Optical fiber state monitoring method, device and system based on genetic optimization coding and strong reflection suppression and storage medium

Abstract

The invention discloses an optical fiber state monitoring method, device and system based on genetic optimization coding and strong reflection suppression and a storage medium, and relates to the technical field of optical fiber testing, and the optical fiber state monitoring method based on genetic optimization coding and strong reflection suppression comprises the steps: obtaining a laser pulse signal, performing genetic optimization coding modulation on the laser pulse signal to obtain a pulse sequence signal; transmitting the pulse sequence signal to a to-be-measured optical fiber through a connector to obtain a backscattering signal, the connector using a weak reflection design or a free space optical coupling technology; performing signal conversion and signal enhancement on the backscattering signal to obtain a digital signal; performing genetic optimization decoding on the digital signal to obtain a monopulse response signal; and analyzing the monopulse response signal to obtain state information of the optical fiber to be detected. According to the invention, the state of the optical fiber can be monitored with high sensitivity and high reliability, and strong reflection interference at the joint of the hollow-core optical fiber can be effectively inhibited.

Description

Technical Field

[0001] The present application relates to the field of optical fiber testing technology, and in particular to an optical fiber status monitoring method, device, system and storage medium based on genetic optimization coding and strong reflection suppression. Background Art

[0002] As the demand for data transmission bandwidth and reliability in modern communication networks continues to increase, optical fiber has become a core component. Traditional commercial quartz optical fiber will gradually be unable to meet the needs of modern communications, and people are increasingly in need of high-bandwidth, low-loss communication media. Antiresonant hollow-core optical fiber is based on the principle of antiresonance. It guides light through the air, effectively avoiding the intrinsic defects of quartz materials. In theory, it can provide dozens of times of capacity growth. Due to its superior low loss and high bandwidth characteristics, it has gradually received attention. However, due to the thin-walled, hollow microstructure of antiresonant hollow-core optical fiber, it is more susceptible to damage during use. To ensure its reliability, effective fiber status monitoring and fault diagnosis must be performed on hollow-core optical fiber to ensure the stability and reliability of the communication network.

[0003] At present, optical fiber status monitoring mainly relies on OTDR (Optical Time-Domain Reflectometer) technology. However, the existing OTDR technology has significant deficiencies in antiresonant hollow-core optical fiber monitoring. First, due to the extremely low Rayleigh scattering of air, the backscattering signal of antiresonant hollow-core optical fiber is about 30dB lower than that of conventional single-mode optical fiber. It is difficult for existing OTDR equipment to detect such a weak signal, resulting in insufficient monitoring sensitivity. Secondly, the existing OTDR technology is limited by nonlinear effects, and the power of the detection optical signal is limited, resulting in an inherent contradiction between the dynamic range and spatial resolution of the system. In addition, strong reflections often occur at the connection between the single-mode optical fiber and the antiresonant hollow-core optical fiber, which will cause the saturation of the detector at the receiving end, thereby making the OTDR invalid and unable to accurately obtain the backscattering signal inside the optical fiber. Therefore, how to monitor the optical fiber status with high sensitivity and high reliability, while effectively suppressing the strong reflection interference at the connection of the hollow-core optical fiber, has become an urgent problem to be solved.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The purpose of this application is to provide a method, device, system and storage medium for optical fiber status monitoring based on genetic optimization coding and strong reflection suppression, aiming to solve the technical problem of how to monitor the status of optical fiber with high sensitivity and high reliability while effectively suppressing strong reflection interference at the connection of hollow-core optical fiber.

[0006] To achieve the above objectives, the present application proposes a method for monitoring optical fiber status based on genetic optimization coding and strong reflection suppression, the method comprising:

[0007] Acquiring a laser pulse signal, and performing genetic optimization coding modulation on the laser pulse signal to obtain a pulse sequence signal;

[0008] The pulse sequence signal is transmitted to the optical fiber to be tested through a connector to obtain a backscattered signal, wherein the connector uses a weak reflection design or a free space optical coupling technology;

[0009] Performing signal conversion and signal enhancement on the backscattered signal to obtain a digital signal;

[0010] Performing genetic optimization decoding on the digital signal to obtain a single pulse response signal;

[0011] The single pulse response signal is analyzed to obtain the state information of the optical fiber to be tested.

[0012] In one embodiment, the step of acquiring a laser pulse signal and performing genetically optimized coding modulation on the laser pulse signal to obtain a pulse sequence signal comprises:

[0013] Acquire multiple continuous laser pulse signals;

[0014] Randomly generate multiple coding sequences, and search the coding sequences through a genetic algorithm to obtain an initial coding sequence;

[0015] Upsampling the initial coding sequence to obtain a target coding sequence;

[0016] The laser pulse signal is modulated according to the target coding sequence to obtain a pulse sequence signal.

[0017] In one embodiment, the step of randomly generating a plurality of coding sequences and searching the coding sequences by a genetic algorithm to obtain an initial coding sequence comprises:

[0018] Initialize the number of iterations and the first preset number of populations;

[0019] Randomly generating a second preset number of coding sequences within the population, wherein the length of the coding sequences is a preset length;

[0020] Calculating a noise scaling factor for the coded sequence;

[0021] Repeatedly screening, randomly crossing, randomly mutating, and randomly migrating the coding sequence, and increasing the number of iterations until the number of iterations reaches a preset iteration threshold or the minimum value of the noise scaling factor is less than a preset noise scaling factor threshold;

[0022] The coding sequence corresponding to the minimum value of the noise scaling factor is used as the initial coding sequence.

[0023] In one embodiment, the step of repeatedly screening, randomly crossing, randomly mutating, and randomly migrating the coding sequence, and increasing the number of iterations until the number of iterations reaches a preset iteration threshold or the minimum value of the noise scaling factor is less than a preset noise scaling factor threshold comprises:

[0024] Randomly selecting and retaining a portion of the coding sequences in the population according to the noise scaling factor to obtain a selected coding sequence;

[0025] Performing random crossover on the selected coding sequence within the population to obtain a crossover coding sequence;

[0026] Randomly mutating the crossover coding sequence within the population;

[0027] When the random mutation is completed, updating the noise scaling factor, and recording the minimum value of the updated noise scaling factors as the minimum noise scaling factor;

[0028] Randomly migrating the coding sequences between the populations;

[0029] Return to the step of randomly selecting and retaining a portion of the coding sequences within the population according to the noise scaling factor to obtain a selected coding sequence, and increment the number of iterations until the number of iterations reaches a preset iteration threshold or the minimum noise scaling factor is less than a preset noise scaling factor threshold.

[0030] In one embodiment, the step of performing genetic optimization decoding on the digital signal to obtain a single pulse response signal comprises:

[0031] Performing Fourier transform on the digital signal to obtain a frequency domain signal;

[0032] An inverse Fourier transform is performed on the ratio of the frequency domain signal to the frequency domain representation of the target coding sequence to obtain a single pulse response signal.

[0033] In one embodiment, the state information includes an attenuation coefficient, a physical state, and a fault point location, and the step of analyzing the single pulse response signal to obtain the state information of the optical fiber to be tested includes:

[0034] measuring the signal strength of the single pulse response signal at different distances;

[0035] Calculating the attenuation coefficient of the optical fiber to be tested according to the signal strength;

[0036] Performing waveform analysis on the single pulse response signal to determine the physical state of the optical fiber to be tested;

[0037] The time delay of the single pulse response signal is measured to determine the fault point position of the optical fiber to be tested.

[0038] In one embodiment, the step of performing signal conversion and signal enhancement on the backscattered signal to obtain a digital signal comprises:

[0039] Converting the backscattered signal from an optical signal to an electrical signal to obtain an initial electrical signal;

[0040] Performing signal enhancement on the initial electrical signal to obtain an enhanced electrical signal;

[0041] filtering the enhanced electrical signal to obtain a target electrical signal;

[0042] The target electrical signal is converted from an analog signal to a digital signal.

[0043] In addition, to achieve the above purpose, the present application also proposes an optical fiber state monitoring device based on genetic optimization coding and strong reflection suppression, the device comprising:

[0044] A coding and modulation module is used to obtain a laser pulse signal and perform genetic optimization coding and modulation on the laser pulse signal to obtain a pulse sequence signal;

[0045] A scattering module, used to transmit the pulse sequence signal to the optical fiber to be tested through a connector to obtain a backscattered signal, wherein the connector uses a weak reflection design or a free space optical coupling technology;

[0046] A signal conversion and enhancement module, used for performing signal conversion and signal enhancement on the backscattered signal to obtain a digital signal;

[0047] A decoding module, used for performing genetic optimization decoding on the digital signal to obtain a single pulse response signal;

[0048] The signal analysis module is used to analyze the single pulse response signal to obtain the state information of the optical fiber to be tested.

[0049] In addition, to achieve the above-mentioned objectives, the present application also proposes an optical fiber state monitoring device based on genetic optimized coding and strong reflection suppression, the device comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the optical fiber state monitoring method based on genetic optimized coding and strong reflection suppression as described above.

