Optical cable icing galloping online monitoring system based on BOTDR (Brillouin Optical Time Domain Reflectometer)

Abstract

The invention discloses an optical cable icing galloping online monitoring system based on a BOTDR, and belongs to the technical field of distributed optical fiber sensing. The system comprises an optical module, a signal processing module and a data processing module, the optical module is used for generating a detection light signal and interacting with a sensing optical fiber to obtain a Brillouin scattering signal, and then the Brillouin scattering signal is converted into an electric signal; the signal processing module is used for performing frequency down-conversion, signal filtering and digital acquisition on the electric signal to obtain multiple groups of Brillouin scattering signal data; the data processing module is used for processing the multiple groups of Brillouin scattering signal data and extracting a Brillouin frequency shift center frequency; temperature and strain distribution along the optical cable is obtained according to the frequency shift, and temperature and strain states are transmitted to a QT upper computer through PCIE for real-time display. According to the invention, high-precision and long-distance optical cable icing galloping monitoring can be realized, reliable early warning and safety guarantee are provided, and technical support is provided for maintenance and management of an electric power system.

Description

Technical Field

[0001] The present invention relates to the technical field of optical fiber sensing, and in particular to an on-line monitoring system for ice coating and galloping of optical cable based on Brillouin Optical Time Domain Reflectometer (BOTDR). Background Art

[0002] With the rapid development of power communication networks, as an important communication carrier, the operating state of optical cables directly affects the stability of the system. Harsh climate conditions (such as ice and snow and strong winds) may cause ice coating and galloping of optical cables, thus endangering the safe operation of optical cables. At present, traditional optical cable monitoring technologies mostly use point sensors, which have problems such as complex wiring, high cost, and limited coverage, and are difficult to meet the requirements of large-scale and distributed optical cable monitoring.

[0003] BOTDR is an advanced distributed optical fiber sensing technology, which can realize real-time monitoring of temperature and strain along the optical cable through the frequency shift characteristics of Brillouin scattering signals. Although BOTDR technology has been preliminarily applied in power communication systems, there are still the following problems: insufficient signal-to-noise ratio, resulting in limited monitoring accuracy; slow data processing speed, making it difficult to meet the requirements of real-time warning; limited system spatial resolution, unable to accurately locate abnormal areas. Summary of the Invention

[0004] To solve the above problems, the present invention provides an on-line monitoring system for ice coating and galloping of optical cable based on BOTDR, which improves the system signal-to-noise ratio by performing various denoising processes on the scattered signals, uses a ZYNQ7100 chip to process data in parallel, greatly increases the data processing speed, and meets the system warning requirements accordingly.

[0005] The technical solution of the present invention is: an on-line monitoring system for ice coating and galloping of optical cable based on BOTDR, including an optical module, a signal processing module, and a data processing module.

[0006] The optical module is used to generate a detection optical signal and interact with the sensing optical fiber to obtain a Brillouin scattering signal, and then convert it into an electrical signal.

[0007] The signal processing module is used to perform frequency down-conversion, signal filtering, and digital acquisition on the electrical signal to obtain multiple groups of Brillouin scattering signal data.

[0008] The data processing module is used to process multiple groups of Brillouin scattering signal data, extract the central frequency of Brillouin frequency shift; obtain the temperature and strain distributions along the optical cable according to the frequency shift, and then transmit the temperature and strain states to the QT upper computer through PCIE for real-time display.

[0009] The optical module includes a dual-wavelength laser source, a coupler, an optical variable attenuator, a gain-switching optical modulator, a pulsed erbium-doped fiber amplifier 1, an optical circulator, an erbium-doped fiber amplifier 2, a dual-channel polarization diversity device, and a dual-balanced detector.

[0010] The dual-wavelength laser source is divided into a detection path and a reference path by the coupler.

[0011] Among them, the continuous light entering the detection path sequentially passes through the optical variable attenuator, the gain-switching optical modulator, the pulsed erbium-doped fiber amplifier 1, and the optical circulator, and then enters the sensing fiber.

[0012] The Brillouin scattering signal generated in the sensing fiber enters the erbium-doped fiber amplifier 2 after passing through the output end of the optical circulator, and then beats with the reference light in the reference path.

