Optical fiber temperature and pressure sensing signal processing method and system

Abstract

This invention proposes a fiber optic temperature and pressure sensing signal processing method and system, belonging to the field of signal processing technology. The core lies in achieving high-precision demodulation of temperature and pressure parameters through the synergistic optimization of spatial coherence distribution characteristics and coupling effectiveness models. First, a theoretical spatial coherence distribution model is calculated based on multimode fiber parameters, and LSTM neural network correction is performed through experimental measurements. Second, finite element simulation is used to analyze the sensor's micro-deformation under force and heat conditions, establishing a functional relationship between coupling effectiveness and temperature and pressure, and Bayesian optimization is used to calibrate and compensate parameters. Furthermore, by integrating the spatial coherence correction coefficient and coupling effectiveness compensation parameters, an adaptive bandpass filter is designed, and frequency domain processing significantly improves the quality of the interference signal. This method effectively solves the problem of decreased measurement accuracy caused by environmental interference in multimode fiber optic sensing systems, providing reliable technical support for real-time temperature and pressure monitoring in fields such as high-voltage power equipment and oil extraction.

Description

Technical Field

[0001] This invention relates to the field of visual analysis technology, and in particular to a fiber optic temperature and pressure sensing signal processing method and system. Background Technology

[0002] Currently, fiber optic sensors are widely used in temperature and pressure measurement. However, traditional fiber optic temperature and pressure sensors still face problems such as insufficient accuracy and poor stability in complex environments (such as high temperature, high pressure, and strong electromagnetic interference). Existing technologies mainly rely on a single physical parameter (such as wavelength drift) for measurement, making it difficult to simultaneously and accurately demodulate both temperature and pressure parameters. Furthermore, they are susceptible to environmental interference (such as fiber micro-bending and thermal expansion), leading to significant measurement errors. Multimode fiber optic temperature and pressure sensors, in particular, suffer from complex noise and crosstalk in their interference spectrum signals due to their multimode characteristics. Traditional signal processing methods struggle to effectively separate and compensate for these interference factors, resulting in temperature and pressure measurement accuracies typically below ±1℃ / ±0.1MPa. In addition, existing technologies lack a systematic analysis of spatial coherence distribution characteristics and coupling efficiency variations, making it impossible to establish accurate environmental impact compensation models. In critical fields such as high-voltage power equipment and oil extraction, the accuracy of temperature and pressure monitoring is directly related to the safe operation of equipment and production efficiency. There is an urgent need to develop a high-precision processing method that can effectively process multi-mode fiber optic temperature and pressure sensing signals in order to break through the bottlenecks of traditional technology and meet the urgent needs of industrial sites for high-precision and high-reliability temperature and pressure monitoring. Summary of the Invention

[0003] The purpose of this invention is to provide a fiber optic temperature and pressure sensing signal processing method and system, which solves the problem of decreased measurement accuracy caused by environmental interference in multimode fiber optic sensing systems.

[0004] This application proposes a fiber optic temperature and pressure sensing signal processing method, which includes:

[0005] S1: Generate a first interference spectral signal by illumination, acquire the first interference spectral signal and perform correction and optimization processing to obtain the target interference spectral dataset;

[0006] S2: Determine the theoretical spatial coherence distribution model and spatial coherence experimental data, and generate a spatial coherence correction coefficient table through the first comparison process;

[0007] S3: Establish the first coupling performance relationship based on the first micro-deformation data to generate a coupling performance compensation parameter table;

[0008] S4: Perform a first compensation process on the target interferometric spectrum dataset according to the spatial coherence correction coefficient table and the coupling effectiveness compensation parameter table to obtain a first time-domain interferometric spectrum.

[0009] Preferably, the S1 includes:

[0010] S11: Irradiate the multimodal fiber optic temperature and pressure sensor with a first broadband light source to generate a first interference spectrum signal;

[0011] S12: Perform a first preprocessing operation on the first interference spectrum signal to obtain a first interference spectrum data set;

[0012] S13: Perform a second preprocessing operation on the first interference spectrum data set to obtain a target interference spectrum data set.

