An optical fiber core automatic calibration method, device and medium

Abstract

The application relates to the field of communication monitoring, and discloses a kind of optical fiber core automatic proofreading method, equipment and medium, method includes: sending end emits orthogonal encoding optical signal;Receiving end conversion and preprocessing electric signal;Blind source separation is carried out to signal to eliminate crosstalk;Matching degree is calculated using compressed sensing matching algorithm;Mapping table is constructed and numbered;Output proofreading and diagnostic results.The system uses Zadoff-Chu sequence and advanced signal processing technology, effectively solves the identification problem of multi-core crosstalk and attenuation, realizes efficient, accurate, automated optical fiber proofreading and fault diagnosis, significantly improves the intelligentialization and reliability of optical network operation.

Description

Technical Field

[0001] This invention relates to the field of communication monitoring, and in particular to an automatic optical fiber core calibration method, device and medium. Background Technology

[0002] As a core carrier of modern information transmission, fiber optic communication's reliability directly impacts the stable operation of critical infrastructure such as power, communications, and finance. In complex fiber optic networks, especially in multi-fiber applications like smart substations and data centers, ensuring the correct connection of each fiber core between the transmitting and receiving ends is crucial. Fiber core alignment, confirming the one-to-one correspondence between the cores at both ends of the fiber, is a fundamental and critical step in the construction, operation, and maintenance of optical networks. Traditionally, this process relied on manual operation; however, with the increasing number of fibers and the increasing complexity of network structures, the limitations of manual alignment have become increasingly apparent, necessitating the introduction of automated and intelligent alignment methods to improve efficiency and accuracy.

[0003] Current automated or semi-automated fiber core alignment technologies are mostly based on methods such as optical time domain reflectometry (OTDR), optical power detection, or simple optical signal pulse timing identification. For example, some schemes determine the fiber core correspondence by sequentially sending optical signals at the transmitting end and detecting the arrival order or intensity of the signals at the receiving end. However, these methods have significant drawbacks: first, they have poor anti-interference capabilities and are easily affected by factors such as ambient light, uneven link attenuation, and connector loss; second, they are inefficient when processing multiple signals and cannot achieve true parallel processing; third, the identification accuracy drops significantly in scenarios with inter-fiber crosstalk, signal coupling, or partial faults; and fourth, most existing methods can only perform basic connectivity checks and lack the ability to diagnose deeper information such as link quality and crosstalk levels. These limitations severely restrict the widespread application of automated alignment technology in high-speed, high-reliability optical networks. Summary of the Invention

[0004] The purpose of this invention is to propose an automatic fiber core alignment method, device, and medium to solve the technical problem of how to achieve fast, accurate, and interference-resistant automatic fiber core alignment in high-density, multi-core fiber optic links.

[0005] Specifically, the present invention provides an automatic optical fiber core alignment method, comprising: S1. Control multiple light sources at the transmitting end to transmit a pre-coded sequence of probe light signals with orthogonal characteristics to the fiber optic link under test; S2. The optical signal sequence is received at the receiving end through a photoelectric conversion module and converted into an analog electrical signal; S3. The analog electrical signal is amplified and filtered preprocessed, and then converted into a digital signal sequence by an analog-to-digital converter (ADC). S4. Perform blind source separation (BSS) processing on the digital signal sequence of each optical fiber to separate the mixed signal from the transmitting end through different paths coupled or crosstalked, and obtain the separated signal components. S5. Extract the feature codes of the separated signal components described in step S4, and perform a matching tracking algorithm based on compressed sensing with the original encoded sequence of the transmitting end to calculate its matching degree. S6. Based on the matching degree calculation results, construct a mapping table of the correspondence between the fiber cores of the transmitting end and the receiving end, and automatically number the fiber cores. S7. Output the calibration results, including normally connected fiber core pairs, abnormal fiber cores, and fiber core pairs with crosstalk.

[0006] A storage device that stores instructions and data for implementing an automatic fiber core alignment method.

