Pipeline geological disaster early warning system based on phi-OTDR and application

Abstract

The invention provides a pipeline geological disaster early warning system based on phi-OTDR, which relates to the technical field of geological disaster monitoring and comprises a light source module, a modulator, a photoelectric detector, a single-mode communication optical fiber, a data acquisition and processing unit, a control and analysis unit and a remote monitoring platform. The light source module generates a light pulse signal, the light pulse signal is modulated by the modulator and then transmitted through the single-mode communication optical fiber, the photoelectric detector receives backscattered light and converts the backscattered light into an electric signal, the data acquisition and processing unit amplifies, filters and digitalizes the electric signal, and the control and analysis unit analyzes the processed data and generates an early warning signal. And the remote monitoring platform receives and displays the early warning signal. The system realizes real-time, high-precision and long-distance monitoring along the pipeline, and solves the problems of limited monitoring range, long response time, high maintenance cost and weak anti-interference capability in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of geological disaster monitoring, and in particular to a pipeline geological disaster early warning system based on φ-OTDR and its application. Background Art

[0002] With the continuous expansion of the oil pipeline network, the threat of geological disasters faced by the safe operation of pipelines is increasing. Geological disasters such as earthquakes, landslides, and surface subsidence can not only cause pipeline deformation and rupture, but may also trigger secondary disasters, causing significant economic losses and environmental pollution. Therefore, real-time monitoring and early warning of geological activities along the pipeline are of great significance.

[0003] At present, pipeline geological disaster monitoring mainly relies on surface sensors and satellite remote sensing technology. Surface sensors usually use strain gauges, inclinometers and other equipment to obtain geological activity information by deploying multiple monitoring points at key locations along the pipeline. Satellite remote sensing technology uses radar interferometry and other means to monitor surface deformation over a large area. These traditional monitoring methods have the following problems: First, the monitoring range of surface sensors is limited, making it difficult to achieve continuous monitoring along the pipeline; second, satellite remote sensing technology is greatly affected by weather conditions, and the data update cycle is long, which cannot meet the needs of real-time monitoring; third, surface sensors need to be regularly maintained and replaced, and the maintenance cost is high; finally, traditional monitoring methods are easily affected by external interference in complex geological environments, affecting the reliability and accuracy of monitoring.

[0004] In addition, the existing monitoring system generally has the problem of insufficient monitoring accuracy. Because geological disasters often occur before they occur, and traditional monitoring methods are difficult to capture these subtle changes, resulting in delayed warnings. At the same time, the existing system lacks the ability to comprehensively analyze multi-source monitoring data, making it difficult to accurately identify and predict potential geological disasters. Summary of the invention

[0005] The purpose of the present invention is to provide an early warning system that can monitor geological activities along the pipeline in real time and accurately, so as to improve the timeliness, accuracy and reliability of geological disaster monitoring. The system should have long-distance monitoring capabilities, high sensitivity, strong anti-interference, and be able to perform intelligent analysis on multi-source monitoring data, so as to achieve early warning of geological disasters.

[0006] To achieve the above object, the present invention is implemented through the following technical solutions:

[0007] A pipeline geological disaster early warning system based on φ-OTDR, comprising:

[0008] A light source module, used for generating an optical pulse signal;

[0009] A modulator, connected to the light source module, and used to modulate the optical pulse signal;

[0010] Photodetector, used to receive light signals and convert them into electrical signals;

[0011] A single-mode communication optical fiber, arranged between the modulator and the photodetector, for transmitting the optical pulse signal and acquiring backscattered light;

[0012] A data acquisition and processing unit, connected to the photoelectric detector, for amplifying, filtering and digitalizing the electrical signal;

[0013] A control and analysis unit, connected to the data acquisition and processing unit, for analyzing the processed data and generating an early warning signal;

[0014] The remote monitoring platform is connected to the control and analysis unit for receiving and displaying the warning signal.

[0015] Furthermore: the light source module is a narrow linewidth laser.

[0016] Further: the control and analysis unit includes:

[0017] A data preprocessing module, used for normalizing and denoising the processed data;

[0018] A feature extraction module is used to extract vibration signal features, strain signal features and topographic features from the preprocessed data;

[0019] The multimodal fusion module is used to fuse and analyze the vibration signal characteristics, strain signal characteristics and terrain characteristics and generate the warning signal.

