Optical fiber distributed measurement method and system based on background deduction technology
Through the distributed measurement method of fiber optic fiber based on background subtraction technology, the phase model is constructed and updated to identify signal types, and the problem of quasi-static signal measurement under noise interference in fiber optic sensing system is solved, and the measurement of accurate identification of quasi-static and dynamic signals is realized. .
Patent Information
- Application Number
- CN202411363821.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-28
- Publication Date
- 2025-01-03
AI Technical Summary
When measuring stress strain and temperature, existing fiber distributed sensing systems are affected by the light source phase noise, polarization noise, electrical noise and environmental noise, resulting in high randomness of backward Rayleigh scattered signals, making it difficult to demodulate quasi-static information. .
Adopting a distributed fiber measurement method based on background deduction technology, the phase model is constructed through noise statistical analysis, the background model is updated when there is no external signal to be measured, the signal type is matched when there is a signal to be measured, the background point and foreground point are identified, and the background is updated. The model is used to extract the phase changes and positions of the signal to be measured.
Effectively identifying quasi-static and dynamic phase signals in a large number of random noises solves the problem that the existing Rayleigh OTDR system cannot measure quasi-static signals such as temperature and strain, and improves the accuracy of signal measurement.
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Figure CN119245703A_ABST
Abstract
Description
Technical Field
[0001] The invention belongs to the technical field of signal processing, and in particular relates to an optical fiber distributed measurement method and system based on background subtraction technology. Background Art
[0002] The stress, strain, temperature and vibration of the fiber optic distributed sensing system based on Rayleigh backscattering have great development prospects in engineering applications, especially in the health monitoring of some large infrastructure, such as roads and bridges, aviation and aerospace, large machinery, and oil and gas transportation pipelines. Due to the influence of light source phase noise, polarization noise, electrical noise of photodetectors and environmental noise, the backscattered Rayleigh signal received by the system has a certain randomness, so it is difficult to demodulate quasi-static information such as stress, strain and temperature from it.
[0003] The present invention will introduce a fiber optic DAS distributed measurement method and device based on background subtraction technology, construct a phase model of the sensing system through noise statistical analysis, and use background subtraction technology to obtain the position and phase information of the external signal to be measured, focusing on the measurement of quasi-static signals. Summary of the invention
[0004] The present invention aims to solve the deficiencies of the prior art and provides the following solutions:
[0005] An optical fiber distributed measurement method based on background subtraction technology comprises the following steps:
[0006] When there is no external signal to be measured, K groups of M×N first optical fiber sensing signals are collected, and a background model is made based on the first optical fiber sensing signals, and the background model and model parameters are updated;
[0007] When there is an external signal to be measured, j groups of M×N second optical fiber sensing signals are collected, and the second optical fiber sensing signals are matched with the background model to determine the pixel type of the current signal;
[0008] If the second optical fiber sensing signal matches the background model, the pixel point is a background point, and the background point is used to continue updating the background model;
[0009] If the second optical fiber sensing signal does not match the background model, the pixel point is a foreground point, and the foreground point is used to replace the background point;
[0010] Based on the update information of the background model, the magnitude and occurrence position of the phase change of the optical fiber sensing signal are obtained, thereby obtaining the magnitude and occurrence position of the external signal to be measured, and completing the distributed measurement.
[0011] Preferably, the first optical fiber sensing signal and the second optical fiber sensing signal are two-dimensional arrays, including noise and a signal to be measured;
[0012] Wherein, M represents the number of columns of the two-dimensional array, corresponding to the position along the optical fiber; N represents the number of rows of the two-dimensional array, corresponding to the number of backscattered signals.
[0013] Preferably, the first optical fiber sensing signal is processed to calculate the mean μ of the first group of the two-dimensional arrays, and the standard deviation is set to 0;
[0014] Let the background model be B N (X, Y), the current two-dimensional array is I(X, Y), the mean value μ of the Kth group background model N (X, Y), standard deviation σ of the Kth group background model N (X, Y), update the background model and the model parameters:
[0015] B N+1 (X, Y) = (1-α)B N (X, Y) + I (X, Y),
[0016] μ N+1 (X, Y) = (1-α)μ N (X, Y)+I N+1 (X, Y),
[0017] σ N+1 (X, Y) = (1-α)σ N (X, Y)+σ(I N+1 (X, Y)-μ N (X, Y) 2 , where B N+1 (X, Y) represents the updated background model, α represents the update speed, μ N+1 (X, Y) represents the updated mean, σ N+1 (X, Y) represents the updated standard deviation, I N+1 (X, Y) represents the foreground point.
