Broken fiber automatic identification and positioning method and device, computer equipment and storage medium

Abstract

The invention relates to a broken fiber automatic identification and positioning method and device, computer equipment and a storage medium. The light intensity matrix is converted into a decibel value through inverse logarithmic transformation, the dynamic range of the light intensity can be compressed to amplify a broken fiber signal and enhance the characteristics of a broken fiber point, a low-pass filtering method is adopted to eliminate high-frequency noise to obtain a smooth logarithmic light intensity matrix, and quadratic polynomial fitting is carried out on light intensity data in a specified space channel range of the front section to obtain a decibel value; a theoretical attenuation model in a normal optical fiber transmission state is obtained, a light intensity abrupt change point can be rapidly identified, and accurate identification and real-time monitoring of an optical fiber fracture position are realized. Compared with a traditional method depending on a fixed threshold value or manual interpretation, the method has high adaptive capacity and robustness, and can stably work under various noise environments and transmission conditions. Meanwhile, the algorithm is low in calculation amount and suitable for being deployed in edge equipment or a real-time monitoring system, and efficient and reliable support is provided for operation and maintenance of an actual optical fiber network.

Description

Technical Field

[0001] The present application relates to the field of optical fiber technology, and in particular to a method, device, computer equipment, and storage medium for automatically identifying and locating broken fibers. Background Art

[0002] With the widespread application of fiber-optic sensing technology in oil and gas pipelines, bridges and tunnels, and border protection, distributed optical and acoustic sensing (DAS) systems have become an important means of long-distance, real-time monitoring. However, in actual operation, optical fibers often break or suffer severe wear and tear due to environmental stress, construction damage, and natural aging, leading to signal interruptions and compromising system stability and data reliability.

[0003] Traditional methods for detecting fiber breaks typically include OTDRs, power monitoring, and machine learning. OTDRs only scan intermittently and cannot monitor in real time. Power detection, which detects a significant drop in optical power at the input / output, is a crude method that can only determine if a fiber is broken but cannot pinpoint its location. Machine learning or deep learning-based methods require extensive training data and are computationally intensive. Therefore, a method that requires minimal computation and can identify and locate fiber breaks in real time is urgently needed. Summary of the Invention

[0004] Based on this, a method, device, computer equipment and storage medium for automatically identifying and locating broken fibers are provided to solve the technical problem that traditional methods for breaking fibers cannot identify and locate broken fibers in real time.

[0005] In one aspect, a method for automatically identifying and locating a broken fiber is provided, the method comprising: Collect the light intensity data of each time sampling point in the fiber optic distributed acousto-optic sensing system to form a two-dimensional light intensity matrix; Performing an inverse logarithmic transformation on the light intensity matrix and using a low-pass filtering method to eliminate high-frequency noise to obtain a smooth logarithmic light intensity matrix; For each frame of data in the logarithmic light intensity matrix, a quadratic polynomial fitting is performed on the light intensity data within the range of the spatial channel specified in the previous section to obtain a theoretical attenuation model under a normal optical fiber transmission state; Adding a positive offset to the theoretical attenuation model to obtain a fitting curve, ensuring that the fitting light intensity theoretical value of the fitting curve is greater than the actual light intensity value within the normal range of the spatial channel, and the fitting light intensity theoretical value of the fitting curve is less than or equal to the actual light intensity value within the abnormal range of the spatial channel; Obtain the actual light intensity data and fitting curve of each spatial channel, compare the actual light intensity value with the fitted light intensity theoretical value, and determine whether there is an abnormal deviation in the optical fiber signal. If there is an abnormal deviation, determine the spatial channel location where the fiber break occurs.

[0006] In one embodiment, collecting light intensity data at each time sampling point in the optical fiber distributed acousto-optic sensing system to form a two-dimensional light intensity matrix includes: The sampling rate of each time sampling point in the optical fiber distributed acousto-optic sensing system is set to 10 Hz, and the light intensity data of each spatial channel is collected to form a two-dimensional light intensity matrix. The light intensity data matrix of the two-dimensional light intensity matrix is obtained as follows: = (T, N), where T is the number of sampling frames and N is the number of spatial channels.

[0007] In one embodiment, performing an inverse logarithmic transformation on the light intensity matrix includes: Loading the light intensity data matrix of the two-dimensional light intensity matrix; Perform inverse logarithmic transformation on the light intensity data .

[0008] In one embodiment, the low-pass filtering method is used to eliminate high-frequency noise to obtain a smooth logarithmic light intensity matrix, which includes: Set the order of the Butterworth filter to 2, set the cutoff frequency according to the length of the identified fiber, and set the normalized frequency value of the cutoff frequency; The Butterworth filter is used to perform zero-phase low-pass filtering on the light intensity data after the inverse logarithmic transformation to obtain a smoothed logarithmic light intensity matrix.