[0020] Further: the feature extraction module includes:

[0021] Wavelet analysis unit, used to extract wavelet features of vibration signals;

[0022] A principal component analysis unit, used to extract principal component features of strain signals;

[0023] Short-time Fourier transform unit is used to extract the frequency domain characteristics of strain signals.

[0024] Further: the multimodal fusion module includes:

[0025] A feature layer fusion unit, used for fusing the vibration signal features, strain signal features and topographic features;

[0026] The fusion layer fusion unit is used to perform weight assignment and CNN feature extraction on the data after feature fusion;

[0027] The decision layer fusion unit is used to perform weighted average fusion on the prediction results of multiple modalities.

[0028] Further: the decision layer fusion unit adopts the following formula to perform weighted average fusion:

[0029] P=w1P1+w2P2+w3P3

[0030] Among them, P is the final prediction probability, P1 is the prediction probability of the vibration signal model, P2 is the prediction probability of the terrain model, P3 is the prediction probability of the strain data model, and w1, w2, and w3 are the corresponding weight coefficients respectively.

[0031] Further: the remote monitoring platform includes:

[0032] Cloud servers for storing and processing monitoring data;

[0033] Client, used to display monitoring data and warning information.

[0034] Furthermore: it also includes an emergency response module, which is connected to the control and analysis unit and is used to activate a preset emergency plan when a high-risk geological disaster is detected.

[0035] Furthermore: the emergency response module is connected to the pipeline control system and is used to control the closure of relevant valves when a high-risk geological disaster is detected.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] 1. This system uses φ-OTDR technology to perform distributed optical fiber sensing detection along the pipeline, and uses single-mode communication optical fiber as both a sensing medium and a transmission channel to achieve continuous monitoring along the pipeline. Compared with the traditional point-type monitoring method, the present invention significantly expands the monitoring range, can achieve long-distance, real-time monitoring without blind spots, and effectively solves the problem of limited monitoring range in the prior art.

[0038] Second, the present invention adopts multimodal data fusion technology, and through the three-layer fusion architecture of feature layer, fusion layer and decision layer, deeply fuses and analyzes vibration signals, strain signals and topographic features. This fusion analysis method of multi-source data significantly improves the monitoring accuracy and reliability of the system, can effectively identify small signs of geological activities, and provide a more accurate basis for geological disaster warning.

[0039] 3. This system uses passive optical fiber sensing technology, which does not require the installation of power supply equipment and signal processing equipment along the optical fiber, significantly reducing the maintenance cost of the system. At the same time, optical fiber sensors have the characteristics of anti-electromagnetic interference and corrosion resistance, and can still maintain stable monitoring performance in complex geological environments, improving the anti-interference ability of the system.

[0040] 4. The intelligent early warning algorithm designed in the present invention can analyze monitoring data in real time, promptly discover abnormal situations and accurately locate the location of geological activities. Combined with the emergency response module, it can quickly initiate the corresponding emergency plan, effectively shortening the response time of the system and providing sufficient early warning time for preventing geological disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A schematic diagram of a framework diagram of a pipeline geological disaster early warning system based on φ-OTDR in an embodiment;

[0042] Figure 2 A schematic diagram of a flow chart of an application method of a pipeline geological disaster early warning system based on φ-OTDR in an embodiment;

[0043] In the figure:

[0044] 1. Light source module; 2. Modulator; 3. Photodetector; 4. Single-mode communication optical fiber; 5. Data acquisition and processing unit; 6. Control and analysis unit; 7. Remote monitoring platform. DETAILED DESCRIPTION

[0045] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0047] like Figure 1As shown, the pipeline geological disaster early warning system based on φ-OTDR provided by the present invention includes a light source module 1, a modulator 2, a photodetector 3, a single-mode communication optical fiber 4, a data acquisition and processing unit 5, a control and analysis unit 6 and a remote monitoring platform 7. Among them, the light source module 1 adopts a narrow linewidth laser to generate a stable optical pulse signal. The modulator 2 is connected to the light source module 1 to finely modulate the optical pulse signal to meet the requirements of the φ-OTDR technology. The single-mode communication optical fiber 4 is arranged between the modulator 2 and the photodetector 3, which serves as both a transmission channel for the optical pulse signal and a distributed sensing medium for capturing tiny vibrations caused by geological activities. The photodetector 3 receives the backscattered light transmitted back through the single-mode communication optical fiber 4 and converts it into an electrical signal. The data acquisition and processing unit 5 is connected to the photodetector 3 to amplify, filter and digitize the received electrical signal.

