Optical fiber synaesthesia data processing module, monitoring device and monitoring positioning method

Abstract

The present invention relates to an optical fiber synaesthesia data processing module, a monitoring device and a monitoring and positioning method. The module comprises a mainboard chip, wherein the mainboard chip is used to calculate a two-dimensional phase matrix and a two-dimensional intensity matrix in high-purity data form from acquired DAS digital signal data and DVS digital signal data. The monitoring and positioning method comprises fusing the phase characteristics of the DAS and the intensity characteristics of the DVS, enhancing sensitivity through coherent interference, performing noise suppression on continuous multimodal signals through phase compensation to obtain a fusion matrix, extracting and calculating the characteristic value of each disturbance position, and screening out significant disturbance positions through threshold detection. The method also maps time delay to spatial coordinates through a formula, and the final disturbance position is represented by a set of coordinates.

Description

Technical Field

[0001] The present invention relates to the technical field of acoustic vibration monitoring, and in particular to an optical fiber synaesthesia data processing module, a monitoring device and a monitoring positioning method. Background Art

[0002] Distributed fiber optic sensing technology has been widely used in acoustic and vibration monitoring in recent years. This includes Rayleigh scattering-based distributed acoustic sensing (DAS) and distributed vibration sensing (DVS), as well as optical time-domain reflectometry (OTDR)-based positioning technology. These technologies, which utilize optical fiber as a continuous sensing medium, are widely used in oil and gas pipeline leak monitoring, railway safety monitoring, slope stability monitoring, and power line fault detection.

[0003] Among existing technologies, DAS technology detects tiny phase changes in optical fibers to acquire low-frequency acoustic signals, making it suitable for monitoring acoustic source events such as pipeline leaks and geological activities. DVS technology primarily detects high-frequency vibration signals through changes in light intensity or polarization, and is often used for identifying events such as intrusion detection and mechanical disturbances. OTDR technology analyzes the time delay characteristics of backscattered light to accurately locate the location of an event, with a spatial resolution typically reaching meters.

[0004] Although existing technologies have achieved certain results, they still have the following significant shortcomings:

[0005] 1. Limitations of a Single Sensing Mode: Most existing systems utilize only DAS or DVS, lacking the ability to integrate acoustic and vibration signals, making it difficult to provide comprehensive monitoring in complex scenarios such as leaks accompanied by mechanical disturbances. Furthermore, DAS's response to high-frequency vibrations is weak, while DVS's ability to discern low-frequency sound waves is limited, making it difficult to achieve unified, high-sensitivity detection across a wide frequency band.

[0006] 2. Low data processing efficiency: Distributed fiber-optic sensing systems generate enormous amounts of data. Existing technologies face bottlenecks in real-time noise removal, feature extraction, and data compression, impacting the timeliness of event identification and system resource efficiency. Furthermore, the OTDR's positioning accuracy in complex environments is limited by pulse width and signal-to-noise ratio, making it prone to errors.

[0007] 3. Inaccurate event identification and interval determination: Current systems often rely on static thresholds or template matching to classify events, which can easily lead to false positives and false negatives. When faced with persistent or diffuse disturbances (such as a gradual leak along a pipeline), there is a lack of effective means to define the start and end intervals of the event, thus affecting the timeliness and accuracy of response strategies.

[0008] 4. Poor system integration and scalability: Existing monitoring equipment typically has limited functionality and lacks modular design, making it difficult to quickly adapt to different scenarios (such as long-distance pipeline corridors and short-distance tunnels). Inconvenient hardware upgrades and high maintenance costs also hinder its widespread application in large-scale, multi-type projects.

[0009] 5. Insufficient environmental adaptability: Under complex or harsh environmental conditions, such as strong wind interference, drastic temperature changes, strong background noise, etc., the system signal-to-noise ratio drops significantly, affecting the stability and reliability of monitoring data, thereby limiting the coverage capability and alarm accuracy in actual applications.

[0010] In summary, the existing distributed acoustic and vibration monitoring technology still has much room for improvement in terms of signal type coverage, data processing efficiency, event recognition accuracy and system scalability. There is an urgent need for a distributed acoustic and vibration joint monitoring device that integrates DAS and DVS to better meet the intelligent and multi-dimensional monitoring needs in complex scenarios. Summary of the Invention

[0011] The technical problem to be solved by the present invention is to provide a fiber optic synaesthesia data processing module, a monitoring device and a monitoring positioning method to overcome the deficiencies in the above-mentioned prior art.

