CN115790890A - Signal processing method and terminal of Brillouin optical time domain analysis system - Google Patents
Signal processing method and terminal of Brillouin optical time domain analysis system Download PDFInfo
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Abstract
The invention discloses a signal processing method of a Brillouin optical time domain analysis system, which comprises the following steps of: s1, acquiring a Brillouin gain spectrum, and performing two-dimensional variational modal decomposition and reconstruction; s2, performing spectrum type correction through time slot difference, and correcting the distorted Brillouin gain spectrum into a Lorentz spectrum; and S3, extracting temperature information from the corrected Brillouin gain spectrum based on the data model. The three functions of noise reduction, spectrum type correction and temperature information extraction are realized, and the defects of poor measurement precision and long processing time of the traditional fitting algorithm are overcome.
Description
Technical Field
The invention relates to the technical field of signal processing, in particular to a signal processing method and a terminal of a Brillouin optical time domain analysis system.
Background
The distributed optical fiber sensing system based on the Brillouin scattering has the advantages of large sensing dynamic range, long monitoring distance, continuous distributed measurement and the like, and is widely applied to health state diagnosis in the fields of bridges, oil pipelines, power transmission lines and the like. A Brillouin optical time domain analysis system (BOTDA) realizes optical fiber line monitoring according to the function relation between Brillouin frequency shift and temperature/strain, so that it is very important to accurately acquire the Brillouin frequency shift. The long-distance Brillouin optical time domain sensing system is easily influenced by non-local effects, and the Brillouin gain spectrum of the long-distance Brillouin optical time domain sensing system is distorted. When the sensing signal is distorted and the signal-to-noise ratio is low, the conventional signal processing method has the problems of poor measurement precision, insufficient denoising performance, loss of spectral pattern details, long processing time and the like.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: aiming at the conditions of sensing signal distortion and low signal-to-noise ratio, a signal processing method and a terminal of a Brillouin optical time domain sensing system are provided, and the measurement accuracy of temperature information along an optical fiber is improved.
In order to solve the technical problems, the invention adopts a technical scheme that:
a signal processing method of a Brillouin optical time domain analysis system comprises the following steps:
s1, acquiring a Brillouin gain spectrum, and performing two-dimensional variational modal decomposition and reconstruction;
s2, performing spectrum type correction through time slot difference, and correcting the distorted Brillouin gain spectrum into a Lorentz spectrum;
and S3, extracting temperature information from the corrected Brillouin gain spectrum based on the data model.
In order to solve the technical problem, the invention adopts another technical scheme as follows:
a signal processing terminal of a brillouin optical time domain analysis system, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the signal processing method of the brillouin optical time domain analysis system when executing the computer program.
The invention has the beneficial effects that: the signal processing method and the terminal thereof of the Brillouin optical time domain sensing system are provided, three functions of noise reduction, spectrum type correction and temperature information extraction are realized, the signal-to-noise ratio is improved, and the edge characteristics of Brillouin gain spectrum image signals are kept; the distorted Brillouin gain spectrum presents a good Lorentz shape after being subjected to the spectrum type correction processing of time slot difference, and the temperature extraction precision is improved; and the temperature information along the optical fiber is directly extracted based on the data model, so that the defects of poor measurement precision and long processing time of the traditional fitting algorithm are overcome.
Drawings
Fig. 1 is a schematic diagram of stimulated brillouin scattering of a single-mode optical fiber in a signal processing method of a brillouin optical time domain analysis system according to an embodiment of the present invention;
fig. 2 is a noise reduction flowchart of a signal processing method of a brillouin optical time domain analysis system according to an embodiment of the present invention;
fig. 3 is an optimization flowchart of a signal processing method of a brillouin optical time domain analysis system according to an embodiment of the present invention;
fig. 4 is a general flowchart of a signal processing method of a brillouin optical time domain analysis system according to an embodiment of the present invention;
fig. 5 is an architecture diagram of a signal processing terminal of a brillouin optical time domain analysis system according to an embodiment of the present invention.
