Optical time domain reflectometer event detection method and system

Abstract

This invention discloses a method and system for event detection using an optical time-domain reflectometry (OTDR). The method includes the following steps: S1: Measuring the optical fiber using an OTD to obtain an OTD test curve; S2: Denoising the test curve data to obtain denoised test curve data; S3: Performing discrete wavelet transform and a constant false alarm rate (CFAR) algorithm based on the transform exponent to obtain wavelet coefficient reflection event points and wavelet coefficient non-reflection event points; S4: Merging the wavelet coefficient reflection event points and wavelet coefficient non-reflection event points and sorting them according to their positions; S5: Mapping the sorted wavelet coefficient reflection event points and wavelet coefficient non-reflection event points onto the test curve to obtain the true positions of the reflection event points and non-reflection event points, thus completing the OTD event detection. This invention can quickly and accurately locate targets, including nearby multiple targets and weak non-reflection events.

Description

Technical Field

[0001] This invention belongs to the field of optical time domain reflectometer event detection technology, specifically relating to an optical time domain reflectometer event detection method and system. Background Technology

[0002] With the development of fiber optic communication, and the major trends of 5G and fiber-to-the-home (FTTH), the use of fiber optics has increased significantly, making deployment more complex. Fiber optic communication networks typically consist of numerous optical fibers, connectors, splitters, and other optical components. These components are intertwined in long-distance fiber optic links, complicating network maintenance and troubleshooting. Optical Time Domain Reflectometers (OTDRs), as single-ended, lossless fiber optic measurement instruments, are widely used. OTDR technology provides engineers with a reliable means to detect and locate events and faults in fiber optic links, helping to maintain network performance and reliability. It is also one of the primary methods for locating faults in fiber optic links; whether the fault is caused by fiber breakage, loose connection, bending, scratches, or other factors, the OTDR can pinpoint the location of the fault, facilitating rapid repair.

[0003] Existing OTDR methods are mainly based on the two-point method and the least squares method. Although these methods are fast and efficient, their detection performance degrades significantly for curves with high noise. More advanced methods include support vector machine algorithms and neural networks to detect OTDR events, but these algorithms have very high computational requirements and time demands, which are not conducive to the fast response required by fiber optic detection. Therefore, an optical time-domain transmitter event detection method and system have been invented, which can quickly and accurately locate targets, including nearby multiple targets and weak non-reflective events. Summary of the Invention

[0004] This invention aims to address the shortcomings of existing technologies by proposing a method and system for detecting events in an optical time-domain reflectometer, enabling rapid and accurate location of events in an optical time-domain reflectometer.

[0005] To achieve the above objectives, the present invention provides the following solution:

[0006] An event detection method for an optical time-domain emitter includes the following steps:

[0007] S1: Use an optical time domain reflectometer to measure the optical fiber and obtain the optical time domain reflectometer test curve;

[0008] S2: Denoise the test curve data to obtain the denoised optical time domain reflectometer test curve data;

[0009] S3: Perform discrete wavelet transform and constant false alarm rate (CFAR) algorithm based on transform exponent on the denoised test curve data to obtain wavelet coefficient reflection event points and wavelet coefficient non-reflection event points.

[0010] S4: Merge the wavelet coefficient reflection event points and the wavelet coefficient non-reflection event points, and sort them according to their positions;

[0011] S5: Map the wavelet coefficient reflection event points and wavelet coefficient non-reflection event points, which have been sorted by position, onto the test curve to obtain the true positions of the reflection event points and non-reflection event points, and complete the optical time domain reflectometer event detection.

[0012] Preferably, in step S3, the method for obtaining the wavelet coefficient reflection event point is as follows:

[0013] Discrete wavelet transform is performed on the data of the denoised test curve, where the wavelet basis is Haar and the wavelet decomposition level is 4. The high-frequency component wavelet coefficients A of the 4th level are extracted.

