Fiber optic testing methods, electronic equipment and computer program products

Abstract

This application discloses an optical fiber detection method, electronic device, and computer program product, relating to the field of optical fiber detection technology. The method includes: scanning and probing a target optical fiber link using pulse compression technology to obtain multiple Rayleigh scattering curves of the target optical fiber link, wherein each Rayleigh scattering curve is obtained by windowing with different window functions; and performing link feature detection on the target optical fiber link based on each Rayleigh scattering curve to obtain the link feature detection result of the target optical fiber link. This application can improve the accuracy of optical fiber link feature detection in FTTR.

Description

Technical Field

[0001] This application relates to the field of optical fiber testing technology, and in particular to optical fiber testing methods, electronic equipment and computer program products. Background Technology

[0002] In high-density, high-reliability optical access networks such as Fiber to the Room (FTTR), accurate sensing of fiber optic link status is crucial for ensuring service quality and operational efficiency. Optical reflectometer technology analyzes Rayleigh scattering signals returned from the fiber optic link to locate and assess events such as breakpoints, connectors, and bends. To balance detection distance and spatial resolution, pulse compression technology is widely used. This technology effectively improves the signal-to-noise ratio and dynamic range by modulating long pulses and performing matched filtering at the receiver.

[0003] However, in practical applications, even with pulse compression technology, the obtained Rayleigh scattering curves are still susceptible to factors such as signal processing methods, system response characteristics, and environmental noise, leading to uncertainties in the extraction of link features. Especially in complex cabling scenarios, weak or dense link events can easily be masked or misjudged, making it difficult for the detection results to meet the requirements of high-precision operation and maintenance in terms of accuracy, stability, and resolution. This, in turn, restricts the comprehensive and reliable assessment of the health status of fiber optic links. Summary of the Invention

[0004] The main objective of this application is to provide a fiber optic detection method, electronic device, and computer program product, which aim to improve the accuracy of fiber optic link feature detection in FTTR.

[0005] To achieve the above objectives, this application provides an optical fiber detection method, the method comprising: The target optical fiber link is scanned and detected using pulse compression technology to obtain multiple Rayleigh scattering curves of the target optical fiber link. Each Rayleigh scattering curve is obtained by windowing with a different window function. Based on the Rayleigh scattering curves, link feature detection is performed on the target optical fiber link to obtain the link feature detection results of the target optical fiber link.

[0006] In addition, to achieve the above objectives, this application also provides an electronic device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the fiber optic detection method as described above.

[0007] In addition, to achieve the above objectives, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the fiber optic detection method described above.

[0008] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the fiber optic detection method described above.

[0009] This application provides an optical fiber detection method, electronic device, and computer program product. The optical fiber detection method includes: scanning and detecting a target optical fiber link using pulse compression technology to obtain multiple Rayleigh scattering curves of the target optical fiber link, wherein each Rayleigh scattering curve is obtained by windowing with a different window function; and performing link feature detection on the target optical fiber link based on each Rayleigh scattering curve to obtain the link feature detection result of the target optical fiber link.

[0010] This application's embodiments fundamentally overcome the feature discrimination ambiguity problem caused by the limitation of traditional fiber optic detection methods to a single signal processing path by introducing a multi-window function collaborative analysis mechanism. Its core innovation lies in: based on pulse compression detection, acquiring multiple Rayleigh scattering curves of the same fiber optic link after windowing with different window functions, and constructing a multi-dimensional observation perspective—different window functions have complementary characteristics in terms of main lobe width, side lobe suppression capability, and noise sensitivity. For example, high-resolution windows such as rectangular windows easily introduce side lobe artifacts but can preserve event details; while strong side lobe suppression windows such as Hanning windows sacrifice some spatial resolution but can effectively filter out spurious components. This application's technical solution does not simply choose one type of window function, but uses these multiple curves with inherent performance tension as a joint criterion. By analyzing the changing patterns of response characteristics of the same physical location in different curves, such as peak intensity, morphological consistency, and presence or absence, reliable distinction between real link events and processing artifacts is achieved. This detection logic based on "differential response consistency verification" essentially transforms the original single-point judgment that relied on prior assumptions or empirical thresholds into a cross-validation process based on multi-source observation evidence, thereby significantly improving the accuracy, robustness, and anti-interference ability of detecting link features including but not limited to reflection events, non-reflection events, and average loss coefficients.

[0011] Therefore, the system is no longer constrained by the inherent performance trade-offs of a single window function. Instead, without increasing hardware complexity, it achieves a fundamental leap in detection accuracy and reliability by expanding the dimensions of signal processing, thus solving the fundamental defects of related technologies such as feature misjudgment, missed detection, or positioning deviation caused by the single processing method. Attached Figure Description

[0012] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0013] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0014] Figure 1 This is a flowchart illustrating the first embodiment of the fiber optic detection method of this application; Figure 2 This is a flowchart illustrating the second embodiment of the fiber optic detection method of this application. Figure 3 This is a flowchart illustrating the third embodiment of the fiber optic detection method of this application. Figure 4 This is a system structure block diagram of a time-gated digital optical frequency domain reflectometer in a specific embodiment of this application; Figure 5 This is a schematic diagram of the topology of the target optical fiber link in a specific embodiment of this application; Figure 6 A comparison diagram of different normalized window shapes provided for a specific embodiment of this application; Figure 7 A comparison diagram of normalized compressed pulses under different windows provided for a specific embodiment of this application; Figure 8 A magnified comparison of the main lobe of the compressed pulse under different windows provided in a specific embodiment of this application; Figure 9 This is a schematic diagram of the Rayleigh scattering curve obtained by scanning detection in a specific embodiment of this application; Figure 10 A comparison diagram of reflection event recognition provided in a specific embodiment of this application; Figure 11 This is a schematic diagram of the reflection peak at the end of the second-stage beam splitter under different window function modulations in a specific embodiment of this application; Figure 12 This is a schematic diagram of the electronic equipment structure of the hardware operating environment involved in the fiber optic detection method in this application embodiment.

[0015] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0016] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0017] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0018] Currently, in high-density, high-reliability optical access networks such as Fiber to the Room (FTTR), even with pulse compression technology, the obtained Rayleigh scattering curves are still susceptible to factors such as signal processing methods, system response characteristics, and environmental noise, leading to uncertainties in the extraction of link features. Especially in complex cabling scenarios, weak or dense link events can easily be masked or misjudged, making it difficult for the detection results to meet the requirements of high-precision operation and maintenance in terms of accuracy, stability, and resolution. This, in turn, restricts the comprehensive and reliable assessment of the health status of fiber optic links.

[0019] In response, the solution provided in this application is an optical fiber detection method, comprising: scanning and detecting a target optical fiber link using pulse compression technology to obtain multiple Rayleigh scattering curves of the target optical fiber link, wherein each Rayleigh scattering curve is obtained by windowing with different window functions; and performing link feature detection on the target optical fiber link based on each Rayleigh scattering curve to obtain the link feature detection result of the target optical fiber link.

[0020] This application's embodiments fundamentally overcome the feature discrimination ambiguity problem caused by the limitation of traditional fiber optic detection methods to a single signal processing path by introducing a multi-window function collaborative analysis mechanism. Its core innovation lies in: based on pulse compression detection, acquiring multiple Rayleigh scattering curves of the same fiber optic link after windowing with different window functions, and constructing a multi-dimensional observation perspective—different window functions have complementary characteristics in terms of main lobe width, side lobe suppression capability, and noise sensitivity. For example, high-resolution windows such as rectangular windows easily introduce side lobe artifacts but can preserve event details; while strong side lobe suppression windows such as Hanning windows sacrifice some spatial resolution but can effectively filter out spurious components. This application's technical solution does not simply choose one type of window function, but uses these multiple curves with inherent performance tension as a joint criterion. By analyzing the changing patterns of response characteristics of the same physical location in different curves, such as peak intensity, morphological consistency, and presence or absence, reliable distinction between real link events and processing artifacts is achieved. This detection logic based on "differential response consistency verification" essentially transforms the original single-point judgment that relied on prior assumptions or empirical thresholds into a cross-validation process based on multi-source observation evidence, thereby significantly improving the accuracy, robustness, and anti-interference ability of detecting link features including but not limited to reflection events, non-reflection events, and average loss coefficients.

[0021] Therefore, the system is no longer constrained by the inherent performance trade-offs of a single window function. Instead, without increasing hardware complexity, it achieves a fundamental leap in detection accuracy and reliability by expanding the dimensions of signal processing, thus solving the fundamental defects of related technologies such as feature misjudgment, missed detection, or positioning deviation caused by the single processing method.

[0022] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0023] This application proposes an embodiment of an optical fiber detection method.

[0024] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the fiber optic detection method of this application.

[0025] In this embodiment, the fiber optic detection method includes steps S100~S300: Step S100: The target optical fiber link is scanned and detected using pulse compression technology to obtain multiple Rayleigh scattering curves of the target optical fiber link. Each Rayleigh scattering curve is obtained by windowing with different window functions. As those skilled in the art will recognize, FTTR (Fiber to the Room) is an optical access network deployment method that extends optical fiber further into individual rooms or specific areas within a building. Compared to the traditional FTTH (Fiber to the Home), FTTR replaces the connection medium between the Optical Network Terminal (ONT) and the end information outlet with optical fiber instead of copper cable or network cable, aiming to provide an all-optical connectivity experience with ultra-high bandwidth, ultra-low latency, and immunity to electromagnetic interference for each independent space.

[0026] An optical fiber link is a physical channel for carrying optical signal transmission, consisting of one or more optical fibers connected end to end. It typically includes trunk optical cables, distribution optical cables, branch optical cables, optical fiber connectors, splitters, and various fusion splices. In the FTTR scenario, the link topology is complex, with numerous branches and potential hidden fault points.

[0027] Pulse compression technology refers to a signal processing technique that transmits a wide pulse signal with a specific modulation pattern at the transmitting end and compresses it into a narrow pulse signal at the receiving end through matched filtering or correlation processing, thereby simultaneously obtaining the high dynamic range of long pulses and the high spatial resolution of short pulses. In the field of fiber optic testing, this technology solves the contradiction between dynamic range and spatial resolution that is difficult to achieve simultaneously in traditional optical time domain reflectometers (OTDRs).

[0028] Scanning detection refers to the process of using a preset detection signal to initiate a complete physical signal excitation and echo acquisition process along the optical fiber link from the near end to the far end in order to obtain the scattering response of the entire link, i.e., the Rayleigh scattering curve.

[0029] The Rayleigh scattering curve is a distance-intensity function obtained by collecting Rayleigh backscattered (RBS) light signals caused by microscopic density fluctuations in optical fibers, and then demodulating and processing them. It reflects the physical state and geometric characteristics of the optical fiber link. The curve is typically plotted with distance on the horizontal axis and logarithmically or linearly shifted scattered light intensity on the vertical axis. Changes in the slope of the curve correspond to the transmission loss of the optical link, and reflection peaks formed by intensity increases correspond to locations of abrupt changes in refractive index, such as physical connection points or fracture surfaces. In fiber segments where no reflection events occur, the Rayleigh scattering curve exhibits continuous floor noise and a smooth exponential decay trend.

