Method and system for signal demodulation of fiber optic sensors

Abstract

The application discloses a signal demodulation method and system of an optical fiber sensor, and particularly relates to the technical field of optical fiber sensing measurement, and is used for solving the technical problem that the non-reciprocal phase modulation of a common transmission fiber section of an existing interference type optical fiber sensor array is caused by dynamic disturbance, which leads to mutual crosstalk of multi-channel measurement results; the method is achieved by acquiring the phase modulation signals of each sensing channel, identifying the non-reciprocal phase modulation interval on the common transmission fiber section, analyzing the energy propagation characteristics of the interval on the spatial sequence of the sensor array to determine the disturbance source type, calculating the bias coefficient of the frequency domain energy distribution, constructing the crosstalk evaluation factor between channels, and selectively suppressing the demodulation results of the corresponding sensing channels according to the factor to accurately distinguish the real sensing signals and the transmission disturbance signals, effectively eliminating the influence of the crosstalk between channels on the measurement results, and improving the measurement accuracy and data reliability of the distributed optical fiber sensing system.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of optical fiber sensing measurement, in particular to a signal demodulation method and system of an optical fiber sensor. BACKGROUND

[0002] Optical fiber sensors have unique advantages such as anti-electromagnetic interference, small size, and flexibility, and can be used as key sensing tools in fields such as minimally invasive surgical instrument force feedback, in-vivo physiological parameter monitoring, and industrial equipment state monitoring. Among them, an interference type optical fiber sensor array based on time division multiplexing technology can realize distributed measurement of dozens of measurement points of physical quantities such as vibration and sound waves using a single optical fiber, and plays an important role in Internet of Things and smart city applications such as perimeter security, pipeline monitoring, and large-scale structure health monitoring. By analyzing the time sequence signals returned by the optical pulse in the multiple interferometer sensors connected in series, the demodulation and reconstruction of sensing information at different spatial positions are realized.

[0003] However, the existing demodulation technology is based on the ideal assumption of optical wave transmission reciprocity. When external dynamic disturbance acts on the common transmission optical fiber between the sensors, the symmetry of the go-and-return optical paths is destroyed, and non-reciprocal phase modulation is generated. This modulation signal will be coupled into the demodulation results of multiple sensing channels at the same time, causing the system to incorrectly identify the disturbance of the connecting optical fiber as a change in the physical quantity of the sensor itself, resulting in false event signals and positioning deviations. This is a deep measurement defect caused by the mismatch between the ideal model of the system architecture and the actual physical environment, which seriously affects the data reliability and measurement accuracy of the multiplexed optical fiber sensing system in complex field environments. SUMMARY

[0004] The present application provides a signal demodulation method and system for optical fiber sensors. The present application realizes accurate discrimination between real sensing signals and transmission disturbance signals, effectively eliminates the influence of channel crosstalk on measurement results, and improves the measurement accuracy and data reliability of the distributed optical fiber sensing system.

[0005] The technical solution of the present application to solve the above technical problems is as follows:

[0006] The signal demodulation method of the optical fiber sensor comprises:

[0007] S1, acquiring the phase modulation signals returned by each sensing channel in the time division multiplexing interference type sensor array;

[0008] S2, identifying the non-reciprocal phase modulation interval caused by dynamic disturbance on the common transmission optical fiber section;

[0009] S3. Analyze the energy propagation characteristics of the non-reciprocal phase modulation interval on the spatial sequence of the sensor array. Calculate the energy distribution ratio of its forward propagation and backward propagation through principal component analysis. Determine the type of disturbance source based on whether the energy distribution ratio meets the preset condition of consistency of disturbance propagation direction.

[0010] S4. Analyze the frequency domain energy distribution of the signal within the non-reciprocal phase modulation interval, and calculate the bias coefficients of its low-frequency energy and high-frequency energy.

[0011] S5. Based on the judgment results of the disturbance source type and the bias coefficient, construct the inter-channel crosstalk evaluation factor;

[0012] S6. Selectively suppress the demodulation results of the corresponding sensing channels based on the inter-channel crosstalk evaluation factor.

[0013] Furthermore, the phase modulation signals returned by each sensing channel in the time-division multiplexed interferometric sensor array are acquired, including:

[0014] Receive composite optical signals from a time-division multiplexed interferometric sensor array;

[0015] Based on the time division multiplexing timing sequence, the composite optical signal is demultiplexed to separate the optical signal corresponding to each sensing channel;

[0016] The optical signals from each sensor channel are converted into electrical signals via a photoelectric converter;

[0017] The electrical signal is processed by analog-to-digital conversion to obtain the digital phase modulation signal of each sensing channel.

[0018] Furthermore, identifying non-reciprocal phase modulation intervals caused by dynamic disturbances on the common transmission fiber segment includes:

[0019] Calculate the rate of change of the phase modulation signal of each sensing channel in the time domain;

[0020] The independent disturbance range of each sensing channel is determined based on the range where the rate of change exceeds the preset rate of change threshold;

[0021] Compare the overlapping portions of the independent disturbance intervals of adjacent sensing channels in the time domain;

[0022] When the time span of the overlapping portion meets the preset conditions, the overlapping portion is determined to be a non-reciprocal phase modulation interval on the common transmission fiber segment caused by dynamic disturbance.

[0023] Furthermore, the energy propagation characteristics of the non-reciprocal phase modulation interval in the spatial sequence of the sensor array are analyzed. Principal component analysis is used to calculate the energy distribution ratio of its forward and backward propagation. The type of disturbance source is determined based on whether the energy distribution ratio meets the preset condition for consistency of disturbance propagation direction, including:

[0024] The perturbation signal matrix of the sensor array spatial sequence is constructed based on the identified non-reciprocal phase modulation intervals;

[0025] Singular value decomposition is performed on the disturbance signal matrix to extract principal component eigenvectors representing the direction of energy propagation;

[0026] The ratio of forward propagation energy to backward propagation energy is calculated based on the symbol distribution pattern of the principal component eigenvectors in the spatial sequence of the sensor array.