[0014] The high-frequency component wavelet coefficient A is divided into positive wavelet coefficient curves and negative wavelet coefficient curves for the reflection event, with zero as the boundary.

[0015] The constant false alarm rate algorithm based on the transform exponent is used to detect the positive wavelet coefficient curve of the reflection event, and the modulus maxima points exceeding the reflection event threshold are screened out to obtain the modulus maxima sequence of the reflection event.

[0016] The constant false alarm rate algorithm based on the transform exponent is used to detect the negative wavelet coefficient curve of the reflection event, and the points with modulo minima exceeding the threshold of the reflection event are screened out to obtain the modulo minima series of the reflection event.

[0017] Based on the matching threshold, the maximum points of the reflection event modulus in the column of maximum reflection event modulus are matched with the minimum points of the reflection event modulus in the column of minimum reflection event modulus to obtain the range of extreme points of the wavelet modulus of the reflection event.

[0018] Based on the extreme point interval of the wavelet modulus of the reflection event, the wavelet coefficient reflection event point is obtained.

[0019] Preferably, in step S3, the method for obtaining the non-reflection event points of the wavelet coefficients is as follows:

[0020] Discrete wavelet transform is performed on the data of the denoised test curve, where the wavelet basis is rbio3.1, the wavelet decomposition level is 4, and the high-frequency component wavelet coefficient B of the 4th level is extracted.

[0021] The negative part of the high-frequency component wavelet coefficient B is used as the negative wavelet coefficient curve of the non-reflection event. The non-reflection event negative wavelet coefficient curve is detected by the constant false alarm algorithm based on the transform exponent. The non-reflection event modulus minimum points that exceed the non-reflection event threshold are screened to obtain the non-reflection event modulus minimum value series.

[0022] Remove the non-reflection event modulus minimum points that are located in the interval of the wavelet modulus extreme points of the reflection event to obtain the non-reflection event points of the wavelet coefficients.

[0023] Preferably, the method for obtaining the reflection event threshold is as follows:

[0024] The system includes a reference unit, a protection unit, and a false alarm rate; the protection unit is located on both sides of the unit being detected.

[0025] Statistic VI is calculated based on the number of reference units, the mean of the reference unit samples, the variance of the reference unit samples, and the reference unit data.

[0026] The statistic MR is calculated based on the mean of the previous reference unit and the mean of the subsequent reference unit.

[0027] Calculate the threshold factor based on the false alarm rate and the reference unit;

[0028] Set judgment threshold T VI Based on the decision threshold T VI Using the statistic VI, determine whether the front reference unit and the back reference unit are uniform;

[0029] Set judgment threshold T MR Based on the decision threshold T MR Using the statistic MR, determine whether the mean of the previous reference unit is the same as the mean of the subsequent reference unit;

[0030] Based on the judgment results of whether the reference unit is uniform and whether the mean is the same, the threshold of the reflection event is obtained through a threshold calculation selection method.

[0031] Preferably, the formula for calculating the statistic VI is:

[0032]

[0033] In the formula, n is the number of reference units. x is the mean of the reference unit samples. i For reference cell data, The variance of the reference unit sample;

[0034] The formula for calculating the statistic MR is:

[0035]

[0036] In the formula, This is the mean of the first half of the reference unit. This is the mean of the second half of the reference unit.

[0037] Preferably, the threshold factor is calculated using the following formula:

[0038]

[0039] In the formula, α is the threshold factor, N is the reference unit, and Pfa is the false alarm rate.

[0040] Preferably, the threshold calculation and selection method is as follows:

[0041] Calculate the sum of the data in the preceding reference cell, ∑A; the sum of the data in the following reference cell, ∑B; and the sum of the data in the entire reference cell, ∑AB.