[0013] Preferably, the specific operation process of the second preprocessing operation includes baseline correction and noise removal.

[0014] Preferably, the S2 includes:

[0015] S21: Obtain a theoretical spatial coherence degree distribution model based on the first geometric parameters of the multimodal fiber optic temperature and pressure sensor;

[0016] S22: Build a spatial coherence degree experimental system and measure and obtain spatial coherence degree experimental data;

[0017] S23: Based on the theoretical spatial coherence degree distribution model, obtain spatial coherence degree theoretical data corresponding to the spatial coherence degree experimental data, and perform a first comparison process on the two to generate a spatial coherence degree correction coefficient table.

[0018] Preferably, the S3 includes:

[0019] S3_{1}: Obtain first micro-deformation data by finite element simulation analysis of the micro-deformation of the sensor structure in a force-heat environment; [[ID=_{32}}]

[0020] S32: Based on the first micro-deformation data, determine the change value of the beam coupling efficiency and establish a first coupling efficiency relation;

[0021] S33: Generate a coupling efficiency compensation parameter table according to the first experimental data and the first coupling efficiency relation.

[0022] Preferably, the S4 includes:

[0023] S41: Perform a frequency domain conversion process on the target interference spectrum data set to identify effective interference frequency components;

[0024] S42: Determine an adaptive band-pass filter according to the spatial coherence degree correction coefficient table and the coupling efficiency compensation parameter table;

[0025] Note: In the translation of S31, "S3_{1}" is used to match the original text's subscript format. If there is a specific requirement for this format in the actual context, it may need to be adjusted according to the standard. Here, it is mainly to show the correspondence.S43: Based on the adaptive bandpass filter, the first frequency domain signal is determined, and the first frequency domain signal is subjected to inverse Fourier transform to obtain the first time domain interference spectrum.

[0026] This application also proposes an optical fiber temperature and pressure sensing signal processing system for implementing the aforementioned optical fiber temperature and pressure sensing signal processing method.

[0027] This invention proposes a fiber optic temperature and pressure sensing signal processing method and system, belonging to the field of signal processing technology. The core lies in achieving high-precision demodulation of temperature and pressure parameters through the synergistic optimization of spatial coherence distribution characteristics and coupling effectiveness models. First, a theoretical spatial coherence distribution model is calculated based on multimode fiber parameters, and LSTM neural network correction is performed through experimental measurements. Second, finite element simulation is used to analyze the sensor's micro-deformation under force and heat conditions, establishing a functional relationship between coupling effectiveness and temperature and pressure, and Bayesian optimization is used to calibrate and compensate parameters. Furthermore, by integrating the spatial coherence correction coefficient and coupling effectiveness compensation parameters, an adaptive bandpass filter is designed, and frequency domain processing significantly improves the quality of the interference signal. This method effectively solves the problem of decreased measurement accuracy caused by environmental interference in multimode fiber optic sensing systems, providing reliable technical support for real-time temperature and pressure monitoring in fields such as high-voltage power equipment and oil extraction. Attached Figure Description

[0028] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0029] Figure 1 This is an execution flowchart of a fiber optic temperature and pressure sensing signal processing method according to the present invention. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. The illustrative embodiments and descriptions are only used to explain the present invention and are not intended to limit the present invention.

[0032] The following is a detailed description of a fiber optic temperature and pressure sensing signal processing method and system according to the present invention.

[0033] This embodiment proposes a fiber optic temperature and pressure sensing signal processing method, the specific process of which is as follows: Figure 1 As shown.

[0034] S1: Generate a first interference spectral signal by illumination, acquire the first interference spectral signal and perform correction and optimization processing to obtain the target interference spectral dataset.

