Intelligent fiber optic detection system and method

Abstract

The application discloses a wavelength-selected optical fiber fault detection method and system, and belongs to the technical field of optical communication monitoring.The method adopts a multi-wavelength OTDR (optical time domain reflectometer) technology, analyzes OTDR echo signals of different wavelengths through an AI module, intelligently selects a wavelength most suitable for detecting a specific type of optical fiber fault, and improves detection precision and efficiency.The application can analyze historical data by using a machine learning algorithm, optimizes a wavelength use strategy, and generates a comprehensive optical fiber state report in combination with multi-wavelength measurement data.The application can significantly improve the intelligent level of optical fiber fault detection, and is particularly suitable for maintenance and management of a large-scale optical fiber network.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of communication, in particular to an intelligent optical fiber detection system and method. BACKGROUND

[0002] In modern communication networks, optical fibers bear important data transmission tasks. However, optical fibers may fail due to aging, external damage, excessive bending, etc. during use. Traditional optical fiber fault detection methods have many problems: optical time domain reflectometer (OTDR) needs to interrupt service for testing, and the fault location is not accurate enough in complex network environment; the manual step-by-step troubleshooting method of optical fiber failure not only has low efficiency, but also increases labor cost. Therefore, there is an urgent need for a detection technology that can quickly and accurately locate the fault position of optical fiber without affecting the service. SUMMARY

[0003] In view of the problems in the prior art, the present application provides an intelligent optical fiber detection system, which comprises a light source, a circulator, a detection module, a first optical fiber to be tested and a corresponding first optical filter, a second optical fiber to be tested and a corresponding second optical filter; the light source is used to emit laser beams of different wavelengths; the circulator guides the laser beams into the optical fiber to be tested; the detection module is used to record the time information of the received laser beams; the first optical filter is arranged on the transmission path of the first optical fiber to be tested; the second optical filter is arranged on the transmission path of the second optical fiber to be tested; the first optical filter and the second optical filter reflect laser beams of preset wavelengths and allow laser beams of other wavelengths to pass through at the same time.

[0004] Preferably, the system further comprises an AI module, which is used to automatically select the wavelength most suitable for detecting a specific type of optical fiber failure by analyzing OTDR echo signals of different wavelengths.

[0005] Preferably, the preset wavelength reflected by the first optical filter is different from the preset wavelength reflected by the second optical filter.

[0006] Preferably, the preset wavelength reflected by the first optical filter is spaced apart from the preset wavelength reflected by the second optical filter by 20-100 nm.

[0007] Preferably, the first optical fiber to be tested and the second optical fiber to be tested are connected in series.

[0008] The present application also provides a detection method using the above system, which comprises the following steps:

[0009] S1: the light source emits laser beams of two wavelengths λ1 and λ2;

[0010] S2: the laser λ1 and the laser λ2 enter the first fiber to be tested and the second fiber to be tested through the circulator;

[0011] S3: in the first fiber to be tested, when the laser λ1 and the laser λ2 reach the first optical filter, the laser λ1 is reflected back and finally received by the detection module, and the arrival time t1 is recorded; the laser λ2 continues to advance until it meets the obstacle point and returns, and finally is received by the detection module, and the arrival time t2 is recorded; and the specific position of the fault is obtained through the fault point formula

[0012] L = v * (t2-t1) / 2, wherein v is the speed of light propagation in the optical fiber;

[0013] S4: in the second fiber to be tested, when the laser λ1 and the laser λ2 reach the second optical filter, the laser λ2 is reflected back and finally received by the detection module, and the arrival time t2 is recorded; the laser λ1 continues to advance until it meets the obstacle point and returns, and finally is received by the detection module, and the arrival time t1 is recorded; and the specific position of the fault is obtained through the fault point formula

[0014] L = v * (t1-t2) / 2, wherein v is the speed of light propagation in the optical fiber;

[0015] Before step S1, an AI module is also used to automatically select the wavelength most suitable for detecting the fault of a specific type of optical fiber by analyzing the OTDR echo signals of different wavelengths.

[0016] Preferably, the preset wavelength reflected by the first optical filter is different from the preset wavelength reflected by the second optical filter.

[0017] Preferably, the preset wavelength reflected by the first optical filter is spaced 20-100 nm from the preset wavelength reflected by the second optical filter.