[0042] If the front reference unit is uniform, the rear reference unit is uniform, and the mean values ​​of the front and rear reference units are different, then the reflection event threshold is α. N / 2 max(∑A, ∑B);

[0043] If the front reference cell is non-uniform and the rear reference cell is uniform, then the reflection event threshold is α. N / 2 ∑B;

[0044] If the front reference cell is uniform and the rear reference cell is non-uniform, then the reflection event threshold is α. N / 2 ∑A;

[0045] If the front reference cell is non-uniform and the rear reference cell is non-uniform, then the reflection event threshold is α. N / 2min(∑A,∑B).

[0046] The present invention also provides an event detection system for an optical time-domain reflectometer, wherein the method used in the system includes: an optical time-domain reflectometer curve data acquisition module, a noise reduction module, an event detection module, a sorting module, and a mapping module;

[0047] The optical time domain reflectometer curve data acquisition module is used to measure optical fiber using an optical time domain reflectometer and obtain the optical time domain reflectometer test curve.

[0048] The denoising module is used to denoise the test curve data to obtain denoised optical time domain reflectometer test curve data.

[0049] The event detection module is used to perform discrete wavelet transform and constant false alarm rate algorithm based on transform exponent on the denoised test curve data to obtain wavelet coefficient reflection event points and wavelet coefficient non-reflection event points.

[0050] The sorting module is used to merge the wavelet coefficient reflection event points and the wavelet coefficient non-reflection event points, and sort them according to their positions.

[0051] The mapping module is used to map the wavelet coefficient reflection event points and the wavelet coefficient non-reflection event points, which have been sorted by position, onto the test curve to obtain the true positions of the reflection event points and non-reflection event points, and to complete the event detection of the optical time domain reflectometer.

[0052] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0053] Compared to traditional OTDR event detection algorithms, using discrete wavelet transform technology to detect OTDR event point locations can provide better resolution, accuracy, and multi-scale analysis capabilities, helping to eliminate noise and interference. It can also better handle non-reflection events, and the constant false alarm rate algorithm can provide adaptive threshold processing, thus having significant advantages in fiber optic event point detection.

[0054] Better resolution and accuracy: Discrete wavelet transform can analyze signals at different scales, thus providing better time and frequency resolution. This allows it to more accurately locate event points in optical fibers, whether small reflection events or attenuation events.

[0055] Multi-scale analysis capability: Discrete wavelet transform can decompose signals at multiple scales, meaning it can capture features at different scales, making detection more comprehensive and accurate. This is very useful when dealing with different types of events, adapting well to both large-scale and small-scale events.

[0056] Noise and interference elimination: Discrete wavelet transform can decompose a signal into different frequency components, thereby filtering out some noise and interference during processing, making the detection of event points more stable and reliable.

[0057] Adaptive thresholding: The constant false alarm rate (CFAR) algorithm based on the transform exponent helps determine an appropriate threshold for detecting valid event points. This adaptive thresholding helps reduce false alarms and false negatives, improving detection accuracy.

[0058] Better detection of non-reflective events: Discrete wavelet transform can capture the descent characteristics of non-reflective events, making the detection of fiber bending, damage and other conditions more sensitive and effective.

[0059] Fast computation and implementation: Discrete wavelet transform can be computed quickly using efficient algorithms, making it suitable for scenarios requiring real-time or high-speed processing.

[0060] Saves human resources: No need for professional technicians to analyze curves, laying the foundation for automation and intelligence. Attached Figure Description

[0061] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0062] Figure 1 This is a flowchart of the event detection method for an optical time-domain transmitter according to an embodiment of the present invention;

[0063] Figure 2 This is a flowchart of the reflection event detection process according to an embodiment of the present invention;

[0064] Figure 3 This is a flowchart of a non-reflection event according to an embodiment of the present invention;

[0065] Figure 4 This is the OTDR data curve of an embodiment of the present invention;

[0066] Figure 5 This is the OTDR data curve after noise reduction according to an embodiment of the present invention;

[0067] Figure 6 This is a diagram of the wavelet coefficients of the fourth layer of Haar discrete wavelet transform according to an embodiment of the present invention;

