Optical fiber state monitoring method and apparatus, device, medium, and product

Abstract

The present application discloses an optical fiber state monitoring method and apparatus, a device, a medium, and a product. The method comprises: detecting a polarization state of signal light transmitted by an optical fiber link, and monitoring a polarization state change event; upon detecting the polarization state change event, acquiring state data of the optical fiber link at a preset first frequency, wherein the first frequency is higher than an initial acquisition frequency of the state data; and determining an intruded position of an optical fiber on the basis of the state data.

Description

A method, apparatus, equipment, medium, and product for fiber optic condition monitoring.

[0001] This patent application claims priority to Chinese Patent Application No. 202411201405.1, filed on August 29, 2024, by China Mobile Research Institute and China Mobile Communications Group Co., Ltd., entitled "A Method, Apparatus, Device, Medium and Product for Fiber Optic Status Monitoring", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of optical transmission and bearer technology, and to an optical fiber status monitoring method, device, equipment, medium and product. Background Technology

[0003] Fiber optic links, as the physical layer medium of optical transmission systems, are the foundation of the entire optical communication system. With the development and expansion of optical network technology, massive amounts of fiber optic cables have been laid in various environments. Currently, from access points to metropolitan areas and then to the backbone, they essentially cover all cities and rural areas, carrying the various kinds of information essential for daily life. However, during use, fiber optic links are increasingly vulnerable due to over 60% of cable breaks caused by construction projects such as municipal engineering, agricultural irrigation, road construction, and building construction. These breaks are further exacerbated by natural disasters and cable aging.

[0004] Because existing technologies can only respond after signal transmission is interrupted when monitoring the condition of optical fibers, they cannot prevent optical fiber line faults.

[0005] Application content

[0006] To address the aforementioned issues, this application proposes a method, apparatus, equipment, medium, and product for optical fiber status monitoring.

[0007] This application provides a fiber optic condition monitoring method, the method comprising:

[0008] Polarization state detection is performed on the signal light transmitted through the fiber optic link, and events causing changes in the polarization state are monitored.

[0009] When a change in polarization state is detected, status data of the optical fiber link is acquired at a first frequency; wherein the first frequency is higher than the initial acquisition frequency of the status data.

[0010] The location of the intrusion on the optical fiber is determined based on the status data.

[0011] In the above scheme, polarization state detection is performed on the signal light transmitted through the fiber optic link, and events causing changes in polarization state are monitored, including:

[0012] The signal light transmitted through the optical fiber link is coherently coupled with the locally generated local oscillator light;

[0013] The difference in polarization state between the signal light and the local oscillator light is detected, and events indicating changes in polarization state are identified.

[0014] In the above scheme, detecting the difference in polarization state between the signal light and the local oscillator light, and identifying events indicating changes in polarization state, includes:

[0015] The direction difference between the electric vector directions of the signal light and the local oscillator light is detected;

[0016] When the direction difference is not within a preset first range, a change in polarization state is identified.

[0017] In the above scheme, collecting the status data of the optical fiber link at a first frequency includes:

[0018] The sampling frequency of the built-in optical time domain reflectometer is adjusted from the initial second frequency to the first frequency, and the attenuation curve of the optical signal intensity with distance on the optical fiber link is collected as the state data.

[0019] The first frequency is higher than the second frequency.

[0020] In the above scheme, determining the location of the intruded fiber based on the status data includes:

[0021] By comparing the attenuation curve of the optical signal in the state data with the standard attenuation curve in the preset standard state data, the intrusion event that meets the preset conditions is identified.

[0022] The location of the intrusion is determined based on the distribution of the intrusion events.

[0023] In the above scheme, the attenuation curve of the optical signal in the state data is compared with the standard attenuation curve in the preset standard state data to identify the intrusion event that meets the preset conditions, including:

[0024] Calculate the attenuation rate of the attenuation curve at different positions, and the attenuation rate of the standard attenuation curve at different positions;

[0025] Calculate the error rate between the attenuation curve and the standard attenuation curve at the corresponding position;

[0026] When the error rate at a certain location exceeds a preset fluctuation range, the intrusion event is identified.

[0027] The method in the above scheme further includes:

[0028] Extract the abnormal status data of the optical fiber link with a preset length where the intruded location is located.

[0029] The attenuation characteristics of the abnormal state data are input into a pre-trained vibration recognition model to identify the vibration intensity and frequency at the disturbed location.

[0030] The method in the above scheme further includes:

[0031] Based on the vibration intensity and the vibration frequency, the corresponding intrusion event type is matched from a preset event type library.

[0032] The above scheme also includes the following methods:

[0033] Collect different state data of optical signals in optical fiber links subjected to different vibration intensities and frequencies;

[0034] The intrusion features corresponding to the intrusion locations in the different state data are extracted as training data;

[0035] The extracted training data are used as input, and the corresponding vibration intensity and vibration frequency are used as labels to train the preset deep learning model.

[0036] The trained model is used as the new vibration recognition model.