Detailed Description
In order to explain technical contents, achieved objects, and effects of the present invention in detail, the following description is made with reference to the accompanying drawings in combination with the embodiments.
Referring to fig. 1 to 4, an embodiment of the present invention provides a signal processing method of a brillouin optical time domain analysis system, including the following steps:
s1, acquiring a Brillouin gain spectrum, and performing two-dimensional variational modal decomposition and reconstruction;
s2, performing spectrum type correction through time slot difference, and correcting the distorted Brillouin gain spectrum into a Lorentz spectrum;
and S3, extracting temperature information from the corrected Brillouin gain spectrum based on the data model.
From the above description, the beneficial effects of the present invention are: the signal processing method of the Brillouin optical time domain sensing system is provided, three functions of noise reduction, spectrum type correction and temperature information extraction are realized, the signal-to-noise ratio is improved, and the edge characteristics of Brillouin gain spectrum image signals are kept; the distorted Brillouin gain spectrum presents a good Lorentz shape after being subjected to the spectrum type correction processing of time slot difference, and the temperature extraction precision is improved; and the temperature information along the optical fiber is directly extracted based on the data model, so that the defects of poor measurement precision and long processing time of the traditional fitting algorithm are overcome.
Further, the acquiring the brillouin gain spectrum in step S1 specifically includes:
the BOTDA system is obtained through frequency sweeping, M groups of one-dimensional Brillouin gain spectrum signals along the optical fiber are obtained, the one-dimensional Brillouin gain spectrum signals are M columns and n rows of two-dimensional matrix M, and the matrix M comprises local Brillouin gain spectrums at various positions of the optical fiber:
wherein f (v) i ,z j ) (i is more than or equal to 1 and less than or equal to m, and j is more than or equal to 1 and less than or equal to n) represents the ith frequency sweepThe Brillouin gain spectrum at the jth sampling point in the frequency point and optical fiber length range.
As can be seen from the above description, the BOTDA system can obtain a one-dimensional brillouin gain curve of each spatial position point along the optical fiber corresponding to different scanning frequencies, and decompose a two-dimensional brillouin gain spectrum signal obtained from the frequency sweep into a plurality of sub-modes, so that the center frequencies of the modes are different, that is, the collected brillouin gain spectrum can be regarded as a two-dimensional signal distributed along with the frequency and the distance.
Further, the performing two-dimensional variational modal decomposition and reconstruction in step S1 specifically includes:
f (v) i ,z j ) (i is more than or equal to 1 and less than or equal to m, j is more than or equal to 1 and less than or equal to n) is set as f (x), and a constraint variational model is set as follows:
in the formula u k ＝{u 1 ,u 2 ,…,u k Is the decomposed K mode functions, w k ＝{w 1 ,w 2 ,…,w k The center frequency is the corresponding center frequency;
introducing a secondary penalty factor and a Lagrange multiplier lambda, and optimizing the constraint variation model by using an alternating direction multiplier method to obtain an unconstrained problem-expanded Lagrange function:
alternate update
Until it is satisfied
When the iteration is stopped andoutputting each IMF component:
in the formula, tau is a residual error,
and
each represents u k And w k The frequency domain property of;
and combining the IMF components obtained by decomposition to obtain a reconstructed signal.
As can be seen from the above description, the reconstructed signal removes the noise component of the original signal and has a good signal-to-noise ratio.
Further, step S1 further includes performing transform domain noise reduction processing on the signal with low signal-to-noise ratio, specifically:
s101, inputting a noisy two-dimensional Brillouin gain spectrum signal acquired by a BOTDA system, and setting an initial value of the number of decomposition layers to be 2;
s102, setting a correlation coefficient screening threshold value to be 0.3, calculating a correlation coefficient between IMF1 of modal decomposition and an original signal under a current value, judging whether the correlation coefficient between the IMF1 and the original signal is larger than the threshold value, if so, determining that K = K-1 is the optimal decomposition number, otherwise, K = K +1, and continuing to execute the step S102;
and S103, executing a decomposition process by adopting the value determined in the step S102 to obtain and reconstruct the modal signal.