[0027] The calculated energy distribution ratio is compared with a preset distribution threshold.

[0028] When the energy distribution ratio is greater than the preset distribution threshold, it is determined to be a fiber optic disturbance; when the energy distribution ratio is less than or equal to the preset distribution threshold, it is determined to be a real sensor event.

[0029] Furthermore, the singular value decomposition of the perturbation signal matrix is ​​performed to extract the principal component eigenvectors representing the direction of energy propagation. This includes: performing singular value decomposition mathematical operations on the constructed perturbation signal matrix to obtain a left singular vector matrix, a singular value matrix, and a right singular vector matrix; and selecting the column vector corresponding to the largest singular value from the left singular vector matrix as the principal component eigenvectors representing the direction of energy propagation based on the order of the singular values ​​in the singular value matrix.

[0030] Furthermore, the frequency domain energy distribution of the signal within the non-reciprocal phase modulation interval is analyzed, and the bias coefficients of its low-frequency and high-frequency energies are calculated, including:

[0031] The spectrum is obtained by performing a Fourier transform on the phase-modulated signal within the non-reciprocal phase-modulation interval;

[0032] The spectrum is divided into a preset low-frequency band and a preset high-frequency band;

[0033] Calculate the sum of squares of the spectral amplitudes within the preset low-frequency band as the low-frequency energy, and calculate the sum of squares of the spectral amplitudes within the preset high-frequency band as the high-frequency energy;

[0034] The bias coefficients of low-frequency energy and high-frequency energy are obtained by taking the logarithm of the ratio of low-frequency energy to high-frequency energy.

[0035] Furthermore, the Fourier transform of the phase modulation signal within the non-reciprocal phase modulation interval to obtain the spectrum includes: applying the Fast Fourier Transform algorithm to the phase modulation signal within the non-reciprocal phase modulation interval to convert the time-domain phase modulation signal into a frequency-domain representation to obtain the spectrum.

[0036] Furthermore, based on the determination of the disturbance source type and the bias coefficient, an inter-channel crosstalk assessment factor is constructed, including:

[0037] The judgment result of the fiber optic disturbance is mapped to the first weighting coefficient, and the judgment result of the real sensor event is mapped to the second weighting coefficient.

[0038] The bias coefficient is obtained by multiplying the bias coefficient with the corresponding weight coefficient.

[0039] The weighted bias coefficients are normalized to generate inter-channel crosstalk evaluation factors.

[0040] Furthermore, selective suppression is applied to the demodulation results of the corresponding sensing channels based on the inter-channel crosstalk evaluation factor, including:

[0041] Compare the inter-channel crosstalk evaluation factor with the preset suppression threshold;

[0042] When the inter-channel crosstalk evaluation factor is greater than the preset suppression threshold, suppression processing is applied to the phase modulation signal of the corresponding sensing channel.

[0043] When the inter-channel crosstalk evaluation factor is less than or equal to the preset suppression threshold, the phase modulation signal of the corresponding sensing channel remains unchanged.

[0044] On the other hand, the present invention provides a signal demodulation system for an optical fiber sensor, comprising:

[0045] The signal acquisition module is used to acquire the phase modulation signals returned by each sensing channel in the time-division multiplexed interferometric sensor array;

[0046] The interval identification module is used to identify non-reciprocal phase modulation intervals caused by dynamic disturbances on the common transmission fiber segment;

[0047] The type determination module is used to analyze the energy propagation characteristics of the non-reciprocal phase modulation interval on the spatial sequence of the sensor array. It calculates the energy distribution ratio of its forward propagation and backward propagation through principal component analysis, and determines the type of disturbance source based on whether the energy distribution ratio meets the preset disturbance propagation direction consistency condition.

[0048] The coefficient calculation module is used to analyze the frequency domain energy distribution of the signal within the non-reciprocal phase modulation interval and calculate the bias coefficients of its low-frequency energy and high-frequency energy.

[0049] The factor construction module is used to construct inter-channel crosstalk evaluation factors based on the judgment results of the disturbance source type and the bias coefficient.

[0050] The suppression execution module is used to selectively suppress the demodulation results of the corresponding sensing channels based on the inter-channel crosstalk evaluation factor.

[0051] The beneficial effects of this invention are:

[0052] 1. By identifying the non-reciprocal phase modulation interval on the common transmission fiber segment, the actual sensor-perceived signal and the crosstalk signal caused by transmission line disturbances are effectively distinguished. This solves the measurement distortion problem caused by transmission path disturbances in multiplexed fiber optic sensing systems from the perspective of measurement principle. By analyzing the energy propagation characteristics of the non-reciprocal phase modulation interval on the spatial sequence of the sensor array, the type of disturbance source can be accurately determined. This enables precise identification of signals of different properties during the signal demodulation stage, significantly improving the system's reliability in detecting changes in real physical quantities.

[0053] 2. By constructing an inter-channel crosstalk evaluation factor and implementing selective suppression, the cross-influence of transmission line disturbances on multi-channel measurement results is effectively eliminated while maintaining the signal integrity of normal sensing channels. Starting from the physical characteristics of the fiber optic sensing system, a crosstalk suppression mechanism conforming to the light wave transmission law is established by analyzing the frequency domain energy distribution characteristics and spatial propagation characteristics. This enables the interferometric fiber optic sensor array to maintain stable measurement accuracy and data reliability under complex working conditions, providing a more reliable sensing data foundation for IoT and smart city applications. Attached Figure Description

[0054] Figure 1 This is a flowchart of the signal demodulation method for the fiber optic sensor of the present invention;

[0055] Figure 2 This is a schematic diagram of the signal demodulation system of the fiber optic sensor of the present invention. Detailed Implementation

[0056] 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.