[0012] The technical solution of the present invention to solve the above technical problems is as follows: a fiber optic synaesthesia data processing module based on multimodal coherence, including a mainboard chip, which is used to calculate the acquired DAS digital signal data and DVS digital signal data through formulas to obtain a two-dimensional phase matrix in the form of high-purity data. and the two-dimensional intensity matrix ;

[0013] Two-dimensional phase matrix The calculation formula is:

[0014] ;

[0015] in, is the optimized phase signal, indicating that ,Location The phase value at ; Provides the amplitude of the signal, which is affected by the scattering characteristics of the optical fiber; is the frequency, For time; is the optimized phase; is the spatial position of the signal;

[0016] Two-dimensional intensity matrix The calculation formula is:

[0017] ;

[0018] in, is the optimized vibration intensity signal, at time and spatial location The value at is the transient vibration amplitude after optimization, at position ,time The value at For time Gaussian transient pulse at .

[0019] The present invention has the following beneficial effects: its optimization strategy enhances the applicability of DAS and DVS systems in a variety of engineering scenarios. DAS is suitable for scenarios requiring high-precision, low-frequency signals, such as pipeline leak monitoring, seismic wave analysis, and geological exploration; while DVS is suitable for scenarios requiring rapid capture of high-frequency vibrations, such as intrusion detection, machinery condition monitoring, and traffic flow analysis. The optimized, high-purity signal matrix provides a reliable foundation for subsequent data analysis, such as pattern recognition and event classification.

[0020] On the basis of the above technical solution, the present invention can also be improved as follows.

[0021] Furthermore, the two-dimensional phase matrix The calculation formula is determined as follows:

[0022] DAS raw data records low-frequency acoustic disturbances along the optical fiber in the form of a time-space phase matrix. Its core signal model is:

[0023] ;

[0024] in, For location ,time The phase signal, the unit is radian; is the local scattering amplitude, normalized to [0,1]; is the disturbance frequency; is the phase shift caused by the sound wave; : Initial variance ;

[0025] After discretization by the acquisition card, the data forms a two-dimensional matrix:

[0026] ;

[0027] For coherent noise , using adaptive compensation technology to refine the phase:

[0028] ;

[0029] in, is the weight, and the optimization is:

[0030] ;

[0031] ;

[0032] in, is the independent component index of the coherent light field disturbance, ,in is the quantity of portion; is the mean square expectation of the phase error; is an ideal phase signal;

[0033] The optimized DAS digital signal data is output in the form of a high-purity phase matrix, and a two-dimensional phase matrix is ​​obtained. The calculation formula of .

[0034] Furthermore, the two-dimensional intensity matrix The calculation formula is determined as follows:

[0035] DVS raw data records high-frequency vibration disturbances along the optical fiber in the form of a time-space intensity matrix. Its core signal model is:

[0036] ;

[0037] in, For location ,time The vibration intensity signal is in normalized value or dB; is the transient vibration amplitude, reflecting the disturbance intensity; is a Gaussian transient pulse, representing the vibration peak:

[0038] ;

[0039] is the peak moment; is the pulse width, typical value is 0.5ms; is the background noise, the initial variance ;

[0040] For background noise , using multi-component compensation technology to refine the vibration amplitude, the formula logic progresses from the original signal to the optimized output:

[0041] ;

[0042] in, is the weight, is the independent component index of the coherent light field disturbance, ,in is the quantity of portion;

[0043] Optimized two-dimensional intensity matrix The calculation formula of .

[0044] The present invention also discloses a fiber optic synaesthesia intelligent monitoring device based on multimodal coherence, comprising the above-mentioned fiber optic synaesthesia data processing module, a distributed fiber optic sensing unit, a light source and modulation unit, a signal detection and processing unit, and an intelligent monitoring and positioning analysis unit;

[0045] Distributed fiber optic sensing unit: Used to use a single optical fiber as a continuous distributed sensor. Based on the principle of Rayleigh scattering in optical fiber and combined with coherent optical time-domain reflectometry technology, it builds a high-precision, high-sensitivity distributed sensing system to capture sound and vibration signals at construction sites and cable tunnels.

[0046] Light source and modulation unit: used to generate high-coherence pulsed light;

[0047] Signal detection and processing unit: used to extract sound waves, vibration and position information from optical fiber scattered signals, and perform real-time denoising, demodulation and feature analysis to provide high-quality data for subsequent intelligent monitoring and positioning;

[0048] The intelligent monitoring and positioning analysis unit is used for intelligent identification, alarm triggering, and spatial positioning of abnormal events such as sound waves and vibrations. It intelligently analyzes signal data obtained from the front-end sensing system and combines the spatial variation characteristics of distributed signals to achieve classification, judgment, and precise positioning of fault events.

[0049] The data of the distributed optical fiber sensing unit, light source and modulation unit, signal detection and processing unit, and intelligent monitoring and positioning analysis unit are calculated and processed by the optical fiber synaesthesia data processing module.

