Optical fiber temperature measurement method and system

Abstract

The invention relates to the technical field of optical fiber sensing, in particular to an optical fiber temperature measurement method and system, and the method comprises the steps: injecting a multi-scale dynamic modulation coding laser signal into a to-be-measured optical fiber and a reference optical fiber respectively, collecting two paths of optical signals, and generating frequency domain data containing modulation coding characteristics through fast Fourier transform; constructing a virtual reference channel, fusing pre-stored historical data through digital matched filtering and self-organizing mapping, and realizing stable output of a reference signal; adaptive calibration is performed on data to be measured by using extended Kalman filtering in combination with fuzzy logic, and calibrated phase data accurately reflecting temperature change is output; and finally, outputting high-precision temperature distribution data based on the temperature inversion model and multi-dimensional correlation analysis. The temperature measurement precision and the system robustness are remarkably improved, and the method is suitable for the fields of industrial and environmental monitoring and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of optical fiber sensing, and particularly to an optical fiber temperature measurement method and system. Background Art

[0002] Optical fiber temperature measurement technology utilizes the excellent characteristics of optical fibers themselves, such as high temperature resistance, corrosion resistance, and electromagnetic interference resistance, and plays an important role in fields such as electric power, petroleum, civil engineering, and disaster monitoring. Traditional optical fiber temperature measurement methods mainly involve inputting a laser pulse signal into the optical fiber, collecting anti-Stokes (AST) signals and Stokes (ST) signals, and reflecting temperature changes through the ratio of the light intensity values of the two.

[0003] However, existing technologies generally require the winding length of the optical fiber at the temperature measurement point to reach a minimum physical distance to ensure temperature measurement accuracy; when the size of the object to be measured is small or the environmental requirements are compact in practical applications, insufficient winding length of the optical fiber often leads to non-linear changes in the ratio of AST and ST signals due to transmission attenuation, thereby introducing measurement errors and restricting the effective detection distance of the overall optical fiber.

[0004] In addition, traditional methods usually adopt simple signal ratio compensation techniques or rely on deep learning models to compensate the collected data, but these methods have defects such as insufficient training data, poor model generalization ability, and sensitivity to environmental noise, resulting in difficulty in meeting high requirements for temperature measurement accuracy in practical applications. Based on the above background, there is an urgent need for a new type of optical fiber temperature measurement technology that can overcome the strict requirements of traditional methods for the winding length of optical fibers and can achieve real-time and accurate detection of temperature changes in complex environments, thereby expanding the effective detection distance of optical fibers and improving the overall robustness and stability of the system. Summary of the Invention

[0005] In view of the many problems existing in the above-mentioned prior art, the present invention provides an optical fiber temperature measurement method and system. The present invention embeds multi-scale modulation coding features in the laser signal, constructs a virtual reference channel using reference fiber data, then realizes adaptive calibration through extended Kalman filtering combined with fuzzy logic, and finally converts the calibrated phase data into temperature values through a temperature inversion model. The present invention effectively eliminates the signal attenuation problem caused by insufficient winding length of the optical fiber in traditional optical fiber temperature measurement, and realizes dynamic adaptive adjustment of system parameters through closed-loop feedback control, thereby obtaining high-precision temperature distribution data.

[0006] An optical fiber temperature measurement method includes the following steps: Inject laser signals modulated and encoded with multi-scale dynamics into the optical fiber to be measured and the reference optical fiber respectively, and collect the optical signals of each channel and generate frequency domain data containing modulation coding features through fast Fourier transform; Construct a virtual reference channel, where the frequency-domain data collected from the reference optical fiber is demodulated and denoised, and the demodulated data is fused with the pre-stored historical data to form virtual reference frequency-domain data. Then, the frequency-domain data of the optical fiber under test is compared with the virtual reference frequency-domain data at the same sampling position, and adaptive calibration is performed using an adaptive calibration algorithm. The adaptive calibration algorithm uses an extended Kalman filter combined with fuzzy logic to correct the signal of the optical fiber under test to obtain calibrated phase data reflecting temperature changes; Perform temperature inversion on the calibrated phase data based on a pre-established temperature inversion model, and optimize the preliminary temperature data using multi-dimensional correlation analysis, so as to output high-precision temperature distribution data.

[0007] Preferably, in the multi-scale dynamic modulation coding step, a high-speed digital signal processor is used to perform sine-wave modulation on the output signal of the laser. The frequency of the sine-wave modulation is set to be not less than 1000 Hz and not higher than 5000 Hz. At the same time, a pulse signal is superimposed through a dedicated pulse coding circuit. The pulse width is set to be not less than 5 μs and not higher than 50 μs, and the pulse repetition period is determined according to the temperature response characteristics of the environment under test, so as to generate a laser signal containing fixed-frequency components and pulse coding components in the spectrum. The laser signal is used to extract temperature change indicators in the subsequent frequency-domain data.

[0008] Preferably, in the step of performing fast Fourier transform after collecting the optical signal, the collected time-domain data is first subjected to Hanning window weighting processing, and then the Fourier transform is performed using a digital signal processing algorithm to generate frequency-domain data, so as to ensure that the modulation coding characteristics are retained in the frequency-domain data.

[0009] Preferably, in the step of constructing the virtual reference channel, the frequency-domain data collected from the reference optical fiber is demodulated and noise-suppressed using a digital matched filter, and the self-organizing mapping algorithm combined with the fuzzy clustering algorithm is used to fuse the demodulated data with the pre-stored historical reference data to form virtual reference frequency-domain data reflecting the ideal reference state.

[0010] Preferably, in the adaptive calibration step, the frequency-domain data of the optical fiber under test is compared with the virtual reference frequency-domain data at the same sampling position to calculate the initial phase error signal, and the extended Kalman filter combined with fuzzy logic is used to perform recursive prediction and update on the initial phase error signal. At the same time, the process noise covariance and the measurement noise covariance are adaptively adjusted online to obtain calibrated phase data accurately reflecting temperature changes.

[0011] Preferably, in the temperature inversion step, a temperature inversion model established based on offline calibration data is used. The temperature inversion model uses quadratic polynomial fitting to convert the calibrated phase data into temperature values, so as to generate preliminary temperature data.

[0012] Preferably, in the multidimensional correlation analysis step, cross-spectrum analysis is performed on the preliminary temperature data and the modulation coding characteristic data, the nonlinear correlation between the two is extracted through normalization processing and correlation coefficient calculation, and the preliminary temperature data is robustly optimized using a random sampling consistency algorithm to obtain corrected temperature data.

[0013] Preferably, the calibrated phase data is monitored in real time through a closed-loop feedback system, which transmits the filtering residuals generated during the calibration process to the multi-scale dynamic modulation coding and virtual reference channel construction steps, and uses an online minimum mean square error algorithm to perform dynamic adaptive adjustment of the modulation parameters and virtual reference construction parameters.

[0014] Preferably, the temperature inversion step further includes iteratively adjusting the temperature inversion model parameters using an online minimum mean square error algorithm, wherein the online minimum mean square error algorithm uses the error between the temperature value obtained by converting the auxiliary temperature reference data and the calibrated phase data as feedback to reduce the error and improve the accuracy of the temperature inversion.

