A high-speed fabry-perot signal demodulation method and system based on sparse spectrum

Abstract

The application belongs to the technical field of optical fiber sensing, and discloses a high-speed F-P signal demodulation method and system based on sparse spectrum. The method comprises the following steps: selecting a cavity length range of a F-P cavity, and generating a cavity length sequence; repeatedly simulating to obtain the reflection spectrum of the F-P cavity under all cavity lengths in the cavity length sequence; adding noise to the reflection spectrum to obtain a simulated acquisition spectrum; selecting a channel according to the output wavelength of a wavelength division multiplexer to obtain a sparse spectrum, and constructing a data set; training a neural network model through the data set; after the training is completed, obtaining the reflection signal of the F-P cavity through the wavelength division multiplexer and a photoelectric detector array and converting the reflection signal into a sparse spectrum; inputting the obtained sparse spectrum into the neural network model to obtain the cavity length of the fiber F-P sensor, and realizing F-P signal demodulation. The application not only improves the demodulation rate and monitoring efficiency of the fiber F-P sensor, but also provides an efficient and reliable remote monitoring solution for multiple fields.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of optical fiber sensing, and more particularly to a high-speed F-P signal demodulation method and system based on sparse spectrum. BACKGROUND

[0002] With the rapid development of aerospace, industrial manufacturing and other fields, the monitoring of high time-varying physical quantities such as noise, vibration, strain and pressure puts higher requirements on sensors and corresponding signal processing technology. Optical fiber F-P sensor has become the first choice for exploring high-speed measurement sensors due to its advantages of anti-electromagnetic interference, fast response, high sensitivity, intrinsic passivity, high temperature resistance and the like.

[0003] There are many demodulation methods for optical fiber F-P sensors. From the demodulated information, the demodulation methods can be divided into intensity demodulation, phase demodulation and wavelength demodulation. Different demodulation methods have different applicable scenarios. In the face of the test characteristics of high frequency and large impact of rocket engines, a high-speed and large dynamic range demodulation method is needed. The existing intensity demodulation method can realize fast demodulation by means of working point control or phase quadrature, but it is only applicable to F-P signals with small changes in cavity length. The phase demodulation method can realize a large dynamic range by means of full spectrum information or a reference interferometer, but the low speed of the spectrometer and the complex program limit the demodulation rate. The wavelength demodulation uses sparse spectrum to obtain cavity length information, avoiding the limitation that the working point can only work in the linear working area, and does not need a spectrometer to obtain full spectrum information. However, due to the limited characteristics of sparse spectrum, the resolution of this method is relatively low. In addition, the existing technologies do not have the functions of multi-end display and remote monitoring, and cannot efficiently demodulate the optical fiber F-P sensor. Therefore, it is necessary to explore a demodulation method that meets the use requirements.

[0004] Therefore, it is necessary to provide a method and a corresponding system for high-speed and high-precision demodulation of F-P signals to solve the above problems. SUMMARY

[0005] In order to solve the deficiencies of the existing technology of optical fiber F-P sensor demodulation method in demodulation speed and demodulation accuracy, the present application proposes a high-speed F-P signal demodulation method and system based on sparse spectrum to improve the demodulation speed and accuracy and realize high-speed and high-precision demodulation.

[0006] In order to solve the above technical problems, the technical scheme adopted by the present application is as follows: a high-speed F-P signal demodulation method based on sparse spectrum, comprising the following steps:

[0007] Step 1: selecting a cavity length range of the F-P cavity and generating a cavity length sequence;

[0008] Step 2: Extract a cavity length value from the cavity length sequence, perform multi-beam interference simulation to obtain the reflection spectrum of the F-P cavity at this cavity length; repeat the simulation to obtain the reflection spectrum of the F-P cavity at all cavity lengths in the cavity length sequence;

[0009] Step 3: Add noise to the reflection spectrum to obtain the simulated acquisition spectrum;

[0010] Step 4: According to the output wavelength selection channel of the wavelength division multiplexer, take out the reflection intensity corresponding to each channel from the simulated acquisition spectrum to form a sparse spectrum; take each cavity length in the cavity length sequence as the output and the corresponding sparse spectrum as the input to construct a data set; and pre-process the data set;

[0011] Step 5: Initialize the neural network, take the sparse spectrum corresponding to each cavity length as the input, and take the cavity length as the output, and train the neural network model through the data set data;

[0012] Step 6: After training, the output signal of the fiber F-P sensor is received through the wavelength division multiplexer, the reflection signals output by each channel of the wavelength division multiplexer are received by the photodetector array, and the reflection signals are converted into a sparse spectrum;

[0013] Step 7: Input the obtained sparse spectrum into the neural network model, obtain the cavity length of the fiber F-P sensor through the neural network model, and realize F-P signal demodulation.

