A multimodal scattering based fusion type optical fiber disturbance detection system and method

Abstract

The present application relates to the technical field of optical fiber sensing, and particularly relates to a fusion type optical fiber disturbance detection system and method based on multi-modal scattering. The system comprises a light source module, an optical fiber sensing network, an optical signal demodulation module, a data processing unit and an adaptive disturbance recognition module. By simultaneously extracting characteristic information of Rayleigh scattering and Raman scattering, the detection sensitivity and accuracy of different types of disturbances can be improved. The system adopts an adaptive disturbance recognition algorithm, dynamically adjusts the fusion weight of Rayleigh scattering and Raman scattering by using information entropy calculation, so as to adapt to the disturbance detection requirements under different environmental conditions, improve the adaptability and reliability of the system, and effectively reduce the false alarm rate. In addition, the data processing unit combines the principle of optical time domain reflectometry (OTDR), calculates the time difference of the disturbance signal arriving at the demodulation module, and realizes high-precision disturbance positioning.

Description

Technical Field

[0001] This invention relates to the field of fiber optic sensing technology, and in particular to a fusion-based fiber optic disturbance detection system and method based on multimode scattering. Background Technology

[0002] Fiber optic sensors are widely used in many fields due to their advantages such as resistance to electromagnetic interference, high sensitivity, and ease of deployment. Fiber optic sensing technology, based on principles such as Rayleigh scattering and Raman scattering, uses the fiber itself as a sensing medium to transmit specific light pulses into the fiber, enabling real-time monitoring of disturbances at multiple points along the fiber. When an external disturbance occurs, the characteristics of the scattered light signal change, and the demodulation system can accurately analyze these changes to determine the location, type, and intensity of the disturbance. However, existing fiber optic sensors based on a single scattering mode have some limitations in practical applications. For example, Rayleigh scattering has limited sensitivity to detect weak disturbances, and Raman scattering signals are weak under certain environmental conditions, leading to insufficient detection accuracy and reliability. Summary of the Invention

[0003] In view of the problems existing in the prior art, the present invention provides a fusion-type fiber optic disturbance detection system based on multimodal scattering, the system comprising a light source module, a fiber optic sensing network, an optical signal demodulation module, a data processing unit, and an adaptive disturbance identification module;

[0004] The light source module is used to emit light pulses with specific wavelengths and pulse widths to the fiber optic sensing network.

[0005] The fiber optic sensing network transmits the light pulses emitted by the light source module to the area to be monitored, and transmits the returned scattered light signals to the optical signal demodulation module.

[0006] The optical signal demodulation module receives the scattered optical signal returned from the optical fiber sensing network, demodulates it, and extracts the characteristic parameters of Rayleigh scattering and Raman scattering.

[0007] The data processing unit processes and analyzes the received Rayleigh and Raman scattering feature parameters, and combines them with the algorithm of the adaptive disturbance identification module to accurately determine the location, type, and intensity of the disturbance.

[0008] The adaptive disturbance recognition module dynamically adjusts the parameters of the feature extraction algorithm based on the real-time collected scattered light signal to adapt to the disturbance detection requirements under different environmental conditions.

[0009] Preferably, the data processing unit and the adaptive disturbance identification module jointly calculate the information entropy H of the Rayleigh signal features and the Raman signal features. R and H L Dynamically adjust the fusion weights: WR = H R / (H R + H L + ∈), W L = 1 - W R , ∈ is a small constant to ensure the stability of the weight in the noise environment.

[0010] Preferably, the data processing unit and the adaptive disturbance identification module calculate the disturbance position through the formula L = c * Δt / 2n based on the time difference Δt of the disturbance signal reaching the demodulation module using the optical time domain reflection principle, wherein n is the refractive index of the optical fiber, and c is the speed of light.

[0011] Preferably, the data processing unit and the adaptive disturbance identification module determine the vibration disturbance through the low-frequency vibration of the Rayleigh phase and the high-frequency strain component of the Raman frequency shift.