[0018] Preferably, the AI module uses K-Means clustering to classify OTDR echo data points, distinguish normal signals, attenuation signals, and sudden change signals (breakage), and analyze the OTDR data of different wavelengths to determine which wavelengths are most sensitive to the current fault.

[0019] Preferably, the first fiber to be tested and the second fiber to be tested are connected in series.

[0020] Preferably, in steps S3 and S4, a dispersion compensation algorithm can be used for correction.

[0021] Compared with the prior art, the present application has at least the following beneficial effects:

[0022] 1) The invention utilizes a light source to emit multiple different wavelengths of laser light. Each wavelength of laser light, when propagating in the optical fiber, encounters an optical filter and is reflected according to its pre-set wavelength characteristics. The detection module records the first time when the different wavelengths of laser light are received (the time when the laser light directly emitted from the light source arrives) and the second time of each reflected laser light. By calculating the time difference between the first time and each second time, combined with the propagation speed of light in the optical fiber, the distance of the fault point from the light source can be accurately calculated. This method avoids the problem of inaccurate positioning caused by the increasing error between the length of the optical cable and the length of the ground projection in traditional OTDR technology, especially in long-distance optical fiber detection. It can achieve high-precision fault positioning and solve the problem of insufficient precision in existing optical fiber fault positioning.

[0023] 2) The invention integrates components such as light sources, circulators, detection modules, optical fibers to be tested, and optical filters together to form a complete intelligent detection system. Each component works cooperatively through specific connections and signal transmission methods. For example, the circulator not only accurately directs the laser light emitted by the light source into the optical fiber to be tested, but also guides the reflected laser light from the optical filter to the detection module, ensuring complete signal transmission. The cooperation between the detection module and the light source can accurately record time information. This integrated design enables the system to automatically complete the entire process from laser emission to fault positioning without human intervention, greatly improving detection efficiency and solving the problems of equipment dispersion, complex operation, and low efficiency in existing optical fiber detection, achieving automation and intelligence in optical fiber detection.

[0024] 3) Adding intelligent optimization wavelength selection in optical fiber multi-wavelength detection can greatly improve detection accuracy: traditional OTDR relies on manual wavelength selection, which may lead to blind areas in fault detection. AI selects wavelengths adaptively to ensure optimal detection schemes. In addition, AI automatically analyzes echo data, reduces human error, and improves optical fiber maintenance efficiency. Finally, this embodiment can be applied to different optical fiber scenarios to improve adaptability: it is suitable for data centers, urban optical fiber networks (micro-bend faults), and long-distance trunk optical fibers (long-distance attenuation monitoring). BRIEF DESCRIPTION OF DRAWINGS

[0025] Figure 1 Figure 1 is a structural diagram of the intelligent optical fiber detection system of the invention;

[0026] Figure 1 is a structural diagram of the intelligent optical fiber detection system of the invention;

[0027] The application will be further described in detail below. However, the following examples are only simple examples of the application and do not represent or limit the protection scope of the application, and the protection scope of the application is subject to the claims. Specific embodiments

[0028] The technical solutions of the application will be further described below with reference to the drawings and through specific embodiments.

[0029] As shown in Figure 1 The intelligent optical fiber detection system of the application includes a light source 1, a circulator 2, a detection module 3, at least one optical fiber to be detected, and at least one optical filter. The light source inputs laser beams of different wavelengths to the first end of the circulator and the first input end of the detection module; the circulator inputs the laser beams to the optical fiber to be detected; the optical fiber to be detected transmits the laser beams input by the circulator to the side of the optical filter; the optical filter reflects laser beams of a preset wavelength back to the optical fiber to be detected and transmits laser beams other than the preset wavelength; the optical fiber to be detected transmits the reflected laser beams of the optical filter to the side of the circulator; the circulator inputs the reflected laser beams to the detection module; the detection module records a first time when the laser beams input by the light source are received and each second time when each reflected laser beam is received, and determines the faulty optical fiber according to the first time and each second time.

[0030] Embodiment one

[0031] The single optical fiber to be detected, the light source emits laser beams of two different wavelengths, which are λ1 and λ2 respectively. The optical filter is preset to reflect a wavelength of λ1. When the laser beams enter the optical fiber to be detected through the circulator, the laser beams with a wavelength of λ1 are reflected back to the optical fiber to be detected, and the laser beams with a wavelength of λ2 continue to be transmitted. The detection module records the time t1 when the reflected laser beams with a wavelength of λ1 are received and the time t2 when the laser beams with a wavelength of λ2 are received. According to the propagation speed v of light in the optical fiber and the time difference Δt=t2-t1, the distance L of the fault point from the light source can be calculated as L=v×Δt / 2. In this way, the fault position in the single optical fiber can be quickly determined.