[0014] In step 2, the reflection spectrum of the F-P cavity in the C band is obtained by simulating the multi-beam interference of the F-P cavity through a Python script; the reflection spectrum is composed of the reflection output intensity I R of the C band. R The reflection spectrum is expressed as:

[0015]

[0016] Where F is the fineness coefficient of the F-P cavity, δ represents the phase difference between adjacent light rays, and I0 represents the incident light intensity.

[0017] In step 3, the specific method for adding noise to the reflection spectrum to obtain the simulated acquisition spectrum is as follows:

[0018] Generate Gaussian white noise through the random number generator in the Numpy library, superimpose it with the reflection spectrum, and obtain the spectrum as the simulated acquisition spectrum.

[0019] In step 4, the pre-processing steps of the data set include:

[0020] Normalizing the data in the data set;

[0021] Divide the normalized data set into a training set and a test set.

[0022] In step 5, the specific method for training and testing the neural network model by the dataset data is as follows:

[0023] Step 5.1: initialize the neural network, select the number of hidden layers and neurons;

[0024] Step 5.2: calculate the loss function LOSS between the predicted results calculated by the neural network and the actual cavity length, and update the weights and biases using the Adam optimizer, set the number of iterations, and obtain the final loss function LOSS by continuous loop iteration;

[0025] Step 5.3: determine whether the final loss function LOSS meets the expectation, if yes, the training is completed, the neural network model is exported, if not, the number of hidden layers and neurons of the neural network is modified, and the loop iteration is restarted until the neural network model with the loss function LOSS meeting the expected effect is obtained.

[0026] In addition, the application also provides a high-speed F-P signal demodulation system based on sparse spectrum, which is used for executing the high-speed F-P signal demodulation method based on sparse spectrum, and comprises a light source, a fiber F-P sensor, a fiber circulator, a wavelength division multiplexer, a photodetector array, a synchronous acquisition card and a computing unit.

[0027] The C-band broadband light output by the light source is incident on the fiber F-P sensor after passing through the fiber circulator, and the reflection signal generated in the fiber F-P sensor is incident on the wavelength division multiplexer again after passing through the fiber circulator, the reflection signal is subjected to spectral wavelength division by the wavelength division multiplexer, then the reflection signal output by each channel of the wavelength division multiplexer is received by the photodetector array and subjected to photoelectric conversion, and a sparse spectrum with multiple different wavelengths of light is obtained; the sparse spectrum is subjected to data acquisition by the synchronous acquisition card, and then sent to the computing unit for demodulation.

[0028] The value of n is 3-20.

[0029] The high-speed F-P signal demodulation system based on sparse spectrum further comprises a power supply and a voltage conversion module; the power supply is used for providing voltage, and the voltage conversion module is used for converting the voltage provided by the power supply to supply power to the system.

[0030] The high-speed F-P signal demodulation system based on sparse spectrum further comprises a network card, a cloud platform and a display unit; after the data demodulation is completed by the calculation unit, the demodulated F-P cavity length data is uploaded to the cloud platform through the network card; the cloud platform is used for real-time drawing of the F-P cavity length data on a webpage end, and the display unit is connected to the Internet to access the webpage, so that remote multi-end monitoring is realized.

[0031] Compared with the prior art, the present application has the following beneficial effects:

[0032] 1. The present application uses the sparse spectrum of the fiber F-P sensor for demodulation instead of using the complete interference spectrum, solves the problem of low demodulation rate caused by the sampling rate limitation of the spectrometer, and avoids the waste of redundant information generated by the complete spectrum, thereby improving the demodulation rate of the F-P signal.

[0033] 2. The present application uses a neural network algorithm to realize end-to-end demodulation from the sparse spectrum to the F-P cavity length, solves the problems of complex calculation and long calculation time caused by the traditional demodulation algorithm relying on cyclic processing and phase correction. At the same time, the model is inferred by using the TensorRT framework to ensure that the calculation time of batch data is much smaller than the acquisition time, avoiding the memory crisis caused by data accumulation due to long-time operation of the traditional algorithm, and providing a reference value for stable demodulation of long-time F-P signal.

