Linear frequency modulation phi-OTDR vibration signal extraction method and system based on FPGA

Abstract

The invention relates to the technical field of signal extraction of phase-sensitive optical time domain reflectometers, and discloses a linear frequency modulation phi-OTDR vibration signal extraction method and system based on an FPGA, and the system comprises an acquisition card, a data preprocessing unit, the FPGA, a storage unit, and a display unit. A data receiving and checking unit, an edge detection unit, an FIFO (First In First Out) reading unit, an FIFO writing unit, a VGA (Video Graphics Array) scanning unit, a bit width conversion unit and an HDMI (High Definition Multimedia Interface) image data conversion unit are arranged in the FPGA; according to the invention, the real-time extraction of the vibration signal is realized based on the FPGA, and the signal demodulation rate is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of signal extraction of a phase-sensitive optical time domain reflectometer, and in particular to a method and system for extracting linear frequency modulation (FPM) φ-OTDR vibration signals based on FPGA, which processes image data in real time through FPGA. Background Art

[0002] Phase-sensitive optical time-domain reflectometry (φ-OTDR) is a new type of distributed fiber optic sensing technology with the characteristics of simple structure, high sensitivity, good real-time performance, and wide dynamic range. It plays an important role in long-distance perimeter security, oil and gas pipeline safety status monitoring, and large-scale structural health monitoring.

[0003] Currently, φ-OTDR technology is primarily divided into two categories: phase-based demodulation systems and intensity-based demodulation systems. Various phase demodulation methods have been proposed for φ-OTDR technology, including digital coherent demodulation, IQ quadrature demodulation, 3×3 coupler demodulation, and phase-generated carrier demodulation. While these methods theoretically offer high-precision measurements, in practice, issues such as phase jumps during the phase unwinding process can easily lead to measurement errors. In contrast, intensity-based demodulation systems analyze changes in scattered signal intensity to infer changes in external parameters along the fiber, offering a simpler system design. Currently, intensity-based demodulation systems utilize the time-domain difference between two adjacent scattered signal frames to achieve signal demodulation. While this method effectively identifies vibration locations, the nonlinear relationship between scattered signal intensity and vibration signal intensity makes it difficult to accurately obtain specific vibration information.

[0004] To overcome these limitations, the proposed φ-OTDR system based on linear frequency-modulated pulses offers new possibilities for waveform reconstruction based on intensity demodulation. In this system, a linear frequency-modulated pulse is used as the detection signal to convert the phase change of the Rayleigh scattering signal caused by the disturbance into a horizontal offset of the scattered signal along the fiber. This offset is linearly related to the vibration signal intensity, allowing accurate reconstruction of the specific vibration waveform. To address the time shift of the scattered signal within the vibration region, researchers have proposed various demodulation methods based on the temporal similarity of the scattered signals. J. Graells et al. proposed using a time-domain correlation algorithm to measure dynamic strain waveforms by injecting chirped pulses through the laser current, achieving long-term stable measurements. Z. Ma et al. introduced a virtual block array phase analysis method and used a sliding window cross-correlation algorithm to achieve waveform reconstruction with a high signal-to-noise ratio in noisy environments. S. Wang et al. performed sliding window cross-correlation calculations on a GPU, leveraging the GPU's computing units for parallel processing, significantly improving data processing speed. Y. Wang et al. proposed using a random fiber grating array (RFGA) to enhance the scattered signal, achieving high-precision identification and quantification of physical changes at different locations along the fiber, improving measurement stability and accuracy. B. Lu et al. injected linear frequency-modulated pulses into a φ-OTDR system and combined them with a matched filtering algorithm for demodulation, achieving dynamic measurements with a spatial resolution of 30 cm and effectively improving the system's signal-to-noise ratio. While these schemes can effectively detect and quantify signal time shifts, they are all based on the translational similarity of the scattered signal, relying on the translational similarity of the one-dimensional raw data to demodulate the vibration signal. During the demodulation process, a sliding window cross-correlation operation is required to repeatedly compare similar features of the one-dimensional raw data, resulting in long demodulation times. Furthermore, these schemes all utilize computer software to perform offline demodulation of the collected scattered signals, further compromising the system's real-time performance.

