Long-distance oil and gas pipeline fault early warning and positioning method and system

Abstract

The invention relates to the technical field of oil and gas pipeline monitoring and fault early warning, in particular to a long-distance oil and gas pipeline fault early warning and positioning method and system. The method comprises the steps of magnetoacoustic coupling excitation signal generation and acquisition, weighted complementary correlation matrix fusion, multi-modal deep integration network fault classification, iterative error correction and positioning and fault trend prediction and early warning. By fusing the multi-modal features and dynamically adjusting the early warning threshold, the fault detection precision and the early warning flexibility are improved, efficient detection and accurate positioning of complex faults of long-distance oil and gas pipelines of more than 50 kilometers are achieved, missing report and false report are effectively reduced, and the safety and reliability of pipeline operation are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas pipeline monitoring and fault early warning, and in particular to a method and system for long-distance oil and gas pipeline fault early warning and positioning. Background Art

[0002] Fault warning and location of long-distance oil and gas pipelines are of great significance to the safety and economy of oil and gas transportation, especially for pipelines over 50 kilometers in length, the difficulty of fault monitoring increases significantly. The operating environment of long-distance oil and gas pipelines is complex, and various factors such as corrosion, cracks, and external damage may cause pipeline leakage or rupture. These problems need to be detected and located in a timely manner to avoid serious environmental pollution and economic losses.

[0003] At present, the commonly used oil and gas pipeline monitoring technology (Chinese invention patent, publication number: CN117553852A, name: a fiber optic multi-parameter monitoring sensor system) mainly relies on fiber optic sensing technology, such as fiber optic multi-parameter monitoring system. These systems usually use fiber optic sensors to collect data on multiple physical parameters such as temperature, pressure, strain, etc., and provide early warnings based on the setting of fixed thresholds or floating thresholds. However, this method has the following defects:

[0004] The data processing of fiber optic sensors is complex and greatly affected by noise, resulting in insufficient fault location accuracy, especially on long-distance pipelines, where signal attenuation is significant; existing systems usually rely on fixed thresholds and are unable to dynamically adjust warning thresholds based on real-time monitoring data, which can easily lead to false alarms or missed alarms; the use of a single-mode data processing method cannot effectively integrate multiple feature data, resulting in low information utilization. Summary of the invention

[0005] In view of the many problems existing in the above-mentioned prior art, the present invention provides a long-distance oil and gas pipeline fault warning and positioning method and system. The present invention is based on the acquisition of magneto-acoustic coupled waveguide signals, combined with a multimodal deep integration network and a cross-modal self-attention mechanism, and through multiple iterative error corrections, it achieves high-precision positioning of long-distance oil and gas pipeline faults, and significantly improves the accuracy and timeliness of the warning by dynamically adjusting the warning strategy.

[0006] A long-distance oil and gas pipeline fault early warning and positioning method comprises the following steps:

[0007] The adaptively modulated waveguide signal is generated by the magnetoacoustic coupling exciter, and the fiber Bragg grating sensor arranged on the oil and gas pipeline is used to collect the strain change data caused by the waveguide propagation in real time. The metamaterial coating is applied at a specific location to enhance the reflected wave field of a specific frequency, and the preliminary processed data after synchronization is obtained.

[0008] The synchronized preliminary processed data is subjected to weighted complementary correlation matrix fusion processing to extract and enhance multi-scale features, and normalized feature data is obtained through adaptive feature screening and normalization processing;

[0009] The normalized feature data is input into the multimodal deep integration network, the multimodal features are fused using the cross-modal self-attention mechanism, and fault classification data is generated through recursive analysis and adaptive classification;

[0010] By using the fault classification data, iterative error correction and multi-modal feature constraint optimization are performed through inverse wave propagation calculation to obtain optimized fault location data and achieve accurate positioning of the fault position.

[0011] Based on the optimized fault location data and fault classification data, a fault trend prediction model is constructed to generate fault trend prediction data. The fault warning is triggered by dynamically adjusting the warning threshold to generate fault warning data, and the fault warning data is fed back and updated to optimize the warning strategy.

[0012] Preferably, the frequency and amplitude of the waveguide signal generated by the magnetoacoustic coupling exciter are dynamically adjusted according to the real-time operating status of the oil and gas pipeline by adjusting the magnetic field strength and frequency of the magnetoacoustic coupling exciter, so that the waveguide signal generated by the magnetoacoustic coupling exciter can adapt to the changing propagation path and material properties under different environmental conditions.

