Optical fiber sensing monitoring system suitable for complex environment

Abstract

The invention discloses an optical fiber sensing monitoring system suitable for a complex environment, and aims to solve the problems of insufficient demodulation precision, temperature-strain cross sensitivity, lack of a dynamic environment disturbance real-time compensation mechanism and fixed and rigid alarm strategy of a traditional optical fiber sensing system under a strong noise background. Comprising a broadband light source module, an optical fiber sensing array module, a signal demodulation and acquisition module, an adaptive signal processing module, an intelligent discrimination and alarm module and a system control and data management module. Through an adaptive signal processing chain integrating multi-scale noise suppression, cross-sensitive decoupling and dynamic disturbance compensation, the measurement precision and long-term stability of the system in strong noise, multi-physical field coupling and dynamic disturbance environments are significantly improved, the strain measurement error is reduced to + / -2 [mu] epsilon, and the temperature measurement error is reduced to + / -0.5 DEG C. A self-adaptive alarm threshold mechanism based on environment complexity perception is adopted, the limitation of a fixed threshold strategy is overcome, false alarm and missing alarm are reduced, and the reliability of a monitoring result is improved.

Description

Technical Field

[0001] This invention relates to the field of fiber optic sensing technology, and in particular to a fiber optic sensing and monitoring system suitable for complex environments. Background Technology

[0002] Fiber optic sensing technology, due to its advantages such as resistance to electromagnetic interference, intrinsic safety, distributed sensing, and long-distance monitoring, has been widely applied in the health monitoring of critical infrastructure in civil engineering, energy and power, rail transportation, and national defense. In complex environmental conditions, such as strong vibration, high humidity, extreme temperatures, or chemically corrosive environments, the need for real-time and accurate sensing of structural deformation, temperature field distribution, and stress state is increasingly urgent. This makes high robustness, high sensitivity, and adaptive compensation capability the core performance indicators of fiber optic sensing systems.

[0003] Among them, fiber optic sensing and monitoring systems suitable for complex environments aim to achieve stable and reliable extraction of measured physical quantities through multi-parameter fusion demodulation and environmental disturbance suppression mechanisms. These systems are typically based on principles such as Brillouin scattering, Raman scattering, or fiber grating reflection spectra, utilizing the frequency shift, intensity, or phase changes caused by the modulation of the optical signal by the external environment during propagation in the optical fiber to invert environmental parameters. However, their actual deployment effectiveness is highly dependent on the signal-to-noise ratio of the sensing signal, the stability of the demodulation algorithm, and the system's ability to handle cross-sensitivity issues.

[0004] Existing technologies generally suffer from the following prominent problems: First, traditional demodulation methods, such as edge filtering or matched filtering, are prone to significant errors in strong noise environments, making it difficult to meet the accuracy requirements under complex working conditions. Second, most systems lack an effective temperature-strain cross-sensitivity decoupling model, leading to measurement distortion in multi-physics coupled environments. Third, there is a lack of real-time compensation mechanisms for dynamic environmental disturbances (such as random vibrations or transient temperature changes), resulting in insufficient long-term system stability. Finally, existing architectures typically employ fixed threshold alarm strategies, failing to adaptively adjust sensitivity and response logic based on environmental complexity. These deficiencies make it difficult for existing fiber optic sensing systems to achieve high-confidence continuous monitoring in high-risk and complex scenarios such as tunnels, bridges, and oil and gas pipelines. Therefore, a new monitoring system with environmental adaptability, multi-parameter decoupling capabilities, and intelligent discrimination mechanisms is urgently needed. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a fiber optic sensing and monitoring system suitable for complex environments. This system can effectively solve the problems of insufficient demodulation accuracy, temperature-strain cross-sensitivity, lack of real-time compensation mechanism for dynamic environmental disturbances, and fixed and rigid alarm strategies in traditional fiber optic sensing systems in the background.