[0050] In addition, to achieve the above-mentioned purpose, the present application also proposes a fiber state monitoring system based on genetic optimization coding and strong reflection suppression, the system comprising: a laser, a pulse code modulator, a circulator, a single-mode optical fiber, a connector, an optical fiber to be tested, a control module, a data processing module, an analog-to-digital conversion module, a bandpass filter, a signal amplifier and a high-sensitivity detector, the laser is respectively connected to the pulse code modulator and the control module, the pulse code modulator is respectively connected to the control module and the circulator, the circulator is respectively connected to the single-mode optical fiber and the high-sensitivity detector, the single-mode optical fiber is connected to the connector, the connector is connected to the optical fiber to be tested, the high-sensitivity detector is connected to the signal amplifier, the signal amplifier is connected to the bandpass filter, the bandpass filter is connected to the analog-to-digital conversion module, the analog-to-digital conversion module is connected to the data processing module, the data processing module is connected to the control module, and the system executes the fiber state monitoring method based on genetic optimization coding and strong reflection suppression.

[0051] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the optical fiber state monitoring method based on genetic optimization coding and strong reflection suppression as described above are implemented.

[0052] In addition, to achieve the above objectives, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the optical fiber state monitoring method based on genetic optimization coding and strong reflection suppression as described above.

[0053] One or more technical solutions proposed in this application have at least the following technical effects:

[0054] First, the optical fiber status monitoring system obtains the laser pulse signal and performs genetic optimization coding modulation on it to generate a pulse sequence signal, which significantly improves the dynamic range and signal-to-noise ratio of the signal, enabling the system to more effectively detect low-intensity backscattered signals. Secondly, the system transmits the pulse sequence signal to the optical fiber to be tested through a connector to obtain a backscattered signal. The connector adopts a weak reflection design or free space optical coupling technology, which effectively reduces the strong reflection interference at the optical fiber connection, avoids the detector saturation problem caused by the traditional connection method, and improves the linear performance and monitoring accuracy of the system. Then, the system performs signal conversion and signal enhancement on the backscattered signal to obtain a digital signal, which improves the signal strength and signal-to-noise ratio. Next, the system performs genetic optimization decoding on the digital signal to recover a clear single pulse response signal from the complex digital signal, improves the dynamic range and monitoring sensitivity of the system, and enables the system to more accurately extract the optical fiber status information. Finally, the system analyzes the single pulse response signal to obtain the status information of the optical fiber to be tested, providing comprehensive and accurate data support for the maintenance and fault diagnosis of the optical fiber. Through the above steps, the system can monitor the status of the optical fiber with high sensitivity and reliability, while effectively suppressing the strong reflection interference at the connection of the hollow-core optical fiber. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0056] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0057] Figure 1 A flow chart of Embodiment 1 of the optical fiber state monitoring method based on genetic optimization coding and strong reflection suppression provided in the present application;

[0058] Figure 2 A flow chart of Embodiment 2 of the optical fiber state monitoring method based on genetic optimization coding and strong reflection suppression provided in this application;

[0059] Figure 3 This is a schematic diagram of the module structure of an optical fiber state monitoring device based on genetic optimization coding and strong reflection suppression according to an embodiment of the present application;

[0060] Figure 4 This is a schematic diagram of the structure of an optical fiber state monitoring system based on genetic optimization coding and strong reflection suppression according to an embodiment of the present application;

[0061] Figure 5This is a schematic diagram of the device structure of the hardware operating environment involved in the optical fiber state monitoring method based on genetic optimization coding and strong reflection suppression in the embodiment of the present application.

[0062] Description of Figure Numbers:

[0063] 1. Laser; 2. Pulse code modulator; 3. Circulator; 4. Single-mode optical fiber; 5. Connector; 6. Optical fiber to be tested; 7. Control module; 8. Data processing module; 9. Analog-to-digital conversion module; 10. Bandpass filter; 11. Signal amplifier; 12. High-sensitivity detector.

[0064] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0065] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0066] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0067] As the demand for high-bandwidth and low-loss communication media grows, antiresonant hollow-core fiber has attracted attention due to its superior performance, but its thin-walled hollow structure is vulnerable and needs to be effectively monitored to ensure the stability and reliability of the communication network. Traditional OTDR technology monitors the state of the optical fiber by detecting the backscattered light signal, but it faces challenges when applied to antiresonant hollow-core fiber, such as the extremely low Rayleigh scattering of air, which makes signal detection difficult, the nonlinear effect limits the detection light power, and the strong reflection problem at the connection between the single-mode fiber and the hollow-core fiber. These problems make it difficult for traditional OTDR to effectively monitor the state of the antiresonant hollow-core fiber.

[0068] The main solution of the embodiment of the present application is: first, the laser pulse signal is modulated by genetic optimization coding to generate a pulse sequence with high dynamic range and signal-to-noise ratio, so as to more effectively detect low-intensity backscattered signals. A connector with weak reflection design or free space optical coupling technology is used to transmit the pulse sequence to the optical fiber to be tested, and reduce strong reflection interference, improve linear performance and monitoring accuracy. The obtained backscattered signal is then converted and enhanced, converted into a digital signal and further genetically optimized and decoded to restore a clear single pulse response signal, thereby improving the dynamic range and monitoring sensitivity. Finally, by analyzing the single pulse response signal, the status information of the optical fiber is accurately extracted, providing comprehensive and accurate data support for optical fiber maintenance and fault diagnosis.

[0069] It should be noted that the execution subject of the embodiment of the present application may be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a time domain reflectometer, an optical fiber state monitoring system, etc. The optical fiber state monitoring system is taken as an example to illustrate this embodiment and the following embodiments.

[0070] Based on this, the embodiment of the present application provides a method for monitoring the state of an optical fiber based on genetic optimization coding and strong reflection suppression, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the optical fiber state monitoring method based on genetic optimization coding and strong reflection suppression of the present application.

[0071] In this embodiment, the optical fiber state monitoring method based on genetic optimization coding and strong reflection suppression includes steps S10 to S50:

[0072] Step S10, acquiring a laser pulse signal, and performing genetic optimization coding modulation on the laser pulse signal to obtain a pulse sequence signal.

[0073] It should be noted that the laser pulse signal refers to an optical signal with a specific wavelength, pulse width and repetition frequency generated by a laser. Specifically, the laser (such as a DFB laser with a central wavelength of 1550nm±0.3nm) generates narrow linewidth optical pulses under the drive of the control module (narrow linewidth means that the spectral width of the laser is very narrow, that is, the frequency or wavelength distribution of the laser is very concentrated in a very small range). These optical pulses are the basic signal source of the OTDR system and the input signal of the subsequent genetically optimized coding modulation. They are used to inject into the optical fiber to be tested and obtain the status information of the optical fiber through the backscattering effect in the optical fiber. The characteristics of the laser pulse signal (such as pulse width and repetition frequency) directly affect the resolution and measurement range of the OTDR system.

[0074] Genetic algorithm is an advanced search algorithm used to optimize pulse coding sequences to improve the dynamic range and signal-to-noise ratio of the OTDR system. In this embodiment, the genetic optimization coding modulator uses SOA (Semiconductor Optical Amplifier) ​​as the core device, receives the coding control signal through the control module, and modulates the input laser pulse signal into a pulse signal with a specific coding sequence.

[0075] The pulse train signal refers to an optical signal that has been modulated by genetically optimized coding. In this embodiment, the laser pulse signal is processed by the genetically optimized coding modulator to generate a series of pulse signals with a specific coding sequence. The design of the pulse train signal aims to improve the dynamic range and signal-to-noise ratio of the system by optimizing the coding sequence, thereby more effectively detecting the backscattered signal in the optical fiber. Compared with the traditional single pulse signal, the pulse train signal significantly enhances the detection capability of weak reflection signals through genetically optimized coding technology, so that the OTDR system can more accurately monitor the status and fault location of the anti-resonant hollow core optical fiber.

[0076] It can be understood that, firstly, the optical fiber status monitoring system controls the laser through the control module to generate a narrow linewidth laser pulse signal with a central wavelength of 1550nm. The signal has stable optical power and specific pulse width, providing a high-quality basic light source for subsequent signal processing. Secondly, the system sends the laser pulse signal to the pulse code modulator, which modulates it based on the genetic optimization coding algorithm to generate a pulse sequence signal with a specific coding pattern. This process not only significantly improves the dynamic range and signal-to-noise ratio of the system, but also enhances the detection capability of low backscattering signals, thereby achieving high-sensitivity monitoring of the optical fiber status.

[0077] In this embodiment, other coding technologies (such as Simplex code, Golay code, Barker code, etc.) can also be used in the coding scheme to enhance the signal dynamic range by adjusting the coding rules, or a coding optimization method based on machine learning can be used to generate a coding sequence that is more suitable for a specific application scenario through data-driven means, thereby further improving the dynamic range and signal-to-noise ratio of the system.

[0078] Step S20, transmitting the pulse sequence signal to the optical fiber to be tested through a connector to obtain a backscattered signal, wherein the connector uses a weak reflection design or a free space optical coupling technology.

[0079] It should be noted that a connector refers to a device used to connect a single-mode optical fiber to an antiresonant hollow-core optical fiber. Its main function is to ensure efficient and low-loss transmission of optical signals between the two optical fibers while minimizing reflection interference at the connection.

[0080] The optical fiber to be tested refers to the optical fiber that needs to be monitored for status and fault diagnosis. In this embodiment, it is an antiresonant hollow-core optical fiber. The antiresonant hollow-core optical fiber is based on the antiresonance principle and guides light through the air. It has the advantages of low loss and high bandwidth, but its backscattered signal is extremely low (about 30dB lower than conventional single-mode optical fiber), so highly sensitive monitoring equipment and technology are required.