[0015] An optical fiber temperature measurement system, used to implement the optical fiber temperature measurement method, the system comprising: A laser module, used for outputting a laser signal that has been dynamically modulated and encoded at multiple scales; An optical path distribution module, used for equally dividing the modulated laser signal and injecting it into the optical fiber to be tested and the reference optical fiber respectively, wherein the optical fiber to be tested is used for transmitting to the object to be tested, and the reference optical fiber is placed in a temperature-controlled and low-vibration environment; A signal detection module, used to collect the return optical signals of the optical fiber to be tested and the reference optical fiber, and convert the optical signals into digital time domain data; A digital signal processing module, used for performing a fast Fourier transform on the digital time domain data, and generating frequency domain data containing modulation coding features after Hanning window weighted processing; A virtual reference channel construction module is used to demodulate and suppress noise on the frequency domain data collected by the reference optical fiber, and fuse the demodulated data with the pre-stored historical reference data to form virtual reference frequency domain data reflecting an ideal reference state; The adaptive calibration module is used to compare the frequency domain data of the optical fiber to be tested with the virtual reference frequency domain data at the same sampling position, recursively predict and update the initial phase error signal using the extended Kalman filter combined with fuzzy logic, and perform online adaptive adjustment on the process noise covariance and the measurement noise covariance, thereby outputting calibrated phase data reflecting temperature changes; A temperature inversion module, used to convert the calibrated phase data into temperature values ​​based on a pre-established temperature inversion model, and optimize the preliminary temperature data using multi-dimensional correlation analysis to output high-precision temperature distribution data; The closed-loop feedback control module is used to receive in real time the error information output by the adaptive calibration module and the temperature inversion module, and feedback the error information to the laser module, the virtual reference channel construction module and the adaptive calibration module to achieve dynamic adaptive adjustment of the parameters of each module.

[0016] Compared with the prior art, the advantages and beneficial effects of the present invention are as follows: Through the multi-scale dynamic modulation coding technology, the stable embedding of sine modulation and pulse coding features in the laser signal is realized, so as to form a clear modulation identifier in the frequency domain; Through the digital matched filter combined with the self-organizing mapping and fuzzy clustering algorithms, the accurate demodulation of the data collected by the reference optical fiber and the fusion of historical data are realized, so as to construct a virtual reference channel reflecting the ideal reference state; Through the adaptive calibration algorithm combining the extended Kalman filter and fuzzy logic, the real-time recursive correction of the initial phase error between the optical fiber signal to be measured and the virtual reference signal is realized, effectively compensating for the errors caused by optical fiber transmission attenuation, laser drift and environmental noise, thus greatly improving the accuracy of temperature inversion and the robustness of the system. Description of the Drawings

[0017] Figure 1 It is a schematic diagram of the method flow of the present invention; Figure 2 It is a schematic diagram of the virtual reference channel construction in the present invention; Figure 3 It is a schematic diagram of the adaptive calibration in the present invention; Figure 4 It is a block diagram of the system structure of the present invention. Detailed Embodiments

[0018] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the following detailed description, for the sake of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, obviously, one or more embodiments can also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessarily confusing the concepts of the present disclosure.

[0019] The terms used herein are merely for describing specific embodiments and are not intended to limit the present disclosure. The terms "including", "comprising" and the like used herein indicate the presence of the described features, steps, operations and / or components, but do not exclude the presence or addition of one or more other features, steps, operations or components.

[0020] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those of ordinary skill in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification, and should not be interpreted in an idealized or overly rigid manner.

[0021] An optical fiber temperature measurement method includes the following steps: Utilize laser signals that have undergone multi-scale dynamic modulation coding to be respectively injected into the optical fiber to be measured and the reference optical fiber, and after collecting the optical signals of each channel, generate frequency-domain data containing modulation coding characteristics through fast Fourier transform. The present invention proposes an optical fiber temperature measurement method. The core idea lies in performing multi-scale dynamic modulation coding processing on the laser signal, and respectively injecting the processed laser signal into the optical fiber to be measured and the reference optical fiber. Then, collect the two-way optical signals, and use digital signal processing technology to achieve fast Fourier transform, generating frequency-domain data containing modulation coding characteristics, thereby providing a reliable signal basis for the detection of temperature changes.

[0022] The overall process of the present invention includes multiple links such as laser signal modulation, optical signal injection, signal collection and analog-to-digital conversion, window function weighting, and fast Fourier transform. Each link is carefully designed to ensure that the signal characteristics caused by temperature changes can still be accurately extracted under complex environments. Specifically, first, utilize laser signals that have undergone multi-scale dynamic modulation coding to be respectively injected into the optical fiber to be measured and the reference optical fiber. The optical fiber to be measured is usually laid or wound in a temperature-sensitive area, while the reference optical fiber is installed in a temperature-controlled and low-vibration environment as the system calibration reference. During the transmission process, both optical fibers will be affected by the external environment, mechanical stress, and system noise, resulting in slight changes in parameters such as the intensity and phase of the optical signal. Therefore, in the collection stage, convert these two-way returned optical signals into discrete analog signals through a high-speed optical receiver, and convert them into digital signals by a high-speed analog-to-digital converter (ADC). Subsequently, the digital signal processing module preprocesses the obtained time-domain data, including applying window function weighting processing to reduce spectral leakage, and then using the fast Fourier transform algorithm to convert the preprocessed time-domain data into frequency-domain data, obtaining frequency-domain data containing modulation coding characteristics. This frequency-domain data not only retains the amplitude and phase information of the optical signal itself, but also due to the effect of modulation coding, obvious frequency peaks and pulse spectral lines are formed at specific frequencies, facilitating subsequent analysis.

[0023] The advantage of the whole process is that by pre-injecting specific modulation coding information at the laser end, the system forms stable identification features in the frequency domain, which can be used to distinguish signal changes caused by ambient temperature changes and interference caused by system noise, laser drift and other factors. The present invention relies on high-speed digital signal processing and advanced Fourier transform technology, and can accurately extract weak signals caused by temperature changes even in long-distance transmission and high-noise environments, thereby achieving high-precision temperature monitoring. In short, the present invention provides a novel and efficient technical approach for temperature measurement by organically combining the modulation, transmission, acquisition and frequency domain conversion of optical signals, and has good real-time and robustness, and can be widely used in various industrial and environmental monitoring fields, providing a solid data foundation for subsequent adaptive calibration and temperature inversion.

[0024] Preferably, in the multi-scale dynamic modulation coding step, a high-speed digital signal processor is used to perform sinusoidal wave modulation on the laser output signal, and the frequency of the sinusoidal wave modulation is set to be not less than 1000 Hz and not more than 5000 Hz. At the same time, a pulse signal is superimposed through a dedicated pulse coding circuit, and the pulse width is set to be not less than 5 microseconds and not more than 50 microseconds. The pulse repetition period is determined according to the temperature response characteristics of the environment to be measured, thereby generating a laser signal containing a fixed frequency component and a pulse coding component in the spectrum, and the laser signal is used to extract temperature change identification in subsequent frequency domain data.

[0025] In the multi-scale dynamic modulation coding step, the present invention uses a high-speed digital signal processor to implement a superposition of sinusoidal wave modulation and pulse coding on the laser output signal, so as to embed a specific modulation feature in the optical signal, thereby forming a fixed frequency component and a pulse coding component in the frequency domain, providing a unique identifier for the subsequent detection of temperature changes. In the specific implementation process, a high-speed digital signal processor is first used to generate a sinusoidal wave modulation signal, and its modulation frequency is set to , and satisfies This frequency range has been experimentally verified to avoid overlap with common environmental noise while ensuring sufficient time resolution. The principle of sinusoidal wave modulation can be expressed as , in, represents the average output amplitude of the laser, represents the modulation depth, is a time variable. This formula shows that after the laser signal is modulated by a sine wave, its output amplitude will change periodically and produce a corresponding frequency peak in the frequency domain.