[0006] To achieve the above objectives, the present invention provides the following technical solution: On one hand, a fiber optic sensing monitoring system suitable for complex environments, comprising the following components: a broadband light source module for generating continuous optical signals covering a specific wavelength band; a fiber optic sensing array module, consisting of different types of fiber optic sensors arranged in a preset topology within the measured environment, for sensing environmental physical quantities and modulating them into optical signal characteristic changes; a signal demodulation and acquisition module connected to the fiber optic sensing array module for receiving and converting the optical signal characteristic changes into digital electrical signals, and performing preliminary filtering and amplification on the digital electrical signals; an adaptive signal processing module, with its input connected to the signal demodulation and acquisition module, for performing multi-scale noise suppression, cross-sensitivity decoupling, and dynamic disturbance compensation processing on the digital electrical signals; an intelligent discrimination and alarm module, with its input connected to the adaptive signal processing module, for performing state evaluation based on the processed signal characteristics and generating adaptive alarm commands; and a system control and data management module, which communicates bidirectionally with the broadband light source module, signal demodulation and acquisition module, adaptive signal processing module, and intelligent discrimination and alarm module, for coordinating the operation of each system component, configuring operating parameters, and storing monitoring data.

[0007] Preferably, the broadband light source module adopts a superluminescent diode or an erbium-doped fiber light source, with an output spectral width greater than 40 nm, a center wavelength stability better than ±0.02 nm / ℃, and an output power fluctuation of less than 0.05 dB. The module integrates a temperature control unit and a power feedback unit, and adjusts the drive current and thermoelectric cooler operating status in real time through a proportional-integral-derivative controller to ensure the long-term power and spectral shape stability of the output optical signal.

[0008] Furthermore, the fiber optic sensing array module includes distributed fiber optic sensors and quasi-distributed fiber Bragg grating sensors. The distributed fiber optic sensors are based on stimulated Brillouin scattering, and their sensing fibers are specially designed with polyimide coating to enhance mechanical strength and temperature sensitivity, achieving a spatial resolution of 1 meter, a strain measurement accuracy of ±2με, and a temperature measurement accuracy of ±0.5℃. The quasi-distributed fiber Bragg grating sensor array is composed of multiple fiber Bragg gratings connected in series with different Bragg wavelengths. The reflectivity of each fiber Bragg grating is between 3% and 5%, and the wavelength spacing is not less than 0.5nm. It is encapsulated in a metal capillary or polymer matrix to improve its compressive and corrosion resistance.

[0009] Furthermore, the signal demodulation and acquisition module includes a photodetector array, a transimpedance amplifier, an anti-aliasing filter, and a high-speed analog-to-digital converter. The photodetector array uses InGaAs material with a responsivity greater than 0.9 A / W and a 3dB bandwidth of not less than 100 MHz. The transimpedance amplifier has a programmable gain ranging from 1 kΩ to 1 MΩ to accommodate input photocurrents of varying intensities. The anti-aliasing filter is an 8th-order Chebyshev low-pass filter with a cutoff frequency dynamically set based on the highest frequency component of the signal. The high-speed analog-to-digital converter has a resolution of not less than 16 bits and a sampling rate of up to 250 MSPS, ensuring high-fidelity digitization of weak signals.

[0010] Preferably, the adaptive signal processing module integrates a digital signal processor and a field-programmable gate array (FPGA). The processing algorithms running inside include: a multi-scale noise suppression unit based on wavelet packet transform and improved empirical mode decomposition, used to separate the noise intrinsic mode functions in the signal; a temperature-strain cross-sensitivity decoupling unit based on dual-parameter matrix operation and support vector regression machine, used to establish and solve the independent mapping relationship between strain and temperature; and a dynamic disturbance compensation unit based on Kalman filter and long short-term memory neural network, used to predict and cancel signal drift caused by random vibration or transient temperature change.

[0011] Furthermore, the intelligent discrimination and alarm module includes a feature extraction unit, a pattern recognition unit, and a decision logic unit. The feature extraction unit extracts time-domain statistical features, frequency-domain energy features, and joint time-frequency features from the adaptively processed signal. The pattern recognition unit uses a support vector machine classifier based on radial basis function kernels to classify the extracted feature vectors into three states: normal, warning, and abnormal. The decision logic unit dynamically adjusts the alarm threshold and response delay time based on the current environmental complexity index and historical state sequences. Its adaptive threshold adjustment follows the formula: ,in The final alarm threshold, Based on the threshold, For environmental noise variance, The rate of change of the signal trend. and The weighting coefficients are determined through training with historical data.