[0012] Preferably, the data processing unit and the adaptive disturbance identification module determine the temperature disturbance through the significant change of the Raman intensity ratio and the stability of the Rayleigh phase.

[0013] Preferably, the data processing unit and the adaptive disturbance identification module determine the compound event through the phase mutation accompanied by a sudden temperature rise.

[0014] The application also provides a detection method implemented by the above system, which comprises the following steps:

[0015] S1: The light source module is used to emit optical pulses of specific wavelength and pulse width to the optical fiber sensing network;

[0016] S2: The optical fiber sensing network transmits the optical pulses emitted by the light source module to the area to be monitored and transmits the returned scattered light signals to the optical signal demodulation module;

[0017] S3: The optical signal demodulation module receives the scattered light signals returned from the optical fiber sensing network and demodulates and processes them to extract the characteristic parameters of Rayleigh scattering and Raman scattering;

[0018] S4: The data processing unit processes and analyzes the received Rayleigh scattering and Raman scattering characteristic parameters, and combines the algorithm of the adaptive disturbance identification module to realize accurate judgment of the position, type and intensity of the disturbance.

[0019] Preferably, the data processing unit and the adaptive disturbance identification module dynamically adjust the fusion weight W R = H L / (H R + H R + ∈).R +H L +∈), W L =1-W R ,∈ is a small constant to prevent division by zero, ensuring the stability of the weight in the noise environment.

[0020] Preferably, the data processing unit and the adaptive disturbance identification module calculate the disturbance position through the formula L = c * Δt / 2n based on the time difference Δt of the disturbance signal reaching the demodulation module using the principle of optical time domain reflection, wherein n is the refractive index of the optical fiber, and c is the speed of light.

[0021] Preferably, the data processing unit and the adaptive disturbance identification module determine vibration disturbance through the low-frequency vibration of the Rayleigh phase and the high-frequency strain component of the Raman frequency shift.

[0022] Preferably, the data processing unit and the adaptive disturbance identification module determine temperature disturbance through the significant change of the Raman intensity ratio and the stability of the Rayleigh phase.

[0023] Preferably, the data processing unit and the adaptive disturbance identification module determine a compound event through the phase mutation accompanied by a sudden temperature rise.

[0024] Compared with the prior art, the present application has at least the following beneficial effects:

[0025] 1) The present application proposes a multi-modal scattered optical fiber disturbance detection technology that fuses Rayleigh scattering and Raman scattering, which improves the detection sensitivity and accuracy of different intensity and type disturbances by simultaneously utilizing the advantages of the two scattering modes, and solves the detection limitation problem of existing single scattering mode optical fiber sensors in complex environments.

[0026] 2) The adaptive disturbance feature extraction and identification algorithm can dynamically adjust the feature extraction strategy according to real-time signals to adapt to the disturbance detection requirements under different environmental conditions, improve the adaptability and reliability of the system, and effectively reduce the false alarm rate. The system uses the characteristic information entropy (HR and HL) of Rayleigh scattering and Raman scattering to dynamically adjust the feature fusion weight, wherein Rayleigh scattering mainly reflects the small deformation of the optical fiber, and Raman scattering is more sensitive to temperature changes. By calculating the relative size of HR and HL, and using the weight distribution strategy of WR = HR / (HR+HL+∈) and WL = 1-WR (where ∈ is a small constant to prevent division by zero), the system can maintain stable signal weight distribution under different noise environments. This ensures that when the Rayleigh scattering signal is strong, the system can preferentially identify vibration disturbance, and when the Raman signal is dominant, it pays more attention to temperature changes. At the same time, the adaptive algorithm can dynamically adjust the feature extraction strategy according to the changes of real-time signals, thereby improving the recognition accuracy of disturbance events under complex environments, reducing the false alarm rate, and ensuring that the system can maintain stable and reliable monitoring capability in various application scenarios.