[0032] The light source is one of the key components of the system, responsible for emitting laser beams of different wavelengths. In order to meet different detection needs, the light source can use a tunable laser, which can adjust the wavelength of the emitted laser beams as needed. In actual application, the wavelength selection of the light source should be determined according to the characteristics of the optical fiber and the reflection wavelength range of the optical filter.

[0033] The function of the circulator is to guide the laser beams emitted by the light source into the optical fiber to be detected, and guide the laser beams reflected by the optical filter to the detection module. The performance of the circulator directly affects the transmission efficiency of the laser beams and the accuracy of the detection. Therefore, the circulator should have the characteristics of low insertion loss and high isolation to ensure the signal quality of the laser beams during transmission.

[0034] The detection module is used to record the received laser time information. It includes a high-precision photodetector and a time measurement unit. The photodetector can convert the received laser signal into an electrical signal, and the time measurement unit is used to accurately measure the time of receiving different wavelength lasers. The accuracy of the detection module directly determines the accuracy of fault location. In practical applications, the detection module should have high sensitivity and high time resolution.

[0035] The function of the optical filter is to reflect the preset wavelength of laser, while allowing other wavelengths of laser to pass through. The performance of the optical filter is crucial to the accuracy of fault location. It should have high reflectivity and high selectivity, ensuring that only the preset wavelength of laser is reflected, while other wavelengths of laser can be transmitted normally. The reflected wavelength of the optical filter can be customized according to the actual application requirements.

[0036] The fiber under test is the object of detection, and its quality directly affects the performance of the communication network. During the detection process, the fiber under test should maintain good connection state to avoid signal transmission problems caused by poor connection. At the same time, the length and characteristics of the fiber under test will also affect the detection results, so in practical applications, the corresponding parameter settings need to be made according to the specific situation of the fiber.

[0037] This method is actually an improvement of optical time domain reflectometry (OTDR, Optical Time Domain Reflectometry), which uses the propagation characteristics of different wavelengths of light in the optical fiber to determine the location of the fault point. It has the following advantages: (1) It does not need to measure along the optical fiber, but can directly calculate the fault point position through the time difference. (2) As long as the time resolution of the light source and the detection module is high enough, high-precision positioning can be achieved. (3) OTDR equipment can usually measure optical fibers within tens of kilometers, and this method can also be extended to similar application scenarios.

[0038] Example Two

[0039] In actual communication networks, there are often multiple optical fibers. The system of the present invention can detect multiple optical fibers simultaneously. Assuming there are multiple fibers under test, each fiber is connected to an optical filter, and each optical filter has a different preset reflected wavelength. The light source emits different wavelengths of laser in turn, and the detection module records the reception time of each wavelength of laser and the reception time of the reflected laser. By comparing the time difference corresponding to each fiber, the location of the fault point in each fiber can be calculated respectively. This method greatly improves the efficiency of optical fiber detection, and is especially suitable for the maintenance of large-scale optical fiber networks.

[0040] Since the reflection wavelengths of each optical fiber are different, the system can quickly poll multiple wavelengths, realize the simultaneous detection of multiple optical fibers, and avoid the inefficient mode of testing each fiber one by one. In a data center, metropolitan area network or backbone optical fiber network, the number of optical fibers is large, and this method can detect the health status of multiple optical fiber channels in a short time, improving the operation and maintenance efficiency. Combined with automatic optical fiber switching equipment (such as optical switch), a fully automated optical fiber monitoring system can be built to realize remote fault location and alarm.

[0041] A suitable wavelength interval is selected to avoid signal confusion caused by filter bandwidth overlap. The speed of light in an optical fiber is about 2*10 8 meters per second, and the refractive index of light in an optical fiber is n≈1.5.

[0042] The number of optical fibers to be tested is 3, each optical fiber is connected to a different optical filter, and the preset reflection wavelengths of the optical filter are as follows: the first optical fiber, λ1=1550nm; the second optical fiber, λ2=1570nm; the third optical fiber: λ3=1590nm; the time resolution of the detection module: 10ps. Based on the resolution of the existing detection module, the reflection wavelength interval is more appropriate between 20-100nm.