[0026] Meanwhile, to enhance the multi-scale characteristics of the modulation signal, the system uses a dedicated pulse coding circuit to superimpose pulse signals. The key parameters of pulse coding are the pulse width and the pulse repetition period, where the pulse width is set to be not less than 5  and not more than 50 . The pulse signal can be denoted as: , In the above formula, represents the pulse amplitude, represents the pulse repetition period, represents the pulse width, and the function is a rectangular pulse function, which is defined to take the value 1 in the interval and 0 for the rest. The pulse repetition period is reasonably determined according to the temperature response characteristics of the environment to be measured to balance the time resolution and data redundancy.

[0027] After superimposing the sine wave modulation signal and the pulse coding signal, the optical signal output by the laser can be expressed as: , This composite signal exhibits obvious sine modulation peaks and periodic pulse coding spectral lines in the frequency domain. The advantage of the modulation coding signal is that during the transmission of the optical signal through the fiber to be measured, temperature changes will cause slight phase and intensity offsets in these modulation components, and these offsets can be accurately identified through frequency domain analysis, thus reflecting the changes in the ambient temperature. In particular, since the reference fiber is in a relatively stable environment, the change in its modulation coding signal is small and can be used as an important reference for calibrating the signal of the fiber to be measured.

[0028] During the implementation process, the high-speed digital signal processor and the dedicated pulse coding circuit need to cooperate with a high-precision clock system to ensure that both the phase and amplitude of the sine modulation and pulse coding are strictly controlled within the design parameter range. Through offline calibration and online correction, the system can monitor and adjust the modulation parameters in real time to ensure that the characteristics formed by the generated modulation coding signal in the frequency domain are stable and reproducible. In summary, the multi-scale dynamic modulation coding step provides a unique and reliable signal identification for the fiber optic temperature measurement method, greatly improving the accuracy of subsequent temperature data extraction and the anti-interference ability of the system.

[0029] Preferably, in the step of performing fast Fourier transform after collecting the optical signal, the collected time-domain data is first subjected to Hanning window weighting processing, and then the Fourier transform is performed using a digital signal processing algorithm to generate frequency-domain data, so as to ensure that the modulation coding characteristics are retained in the frequency-domain data.

[0030] In the step of performing the fast Fourier transform after collecting the optical signal, first, the optical signals returned by the fiber under test and the reference fiber need to be converted into discrete digital time-domain data through an analog-to-digital converter. To ensure that the frequency-domain data can accurately reflect the characteristics of multi-scale dynamic modulation coding, usually before performing the discrete Fourier transform, a Hanning window weighting process is applied to the time-domain data. The mathematical expression of the Hanning window function is , where represents the index of discrete sampling points, represents the window function length. Through this window function, the sampled data can show a smooth attenuation effect at both ends of the data, reducing the spectral leakage caused by signal truncation.

[0031] Multiply the original time-domain data by the Hanning window function to obtain the weighted data , and its expression is: , Next, convert the weighted data to the frequency domain through the fast Fourier transform algorithm to obtain the frequency-domain representation , that is: , In this process, the fast Fourier transform algorithm uses the divide-and-conquer strategy to perform efficient calculations on the discrete data, and the output frequency-domain data will clearly show the fixed-frequency components generated by sine-wave modulation and the periodic spectral lines introduced by pulse coding. For example, near the sine modulation frequency , obvious spectral peaks can be observed; while pulse coding shows periodic spectral lines at its harmonic positions. These modulation coding characteristics provide reliable anchor points for subsequent temperature change detection, because when the optical signal is transmitted through the fiber under test, temperature changes will cause slight shifts in the amplitude or position of these spectral lines, thereby reflecting the temperature change.

[0032] To ensure the accuracy and resolution of the Fourier transform, the system requires the use of a high-sampling-rate analog-to-digital converter in the hardware design. The sampling rate is usually set to not less than 100 MHz, and the conversion bit depth is not less than 14 bits; at the same time, the number of points needs to be selected according to the actual sampling data length and the expected frequency-domain resolution to ensure that the modulation coding characteristics can be accurately resolved in the frequency domain.

[0033] In the actual operation process, the digital signal processing module first stores the collected time-domain data and, according to the set window function length Calculate the Hamming window weights for each sampling point, then multiply them point by point with the original data to generate weighted data with a smooth transition. Subsequently, input the weighted data into the fast Fourier transform algorithm to obtain frequency-domain data through a divide-and-conquer calculation method. To verify whether the modulation coding features are retained, the system will detect the spectral lines in the output frequency-domain data. If it is found that the preset frequency components and pulse coding components do not meet the expectations, it can be determined that there are abnormalities in the sampling or modulation process, and corresponding correction measures will be initiated.

[0034] In summary, the steps of performing fast Fourier transform after collecting the optical signal are achieved through the combination of Hamming window weighting and an efficient algorithm, realizing an accurate conversion from discrete-time domain data to frequency-domain data, ensuring that the frequency-domain data contains stable and clear multi-scale modulation coding features, providing a solid data foundation for subsequent virtual reference construction, adaptive calibration, and temperature inversion, and thus achieving high-precision fiber optic temperature measurement.

[0035] Construct a virtual reference channel, where the frequency-domain data collected from the reference fiber is demodulated and denoised, and the demodulated data is fused with the pre-stored historical data to form virtual reference frequency-domain data. Then, at the same sampling position, the frequency-domain data of the fiber under test is compared with the virtual reference frequency-domain data, and an adaptive calibration algorithm is used for adaptive calibration. The adaptive calibration algorithm uses an extended Kalman filter combined with fuzzy logic to correct the signal of the fiber under test to obtain calibrated phase data reflecting temperature changes. The core step of the present invention lies in constructing a virtual reference channel and using an adaptive calibration algorithm to correct the frequency-domain data collected from the fiber under test to obtain high-precision calibrated phase data reflecting temperature changes. Overall, this step first demodulates and suppresses the noise of the frequency-domain data collected from the reference fiber, and then fuses the demodulated data with the pre-stored historical reference data to construct virtual reference frequency-domain data. Next, at the same sampling position, the frequency-domain data collected from the fiber under test is compared with the constructed virtual reference frequency-domain data to calculate the initial phase error signal.

[0036] Using an adaptive calibration algorithm, which is based on an extended Kalman filter combined with fuzzy logic, corrects the initial phase error signal through recursive prediction and update, and adaptively adjusts the process noise covariance and measurement noise covariance online to gradually eliminate the interference caused by environmental temperature changes, laser drift, and system noise, and finally outputs calibrated phase data that accurately reflects temperature changes. This core step plays a crucial role in the entire fiber optic temperature measurement method because only the accurately calibrated phase data can be input into the temperature inversion module to achieve high-precision measurement of the temperature field distribution.