[0007] An automatic fiber core alignment device includes: a processor and a storage device; the processor loads and executes instructions and data in the storage device to implement an automatic fiber core alignment method.

[0008] The beneficial effects of this invention are as follows: By introducing advanced signal processing technologies such as orthogonal coded optical signals, blind source separation (BSS), and compressed sensing, an innovative automatic fiber core alignment method and system are constructed. This solution not only enables efficient, parallel, and non-contact alignment of multiple fiber cores, significantly improving operational efficiency, but also possesses strong anti-interference and anti-crosstalk capabilities, maintaining high accuracy even in complex environments. Furthermore, this invention can quantitatively assess fiber connection quality, identify potential faults such as attenuation anomalies and crosstalk sources, and generate detailed alignment and diagnostic reports, greatly enhancing the intelligent level of fiber optic link operation and maintenance. This technology can be widely applied in fields such as smart substations, optical communication networks, and data centers, demonstrating significant technological advancement and engineering application value. Attached Figure Description

[0009] Figure 1 This is a simplified schematic diagram of the method flow of the present invention; Figure 2 This is a schematic diagram of the hardware device of the present invention. Detailed Implementation

[0010] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0011] Before formally describing the present invention, a general description of the solution of the present invention will be given first to facilitate understanding.

[0012] Please refer to Figure 1 The present invention provides an automatic optical fiber core alignment method, comprising: S1. Control multiple light sources at the transmitting end to transmit a pre-coded sequence of probe light signals with orthogonal characteristics to the fiber optic link under test; Specifically, at the transmitting end, a signal generation module (such as an FPGA) generates multiple mutually orthogonal probe optical signals. Orthogonality is to ensure that even if the signals are mixed at the receiving end, they can be effectively separated by an algorithm.

[0013] In step S1, the pre-encoded probe light signal sequence with orthogonal characteristics is a sequence encoded using a linear frequency modulated Zadoff-Chu sequence as the baseband, and its expression is as follows:

[0014] in Let u be the sequence length, and c be the sequence index that is coprime to the root index. f With a fixed offset, q is any integer, and j is the imaginary unit; this baseband code is applied to the luminous intensity of each light source through intensity modulation to generate orthogonal photodetector signals.

[0015] Among them, the Zadoff-Chu (ZC) sequence is a known constant envelope zero autocorrelation (CAZAC) sequence in the field of communications. This sequence has perfect autocorrelation and good cross-correlation, making it very suitable as an identifier to distinguish different light sources. The amplitude information of the complex baseband ZC sequence is loaded onto the light wave by intensity modulation (i.e., directly controlling the luminous power of the light source with an electrical signal) to form a detection signal in the optical domain. Figure 2 The time-domain waveform of a ZC sequence is shown.

[0016] S2. The optical signal sequence is received at the receiving end through a photoelectric conversion module and converted into an analog electrical signal; Specifically, at the receiving end, the photoelectric conversion module (which typically includes a photodiode and a transimpedance amplifier) ​​converts the weak optical signal into an analog current or voltage signal.

[0017] S3. The analog electrical signal is amplified and filtered preprocessed, and then converted into a digital signal sequence by an analog-to-digital converter (ADC). Specifically, the signal conditioning circuit amplifies and filters the analog electrical signal to improve the signal-to-noise ratio and remove out-of-band noise. The analog-to-digital converter (ADC) then digitizes it for use by subsequent digital signal processing units.

[0018] S4. Perform blind source separation (BSS) processing on the digital signal sequence of each optical fiber to separate the mixed signal from the transmitting end through different paths coupled or crosstalked, and obtain the separated signal components. Specifically, this step is crucial for resolving signal crosstalk. Due to the extremely close proximity of multiple fiber cores, coupling is unavoidable, resulting in any detector at the receiving end receiving a mixture of multiple transmitted signals. Blind Source Separation (BSS) technology is precisely the process of recovering the original independent signal components from the received mixed signal alone, even in the absence of an known mixed channel.

