Method and system for testing and evaluating optical fiber acceleration sensor based on feedback analysis

Abstract

The invention discloses an optical fiber acceleration sensor test evaluation method and system based on feedback analysis, and the method comprises the steps: carrying out the test through deploying a plurality of optical fiber acceleration sensors at a target object, collecting a multi-dimensional vibration acceleration signal, and constructing a core parameter set; marking abnormal time periods through linear regression feedback analysis, and serializing abnormal parameters and acceleration results after screening overlapped abnormal time periods; nonlinear sequence points are positioned through nonlinear analysis, and finally abnormal sensors and corresponding parameters are screened through deviation analysis. According to the method, the accuracy and reliability of sensor test evaluation are improved, efficient technical support is provided for performance verification and troubleshooting of the optical fiber acceleration sensor, and the method is suitable for a test scene of the multi-array optical fiber acceleration sensor.

Description

Technical Field

[0001] This invention relates to the field of fiber optic sensor application evaluation, and more specifically, to a method and system for testing and evaluating fiber optic accelerometers based on feedback analysis. Background Technology

[0002] Fiber optic accelerometers, with their advantages of high sensitivity, strong anti-interference ability, and remote transmission capability, have been widely used in key fields such as aerospace, rail transportation, energy extraction, and civil engineering. Their performance stability and measurement accuracy are directly related to the safe operation and reliable decision-making of systems in various fields.

[0003] Existing fiber optic accelerometer testing and evaluation technologies have several shortcomings: First, data acquisition is limited to a single dimension, focusing primarily on the core signal of vibration acceleration while neglecting multi-dimensional testing and analysis. This results in biased evaluation results that fail to fully reflect the sensor's true performance under complex operating conditions. Second, there is a lack of in-depth correlation feature analysis for large-scale multi-sensor systems. The testing process cannot accurately distinguish between normal and abnormal sensor operating states, and the evaluation models are fixed, relying on manual experience for adjustment. They lack automated analysis and adjustment capabilities and cannot perform anomaly analysis and optimization testing based on test data feedback from application scenarios, making it difficult to meet the current forward-looking testing needs of fiber optic accelerometers. Summary of the Invention

[0004] This invention overcomes the shortcomings of the prior art and proposes a test and evaluation method and system for fiber optic accelerometers based on feedback analysis.

[0005] The first aspect of this invention provides a test and evaluation method for fiber optic accelerometers based on feedback analysis, comprising: S11. Deploy multiple fiber optic accelerometers in the target object and record their relative positions with preset points. Set the test vibration parameters of the target object according to the preset scheme. Collect vibration acceleration signals from multiple fiber optic accelerometers during multiple test periods. S12. Based on the vibration acceleration signal, perform fiber optic signal analysis and data preprocessing. Construct a core parameter set for multiple time periods based on light intensity, phase difference, and center wavelength offset parameters. Analyze the correlation between the core parameter set and the acceleration calculation results through linear regression analysis. Mark low-correlation time periods as abnormal time periods. Introduce multi-sensor time period analysis to screen out overlapping abnormal time periods. S13. Extract the core parameters corresponding to the overlapping abnormal time periods in each sensor to obtain an abnormal parameter set. Sort the abnormal parameter set and acceleration calculation results according to the sorting results to obtain the first sequence and the second sequence. Identify the nonlinear sequence points that exist simultaneously in the first sequence and the second sequence. S14. In the second sequence, extract the nonlinear sequence points as abnormal result values, perform feedback deviation analysis on the abnormal result values ​​and the acceleration comparison values, and screen out the abnormal fiber optic accelerometers and the corresponding abnormal nonlinear segment parameters.

[0006] In this solution, S11 includes: Set a preset point in the target object; the preset point is used as the test vibration point. The vibration parameters tested include vibration frequency, vibration amplitude, vibration direction, and acceleration. Multiple fiber optic accelerometers are deployed and their relative positions to preset points are recorded. The relative positions can be represented by three-dimensional coordinates and distance.

[0007] In this scheme, the vibration acceleration signal includes time-domain waveform, frequency-domain spectrum data, fiber optic grating wavelength offset, center wavelength offset, and phase difference data.