[0081] Backscatter signal refers to the reverse propagation of optical signal due to the inhomogeneity and structural defects inside the optical fiber when the optical signal propagates in the optical fiber. In OTDR technology, backscatter signal is a key source of information for monitoring the status of the optical fiber.

[0082] Weak reflection design means reducing reflections at the fiber optic connection and avoiding detector saturation by optimizing the end face structure of the connector (by precisely processing the end face of the connector to achieve high-precision surface flatness and angle requirements) or applying an anti-reflection coating (coating a special anti-reflection coating on the end face of the connector, which can effectively reduce the reflectivity of the optical signal at the interface and improve the transmission efficiency of the optical signal), thereby improving the linear performance and monitoring accuracy of the system.

[0083] Free-space optical coupling technology refers to the efficient coupling of optical signals in single-mode optical fibers into antiresonant hollow-core optical fibers through the free propagation of optical signals. This method avoids the reflection interference caused by the traditional physical connection interface. The specific implementation methods include: (1) Spatial optical coupler: Use optical lenses or other optical elements to focus and couple the optical signal output from the single-mode optical fiber to the input end of the antiresonant hollow-core optical fiber. (2) Alignment accuracy: Through a high-precision alignment mechanism, ensure that the optical signal is efficiently transmitted in free space, reducing energy loss and reflection. Free-space optical coupling technology can fundamentally eliminate the reflection interference caused by the physical connection interface, further improving the linear performance and monitoring reliability of the system.

[0084] It can be understood that, first, the fiber state monitoring system injects the pulse sequence signal after genetic optimization coding modulation into the single-mode optical fiber through a circulator, and then transmits the pulse sequence signal in the single-mode optical fiber to the optical fiber to be tested through a connector. Secondly, in order to ensure that reflection interference is minimized during signal transmission, the system uses a connector with weak reflection design or free space optical coupling technology. Finally, when the pulse sequence signal propagates in the optical fiber to be tested, it generates backscattered signals due to Rayleigh scattering (referring to the scattering that occurs when light waves pass through particles much smaller than their wavelength (such as gas molecules, tiny dust, etc.)) or structural defects. These signals are received by the system and used to analyze the optical fiber state. In this way, the system can effectively suppress strong reflection interference and improve signal quality, thereby achieving high-sensitivity and high-reliability monitoring of the optical fiber state.

[0085] In this embodiment, in the strong reflection suppression scheme, the interference of strong reflection signals can also be avoided through gating technology. By adding a gating module in the signal transmission path, the detection of strong reflection signals can be shielded within a specific time period, thereby reducing the impact of strong reflections on the overall system performance, effectively avoiding the dynamic range compression problem caused by strong reflection saturation of the detector, while retaining the high-sensitivity detection capability of weak scattered signals.

[0086] Step S30, performing signal conversion and signal enhancement on the backscattered signal to obtain a digital signal.

[0087] It should be noted that signal conversion refers to the process of converting the received backscattered light signal into an electrical signal. Specifically, the backscattered signal is guided from the optical fiber to be tested to a high-sensitivity detector through a connector, a single-mode optical fiber, and a circulator. The backscattered light signal is received by a high-sensitivity detector (such as an APD detector or a single-photon detector) and converted into an electrical signal. The selection of a high-sensitivity detector is to ensure that the extremely low-intensity backscattered light signal in the anti-resonant hollow-core optical fiber can be detected, thereby improving the sensitivity of the system.

[0088] Signal enhancement refers to the process of amplifying the converted electrical signal to increase the strength of the signal and make it more suitable for subsequent processing and analysis. In this embodiment, signal enhancement is achieved by a signal amplifier, which adaptively adjusts the amplification factor according to the amplitude of the detector output signal. This adaptive amplification technology can dynamically adjust the amplification factor according to the actual strength of the signal, thereby maintaining the best signal quality under different signal strengths. The purpose of signal enhancement is to improve the signal-to-noise ratio of the signal to ensure that subsequent processing can accurately extract the fiber status information.

[0089] Digital signal refers to the signal processed by ADC (Analog-to-Digital Converter). In this embodiment, the electrical signal after signal enhancement is sent to the analog-to-digital conversion module, which converts the analog signal into a digital signal. Digital signal has discrete time and amplitude characteristics, which is convenient for digital signal processing, such as decoding and data analysis.

[0090] It can be understood that, first, the system receives the backscattered light signal returned from the optical fiber to be tested through a high-sensitivity detector and converts it into a processable electrical signal. Then, in order to improve the signal strength and signal-to-noise ratio, the system uses a signal amplifier to amplify the converted electrical signal to ensure that the signal can be accurately identified and analyzed in subsequent processing. Finally, the enhanced electrical signal is sent to the analog-to-digital conversion module to convert the continuous analog signal into a discrete digital signal for further digital signal processing and data analysis, thereby obtaining a digital signal that can be used for optical fiber status monitoring.

[0091] As an example, the steps of performing signal conversion and signal enhancement on the backscattered signal to obtain a digital signal include: converting the backscattered signal from an optical signal to an electrical signal to obtain an initial electrical signal; performing signal enhancement on the initial electrical signal to obtain an enhanced electrical signal; filtering the enhanced electrical signal to obtain a target electrical signal; and converting the target electrical signal from an analog signal to a digital signal.

[0092] The initial electrical signal refers to the electrical signal converted from the backscattered light signal by a high-sensitivity detector. This signal is the direct output after the optical signal is converted into an electrical signal. It usually has a lower intensity and a higher noise level because it directly reflects the weak backscattered light signal in the optical fiber to be tested.

[0093] The enhanced electrical signal refers to the initial electrical signal after being processed by the signal amplifier. The signal amplifier adaptively adjusts the amplification factor according to the amplitude of the initial electrical signal, thereby increasing the strength of the signal and making it more suitable for subsequent processing.

[0094] The target electrical signal refers to the enhanced electrical signal after filtering. In this embodiment, the bandpass filter is used to filter out the high-frequency and low-frequency noise in the enhanced electrical signal, and only retain the target signal frequency band (such as the signal frequency band with a central wavelength of 1550nm). By filtering, noise interference can be further reduced, the purity and reliability of the signal can be improved, and more accurate fiber status information can be obtained.

[0095] An analog signal refers to a signal that changes continuously in time and amplitude, as opposed to a digital signal. In this embodiment, both the initial electrical signal and the enhanced electrical signal are analog signals because they change continuously in time and amplitude. Before being converted into a digital signal, the analog signal needs to be quantized and encoded by an analog-to-digital conversion module to obtain a discrete digital signal, which is convenient for subsequent digital signal processing and data analysis.

[0096] First, the system uses a high-sensitivity detector to receive the backscattered light signal in the optical fiber to be tested and converts it into an initial electrical signal. This process converts the intensity of the optical signal into the amplitude of the electrical signal, providing a basis for subsequent processing. Secondly, the system amplifies the initial electrical signal through a signal amplifier, adaptively adjusts the amplification factor to increase the signal strength, and thus obtains an enhanced electrical signal. This step is to improve the signal-to-noise ratio of the signal and ensure that the signal can be accurately identified and analyzed in subsequent processing. Then, the enhanced electrical signal is filtered through a bandpass filter to remove high-frequency and low-frequency noise, retaining only the target signal frequency band to obtain the target electrical signal. This process further reduces noise interference and improves the purity and reliability of the signal. Finally, the target electrical signal is quantized and encoded through an analog-to-digital conversion module, converting it from an analog signal to a digital signal, which is convenient for subsequent digital signal processing and data analysis, thereby achieving accurate monitoring of the optical fiber status and fault diagnosis.

[0097] Step S40, performing genetic optimization decoding on the digital signal to obtain a single pulse response signal.

[0098] It should be noted that genetic optimization decoding refers to the process of decoding the digital signal after genetic optimization coding modulation, and the digital signal is processed through a series of operations to restore the original single pulse response signal. The single pulse response signal refers to the original pulse signal recovered from the digital signal through the genetic optimization decoding algorithm. It is a direct reflection of the backscattered signal in the optical fiber and contains key information about the optical fiber status.

[0099] It can be understood that, first, the system inputs the digital signal after analog-to-digital conversion into the data processing module. Then, the data processing module uses the genetic optimization decoding algorithm to decode the single pulse response signal from the digital signal.

[0100] As an example, the step of performing genetic optimization decoding on the digital signal to obtain a single pulse response signal includes: performing Fourier transform on the digital signal to obtain a frequency domain signal; performing inverse Fourier transform on the ratio of the frequency domain signal to the frequency domain representation of the target coding sequence to obtain a single pulse response signal.

[0101] Fourier transform refers to the mathematical operation of converting digital signals from the time domain to the frequency domain. Specifically, the optical fiber status monitoring system decomposes the collected digital signals into a combination of different frequency components through Fourier transform, thereby obtaining frequency domain signals. The purpose is to simplify the complex time domain signals into frequency components in the frequency domain, which is convenient for subsequent signal processing and analysis.

[0102] Frequency domain signal refers to the signal obtained after Fourier transform, which represents the amplitude and phase distribution of the original signal at different frequencies. In the frequency domain, the characteristics of the signal are more intuitive, and it is easier to identify and process noise, interference and other components.

[0103] The ratio refers to the result of dividing the frequency domain signal obtained by Fourier transforming the digital signal by the frequency domain representation of the target coding sequence point by point.