[0009] In one embodiment, setting the cutoff frequency according to the length of the identified optical fiber includes: Obtain the length of the identified optical fiber. When the length of the identified optical fiber is 0-10 km, set the cutoff frequency to 0.05; when the length of the identified optical fiber is 10-50 km, set the cutoff frequency to 0.001; when the length of the identified optical fiber is 50-100 km, set the cutoff frequency to 0.0005.

[0010] In one embodiment, performing quadratic polynomial fitting on the light intensity data within the specified spatial channel range of each frame of data in the logarithmic light intensity matrix to obtain a theoretical attenuation model under normal optical fiber transmission conditions includes: The fitting function used in the process of performing quadratic polynomial fitting on the light intensity data is set to a quadratic polynomial function, and the light intensity data within the specified spatial channel range of the optical fiber front section is curve fitted to form a theoretical attenuation curve.

[0011] In one embodiment, obtaining actual light intensity data and a fitting curve of each spatial channel, comparing the actual light intensity value with the fitted light intensity theoretical value, determining whether there is an abnormal deviation in the optical fiber signal, and if there is an abnormal deviation, determining the spatial channel location where the fiber break occurs includes: Comparing the actual light intensity value with the fitting intensity theoretical value of the fitting curve; When all actual light intensity values corresponding to each spatial channel are less than or equal to the fitting intensity theoretical value of the fitting curve, it is determined that there is no fiber breakage; When all actual light intensity values corresponding to any spatial channel are greater than the fitting intensity theoretical value of the fitting curve, the abnormal point where the actual light intensity value within the valid channel range is higher than the fitting intensity theoretical value is identified, it is determined that a fiber break occurs, and the spatial channel position where the fiber break occurs is returned.

[0012] On the other hand, a device for automatically identifying and locating broken fibers is provided, the device comprising: A data acquisition module is used to collect light intensity data at each time sampling point in the optical fiber distributed acousto-optic sensing system to form a two-dimensional light intensity matrix; A data processing module is used to perform an inverse logarithmic transformation on the light intensity matrix and use a low-pass filtering method to eliminate high-frequency noise to obtain a smooth logarithmic light intensity matrix; A module for obtaining a theoretical attenuation model is used to perform a quadratic polynomial fitting on the light intensity data within the range of the spatial channel specified in the previous section for each frame of data in the logarithmic light intensity matrix to obtain a theoretical attenuation model under normal optical fiber transmission conditions; A fitting curve processing module is used to add a positive offset to the theoretical attenuation model to obtain a fitting curve, ensuring that the theoretical value of the fitting light intensity of the fitting curve is greater than the actual light intensity value within the normal range of the spatial channel, and that the theoretical value of the fitting light intensity of the fitting curve is less than or equal to the actual light intensity value within the abnormal range of the spatial channel; The fiber break identification module is used to obtain the actual light intensity data and fitting curve of each spatial channel, compare the actual light intensity value with the fitted light intensity theoretical value, and determine whether there is an abnormal deviation in the optical fiber signal. If there is an abnormal deviation, the spatial channel location where the fiber break occurred is determined.

[0013] On the other hand, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for automatically identifying and locating broken fibers when executing the computer program.

[0014] In another aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for automatically identifying and locating a broken fiber are implemented.