[0035] In this step, a broadband light source is used to illuminate the multimode fiber optic thermo-baric sensor to obtain the first interference spectrum signal. After multiple rounds of processing, such as formatting, baseline correction, and noise extraction, the target interference spectrum dataset is formed.

[0036] S1 includes the following sub-steps:

[0037] S11: Illuminate the multimode fiber optic temperature and pressure sensor using a first broadband light source to generate a first interference spectral signal.

[0038] In this step, a broadband light source is set up to generate the first interference spectrum signal, that is, the relationship between optical power and wavelength.

[0039] The broadband light source is set up in the following way: a superluminescent diode (SLD) with a wavelength range of 1250-1350nm is used as the broadband light source, with a stable output power of 2mW±0.05mW and a spectral half-width of 40nm, to ensure that a sufficiently strong interference effect is generated in the multimode fiber.

[0040] To avoid interference caused by the light source directly entering the detector, the SLD is coupled to the input end of the multimode fiber temperature and pressure sensor through a single-mode fiber. The light source and the detection system are separated by a fiber coupler with a coupling ratio of 1:1.

[0041] An optical fiber collimator is used at the sensor output to collimate the optical fiber output beam, ensuring that the interference spectral signal can be accurately received by the high-precision spectrometer. The system is equipped with a real-time power monitoring module to ensure that the fluctuation of the light source output power is controlled within ±2%, providing stable light source conditions for interference spectral signal acquisition.

[0042] S12: Perform a first preprocessing operation on the first interference spectrum signal to obtain a first interference spectrum dataset.

[0043] In step S11, the first interference spectrum signal has been generated by the illumination of a broadband light source. In this step, the first interference spectrum signal needs to be acquired, preprocessed and stored.

[0044] The process of the first preprocessing operation is as follows:

[0045] An Agilent 86120C high-resolution spectrometer was used, with the following parameters: wavelength resolution better than 0.01 nm, dynamic range up to 100 dB, sampling interval of 0.02 nm, and spectral scan rate of 200 ms / s. An adjustable optical attenuator was configured at the spectrometer input, with an attenuation range preferably of 0-30 dB and an accuracy preferably of 0.1 dB, to ensure that the signal intensity remained within the linear operating range of the spectrometer.

[0046] An automated data acquisition program was written using Python scripts to collect spectral data every 50ms, recording the acquisition timestamp and device ID simultaneously. The data is stored in HDF5 format, containing arrays of wavelengths, optical power, timestamps, and device identifiers to ensure data traceability.

[0047] Preferably, a preliminary verification is required before storing spectral data to exclude abnormal data points, such as points with a spectral intensity of 0 or exceeding the measurement range.

[0048] S13: Perform a second preprocessing operation on the first interference spectrum dataset to obtain the target interference spectrum dataset.

[0049] The first interference spectrum dataset obtained in S12 inevitably has baseline deviation and excessive noise. Therefore, in this step, corresponding signal optimization processing is required to remove the interference from the environmental background.

[0050] The specific operation flow of the second preprocessing operation includes baseline correction and noise extraction, specifically including:

[0051] A baseline correction algorithm based on wavelet transform is adopted:

[0052] First, the interference spectrum was decomposed into 5 levels using the db4 wavelet of the pywavelets library, and the low-frequency components were extracted as the baseline.

[0053] Then, the baseline is fitted using the least squares method to obtain the baseline correction function;

[0054] Finally, the baseline is subtracted from the original spectrum to obtain the corrected spectrum.

[0055] Noise filtering employs an adaptive wavelet threshold denoising method:

[0056] The signal-to-noise ratio (SNR) of the spectrum is calculated, and a threshold is dynamically determined based on the SNR. The preferred threshold is calculated as standard deviation × sqrt(2 × log(N)), where N is the number of data points. Soft thresholding is applied to high-frequency components. During preprocessing, a sliding window statistical analysis is used to calculate the SNR of the spectrum. If the SNR is below 15 dB, adaptive thresholding is applied for denoising; if the SNR is above 15 dB, only baseline correction is performed. The preprocessed data is stored as a new HDF5 file, and a preprocessing quality report is generated, including metrics such as SNR, baseline correction error, and noise filtering effect.