[0043] In the first optical fiber, the reflected light (t1) reflected by the first filter 61 arrives at a time of 20.000ns, and the reflected light (t2) of the fault point 41 arrives at a time of 20.600ns, the time difference: Δt1=t2-t1=20.600-20.000=0.600ns;

[0044] In the second optical fiber, the reflected light (t1) reflected by the second filter 62 arrives at a time of 19.500ns, and the reflected light (t2) of the fault point 42 arrives at a time of 20.300ns, the time difference: Δt1=t2-t1=20.300-19.500=0.800ns;

[0045] In the third optical fiber, the reflected light (t1) reflected by the third filter 63 arrives at a time of 21.000ns, and the reflected light (t2) of the fault point 43 arrives at a time of 21.400ns, the time difference: Δt1=t2-t1=21.400-21.000=0.400ns.

[0046] Table 1 Optical fiber fault point position detection data table using different wavelengths

[0047]

[0048] In order to measure such time intervals, photodetectors need to have extremely high time resolution. Usually includes: photodiode (PD, Photodiode). Common types: avalanche photodiode (APD, Avalanche Photodiode); response time: tens of picoseconds (ps). For example: InGaAs APD (Indium Gallium Arsenide Avalanche Photodiode) can reach 10 ps level response speed, suitable for 1.3 μm and 1.55 μm communication wavelength. In addition, the photodetector itself only converts the optical signal to an electrical signal, and measures the nanosecond- picosecond time difference, such as a high-speed oscilloscope, which requires a bandwidth of > 10 GHz (ideally > 40 GHz). Different wavelengths may produce different dispersion effects in long-distance optical fibers, affecting measurement accuracy, and dispersion compensation algorithms can be used for correction.

[0049] Embodiment three

[0050] In the above embodiments one and two, the test personnel usually need to manually select the test wavelength and rely on experience to analyze the OTDR echo signal to determine the type and location of the optical fiber fault. However, due to the variety of optical fiber fault types, such as microbend loss, breakage, fusion loss, etc., a single wavelength OTDR may have a detection blind area in some scenarios. Therefore, this embodiment proposes an adaptive wavelength selection OTDR fault detection system based on artificial intelligence (AI) optimization to improve detection accuracy and efficiency.

[0051] The OTDR system of this embodiment three includes: a multi-wavelength laser light source module (supporting 1310 nm, 1550 nm, 1625 nm, etc.); an optical signal detection module (high-precision APD or SPAD as a detector); a data processing and AI analysis unit (deep learning model); and a control and decision unit (dynamically selecting the optimal wavelength according to the analysis results).

[0052] Working steps:

[0053] 1. Initial test: OTDR transmits laser pulses of different wavelengths such as 1310 nm, 1550 nm, 1625 nm, etc. in turn, and records the echo signal.

[0054] 2. AI analysis: Use machine learning models to analyze OTDR echo data at different wavelengths to determine possible fault types in the optical fiber. The AI pre-analysis here is to apply mathematical models to analyze OTDR echo data at different wavelengths in this field. For example: classical machine learning (ML) method. This classical machine learning method is suitable for small-scale data analysis and can quickly identify common optical fiber fault patterns.

[0055] For example, K-Means clustering is used. The principle is to classify OTDR echo data points, such as distinguishing normal signals, attenuated signals, and sudden signal changes (breaks). By clustering analysis of OTDR data at different wavelengths, it is determined which wavelengths are most sensitive to the current fault.

[0056] After detecting multiple wavelength OTDR signals, K-Means can find that 1310nm has an abnormally large attenuation when there is a micro-bend fault, and automatically selects 1310nm for the next step of fine scanning. If a micro-bend loss is identified, 1310nm is preferred for detailed testing. If a long-distance attenuation problem is identified, 1550nm or 1625nm is preferred for long-distance detection. If a possible optical fiber break or severe loss is identified, multiple wavelengths are combined for analysis to improve the accuracy of fault location. Final fault diagnosis and report: using the above method combined with multiple wavelength OTDR data, the fault point location and possible fault type are accurately calculated.