[0034] 3. The present application uses the Internet of Things technology to realize remote multi-end monitoring of the F-P sensor demodulation. The traditional demodulation method relies on local hardware and manual operation, has poor monitoring flexibility, low real-time performance and high maintenance cost. Through remote communication, multi-end synchronous access, intelligent early warning and fault diagnosis, dynamic configuration optimization and easy extension and maintenance, the present application not only improves the monitoring efficiency and response speed, promotes information sharing and team cooperation, but also realizes the optimization of resources and reduces the operating cost, thereby providing an efficient and reliable remote monitoring solution for multiple fields. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 A flowchart of a high-speed F-P signal demodulation method based on sparse spectrum provided for the first embodiment of the present application is shown in the figure;

[0036] Figure 2 A demodulation principle diagram of the neural network in the present application is shown in the figure;

[0037] Figure 3 A structure diagram of the demodulation device used in the first embodiment of the present application is shown in the figure;

[0038] Figure 4 A comparison diagram of neural network model prediction is shown in the figure;

[0039] Figure 5A structure schematic diagram of a high-speed Fabry-Perot signal demodulation system based on sparse spectrum is provided for the first embodiment of the present application.

[0040] Figure 6 A remote multi-end monitoring flowchart is provided.

[0041] In the figure: 1-power supply, 2-voltage conversion module, 3-light source, 4-optical fiber Fabry-Perot sensor, 5-optical fiber circulator, 6-wave division multiplexer, 7-optical detector, 8-synchronous acquisition card, 9-computing unit, 10-network card, 11-cloud platform, 12-display unit. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be described clearly and completely below. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0043] Embodiment one

[0044] As shown in the figure, the first embodiment of the present application provides a high-speed Fabry-Perot signal demodulation method based on sparse spectrum, which comprises the following steps: Figure 1

[0045] Step 1: select the cavity length range of the Fabry-Perot cavity, and generate a cavity length sequence.

[0046] Different cavity lengths will produce different reflection spectra, and the greater the cavity length range will bring more data sets and increase the training pressure of the model, but will improve the demodulation ability of the model to different cavity length Fabry-Perot cavities. Generate a cavity length sequence at a certain interval within the selected cavity length range. The smaller the interval, the higher the demodulation resolution, but the greater the model training pressure.

[0047] Step 2: extract a cavity length value from the cavity length sequence, perform multi-beam interference simulation, and obtain the reflection spectrum of the Fabry-Perot cavity at the cavity length; repeat the simulation to obtain the reflection spectrum of the Fabry-Perot cavity at all cavity lengths in the cavity length sequence.

[0048] In the step 2, the Fabry-Perot cavity is simulated by a Python script to obtain the reflection spectrum of the Fabry-Perot cavity in the C band; the emission spectrum is composed of the reflection output intensity I R of the C band, and I R is expressed as:

[0049]

[0050] ​Where F is the finesse factor of the Fabry-Perot cavity, δ represents the phase difference between adjacent light rays, and I0 represents the incident light intensity. The expression of the finesse factor F is:

[0051]

[0052] Where R is the reflectivity of the Fabry-Perot cavity.

[0053] When the medium in the Fabry-Perot cavity is air, that is, when the refractive index of the medium is approximately 1, the phase difference δ between adjacent light rays can be expressed as:

[0054]

[0055] Where h is the Fabry-Perot cavity length and λ is the wavelength.

[0056] Step 3: Add noise to the reflection spectrum to obtain the simulated acquisition spectrum.

[0057] In step 3, the specific method of adding noise to the reflection spectrum to obtain the simulated acquisition spectrum is:

[0058] Gaussian white noise is generated by the random number generator in the Numpy library and superimposed with the reflection spectrum to obtain a spectrum as the simulated acquisition spectrum.

[0059] Step 4: Select channels based on the output wavelength of the wavelength division multiplexer, extract the reflected light intensity corresponding to each channel from the simulated acquisition spectrum, and construct a sparse spectrum. Use each cavity length in the cavity length sequence as output and the corresponding sparse spectrum as input to construct a data set. Preprocess the data set.

[0060] In step 4, the step of preprocessing the data set includes:

[0061] Normalize the data of the dataset;

[0062] The normalized dataset is divided into training set and test set.

[0063] Step 5: Initialize the neural network, take the sparse spectrum corresponding to each cavity length as input and the cavity length as output, and train the neural network model using the data set data.

[0064] like Figure 2 The figure shows the principle diagram of neural network data training.