[0057] S2: Determine the theoretical spatial coherence distribution model and spatial coherence experimental data, and generate a spatial coherence correction coefficient table through the first comparison process.

[0058] Spatial coherence is one of the important factors affecting spectral acquisition. Therefore, in order to obtain accurate spectral parameters in the future, it is necessary to analyze the spatial coherence of the multimode fiber optic temperature and pressure sensor in this step.

[0059] S2 includes the following sub-steps:

[0060] S21: Based on the first geometric parameters of the multimode fiber optic temperature and pressure sensor, obtain the theoretical spatial coherence distribution model.

[0061] In this step, the multimode fiber thermobaric sensor that generates the target interferometric spectral dataset needs to be analyzed to obtain a theoretical spatial coherence distribution model.

[0062] The analysis process is as follows:

[0063] Based on the geometric parameters and material properties of multimode optical fibers, a theoretical spatial coherence distribution model is established. Exemplary geometric parameters include: core diameter 200 μm, numerical aperture 0.22, length 50 cm, and refractive index 1.46.

[0064] In practical implementation, a MATLAB program can be used to calculate the spatial coherence distribution based on scalar diffraction theory and mode coupling theory of multimode fiber. Specific calculation methods can be found in existing technologies and will not be elaborated upon here.

[0065] In the calculation process, the Monte Carlo method is used to simulate the random coupling of modes in the optical fiber, taking into account the small inhomogeneities in the optical fiber manufacturing process, thereby improving the accuracy of the theoretical model.

[0066] S22: Build a spatial coherence experimental system and measure and obtain spatial coherence experimental data.

[0067] In this step, it is necessary to measure the spatial coherence distribution data based on the established measurement experimental system.

[0068] The measurement process for the spatial coherence experimental data is as follows:

[0069] First, an experimental system based on the Mach-Zehnder interferometer was built: a 1310nm laser was used as a coherent light source, which was split into a reference arm and a measurement arm by an optical fiber coupler; the measurement arm was connected to the output of a multimode fiber thermo-baric sensor, and the reference arm was connected to a standard optical fiber; a high-resolution CCD camera was configured at the output of the interferometer to acquire interference fringe images.

[0070] Then, by moving the optical path of the reference arm, interference fringes under different optical path differences are recorded, and spatial coherence is calculated. Specifically, a stepper motor is used to precisely control the optical path of the reference arm, moving it 0.5 μm at a time, acquiring 1000 interference images under different optical path differences; the images are processed using the OpenCV library, the contrast of the interference fringes is calculated, and the spatial coherence experimental data is determined by the relationship between contrast and optical path difference. A temperature control system is required to ensure stable experimental temperature.

[0071] S23: Based on the theoretical spatial coherence distribution model, obtain the spatial coherence theoretical data corresponding to the spatial coherence experimental data, and perform a first comparison process on the two to generate a spatial coherence correction coefficient table.

[0072] In S21 and S22, the theoretical and measured values ​​of spatial coherence are determined respectively. In this step, the two values ​​need to be compared to generate a spatial coherence correction coefficient table.

[0073] The specific comparison process is as follows:

[0074] Based on the aforementioned spatial coherence experimental data, the corresponding theoretical spatial coherence data are calculated from the theoretical spatial coherence distribution model using the same experimental conditions such as contrast and optical path difference.

[0075] The theoretical spatial coherence distribution is compared with experimental measurement data, and the mean square error and correlation coefficient are calculated.