[0012] In addition, the system control and data management module runs an embedded real-time operating system, providing a graphical user interface for parameter configuration and status monitoring; the module has a built-in relational database for storing raw sensor data, processing intermediate results, alarm event logs, and system operation logs; at the same time, the module integrates a network communication interface based on Transmission Control Protocol / Internet Protocol, supporting remote data access and firmware online upgrade functions.

[0013] On the other hand, a fiber optic sensing monitoring method suitable for complex environments includes the following steps: Step S110, injecting a probe light signal into a fiber optic sensing array deployed in the monitoring area using a broadband light source, and receiving backscattered or reflected light signals generated by environmental physical quantities modulation; Step S120, converting the received light signal into a digital electrical signal using a signal demodulation and acquisition unit, and performing preliminary signal conditioning, including gain adjustment and band-limited filtering; Step S130, inputting the digital electrical signal into an adaptive signal processing unit, and sequentially performing multi-scale noise suppression processing to improve the signal-to-noise ratio and temperature-susceptibility ratio. The process includes: variable cross-sensitivity decoupling to separate strain and temperature components, and dynamic disturbance compensation to suppress transient environmental interference; step S140: extracting multi-dimensional feature vectors based on the adaptively processed signal and inputting them into a pre-trained classification model for state identification and risk assessment; step S150: dynamically calculating alarm thresholds and generating corresponding alarm commands and maintenance suggestions based on the identified state category and environmental complexity; step S160: storing all data, processing results, and alarm events during the monitoring process locally, and supporting remote data transmission and system status query via wired or wireless networks.

[0014] The beneficial effects of this invention are:

[0015] 1. By integrating an adaptive signal processing chain that combines multi-scale noise suppression, cross-sensitive decoupling, and dynamic disturbance compensation, the measurement accuracy and long-term stability of the system under strong noise, multi-physics coupling, and dynamic disturbance environments are significantly improved. The strain measurement error is reduced to ±2με, and the temperature measurement error is reduced to ±0.5℃.

[0016] 2. An adaptive alarm threshold mechanism based on environmental complexity awareness is adopted, which overcomes the limitations of the fixed threshold strategy, enabling the system to flexibly adjust sensitivity according to actual working conditions, reduce false alarms and missed alarms, and improve the reliability of monitoring results.

[0017] 3. The system architecture is highly modular, compatible with distributed and quasi-distributed sensing, and has strong scalability and maintainability. It can adapt to the long-term, high-confidence continuous monitoring needs of various complex and high-risk scenarios such as tunnels, bridges, and oil and gas pipelines. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the overall technical solution architecture of the present invention;

[0019] Figure 2 This is a schematic diagram of the core principle framework of adaptive signal processing and intelligent judgment alarm in this invention. Detailed Implementation

[0020] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. The illustrative embodiments and descriptions herein are used to explain the present invention, but are not intended to limit the present invention.

[0021] Example 1:

[0022] In long-distance oil and gas pipeline monitoring scenarios, this system uses fiber optic sensor array modules to continuously monitor the strain distribution and temperature field along the pipeline around the clock. (See also...) Figure 1 The overall system architecture comprises a broadband light source module, a fiber optic sensor array module, a signal demodulation and acquisition module, an adaptive signal processing module, an intelligent discrimination and alarm module, and a system control and data management module. The broadband light source module uses an erbium-doped fiber optic light source as its core light-emitting device, with an output spectrum covering the 1520 nm to 1560 nm band, a spectral width of 45 nm, and a stable center wavelength of 1540 nm. The integrated temperature control unit within this module actively controls the laser chip temperature via a thermoelectric cooler, achieving a temperature control accuracy of ±0.1 degrees Celsius. Simultaneously, the power feedback unit monitors the output optical power in real time and adjusts the drive current via a proportional-integral-derivative controller to ensure the output power remains stable within the range of 10 milliwatts ±0.03 dB.