[0027] 3) The fusion type optical fiber disturbance detection system and method of the present application is not only suitable for safety monitoring of long distance optical fiber communication link, but also can be widely applied to industrial equipment state monitoring, perimeter security, environmental monitoring and other fields, and has wide application prospect. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 It is a structural schematic diagram of the fusion type optical fiber disturbance detection system of the present application.

[0029] The present application will be further described in detail below. However, the following examples are only simple examples of the present application, and do not represent or limit the protection scope of the present application, and the protection scope of the present application is subject to the claims. DETAILED EMBODIMENT

[0030] The technical solutions of the present application will be further described below in combination with the drawings and through specific embodiments.

[0031] The fusion type optical fiber disturbance detection system of the present application comprises a light source module, an optical fiber sensing network, an optical signal demodulation module, a data processing unit and an adaptive disturbance recognition module. The light source module emits light pulses of specific wavelength and pulse width to the optical fiber sensing network; the optical fiber sensing network serves as a sensing medium, and transmits the light pulses to a monitoring area; the optical signal demodulation module receives and demodulates scattered light signals returned from the optical fiber sensing network, and extracts characteristic parameters of Rayleigh scattering and Raman scattering; the data processing unit processes and analyzes the demodulated characteristic parameters, and realizes accurate judgment of the position, type and intensity of disturbance in combination with the algorithm of the adaptive disturbance recognition module.

[0032] Embodiment one

[0033] In the long distance optical fiber communication link, the fusion type optical fiber sensing network is laid, and the environmental disturbance and human interference along the line are monitored. The light source module emits light pulses with a wavelength of 1550 nm, and after passing through the optical fiber sensing network, the Rayleigh scattering and Raman scattering light signals are received by the optical signal demodulation module. The demodulation module extracts the phase change of Rayleigh scattering through coherent demodulation technology, and extracts the frequency shift and intensity change of Raman scattering through spectrum analysis. The data processing unit fuses and processes the characteristic parameters of the two scattering modes, when detecting weak vibration disturbance at a certain place along the line, the phase change of Rayleigh scattering is small, but the frequency shift and intensity change of Raman scattering are obvious, the fusion algorithm accurately judges the position and type of the disturbance by comprehensively analyzing the two scattering signals, and provides real-time early warning for the safe operation of the communication link.

[0034] The principle of the fusion type optical fiber sensing network is based on the cooperative work of two scattering effects in the optical fiber, and combines signal demodulation and data fusion technology to realize accurate monitoring of environmental disturbance. The specific principle can be divided into the following levels:

[0035] Rayleigh scattering is an elastic scattering caused by microscopic inhomogeneities in the density of optical fiber materials, and its wavelength remains constant. Its phase is sensitive to minute strains in the optical fiber (such as vibration or pressure), and the phase change is positively correlated with the intensity of the disturbance.

[0036] Raman scattering is caused by inelastic collisions between photons and the vibrations (phonons) of a fiber lattice, resulting in a frequency shift (Stokes light and anti-Stokes light). The amount and intensity of this frequency shift are sensitive to temperature and strain; temperature changes significantly affect the frequency shift and intensity ratio of Raman scattering.

[0037] The light source module emits 1550nm narrow pulse light (low-loss window). When the light pulse propagates in the optical fiber, Rayleigh scattering light (backward propagation) and Raman scattering light (bidirectional propagation) are captured by the optical signal demodulation module.

[0038] The optical signal demodulation module uses coherent detection technology (such as...) -OTDR (Rayleigh scattering demodulation): Extracts phase changes through the interference of local oscillating light and Rayleigh scattered light. Tiny phase fluctuations (e.g., 0.01 rad) correspond to nanoscale fiber deformation, which can detect weak vibrations (such as human walking or mechanical excavation).

