Dynamic signal demodulation method and device for high-sensitivity wide-range optical fiber interference type sensor based on symmetric windowing period splicing, program and storage medium

Abstract

The invention discloses a high-sensitivity wide-range optical fiber interference type sensor dynamic signal demodulation method and device based on symmetric windowing period splicing, a program and a storage medium. According to the method, symmetrical windowing period positioning is carried out on dynamic sensing signals output by an optical fiber interference type sensor under the sine excitation condition, and accurate analysis of signal frequency is achieved; furthermore, the discrete trajectory length in unit time is calculated by using the time domain signals after periodic splicing, so that high-sensitivity and wide-range demodulation of the signal intensity is realized. According to the invention, the problems of inaccurate frequency positioning, signal saturation and the like of a high-sensitivity optical fiber interference type sensor under large-dynamic-range dynamic signal excitation are effectively solved, and synchronous measurement of frequency and amplitude is realized.

Description

A method, apparatus, program, and storage medium for dynamic signal demodulation of a high-sensitivity, large-range fiber optic interferometer sensor based on symmetrical windowing and periodic splicing. Technical Field

[0001] This invention relates to the field of fiber optic sensing and signal processing technology, and in particular to a method, apparatus, program, and storage medium for dynamic signal demodulation of a high-sensitivity, large-range fiber optic interferometric sensor based on symmetrical windowing periodic splicing. Background Technology

[0002] Fiber optic interferometric sensors offer advantages such as resistance to electromagnetic interference, high sensitivity, compact structure, and suitability for long-distance transmission, making them widely used in precision manufacturing, energy equipment, structural health monitoring, biomedical detection, and intelligent sensing. With increasing demands for weak alternating signal detection capabilities and dynamic response performance, these sensors are evolving towards even higher sensitivity, demonstrating significant advantages in sensing weak excitations.

[0003] However, increased sensitivity is often accompanied by a reduction in the effective measurement range. When the amplitude of the external alternating excitation is large or the dynamic range is wide, high-sensitivity fiber optic interferometric sensors are prone to problems such as nonlinear modulation of the interference signal, period crossing, or even saturation, leading to response distortion and limiting their application in the measurement of alternating signals with a large dynamic range. Therefore, achieving stable demodulation over a large range while maintaining high sensitivity has become a key problem that urgently needs to be solved in this field.

[0004] Existing technologies primarily improve upon sensor structures through two main improvements: sensor architecture design and signal demodulation algorithms. Structurally, researchers typically introduce cascaded or composite sensor structures to overcome the limitations of the free spectral range (FSR) of a single interferometer, thereby expanding the effective measurement range. For example, in 2025, Zhao et al.... A vernier-type sensing structure based on a femtosecond laser-processed FBG cascaded with a C-type fiber was proposed. By constructing a multi-scale spectral response and using the FBG to identify the interference orders corresponding to different temperature ranges, the spectral aliasing problem was effectively alleviated, extending the temperature measurement range from the traditional narrow range to 6.242℃–66.223℃. However, such structures are usually accompanied by increased system complexity, larger device size, and coupling interference between multiple sensing units. At the signal demodulation algorithm level, researchers have attempted to overcome the dependence of traditional methods on single spectral features by introducing intelligent demodulation or multi-feature joint demodulation strategies. In 2023, Zhang et al. A temperature sensor based on an external cavity Fabry-Perot interferometer (EFPI) using thermally expanding fiber is proposed. A multi-valley averaging demodulation algorithm based on dynamic estimation of interference fringe order is employed. By jointly utilizing information from multiple interference valleys, the temperature resolution is improved to a higher level. The above-mentioned algorithm optimization methods are mostly designed for DC or quasi-static signals and rely on the assumption that the interference fringes change monotonically with the measured quantity. They are difficult to apply directly to periodic, alternating positive and negative signals and are prone to demodulation ambiguity or failure.

[0005] To address this, the present invention proposes a dynamic signal demodulation method, device, program, and storage medium for a high-sensitivity, large-range fiber optic interferometer sensor based on symmetrical windowing periodic splicing. Without changing the sensor hardware, it achieves synchronous and accurate demodulation of alternating excitation frequency and amplitude, effectively expanding the usable dynamic range of the sensor. This provides a new technical solution for resolving the contradiction between sensitivity and range in alternating signal measurement of high-sensitivity fiber optic interferometer sensors. Summary of the Invention

[0006] The purpose of this invention is to propose a dynamic signal demodulation method, device, program, and storage medium for a high-sensitivity, large-range fiber optic interferometer sensor based on symmetrical windowing periodic splicing, so as to resolve the contradiction between high sensitivity and large range, and realize stable synchronous measurement of frequency and amplitude under alternating excitation.