[0068] Figure 7 This is a detection diagram of the fourth layer of positive wavelet coefficients in the Haar discrete wavelet transform according to an embodiment of the present invention;

[0069] Figure 8 This is a diagram of the wavelet coefficients of the fourth layer of wavelet transform in embodiment rbior3.1 of the present invention;

[0070] Figure 9 This is the detection diagram of the negative wavelet coefficients of the fourth layer of wavelet transform in embodiment rbior3.1 of the present invention;

[0071] Figure 10 This is an OTDR event point detection diagram according to an embodiment of the present invention. Detailed Implementation

[0072] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0073] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0074] Example 1

[0075] like Figure 1 As shown, an event detection method for an optical time-domain emitter includes the following steps:

[0076] S1: Measure the optical fiber using an optical time domain reflectometer (OTDR) to obtain the OTDR test curve. Figure 4 This is a graph of OTDR test data;

[0077] S2: Denoise the test curve data (smooth it) to obtain the denoised optical time-domain reflectometer test curve data. Figure 5 This is the denoised curve image;

[0078] S3: Perform discrete wavelet transform and constant false alarm rate (CFAR) algorithm based on transform exponent on the denoised test curve data to obtain wavelet coefficient reflection event points and wavelet coefficient non-reflection event points.

[0079] A further implementation method is, such as Figure 2 As shown, in step S3, the method for obtaining the wavelet coefficient reflection event points is as follows:

[0080] Discrete wavelet transform was performed on the denoised optical time-domain reflectometry curve data, with Haar as the wavelet basis and four wavelet decomposition levels. The high-frequency component wavelet coefficients A of the fourth level were extracted, as follows: Figure 6 As shown;

[0081] The wavelet coefficients A of the high-frequency components are divided into positive wavelet coefficient curves and negative wavelet coefficient curves for the reflection event, with zero as the boundary.

[0082] The constant false alarm rate algorithm based on the transform exponent (VI-CFAR) is used to detect the positive wavelet coefficient curve of the reflection event, and the modulus maxima points exceeding the reflection event threshold are screened out to obtain the modulus maxima sequence of the reflection event.

[0083] The constant false alarm rate algorithm based on the transform exponent is used to detect the negative wavelet coefficient curve of the reflection event, and the points with modulo minima exceeding the threshold of the reflection event are screened out to obtain the modulo minima series of the reflection event.

[0084] Based on the matching threshold S, the maximum points of the reflection event modulus in the column of maximum values ​​of the reflection event modulus are matched with the minimum points of the reflection event modulus in the column of minimum values ​​of the reflection event modulus to obtain the range of extreme points of the wavelet modulus of the reflection event.

[0085] Based on the extreme point interval of the wavelet modulus of the reflection event, the reflection event point of the wavelet coefficient is obtained.

[0086] Specifically, a distance S is set, which is the matching threshold S. The distances between the modulo maxima and modulo minima are compared one by one. For modulo maxima x within the distance S, ... a Minimum point of the modulus x b Select the minimum and maximum points of the modulus with the smallest distance to form a pair of modulus extrema intervals [minimum, maximum]. Continue this process to obtain a set of wavelet modulus extrema intervals for reflection events [minimum, maximum]. Modulus maximum point [Modulus minimum point] Modulus maximum point ],......];

[0087] A further implementation method is to obtain the reflection event threshold as follows:

[0088] The reference unit N, the protection unit PN, and the false alarm rate Pfa are set; the protection unit is set on both sides of the unit being detected.

[0089] Based on the number of reference units, the mean of the reference unit samples, the variance of the reference unit samples, and the data of the reference units, the statistic VI is calculated. The statistic VI is used to determine whether the sampled data in the reference unit comes from a uniform environment. Specifically, the formula for calculating the statistic VI is:

[0090]

[0091] In the formula, n is the number of reference units. x is the mean of the reference unit samples. i For reference cell data, The variance of the reference unit sample.