[0048] In some embodiments, the control and analysis unit 6 includes a data preprocessing module, a feature extraction module and a multimodal fusion module.

[0049] The data preprocessing module first normalizes the collected data and converts data of different dimensions to the same dimension. For the value x, the normalization process uses the following formula:

[0050]

[0051] Where x' is the normalized value, min(x) and max(x) are the minimum and maximum values ​​of this type of data, respectively.

[0052] Then, wavelet transform is used to denoise the signal to improve the signal-to-noise ratio. The specific formula is as follows:

[0053]

[0054] When the absolute value of the signal |x| is greater than the threshold λ:

[0055] preserve the sign of the signal (sgn(x)),

[0056] Subtract the threshold (|x|-λ) from the signal amplitude,

[0057] If the result is negative, take 0, if it is positive, keep the value (max(...,0));

[0058] When the absolute value of the signal |x| is less than or equal to the threshold λ, the signal value is directly set to 0.

[0059] In one embodiment, the feature extraction module includes a wavelet analysis unit, a principal component analysis unit and a short-time Fourier transform unit. The wavelet analysis unit uses Daubechies wavelet to analyze the preprocessed vibration signal and extracts signal features at different scales and translation positions. The specific calculation formula is as follows:

[0060]

[0061] Among them, f(t) represents the original monitoring data signal, a is the scale parameter that determines the degree of expansion and contraction of the wavelet function, b is the translation parameter that determines the position of the wavelet function on the time axis, and ψ(t) is the wavelet basis function.

[0062] The principal component analysis unit extracts the main features of the strain signal by calculating the data covariance matrix and performing eigenvalue decomposition, achieving data dimensionality reduction while retaining important information. The specific calculation method is as follows:

[0063]

[0064] Among them, m is the number of samples, X is the original data matrix, and μ is the sample mean. Then the covariance matrix C is decomposed by eigenvalue:

[0065] C=UΛU T

[0066] Among them, Λ is the diagonal matrix composed of eigenvalues, and U is the corresponding eigenvector matrix.

[0067] The short-time Fourier transform unit performs time-frequency analysis on the strain data, and obtains the frequency characteristics of the signal in different time periods by windowing the signal in the time domain and performing Fourier transform. The specific formula is as follows:

[0068]

[0069] Among them, x(τ) is the input signal and w(τ-t) is the window function.

[0070] In some other embodiments, the multimodal fusion module adopts a three-layer fusion architecture, including a feature layer fusion unit, a fusion layer fusion unit and a decision layer fusion unit. The decision layer fusion unit uses the following formula for weighted average fusion:

[0071] P=w1P1+w2P2+w3P3

[0072] Among them, P is the final prediction probability, P1 is the prediction probability of the vibration signal model, P2 is the prediction probability of the terrain model, P3 is the prediction probability of the strain data model, w1, w2, w3 are the corresponding weight coefficients, and w1+w2+w3=1 is satisfied. According to experience, w1>w3>w2 is usually set.

[0073] In the feature layer, the wavelet features and principal component features of the vibration signal are concatenated to obtain the feature vector V1, the topographic features are set as V2, and the Fourier features and principal component features of the strain signal are concatenated to obtain the feature vector V3. In the fusion layer, CNN is used to unify the feature vectors, and weights are assigned according to the importance of different features for weighted fusion. In the decision layer, the weighted average method is used to fuse the prediction results of each modal model, in which the vibration signal model has the largest weight, followed by the strain signal, and the topographic weight is the smallest. The specific calculation formula is:

[0074] V fusion =w1V1+w2V2+w3V3

[0075] Among them, V fusion is the final prediction feature.

[0076] In other embodiments, the remote monitoring platform 7 includes a cloud server and a client. The cloud server is responsible for storing and processing monitoring data and pushing the processing results to the client. The client can display monitoring data and early warning information in real time, so that managers can grasp the geological conditions along the pipeline in a timely manner.

[0077] In another embodiment, the present invention further comprises an emergency response module connected to the control and analysis unit 6. When the system detects a high-risk geological disaster, the emergency response module will automatically start a preset emergency plan. At the same time, the emergency response module is connected to the pipeline control system, and can automatically control the closure of related valves when necessary to reduce the losses that may be caused by geological disasters.