[0057] Example 1: Figure 1 The present invention provides a signal demodulation method for the fiber optic sensor, comprising:

[0058] S1. Acquire the phase modulation signal returned by each sensing channel in the time-division multiplexed interferometric sensor array;

[0059] S2. Identify the non-reciprocal phase modulation intervals caused by dynamic disturbances on the common transmission fiber segment;

[0060] S3. Analyze the energy propagation characteristics of the non-reciprocal phase modulation interval on the spatial sequence of the sensor array. Calculate the energy distribution ratio of its forward propagation and backward propagation through principal component analysis. Determine the type of disturbance source based on whether the energy distribution ratio meets the preset condition of consistency of disturbance propagation direction.

[0061] S4. Analyze the frequency domain energy distribution of the signal within the non-reciprocal phase modulation interval, and calculate the bias coefficients of its low-frequency energy and high-frequency energy.

[0062] S5. Based on the judgment results of the disturbance source type and the bias coefficient, construct the inter-channel crosstalk evaluation factor;

[0063] S6. Selectively suppress the demodulation results of the corresponding sensing channels based on the inter-channel crosstalk evaluation factor.

[0064] S1. Obtain the phase modulation signal returned by each sensing channel in the time-division multiplexed interferometric sensor array, specifically as follows:

[0065] During implementation, a composite optical signal is first received from a time-division multiplexed interferometric sensor array. This composite optical signal is an interference signal formed by superimposing optical pulses from multiple sensing channels according to a time-division multiplexing sequence. The receiving operation is completed through an optical interface, which is physically connected to the output optical fiber of the sensor array via fiber optic fusion splicing to ensure that the optical signal is transmitted to the signal processing link with minimal loss. The composite optical signal contains interference signals from each sensing channel, and its light intensity fluctuation range is typically between 10 microwatts and 100 microwatts, with a phase change range covering 0 to 2π radians. The received composite optical signal immediately enters the demultiplexing stage.

[0066] The composite optical signal is demultiplexed based on time-division multiplexing timing to separate the optical signals corresponding to each sensing channel. The time-division multiplexing timing is generated by a field-programmable gate array (FPGA), which generates a pulse control sequence with a fixed period, where each pulse corresponds to the activation period of one sensing channel, typically with a period of 100 microseconds. The demultiplexing process is implemented through time-domain gating, which selects the optical signal of the corresponding channel within a specific time window based on the timing signal. For example, when the system contains 32 sensing channels, the duration of each channel is set to 3.125 microseconds. After demultiplexing, the optical signal of each sensing channel is separated into independent timing segments, each segment containing interference information from a single channel. The separated optical signals are immediately transmitted to the photoelectric conversion stage.

[0067] The optical signals from each sensing channel are converted into electrical signals by a photoelectric converter. The photoelectric converter uses indium gallium arsenide (IGaAs) photodiodes with a response wavelength range covering 1500 nm to 1600 nm. The separated optical signals from each sensing channel are input to an independent photodiode unit, which linearly converts changes in light intensity into a current signal. During the conversion, the photodiode's response time is set to 1 microsecond to ensure signal conversion is completed within the minimum interval of the time-division multiplexing sequence. The converted electrical signal is an analog current signal, typically ranging from 10 μA to 100 μA. This current signal is then converted into a voltage signal by a transimpedance amplifier, with the voltage range adjusted to 0 V to 5 V to meet the input requirements of the subsequent analog-to-digital converter.

[0068] The electrical signal undergoes analog-to-digital conversion (ADC) to obtain the digitized phase-modulated signal for each sensing channel. The ADC employs a high-speed 16-bit resolution chip, with its sampling rate dynamically adjusted according to the signal bandwidth. For example, when the highest frequency component of the signal is 2000 Hz, the sampling rate is set to 5000 Hz to satisfy the Nyquist sampling theorem. During sampling, the ADC discretizes the continuous analog voltage signal into a digital sequence, with each sampling point corresponding to a precise voltage value. The converted digital sequence undergoes digital filtering to remove out-of-band noise. Finally, the digitized phase-modulated signal for each sensing channel is stored in memory in binary format. Each channel's data block contains three fields: timestamp, channel identifier, and phase value, for subsequent signal analysis.

[0069] S2. Identify the non-reciprocal phase modulation intervals caused by dynamic disturbances on the common transmission fiber segment, specifically implemented as follows:

[0070] The rate of change of the phase modulation signal of each sensing channel in the time domain is calculated. The calculation process uses the digitized phase modulation signal acquired in stage S1 as the input data source. The digitized phase modulation signal includes a timestamp sequence and a phase value sequence. The center difference method is used for time-domain differentiation, which calculates the instantaneous rate of change at the intermediate point using the phase values ​​of three adjacent sampling points. Specifically, for the phase sequence of each sensing channel, three consecutive sampling points are taken to form a calculation window. The difference between the phase value of the subsequent point and the phase value of the preceding point is divided by twice the sampling interval time to obtain the rate of change value at the intermediate sampling point. The sampling interval time is determined by the sampling rate of the analog-to-digital converter in stage S1; for example, when the sampling rate is 5000 Hz, the sampling interval time is 0.0002 seconds. The rate of change calculation results form a new time series with the physical dimension of radians per second. This series characterizes the rate of change of the phase signal with time. For the processing of sequence boundary points, the starting point is calculated using the forward difference method, and the ending point is calculated using the backward difference method to ensure the continuity of the rate of change throughout the sequence. After the rate of change is calculated, the results are stored in a cache for subsequent analysis.