[0018] The present invention also provides a fiber optic distributed measurement system based on background subtraction technology, the system applying any of the above methods, including: a background model building module, a matching module and an information acquisition module;
[0019] When there is no external signal to be measured, the background model building module collects K groups of M×N first optical fiber sensing signals, creates a background model based on the first optical fiber sensing signals, and updates the background model and model parameters;
[0020] When there is an external signal to be tested, the matching module collects j groups of M×N second optical fiber sensor signals, and matches the second optical fiber sensor signals with the background model to determine the pixel point type of the current signal; if the second optical fiber sensor signal matches the background model, the pixel point is a background point, and the background point is used to continue to update the background model; if the second optical fiber sensor signal does not match the background model, the pixel point is a foreground point, and the foreground point is used to replace the background point;
[0021] The information acquisition module obtains the magnitude and occurrence position of the phase change of the optical fiber sensing signal based on the updated information of the background model, thereby obtaining the magnitude and occurrence position of the external signal to be measured, and completing the distributed measurement.
[0022] Preferably, the first optical fiber sensing signal and the second optical fiber sensing signal are two-dimensional arrays, including noise and a signal to be measured;
[0023] Wherein, M represents the number of columns of the two-dimensional array, corresponding to the position along the optical fiber; N represents the number of rows of the two-dimensional array, corresponding to the number of backscattered signals.
[0024] Preferably, in the background model construction module,
[0025] Processing the first optical fiber sensing signal, calculating the mean μ of the first group of the two-dimensional arrays, and setting the standard deviation to 0;
[0026] Let the background model be B N (X, Y), the current two-dimensional array is I(X, Y), the mean value μ of the Kth group background model N (X, Y), standard deviation σ of the Kth group background model N (X, Y), update the background model and the model parameters:
[0027] B N+1 (X, Y) = (1-α)B N (X, Y) + I (X, Y),
[0028] μ N+1 (X, Y) = (1-α)μ N (X, Y)+I N+1 (X, Y),
[0029] σ N+1 (X, Y) = (1-α)σ N (X, Y)+σ(I N+1 (X, Y)-μ N (X, Y) 2 ,
[0030] Among them, BN+1 (X, Y) represents the updated background model, α represents the update speed, μ N+1 (X, Y) represents the updated mean, σ N+1 (X,Y) represents the updated standard deviation, I N+1 (X,Y) represents the foreground point.
[0031] Compared with the prior art, the present invention has the following beneficial effects:
[0032] The present invention is aimed at the optical fiber sensing system, adopts the model transfer and background subtraction algorithm, can identify the quasi-static and dynamic phase signals in a large amount of random noise, and solves the problem that the existing Rayleigh OTDR system cannot measure the quasi-static signals such as temperature and strain. BRIEF DESCRIPTION OF THE DRAWINGS
[0033] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in 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 paying creative labor.
[0034] Figure 1 A schematic diagram of a method flow of an embodiment of the present invention;
[0035] Figure 2 The figure is a flowchart of a method according to an embodiment of the present invention. DETAILED DESCRIPTION
[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0037] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.
[0038] Embodiment 1
[0039] In this embodiment, if Figure 1 , Figure 2 As shown, a fiber optic distributed measurement method based on background subtraction technology includes the following steps:
[0040] S1. When there is no external signal to be measured, K groups of M×N first optical fiber sensing signals are collected, and a background model is created based on the first optical fiber sensing signals, and the background model and model parameters are updated.
[0041] The first optical fiber sensing signal is a two-dimensional array. In the present embodiment, the type of the first optical fiber sensing signal may be a phase signal or an amplitude signal. These two signals may be used separately or simultaneously. The first optical fiber sensing signal includes noise and a signal to be measured. M represents the number of columns of the two-dimensional array, corresponding to the position along the optical fiber. N represents the number of rows of the two-dimensional array, corresponding to the number of backscattered signals.
[0042] Process the first optical fiber sensing signal, calculate the mean μ of the first two-dimensional array, and set the standard deviation to 0; set the background model to B N (X, Y), the current two-dimensional array is I(X, Y), the mean value μ of the Kth group background model N (X, Y), standard deviation σ of the Kth group background model N (X, Y), update the background model and model parameters:
[0043] B N+1 (X, Y) = (1-α)B N (X, Y) + I (X, Y),
[0044] μ N+1 (X, Y) = (1-α)μ N (X, Y)+I N+1 (X, Y),
[0045] σ N+1 (X, Y) = (1-α)σ N (X, Y)+σ(I N+1 (X, Y)-μ N (X, Y) 2 ,
[0046] Among them, B N+1 (X, Y) represents the updated background model, α represents the update speed, μ N+1 (X, Y) represents the updated mean, σ N+1 (X, Y) represents the updated standard deviation, I N+1 (X, Y) represents the foreground point.