[0104] Inverse Fourier transform refers to the mathematical operation of converting frequency domain signals back to time domain signals. The system performs inverse Fourier transform on frequency domain signals to obtain a single pulse response signal. The purpose is to restore the signal after frequency domain processing to a pulse signal in the time domain, so as to further analyze the status information of the optical fiber.

[0105] First, the system performs a Fourier transform on the collected digital signal. This step converts the signal from the time domain to the frequency domain to obtain a frequency domain signal, making the frequency component of the signal clear and facilitating the analysis of the frequency characteristics of the signal. Then, the system divides the obtained frequency domain signal by the frequency domain representation of the target coding sequence point by point and calculates their ratio. This process actually decodes the signal in the frequency domain, with the purpose of separating the signal components related to the coding sequence while suppressing noise and interference. Finally, the system performs an inverse Fourier transform on the obtained ratio, converting the signal from the frequency domain back to the time domain to obtain a single pulse response signal. This signal reflects the response characteristics of the optical fiber to a single pulse input and contains the status information of the optical fiber, such as loss and fault location, thereby achieving accurate monitoring of the optical fiber status.

[0106] Step S50: Analyze the single pulse response signal to obtain status information of the optical fiber to be tested.

[0107] It should be noted that the status information refers to various characteristics and parameters of the optical fiber under test extracted by analyzing the single pulse response signal. This information can reflect the health status, performance indicators and whether there are faults or defects of the optical fiber.

[0108] It can be understood that the system analyzes the intensity, waveform and other characteristics of the obtained single pulse response signal, and can comprehensively obtain the state information of the optical fiber to be tested according to its changes.

[0109] As an example, the status information includes an attenuation coefficient, a physical state, and a fault point location, and the step of analyzing the single pulse response signal to obtain the status information of the optical fiber to be tested includes: measuring the signal strength of the single pulse response signal at different distances; calculating the attenuation coefficient of the optical fiber to be tested based on the signal strength; performing waveform analysis on the single pulse response signal to determine the physical state of the optical fiber to be tested; and measuring the time delay of the single pulse response signal to determine the fault point location of the optical fiber to be tested.

[0110] The attenuation coefficient refers to the attenuation of the optical signal intensity within a unit length of the optical fiber, usually expressed in dB / km. It is an important parameter for measuring the transmission loss of optical fiber, reflecting the energy loss of the optical fiber during the transmission process, and can help evaluate the aging degree of the optical fiber, the transmission efficiency, and whether there are local loss points. By measuring the signal strength of the single pulse response signal at different distances, the attenuation coefficient of the optical fiber can be calculated, thereby evaluating the transmission performance of the optical fiber. The calculation formula for the attenuation coefficient is:

[0111]

[0112] Where I0 is the initial strength of the transmitted signal, and I(z) is the signal strength at distance z.

[0113] The physical state refers to the physical integrity of the optical fiber, including whether there are structural defects such as bends, breaks, microcracks, and poor joints. The physical state of the optical fiber can be determined by waveform analysis of the single pulse response signal (referring to the process of analyzing the shape and characteristics of the single pulse response signal). For example, a sudden change or abnormal fluctuation in the signal waveform may indicate that the optical fiber is bent or the joint is damaged; while a sudden interruption of the signal may mean that the optical fiber is broken.

[0114] The fault point location refers to the specific location of the fault in the optical fiber, usually expressed as the length from the starting point of the optical fiber. By measuring the time delay of the single pulse response signal, combined with the refractive index and speed of light of the optical fiber, the location of the fault point can be calculated. This information is crucial for the maintenance and repair of optical fibers because it can help technicians quickly locate and repair faults. The calculation formula is:

[0115]

[0116] Where c is the speed of light, n is the refractive index of the optical fiber, and t0 and t1 are the times for transmitting and receiving signals, respectively.

[0117] Signal strength refers to the light intensity or level of a single pulse response signal at different locations. In optical fiber monitoring, changes in signal strength reflect the backscattering inside the optical fiber. By measuring the signal strength at different distances, the loss characteristics of the optical fiber can be analyzed and the attenuation coefficient can be calculated.

[0118] Delay refers to the time required for an optical signal to propagate in an optical fiber. By measuring the delay of a single pulse response signal, combined with the refractive index of the optical fiber and the speed of light, the distance the optical signal propagates in the optical fiber can be calculated. This parameter is crucial for determining the location of the optical fiber fault point because it can help the system accurately locate the specific location where the fault occurs.

[0119] First, the system measures the intensity of the single pulse response signal at different positions along the optical fiber and records the signal intensity value at each position. This process can help quantify the attenuation of the optical signal during optical fiber transmission. Secondly, the system calculates the attenuation coefficient of the optical fiber to be tested based on these measured signal intensity values ​​using the attenuation formula. This calculation process can quantify the transmission loss characteristics of the optical fiber and thus evaluate the overall health of the optical fiber. Then, the system analyzes the waveform of the single pulse response signal and determines whether the optical fiber has physical state problems such as bending, breaking, or poor joints by observing the changes in the waveform. This analysis process can intuitively reflect the structural integrity of the optical fiber. Finally, the system measures the delay of the single pulse response signal, that is, the time difference from the transmission to the reception of the signal, and combines the refractive index of the optical fiber and the speed of light to calculate the distance from the fault point to the measurement starting point, thereby accurately locating the fault point in the optical fiber. This process provides accurate guidance for the maintenance and repair of the optical fiber, and significantly improves the efficiency and accuracy of optical fiber monitoring.

[0120] This embodiment provides a method for monitoring the state of an optical fiber based on genetic optimization coding and strong reflection suppression. First, the optical fiber state monitoring system obtains a laser pulse signal and modulates it with genetic optimization coding to generate a pulse sequence signal, which significantly improves the dynamic range and signal-to-noise ratio of the signal, so that the system can more effectively detect low-intensity backscattered signals. Secondly, the system transmits the pulse sequence signal to the optical fiber to be tested through a connector to obtain a backscattered signal. The connector adopts a weak reflection design or free space optical coupling technology, which effectively reduces the strong reflection interference at the optical fiber connection, avoids the detector saturation problem caused by the traditional connection method, and improves the linear performance and monitoring accuracy of the system. Then, the system performs signal conversion and signal enhancement on the backscattered signal to obtain a digital signal, which improves the signal strength and signal-to-noise ratio. Next, the system performs genetic optimization decoding on the digital signal, recovers a clear single pulse response signal from the complex digital signal, improves the dynamic range and monitoring sensitivity of the system, and enables the system to more accurately extract the optical fiber state information. Finally, the system analyzes the single pulse response signal to obtain the state information of the optical fiber to be tested, providing comprehensive and accurate data support for the maintenance and fault diagnosis of the optical fiber. Through the above steps, the system can monitor the status of the optical fiber with high sensitivity and high reliability, while effectively suppressing the strong reflection interference at the connection of the hollow-core optical fiber.

[0121] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can refer to the above introduction, and will not be repeated later. Figure 2 , Figure 2This is a flow chart of the second embodiment of the optical fiber state monitoring method based on genetic optimization coding and strong reflection suppression of the present application. Step S10 of the optical fiber state monitoring method based on genetic optimization coding and strong reflection suppression includes steps S11 to S14:

[0122] Step S11, acquiring a plurality of continuous laser pulse signals.

[0123] It can be understood that the system first generates a plurality of continuous laser pulse signals through a laser, and these pulse signals have the same wavelength and pulse width and are continuous in time.

[0124] Step S12: randomly generate multiple coding sequences, and search the coding sequences through a genetic algorithm to obtain an initial coding sequence.

[0125] It should be noted that the coding sequence refers to a specific set of digital or symbol sequences used to modulate the laser pulse signal. These sequences are generated by specific coding rules in order to improve the dynamic range and anti-interference ability of the signal during signal transmission. The coding sequence is usually composed of a series of binary digits (0 and 1), which are convolved with the laser pulse signal to generate a modulated signal with specific characteristics. The initial coding sequence refers to the coding sequence with the smallest noise scaling factor among all coding sequences obtained after being processed by the genetic optimization algorithm. The genetic optimization algorithm continuously optimizes the coding sequence through a series of operations to find the optimal solution that can maximize the system dynamic range and signal-to-noise ratio.

[0126] It is understandable that, first, the system randomly generates multiple coding sequences, each sequence is composed of random binary numbers, and the length is fixed to ensure diversity and coverage. Then, the system searches for these coding sequences by genetic algorithm. After multiple rounds of iterative optimization, the system obtains the initial coding sequence, which is the initial coding sequence after being processed by the genetic optimization algorithm, which can significantly improve the dynamic range and signal-to-noise ratio of the system, can more accurately detect weak signals, and make the system more sensitive and reliable when monitoring the optical fiber to be tested (antiresonance hollow-core optical fiber in the present embodiment) of low backscattering signal.

[0127] As an example, the steps of randomly generating multiple coding sequences and searching the coding sequences through a genetic algorithm to obtain an initial coding sequence include: initializing the number of iterations and a first preset number of populations; randomly generating a second preset number of coding sequences within the population, wherein the length of the coding sequence is a preset length; calculating the noise scaling factor of the coding sequence; repeatedly screening, randomly crossing, randomly mutating, and randomly migrating the coding sequence, and increasing the number of iterations until the number of iterations reaches a preset iteration threshold or the minimum value of the noise scaling factor is less than a preset noise scaling factor threshold; and taking the coding sequence corresponding to the minimum value of the noise scaling factor as the initial coding sequence.