[0050] The beneficial effects of the present invention are: high precision and high sensitivity: the multimodal signal processing of DAS, DVS and COTDR is integrated to achieve accurate capture of weak disturbances; precise positioning: through feature fusion and time-space mapping, high-precision disturbance position coordinates are output; strong noise resistance: the adaptive optimization algorithm significantly improves the signal-to-noise ratio and adapts to complex environments; intelligent monitoring: automatic identification, classification and alarm, reducing manual intervention; wide spectrum adaptability: covering low-frequency to high-frequency signals, suitable for multi-scenario applications; efficient data processing: modular design and unified mathematical framework support real-time analysis; high cost-effectiveness: a single optical fiber realizes distributed monitoring, reducing deployment and maintenance costs.

[0051] The present invention also discloses a multi-modal coherence-based optical fiber synaesthesia intelligent monitoring and positioning method, which uses the above-mentioned optical fiber synaesthesia intelligent monitoring device for positioning;

[0052] Step S01: Fusion of DAS phase features Intensity characteristics with DVS , and based on the high frequency carrier of COTDR, the sensitivity is enhanced by coherent interference to obtain multimodal signals ;

[0053] Step S02: Coherence optimization factor based on COTDR , through phase compensation for continuous multimodal signals Perform noise suppression and convert it into a discrete mathematical structure to obtain a time-space fusion matrix ;

[0054] Step S03: From the fusion matrix Extract the disturbance position ; Calculate each disturbance position The eigenvalue of , and screen out significant disturbance locations through threshold detection ;

[0055] Step S04: Based on the significant disturbance position , the time delay is calculated by the formula Mapping to spatial coordinates , the final perturbation position is a set of coordinates express.

[0056] The beneficial effects of the present invention are: high-precision positioning: through multimodal signal fusion and time-space mapping, high-precision spatial positioning of the disturbance point is achieved; high sensitivity and noise resistance: COTDR coherence optimization and adaptive noise suppression enhance weak signal detection and reduce false alarm rate; intelligence and automation: automatic feature extraction and threshold detection realize intelligent identification and positioning of disturbance events, reducing manual intervention; wide spectrum adaptability: covering low-frequency to high-frequency signals, meeting the needs of multiple scenarios such as pipeline monitoring, security, geological exploration, etc.; efficient data processing: modular design and unified mathematical framework support real-time and efficient signal processing; high cost-effectiveness: a single optical fiber realizes distributed monitoring, reducing deployment and maintenance costs; strong robustness: adaptive algorithm ensures stable performance in complex environments.

[0057] On the basis of the above technical solution, the present invention can also be improved as follows.

[0058] Furthermore, step S01 specifically includes:

[0059] Introducing coherent modulation terms , the high frequency carrier of COTDR is enhanced by coherent interference and The sensitivity of the system is high, and a narrow pulse width coherent pulse is generated by an acousto-optic modulator to stimulate multimodal signals including Rayleigh scattering of DAS and DVS. , the formula is as follows:

[0060] ;

[0061] Where, is the coherent modulation term; is the interference intensity factor, which optimizes the detectability of weak perturbations; is the speed of light in the optical fiber.

[0062] Further, step S02 specifically includes:

[0063] Introducing phase correction term , converting the continuous scattering signal into a discrete mathematical structure and generating a time-space matrix, the formula is as follows:

[0064] ;

[0065] Where, is the coherence optimization factor of COTDR.

[0066] Furthermore, step S03 specifically includes:

[0067] Extract comprehensive features through signal processing modules:

[0068] ;

[0069] Where, is the disturbance position The eigenvalue is the mathematical kernel of the disturbance feature, representing the position signal energy; is the energy accumulation of the intensity component; is the dynamic gradient of the phase component; 、 is the adaptive weight;

[0070] By placing each Perform threshold detection to screen out significant disturbance locations .

[0071] Further, step S04 is specifically as follows:

[0072] Coherent mixing based on COTDR and amplification through interference matrix The real and imaginary parts of the time delay are mathematically mapped to the spatial coordinates to obtain the spatial coordinates , the formula is as follows:

[0073] ;

[0074] Where, is the spatial coordinate of the disturbance point; is the speed of light in the optical fiber; It is the round trip time delay of the pulse, which accurately represents the distance from the disturbance point to the starting point of the optical fiber; is the correction term, is the intensity feedback factor of COTDR;

[0075] Finally, the disturbance position along the fiber is located as a set of coordinates express. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 It is the overall structural diagram of the present invention;

[0077] Figure 2 This is a schematic diagram of the data acquisition principle of the present invention;

[0078] Figure 3 It is the OTDR principle diagram of the present invention;

[0079] Figure 4 This is a schematic diagram of the coherent phase processing principle of the present invention;

[0080] Figure 5 This is the original data collection principle diagram of the present invention;

[0081] Figure 6 is the original water level map of the present invention;

[0082] Figure 7 This is a one-dimensional diagram of the data of the key disturbance points of the present invention. DETAILED DESCRIPTION

[0083] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0084] like Figures 1 to 7 As shown in Example 1, a fiber optic synaesthesia data processing module based on multimodal coherence includes a motherboard chip, which is used to calculate the acquired DAS digital signal data and DVS digital signal data through formulas to obtain a two-dimensional phase matrix in the form of high-purity data. and the two-dimensional intensity matrix ;