[0015] The aforementioned automatic fiber break identification and location method, device, computer equipment, and storage medium convert the optical intensity matrix into decibel values by performing an inverse logarithmic transformation. This compresses the dynamic range of optical intensity to amplify the fiber break signal and enhance the characteristics of the fiber break point. Low-pass filtering is then used to eliminate high-frequency noise, resulting in a smoothed logarithmic optical intensity matrix. A quadratic polynomial fit is performed on the optical intensity data within a specified spatial channel range in the preceding section to obtain a theoretical attenuation model under normal optical fiber transmission conditions. This method can quickly identify the optical intensity mutation point, enabling accurate identification and real-time monitoring of the fiber break location. Compared to traditional technologies, this method is more economical and suitable for real-time monitoring, particularly in scenarios requiring rapid fault response, such as long-distance trunk lines and data centers. It simultaneously meets the dual requirements of reducing operation and maintenance costs and improving network availability. Compared to traditional methods that rely on fixed thresholds or manual interpretation, this method possesses greater adaptability and robustness, operating stably in a variety of noise environments and transmission conditions. Furthermore, the algorithm's low computational complexity makes it suitable for deployment in edge devices or real-time monitoring systems, providing efficient and reliable support for the operation and maintenance of actual optical fiber networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 This is a flow chart of a method for automatically identifying and locating broken fibers in one embodiment of the present application; Figure 2 This is a data processing and analysis logic diagram of a method for automatically identifying and locating broken fibers in one embodiment of the present application; Figure 3 This is a schematic diagram of the process of identifying and locating a short-distance optical fiber break in Example 1 of the present application; Figure 4 This is a flow chart of identifying and locating a long-distance optical fiber break in Example 2 of the present application; Figure 5 This is a schematic diagram of a process for identifying whether a normal optical fiber is broken in Example 3 of the present application; Figure 6 This is a structural block diagram of a device for automatically identifying and locating broken fibers in one embodiment of the present application; Figure 7 This is a diagram of the internal structure of a computer device in one embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0019] In one embodiment, Figure 1 As shown, a method for automatically identifying and locating a broken fiber is provided, comprising the following steps: Step S1, collecting light intensity data of each time sampling point in the optical fiber distributed acousto-optic sensing system to form a two-dimensional light intensity matrix; Step S2, performing an inverse logarithmic transformation on the light intensity matrix and using a low-pass filtering method to eliminate high-frequency noise to obtain a smoothed logarithmic light intensity matrix; Step S3, performing quadratic polynomial fitting on the light intensity data within the range of the previously specified spatial channel for each frame of data in the logarithmic light intensity matrix to obtain a theoretical attenuation model under normal optical fiber transmission conditions; Step S4, adding a positive offset to the theoretical attenuation model to obtain a fitting curve, ensuring that the fitting light intensity theoretical value of the fitting curve is greater than the actual light intensity value within the normal range of the spatial channel, and the fitting light intensity theoretical value of the fitting curve is less than or equal to the actual light intensity value within the abnormal range of the spatial channel; Step S5, obtain the actual light intensity data and fitting curve of each spatial channel, compare the actual light intensity value with the fitted light intensity theoretical value, and determine whether there is an abnormal deviation in the optical fiber signal. If there is an abnormal deviation, determine the spatial channel position where the fiber break occurs.

[0020] Specifically, by performing an inverse logarithmic transformation on the optical intensity matrix and converting it into decibel values, the dynamic range of the optical intensity can be compressed to amplify the fiber break signal, enhancing the fiber break point characteristics. A low-pass filtering method is used to eliminate high-frequency noise, resulting in a smooth logarithmic optical intensity matrix. A quadratic polynomial fitting is performed on the optical intensity data within the specified spatial channel range of the previous section to obtain a theoretical attenuation model under normal optical fiber transmission conditions. This allows for rapid identification of optical intensity mutation points, enabling accurate identification and real-time monitoring of the optical fiber break location. Compared to traditional technologies, this method is more economical and suitable for real-time monitoring, and is particularly suitable for scenarios requiring rapid response to faults, such as long-distance trunk lines and data centers. It also meets the dual needs of reducing operation and maintenance costs and improving network availability. Compared to traditional methods that rely on fixed thresholds or manual interpretation, this method has stronger adaptability and robustness, and can operate stably in a variety of noise environments and transmission conditions. At the same time, the algorithm has a low computational complexity and is suitable for deployment in edge devices or real-time monitoring systems, providing efficient and reliable support for actual optical fiber network operation and maintenance.

[0021] In this embodiment, collecting light intensity data at each time sampling point in the optical fiber distributed acousto-optic sensing system to form a two-dimensional light intensity matrix includes: The sampling rate of each time sampling point in the optical fiber distributed acousto-optic sensing system is set to 10 Hz, and the light intensity data of each spatial channel is collected to form a two-dimensional light intensity matrix. The light intensity data matrix of the two-dimensional light intensity matrix is obtained as follows: = (T, N), where T is the number of sampling frames and N is the number of spatial channels.

[0022] In this embodiment, performing an inverse logarithmic transformation on the light intensity matrix includes: Loading the light intensity data matrix of the two-dimensional light intensity matrix; Perform inverse logarithmic transformation on the light intensity data .

[0023] The inverse logarithmic transformation , can compress the dynamic range of light intensity, compress large values, and amplify small values, making the data smoother and easier to analyze. , can enhance the fiber break point characteristics. The signal will drop significantly after the fiber break point, and this mutation will become more obvious after taking the logarithm, which is beneficial for subsequent fitting and judgment. , you can invert the intensity, mapping high values to low values, and enhancing weak signals.

[0024] In this embodiment, the low-pass filtering method is used to eliminate high-frequency noise to obtain a smooth logarithmic light intensity matrix, which includes: Set the order of the Butterworth filter to 2, set the cutoff frequency according to the length of the identified fiber, and set the normalized frequency value of the cutoff frequency; The Butterworth filter is used to perform zero-phase low-pass filtering on the light intensity data after the inverse logarithmic transformation to obtain a smoothed logarithmic light intensity matrix.

[0025] In this embodiment, setting the cutoff frequency according to the length of the identified optical fiber includes: Obtain the length of the identified optical fiber. When the length of the identified optical fiber is 0-10 km, set the cutoff frequency to 0.05; when the length of the identified optical fiber is 10-50 km, set the cutoff frequency to 0.001; and when the length of the identified optical fiber is 50-100 km, set the cutoff frequency to 0.0005. The details are shown in Table 1.