[0128] The number of iterations refers to the number of cycles of the genetic optimization algorithm, which is used to control the execution progress and optimization depth of the algorithm. Each iteration will perform a series of optimization operations on the coding sequence until the termination condition is met. The first preset number refers to the size of the initial population, that is, the number of sets of coding sequences generated at the beginning of the algorithm. For example, if the first preset number is 10, then the algorithm will initialize 10 populations, each population containing a certain number of coding sequences. A population refers to a set containing multiple coding sequences, which is used to simulate a population in the process of biological evolution. The coding sequences in each population will be optimized through genetic operations (such as crossover, mutation, etc.). The second preset number refers to the number of coding sequences in each population. For example, if the second preset number is 50, then each population will randomly generate 50 coding sequences.

[0129] The preset length refers to the length of each coding sequence, that is, the number of binary digits contained in the coding sequence. For example, if the preset length is 120, then each coding sequence consists of 120 binary digits.

[0130] The fitness value is an indicator to measure the performance of the coding sequence, which is usually related to the performance of the coding sequence in signal processing, such as the dynamic range of the signal, signal-to-noise ratio, etc. In the genetic optimization coding process, the fitness value is used to evaluate the performance of each coding sequence. The larger the fitness value, the better the performance of the coding sequence, that is, the better the performance of the sequence in signal processing, and the higher the signal-to-noise ratio and the larger the dynamic range.

[0131] The noise scaling factor, or Q value, is a parameter that is inversely proportional to the fitness value. In genetic optimization coding, the Q value is used to describe the noise level of the coding sequence. The smaller the Q value, the lower the noise level of the coding sequence, and thus the larger the fitness value, the better the performance of the coding sequence. In other words, the coding sequence with a smaller Q value can provide a clearer signal and less noise interference in signal processing, so it is more likely to be retained and further optimized during the selection process of the genetic algorithm. The calculation method of the Q value is:

[0132]

[0133] Where U(k) is the N of the encoding sequence u(n) r Point discrete Fourier transform, N r is the number of discrete Fourier transform points. Q is inversely proportional to the fitness value of the individual. The smaller Q is, the higher the survival probability of the individual is.

[0134] Screening refers to the process of selecting coding sequences based on fitness values, retaining coding sequences with smaller Q values ​​and eliminating coding sequences with larger Q values. It simulates the process of natural selection and ensures that excellent coding sequences can enter the next generation.

[0135] Random crossover refers to randomly selecting two sequences as parents from the retained sequence, and the crossover method is two-point crossover. Specifically, the two-point crossover operation randomly selects two crossover points, exchanges the parts of the coding sequences of the two parent individuals between the crossover points, and generates two new offspring individuals. For example, when the sequence length is 120, the 30th and 80th positions are selected as crossover points. Exchange the parts of the two parent sequences between the crossover points to generate two new offspring sequences. For example, the parent sequence A is 1111111111, the parent sequence B is 0000000000, and the crossover points are the 3rd and 8th positions, then the generated offspring sequences are 1100000011 and 0011111100. Random crossover simulates the crossover operation in biological evolution and is used to increase the diversity of the population.

[0136] In this embodiment, the random operation can be implemented by a random function in each program programming language.

[0137] Random mutation refers to randomly changing the values ​​of certain bits in the coding sequence to introduce new features. It simulates the mutation operation in biological evolution and is used to prevent the algorithm from falling into the local optimal solution. The mutation method is bit flipping. Specifically, the mutation operation randomly selects multiple bits in the coding sequence with a certain probability and flips their values ​​(0 flips to 1, 1 flips to 0). This operation can increase the diversity of the population and prevent the algorithm from falling into the local optimal solution. Assuming that the offspring sequence is 1100000011, the 2nd and 5th bits are randomly selected for mutation, then the mutated sequence is 1000100011.

[0138] The core mechanism of random migration is based on the ring topology to implement the elite migration strategy between subpopulations: the m individuals with the highest fitness value in the current subpopulation are copied to the m positions with the lowest fitness value in the adjacent subpopulation with a certain probability, forming an optimization mechanism of "advantageous individuals expelling inferior individuals". Its operation process is divided into three steps: first, subpopulation i is sorted in ascending order of fitness value and the best individual index is extracted; then the next subpopulation j connected in the ring is determined, and the worst individual index is extracted in descending order of fitness value; finally, the inferior individual position of subpopulation j is covered with the high-quality individuals of subpopulation i with a certain probability, realizing the propagation of high-quality genes across populations.

[0139] The preset iteration threshold refers to the maximum number of iterations of the algorithm. When the number of iterations reaches this threshold, the algorithm stops running. For example, the preset iteration threshold can be set to 1000 iterations. The minimum value refers to the minimum value of the Q values ​​of all current coding sequences, which reflects the Q value of the coding sequence with the best performance in the current population. The preset noise scaling factor threshold refers to a preset Q value threshold. When the Q value of a coding sequence is less than this threshold, the algorithm believes that a good enough solution has been found and stops running. For example, when the length of each sequence is 120 bits, the preset noise scaling factor threshold can be set to 0.02.

[0140] First, the system sets the initial number of iterations to 0 and creates 60 populations, each of which contains 60 randomly generated coding sequences, each of which is 120 bits long, to provide a diverse initial sample for the genetic algorithm. Then, the system calculates the Q value of each coding sequence, and selects sequences with better performance by evaluating its performance in signal processing, and eliminates sequences with poor performance, thereby gradually optimizing the population. Next, the system randomly crosses the selected sequences, selects two sequences to exchange part of the information at random positions to generate new sequences, and increases the diversity of the population; performs random mutation, randomly changes the values ​​of certain bits in the sequence to introduce new features, and avoids the algorithm from falling into local optimality; and randomly migrates, exchanges excellent sequences between different populations to promote information exchange and improve global search capabilities. After each operation, the number of iterations is increased by 1 until the number of iterations reaches 1000 or the minimum value in the Q value is less than 0.02, indicating that a sufficiently good coding sequence has been found. Finally, the system uses the coding sequence with the smallest Q value as the initial coding sequence for the subsequent pulse modulation process, which significantly improves the dynamic range and signal-to-noise ratio of the system and achieves high-sensitivity fiber status monitoring. (The numbers in this application are for example only and are not intended to be limiting)

[0141] As an example, the step of repeatedly screening, randomly crossing, randomly mutating and randomly migrating the coding sequences, and increasing the number of iterations until the number of iterations reaches a preset iteration threshold or the minimum value of the noise scaling factor is less than the preset noise scaling factor threshold includes: randomly selecting and retaining a portion of the coding sequences in the population according to the noise scaling factor to obtain a selected coding sequence; randomly crossing the selected coding sequences in the population to obtain a crossed coding sequence; randomly mutating the crossed coding sequences in the population; when the random mutation is completed, updating the noise scaling factor, and recording the minimum value of the updated noise scaling factor as the minimum noise scaling factor; randomly migrating the coding sequences between the populations; returning to the step of randomly selecting and retaining a portion of the coding sequences in the population according to the noise scaling factor to obtain a selected coding sequence, and increasing the number of iterations until the number of iterations reaches the preset iteration threshold or the minimum noise scaling factor is less than the preset noise scaling factor threshold.

[0142] The random selection of coding sequences mentioned in the retention part refers to selecting which sequences will be retained in the next generation population based on the fitness value of each coding sequence in the current population. This process is usually implemented using a "roulette wheel" selection method, in which sequences with higher fitness values ​​have a higher probability of being selected, while sequences with lower fitness values ​​have a lower probability of being selected. For example, suppose there is a population of 60 coding sequences, each with a fitness value, and you want to select new 60 sequences from them to form the next generation population. First, calculate the normalized fitness value of each sequence and determine its selection probability based on it. Then, construct a roulette wheel in which the size of the area occupied by each sequence is proportional to its selection probability. By spinning this roulette wheel multiple times, sequences can be randomly but biasedly selected to form a new population. In this process, sequences with higher fitness values ​​are more likely to be selected multiple times and thus appear repeatedly in the new population, while sequences with lower fitness values ​​may be eliminated or only selected once. The purpose of this is to allow coding sequences with better performance to have a higher chance of being passed to the next generation, while maintaining a certain degree of diversity to avoid the algorithm converging to a local optimal solution too early.

[0143] In this embodiment, the selection algorithm in the random selection retention is the "roulette" method. First, the Q value of each individual (i.e., the coding sequence) is calculated, and its selection probability is calculated based on the Q value. The calculation formula for the selection probability is:

[0144]

[0145] Where Pi is the probability of the i-th individual being selected in each round of selection. Afterwards, a virtual roulette wheel is constructed, and the size of the area occupied by each individual on the roulette wheel is proportional to its selection probability. Finally, by randomly generating a random number between 0 and 1, the rotation of the roulette wheel is simulated, and the corresponding individual is selected according to the area on the roulette wheel where the random number falls. Repeat this process β times, each time selecting an individual as the parent of the next generation of the population. Through roulette selection, individuals with higher fitness values ​​(i.e. individuals with smaller Q values) have a greater probability of being selected, thereby ensuring that excellent genes can be passed on to the next generation with a greater probability while maintaining the diversity of the population. Finally, the selected β individuals will be used for subsequent crossover and mutation operations.