[0023] The fiber optic sensing array module is laid along the outer wall of the oil and gas pipeline and includes two types: distributed fiber optic sensors and quasi-distributed fiber optic grating sensors. The distributed fiber optic sensors are based on stimulated Brillouin scattering (SBS). Their sensing fibers employ a double-layer polyimide coating structure, with an inner coating thickness of 15 micrometers and an outer coating thickness of 25 micrometers. This special design allows the fiber to maintain temperature sensitivity while increasing its tensile strength to 100 klb / s². The sensing fibers are fixed to the pipeline surface in a helical winding manner with a winding spacing of 20 cm, ensuring a spatial resolution of 1 meter. The quasi-distributed fiber optic grating sensor array consists of 48 fiber gratings connected in series. These fiber gratings have a uniformly distributed Bragg wavelength from 1525 nm to 1545 nm, with an adjacent grating wavelength spacing of 0.5 nm. The reflectivity of each grating is controlled at 4% ± 0.5%. The fiber grating is encapsulated in a 316L stainless steel capillary tube with an outer diameter of 1.2 mm and an inner diameter of 0.8 mm. Both ends are sealed by laser welding to ensure structural integrity even when the pressure difference between the inside and outside of the pipe reaches 10 MPa.

[0024] The signal demodulation and acquisition module is connected to the fiber optic sensing array module via a single-mode fiber optic patch cord. The photodetector array inside this module is made of indium gallium arsenide (IGaAs), with each detector unit having a photosensitive surface diameter of 80 micrometers, a responsivity of 0.95 amperes per watt at a wavelength of 1550 nanometers, and a 3 dB bandwidth of 120 MHz. The transimpedance amplifier employs a programmable gain architecture, with 256 adjustable gain levels ranging from 1 kiloohm to 1 megaohm, automatically selecting the optimal gain value based on the input photocurrent intensity. The anti-aliasing filter uses an 8th-order Chebyshev low-pass filter design, with the cutoff frequency dynamically adjusted within the range of 1 MHz to 50 MHz based on the highest frequency component of the signal. The passband ripple is less than 0.1 dB, and the stopband attenuation is greater than 80 dB. The high-speed analog-to-digital converter uses a successive approximation architecture, with a resolution of 18 bits and a maximum sampling rate of 250 megasamples per second. In normal operating mode, it digitizes the signal at a rate of 100 megasamples per second, achieving an effective bit depth of 16.5 bits.

[0025] The adaptive signal processing module integrates a digital signal processor and a field-programmable gate array (FPGA) to work together. See also... Figure 2The multi-scale noise suppression unit within this module first performs wavelet packet transform on the input digital electrical signal, then uses the db8 wavelet basis function for a 6-level decomposition, resulting in 64 sub-band signals. An improved empirical mode decomposition algorithm then processes each sub-band signal, introducing an adaptive noise complete set empirical mode decomposition method to decompose the signal into 12 intrinsic mode function components. The algorithm identifies the dominant noise component based on the sample entropy value of each component. The embedding dimension is set to 2 during sample entropy calculation (based on Takens' theorem's recommendation for low-dimensional system reconstruction), and the similarity tolerance is set to 0.2 times the signal standard deviation (referencing the empirical ratio of noise level to tolerance in "Nonlinear Time Series Analysis"). Threshold filtering is then applied to the identified noise components. The temperature-strain cross-sensitivity decoupling unit establishes an independent mapping relationship between strain and temperature based on dual-parameter matrix operations. This unit first constructs a 2xn matrix of sensing coefficients, where n is the number of sensing points. The matrix elements are determined through calibration experiments. The strain coefficient of the distributed fiber optic sensor is 0.05 MΩ per microstrain, and the temperature coefficient is 1.2 MΩ per degree Celsius. The strain sensitivity of the fiber optic grating sensor is 1.2 picometers per microstrain, and the temperature sensitivity is 10 picometers per degree Celsius. The support vector regression machine uses a radial basis function kernel with a kernel width parameter of 0.5 and a penalty factor of 100. A separation model for strain and temperature is obtained by solving the dual problem. The dynamic disturbance compensation unit combines a Kalman filter and a long short-term memory neural network. The Kalman filter state vector contains four state variables, representing the true strain value, the true temperature value, the strain rate of change, and the temperature rate of change, respectively. The observation noise covariance matrix is ​​determined through statistical analysis of historical data. The Long Short-Term Memory (LSTM) neural network has an input layer with 20 neurons, a hidden layer with 50 neurons, and an output layer with 2 neurons. The network uses observation data from the past 60 time steps as input to predict the signal drift trend for the next 10 time steps. During training, an adaptive moment estimation algorithm is used with an initial learning rate of 0.001 (decreasing to 0.9 times every 10 rounds) and a batch size of 32. The training set contains 5000 time-series samples (covering high / medium / low noise scenarios) and uses the mean squared error loss function. The validation set accounts for 20% of the total training time. The early stopping strategy is activated when the validation loss does not decrease for 5 consecutive rounds.