[0037] This application embodiment also provides an optical fiber status monitoring device, the device comprising:

[0038] The polarization state monitoring module is configured to detect the polarization state of the signal light transmitted through the fiber optic link and monitor events that change the polarization state.

[0039] The data acquisition module is configured to acquire state data of the optical fiber link at a first frequency when a change in polarization state is detected; wherein the first frequency is higher than the initial acquisition frequency of the state data.

[0040] The location determination module is configured to determine the location of the intrusion into the optical fiber based on the status data.

[0041] In the above scheme, the polarization state monitoring module is configured as follows:

[0042] The signal light transmitted through the optical fiber link is coherently coupled with the locally generated local oscillator light;

[0043] The difference in polarization state between the signal light and the local oscillator light is detected, and events indicating changes in polarization state are identified.

[0044] In the above scheme, the polarization state monitoring module is further configured as follows:

[0045] The direction difference between the electric vector directions of the signal light and the local oscillator light is detected;

[0046] When the direction difference is not within a preset first range, a change in polarization state is identified.

[0047] In the above scheme, the data acquisition module is configured as follows:

[0048] The sampling frequency of the built-in optical time domain reflectometer is adjusted from the initial second frequency to the first frequency, and the attenuation curve of the optical signal intensity with distance on the optical fiber link is collected as the state data.

[0049] The first frequency is higher than the second frequency.

[0050] In the above scheme, the location determination module is configured as follows:

[0051] By comparing the attenuation curve of the optical signal in the state data with the standard attenuation curve in the preset standard state data, the intrusion event that meets the preset conditions is identified.

[0052] The location of the intrusion is determined based on the distribution of the intrusion events.

[0053] In the above scheme, the location determination module is further configured as follows:

[0054] Calculate the attenuation rate of the attenuation curve at different positions, and the attenuation rate of the standard attenuation curve at different positions;

[0055] Calculate the error rate between the attenuation curve and the standard attenuation curve at the corresponding position;

[0056] When the error rate at a certain location exceeds a preset fluctuation range, the intrusion event is identified.

[0057] In the above scheme, the device further includes a vibration recognition module, configured as follows:

[0058] Extract the abnormal status data of the optical fiber link with a preset length where the intruded location is located.

[0059] The attenuation features of the abnormal state data are extracted and input into a pre-trained vibration recognition model to identify the vibration intensity and frequency at the disturbed location.

[0060] In the above scheme, the device further includes an intrusion detection module, configured as follows:

[0061] Based on the vibration intensity and the vibration frequency, the corresponding intrusion event type is matched from a preset event type library.

[0062] In the above scheme, the vibration recognition model training process includes:

[0063] Collect different state data of optical signals in optical fiber links subjected to different vibration intensities and frequencies;

[0064] The intrusion features corresponding to the intrusion locations in the different state data are extracted as training data;

[0065] The extracted training data are used as input, and the corresponding vibration intensity and vibration frequency are used as labels to train the preset deep learning model.

[0066] The trained model is used as the vibration recognition model.

[0067] This application also provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the fiber optic status monitoring method as described in any of the above embodiments.

[0068] This application also provides a computer-readable storage medium, which includes a stored computer program, wherein the computer program, when running, controls the device where the computer-readable storage medium is located to execute the fiber optic status monitoring method as described in any of the above embodiments.

[0069] This application also provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of any of the methods described above.

[0070] This application provides a method, apparatus, device, medium, and product for optical fiber status monitoring. It involves detecting the polarization state of signal light transmitted through an optical fiber link and monitoring events indicating changes in the polarization state. Upon detecting a change in polarization state, status data of the optical fiber link is collected at a preset first frequency, where the first frequency is higher than the initial collection frequency of the status data. The location of any interference in the optical fiber is determined based on the status data. This application's solution proactively triggers the status monitoring process by monitoring polarization state change events, enabling interference identification and allowing for the identification of abnormal states before a fault occurs, thus preventing optical fiber transmission interruptions. Attached Figure Description

[0071] Figure 1 is a flowchart illustrating an optical fiber condition monitoring method provided in an embodiment of this application;

[0072] Figure 2 is a schematic diagram of the optical fiber transmission link provided in an embodiment of this application;

[0073] Figure 3 is a schematic diagram comparing the attenuation curves provided in the embodiments of this application;

[0074] Figure 4 is a schematic diagram of the structure of an optical fiber condition monitoring device provided in an embodiment of this application;

[0075] Figure 5 is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. Detailed Implementation

[0076] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0077] Fiber optic links, as the physical layer medium of optical transmission systems, are the foundation of the entire optical communication system. With the development and expansion of optical network technology, massive amounts of fiber optic cables have been laid in various environments. Fiber optic links cover all cities and rural areas, carrying the various kinds of information essential for human daily life.

[0078] The extensive fiber optic links are also experiencing frequent failures due to various construction projects, natural disasters, or aging damage, making the security of optical cables increasingly vulnerable. Current fiber optic fault detection technologies only detect fault events after the fiber has been abnormally interrupted or damaged, failing to provide proactive fault monitoring.