[0047] S2. When there is an external signal to be measured, collect j groups of M×N second optical fiber sensor signals, and match the second optical fiber sensor signals with the background model to determine the pixel point type of the current signal; if the second optical fiber sensor signal matches the background model, the pixel point is a background point, and the background point is used to continue to update the background model; if the second optical fiber sensor signal does not match the background model, the pixel point is a foreground point, and the foreground point is used to replace the background point.
[0048] The second optical fiber sensing signal is a two-dimensional array. In the present embodiment, the type of the second optical fiber sensing signal may be a phase signal or an amplitude signal. These two signals may be used separately or simultaneously. The second optical fiber sensing signal includes noise and a signal to be measured. M represents the number of columns of the two-dimensional array, corresponding to the position along the optical fiber. N represents the number of rows of the two-dimensional array, corresponding to the number of backscattered signals.
[0049] Among them, the foreground attractions are:
[0050] I N+1 (X, Y), when When , it is determined that the foreground point is outside the template distribution, and the foreground point is not updated. The current point is used as the foreground point. T is the threshold of the judgment condition, which can be set by yourself.
[0051] Background points are points in the background model: B N+1 (X, Y).
[0052] S3. Based on the updated information of the background model, the magnitude and occurrence position of the phase change of the optical fiber sensing signal are obtained, thereby obtaining the magnitude and occurrence position of the external signal to be measured, and completing the distributed measurement.
[0053] Embodiment 2
[0054] In this embodiment, a fiber optic distributed measurement system based on background subtraction technology includes: a background model building module, a matching module and an information acquisition module;
[0055] When there is no external signal to be measured, the background model building module collects K groups of M×N first optical fiber sensing signals, creates a background model based on the first optical fiber sensing signals, and updates the background model and model parameters.
[0056] The first optical fiber sensing signal is a two-dimensional array. In the present embodiment, the type of the first optical fiber sensing signal may be a phase signal or an amplitude signal. These two signals may be used separately or simultaneously. The first optical fiber sensing signal includes noise and a signal to be measured. M represents the number of columns of the two-dimensional array, corresponding to the position along the optical fiber. N represents the number of rows of the two-dimensional array, corresponding to the number of backscattered signals.
[0057] Process the first optical fiber sensing signal, calculate the mean μ of the first two-dimensional array, and set the standard deviation to 0; set the background model to B N (X, Y), the current two-dimensional array is I(X, Y), the mean value μ of the Kth group background model N (X, Y), standard deviation σ of the Kth group background model N (X, Y), update the background model and model parameters:
[0058] B N+1(X, Y) = (1-α)B N (X, Y) + I (X, Y),
[0059] μ N+1 (X, Y) = (1-α)μ N (X, Y)+I N+1 (X, Y),
[0060] σ N+1 (X, Y) = (1-α)σ N (X, Y)+σ(I N+1 (X, Y)-μ N (X, Y) 2 ,
[0061] Among them, B N+1 (X,Y) represents the updated background model, α represents the update speed, μ N+1 (X,Y) represents the updated mean, σ N+1 (X,Y) represents the updated standard deviation, I N+1 (X,Y) represents the foreground point.
[0062] When there is an external signal to be tested, the matching module collects j groups of M×N second optical fiber sensor signals, and matches the second optical fiber sensor signals with the background model to determine the pixel point type of the current signal; if the second optical fiber sensor signal matches the background model, the pixel point is a background point, and the background point is used to continue to update the background model; if the second optical fiber sensor signal does not match the background model, the pixel point is a foreground point, and the foreground point is used to replace the background point.
[0063] The second optical fiber sensing signal is a two-dimensional array. In the present embodiment, the type of the second optical fiber sensing signal may be a phase signal or an amplitude signal. These two signals may be used separately or simultaneously. The second optical fiber sensing signal includes noise and a signal to be measured. M represents the number of columns of the two-dimensional array, corresponding to the position along the optical fiber. N represents the number of rows of the two-dimensional array, corresponding to the number of backscattered signals.
[0064] Among them, the foreground attractions are:
[0065] I N+1 (X,Y), when When , it is determined that the foreground point is outside the template distribution, and the foreground point is not updated. The current point is used as the foreground point. T is the threshold of the judgment condition, which can be set by yourself.
[0066] Background points are points in the background model: B N+1 (X,Y).
[0067] The information acquisition module obtains the magnitude and occurrence position of the phase change of the optical fiber sensing signal based on the updated information of the background model, thereby obtaining the magnitude and occurrence position of the external signal to be measured and completing the distributed measurement.
[0068] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.