[0037] The entire process utilizes the complementary characteristics of dual-channel data. Considering that the optical fiber is in a stable environment, its frequency-domain data can serve as an ideal reference after demodulation and noise suppression processing, while the data of the optical fiber under test contains minute phase shifts caused by temperature changes. By comparing the two at the same sampling positions, the phase difference, i.e., the initial phase error signal, can be calculated and expressed by the mathematical formula: , where, is the frequency-domain data of the optical fiber under test, is the constructed virtual reference frequency-domain data, represents phase extraction. Then, an extended Kalman filter combined with fuzzy logic is used to perform recursive prediction and update to achieve adaptive calibration. This process not only predicts the state variables based on a mathematical model but also continuously adjusts the noise parameters through online feedback to ensure that the output phase data after calibration can accurately reflect temperature changes.

[0038] Overall, this step fully integrates multiple links such as demodulation, data fusion, comparison, and adaptive filtering, constituting a complete closed-loop feedback control link, providing high-quality and low-noise basic data for subsequent temperature inversion, thereby greatly improving the accuracy and robustness of the optical fiber temperature measurement system. Through this core step, the system can not only effectively eliminate the errors caused by laser drift and environmental noise but also exhibit extremely high sensitivity in detecting minute phase shifts caused by temperature changes. Generally speaking, through the construction of a virtual reference channel and adaptive calibration, the present invention realizes the effective complementarity of the data of the optical fiber under test and the reference optical fiber, ensuring that high-precision temperature measurement results can be obtained in practical applications, providing a reliable and efficient technical solution for fields such as industrial monitoring and environmental detection.

[0039] Preferably, as Figure 2 shown, in the step of constructing the virtual reference channel, the frequency-domain data collected from the reference optical fiber is processed by a digital matched filter for demodulation and noise suppression, and the self-organizing mapping algorithm combined with the fuzzy clustering algorithm is used to fuse the demodulated data with the pre-stored historical reference data to form the virtual reference frequency-domain data reflecting the ideal reference state.

[0040] In the step of constructing the virtual reference channel, to ensure that the frequency-domain data collected from the reference optical fiber can effectively reflect the ideal reference state, the system first performs demodulation and noise suppression processing on the original frequency-domain data collected from the reference optical fiber. Let the original frequency-domain data collected from the reference optical fiber be , where represents the index of the frequency-domain sampling points. To extract the key components in the modulated coding signal, a digital matched filter is used to process Perform a convolution operation with a preset modulation template The mathematical expression is as follows: , Here, the symbol represents the convolution operation, is the reference frequency-domain data after demodulation and noise suppression. Through the convolution with the modulation template, the digital matched filter can highlight the expected sine modulation and pulse coding components in the frequency domain, thereby effectively reducing the influence of system noise. Next, in order to construct the virtual reference frequency-domain data in the ideal state, the system fuses the above-mentioned demodulated reference data with the pre-stored historical reference data. The pre-stored historical reference data is denoted as , which is a high-quality reference signal collected when the system is running stably, and its characteristics represent the ideal reference state.

[0041] The fusion process is processed by using the self-organizing mapping algorithm (SOM) combined with the fuzzy clustering algorithm. This algorithm can cluster and summarize the current demodulated data and historical data in the high-dimensional feature space, so as to extract the most representative reference features. The fusion process can be expressed as: , where represents the processing process of the self-organizing mapping algorithm, and the output is the constructed virtual reference frequency-domain data. Through this method, the constructed virtual reference data not only contains the effective modulation coding features in the current reference optical fiber data, but also integrates the stable feature information in the historical data, thus forming a reference data with a high signal-to-noise ratio and capable of reflecting the ideal reference state in the frequency domain.

[0042] In actual operation, to ensure the data fusion effect, the parameters of the self-organizing mapping algorithm (such as the number of clustering centers, learning rate, etc.) need to be determined in advance during the offline calibration stage and adjusted dynamically according to data changes during the online operation process. This fusion process has good robustness and adaptability, and can output a stable virtual reference signal under various environmental interferences, providing an accurate benchmark for subsequent adaptive calibration. In addition, the digital matched filter needs to fully consider the sampling rate and modulation template matching degree during the processing to ensure that the modulation coding features are obvious in the convolution operation result while suppressing non-target noise. Through fine parameter settings and strict hardware selection, this step realizes a smooth transition from the original reference data to the virtual reference data, and its output data is a crucial calibration benchmark in the entire optical fiber temperature measurement system.

[0043] The role played by the step of constructing the virtual reference channel in the system is crucial because it provides an ideal comparison standard for the correction of the optical fiber data to be measured. By fusing the current demodulation data with the historical reference data, the system can automatically compensate for errors caused by signal attenuation, laser drift, and environmental noise during the optical fiber transmission process, making the virtual reference signal closer to the theoretical ideal state. Ultimately, the implementation of this step not only improves the stability of the reference signal but also provides accurate input for the subsequent adaptive calibration module, thereby greatly enhancing the overall accuracy and robustness of temperature measurement. The entire process requires high-speed digital signal processing capabilities and high-precision matched filters in terms of hardware during implementation. At the same time, efficient calculations of self-organizing mapping and fuzzy clustering algorithms need to be realized in terms of software algorithms to ensure that virtual reference frequency-domain data can be output in real time during the online operation process.

[0044] Preferably, as Figure 3 shown, in the adaptive calibration step, the frequency-domain data of the optical fiber to be measured and the virtual reference frequency-domain data are compared at the same sampling position to calculate the initial phase error signal, and the extended Kalman filter combined with fuzzy logic is used to recursively predict and update the initial phase error signal. At the same time, online adaptive adjustment is performed on the process noise covariance and the measurement noise covariance to obtain calibrated phase data that accurately reflects the temperature change.

[0045] In the adaptive calibration step, the system calculates the initial phase error signal by comparing the frequency-domain data collected from the optical fiber to be measured with the constructed virtual reference frequency-domain data at the same sampling position, and uses the extended Kalman filter combined with fuzzy logic to recursively predict and update the initial phase error signal to obtain calibrated phase data that accurately reflects the temperature change. Specifically, let the frequency-domain data of the optical fiber to be measured be while the virtual reference frequency-domain data is At the same sampling position, the phases of the two are denoted as and The initial phase error signal is then defined as: , This initial phase error signal reflects the difference caused by temperature change between the signal of the optical fiber to be measured and the ideal reference signal. To correct this error signal, the system uses an extended Kalman filter, which recursively estimates the phase error based on the state space model. Let the state variable represent the true phase error at the sampling point , then the state transition equation can be written as: , where is the state transition matrix, is the process noise; the measurement model is: , wherein, is the measured initial phase error signal, is the observation matrix, is the measurement noise. The prediction step of the extended Kalman filter is: , , wherein, represents the predicted state, is the predicted error covariance, is the process noise covariance matrix. The update step is: , , , wherein, is the Kalman gain, is the measurement noise covariance matrix, is the identity matrix. In order to adapt to the changes in the environment and system state during actual operation, the present invention introduces a fuzzy logic mechanism in the extended Kalman filter to perform online adaptive adjustment on the noise covariance matrices and . Specifically, when the filter residual increases, the fuzzy rule will automatically adjust the value of to increase the prediction uncertainty or adjust the value of to reduce the dependence on the current measurement, so that the filter can quickly respond to environmental changes. After multiple prediction and update iterations, the finally output state estimate is the calibrated phase data, which can accurately reflect the phase shift caused by temperature changes.