[0079] To address the aforementioned technical problems, this embodiment provides a fiber optic condition monitoring method. Referring to Figure 1, which is a flowchart illustrating a fiber optic condition monitoring method provided in this embodiment, the method includes:

[0080] Step S101: Detect the polarization state of the signal light transmitted through the optical fiber link and monitor events that change the polarization state.

[0081] In this embodiment, under idealized optical fiber transmission, the state of polarization (SOP) of the signal light is closely related to the ellipticity of the fiber core. If the ellipticity of the fiber core end face does not change, the SOP will not change either. However, the external environment can have varying degrees of impact on the laid optical cables. For example, ground construction affects buried optical cables, vibrations from passing trains affect optical cables laid along railways, waves impact submarine optical cables, and strong winds affect overhead optical cables. These factors can cause the optical fibers in the cable to be compressed, and the fiber end face to deform, resulting in a rapid change in the polarization state of the transmitted light. Since the polarization state of the local oscillator light is fixed, while the polarization state of the signal light can change due to abnormal conditions during signal transmission in the optical fiber, the receiving end can detect changes in the polarization state of the signal light during optical fiber transmission by monitoring the polarization state of the signal light.

[0082] It should be noted that in identifying this change event, the signal light is identified by the change in polarization state due to external interference in the optical fiber link. However, this process cannot identify the cause of the interference or the location of the interference. It can only identify the impact of ground construction on buried optical cables, vibrations from passing trains on optical cables laid along railways, waves crashing on submarine optical cables, and strong winds on overhead optical fiber links. When there is external interference, the optical fiber is vibrated or squeezed, causing the degree of ellipticity of the optical fiber end face to change, which ultimately affects the distribution of the polarization state of the transmitted signal light.

[0083] In this embodiment of the application, during implementation, the polarization state of the transmitted signal light can be detected, combined with the polarization state of the signal light output by the transmitting end, and the polarization state of the signal light before and after transmission through the optical fiber link can be determined to have detected a change event after the detected polarization state change meets the preset event conditions.

[0084] Step S102: When a change in polarization state is detected, the state data of the optical fiber link is collected at a preset first frequency.

[0085] In this embodiment, relying solely on fiber optic link status monitoring only enables post-fault monitoring. Therefore, this application uses polarization state monitoring to detect changes in polarization state. Upon triggering a change event, status data is collected at a preset first frequency. It should be noted that the preset first frequency can be set to the second level to trigger active monitoring of abnormal interference, enabling early detection of the interference location before the fiber optic link experiences a fault. The first frequency is higher than the initial collection frequency of the status data.

[0086] In this embodiment, after detecting a change event, the application further acquires the fiber optic link status by triggering the change event, and uses distributed sensing devices to acquire the optical time domain reflectometer (OTDR) curve of the fiber under test. An OTDR is a fiber optic instrument used for characteristic analysis, fault diagnosis, and maintenance of optical communication networks. It includes a laser diode light source, an avalanche diode, and a highly accurate timing circuit (or time base).

[0087] In the OTDR curve acquisition process, a pulse signal of a specific length is emitted into the optical fiber under test via an optical time-domain reflectometer transmitter. As the pulse signal propagates along the fiber, Rayleigh scattering occurs. The injected light pulse is scattered in all directions at every point along the fiber's length. A portion of this backscattered light returns to the OTDR's detection unit, and the OTDR collects and measures this backscattered light. The backscattered signal decreases exponentially with increasing fiber distance. Converting the backscattered signal to dB exponential space, the OTDR sampling curve shows a linear and uniform decrease, thus the backscattered signal is used as the OTDR curve.

[0088] In the OTDR curve, when a splice, splice loss or damage occurs on the optical fiber link, the sampling signal curve at the corresponding position on the OTDR curve will show a non-linear change. By identifying this non-linear change in the signal, the optical fiber event can be automatically identified.

[0089] It should be noted that in this implementation, the OTDR curve can be used as the detected status data to characterize the changes in the fiber optic link status. In other embodiments, various schemes can be used for status data monitoring.

[0090] Existing fiber optic cable monitoring systems comprehensively utilize various technologies such as Geographic Information System (GIS), Global Positioning System (GPS), Optical Time Domain Reflectometer (OTDR), Wavelength Division Multiplexing (WDM), and data mining to promptly grasp the operational status of fiber optic networks.

[0091] Monitoring systems are a necessity for the development of optical cable line maintenance in the new era. They bring optical cable lines under real-time centralized monitoring and maintenance. No matter how much change occurs on the line, monitoring is carried out at the minute level. Transmission monitoring can detect equipment failures, but it is a reactive measure.

[0092] Step S103: Determine the location of the intrusion on the optical fiber based on the status data.

[0093] In this embodiment of the application, the location of the disturbance can be determined by analyzing the status data and determining the location represented by the difference between the status data and the standard status data.