[0013] Preferably, the strain change data collected by the fiber Bragg grating sensor is subjected to noise filtering through a multi-stage filter, wherein the multi-stage filter includes a low-pass filter, a high-pass filter and a band-pass filter to respectively remove low-frequency background noise, high-frequency sharp noise and interference signals in non-target frequency bands, thereby generating preliminary processed data with optimized signal-to-noise ratio.

[0014] Preferably, the weighted coefficient w of the weighted complementary correlation matrix fusion process is i Calculated by the following formula:

[0015]

[0016] Among them, w i represents the weight coefficient of the i-th data source; C i represents the relevance value of the i-th data source; n represents the total number of data sources; j represents the index of each data source traversed during the weighted summation process.

[0017] Preferably, multi-scale feature extraction is achieved by discrete wavelet transform, which decomposes the synchronized preliminary processed data f(t) into sub-band signals of multiple scales, and its transformation formula is:

[0018]

[0019] Where f(t) is the original signal; ψ j，k (t) represents discrete wavelet basis function; c j，k represents the corresponding decomposition coefficient; j represents the scale parameter; k represents the translation parameter.

[0020] Preferably, the multimodal deep integration network is recursively analyzed by the following recursive calculation formula:

[0021] F n+1 =f(w·F n +b)

[0022] Among them, F n+1 represents the feature vector of the n+1th layer; F n represents the feature vector of the nth layer; W is the weight matrix, which defines the linear combination relationship of the feature vector; b is the bias term, which is used to adjust the value range of the feature vector; f is the activation function, which acts on the result of the linear combination to introduce nonlinear features.

[0023] Preferably, the cross-modal self-attention mechanism adjusts the attention weight according to the similarity and difference of different modal features. The calculation of the similarity and difference is based on the dot product operation and normalization processing of the feature vector to generate an attention weight matrix for guiding the weighted fusion of cross-modal features.

[0024] Preferably, the iterative error correction method uses the following optimization formula for error correction:

[0025]

[0026] Among them, L i+1 is the fault location result of the i+1th iteration; L i is the fault location result of the i-th iteration, indicating the current fault location estimate; α is the learning rate, which determines the step size of each iteration; is the gradient of the fault location error function, which is used to adjust the offset of the fault location estimation.

[0027] Preferably, the multimodal feature constraint optimization is based on the joint constraints of time delay features, phase features and spectrum features, and the accuracy of the finally optimized fault location data is improved by performing multiple fine-tuning on the fault location data after iterative error correction.

[0028] A system for implementing the long-distance oil and gas pipeline fault early warning and positioning method includes the following modules:

[0029] A magneto-acoustic coupling excitation module for generating an adaptively modulated waveguide signal;

[0030] Fiber Bragg grating sensor module, arranged on oil and gas pipelines, is used to collect real-time data on strain changes caused by guided wave propagation;

[0031] Metamaterial coating modules are applied at specific locations on oil and gas pipelines to enhance the reflected wave fields of specific frequencies;

[0032] The data fusion processing module is used to perform weighted complementary correlation matrix fusion processing on the collected synchronized preliminary processed data, extract and enhance multi-scale features, and obtain normalized feature data through adaptive feature screening and normalization processing;

[0033] A multimodal deep integration network module, which receives normalized feature data, fuses multimodal features using a cross-modal self-attention mechanism, and generates fault classification data through recursive analysis and adaptive classification;

[0034] Iterative reverse wave propagation positioning module, which is used to receive fault classification data, perform iterative error correction and multi-modal feature constraint optimization through reverse wave propagation calculation, generate optimized fault location data, and realize accurate positioning of the fault position;

[0035] The fault trend prediction and warning module is used to build a fault trend prediction model based on the optimized fault location data and fault classification data, generate fault trend prediction data, trigger fault warnings by dynamically adjusting the warning threshold, generate fault warning data, and feedback and update the fault warning data to optimize the warning strategy.

[0036] Compared with the prior art, the advantages and beneficial effects of the present invention are:

[0037] The present invention uses iterative error correction and reverse wave propagation calculation to perform high-precision correction on the fault location, thus achieving accurate fault location for long-distance pipelines of more than 50 kilometers.