[0008] In this solution, S12 specifically refers to: For a fiber optic accelerometer, the corresponding vibration acceleration signal is collected and analyzed by fiber optic signal analysis. The spectral information is preprocessed by median filtering. The corresponding light intensity, phase difference, and center wavelength offset parameters are collected and a core parameter set for multiple time periods is constructed. The core parameter set is normalized to the [0,1] interval by min-max to obtain a standardized parameter set. For each time period, the standardized parameter set is weighted and averaged to obtain the mean parameter of multiple time periods. In each time period, the mean parameter of the current time period is fitted with the linear rate of change of the mean parameter of the previous and subsequent time periods to obtain the first rate of change curve. The preset parameters of each time period are extracted based on the acceleration results and fitted with the linear rate of change to obtain the second rate of change curve. The periods when the first rate of change curve and the second rate of change are higher than the preset deviation are marked to obtain the abnormal periods. Abnormal time periods are calculated based on multiple sensors, and overlapping abnormal time periods are filtered out. The determination of overlapping abnormal periods is as follows: if there are two or more fiber optic accelerometers that match an abnormal period, then it is considered an overlapping abnormal period.

[0009] In this solution, S13 specifically refers to: Extract the core parameters corresponding to the overlapping abnormal time periods in each sensor and obtain the abnormal parameter set; In the abnormal parameter set, relative positions are introduced to sort the sensors and map them to the sorting of the abnormal parameter set. If there are multiple overlapping abnormal time periods for the same sensor, they will be sorted in chronological order. The first target parameter is selected from the set of abnormal parameters and serialized. The sequence order follows the sorting result above to obtain the first sequence. Based on the sorting results, the second target parameter is selected from the acceleration calculation results and serialized to obtain the second sequence; Nonlinear analysis was performed using the linear fitting deviation analysis method. Linear equations were used to fit the data for the first and second sequences, with the test period as the independent variable and the target parameter as the dependent variable. The least squares method was used to search for the fitting parameters during the fitting process, and the first and second fitting curves were obtained respectively. In the first and second fitted curves, the deviation between the actual value and the curve fitting value is analyzed. If the deviation rate of a certain sequence point in both fitted curves is greater than the preset curve deviation, then the sequence point is marked as a nonlinear sequence point.

[0010] In this solution, S14 specifically refers to: In the second sequence, nonlinear sequence points are extracted as outlier values. Feedback deviation analysis is performed between abnormal result values ​​and acceleration comparison values. Sequence points with deviations greater than preset deviations are marked, and the corresponding sensors are marked as abnormal fiber optic accelerometer sensors. The abnormal fiber optic accelerometer sensor and the corresponding abnormal nonlinear segment parameters are sent as test results to a preset terminal.

[0011] In this scheme, for each fiber optic accelerometer, a corresponding vibration acceleration signal is collected for each test period, and an acceleration calculation result is obtained for each test period.

[0012] A second aspect of the present invention also provides a test and evaluation system for an optical fiber accelerometer based on feedback analysis. The system includes a memory and a processor. The memory includes a test and evaluation program for an optical fiber accelerometer based on feedback analysis. When executed by the processor, the test and evaluation program for the optical fiber accelerometer based on feedback analysis performs the following steps: S11. Deploy multiple fiber optic accelerometers in the target object and record their relative positions with preset points. Set the test vibration parameters of the target object according to the preset scheme. Collect vibration acceleration signals from multiple fiber optic accelerometers during multiple test periods. S12. Based on the vibration acceleration signal, perform fiber optic signal analysis and data preprocessing. Construct a core parameter set for multiple time periods based on light intensity, phase difference, and center wavelength offset parameters. Analyze the correlation between the core parameter set and the acceleration calculation results through linear regression analysis. Mark low-correlation time periods as abnormal time periods. Introduce multi-sensor time period analysis to screen out overlapping abnormal time periods. S13. Extract the core parameters corresponding to the overlapping abnormal time periods in each sensor to obtain an abnormal parameter set. Sort the abnormal parameter set and acceleration calculation results according to the sorting results to obtain the first sequence and the second sequence. Identify the nonlinear sequence points that exist simultaneously in the first sequence and the second sequence. S14. In the second sequence, extract the nonlinear sequence points as abnormal result values, perform feedback deviation analysis on the abnormal result values ​​and the acceleration comparison values, and screen out the abnormal fiber optic accelerometers and the corresponding abnormal nonlinear segment parameters.