[0094] In this embodiment of the application, in order to give full play to the advantages of coherent reception, the transmitting end of the coherent optical communication system will take optical polarization stabilization measures in advance to maintain the polarization state of the local oscillator light and the signal light within a threshold, so as to ensure that the reception is in the best state.

[0095] The existing optical cable line monitoring system not only replaces manual labor with automation, but also fundamentally improves the level of maintenance. This means that the number of tests has been increased from once or twice a year to several times a year. However, the monitoring cycle is still at a low level. If the monitoring cycle is simply increased, it will require a large amount of data on the detection status, as well as a large amount of analysis, resulting in significant additional losses and resource consumption.

[0096] In this embodiment, by monitoring polarization state change events, a state monitoring process is actively triggered to identify intrusions and detect fiber degradation trends. This enables condition-based maintenance based on operational status and allows for rapid and accurate fault location when a fault occurs. Network maintenance personnel can be notified in advance to take appropriate emergency measures, contact the construction unit to suspend construction to avoid the fiber optic cable being cut, or take manual switching measures in advance to transfer potentially affected services to other fiber optic cables, minimizing the loss of fiber optic cable or services.

[0097] In another embodiment provided in this application, when performing polarization state detection and polarization state change event identification in step S1 above, the following steps are performed:

[0098] S201. Coherently couple the signal light transmitted by the optical fiber link with the local oscillator light generated locally.

[0099] In this embodiment, coherent optical communication technology is commonly used in optical transmission systems. At the transmitting end, external modulation is used to modulate the signal onto an optical carrier for transmission. During optical signal polarization state analysis, when the signal light reaches the receiving end, it is first coherently coupled with the locally generated local oscillator signal.

[0100] Coherent detection is performed by the receiver, which requires that the local oscillator light and the received signal light have the same polarization direction.

[0101] S202. Detect the difference in polarization state between the signal light and the local oscillator light, and identify events that change the polarization state.

[0102] In this embodiment of the application, the change in polarization direction of the signal light and the local oscillator light is detected, and when the change in polarization direction meets the preset change event conditions, the change event of polarization state is identified.

[0103] By associating events such as external forces affecting the optical cable (ground construction operations, geological activities, train or car passing by, etc.), fiber end face deformation, and polarization state changes, the optical digital signal processor in the optical transmitting and receiving units of the transmission equipment will detect the polarization state change event and achieve accurate identification of the polarization state change event.

[0104] Referring to Figure 2, it is a schematic diagram of the optical fiber transmission link provided in an embodiment of this application. In the figure, data transmission is carried out between node A and node Z via optical fiber.

[0105] An erbium-doped optical fiber amplifier (EDFA) is also configured between node A and node Z as an optical amplifier station to perform power compensation on the signal light in the transmission link and ensure transmission quality.

[0106] A single optical fiber link may reuse multiple different optical fibers. Therefore, optical multimode (OM) and optical amplifier (OA) are configured at nodes A and Z to handle optical signals of different modes, and the polarization state is monitored by an optical digital signal processor (oDSP).

[0107] In the embodiment shown in Figure 2, N different modal optical signals are configured. Therefore, both node A and node Z need to be configured with N different optical digital signal processors oDSPTx1~TxN to perform polarization state monitoring and analysis on the different modal optical signals.

[0108] In this embodiment, the signal light transmitted through the optical fiber link is coherently coupled to the locally generated local oscillator light. The difference in polarization state between the signal light and the local oscillator light is detected, and polarization state change events are identified. This coherent coupling process ensures that the signal light and the local oscillator light have the same polarization direction, thereby accurately determining the polarization state difference and improving the monitoring accuracy of polarization state change events.

[0109] In another embodiment provided in this application, when identifying a change event in polarization state based on a change in polarization direction, the following steps are performed:

[0110] S301. Detect the directional difference between the electric vector directions of the signal light and the local oscillator light.

[0111] In this embodiment of the application, the direction difference between the electric vector directions of the signal light and the local oscillator light is detected. Coherent detection requires that the local oscillator light and the received signal light have the same polarization direction. In other words, the electric vector directions of the signal light and the local oscillator light must be the same in order to obtain the high sensitivity provided by coherent reception. Otherwise, the sensitivity of coherent detection will decrease.

[0112] S302. When the direction difference is not within a preset first range, a change in polarization state is identified.

[0113] In this embodiment of the application, the direction difference between the electric vector direction of the signal light and the electric vector direction of the local oscillator light is detected, and when the direction difference is not within a preset first range, a change event in polarization state is identified.

[0114] It should be noted that the setting of the first range can be determined based on the actual detection accuracy. The smaller the first range setting, the more likely false triggering of polarization state change events may occur due to errors. The larger the first range setting, the fewer errors occur, but there may be missed detections, meaning that polarization state change events caused by small intrusion events may be ignored.