[0019] It should be noted that the blind source separation (BSS) process in step S4 is as follows: The Independent Component Analysis (ICA) algorithm based on sparsity constraints is adopted. Its objective function is to minimize the mutual information between the separated signal components, and an L1 norm sparsity constraint term is added:

[0020] Where W is the separation matrix, H(y) i ) is the entropy of the i-th separated output signal, 𝜆 is the sparsity constraint coefficient, y is the separated signal vector, and det(W) represents the determinant of matrix W.

[0021] Independent Component Analysis (ICA) is a classic implementation of BSS. Its goal is to find a linear transformation (separation matrix W) that maximizes the statistical independence of the components of the transformed output signal y. The sparsity constraint is prior knowledge introduced based on the characteristics of fiber optic signals, because in practice, a received signal is mainly composed of a single main transmitted signal (i.e., sparse), and crosstalk energy is relatively weak.

[0022] S5. Extract the feature codes of the separated signal components described in step S4, and perform a matching tracking algorithm based on compressed sensing with the original encoded sequence of the transmitting end to calculate its matching degree. It should be noted that even after BSS separation, it is still necessary to accurately determine which transmitting port each separated signal originates from. This invention employs the matching pursuit algorithm from compressed sensing theory to solve this sparse matching problem.

[0023] It should be noted that the matching and tracking algorithm based on compressed sensing in step S5 is as follows: The received signal is considered as a linear combination of all possible coded sequences from the transmitter in a sparse channel. The best-matching sparse coefficient vector is reconstructed using the Orthogonal Matching Pursuit (OMP) algorithm. The non-zero positions of these coefficients correspond to the source fiber core numbers at the transmitter. The iterative process involves continuously selecting the atom most relevant to the current residual from the sensing matrix and updating the residual until a stopping condition is met. The mathematical model is as follows:

[0024] Where s is the received signal, Φ is the observation matrix, Ψ is the sparse basis matrix composed of all Zadoff-Chu sequences, and 𝜃 is the sparse coefficient vector to be determined. This is the tolerance for error.

[0025] Orthogonal matching pursuit (OMP) is a widely used greedy reconstruction algorithm in compressed sensing.

[0026] The matching degree is calculated in step S5 as follows: Calculate each non-zero coefficient θ in the sparse coefficient vector θ i The reconstructed signal-to-noise ratio is defined as:

[0027] Among them, 𝜓 i The i-th column of the sparse basis matrix Ψ; if the R-SNR value of a certain fiber core is higher than the first threshold, it is determined that the fiber core is correctly matched; if its R-SNR value is between the first threshold and the second threshold, it is determined that the fiber has high attenuation or poor connection; if its R-SNR value is lower than the second threshold, it is determined that the fiber is not connected or broken.

[0028] The formula calculates the reconstructed i-th signal component (θ). i ·、ψ i The R-SNR is the ratio of the energy of the signal to the energy of the reconstruction error (residual), expressed in decibels (dB). A higher R-SNR value indicates better signal quality and a more reliable match. Three levels of fine-grained diagnostics can be achieved by setting two thresholds (e.g., Th_high = 25 dB, Th_low = 10 dB). R-SNR > Th_high: The fiber core is well connected and correctly matched.

[0029] Th_low < R-SNR < Th_high: The fiber core is connected but the attenuation is too high, which may be due to bending, dirty connectors, or aging.

[0030] R-SNR < Th_low: The fiber core is not connected or is broken.

[0031] S6. Based on the matching degree calculation results, construct a mapping table of the correspondence between the fiber cores of the transmitting end and the receiving end, and automatically number the fiber cores. It should be noted that step S6 also includes: for cases in the matching degree calculation where one received signal has a similar matching degree to multiple transmitted signals, this is determined to be inter-fiber crosstalk, and the crosstalk ratio (CTR) is calculated.

[0032] Among them, P i For the expected main fiber core signal power, P j The signal power that crosstalks to adjacent fiber core j; if the CTR is higher than the set threshold, a crosstalk alarm message is output.