[0092] The statistic MR is calculated based on the mean of the previous reference unit and the mean of the subsequent reference unit.

[0093] The MR statistic is used to determine whether the means of the reference cells before and after are the same. The calculation method is as follows:

[0094]

[0095] in This is the mean of the first half of the reference unit. This is the mean of the second half of the reference unit;

[0096] Calculate the threshold factor based on the false alarm rate and the reference cell;

[0097] The formula for calculating the threshold factor is:

[0098]

[0099] In the formula, α is the threshold factor, N is the reference unit, and Pfa is the false alarm rate.

[0100] Set judgment threshold T VI Based on the decision threshold T VI The statistic VI is used to determine whether the preceding and following reference units are uniform; specifically, when the value of the statistic VI is greater than the decision threshold T... VI If the reference cell is non-uniform, it is considered non-uniform; otherwise, it is considered uniform.

[0101] Set judgment threshold T MR Based on the decision threshold T MR The MR statistic is used to determine whether the mean of the previous reference unit is the same as the mean of the subsequent reference unit; when the value of the MR statistic is less than Or greater than T MR If the mean is different, then the mean is considered to be the same; otherwise, the mean is considered to be the same.

[0102] Based on the judgment results of whether the reference cells are uniform and whether the mean values ​​are the same, the threshold of the reflection event is obtained by selecting a threshold calculation method.

[0103] A further implementation method is that the threshold calculation and selection method is specifically as follows:

[0104] Calculate the sum of the data in the preceding reference cell, ∑A; the sum of the data in the following reference cell, ∑B; and the sum of the data in the entire reference cell, ∑AB.

[0105] If the front reference cell is uniform, the rear reference cell is uniform, and the mean values ​​of the front and rear reference cells are different, then the reflection event threshold is α. N / 2 max(∑A, ∑B);

[0106] If the front reference cell is non-uniform and the rear reference cell is uniform, then the reflection event threshold is α. N / 2 ∑B;

[0107] If the front reference cell is uniform and the rear reference cell is non-uniform, then the reflection event threshold is α. N / 2 ∑A;

[0108] If both the front and rear reference cells are non-uniform, then the reflection event threshold is α. N / 2 min(∑A, ∑B).

[0109] The details are shown in Table 1:

[0110] Table 1

[0111]

[0112] The adaptive reflection event threshold is finally obtained through calculation, where the fourth-level positive wavelet coefficients of the Haar wavelet transform are detected as follows: Figure 7 As shown;

[0113] A further implementation method is, such as Figure 3 As shown, in step S3, the method for obtaining non-reflection event points is as follows:

[0114] Discrete wavelet transform was performed on the denoised optical time-domain reflectometry curve data, with the wavelet basis being rbio3.1 and the wavelet decomposition level being 4. The high-frequency component wavelet coefficients B of the 4th level were extracted, as follows: Figure 8 As shown; then take the wavelet coefficients less than 0, and set all the wavelet coefficients greater than 0 to 0 to obtain the negative wavelet coefficients;

[0115] The negative part of the wavelet coefficient B of the high-frequency component is used as the negative wavelet coefficient curve of the non-reflection event. The constant false alarm rate algorithm based on the transform exponent is used to detect the negative wavelet coefficient curve of the non-reflection event, and the non-reflection event modulus minima exceeding the non-reflection event threshold is screened to obtain the non-reflection event modulus minima column.

[0116] For negative wavelet coefficients, a constant false alarm rate (CFAR) algorithm based on transform index (VI-CFAR) is applied. The reference cell is N, the guard cell is PN, and the false alarm rate is Pfa. The detection results are as follows: Figure 9 As shown, a set of modulo minima is finally obtained [modulo minima]. Modulus minimum point ...];

[0117] Remove the non-reflection event modulus minimum points that are located in the interval of the wavelet modulus extrema of the reflection event to obtain the non-reflection event points of the wavelet coefficients.