[0013] A third aspect of the present invention also provides a computer-readable storage medium including a fiber optic accelerometer test and evaluation program based on feedback analysis, wherein when the fiber optic accelerometer test and evaluation program based on feedback analysis is executed by a processor, it implements the steps of the fiber optic accelerometer test and evaluation method based on feedback analysis as described in any of the preceding claims.

[0014] This invention discloses a testing and evaluation method and system for fiber optic accelerometers based on feedback analysis. The method involves deploying multiple fiber optic accelerometers on a target object to collect multi-dimensional vibration acceleration signals and construct a core parameter set. Abnormal time periods are identified through linear regression feedback analysis, and after filtering overlapping abnormal time periods, abnormal parameters and acceleration results are serialized. Nonlinear analysis is used to locate nonlinear sequence points, and finally, deviation analysis is used to filter abnormal sensors and their corresponding parameters. This method improves the accuracy and reliability of sensor testing and evaluation, providing efficient technical support for performance verification and fault diagnosis of fiber optic accelerometers. It is applicable to testing scenarios involving multi-array fiber optic accelerometers and can solve the problems of low anomaly identification accuracy, ambiguous sensor fault location, and insufficient parameter correlation analysis in existing testing methods. Attached Figure Description

[0015] Figure 1 A flowchart of a fiber optic accelerometer testing and evaluation method based on feedback analysis according to the present invention is shown. Figure 2 The flowchart of the nonlinear sequence point analysis of the present invention is shown; Figure 3 A block diagram of a fiber optic accelerometer test and evaluation system based on feedback analysis according to the present invention is shown. Detailed Implementation

[0016] To better understand the above-mentioned objects, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. In the embodiments, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0017] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0018] Figure 1 A flowchart of a fiber optic accelerometer testing and evaluation method based on feedback analysis according to the present invention is shown.

[0019] like Figure 1 As shown, the first aspect of the present invention provides a test and evaluation method for fiber optic accelerometers based on feedback analysis, comprising: S11. Deploy multiple fiber optic accelerometers in the target object and record their relative positions with preset points. Set the test vibration parameters of the target object according to the preset scheme. Collect vibration acceleration signals from multiple fiber optic accelerometers during multiple test periods. S12. Based on the vibration acceleration signal, perform fiber optic signal analysis and data preprocessing. Construct a core parameter set for multiple time periods based on light intensity, phase difference, and center wavelength offset parameters. Analyze the correlation between the core parameter set and the acceleration calculation results through linear regression analysis. Mark low-correlation time periods as abnormal time periods. Introduce multi-sensor time period analysis to screen out overlapping abnormal time periods. S13. Extract the core parameters corresponding to the overlapping abnormal time periods in each sensor to obtain an abnormal parameter set. Sort the abnormal parameter set and acceleration calculation results according to the sorting results to obtain the first sequence and the second sequence. Identify the nonlinear sequence points that exist simultaneously in the first sequence and the second sequence. S14. In the second sequence, extract the nonlinear sequence points as abnormal result values, perform feedback deviation analysis on the abnormal result values ​​and the acceleration comparison values, and screen out the abnormal fiber optic accelerometers and the corresponding abnormal nonlinear segment parameters.

[0020] According to an embodiment of the present invention, S11 includes: Set a preset point in the target object; the preset point is used as the test vibration point. The vibration parameters tested include vibration frequency, vibration amplitude, vibration direction, and acceleration. Multiple fiber optic accelerometers are deployed and their relative positions to preset points are recorded. The relative positions can be represented by three-dimensional coordinates and distance.