[0007] This invention proposes a dynamic signal demodulation method, device, program, and storage medium for a high-sensitivity, large-range fiber optic interferometer sensor based on symmetrical windowing and periodic splicing. The core technical solutions include the following:

[0008] A dynamic signal demodulation method for a high-sensitivity, large-range fiber optic interferometer sensor based on symmetrical windowing periodic splicing includes the following steps:

[0009] Step 1: Set a fixed observation time, obtain the time domain sequence and sampling rate, and set the candidate window length range according to the sampling rate to obtain the candidate window length set.

[0010] Step 2: Perform symmetry analysis and extended repeatability analysis on each candidate window length in the candidate window length set to obtain the symmetry correlation coefficient and repeatability correlation coefficient corresponding to each window length.

[0011] Step 3: Calculate the comprehensive score for each window length based on the symmetry correlation coefficient and the repeatability correlation coefficient.

[0012] Step 4: Determine if all candidate window lengths have been traversed. If yes, proceed to the next step; otherwise, return to step 2.

[0013] Step 5: Based on the comprehensive score of each window length, construct a curve showing the change of the comprehensive score with the window length, and perform local peak detection. Based on the preset threshold ratio coefficient, filter reliable peaks.

[0014] Step 6: Select the window length corresponding to the first peak among all reliable peaks as the optimal effective window length, and calculate the true excitation frequency.

[0015] Step 7: Determine the sensor calibration model based on the actual excitation frequency; and extract the time-intensity trajectory signal sequence based on the fixed observation time, and calculate the unit time length of the time-intensity trajectory signal sequence.

[0016] Step 8: Based on the actual excitation frequency and the discrete trajectory length per unit time, and combined with the determined sensor calibration model, calculate the excitation intensity and output the demodulation results.

[0017] Furthermore, the symmetry analysis method described in step 2 specifically includes:

[0018] For each candidate window length, the number of sampling points extracted from the temporal sequence is... The signal segment sequence is calculated, and its time-reversed sequence is calculated.

[0019] Based on the signal segment sequence and the time-reversed sequence, calculate the symmetry correlation coefficient corresponding to the window length. ;

[0020]

[0021] in, For the first The light intensity value at each sampling point The mean of the signal segment sequence. This is the mean of the time-reversed sequence.

[0022] Furthermore, the method for extension repeatability analysis described in step 2 specifically includes:

[0023] For each candidate window length, the period of the signal segment sequence is extended to obtain an extended sequence.

[0024] Based on the signal segment sequence and the extended sequence, calculate the repeatability correlation coefficient corresponding to the window length. ;

[0025]

[0026] in, is the mean of the extended sequence.

[0027] Furthermore, step 3 involves calculating the comprehensive score for each window length. The methods specifically include:

[0028]

[0029] in, These are symmetric weights and repeated weights, respectively. .

[0030] Furthermore, the actual excitation frequency described in step 6 The specific calculation methods include:

[0031]

[0032]

[0033] in, The frequency of the sensor output signal. The optimal effective window length.

[0034] Furthermore, the unit time length of the time-intensity trajectory signal sequence described in step 7... The specific calculation methods include:

[0035]

[0036]

[0037] in, The total trajectory length, To fix the observation time, For the first The time for each sampling point For the first Voltage values ​​at each sampling point This represents the total number of trajectory sampling points.

[0038] A computer device includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described above.

[0039] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0040] A computer program product includes computer instructions that, when executed by a processor, implement the steps of the above-described method.

[0041] The beneficial effects of this invention are as follows: This invention accurately identifies the signal period through a comprehensive scoring mechanism that combines symmetry analysis and extended repeatability analysis, avoiding frequency misjudgment under high sensitivity; by calculating the discrete trajectory line length per unit time, a stable mapping with the excitation intensity is established, and high sensitivity and large range demodulation are achieved simultaneously without changing the hardware, effectively expanding the application range of fiber optic interferometric sensors in alternating signal measurement. Attached Figure Description

[0042] Figure 1 is a schematic diagram of the time-domain signal acquisition principle of the present invention.

[0043] Figure 2 is a visualization of the time-intensity trajectory under a large excitation amplitude (A=4000 nm) according to an embodiment of the present invention.

[0044] Figure 3 is a flowchart of the method of the present invention.

[0045] Figure 4 is a schematic diagram of the windowing method of the present invention.

[0046] Figure 5 shows the comprehensive scoring curve and peak screening frequency analysis diagram of the present invention.