[0071] The independent disturbance intervals of each sensing channel are determined based on the intervals where the rate of change exceeds a preset rate of change threshold. The preset rate of change threshold is obtained through a statistical learning method based on undisturbed historical data collected during system initialization. In practice, under stable operating conditions of the fiber optic sensor array, phase change rate samples from each channel are continuously collected, with at least 1000 samples. The arithmetic mean and standard deviation of these samples are calculated, and the sum of the arithmetic mean and three times the standard deviation is used as the preset rate of change threshold. This setting, based on statistical principles, can cover more than 99.7% of noise fluctuations. For example, when the statistically obtained arithmetic mean of the rate of change is 0 radians per second and the standard deviation is 0.05 radians per second, the preset rate of change threshold is set to 0.15 radians per second. When identifying independent disturbance intervals, a sliding window detection is performed on the rate of change sequence. The width of the sliding window is set to five consecutive sampling points, determined based on signal stability requirements, effectively filtering out transient interference. When the rate of change values ​​of all sampling points within the window exceed the preset rate of change threshold, the time period corresponding to that window is determined to be a valid independent disturbance interval. Each sensing channel executes this detection process independently, and the final output is a set of independent perturbation intervals containing the start and end timestamps.

[0072] The process compares the overlapping portions of independent disturbance intervals in adjacent sensing channels in the time domain. Adjacent channels are determined based on the cascading order in the fiber optic topology; for example, channel number n and channel number n+1 form an adjacent channel pair. The comparison process employs time-domain intersection operations, which determine overlap by comparing the coordinates of the endpoints of the time intervals. Specifically, a unified time axis coordinate system is established, mapping the start and end timestamps of each independent disturbance interval to coordinate points on the time axis. For any two adjacent channels' independent disturbance intervals, the overlap is determined by comparing the numerical relationship between the start and end timestamps. If the end timetamp of the first interval is greater than the start timetamp of the second interval, and the start timetamp of the first interval is less than the end timetamp of the second interval, then the two intervals are considered to have an overlapping portion. The time span of the overlapping portion is calculated by taking the maximum value of the start timestamps and the minimum value of the end timestamps of the two intervals. This process is repeated until all adjacent channel pairs are compared, generating a set of comparison results containing multiple sets of overlapping portions.

[0073] When the time span of the overlapping portion meets a preset condition, the overlapping portion is determined to be a non-reciprocal phase modulation interval caused by dynamic disturbance on the common transmission fiber segment. The preset condition is determined through experimental calibration methods. Specifically, the ratio of the time span of the overlapping portion to the time span of the corresponding independent disturbance interval reaches a preset ratio threshold, which is set at 60%. This value is derived from statistical analysis of a large amount of experimental data and can effectively distinguish between common fiber disturbances and independent sensor events. In practice, the time span value of each overlapping portion is first calculated by subtracting the start timestamp from the end timestamp. At the same time, the time span value of the corresponding independent disturbance interval is calculated. Then, the time span of the overlapping portion is divided by the time span of the independent disturbance interval to obtain the actual ratio. The actual ratio is compared with the preset ratio threshold. When the actual ratio is greater than or equal to 60%, the preset condition is determined to be met. The overlapping portion that meets the condition is marked as a non-reciprocal phase modulation interval caused by dynamic disturbance on the common transmission fiber segment, and its spatial location information is recorded. The spatial location is jointly determined by the adjacent channel numbers that generated the overlapping portion. The non-reciprocal phase modulation interval caused by dynamic disturbances on the final output common transmission fiber segment will be used as input data for subsequent signal analysis.

[0074] S3. Analyze the energy propagation characteristics of the non-reciprocal phase modulation interval in the spatial sequence of the sensor array. Calculate the energy distribution ratio of its forward and backward propagation through principal component analysis. Determine the type of disturbance source based on whether the energy distribution ratio meets the preset condition for consistency of disturbance propagation direction. The specific implementation is as follows:

[0075] A perturbation signal matrix for the spatial sequence of the sensor array is constructed based on the identified non-reciprocal phase modulation intervals. The construction process uses the non-reciprocal phase modulation intervals output from stage S2 as the input data source. These intervals contain time range information and corresponding sensor channel number information. Specifically, the arrangement order of the sensor array spatial sequence is first determined. This spatial sequence is based on the physical location relationship of each sensor channel on the optical fiber link; for example, channel numbers from smallest to largest correspond to the spatial distribution of the optical fiber from near to far end. For each non-reciprocal phase modulation interval, phase modulation signal segments from all sensor channels within that interval are extracted. These phase modulation signal segments originate from the digitized phase modulation signals acquired in stage S1. The extracted phase modulation signal segments are arranged in spatial sequence order into a matrix. The row dimension of the matrix corresponds to the sensor channel number, the column dimension corresponds to the time sampling point, and the matrix element values ​​are the phase values ​​of each channel at each time point. The number of rows in the matrix equals the number of sensing channels involved in the construction. The number of columns is determined by the time span of the non-reciprocal phase modulation interval and the sampling rate. For example, when the time span is 100 milliseconds and the sampling rate is 5000 Hz, the matrix has 500 columns. The constructed perturbation signal matrix is ​​stored in memory as input data for subsequent analysis.