[0005] Therefore, it is necessary to invent a new linear frequency modulation A vibration signal extraction method is proposed to address the defects of existing methods. Summary of the Invention

[0006] In order to solve the above technical problems, the present invention proposes a linear frequency modulation φ-OTDR vibration signal extraction method and system based on FPGA. The original data of multiple Rayleigh scattering traces generated by multiple detection pulses in the sensing optical fiber is constructed into a two-dimensional image, and the vibration signal is extracted by edge detection of the two-dimensional image. It is precisely through the matrix operation in the image edge detection process that the original data structure in the one-dimensional data demodulation process is changed. Combined with the parallel computing architecture and customizable logic resources of FPGA, the signal extraction speed is significantly improved compared with the existing methods.

[0007] In order to solve the above technical problems, the technical solution adopted by the present invention is: a linear frequency modulation φ-OTDR vibration signal extraction method based on FPGA, comprising the following steps:

[0008] Step 1: The backscattered Rayleigh signal is collected by the acquisition card and sent to the data preprocessing unit, which performs Hilbert transform on the collected backscattered Rayleigh signal to extract the signal envelope;

[0009] Step 2: The extracted envelope data is differentially located by the data preprocessing unit to obtain the vibration position;

[0010] Step 3: The data pre-processing unit intercepts image data within a first distance before and after the vibration position, sets the pixels of the image, obtains the image data, smoothes it, and sends it to the FPGA via Ethernet;

[0011] Step 4: FPGA performs edge detection on the received image data and sends it to the FIFO buffer;

[0012] Step 5: When the FIFO is full of data for a burst, the data for a burst is read out and the data burst is written into the DDR3 SDRAM.

[0013] Step 6: Read the data stored in the DDR3 SDRAM through the FPGA's FIFO and display it on the monitor;

[0014] Step 7: Process the coordinates of discrete pixel points, connect the coordinate points in sequence and perform smoothing.

[0015] Step 8: Convert the coordinate curve into a "time-amplitude" curve based on the time delay coefficient and the gauge factor to obtain the specific waveform of the vibration.

[0016] In step 3, the processed image data is packaged according to the TCP / IP protocol, and each frame of the image is divided into multiple data packets and sent to the FPGA through the Ethernet interface.

[0017] In step 4, the FPGA performs edge detection through dual FIFO pipeline operation.

[0018] In step 4, the specific method of FPGA performing edge detection is:

[0019] Step 401: converting the received color image into a grayscale image;

[0020] Step 402: Perform Gaussian filtering on the grayscale image;

[0021] Step 403: Calculate gradients: Calculate the horizontal gradient Gx and vertical gradient Gy of the image through dual FIFO pipeline operation and using the Sobel operator;

[0022] Step 404: Compare adjacent pixels in the gradient direction, suppress pixels that are not local maxima, and retain the maximum pixel points on the edge to achieve non-maximum suppression;

[0023] Step 405: Double threshold processing is performed on the image after non-maximum suppression processing, that is, edge pixels are classified by high and low double thresholds, strong edge pixels are retained, and weak edge pixels are connected to strong edges to ensure continuity. The result after double threshold processing is used as the final edge detection result.

[0024] In addition, the present invention also provides a linear frequency modulation φ-OTDR vibration signal extraction system based on FPGA, comprising: an acquisition card, a data preprocessing unit, an FPGA, a storage unit and a display unit, wherein the FPGA is provided with a data receiving and verification unit, an edge detection unit, a FIFO reading unit, a FIFO writing unit, a VGA scanning unit, a bit width conversion unit, and an HDMI image data conversion unit;

[0025] The data preprocessing unit is used to preprocess the data collected by the acquisition card and send the obtained image data to the FPGA;

[0026] The data receiving and verifying unit is connected to the data pre-processing unit via Ethernet communication, and is used to verify the image data sent by the data pre-processing unit and then send it to the edge detection unit;

[0027] The edge detection unit is used to perform edge detection on the received image data and then send it to the FIFO write unit for temporary storage. The FIFO write unit is used to send the data in the FIFO to the storage unit for storage when the data in the FIFO meets the burst data volume.