[0076] If the mean squared error is greater than 0.05 or the correlation coefficient is less than 0.9, a model correction algorithm based on an LSTM neural network is used: The theoretical model parameters are used as input, and experimental data are used as labels to train the LSTM network. The optimal network parameters are: 128 hidden layer units, a learning rate of 0.001, and 2000 iterations, generating a corrected spatial coherence distribution model. The LSTM network input includes environmental factors such as fiber optic parameters, temperature, and pressure, and the output is the corrected spatial coherence distribution. The training data comes from 500 sets of experimental measurement data under different environmental conditions to ensure the model's generalization ability. After correction, a spatial coherence correction coefficient table is generated, where the correction coefficient = theoretical value / experimental value.

[0077] S3: Establish the first coupling performance relationship based on the first micro-deformation data to generate a coupling performance compensation parameter table.

[0078] Under mechanical and thermal conditions, the sensor may undergo slight structural deformation. In this step, it is necessary to calculate the impact of the coupling efficiency caused by the slight deformation and apply corresponding compensation to improve the measurement accuracy.

[0079] S3 specifically includes the following sub-steps:

[0080] S31: The micro-deformation of the sensor structure under force and heat environment is analyzed by finite element simulation to obtain the first micro-deformation data.

[0081] In this step, it is necessary to analyze the micro-deformation of the sensor structure through finite element simulation.

[0082] Multiphysics coupling simulation was performed using ANSYS Workbench: a three-dimensional finite element model of a multimode fiber optic thermobaric sensor was established, including the fiber, packaging material, and sensor housing.

[0083] Set boundary conditions: temperature range -20℃ to 120℃, pressure range 0 to 10MPa, coefficient of thermal expansion (optical fiber: 5×10^-6 / ℃, encapsulation material: 20×10^-6 / ℃), material elastic modulus (optical fiber: 70GPa, encapsulation material: 3GPa).

[0084] A thermo-structural coupling analysis was employed to calculate micro-deformations under different temperatures and pressures. Specifically, ANSYS's thermal-structural coupling function was used for transient thermal analysis, followed by structural analysis, outputting deformation contour maps and displacement data. Through parametric scanning, a temperature-pressure-deformation relationship table was generated, recording the micro-deformation at key points such as the fiber optic endface.

[0085] S32: Based on the first micro-deformation data, determine the change value of beam coupling efficiency and establish the first coupling efficiency relationship.

[0086] In this step, it is necessary to obtain the change in beam coupling efficiency based on the first micro-deformation data.

[0087] The effect of micro-deformation on beam propagation was simulated using COMSOL Multiphysics, and the coupling effectiveness was calculated. The relationship between temperature, pressure, and coupling effectiveness η(T,P) was fitted using the least squares method, resulting in the first coupling effectiveness equation: η(T,P)=a0+a1T+a2P+a3T²+a4P²+a5TP, where T is temperature, P is pressure, and a0…a5 are coefficients of each order. The fitting coefficients were obtained through training with 500 sets of simulation data to ensure model accuracy.

[0088] S33: Generate a coupling performance compensation parameter table based on the first experimental data and the first coupling performance relationship.

[0089] In this step, coupling performance compensation parameters need to be generated based on the first coupling performance relationship.

[0090] Under laboratory conditions, a temperature-pressure calibration experiment was conducted on the sensor: within the range of -20℃ to 120℃ and 0 to 10MPa, a set of coupling performance data was collected every 10℃ and 1MPa. The collected experimental data were used to calibrate the coupling performance model. A parameter adjustment method based on Bayesian optimization was used to optimize the model parameters to minimize the mean square error between the predicted and experimental values.

[0091] Implementation details: Bayesian optimization is performed using the scikit-optimize library, defining the objective function as the mean squared error and the search space as a reasonable range of fitted coefficients. After calibration, a coupling efficiency compensation parameter table is generated, where the compensation parameter = 1 / η(T,P). Simultaneously, cross-validation is used to evaluate the model's generalization ability and ensure the reliability of the compensation parameters.

[0092] S4: Perform a first compensation process on the target interferometric spectrum dataset according to the spatial coherence correction coefficient table and the coupling effectiveness compensation parameter table to obtain a first time-domain interferometric spectrum.