[0085] Two-dimensional phase matrix The calculation formula is:

[0086] ;

[0087] in, is the optimized phase signal, indicating that ,Location The phase value at ; Provides the amplitude of the signal, which is affected by the scattering characteristics of the optical fiber; is the frequency, For time, and Together they determine the temporal periodicity of the signal; It is the optimized phase, which reduces noise interference; is the spatial position of the signal;

[0088] Two-dimensional intensity matrix The calculation formula is:

[0089] ;

[0090] in, is the optimized vibration intensity signal, at time and spatial location The value at , in normalized units or dB, is consistent with the original signal, indicating a high-quality vibration signal with background noise removed, which is convenient for subsequent analysis and application; is the transient vibration amplitude after optimization, at position ,time The value at For time The Gaussian transient pulse at the position retains the time domain characteristics of the original vibration signal and describes the transient behavior of the vibration; the optimized Preserves the temporal shape of the Gaussian pulse ,but More accurate, noise interference is significantly reduced; output data is stored in a time-space matrix format, making it easier to visualize, analyze, and identify vibration events (such as intrusion, mechanical failure, etc.).

[0091] The optimization strategy presented in this paper enhances the applicability of DAS and DVS systems in a variety of engineering scenarios. DAS is suitable for scenarios requiring high-precision, low-frequency signals, such as pipeline leak monitoring, seismic wave analysis, and geological exploration. DVS is suitable for scenarios requiring rapid capture of high-frequency vibrations, such as intrusion detection, machinery condition monitoring, and traffic flow analysis. The optimized, high-purity signal matrix provides a reliable foundation for subsequent data analysis, such as pattern recognition and event classification.

[0092] Example 2: This example is a further improvement on Example 1, and its details are as follows:

[0093] Two-dimensional phase matrix The calculation formula is determined as follows:

[0094] DAS raw data records low-frequency acoustic disturbances along the optical fiber in the form of a time-space phase matrix. Its core signal model is:

[0095] ;

[0096] in, For location ,time The phase signal, the unit is radian; is the local scattering amplitude, normalized to [0,1]; is the disturbance frequency; is the phase shift caused by the sound wave; : Initial variance ;

[0097] After discretization by the acquisition card, the data forms a two-dimensional matrix:

[0098] ;

[0099] For coherent noise , using adaptive compensation technology to refine the phase:

[0100] ;

[0101] in, is the weight, and the optimization is:

[0102]

[0103]

[0104] in, is the independent component index of the coherent light field disturbance, ,in is the number of components (typical value is 3~5, depending on the complexity of the optical fiber environment); coherent noise Originated from multipath scattering and polarization drift in optical fiber, manifested as multiple independent random disturbance components , each component has different frequency or phase characteristics (such as mechanical vibration aliasing at a construction site, electromagnetic pulse interference in a cable tunnel); is the mean square expectation of the phase error; is an ideal phase signal;

[0105] The optimized DAS digital signal data is output in the form of a high-purity phase matrix, and a two-dimensional phase matrix is ​​obtained. The calculation formula is:

[0106] .

[0107] Figure 7 It represents the one-dimensionalization of the position data in the optimization matrix, and the key perturbation points are in the boxes of the timing diagram.

[0108] Example 3: This example is a further improvement on Example 1, and its details are as follows:

[0109] Two-dimensional intensity matrix The calculation formula is determined as follows:

[0110] DVS raw data records high-frequency vibration disturbances along the optical fiber in the form of a time-space intensity matrix. Its core signal model is:

[0111] ;

[0112] in, For location ,time The vibration intensity signal is in normalized value or dB; is the transient vibration amplitude, reflecting the disturbance intensity; is a Gaussian transient pulse, representing the vibration peak:

[0113] ;

[0114] is the peak moment; is the pulse width, with a typical value of 0.5ms (corresponding to 4kHz vibration); is the background noise, the initial variance ;

[0115] For background noise (including polarization drift and environmental interference), multi-component compensation technology is used to refine the vibration amplitude, and the formula logic is progressive from the original signal to the optimized output:

[0116] ;

[0117] Optimized two-dimensional intensity matrix The calculation formula is:

[0118] .