[0030] A window function is a real function that takes zero values ​​outside a given interval and has a specific shape within that interval. It is widely used in digital signal processing to truncate infinitely long sequences or suppress spectral leakage. In the pulse compression system described in this embodiment, the window function directly affects the time-domain envelope of the probe signal or the response shape of the demodulation filter. Commonly used window functions include, but are not limited to, rectangular windows, Hanning windows, Hamming windows, Taylor windows, Gaussian windows, Kaiser windows, and Tukey windows. Different window functions exhibit varying combinations of characteristics in terms of main lobe width, side lobe level, and side lobe attenuation rate in the time domain.

[0031] It should be noted that in this embodiment, the window function can also be a composite window function, or a hybrid window function, composed of multiple window functions. A composite window function refers to a new window function form created by merging two or more basic window functions through mathematical operations. The design goal of a composite window function is to simultaneously take into account the advantages of different basic window functions, such as further reducing the leakage level of distant side lobes while maintaining a moderately narrow main lobe width. Common composite windows include the Blackman window, the Kaiser window, and the Chebyshev window. In this embodiment, the system can also incorporate several composite window functions with independent statistical characteristics into the windowing processing set to further enrich the diverse observation perspectives of Rayleigh scattering curves and provide a more discriminative signal representation basis for subsequent multi-curve collaborative analysis and feature cross-validation.

[0032] Windowing refers to a mathematical operation that involves multiplying a selected window function point-by-point with a time-domain signal to purposefully modulate or weight its amplitude. In the pulse compression system involved in this embodiment, the purpose of windowing is to change key time-domain characteristics of the final compressed pulse, such as the main lobe width, side lobe level, and descent rate.

[0033] In this embodiment, the system first identifies the target fiber optic link to be detected, which is located in the currently operating FTTR environment. After obtaining access permissions to the target fiber optic link, the system uses a pulse compression-based optical reflector, such as a time-gated digital optical frequency domain reflector or a coherent optical time domain reflector, to inject specially encoded probe light pulses, also known as probe signals, into the link.

[0034] Unlike conventional detection methods that use only a single window function or no additional window function modulation, this embodiment actively introduces multiple different window functions for windowing processing at the transmitting and / or receiving ends of the optical reflector. For example, in the same detection task, the system can generate corresponding detection signal templates or demodulation filter templates based on rectangular windows, Hanning windows, Taylor windows, and Gaussian windows, respectively. When the probe light propagates in the optical fiber link and generates a return optical signal due to Rayleigh scattering, the receiving end captures these weak back echoes and converts them into electrical signals. The subsequent digital signal processing unit uses the demodulation algorithm corresponding to each windowing process to calculate the Rayleigh scattering curves corresponding to different window functions. Thus, for the same optical fiber link and under the same physical state, the system collects or calculates multiple sets of data—multiple Rayleigh scattering curves with different effective spatial resolutions, different sidelobe suppression degrees, and different signal-to-noise ratios. The collection of these multiple curves constitutes a multi-dimensional and differentiated characterization system for the same optical fiber link state.

[0035] In principle, the effect of different window functions on pulse compression lies in the inherent trade-off between their time-domain truncation effect and frequency-domain sidelobe suppression capability. Taking the rectangular window as an example, it applies equal weights to each point of the signal, equivalent to convolving with the Singer function in the frequency domain. This results in the narrowest main lobe width and extremely high spatial resolution after compression. However, its sidelobe level is high and decays slowly, causing the sidelobe energy generated by strong reflection events to diffuse along the distance axis, forming artifacts and masking nearby weak events or triggering false event identification. In contrast, the Hanning window smooths the edge discontinuities of the time-domain truncation through cosine weighting, resulting in a main lobe width approximately twice that of the rectangular window. While spatial resolution decreases significantly, it also greatly suppresses sidelobe levels and significantly increases the decay rate, effectively isolating energy crosstalk between adjacent events.

[0036] This embodiment actively constructs and acquires multiple Rayleigh scattering curves affected by different window functions during the detection and acquisition phase. The purpose is not to improve the absolute signal-to-noise ratio of the system at the physical hardware level, but rather to endow the system with the ability to "change observation conditions" from a signal processing perspective. The response intensity and neighborhood interference of the same physical event will exhibit regular differences under different window function conditions. In particular, spurious events introduced by sidelobe crosstalk or Rayleigh fluctuations will show significant instability in different curves due to the lack of stable physical counterparts. This acquisition of differentiated characterization provides a multi-dimensional set of decision criteria for subsequent link feature detection steps, enabling the system to break free from path dependence on the results of a single window function.

[0037] Step S200: Based on each Rayleigh scattering curve, perform link feature detection on the target optical fiber link to obtain the link feature detection results of the target optical fiber link.

[0038] It should be noted that, in this embodiment, link characteristics refer to detectable and quantifiable attribute identifiers that can characterize the physical state, topology, or operational health of the target optical fiber link, and may include, but are not limited to, reflection events, non-reflection events, average loss coefficient, etc.

[0039] A reflection event is an optical phenomenon caused by a physical point in an optical fiber link where the refractive index changes abruptly. In a Rayleigh scattering curve, a reflection event manifests as a significant and sharp energy rise peak at a specific distance relative to the surrounding smooth Rayleigh scattering substrate; this is the reflection peak. The amplitude of this reflection peak directly reflects the optical power reflectivity at that location, while the width and shape of the peak are related to the physical morphology of the event. The mechanism of a reflection event is that when a transmitted optical pulse encounters an interface with a change in refractive index, a portion of the optical energy undergoes Fresnel reflection and returns to the detector along its original path. By identifying the location and intensity of reflection events, connection points, adapters, or break points in an optical fiber link can be accurately located, making it one of the core indicators for evaluating link connection quality and fault location.

[0040] Non-reflective events, also known as non-reflective loss events, refer to optical phenomena caused by physical points in an optical fiber link where optical energy undergoes non-reflective attenuation. In a Rayleigh scattering curve, a non-reflective event manifests as a steep, step-like abrupt drop along the signal propagation direction at a specific distance. That is, the Rayleigh scattering baseline power level after the event point is significantly lower than the level before the event point, forming a downward discontinuous step, without any accompanying rise in the reflection peak. The mechanism of non-reflective events lies in the fact that when an optical pulse passes through a region with additional loss, the backscattered Rayleigh power after that point is permanently reduced, resulting in a sharp drop in power on the curve. The magnitude of this step-like drop precisely quantifies the additional loss value of the event. By identifying the location and loss magnitude of non-reflective events, quantitative assessment and diagnosis of splice quality, bending, and mechanical damage in optical fiber links can be performed.

[0041] The average loss factor is a comprehensive indicator characterizing the average attenuation capability of optical signal transmission per unit length of optical fiber in an optical fiber link, usually measured in decibels per kilometer. Its physical meaning lies in quantifying the linear attenuation rate of optical power with distance caused by factors such as absorption, Rayleigh scattering, and imperfections in waveguide structure during optical signal transmission in the optical fiber medium. This factor can be calculated by statistically analyzing the ratio of total loss to the corresponding length on the entire link or a clean fiber segment. It is a fundamental parameter for assessing the inherent transmission quality of the optical fiber and determining whether there is abnormal additional loss. A high average loss factor may indicate manufacturing defects, aging, or long-term compression damage to the optical fiber, while the consistency and stability of this factor are important indicators for judging the overall health of the link.

[0042] Link feature detection refers to the process of extracting, identifying, and labeling link features from Rayleigh scattering curves using specific signal processing and feature analysis algorithms. This process takes the Rayleigh scattering curve as input and the link feature detection results as output. The accuracy and reliability of these results directly affect subsequent operational decisions regarding optical distribution network topology reconfiguration, fault location, and health assessment. Specifically, the link feature detection results may include a list of reflection events, a list of non-reflection events, and the average loss coefficient.

[0043] It is worth mentioning that, in this embodiment, each reflection event in the reflection event list may include parameters such as distance location, reflection peak amplitude, and reflectivity, while each non-reflection event in the non-reflection event list may include parameters such as distance location and step descent amplitude.

[0044] In this embodiment, the core of link feature detection based on multiple Rayleigh scattering curves lies in utilizing the known influence of different window functions on the detected link features. This allows for cross-calibration and fusion of the feature parameters initially extracted from each individual curve, thereby achieving a measurement accuracy superior to that achievable with only a single curve. The underlying mechanism is as follows: due to differences in their time-domain truncation characteristics and sidelobe suppression capabilities, different window functions will predictably affect the characterization of various link features on the Rayleigh scattering curves during pulse compression and demodulation.

[0045] For example, for reflection events, under a rectangular window plus a window, the main lobe of the reflection peak is the narrowest and the position determination accuracy is the highest, but its sidelobe level is high, which may cause nearby weak reflection events to be masked or generate false events; under a Hanning window plus a window, the main lobe of the reflection peak is widened and the position determination resolution decreases, but the sidelobes are effectively suppressed and false events are greatly reduced.

[0046] Similarly, for non-reflective events, different window functions affect the steepness of the step-like falling edge and the accuracy of the additional loss measurement. Taking a rectangular window as an example, its narrow main lobe in the time domain provides strong spatial localization capability for non-reflective events, enabling relatively accurate identification of the event's distance point. However, the high sidelobe characteristics of the rectangular window may cause small fluctuations near the step-like falling edge due to sidelobe oscillations, thus interfering with the accurate reading of the event's true loss amplitude. In contrast, the Rayleigh scattering curve processed by a Hanning or Gaussian window has a wider main lobe in the time domain, and the power baseline after the non-reflective event is smoothed, making the step-like falling edge gentler rather than steep. While this may obscure the precise location of the event to some extent, it effectively eliminates spurious fluctuations near the step, making the additional loss value calculated from the difference in average power before and after the step more stable and accurate. In other words, a strong sidelobe suppression window is beneficial for obtaining more reliable quantification results of non-reflective event loss, while a low sidelobe suppression window is more suitable for preliminary screening of event locations. This embodiment can optimize the confidence level of event loss measurement by utilizing the performance differences of different window functions in non-reflective event detection and through multi-curve cross-validation, based on actual detection needs.

[0047] In other words, the detection performance of the same link feature under different window functions exhibits deterministic and predictable differences, rather than random fluctuations.

[0048] Based on this understanding, in one feasible implementation, the system can pre-determine the influence characteristics of the link to be detected under different window functions through experimental calibration or theoretical derivation. This file specifically records, for a certain type of link characteristic, the system deviation, resolution changes, and detection confidence differences compared to the reference value (i.e., the true or theoretical value of a known standard fiber optic link) when using different window functions such as rectangular windows, Hanning windows, and Gaussian windows, respectively. This influence characteristic file can be either an analytical formula or a lookup table-type mapping relationship, and its function is to provide correction parameters and weighting basis for subsequent multi-curve fusion.

[0049] Furthermore, in the actual detection of the target fiber optic link, the system first independently performs a preliminary extraction operation on the target link features for each Rayleigh scattering curve obtained in step S100, resulting in a set of candidate detection results corresponding one-to-one with the window function. Subsequently, the system calls the aforementioned preset influence characteristic file to cross-calibrate and fuse the candidate detection results of the target link features under each Rayleigh scattering curve, thereby obtaining the final detection result of the target link features.