From the above description, the low-frequency part and the high-frequency part are separated by using the correlation coefficient, and the two-dimensional brillouin gain spectrum signal after noise reduction can be obtained by using the low-frequency part for reconstruction.
Further, step S2 specifically includes:
setting the Brillouin gain spectrum after noise reduction as M':
randomly selecting a row from time slot spectrum data which do not carry stimulated Brillouin scattering information, and marking the row as C:
C＝[F(v 1 )F(v 2 )…F(v m )]
and (4) subtracting each row of data in the matrix M' from C to obtain corrected Lorentz-shaped Brillouin gain spectrum data.
From the above description, for the distorted brillouin gain spectrum caused by the non-local effect, a time slot differential spectrum type correction scheme is adopted to correct the distorted spectrum into a lorentzian spectrum type. Because the repetition time of the pump pulse of the system is longer than the time of the pulse traversing the length of the optical fiber and returning the scattered signal, a time slot exists in each sweep measurement period, and the photodetector collects continuous optical signals which are not met by the pump pulse light in the time slot and do not carry SBS information. The spectrum type can be corrected by differentiating the Brillouin gain spectrum of each position along the optical fiber with the continuous optical spectrum without SBS.
Further, the step S3 of directly extracting temperature information from the brillouin gain spectrum based on the data model specifically includes:
s301, establishing a data model, and continuously learning and training to obtain a mapping function relation between the Brillouin frequency shift and the temperature;
s302, generating L training samples (A) through simulation and laboratory calibration i ,T i ) I =1,2, …, L, where a i ＝[a i (v 1 ),a i (v 2 ),…,a i (v m )]The method comprises the steps of obtaining an ith sample, namely a Brillouin gain spectrum under different frequency sweeps; t is i For corresponding output vectorI.e. temperature value; if the number of hidden nodes is N, the output vector is as follows:
in the formula, w j Is an input weight, beta, between an input node and a hidden node j As output weights between hidden nodes and output nodes, b j For the bias of the jth hidden node, g (-) is the activation function, let β = [ β ] 1 β 2 …β N ] T ，T＝[T 1 T 2 …T L ]If the hidden layer node outputs:
the output vector is expressed in matrix form as: h β = T;
s303, the number of the sampling points of the optical fiber along the line is n, the Brillouin gain spectrum of each sampling point of the optical fiber is selected and input into the trained model for testing, and the corresponding temperature, namely the temperature information along the optical fiber, is obtained.
As can be seen from the above description, in the initial measurement stage or the calibration stage in the laboratory, the mapping function relationship between the brillouin frequency shift spectrum and the temperature is established by learning and training the brillouin spectrum data acquired under different temperature conditions, so that the brillouin frequency shift does not need to be determined in the actual monitoring process, and the temperature information along the optical fiber can be directly acquired through the trained function relationship. The measurement precision is improved, and meanwhile, the data processing time is effectively reduced.
Further, step S3 further includes optimizing the input weight w and the offset b: and taking the root mean square error of the prediction result of the training set as a fitness function, setting the target of parameter optimization to minimize the root mean square error of the prediction result, and jumping out of the loop when the fitness value is minimum or the maximum iteration times are met.
As can be seen from the above description, the prediction accuracy of the model is improved by optimizing the input weight w and the bias b.
Referring to fig. 5, another embodiment of the present invention provides a signal processing terminal of a brillouin optical time domain analysis system, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the steps in the signal processing method of the brillouin optical time domain analysis system.