[0021] In this embodiment, the target object can be a high-precision standard vibration table or similar device used for testing acceleration and vibration states. Test vibration parameters include vibration frequency (50Hz-500Hz, increasing in 50Hz increments), vibration amplitude (0.1mm-2.0mm, increasing in 0.1mm increments), vibration direction (axial, radial, tangential), and corresponding acceleration comparison values, which can be adjusted appropriately based on testing requirements. The testing environment is generally set to a standard environment (25℃, 50%RH).

[0022] According to an embodiment of the present invention, the vibration acceleration signal includes time-domain waveform, frequency-domain spectrum data, wavelength offset of fiber optic grating, center wavelength offset, and phase difference data.

[0023] According to an embodiment of the present invention, step S12 specifically includes: For a fiber optic accelerometer, the corresponding vibration acceleration signal is collected and analyzed by fiber optic signal analysis. The spectral information is preprocessed by median filtering. The corresponding light intensity, phase difference, and center wavelength offset parameters are collected and a core parameter set for multiple time periods is constructed. The core parameter set is normalized to the [0,1] interval by min-max to obtain a standardized parameter set. For each time period, the standardized parameter set is weighted and averaged to obtain the mean parameter of multiple time periods. In each time period, the mean parameter of the current time period is fitted with the linear rate of change of the mean parameter of the previous and subsequent time periods to obtain the first rate of change curve. The preset parameters of each time period are extracted based on the acceleration results and fitted with the linear rate of change to obtain the second rate of change curve. The periods when the first rate of change curve and the second rate of change are higher than the preset deviation are marked to obtain the abnormal periods. Abnormal time periods are calculated based on multiple sensors, and overlapping abnormal time periods are filtered out. The determination of overlapping abnormal periods is as follows: if there are two or more fiber optic accelerometers that match an abnormal period, then it is considered an overlapping abnormal period.

[0024] As can be understood in this embodiment, light intensity, phase difference, and center wavelength offset are used as core parameters for evaluation. Based on the differences in acceleration calculation modes of different fiber optic sensors, different parameters can be appropriately selected as core parameters for fiber optic signal anomaly assessment. The weighted average involves performing a weighted average of the standardized data of light intensity, phase difference, and center wavelength offset for each time period (one or more parameters can be selected; if only one is selected, the weighted calculation step can be ignored). The preset weight values ​​are equal by default and can be adjusted based on the importance of the parameters.

[0025] Here, the acceleration calculation results may include parameters such as vibration frequency, amplitude, and acceleration value. One of the preset parameters can be selected for calculation. The preset deviation setting is generally less than or equal to 20%.

[0026] In this embodiment, the sensor specifically refers to a fiber optic accelerometer. It is understood that the fiber optic accelerometer may include various fiber optic computing modes (such as interferometric and fiber Bragg grating types), and the magnitude of the acceleration parameters is affected by different parameters, as detailed below: Light intensity parameter: denoted as I, with units of dBm and mW, is a direct reflection of the changes in the interference signal of the fiber optic sensor and reflects the stability of signal transmission; Phase difference parameter: denoted as φ, in rad. The fiber length deformation caused by acceleration is directly converted into a phase difference change. Center wavelength offset: denoted as λ, in nm, reflects the change in grating period caused by vibration stress / strain, which in turn causes wavelength offset. This value represents the degree of wavelength drift.

[0027] Figure 2 A flowchart of the nonlinear sequence point analysis of the present invention is shown.

[0028] According to an embodiment of the present invention, step S13 specifically includes: Extract the core parameters corresponding to the overlapping abnormal time periods in each sensor and obtain the abnormal parameter set; In the abnormal parameter set, relative positions are introduced to sort the sensors and map them to the sorting of the abnormal parameter set. If there are multiple overlapping abnormal time periods for the same sensor, they will be sorted in chronological order. The first target parameter is selected from the set of abnormal parameters and serialized. The sequence order follows the sorting result above to obtain the first sequence. Based on the sorting results, the second target parameter is selected from the acceleration calculation results and serialized to obtain the second sequence; Nonlinear analysis was performed using the linear fitting deviation analysis method. Linear equations were used to fit the data for the first and second sequences, with the test period as the independent variable and the target parameter as the dependent variable. The least squares method was used to search for the fitting parameters during the fitting process, and the first and second fitting curves were obtained respectively. In the first and second fitted curves, the deviation between the actual value and the curve fitting value is analyzed. If the deviation rate of a certain sequence point in both fitted curves is greater than the preset curve deviation, then the sequence point is marked as a nonlinear sequence point.