[0050] By employing this strategy of "independent detection followed by cross-calibration and fusion based on known window function influence characteristics," this embodiment transforms the inherent performance limitations of different window functions into complementary observation channels. The final output link feature detection result is no longer a single measurement value given by a specific Rayleigh scattering curve, but rather the optimal estimate after multi-source evidence fusion and systematic bias correction. This approach is not only applicable to the authenticity identification and precise parameter determination of reflection events, but also to any other link feature type with predictable window function modulation—as long as the detection bias characteristics of the link feature under different window functions can be known in advance, it can be incorporated into the collaborative analysis framework of this embodiment, achieving a dual improvement in detection accuracy and robustness.

[0051] Taking the link feature of reflection events as an example, the core of this embodiment for link feature detection based on multiple Rayleigh scattering curves lies in using the differentiated sidelobe suppression effect introduced by windowing with different window functions to cross-validate and eliminate false detections in the process of identifying reflection events.

[0052] Specifically, the system performs joint analysis and comparison of multiple Rayleigh scattering curves obtained in step S100. This analysis logic is based on the following physical facts: a real reflection event has a unique and fixed longitudinal geometric position in the fiber optic link, and a stable relative reflectivity determined by the end-face reflectivity. Therefore, regardless of the window function applied to the probe signal or demodulation filter, as long as the demodulation algorithm correctly maps the corresponding distance, the real reflection event should exhibit a statistically significant energy rise relative to the surrounding Rayleigh scattering substrate at its corresponding physical position in each Rayleigh scattering curve, i.e., a reflection peak. Although different window functions may cause differences in the absolute peak height, main lobe width, and side lobe morphology of this reflection peak, the reflection peak itself is stable in each curve, and its positioning on the distance axis remains highly consistent.

[0053] In stark contrast, whether spurious reflection peaks caused by the diffusion of sidelobes from strong reflection events, or pseudo-peak waveforms introduced by random fluctuations in Rayleigh scattering, can form a reflection event-like pattern exceeding the decision threshold in the curve depends heavily on the ability of the window function used to suppress sidelobe energy and smooth local noise. For example, in the Rayleigh scattering curve processed by a rectangular window, the peak height of a sidelobe of a strong reflection event may exceed the conventional event detection threshold, thus being misidentified as an independent reflection event; however, in the curve processed by a Hanning window, because the sidelobe energy is significantly suppressed, the intensity at the same distance will drop below the threshold, or even disappear completely into the floor noise. Similarly, peak-like waveforms accidentally formed by random fluctuations in Rayleigh scattering have extremely low reproducibility across curves, exhibiting an unstable state present in some curves but absent in others.

[0054] The significant differences in response intensity and presence at the same distance location across multiple Rayleigh scattering curves constitute the natural decision boundary for distinguishing genuine reflection events from artifacts in this embodiment. The detection of reflection events in this embodiment is precisely based on this: by analyzing the consistency and stability of the response intensity and presence at the same candidate reflection peak location across multiple Rayleigh scattering curves obtained by windowing with different window functions, a systematic elimination of false events is achieved, ultimately outputting a confirmed list of genuine reflection events and the precise location of each event. Under this decision framework, genuine reflection events, due to their physical reality, exhibit high response consistency and low intensity fluctuations in multi-curve comparisons; while false reflection events, due to the window function sensitivity of sidelobe energy or the low reproducibility of random fluctuations, are judged as non-genuine link features and excluded from the detection results. Therefore, the detection of link features such as reflection events no longer relies on empirical judgments of fixed thresholds on a single curve, but is transformed into a reliable identification process based on the consistency verification of multi-source observation evidence.

[0055] For other types of link features such as non-reflection events and average loss coefficients, although the corresponding refined extraction algorithms are not yet elaborated in the multi-window function collaborative analysis framework of this embodiment, those skilled in the art will understand that multiple Rayleigh scattering curves processed by different window functions can also provide multi-perspective references for the extraction of these link features. For example, when analyzing non-reflection events, the accurate location under a high spatial resolution window function and the basis stability under a strong sidelobe suppression window function can be combined for comprehensive judgment to eliminate misjudgments caused by local noise fluctuations. The "differential response consistency verification" idea adopted by this embodiment for reflection event detection can be smoothly extended to the identification process of other link feature types. This embodiment does not make specific limitations on this to retain the scalability and adaptability of the technical solution in diverse application scenarios.

[0056] This embodiment fundamentally overcomes the feature discrimination ambiguity problem caused by the limitation of traditional fiber optic detection methods to a single signal processing path by introducing a multi-window function collaborative analysis mechanism. Its core innovation lies in: based on pulse compression detection, acquiring multiple Rayleigh scattering curves of the same fiber optic link after windowing with different window functions, and constructing a multi-dimensional observation perspective—different window functions have complementary characteristics in terms of main lobe width, side lobe suppression capability, and noise sensitivity. For example, high-resolution windows such as rectangular windows are prone to introducing side lobe artifacts, but can preserve event details; while strong side lobe suppression windows such as Hanning windows, although sacrificing some spatial resolution, can effectively filter out spurious components. This technical solution does not simply choose to use a certain type of window function, but uses these multiple curves with inherent performance tension as a joint criterion. By analyzing the changing patterns of response characteristics of the same physical location in different curves, such as peak intensity, morphological consistency, and presence or absence, reliable distinction between real link events and processing artifacts is achieved. This detection logic based on "differential response consistency verification" essentially transforms the original single-point judgment that relied on prior assumptions or empirical thresholds into a cross-validation process based on multi-source observation evidence, thereby significantly improving the accuracy, robustness, and anti-interference ability of detecting link features including but not limited to reflection events, non-reflection events, and average loss coefficients.

[0057] Therefore, the system is no longer constrained by the inherent performance trade-offs of a single window function. Instead, without increasing hardware complexity, it achieves a fundamental leap in detection accuracy and reliability by expanding the dimensions of signal processing, thus solving the fundamental defects of related technologies such as feature misjudgment, missed detection, or positioning deviation caused by the single processing method.

[0058] It is worth mentioning that the method in this embodiment is not only applicable to the accurate location and false detection elimination of reflection events in high-density, multi-branch access networks such as FTTR, but also, based on the core inventive concept of "multi-window collaborative cross-validation," can be smoothly extended to other link characteristic detection scenarios such as reflection event identification in point-to-point backbone optical cable monitoring, accurate extraction of reflection events at the end of passive optical network (ODN) link branches, and reliability assessment of non-reflective loss events introduced by micro-bends or connectors in optical fiber links. In actual deployment, the generation of different window functions and the corresponding demodulation operations can be completed in real time through software configuration within the digital signal processing module of the optical reflectometer device, or offline batch processing of pre-acquired and stored raw echo waveform data can be achieved with the help of external computing devices. This embodiment does not impose specific limitations on this, in order to retain the diversity of technical implementation paths and the flexibility of actual deployment.

[0059] In one feasible implementation, the link feature detection result includes a list of reflection events. The above-mentioned step S200, based on each Rayleigh scattering curve, performs link feature detection on the target optical fiber link to obtain the link feature detection result of the target optical fiber link, and may include steps S210~S250: Step S210: Identify the reflection peak to be verified of the target optical fiber link from each Rayleigh scattering curve, wherein the same reflection peak to be verified has the same position in each Rayleigh scattering curve. As those skilled in the art will know, a reflection peak refers to a local waveform structure in the Rayleigh scattering curve where the backscattered light power is significantly enhanced due to a sudden change in the local refractive index of the fiber link, resulting in a significant positive amplitude increase relative to the surrounding smooth Rayleigh scattering substrate. The shape of the reflection peak is usually approximately a Gaussian pulse.

[0060] The reflection peak location refers to the physical distance coordinates of the optical fiber link corresponding to the highest point of the reflection peak waveform structure.

[0061] The peak-to-peak value of a reflection peak refers to the absolute signal strength value at the highest point of the reflection peak waveform structure.

[0062] The reflection peak height refers to the relative increase in the peak value of the reflection peak compared to the average intensity of its neighboring substrate (i.e., the smooth Rayleigh scattering substrates on both sides of the reflection peak that are not affected by the reflection event). This indicator eliminates the influence of the overall attenuation trend of the optical fiber link and is a core quantitative parameter for measuring the severity of reflection events.

[0063] It should be noted that, in this embodiment, the reflection peak to be verified refers to the waveform structure of the reflection peak initially identified from each Rayleigh scattering curve and marked as a suspected reflection event. It is a set of candidate objects for subsequent authenticity verification operations. The identification process can refer to the general description of step S200 in the first embodiment: First, for each Rayleigh scattering curve obtained in step S100, such as curves obtained by adding windows through rectangular windows, Hanning windows, and Taylor windows respectively, the system independently executes a peak search algorithm to initially locate all local peaks that have a significant intensity increase relative to the neighborhood substrate; then, the system performs spatial alignment and matching on these independent peak position sets from different curves, and identifies those coordinate points that show an intensity increase at the same physical distance coordinate or within a very small distance tolerance range in multiple Rayleigh scattering curves as reflection peaks to be verified, and records their common reflection peak positions.

[0064] It is worth mentioning that in this embodiment, when the distance between the reflection peak positions in different Rayleigh scattering curves is less than a preset value, or when the reflection peak positions in different Rayleigh scattering curves are within a very small distance tolerance range, it is considered that the reflection peak positions in each Rayleigh scattering curve are the same. The specific values ​​of the preset value and the distance tolerance range can be flexibly set according to actual conditions, and this embodiment does not limit them.

[0065] The premise of this "multi-curve reflection peak position cross-matching" preprocessing operation is that if a real reflection event does exist at a certain physical location, then the reflection event should be traceable in all curves, even though the height and shape of its reflection peak may vary with different window functions; conversely, if a peak is detected only in a few curves and disappears completely in other curves, then the peak is very likely a noise fluctuation or an algorithm anomaly introduced by a specific window function, and can be directly identified as a false event without needing to proceed to the subsequent fine verification stage.

[0066] Therefore, this implementation method significantly reduces the number of events to be verified through preliminary "existence" intersection screening, and focuses limited computing resources on candidate peaks that have a certain performance in multiple curves but still need further verification, thus laying an efficient and robust screening foundation for subsequent quantitative analysis.

[0067] Step S220: Based on the reflection peak height of each reflection peak to be verified in each Rayleigh scattering curve, determine the minimum reflection peak height of each reflection peak to be verified; It should be noted that, in this embodiment, the lowest reflection peak height refers to: for the same reflection peak to be verified, among the set of reflection peak height values ​​presented by the multiple Rayleigh scattering curves generated in step S100, the smallest value is selected as the conservative intensity characterization of the reflection peak to be verified. This is determined by iterating through the reflection peak heights of the reflection peak at the corresponding positions in each curve and finding the minimum value.