[0093] In this step, the target interferometric spectral dataset needs to be subjected to a first compensation process based on the spatial coherence correction coefficient table and the coupling effectiveness compensation parameter table, and finally the first time-domain interferometric spectrum is obtained.

[0094] S4 includes the following sub-steps:

[0095] S41: Perform frequency domain transformation processing on the target interference spectrum dataset to identify the effective interference frequency components.

[0096] To perform compensation processing, the target interferometric spectral dataset first needs to be converted to the frequency domain.

[0097] A Fast Fourier Transform (FFT) was applied to the preprocessed target interferometric spectrum dataset: the time-domain interferometric spectrum was converted to a frequency-domain signal using the `fft` function from the NumPy library. The FFT point count was set to 2048 to ensure a frequency domain resolution of less than 0.01 nm. In the frequency domain, the effective interferometric frequency components, corresponding to the characteristic frequencies of the sensor microcavity, were identified. The location of the main frequency peak was determined by calculating the power spectral density of the spectrum. An adaptive thresholding method was used to identify the effective frequency components, i.e., threshold = mean PSD + 3 × standard deviation. Simultaneously, wavelet packet decomposition was applied to further analyze the frequency domain signal and extract key frequency features.

[0098] S42: Determine the adaptive bandpass filter based on the spatial coherence correction coefficient table and the coupling performance compensation parameter table.

[0099] Based on the spatial coherence correction coefficient table and coupling effectiveness compensation parameter table generated in S23 and S33, an adaptive bandpass filter is designed: First, the effective interference frequency range is determined, such as 1310nm ± 5nm. Then, the center frequency and bandwidth of the filter are adjusted according to the spatial coherence correction coefficients. An FIR filter is designed using scipy.signal, with a center frequency f0 = 1310nm, bandwidth B = 10nm, and filter order N = 50.

[0100] In the specific compensation process, it is necessary to determine the corresponding spatial coherence correction coefficient and coupling effectiveness compensation parameter, so as to determine the frequency domain filter transfer function of the adaptive bandpass filter.

[0101] The final optimized relationship is as follows:

[0102] S_filtered = FFT - ¹{H_final(ω) × FFT(S_coupling)};

[0103] Where S_filtered is the enhanced frequency domain interference spectrum; H_final(ω) is the final frequency domain filter transfer function; FFT(S_coupling) is the discrete Fourier transform of S_coupling; and S_coupling is the interference signal after coupling efficiency compensation.

[0104] S_coupling = S_spatial × (1 + K_coupling(ω));

[0105] Where S_spatial is the interference signal after spatial coherence compensation; K_coupling(ω) is the compensation parameter for coupling effectiveness compensation;

[0106] S_spatial = S_raw(ω) × (1 + K_spatial(ω));

[0107] Wherein, S_raw(ω) is the effective interference frequency component; K_spatial(ω) is the compensation parameter for spatial coherence compensation.

[0108] S43: Based on the adaptive bandpass filter, the first frequency domain signal is determined, and the first frequency domain signal is subjected to inverse Fourier transform to obtain the first time domain interference spectrum.

[0109] An inverse fast Fourier transform (IFFT) is applied to the enhanced frequency-domain interferometric spectral data: using the `ifft` function from the NumPy library, the frequency-domain signal is transformed back to the time domain. Before the inverse transform, phase correction is applied to ensure the phase continuity of the time-domain signal. An adaptive phase correction algorithm is used: the phase spectrum of the frequency-domain signal is calculated, the phase is smoothed, and then the inverse transform is performed. The processed time-domain interferometric spectrum will have a higher signal-to-noise ratio and clearer interference characteristics.

[0110] This application also proposes an optical fiber temperature and pressure sensing signal processing system for executing the aforementioned optical fiber temperature and pressure sensing signal processing method.