[0046] In a preferred embodiment, the adaptive calibration step further includes continuously monitoring the calibration result by using a feedback mechanism during the online processing, and further iteratively optimizing the filter parameters through an online least mean square error algorithm. Through this closed-loop control, the system can adjust the parameters of the adaptive calibration algorithm in real time to ensure that the calibrated phase data maintains high precision and low noise during long-term operation. The successful implementation of this step depends on the support of a high-precision digital signal processor and sufficient offline calibration work to determine the initial parameters through experiments and then continuously update them through online feedback. In practical applications, the response speed and accuracy of the filter can be finely controlled by setting the sampling period, update frequency, and error threshold, so as to accurately capture and correct the influence of temperature changes on the phase signal. Overall, based on the stable reference data provided by constructing a virtual reference channel, the adaptive calibration step uses extended Kalman filtering and fuzzy logic to achieve fine correction of the signal to be measured, providing extremely reliable data input for subsequent temperature inversion, so that the entire optical fiber temperature measurement system can still output high-precision temperature distribution data in a complex environment.

[0047] Perform temperature inversion on the calibrated phase data based on a pre-established temperature inversion model, and optimize the preliminary temperature data using multi-dimensional correlation analysis, so as to output high-precision temperature distribution data.

[0048] The present invention aims to achieve high-precision real-time measurement of the temperature field distribution in optical fiber temperature measurement. The core idea is to perform temperature inversion on the calibrated phase data after adaptive calibration processing based on a pre-established temperature inversion model, and at the same time combine multi-dimensional correlation analysis to further optimize the preliminary temperature data, so as to output high-precision temperature distribution data reflecting temperature changes.

[0049] Overall, the present invention first digitizes the optical signals collected from the optical fiber to be measured and the reference optical fiber in the early stage to obtain frequency-domain data containing modulation coding characteristics, and uses a virtual reference channel construction and an adaptive calibration module to eliminate system noise, laser drift, and external environmental interference, so that the output calibrated phase data accurately reflects the temperature changes in the area to be measured. Subsequently, based on the temperature inversion model obtained through offline calibration, the calibrated phase data is converted into temperature values to obtain preliminary temperature data; then, cross-spectrum analysis is performed on the preliminary temperature data and the modulation coding characteristic data through multi-dimensional correlation analysis to extract the non-linear correlation between the two, and the random sample consensus algorithm is used to robustly optimize the preliminary temperature data, and finally the corrected temperature data is obtained.

[0050] In addition, in order to ensure the accuracy and stability of the system during long-term operation, the present invention also adopts a closed-loop feedback control mechanism to feed back the filter residuals generated during the adaptive calibration process to the multi-scale dynamic modulation coding and virtual reference channel construction modules of the front end in real time, and uses an online minimum mean square error algorithm to implement dynamic adaptive adjustment of relevant parameters. Overall, the present invention achieves high-precision capture and accurate inversion of small phase changes caused by temperature by constructing a virtual reference channel, adaptive calibration, temperature inversion and multidimensional correlation analysis, and an organic combination of closed-loop feedback mechanisms. This solution makes full use of the complementary information between the optical fiber to be tested and the reference optical fiber during the transmission process, which not only ensures that the modulation and coding characteristics of the signal in the frequency domain are completely retained, but also effectively eliminates interference factors inside and outside the system through adaptive calibration, so that the temperature inversion result has high accuracy and robustness.

[0051] Through the present invention, even in the case of complex environmental noise or long optical fiber transmission distance, accurate temperature distribution data can be stably output, providing a reliable and efficient solution for application scenarios such as industrial process monitoring, environmental monitoring, and safety warning. The overall situation of the present invention is to achieve high-precision conversion from the original calibrated phase data to the final temperature distribution data, in which each link achieves optimal performance through offline calibration and online adaptive adjustment, and ensures accurate recovery and real-time monitoring of temperature information through advanced technical means such as multi-dimensional data fusion and cross-spectral analysis, forming a complete, closed-loop feedback temperature measurement system.

[0052] Preferably, in the temperature inversion step, a temperature inversion model established based on offline calibration data is used, and the temperature inversion model uses quadratic polynomial fitting to convert the calibrated phase data into temperature values, thereby generating preliminary temperature data.

[0053] In the temperature inversion step, the system uses a temperature inversion model based on offline calibration data to convert the calibrated phase data into temperature values, thereby generating preliminary temperature data. Specifically, in the offline calibration stage, the experimenter collects calibrated phase data under known temperature conditions to establish a mapping relationship between temperature and phase change. This mapping relationship is usually modeled using a quadratic polynomial fitting method, and its mathematical expression is: , in, Indicates the temperature value, It indicates the phase shift in the calibrated phase data reflecting the temperature change. , as well as They are fitting coefficients obtained through offline calibration. In practical applications, the system inputs the calibrated phase data into the temperature inversion module, which calculates according to the above quadratic polynomial model, converts the phase offset of each sampling point into the corresponding temperature value, and thus generates preliminary temperature data. To ensure the accuracy and adaptability of the model, sufficient sample data should be collected during offline calibration and cover the expected temperature range; meanwhile, during the operation of the system, the coefficients of the temperature inversion model can be updated by means of regular calibration or real-time feedback correction to ensure that the inversion results always reflect the real temperature changes.

[0054] The operable details of this step include: First, obtain the calibrated phase data through the data acquisition module, and its data form is a discrete sequence of phase values; Second, perform operations on this phase data according to the preset quadratic polynomial model to calculate the temperature value of each sampling point; Third, the output preliminary temperature data is a sequence of temperature values corresponding to each sampling point, which can be directly used for subsequent optimization processing. In the embodiment, if the calibrated phase data of a certain sampling point is , then the corresponding temperature value can be calculated by the formula .

[0055] In practical applications, the digital signal processing module performs this quadratic fitting operation through the built-in algorithm and outputs the temperature data in real time on the high-speed processor. The advantage of using the quadratic polynomial fitting method for the temperature inversion model is that its computational complexity is low and it can better describe the nonlinear relationship between temperature and phase changes in most cases. In addition, the model structure is simple, which is convenient for offline calibration and online update, thus ensuring the inversion accuracy and facilitating hardware implementation in actual temperature measurement. Through this temperature inversion step, the preliminary temperature data reflects the temperature information in the calibrated phase data, but due to possible measurement errors and noises in the actual environment, there may still be certain uncertainties in the preliminary temperature data. Therefore, this step lays a foundation for further optimizing the preliminary temperature data by using multi-dimensional correlation analysis later to ensure that the finally output temperature data has high precision and low noise.

[0056] Preferably, in the multi-dimensional correlation analysis step, cross-spectrum analysis is performed on the preliminary temperature data and the modulation coding feature data, the nonlinear correlation between the two is extracted through normalization processing and correlation coefficient calculation, and the random sample consensus algorithm is used to robustly optimize the preliminary temperature data, so as to obtain the corrected temperature data.