[0146] The minimum noise scaling factor refers to the minimum value of the Q values ​​of all coding sequences in all current populations. It reflects the Q value of the best performing coding sequence in all current populations. For example, suppose there are currently 10 populations, each with 50 coding sequences, and each coding sequence has a Q value. Among all these coding sequences, find the one with the smallest Q value, which is the minimum noise scaling factor. The minimum noise scaling factor is used to determine whether the algorithm has achieved the optimization goal. If the minimum noise scaling factor is less than the preset noise scaling factor threshold (such as 0.02), it is considered that a sufficiently good coding sequence has been found and the algorithm can stop running.

[0147] Incrementing means that the number of iterations is increased by 1 during each iteration. The incrementing operation ensures that the algorithm can gradually optimize the coding sequence until the termination condition is met.

[0148] First, the system uses the roulette wheel method to randomly select and retain some coding sequences in the population according to the Q value of each coding sequence to form a new generation of population, in which sequences with smaller Q values ​​have a higher probability of being selected, which can increase the proportion of excellent features in the population. Next, the selected coding sequences are randomly crossed, and new coding sequences are generated by exchanging some elements in the sequence in the hope of combining sequences with better performance. Then, random mutation is performed on the crossed coding sequences, that is, the values ​​of certain bits in the sequence are randomly changed to introduce new genetic diversity and avoid local optimality. After that, the Q values ​​of all coding sequences are updated, and the minimum noise scaling factor is recorded. This value reflects the performance of the initial coding sequence in the current population. Next, the coding sequences with smaller Q values ​​between populations are randomly migrated to promote the exchange of excellent genes between different populations and improve the adaptability of the overall population. Finally, the above selection, crossover, mutation and migration steps are repeated, and the number of iterations is increased until the number of iterations reaches the preset threshold or the minimum noise scaling factor is less than the preset Q value threshold, so as to ensure that the algorithm can terminate in time when a sufficiently excellent coding sequence is found.

[0149] Step S13, upsampling the initial coding sequence to obtain a target coding sequence.

[0150] It should be noted that upsampling refers to interpolating the initial coding sequence to increase the number of sampling points of the sequence, thereby improving its time resolution. Specifically, upsampling generates new sample points by inserting additional zero values ​​between adjacent samples of the sequence or by an interpolation algorithm (such as linear interpolation, spline interpolation, etc.). For example, assuming that the initial coding sequence is [1,0,1,0], upsampling can generate [1,0,0,0,1,0,0,0]. The purpose of upsampling is to increase the time resolution of the signal without changing the frequency characteristics of the original signal, so that the signal can be processed more finely in subsequent convolution operations.

[0151] The target coding sequence refers to the coding sequence after upsampling. This sequence has a higher resolution in time or space and can more accurately describe the characteristics of the signal. For example, if the initial coding sequence is a simple pulse sequence, after upsampling, the target coding sequence will contain more detailed information, so that the original signal can be restored more accurately in the subsequent signal processing and decoding process. The generation of the target coding sequence is to optimize the performance of the system, especially when processing antiresonant hollow core fibers with low backscattering signals, so that the fiber state information can be more effectively extracted.

[0152] It is understandable that the system performs upsampling on the initial coding sequence, increasing the sampling rate and time resolution of the sequence by inserting additional zero values ​​or other preset values ​​between adjacent coding bits. For example, for an initial coding sequence of length 120, upsampling may expand it to a sequence of length 240 or more. The resulting target coding sequence has a higher time resolution and can more accurately control the shape and time interval of the pulse signal, thereby generating a clearer backscattered signal after injection into the optical fiber, improving the dynamic range and signal-to-noise ratio of the system, and enhancing the sensitivity and reliability of optical fiber status monitoring.

[0153] Step S14, modulating the laser pulse signal according to the target coding sequence to obtain a pulse sequence signal.

[0154] It should be noted that, in this embodiment, modulation is achieved by convolving the target coding sequence with the laser pulse signal. The convolution operation is a mathematical operation that multiplies and accumulates each element in the coding sequence with the laser pulse signal to generate a new pulse sequence signal. For example, if the target coding sequence is [1, 0, 1, 1] and the laser pulse signal is [p1, p2, p3, p4], then the convolution operation will generate a new pulse sequence signal [p1, 0, p3, p4].

[0155] It can be understood that, first, the system multiplies the target coding sequence and the laser pulse signal bit by bit to generate a series of weighted pulse signals. Secondly, the system accumulates these weighted pulse signals to form the final pulse sequence signal. Finally, through this modulation method, the system can embed the genetically optimized coding information into the laser pulse signal, so that the signal injected into the optical fiber has a higher dynamic range and signal-to-noise ratio, thereby improving the sensitivity and reliability of monitoring.

[0156] Assuming u(n) is the initial coding sequence, after upsampling, the target coding sequence d(n) is obtained, and convolved with the pulse p(n), the signal injected into the optical fiber (that is, the pulse sequence signal transmitted to the optical fiber to be tested) is c(n):

[0157]

[0158] The received signal model (time domain) of the detected optical fiber response signal (i.e., backscattered signal) is:

[0159]

[0160] Among them, r c (n) is the received signal, c(n) is the transmitted pulse sequence, h(n) is the ideal pulse response of the optical fiber, e c (n) is the noise, Represents a convolution operation.

[0161] The transmitted pulse sequence is decomposed (time domain):

[0162]

[0163] Among them, d(n) is the data signal (i.e., the target coding sequence, which is a sequence of 0s and 1s), and p(n) is the pulse signal.

[0164] Combining the above formulas, we can get the time domain formula:

[0165]

[0166] Perform Fourier transform on the time domain formula to obtain the frequency domain formula (time domain convolution, corresponding frequency domain multiplication):

[0167] R c (k)=D(k)P(k)H(k)+E C (k)

[0168] Among them, R c (k) is the frequency domain representation of the received signal, D(k) is the frequency domain representation of the data signal (i.e., the target coding sequence), P(k) is the frequency domain representation of the pulse signal, H(k) is the frequency domain representation of the ideal pulse response of the optical fiber, and Ec (k) is the frequency domain representation of the noise.

[0169] The single pulse response is calculated by inverse Fourier transform (IDFT) (the two steps of Fourier transform and inverse Fourier transform are the genetic optimization decoding process):

[0170]

[0171] in, is the estimated single pulse response (i.e., the single pulse response signal obtained after genetic optimization decoding of the backscattered signal), r p (n) is the ideal single pulse response, is the effect of noise on the estimated single pulse response.

[0172] In summary, by dividing by the frequency domain representation D(k) of the data signal, the influence of the data signal on the received signal is eliminated, thereby separating the impulse response and the noise. Then, the processed frequency domain signal is converted back to the time domain through inverse Fourier transform to obtain the estimated impulse response.

[0173] This embodiment first obtains multiple continuous laser pulse signals, which have the same wavelength and pulse width. Through multiple sampling and accumulation, the signal-to-noise ratio of the signal can be improved, and the detection capability of weak backscattered signals can be enhanced. Then, multiple coding sequences are randomly generated, and these coding sequences are searched by genetic algorithms. The coding sequences are gradually optimized by selection, crossover, mutation and other operations, and finally the initial coding sequence is obtained. This process can significantly improve the dynamic range and signal-to-noise ratio of the signal, so that the system can more effectively detect low-intensity backscattered signals. Then, the system upsamples the initial coding sequence, and increases the time resolution of the sequence by inserting additional zero values ​​or other preset values ​​between adjacent coding bits to obtain the target coding sequence. This step can further improve the accuracy and dynamic range of the signal. Finally, the system modulates the laser pulse signal according to the target coding sequence, and embeds the coding information into the laser pulse signal through the convolution operation to obtain a pulse sequence signal. This modulation process makes the signal injected into the optical fiber have a higher dynamic range and signal-to-noise ratio, thereby improving the sensitivity and reliability of monitoring.

[0174] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the optical fiber state monitoring method based on genetic optimization coding and strong reflection suppression of the present application. More forms of simple transformations based on this technical concept are all within the scope of protection of the present application.

[0175] The present application also provides an optical fiber status monitoring device based on genetic optimization coding and strong reflection suppression, please refer to Figure 3 , the optical fiber state monitoring device based on genetic optimization coding and strong reflection suppression comprises:

[0176] The coding and modulation module 10 is used to obtain a laser pulse signal and perform genetic optimization coding and modulation on the laser pulse signal to obtain a pulse sequence signal;

[0177] A scattering module 20, used to transmit the pulse sequence signal to the optical fiber to be tested through a connector to obtain a backscattered signal, wherein the connector uses a weak reflection design or a free space optical coupling technology;

[0178] A signal conversion and enhancement module 30, used for performing signal conversion and signal enhancement on the backscattered signal to obtain a digital signal;

[0179] A decoding module 40, configured to perform genetic optimization decoding on the digital signal to obtain a single pulse response signal;

[0180] The signal analysis module 50 is used to analyze the single pulse response signal to obtain the state information of the optical fiber to be tested.

[0181] In one embodiment, the coding and modulation module 10 is also used to obtain multiple continuous laser pulse signals; randomly generate multiple coding sequences, and search the coding sequences through a genetic algorithm to obtain an initial coding sequence; upsample the initial coding sequence to obtain a target coding sequence; modulate the laser pulse signal according to the target coding sequence to obtain a pulse sequence signal.

