Given the location, The location of the calibration point is given by denoted ...
[0027] Step S2: Perform DC removal, anti-aliasing, and noise attenuation processing on the preprocessed DAS signal to obtain the denoised DAS signal.
[0028] First, the preprocessed DAS signal includes the DAS signal of each channel. The average value of the DAS signal of each channel is subtracted to achieve DC removal. Each channel corresponds to a pipe position. Then, a low-pass filter is used to process the DAS signal of each channel to achieve anti-aliasing processing; the cutoff frequency of the low-pass filter is greater than the effective frequency band range; here, through prior knowledge or field tests, the main frequency range of vibration generated by the grouting process (grout flow, friction, impact) is analyzed to determine the effective frequency band range.
[0029] Then, principal component analysis is used to attenuate the noise in the DAS signals of each channel to obtain the denoised DAS signals.
[0030] Here, considering that common-mode noise exhibits high consistency across all sensing channels, and that effective vibration signals are typically locally correlated, common-mode noise usually manifests as the first principal component with the highest energy. Therefore, a covariance matrix is constructed for the DAS signal of each channel, and eigenvalue decomposition is performed to obtain eigenvalues and eigenvectors. Based on the eigenvalues, the first principal component corresponding to the highest energy is removed. A cumulative contribution rate threshold is determined, and components with contribution rates less than the cumulative contribution rate threshold are removed. The DAS signal is reconstructed using the retained principal components to obtain the denoised DAS signal.
[0031] Step S3: Convert the denoised DAS signal from strain to strain rate to obtain the converted DAS signal.
[0032] Pipe blockage is essentially the obstruction of fluid movement, leading to a redistribution and violent fluctuations of pressure before and after the blockage point. These pressure fluctuations are immediately transmitted to the pipe wall through the fluid, causing high-frequency, micro-amplitude vibrations. Therefore, strain rate directly corresponds to the vibration velocity and is extremely sensitive to these sudden, high-frequency pressure shocks, clearly capturing the transient characteristics of blockage occurrence and evolution. The core physical response of DAS (Digital Optical Array) is fiber optic strain, and the directly measured vibration signal is often output as strain. Therefore, a physical field conversion from strain to strain rate is required for the acquired DAS signal. Strain rate is the rate of change of strain over time; therefore, the time partial derivative of strain is the strain rate.
[0033] In the above formula, let the fiber optic cable be along... Axis layout, For the spatial location of the optical fiber, For fiber optic cable in position The strain of the particle at point t at time t. Considering that the differential relationship in the above formula is based on the assumption of a continuous medium, and that the DAS is actually a discrete sensing unit, a numerical differential approximation is used for calculation:
[0034] in, Let be the strain rate of the DAS signal (fiber optic signal) at time t. The strain of the DAS signal at time t+1 The strain of the DAS signal at time t-1 The time sampling interval is denoted as .
[0035] Step S4: Perform gain compensation on the converted DAS signal to obtain the gain-compensated DAS signal.
[0036] When a signal propagates in an optical fiber, its intensity decreases exponentially with increasing transmission distance due to factors such as absorption, scattering, and bending losses. This means that at locations farther from the DAS demodulator, the backscattered Rayleigh signal generated by the same physical event will be weaker, resulting in a decrease in the measured amplitude with distance. To achieve signal consistency along the fiber direction, distance gain compensation is required for the acquired DAS signal in long-distance pipeline monitoring.
[0037] The power attenuation of laser light propagating in optical fiber follows the Beer-Lambert law, i.e., the exponential attenuation model:
[0038] in: It is the optical power at the propagation distance z. It is the initial optical power. It is the attenuation coefficient of optical fiber (unit: 1 / m). This is the propagation distance (unit: meters). Based on this, the fiber gain compensation coefficient G(z) is obtained:
[0039] in, For fiber optic propagation distance The compensation coefficient at the meter. This is the optical cable attenuation coefficient.
[0040] Step S5: Calculate the data frequency division energy of the DAS signal after gain compensation, and perform variance feature resampling on the DAS signal based on the data frequency division energy to obtain the resampled feature value signal.