[0115] During implementation, the ratio of identified intrusion locations to the number of identified change events within a preset period can be statistically analyzed to determine the detection accuracy value and adjust the first range. The closer the accuracy value is to 1, the more reasonable the first range setting is, resulting in a lower false alarm rate and a lower false negative rate; in this case, the first range should be maintained. When the accuracy value is less than 1, it indicates that the number of identified intrusion locations is lower than the number of change events, indicating false alarms, and the first range needs to be increased. By detecting whether false negative events occur—that is, whether fiber optic faults are detected without triggering change events—it can be determined whether false negatives exist. If the number of false negatives exceeds a preset threshold within a certain period, the first range should be decreased.

[0116] In this embodiment, the directional difference between the electric vector directions of the signal light and the local oscillator light is detected. When the directional difference is not within a preset first range, a change in polarization state is identified. Thus, by setting a first range, errors in polarization state detection can be prevented from causing false positives and false negatives, thereby improving the detection accuracy of change events.

[0117] In another embodiment provided in this application, after identifying a polarization state change event in step S2, when collecting state data, a rapid state information collection is initiated, including:

[0118] S401. Adjust the sampling frequency of the built-in optical time domain reflectometer from the initial second frequency to the first frequency, and collect the attenuation curve of the optical signal intensity with distance on the optical fiber link as the status data.

[0119] In this embodiment, the sampling frequency of the built-in optical time domain reflectometer is adjusted from the initial second frequency to the first frequency. By detecting the state of the optical fiber of the built-in optical time domain reflectometer, i.e., the intensity of the received scattered light, the attenuation of the scattered light is calculated based on the intensity of the emitted reference light, thereby determining the attenuation curve of the optical signal intensity over time as state data.

[0120] For example, by reducing the interval between collecting and reporting fiber optic status information from once every 15 minutes to once every 1 second, these intrusion events can be detected.

[0121] By analyzing the attenuation curve, the exact location of the disturbance can be determined.

[0122] It should be noted that the first frequency is higher than the second frequency. Before a change event is detected, status data is collected at the preset second frequency for fiber optic monitoring. The second frequency is relatively low, typically used for fault analysis at ten-minute intervals to reduce resource consumption for fault detection. When an intrusion is detected, the detection frequency is increased to the second frequency, generally once per second, for rapid intrusion analysis to identify the intrusion event in the fiber optic link. If no intrusion event is detected within a preset first time period, or after the detected change event disappears, the second frequency is restored.

[0123] In this embodiment, the sampling frequency of the built-in optical time-domain reflectometer is adjusted from the initial second frequency to the first frequency, and the attenuation curve of the optical signal intensity with distance on the optical fiber link is collected as the state data. Thus, by shortening the interval between collecting state data after identifying a change event, these intrusion events can be captured, providing more information for quickly locating the location of the intrusion on the optical fiber. This allows for timely determination of the intrusion location, enabling prevention of faults based on the location of the intrusion.

[0124] In another embodiment provided in this application, when determining the location of the intrusion based on the status data in step S3, the following steps are performed:

[0125] S501. Compare the attenuation curve of the optical signal in the state data with the standard attenuation curve in the preset standard state data to identify the interference event that meets the preset conditions.

[0126] In this embodiment of the application, pre-collected standard state data is obtained. Referring to Figure 3, which is a schematic diagram of attenuation curve comparison provided in this embodiment of the application, the attenuation curve of the optical signal in the state data is compared with the standard attenuation curve in the preset standard state data to identify the interference event that meets the preset conditions.

[0127] When the optical fiber is in a normal state, the eOTDR will have a normal line attenuation curve, with the signal strength slowly decreasing at a certain slope as the distance increases. When the optical fiber is compressed due to external interference, the loss suddenly changes due to the slight bending of the fiber, and there will be an abnormal change in signal strength near the location of the interference.

[0128] S502. Determine the location of the intrusion based on the distribution location of the intrusion event.

[0129] In this embodiment of the application, an intrusion event that meets a preset mutation condition is identified, and the intrusion location is determined based on the distribution location of the intrusion event.

[0130] In this embodiment, the attenuation curve of the optical signal in the state data is compared with the standard attenuation curve in the preset standard state data to identify interference events that meet preset conditions. Thus, after collecting the fiber optic link attenuation performance data, the information is uploaded to the transmission system's management platform. The management platform retains a standard attenuation performance curve of the fiber optic link under normal conditions. By comparing the standard attenuation curve with the attenuation performance curve of the fiber optic link with "glitch" artifacts, the location of external interference can be quickly determined.

[0131] In another embodiment provided in this application, when identifying an intrusion event, the following is performed:

[0132] S601. Calculate the attenuation rate of the attenuation curve at different positions, and the standard attenuation rate of the standard attenuation curve at different positions.

[0133] In this embodiment of the application, when the optical fiber is subjected to a slight bend due to external interference, the loss suddenly changes, and the attenuation rate of the optical fiber will change abruptly, which is reflected as "bristles" on the attenuation curve. Therefore, the attenuation rate at different positions is determined by calculating the attenuation rate of the intensity on the attenuation curve.

[0134] S602. Calculate the error rate of the attenuation rate at the same position on the attenuation curve and the standard attenuation curve.