[0013] After that, the light enters the dual-channel polarization diversity device and is divided into two paths, a and b. Then, the optical signals are uniformly combined through an optical coupler respectively, and then enter the dual-balanced detector to be converted into an electrical signal.

[0014] After the light is split by the polarization beam splitter in the dual-channel polarization diversity device, the two optical fields with orthogonal polarization states formed are respectively:

[0015]

[0016] In the formula, E 1 is the optical field with horizontal polarization, E 2 is the optical field with vertical polarization, E x is horizontal polarization, E y is vertical polarization;

[0017] Each polarization state corresponds to an independent optoelectronic detection channel for each polarization state in the dual-balanced detector, and is respectively converted into electrical signals I x (t) and I y (t), specifically:

[0018] I x (t) = R|E x | 2 , I y (t) = R|E y | 2 (2)

[0019] In the formula, R is the detector responsivity; t is the time;

[0020] Finally, by performing weighted combination on the two electrical signals, the two electrical signals are combined to obtain the output signal I out (t), specifically:

[0021] I out (t) = w x Ix (t) + w y I y (t)(3)

[0022] Wherein, w x and w y are weight coefficients respectively, satisfying

[0023] The signal processing module includes a low-noise amplifier, a mixer, a microwave local oscillator, a band-pass filter and an ADC module.

[0024] The electrical signal output by the double-balanced detector first enters the low-noise amplifier, and then enters the mixer to be mixed with the sine signal generated by the microwave local oscillator. Then, the down-converted electrical signal is frequency-selectively filtered by the band-pass filter. Finally, it is converted by the ADC module to obtain multiple sets of Brillouin scattering signal data.

[0025] The signal processing module uses a band-pass filter with a bandwidth of 87 MHz.

[0026] The bandwidth of the double-balanced detector is designed to be above 12 GHz, and the responsivity is greater than 0.8 A / W.

[0027] The data processing module includes:

[0028] Deploy an overlay averaging denoising module, an adaptive noise suppression module and a Lorentz fitting module on the FPGA.

[0029] Among them, the overlay averaging denoising module is used to perform point-by-point overlay averaging on the data points at the same position of multiple sets of Brillouin scattering signal data, and then output the signal after overlay averaging denoising to the adaptive noise suppression module. After denoising again, the Brillouin frequency shift center frequency is extracted through the Lorentz fitting module.

[0030] Deploy an edge computing node between the FPGA and the QT host computer for data hierarchical processing and remote diagnosis.

[0031] The processed data is hierarchically classified at the edge node. Among them, the key data is immediately transmitted to the QT host computer, and the non-key data is locally stored;

[0032] The key data includes abnormal temperature rise and stress concentration areas, and the non-key data includes periodic statistical information;

[0033] The edge node uploads the data to the host computer for remote diagnosis; the host computer uses machine learning algorithms to generate a diagnostic analysis report, and then feedbacks it to the edge computing node for guiding the adjustment of the monitoring frequency or threshold.

[0034] The FPGA is based on the ZYNQ7100 chip and processes signals in parallel at a sampling rate of 250 MSPS and a precision of 14 bits.

[0035] In the operation of the present invention, a dual-wavelength laser source is used to generate two pump lights with different wavelengths. After passing through an optical fiber coupler and a gain-converted optical modulator, they enter the optical fiber under test. The returned Brillouin scattering signal and the reference signal of the dual-channel polarization diversity receiver that eliminates polarization correlation noise pass through the beat frequency technique, and after being converted into an electrical signal by a double-balanced detector, signal filtering, mixing, acquisition, and analysis are completed. To improve the monitoring accuracy and signal-to-noise ratio, local superposition averaging denoising and adaptive noise suppression algorithms are adopted to effectively remove Gaussian white noise and burst interference. At the same time, the Lorentz fitting method is introduced to extract the central frequency of the Brillouin frequency shift to demodulate the temperature and strain distribution of the optical cable. In addition, the present invention integrates a high-precision ADC module (analog-to-digital converter) and an FPGA based on the ZYNQ7100 chip to achieve high-speed data acquisition and processing. To better feedback the condition of the optical cable to the user, an edge computing node is deployed between the FPGA and the QT upper computer, and it is transmitted to the upper computer through PCIE to achieve hierarchical data processing and remote diagnosis. Only 20% of the compressed data can be uploaded to the cloud, greatly shortening the response time.