[0041] Based on the frequency characteristics (dominant frequency band) of vibrations generated by grout flow, friction against pipe walls, and filling of voids during the grouting process, a short-time Fourier transform is performed on the DAS strain rate signal after gain compensation. The preprocessed signal is decomposed into sub-bands within the dominant frequency band, and the mean square energy of each sub-band signal is calculated, thus realizing the extraction of frequency band energy of non-stationary noise signals at various locations.
[0042] In the specific processing steps, a short-time Fourier transform is performed by selecting an appropriate time window length and number of overlap points to decompose the DAS signal into multiple sub-band signals. After calculation, a complex matrix is obtained, where each column corresponds to a time frame and each row corresponds to a frequency point. The square of the modulus of each element in the complex matrix is calculated to obtain the power spectral density of the DAS signal.
[0043]
[0044] Based on the DAS signal spectrum during grouting, the dominant frequency band for monitoring the grouting pipeline is determined. For each time frame, the power spectrum values at all frequency points within the dominant frequency band are summed.
[0045] in, Represents time frame Data frequency division energy in the dominant frequency band This represents the lower limit of the dominant frequency band. This is the upper limit of the dominant frequency band. The result is the short-time Fourier transform of the DAS signal. The power spectral density of the DAS signal. For time frames, This refers to a frequency point.
[0046] Based on the calculated FBE value, a feature extraction thinning method is used to resample the DAS signal using variance features. The specific formula is as follows:
[0047] in, For the first The variance of each resampled signal. This is the length of the resampling window. ) represents a time frame Data frequency division energy in the dominant frequency band The sampling point number is used for resampling. This represents the number of sampling points recorded in the original DAS signal.
[0048] After variance-based feature resampling, the original DAS signal is downsampled in the time dimension to obtain a dataset that retains the key dynamic features of the grouting process while significantly reducing the amount of data, making it more suitable for long-term monitoring and real-time analysis. This shifts DAS-based grouting pipeline monitoring from "data recording" to "information extraction," laying a solid foundation for subsequent accurate analysis.
[0049] Step S6: Dynamically visualize the resampled feature value signal, set a threshold, and determine the location of the abnormal pipeline and trigger an alarm based on the relationship between the threshold and the feature value.
[0050] First, the processed DAS measurement data is dynamically visualized, displaying the processed feature set in real time as a waterfall plot. The X-axis represents time, the Y-axis represents pipe location, and the color intensity represents the magnitude of the feature values (variance / energy). Operators can intuitively see abnormal information on the pipe during the grouting process through the DAS visualization.
[0051] Based on visualization, feature analysis and event identification are performed. When the slurry flows stably, the generated vibration signal is relatively stable and continuous, appearing as a "background value" with low amplitude and small fluctuations in the feature sequence. When a local blockage occurs in the pipeline, strong, transient vibration events will occur before and after the blockage point due to fluid impact and pressure fluctuations. This will appear as a sudden spike pulse in the feature sequence. Due to the spatial continuity of DAS, the blockage point can be accurately located by analyzing the spatial location (channel number) of the spike pulse. The feature value at the blockage point will be significantly higher than the feature values upstream and downstream. By setting a threshold, when the feature values of one or more adjacent channels continuously and significantly exceed the threshold, the system triggers an alarm. The alarm information should include: alarm time, precise pipeline location (meters) of the blockage point, and event intensity (feature value magnitude).
[0052] In summary, this application proposes a feature extraction and resampling method based on the energy variance of the dominant frequency band. Its core lies in calculating the signal energy variance using a sliding time window, which is then used as the feature sampling point. The variance is extremely sensitive to high-frequency transient vibrations, enabling this application to achieve extreme data compression while prioritizing and completely preserving key dynamic information triggered by events such as grouting. This fundamentally overcomes the technical bottleneck of the difficulty in simultaneously achieving both large signal volume and real-time performance in DAS (Digital Signal Acquisition).
[0053] This application constructs an intelligent decision support system based on spatiotemporal multidimensional information fusion. By integrating the spatial distribution (anomaly point and upstream / downstream comparison) and time series (persistence of transient spikes) characteristics of DAS signals, the system establishes a reliable mechanism for identifying and locating congestion events, realizing the intelligent transformation from massive data to actionable alarm information, and completing the decision-making leap from "perception" to "cognition".