[0039] The optical signal demodulation module employs spectral analysis and intensity demodulation (Raman scattering signal demodulation): by separating the intensity ratio of Stokes light (low frequency) and anti-Stokes light (high frequency), temperature changes are calculated. Frequency shift reflects the strain rate or vibration frequency.

[0040] The data processing unit and the adaptive disturbance identification module extract the phase change amplitude, disturbance frequency, and time position (distance calculated based on the round-trip time of the light pulse) from the Rayleigh signal; and extract the frequency shift, temperature change gradient, and intensity fluctuation pattern from the Raman signal.

[0041] The data processing unit and the adaptive disturbance identification module determine vibration disturbances, such as mechanical vibrations, by using small Rayleigh phase changes (low-frequency vibrations) and significant Raman frequency shifts (high-frequency strain components);

[0042] The data processing unit and the adaptive disturbance identification module use significant changes in Raman intensity ratio and Rayleigh phase stability to determine temperature disturbances, such as fires or heat source intrusions.

[0043] The data processing unit and the adaptive disturbance identification module determine the composite event by the phase change (such as breakage) accompanied by a sudden temperature rise, such as human-caused damage, specifically cutting optical fibers.

[0044] The data processing unit and the adaptive disturbance identification module are based on the optical time domain reflection (OTDR) principle, utilize the time difference At of the disturbance signal arriving at the demodulation module, and calculate the disturbance position (n is the refractive index of the optical fiber) through the formula L = c * At / 2n.

[0045] The Rayleigh signal is sensitive to dynamic strain, and the Raman signal is sensitive to static temperature. The two are complementary and can distinguish between natural interference (such as temperature drift) and human damage. Phase demodulation can detect nanoscale vibrations, and Raman spectroscopy can sense 0.1 °C temperature changes. Through high-speed sampling (kHz level) and parallel processing algorithms, a second-level early warning response is achieved. Multiplexed communication optical fibers are used as sensors, without the need for additional sensor network layout. This technology combines the differences in physical responses of Rayleigh scattering and Raman scattering, combined with coherent detection and spectral analysis, to achieve multi-parameter distributed sensing. The data fusion algorithm integrates the spatial and temporal features through weighted decision or machine learning models (such as SVM, CNN), ultimately achieving high accuracy and low false alarm rate for monitoring targets, providing "self-aware" security for optical fiber communication links.

[0046] Embodiment Two

[0047] Compared with the deterministic disturbance in Embodiment One (such as periodic mechanical vibration, temperature step change), there is a strong physical correlation between its Rayleigh phase and Raman frequency shift (for example, vibration will inevitably cause both phase change and temperature fluctuation). The algorithm relies on a pre-set fixed fusion rule (such as a logic judgment tree) to achieve classification. This embodiment mainly deals with non-stationary disturbances (such as random personnel movement, transient equipment start-stop), and the correlation between Rayleigh and Raman signals may be destroyed by environmental noise. For example: high-frequency vibration of equipment (sensitive to Rayleigh phase) may overlap in the frequency domain with personnel footsteps (sensitive to Raman intensity), and magnetic interference may simultaneously contaminate both scattering signals. At this time, the fixed fusion rule is easily invalidated, and needs to be adjusted dynamically to adapt to the signal statistical characteristics.

[0048] In complex industrial environments, the fusion optical fiber sensor is used to monitor the running state of equipment and personnel activities. Due to the diversity of environmental noise and equipment vibration, a single disturbance feature extraction method is difficult to accurately identify different types of disturbances. The adaptive disturbance identification module dynamically adjusts the parameters of the feature extraction algorithm according to the real-time collected scattered light signals. For example, when periodic high-frequency vibration components are detected in the signal, the algorithm automatically increases the weight of the Rayleigh scattering phase change to improve the detection sensitivity of the equipment vibration; when non-periodic low-frequency disturbances appear in the signal, the algorithm focuses on analyzing the intensity change of the Raman scattering to accurately identify the interference caused by personnel activities. Through the adaptive algorithm, the system can maintain high-precision disturbance identification capability under different environmental conditions, effectively reducing the false alarm rate.