[0076] Singular value decomposition (SVD) is performed on the perturbation signal matrix to extract principal component eigenvectors representing the energy propagation direction. The SVD process decomposes the perturbation signal matrix into a product of three matrices: a left singular vector matrix, a singular value matrix, and a right singular vector matrix. Specifically, the Jacobi iterative algorithm is used to calculate the SVD. The Jacobi iterative algorithm progressively diagonalizes the matrix through multiple orthogonal transformations. The iteration termination condition is set to the change in singular values ​​between two consecutive iterations being less than 1 × 10⁻⁶. -6 During the decomposition process, the column vectors of the left singular vector matrix represent the energy distribution patterns on the spatial sequence of the sensor array, the diagonal elements of the singular value matrix represent the energy magnitude of each pattern, and the row vectors of the right singular vector matrix represent the energy change patterns on the time series. When extracting the principal component eigenvectors, the diagonal elements of the singular value matrix are first sorted in descending order. Then, the column vector corresponding to the largest singular value is selected from the left singular vector matrix as the principal component eigenvector representing the energy propagation direction. This principal component eigenvector is a numerical sequence with a length equal to the number of sensing channels. Each value in the sequence corresponds to a weighting coefficient of a sensing channel during energy propagation. The extracted principal component eigenvectors will be used for subsequent energy distribution calculations.

[0077] The ratio of forward propagation energy to backward propagation energy is calculated based on the sign distribution pattern of the principal component eigenvectors in the spatial sequence of the sensor array. The sign distribution pattern is determined by analyzing the numerical signs of each element in the principal component eigenvectors. Specifically, the sensor array spatial sequence is divided into forward propagation and backward propagation segments. The forward propagation segment corresponds to a continuous group of channels with the same sign for each eigenvector element, while the backward propagation segment corresponds to a group of channels with alternating signs for each eigenvector element. For energy calculation, the forward propagation energy is obtained by accumulating the variance of the phase signals of the corresponding channels in the forward propagation segment, and the backward propagation energy is obtained by accumulating the variance of the phase signals of the corresponding channels in the backward propagation segment. The phase signal variance is calculated based on the phase data of the corresponding channels in the perturbation signal matrix. The variance calculation formula is the sum of the squares of the differences between the phase values ​​of each channel and their mean, divided by the number of data points minus one. The ratio of forward propagation energy to backward propagation energy is obtained by dividing the forward propagation energy value by the backward propagation energy value. When the backward propagation energy is zero, the ratio is defined as 1 × 10⁻¹⁰. 6 This ratio characterizes the direction of energy propagation in a spatial sequence; a ratio greater than 1 indicates that forward propagation is dominant, while a ratio less than 1 indicates that backward propagation is dominant.

[0078] The calculated energy distribution ratio is compared with a preset distribution threshold. This preset threshold is trained using historical data, which includes samples of known types of fiber optic disturbance events and real sensor events. Specifically, during the system calibration phase, at least 200 sets of samples of known disturbance types are collected, with fiber optic disturbance event samples and real sensor event samples each accounting for 50%. The energy distribution ratio is calculated for each sample, and the optimal discrimination point is determined through receiver operating characteristic curve analysis. The energy distribution ratio corresponding to the point on the curve closest to the upper left corner is used as the preset distribution threshold. During the comparison, the real-time calculated energy distribution ratio is compared with the preset distribution threshold. The comparison result is categorized into three cases: greater than the threshold, equal to the threshold, and less than the threshold. A floating-point comparison algorithm is used, with a comparison precision of four decimal places to avoid misjudgments caused by floating-point errors. The comparison result will serve as the direct basis for determining the disturbance source type.

[0079] When the energy distribution ratio is greater than a preset distribution threshold, it is determined to be a fiber optic disturbance; when the energy distribution ratio is less than or equal to the preset distribution threshold, it is determined to be a genuine sensor event. During the determination process, the comparison between the energy distribution ratio and the preset distribution threshold is first checked. If the energy distribution ratio is greater than the preset distribution threshold, the current event is determined to be a fiber optic disturbance, which refers to signal interference caused by external mechanical vibration or temperature changes on the common transmission fiber segment. If the energy distribution ratio is less than or equal to the preset distribution threshold, the current event is determined to be a genuine sensor event, which refers to a signal generated by a change in a physical quantity directly sensed by the sensing unit. The determination result includes a confidence assessment, which is quantified by calculating the absolute distance between the current energy distribution ratio and the preset distribution threshold; the greater the distance, the higher the confidence level. The final determination result is stored together with the original phase data and triggers corresponding alarm or recording operations.

[0080] S4. Analyze the frequency domain energy distribution of the signal within the non-reciprocal phase modulation interval, and calculate the bias coefficients of its low-frequency energy and high-frequency energy. The specific implementation is as follows:

[0081] The spectrum is obtained by performing a Fourier transform on the phase modulation signal within the non-reciprocal phase modulation interval. This process uses the non-reciprocal phase modulation interval identified in stage S2 as the analysis object, extracting the phase modulation signal of each sensing channel within this interval. Specifically, a Fast Fourier Transform (FFT) algorithm is used to convert the time-domain phase modulation signal into a frequency-domain representation. The number of FFT points is determined based on the signal length and frequency resolution requirements; for example, a 512-point FFT is used when the signal contains 512 sampling points. Before the transform, a Hanning window function is applied to the signal to reduce spectral leakage. The coefficients of the Hanning window function are calculated using a cosine function, and the window function length is consistent with the signal length. The resulting spectrum contains a real part sequence and an imaginary part sequence. The amplitude spectrum sequence is obtained by calculating the square root of the sum of the squares of the real and imaginary parts. The frequency range of the spectrum is determined by the sampling rate; for example, when the sampling rate is 5000 Hz, the effective frequency range of the spectrum is 0 Hz to 2500 Hz. The obtained spectral data is stored in array form, and the amplitude spectrum sequence is mainly used for subsequent analysis.