[0028] The FIFO reading unit is used to read data from the storage unit and store it in the FIFO. The VGA scanning unit is used to configure the scanning logic to adjust the timing of the image data according to the resolution and refresh rate of the display, and read the data in the FIFO reading unit according to the horizontal and vertical synchronization signals and send them to the bit width conversion unit.

[0029] The bit width conversion unit is used to convert the image data sent by the VGA scanning unit into bit width and then send it to the HDMI image data conversion unit. The HDMI image data conversion unit is used to convert the image data after bit width conversion and then send it to the display unit for display.

[0030] The data preprocessing unit is a host computer.

[0031] The storage unit is a DDR3 SDRAM memory.

[0032] The method for the data preprocessing unit to perform data processing is as follows:

[0033] The collected Rayleigh backscattered signal is filtered, and then the analytical signal of the signal is extracted using Hilbert transform, and then the real part of the analytical signal is extracted to obtain the signal envelope data;

[0034] The vibration position is obtained by differential positioning of the signal envelope data;

[0035] Image data within a first distance is intercepted before and after the vibration position, and after setting the pixels of the image, it is smoothed to obtain image data; the horizontal coordinate of the image data is the detection time and the vertical coordinate is the optical fiber length, and the pixel point value is the signal intensity.

[0036] Furthermore, the acquisition card collects the backscattered Rayleigh signal through a linear frequency modulation φ-OTDR device, wherein the linear frequency modulation φ-OTDR device includes a narrow linewidth laser, a first optical coupler, an IQ modulator, an arbitrary waveform generator, an erbium-doped fiber pulse amplifier, an optical fiber circulator, a sensing fiber, an erbium-doped fiber amplifier, a second optical coupler, and a balanced photodetector;

[0037] The laser light emitted by the narrow linewidth laser is divided into two beams after passing through the first optical coupler, one of which is used as detection light. The detection light enters the sensing optical fiber after passing through the IQ modulator, the erbium-doped fiber pulse amplifier, and the optical fiber circulator. The backscattered Rayleigh signal generated in the sensing optical fiber is output through the optical fiber circulator, then amplified by the erbium-doped fiber amplifier and incident on the second optical coupler. The other beam is used as reference light. After the reference light is incident on the second optical coupler, it is detected by the balanced photodetector together with the backscattered Rayleigh signal, and the detection signal is collected by the acquisition card.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] 1. The present invention provides a linear frequency modulation φ-OTDR vibration signal extraction method based on FPGA real-time processing. By converting one-dimensional raw data into two-dimensional image data, the parallel computing architecture and hardware implementation resources of FPGA are combined to realize matrix operations in the image edge detection process and complete the extraction of vibration signals. This overcomes the problem of poor real-time performance of one-dimensional data demodulation schemes and significantly improves the signal extraction speed compared with existing methods.

[0040] 2. This invention utilizes FPGA hardware to demodulate vibration signals, overcoming the problem of traditional linear frequency modulation (FFM) φ-OTDR systems that rely on offline computer software to demodulate the collected scattered signals. This facilitates the overall integration and miniaturization of FFM φ-OTDR systems. Furthermore, utilizing FPGA hardware to demodulate vibration signals reduces computer floating-point arithmetic errors, further improving the quality of vibration signal waveform reconstruction. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A linear frequency modulation based on FPGA provided by the embodiment of the present invention Flowchart of the vibration signal extraction method;

[0042] Figure 2 A schematic diagram of a linear frequency modulated pulse φ-OTDR device used in an embodiment of the present invention;

[0043] Among them are narrow linewidth laser 1, 90:10 optical coupler 2, IQ modulator 3, arbitrary waveform generator 4, erbium-doped fiber pulse amplifier 5, fiber circulator 6, sensing fiber 7, erbium-doped fiber amplifier 8, 50:50 optical coupler 9, balanced photodetector 10, data acquisition card 11, and FPGA demodulation unit 12.

[0044] Figure 3 100Hz and 500Hz sinusoidal signal demodulation results obtained in the embodiment of the present invention;

[0045] Figure 4 This is a structural diagram of a linear frequency modulation φ-OTDR vibration signal extraction system based on FPGA provided in the second embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, not all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0047] Example 1

[0048] like Figure 1 As shown, the first embodiment of the present invention provides a linear frequency modulation φ-OTDR vibration signal extraction method based on FPGA, comprising the following steps:

[0049] Step 1: The backscattered Rayleigh signal is collected by the acquisition card and sent to the data preprocessing unit. The data preprocessing unit performs Hilbert transform on the collected backscattered Rayleigh signal to extract the signal envelope.