[0068] This implementation selects the minimum value, rather than the average or maximum value, as the evaluation criterion, reflecting clear physical considerations and engineering strategies. From a physical mechanism perspective, different window functions, due to their varying degrees of main lobe energy dispersion and side lobe leakage attenuation rates, will systematically modulate the reflection peak height of the same real reflection event. However, this modulation has an upper limit: the energy of a real reflection peak originates from objectively existing Fresnel reflection, and its main lobe peak energy is concentrated at the physical event point. Even when applying a window function with the most significant main lobe broadening, its reflection peak height will decrease, but the decrease is relatively limited. In contrast, the energy of a spurious reflection peak is essentially a spatial leakage of main peak energy, and it is extremely sensitive to the sidelobe suppression capability of the window function. When a window function with extremely strong sidelobe suppression capability is applied, the leakage energy at the location of the spurious reflection peak will be significantly reduced, and its reflection peak height will experience a precipitous drop. Therefore, after traversing all curves, the minimum reflection peak height of a spurious reflection peak is usually much lower than the minimum value achievable by a real reflection peak. Choosing the minimum value as the criterion is precisely to maximize the energy difference between true and false events under the "most unfavorable observation conditions," thereby constructing the most discriminative feature dimension for subsequent dual-threshold decisions.

[0069] Step S230: Based on the peak values ​​of each reflection peak to be verified in each Rayleigh scattering curve, determine the peak fluctuation degree of each reflection peak to be verified. It should be noted that, in this embodiment, the peak fluctuation refers to a statistical measure used to quantitatively characterize the drastic change in the peak intensity of the same reflection peak in various Rayleigh scattering curves. This measure aims to capture the impact of different window functions on the consistency of the response energy at that location. Common quantification methods include, but are not limited to, calculating the standard deviation, variance, range, or coefficient of variation of the peak value of the reflection peak in each curve. Regardless of the specific statistical measure used, the core purpose is to transform the "response stability of the candidate peak under different observation perspectives" into a calculable numerical index.

[0070] The principle behind introducing the peak variability dimension in this step is as follows: Real reflection events originate from deterministic physical interfaces, and their reflected light energy is concentrated at the event point itself. While different window functions affect the absolute height and width of the reflection peak, the range of peak intensity variation is relatively concentrated due to the convergence of the main lobe energy, exhibiting low variability. Conversely, spurious reflection peaks, especially those originating from energy leakage from strong sidelobes or random fluctuations in Rayleigh scattering, have a statistical or path-dependent generation mechanism. When the window function changes, the phase superposition relationship and spatial distribution of sidelobe leakage energy change drastically. This results in some window functions having strong sidelobe superposition at that point, manifesting as a high peak value; while in other window functions, the sidelobes are effectively suppressed, manifesting as a low peak value. This high sensitivity to window function selection is directly reflected in the drastic jumps in peak intensity between curves for the candidate peak, i.e., high variability. Therefore, peak variability effectively quantifies the physical stability of real events and the processing dependence of spurious events into distinctly different numerical characteristics from the perspective of "response consistency."

[0071] Step S240: Obtain the pre-calibrated reflection peak height threshold and reflection peak fluctuation threshold, and determine the reflection peak to be verified that has a minimum reflection peak height greater than the reflection peak height threshold and a peak fluctuation degree less than the reflection peak fluctuation threshold as the true reflection peak of the target optical fiber link; It should be noted that, in this embodiment, the reflection peak height threshold refers to a pre-set minimum relative intensity threshold value that must be exceeded to determine whether a signal rise has event significance. Its function is to filter out all candidate targets with too low absolute intensity that do not have physical event value, ensuring that the candidate peaks entering the final decision loop exhibit an intensity level consistent with the actual reflection event under at least one observation condition.

[0072] The reflection peak fluctuation threshold is a pre-set upper limit for judging whether the consistency of a signal rise under different window function observations is acceptable in terms of intensity variation. Its function is to eliminate "unstable" candidate peaks that meet the intensity standard in one curve but whose intensity decreases sharply in other curves.

[0073] A true reflection peak refers to a reflection peak structure that, after being screened by the above dual thresholds, is finally confirmed to correspond to an objectively existing physical reflection event, rather than a sidelobe artifact or random noise.

[0074] This implementation method fully executes the "differential response consistency verification" logic proposed in the first embodiment through a joint decision based on two dimensions: "lowest reflection peak height" and "peak fluctuation degree." Its underlying decision principle can be understood as a two-stage cascaded filter: the first stage is an absolute intensity screening to ensure the physical significance of the reflection event; the second stage is a relative stability screening to ensure the observational consistency of the reflection event. Only when a candidate reflection peak, i.e., the reflection peak to be verified, simultaneously satisfies both conditions—"its intensity is still sufficiently significant even under the most unfavorable window function for the event" and "its response intensity is sufficiently stable regardless of the window function used for observation"—is it confirmed as a true reflection peak.

[0075] This decision logic has a deep supporting relationship with the "influence characteristic profile of the detected link feature under different window functions" in the first embodiment. The influence characteristic profile records prior knowledge about the system attenuation ratio of the reflection peak height and the peak fluctuation range compared to the baseline value for a known real reflection event under different window functions such as rectangular window, Hanning window, and Gaussian window. The calibration of the reflection peak fluctuation threshold is determined based on the "upper bound of the expected maximum fluctuation range of the real reflection event" provided by the influence characteristic profile. The calibration of the reflection peak height threshold relies on the core information revealed by the influence characteristic profile: "theoretically, the lowest reflection peak height of the real reflection event should fall into which numerical range after applying various window functions." In other words, the influence characteristic profile provides an objective, non-empirical, and traceable calibration basis for the specific numerical selection of the dual thresholds, transforming the threshold configuration, which might otherwise be subjectively arbitrary, into a system calibration process with physical basis and data support. This mapping from "prior knowledge calibration" to "dual threshold decision" gives each link in the entire decision chain a profound theoretical and experimental foundation.

[0076] This implementation method, through this dual-threshold decision mechanism, elevates the multi-window function collaborative analysis from a qualitative perspective to a programmable, quantifiable, and reproducible automatic decision strategy, thereby fully implementing the technical ideas of the first embodiment at the engineering implementation level.

[0077] Step S250: Based on the actual reflection peaks, determine the list of reflection events for the target fiber optic link.

[0078] In this embodiment, the reflection event list refers to the final detection result that systematically describes all confirmed physical reflection events in the target fiber optic link, based on the set of real reflection peaks confirmed in step S240, after parameter extraction and structured processing. This list is one of the core components of the link feature detection results and can be directly used as a reliable input basis for subsequent operation and maintenance decisions, topology reconfiguration, and health assessment.

[0079] Each record in the reflection event list uniquely corresponds to a true reflection peak confirmed by the dual-threshold mechanism of this embodiment, and may include at least the following parameters: the precise distance and location of the true reflection peak, the height of the reflection peak, and optionally, the reflectivity estimated using the Fresnel reflection formula. The value of the reflection peak height can be a measurement under a representative window function (such as a rectangular window), or it can be the optimal estimate after weighted fusion of multiple curves; this embodiment does not limit this. The reflectivity can be calculated through the calibration relationship between the reflection peak height and the reflection peak height of a known standard reflective end face, and is used to quantify the physical intensity of the reflection event.

[0080] It is worth noting that the reflection event list generated in this embodiment is fundamentally different from the reflection event list obtained by related technologies based solely on a single curve and a fixed threshold. Lists output by traditional methods often contain a large number of false events caused by sidelobe crosstalk, Rayleigh fluctuations, or noise spikes, resulting in a high false detection rate and requiring maintenance personnel to expend considerable effort in manual screening and removal. In contrast, the reflection event list output by this embodiment has undergone two rigorous filtering processes—"differential response consistency verification" and "dual threshold joint decision"—under the multi-window function collaborative analysis framework, systematically identifying and eliminating false events. Therefore, the event entries in this reflection event list are highly reliable, concise, and accurately located, truly and purely reflecting every objectively existing physical reflection point in the target fiber optic link, providing a solid information foundation for efficient operation and maintenance and accurate fault location of FTTR networks.

[0081] In summary, this implementation fully reveals how to implement the overarching concept of "link feature detection based on multi-window function collaborative analysis" proposed in the first embodiment into a highly reliable identification of reflection events through a specific and operable dual-threshold decision process. This implementation completes the initial screening and existence verification of candidate events through "multi-curve position cross-matching" in step S210; constructs a conservative intensity feature characterizing the physical salience of the event through "minimum reflection peak height" extraction in step S220; constructs a stability feature characterizing the observation consistency of the event through "peak fluctuation degree" calculation in step S230; furthermore, through step S240, it introduces a pre-calibrated reflection peak height threshold and reflection peak fluctuation threshold based on the "influence characteristic profile of the link feature under different window function conditions," jointly deciding on the above two feature dimensions to accurately identify the true reflection peak; finally, through step S250, it outputs a highly reliable, non-spurious, structured list of reflection events. The entire implementation compromises the inherent performance of different window functions, transforming them into complementary multidimensional decision evidence. This makes side lobe artifacts and random spurious events, which are easily masked or misjudged by a single window function, impossible to hide under the joint screening of "strength-stability". This achieves a fundamental leap in the detection of reflection events from fuzzy single-point empirical judgment to accurate multi-source cross-validation.

[0082] Based on the above embodiments, this application proposes a second embodiment of an optical fiber detection method.

[0083] In the second embodiment of this application, the same or similar content as in the above embodiments can be referred to the above description, and will not be repeated hereafter.

[0084] Please refer to Figure 2 , Figure 2 This is a flowchart illustrating the second embodiment of the fiber optic detection method of this application.

[0085] In this embodiment, step S100 above uses pulse compression technology to scan and detect the target optical fiber link to obtain multiple Rayleigh scattering curves of the target optical fiber link, and may include steps S110~S130: Step S110: Obtain a preset basic modulation signal, and based on the basic modulation signal and multiple different window functions, modulate to obtain a multi-channel windowed detection signal; As those skilled in the art will know, a modulated signal is an electrical signal with a specific waveform used to carry information to be transmitted in a communication or sensing system. In an optical fiber detection system based on pulse compression technology, the modulated signal typically refers to a radio frequency electrical signal applied to a light source or optical modulator, and its waveform parameters directly determine the time-frequency characteristics of the probe light pulse injected into the optical fiber.

[0086] The demodulated signal is a reference signal template used at the receiving end to perform specific operations with the acquired echo signal, such as convolution, correlation, or matched filtering, to recover or extract specific information. In pulse compression systems, the design of the demodulated signal is closely related to the modulation signal; its function is to compress the time-domain broadened received echo into a narrow pulse, thereby achieving high spatial resolution.

[0087] Modulation refers to the process of applying a modulation signal to a light source or optical modulator so that the output optical pulse signal carries the amplitude, phase, or frequency change information encoded by the modulation signal.

[0088] Demodulation refers to the process of reconstructing the scattering intensity distribution information (i.e., Rayleigh scattering curve) along the optical fiber link from the echo signal received by the detector by performing matched filtering or correlation operations with the demodulated signal.

[0089] The probe signal refers to the optical pulse signal actually injected into the optical fiber link.