[0038] The present invention dynamically adjusts the warning threshold based on the fault trend prediction model to adapt to the changing trend of fault development and reduce false alarms and missed alarms;

[0039] The present invention utilizes a multimodal deep integration network to fuse multiple feature data to improve the ability to identify complex faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a schematic diagram of the process of the present invention;

[0041] Figure 2 A schematic diagram of the coordinated application of the magnetoacoustic coupling actuator, metamaterial coating and fiber Bragg grating sensor in the present invention;

[0042] Figure 3 This is a flow chart of multi-modal deep integration network fault classification and recursive analysis in the present invention;

[0043] Figure 4 It is a flowchart of iterative error correction and fault location in the present invention;

[0044] Figure 5 This is a system structure block diagram of the present invention. DETAILED DESCRIPTION

[0045] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is obvious that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0046] like Figure 1 As shown, a long-distance oil and gas pipeline fault early warning and positioning method includes the following steps:

[0047] The adaptively modulated waveguide signal is generated by the magnetoacoustic coupling exciter, and the fiber Bragg grating sensor arranged on the oil and gas pipeline is used to collect the strain change data caused by the waveguide propagation in real time. The metamaterial coating is applied at a specific location to enhance the reflected wave field of a specific frequency, and the preliminary processed data after synchronization is obtained.

[0048] In the present invention, the long-distance oil and gas pipeline fault warning and positioning adopts a magneto-acoustic coupled waveguide exciter to generate an adaptively modulated waveguide signal, and the fiber Bragg grating sensor arranged on the pipeline collects the strain change data caused by waveguide propagation in real time, and at the same time, a metamaterial coating is applied at a specific location to enhance the reflection wave field of a specific frequency. These technical means are combined to provide an efficient solution for pipeline fault detection and positioning, which is suitable for long-distance oil and gas pipelines of more than 50 kilometers. The long distance referred to in the present invention refers to more than 50 kilometers.

[0049] The magnetoacoustic coupling exciter uses the magnetoacoustic effect to generate waveguide signals in oil and gas pipelines. These waveguide signals modulate the local vibration of the pipeline through changes in the magnetic field, allowing the waveguide to propagate along the pipeline. Adaptive modulation means that the magnetoacoustic coupling exciter can dynamically adjust the frequency and amplitude of the waveguide according to the operating status of the pipeline (such as pressure, temperature, etc.) and material properties, so that the waveguide can adapt to changes in the propagation path under different environmental conditions. This adaptive modulation can enhance the signal's penetration ability and propagation distance, and effectively reduce the signal's attenuation during long-distance propagation.

[0050] Fiber Bragg grating sensors are evenly arranged on the oil and gas pipelines to collect real-time strain change data caused by waveguide propagation. When the waveguide propagates in the pipeline, if it encounters defects or faults in the pipeline (such as corrosion, cracks, etc.), the propagation characteristics of the waveguide (such as frequency, phase, amplitude) will change. Fiber Bragg grating sensors are sensitive to these changes and can record subtle strain changes, reflecting the size and position of the strain through the wavelength drift of the optical fiber. After preliminary processing, these strain change data form synchronized preliminary processing data, which provides basic data for subsequent multi-scale feature extraction and fault identification.

[0051] Metamaterial coatings are applied at specific locations of the pipeline to enhance the reflection effect of the waveguide. Metamaterials have special electromagnetic or acoustic properties and can be designed to have high reflectivity at specific frequencies. In the present invention, the metamaterial coating can be customized to produce strong reflections for waveguides of specific frequencies, so that the propagation of the waveguides in the pipeline is effectively enhanced. This enhancement is particularly important in long-distance detection because it can significantly reduce signal attenuation, improve detection sensitivity and signal-to-noise ratio. This enhancement not only increases the detection distance, but also provides clearer reflection signals when the waveguide passes through the fault area, which helps to accurately identify the fault location.

[0052] In practical applications, the materials involved in the practical application of the metamaterial coating referred to in the present invention generally include the following types, which are designed to exhibit excellent acoustic or electromagnetic properties at specific frequencies to enhance the reflection effect of guided waves:

[0053] Metal-dielectric composite materials: This material is composed of metal particles and dielectric materials (such as ceramics, polymers, etc.). By changing the arrangement of metal particles and the thickness of dielectric materials, the reflection properties of the material can be adjusted so that it exhibits high reflectivity at specific frequencies.

[0054] Phononic crystal materials: Phononic crystals are materials with periodic structures that can adjust the propagation path and frequency-selective reflection characteristics of sound waves. By designing phononic crystal structures with different periods and geometric shapes, the reflection enhancement effect at specific frequencies of guided waves can be customized.