Claims
1. A fiber distributed measurement method based on background subtraction technology, characterized in that: The following steps are involved: When there is no external signal to be measured, K groups of M×N first optical fiber sensing signals are collected, and a background model is made based on the first optical fiber sensing signals, and the background model and model parameters are updated; When there is an external signal to be measured, j groups of M×N second optical fiber sensing signals are collected, and the second optical fiber sensing signals are matched with the background model to determine the pixel type of the current signal; If the second optical fiber sensing signal matches the background model, the pixel point is a background point, and the background point is used to continue updating the background model; If the second optical fiber sensing signal does not match the background model, the pixel point is a foreground point, and the foreground point is used to replace the background point; Based on the update information of the background model, the magnitude and occurrence position of the phase change of the optical fiber sensing signal are obtained, thereby obtaining the magnitude and occurrence position of the external signal to be measured, and completing the distributed measurement.
2. According to claim 1, a fiber distributed measurement method based on background subtraction technology is characterized in that: The first optical fiber sensing signal and the second optical fiber sensing signal are two-dimensional arrays, including noise and a signal to be measured; Wherein, M represents the number of columns of the two-dimensional array, corresponding to the position along the optical fiber; N represents the number of rows of the two-dimensional array, corresponding to the number of backscattered signals.
3. According to claim 1, a fiber distributed measurement method based on background subtraction technology is characterized in that: Processing the first optical fiber sensing signal, calculating the mean μ of the first group of the two-dimensional arrays, and setting the standard deviation to 0; Let the background model be B N (X,Y), the current two-dimensional array is I(X,Y), the mean value μ of the Kth group background model N (X,Y), standard deviation σ of the Kth group background model N (X, Y), update the background model and the model parameters: B N+1 (X,Y)=(1-α)B N (X,Y)+I(X,Y), m N+1 (X,Y)=(1-a)μ N (X,Y)+I N+1 (X,Y), σ N+1 (X,Y)=(1-α)σ N (X,Y)+σ(I N+1 (X,Y)-μ N (X,Y) 2 , where B N+1 (X,Y) represents the updated background model, α represents the update speed, μ N+1 (X,Y) represents the updated mean, σ N+1 (X,Y) represents the updated standard deviation, I N+1 (X,Y) represents the foreground point.
4. A fiber optic distributed measurement system based on background subtraction technology, the system applying the method according to any one of claims 1 to 3, characterized in that: include: Background model building module, matching module and information acquisition module; When there is no external signal to be measured, the background model building module collects K groups of M×N first optical fiber sensing signals, creates a background model based on the first optical fiber sensing signals, and updates the background model and model parameters; When there is an external signal to be measured, the matching module collects j groups of M×N second optical fiber sensing signals, and matches the second optical fiber sensing signals with the background model to determine the pixel type of the current signal; If the second optical fiber sensing signal matches the background model, the pixel point is a background point, and the background point is used to continue updating the background model; if the second optical fiber sensing signal does not match the background model, the pixel point is a foreground point, and the foreground point is used to replace the background point; The information acquisition module obtains the magnitude and occurrence position of the phase change of the optical fiber sensing signal based on the updated information of the background model, thereby obtaining the magnitude and occurrence position of the external signal to be measured, and completing the distributed measurement.
5. The optical fiber distributed measurement system based on background subtraction technology according to claim 4, characterized in that: The first optical fiber sensing signal and the second optical fiber sensing signal are two-dimensional arrays, including noise and a signal to be measured; Wherein, M represents the number of columns of the two-dimensional array, corresponding to the position along the optical fiber; N represents the number of rows of the two-dimensional array, corresponding to the number of backscattered signals.
6. The optical fiber distributed measurement system based on background subtraction technology according to claim 4, characterized in that: In the background model construction module, Processing the first optical fiber sensing signal, calculating the mean μ of the first group of the two-dimensional arrays, and setting the standard deviation to 0; Let the background model be B N (X, Y), the current two-dimensional array is I(X, Y), the mean value μ of the Kth group background model N (X, Y), standard deviation σ of the Kth group background model N (X, Y), update the background model and the model parameters: B N+1 (X,Y)=(1-α)B N (X,Y)+I(X,Y), m N+1 (X,Y)=(1-a)μ N (X,Y)+I N+1 (X,Y), s N+1 (X,Y)=(1-a)σ N (X,Y)+σ(I N+1 (X, Y)-μ N (X,Y)) 2 , Among them, B N+1 (X, Y) represents the updated background model, α represents the update speed, μ N+1 (X, Y) represents the updated mean, σ N+1 (X, Y) represents the updated standard deviation, I N+1 (X, Y) represents the foreground point.