[0118] For the obtained wavelet coefficient reflection event wavelet modulus extremum point interval [[modulus minimum point] Modulus maximum point [Modulus minimum point] Modulus maximum point [, ...], only the minimum modulus points are taken to calculate the final reflection event point location, thus obtaining [minimum modulus points]. Modulus minimum point ...], through formula

[0119] realLocation = x × 2 n

[0120] Obtain the final reflection event point location Where x is the position of the modulus extremum point, and n is the wavelet decomposition level;

[0121] Similarly, this formula can be used to represent the non-reflection event points of wavelet coefficients [modulus minimum points]. Modulus minimum point ...], calculated

[0122] S4: Merge wavelet coefficient reflection event points and wavelet coefficient non-reflection event points, and sort them according to their positions;

[0123] S5: Map the wavelet coefficient reflection event points and wavelet coefficient non-reflection event points that have completed position sorting onto the optical time domain reflectometer test curve to obtain the true positions of the reflection event points and non-reflection event points, thus completing the optical time domain reflectometer event detection.

[0124] Specifically, the obtained wavelet coefficient reflection event points and wavelet coefficient non-reflection event point locations are merged into the final detected OTDR events. The event point detection results are as follows: Figure 10 As shown, the loss, attenuation coefficient, etc. of the detected events are finally calculated, integrated, and displayed on the OTDR interface.

[0125] Example 2

[0126] The present invention also provides an event detection system for an optical time-domain reflectometer, wherein the method used in the system includes: an optical time-domain reflectometer curve data acquisition module, a noise reduction module, an event detection module, a sorting module, and a mapping module;

[0127] The optical time domain reflectometer curve data acquisition module is used to measure optical fiber using an optical time domain reflectometer and obtain the optical time domain reflectometer test curve.

[0128] The denoising module is used to denoise the test curve data to obtain denoised optical time domain reflectometer test curve data.

[0129] The event detection module is used to perform discrete wavelet transform and constant false alarm rate algorithm based on transform exponent on the denoised test curve data to obtain wavelet coefficient reflection event points and wavelet coefficient non-reflection event points.

[0130] The sorting module is used to merge the wavelet coefficient reflection event points and the wavelet coefficient non-reflection event points, and sort them according to their positions.

[0131] The mapping module is used to map the wavelet coefficient reflection event points and the wavelet coefficient non-reflection event points, which have been sorted by position, onto the test curve to obtain the true positions of the reflection event points and non-reflection event points, and to complete the event detection of the optical time domain reflectometer.

[0132] The method for obtaining the wavelet coefficient reflection event points is as follows:

[0133] Discrete wavelet transform was performed on the denoised optical time-domain reflectometry test curve data, where the wavelet basis was Haar and the wavelet decomposition level was 4. The high-frequency component wavelet coefficients A of the 4th level were extracted, as shown below. Figure 6 As shown;

[0134] The wavelet coefficients A of the high-frequency components are divided into positive wavelet coefficient curves and negative wavelet coefficient curves for the reflection event, with zero as the boundary.

[0135] A constant false alarm rate algorithm based on transform exponent (VI-CFAR) is used to transform the wavelet coefficient curve of the reflection event, and the modulus maxima points exceeding the reflection event threshold are screened out to obtain the modulus maxima sequence of the reflection event.

[0136] A constant false alarm rate (CFAR) algorithm based on the transform exponent is used to transform the negative wavelet coefficient curve of the reflection event, and the modulus minimum points exceeding the threshold of the reflection event are screened out to obtain the modulus minimum value series of the reflection event.

[0137] Based on the matching threshold S, the maximum points of the reflection event modulus in the column of maximum values ​​of the reflection event modulus are matched with the minimum points of the reflection event modulus in the column of minimum values ​​of the reflection event modulus to obtain the range of extreme points of the wavelet modulus of the reflection event.

[0138] Based on the extreme point interval of the wavelet modulus of the reflection event, the reflection event point of the wavelet coefficient is obtained.