[0055] Nanostructured materials: Using nanoscale materials, such as nanowires, nanoparticles, or nanofilms, these materials can control the reflection characteristics of guided waves by changing their microstructures. Nanostructured materials usually have excellent acoustic properties and can significantly enhance the reflection signal of guided waves.

[0056] Magnetic materials: Some metamaterials with magnetic properties can change their acoustic properties under the action of a magnetic field. Such materials can be combined with the working principle of magneto-acoustic coupling actuators to further adjust the reflection effect of guided waves at specific frequencies and adapt to different detection environments.

[0057] The specific selection and design of these metamaterial coatings will be customized according to the material properties of the pipeline, the frequency range of the waveguide, and the requirements of the fault type to achieve the best reflection enhancement effect.

[0058] like Figure 2 As shown in the figure, the magneto-acoustic coupling exciter is used to generate guided wave signals on oil and gas pipelines. These signals are adaptively modulated by adjusting the strength and frequency of the magnetic field, covering multiple frequency ranges to better meet the needs of different types of pipeline fault detection. When the guided wave signals propagate in the pipeline, they pass through metamaterial coatings set at specific locations. The reflection properties of these coatings are carefully designed to produce enhanced reflection effects at specific frequencies of the guided wave signals.

[0059] Metamaterial coatings are applied to key locations on pipelines, usually areas where detection is difficult or signals are prone to attenuation. By using metal-dielectric composite materials, phononic crystals, nanostructured materials, etc., the coating can significantly increase the reflection intensity of the waveguide signal within a specific frequency range. When the waveguide signal passes through the metamaterial coating, the high reflectivity of the coating causes the signal to be amplified at these locations, thereby reducing the attenuation effect during long-distance propagation. This enhancement can make the signal collected by the sensor clearer and effectively improve the detection sensitivity of the system.

[0060] Fiber Bragg grating sensors arranged along oil and gas pipelines are used to collect strain change data of waveguide signals in real time. When the waveguide signal passes through the reflection area enhanced by the metamaterial coating, the sensor can record a stronger reflection signal, which helps detect subtle changes or early fault characteristics in the pipeline. The sensor is usually arranged in a position corresponding to or adjacent to the position of the metamaterial coating to better capture the characteristic changes of the waveguide signal. By coordinating with the coating position, the sensor can effectively reduce noise interference and improve the signal-to-noise ratio of the signal.

[0061] The magnetoacoustic coupling exciter generates a waveguide signal, the metamaterial coating enhances the reflection of the signal, and the fiber Bragg grating sensor records the changes in the reflected signal in real time, forming a complete detection link. Through this linkage, the system can effectively improve the propagation distance and detection sensitivity of the waveguide signal. By adaptively modulating the frequency of the waveguide signal, the signal is matched with the enhanced frequency of the metamaterial coating, maximizing the use of the reflection enhancement effect and improving the reliability of long-distance oil and gas pipeline fault detection. The enhanced signal collected by the sensor can be combined with data from other modes (such as frequency characteristics and phase characteristics) to further improve the accuracy of fault location and the reliability of trend prediction.

[0062] Preferably, the frequency and amplitude of the waveguide signal generated by the magnetoacoustic coupling exciter are dynamically adjusted according to the real-time operating status of the oil and gas pipeline by adjusting the magnetic field strength and frequency of the magnetoacoustic coupling exciter, so that the waveguide signal generated by the magnetoacoustic coupling exciter can adapt to the changing propagation path and material properties under different environmental conditions.

[0063] In the present invention, the magneto-acoustic coupling exciter is used to generate an adaptively modulated waveguide signal. By adjusting the magnetic field strength and frequency, the frequency and amplitude of the waveguide signal are dynamically adjusted to adapt to the real-time operating status of the oil and gas pipeline. This adjustment mechanism enables the waveguide signal to propagate stably under different environmental conditions and adapt to the changing propagation path and material properties, thereby meeting the needs of long-distance (more than 50 kilometers) oil and gas pipeline fault warning and positioning.

[0064] The working principle of the magnetoacoustic coupling exciter is based on the magnetoacoustic effect, that is, elastic waves are excited in the pipeline by applying an alternating magnetic field. When the waveguide propagates in the pipeline, the frequency and amplitude of the wave can be controlled by adjusting the frequency and intensity of the external magnetic field. When the frequency of the magnetic field changes, the frequency of the waveguide changes accordingly; when the magnetic field intensity increases, the amplitude of the waveguide also increases, thereby affecting the penetration ability and propagation distance of the signal. In order to ensure that the waveguide signal can maintain efficient propagation characteristics during long-distance propagation, the present invention monitors the operating status of the oil and gas pipeline in real time through an adaptive modulation mechanism, such as temperature, pressure and other working conditions, and adjusts the working parameters of the magnetoacoustic coupling exciter according to these status information, so that the frequency and amplitude of the waveguide signal can adapt to various propagation conditions.