[0065] In step 5, the specific method for training and testing the neural network model using the data set data is:

[0066] Step 5.1: Initialize the neural network and select the number of hidden layers and neurons;

[0067] Step 5.2: Calculate the loss function LOSS for the predicted result obtained by the neural network calculation and the actual cavity length, and use the Adam optimizer to update the weights and biases. Set the number of loops and obtain the final loss function LOSS through continuous iteration.

[0068] Step 5.3: Determine whether the final loss function LOSS meets expectations. If so, the training ends and the neural network model is derived. If not, the number of hidden layers and neurons in the neural network is modified, and the loop is iterated again until a neural network model with a loss function LOSS that meets the expected effect is obtained.

[0069] The model prediction results are compared Figure 4 As shown, the linear fitting line R 2 =1, which has a low prediction error, indicating that the neural network model of the embodiment of the present invention can achieve accurate demodulation of the cavity length.

[0070] Step 6: After the training is completed, the sensing signal output by the fiber Fabry-Perot sensor is received through a wavelength division multiplexer, and the reflected signal output by each channel of the wavelength division multiplexer is received by a photodetector array and converted into a sparse spectrum.

[0071] like Figure 3 Figure 2 shows a schematic diagram of using a wavelength division multiplexer (WDM) and a photodetector array to receive reflected signals and obtain a sparse spectrum. WDM 6 is a dense WDM, with n channels of different wavelengths configured at its output port. The wavelengths of each channel are the same as those used to construct the sparse spectrum by extracting the reflected light intensity from the simulated spectrum in step 4.

[0072] Step 7: Input the obtained sparse spectrum into the neural network model, obtain the cavity length of the optical fiber Fabry-Perot sensor through the neural network model, and realize Fabry-Perot signal demodulation.

[0073] Specifically, the neural network model is set in a computing unit, and specifically, the computing unit is a desktop GPU or an embedded GPU.

[0074] Specifically, step 7 includes the following steps: converting the model obtained in step 5 to obtain a model in a universal format, using TensorRT for inference acceleration, thereby deploying the neural network model to a desktop GPU or an embedded GPU, and then inputting the obtained sparse spectrum into the desktop GPU or the embedded GPU, obtaining the cavity length of the fiber Fabry-Perot sensor through the neural network model in the desktop GPU or the embedded GPU, and realizing Fabry-Perot signal demodulation.

[0075] Example 2

[0076] like Figure 5As shown, the second embodiment of the present application provides a high-speed fiber Fabry-Perot signal demodulation system based on sparse spectrum, which is used to execute the high-speed fiber Fabry-Perot signal demodulation method based on sparse spectrum in the first embodiment, and includes a light source 3, a fiber Fabry-Perot sensor 4, a fiber circulator 5, a wavelength division multiplexer 6, a photodetector array 7, a synchronous acquisition card 8, and a computing unit 9. The wavelength division multiplexer 6 is a dense wavelength division multiplexer, and the output port of the wavelength division multiplexer 6 is provided with n channels with different wavelengths.

[0077] The C-band broadband spectrum light output by the light source 3 is incident to the fiber Fabry-Perot sensor 4 after passing through the fiber circulator 5. The reflection signal generated in the fiber Fabry-Perot sensor 4 is incident to the wavelength division multiplexer 6 again after passing through the fiber circulator 5. The reflection signal is subjected to spectral wavelength division by the wavelength division multiplexer 6. Then, the reflection signal output by each channel of the wavelength division multiplexer is received by the photodetector array 7 and subjected to photoelectric conversion, so as to obtain a sparse spectrum with multiple different wavelength lights. The sparse spectrum is subjected to data acquisition by the synchronous acquisition card 8, and then is sent to the computing unit 9 for demodulation. The computing unit 9 is provided with a neural network model for demodulation.

[0078] Specifically, in the present embodiment, the value range of n is 3-20.

[0079] Further, the high-speed fiber Fabry-Perot signal demodulation system based on sparse spectrum also includes a power supply 1 and a voltage conversion module 2. The power supply 1 is used to provide voltage, and the voltage conversion module 2 is used to convert the voltage provided by the power supply 1 to supply power to the system.

[0080] In the present embodiment, the computing unit 9 realizes high-speed demodulation of the sparse spectrum by using the deployed neural network model. The input of the neural network model is the light intensity of n wavelengths included in the sparse spectrum, and the output is the fiber Fabry-Perot cavity length.