[0026] The intelligent discrimination and alarm module extracts multi-dimensional feature vectors from the adaptively processed signal. The feature extraction unit calculates time-domain statistical features including mean, variance, skewness, and kurtosis; frequency-domain energy features are calculated using Fast Fourier Transform (FFT) to determine the energy proportions of eight sub-bands within the 0-10 MHz frequency band; and joint time-frequency features are extracted using wavelet transform to extract wavelet coefficient energy at five scales. The pattern recognition unit uses a support vector machine classifier based on radial basis function kernels to classify the extracted feature vectors into three states: normal, warning, and abnormal. The decision logic unit dynamically adjusts the alarm threshold and response delay time based on the current environmental complexity index and historical state sequences. Its adaptive threshold adjustment follows the formula: ,in The final alarm threshold, The base threshold (preset based on the characteristics of the monitored object). The environmental noise variance is calculated by measuring the variance of the signals from the most recent N sampling points, where N=100. The rate of change of the signal trend (the slope is calculated by linear regression analysis of the most recent M sampling points, M=30). and The weighting coefficients are determined through training on historical datasets: collect K groups of labeled samples, use the least squares method to optimize α and β to minimize the false alarm rate, K≥1000.

[0027] The pattern recognition unit employs a support vector machine classifier based on a radial basis function kernel, with a kernel parameter of 0.1 and a penalty factor of 10. The classification model was trained using 5000 labeled samples, achieving a classification accuracy of 98.7% on the test set. The decision logic unit dynamically adjusts alarm thresholds based on environmental complexity indicators. Environmental noise variance is calculated using the signal variance of the most recent 100 sampling points, and the signal trend change rate is calculated using linear regression analysis of the slope value of the most recent 30 sampling points. The base thresholds in the adaptive threshold adjustment formula are set based on the pipe material characteristics: a base strain threshold of 150 microstrains and a base temperature threshold of 80 degrees Celsius for carbon steel pipes. Weighting coefficients are determined through training with historical data, with an environmental noise weighting coefficient of 0.6 and a signal trend weighting coefficient of 0.4. When the system identifies an abnormal state, it generates different response strategies based on the abnormality level. Level 1 abnormalities trigger audible and visual alarms and send SMS notifications; level 2 abnormalities automatically initiate emergency procedures and generate maintenance work orders.

[0028] The system control and data management module runs an embedded real-time operating system with a system clock accuracy of 1 microsecond. The internal relational database employs a multi-table structure. The raw data table stores raw sensor data in blocks per minute, with each block containing 6000 sampling points. The processing result table stores adaptively processed feature data. The alarm event table records the time, location, type, and processing status of all alarm events. The system operation log table records the operating status and performance indicators of each module. The network communication interface supports both Transmission Control Protocol (TCP) and User Datagram Protocol (UDP) transmission modes, defaulting to TCP for reliable data transmission. The data packet format uses a custom binary protocol, with each data packet containing an 8-byte header, a 1024-byte payload, and a 4-byte checksum. The graphical user interface provides real-time data visualization, displaying strain distribution curves along the pipeline as line graphs and temperature field distribution as heat maps. It also provides historical data query and report generation functions.