[0033] This invention adds a quantitative analysis function for crosstalk. If the CTR is higher than a set threshold (e.g., -30 dB), it indicates that fiber core j is subjected to non-negligible crosstalk from fiber core i, and the system will output an alarm, indicating that there may be fiber bundle compression, excessively tight bundling, or optical cable quality problems.

[0034] S7. Output the calibration results, including normally connected fiber core pairs, abnormal fiber cores, and fiber core pairs with crosstalk.

[0035] It should be noted that, based on the matching results, the system automatically generates a fiber core mapping table and further outputs a comprehensive report containing quality assessment and fault diagnosis information through advanced signal processing (such as calculating signal-to-noise ratio and crosstalk ratio), which goes far beyond simple continuity / disconnection judgment.

[0036] Example 1: Automatic calibration of 96-core optical fiber cable in intelligent substation This invention is applied to: a smart substation that needs to conduct final acceptance testing on a bundle of 96-core outdoor optical cables.

[0037] The system configuration is as follows: Transmitting equipment: An integrated array of 12 fixed-wavelength (1550nm) DFB lasers, each driven by an independent APC / ATC circuit. A Xilinx Artix-7 FPGA generates 8 sets of ZC sequences with different root indices u (length N_{ZC}=97), which are modulated by 8 lasers respectively through an 8-channel DAC (96-core tests were completed in 12 batches).

[0038] Receiver equipment: Includes 8 high-sensitivity InGaAs photodetectors, analog front-end, and high-speed ADC. A TITMS320C6678 multi-core DSP serves as the main processor, responsible for running BSS-ICA and OMP algorithms.

[0039] Software: An embedded Linux system is used to run the calibration program.

[0040] Work process: The operator connects both ends of the optical cable to the transmitting and receiving devices respectively; the test is then initiated in the software interface. The transmitting FPGA controls the first batch of eight lasers to simultaneously emit optical signals loaded with different ZC sequences. The receiving DSP acquires the eight mixed signals, performs ICA separation, and then executes the OMP algorithm on each separated signal to search for matching terms in the sparse basis matrix (containing all eight ZC sequences). The R-SNR value of each matching pair is calculated. In this batch of tests, seven cores have an R-SNR > 30dB, which is considered excellent; one core has an R-SNR of 15dB, and the software marks this core as having "abnormal attenuation." The system automatically switches to the next batch of lasers, repeating the process until all 96 cores have been tested. Analysis of the overall data reveals that the CTR of core 23 to core 24 is -28dB, exceeding the -30dB threshold, and the software generates an "Inter-core Crosstalk Warning" in the final report. A PDF report containing a core mapping table, quality level, abnormal alarms, and test parameters is automatically generated.

[0041] In this embodiment of the invention, the entire 96-core calibration process was completed within 3 minutes, and one attenuation anomaly and one crosstalk hazard were accurately located. The efficiency and accuracy are far superior to the traditional manual method, which fully demonstrates the technical advantages of the present invention.

[0042] Please see Figure 2 , Figure 2 This is a schematic diagram of the hardware device in operation according to an embodiment of the present invention. The hardware device specifically includes: an automatic optical fiber core alignment device 401, a processor 402, and a storage device 403.

[0043] An automatic fiber core alignment device 401: The automatic fiber core alignment device 401 implements the automatic fiber core alignment method.

[0044] Processor 402: The processor 402 loads and executes the instructions and data in the storage device 403 to implement the automatic fiber core alignment method.

[0045] Storage device 403: The storage device 403 stores instructions and data; the storage device 403 is used to implement the automatic fiber core alignment method.