[0050] In this embodiment, the acquisition card collects the Rayleigh backscattered signal through the linear frequency modulation φ-OTDR device, such as Figure 2 As shown, the linear frequency modulation φ-OTDR device includes a narrow linewidth laser 1, a first optical coupler 2, an IQ modulator 3, an arbitrary waveform generator 4, an erbium-doped fiber pulse amplifier 5, an optical fiber circulator 6, a sensing fiber 7, an erbium-doped fiber amplifier 8, a second optical coupler 9, and a balanced photodetector 10;

[0051] The laser light emitted by the narrow linewidth laser 1 is divided into two beams after passing through the first optical coupler 2. One beam is used as detection light. The detection light enters the sensing optical fiber 7 after passing through the IQ modulator 3, the erbium-doped fiber pulse amplifier 5, and the optical fiber circulator 6. The backscattered Rayleigh signal generated in the sensing optical fiber 7 is output through the optical fiber circulator 6, then amplified by the erbium-doped fiber amplifier 8 and incident on the second optical coupler 9. The other beam is used as reference light. After the reference light is incident on the second optical coupler 9, it is detected by the balanced photodetector 10 together with the backscattered Rayleigh signal, and the detection signal is collected by the acquisition card.

[0052] After the acquisition card collects the raw data of the optical fiber backscattered Rayleigh signal, it first filters the data, then applies Hilbert transform to the filtered signal to extract the analytical signal of the signal, and extracts the signal envelope by extracting the real part of the analytical signal.

[0053] Step 2: The extracted envelope data is differentially located by the data preprocessing unit to obtain the vibration position.

[0054] Step 3: The data pre-processing unit intercepts image data within the first distance before and after the vibration position, sets the pixels of the image, obtains the image data, smoothes it, and then sends it to the FPGA via Ethernet.

[0055] In step 3, the first distance is equal to the distance corresponding to a spatial resolution, and the pixel value of the image is set to the number of detection pulses×the number of data points, where the number of data points is the number of data points corresponding to the image data obtained by the screenshot.

[0056] Based on the determined vibration location, envelope data within a spatial resolution of 10m is captured both before and after to ensure the entire vibration region is captured. The data is saved as a two-dimensional (time-distance) image, with the abscissa representing the detection time (which can be converted to the number of detection pulses using the detection pulse period) and the ordinate representing the fiber length.

[0057] Specifically, in this embodiment, the number of detection pulses of the original data is 1000, and the number of data points saved in step 3 is 600, so the pixels of the image intercepted in step 3 are set to 1000×600; and the image is smoothed using a suitable averaging filter.

[0058] Specifically, the smoothed image data is packaged according to the TCP / IP protocol. Each frame of the image is divided into multiple data packets and sent to the FPGA via the Ethernet interface.

[0059] Step 4: Perform edge detection on the received image data through FPGA and send it to the FIFO buffer.

[0060] In step 4, the FPGA performs edge detection through dual FIFO pipeline operations. Furthermore, the FPGA is equipped with an Ethernet receiver module to receive data packets transmitted from the host computer. Upon receiving the data packets, the FPGA unpacks and reassembles them into complete image data, which is then stored in the on-chip DDR3 SDRAM. To ensure data integrity and transmission reliability, CRC checksums or other error detection mechanisms are used during the reception process.

[0061] Specifically, in step 4, the specific method for FPGA to perform edge detection is:

[0062] Step 401: Receive image data through CRC check or other error detection mechanisms, and then convert the received color image into a grayscale image.

[0063] Use the weighted average method to convert the color information of the RGB three channels into grayscale values. The conversion formula is:

[0064] Gray=0.299×R+0.587×G+0.114×B (1)

[0065] Through this formula, the RGB value of each pixel is mapped to a grayscale value to form a grayscale image.

[0066] Step 402: Perform Gaussian filtering to reduce noise on the grayscale image.