[0026] Table 1 Cutoff frequency setting reference table

[0027] In this embodiment, performing quadratic polynomial fitting on the light intensity data within the range of the previously specified spatial channel for each frame of data in the logarithmic light intensity matrix to obtain a theoretical attenuation model under normal optical fiber transmission conditions includes: The fitting function used in the process of performing quadratic polynomial fitting on the light intensity data is set to a quadratic polynomial function, and the light intensity data within the specified spatial channel range of the optical fiber front section is curve fitted to form a theoretical attenuation curve.

[0028] The quadratic polynomial function is in the form The added positive offset can make the fitting theoretical value always higher than the normal value, avoiding misjudgment when identifying and locating broken fibers based on strength comparison. That is, the fitting curve is: In this embodiment, obtaining actual light intensity data and a fitting curve for each spatial channel, comparing the actual light intensity value with the fitted light intensity theoretical value, determining whether there is an abnormal deviation in the optical fiber signal, and if there is an abnormal deviation, determining the spatial channel location where the fiber break occurs includes: Comparing the actual light intensity value with the fitting intensity theoretical value of the fitting curve; When all actual light intensity values corresponding to each spatial channel are less than or equal to the fitting intensity theoretical value of the fitting curve, it is determined that there is no fiber breakage; When all actual light intensity values corresponding to any spatial channel are greater than the fitting intensity theoretical value of the fitting curve, the abnormal point where the actual light intensity value within the valid channel range is higher than the fitting intensity theoretical value is identified, it is determined that a fiber break occurs, and the spatial channel position where the fiber break occurs is returned.

[0029] During breakpoint identification and location, the preprocessed light intensity is normally lower than the theoretical value. When a fiber break occurs, the light intensity suddenly increases and exceeds the fitted value. The last channel within the valid channel range (excluding the interference segments at the beginning and end) where the actual light intensity is lower than the fitted value is selected as the starting location of the fiber break.

[0030] In the aforementioned automatic fiber break identification and location method, by performing an inverse logarithmic transformation on the optical intensity matrix and converting it into decibel values, the dynamic range of the optical intensity is compressed to amplify the fiber break signal and enhance the characteristics of the fiber break point. Low-pass filtering is then used to eliminate high-frequency noise, resulting in a smoothed logarithmic optical intensity matrix. A quadratic polynomial fit is then performed on the optical intensity data within the specified spatial channel range in the preceding section to obtain a theoretical attenuation model under normal optical fiber transmission conditions. This method can quickly identify the optical intensity mutation point, enabling accurate identification and real-time monitoring of the fiber break location. Compared to traditional technologies, this method is more economical and suitable for real-time monitoring, particularly in scenarios requiring rapid fault response, such as long-distance trunk lines and data centers. It simultaneously meets the dual requirements of reducing operation and maintenance costs and improving network availability. Compared to traditional methods that rely on fixed thresholds or manual interpretation, this method possesses greater adaptability and robustness, operating stably in a variety of noise environments and transmission conditions. Furthermore, the algorithm's low computational complexity makes it suitable for deployment in edge devices or real-time monitoring systems, providing efficient and reliable support for the operation and maintenance of actual optical fiber networks.

[0031] The data processing and analysis process of the present invention is as follows Figure 2 As shown, it covers the entire process of data acquisition, preprocessing, fitting modeling, identification and positioning.

[0032] Example 1: Identifying and locating short-distance fiber breaks like Figure 3 As shown in the figure, taking a 25km optical fiber as an example, the spatial sampling interval is 0.2m / channel, which means a total of 125,000 channels. The actual channel number where the fiber break occurred is: 100477. The implementation steps are as follows: S11: Collect 10 frames (sampling rate 10 Hz) of light intensity data through the distributed fiber optic sensing system; S12: Load light intensity data matrix; S13: Perform inverse logarithmic transformation on the light intensity data ; S14: Set the filter order to 2 and the cutoff frequency to a Butterworth low-pass filter less than 0.001; S15: Perform zero-phase low-pass filtering on the data; S16: Take one frame of data for analysis and set the number of frames to 3; the actual value of the third frame of data extracted is raw_data=log_matrix[3, N]; S17: Perform a quadratic function based on the normal data within the normal channel (K1, K2) of the previous section Fitting modeling, setting K1=10000, K2=30000, after fitting calculation, within the normal channel range, the slope of the fitting curve and the actual light intensity curve are similar; S18: Add a positive offset of offset=4.2 to the quadratic function obtained by fitting, and the fitting value on the fitting curve is , ensure that in the normal channel range, the fitting value is greater than the actual value, and in the abnormal range, the fitting value is less than the actual value; S19: Compare the theoretical value and the actual value of each channel, find all channel number indexes whose actual values are smaller than the fitted values, and the last index position is the channel number where the fiber break starts; S110: After calculation, the channel number of the theoretical fiber break point is 100399, which differs from the actual fiber break point 10477 by 78 channels, or 15.6 meters.