[0057] For example, in the AI identification of micro-bend faults: Micro-bend fault detection optimizes actual fiber communication networks, where external pressure (such as wire piercing or bending) can cause micro-bend loss in the fiber. This type of fault is usually more sensitive to 1310nm wavelength, while 1550nm and 1625nm have less impact. Test procedure: 1310nm OTDR signal is found to have greater attenuation than 1550nm, suggesting that it may be a micro-bend loss. The system automatically selects 1310nm wavelength for further scanning to improve detection accuracy. By learning the echo characteristics, the severity of the micro-bend loss is determined, and repair recommendations are provided.

[0058] In addition to the above application scenarios, in the optimization of long-distance fiber attenuation monitoring, for example, for long-distance trunk fibers, the overall attenuation of the fiber is a key issue. Generally, 1550nm or 1625nm wavelength has less attenuation when propagating over long distances, making it more suitable for long-distance monitoring. Test procedure: the model finds that the OTDR echo signal strength of 1550nm is higher than that of 1310nm, indicating that 1550nm is more suitable for long-distance monitoring of this line.

[0059] The above use of AI to find 1550nm echoes can also be applied to long-distance line monitoring, including:

[0060] (1) Data collection:

[0061] OTDR sends optical pulses at 1310nm, 1550nm, and 1625nm wavelengths in turn and records the echo signals at each wavelength.

[0062] The key data includes: backscatter level (BS), attenuation (ATT), signal noise ratio (SNR), event points (EP) (such as joints, bends, breaking points)

[0063] (2) AI calculates the attenuation of each wavelength:

[0064] AI uses regression analysis + machine learning to calculate the signal attenuation of each wavelength at different distances:

[0065]

[0066] Where: ATT λ : Attenuation value of a certain wavelength (dB / km); P input : Input optical power; P output : Backscatter optical power L: Fiber length (km).

[0067] AI compares the average attenuation values of each wavelength:

[0068] If ATT(1310nm)>ATT(1550nm), it means that 1550nm has lower loss and is more suitable for long-distance monitoring.

[0069] If ATT(1550nm)≈ATT(1625nm) and SNR(1550nm)>SNR(1625nm), then 1550nm is more suitable for long-distance monitoring than 1625nm.

[0070] (3) AI calculates signal intensity ratio:

[0071] AI calculates the backscatter signal intensity ratio of 1550nm and 1310nm:

[0072]

[0073] If R>1 (i.e. the OTDR backscatter signal intensity of 1550nm is higher than that of 1310nm), further calculation of applicability is needed:

[0074] R>>1R (such as 1.5 or more): 1550nm is suitable for long-distance monitoring and is recommended as the main wavelength.

[0075] 1.0<R<1.5: 1550nm and 1310nm have similar applicability, and it is recommended to make a decision in combination with other parameters.

[0076] R≤1: 1550nm is affected by abnormal attenuation and is not recommended as a long-distance monitoring wavelength.

[0077] (4) AI combines signal-to-noise ratio (SNR) to confirm the final wavelength:

[0078] AI calculates the signal-to-noise ratio of each wavelength:

[0079]

[0080] If SNR(1550nm) > SNR(1310nm), it means that 1550nm is more stable on this line, suitable for long-distance monitoring.

[0081] If the SNR of 1550nm is lower than that of 1310nm, there may be nonlinear effects, bending loss, which needs to be confirmed manually.

[0082] AI selects 1550nm as the decision logic for long-distance monitoring wavelength: AI uses a decision tree model to automatically select the optimal wavelength:

[0083]

[0084]

[0085] If the above conditions are met, AI recommends 1550nm as the long-distance monitoring wavelength.

[0086] AI automatically adjusts the OTDR measurement strategy:

[0087] After AI finds that 1550nm is suitable for long-distance, it automatically optimizes the OTDR measurement parameters: reduces the test frequency of 1310nm, saves power consumption. Improve the test resolution of 1550nm, refine the long-distance monitoring. In the optical fiber aging monitoring, combined with 1625nm for auxiliary analysis.

[0088] The above AI automatically identifies the most suitable OTDR wavelength for long-distance monitoring through attenuation calculation, signal strength comparison, and SNR analysis. Use 1550nm as the main wavelength, optimize the OTDR monitoring scheme, and improve the accuracy of long-distance measurement. AI dynamically adjusts the measurement strategy to improve the efficiency of optical fiber monitoring and reduce human judgment errors. In this way, AI can automatically determine whether 1550nm is the most suitable for long-distance monitoring and optimize the OTDR signal processing strategy.