[0182] In one embodiment, the coding modulation module 10 is also used to initialize the number of iterations and a first preset number of populations; randomly generate a second preset number of coding sequences within the population, and the length of the coding sequence is a preset length; calculate the noise scaling factor of the coding sequence; repeatedly screen, randomly cross, randomly mutate and randomly migrate the coding sequence, and increase the number of iterations until the number of iterations reaches a preset iteration threshold or the minimum value of the noise scaling factor is less than a preset noise scaling factor threshold; and use the coding sequence corresponding to the minimum value of the noise scaling factor as the initial coding sequence.

[0183] In one embodiment, the coding and modulation module 10 is further used to randomly select and retain a portion of the coding sequences within the population according to the noise scaling factor to obtain a selected coding sequence; randomly cross the selected coding sequences within the population to obtain a crossed coding sequence; randomly mutate the crossed coding sequences within the population; when the random mutation is completed, update the noise scaling factor, and record the minimum value of the updated noise scaling factor as the minimum noise scaling factor; randomly migrate the coding sequences between each of the populations; return to the step of randomly selecting and retaining a portion of the coding sequences within the population according to the noise scaling factor to obtain a selected coding sequence, and increment the number of iterations until the number of iterations reaches a preset iteration threshold or the minimum noise scaling factor is less than a preset noise scaling factor threshold.

[0184] In one embodiment, the decoding module 40 is further used to perform Fourier transform on the digital signal to obtain a frequency domain signal; and perform inverse Fourier transform on the ratio of the frequency domain signal to the frequency domain representation of the target coding sequence to obtain a single pulse response signal.

[0185] In one embodiment, the signal analysis module 50 is further used to measure the signal strength of the single pulse response signal at different distances; calculate the attenuation coefficient of the optical fiber to be tested based on the signal strength; perform waveform analysis on the single pulse response signal to determine the physical state of the optical fiber to be tested; and measure the time delay of the single pulse response signal to determine the fault point location of the optical fiber to be tested.

[0186] In one embodiment, the signal conversion and enhancement module 30 is also used to convert the backscattered signal from an optical signal to an electrical signal to obtain an initial electrical signal; perform signal enhancement on the initial electrical signal to obtain an enhanced electrical signal; filter the enhanced electrical signal to obtain a target electrical signal; and convert the target electrical signal from an analog signal to a digital signal.

[0187] The optical fiber state monitoring device based on genetic optimization coding and strong reflection suppression provided by the present application adopts the optical fiber state monitoring method based on genetic optimization coding and strong reflection suppression in the above-mentioned embodiment, which can solve the technical problem of how to monitor the state of the optical fiber with high sensitivity and high reliability, while effectively suppressing the strong reflection interference at the connection of the hollow-core optical fiber. Compared with the prior art, the beneficial effects of the optical fiber state monitoring device based on genetic optimization coding and strong reflection suppression provided by the present application are the same as the beneficial effects of the optical fiber state monitoring method based on genetic optimization coding and strong reflection suppression provided by the above-mentioned embodiment, and the other technical features of the optical fiber state monitoring device based on genetic optimization coding and strong reflection suppression are the same as the features disclosed in the above-mentioned embodiment method, which will not be repeated here.

[0188] like Figure 4 As shown, Figure 4 The schematic diagram of the structure of the optical fiber state monitoring system based on genetic optimization coding and strong reflection suppression in the embodiment of the present application is as follows: a laser 1, a pulse code modulator 2, a circulator 3, a single-mode optical fiber 4, a connector 5, an optical fiber to be tested 6 (in the present embodiment, the optical fiber to be tested 6 is an anti-resonant hollow-core optical fiber), a control module 7, a data processing module 8, an analog-to-digital conversion module 9, a bandpass filter 10, a signal amplifier 11 and a highly sensitive detector 12. The laser 1 is connected to the pulse code modulator 2 and the control module 7 respectively, and the pulse code modulator 2 is connected to the control module 7 and the circulator 3 respectively. The circulator 3 is connected to the single-mode optical fiber 4 and the high-sensitivity detector 12 respectively, the single-mode optical fiber 4 is connected to the connector 5, the connector 5 is connected to the optical fiber to be tested 6, the high-sensitivity detector 12 is connected to the signal amplifier 11, the signal amplifier 11 is connected to the bandpass filter 10, the bandpass filter 10 is connected to the analog-to-digital conversion module 9, the analog-to-digital conversion module 9 is connected to the data processing module 8, the data processing module 8 is connected to the control module 7, and the system executes the optical fiber state monitoring method based on genetic optimization coding and strong reflection suppression.

[0189] It contains several key components: Laser 1 in this embodiment is a DFB (Distributed Feedback) laser with a central wavelength of 1550nm±0.3nm, which is used to generate a narrow line width (referring to the very narrow spectral width of the laser, that is, the frequency or wavelength distribution of the laser is very concentrated in a very small range, and the narrow line width is limited to ensure the purity and stability of the signal spectrum, reduce the influence of the dispersion effect, have better coherence, improve the signal-to-noise ratio, enhance the propagation characteristics of the signal, optimize the modulation effect, and improve the system sensitivity) laser pulse signal, and its central wavelength is 1550nm±0.3nm. Laser 1 is connected to the control module 7 through an electrical signal, which is used to receive the control signal to realize the switching and power adjustment of the laser. At the same time, the laser signal output by the laser 1 is connected to the pulse code modulator 2 for the modulator to perform pulse code modulation.

[0190] The pulse code modulator 2 uses a modulation method based on genetic optimization coding to modulate the continuous laser signal emitted by the laser 1 into a pulse signal with a specific coding sequence to generate a higher gain than a single pulse and achieve a larger dynamic range. The modulator uses a semiconductor optical amplifier (SOA) as a core device, can generate a pulse code sequence signal with a high signal-to-noise ratio, receives the coding control signal through the control module 7 and completes the efficient modulation of the laser signal.

[0191] The circulator 3 is an optical signal separator. In this embodiment, a single-mode fiber 1X2 fiber circulator is selected, and its first port is connected to the pulse code modulator 2, the second port is connected to the single-mode fiber 4, and the third port is connected to the high-sensitivity detector 12. The circulator is used to guide the modulated optical signal to the single-mode fiber 4, and at the same time separate the backscattered light signal returned from the optical fiber and transmit it to the high-sensitivity detector 12.

[0192] The single-mode optical fiber 4 is used as a connection channel between the modulated signal and the anti-resonant hollow core optical fiber 6. In this embodiment, a standard single-mode optical fiber is selected with a length of less than 10m to ensure low loss and efficient coupling of signal transmission. One end of the single-mode optical fiber is connected to the circulator 3, and the other end is connected to the connector 5.

[0193] The connector 5 adopts the industry's advanced weak reflection connector or spatial optical coupling connection method, which is used to connect the single-mode optical fiber 4 with the anti-resonance hollow core optical fiber 6, minimize the strong reflection interference at the connection point, ensure the linear characteristics of the system, and achieve effective coding modulation and demodulation. The anti-resonance hollow core optical fiber 6 is the optical fiber under test, and its backscattering is about 30dB lower than that of the conventional single-mode optical fiber, and its main backscattering source is air. In this embodiment, the high-sensitivity detector 12 uses an APD (Avalanche Photodiode) detector or a single-photon detector to receive the echo signal transmitted by the circulator 3 and convert the optical signal into an electrical signal. The signal amplifier 11 adaptively adjusts the amplification factor to adapt to the change in signal strength according to the amplitude of the output signal of the high-sensitivity detector 12. The amplified signal is transmitted to the bandpass filter 10.

[0194] In this embodiment, the bandpass filter 10 uses a filter with a central wavelength of 1550nm to filter out high-frequency and low-frequency noise in the signal, and only retains the target signal frequency band (central wavelength of 1550nm). The analog-to-digital conversion module 9 converts the analog signal output by the bandpass filter 10 into a digital signal for analysis and processing by the data processing module 8. The data processing module 8 is used to implement signal processing. Based on the genetic optimization coding and decoding algorithm, the digital signal of the analog-to-digital conversion module 9 is decoded and the time domain signal is reconstructed to extract the state information and fault location of the optical fiber. The processing results are coordinated and output and fed back through the control module 7. The control module 7 is responsible for the overall coordination of the system, including controlling the output of the laser 1, configuring the coding sequence of the pulse code modulator 2, adjusting the working state of the high-sensitivity detector 12, and outputting the analysis results of the data processing module 8 to the user interface. These components work together to achieve high-sensitivity monitoring of the optical fiber state and accurate fault diagnosis.

[0195] The present application provides an optical fiber state monitoring device based on genetic optimization coding and strong reflection suppression, and the optical fiber state monitoring device based on genetic optimization coding and strong reflection suppression includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the optical fiber state monitoring method based on genetic optimization coding and strong reflection suppression in the above-mentioned embodiment 1.

[0196] Reference below Figure 5 , which shows a schematic diagram of the structure of an optical fiber state monitoring device based on genetic optimization coding and strong reflection suppression suitable for implementing the embodiment of the present application. The optical fiber state monitoring device based on genetic optimization coding and strong reflection suppression in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The optical fiber state monitoring device based on genetic optimization coding and strong reflection suppression shown is only an example and should not bring any limitation to the function and scope of use of the embodiments of the present application.