[0065] In the specific implementation process, the system uses sensors installed on the oil and gas pipelines to collect real-time operating data and inputs this data into the dynamic adjustment algorithm. The algorithm will automatically calculate and adjust the magnetic field strength and frequency according to the changes in working conditions reported by the sensors, such as the decrease in the elastic modulus of the material due to the increase in temperature, to compensate for the changes in propagation speed and signal strength caused by changes in material properties. Through this adaptive modulation mechanism, when the waveguide passes through the curved section or other complex structures of the pipeline, the system can automatically reduce the frequency of the waveguide and extend the wavelength to reduce the attenuation effect of the bend on the signal, while increasing the magnetic field strength to further enhance the propagation effect of the waveguide signal.

[0066] In addition, the dynamic adjustment algorithm can also make real-time corrections based on the attenuation of the signal to maintain the signal-to-noise ratio of the waveguide signal during long-distance propagation. For example, when a large fluctuation in pipeline pressure is detected, the algorithm will appropriately reduce the frequency of the magnetic field to reduce the scattering effect caused by material inhomogeneity, thereby maintaining signal stability. In this way, the adaptively modulated waveguide signal can provide stable and reliable propagation performance under different working conditions, making the system more sensitive to the detection of various fault types and improving the accuracy of fault location.

[0067] In a specific embodiment, the magnetoacoustic coupling exciter is installed at several key nodes (every 5-10 kilometers) on an oil pipeline as long as 100 kilometers. When the temperature of a certain section of the pipeline exceeds 60°C, the system automatically increases the magnetic field strength by 20% and increases the frequency of the waveguide by 5% to adapt to the changes in material properties caused by temperature changes. If the pressure fluctuates violently, the system will reduce the frequency of the magnetic field to reduce the impact of signal scattering. Through this adaptive adjustment method, the waveguide signal can still maintain efficient propagation characteristics under different environmental conditions, ensuring the accuracy and reliability of fault warning and positioning in long-distance oil and gas pipelines. This solution achieves high precision and high stability in long-distance pipeline fault detection by combining magnetoacoustic coupling, dynamic adjustment algorithm and adaptive modulation technology.

[0068] Preferably, the strain change data collected by the fiber Bragg grating sensor is subjected to noise filtering through a multi-stage filter, wherein the multi-stage filter includes a low-pass filter, a high-pass filter and a band-pass filter to respectively remove low-frequency background noise, high-frequency sharp noise and interference signals in non-target frequency bands, thereby generating preliminary processed data with optimized signal-to-noise ratio.

[0069] In the present invention, the fiber Bragg grating sensor is used to collect strain change data caused by waveguide propagation in long-distance oil and gas pipelines. These data contain key information of the waveguide signal and are used to identify and locate potential faults in the pipeline. However, since the signal will be interfered by various noises in practical applications, such as low-frequency background noise, high-frequency sharp noise and interference signals in non-target frequency bands, it is necessary to filter the collected strain change data through a multi-stage filter to improve the signal-to-noise ratio and generate optimized preliminary processing data. This process is crucial in the early warning and positioning of long-distance oil and gas pipeline faults, because high-quality signal data is the basis for subsequent fault identification and positioning.

[0070] Fiber Bragg grating sensors reflect strain changes in pipelines by monitoring the Bragg wavelength drift in optical fibers. When guided waves propagate in pipelines, if they encounter faults or defects (such as cracks or corrosion), changes in guided wave characteristics will cause strain changes. The sensor can record this change through the fiber Bragg grating and convert it into a strain signal. However, these signals are often mixed with various noises, making the original strain change data contain a variety of irrelevant interference components. Therefore, these raw data must be processed using multi-stage filters to extract useful signals and eliminate noise.

[0071] The multi-stage filter includes a low-pass filter, a high-pass filter and a band-pass filter, which process noise in different frequency bands respectively. The low-pass filter is used to remove low-frequency background noise, such as the slow drift signal caused by ambient temperature changes, which will mask small changes in fault signals in long-distance pipeline monitoring; the high-pass filter is used to eliminate high-frequency sharp noise, such as high-frequency signals caused by electromagnetic interference or mechanical vibration, which will interfere with the edge characteristics of the signal and affect the accuracy of fault location; the band-pass filter is used to filter out interference signals in non-target frequency bands and only retain the waveguide signals within a specific frequency range. These signals correspond to the frequency changes of the waveguide when encountering a fault and are key information for fault detection.