[0049] The optical signal demodulation module utilizes both Rayleigh scattering (strain-sensitive) and Raman scattering (temperature-sensitive) in the optical fiber, but separates the responses of the two types of signals to disturbances through a dynamic decoupling mechanism:

[0050] Rayleigh scattering primarily captures high-frequency vibrations (equipment operating frequency > 10 Hz) and rapid strains (such as mechanical shocks). Raman scattering focuses on sensing low-frequency disturbances (personal activity frequency < 5 Hz) and gradual temperature changes (such as localized temperature rise caused by human proximity). Frequency domain filtering (such as wavelet packet decomposition) is used to eliminate the coupling effect of equipment heating on the Rayleigh signal.

[0051] The optical signal demodulation module uses bidirectional Raman scattering demodulation (BOTDR) and phase-sensitive time-domain reflectometry (TDR). The architecture is a hybrid of BOTDR and φ-OTDR. BOTDR calculates the temperature distribution by using the inverse Stokes / Stokes light intensity ratio to dynamically compensate for the influence of environmental temperature drift on vibration detection. φ-OTDR is based on phase resolution of coherent detection to achieve distributed measurement of sub-nanometer vibrations.

[0052] The data processing unit and adaptive perturbation identification module extract phase change rate, local extremum density, and Hilbert marginal spectral entropy from Rayleigh signals; and extract intensity fluctuation variance, spatial correlation of temperature gradient, and event duration from Raman signals. They calculate the time-frequency matrix of the signal in real time (STFT or Wigner-Ville distribution) and select characteristic frequency bands based on energy concentration regions.

[0053] Calculate the information entropy H of the two types of signal features R (Rayleigh) and H L (Raman), dynamically adjust fusion weights: W R =H R / (H R +H L +∈), W L =1-W R ∈ is a small constant to prevent division by zero, ensuring weight stability in noisy environments.

[0054] For example, Rayleigh signal entropy H R A significant increase in W R A significant increase occurs; the system prioritizes analyzing the phase change rate to identify the vibration frequency (e.g., motor speed). Raman signal entropy value H L The sudden increase made W L Significantly increased, the system focuses on pulse patterns of intensity (e.g., step interval detection).

[0055] The data processing unit and adaptive perturbation recognition module combine a rule engine with a lightweight neural network (such as TinyML).

[0056] Lightweight neural network includes: rule layer: pre-defined event templates (e.g. "motor overload" corresponds to frequency 10-100Hz, duration >30s). Learning layer: update classification boundary using online incremental learning, adapt to new disturbance patterns.

[0057] Data processing unit and adaptive disturbance recognition module finally perform confidence evaluation, output disturbance type probability (e.g. P(equipment vibration) = 0.92, P(person activity) = 0.08).

[0058] Adopt compressed sensing (Compressed Sensing) technology, according to the signal sparsity adaptive adjustment of sampling rate: (1) high frequency vibration segment, sampling rate is improved to 1MHz (satisfy Nyquist theorem); (2) smooth segment, reduce to 10kHz (reduce data redundancy).

[0059] Use dynamic time warping (DTW) algorithm, solve the time delay mismatch problem caused by the difference of Rayleigh and Raman signal propagation speed.

[0060] The light source module is one of the core components of the system, responsible for transmitting light pulses of specific wavelength and pulse width to the optical fiber sensing network. Using a tunable pulsed laser, the parameters of the light pulse can be adjusted according to the detection requirements, ensuring that sufficient scattered light signal intensity can be obtained under different environmental conditions. The output light pulse of the light source module is injected into the optical fiber sensing network through the coupler.