[0082] The spectrum is divided into a preset low-frequency band and a preset high-frequency band. The boundaries between these bands are determined by analyzing the response characteristics of the fiber optic sensor system to disturbances at different frequencies, specifically based on the energy distribution characteristics of the signal in the frequency domain. For example, for common mechanical vibration interference, 0 Hz to 100 Hz is designated as the preset low-frequency band, and 100 Hz to the highest frequency of the spectrum is designated as the preset high-frequency band. The division process is based on the frequency coordinate sequence of the spectrum. First, the frequency resolution of the spectrum is determined, which is equal to the sampling rate divided by the number of Fast Fourier Transform (FFT) points. For example, when the sampling rate is 5000 Hz and the FFT points are 512, the frequency resolution is approximately 9.76 Hz. Then, the corresponding frequency indices are calculated based on the preset boundary frequency values. The index range from the DC component to the boundary frequency in the spectrum sequence is designated as the preset low-frequency band, and the index range from the boundary frequency to the Nyquist frequency is designated as the preset high-frequency band. After division, the spectrum is divided into two continuous frequency bands, each containing several adjacent frequency components.

[0083] The low-frequency energy is calculated by summing the squares of the spectral amplitudes within a preset low-frequency band, and the high-frequency energy is calculated by summing the squares of the spectral amplitudes within a preset high-frequency band. During the calculation, the spectral amplitude refers to the value of each frequency component in the amplitude spectrum sequence. The low-frequency energy is calculated by iterating through all frequency indices within the preset low-frequency band, squaring the amplitude value corresponding to each frequency index, and then summing the results. The high-frequency energy is calculated using the same method, iterating through all frequency indices within the preset high-frequency band and summing the squared amplitude values. Normalization processing using the Fast Fourier Transform (FFT) must be considered during energy calculation. For example, for an N-point FFT, the result needs to be divided by N to maintain energy conservation. The calculated low-frequency and high-frequency energies are dimensionless relative energy values, and their magnitude reflects the degree of energy concentration of the signal in different frequency bands. The two energy values ​​are stored in designated memory units for subsequent bias coefficient calculations.

[0084] The logarithm of the ratio of low-frequency energy to high-frequency energy is used to obtain the bias coefficients for low-frequency and high-frequency energy. The ratio calculation uses the low-frequency energy value as the numerator and the high-frequency energy value as the denominator. To avoid division by zero errors, when the high-frequency energy value is less than 1 × 10⁻⁶... -12 At that time, the high-frequency energy value was set to 1×10. -12 .

[0085] Logarithmic operations are performed using the common base-10 logarithm. The specific formula for calculating the bias coefficient is as follows: Bias coefficient = 10 × log10 (low-frequency energy / high-frequency energy). The purpose of the logarithmic transformation is to convert the linear ratio into a decibel value, facilitating subsequent processing and analysis. Numerical stability must be considered during the calculation; numerical truncation can be used when the energy ratio is too large or too small. The final bias coefficient is a real number, and its numerical characteristics are as follows: a positive bias coefficient indicates that the signal energy is mainly concentrated in the low-frequency range, a negative bias coefficient indicates that the signal energy is mainly concentrated in the high-frequency range, and a zero value indicates that the low-frequency energy is roughly equal to the high-frequency energy. The calculated bias coefficient result is stored together with the corresponding sensor channel information as input parameters for subsequently constructing the inter-channel crosstalk evaluation factor.

[0086] S5. Based on the judgment result of the disturbance source type and the bias coefficient, construct the inter-channel crosstalk evaluation factor, specifically implemented as follows:

[0087] The judgment results for fiber optic disturbances are mapped to a first weighting coefficient, and the judgment results for actual sensor events are mapped to a second weighting coefficient. The mapping process is based on the disturbance source type judgment results obtained in stage S3, which include both fiber optic disturbances and actual sensor events. The specific values ​​of the first and second weighting coefficients are determined through historical data analysis, which includes multiple sample events with known crosstalk levels. In practice, the crosstalk levels between channels corresponding to different event types are first statistically analyzed. Fiber optic disturbance events typically cause higher crosstalk, thus receiving a larger first weighting coefficient, while actual sensor events typically cause lower crosstalk, thus receiving a smaller second weighting coefficient. For example, by analyzing 100 sets of historical data samples, it is determined that the average crosstalk level of fiber optic disturbance events is 2.5 times that of actual sensor events; therefore, the first weighting coefficient is set to 2.5, and the second weighting coefficient is set to 1.0. The weighting coefficients are stored as a lookup table structure, with the key being the disturbance source type and the value being the corresponding weighting coefficient value. When a new disturbance source type judgment result is received, the corresponding weighting coefficient is obtained by querying this lookup table.

[0088] The bias coefficient is obtained by multiplying the bias coefficient with the corresponding weighting coefficient. The bias coefficient comes from the bias coefficients of low-frequency and high-frequency energy calculated in stage S4, and the weighting coefficient comes from the first or second weighting coefficient mapped in the previous step. The product operation is implemented using scalar multiplication, multiplying the bias coefficient value with the corresponding weighting coefficient value to obtain the weighted bias coefficient. In specific implementation, the appropriate weighting coefficient is selected according to the disturbance source type of the current channel. If the judgment result is a fiber optic disturbance, the first weighting coefficient is selected; if the judgment result is a real sensor event, the second weighting coefficient is selected. Floating-point arithmetic is used to maintain numerical precision during the product operation to avoid precision loss. For example, when the bias coefficient is 3.2 and the corresponding weighting coefficient is 2.5, the calculated weighted bias coefficient is 8.0. The weighted bias coefficient reflects the frequency domain energy distribution characteristics after being weighted by the disturbance source type, and its numerical range expands with the introduction of weighting coefficients.