[0067] Before performing edge detection, the grayscale image is first Gaussian filtered to reduce high-frequency noise in the image. This is accomplished by implementing a two-dimensional convolution operation in the FPGA. The size and standard deviation of the Gaussian filter are adjusted according to the actual application.

[0068] Step 403: Calculate gradients: Calculate the horizontal gradient Gx and vertical gradient Gy of the image through dual FIFO pipeline operation and using the Sobel operator. The calculation formula is:

[0069]

[0070] The calculation formula of the gradient amplitude Grad is:

[0071]

[0072] Step 404: Compare adjacent pixels in the gradient direction, suppress pixels that are not local maxima, retain the maximum pixel points on the edge, and achieve non-maximum suppression; this operation is implemented in parallel in the pipeline through the condition judgment module.

[0073] Step 405: Double threshold processing is performed on the image after non-maximum suppression processing, that is, edge pixels are classified by high and low double thresholds, strong edge pixels are retained, and weak edge pixels are connected to strong edges to ensure continuity. The result after double threshold processing is used as the final edge detection result.

[0074] After edge detection, the data is first sent to the FPGA's internal FIFO for temporary storage. This buffer effectively smooths the data flow and avoids overflow problems caused by bursts of data.

[0075] Step 5: When the FIFO write module stores the full amount of data for one burst, the data for one burst is read out and the data burst is written into the DDR3 SDRAM.

[0076] Step 6: Read the data stored in the DDR3 SDRAM through the FIFO of the FPGA and display it on the monitor.

[0077] The step 6 specifically includes the following steps:

[0078] Step 601: Data read: read image data from DDR3 SDRAM and write it into the FIFO of the data cache.

[0079] Step 602, data buffering: buffering pixel data so as to directly send a row of pixels to the display module to achieve continuous display of the image.

[0080] Step 603, image timing scanning: configure the scanning logic to adjust the timing of the image data according to the resolution and refresh rate of the display, and read the data in the data buffer FIFO according to the horizontal and vertical synchronization signals. This step is implemented by the VGA_timing module.

[0081] Step 604, HDMI image display: The data read out in the previous step is transmitted to the HDMI display for display after being encoded, converted from parallel to serial, and converted from serial to differential.

[0082] Step 7: Process the coordinates of discrete pixel points, connect the coordinate points in sequence and perform smoothing.

[0083] Step 8: Convert the coordinate curve into a "time-amplitude" curve based on the time delay coefficient and gauge factor to obtain the specific waveform of the vibration. The experimental coefficient and gauge factor can be determined through calibration experiments.

[0084] Specifically, steps 7 and 8 can be implemented by a host computer.

[0085] In this embodiment, the extracted signal amplitude represents the variation range of the pixel points, and the variation range of the pixel points reflects the variation of the distance along the optical fiber. The time delay can be extracted by converting distance and time. Then, the vibration applied to the optical fiber can be demodulated based on the correspondence between the time delay Δt and the optical fiber strain ε caused by the vibration (such as Formula 10).

[0086]

[0087] The choice of sign depends on the slope of the LFM pulse. It is positive when the modulated optical frequency of the LFM pulse increases linearly from low frequency to high frequency, and negative when it decreases linearly. μ represents the absolute value of the modulation slope, which is the starting frequency of the LFM pulse. K ε is the gauge factor.

[0088] like Figure 3 As shown in (a) and (b), in this embodiment, 100Hz and 500Hz sinusoidal vibration signals are applied to the sensing optical fiber through PZT and demodulated through the above scheme. Through the above steps, better vibration signal extraction can be achieved. Specifically, the present invention uses a linear frequency modulation φ-OTDR vibration signal extraction method that processes image data in real time through FPGA. Compared with other linear frequency modulation methods, Signal extraction algorithm: On the one hand, through image edge detection and the high-speed parallel processing capabilities of FPGA, the signal extraction speed is significantly improved compared with existing methods; on the other hand, the quality of vibration signal waveform reconstruction of the linear frequency modulation φ-OTDR system is improved. Therefore, the present invention can realize online real-time extraction.