[0033] Example 2: Identifying and locating long-distance optical fiber breaks like Figure 4 As shown in the figure, taking a 100km optical fiber as an example, the spatial sampling interval is 0.2m / channel, that is, a total of 500,000 channels. The actual channel number where the fiber break occurred is: 299180. The implementation steps are as follows: S21: Collect 10 frames (sampling rate 10 Hz) of light intensity data through the distributed fiber optic sensing system; S22: Load light intensity data matrix; S23: Perform inverse logarithmic transformation on the light intensity data ; S24: Set the filter order to 2 and the cutoff frequency to a Butterworth low-pass filter less than 0.0005; S25: perform zero-phase low-pass filtering on the data; S26: Take one frame of data for analysis, set the number of frames to 3; the actual value of the third frame of data extracted is raw_data=log_matrix[3, N]; S27: Perform a quadratic function based on the normal data within the normal channel (K1, K2) of the previous section Fitting modeling, setting K1=10000, K2=250000, after fitting calculation, within the normal channel range, the slope of the fitting curve and the actual light intensity curve are similar; S28: Add a positive offset of offset=1.6 to the quadratic function obtained by fitting, and the fitting value on the fitting curve is , ensure that in the normal channel range, the fitting value is greater than the actual value, and in the abnormal range, the fitting value is less than the actual value; S29: Compare the theoretical value and the actual value of each channel, find all channel number indexes whose actual values are smaller than the fitted values, and the last index position is the channel number where the fiber break starts; S210: After calculation, the channel number of the theoretical fiber break point is 299027, which differs from the actual fiber break point 299180 by 153 channels, or 30.6 meters.

[0034] Example 3: Identifying whether a normal optical fiber is broken like Figure 5 As shown in the figure, taking an 80km section of normal optical fiber as an example, it identifies whether there is a fiber break. If there is a fiber break, it returns the location of the fiber break point. If there is no fiber break, it returns None. The implementation steps are as follows: S31: Collect 10 frames (sampling rate 10 Hz) of light intensity data through the distributed fiber optic sensing system; S32: Load light intensity data matrix; S33: Perform inverse logarithmic transformation on light intensity data ; S34: Set the filter order to 2 and the cutoff frequency to a Butterworth low-pass filter less than 0.0005; S35: performing zero-phase low-pass filtering on the data; S36: Take one frame of data for analysis, set the number of frames to 3; the actual value of the third frame of data extracted is raw_data=log_matrix[3, N]; S37: Perform a quadratic function based on the normal data within the normal channel (K1, K2) of the previous section Fitting modeling, setting K1=10000, K2=250000, after fitting calculation, within the normal channel range, the slope of the fitting curve and the actual light intensity curve are similar; S38: Add a positive offset of offset=1.6 to the quadratic function obtained by fitting, and the fitting value on the fitting curve is , ensure that in the normal channel range, the fitting value is greater than the actual value, and in the abnormal range, the fitting value is less than the actual value; S39: Compare the theoretical value and the actual value of each channel, find all channel number indexes whose actual values are smaller than the fitted values, and the last index position is the channel number where the fiber break starts; S310: After calculation, the fiber point channel position is: None, no fiber breakage.

[0035] Compared with existing methods for identifying and locating broken fibers, this application has the following advantages: High-precision positioning: This method models the spatial variation trend of light intensity (e.g., quadratic curve) and can still fit the normal attenuation trend before the fiber break point in noisy or weak signal environments, thereby more accurately identifying the deviation point; Strong real-time performance. Compared with the periodic measurement and complex optical system of OTDR, this method is completely based on the light intensity data in the existing DAS system. The required computational effort is low, and operations such as logarithmic compression, filtering, fitting, and comparison can be processed quickly, making it suitable for real-time fiber break alarms. Good robustness: this method combines filtering and logarithmic compression, and has strong noise resistance; With strong adaptability and good resistance to local interference, this embodiment is insensitive to abnormal fluctuations caused by sudden changes in local light intensity, and can effectively avoid interference from local noise misjudgment. Parameter adjustment is simple, and the speed of adapting to different scenarios is fast, with strong practical application flexibility.

[0036] In one embodiment, Figure 6 As shown, a device 10 for automatically identifying and locating broken fibers is provided, comprising: a data acquisition module 1, a data processing module 2, a module for acquiring a theoretical attenuation model 3, a fitting curve processing module 4, and a broken fiber identification module 5.

[0037] The data acquisition module 1 is used to collect light intensity data at each time sampling point in the optical fiber distributed acousto-optic sensing system to form a two-dimensional light intensity matrix.