[0029] The monitoring method is executed according to predetermined steps: In step S110, the broadband light source module injects a probe light signal into the fiber optic sensing array laid on the surface of the pipe. When the light signal is transmitted in the sensing fiber, the pipe strain change causes a Brillouin frequency shift, and the temperature change causes a Brillouin power change. The Bragg wavelength of the fiber Bragg grating drifts with strain and temperature changes. The backscattered Brillouin light and the reflected light from the fiber Bragg grating carry this modulation information back to the signal demodulation and acquisition module. In step S120, the photodetector array converts the light signal into an analog electrical signal. The transimpedance amplifier automatically selects a gain of 500 kΩ based on the signal strength, the anti-aliasing filter is set to a cutoff frequency of 20 MHz, and the high-speed analog-to-digital converter digitizes the signal at a sampling rate of 100 megasamples per second. In step S130, the adaptive signal processing module sequentially performs multi-scale noise suppression, temperature strain cross-sensitivity decoupling, and dynamic disturbance compensation processing on the digital electrical signal, with the processing delay controlled within 50 milliseconds. In step S140, the intelligent discrimination and alarm module extracts a 25-dimensional feature vector from the processed signal and inputs it into a pre-trained classification model for state recognition. The model inference time is less than 10 milliseconds. In step S150, the system dynamically calculates the alarm threshold based on the current environmental noise level and signal change trend. When the local strain of the pipeline exceeds the threshold, a corresponding alarm command is generated, and the inspection personnel are advised to go to the designated station for detailed inspection. In step S160, the system stores complete monitoring data in a local database and uploads key alarm information and statistical reports to the monitoring center via industrial Ethernet, supporting remote clients to query system status and historical data in real time.

[0030] Example 2:

[0031] In the application of large-scale bridge structural health monitoring, this system performs distributed strain and temperature monitoring on key components such as the main girder, towers, and cables. The broadband light source module uses superluminescent diodes (SLEDs) with an output spectral width of 50 nanometers, a center wavelength of 1550 nanometers, and an output power of 15 milliwatts. The fiber optic sensor array module is arranged in a grid pattern on the surface of the main bridge structure with a grid spacing of 2 meters, and the grid is denser in stress concentration areas such as cable anchorage zones, with a spacing of 0.5 meters. The distributed fiber optic sensors employ a carbon fiber composite coating to enhance adhesion to the concrete structure. The quasi-distributed fiber optic grating sensor array is specifically designed for cable monitoring, with four fiber optic gratings deployed on each cable at 90-degree intervals to monitor the uniformity of cable stress.

[0032] To address the long monitoring distances of bridges, the signal demodulation and acquisition module employs coherent detection technology to improve the signal-to-noise ratio. The 3 dB bandwidth of the photodetector array is extended to 150 MHz, and the maximum gain of the transimpedance amplifier is increased to 2 megaohms. The anti-aliasing filter uses an elliptic filter design, with a passband ripple of less than 0.05 dB and a stopband attenuation of greater than 100 dB. The high-speed analog-to-digital converter is set to a sampling rate of 200 megasamples per second, maintaining an 18-bit resolution.

[0033] The adaptive signal processing module optimizes algorithm parameters for bridge vibration environments. The multi-scale noise suppression unit uses the sym8 wavelet basis function, increasing the decomposition level to 8 layers. The temperature-strain cross-sensitivity decoupling unit establishes mapping models for the different thermal expansion coefficients of steel and concrete structures. The dynamic disturbance compensation unit has been particularly enhanced to compensate for transient strains caused by traffic loads, and the input sequence length of the long short-term memory neural network has been extended to 100 time steps.

[0034] The intelligent discrimination and alarm module adds fatigue damage assessment functionality to address the specific characteristics of bridge monitoring. The feature extraction unit adds crack propagation features and vibration modal features. The pattern recognition unit employs a multi-class support vector machine to subdivide the bridge condition into four levels: normal, minor damage, moderate damage, and severe damage. In the adaptive threshold adjustment formula of the decision logic unit, the basic threshold is determined according to bridge design specifications, and the weighting coefficients are obtained through training with finite element model simulation data.

[0035] The system control and data management module includes a dedicated database table for bridges, storing bridge structural parameters, design load information, and historical inspection records. The network communication interface supports dual-link backup of 4G and fiber optic connections, ensuring communication reliability even in adverse weather conditions. The graphical user interface integrates a 3D model of the bridge, displaying real-time stress distribution cloud maps and deformation trends.