[0089] Example 2

[0090] like Figure 4 As shown, the second embodiment of the present invention provides an FPGA-based linear frequency modulation φ-OTDR vibration signal extraction system, including: an acquisition card, a data preprocessing unit, an FPGA, a storage unit and a display unit, wherein the FPGA is provided with a data receiving and verification unit, an edge detection unit, a FIFO reading unit, a FIFO writing unit, a VGA scanning unit, a bit width conversion unit, and an HDMI image data conversion unit;

[0091] The data preprocessing unit is used to preprocess the data collected by the acquisition card and send the obtained image data to the FPGA;

[0092] The data receiving and verifying unit is connected to the data pre-processing unit via Ethernet communication, and is used to verify the image data sent by the data pre-processing unit and then send it to the edge detection unit;

[0093] The edge detection unit is used to perform edge detection on the received image data and then send it to the FIFO write unit for temporary storage. The FIFO write unit is used to send the data in the FIFO to the storage unit for storage when the data in the FIFO meets the burst data volume.

[0094] The FIFO reading unit is used to read data from the storage unit and store it in the FIFO. The VGA scanning unit is used to configure the scanning logic to adjust the timing of the image data according to the resolution and refresh rate of the display, and read the data in the FIFO reading unit according to the horizontal and vertical synchronization signals and send them to the bit width conversion unit.

[0095] The bit width conversion unit is used to convert the image data sent by the VGA scanning unit into bit width and then send it to the HDMI image data conversion unit. The HDMI image data conversion unit is used to convert the image data after bit width conversion and then send it to the display unit for display.

[0096] Specifically, the data preprocessing unit is a host computer.

[0097] Specifically, the storage unit is a DDR3 SDRAM memory.

[0098] Specifically, the data preprocessing unit performs data processing in the following manner:

[0099] The collected Rayleigh backscattered signal is filtered, and then the analytical signal of the signal is extracted using Hilbert transform, and then the real part of the analytical signal is extracted to obtain the signal envelope data;

[0100] The vibration position is obtained by differential positioning of the signal envelope data;

[0101] Image data within a first distance is intercepted before and after the vibration position, and after setting the pixels of the image, it is smoothed to obtain image data; the horizontal coordinate of the image data is the detection time and the vertical coordinate is the optical fiber length, and the pixel point value is the signal intensity.

[0102] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A linear frequency modulation φ-OTDR vibration signal extraction method based on FPGA, characterized in that: The following steps are involved: Step 1: The backscattered Rayleigh signal is collected by the acquisition card and sent to the data preprocessing unit, and the Hilbert transform of the collected backscattered Rayleigh signal is performed by the data preprocessing unit to extract the signal envelope; Step 2: The extracted envelope data is differentially located by a data preprocessing unit to obtain the vibration position; Step 3: intercepting image data within the first distance before and after the vibration position through the data preprocessing unit, setting the pixels of the image, and then obtaining the image data and smoothing it before sending it to the FPGA through Ethernet; Step 4: Perform edge detection on the received image data through FPGA and send it to the FIFO buffer; Step 5: When the FIFO is full of data of one burst, the data of one burst is read out, and the data burst is written into the DDR3 SDRAM; Step 6: Read the data stored in DDR3 SDRAM through FPGA's FIFO and display it on the display; Step 7: Process the coordinates of discrete pixel points, connect the coordinate points in sequence and perform smoothing; Step 8: According to the time delay coefficient and the strain coefficient, the coordinate curve is converted into a "time-amplitude" curve to obtain the specific waveform of the vibration.

2. The method for extracting linear frequency modulation φ-OTDR vibration signals based on FPGA according to claim 1, characterized in that: In step 3, the processed image data is packaged according to the TCP / IP protocol, and each frame of the image is divided into multiple data packets and sent to the FPGA through the Ethernet interface.

3. The method for extracting linear frequency modulation φ-OTDR vibration signals based on FPGA according to claim 1, characterized in that: In step 4, the FPGA performs edge detection through dual FIFO pipeline operation.