[0061] The optical fiber sensing network, as a sensing medium, is laid along the area to be monitored, transmitting the light pulses emitted by the light source module to the area to be monitored, and transmitting the returned scattered light signals to the optical signal demodulation module. The optical fiber sensing network can select different types and structures of optical fibers according to actual application requirements, such as single-mode optical fiber, multi-mode optical fiber or special optical fiber, to adapt to different detection environments and requirements.

[0062] The optical signal demodulation module receives the scattered light signals returned from the optical fiber sensing network and performs demodulation processing, extracting the characteristic parameters of Rayleigh scattering and Raman scattering. Coherent demodulation technology is used to extract the phase change of Rayleigh scattering, and spectral analysis is used to extract the frequency shift and intensity change of Raman scattering. The demodulation module transmits the extracted characteristic parameters to the data processing unit for further processing and analysis.

[0063] The data processing unit processes and analyzes the received Rayleigh scattering and Raman scattering characteristic parameters, combines the algorithm of the adaptive disturbance recognition module, and realizes the accurate judgment of the position, type and intensity of the disturbance. Using multi-modal data fusion technology, the characteristic parameters of the two scattering modes are comprehensively analyzed, and by establishing a disturbance model and a feature library, the rapid recognition and classification of different disturbance patterns are realized.

[0064] The adaptive disturbance recognition module dynamically adjusts the parameters of the feature extraction algorithm according to the real-time collected scattered light signals, to adapt to the disturbance detection requirements under different environmental conditions. Machine learning algorithms such as support vector machine (SVM) or neural network are used to classify and recognize the disturbance features. By continuously learning and optimizing the algorithm parameters, the disturbance recognition accuracy and real-time performance of the system are improved.

[0065] The specific hardware configuration of each embodiment is as follows:

[0066] 1. Light source module

[0067]

[0068] 2. Optical fiber sensing network

[0069]

[0070]

[0071] 3. Optical signal demodulation module

[0072]

[0073] 4. Data processing unit

[0074]

[0075]

[0076] 5. Adaptive disturbance recognition module

[0077]

[0078]

[0079] Two embodiments maintain independence under the modular architecture, and realize deep collaboration through hardware resource sharing, algorithm knowledge migration, and data joint inference. Embodiment one focuses on wide-area low-power monitoring, and realizes stable operation over long distances relying on fixed parameter optimization. Embodiment two emphasizes local high-precision perception, and responds to open complex environments through a dynamic adaptive mechanism. The fusion of the two can construct a "point-line-surface" three-dimensional monitoring network, and provide a complete optical fiber sensing solution for the intelligentization of new infrastructure.

[0080] Improve detection sensitivity and accuracy: the multimodal scattering detection technology that fuses Rayleigh scattering and Raman scattering can simultaneously utilize the advantages of the two scattering modes, improve the detection sensitivity and accuracy of different intensity and type disturbances, and solve the detection limitation problem of existing single scattering mode optical fiber sensors in complex environments.

[0081] Enhance system adaptability and reliability: adaptive disturbance feature extraction and recognition algorithm can dynamically adjust the feature extraction strategy according to the real-time signal, adapt to the disturbance detection demand under different environmental conditions, improve the adaptability and reliability of the system, and effectively reduce the false alarm rate.

[0082] Expand application range: the fusion type optical fiber disturbance detection system and method of the application is not only suitable for safety monitoring of long distance optical fiber communication link, but also can be widely applied to industrial equipment state monitoring, perimeter security, environmental monitoring and other fields, and has wide application prospect.

[0083] The above describes the preferred embodiments of the application, but the application is not limited to the specific details in the above embodiments, and various simple modifications can be made to the technical solutions of the application within the technical concept of the application, and these simple modifications all belong to the protection scope of the application.

[0084] In addition, it should be noted that various specific technical features described in the above specific embodiments can be combined in any appropriate manner without contradiction, and in order to avoid unnecessary repetition, the application will not further describe various possible combinations.