[0111] This invention proposes a fiber optic temperature and pressure sensing signal processing method and system, belonging to the field of signal processing technology. The core lies in achieving high-precision demodulation of temperature and pressure parameters through the synergistic optimization of spatial coherence distribution characteristics and coupling effectiveness models. First, a theoretical spatial coherence distribution model is calculated based on multimode fiber parameters, and LSTM neural network correction is performed through experimental measurements. Second, finite element simulation is used to analyze the sensor's micro-deformation under force and heat conditions, establishing a functional relationship between coupling effectiveness and temperature and pressure, and Bayesian optimization is used to calibrate and compensate parameters. Furthermore, by integrating the spatial coherence correction coefficient and coupling effectiveness compensation parameters, an adaptive bandpass filter is designed, and frequency domain processing significantly improves the quality of the interference signal. This method effectively solves the problem of decreased measurement accuracy caused by environmental interference in multimode fiber optic sensing systems, providing reliable technical support for real-time temperature and pressure monitoring in fields such as high-voltage power equipment and oil extraction.

[0112] The above description is only a preferred embodiment of the present invention. Therefore, all equivalent changes or modifications made to the structure, features and principles described in the claims of this patent application are included in the scope of this patent application.

Claims

1. A fiber optic temperature and pressure sensing signal processing method, characterized in that, The method includes: S1: Generate a first interference spectral signal by illumination, acquire the first interference spectral signal and perform correction and optimization processing to obtain the target interference spectral dataset; S2: Determine the theoretical spatial coherence distribution model and spatial coherence experimental data, and generate a spatial coherence correction coefficient table through the first comparison process; S3: Establish the first coupling performance relationship based on the first micro-deformation data to generate a coupling performance compensation parameter table; S4: Perform a first compensation process on the target interferometric spectrum dataset according to the spatial coherence correction coefficient table and the coupling effectiveness compensation parameter table to obtain a first time-domain interferometric spectrum; S1 includes: S11: Illuminate the multimode fiber optic temperature and pressure sensor using a first broadband light source to generate a first interference spectral signal; S12: Perform a first preprocessing operation on the first interference spectrum signal to obtain a first interference spectrum dataset; S13: Perform a second preprocessing operation on the first interferometric spectrum dataset to obtain the target interferometric spectrum dataset; S2 includes: S21: Based on the first geometric parameters of the multimode fiber optic temperature and pressure sensor, obtain the theoretical spatial coherence distribution model; S22: Build a spatial coherence experimental system and measure and obtain spatial coherence experimental data; S23: Based on the theoretical spatial coherence distribution model, obtain the spatial coherence theoretical data corresponding to the spatial coherence experimental data, and perform a first comparison process on the two to generate a spatial coherence correction coefficient table.

2. The fiber optic temperature and pressure sensing signal processing method according to claim 1, characterized in that, The specific operation procedure of the second preprocessing operation includes baseline correction and noise extraction.

3. The fiber optic temperature and pressure sensing signal processing method according to claim 1, characterized in that, S3 includes: S31: The micro-deformation of the sensor structure under force and heat environment is analyzed by finite element simulation to obtain the first micro-deformation data; S32: Based on the first micro-deformation data, determine the change value of beam coupling efficiency and establish the first coupling efficiency relationship; S33: Generate a coupling performance compensation parameter table based on the first experimental data and the first coupling performance relationship.

4. The fiber optic temperature and pressure sensing signal processing method according to claim 1, characterized in that, S4 includes: S41: Perform frequency domain transformation processing on the target interference spectrum dataset to identify the effective interference frequency components; S42: Determine the adaptive bandpass filter based on the spatial coherence correction coefficient table and the coupling effectiveness compensation parameter table; S43: Based on the adaptive bandpass filter, the first frequency domain signal is determined, and the first frequency domain signal is subjected to inverse Fourier transform to obtain the first time domain interference spectrum.

5. A fiber optic temperature and pressure sensing signal processing system for implementing the fiber optic temperature and pressure sensing signal processing method according to any one of claims 1-4.

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