[0057] In the multi-dimensional correlation analysis step, the system further performs a joint analysis on the preliminary temperature data and the modulation coding feature data to extract the non-linear correlation between the two, and uses the Random Sample Consensus (RANSAC) algorithm to robustly optimize the preliminary temperature data, thereby obtaining the corrected temperature data. First, the preliminary temperature data is generated by the temperature inversion step and represents the temperature estimate obtained after quadratic polynomial fitting. However, due to factors such as system noise, optical fiber transmission loss, and environmental interference, there may still be certain random errors in the preliminary temperature data. To further eliminate these interference factors, the system performs cross-spectrum analysis on the preliminary temperature data and the modulation coding feature data obtained in the multi-scale dynamic modulation coding step. Cross-spectrum analysis is a method for detecting the correlation between two sets of signals in the frequency domain. By normalizing the preliminary temperature data and the modulation coding feature data and calculating the correlation coefficient, the non-linear correlation information between the two can be extracted, thereby effectively identifying the frequency domain features related to temperature changes. Assume the preliminary temperature data is , and the modulation coding feature data is . Then the cross-spectrum function can be expressed as: , where represents the complex conjugate of the modulation coding feature data, and represents the amplitude. By calculating the cross-spectrum function, a correlation coefficient can be obtained, which reflects the correlation between the preliminary temperature data and the modulation coding feature data at each frequency domain sampling point. Subsequently, the system uses the Random Sample Consensus (RANSAC) algorithm to robustly optimize the preliminary temperature data. This algorithm searches for temperature data consistent with the cross-spectrum analysis results among a large number of random samples, thereby removing outliers and noise interference and optimizing the accuracy of the preliminary temperature data. The core of the Random Sample Consensus (RANSAC) algorithm lies in screening and clustering the data set through multiple iterations to obtain a set of temperature data with high cohesion, denoted as , and this data is the corrected temperature data after robust optimization.

[0058] The combined method of multi-dimensional correlation analysis and robust optimization has significant advantages. It not only utilizes the stability presented by modulation coding features in the frequency domain but also reveals the non-linear correlation information in temperature data through cross-spectrum analysis, thereby effectively reducing noise interference and random errors in complex environments. In actual operation, this step requires the digital signal processing module to have high-speed computing capabilities and preprocess, normalize, and calculate the correlation coefficients of the collected data. For example, if the correlation coefficient between the preliminary temperature data and the modulation coding feature data at a certain frequency domain sampling point reaches 0.85, the system considers the temperature data at this point to be relatively reliable; if the correlation coefficient is low, the system will eliminate the data at this point through the random sample consensus algorithm. Finally, through multi-dimensional correlation analysis and robust optimization, the corrected temperature data output by the system can not only accurately reflect temperature changes but also has high robustness and consistency, providing high-quality temperature information for the entire temperature measurement system. The implementation process of this step requires careful debugging of the normalization method involved in cross-spectrum analysis, the correlation coefficient calculation formula, and the parameters of the random sample consensus algorithm to ensure stable effects under various measurement conditions, thereby providing a solid data foundation for the subsequent feedback of temperature data and the overall closed-loop control of the system.

[0059] Preferably, the calibrated phase data is monitored in real time through a closed-loop feedback system. The closed-loop feedback system transmits the filtering residuals generated during the calibration process to the multi-scale dynamic modulation coding and virtual reference channel construction steps and dynamically adjusts the modulation parameters and virtual reference construction parameters using the online least mean square error algorithm.

[0060] In the subsequent process of the temperature inversion step, to further ensure the long-term stability and high precision of the temperature measurement system, the present invention designs a closed-loop feedback system to monitor the calibrated phase data in real time and transmits the filtering residuals generated during the calibration process to the multi-scale dynamic modulation coding and virtual reference channel construction steps, thereby realizing online adaptive adjustment of parameters. Specifically, when the system performs temperature inversion, it uses a pre-established temperature inversion model to convert the calibrated phase data into preliminary temperature data, and then outputs corrected temperature data after multi-dimensional correlation analysis optimization. During this process, any errors caused by external environmental changes or system drift will be manifested as residual signals in the filter. Let the residual signal output by the filter be , the residual signal reflects the error situation of the extended Kalman filter during the adaptive calibration process. When it is detected that the residual signal exceeds a preset threshold, the closed-loop feedback system will automatically transmit this information to the multi-scale dynamic modulation coding module and the virtual reference channel construction module at the front end. The specific feedback mechanism is based on the online least mean square error algorithm, which adjusts the laser signal modulation parameters and the virtual reference construction parameters by calculating the error between the currently output temperature of the system and the auxiliary temperature reference data in real time. The feedback control process can be expressed by the following mathematical expression: , where, represents the parameter to be adjusted (such as the sine wave modulation depth, the pulse repetition period, or the clustering center parameter of the virtual reference construction), represents the current temperature error, is the learning rate. Through this iterative adjustment mechanism, the system can optimize the front-end parameter settings in real time, so that the modulation coding features in the frequency-domain data of the optical fiber to be measured are kept in the best match with the data of the reference channel, and further enable the extended Kalman filter to correct the phase error more accurately during the adaptive calibration process.

[0061] In practical applications, the closed-loop feedback system requires data acquisition, processing, and feedback control to be completed within milliseconds to ensure that the system can quickly respond to environmental changes. Hardware-wise, high-speed data processors and real-time control units are needed, and software-wise, the online least mean square error algorithm and the dynamic gradient descent adjustment mechanism need to be implemented. The parameters after feedback adjustment are immediately applied to laser modulation and virtual reference data construction, forming a dynamically adaptive closed-loop control system, so that the entire temperature measurement process can still maintain the high precision and stability of the output temperature data in the face of laser drift, environmental noise, or changes in optical fiber transmission loss. Overall, this closed-loop feedback and online adaptive adjustment mechanism effectively compensates for the drift and error accumulation problems that may occur in traditional open-loop systems during long-term operation by monitoring the filtering residuals in real time and using the online least mean square error algorithm to dynamically correct the front-end parameters, providing extremely high stability and robustness for the entire optical fiber temperature measurement system. Through actual experimental verification, this mechanism can operate stably under various working conditions, ensure that the temperature inversion results are always consistent with the actual environmental temperature, and significantly improve the practical application performance of the system.

[0062] Preferably, the temperature inversion step further includes iteratively adjusting the temperature inversion model parameters using the online least mean square error algorithm, and the online least mean square error algorithm uses the error between the auxiliary temperature reference data and the temperature value obtained by converting the calibrated phase data as feedback to reduce the error and improve the accuracy of temperature inversion.

[0063] The temperature inversion step further iteratively adjusts the parameters of the temperature inversion model through an online least mean square error algorithm to reduce the error between the auxiliary temperature reference data and the temperature value converted from the calibrated phase data, thereby improving the accuracy of temperature inversion. Specifically, in the preliminary temperature inversion step, the system uses a temperature inversion model established based on offline calibration data and adopts a quadratic polynomial fitting method to convert the calibrated phase data into preliminary temperature data. The model can be expressed as , where, represents the temperature value, represents the phase shift reflecting temperature change in the calibrated phase data, , and are the fitting coefficients determined during offline calibration. However, during actual operation, due to the influence of external environment, equipment aging or other non-ideal factors, there may be certain deviations in this temperature inversion model. Therefore, the system introduces an online least mean square error algorithm to iteratively adjust the parameters of the temperature inversion model. The online least mean square error algorithm continuously calculates the error between the auxiliary temperature reference data and the temperature value converted from the calibrated phase data, and then uses this error to update the model parameters in a gradient descent manner. The formula can be expressed as: , where, represents the th coefficient in the temperature inversion model, represents the mean square value of the temperature error, is the learning rate. This online adjustment mechanism enables the temperature inversion model to adaptively correct parameter deviations, ensuring that the error between the converted temperature value and the actual measured value of the auxiliary temperature reference data is as small as possible. During implementation, the system will periodically or real-time obtain the auxiliary temperature reference data, compare it with the current temperature value calculated by the model, and use the calculated error as feedback input to the online least mean square error algorithm to continuously update the model parameters. The specific operation steps include: First, collect the calibrated phase data and calculate the preliminary temperature data using the initial temperature inversion model; Second, obtain the auxiliary temperature reference data and calculate the error between the preliminary temperature data and the reference data; Third, use the error calculation result to update the coefficients of the temperature inversion model through the gradient descent method; Finally, recalculate the temperature value using the updated model and continue to iterate until the error converges to the preset allowable range.