[0072] During the implementation process, fiber Bragg grating sensors are arranged along the oil and gas pipelines, and real-time data on strain changes caused by guided wave propagation is collected. The system first uses a low-pass filter to filter out low-frequency background noise, which mainly comes from the slow signal drift caused by temperature changes during pipeline operation. Next, a high-pass filter is used to eliminate high-frequency noise caused by mechanical vibration or electromagnetic interference to ensure that the edge characteristics and response speed of the signal are not affected. Finally, a bandpass filter is used to select the target frequency range of the guided wave and filter out interference signals in other irrelevant frequency bands. This staged multi-stage filtering process can remove noise interference to the maximum extent and retain useful signal features related to the fault.

[0073] In a specific embodiment, fiber Bragg grating sensors are arranged on a section of an 80-kilometer oil pipeline, with a sensor node arranged every 1 kilometer to form a dense monitoring network. The raw strain change data collected by the system is first processed by a low-pass filter to filter out background noise with a frequency lower than 0.5Hz; then, a high-pass filter is used to remove sharp noise with a frequency higher than 1000Hz; finally, a bandpass filter is used to select the waveguide signal frequency band between 20Hz and 500Hz, because the signal changes in this frequency band are closely related to tiny cracks or local corrosion faults in the pipeline. After multi-stage filter processing, the signal-to-noise ratio of the signal is significantly improved, providing high-quality preliminary processing data for subsequent feature extraction and fault location.

[0074] The synchronized preliminary processed data is subjected to weighted complementary correlation matrix fusion processing to extract and enhance multi-scale features, and normalized feature data is obtained through adaptive feature screening and normalization processing;

[0075] In the present invention, in order to realize the fault warning and positioning of long-distance oil and gas pipelines, the synchronized preliminary processed data is subjected to weighted complementary correlation matrix fusion processing to extract and enhance multi-scale features, and normalized feature data is obtained through adaptive feature screening and normalization processing. This process is mainly to make full use of the information in multi-source data, extract key features related to faults, and thus improve the accuracy and robustness of fault detection.

[0076] In oil and gas pipeline monitoring, the initial processing data usually comes from different sensor nodes, reflecting the various characteristics of the waveguide signal (such as frequency, amplitude, phase, etc.). There is a certain complementarity between these characteristic data, which can be integrated through correlation analysis to extract more effective information. The core principle of weighted complementary correlation matrix fusion processing is to use the mutual correlation between different data sources, and perform weighted fusion by calculating weights, so as to construct a comprehensive feature matrix and enhance the significance of multi-scale features.

[0077] Specifically, the system first calculates the correlation matrix of each data source, evaluates the degree of correlation of different data sources at each feature scale, and then assigns weighting coefficients to each data source based on these correlation values. The weighting coefficients are assigned in accordance with the principle of complementarity, that is, if a feature has a large difference in performance between different data sources, the weight of the feature will be increased accordingly to enhance its role in the fused feature matrix. In this way, feature information that is significant at different scales can be extracted.

[0078] Next, adaptive feature screening and normalization are performed. Adaptive feature screening is to dynamically adjust the features according to their significance and discrimination to ensure that the selected features have high fault identification capabilities. Normalization is to eliminate the dimensional differences between different features and adjust all feature values ​​to a unified range to avoid unnecessary impact on subsequent analysis due to the large numerical range of some features.

[0079] In the specific implementation process, the system first constructs the correlation matrix of each sensor node based on the collected synchronized preliminary processed data. For example, in a monitoring network of an 80-kilometer oil pipeline, the guided wave signals from different sensors will reflect the environmental changes and pipeline characteristics of their respective locations. The multi-scale characteristics of these data include frequency, the trend of time domain signal changes, waveform envelope, etc. The system generates an initial correlation matrix by calculating the mutual correlation between these multi-scale features, and then assigns weight coefficients to different data sources based on the values ​​of each element in the correlation matrix.

[0080] In the process of weighted complementary correlation matrix fusion, the feature matrix of each data source is multiplied and summed with the weighting coefficient to construct a comprehensive fusion feature matrix, which can effectively enhance the significance of multi-scale features. For example, at a certain node in the pipeline, if the correlation of frequency features is high and the correlation of phase features is low, a larger weight will be assigned to the frequency features during the fusion process to highlight the role of frequency features. After the fusion is completed, the system performs adaptive feature screening to select more representative feature data based on the significance and discrimination of the features.