[0089] The weighted bias coefficients are normalized to generate inter-channel crosstalk evaluation factors. The normalization process uses a maximum-minimum normalization method, which linearly transforms the weighted bias coefficients to the range of 0 to 1. Specifically, the weighted bias coefficients of all channels in the current processing batch are first obtained, and the maximum and minimum values ​​are identified. Then, a normalization formula is applied to each weighted bias coefficient: the current weighted bias coefficient minus the minimum value, divided by the difference between the maximum and minimum values. When the maximum and minimum values ​​are equal, all inter-channel crosstalk evaluation factors are set to 0.5. The normalized value is the inter-channel crosstalk evaluation factor, a dimensionless value between 0 and 1; a larger value indicates a higher degree of inter-channel crosstalk. The final generated inter-channel crosstalk evaluation factor is stored along with the corresponding channel number as input parameters for subsequent selective suppression processing.

[0090] S6. Selectively suppress the demodulation results of the corresponding sensing channels based on the inter-channel crosstalk evaluation factor, specifically as follows:

[0091] The inter-channel crosstalk assessment factor is compared with a preset suppression threshold. The comparison process uses the inter-channel crosstalk assessment factor generated in stage S5 as input data; this factor is a dimensionless value between 0 and 1. The preset suppression threshold is determined through a balance analysis between the system's false alarm rate and signal quality requirements. In practice, inter-channel crosstalk assessment factor data is collected over multiple operating cycles, and the signal distortion and interference suppression effects under different thresholds are analyzed. The value that achieves the optimal balance between signal quality loss and interference residue is selected as the preset suppression threshold. For example, analysis of 2000 sets of historical operating data shows that when the preset suppression threshold is set to 0.65, the system can suppress 80% of crosstalk interference while maintaining 95% of the effective signal. The comparison operation uses a double-precision floating-point comparison algorithm, with a comparison tolerance of 1 × 10⁻⁶. -6 To eliminate the impact of floating-point operation errors on the comparison results, the comparison process is performed independently for each sensing channel, generating a binary comparison result. A logical true value indicates that the inter-channel crosstalk evaluation factor is greater than the preset suppression threshold, while a logical false value indicates that the inter-channel crosstalk evaluation factor is less than or equal to the preset suppression threshold.

[0092] When the inter-channel crosstalk evaluation factor is greater than the preset suppression threshold, suppression processing is applied to the phase modulation signal of the corresponding sensing channel. The suppression processing adopts an adaptive attenuation method, which calculates the attenuation coefficient based on the relative magnitude of the inter-channel crosstalk evaluation factor and the preset suppression threshold.

[0093] The attenuation coefficient is calculated using the inter-channel crosstalk evaluation factor and a preset suppression threshold. The formula is: Attenuation coefficient = 1 - (Inter-channel crosstalk evaluation factor - Preset suppression threshold) / (1 - Preset suppression threshold). This ensures the attenuation coefficient increases monotonically with the degree of crosstalk. Suppression processing is applied to each sampling point of the phase-modulated signal in the time domain, multiplying the original signal amplitude by the corresponding attenuation coefficient. The processed phase-modulated signal retains its original phase characteristics and time-series structure, but the signal energy is reduced proportionally to the attenuation coefficient. During the suppression process, both the attenuation coefficient and the processing timestamp are recorded, forming a complete signal processing log.

[0094] When the inter-channel crosstalk evaluation factor is less than or equal to the preset suppression threshold, the phase modulation signal of the corresponding sensing channel remains unchanged. The hold operation means that no modifications are made to the original phase modulation signal, including maintaining the integrity of the signal amplitude, phase information, and time series. In practice, the system marks such channels as high-quality signal channels and directly transmits their phase modulation signals to subsequent processing stages. During the hold process, the signal sampling rate, quantization accuracy, and time-domain characteristics are fully preserved, while the channel's channel state information is recorded. All phase modulation signals that maintain their original state are synchronized with the suppressed signals according to a unified time base, forming the final multi-channel output dataset. Each channel in the output dataset is accompanied by a processing status identifier; a value of 0 indicates that the signal maintains its original state, and a value of 1 indicates that the signal has undergone suppression processing. This identifier is used for subsequent signal analysis and system performance evaluation.

[0095] Example 2: Figure 2 A schematic diagram of the signal demodulation system for the fiber optic sensor of the present invention is provided. The signal demodulation system for the fiber optic sensor includes:

[0096] The signal acquisition module is used to acquire the phase modulation signals returned by each sensing channel in the time-division multiplexed interferometric sensor array;

[0097] The interval identification module is used to identify non-reciprocal phase modulation intervals caused by dynamic disturbances on the common transmission fiber segment;

[0098] The type determination module is used to analyze the energy propagation characteristics of the non-reciprocal phase modulation interval on the spatial sequence of the sensor array. It calculates the energy distribution ratio of its forward propagation and backward propagation through principal component analysis, and determines the type of disturbance source based on whether the energy distribution ratio meets the preset disturbance propagation direction consistency condition.

[0099] The coefficient calculation module is used to analyze the frequency domain energy distribution of the signal within the non-reciprocal phase modulation interval and calculate the bias coefficients of its low-frequency energy and high-frequency energy.

[0100] The factor construction module is used to construct inter-channel crosstalk evaluation factors based on the judgment results of the disturbance source type and the bias coefficient.

[0101] The suppression execution module is used to selectively suppress the demodulation results of the corresponding sensing channels based on the inter-channel crosstalk evaluation factor.

[0102] All calculations involved in the embodiments are dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0103] It should be noted that this invention can be deployed on the device itself to realize embedded applications, or it can run on a PC or other terminal with a user interface, thereby meeting various hardware environments and usage requirements.