[0064] In practical applications, this online minimum mean square error algorithm requires the system to have efficient data acquisition and processing capabilities, while ensuring that the update frequency is high enough to cope with environmental changes. Through actual implementation examples, it is verified that online iterative adjustment can not only significantly reduce the system error of temperature inversion, but also maintain the stability and accuracy of temperature data output during long-term monitoring. This mechanism enables the temperature inversion model to dynamically adapt to changes in external conditions, form a closed-loop feedback, and work together with other modules (such as multi-scale dynamic modulation coding and virtual reference channel construction) to further improve the performance of the overall optical fiber temperature measurement system. Through this method of online iterative adjustment of parameters, the system realizes high-precision conversion from calibrated phase data to temperature values, provides real-time adaptive correction function for the entire temperature measurement process, and ensures that the temperature inversion results can maintain high accuracy and low error in various complex environments.

[0065] like Figure 4 As shown, an optical fiber temperature measurement system is used to implement the optical fiber temperature measurement method, and the system includes: Laser module, used to output laser signals that have been dynamically modulated and encoded at multiple scales; the system is equipped with a laser module. This module uses a high-coherence, frequency-adjustable continuous wave laser, which outputs laser signals that have been dynamically modulated and encoded at multiple scales after precise control. In the hardware design, the laser drive circuit works in conjunction with the digital signal processor, and the superposition of sinusoidal wave modulation and pulse coding is achieved through the built-in modulation circuit. Specifically, a high-speed digital signal processor is embedded in the laser module, which generates a modulation signal, and the frequency of the sinusoidal wave modulation part is set to meet 1000 Hz ≤ ≤5000 Hz, and generate pulse signals through a dedicated pulse coding circuit, with a pulse width between 5 microseconds and 50 microseconds, and set the pulse repetition period according to the temperature response characteristics of the environment to be measured. The output laser signal has both sinusoidal modulation and pulse coding characteristics in the time domain, and can be displayed as a fixed frequency component and periodic pulse spectrum in the frequency domain, providing obvious identification information for subsequent signal processing.

[0066] An optical path distribution module is used to evenly split the modulated laser signal and inject it into the fiber under test and the reference fiber respectively. The fiber under test is used to transmit to the object to be measured, and the reference fiber is placed in a temperature-controlled and low-vibration environment. The optical path distribution module is responsible for evenly splitting the modulated laser signal output by the laser module and injecting it into two different fiber channels. This module uses a low-loss and high-precision optical splitter to divide the modulated signal into two parts: one part is injected into the fiber under test, which directly transmits to the area where the object to be measured is located, for collecting the change of the optical signal caused by the environmental temperature change; the other part is injected into the reference fiber, which is placed in a temperature-controlled and low-vibration environment, for collecting a stable reference optical signal to provide a system calibration reference. When designing the optical path distribution module, it is necessary to ensure that the two optical signals have the same optical power and phase stability, and at the same time, the selection of devices such as fiber optic connectors and couplers is strictly required to avoid introducing additional interference due to device errors.

[0067] A signal detection module is used to collect the return optical signals of the fiber under test and the reference fiber and convert the optical signals into digital time-domain data. The signal detection module of the system uses a high-speed optical receiver and a high-precision analog-to-digital converter (ADC) to collect the return optical signals of the fiber under test and the reference fiber. The optical receiver converts the optical signal into an electrical signal, which is then amplified by a low-noise amplifier, and then the ADC converts the analog signal into digital time-domain data. In terms of hardware, this module usually requires the sampling rate of the ADC to be not less than 100 MHz and the resolution to be not less than 14 bits to ensure that the collected digital data has sufficient dynamic range and resolution to completely capture the subtle changes in the modulated coding signal. The data output of the signal detection module is the digital time-domain data of the two channels, providing the original input for subsequent digital signal processing.

[0068] A digital signal processing module is used to perform a fast Fourier transform on the digital time-domain data, and generate frequency-domain data containing modulation coding characteristics after being weighted by a Hanning window; the digital signal processing module is responsible for performing a fast Fourier transform (FFT) on the collected digital time-domain data. This module internally integrates a high-speed digital signal processor (DSP) or a field-programmable gate array (FPGA) to achieve real-time data processing. This module first applies a Hanning window weighting process to the time-domain data.

[0069] A virtual reference channel construction module is used to demodulate and suppress noise on the frequency-domain data collected by the reference fiber, and fuse the demodulated data with pre-stored historical reference data to form virtual reference frequency-domain data reflecting the ideal reference state; the virtual reference channel construction module demodulates and suppresses noise on the frequency-domain data collected by the reference fiber, and fuses the demodulated data with pre-stored historical reference data to form virtual reference frequency-domain data reflecting the ideal reference state. In terms of hardware, this module uses a dedicated digital signal processor and a matched filter.

[0070] An adaptive calibration module, which is used to compare the frequency-domain data of the optical fiber under test with the virtual reference frequency-domain data at the same sampling position, recursively predict and update the initial phase error signal by using the extended Kalman filter combined with fuzzy logic, and perform online adaptive adjustment on the process noise covariance and the measurement noise covariance, so as to output the calibrated phase data reflecting the temperature change; the adaptive calibration module is mainly used to compare the frequency-domain data of the optical fiber under test with the virtual reference frequency-domain data at the same sampling position, recursively predict and update the initial phase error signal by using the adaptive calibration algorithm, and then output the calibrated phase data reflecting the temperature change. This module is usually composed of a high-performance embedded processor, which has an extended Kalman filter and a fuzzy logic controller built in.

[0071] A temperature inversion module, which is used to convert the calibrated phase data into a temperature value based on a pre-established temperature inversion model, and optimize the preliminary temperature data by using multi-dimensional correlation analysis to output high-precision temperature distribution data; the temperature inversion module performs temperature inversion on the calibrated phase data based on a pre-established temperature inversion model, and optimizes the preliminary temperature data by using multi-dimensional correlation analysis to output high-precision temperature distribution data. In terms of hardware, this module is composed of a dedicated embedded processor and a memory, and its temperature inversion model usually adopts the quadratic polynomial fitting method. The calibrated phase data is substituted into the above model by a digital signal processor to calculate the preliminary temperature data. Then, the temperature inversion module performs cross-spectrum analysis on the preliminary temperature data and the modulation coding feature data by using multi-dimensional correlation analysis, extracts the non-linear correlation between the two through normalization and correlation coefficient calculation, and then uses the random sample consensus algorithm to perform robust optimization on the preliminary temperature data, and finally outputs the corrected temperature data. The hardware implementation of this module requires high computing power and storage bandwidth to process large-scale data in real time, and continuously updates and optimizes the model parameters through the built-in algorithm to ensure the high precision and stability of the temperature data.