[0119] Example 4, a fiber optic synaesthesia monitoring device based on multimodal coherence, comprising the fiber optic synaesthesia data processing module, distributed fiber optic sensing unit, light source and modulation unit, signal detection and processing unit, and intelligent monitoring and positioning analysis unit according to any one of Examples 1 to 3;

[0120] Distributed fiber optic sensing units (DFSs) utilize a single optical fiber as a continuous distributed sensor. Based on the principle of Rayleigh scattering in optical fibers and combined with coherent optical time-domain reflectometry (C-OTDR), they create a high-precision, high-sensitivity distributed sensing system to capture acoustic and vibration signals at construction sites and cable tunnels. Rayleigh scattering is caused by tiny random refractive index inhomogeneities within optical fibers. When an incident light pulse encounters these microstructures during propagation, it generates backscattered signals. The system detects minute changes in the phase (or intensity or polarization) of these scattered signals, revealing the dynamic behavior of external disturbances. The DFSs consist of a DAS, a DVS, and the fiber under test. The DAS monitors phase perturbations in Rayleigh scattered light, achieving extremely high sensitivity to low-frequency, continuous acoustic signals. The DVS captures transient, high-frequency vibration signals by detecting changes in scattered light intensity or polarization state.

[0121] The light source and modulation unit is used to generate highly coherent pulsed light, providing stable, high-quality optical signals for DAS, DVS, and OTDR functions. This module uses an acousto-optic modulator (AOM) as its core driver, leveraging the acousto-optic effect to rapidly modulate optical signals. The acousto-optic effect refers to the interaction between periodic refractive index changes generated by ultrasound in a medium and light waves, resulting in light diffraction and frequency shifts, thereby generating precise pulsed light sequences. These pulsed light excites Rayleigh scattering in the optical fiber. DAS detects acoustic signals through phase changes in the scattered light, while DVS captures vibration signals through intensity or polarization changes. The OTDR, based on the principle of optical time-domain reflectometry, measures the return time of scattered light to construct a spatiotemporal map along the optical fiber, enabling precise location of events. The light source and modulation unit includes an acousto-optic modulator (AOM) driver and a DAS laser. The AOM driver utilizes the acousto-optic effect, driven by ultrasound to form a dynamic grating in the modulation medium. This rapidly diffracts and frequency-modulates the incident light, generating a high-precision pulsed light sequence.

[0122] Signal detection and processing unit: used to extract sound waves, vibrations and position information from optical fiber scattered signals, and perform real-time denoising, demodulation and feature analysis to provide high-quality data for subsequent intelligent monitoring and positioning; this module is based on the physical properties of Rayleigh scattering, capturing phase, intensity or polarization changes in optical fibers caused by sound waves and vibrations. DAS uses coherent fading technology to detect phase changes in Rayleigh scattered light and perceive low-frequency sound waves (such as crack sounds at construction sites and partial discharge sounds in cable tunnels), while DVS monitors high-frequency vibrations (such as mechanical vibrations at construction sites and external excavation of cable tunnels) through intensity or polarization changes. Coherent fading technology utilizes the interference effect of optical signals to significantly improve the detection sensitivity of DAS to weak sound waves by enhancing the coherence of scattered light. The signal detection and processing unit includes an acquisition card, a USB adapter and some DAS signal demodulation modules;

[0123] OTDR signal processing analyzes the return time of high-precision pulsed light sequences generated by an acousto-optic modulator (AOM) to construct a spatiotemporal map along the fiber, enabling precise location of events. Core components include an acquisition card, USB adapter, and selected DAS signal demodulation modules, working together to ensure efficient acquisition and processing of Rayleigh scattering signals, providing stable support for multimodal monitoring using DAS, DVS, and OTDR.

[0124] Acquisition card: Responsible for capturing Rayleigh scattering signals in optical fibers, supporting coherent detection (phase change) of DAS, intensity / polarization detection of DVS, and spatiotemporal signal analysis of OTDR.

[0125] USB adapter: Provides a high-speed, stable data transmission interface to quickly transmit the original signal captured by the acquisition card to the processing unit or external system.

[0126] Partial DAS signal demodulation module: responsible for demodulating the phase information of the Rayleigh scattering signal, extracting the time domain and frequency domain characteristics of the DAS acoustic wave signal, and supporting the accurate identification of weak sound waves.

[0127] The intelligent monitoring and positioning analysis unit is used for intelligent identification, alarm triggering, and spatial positioning of abnormal events such as sound waves and vibrations. It intelligently analyzes signal data obtained from the front-end sensing system and combines the spatial variation characteristics of distributed signals to achieve classification, judgment, and precise positioning of fault events.

[0128] The data of the distributed optical fiber sensing unit, light source and modulation unit, signal detection and processing unit, and intelligent monitoring and positioning analysis unit are calculated and processed by the optical fiber synaesthesia data processing module.

[0129] The present invention has high precision and high sensitivity: it integrates the multimodal signal processing of DAS, DVS and COTDR to achieve accurate capture of weak disturbances; precise positioning: through feature fusion and time-space mapping, it outputs high-precision disturbance position coordinates; strong noise resistance: the adaptive optimization algorithm significantly improves the signal-to-noise ratio and adapts to complex environments; intelligent monitoring: automatic identification, classification and alarm, reducing human intervention; wide spectrum adaptability: covering low-frequency to high-frequency signals, suitable for multi-scenario applications; efficient data processing: modular design and unified mathematical framework support real-time analysis; high cost-effectiveness: a single optical fiber realizes distributed monitoring, reducing deployment and maintenance costs.