[0090] Rayleigh scattering signal refers to the echo signal formed when backscattered light caused by material micro-density fluctuations is received by the detector and converted into an electrical signal during the transmission of the probe signal in the optical fiber. It carries information about the loss distribution and local refractive index changes of the entire optical fiber link.

[0091] In this embodiment, the basic modulation signal refers to the original radio frequency modulation signal template preset by the system and not weighted by an additional window function. It has definite basic parameters such as start frequency, end frequency, sweep rate and time domain length.

[0092] A windowed modulated signal is a modulated signal whose time domain envelope is shaped by a specific window function. It is in the radio frequency or digital baseband stage and has not yet been loaded onto the optical carrier.

[0093] The basic probe signal refers to the probe optical signal generated directly by the basic modulation signal without applying additional window function time-domain envelope shaping. It carries the basic time-frequency code required by the pulse compression system and serves as the unified optical domain reference for subsequently deriving various windowed probe signals in the optical domain.

[0094] A windowed probe signal is a probe signal whose time-domain envelope of the probe light pulse is shaped by a specific window function.

[0095] In this embodiment, the system first acquires a preset basic modulation signal, which serves as a unified reference for all subsequent windowing operations. Then, the system applies the envelope characteristics of different window functions to the generation path of the probe signal to obtain multiple windowed probe signals. The specific location of application can be the radio frequency modulation signal itself, i.e., windowing the basic modulation signal in the electrical domain to obtain a windowed modulation signal, which is then modulated into an optical signal; or it can be a modulated optical signal, i.e., envelope shaping of the basic probe signal in the optical domain. This embodiment does not limit this. Each windowed probe signal is injected sequentially or in parallel into the target fiber optic link. Their common essence is that each windowed probe signal, carrying the temporal envelope characteristics of a specific window function, possesses a pulse shape distinct from other windowed probe signals.

[0096] This embodiment introduces multiple different window functions during the probe signal generation stage, enabling differentiation from the signal source to be established for multiple scans of the same fiber optic link. Due to the varying effects of the window functions on the main lobe energy concentration and sidelobe leakage suppression capabilities, the windowed probe signals will exhibit systematic differences in reflection peak width, sidelobe level, and signal-to-noise ratio in the subsequently demodulated Rayleigh scattering curves. This provides the most direct signal source guarantee for the multi-curve collaborative analysis and cross-validation described in the first embodiment.

[0097] Step S120: Scan the target fiber optic link using each windowed detection signal to obtain the Rayleigh scattering signal corresponding to each windowed detection signal; In this embodiment, the system injects the multiple windowed probe signals generated in step S110 sequentially or through parallel mechanisms such as wavelength division multiplexing into the target fiber optic link. Each windowed probe signal propagates independently in the fiber and continuously excites Rayleigh backscattering. The optical receiver synchronously acquires the returned optical signals generated by each scan probe and converts them into electrical signals to obtain Rayleigh scattering signals corresponding one-to-one with each windowed probe signal. Each Rayleigh scattering signal carries different temporal information characteristics due to the envelope difference of the corresponding windowed probe signal, which is the original data basis for subsequently demodulating Rayleigh scattering curves under different window functions.

[0098] Step S130: Based on the Rayleigh scattering signals corresponding to each windowed detection signal, demodulate to obtain multiple Rayleigh scattering curves of the target optical fiber link.

[0099] In this embodiment, the system performs pulse compression demodulation operation on each Rayleigh scattering signal acquired in step S120, and finally obtains multiple Rayleigh scattering curves that correspond one-to-one with the window function.

[0100] There are several feasible variations in the specific implementation path of demodulation. The following describes several typical implementation methods, each with different technical focuses in terms of computing architecture, resource consumption, and system flexibility.

[0101] In a first feasible implementation, step S130 may include steps S131-S132: Step S131: Generate a basic demodulated signal based on the basic modulation signal; It should be noted that, in this embodiment, the basic demodulated signal refers to the original demodulated signal template derived from the basic modulation signal without any window function weighting. In a standard pulse compression matched filter architecture, the basic demodulated signal is usually designed as a time-domain conjugate inverted version of the basic modulation signal, serving as a unified reference template for generating various windowed or unwindowed demodulated signals.

[0102] The core design choice of this implementation is that the demodulation end maintains only a common basic demodulation signal, instead of customizing a matching demodulation filter for each windowed probe signal. This "unified demodulation core" design concentrates the differentiated processing of the window function entirely at the transmitting end, greatly simplifying the signal processing flow at the receiving end.

[0103] Step S132: Based on the basic demodulated signal and the Rayleigh scattering signal corresponding to each windowed detection signal, multiple Rayleigh scattering curves of the target optical fiber link are demodulated.

[0104] In this embodiment, the system demodulates the same basic demodulated signal generated in step S131 with each of the Rayleigh scattering signals acquired in step S120. Although the demodulated signal itself is not windowed, since each Rayleigh scattering signal is excited by a windowed probe signal carrying a different window function envelope, their respective time-frequency characteristics already incorporate the differences in the window functions. Therefore, the Rayleigh scattering curves obtained after demodulation naturally exhibit differences in reflection peak width, sidelobe level, and signal-to-noise ratio corresponding to different window functions.

[0105] The technical advantage of this implementation lies in the extreme simplification and unification of the receiver processing architecture. Since only a single basic demodulated signal needs to be stored and retrieved, the storage resources and parallel computing channel overhead of the receiver's digital signal processing module are significantly reduced. Furthermore, when it is necessary to add or change the window function type, only the windowing configuration at the transmitter needs to be modified; the receiver's hardware and software require no adaptation adjustments, significantly improving the flexibility and maintainability of system upgrades. This architecture is particularly suitable for application scenarios where the transmitter has ample hardware resources, but the receiver prioritizes low power consumption and low cost, or is already deployed on a large scale and difficult to upgrade uniformly.

[0106] In a second feasible implementation, step S130 may include steps S133-S134: Step S133: Generate a basic demodulated signal based on the basic modulation signal, and apply windowing to the basic demodulated signal using different window functions to obtain multiple windowed demodulated signals; It should be noted that a windowed demodulated signal refers to a demodulated signal template that has been shaped by applying a specific window function to the time domain envelope of the demodulated signal.

[0107] This implementation, based on the second embodiment where multiple windowed probe signals are generated at the transmitting end, further introduces window function processing at the receiving end. Its core design feature is that the application of the window function is not limited to the transmitting end, but simultaneously applies to both the probe signal generation link at the transmitting end and the demodulation signal generation link at the receiving end, forming a collaborative windowing processing architecture at both ends. Under this architecture, the window function used for each windowed demodulated signal is not required to be consistent with the window function of the corresponding windowed probe signal. They can be the same to achieve matched filtering, or different to form an asymmetric combination of window functions. The specific choice depends on the system's comprehensive trade-off requirements for performance indicators such as main lobe width, side lobe suppression capability, and signal-to-noise ratio.

[0108] Step S134: Based on each windowed demodulated signal and the Rayleigh scattering signal corresponding to each windowed detection signal, multiple Rayleigh scattering curves of the target optical fiber link are obtained by demodulation.

[0109] In this embodiment, the system performs matched filtering or correlation operations on each windowed demodulated signal with its corresponding Rayleigh scattering signal. Since the windowed demodulated signal itself also carries window function information, when it performs matched filtering or correlation operations with the Rayleigh scattering signal that already carries the characteristics of the transmitting end window function, the window function effects at both ends will jointly affect the final pulse compression result.

[0110] Compared to the scheme in steps S131-S132 above where "the receiver only uses a unified basic demodulation signal for demodulation," the significant difference in this implementation is that the receiver no longer passively accepts the curve differences caused by the windowing at the transmitter, but actively introduces a second, independently configurable window function dimension. This mechanism of "independent windowing at both the transmitter and receiver" gives the system a richer and more flexible control space in the signal processing dimension. For example, the system can use a rectangular window at the transmitter to ensure the highest spatial resolution for event localization, while using a Hanning window at the receiver to suppress sidelobe interference for event authenticity identification. The asymmetric combination of the window functions at both ends allows the final generated Rayleigh scattering curves to obtain new performance combinations beyond the trade-off between resolution and sidelobe suppression. Of course, if the same window function is selected at both ends, matching enhancement can be achieved, further amplifying the characteristic differences between the curves. The resulting Rayleigh scattering curves, due to different combinations of window functions at both the transmitting and receiving ends, will exhibit richer and more diverse characteristics in dimensions such as reflection peak width, sidelobe suppression degree, and signal-to-noise ratio. This provides a more discriminative and flexible set of observation samples for multi-curve collaborative analysis and cross-validation in the first embodiment. The trade-off is that the receiver needs to maintain and call the corresponding windowed demodulated signal for each Rayleigh scattering signal, increasing the storage and parallel computing load of the receiver's digital signal processing module. This approach is suitable for applications requiring higher detection accuracy and with sufficient computing power at the receiver.

[0111] In one feasible implementation, the step of obtaining a multi-windowed detection signal based on a basic modulation signal and multiple different window functions in step S110 above may include steps S111 to S112: Step S111: Window the basic modulation signal using different window functions to obtain multi-channel windowed modulation signals; In this embodiment, during the digital signal processing stage, the system inputs the fundamental modulation signal into multiple different windowed processing channels. Each channel calls a different window function to perform point-by-point amplitude modulation on the fundamental modulation signal. This results in multiple windowed modulation signals with differentiated time-domain envelopes. These windowed modulation signals share the same fundamental frequency modulation pattern, but because their envelopes are shaped by different window functions, their energy gradient trends at the beginning and end of the time domain differ.

[0112] This step completely confines the differentiated processing of "multi-window" to the baseband modulation signal stage, serving as a direct precursor to the subsequent generation of multiple windowed detection signals. Its technical significance lies in the fact that the window function is applied to a pure electrical signal that has not yet undergone electro-optic modulation; all calculations are performed in the digital domain, unaffected by the nonlinearity, noise, and bandwidth limitations of optoelectronic devices. Therefore, the generation of the window function envelope is highly accurate and reproducible, and modifications to the window function type or parameters require no changes to the hardware configuration, only software reconfiguration, giving the system extremely high flexibility and scalability.

[0113] Step S112: Multi-channel windowed detection signals are obtained by modulating each windowed modulation signal accordingly.

[0114] In this embodiment, the system applies each windowed modulation signal generated in step S111 sequentially or in parallel to the light source or optical modulator to modulate and generate corresponding multi-channel windowed probe optical signals. Specifically, when using direct modulation, the windowed modulation signal directly drives the injection current of the light source, causing the output optical power to vary with the amplitude of the modulation signal; when using external modulation, the windowed modulation signal is applied to the RF input of the optical modulator to modulate the intensity or phase of continuous or pulsed carrier light. Regardless of the modulation method used, the purpose is to completely transfer the time-frequency coding and window function envelope information carried by each windowed modulation signal to the optical domain to generate the corresponding windowed probe signal.

[0115] The process from step S111 to step S112 completes the transformation of the window function from "digital baseband characteristics" to "optical domain physical waveform", providing a direct optical signal carrier for injecting probe light carrying differentiated window function characteristics into the optical fiber link in step S120.