[0038] The data processing module 2 is used to perform an inverse logarithmic transformation on the light intensity matrix and use a low-pass filtering method to eliminate high-frequency noise to obtain a smooth logarithmic light intensity matrix.

[0039] The module 3 for obtaining the theoretical attenuation model is used to perform quadratic polynomial fitting on the light intensity data within the range of the preceding specified spatial channel for each frame of data in the logarithmic light intensity matrix, so as to obtain the theoretical attenuation model under normal optical fiber transmission conditions.

[0040] The fitting curve processing module 4 is used to add a positive offset to the theoretical attenuation model to obtain a fitting curve, ensuring that the theoretical value of the fitting light intensity of the fitting curve is greater than the actual light intensity value within the normal range of the spatial channel, and the theoretical value of the fitting light intensity of the fitting curve is less than or equal to the actual light intensity value within the abnormal range of the spatial channel.

[0041] The fiber break identification module 5 is used to obtain the actual light intensity data and fitting curve of each spatial channel, compare the actual light intensity value with the fitted light intensity theoretical value, and determine whether there is an abnormal deviation in the optical fiber signal. If there is an abnormal deviation, the spatial channel position where the fiber break occurs is determined.

[0042] In this embodiment, collecting light intensity data at each time sampling point in the optical fiber distributed acousto-optic sensing system to form a two-dimensional light intensity matrix includes: The sampling rate of each time sampling point in the optical fiber distributed acousto-optic sensing system is set to 10 Hz, and the light intensity data of each spatial channel is collected to form a two-dimensional light intensity matrix. The light intensity data matrix of the two-dimensional light intensity matrix is obtained as follows: = (T, N), where T is the number of sampling frames and N is the number of spatial channels.

[0043] In this embodiment, performing an inverse logarithmic transformation on the light intensity matrix includes: Loading the light intensity data matrix of the two-dimensional light intensity matrix; Perform inverse logarithmic transformation on the light intensity data .

[0044] In this embodiment, the low-pass filtering method is used to eliminate high-frequency noise to obtain a smooth logarithmic light intensity matrix, which includes: Set the order of the Butterworth filter to 2, set the cutoff frequency according to the length of the identified fiber, and set the normalized frequency value of the cutoff frequency; The Butterworth filter is used to perform zero-phase low-pass filtering on the light intensity data after the inverse logarithmic transformation to obtain a smoothed logarithmic light intensity matrix.

[0045] In this embodiment, setting the cutoff frequency according to the length of the identified optical fiber includes: Obtain the length of the identified optical fiber. When the length of the identified optical fiber is 0-10 km, set the cutoff frequency to 0.05; when the length of the identified optical fiber is 10-50 km, set the cutoff frequency to 0.001; when the length of the identified optical fiber is 50-100 km, set the cutoff frequency to 0.0005.

[0046] In this embodiment, performing quadratic polynomial fitting on the light intensity data within the range of the previously specified spatial channel for each frame of data in the logarithmic light intensity matrix to obtain a theoretical attenuation model under normal optical fiber transmission conditions includes: The fitting function used in the process of performing quadratic polynomial fitting on the light intensity data is set to a quadratic polynomial function, and the light intensity data within the specified spatial channel range of the optical fiber front section is curve fitted to form a theoretical attenuation curve.

[0047] In this embodiment, obtaining actual light intensity data and a fitting curve for each spatial channel, comparing the actual light intensity value with the fitted light intensity theoretical value, determining whether there is an abnormal deviation in the optical fiber signal, and if there is an abnormal deviation, determining the spatial channel location where the fiber break occurs includes: Comparing the actual light intensity value with the fitting intensity theoretical value of the fitting curve; When all actual light intensity values corresponding to each spatial channel are less than or equal to the fitting intensity theoretical value of the fitting curve, it is determined that there is no fiber breakage; When all actual light intensity values corresponding to any spatial channel are greater than the fitting intensity theoretical value of the fitting curve, the abnormal point where the actual light intensity value within the valid channel range is higher than the fitting intensity theoretical value is identified, it is determined that a fiber break occurs, and the spatial channel position where the fiber break occurs is returned.

[0048] In the aforementioned automatic fiber break identification and location device, by performing an inverse logarithmic transformation on the optical intensity matrix and converting it into decibel values, the dynamic range of the optical intensity is compressed to amplify the fiber break signal, enhancing the characteristics of the fiber break point. Low-pass filtering is then used to eliminate high-frequency noise, resulting in a smoothed logarithmic optical intensity matrix. A quadratic polynomial fit is then performed on the optical intensity data within the specified spatial channel range in the preceding section to obtain a theoretical attenuation model under normal optical fiber transmission conditions. This allows for rapid identification of optical intensity mutation points, enabling accurate identification and real-time monitoring of the fiber break location. Compared to traditional technologies, this method is more economical and suitable for real-time monitoring, making it particularly suitable for scenarios requiring rapid fault response, such as long-distance trunk lines and data centers. It simultaneously meets the dual requirements of reducing operation and maintenance costs and improving network availability. Compared to traditional methods that rely on fixed thresholds or manual interpretation, this method possesses greater adaptability and robustness, operating stably in a variety of noise environments and transmission conditions. Furthermore, the algorithm's low computational complexity makes it suitable for deployment in edge devices or real-time monitoring systems, providing efficient and reliable support for the operation and maintenance of actual optical fiber networks.