[0047] Figure 6 is a simulation diagram of the sensor calibration model according to an embodiment of the present invention. Detailed Implementation

[0048] Referring to Figure 3, a dynamic signal demodulation method for a high-sensitivity, large-range fiber optic interferometer sensor based on symmetrical windowing periodic splicing is presented:

[0049] Step 1: Set a fixed observation time, obtain the time domain sequence and sampling rate, and set the candidate window length range according to the sampling rate to obtain the candidate window length set.

[0050] Step 2: Perform symmetry analysis and extended repeatability analysis on each candidate window length in the candidate window length set to obtain the symmetry correlation coefficient and repeatability correlation coefficient corresponding to each window length.

[0051] The symmetry analysis method specifically includes:

[0052] For each candidate window length, the number of sampling points extracted from the temporal sequence is... The signal segment sequence is calculated, and its time-reversed sequence is calculated.

[0053] Based on the signal segment sequence and the time-reversed sequence, calculate the symmetry correlation coefficient corresponding to the window length. ;

[0054]

[0055] in, For the first The light intensity value at each sampling point The mean of the signal segment sequence. This is the mean of the time-reversed sequence.

[0056] The method for the continuation repeatability analysis specifically includes:

[0057] For each candidate window length, the period of the signal segment sequence is extended to obtain an extended sequence.

[0058] Based on the signal segment sequence and the extended sequence, calculate the repeatability correlation coefficient corresponding to the window length. ;

[0059]

[0060] in, is the mean of the extended sequence.

[0061] Step 3: Calculate the comprehensive score for each window length based on the symmetry correlation coefficient and the repeatability correlation coefficient.

[0062] The comprehensive score for calculating each window length is as follows. The methods specifically include:

[0063]

[0064] in, These are symmetric weights and repeated weights, respectively. .

[0065] Step 4: Determine if all candidate window lengths have been traversed. If yes, proceed to the next step; otherwise, return to step 2.

[0066] Step 5: Based on the comprehensive score of each window length, construct a curve showing the change of the comprehensive score with the window length, and perform local peak detection. Based on the preset threshold ratio coefficient, filter reliable peaks.

[0067] Step 6: Select the window length corresponding to the first peak among all reliable peaks as the optimal effective window length, and calculate the true excitation frequency.

[0068] The actual excitation frequency The specific calculation methods include:

[0069]

[0070]

[0071] in, The frequency of the sensor output signal. The optimal effective window length.

[0072] Step 7: Determine the sensor calibration model based on the actual excitation frequency; and extract the time-intensity trajectory signal sequence based on the fixed observation time, and calculate the unit time length of the time-intensity trajectory signal sequence.

[0073] The unit time length of the time-intensity trajectory signal sequence The specific calculation methods include:

[0074]

[0075]

[0076] in, The total trajectory length, To fix the observation time, For the first The time for each sampling point For the first Voltage values ​​at each sampling point This represents the total number of trajectory sampling points.

[0077] Step 8: Based on the actual excitation frequency and the discrete trajectory length per unit time, and combined with the determined sensor calibration model, calculate the excitation intensity and output the demodulation results.

[0078] Example

[0079] The following illustrative simulation will illustrate the light intensity change mechanism of the fiber optic interferometric sensor of the present invention under AC excitation.

[0080] This embodiment uses a Fabry-Perot interference structure as an example, and its reflected light intensity is expressed as follows:

[0081]

[0082] in Indicates the intensity of reflected light. Indicates the intensity of the incident light. Indicates the operating wavelength. Indicates reflectivity, Indicates the length of the external excitation cavity. This represents the initial phase of the sensor.

[0083] External sinusoidal stimulus Under the influence of the light, the intensity of the reflected light changes periodically, among which... Indicates the initial cavity length. For the amplitude of the applied excitation intensity, For the external excitation frequency, For time. Since the cavity length undergoes two changes, one positive and one negative, within one excitation cycle, the light intensity response exhibits full-wave characteristics, and its dominant frequency is twice the excitation frequency.

[0084] To illustrate the above mechanism intuitively, this embodiment presents a schematic simulation analysis of the light intensity change process under AC excitation. The relevant parameters selected in the simulation are only used to illustrate the relationship between cavity length modulation and light intensity change, and do not constitute a limitation on the present invention. The simulation results are shown in Figure 1.

[0085] To further illustrate the applicability of the method of the present invention under a wide range of conditions, A = 4000 nm was selected. =50Hz, =1550nm, = , A simulation example was performed at 203050nm, and the corresponding visualization results are shown in Figure 2.