[0029] In this embodiment, the abnormal parameter set stores the overlapping time period parameters obtained from the first multi-time period common abnormal optical connection analysis based on fiber optic acquisition parameters (such as light intensity, phase difference, center wavelength offset, etc.). This parameter set is a relatively large set of parameters, which can basically cover the abnormal data segments of the tested sensor fiber optics. However, some normal data segments may exist, resulting in a certain degree of out-of-range judgment. But the accuracy of screening out abnormal time periods is relatively high. Subsequently, this embodiment of the invention will use the relationship of their relative positions to introduce nonlinearity to mine the existing abnormal data points. This invention uses linear fitting of relative distance to identify the existence of nonlinear segments. Due to the addition of multi-point sensors... In the velocity analysis process, for different relative distances to the vibration source (preset point), the collected acceleration data exhibit a certain time-series correlation and linear variation law. Furthermore, this invention sorts the sensors based on their relative positions and sequentially maps and serializes the parameters of the corresponding overlapping abnormal time periods to obtain an abnormal dataset with positional arrangement characteristics, which is represented as a sequence. Subsequently, nonlinear evaluation is used to screen out abnormal sensors, and individual tests are performed on the abnormal sensors to improve the testing efficiency of the sensors and the screening accuracy of abnormal states. Moreover, it can be widely applied to distributed, multi-array sensor applications and multi-sensor abnormal monitoring scenarios, making it highly practical.

[0030] The sorting here is based on sensors with overlapping abnormal time periods. A single sensor may have multiple overlapping abnormal time periods (i.e., a specific test period). The first and second target parameters can be preset by the user. For anomaly analysis of the core parameters, subsequent weighted averaging can characterize the fiber optic parameter features to form multi-dimensional evaluation values. While weighted averaging increases computational complexity by characterizing the characteristic sequence of relevant fiber optic data based on multi-dimensional parameters, it offers high accuracy in characterizing multi-dimensional fiber optic signal parameters. This allows for efficient screening of abnormal sensors and corresponding abnormal data segments, and enables effective sensor manufacturing quality analysis and component quality inspection evaluation.

[0031] In the process of selecting target parameters from the acceleration calculation results for serialization, the target parameters can be selected as the vibration frequency or the acceleration value measured by the sensor in a certain direction. The selected parameters are used as the serialization parameters, and the acceleration calculation results (parameters) obtained from the core parameters corresponding to the abnormal overlap period are used for serialization. The preset curve deviation can be set to 20%-30%.

[0032] In the above sorting process, the sensor's relative position (i.e., relative position and relative distance to the preset point, sorted in ascending order) is given priority. If there are multiple overlapping abnormal time periods for the same sensor, the abnormal dataset is sorted according to the time dimension of the multiple overlapping abnormal time periods to correspond to the abnormal parameters.

[0033] In the nonlinear sequence point discrimination, the linear fitting deviation analysis method is used for nonlinear analysis. Linear equations (y=ax+b, where x is the time point (test period), y is the parameter value / acceleration value, and a and b are linear fitting parameters) are fitted to the first sequence and the second sequence respectively. The relative deviation rate between the actual value and the predicted value of the fitted equation at each time point is calculated. A preset curve deviation is introduced for error control. Time points with relative deviation rates exceeding the expectations are marked as nonlinear sequence points. Finally, nonlinear sequence points that exist in both the first sequence and the second sequence are selected.

[0034] According to an embodiment of the present invention, S14 specifically includes: In the second sequence, nonlinear sequence points are extracted as outlier values. Feedback deviation analysis is performed between abnormal result values ​​and acceleration comparison values. Sequence points with deviations greater than preset deviations are marked, and the corresponding sensors are marked as abnormal fiber optic accelerometer sensors. The abnormal fiber optic accelerometer sensor and the corresponding abnormal nonlinear segment parameters are sent as test results to a preset terminal.