[0116] Furthermore, in a feasible implementation, step S130 above may include steps S135-S136: Step S135: Based on each windowed modulation signal, generate the corresponding windowed demodulation signal for each windowed detection signal; It should be noted that, in this embodiment, the windowed demodulated signal is a time-domain conjugate inverted matched demodulation template constructed directly based on the windowed modulation signals generated in step S111. Since the windowed modulation signal itself already embeds the envelope features of the corresponding window function, the derived windowed demodulated signal naturally carries the same window function information.

[0117] Unlike the open architecture in steps S133-S134 where the transmitting and receiving ends can independently select window functions, the processing link in this embodiment has clear limitations and symmetry: the windowed demodulated signal is not generated by arbitrarily windowing the basic modulation signal, but is directly derived from the already windowed modulation signal. This design ensures that the window function characteristics in the probe link and demodulation link are strictly homologous, with both ends using the same window function, forming a closed-loop processing with complete matching between transmitting and receiving.

[0118] Step S136: Based on the windowed demodulated signal and Rayleigh scattering signal corresponding to each windowed detection signal, multiple Rayleigh scattering curves of the target optical fiber link are demodulated.

[0119] In this embodiment, the system convolves and demodulates each Rayleigh scattering signal with its corresponding windowed demodulated signal. Since the probe signal and the demodulated signal both originate from the same windowed modulation signal, they form a perfect conjugate match in the time domain envelope, and the pulse compression effect after demodulation can reach the theoretical optimum for this type of window function.

[0120] Compared to the asymmetric windowing scheme at both the transmitting and receiving ends in steps S133-S134, the distinguishing feature of this implementation is its focus on "matching accuracy" rather than "combination flexibility." The homology of the window functions at both ends completely avoids compression performance degradation caused by minor mismatches that may be introduced by independent generation. Under this architecture, the suppression of sidelobe energy is achieved entirely according to the theoretical design value of the corresponding window function. There is no additional sidelobe lifting or main lobe broadening caused by mismatch between the transmitting and receiving window functions. Therefore, the detection sensitivity for weak reflection events can reach the physical limit under this window function type, maximizing the pulse compression processing gain and making it suitable for application scenarios with extremely stringent detection accuracy requirements.

[0121] In one feasible implementation, the step of obtaining a multi-windowed detection signal based on a basic modulation signal and multiple different window functions in step S110 above may include steps S113-S114: Step S113: Obtain the basic detection signal by modulating the basic modulation signal; In this embodiment, the system applies a basic modulation signal to a light source or optical modulator to generate a basic detection signal. This basic detection signal carries the basic time-frequency code required by the pulse compression system, but its time-domain envelope retains the original form of the basic modulation signal, which is equivalent to applying a rectangular window. It serves as a unified optical domain reference for subsequently deriving various windowed detection signals in the optical domain.

[0122] The core design feature of this implementation is that the system generates only one basic detection signal in the electrical domain, and postpones the differential processing of the window function to the optical domain, thereby greatly simplifying the complexity of the electrical link at the transmitting end.

[0123] Step S114 involves applying different window functions to the basic detection signal to obtain multiple windowed detection signals.

[0124] In this embodiment, the system performs time-domain amplitude shaping on the basic probe signal generated in step S113 in the optical domain. Specifically, a controllable intensity modulator can be introduced into the optical path to perform real-time amplitude trimming or shaping on the waveform of the basic probe light pulse according to the time-domain envelope curves of different window functions, thereby obtaining multiple windowed probe signals with different window function envelope characteristics.

[0125] Compared to the scheme of adding windowing in the electrical baseband in steps S111-S112, this embodiment shifts the execution point of the windowing operation to after optical modulation is completed. Its technical focus is on the fact that when the bandwidth or sampling rate of the system's transmitting electrical module is insufficient to generate high-frequency RF signals with complex window function envelopes with high precision, the high-precision envelope shaping task can be transferred to the optical domain processing stage, which has more bandwidth or is specifically optimized. Simultaneously, since only one basic probe signal needs to be generated, the electrical transmission channel is simplified, and the switching and diversified generation of window functions are entirely implemented at the optical level, making it suitable for system architectures with limited transmitting electrical link performance but strong optical control capabilities. Furthermore, optical domain windowing can flexibly adapt to the different requirements of window function types for different fiber optic links without modifying the electrical transmission link, improving the system's adaptability in different deployment environments.

[0126] Based on the above embodiments, this application proposes a third embodiment of an optical fiber detection method.

[0127] In the third embodiment of this application, the same or similar content as the above embodiments can be referred to the above description, and will not be repeated hereafter.

[0128] Please refer to Figure 3 , Figure 3 This is a flowchart illustrating the third embodiment of the fiber optic detection method of this application.

[0129] In this embodiment, step S100 above uses pulse compression technology to scan and detect the target optical fiber link to obtain multiple Rayleigh scattering curves of the target optical fiber link, and may include steps S140~S170: Step S140: Obtain a preset basic modulation signal, and obtain a basic detection signal by modulating the basic modulation signal; In this embodiment, after acquiring a preset basic modulation signal, the system applies it to a light source or optical modulator to generate a basic detection signal. This basic detection signal carries the basic time-frequency code required by the pulse compression system, but its time-domain envelope retains the original shape of the basic modulation signal, which is equivalent to applying a rectangular window without applying any additional window function time-domain envelope shaping.

[0130] Unlike the second embodiment, which generates multiple windowed probe signals at the transmitting end, this embodiment generates only a single basic probe signal at the transmitting end, with the differential processing of the window function completely deferred to the demodulation stage at the receiving end. The direct result of this design choice is that the hardware architecture and signal generation process at the transmitting end are simplified to their bare minimum. There is no need to configure multiple parallel windowing processing channels, nor to perform multiple scan probes or time-division multiplexing switching, significantly reducing the complexity and cost of the transmitting unit.

[0131] Step S150: Scan the target fiber optic link using the basic detection signal to obtain the Rayleigh scattering signal corresponding to the basic detection signal; In this embodiment, the system injects the unique basic detection signal generated in step S140 into the target optical fiber link, performs a complete scan detection, and collects the returned Rayleigh backscattered light by the receiving end, converts it into an electrical signal, and obtains the Rayleigh scattering signal corresponding to the basic detection signal.

[0132] Since the transmitter only performs one detection, the receiver only needs to acquire one Rayleigh scattering signal. Compared to the second embodiment, which requires sequential or parallel transmission of multiple windowed detection signals and corresponding acquisition of multiple Rayleigh scattering signals, this embodiment minimizes the time consumed in the detection and acquisition process, while also avoiding inconsistencies that may be introduced between multiple detections due to time-varying fiber optic link states. This characteristic makes this embodiment particularly suitable for applications with high real-time detection requirements or where the fiber optic link state may fluctuate within a short period of time.

[0133] Step S160: Generate a basic demodulated signal based on the basic modulation signal, and apply windowing to the basic demodulated signal using different window functions to obtain multiple windowed demodulated signals; In this embodiment, after the system generates a basic demodulated signal based on the basic modulation signal at the receiving end, it does not directly use the basic demodulated signal for demodulation. Instead, it inputs it into multiple different windowing processing channels. Each channel calls a different window function to perform point-by-point time-domain amplitude weighting on the basic demodulated signal, resulting in multiple windowed demodulated signals with different envelope characteristics.

[0134] Compared to the architecture of collaborative windowing at both the transmitting and receiving ends in steps S133-S134 of the second embodiment, the core difference in this embodiment is that the differential processing of window functions is entirely concentrated at the receiving end, and the transmitting end is unaware of and does not participate in it. The type of window function used for each windowed demodulated signal is entirely determined by the digital signal processing module at the receiving end at the software level, without any coordination or matching constraints with the transmitting end. The selection, addition, deletion, and parameter adjustment of window functions can all be completed independently at the receiving end, giving the system great flexibility and scalability in subsequent multi-curve analysis dimensions.

[0135] In principle, the mechanism for multi-window observation in this embodiment lies in the fact that although only one basic detection signal is injected into the optical fiber and only one Rayleigh scattering signal is acquired, when the receiver demodulates the same Rayleigh scattering signal with demodulated signals weighted by different window functions, the different time-domain envelopes of each windowed demodulated signal will produce differentiated demodulation results with the different time-frequency components of the measured Rayleigh scattering signal. Specifically, the modulation effect of different window functions on the main lobe width and sidelobe level of the basic demodulated signal will be directly mapped onto the demodulated Rayleigh scattering curve, resulting in multiple Rayleigh scattering curves with systematic differences in reflection peak width, sidelobe suppression degree, and signal-to-noise ratio after demodulating the same Rayleigh scattering signal. This "one transmit, multiple receive, software multi-window" architecture shifts the physical implementation of multi-window observation from the hardware dimension requiring multiple detections to the software dimension requiring only a single detection combined with multiple parallel digital demodulations.

[0136] Step S170: Based on each windowed demodulated signal and the Rayleigh scattering signal corresponding to the basic detection signal, multiple Rayleigh scattering curves of the target optical fiber link are demodulated.

[0137] In this embodiment, the system demodulates the multiple windowed demodulated signals generated in step S160 with the same Rayleigh scattering signal acquired in step S150. Since the window function envelope characteristics carried by each windowed demodulated signal are different, their pulse compression effects on the same Rayleigh scattering signal also differ, ultimately resulting in multiple Rayleigh scattering curves with differentiated window function characteristics. Although these curves originate from the same detection and acquisition, they exhibit diverse representations corresponding one-to-one with the multiple window functions due to the different window functions at the demodulation end, achieving the same multi-dimensional observation effect as the second embodiment's multi-windowing at the transmitting end.

[0138] The core technological value of this embodiment lies in achieving an ultimate balance between "minimal hardware" and "richest software observation dimensions." Throughout the entire process described above, the transmitter maintains its simplest form: it only needs to generate one basic detection signal, perform one scan detection, and acquire one Rayleigh scattering signal, minimizing both hardware costs and detection time. Meanwhile, the receiver, through parallel software processing, applies demodulation of the same Rayleigh scattering signal using multiple different window functions, virtually constructing an effect in the digital domain equivalent to multiple detections with different window functions. This "one-time detection, multi-dimensional demodulation" architecture not only completely eliminates the time-varying errors that may exist between multiple detections because all demodulation operations are applied to the echo data of the same detection, making the differences between Rayleigh scattering curves purely reflect the window function effect rather than the link state change, thus providing the purest set of observation samples for multi-curve collaborative analysis and cross-validation in the first embodiment; it also gives the system extremely high flexibility after deployment—the type, number, and parameter adjustment of the window function are all completed at the receiver software level, without requiring any modification or recalibration of the deployed transmitter hardware, greatly reducing the cost and risk of system upgrades and maintenance.

[0139] In summary, compared to the second embodiment's multi-window approach at the transmitting end, this embodiment represents an independent technical path for realizing the "multi-window collaborative cross-validation" concept. The advantage of the second embodiment lies in the transmitting end's proactive shaping of differentiated detection signals, which can endow each curve with stronger feature differentiation from the signal source. The advantage of this embodiment lies in the extreme simplification of the transmitting end and the complete software-defined nature of the receiving end. It has significant advantages in hardware cost, real-time detection performance, consistency of multiple curves originating from the same source, and system maintainability. It is particularly suitable for application scenarios with strict constraints on the hardware cost and complexity of the transmitting end, or where basic detection equipment has already been deployed on a large scale, and where multi-window collaborative analysis capabilities can be acquired simply through a software upgrade at the receiving end. These two technical paths complement each other, together constituting the complete technical solution system of this application for realizing the core inventive concept of "multi-window collaborative analysis."