[0135] In this embodiment of the application, the attenuation rate of the attenuation curve and the standard attenuation curve at different positions are compared to calculate the error of the attenuation rate of the attenuation curve at the same position on the standard attenuation curve. The error rate is calculated based on the error and the error rate is judged.

[0136] S603. When the error rate at a certain location exceeds a preset fluctuation range, the intrusion event is identified.

[0137] In this embodiment of the application, when the error rate between the attenuation curve and the corresponding position at a certain position exceeds a preset fluctuation range, an intrusion event is identified at that position.

[0138] It should be noted that continuous portions with error rates exceeding the fluctuation range are identified as the same intrusion event, and the location of the intrusion of the same intrusion event is determined by the middle position of the continuous portion.

[0139] It should be noted that the fluctuation range setting can be adjusted according to the actual situation, and there is no restriction here. Those skilled in the art can set the fluctuation range to 5%, 10% or 15% as appropriate to deal with detection errors at different times.

[0140] In this embodiment, the error rate between the attenuation curve and the standard attenuation curve at the same position is calculated. When the error rate at a certain position exceeds a preset fluctuation range, the intrusion event is identified. Thus, by comparing the attenuation rate at the same position on the attenuation curve, the location of abnormal loss in the optical fiber link can be accurately identified.

[0141] In another embodiment provided in this application, after identifying the location of the intrusion and the intrusion event, this application further includes identifying the intrusion event, including:

[0142] S701. Extract the abnormal status data of the optical fiber link with a preset length where the intruded location is located.

[0143] In this embodiment of the application, abnormal state data of the optical fiber link with a preset length where the harassed location is located is extracted. That is, after the harassed location is identified, the preset length of half the length before and after the harassed location is determined as the abnormal state data, and the corresponding abnormal attenuation curve in the attenuation curve is extracted.

[0144] Among them, the decay features in the abnormal decay curve are extracted, that is, the features of the "spicule" part in the abnormal decay curve are extracted. During identification, the features of the mutation curve where the same intrusion event is located are extracted. The extracted features include: the range of mutation extreme values, that is, the difference between the maximum and minimum values, the duration distance, the maximum mutation rate and the minimum mutation rate of the decay rate, the number of extreme values, etc.

[0145] S702. Input the attenuation characteristics of the abnormal state data into the pre-trained vibration recognition model to identify the vibration intensity and vibration frequency at the disturbed location.

[0146] In this embodiment of the application, the extracted attenuation features are input into a pre-trained vibration recognition model to identify the vibration intensity and vibration frequency at the disturbed location.

[0147] The vibration recognition model is a model trained in advance using deep learning, which can identify different input features and determine the vibration intensity and frequency at the disturbed location.

[0148] Through pre-training, it is possible to identify the vibration frequency and amplitude at the intrusion location.

[0149] Because the degree of deformation or damage to the fiber core varies under different vibration amplitudes and frequencies, the corresponding optical signal loss will also be different, which is reflected in the attenuation curve. By analyzing the abnormal characteristics of the attenuation curve, the vibration intensity and frequency at the location of the disturbance can be identified.

[0150] In another embodiment provided in this application, after identifying the vibration intensity and vibration frequency, the corresponding intrusion event type is matched from a preset event type library according to the vibration intensity and vibration frequency.

[0151] That is, the vibration intensity and frequency generated on the optical fiber are different under different types of intrusion. Different types of intrusion are pre-classified according to the different vibration intensity and frequency.

[0152] For example, during ground construction operations, when trains or cars pass by, the vibration frequency and intensity generated by ground transmission will differ. Correspondingly, a mapping relationship between vibration intensity and vibration frequency and intrusion type should be constructed, and an event type library should be built.

[0153] Based on the vibration intensity and frequency, the corresponding intrusion event type is matched from the correspondence in the event type database.

[0154] In this embodiment, abnormal state data of the optical fiber link along a preset length where the intrusion location is located is extracted. The attenuation characteristics of the abnormal state data are input into a pre-trained vibration identification model to identify the vibration intensity and frequency at the intrusion location. Based on the vibration intensity and frequency, a corresponding intrusion event type is matched from a preset event type library. Thus, by analyzing the vibration intensity and frequency at the intrusion location, the intrusion type is identified in addition to the intrusion event itself, facilitating rapid response to intrusions, corresponding management work, timely damage mitigation, and prevention of continuous intrusion leading to optical fiber damage.

[0155] In another embodiment provided in this application, the vibration recognition model is trained by performing the following steps:

[0156] S801: Collect different state data of optical signals in optical fiber links with different vibration intensities and frequencies.

[0157] S802. Extract the intrusion features corresponding to the intrusion positions from the different state data as training data.

[0158] In this embodiment of the application, training data is collected, that is, the attenuation curves of optical signals in optical fiber links with different vibration intensities and frequencies are collected under simulation and used as training state data.

[0159] The abnormal part of the curve is extracted from the collected attenuation curve. The process of identifying the abnormal part can adopt the method of identifying the intrusion event in the previous embodiment, which will not be described in detail here.