[0130] Example 5, a fiber optic synaesthesia intelligent monitoring and positioning method based on multimodal coherence, using the fiber optic synaesthesia intelligent monitoring device of Example 4 for positioning;

[0131] Step S01: Fusion of DAS phase features Intensity characteristics with DVS , and based on the high frequency carrier of COTDR, the sensitivity is enhanced by coherent interference to obtain multimodal signals ;

[0132] Step S02: Coherence optimization factor based on COTDR , through phase compensation for continuous multimodal signals Perform noise suppression and convert it into a discrete mathematical structure to obtain a time-space fusion matrix ;

[0133] Step S03: From the fusion matrix Extract the disturbance position ; Calculate each disturbance position The eigenvalue of , and screen out significant disturbance locations through threshold detection ;

[0134] Step S04: Based on the significant disturbance position , the time delay is calculated by the formula Mapping to spatial coordinates , the final perturbation position is a set of coordinates In the specific implementation, the time delay Get the timestamp from the capture card.

[0135] High-precision positioning: Through multimodal signal fusion and time-space mapping, high-precision spatial positioning of the disturbance point is achieved; High sensitivity and noise resistance: COTDR coherence optimization and adaptive noise suppression enhance weak signal detection and reduce false alarm rate; Intelligence and automation: Automatic feature extraction and threshold detection realize intelligent identification and positioning of disturbance events, reducing manual intervention; Wide spectrum adaptability: Covering low-frequency to high-frequency signals, meeting the needs of multiple scenarios such as pipeline monitoring, security, geological exploration, etc.; Efficient data processing: Modular design and unified mathematical framework support real-time and efficient signal processing; High cost-effectiveness: Distributed monitoring is achieved through a single optical fiber, reducing deployment and maintenance costs; Strong robustness: Adaptive algorithms ensure stable performance in complex environments.

[0136] Example 6: This example is a further improvement on Example 5, and its details are as follows:

[0137] Step S01 specifically includes:

[0138] Introducing coherent modulation terms , the high frequency carrier of COTDR is enhanced by coherent interference and The sensitivity is high, and a narrow pulse width coherent pulse is generated by an acousto-optic modulator (AOM) to stimulate multimodal signals including Rayleigh scattering of DAS and DVS. , the formula is as follows:

[0139] ;

[0140] Where, is the coherent modulation term; is the interference intensity factor, which optimizes the detectability of weak perturbations; is the speed of light in the optical fiber.

[0141] Example 7: This example is a further improvement on Example 6, and its details are as follows:

[0142] Step S02 specifically includes:

[0143] Introducing phase correction term , the coherence optimization factor of COTDR , random noise is suppressed by phase supplementation, and the mathematical stability of the matrix is ​​enhanced; the continuous scattering signal is converted into a discrete mathematical structure to generate a time-space matrix, the formula is as follows:

[0144] ;

[0145] Where, is the coherence optimization factor of COTDR; DAS characteristics: phase Characterizes the wavefront evolution of periodic disturbances, following the coherent statistical law; DVS characteristics: intensity Characterizes the amplitude peak of transient disturbances and follows a non-stationary random model; COTDR characteristics: The coherence correction term optimizes the phase and intensity components of weak signals and improves the signal-to-noise ratio of the matrix.

[0146] Example 8: This example is a further improvement on Example 7, and its details are as follows:

[0147] Step S03 specifically includes:

[0148] The core of positioning is to The disturbance location is extracted through multimodal feature fusion and spatiotemporal mapping.

[0149] Extract comprehensive features through signal processing modules:

[0150] ;

[0151] Where, is the disturbance position The eigenvalue is the mathematical kernel of the disturbance feature, representing the position signal energy; It accumulates the energy of the intensity component, captures the transient disturbance characteristics of DVS, and amplifies the significant signal through square operation; It is the dynamic gradient of the phase component, capturing the periodic disturbance changes of DAS, and the partial derivative operation highlights the frequency characteristics of phase fluctuations; 、 For adaptive weights, mathematical optimization (such as gradient descent) is used to balance the contributions of DVS and DAS to ensure the stability of feature fusion;

[0152] By placing each Perform threshold detection to screen out significant disturbance locations .

[0153] Example 9: This example is a further improvement on Example 8, and its details are as follows:

[0154] Step S04 is specifically as follows:

[0155] Coherent mixing based on COTDR and amplification through interference matrix The real and imaginary parts of the time delay are mathematically mapped to the spatial coordinates to obtain the spatial coordinates , the formula is as follows:

[0156] ;

[0157] Where, is the spatial coordinate of the disturbance point position, in meters (m), through the time delay and COTDR intensity feedback factor are calculated; The speed of light in the optical fiber, in m / s; The time delay of the pulse round trip accurately represents the distance from the disturbance point to the starting point of the optical fiber and is obtained from the acquisition card timestamp; is the correction term, is the intensity feedback factor of COTDR;

[0158] Finally, the disturbance position along the fiber is located as a set of coordinates express.