[0140] During the deployment and operation of optical distribution networks, operators often struggle to accurately ascertain the actual network topology, a problem particularly prominent in PON networks and FTTR optical link quality testing scenarios. Rayleigh backscattered (RBS) optical reflectors are a common link testing method. Among them, coherent receiver optical reflector systems based on pulse compression technology possess both extremely high spatial resolution and dynamic range, meeting the refined testing requirements of long-distance fiber optic links.

[0141] Physical events in fiber optic links typically manifest as local fluctuations in reflectivity. On the Rayleigh scattering curve, a reflection event at the corresponding distance exhibits an approximately Gaussian-shaped curve rise, generally referred to as a reflection peak. The difference in peak intensity of the reflection peak relative to the intensity of the surrounding Rayleigh scattering substrate is determined by the severity of the abrupt change in refractive index at that reflection point. In related technologies, algorithms for identifying reflection events are usually based on the absolute or relative intensity of the reflection peak. The presence of a reflection event is determined by whether the average intensity difference between the peak value and the substrates on either side of it exceeds a certain fixed threshold, commonly chosen as 3 dB.

[0142] However, the above detection scheme based on a fixed threshold of a single curve has many drawbacks: First, in coherent receiving systems based on pulse compression technology, due to the inherent characteristics of pulse compression processing and the influence of optical path diffraction, side lobes are generated on both sides of the main lobe after compression. The energy level of the side lobes is positively correlated with the reflectivity of the reflection point and the specific pulse coding method used. When a strong reflection event exists in the optical fiber link, the high-energy side lobes it generates may be misidentified as independent reflection events, leading to event misjudgment.

[0143] Secondly, when multiple reflection events are located on different branches of an optical fiber link and are physically close to each other, their corresponding reflection peaks partially or completely overlap on the Rayleigh scattering curve. The overlap of reflection peaks that are close to each other will lead to an increase in the sidelobe energy superposition in this region, which in turn will trigger the identification of additional false reflection events in the vicinity, exacerbating the risk of event misjudgment.

[0144] Finally, Rayleigh scattering in optical fibers inherently exhibits random coherent fluctuations. This characteristic means that even in pristine fiber segments devoid of any physical reflection events, the Rayleigh scattering curve will display inherent, irregular local intensity fluctuations. In curve segments near the link end, where the scattered signal gradually attenuates and approaches the noise floor, these random intensity fluctuations are more easily misinterpreted as false reflection peaks by fixed-threshold algorithms, further increasing the probability of event misjudgment.

[0145] Based on an in-depth analysis of the three major sources of misjudgment in the aforementioned related technologies—sidelobe interference, multi-event crosstalk, and Rayleigh coherent fluctuations—this application proposes the multi-window function collaborative analysis and cross-validation detection scheme described in the aforementioned embodiments to systematically solve the above-mentioned technical problems.

[0146] To facilitate a further understanding of the core concepts of the above embodiments of this application, a specific embodiment is provided: This specific embodiment relates to a method for detecting link events in an optical reflector based on pulse compression technology. It is mainly aimed at fault location and event identification in scenarios such as optical distribution networks (ODN) of passive optical networks (PON), backbone optical fibers, and fiber to the room (FTTR), especially the erroneous identification of reflection peaks and suppression of sidelobe interference caused by crosstalk between multiple strong reflection events.

[0147] like Figure 4 As shown, the system components of the time-gated digital optical frequency domain reflectometer used in this specific embodiment include: a laser, a coupler, a radio frequency signal source, an acousto-optic modulator, an erbium-doped fiber amplifier, a circulator, a target fiber optic link, a polarization diversity receiver, and a data acquisition card.

[0148] The system comprises the following components: a laser for outputting a continuous carrier optical signal; a coupler for splitting the laser output signal into two paths according to a preset ratio, one path serving as a local reference light and the other path entering the subsequent modulation link; a radio frequency (RF) signal source for generating a basic modulation signal; an acousto-optic modulator for receiving the basic modulation signal from the RF signal source and modulating the injected optical carrier to generate a probe signal carrying time-frequency coding information; an erbium-doped fiber amplifier for amplifying the modulated probe signal to increase the signal energy injected into the target fiber link; a circulator for unidirectionally coupling the amplified probe signal to the target fiber link and guiding the Rayleigh scattering signal from the target fiber link to the receiver; a polarization diversity receiver for receiving the Rayleigh scattering signal and coherently beating it with the local reference light to convert the optical signal into an electrical signal; and a data acquisition card for performing analog-to-digital conversion and acquiring the electrical signal output from the receiver, which is then used by the subsequent digital signal processing unit to perform pulse compression demodulation and multi-window function collaborative analysis.

[0149] It should be noted that in actual deployment, the erbium-doped fiber amplifier in the above hardware structure can be replaced with a semiconductor fiber amplifier to achieve the same or similar optical amplification function; the polarization diversity receiver can be replaced with a photodetector to adapt to system configurations with different performance requirements or cost constraints. The replacement of the above hardware components does not affect the technical concept and implementation effect of this specific embodiment.

[0150] It should also be noted that the pulse compression demodulation algorithm and multi-window function collaborative analysis process involved in this specific embodiment can be uniformly implemented by a computer or equivalent digital processing device through software programming after the data acquisition card completes the Rayleigh scattering signal acquisition. That is, the signal demodulation and link feature detection are completed using an offline post-processing architecture. This method does not require the demodulation algorithm to run synchronously and in real time with the data acquisition process, thereby reducing the real-time computing power requirements of the embedded processing unit and improving the flexibility of algorithm deployment.

[0151] like Figure 5 As shown, in this specific embodiment, the structure of the target fiber optic link is as follows: two standard single-mode optical fibers, each 10 km long, also known as the fiber under test (FUT), are coupled together by a 1:8 splitter; after the second standard single-mode optical fiber, a second-stage 1:8 splitter is connected, with a 1.5 m long APC (Angled Physical Contact)-UPC (Ultra Physical Contact) jumper and a 4 m long APC-UPC jumper connected to the two branches of the second-stage 1:8 splitter.

[0152] This specific embodiment uses a time-gated digital optical frequency domain reflectometry (TGD-OFDR) system as an example. The system architecture can be referred to. Figure 4 .

[0153] The basic principle of this specific embodiment is based on amplitude modulation of the basic modulation signal. By windowing the basic modulation signal with different window functions, multiple windowed modulation signals are obtained, which in turn generate multiple windowed detection signals to obtain intensity change information of reflection events in multiple Rayleigh scattering curves. Finally, based on this information, the reflection peak height threshold and the reflection peak fluctuation threshold are used for joint discrimination to eliminate false reflection events.

[0154] That is, the target optical fiber link is scanned and detected using pulse compression technology to obtain multiple Rayleigh scattering curves of the target optical fiber link. Each Rayleigh scattering curve is obtained by windowing with different window functions. Based on each Rayleigh scattering curve, the link feature of the target optical fiber link is detected to obtain a list of reflection events of the target optical fiber link.

[0155] The basic principle at the signal level is as follows: The windowed modulation signal is represented as: ; In the formula, It is a window function. Let be the pulse width, t be the time variable, e be the base of the natural logarithm, j be the imaginary unit, and π be the mathematical constant pi. The starting frequency, The sweep rate is given by the pulse signal duration from t=0 to t= .

[0156] A windowed modulation signal is applied to the light source to obtain a local reference signal. and windowed detection signal : ; ; In the formula, P L P is the output optical power of the local reference signal. P To increase the output optical power of the windowed detection signal, ω is the angular frequency of the optical carrier wave.

[0157] After the windowed probe signal is injected into the target fiber optic link, the returned Rayleigh scattering signal coherently beats with the local reference signal and is converted into an electrical signal. Its expression is: ; In the formula, N is the total number of scattering points in the target fiber optic link, and i is the index of the scattering point. For amplitude, For time delay, Let be the amplitude of the Rayleigh scattering signal corresponding to the i-th scattering point after it has been converted into an electrical signal. Let be the round-trip time delay of the signal corresponding to the i-th scattering point.

[0158] The phase of the real electrical signal is extracted using the Hilbert transform and converted into a complex signal, which is then rewritten in integral form: ; In the formula, The total round-trip time of the windowed detection signal in the target fiber optic link. To delay Let h(t) be the scattering amplitude function of the independent variable, h(t) be the fiber impulse response, and s(t) be the windowed modulation signal.

[0159] ; In the formula, (t) is the scattering amplitude function with time t as the independent variable.

[0160] A windowed demodulated signal is generated in the digital domain. Taking matched filtering as an example, the windowed demodulated signal has the same start frequency and sweep rate as the windowed modulated signal, but with opposite time phases, and its form is as follows: , where * is the complex conjugate symbol.

[0161] The windowed demodulated signal is convolved with i(t) to complete pulse compression demodulation: ; In the formula, I(t) is the Rayleigh scattering curve obtained by demodulation, and S(t) is the compressed pulse.

[0162] When different window functions are used to generate multi-channel windowed modulation signals and thus obtain multi-channel windowed detection signals, it is equivalent to changing the parameters in s(t). This alters the shape of the compressed pulse S(t), causing intensity fluctuations in the sidelobes of the demodulated Rayleigh scattering curve I(t). The sidelobe levels and attenuation rates exhibit significant differences under different window functions, thus enabling multi-window differentiated observations.

[0163] The window functions applied in this specific embodiment include: rectangular window, Taylor window, Hanning window, Gaussian window, and Tukey window. The normalized time-domain shape of each of the above window functions can be referred to... Figure 6 The compressed pulse S(t) obtained by convolving the windowed modulated signal with the windowed demodulated signal after applying different window functions can be referred to... Figure 7 The compressed pulse has undergone intensity normalization. Figure 8 The magnified spectrum near the main lobe of the compressed pulse S(t) is shown. It can be seen that applying different window functions significantly affects the sidelobe level and decay rate of the compressed pulse S(t), resulting in differentiated sidelobe intensity distributions for the compressed pulse under each window function.

[0164] The target fiber optic link scanned and detected in this specific embodiment can be referred to... Figure 5 The Rayleigh scattering curve obtained by scanning and probing the fiber optic link of the target can be referenced. Figure 9 The traditional approach, which relies solely on comparing reflection peak heights based on a single fixed threshold, yields reflection event discrimination results that can be referenced... Figure 10 The red asterisks indicate a large number of misjudged reflection events.