[0036] The present invention can achieve high-precision and long-distance monitoring of ice coating and galloping of optical cables, provide reliable early warnings and safety guarantees, provide technical support for the maintenance and management of power systems, and is applicable to the intelligent monitoring requirements of power lines under extreme climate conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required in the description of the embodiments or the prior art.

[0038] Figure 1 It is the system block diagram of the present invention;

[0039] Figure 2 It is the principle block diagram of the signal processing module in the present invention;

[0040] Figure 3 It is the principle block diagram of the data processing module in the present invention. SPECIFIC IMPLEMENTATION METHODS

[0042] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. In other cases, the detailed descriptions of devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0043] In the present invention, the monitoring information refers to the information that can cause changes in the state of the optical fiber to generate Brillouin scattering. It includes the information on the change in the ambient temperature caused by the ice covering on the optical cable, as well as the relevant information such as the stress acting on the optical cable due to strong winds or other human factors. Using this system, the fluctuation points of the optical cable can be accurately calculated to achieve the detection of the optical cable.

[0044] As Figure 1 shown, the present invention provides an online monitoring system for ice-covered galloping of optical cables based on BOTDR, including an optical module, a signal processing module, and a data processing module.

[0045] The optical module is used to generate a probe optical signal, interact with the sensing optical fiber to obtain a Brillouin scattering signal, and then convert it into an electrical signal.

[0046] The signal processing module is used to perform frequency down-conversion, signal filtering, and digital acquisition on the electrical signal to obtain multiple groups of Brillouin scattering signal data.

[0047] The data processing module is used to process multiple groups of Brillouin scattering signal data, extract the central frequency of the Brillouin frequency shift; obtain the temperature and strain distributions along the optical cable according to the frequency shift, and then transmit the temperature and strain states to the QT upper computer through PCIE for real-time display.

[0048] The dual-wavelength laser source (Laser) emits two different continuous lights, which are divided into two paths through a 90:10 coupler. One of the 90% paths is used as the probe path to generate the probe pulse entering the sensing optical fiber, and the other path is used as the reference path to generate the reference light. The continuous light entering the probe path first needs to pass through a variable optical attenuator (VOA) to reduce the power below the maximum incident power based on the gain conversion optical modulator (GCOM), and then enter the GCOM to modulate the pulse. Since the peak power of the pulse output by the GCOM is relatively low, it needs to enter the pulsed erbium-doped fiber amplifier 1 (EDFA1) to be amplified to a certain power, and then pass through an optical circulator (OC) to enter the sensing optical fiber. Since the Brillouin scattering signal is weak, the Brillouin scattering signal generated in the optical fiber needs to enter the erbium-doped fiber amplifier 2 (EDFA2) through the output end of the optical circulator for amplification, and then beat with the reference light.

[0049] For the optical module, a dual-wavelength laser source (λ 1 and λ 2 ) is adopted. By emitting probe lights with two fixed different wavelengths, they respectively propagate along the sensing optical fiber and undergo Brillouin reflection, and the complete decoupling of temperature and strain is realized by time-sharing wavelength switching. The wavelengths utilize the sensitivity coefficient differences of different bands to temperature / strain (such as 1550 nm is more sensitive to temperature). Through the differential algorithm, the cross-interference is eliminated, and the demodulation error is reduced by more than 40% to optimize the response characteristics and sensitivity of the scattering signal.

[0050] The dual-wavelength differential detection technology is adopted. By emitting two kinds of detection lights with two fixed different wavelengths, they propagate along the sensing optical fiber respectively and interact with the Brillouin scattering signal. The time delay difference between the two signals can be compensated and eliminated by a dual-channel polarization diversity combiner, which separates the horizontal and vertical polarization components of the incident light into two independent signals and collects them in parallel through a dual-balanced detector, effectively reducing the influence of random polarization noise. The beat frequency signal generated after interference can significantly improve the signal-to-noise ratio (SNR), and at the same time suppress low-frequency interferences such as environmental vibration.