[0160] Feature extraction is performed on the extracted curve. The feature extraction method can be the same as the feature extraction scheme in the previous embodiments, and will not be described in detail here.

[0161] S803. The extracted different training data are used as input, and the corresponding vibration intensity and vibration frequency are used as labels to train the preset deep learning model.

[0162] In this embodiment of the application, the intrusion features of different training data extracted are used as input, and the corresponding vibration intensity and vibration frequency are used as labels to train the preset deep learning model.

[0163] S804. Use the trained model as the new vibration recognition model.

[0164] In this embodiment of the application, after the training termination condition is met, the trained model is used as the vibration recognition model to complete the model training.

[0165] Increasing the amount of training data used in model training can improve the model's recognition accuracy.

[0166] In this embodiment, different state data of optical signals in an optical fiber link subjected to different vibration intensities and frequencies are collected. The intrusion features corresponding to the intrusion locations in the different state data are extracted as training data. The extracted training data is used as input, and the corresponding vibration intensities and frequencies are used as labels to train a preset deep learning model. The trained model is then used as the new vibration recognition model. In this way, training the deep learning model with different training data allows it to learn diverse intrusion feature data, thereby improving its recognition performance.

[0167] Referring to Figure 4, which is a structural schematic diagram of an optical fiber status monitoring device provided in an embodiment of this application, the device includes:

[0168] The polarization state monitoring module is configured to detect the polarization state of the signal light transmitted through the fiber optic link and monitor events that change the polarization state.

[0169] The data acquisition module is configured to acquire state data of the optical fiber link at a preset first frequency when a change in polarization state is detected; wherein the first frequency is higher than the initial acquisition frequency of the state data.

[0170] The location determination module is used to determine the location of the intrusion into the optical fiber based on the status data.

[0171] In this embodiment of the application, the polarization state monitoring module is configured to coherently couple the signal light transmitted by the optical fiber link with the locally generated local oscillator light;

[0172] The difference in polarization state between the signal light and the local oscillator light is detected, and events indicating changes in polarization state are identified.

[0173] In this embodiment of the application, the polarization state monitoring module is configured to detect the directional difference between the electric vector directions of the signal light and the local oscillator light;

[0174] When the direction difference is not within a preset first range, a change in polarization state is identified.

[0175] In this embodiment of the application, the data acquisition module is configured to adjust the sampling frequency of the built-in optical time domain reflectometer from the initial second frequency to the first frequency, and acquire the attenuation curve of the optical signal intensity with distance on the optical fiber link as the status data;

[0176] The first frequency is higher than the second frequency.

[0177] In this embodiment of the application, the location determination module is configured to compare the attenuation curve of the optical signal in the state data with the standard attenuation curve in the preset standard state data to identify an intrusion event that meets the preset conditions.

[0178] The location of the intrusion is determined based on the distribution of the intrusion events.

[0179] In this embodiment of the application, the polarization state monitoring module is configured to calculate the attenuation rate of the attenuation curve at different positions and the attenuation rate of the standard attenuation curve at different positions.

[0180] Calculate the error rate between the attenuation curve and the standard attenuation curve at the corresponding position;

[0181] When the error rate at a certain location exceeds a preset fluctuation range, the intrusion event is identified.

[0182] In this embodiment of the application, the data acquisition module is configured to extract abnormal status data of the optical fiber link of a preset length where the harassed location is located.

[0183] The attenuation characteristics of the abnormal state data are input into a pre-trained vibration recognition model to identify the vibration intensity and frequency at the disturbed location.

[0184] In this embodiment of the application, the polarization state monitoring module is configured to match the corresponding intrusion event type from a preset event type library based on the vibration intensity and the vibration frequency.

[0185] In this embodiment, the data acquisition module is configured to acquire different state data of optical signals in an optical fiber link subjected to different vibration intensities and frequencies; extract the intrusion features corresponding to the intrusion locations in the different state data as training data; use the extracted different training data as input, and the corresponding vibration intensities and frequencies as labels, to train a preset deep learning model; and use the trained model as a new vibration recognition model.

[0186] The optical fiber status monitoring device provided in this embodiment can perform all the steps and functions of the optical fiber status monitoring method provided in any of the above embodiments, and the functions of the device will not be described in detail here.

[0187] Referring to Figure 5, it is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. The terminal device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a fiber optic status monitoring program. When the processor executes the computer program, it implements the steps in each of the above-described embodiments of the fiber optic status monitoring method, such as steps S101 to S103 shown in Figure 1. Alternatively, when the processor executes the computer program, it implements the functions of each module in the above-described device embodiments.

[0188] For example, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete this application. The one or more modules can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the fiber optic condition monitoring device. For example, the computer program can be divided into several modules, the functions of which have been described in detail in the fiber optic condition monitoring method provided in any of the above embodiments, and the functions of the device will not be repeated here.