[0081] Normalization is to adjust all the filtered feature data to a relatively uniform numerical range, such as standardizing the data range to between 0 and 1, so as to prevent the dimensional differences between the features from affecting the analysis results. Through these processing steps, high-quality normalized feature data is generated, providing a solid foundation for subsequent fault classification and location.

[0082] Preferably, the weighted coefficient w of the weighted complementary correlation matrix fusion process is i Calculated by the following formula:

[0083]

[0084] Among them, w i represents the weight coefficient of the i-th data source; C i represents the relevance value of the ith data source; n represents the total number of data sources; j represents the index of each data source traversed during the weighted summation process. This formula aims to assign weights to each data source according to their relative relevance values, thereby highlighting feature information with higher relevance during fusion processing.

[0085] In the present invention, in order to achieve fault warning and location of long-distance oil and gas pipelines, weighted complementary correlation matrix fusion processing is used to comprehensively analyze the feature information of multi-source data to extract more significant multi-scale features. The calculation of weighted coefficients is the key to fusion processing. By assigning appropriate weights to each data source, the features related to the fault can be enhanced and irrelevant information can be suppressed.

[0086] The core of weighted complementary correlation matrix fusion processing is to use the complementary characteristics between multi-source data to extract features that are important for fault detection. Oil and gas pipeline monitoring data usually comes from multiple sensors, reflecting the changes in guided wave signals at different locations and with different characteristics (such as frequency, phase, amplitude, etc.). There is a certain correlation between the features of these data from different sensors. Using the correlation value C i As an indicator to measure the importance of data source information, multi-scale features that are sensitive to fault characteristics can be effectively extracted.

[0087] In the specific implementation, the system first calculates the correlation matrix based on the preliminary processed data of each data source. Each element of the matrix represents the degree of correlation between different data sources. By analyzing the correlation matrix, the correlation value C of each data source can be determined. i , reflecting its contribution to the comprehensive feature matrix. If the feature of a data source shows a strong correlation between different data sources, it means that the feature has a strong explanatory power for the fault signal, so a larger weight can be assigned; conversely, if the correlation of a data source is weak, its influence on the final fusion feature will be appropriately reduced.

[0088] The calculation formula of the weighting coefficient is normalized so that the sum of the weights of each data source is 1, so that the overall feature ratio consistency can be maintained during fusion. Specifically, the system calculates the weighting coefficient w i When the correlation value C of each data source is i Divided by the sum of all data source relevance values Ensure that the weight values ​​are relatively consistent. This processing method can not only highlight the significant features related to the fault, but also reduce the influence of irrelevant or noise features, thereby enhancing the quality of the fused multi-scale feature matrix.

[0089] In specific applications, for example, in an 80-kilometer oil and gas pipeline monitoring system, a sensor node is arranged every 1 kilometer. The waveguide signals collected by these nodes contain multiple features (such as frequency, amplitude, and time domain envelope, etc.). The system first constructs a correlation matrix for these features to evaluate the complementarity of data from different nodes. In an example, assuming that the correlation between the frequency features of some nodes is strong, while the correlation between the phase features is weak, the system will calculate the frequency feature with a larger weight and the phase feature with a smaller weight according to the formula.

[0090] After the weighted coefficients are calculated, the system performs matrix fusion of the preliminary processed data and the corresponding weighted coefficients to generate a multi-scale fusion feature matrix. This fusion feature matrix combines the advantages of each data source on the basis of weighted processing, and improves the extraction effect of the waveguide signal features. For example, at a specific fault location, there may be a significant change in the frequency feature, while other features change less. At this time, weighted fusion can amplify the influence of the frequency feature and make the fault signal more obvious.

[0091] In a specific embodiment, the monitoring system of a long-distance oil and gas pipeline is deployed on a 100-kilometer pipeline, with a sensor node arranged every 1 kilometer to collect changes in various features during waveguide propagation. The system first constructs a correlation matrix for each sensor based on the collected preliminary processing data and analyzes the mutual correlation between different nodes. For example, if the high-frequency features of some nodes show a strong correlation in the surrounding nodes, the system will assign a higher weighting coefficient to these high-frequency features, and vice versa. Subsequently, the system performs weighted fusion processing on the preliminary processing data of each node according to the calculated weighting coefficient to generate an enhanced multi-scale fusion feature matrix.