[0197] like Figure 5As shown, the optical fiber state monitoring device based on genetic optimization coding and strong reflection suppression may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to the program stored in ROM (Read Only Memory) 1002 or the program loaded from the storage device 1003 to RAM (Random Access Memory) 1004. Various programs and data required for the operation of the optical fiber state monitoring device based on genetic optimization coding and strong reflection suppression are also stored in RAM1004. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, an LCD (Liquid Crystal Display), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the optical fiber state monitoring device based on genetic optimization coding and strong reflection suppression to communicate wirelessly or wired with other devices to exchange data. Although the optical fiber state monitoring device based on genetic optimization coding and strong reflection suppression with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0198] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0199] The optical fiber state monitoring device based on genetic optimization coding and strong reflection suppression provided by the present application adopts the optical fiber state monitoring method based on genetic optimization coding and strong reflection suppression in the above-mentioned embodiment, which can solve the technical problem of how to monitor the state of the optical fiber with high sensitivity and high reliability, while effectively suppressing the strong reflection interference at the connection of the hollow-core optical fiber. Compared with the prior art, the beneficial effects of the optical fiber state monitoring device based on genetic optimization coding and strong reflection suppression provided by the present application are the same as the beneficial effects of the optical fiber state monitoring method based on genetic optimization coding and strong reflection suppression provided by the above-mentioned embodiment, and the other technical features of the optical fiber state monitoring device based on genetic optimization coding and strong reflection suppression are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0200] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0201] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0202] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, the computer-readable program instructions being used to execute the optical fiber state monitoring method based on genetic optimization coding and strong reflection suppression in the above-mentioned embodiment.

[0203] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable Read Only Memory, Erasable Programmable Read Only Memory or Flash Memory), optical fiber, CD-ROM (CD-Read Only Memory, portable compact disk read-only memory), optical storage device, magnetic storage device, or any suitable combination of the above. In the present embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0204] The computer-readable storage medium may be included in the optical fiber state monitoring device based on genetic optimization coding and strong reflection suppression; or may exist independently without being assembled into the optical fiber state monitoring device based on genetic optimization coding and strong reflection suppression.

[0205] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the optical fiber state monitoring device based on genetic optimization coding and strong reflection suppression, the optical fiber state monitoring device based on genetic optimization coding and strong reflection suppression: obtains a laser pulse signal, and performs genetic optimization coding modulation on the laser pulse signal to obtain a pulse sequence signal; transmits the pulse sequence signal to the optical fiber to be tested through a connector to obtain a backscattered signal, and the connector uses a weak reflection design or free space optical coupling technology; performs signal conversion and signal enhancement on the backscattered signal to obtain a digital signal; performs genetic optimization decoding on the digital signal to obtain a single pulse response signal; and analyzes the single pulse response signal to obtain state information of the optical fiber to be tested.

[0206] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a LAN (Local Area Network) or a WAN (Wide Area Network), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0207] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0208] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0209] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned optical fiber state monitoring method based on genetic optimization coding and strong reflection suppression, and can solve the technical problem of how to monitor the state of the optical fiber with high sensitivity and high reliability, while effectively suppressing the strong reflection interference at the connection of the hollow-core optical fiber. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the optical fiber state monitoring method based on genetic optimization coding and strong reflection suppression provided by the above-mentioned embodiment, and will not be repeated here.

[0210] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the optical fiber state monitoring method based on genetic optimization coding and strong reflection suppression as described above.

[0211] The computer program product provided by the present application can solve the technical problem of how to monitor the state of an optical fiber with high sensitivity and high reliability, while effectively suppressing the strong reflection interference at the connection of the hollow-core optical fiber. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the optical fiber state monitoring method based on genetic optimization coding and strong reflection suppression provided by the above-mentioned embodiment, and will not be elaborated here.

[0212] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for monitoring optical fiber status based on genetic optimization coding and strong reflection suppression, characterized in that: The method comprises: Acquiring a laser pulse signal, and performing genetic optimization coding modulation on the laser pulse signal to obtain a pulse sequence signal; The pulse sequence signal is transmitted to the optical fiber to be tested through a connector to obtain a backscattered signal, wherein the connector uses a weak reflection design or a free space optical coupling technology; Performing signal conversion and signal enhancement on the backscattered signal to obtain a digital signal; Performing genetic optimization decoding on the digital signal to obtain a single pulse response signal; The single pulse response signal is analyzed to obtain the state information of the optical fiber to be tested.

2. The method according to claim 1, characterized in that The step of acquiring a laser pulse signal and performing genetic optimization coding modulation on the laser pulse signal to obtain a pulse sequence signal comprises: Acquire multiple continuous laser pulse signals; Randomly generate multiple coding sequences, and search the coding sequences through a genetic algorithm to obtain an initial coding sequence; Upsampling the initial coding sequence to obtain a target coding sequence; The laser pulse signal is modulated according to the target coding sequence to obtain a pulse sequence signal.

3. The method according to claim 2, characterized in that The step of randomly generating a plurality of coding sequences and searching the coding sequences by a genetic algorithm to obtain an initial coding sequence comprises: Initialize the number of iterations and the first preset number of populations; Randomly generating a second preset number of coding sequences within the population, wherein the length of the coding sequences is a preset length; Calculating a noise scaling factor for the coded sequence; Repeatedly screening, randomly crossing, randomly mutating, and randomly migrating the coding sequence, and increasing the number of iterations until the number of iterations reaches a preset iteration threshold or the minimum value of the noise scaling factor is less than a preset noise scaling factor threshold; The coding sequence corresponding to the minimum value of the noise scaling factor is used as the initial coding sequence.

4. The method according to claim 3, characterized in that The step of repeatedly screening, randomly crossing, randomly mutating and randomly migrating the coding sequence, and increasing the number of iterations until the number of iterations reaches a preset iteration threshold or the minimum value of the noise scaling factor is less than a preset noise scaling factor threshold comprises: Randomly selecting and retaining a portion of the coding sequences in the population according to the noise scaling factor to obtain a selected coding sequence; Performing random crossover on the selected coding sequence within the population to obtain a crossover coding sequence; Randomly mutating the crossover coding sequence within the population; When the random mutation is completed, updating the noise scaling factor, and recording the minimum value of the updated noise scaling factors as the minimum noise scaling factor; Randomly migrating the coding sequences between the populations; Return to the step of randomly selecting and retaining a portion of the coding sequences within the population according to the noise scaling factor to obtain a selected coding sequence, and increment the number of iterations until the number of iterations reaches a preset iteration threshold or the minimum noise scaling factor is less than a preset noise scaling factor threshold.

5. The method according to claim 2, characterized in that The step of performing genetic optimization decoding on the digital signal to obtain a single pulse response signal comprises: Performing Fourier transform on the digital signal to obtain a frequency domain signal; An inverse Fourier transform is performed on the ratio of the frequency domain signal to the frequency domain representation of the target coding sequence to obtain a single pulse response signal.

6. The method according to claim 1, characterized in that The state information includes an attenuation coefficient, a physical state, and a fault point location. The step of analyzing the single pulse response signal to obtain the state information of the optical fiber to be tested includes: measuring the signal strength of the single pulse response signal at different distances; Calculating the attenuation coefficient of the optical fiber to be tested according to the signal strength; Performing waveform analysis on the single pulse response signal to determine the physical state of the optical fiber to be tested; The time delay of the single pulse response signal is measured to determine the fault point position of the optical fiber to be tested.

7. The method according to any one of claims 1 to 6, characterized in that The step of performing signal conversion and signal enhancement on the backscattered signal to obtain a digital signal comprises: Converting the backscattered signal from an optical signal to an electrical signal to obtain an initial electrical signal; Performing signal enhancement on the initial electrical signal to obtain an enhanced electrical signal; filtering the enhanced electrical signal to obtain a target electrical signal; The target electrical signal is converted from an analog signal to a digital signal.

8. An optical fiber status monitoring device based on genetic optimization coding and strong reflection suppression, characterized in that: The device comprises: A coding and modulation module is used to obtain a laser pulse signal and perform genetic optimization coding and modulation on the laser pulse signal to obtain a pulse sequence signal; A scattering module, used to transmit the pulse sequence signal to the optical fiber to be tested through a connector to obtain a backscattered signal, wherein the connector uses a weak reflection design or a free space optical coupling technology; A signal conversion and enhancement module, used for performing signal conversion and signal enhancement on the backscattered signal to obtain a digital signal; A decoding module, used for performing genetic optimization decoding on the digital signal to obtain a single pulse response signal; The signal analysis module is used to analyze the single pulse response signal to obtain the state information of the optical fiber to be tested.

9. An optical fiber state monitoring system based on genetic optimization coding and strong reflection suppression, characterized in that: The system comprises: a laser, a pulse code modulator, a circulator, a single-mode optical fiber, a connector, an optical fiber to be tested, a control module, a data processing module, an analog-to-digital conversion module, a bandpass filter, a signal amplifier and a highly sensitive detector. The laser is connected to the pulse code modulator and the control module respectively, the pulse code modulator is connected to the control module and the circulator respectively, the circulator is connected to the single-mode optical fiber and the highly sensitive detector respectively, the single-mode optical fiber is connected to the connector, the connector is connected to the optical fiber to be tested, the highly sensitive detector is connected to the signal amplifier, the signal amplifier is connected to the bandpass filter, the bandpass filter is connected to the analog-to-digital conversion module, the analog-to-digital conversion module is connected to the data processing module, the data processing module is connected to the control module, and the system executes the optical fiber state monitoring method based on genetic optimization coding and strong reflection suppression as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the optical fiber state monitoring method based on genetic optimization coding and strong reflection suppression as described in any one of claims 1 to 7 are implemented.

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