The signal processing method of the brillouin optical time domain sensing system and the terminal thereof of the present invention can effectively improve the measurement accuracy of the brillouin optical time domain analysis system, and the following description is made through specific embodiments:
example one
Referring to fig. 4, a signal processing method of a brillouin optical time domain analysis system includes the following steps:
s1, acquiring a Brillouin gain spectrum, and performing two-dimensional variational modal decomposition and reconstruction;
the step S1 of acquiring the brillouin gain spectrum specifically includes:
the BOTDA acquisition system obtains m groups of one-dimensional Brillouin gain spectrum signals along the optical fiber through frequency sweeping, and specifically, the BOTDA acquisition system can obtain one-dimensional Brillouin gain curves of all spatial position points along the optical fiber corresponding to different scanning frequencies:
storing the one-dimensional signals of M groups of frequency sweep frequency points according to columns to obtain a two-dimensional matrix M with M columns and n rows, wherein the matrix M comprises local Brillouin gain spectrums at various positions of optical fibers, and the method comprises the following steps:
v 1 ,v 2 ,…,v m for scanning the frequency, z 1 ,z 2 ,…,z n As position of the optical fiber, S 1 ,S 2 ,…,S m For sweeping the frequency v 1 ,v 2 ,…,v m And m groups of one-dimensional signals are obtained.
The two-dimensional Brillouin gain spectrum signal obtained by the frequency sweep is decomposed into a plurality of submodes, the center frequencies of the modes are different, and the acquired Brillouin gain spectrum can be regarded as a two-dimensional signal distributed along with the frequency and the distance.
The one-dimensional Brillouin gain spectrum signal is a two-dimensional matrix M with M columns and n rows, wherein the matrix M comprises local Brillouin gain spectrums at various positions of the optical fiber:
wherein f (v) i ,z j ) And (i is more than or equal to 1 and less than or equal to m, and j is more than or equal to 1 and less than or equal to n) represents the Brillouin gain spectrum at the jth sampling point in the ith frequency sweep point and the optical fiber length range.
The two-dimensional variational modal decomposition and reconstruction in the step S1 specifically comprises:
to simplify the description, let f (v) i ,z j ) (i is more than or equal to 1 and less than or equal to m, j is more than or equal to 1 and less than or equal to n) is set as f (x), and a constraint variational model is set as follows:
in the formula u k ＝{u 1 ,u 2 ,…,u k Is the decomposed K mode functions, w k ＝{w 1 ,w 2 ,…,w k -is the corresponding center frequency;
introducing a secondary penalty factor and a Lagrange multiplier lambda, and optimizing the constraint variation model by using an alternating direction multiplier method to obtain an unconstrained problem-an expanded Lagrange function:
alternate update
Until it is satisfied
Then, the iteration stops and outputs each IMF component:
in the formula, tau is a residual error,
and
each represents u k And w k The frequency domain property of;
and combining the IMF components obtained by decomposition to obtain a reconstructed signal. The reconstructed signal removes the noise component of the original signal and has a good signal-to-noise ratio.
Referring to fig. 2, step S1 further includes performing transform domain noise reduction processing on the signal with low signal-to-noise ratio, specifically:
s101, inputting a two-dimensional Brillouin gain spectrum signal containing noise acquired by a BOTDA system, and setting an initial value of the number of decomposition layers to be 2;
s102, setting a correlation coefficient screening threshold value to be 0.3, calculating a correlation coefficient between IMF1 of modal decomposition and an original signal under a current value, judging whether the correlation coefficient between the IMF1 and the original signal is larger than the threshold value, if so, determining that K = K-1 is the optimal decomposition number, otherwise, K = K +1, and continuing to execute the step S102;
and S103, executing a decomposition process by adopting the value determined in the step S102 to obtain and reconstruct the modal signal.
Specifically, the sub-modal frequencies are sequentially reduced, so the center frequency of the IMF1 is the highest, and when over-decomposition occurs, the IMF1 corresponds to the decomposed pseudo component. In which both the effective information and the edge portions remain in the low-frequency modes, while the noise to be removed is concentrated in the high-frequency modes. And separating the low-frequency part from the high-frequency part by using the correlation coefficient, and reconstructing by using the low-frequency part to obtain the noise-reduced two-dimensional Brillouin gain spectrum signal.