[0049] The specific definitions of the automatic fiber break identification and location device can be found in the definitions of the automatic fiber break identification and location method above and will not be repeated here. Each module in the automatic fiber break identification and location device described above can be implemented in whole or in part through software, hardware, or a combination thereof. Each of these modules can be embedded in or independent of a processor in a computer device in hardware form, or stored in a computer device memory in software form, allowing the processor to call and execute the corresponding operations of each module.

[0050] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 7 As shown. The computer device includes a processor, memory, network interface, and database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store data for automatic fiber break identification and location. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for automatic fiber break identification and location is implemented.

[0051] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0052] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are performed: Collect the light intensity data of each time sampling point in the fiber optic distributed acousto-optic sensing system to form a two-dimensional light intensity matrix; Performing an inverse logarithmic transformation on the light intensity matrix and using a low-pass filtering method to eliminate high-frequency noise to obtain a smooth logarithmic light intensity matrix; For each frame of data in the logarithmic light intensity matrix, a quadratic polynomial fitting is performed on the light intensity data within the range of the spatial channel specified in the previous section to obtain a theoretical attenuation model under a normal optical fiber transmission state; Adding a positive offset to the theoretical attenuation model to obtain a fitting curve, ensuring that the fitting light intensity theoretical value of the fitting curve is greater than the actual light intensity value within the normal range of the spatial channel, and the fitting light intensity theoretical value of the fitting curve is less than or equal to the actual light intensity value within the abnormal range of the spatial channel; Obtain the actual light intensity data and fitting curve of each spatial channel, compare the actual light intensity value with the fitted light intensity theoretical value, and determine whether there is an abnormal deviation in the optical fiber signal. If there is an abnormal deviation, determine the spatial channel location where the fiber break occurs.

[0053] For specific limitations on the steps implemented when the processor executes the computer program, please refer to the above limitations on the method for automatic fiber break identification and location, which will not be repeated here.

[0054] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: Collect the light intensity data of each time sampling point in the fiber optic distributed acousto-optic sensing system to form a two-dimensional light intensity matrix; Performing an inverse logarithmic transformation on the light intensity matrix and using a low-pass filtering method to eliminate high-frequency noise to obtain a smooth logarithmic light intensity matrix; For each frame of data in the logarithmic light intensity matrix, a quadratic polynomial fitting is performed on the light intensity data within the range of the spatial channel specified in the previous section to obtain a theoretical attenuation model under a normal optical fiber transmission state; Adding a positive offset to the theoretical attenuation model to obtain a fitting curve, ensuring that the fitting light intensity theoretical value of the fitting curve is greater than the actual light intensity value within the normal range of the spatial channel, and the fitting light intensity theoretical value of the fitting curve is less than or equal to the actual light intensity value within the abnormal range of the spatial channel; Obtain the actual light intensity data and fitting curve of each spatial channel, compare the actual light intensity value with the fitted light intensity theoretical value, and determine whether there is an abnormal deviation in the optical fiber signal. If there is an abnormal deviation, determine the spatial channel location where the fiber break occurs.

[0055] For specific limitations on the steps implemented when the computer program is executed by the processor, please refer to the above limitations on the method for automatic fiber break identification and location, which will not be repeated here.

[0056] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0057] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0058] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and such modifications and improvements are intended to fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for automatically identifying and locating broken fibers, characterized in that: include: Collect the light intensity data of each time sampling point in the fiber optic distributed acousto-optic sensing system to form a two-dimensional light intensity matrix; Performing an inverse logarithmic transformation on the light intensity matrix and using a low-pass filtering method to eliminate high-frequency noise to obtain a smooth logarithmic light intensity matrix; For each frame of data in the logarithmic light intensity matrix, a quadratic polynomial fitting is performed on the light intensity data within the range of the previously specified spatial channel to obtain a theoretical attenuation model under a normal optical fiber transmission state; Adding a positive offset to the theoretical attenuation model to obtain a fitting curve, ensuring that the fitting light intensity theoretical value of the fitting curve is greater than the actual light intensity value within the normal range of the spatial channel, and the fitting light intensity theoretical value of the fitting curve is less than or equal to the actual light intensity value within the abnormal range of the spatial channel; Obtain the actual light intensity data and fitting curve of each spatial channel, compare the actual light intensity value with the fitted light intensity theoretical value, and determine whether there is an abnormal deviation in the optical fiber signal. If there is an abnormal deviation, determine the spatial channel location where the fiber break occurs.