[0089] The system automatically uses 1550nm (or 1625nm) wavelength for accurate scanning to improve the ranging ability.

[0090] AI combines data from different wavelengths to analyze the aging state of optical fibers and provide early warning of potential problems.

[0091] The intelligent optical fiber detection system and method of the present application effectively solves the problems existing in the current optical fiber fault detection through the detailed invention points and innovative technical solutions. It can quickly and accurately locate the optical fiber fault position without affecting the communication service, reducing the labor cost and improving the optical cable maintenance efficiency. The present application has wide application prospect and is of great significance to ensure the stable operation of the communication network.

[0092] The preferred embodiments of the present application are described in detail above, but the present application is not limited to the specific details in the above-described embodiments, and various simple modifications can be made to the technical solutions of the present application within the technical concept of the present application, and these simple modifications all belong to the protection scope of the present application.

[0093] In addition, it should be noted that each specific technical feature described in the above specific embodiments can be combined in any appropriate manner without contradiction, and in order to avoid unnecessary repetition, the present application will not further describe various possible combinations.

[0094] In addition, various different embodiments of the present application can also be combined in any manner, as long as it does not deviate from the idea of the present application, and it should also be considered as disclosed by the present application.

Claims

1. A method for detecting using an intelligent optical fiber detection system, characterized in that: The intelligent optical fiber detection system includes a light source, a circulator, a detection module, a first optical fiber to be tested and a second optical fiber to be tested and their corresponding first and second optical filters, and an AI module; The circulator guides the laser light into the optical fiber to be tested; The detection module is used to record the received laser time information; The first and second optical filters are respectively arranged on the transmission paths of the first and second optical fibers to be tested; and are used to reflect laser light of a preset wavelength while allowing laser light of other wavelengths to pass through; The AI ​​module is used to automatically select the wavelength most suitable for detecting a specific type of optical fiber fault by analyzing OTDR echo signals of different wavelengths before detection, for emission by the light source; The method comprises the following steps: S0: The AI ​​module analyzes OTDR echo signals of different wavelengths and classifies OTDR echo data points using K-Means clustering to distinguish normal signals, attenuated signals, and mutation signals. It can then determine and automatically select the first wavelength laser λ1 and the second wavelength laser λ2, which are most sensitive to the current fault. S1: The light source emits two wavelengths of laser light λ1 and laser light λ2; S2: the laser light λ1 and the laser light λ2 enter the first optical fiber to be tested and the second optical fiber to be tested through the circulator; S3: In the first optical fiber to be tested, when the laser light λ1 and the laser light λ2 reach the first optical filter, the laser light λ1 is reflected back and eventually received by the detection module, and the arrival time t1 is recorded; the laser light λ2 continues to move forward until it encounters an obstacle and returns, and eventually is received by the detection module, and the arrival time t2 is recorded; the specific location of the fault is obtained using the fault point formula L=v*(t2-t1) / 2, where v is the speed of light propagation in the optical fiber; S4: In the second optical fiber to be tested, when the laser light λ1 and the laser light λ2 reach the second optical filter, the laser light λ2 is reflected back and is eventually received by the detection module, and the arrival time t2 is recorded; the laser light λ1 continues to move forward until it encounters an obstacle point and returns, and is eventually received by the detection module, and the arrival time t1 is recorded; and the specific location of the fault is obtained through the fault point formula L=v*(t1-t2) / 2, where v is the speed of light propagation in the optical fiber.

2. The method according to claim 1, wherein: The predetermined wavelength reflected by the first optical filter is different from the predetermined wavelength reflected by the second optical filter.

3. The method according to claim 1, wherein: The first optical fiber to be tested and the second optical fiber to be tested are connected in series.

4. The method according to claim 1, wherein: In step S3 and step S4, a dispersion compensation algorithm is used to perform correction.

5. The method according to claim 1, wherein: The AI ​​module in the intelligent optical fiber detection system automatically selects the wavelength most suitable for detecting a specific type of optical fiber fault by analyzing OTDR echo signals of different wavelengths.

6. The method according to claim 2, wherein: The preset wavelength reflected by the first optical filter and the preset wavelength reflected by the second optical filter are spaced 20-100 nm apart.

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