[0054] This application is used to process and interpret physical simulation experimental data of long-distance grouting pipelines. Figure 2 The raw DAS signal of the grouting pipeline monitoring is shown. It is the DAS vibration signal sensed by the optical fiber wound on the grouting pipeline during the grouting process, which was collected using a distributed optical fiber device. The sampling rate of the recording is 250 μs, with a total of 79 channels, and each channel records 10 seconds of data. Figure 3 The results of DAS signal preprocessing are shown. Figure 2 The original record shown is the preprocessed record, which includes channel selection and fiber calibration. Figure 4 The image shows the DAS signal after noise suppression. It is a record of the grouting DAS signal after noise attenuation. The noise attenuation includes common-mode noise and random noise. Figure 5The results of FBE in the dominant frequency band after resampling of DAS signals based on variance features are shown. The 8-minute FBE record after resampling is displayed. This record is the result of resampling 48 DAS signals of 10 seconds each after calculating the FBE value in the dominant frequency band based on variance features. From this record, an abnormal point at 35 meters can be clearly identified during the grouting process, which is determined to be a blockage point.
[0055] The technical effects achieved by this application are significant, specifically reflected in the following aspects: First, by converting the strain signal into a strain rate signal that is more sensitive to blockage based on the physical mechanism, and combining systematic noise attenuation with energy focusing in the advantageous frequency band, the ability to capture the precursor of blockage in grouting pipelines—micro-amplitude, high-frequency vibration signals—is greatly enhanced, significantly improving the signal-to-noise ratio and monitoring sensitivity. Secondly, by using the characteristic resampling method based on the variance of the root mean square energy in the advantageous frequency band, the TB-level raw data collected by DAS is compressed into a MB-level high-value feature information set, reducing the data volume by several orders of magnitude. This breaks through the bottleneck of real-time transmission, storage, and computation of massive data, making long-term, uninterrupted real-time monitoring possible and realizing a fundamental shift from passive "data recording" to active "information extraction".
[0056] Ultimately, in terms of engineering application value, this application provides operators with intuitive global pipeline status awareness and precise decision support through dynamic visualization waterfall charts and intelligent threshold alarm mechanisms. This enables early warning and rapid response to blockage risks, significantly improving the safety, reliability, and intelligent management level of grouting operations, and effectively avoiding project delays and economic losses caused by pipeline blockages. In summary, this invention provides an end-to-end, efficient, accurate, and reliable automated solution for grouting pipeline monitoring.
[0057] Based on the same inventive concept as the distributed optical fiber vibration signal processing and interpretation method for grouting pipeline monitoring, this embodiment also provides a corresponding distributed optical fiber vibration signal processing and interpretation device for grouting pipeline monitoring, including: The data preprocessing module is used to acquire the raw DAS signal of the grouting pipeline and preprocess the raw DAS signal to obtain the preprocessed DAS signal; the preprocessed DAS signal includes the raw DAS signal at each location of the grouting pipeline. The noise reduction module is used to perform DC removal, anti-aliasing, and noise attenuation on the preprocessed DAS signal to obtain a noise-reduced DAS signal. The conversion module is used to convert the denoised DAS signal from strain to strain rate to obtain the converted DAS signal. The gain compensation module is used to compensate the gain of the converted DAS signal to obtain the gain-compensated DAS signal. The resampling module is used to calculate the data frequency division energy of the DAS signal after gain compensation, and to perform variance feature resampling on the DAS signal based on the data frequency division energy to obtain the resampled feature value signal. The anomaly detection module is used to dynamically visualize the resampled feature value signal, set a threshold, and determine the location of the abnormal pipeline based on the relationship between the threshold and the feature value, and trigger an alarm.
[0058] The distributed optical fiber vibration signal processing and interpretation device for grouting pipeline monitoring in this embodiment has the same inventive concept as the distributed optical fiber vibration signal processing and interpretation method for grouting pipeline monitoring described above. Therefore, the specific implementation of this device can be found in the embodiment section of the distributed optical fiber vibration signal processing and interpretation method for grouting pipeline monitoring described above, and its technical effects correspond to the technical effects of the above method, so it will not be repeated here.