[0139] The method for obtaining the non-reflection event points of wavelet coefficients is as follows:

[0140] Discrete wavelet transform was performed on the denoised optical time-domain reflectometry test curve data, where the wavelet basis was rbio3.1 and the wavelet decomposition level was 4. The high-frequency component wavelet coefficients B of the 4th level were extracted, as follows. Figure 8 As shown; then take the wavelet coefficients less than 0, and set all the wavelet coefficients greater than 0 to 0 to obtain the negative wavelet coefficients;

[0141] The negative part of the wavelet coefficient B of the high-frequency component is used as the negative wavelet coefficient curve of the non-reflection event. The constant false alarm rate algorithm based on the transform exponent is applied to the negative wavelet coefficient curve of the non-reflection event to filter the modulus minima of the non-reflection event that exceeds the threshold of the non-reflection event, and obtain the modulus minima column of the non-reflection event.

[0142] A constant false alarm rate (VI-CFAR) algorithm based on transform exponent is used to perform a single transform exponent on negative wavelet coefficients. The reference cell is N, the guard cell is PN, and the false alarm rate is Pfa. The detection results are as follows: Figure 9 As shown, a set of modulo minima is finally obtained [modulo minima]. Modulus minimum point ...];

[0143] Remove the non-reflection event modulus minimum points located within the wavelet modulus extreme point interval of the reflection event to obtain the wavelet coefficient non-reflection event points. The above-described embodiments are merely preferred embodiments of the present invention and are not intended to limit the scope of the invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for event detection in an optical time-domain emitter, characterized in that, Includes the following steps: S1: Use an optical time domain reflectometer to measure the optical fiber and obtain the optical time domain reflectometer test curve; S2: Denoise the test curve data to obtain the denoised optical time domain reflectometer test curve data; S3: Perform discrete wavelet transform and constant false alarm rate (CFAR) algorithm based on transform exponent on the denoised test curve data to obtain wavelet coefficient reflection event points and wavelet coefficient non-reflection event points; S4: Merge the wavelet coefficient reflection event points and the wavelet coefficient non-reflection event points, and sort them according to their positions; S5: Map the wavelet coefficient reflection event points and the wavelet coefficient non-reflection event points that have been sorted into positions onto the test curve to obtain the true positions of the reflection event points and non-reflection event points, and complete the optical time domain reflectometer event detection. In step S3, the method for obtaining the wavelet coefficient reflection event point is as follows: Discrete wavelet transform is performed on the data of the denoised test curve, where the wavelet basis is Haar and the wavelet decomposition level is 4. The high-frequency component wavelet coefficients A of the 4th level are extracted. The high-frequency component wavelet coefficient A is divided into positive wavelet coefficient curves and negative wavelet coefficient curves for the reflection event, with zero as the boundary. The constant false alarm rate algorithm based on the transform exponent is used to detect the positive wavelet coefficient curve of the reflection event, and the modulus maxima points exceeding the reflection event threshold are screened out to obtain the modulus maxima sequence of the reflection event. The constant false alarm rate algorithm based on the transform exponent is used to detect the negative wavelet coefficient curve of the reflection event, and the points with modulo minima exceeding the threshold of the reflection event are screened out to obtain the modulo minima series of the reflection event. Based on the matching threshold, the maximum values ​​of the reflection event modulus in the column of maximum values ​​of the reflection event modulus are matched with the minimum values ​​of the reflection event modulus in the column of minimum values ​​of the reflection event modulus to obtain the range of extreme values ​​of the wavelet modulus of the reflection event. Based on the extreme point interval of the wavelet modulus of the reflection event, the wavelet coefficient reflection event point is obtained; In step S3, the method for obtaining the non-reflection event points of the wavelet coefficients is as follows: Discrete wavelet transform is performed on the data of the denoised test curve, where the wavelet basis is rbio3.1, the wavelet decomposition level is 4, and the high-frequency component wavelet coefficient B of the 4th level is extracted. The negative part of the high-frequency component wavelet coefficient B is used as the negative wavelet coefficient curve of the non-reflection event. The non-reflection event negative wavelet coefficient curve is detected by the constant false alarm algorithm based on the transform exponent. The non-reflection event modulus minimum points that exceed the non-reflection event threshold are screened to obtain the non-reflection event modulus minimum value series. Remove the non-reflection event modulus minimum points that are located in the interval of the wavelet modulus extreme points of the reflection event to obtain the non-reflection event points of the wavelet coefficients.