[0051] In the signal processing module, a dual-balanced detector is used to convert the optical signal after beating the Brillouin scattering signal with the reference light into an electrical signal. To completely recover the Brillouin frequency shift information, the detector bandwidth is designed to be above 12 GHz, and the responsivity is greater than 0.8 A / W. The band-pass filter can filter out the useless frequency bands after mixing and extract the Brillouin frequency shift signal with a center frequency of 11 GHz. The filtering bandwidth is designed to be 87 MHz to maintain a high spatial resolution. An ADC with a resolution of 14 bit and a sampling rate of 250 MSPS is used to convert the analog signal into a digital signal to ensure that the accuracy of the collected data meets the analysis requirements.

[0052] The light continuously changes its polarization state through the dual-channel polarization diversity combiner to eliminate the polarization fading noise caused by different polarization states. Then it enters the dual-balanced detectors (PD1 and PD2) to be converted into electrical signals. The electrical signals output by the dual-balanced detectors first need to enter a low-noise amplifier (LNA) for amplification, and then enter a mixer (Mixer) to mix with the sine signal generated by a microwave local oscillator (ELO). The down-converted electrical signal then passes through a band-pass filter (BPF) for frequency selection and filtering. By stepwise adjusting the output frequency of the microwave local oscillator, the frequency scanning of the Brillouin scattering signal can be completed, and thus the Brillouin scattering spectrum can be reconstructed. By extracting the center frequency of the Brillouin scattering spectrum along the optical fiber, the temperature and strain information at different positions along the entire optical fiber can be demodulated.

[0053] As Figure 2 shown, in optical fiber communication, due to the birefringence effect of the optical fiber and external environmental disturbances, the state of polarization (SOP) of the signal light will change randomly, resulting in signal power fluctuations and polarization-dependent loss (PDL) at the receiving end. The dual-channel polarization diversity reception eliminates the influence of the polarization state fluctuation by simultaneously receiving the signals of two orthogonal polarization states and combining them, thereby improving the receiving sensitivity and system stability. The core idea is to suppress the signal fading caused by the random change of the polarization state through diversity reception and combination, and improve the signal-to-noise ratio (SNR). The polarization beam splitter (PBS) divides the input optical signal into two orthogonal polarization states (such as horizontal polarization E x and vertical polarization E y ), and after being split by the PBS, the optical fields of the two orthogonal polarization states are:

[0054]

[0055] In the formula, E 1 is the optical field with horizontal polarization, and E 2 is the optical field with vertical polarization;

[0056] The dual-channel corresponds to each polarization state and is received by an independent photoelectric detection channel (PD1 and PD2), and is respectively converted into electrical signals I x (t) and I y (t), and the output signals are:

[0057] I x (t) = R|E x | 2 , I y (t) = R|E y | 2 )

[0058] In the formula, R is the detector responsivity; t is time.

[0059] Finally, by performing weighted combination on the two electrical signals, the two electrical signals are combined:

[0060] I out (t) = w x I x (t) + w y I y (t)(3)

[0061] In the formula, w x and w y are weight coefficients respectively, and satisfy

[0062] The data processing module performs efficient parallel computing based on the FPGA platform, including signal denoising, frequency extraction, and temperature and strain demodulation. The specific steps are divided into denoising and demodulation. Denoising combines the local superposition averaging algorithm and the adaptive noise suppression algorithm. The algorithm improves the signal-to-noise ratio while avoiding the processing delay introduced by the local superposition averaging algorithm. The extraction part is to perform spectral fitting on the denoised Brillouin scattering signal through the Lorentz fitting algorithm to extract the Brillouin frequency shift center frequency (BFS). This algorithm can quickly and accurately demodulate temperature and strain information, and its root mean square error is reduced from 1.531 MHz before denoising to 0.736 MHz. Combining the linear characteristics of the Brillouin frequency shift, the system converts the frequency information into the temperature and strain distributions along the optical cable.

[0063] The deployed local superposition averaging algorithm can reduce the Gaussian white noise in the Brillouin scattering signal, and further extract weak signals in combination with the dynamic adaptive noise suppression algorithm to improve the signal-to-noise ratio and maintain high spatial resolution.