[0104] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions according to the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wireless or wired transmission; wired transmission methods include optical fiber, twisted pair, coaxial cable, etc.; wireless transmission includes infrared, microwave, etc. Computer-readable storage media can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

Claims

1. A method of signal demodulation of a fiber optic sensor, characterized by, The method comprises the following steps: S1, obtaining the phase modulation signals returned by each sensing channel in the time division multiplexing interferometric sensor array; S2, identifying the non-reciprocal phase modulation interval caused by dynamic disturbance on the common transmission fiber segment, comprising: calculating the rate of change of the phase modulation signals of each sensing channel in the time domain; determining the independent disturbance interval of each sensing channel according to the interval whose rate of change exceeds the preset rate of change threshold; comparing the overlapping part of the independent disturbance intervals of adjacent sensing channels in the time domain; when the time span of the overlapping part meets the preset condition, it is determined that the overlapping part is the non-reciprocal phase modulation interval caused by dynamic disturbance on the common transmission fiber segment; S3, analyzing the energy propagation characteristics of the non-reciprocal phase modulation interval on the spatial sequence of the sensor array, calculating the energy distribution ratio of forward propagation and backward propagation by principal component analysis, and judging the disturbance source type according to whether the energy distribution ratio meets the preset disturbance propagation direction consistency condition, comprising: constructing the disturbance signal matrix of the spatial sequence of the sensor array based on the identified non-reciprocal phase modulation interval; singular value decomposition is performed on the disturbance signal matrix to extract the principal component feature vector representing the energy propagation direction; calculate the ratio of forward propagation energy to backward propagation energy according to the sign distribution pattern of the principal component feature vector on the spatial sequence of the sensor array; compare the calculated energy distribution ratio with the preset distribution threshold; when the energy distribution ratio is greater than the preset distribution threshold, it is determined as a connected fiber disturbance, and when the energy distribution ratio is less than or equal to the preset distribution threshold, it is determined as a real sensor event; S4, analyzing the frequency energy distribution of the signal in the non-reciprocal phase modulation interval, and calculating the bias coefficient of the low frequency energy and the high frequency energy, comprising: performing Fourier transform on the phase modulation signal in the non-reciprocal phase modulation interval to obtain the frequency spectrum; divide the frequency spectrum into a preset low frequency band and a preset high frequency band; respectively calculate the sum of the square of the frequency spectrum amplitude in the preset low frequency band as the low frequency energy, and the sum of the square of the frequency spectrum amplitude in the preset high frequency band as the high frequency energy; take the logarithm of the ratio of the low frequency energy to the high frequency energy to obtain the bias coefficient of the low frequency energy and the high frequency energy; S5, constructing the inter-channel crosstalk evaluation factor based on the judgment result of the disturbance source type and the bias coefficient, comprising: map the judgment result of the connected fiber disturbance to the first weight coefficient, and map the judgment result of the real sensor event to the second weight coefficient; multiply the bias coefficient and the corresponding weight coefficient to obtain the weighted bias coefficient; normalize the weighted bias coefficient to generate the inter-channel crosstalk evaluation factor; S6, selectively suppressing the demodulation result of the corresponding sensing channel according to the inter-channel crosstalk evaluation factor, comprising: compare the inter-channel crosstalk evaluation factor with the preset suppression threshold; when the inter-channel crosstalk evaluation factor is greater than the preset suppression threshold, the phase modulation signal of the corresponding sensing channel is suppressed; when the inter-channel crosstalk evaluation factor is less than or equal to the preset suppression threshold, the phase modulation signal of the corresponding sensing channel remains unchanged.

2. The method of demodulating a signal of an optical fiber sensor according to claim 1, wherein, Obtaining the phase modulation signals returned by each sensing channel in the time division multiplexing interferometric sensor array, comprising: Receiving a composite optical signal from a time-division multiplexed interferometric sensor array; Based on time-division multiplexing timing, the composite optical signal is demultiplexed, and the optical signal corresponding to each sensing channel is separated out; The optical signal of each sensing channel is converted into an electrical signal by an optoelectronic converter; An analog-to-digital conversion process is performed on the electrical signal to obtain a digitized phase modulation signal for each sensing channel.

3. The method of demodulating a signal of an optical fiber sensor according to claim 1, wherein, The singular value decomposition of the disturbance signal matrix is performed to extract the principal component feature vector representing the energy propagation direction, including: performing singular value decomposition mathematical operation on the constructed disturbance signal matrix to obtain left singular vector matrix, singular value matrix and right singular vector matrix; based on the size of the singular value in the singular value matrix, the column vector corresponding to the maximum singular value in the left singular vector matrix is selected as the principal component feature vector representing the energy propagation direction.

4. The method of demodulating a signal of an optical fiber sensor according to claim 1, wherein, The Fourier transform of the phase modulation signal in the non-reciprocal phase modulation interval includes: applying the fast Fourier transform algorithm to the phase modulation signal in the non-reciprocal phase modulation interval, converting the time domain phase modulation signal into frequency domain representation, and obtaining the frequency spectrum.

5. A signal demodulation system for an optical fiber sensor for implementing the signal demodulation method for an optical fiber sensor according to any one of claims 1 to 4, characterized by Comprising: The signal acquisition module is used for acquiring the phase modulation signal returned by each sensing channel in the time-division multiplexed interferometric sensor array; The interval identification module is used for identifying the non-reciprocal phase modulation interval caused by dynamic disturbance on the common transmission optical fiber segment; The type judgment module is used for analyzing the energy propagation characteristics of the non-reciprocal phase modulation interval on the spatial sequence of the sensor array, calculating the energy distribution ratio of forward propagation and backward propagation by principal component analysis, and judging the disturbance source type according to whether the energy distribution ratio meets the preset disturbance propagation direction consistency condition; The coefficient calculation module is used for analyzing the frequency energy distribution of the signal in the non-reciprocal phase modulation interval, and calculating the bias coefficient of the low frequency energy and the high frequency energy; The factor construction module is used for constructing the crosstalk evaluation factor between channels based on the judgment result of the disturbance source type and the bias coefficient; The suppression execution module is used for selectively suppressing the demodulation result of the corresponding sensing channel according to the crosstalk evaluation factor between channels.

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