[0035] According to an embodiment of the present invention, for each fiber optic accelerometer, a corresponding vibration acceleration signal is acquired for each test period.

[0036] According to an embodiment of the present invention, each test period corresponds to an acceleration calculation result.

[0037] According to an embodiment of the present invention, the first sequence further includes: For the abnormal parameter set, the light intensity, phase difference, and center wavelength offset parameters of the sensor for the corresponding time period are normalized to the [0,1] interval by min-max to obtain the normalized parameter set; The normalized parameter set is weighted and averaged in three dimensions to obtain a weighted evaluation value; The optical fiber signal parameters are characterized by weighted evaluation values, which are then used as sequence points to form a second sequence.

[0038] The weighted calculation is as follows: F=w1×G(I)+w2×G(φ)+w3×G(λ); Where F is the weighted evaluation value, w1, w2, and w3 are the preset weights, G() is the normalization function, and I, φ, and λ are the light intensity, phase difference, and center wavelength offset, respectively.

[0039] The embodiments also include The second sequence is obtained by weighted averaging. For the first and second sequences, the ARIMA prediction model is introduced to calculate the autocorrelation plot and the partial autocorrelation plot. The p, d, and q prediction parameters of the sequence pattern are determined by the correlation diagram; After determining the prediction parameters, the first sequence and the second sequence are predicted based on the ARIMA prediction model to obtain the first predicted sequence and the second predicted sequence. After the S14 process, the proportion of the nonlinear sequence points to the sequence is calculated to obtain the first proportion; Calculate the proportion of nonlinear sequence points in the first and second predicted sequences to obtain the second proportion; The accuracy of nonlinear segment parameter evaluation is assessed by the deviation between the second proportions.

[0040] Here, the parameters p, d, and q are specifically: p represents the autoregressive order of the time series, d represents the differencing order of the time series, and q represents the moving average order of the time series. A larger percentage deviation indicates lower accuracy in estimating the nonlinear segment parameters.

[0041] In this embodiment, a prediction model is introduced to perform certain sequence predictions on the fiber optic signal characterization sequence and the calculation result sequence, and the nonlinearity ratio is evaluated based on the prediction results. The consistency of the nonlinearity ratio can be evaluated in the form of sensor test data trends. If the consistency is higher, it means that the calculation accuracy of the nonlinearity segment is higher, the error of the selected nonlinear segment is lower, and it has a certain level of test effect feedback evaluation, thus improving the foresight of the analysis process.

[0042] Traditional technologies often rely on a large amount of fiber optic test data to evaluate the accuracy of historical data, which has a certain lag and is often inefficient for the determination and anomaly analysis of multi-array sensor modes.

[0043] Figure 3 A block diagram of a fiber optic accelerometer test and evaluation system based on feedback analysis according to the present invention is shown.

[0044] Figure 3 In this invention, the evaluation system is the fiber optic accelerometer test and evaluation system based on feedback analysis.

[0045] A second aspect of the present invention also provides a fiber optic accelerometer testing and evaluation system 3 based on feedback analysis. The system includes a memory 31, a processor 32, and a data interface 33. The memory includes a fiber optic accelerometer testing and evaluation program based on feedback analysis. When executed by the processor, the fiber optic accelerometer testing and evaluation program based on feedback analysis performs the following steps: S11. Deploy multiple fiber optic accelerometers in the target object and record their relative positions with preset points. Set the test vibration parameters of the target object according to the preset scheme. Collect vibration acceleration signals from multiple fiber optic accelerometers during multiple test periods. S12. Based on the vibration acceleration signal, perform fiber optic signal analysis and data preprocessing. Construct a core parameter set for multiple time periods based on light intensity, phase difference, and center wavelength offset parameters. Analyze the correlation between the core parameter set and the acceleration calculation results through linear regression analysis. Mark low-correlation time periods as abnormal time periods. Introduce multi-sensor time period analysis to screen out overlapping abnormal time periods. S13. Extract the core parameters corresponding to the overlapping abnormal time periods in each sensor to obtain an abnormal parameter set. Sort the abnormal parameter set and acceleration calculation results according to the sorting results to obtain the first sequence and the second sequence. Identify the nonlinear sequence points that exist simultaneously in the first sequence and the second sequence. S14. In the second sequence, extract the nonlinear sequence points as abnormal result values, perform feedback deviation analysis on the abnormal result values ​​and the acceleration comparison values, and screen out the abnormal fiber optic accelerometers and the corresponding abnormal nonlinear segment parameters.