[0165] After applying different windowing functions for windowing and demodulation, a local magnification of the reflection peak at the end of the second-stage 1:8 beam splitter can be found by referring to... Figure 11The blue curve represents the end reflection peak curve under a rectangular window (i.e., the baseline case without additional window functions). Five typical locations are marked in the figure: 19579.4m is the reflection peak of the second-stage 1:8 beam splitter itself; 19581.7m is its first sidelobe; 19584.4m is the reflection peak of the 1.5m UPC jumper at the end of the first branch; 19586.8m is the reflection peak of the 4m UPC jumper at the end of the second branch; and 19589.4m is its second sidelobe. These five locations are all identified as reflection events in a traditional single-threshold discrimination scheme.

[0166] After applying different window functions for windowing and demodulation, the three actual reflection events, located at 19579.4m, 19584.4m, and 19586.8m respectively, showed varying reflection peak heights under different window functions. However, the lowest reflection peak height remained above the reflection peak height threshold, and the peak fluctuation did not exceed the reflection peak fluctuation threshold, thus confirming them as true reflection peaks. In other words, the reflection peak to be verified, whose lowest reflection peak height is greater than the reflection peak height threshold and whose peak fluctuation is less than the reflection peak fluctuation threshold, is determined to be the true reflection peak of the target fiber optic link.

[0167] In contrast, the peak fluctuations at the two sidelobe locations of 19581.7m and 19589.4m exceeded the reflection peak fluctuation threshold, and under certain window function modulations, such as the Taylor window, the reflection peak height was below the reflection peak height threshold. Therefore, these were identified as false reflection events and eliminated. Ultimately, only three true reflection events were retained, and the remaining false reflection events were effectively eliminated. The final judgment result of this specific embodiment can be referred to... Figure 10 The green circle mark in the middle.

[0168] Furthermore, using the assumption that only three true reflection events should exist at the end of the known target fiber optic link as the baseline, the impact on the reflection event discrimination results of the entire Rayleigh scattering curve can be observed by adjusting the specific values ​​of the reflection peak height threshold and the reflection peak fluctuation threshold. This allows for further optimization of the threshold settings to obtain more accurate discrimination conditions. This process is also one of the specific practices for threshold calibration based on the "influence characteristic profile of the link to be detected under different window function conditions" in the aforementioned embodiments.

[0169] It should be noted that the specific values ​​of the two thresholds mentioned above can be calibrated through an optical fiber link with a known link structure: with the optimization goal of accurately identifying all reflection events in the known link and having no false sidelobe detections, the specific values ​​of the reflection peak height threshold and intensity change threshold are determined.

[0170] It is worth noting that the windowed demodulation signal used in the above example is typically a matched filter with the same start frequency, sweep rate, and sweep range as the windowed modulation signal. In contrast, the windowed demodulation signal can also be a non-matched filter with a completely different start frequency, sweep rate, or sweep range from the windowed modulation signal.

[0171] When using unmatched filters for convolutional demodulation of Rayleigh scattering signals, the mechanism is similar to that of matched filters: different unmatched filters will also cause differential fluctuations in the intensity of the reflection peak, resulting in changes in sidelobe intensity and broadening of the main lobe width. However, there is a significant difference in the performance trade-off between unmatched and matched filters: unmatched filters have a greater impact on the spatial resolution of the reflection event, i.e., the main lobe broadening is more pronounced, but their ability to suppress sidelobe energy is relatively weak.

[0172] It should be noted that the above embodiments / implementations are only used to assist in understanding this application and do not constitute a limitation on the optical fiber detection method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0173] In addition, please refer to Figure 12 , Figure 12 This is a schematic diagram of the electronic equipment structure of the hardware operating environment involved in the fiber optic detection method in this application embodiment.

[0174] This application also provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of the fiber optic detection method in the above embodiments.

[0175] The following is for reference. Figure 12 The diagram illustrates a structural schematic of an electronic device suitable for implementing the embodiments of this application. The electronic device in the embodiments of this application may include, but is not limited to, devices with fiber optic scanning and detection capabilities such as light reflectors, as well as any electronic device capable of performing the aforementioned functions. Figure 12 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0176] like Figure 12As shown, the electronic device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the electronic device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays, speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tape, hard disks, etc.; and communication devices 1009. The communication device 1009 allows the electronic device to communicate wirelessly or wiredly with other devices to exchange data. Although the diagrams show electronic devices with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented alternatively.

[0177] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0178] The electronic device provided in this application, employing the fiber optic detection method described in the above embodiments, can improve the accuracy of fiber optic link feature detection in FTTR. Compared with related technologies, the beneficial effects of the electronic device provided in this application are the same as those of the fiber optic detection method provided in the above embodiments, and other technical features of this electronic device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0179] It should be understood that the various parts disclosed in this application can be implemented using 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 suitable manner in one or more embodiments or examples.

[0180] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of protection of the above claims.

[0181] In addition, this application also provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to perform the steps of the fiber optic detection method in the above embodiments.

[0182] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory, read-only memory, erasable programmable read-only memory, optical fibers, portable compact disk read-only memory, optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, radio frequency, etc., or any suitable combination thereof.

[0183] The aforementioned computer-readable storage medium may be included in an electronic device or may exist independently without being assembled into an electronic device.

[0184] The aforementioned computer-readable storage medium carries one or more programs. When the aforementioned one or more programs are executed by an electronic device, the electronic device causes the electronic device to: scan and detect the target optical fiber link using pulse compression technology to obtain multiple Rayleigh scattering curves of the target optical fiber link, wherein each Rayleigh scattering curve is obtained by windowing with a different window function; and perform link feature detection on the target optical fiber link based on each Rayleigh scattering curve to obtain the link feature detection result of the target optical fiber link.

[0185] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer, for example, via the Internet using an Internet service provider.

[0186] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0187] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0188] The computer-readable storage medium provided in this application stores computer-readable program instructions for performing the steps of the fiber optic detection method in the above embodiments, which can improve the accuracy of fiber optic link feature detection in FTTR. Compared with related technologies, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the fiber optic detection method provided in the above embodiments, and will not be repeated here.

[0189] Furthermore, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the fiber optic detection method described above.

[0190] The computer program product provided in this application can improve the accuracy of fiber optic link feature detection in FTTR. Compared with related technologies, the beneficial effects of the computer program product provided in this application are the same as those of the fiber optic detection method provided in the above embodiments, and will not be repeated here.

[0191] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A fiber optic detection method, characterized in that, The method includes: The target optical fiber link is scanned and detected using pulse compression technology to obtain multiple Rayleigh scattering curves of the target optical fiber link. Each Rayleigh scattering curve is obtained by applying different window functions. The same link feature has different representation forms in the different Rayleigh scattering curves obtained by applying different window functions. Based on the Rayleigh scattering curves, link feature detection is performed on the target optical fiber link to obtain the link feature detection results of the target optical fiber link.

2. The optical fiber detection method as described in claim 1, characterized in that, The step of scanning and probing the target optical fiber link using pulse compression technology to obtain multiple Rayleigh scattering curves of the target optical fiber link includes: A preset basic modulation signal is acquired, and based on the basic modulation signal and multiple different window functions, a multi-channel windowed detection signal is modulated to obtain the multi-channel windowed detection signal. The target optical fiber link is scanned and detected by each of the windowed detection signals to obtain the Rayleigh scattering signal corresponding to each of the windowed detection signals; Based on the Rayleigh scattering signals corresponding to each of the windowed detection signals, multiple Rayleigh scattering curves of the target optical fiber link are obtained by demodulation.

3. The fiber optic detection method as described in claim 2, characterized in that, The step of modulating a multi-channel windowed detection signal based on the basic modulation signal and multiple different window functions includes: The basic modulation signal is windowed using different window functions to obtain multiple windowed modulation signals. Multiple windowed detection signals are obtained by modulating each of the aforementioned windowed modulation signals.

4. The optical fiber detection method as described in claim 3, characterized in that, The step of demodulating multiple Rayleigh scattering curves of the target optical fiber link based on the Rayleigh scattering signals corresponding to each of the windowed detection signals includes: Based on each of the windowed modulation signals, a corresponding windowed demodulation signal is generated for each of the windowed detection signals. Based on the windowed demodulated signal and Rayleigh scattering signal corresponding to each of the windowed detection signals, multiple Rayleigh scattering curves of the target optical fiber link are obtained by demodulation.

5. The fiber optic detection method as described in claim 2 or 3, characterized in that, The step of demodulating multiple Rayleigh scattering curves of the target optical fiber link based on the Rayleigh scattering signals corresponding to each of the windowed detection signals includes: A basic demodulated signal is generated based on the aforementioned basic modulation signal; Based on the basic demodulated signal and the Rayleigh scattering signal corresponding to each of the windowed detection signals, multiple Rayleigh scattering curves of the target optical fiber link are obtained by demodulation.

6. The optical fiber detection method as described in claim 2 or 3, characterized in that, The step of demodulating multiple Rayleigh scattering curves of the target optical fiber link based on the Rayleigh scattering signals corresponding to each of the windowed detection signals includes: Based on the basic modulation signal, a basic demodulated signal is generated, and the basic demodulated signal is windowed by different window functions to obtain multiple windowed demodulated signals; Based on the windowed demodulated signals and the Rayleigh scattering signals corresponding to the windowed detection signals, multiple Rayleigh scattering curves of the target optical fiber link are obtained through demodulation.

7. The optical fiber detection method as described in claim 1, characterized in that, The step of scanning and probing the target optical fiber link using pulse compression technology to obtain multiple Rayleigh scattering curves of the target optical fiber link includes: A preset basic modulation signal is acquired, and a basic detection signal is obtained by modulating the basic modulation signal with the basic modulation signal; The target optical fiber link is scanned and detected using the basic detection signal to obtain the Rayleigh scattering signal corresponding to the basic detection signal; Based on the basic modulation signal, a basic demodulated signal is generated, and the basic demodulated signal is windowed by different window functions to obtain multiple windowed demodulated signals; Based on the windowed demodulated signals and the Rayleigh scattering signals corresponding to the basic detection signals, multiple Rayleigh scattering curves of the target optical fiber link are obtained through demodulation.

8. The optical fiber detection method as described in claim 1, characterized in that, The link feature detection result includes a list of reflection events. The step of performing link feature detection on the target optical fiber link based on each Rayleigh scattering curve to obtain the link feature detection result of the target optical fiber link includes: The reflection peak to be verified of the target optical fiber link is identified from each of the Rayleigh scattering curves, wherein the same reflection peak to be verified is in the same position in each of the Rayleigh scattering curves. Based on the reflection peak height of each of the reflection peaks to be verified in each of the Rayleigh scattering curves, the minimum reflection peak height of each of the reflection peaks to be verified is determined; Based on the peak values ​​of the reflection peaks of each of the reflection peaks to be verified in each of the Rayleigh scattering curves, the peak fluctuation degree of each of the reflection peaks to be verified is determined. Obtain the pre-calibrated reflection peak height threshold and reflection peak fluctuation threshold, and determine the reflection peak to be verified that has a minimum reflection peak height greater than the reflection peak height threshold and a peak fluctuation degree less than the reflection peak fluctuation threshold as the true reflection peak of the target optical fiber link; Based on the actual reflection peaks, a list of reflection events for the target fiber optic link is determined.

9. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the fiber optic detection method as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the optical fiber detection method as described in any one of claims 1 to 8.

11. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the optical fiber detection method as described in any one of claims 1 to 8.

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