[0085] In addition, various different embodiments of the application can also be combined in any manner, as long as it does not deviate from the idea of the application, and it should also be considered as disclosed by the application.

Claims

1. A fusion-based fiber optic disturbance detection system based on multimode scattering, characterized in that: The system includes a light source module, an optical fiber sensor network, an optical signal demodulation module, a data processing unit, and an adaptive disturbance recognition module. The light source module is used to emit light pulses with specific wavelengths and pulse widths to the fiber optic sensing network. The fiber optic sensing network transmits the light pulses emitted by the light source module to the area to be monitored, and transmits the returned scattered light signals to the optical signal demodulation module. The optical signal demodulation module receives the scattered optical signal returned from the optical fiber sensing network, demodulates it, and extracts the characteristic parameters of Rayleigh scattering and Raman scattering. The data processing unit processes and analyzes the received Rayleigh and Raman scattering feature parameters, and combines them with the algorithm of the adaptive disturbance identification module to accurately determine the location, type, and intensity of the disturbance. The adaptive disturbance recognition module dynamically adjusts the parameters of the feature extraction algorithm based on the real-time acquired scattered light signal to adapt to the disturbance detection requirements under different environmental conditions; the data processing unit and the adaptive disturbance recognition module calculate the information entropy H of the Rayleigh signal features and Raman signal features. R and H L Dynamically adjust the fusion weights: W R =H R / (H R +H L +∈), W L =1-W R ∈ is a small constant to prevent division by zero, ensuring weight stability in noisy environments.

2. The system according to claim 1, characterized in that: The data processing unit and the adaptive disturbance identification module are based on the principle of optical time-domain reflectometry. They use the time difference Δt between the arrival of the disturbance signal and the demodulation module to calculate the disturbance location using the formula L=c*Δt / 2n, where n is the refractive index of the optical fiber and c is the speed of light.

3. The system according to claim 1, characterized in that: The data processing unit and the adaptive disturbance identification module determine vibration disturbances by low-frequency vibrations of the Rayleigh phase and high-frequency strain components of the Raman frequency shift, or by significant changes in the Raman intensity ratio and stability of the Rayleigh phase.

4. The detection method implemented using any one of the systems in claims 1-3, characterized in that: The method includes the following steps: S1: The light source module is used to emit light pulses with specific wavelengths and pulse widths to the optical fiber sensing network; S2: The fiber optic sensing network transmits the light pulses emitted by the light source module to the area to be monitored, and transmits the returned scattered light signal to the optical signal demodulation module; S3: The optical signal demodulation module receives the scattered optical signal returned from the optical fiber sensing network, demodulates it, and extracts the characteristic parameters of Rayleigh scattering and Raman scattering. S4: The data processing unit processes and analyzes the received Rayleigh scattering and Raman scattering characteristic parameters, and combines them with the algorithm of the adaptive disturbance identification module to achieve accurate judgment of the location, type and intensity of the disturbance; The data processing unit and the adaptive disturbance identification module calculate the information entropy H of Rayleigh signal features and Raman signal features. R and H L Dynamically adjust the fusion weights: W R =H R / (H R +H L +∈), W L =1-W R ∈ is a small constant to prevent division by zero, ensuring weight stability in noisy environments.

5. The method according to claim 4, characterized in that: The data processing unit and the adaptive disturbance identification module are based on the principle of optical time-domain reflectometry. They use the time difference Δt between the arrival of the disturbance signal and the demodulation module to calculate the disturbance location using the formula L=c*Δt / 2n, where n is the refractive index of the optical fiber and c is the speed of light.

6. The method according to claim 5, characterized in that: The data processing unit and the adaptive disturbance identification module determine vibration disturbances by low-frequency vibrations of the Rayleigh phase and high-frequency strain components of the Raman frequency shift, or by significant changes in the Raman intensity ratio and stability of the Rayleigh phase.

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