[0046] A third aspect of the present invention also provides a computer-readable storage medium including a fiber optic accelerometer test and evaluation program based on feedback analysis, wherein when the fiber optic accelerometer test and evaluation program based on feedback analysis is executed by a processor, it implements the steps of the fiber optic accelerometer test and evaluation method based on feedback analysis as described in any of the preceding claims.

[0047] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0048] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0049] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0050] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0051] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0052] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A test and evaluation method for fiber optic accelerometers based on feedback analysis, characterized in that, include: S11. Deploy multiple fiber optic accelerometers in the target object and record their relative positions with preset points. Set the test vibration parameters of the target object according to the preset scheme. Collect vibration acceleration signals from multiple fiber optic accelerometers during multiple test periods. S12. Based on the vibration acceleration signal, perform fiber optic signal analysis and data preprocessing. Construct a core parameter set for multiple time periods based on light intensity, phase difference, and center wavelength offset parameters. Analyze the correlation between the core parameter set and the acceleration calculation results through linear regression analysis. Mark low-correlation time periods as abnormal time periods. Introduce multi-sensor time period analysis to screen out overlapping abnormal time periods. S13. Extract the core parameters corresponding to the overlapping abnormal time periods in each sensor to obtain an abnormal parameter set. Sort the abnormal parameter set and acceleration calculation results according to the sorting results to obtain the first sequence and the second sequence. Identify the nonlinear sequence points that exist simultaneously in the first sequence and the second sequence. S14. In the second sequence, extract the nonlinear sequence points as abnormal result values, perform feedback deviation analysis on the abnormal result values ​​and the acceleration comparison values, and screen out the abnormal fiber optic accelerometers and the corresponding abnormal nonlinear segment parameters.

2. The method for testing and evaluating fiber optic accelerometers based on feedback analysis according to claim 1, characterized in that, S11 includes: Set a preset point in the target object; the preset point is used as the test vibration point. The vibration parameters tested include vibration frequency, vibration amplitude, vibration direction, and acceleration. Multiple fiber optic accelerometers are deployed and their relative positions to preset points are recorded. The relative positions can be represented by three-dimensional coordinates and distance.

3. The method for testing and evaluating fiber optic accelerometers based on feedback analysis according to claim 2, characterized in that, The vibration acceleration signal includes time-domain waveform, frequency-domain spectrum data, fiber optic grating wavelength offset, center wavelength offset, and phase difference data.

4. The method for testing and evaluating fiber optic accelerometers based on feedback analysis according to claim 3, characterized in that, Specifically, S12 is as follows: For a fiber optic accelerometer, the corresponding vibration acceleration signal is collected and analyzed by fiber optic signal analysis. The spectral information is preprocessed by median filtering. The corresponding light intensity, phase difference, and center wavelength offset parameters are collected and a core parameter set for multiple time periods is constructed. The core parameter set is normalized to the [0,1] interval by min-max to obtain a standardized parameter set. For each time period, the standardized parameter set is weighted and averaged to obtain the mean parameter of multiple time periods. In each time period, the mean parameter of the current time period is fitted with the linear rate of change of the mean parameter of the previous and subsequent time periods to obtain the first rate of change curve. The preset parameters of each time period are extracted based on the acceleration results and fitted with the linear rate of change to obtain the second rate of change curve. The periods when the first rate of change curve and the second rate of change are higher than the preset deviation are marked to obtain the abnormal periods. Abnormal time periods are calculated based on multiple sensors, and overlapping abnormal time periods are filtered out. The determination of overlapping abnormal periods is as follows: if there are two or more fiber optic accelerometers that match an abnormal period, then it is considered an overlapping abnormal period.