[0189] The terminal device described can be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the schematic diagram is merely an example of a terminal device and does not constitute a limitation on a fiber optic status monitoring device. It may include more or fewer components than illustrated, or combine certain components, or different components. For example, the terminal device may also include input / output devices, network access devices, buses, etc.

[0190] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. This processor is the control center of the fiber optic condition monitoring device, connecting all parts of the device via various interfaces and lines.

[0191] The memory can be used to store the computer program and / or modules. The processor implements various functions of the fiber optic status monitoring device by running or executing the computer program and / or modules stored in the memory and by calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0192] If the integrated module of the fiber optic status monitoring device is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0193] This application also provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the functional network element implementing the method described in the above embodiments.

[0194] The computer program product provided in this embodiment can execute all the steps and functions of the optical fiber status monitoring method provided in any of the above embodiments, and the functions of the product will not be described in detail here.

[0195] It should be noted that those skilled in the art can make several improvements and modifications without departing from the principles of this application, and these improvements and modifications are also considered to be within the scope of protection of this application.

Claims

1. A method for monitoring the condition of an optical fiber, the method comprising: Polarization state detection is performed on the signal light transmitted through the fiber optic link, and events causing changes in the polarization state are monitored. When a change in polarization state is detected, status data of the optical fiber link is acquired at a first frequency; wherein the first frequency is higher than the initial acquisition frequency of the status data. The location of the intrusion on the optical fiber is determined based on the status data.

2. The optical fiber condition monitoring method according to claim 1, wherein, Polarization state detection is performed on the signal light transmitted through the fiber optic link, and events causing changes in the polarization state are monitored, including: The signal light transmitted through the optical fiber link is coherently coupled with the locally generated local oscillator light; The difference in polarization state between the signal light and the local oscillator light is detected, and events indicating changes in polarization state are identified.

3. The optical fiber condition monitoring method according to claim 2, wherein, Detecting the difference in polarization state between the signal light and the local oscillator light, and identifying events indicating changes in polarization state, includes: The direction difference between the electric vector directions of the signal light and the local oscillator light is detected; When the direction difference is not within a preset first range, a change in polarization state is identified.

4. The optical fiber condition monitoring method according to claim 1, wherein, The status data of the optical fiber link is collected at a first frequency, including: The sampling frequency of the built-in optical time domain reflectometer is adjusted from the initial second frequency to the first frequency, and the attenuation curve of the optical signal intensity with distance on the optical fiber link is collected as the state data. The first frequency is higher than the second frequency.

5. The optical fiber condition monitoring method according to claim 1, wherein, Determining the location of the intrusion on the optical fiber based on the status data includes: By comparing the attenuation curve of the optical signal in the state data with the standard attenuation curve in the preset standard state data, the intrusion event that meets the preset conditions is identified. The location of the intrusion is determined based on the distribution of the intrusion events.

6. The optical fiber condition monitoring method according to claim 5, wherein, By comparing the attenuation curve of the optical signal in the state data with the standard attenuation curve in the preset standard state data, intrusion events that meet preset conditions are identified, including: Calculate the attenuation rate of the attenuation curve at different positions, and the attenuation rate of the standard attenuation curve at different positions; Calculate the error rate between the attenuation curve and the standard attenuation curve at the corresponding position; When the error rate at a certain location exceeds a preset fluctuation range, the intrusion event is identified.

7. The optical fiber condition monitoring method according to claim 1, wherein, The method further includes: Extract the abnormal status data of the optical fiber link with a preset length where the intruded location is located. The attenuation characteristics of the abnormal state data are input into a pre-trained vibration recognition model to identify the vibration intensity and frequency at the disturbed location.

8. The optical fiber condition monitoring method according to claim 7, wherein, The method further includes: Based on the vibration intensity and the vibration frequency, the corresponding intrusion event type is matched from a preset event type library.

9. The fiber optic condition monitoring method according to claim 7, characterized in that, The method further includes: Collect different state data of optical signals in optical fiber links subjected to different vibration intensities and frequencies; The intrusion features corresponding to the intrusion locations in the different state data are extracted as training data; The extracted training data are used as input, and the corresponding vibration intensity and vibration frequency are used as labels to train the preset deep learning model. The trained model is used as the new vibration recognition model.

10. A fiber optic condition monitoring device, the device comprising: The polarization state monitoring module is configured to detect the polarization state of the signal light transmitted through the fiber optic link and monitor events that change the polarization state. The data acquisition module is configured to acquire state data of the optical fiber link at a preset first frequency when a change in polarization state is detected; wherein the first frequency is higher than the initial acquisition frequency of the state data. The location determination module is configured to determine the location of the intrusion into the optical fiber based on the status data.

11. A terminal device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the fiber optic status monitoring method as described in any one of claims 1 to 9.

12. A computer-readable storage medium comprising a stored computer program, wherein, When the computer program is executed, it controls the device containing the computer-readable storage medium to perform the fiber optic status monitoring method as described in any one of claims 1 to 9.

13. A computer program product comprising a computer program / instructions that, when executed by a processor, implement the steps of the method of any one of claims 1 to 9.

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