[0064] As Figure 3 shown, first, local superposition averaging denoising is performed on the signal to reduce Gaussian white noise. The theoretical basis is that the mean value of Gaussian white noise approaches zero, and the signal strength increases with the increase of the number of superpositions. Since the returned scattered signal data is huge and the ADC module cannot convert all of it into digital signals at once, multiple sets of Brillouin scattering signal data will be obtained. Each set of data contains an equal number of sampling points. These input data are stored in the FIFO, and then the data points at the same positions of multiple sets of data are averaged point by point. Then, the signal after local superposition averaging denoising is output to the adaptive noise suppression algorithm module to further remove the noise that has not been filtered by the superposition averaging, and better extract the signal characteristics of temperature and strain. The adaptive noise suppression algorithm is a technique that uses an adaptive filter to dynamically adjust parameters to eliminate the noise mixed in the reflected Brillouin signal. Its core idea is to obtain information related to the noise in the reflected Brillouin signal through the reference noise channel, and use the adaptive algorithm to adjust the filter parameters, and finally separate the useful signal from the mixed signal. The core idea is to dynamically adjust the filter weights by minimizing the power (mean square error) of the error signal e(n) to make y(n) approach the noise v 1 (n). Specifically: it contains the useful signal s(n) and the noise v 1 (n), that is, d(n) = s(n) + v 1 (n). Only contains the noise v 1 (n) related to v 2 (n) passes through the adaptive filter to filter v 2 (n) to generate an estimated value y(n) closest to v 1 (n). Finally, the output error signal e(n) = d(n) - y(n) is the denoised signal.

[0065] After data acquisition and preliminary filtering, an edge computing node is deployed between the FPGA and the QT host computer to achieve data hierarchical processing and remote diagnosis. At this stage, noise filtering and signal demodulation are completed to ensure data quality. Edge computing node processing (FPGA node), the FPGA is responsible for receiving the raw data and performing preliminary analysis and processing. With the help of hardware acceleration, the FPGA calculates the temperature and strain distributions in real time and extracts features. In addition, the FPGA interacts with local devices to complete simple diagnosis and alarm functions, such as triggering an alarm when there is an abnormal temperature or strain change. Data classification and storage, the processed data is classified at the edge node. Critical data (such as abnormal changes) is immediately transmitted to the host computer or the cloud, while non-critical data (such as periodic statistical information) is stored locally to relieve the network bandwidth pressure. Remote diagnosis and decision support, the edge node uploads the data to the host computer or the cloud for in-depth analysis and remote diagnosis. The host computer uses methods such as big data analysis and machine learning to generate reports, predict the device status, and assist in decision-making. Feedback and optimization, the host computer feeds back the analysis results to the edge computing node to guide the adjustment of the monitoring frequency or threshold. This data flow mechanism reduces the data transmission delay, improves the response speed, realizes fast decision-making, relieves the server burden, and improves the system efficiency and reliability through edge computing.

[0066] In the above solution, by using pulse modulation technology and optimized filter design, a relatively high spatial resolution is achieved. Combining local superposition averaging and adaptive noise suppression algorithms significantly improves the signal quality. The parallel computing of the FPGA shortens the single measurement time to less than 10 seconds, enabling a quick understanding of the optical cable situation. Through modular design, efficient monitoring of ice accretion and galloping of the optical cable is realized, providing a reliable guarantee for the safe operation of power optical cables and also providing a technical reference for distributed fiber optic sensing applications in other fields.

[0067] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the present invention is not limited to the above specific embodiments. Without departing from the core idea of the present invention, those of ordinary skill in the art can still make various changes and adjustments within the scope of their professional knowledge.

Claims

1. An online monitoring system for optical cable ice dancing based on BOTDR, characterized in that: Including optical module, signal processing module and data processing module, The optical module is used to generate a detection light signal and interact with the sensing optical fiber to obtain a Brillouin scattering signal, which is then converted into an electrical signal; The signal processing module is used to perform frequency down-conversion, signal filtering and digital acquisition on the electrical signal to obtain multiple groups of Brillouin scattering signal data; The data processing module is used to process multiple groups of Brillouin scattering signal data and extract the Brillouin frequency shift center frequency; obtain the temperature and strain distribution along the optical cable according to the frequency shift, and then transmit the temperature and strain status to the QT host computer through PCIE for real-time display.