[0046] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An automatic optical fiber core alignment method, characterized in that: Includes the following steps: S1. Control multiple light sources at the transmitting end to transmit a pre-coded sequence of probe light signals with orthogonal characteristics to the fiber optic link under test; S2. The optical signal sequence is received at the receiving end through a photoelectric conversion module and converted into an analog electrical signal; S3. The analog electrical signal is amplified and filtered preprocessed, and then converted into a digital signal sequence by an analog-to-digital converter (ADC). S4. Perform blind source separation (BSS) processing on the digital signal sequence of each optical fiber to separate the mixed signal from the transmitting end through different paths coupled or crosstalked, and obtain the separated signal components. S5. Extract the feature codes of the separated signal components described in step S4, and perform a matching tracking algorithm based on compressed sensing with the original encoded sequence of the transmitting end to calculate its matching degree. S6. Based on the matching degree calculation results, construct a mapping table of the correspondence between the fiber cores of the transmitting end and the receiving end, and automatically number the fiber cores. S7. Output the calibration results, including normally connected fiber core pairs, abnormal fiber cores, and fiber core pairs with crosstalk.

2. The automatic optical fiber core alignment method as described in claim 1, characterized in that: The pre-encoded probe light signal sequence with orthogonal characteristics in step S1 is a sequence encoded using a linear frequency modulated Zadoff-Chu sequence as the baseband, and its expression is as follows: in Let u be the sequence length, and c be the sequence index that is coprime to the root index. f With a fixed offset, q is any integer and j is the imaginary unit; this baseband code is applied to the luminous intensity of each light source through intensity modulation to generate orthogonal photodetector signals.

3. The automatic optical fiber core alignment method as described in claim 1, characterized in that: The blind source separation (BSS) process in step S4 is as follows: The Independent Component Analysis (ICA) algorithm based on sparsity constraints is adopted. Its objective function is to minimize the mutual information between the separated signal components, and an L1 norm sparsity constraint term is added: Where W is the separation matrix, H(y) i ) is the entropy of the i-th separated output signal, 𝜆 is the sparsity constraint coefficient, y is the separated signal vector, and det(W) represents the determinant of matrix W.

4. The automatic optical fiber core alignment method as described in claim 1, characterized in that: The matching pursuit algorithm based on compressed sensing in step S5 is as follows: The received signal is regarded as a linear combination of all possible coded sequences of the transmitter in a sparse channel. The best matching sparse coefficient vector is reconstructed by the orthogonal matching pursuit (OMP) algorithm. The non-zero position of the sparse coefficient vector corresponds to the source fiber core number of the transmitter. Its iterative process involves continuously selecting the atom most relevant to the current residual from the perception matrix and updating the residual until the stopping condition is met. The mathematical model is as follows: Where s is the received signal, Φ is the observation matrix, Ψ is the sparse basis matrix composed of all Zadoff-Chu sequences, and 𝜃 is the sparse coefficient vector to be determined. This is the error tolerance.

5. The automatic optical fiber core alignment method as described in claim 4, characterized in that: The matching degree is calculated in step S5 as follows: Calculate each non-zero coefficient θ in the sparse coefficient vector θ i The reconstructed signal-to-noise ratio is defined as: Among them, 𝜓 i The i-th column of the sparse basis matrix Ψ; if the R-SNR value of a certain fiber core is higher than the first threshold, it is determined that the fiber core is correctly matched; if its R-SNR value is between the first threshold and the second threshold, it is determined that the fiber has high attenuation or poor connection; if its R-SNR value is lower than the second threshold, it is determined that the fiber is not connected or broken.

6. The automatic optical fiber core alignment method as described in claim 1, characterized in that: Step S6 further includes: for cases in the matching degree calculation where a received signal has a similar matching degree to multiple transmitted signals, this is determined to be inter-fiber crosstalk, and the crosstalk ratio (CTR) is calculated. Among them, P i For the expected main fiber core signal power, P j The signal power that crosstalks to adjacent fiber core j; if the CTR is higher than the set threshold, a crosstalk alarm message is output.

7. A storage device, characterized in that: The storage device stores instructions and data to implement the automatic fiber core alignment method according to any one of claims 1 to 6.

8. An automatic optical fiber core alignment device, characterized in that: include: A processor and a storage device; the processor loads and executes instructions and data in the storage device to implement the automatic fiber core alignment method according to any one of claims 1 to 6.

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