4. The method for extracting linear frequency modulation φ-OTDR vibration signals based on FPGA according to claim 1, characterized in that: In step 4, the specific method of edge detection by FPGA is: Step 401: converting the received color image into a grayscale image; Step 402: Perform Gaussian filtering on the grayscale image; Step 403: Calculate the gradient: Calculate the horizontal gradient Gx and the vertical gradient Gy of the image through dual FIFO pipeline operation and using the Sobel operator; Step 404: Compare adjacent pixels in the gradient direction, suppress pixels that are not local maxima, retain edge maxima pixels, and implement non-maximum suppression; Step 405: perform double threshold processing on the image after non-maximum suppression processing, that is, classify the edge pixels through high and low double thresholds, retain the strong edge pixels, and connect the weak edge pixels to the strong edge to ensure continuity. The result after double threshold processing is used as the final edge detection result.

5. A linear frequency modulation φ-OTDR vibration signal extraction system based on FPGA, characterized in that: include: An acquisition card, a data preprocessing unit, an FPGA, a storage unit and a display unit, wherein the FPGA is provided with a data receiving and checking unit, an edge detection unit, a FIFO reading unit, a FIFO writing unit, a VGA scanning unit, a bit width conversion unit, and an HDMI image data conversion unit; The data preprocessing unit is used to preprocess the data collected by the acquisition card and send the obtained image data to the FPGA; The data receiving and verifying unit is connected to the data preprocessing unit via Ethernet communication, and is used to verify the image data sent by the data preprocessing unit and then send it to the edge detection unit; The edge detection unit is used to perform edge detection on the received image data and then send it to the FIFO write unit for temporary storage. The FIFO write unit is used to send the data in the FIFO to the storage unit for storage when the data in the FIFO meets the burst data amount. The FIFO reading unit is used to read data from the storage unit and store it in FIFO, and the VGA scanning unit is used to configure the scanning logic to adjust the timing of the image data according to the resolution and refresh rate of the display, and read the data in the FIFO reading unit according to the line field synchronization signal and send it to the bit width conversion unit; The bit width conversion unit is used to convert the bit width of the image data sent by the VGA scanning unit and send it to the HDMI image data conversion unit. The HDMI image data conversion unit is used to convert the image data after the bit width conversion and send it to the display unit for display.

6. The linear frequency modulation φ-OTDR vibration signal extraction system based on FPGA according to claim 5, characterized in that: The data preprocessing unit is a host computer.

7. The linear frequency modulation φ-OTDR vibration signal extraction system based on FPGA according to claim 5, characterized in that: The storage unit is a DDR3 SDRAM memory.

8. The linear frequency modulation φ-OTDR vibration signal extraction system based on FPGA according to claim 5, characterized in that: The method for the data preprocessing unit to perform data processing is as follows: The collected backscattered Rayleigh signal is filtered, and then the analytical signal of the signal is extracted by Hilbert transform, and then the real part of the analytical signal is extracted to obtain the signal envelope data; The vibration position is obtained by differential positioning of the signal envelope data; Image data within a first distance are intercepted before and after the vibration position, and after setting the pixels of the image, smoothing is performed to obtain image data; the horizontal coordinate of the image data is the detection time, the vertical coordinate is the optical fiber length, and the pixel point value is the signal intensity.

9. The linear frequency modulation φ-OTDR vibration signal extraction system based on FPGA according to claim 5, characterized in that: The acquisition card acquires the backscattered Rayleigh signal through a linear frequency modulation φ-OTDR device, wherein the linear frequency modulation φ-OTDR device comprises a narrow line width laser (1), a first optical coupler (2), an IQ modulator (3), an arbitrary waveform generator (4), an erbium-doped fiber pulse amplifier (5), an optical fiber circulator (6), a sensing optical fiber (7), an erbium-doped fiber amplifier (8), a second optical coupler (9), and a balanced photodetector (10); The laser light emitted by the narrow linewidth laser (1) is divided into two beams after passing through the first optical coupler (2), one of which is used as detection light. The detection light enters the sensing optical fiber (7) after passing through the IQ modulator (3), the erbium-doped fiber pulse amplifier (5), and the optical fiber circulator (6). The backscattered Rayleigh signal generated in the sensing optical fiber (7) is output through the optical fiber circulator (6), then amplified by the erbium-doped fiber amplifier (8) and incident on the second optical coupler (9). The other beam is used as reference light. After the reference light is incident on the second optical coupler (9), it is detected by the balanced photoelectric detector (10) together with the backscattered Rayleigh signal, and the detection signal is collected by the acquisition card.

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