[0159] In practice, the system injects laser pulses driven by an acousto-optic modulator (AOM) into an optical fiber. Combined with the multi-channel parallel sampling of an acquisition card, it captures the full scattering response of the fiber link, generating a high-fidelity, multi-dimensional raw data stream. This data is presented as a time-series signal matrix, comprehensively characterizing the acoustic waves, vibrations, and spatial position disturbances along the fiber, providing a foundation for subsequent analysis and positioning.

[0160] Data structure:

[0161] ;

[0162] in, For the location Place, time Optical response signal at each moment; is the amplitude term related to the local reflectivity and loss of the optical fiber; is the phase change due to the disturbance, defined as:

[0163] ;

[0164] in, is the effective refractive index of the optical fiber; is the working wavelength; A small change in path length caused by an external disturbance, reflecting the influence of vibration or sound waves.

[0165] The above signal will undergo bandwidth modulation and discrete sampling, and finally form a two-dimensional discrete signal matrix:

[0166] ;

[0167] ;

[0168] ;

[0169] in, is the spatial sampling interval; is the time sampling interval; Indicates the Frame, The data value of a spatial point.

[0170] ;

[0171] is the speed of light in the optical fiber; The pulse width of the AOM driver.

[0172] Time sampling interval:

[0173] ;

[0174] in, is the time sampling rate, which is controlled by the acquisition system and is generally ,Right now .

[0175] Data example:

[0176] Each piece of collected data corresponds to a laser pulse echo, recording the scattering response value of each spatial sampling point in the optical fiber link, and is aggregated in real time by the acquisition card.

[0177] Data is stored as a two-dimensional matrix structure , the rows represent time frames (t), increasing in millisecond intervals; the columns represent spatial points (z), distributed along the length of the fiber.

[0178] The value of each sampling point includes optical signal intensity (DVS), phase offset (DAS), or interference signal value (C-OTDR). The unit is usually dB (intensity), radian (phase), or normalized value. Some DAS signal demodulation modules pre-enhance the phase data.

[0179] Example:

[0180] Frame_001: [0.012, 0.015, 0.019, 0.017, ..., 0.010] / / t=0ms, z=0~N meters;

[0181] Frame_002: [0.011, 0.016, 0.021, 0.018, ..., 0.009] / / t=0.25ms;

[0182] Frame_003: [0.013, 0.017, 0.020, 0.016, ..., 0.011] / / t=0.50ms; ...

[0183] Frame_N : [...];

[0184] Convert to image Figure 6 .

[0185] Data resolution:

[0186] The test resolutions for specific strategies are as follows:

[0187] Spatial resolution: ;

[0188] Time resolution: .

[0189] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A fiber optic synaesthesia data processing module based on multimodal coherence, characterized in that: The mainboard chip is used to calculate the acquired DAS digital signal data and DVS digital signal data through formulas to obtain a two-dimensional phase matrix in the form of high-purity data. and the two-dimensional intensity matrix ; The two-dimensional phase matrix The calculation formula is: ； in, is the optimized phase signal, indicating that ,Location The phase value at ; Provides the amplitude of the signal, which is affected by the scattering characteristics of the optical fiber; is the frequency, For time; is the optimized phase; is the spatial position of the signal; The two-dimensional intensity matrix The calculation formula is: ； in, is the optimized vibration intensity signal, at time and spatial location The value at is the transient vibration amplitude after optimization, at position ,time The value at For time Gaussian transient pulse at ; The two-dimensional phase matrix The calculation formula is determined as follows: DAS raw data records low-frequency acoustic disturbances along the optical fiber in the form of a time-space phase matrix. Its core signal model is: ； in, For location ,time The phase signal, the unit is radian; is the local scattering amplitude, normalized to [0,1]; is the disturbance frequency; is the phase shift caused by the sound wave; : Initial variance ; After discretization by the acquisition card, the data forms a two-dimensional matrix: ； For coherent noise , using adaptive compensation technology to refine the phase: ； in, is the weight, and the optimization is: ； ； in, is the independent component index of the coherent light field disturbance, ,in is the quantity of portion; is the mean square expectation of the phase error; is an ideal phase signal; The optimized DAS digital signal data is output in the form of a high-purity phase matrix, and the two-dimensional phase matrix is ​​obtained. The calculation formula of .