[0036] When the monitoring method is implemented, step S110 pays special attention to the impact of diurnal temperature variation and traffic load on strain monitoring. Step S120 adjusts the cutoff frequency of the anti-aliasing filter to 30 MHz based on the characteristics of bridge vibration signals. The dynamic disturbance compensation unit in step S130 focuses on handling instantaneous strain fluctuations caused by vehicle traffic. Step S140 adds fatigue damage index calculation based on Mainner's theorem for cumulative damage degree. Step S150 generates maintenance recommendations of different levels according to the degree of damage, ranging from routine inspections to graded responses for special tests. In addition to local storage, step S160 also uploads the data in real time to the information system of the bridge maintenance unit to support structural safety assessment and life prediction.

[0037] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

[0038] The technical solutions of the present invention are not limited to the specific embodiments described above. Any technical modifications made in accordance with the technical solutions of the present invention fall within the protection scope of the present invention.

Claims

1. A fiber optic sensing monitoring system suitable for complex environments, characterized in that, The system comprises the following components: a broadband light source module for generating a continuous light signal covering a specific waveband; a fiber-optic sensing array module comprising different types of fiber-optic sensors arranged in a preset topology in the environment to be measured for sensing the physical quantities of the environment and modulating them into changes in the characteristics of the light signal; a signal demodulation and acquisition module connected to the fiber-optic sensing array module for receiving and converting the changes in the characteristics of the light signal into digital electrical signals and performing preliminary filtering and amplification on the digital electrical signals; an adaptive signal processing module having an input end connected to the signal demodulation and acquisition module for performing multi-scale noise suppression, cross-sensitivity decoupling and dynamic disturbance compensation on the digital electrical signals; an intelligent discrimination and alarm module having an input end connected to the adaptive signal processing module for performing state evaluation based on the processed signal characteristics and generating adaptive alarm instructions; a system control and data management module in bidirectional communication with the broadband light source module, the signal demodulation and acquisition module, the adaptive signal processing module and the intelligent discrimination and alarm module for coordinating the operation of the components of the system, configuring the operating parameters and storing the monitoring data.

2. The fiber optic sensing monitoring system suitable for complex environments according to claim 1, wherein, The broadband light source module uses a superluminescent diode or an erbium-doped fiber light source, the output spectrum width of which is greater than 40 nm, the central wavelength stability is better than ±0.02 nm / ℃, and the output power fluctuation is less than 0.05 dB. The module is internally integrated with a temperature control unit and a power feedback unit, and a proportional-integral-derivative controller is used to adjust the driving current and the working state of the thermoelectric cooler in real time to ensure the long-term power and spectral shape stability of the output light signal.

3. The fiber optic sensing monitoring system suitable for complex environments of claim 1, wherein, The fiber-optic sensing array module comprises distributed fiber-optic sensors and quasi-distributed fiber-optic grating sensors. The distributed fiber-optic sensors are based on stimulated Brillouin scattering effect, and the sensing fiber uses a special design with polyimide coating to enhance the mechanical strength and temperature sensitivity, with a spatial resolution of 1 meter, a strain measurement accuracy of ±2με and a temperature measurement accuracy of ±0.5℃. The quasi-distributed fiber-optic grating sensor array is composed of a plurality of fiber-optic gratings with different Bragg wavelengths connected in series, each fiber-optic grating has a reflectivity of 3% to 5% and a wavelength interval of not less than 0.5 nm, and is packaged in a metal capillary or a polymer matrix to improve the pressure resistance and corrosion resistance.

4. The fiber optic sensing monitoring system suitable for complex environment according to claim 1, characterized in that, The signal demodulation and acquisition module comprises a photodetector array, a transimpedance amplifier, an anti-aliasing filter and a high-speed analog-to-digital converter. The photodetector array uses InGaAs material, with a responsivity of greater than 0.9 A / W and a 3dB bandwidth of not less than 100 MHz. The transimpedance amplifier has programmable gain, ranging from 1 kΩ to 1 MΩ to adapt to different intensities of input photocurrent. The anti-aliasing filter is an 8th order Chebyshev low-pass filter, and the cutoff frequency is dynamically set according to the highest frequency component of the signal. The high-speed analog-to-digital converter has a resolution of not less than 16 bits and a sampling rate of up to 250 MSPS, ensuring high-fidelity digitization of weak signals.