S2, performing spectrum type correction through time slot difference, and correcting the distorted Brillouin gain spectrum into a Lorentz spectrum;
wherein, the step S2 specifically comprises the following steps:
setting the Brillouin gain spectrum after noise reduction as M':
randomly selecting a row from time slot spectrum data which do not carry stimulated Brillouin scattering information, and marking the row as C:
C＝[F(v 1 )F(v 2 )…F(v m )]
and (4) subtracting each row of data in the matrix M' from C to obtain corrected Brillouin gain spectrum data in the Lorentz shape. The corrected brillouin gain spectrum data I of the lorentz shape is:
in the formula (f) n (v i ,z j )＝f’(v i ,z j )-F(v i )，i＝1,2,…,m，j＝1,2,…,n。
Specifically, referring to fig. 1, in the BOTDA system, the pump pulse light and the continuous light propagate in opposite directions, and a stimulated brillouin scattering effect (SBS) occurs when the pump pulse light and the continuous light meet each other in the optical fiber. Let the repetition interval of pumping pulse light be t, and the length of sensing fiber be l. One pump pulse light traverseThe optical fiber is continuously met with continuous light along the way to the tail end of the optical fiber, and the optical fiber carries SBS information to return to the head end to be collected by a photoelectric detector, and the used time is set as t 1 Then the remaining time from the next pulse light emission is t-t 1 This time interval is called a slot. The photodetector in the time slot collects continuous optical signals which are not met by the pump pulse light, and the SBS information is not carried. The difference is made between the Brillouin gain spectrum of each position along the optical fiber and the continuous optical spectrum without SBS occurrence, so that the purpose of spectrum type correction can be achieved.
The data collected by the BOTDA system includes two parts: for each scanning frequency, time t in FIG. 1 1 Corresponding to the sampling point z on the length of the optical fiber 1 ,z 2 ,…,z n The SBS effect occurs; in time slot t-t 1 During which the SBS effect does not occur. Forming M by the data of each frequency point along the length of the optical fiber with the SBS effect; the data of each frequency point at any time during the time slot is taken as C.
The core idea is to use time slots to differentiate the spectral signals with or without SBS effect. Because the pump pulse repetition time of the system is greater than the time for the pulse to traverse the length of the fiber and return the scattered signal, there is a time slot during each sweep measurement. The photodetector in the time slot collects the spectrum signal of the continuous light which is not met with the pulse light, the SBS effect does not occur, and the spectrum signal is named as a time slot spectrum. The difference operation is carried out on the Brillouin gain spectrum and the time slot spectrum at each position along the optical fiber, so that the purpose of spectrum type correction can be achieved.
And S3, extracting temperature information from the corrected Brillouin gain spectrum based on the data model.
The step S3 of directly extracting temperature information from the brillouin gain spectrum based on the data model specifically includes:
s301, establishing a data model, and continuously learning and training to obtain a mapping function relation between the Brillouin frequency shift and the temperature; namely, the temperature information along the optical fiber can be directly obtained through the trained functional relation without determining the Brillouin frequency shift in the actual monitoring process.
S302, generating L training samples (A) through simulation and laboratory calibration i ,T i ) I =1,2, …, L, where a i ＝[a i (v 1 ),a i (v 2 ),…,a i (v m )]The ith sample is the Brillouin gain spectrum under different frequency sweeps; t is i Is the corresponding output vector, i.e. temperature value; if the number of hidden nodes is N, the output vector is as follows:
in the formula, w j Is an input weight, beta, between an input node and a hidden node j As output weights between hidden nodes and output nodes, b j For the bias of the jth hidden node, g (-) is an activation function, preferably a Sigmoid function is chosen as the activation function. Let beta = [ beta ] 1 β 2 …β N ] T ，T＝[T 1 T 2 …T L ]If the hidden layer node outputs:
the output vector is expressed in matrix form as: h β = T;
s303, n sampling points are arranged along the optical fiber, a Brillouin gain spectrum at each sampling point of the optical fiber is selected and input into a trained model for testing, and corresponding temperature, namely temperature information along the optical fiber, is obtained.
Specifically, the method directly extracts the temperature information corresponding to the Brillouin gain spectrum, and the temperature information along the optical fiber can be directly obtained without determining the numerical value of the Brillouin center frequency. The method improves the measurement precision and effectively reduces the data processing time. The method has the core idea that in the initial measurement stage or the laboratory calibration stage, the mapping function relationship between the Brillouin frequency shift spectrum and the temperature is established by learning and training Brillouin spectrum data acquired under different temperature conditions. During actual measurement, temperature information may then be output based on the data model obtained from training.