2. According to claim 1, a BOTDR-based optical cable ice dancing online monitoring system is characterized by: The optical module includes a dual-wavelength laser source, a coupler, an adjustable optical attenuator, a gain conversion optical modulator, a pulsed erbium-doped fiber amplifier 1, an optical circulator, an erbium-doped fiber amplifier 2, a dual-channel polarization divider and a dual-balanced detector. The dual-wavelength laser source is divided into a detection path and a reference path through a coupler. The continuous light entering the detection path passes through the adjustable optical attenuator, the gain conversion optical modulator, the pulsed erbium-doped fiber amplifier 1 and the optical circulator in sequence, and then enters the sensing optical fiber; The Brillouin scattered signal generated in the sensing optical fiber enters the erbium-doped fiber amplifier 2 after passing through the output end of the optical circulator, and then beats with the reference light in the reference path; After that, the light enters the dual-channel polarization divider and is divided into two paths, a and b. The optical signals are then evenly combined through optical couplers and then enter the dual-balanced detector to be converted into electrical signals.

3. The BOTDR-based optical cable ice dancing online monitoring system according to claim 2 is characterized by: After the light is split by the polarization beam splitter in the dual-channel polarization diversity device, the two orthogonal polarization states of the light fields are formed: Where E1 is the horizontally polarized light field, E2 is the vertically polarized light field, and E x is horizontal polarization, E y is vertical polarization; Each polarization state corresponds to an independent photoelectric detection channel for each polarization state in the dual-balanced detector, which is converted into an electrical signal I x (t) and I y (t), specifically: I x (t)=R|E x | 2 ,I y (t)=R|E y | 2 (2) Where R is the detector responsivity; t is time; Finally, the two electrical signals are combined by weighted combination to obtain the output signal I out (t), specifically: I out (t)=w x I x (t)+w y I y (t)(3) In the formula, w x and w y are weight coefficients respectively, satisfying 4. The BOTDR-based optical cable ice dancing online monitoring system according to claim 3 is characterized by: The signal processing module includes a low noise amplifier, a mixer, a microwave local oscillator, a bandpass filter and an ADC module. The electrical signal output by the dual-balanced detector first enters a low-noise amplifier, then enters a mixer to mix with a sinusoidal signal generated by a microwave local oscillator, and then the down-converted electrical signal is frequency-selectively filtered by a bandpass filter, and finally, converted by an ADC module to obtain multiple groups of Brillouin scattering signal data.

5. The BOTDR-based optical cable ice dancing online monitoring system according to claim 4 is characterized by: The signal processing module adopts a bandpass filter with a bandwidth of 87 MHz.

6. The BOTDR-based optical cable ice dancing online monitoring system according to claim 4 is characterized by: The bandwidth of the dual-balanced detector is designed to be above 12 GHz, and the responsivity is greater than 0.8 A / W.

7. The BOTDR-based optical cable ice dancing online monitoring system according to claim 4 is characterized by: The data processing module comprises: Deploy the superposition average denoising module, adaptive noise suppression module and Lorentz fitting module on the FPGA. Among them, the superposition average denoising module is used to perform point-by-point superposition and averaging of the data points at the same position of multiple groups of Brillouin scattering signal data, and then output the superposition average denoised signal to the adaptive noise suppression module. After denoising again, the Brillouin frequency shift center frequency is extracted through the Lorentz fitting module.

8. The BOTDR-based optical cable ice dancing online monitoring system according to claim 7 is characterized by: Edge computing nodes are deployed between FPGA and QT host computer for data hierarchical processing and remote diagnosis.

9. The BOTDR-based optical cable ice dancing online monitoring system according to claim 8, characterized in that: The processed data is graded at the edge node, where critical data is instantly transmitted to the QT host computer and non-critical data is stored locally; Key data include abnormal temperature rise and stress concentration areas, and non-key data include periodic statistical information; The edge node uploads data to the host computer for remote diagnosis; the host computer uses machine learning algorithms to generate diagnostic analysis reports, which are then fed back to the edge computing node to guide the adjustment of monitoring frequency or thresholds.

10. The BOTDR-based optical cable ice dancing online monitoring system according to claim 7, characterized in that: The FPGA is based on the ZYNQ7100 chip and processes signals in parallel at a sampling rate of 250MSPS and a precision of 14 bits.

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