5. The fiber optic sensing monitoring system suitable for complex environments of claim 1, wherein, The adaptive signal processing module is integrated with a digital signal processor and a field programmable gate array, and the processing algorithm running inside includes: a multi-scale noise suppression unit based on wavelet packet transform and improved empirical mode decomposition, used for separating noise intrinsic mode functions in the signal; a temperature-strain cross-sensitivity decoupling unit based on double-parameter matrix operation and support vector regression machine, used for establishing and solving the independent mapping relationship of strain and temperature; and a dynamic disturbance compensation unit based on Kalman filter and long short-term memory neural network, used for predicting and offsetting the signal drift caused by random vibration or transient temperature change.

6. The fiber optic sensing monitoring system suitable for complex environments of claim 1, wherein, The intelligent discrimination and alarm module includes a feature extraction unit, a pattern recognition unit and a decision logic unit; the feature extraction unit extracts time domain statistical features, frequency domain energy features and time-frequency domain joint features from the signals processed adaptively; The mode recognition unit adopts a support vector machine classifier based on a radial basis function kernel to classify the extracted feature vectors into three states: normal, early warning, and abnormal. The decision logic unit dynamically adjusts the alarm threshold and response delay time based on the current environmental complexity index and historical state sequence. The adaptive threshold adjustment follows the formula: where is the final alarm threshold, is the base threshold, is the environmental noise variance, is the signal trend change rate, and are weight coefficients determined by training historical data.

7. The fiber optic sensing monitoring system suitable for complex environments of claim 1, wherein, The system control and data management module runs an embedded real-time operating system, provides a graphical user interface for parameter configuration and state monitoring; the module has a built-in relational database for storing raw sensor data, processing intermediate results, alarm event logs and system operation logs; at the same time, the module is integrated with a network communication interface based on transmission control protocol / Internet protocol, supporting remote data access and online firmware upgrade functions.

8. A method of optical fiber sensing monitoring suitable for complex environments, characterized by, The specific steps of the method are: Step S110, injecting a probe light signal into the optical fiber sensing array arranged in the monitoring area through a broadband light source, and receiving the backscattered light or reflected light signal generated after being modulated by the environmental physical quantity; Step S120, converting the received optical signal into a digital electrical signal using a signal demodulation and collection unit, and performing preliminary signal conditioning, including gain adjustment and band-limit filtering; Step S130, inputting the digital electrical signal into an adaptive signal processing unit, sequentially performing multi-scale noise suppression processing to improve the signal-to-noise ratio, temperature-strain cross-sensitivity decoupling processing to separate strain and temperature components, and dynamic disturbance compensation processing to suppress environmental transient interference; Step S140, based on the signals processed adaptively, extracting a multi-dimensional feature vector and inputting it into a pre-trained classification model for state recognition and risk assessment; Step S150, dynamically calculating the alarm threshold according to the recognized state category and environmental complexity, and generating corresponding alarm instructions and maintenance suggestions; Step S160, locally storing all data, processing results and alarm events in the monitoring process, and supporting remote data transmission and system state query through wired or wireless networks.

9. The optical fiber sensing monitoring method suitable for complex environments according to claim 8, characterized in that, The multi-scale noise suppression processing in step S130 includes: performing wavelet packet transform on the input digital electrical signal, using db8 wavelet basis function for 6-layer decomposition to obtain 64 sub-band signals; using the improved empirical mode decomposition algorithm to process each sub-band signal, decomposing the signal into 12 intrinsic mode function components by introducing the adaptive noise complete set empirical mode decomposition method; identifying noise dominant components according to the sample entropy value of each component, embedding dimension setting to 2 when calculating sample entropy, similar tolerance taking 0.2 times the standard deviation of the signal, and performing threshold filtering processing on the identified noise components.

10. The optical fiber sensing monitoring method for complex environment according to claim 8, characterized in that, The dynamic calculation of the alarm threshold in the step S150 is specifically: according to the current environmental noise level and the signal change trend, the environmental noise variance is obtained by calculating the signal variance of the last 100 sampling points, and the signal trend change rate is calculated by linear regression analysis of the slope value of the last 30 sampling points; The basic threshold in the adaptive threshold adjustment formula is set according to the characteristics of the monitored object, and the weight coefficient is determined by historical data training, wherein the environmental noise weight coefficient is 0.6, and the signal trend weight coefficient is 0.4; When the system identifies an abnormal state, different response strategies are generated according to the abnormal level.

Images