2. The optical fiber synaesthesia data processing module based on multimodal coherence according to claim 1, characterized in that: The two-dimensional intensity matrix The calculation formula is determined as follows: DVS raw data records high-frequency vibration disturbances along the optical fiber in the form of a time-space intensity matrix. Its core signal model is: ； in, For location ,time The vibration intensity signal is in normalized value or dB; is the transient vibration amplitude, reflecting the disturbance intensity; is a Gaussian transient pulse, representing the vibration peak: ； is the peak moment; is the pulse width, typical value is 0.5ms; is the background noise, the initial variance ; For background noise , using multi-component compensation technology to refine the vibration amplitude, the formula logic progresses from the original signal to the optimized output: ； in, is the weight, is the independent component index of the coherent light field disturbance, ,in is the quantity of portion; The two-dimensional intensity matrix is ​​optimized The calculation formula of .

3. A fiber optic synaesthesia intelligent monitoring device based on multimodal coherence, comprising the fiber optic synaesthesia data processing module according to any one of claims 1 to 2, a distributed fiber optic sensing unit, a light source and modulation unit, a signal detection and processing unit, and an intelligent monitoring and positioning analysis unit; The distributed optical fiber sensing unit is used to use a single optical fiber as a continuous distributed sensor. Based on the Rayleigh scattering principle in optical fiber and combined with coherent optical time-domain reflectometry technology, a high-precision and high-sensitivity distributed sensing system is constructed to capture sound wave and vibration signals at construction sites and cable tunnels. The light source and modulation unit are used to generate high-coherence pulsed light; The signal detection and processing unit is used to extract sound waves, vibrations, and position information from the optical fiber scattered signal, and perform real-time denoising, demodulation, and feature analysis to provide high-quality data for subsequent intelligent monitoring and positioning; The intelligent monitoring and positioning analysis unit is used for intelligent identification, alarm triggering and spatial positioning of abnormal sound wave and vibration events. It intelligently analyzes the signal data obtained from the front-end sensing system and combines the changing characteristics of the distributed signal in the spatial dimension to achieve classification and accurate positioning of fault events. The data of the distributed optical fiber sensing unit, the light source and modulation unit, the signal detection and processing unit, and the intelligent monitoring and positioning analysis unit are calculated and processed by the optical fiber interoception data processing module.

4. A fiber optic synaesthesia intelligent monitoring and positioning method based on multimodal coherence, using the fiber optic synaesthesia intelligent monitoring device according to claim 3 for positioning; Step S01: Fusion of DAS phase features Intensity characteristics with DVS , and based on the high frequency carrier of COTDR, the sensitivity is enhanced by coherent interference to obtain multimodal signals ; Step S02: Coherence optimization factor based on COTDR , by phase compensation of the continuous multimodal signal Perform noise suppression and convert it into a discrete mathematical structure to obtain a time-space fusion matrix ; Step S03: From the fusion matrix Extract the disturbance position ; Calculate each disturbance position The eigenvalue of , and screen out the significant disturbance positions through threshold detection ; Step S04: Based on the significant disturbance position , the time delay is calculated by the formula Mapping to spatial coordinates , the final perturbation position is a set of coordinates express.

5. The optical fiber synaesthesia intelligent monitoring and positioning method based on multimodal coherence according to claim 4 is characterized in that: The step S01 specifically includes: Introducing coherent modulation terms , the high frequency carrier of COTDR is enhanced by coherent interference and The sensitivity of the system is high, and a narrow pulse width coherent pulse is generated by an acousto-optic modulator to stimulate multimodal signals including Rayleigh scattering of DAS and DVS. , the formula is as follows: ； Where, is the coherent modulation term; is the interference intensity factor, which optimizes the detectability of weak perturbations; is the speed of light in the optical fiber.

6. The optical fiber synaesthesia intelligent monitoring and positioning method based on multimodal coherence according to claim 5 is characterized in that: The step S02 specifically includes: Introducing phase correction term , converting the continuous scattering signal into a discrete mathematical structure and generating a time-space matrix, the formula is as follows: ； Where, is the coherence optimization factor of COTDR.

7. The optical fiber synaesthesia intelligent monitoring and positioning method based on multimodal coherence according to claim 6, characterized in that: The step S03 specifically includes: Extract comprehensive features through signal processing modules: ； Where, is the disturbance position The eigenvalue is the mathematical kernel of the disturbance feature, representing the position signal energy; is the energy accumulation of the intensity component; is the dynamic gradient of the phase component; 、 is the adaptive weight; By placing each Perform threshold detection to screen out the significant disturbance locations .

8. The optical fiber synaesthesia intelligent monitoring and positioning method based on multimodal coherence according to claim 7 is characterized in that: The step S04 is specifically as follows: Coherent mixing based on COTDR and amplification through interference matrix The real and imaginary parts of the time delay are mathematically mapped to the spatial coordinates to obtain the spatial coordinates , the formula is as follows: ； Where, is the spatial coordinate of the disturbance point; is the speed of light in the optical fiber; It is the round trip time delay of the pulse, which accurately represents the distance from the disturbance point to the starting point of the optical fiber; is the correction term, is the intensity feedback factor of COTDR; Finally, the disturbance position along the fiber is located as a set of coordinates express.

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