2. The event detection method for an optical time-domain emitter according to claim 1, characterized in that, The method for obtaining the reflection event threshold is as follows: The system includes a reference unit, a protection unit, and a false alarm rate; the protection unit is located on both sides of the unit being detected. Statistic VI is calculated based on the number of reference units, the mean of the reference unit samples, the variance of the reference unit samples, and the reference unit data. The statistic MR is calculated based on the mean of the previous reference unit and the mean of the subsequent reference unit. Calculate the threshold factor based on the false alarm rate and the reference unit; Setting judgment thresholds Based on the aforementioned decision threshold Using the statistic VI, determine whether the front reference unit and the back reference unit are uniform; Setting judgment thresholds Based on the aforementioned decision threshold Using the statistic MR, determine whether the mean of the previous reference unit is the same as the mean of the subsequent reference unit; Based on the judgment results of whether the reference unit is uniform and whether the mean is the same, the threshold of the reflection event is obtained through a threshold calculation selection method.

3. The event detection method for an optical time-domain transmitter according to claim 2, characterized in that, The formula for calculating the statistic VI is: In the formula, n is the number of reference units. The mean of the reference unit sample. For reference cell data, The variance of the reference unit sample; The formula for calculating the statistic MR is: ， In the formula, This is the mean of the first half of the reference unit. This is the mean of the second half of the reference unit.

4. The optical time-domain transmitter event detection method according to claim 2, characterized in that, The threshold factor is calculated using the following formula: In the formula, α is the threshold factor, and N is the reference unit. This refers to the false alarm rate.

5. The optical time-domain transmitter event detection method according to claim 4, characterized in that, The specific method for calculating and selecting the threshold is as follows: The sum of the reference cell data before calculation The sum of the data from the later reference cells and the sum of the entire reference unit data ; If the front reference unit is uniform, the rear reference unit is uniform, and the mean values ​​of the front and rear reference units are different, then the reflection event threshold is: ; If the front reference unit is non-uniform and the rear reference unit is uniform, then the reflection event threshold is: ; If the front reference unit is uniform and the rear reference unit is non-uniform, then the reflection event threshold is: ; If the front reference cell is non-uniform and the rear reference cell is non-uniform, then the reflection event threshold is: .

6. An event detection system for an optical time-domain emitter, wherein the system applies the method described in any one of claims 1-5, characterized in that, include: The optical time domain reflectometer curve data acquisition module, noise reduction module, event detection module, sorting module, and mapping module; The optical time domain reflectometer curve data acquisition module is used to measure optical fiber using an optical time domain reflectometer and obtain the optical time domain reflectometer test curve. The denoising module is used to denoise the test curve data to obtain denoised optical time domain reflectometer test curve data. The event detection module is used to perform discrete wavelet transform and constant false alarm rate algorithm based on transform exponent on the denoised test curve data to obtain wavelet coefficient reflection event points and wavelet coefficient non-reflection event points. The sorting module is used to merge the wavelet coefficient reflection event points and the wavelet coefficient non-reflection event points, and sort them according to their positions. The mapping module is used to map the wavelet coefficient reflection event points and the wavelet coefficient non-reflection event points, which have been sorted by position, onto the test curve to obtain the true positions of the reflection event points and non-reflection event points, and to complete the event detection of the optical time domain reflectometer.

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