5. The method for testing and evaluating fiber optic accelerometers based on feedback analysis according to claim 4, characterized in that, Specifically, S13 is as follows: Extract the core parameters corresponding to the overlapping abnormal time periods in each sensor and obtain the abnormal parameter set; In the abnormal parameter set, relative positions are introduced to sort the sensors and map them to the sorting of the abnormal parameter set. If there are multiple overlapping abnormal time periods for the same sensor, they will be sorted in chronological order. The first target parameter is selected from the set of abnormal parameters and serialized. The sequence order follows the sorting result above to obtain the first sequence. Based on the sorting results, the second target parameter is selected from the acceleration calculation results and serialized to obtain the second sequence; Nonlinear analysis was performed using the linear fitting deviation analysis method. Linear equations were used to fit the data for the first and second sequences, with the test period as the independent variable and the target parameter as the dependent variable. The least squares method was used to search for the fitting parameters during the fitting process, and the first and second fitting curves were obtained respectively. In the first and second fitted curves, the deviation between the actual value and the curve fitting value is analyzed. If the deviation rate of a certain sequence point in both fitted curves is greater than the preset curve deviation, then the sequence point is marked as a nonlinear sequence point.

6. The method for testing and evaluating fiber optic accelerometers based on feedback analysis according to claim 5, characterized in that, Specifically, S14 is: In the second sequence, nonlinear sequence points are extracted as outlier values. Feedback deviation analysis is performed between abnormal result values ​​and acceleration comparison values. Sequence points with deviations greater than preset deviations are marked, and the corresponding sensors are marked as abnormal fiber optic accelerometer sensors. The abnormal fiber optic accelerometer sensor and the corresponding abnormal nonlinear segment parameters are sent as test results to a preset terminal.

7. The method for testing and evaluating fiber optic accelerometers based on feedback analysis according to claim 6, characterized in that, For each fiber optic accelerometer, a corresponding vibration acceleration signal is collected for each test period.

8. The method for testing and evaluating fiber optic accelerometers based on feedback analysis according to claim 7, characterized in that, Each test period corresponds to one acceleration calculation result.

9. A test and evaluation system for fiber optic accelerometers based on feedback analysis, characterized in that, The system includes a memory and a processor. The memory includes a test and evaluation program for a fiber optic accelerometer based on feedback analysis. When the processor executes the test and evaluation program for the fiber optic accelerometer based on feedback analysis, it performs the following steps: S11. Deploy multiple fiber optic accelerometers in the target object and record their relative positions with preset points. Set the test vibration parameters of the target object according to the preset scheme. Collect vibration acceleration signals from multiple fiber optic accelerometers during multiple test periods. S12. Based on the vibration acceleration signal, perform fiber optic signal analysis and data preprocessing. Construct a core parameter set for multiple time periods based on light intensity, phase difference, and center wavelength offset parameters. Analyze the correlation between the core parameter set and the acceleration calculation results through linear regression analysis. Mark low-correlation time periods as abnormal time periods. Introduce multi-sensor time period analysis to screen out overlapping abnormal time periods. S13. Extract the core parameters corresponding to the overlapping abnormal time periods in each sensor to obtain an abnormal parameter set. Sort the abnormal parameter set and acceleration calculation results according to the sorting results to obtain the first sequence and the second sequence. Identify the nonlinear sequence points that exist simultaneously in the first sequence and the second sequence. S14. In the second sequence, extract the nonlinear sequence points as abnormal result values, perform feedback deviation analysis on the abnormal result values ​​and the acceleration comparison values, and screen out the abnormal fiber optic accelerometers and the corresponding abnormal nonlinear segment parameters.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a fiber optic accelerometer test and evaluation program based on feedback analysis, which, when executed by a processor, implements the steps of the fiber optic accelerometer test and evaluation method based on feedback analysis as described in any one of claims 1 to 8.

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