[0078] In another embodiment, the present invention also provides a specific application of a pipeline geological disaster early warning system based on φ-OTDR, such as Figure 2 As shown in the figure, the system first collects vibration signals, strain signals and other data along the pipeline through φ-OTDR technology. After preprocessing and feature extraction, the collected data is input into the multimodal fusion module for analysis. The system calculates the probability of geological disasters based on the results of the fusion analysis. When the predicted probability exceeds the preset threshold, the system promptly issues a warning signal and initiates the corresponding emergency plan. The whole process realizes real-time monitoring and early warning of geological disasters, providing a strong guarantee for the safe operation of the pipeline.

[0079] Through the above technical scheme, the present invention realizes real-time and accurate monitoring of geological activities along the pipeline, and can issue early warning signals in a timely manner, effectively reducing the threat of geological disasters to pipeline safety.

[0080] The above embodiments are only for illustrating the technical concept and features of the present invention, and their purpose is to enable people familiar with the technology to understand the content of the present invention and implement it accordingly, and they cannot be used to limit the protection scope of the present invention. Any equivalent transformation or modification made according to the spirit of the present invention should be included in the protection scope of the present invention.

Claims

1. A pipeline geological disaster early warning system based on φ-OTDR, characterized in that: include: A light source module, used for generating an optical pulse signal; A modulator, connected to the light source module, and used to modulate the optical pulse signal; Photodetector, used to receive light signals and convert them into electrical signals; A single-mode communication optical fiber, arranged between the modulator and the photodetector, for transmitting the optical pulse signal and acquiring backscattered light; A data acquisition and processing unit, connected to the photoelectric detector, for amplifying, filtering and digitalizing the electrical signal; A control and analysis unit, connected to the data acquisition and processing unit, for analyzing the processed data and generating an early warning signal; The remote monitoring platform is connected to the control and analysis unit for receiving and displaying the warning signal.

2. A pipeline geological disaster early warning system based on φ-OTDR according to claim 1, characterized in that: The light source module is a narrow line width laser.

3. A pipeline geological disaster early warning system based on φ-OTDR according to claim 1, characterized in that: The control and analysis unit comprises: A data preprocessing module, used for normalizing and denoising the processed data; A feature extraction module is used to extract vibration signal features, strain signal features and topographic features from the preprocessed data; The multimodal fusion module is used to fuse and analyze the vibration signal characteristics, strain signal characteristics and terrain characteristics and generate the warning signal.

4. A pipeline geological disaster early warning system based on φ-OTDR according to claim 3, characterized in that: The feature extraction module comprises: Wavelet analysis unit, used to extract wavelet features of vibration signals; A principal component analysis unit, used to extract principal component features of strain signals; Short-time Fourier transform unit is used to extract the frequency domain characteristics of strain signals.

5. The pipeline geological disaster early warning system based on φ-OTDR according to claim 3 is characterized in that: The multimodal fusion module comprises: A feature layer fusion unit, used for fusing the vibration signal features, strain signal features and topographic features; The fusion layer fusion unit is used to perform weight assignment and CNN feature extraction on the data after feature fusion; The decision layer fusion unit is used to perform weighted average fusion on the prediction results of multiple modalities.

6. A pipeline geological disaster early warning system based on φ-OTDR according to claim 5, characterized in that: The decision layer fusion unit uses the following formula for weighted average fusion: P=w1P1+w2P2+w3P3 Among them, P is the final prediction probability, P1 is the prediction probability of the vibration signal model, P2 is the prediction probability of the terrain model, P3 is the prediction probability of the strain data model, and w1, w2, and w3 are the corresponding weight coefficients respectively.

7. The pipeline geological disaster early warning system based on φ-OTDR according to claim 1 is characterized in that: The remote monitoring platform includes: Cloud servers for storing and processing monitoring data; Client, used to display monitoring data and warning information.

8. The pipeline geological disaster early warning system based on φ-OTDR according to claim 1, characterized in that: It also includes an emergency response module, which is connected to the control and analysis unit and is used to activate a preset emergency plan when a high-risk geological disaster is detected.

9. A pipeline geological disaster early warning system based on φ-OTDR according to claim 8, characterized in that: The emergency response module is connected to the pipeline control system and is used to control the closure of relevant valves when a high-risk geological disaster is detected.

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