[0081] Further, the high-speed fiber Fabry-Perot signal demodulation system based on sparse spectrum also includes a network card 10, a cloud platform 11, and a display unit 12. After the computing unit 9 completes data demodulation, the fiber Fabry-Perot cavity length data after demodulation is uploaded to the cloud platform 11 through the network card 10. The cloud platform 11 is used to draw the fiber Fabry-Perot cavity length data in real time on the web page end, and the display unit 12 accesses the web page by connecting to the Internet, so as to realize remote multi-end monitoring.

[0082] Specifically, as shown in the figure, the working steps are as follows: Figure 6

[0083] 1. Turn on the system, and acquire the sparse spectrum from the fiber Fabry-Perot sensor 4 at high speed by the synchronous acquisition card 8;

[0084] 2. Input the sparse spectrum into the deployed neural network model, and output the fiber Fabry-Perot cavity length; ​

[0085] 3. Compressing the data of the cavity length of the Fabry-Perot cavity and uploading it to the cloud platform 11 through the MQTT protocol;

[0086] 4. Refreshing and displaying the batch cavity length data on the webpage;

[0087] 5. Accessing the webpage through networking to view the cavity length data of the fiber Fabry-Perot sensor 4

[0088] 6. Obtaining a batch of sparse spectra, repeating steps 2-4, and realizing real-time remote monitoring.

[0089] In this embodiment, after obtaining a batch of cavity length data, the computing unit 9 compresses and packages the data to reduce the pressure on the network bandwidth. The network card 10 is used to provide networking function for the computing unit 9, and the data is uploaded to the cloud platform 11 through the MQTT protocol. The cloud platform 11 creates a webpage to receive and display the data. The display unit 12 is a display device with networking function, which can access the webpage through the Internet for remote monitoring.

[0090] The working principle of this embodiment is as follows: a large number of data sets of cavity length and corresponding sparse spectra are obtained through multi-beam interference simulation, which are divided into training set and test set. The training set is used to input into the neural network for cyclic iteration optimization of the number of hidden layers and neurons. After the training of the neural network model is completed, it is deployed to the computing unit with GPU through format conversion, TensorRT framework acceleration, etc. When the broadband light source emitted by the light source 3 enters the fiber Fabry-Perot sensor 4 through the fiber circulator 5, the reflected spectrum enters the wavelength division multiplexer 6 through the fiber circulator 5 to complete wavelength division and obtain the sparse spectrum. The photodetector array 7 performs photoelectric conversion on the sparse spectrum, and the synchronous acquisition card 8 acquires the sparse spectrum at high speed and transmits it to the computing unit 9. The neural network model calculates the Fabry-Perot cavity length, uploads it to the cloud platform 11, and realizes remote multi-end monitoring. Avoiding the sampling rate limitation brought by the use of spectrometer, the sampling rate of the original data is improved from the hardware; at the same time, the acceleration processing of the neural network model effectively improves the software processing rate, solving the low-efficiency calculation problem of traditional demodulation method serial calculation and cyclic correction; using the Internet of Things technology to realize remote multi-end monitoring of the fiber Fabry-Perot sensor not only improves the monitoring efficiency and response speed, promotes information sharing and team cooperation, but also realizes the optimization of resource allocation and reduces the operating cost, providing an efficient and reliable remote monitoring solution for multiple fields.

[0091] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions recorded in the above embodiments can be modified, or some or all of the technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A sparse spectrum based high-speed Fabry-Perot signal demodulation method, characterized in that, The method comprises the following steps: Step 1: select the cavity length range of the Fabry-Perot cavity, and generate a cavity length sequence; Step 2: extract a cavity length value from the cavity length sequence, perform multi-beam interference simulation to obtain the reflection spectrum of the Fabry-Perot cavity at the cavity length; repeat the simulation to obtain the reflection spectrum of the Fabry-Perot cavity at all cavity lengths in the cavity length sequence; Step 3: add noise to the reflection spectrum to obtain a simulated acquisition spectrum; Step 4: select a channel according to the output wavelength of the wavelength division multiplexer, and take out the reflection intensity corresponding to each channel from the simulated acquisition spectrum to form a sparse spectrum; Each cavity length in the cavity length sequence is taken as the output, and the corresponding sparse spectrum is taken as the input to construct a data set; the data set is preprocessed; Step 5: initialize the neural network, take the sparse spectrum corresponding to each cavity length as the input, and take the cavity length as the output, and train the neural network model through the data set data; Step 6: after the training is completed, the sensing signal output by the fiber Fabry-Perot sensor is received through the wavelength division multiplexer, the reflection signals output by each channel of the wavelength division multiplexer are received by the photodetector array, and the reflection signals are converted into a sparse spectrum; Step 7: input the obtained sparse spectrum into the neural network model, obtain the cavity length of the fiber Fabry-Perot sensor through the neural network model, and realize Fabry-Perot signal demodulation.