Wherein, the step S3 further comprises optimizing the input weight w and the offset b: and taking the root mean square error of the prediction result of the training set as a fitness function, setting the target of parameter optimization to minimize the root mean square error of the prediction result, and jumping out of the loop when the fitness value is minimum or the maximum iteration times are met. And the prediction precision of the model is improved by optimizing the input weight w and the bias b.
Specifically, referring to fig. 3, the optimization steps are as follows:
step 1: generating a training sample data set (A, T) through simulation experiment calibration, wherein A is Brillouin gain spectrum data and is used as a feature vector; t is the prediction result.
Step 2: initializing parameters, and randomly generating an input weight w and an offset b to obtain an output weight H, wherein the vector of a prediction result is H beta = T;
and step 3: calculating a fitness value and updating a parameter value, wherein a root mean square error is used as a fitness function;
and 4, step 4: comparing whether the current fitness value is superior to the last result, if so, updating the parameter value, otherwise, keeping the parameter value unchanged;
and 5: if the maximum iteration times are reached, ending, and jumping out of the loop to obtain the optimal input weight w and bias b; otherwise, returning to the step 3.
The optimal input weight w and the bias b can be obtained through the optimization process. Selecting a certain position point z of the optical fiber j The data after the noise reduction and the spectrum shape correction are expressed as f (v) 1 ,z j )f”(v 2 ,z j )…f”(v m ,z j )]Inputting the temperature into a well-trained model for testing, and acquiring the corresponding temperature T j And the temperature information monitoring along the optical fiber can be realized by the same principle of other position points.
Example two
Referring to fig. 5, a signal processing terminal of a brillouin optical time domain analysis system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps in the first embodiment when executing the computer program.
In summary, according to the signal processing method and the terminal of the brillouin optical time domain analysis system provided by the invention, the spectral pattern correction is performed through two-dimensional variation modal decomposition and reconstruction and time slot difference, and the temperature information along the optical fiber is directly obtained by using the mapping function relationship between the brillouin frequency shift and the temperature, so that three functions of noise reduction, spectral pattern correction and temperature information extraction are realized, and the defects of poor measurement accuracy and long processing time of the traditional fitting algorithm are overcome.
It should be noted that, for the sake of simplicity, the above-mentioned method embodiments are described as a series of acts or combinations, but those skilled in the art should understand that the present invention is not limited by the described order of acts, as some steps may be performed in other orders or simultaneously according to the present invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no acts or modules are necessarily required of the invention.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes performed by the present specification and drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (8)
1. A signal processing method of a Brillouin optical time domain analysis system is characterized by comprising the following steps:
s1, acquiring a Brillouin gain spectrum, and performing two-dimensional variational modal decomposition and reconstruction;
s2, performing spectrum type correction through time slot difference, and correcting the distorted Brillouin gain spectrum into a Lorentz spectrum;
and S3, extracting temperature information from the corrected Brillouin gain spectrum based on the data model.
2. The signal processing method of the brillouin optical time domain analysis system according to claim 1, wherein the acquiring the brillouin gain spectrum in step S1 specifically is:
the BOTDA system is obtained through frequency sweeping, M groups of one-dimensional Brillouin gain spectrum signals along the optical fiber are obtained, the one-dimensional Brillouin gain spectrum signals are M columns and n rows of two-dimensional matrix M, and the matrix M comprises local Brillouin gain spectrums at various positions of the optical fiber:
wherein f (v) i ,z j ) And (i is more than or equal to 1 and less than or equal to m, and j is more than or equal to 1 and less than or equal to n) represents the Brillouin gain spectrum at the jth sampling point in the ith frequency sweep point and the optical fiber length range.