2. The method for automatically identifying and locating broken fibers according to claim 1, characterized in that: Collect the light intensity data of each time sampling point in the fiber optic distributed acousto-optic sensing system to form a two-dimensional light intensity matrix including: The sampling rate of each time sampling point in the optical fiber distributed acousto-optic sensing system is set to 10 Hz, and the light intensity data of each spatial channel is collected to form a two-dimensional light intensity matrix. The light intensity data matrix of the two-dimensional light intensity matrix is obtained as follows: = (T, N), where T is the number of sampling frames and N is the number of spatial channels.

3. The method for automatically identifying and locating broken fibers according to claim 1, characterized in that: The performing the inverse logarithmic transformation on the light intensity matrix comprises: Loading the light intensity data matrix of the two-dimensional light intensity matrix; Perform inverse logarithmic transformation on the light intensity data .

4. The method for automatically identifying and locating broken fibers according to claim 1, wherein: The method of using a low-pass filtering method to eliminate high-frequency noise and obtain a smooth logarithmic light intensity matrix includes: Set the order of the Butterworth filter to 2, set the cutoff frequency according to the length of the identified fiber, and set the normalized frequency value of the cutoff frequency; The Butterworth filter is used to perform zero-phase low-pass filtering on the light intensity data after the inverse logarithmic transformation to obtain a smoothed logarithmic light intensity matrix.

5. The method for automatically identifying and locating broken fibers according to claim 4, characterized in that: The setting of the cutoff frequency according to the length of the identified optical fiber comprises: Obtain the length of the identified optical fiber. When the length of the identified optical fiber is 0-10 km, set the cutoff frequency to 0.05; when the length of the identified optical fiber is 10-50 km, set the cutoff frequency to 0.001; when the length of the identified optical fiber is 50-100 km, set the cutoff frequency to 0.0005.

6. The method for automatically identifying and locating broken fibers according to claim 1, characterized in that: The performing of quadratic polynomial fitting on the light intensity data within the range of the previously specified spatial channel for each frame of data in the logarithmic light intensity matrix to obtain a theoretical attenuation model under a normal optical fiber transmission state comprises: The fitting function used in the process of performing quadratic polynomial fitting on the light intensity data is set to a quadratic polynomial function, and the light intensity data within the specified spatial channel range of the optical fiber front section is curve fitted to form a theoretical attenuation curve.

7. The method for automatically identifying and locating broken fibers according to claim 6, characterized in that: The obtaining of actual light intensity data and a fitting curve of each spatial channel, comparing the actual light intensity value with the fitted light intensity theoretical value, determining whether there is an abnormal deviation in the optical fiber signal, and if there is an abnormal deviation, determining the spatial channel location where the fiber break occurs includes: Comparing the actual light intensity value with the fitting intensity theoretical value of the fitting curve; When all actual light intensity values corresponding to each spatial channel are less than or equal to the fitting intensity theoretical value of the fitting curve, it is determined that there is no fiber breakage; When all actual light intensity values corresponding to any spatial channel are greater than the fitting intensity theoretical value of the fitting curve, the abnormal point where the actual light intensity value within the valid channel range is higher than the fitting intensity theoretical value is identified, it is determined that a fiber break occurs, and the spatial channel position where the fiber break occurs is returned.

8. A device for automatically identifying and locating broken fibers, characterized in that: The device comprises: A data acquisition module is used to collect light intensity data at each time sampling point in the optical fiber distributed acousto-optic sensing system to form a two-dimensional light intensity matrix; A data processing module is used to perform an inverse logarithmic transformation on the light intensity matrix and use a low-pass filtering method to eliminate high-frequency noise to obtain a smooth logarithmic light intensity matrix; A module for obtaining a theoretical attenuation model is used to perform a quadratic polynomial fitting on the light intensity data within the range of the spatial channel specified in the previous section for each frame of data in the logarithmic light intensity matrix to obtain a theoretical attenuation model under normal optical fiber transmission conditions; A fitting curve processing module is used to add a positive offset to the theoretical attenuation model to obtain a fitting curve, ensuring that the theoretical value of the fitting light intensity of the fitting curve is greater than the actual light intensity value within the normal range of the spatial channel, and that the theoretical value of the fitting light intensity of the fitting curve is less than or equal to the actual light intensity value within the abnormal range of the spatial channel; The fiber break identification module is used to obtain the actual light intensity data and fitting curve of each spatial channel, compare the actual light intensity value with the fitted light intensity theoretical value, and determine whether there is an abnormal deviation in the optical fiber signal. If there is an abnormal deviation, the spatial channel location where the fiber break occurred is determined.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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