[0086] Preferably, the data is processed according to Figure 3 using the following steps:

[0087] Candidate window length set setting, sampling rate setting =10000Hz, set the candidate window length range , =0.001s, =0.05s, and the windowing diagram is shown in Figure 4.

[0088] Calculate the length of each window and Take the weighting coefficient =0.7, =0.3, the overall score is calculated. .

[0089] Referring to Figure 5, construct the scoring curve and set the threshold ratio coefficient. =0.95, the optimal window length was obtained through peak value screening. =0.01s.

[0090] Calculate the frequency multiplication of the output sensor =100Hz, actual excitation frequency =50Hz.

[0091] Cut =1s trajectory, calculate the length of the discrete trajectory per unit time. The value is 4184.386.

[0092] Referring to Figure 6, based on the calibration model Solving for =4028.616, with an error of 0.7%.

[0093] The above embodiments demonstrate the effectiveness of the method of the present invention in demodulating high-sensitivity, large-range alternating signals.

[0094] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0095] References

[0096]

Claims

1. A dynamic signal demodulation method for a high-sensitivity, large-range fiber optic interferometer sensor based on symmetrical windowing and periodic splicing, characterized in that, The process includes the following steps: Step 1: Set a fixed observation time, acquire the temporal domain sequence and sampling rate, and set the candidate window length range according to the sampling rate to obtain a candidate window length set; Step 2: Perform symmetry analysis and extended repeatability analysis on each candidate window length in the candidate window length set to obtain the symmetry correlation coefficient and repeatability correlation coefficient corresponding to each window length; Step 3: Calculate the comprehensive score for each window length based on the symmetry correlation coefficient and repeatability correlation coefficient; Step 4: Determine whether to traverse all candidate window lengths. If yes, proceed to the next step; otherwise, return to Step 2; Step 5: Based on the comprehensive score for each window length, construct a curve showing the comprehensive score changing with the window length, and perform local peak detection. Filter reliable peaks based on a preset threshold ratio coefficient; Step 6: Select the window length corresponding to the first peak among all reliable peaks as the optimal effective window length, and calculate the true excitation frequency; Step 7: Determine the sensor calibration model based on the true excitation frequency; and extract the time-intensity trajectory signal sequence based on the fixed observation time, and calculate the unit time length of the time-intensity trajectory signal sequence; Step 8: Based on the true excitation frequency and the unit time discrete trajectory length, combined with the determined sensor calibration model, calculate the excitation intensity and output the demodulation results.

2. The dynamic signal demodulation method for a high-sensitivity, large-range fiber optic interferometer sensor based on symmetrical windowing periodic splicing as described in claim 1, characterized in that, The symmetry analysis method described in step 2 specifically includes: for each candidate window length, extracting a number of sampling points from the time-domain sequence. The signal segment sequence is obtained, and its time-reversed sequence is calculated; based on the signal segment sequence and the time-reversed sequence, the symmetry correlation coefficient corresponding to the window length is calculated. ； in, For the first The light intensity value at each sampling point The mean of the signal segment sequence is denoted as . This is the mean of the time-reversed sequence.

3. The dynamic signal demodulation method for a high-sensitivity, large-range fiber optic interferometer sensor based on symmetrical windowing periodic splicing according to claim 2, characterized in that, Step 2 of the extended repeatability analysis method specifically includes: for each candidate window length, extending the period of the signal segment sequence to obtain an extended sequence; and calculating the repeatability correlation coefficient corresponding to the window length based on the signal segment sequence and the extended sequence. ； in, is the mean of the extended sequence.

4. The dynamic signal demodulation method for a high-sensitivity, large-range fiber optic interferometer sensor based on symmetrical windowing periodic splicing according to claim 3, characterized in that, Step 3 describes calculating the comprehensive score for each window length. The methods specifically include: in, These are symmetric weights and repeated weights, respectively. 。 5. The dynamic signal demodulation method for a high-sensitivity, large-range fiber optic interferometer sensor based on symmetrical windowing periodic splicing according to claim 1, characterized in that, The actual excitation frequency described in step 6 The specific calculation methods include: in, The frequency of the sensor output signal. The optimal effective window length.

6. The dynamic signal demodulation method for a high-sensitivity, large-range fiber optic interferometer sensor based on symmetrical windowing periodic splicing according to claim 1, characterized in that, The unit time length of the time-intensity trajectory signal sequence described in step 7 The specific calculation methods include: in, The total trajectory length, To fix the observation time, For the first The time for each sampling point For the first Voltage values ​​at each sampling point This represents the total number of trajectory sampling points.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 6.

9. A computer program product comprising computer instructions, characterized in that: When executed by a processor, the computer instructions implement the steps of the method according to any one of claims 1 to 6.

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