3. The signal processing method of the brillouin optical time domain analysis system according to claim 2, wherein the performing the two-dimensional variation modal decomposition and reconstruction in the step S1 specifically includes:
f (v) i ,z j ) (i is more than or equal to 1 and less than or equal to m, j is more than or equal to 1 and less than or equal to n) is set as f (x), and a constraint variational model is set as follows:
in the formula u k ＝{u 1 ,u 2 ,…,u k Is the decomposed K mode functions, w k ＝{w 1 ,w 2 ,…,w k The center frequency is the corresponding center frequency;
introducing a secondary penalty factor and a Lagrange multiplier lambda, and optimizing the constraint variation model by using an alternating direction multiplier method to obtain an unconstrained problem-expanded Lagrange function:
alternate update
Until it is satisfied
Then, the iteration stops and outputs each IMF component:
in the formula, tau is a residual error,
and
each represents u k And w k The frequency domain property of (a);
and combining the IMF components obtained by decomposition to obtain a reconstructed signal.
4. The signal processing method of the brillouin optical time domain analysis system according to claim 3, wherein the step S1 further includes performing transform domain noise reduction processing on the signal with low signal-to-noise ratio, specifically:
s101, inputting a two-dimensional Brillouin gain spectrum signal containing noise acquired by a BOTDA system, and setting an initial value of the number of decomposition layers to be 2;
s102, setting a correlation coefficient screening threshold value to be 0.3, calculating a correlation coefficient between IMF1 of modal decomposition and an original signal under a current value, judging whether the correlation coefficient between the IMF1 and the original signal is larger than the threshold value, if so, determining that K = K-1 is the optimal decomposition number, otherwise, K = K +1, and continuing to execute the step S102;
and S103, executing a decomposition process by adopting the value determined in the step S102 to obtain and reconstruct the modal signal.
5. The signal processing method of the brillouin optical time domain analysis system according to claim 1, wherein the step S2 is specifically:
setting the Brillouin gain spectrum after noise reduction as M':
randomly selecting a row from time slot spectrum data which do not carry stimulated Brillouin scattering information, and marking the row as C:
C＝[F(v 1 ) F(v 2 ) … F(v m )]
and (4) subtracting each row of data in the matrix M' from C to obtain corrected Brillouin gain spectrum data in the Lorentz shape.
6. The signal processing method of the brillouin optical time domain analysis system according to claim 1, wherein the step S3 of directly extracting temperature information from the brillouin gain spectrum based on the data model specifically includes:
s301, establishing a data model, and continuously learning and training to obtain a mapping function relation between the Brillouin frequency shift and the temperature;
s302, generating L trainings through simulation and laboratory calibrationExercise sample (A) i ,T i ) I =1,2, …, L, where a i ＝[a i (v 1 ),a i (v 2 ),…,a i (v m )]The ith sample is the Brillouin gain spectrum under different frequency sweeps; t is a unit of i Is the corresponding output vector, i.e. temperature value; if the number of hidden nodes is N, the output vector is as follows:
in the formula, w j Is an input weight, beta, between an input node and a hidden node j As output weights between hidden nodes and output nodes, b j For the bias of the jth hidden node, g (-) is the activation function, let β = [ β ] 1 β 2 … β N ] T ，T＝[T 1 T 2 … T L ]Then the output of the hidden layer node is:
the output vector is expressed in matrix form as: h β = T;
s303, n sampling points are arranged along the optical fiber, a Brillouin gain spectrum at each sampling point of the optical fiber is selected and input into a trained model for testing, and corresponding temperature, namely temperature information along the optical fiber, is obtained.
7. The signal processing method of the brillouin optical time domain analysis system according to claim 6, wherein the step S3 further comprises optimizing the input weight w and the offset b: and taking the root mean square error of the prediction result of the training set as a fitness function, setting the target of parameter optimization to minimize the root mean square error of the prediction result, and jumping out of the loop when the fitness value is minimum or the maximum iteration times are met.
8. A signal processing terminal of a brillouin optical time domain analysis system, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements each step in a signal processing method of a brillouin optical time domain analysis system according to any one of claims 1 to 7 when executing the computer program.
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2022
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