[0059] The above descriptions are merely various embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
Claims
1. A distributed optical fiber vibration signal processing and interpretation method for grouting pipeline monitoring, characterized in that, include: The raw DAS signal of the grouting pipeline is acquired, and the raw DAS signal is preprocessed to obtain the preprocessed DAS signal. The preprocessed DAS signal includes the original DAS signal at each location of the grouting pipe. The preprocessed DAS signal is subjected to DC removal, anti-aliasing, and noise attenuation processing to obtain a denoised DAS signal; The denoised DAS signal is then converted from strain to strain rate to obtain the converted DAS signal. Gain compensation is performed on the converted DAS signal to obtain a gain-compensated DAS signal; The data frequency division energy is calculated for the DAS signal after gain compensation, and the variance feature is resampled based on the data frequency division energy to obtain the resampled feature value signal. The resampled feature value signal is dynamically visualized, and a threshold is set. Based on the relationship between the threshold and the feature value, the location of the abnormal pipeline is determined and an alarm is triggered.
2. The method as described in claim 1, characterized in that, in, The preprocessed DAS signal is subjected to DC removal, anti-aliasing, and noise attenuation processing to obtain a denoised DAS signal, including: The preprocessed DAS signal includes the DAS signal of each channel. The average value of the DAS signal of each channel is subtracted to achieve DC removal. Each channel corresponds to a pipe position. A low-pass filter is used to process the DAS signal of each channel to achieve anti-aliasing processing; the cutoff frequency of the low-pass filter is greater than the effective frequency band. Principal component analysis was used to attenuate the noise in the DAS signals of each channel to obtain the denoised DAS signals.
3. The method as described in claim 1, characterized in that, The denoised DAS signal is then converted from strain to strain rate to obtain the converted DAS signal using the following formula: in, Let be the strain rate of the DAS signal at time t. The strain of the DAS signal at time t+1 The strain of the DAS signal at time t-1 The time sampling interval is denoted as .
4. The method as described in claim 1, characterized in that, Gain compensation is performed on the converted DAS signal, using a gain compensation coefficient of: in, For fiber optic propagation distance The compensation coefficient at the meter. This is the optical cable attenuation coefficient.
5. The method as described in claim 1, characterized in that, The data frequency division energy of the gain-compensated DAS signal is calculated using the following formula: in, Represents time frame Data frequency division energy in the dominant frequency band This represents the lower limit of the dominant frequency band. This is the upper limit of the dominant frequency band. The result is the short-time Fourier transform of the DAS signal. The power spectral density of the DAS signal. For time frames, This refers to a frequency point.
6. The method as described in claim 1, characterized in that, Based on the data frequency division energy, the DAS signal is resampled using variance features to obtain the resampled feature value signal, using the following formula: in, For the first The variance of each resampled signal. This is the length of the resampling window. ) represents a time frame Data frequency division energy in the dominant frequency band The sampling point number is used for resampling. This represents the number of sampling points recorded in the original DAS signal.
7. A distributed fiber optic vibration signal processing and interpretation device for monitoring grouting pipelines, characterized in that, include: The data preprocessing module is used to acquire the raw DAS signal of the grouting pipeline and preprocess the raw DAS signal to obtain the preprocessed DAS signal. The preprocessed DAS signal includes the original DAS signal at each location of the grouting pipe. The denoising module is used to perform DC removal, anti-aliasing, and noise attenuation processing on the preprocessed DAS signal to obtain a denoised DAS signal. The conversion module is used to convert the denoised DAS signal from strain to strain rate to obtain the converted DAS signal. A gain compensation module is used to perform gain compensation on the converted DAS signal to obtain a gain-compensated DAS signal. The resampling module is used to calculate the data frequency division energy of the gain-compensated DAS signal, and to perform variance feature resampling on the DAS signal based on the data frequency division energy to obtain the resampled feature value signal. The anomaly determination module is used to dynamically visualize the resampled feature value signal, set a threshold, and determine the location of the abnormal pipeline and trigger an alarm based on the relationship between the threshold and the feature value.