2. The sparse spectrum based high-speed Fabry-Perot signal demodulation method according to claim 1, characterized in that, In step 2, the reflection spectrum of the Fabry-Perot cavity in the C band is obtained by simulating multi-beam interference with a Python script Fabry-Perot cavity; the emission spectrum is the reflection output intensity I R consisting of I R is represented as: Wherein, F is the fineness coefficient of the Fabry-Perot cavity, δ represents the phase difference of adjacent light rays, and I0 represents the incident light intensity.

3. The method of claim 1, wherein the method is a sparse spectrum based high-speed Fabry-Perot signal demodulation method. In step 3, the specific method for adding noise to the reflection spectrum to obtain a simulated acquisition spectrum is: Generate Gaussian white noise through the random number generator in the Numpy library, superimpose it with the reflection spectrum, and obtain the spectrum as the simulated acquisition spectrum.

4. The sparse spectrum based high-speed Fabry-Perot signal demodulation method according to claim 1, characterized in that, In step 4, the steps of preprocessing the data set include: Normalize the data of the data set; Divide the normalized data set into a training set and a test set.

5. The sparse spectrum based high-speed Fabry-Perot signal demodulation method according to claim 4, characterized in that, In step 5, the specific method for training and testing the neural network model through the data set data is: Step 5.1: initialize the neural network, select the number of hidden layers and neurons; Step 5.2: calculate the loss function LOSS of the predicted results obtained by the neural network and the true cavity length, and update the weights and biases using the Adam optimizer; set the number of cycles, and obtain the final loss function LOSS through continuous cycle iteration; Step 5.3: determine whether the final loss function LOSS meets the expectation, if yes, the training is completed, the neural network model is exported, if not, the number of hidden layers and neurons of the neural network is modified, and the cycle iteration is restarted until the loss function LOSS meets the expected effect of the neural network model.

6. A sparse spectrum based high-speed Fabry-Perot signal demodulation system, characterized in that, A sparse spectrum-based high-speed Fabry-Perot signal demodulation method for executing the method of claim 1, comprising: a light source (3), a fiber Fabry-Perot sensor (4), a fiber circulator (5), a wavelength division multiplexer (6), a photodetector array (7), a synchronous acquisition card (8), and a computing unit (9); the wavelength division multiplexer (6) is a dense wavelength division multiplexer, and the output port is provided with n channels with different wavelengths; The C-band broadband light output by the light source (3) is incident to the fiber-optic F-P sensor (4) after passing through the fiber-optic circulator (5), the reflection signal generated in the fiber-optic F-P sensor (4) is incident to the wavelength division multiplexer (6) again after passing through the fiber-optic circulator (5), the reflection signal is spectrally wavelength-division processed by the wavelength division multiplexer (6), then the reflection signal output by each channel of the wavelength division multiplexer is received by the photodetector array (7) and is photoelectrically converted, and a sparse spectrum with multiple different wavelengths of light is obtained; the sparse spectrum is subjected to data collection by the synchronous acquisition card (8) and is then sent to the computing unit (9) for demodulation.

7. A sparse spectrum based high-speed Fabry-Perot signal demodulation system according to claim 6, wherein, The value of n ranges from 3 to 20.

8. A sparse spectrum based high-speed Fabry-Perot signal demodulation system according to claim 6, wherein, Further comprising a power supply (1) and a voltage conversion module (2); the power supply (1) is used for providing voltage, and the voltage conversion module (2) is used for converting the voltage provided by the power supply (1) to supply power to the system.

9. The sparse spectrum based high-speed Fabry-Perot signal demodulation system according to claim 6, wherein, Further comprising: a network card (10), a cloud platform (11) and a display unit (12); after the computing unit (9) completes data demodulation, the fiber-optic F-P cavity length data after demodulation is uploaded to the cloud platform (11) through the network card (10); the cloud platform (11) is used for real-time drawing of the fiber-optic F-P cavity length data on a webpage, and the display unit (12) accesses the webpage by connecting to the Internet, thereby realizing remote multi-terminal monitoring.

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