[0072] The closed-loop feedback control module is used to receive in real time the error information output by the adaptive calibration module and the temperature inversion module, and feedback the error information to the laser module, the virtual reference channel construction module and the adaptive calibration module, so as to realize the dynamic adaptive adjustment of the parameters of each module. The closed-loop feedback control module is used to realize the dynamic adaptive adjustment of the system parameters. This module receives in real time the error information output by the adaptive calibration module and the temperature inversion module, such as the filtering residual and the temperature error, and then calculates the adjustment amount through the online least mean square error algorithm. The closed-loop feedback control module transmits the calculated adjustment amount to the laser module, the virtual reference channel construction module and the adaptive calibration module in real time, so as to realize the collaborative online adaptive adjustment of the parameters of each module. In terms of hardware, this module is usually composed of a low-latency controller and a high-speed data communication interface to ensure that the feedback information is transmitted and applied to the front-end parameter adjustment within milliseconds, so that the entire system can still maintain high precision and stability when the environment and system state change.

[0073] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects.

[0074] The above are only the embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. An optical fiber temperature measurement method, characterized in that: The steps include: The laser signal with multi-scale dynamic modulation coding is injected into the optical fiber to be tested and the reference optical fiber respectively, and the optical signal of each channel is collected and then subjected to fast Fourier transform to generate frequency domain data containing modulation coding characteristics; A virtual reference channel is constructed, wherein the reference optical fiber acquisition frequency domain data is demodulated and denoised, and the demodulated data is fused with the pre-stored historical data to form virtual reference frequency domain data, and then the optical fiber frequency domain data to be tested is compared with the virtual reference frequency domain data at the same sampling position, and adaptive calibration is performed using an adaptive calibration algorithm, wherein the adaptive calibration algorithm uses an extended Kalman filter combined with fuzzy logic to correct the optical fiber signal to be tested to obtain calibrated phase data reflecting temperature changes; The temperature inversion of the calibrated phase data is performed based on the pre-established temperature inversion model, and the preliminary temperature data is optimized using multidimensional correlation analysis to output high-precision temperature distribution data.

2. The optical fiber temperature measurement method according to claim 1, characterized in that: In the multi-scale dynamic modulation coding step, a high-speed digital signal processor is used to perform sinusoidal wave modulation on the laser output signal. The frequency of the sinusoidal wave modulation is set to be not less than 1000 Hz and not more than 5000 Hz. At the same time, the pulse signal is superimposed through a dedicated pulse coding circuit. The pulse width is set to be not less than 5 microseconds and not more than 50 microseconds. The pulse repetition period is determined according to the temperature response characteristics of the environment to be measured, thereby generating a laser signal containing a fixed frequency component and a pulse coding component in the spectrum. The laser signal is used to extract temperature change identification in subsequent frequency domain data.

3. The optical fiber temperature measurement method according to claim 1, characterized in that: In the step of performing fast Fourier transform after collecting the optical signal, Hanning window weighted processing is first applied to the collected time domain data, and then Fourier transform is performed using a digital signal processing algorithm to generate frequency domain data, thereby ensuring that the modulation coding features are retained in the frequency domain data.

4. The optical fiber temperature measurement method according to claim 1, characterized in that: In the step of constructing a virtual reference channel, the frequency domain data collected by the reference optical fiber is demodulated and noise suppressed using a digital matched filter, and the demodulated data is fused with the pre-stored historical reference data using a self-organizing mapping algorithm combined with a fuzzy clustering algorithm to form virtual reference frequency domain data that reflects the ideal reference state.

5. The optical fiber temperature measurement method according to claim 1, characterized in that: In the adaptive calibration step, the frequency domain data of the optical fiber to be tested is compared with the virtual reference frequency domain data at the same sampling position to calculate the initial phase error signal, and the extended Kalman filter combined with fuzzy logic is used to recursively predict and update the initial phase error signal. At the same time, the process noise covariance and the measurement noise covariance are adaptively adjusted online to obtain calibrated phase data that accurately reflects temperature changes.

6. The optical fiber temperature measurement method according to claim 1, characterized in that: In the temperature inversion step, a temperature inversion model established based on offline calibration data is used. The temperature inversion model uses quadratic polynomial fitting to convert the calibrated phase data into temperature values, thereby generating preliminary temperature data.

7. The optical fiber temperature measurement method according to claim 1, characterized in that: In the multidimensional correlation analysis step, cross-spectral analysis is performed on the preliminary temperature data and the modulation coding feature data, and the nonlinear correlation between the two is extracted through normalization processing and correlation coefficient calculation. The preliminary temperature data is robustly optimized using a random sampling consistency algorithm to obtain the corrected temperature data.

8. The optical fiber temperature measurement method according to claim 1, characterized in that: The calibrated phase data is monitored in real time through a closed-loop feedback system. The closed-loop feedback system transmits the filtering residuals generated during the calibration process to the multi-scale dynamic modulation coding and virtual reference channel construction steps, and uses an online minimum mean square error algorithm to perform dynamic adaptive adjustment on the modulation parameters and virtual reference construction parameters.

9. The optical fiber temperature measurement method according to claim 1, characterized in that: The temperature inversion step further includes iteratively adjusting the temperature inversion model parameters using an online minimum mean square error algorithm, wherein the online minimum mean square error algorithm uses the error between the temperature value converted from the auxiliary temperature reference data and the calibrated phase data as feedback to reduce the error and improve the accuracy of the temperature inversion.

10. An optical fiber temperature measurement system, used to implement the optical fiber temperature measurement method according to any one of claims 1 to 9, characterized in that: The system includes: A laser module, used for outputting a laser signal that has been dynamically modulated and encoded at multiple scales; An optical path distribution module, used for equally dividing the modulated laser signal and injecting it into the optical fiber to be tested and the reference optical fiber respectively, wherein the optical fiber to be tested is used for transmitting to the object to be tested, and the reference optical fiber is placed in a temperature-controlled and low-vibration environment; A signal detection module, used to collect the return optical signals of the optical fiber to be tested and the reference optical fiber, and convert the optical signals into digital time domain data; A digital signal processing module, used for performing a fast Fourier transform on the digital time domain data, and generating frequency domain data containing modulation coding features after Hanning window weighted processing; A virtual reference channel construction module is used to demodulate and suppress noise on the frequency domain data collected by the reference optical fiber, and fuse the demodulated data with the pre-stored historical reference data to form virtual reference frequency domain data reflecting an ideal reference state; The adaptive calibration module is used to compare the frequency domain data of the optical fiber to be tested with the virtual reference frequency domain data at the same sampling position, recursively predict and update the initial phase error signal using the extended Kalman filter combined with fuzzy logic, and perform online adaptive adjustment on the process noise covariance and the measurement noise covariance, thereby outputting calibrated phase data reflecting temperature changes; A temperature inversion module, used to convert the calibrated phase data into temperature values ​​based on a pre-established temperature inversion model, and optimize the preliminary temperature data using multi-dimensional correlation analysis to output high-precision temperature distribution data; The closed-loop feedback control module is used to receive the error information output by the adaptive calibration module and the temperature inversion module in real time, and feed back the error information to the laser module, the virtual reference channel construction module and the adaptive calibration module to achieve dynamic adaptive adjustment of the parameters of each module.

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