[0092] Preferably, multi-scale feature extraction is achieved by discrete wavelet transform, which decomposes the synchronized preliminary processed data f(t) into sub-band signals of multiple scales, and its transformation formula is:

[0093]

[0094] Where f(t) is the original signal; ψ j，k (t) represents discrete wavelet basis function; c j，k represents the corresponding decomposition coefficient; j represents the scale parameter; k represents the translation parameter.

[0095] In the present invention, multi-scale feature extraction is achieved by discrete wavelet transform (DWT). Discrete wavelet transform decomposes the synchronized preliminary processed data f(t) into sub-band signals of multiple scales, as shown above. Through this decomposition, the original signal can be decomposed into sub-band signals represented on different time scales, so as to extract multi-scale features in the waveguide signal. This method is very useful in long-distance oil and gas pipeline fault warning and positioning, because it can extract fault features that are significant at different scales from the signal, helping to identify the type and location of the fault in the pipeline.

[0096] The core idea of ​​discrete wavelet transform is to decompose the original signal into a series of sub-signals with different frequency components, called sub-band signals. Each sub-band signal corresponds to a specific scale parameter j and translation parameter k, which represents the frequency distribution and temporal position of the signal. This decomposition process is achieved through the wavelet basis function ψ j，k (t) is realized, the basis function is scaled (corresponding to the scale parameter j) and translated (corresponding to the translation parameter k) to match the characteristics of the original signal. Decomposition coefficient c j，k It reflects the energy distribution of the original signal at the corresponding scale and position.

[0097] In long-distance oil and gas pipeline fault monitoring, the characteristics of the waveguide signal will show a variety of different frequency components depending on the fault type and location. For example, a small crack usually causes a significant change in the high-frequency signal, while large-area corrosion may appear as a slow change in the low-frequency signal. Through discrete wavelet transform, the original waveguide signal is decomposed into multiple sub-band signals with different frequency ranges, which can effectively extract these different types of fault characteristics and analyze the signal in both the time domain and the frequency domain.

[0098] In practical applications, the discrete wavelet transform first performs a multi-layer decomposition on the synchronized preliminary processed data f(t). The number of decomposition layers is determined by the length of the pipeline and the complexity of the fault type. Each decomposition can divide the signal into two sub-band signals, low-frequency and high-frequency. The low-frequency part retains the main features of the signal, while the high-frequency part reflects the detailed changes of the signal. This multi-layer decomposition process can be performed recursively so that the different scale features of the signal are extracted layer by layer. For example, for an oil and gas pipeline with a length of 100 kilometers, the waveguide signal can be decomposed into 5 layers of wavelets to extract low-frequency and high-frequency sub-band signals, covering different frequency ranges from macro changes to micro details.

[0099] After each layer of decomposition, the decomposition coefficient c j，k The size reflects the energy concentration of the signal at the corresponding scale and position. If a fault causes a sudden change in the signal within a specific frequency range, the decomposition coefficient of this frequency range will increase significantly, indicating that the frequency component is related to the fault. Based on the changes in these decomposition coefficients, the system can extract the corresponding multi-scale features for further analysis and location of the fault.

[0100] In a specific embodiment, the length of the oil and gas pipeline is 80 kilometers, and the system performs discrete wavelet transform on the synchronized waveguide signals collected by sensors arranged along the pipeline. First, select a suitable wavelet basis function (such as Daubechies wavelet) to perform multi-layer decomposition of the signal. Set the number of decomposition layers to 5 layers to obtain sub-band signals in different frequency ranges. For each layer of decomposition, the high-frequency part is used to capture the instantaneous changes of the signal, such as the enhancement of high-frequency components caused by cracks, while the low-frequency part reflects the changes in the overall structure, such as the changes in low-frequency signals caused by large-area corrosion.

[0101] At a specific node, if the high-frequency decomposition coefficient c of the third layer 3，k The system can identify the possible crack fault at this location and determine the specific location of the crack by combining the changes in the time domain. 1，k If the number of faults increases at the same time, it indicates that there may be large-scale corrosion faults in the area. By analyzing these multi-scale features, the type and severity of the fault can be further determined, thus providing a strong basis for subsequent fault location and treatment.

[0102] like Figure 3 As shown, the normalized feature data is input into the multimodal deep integration network, the multimodal features are fused using the cross-modal self-attention mechanism, and fault classification data is generated through recursive analysis and adaptive classification;

[0103] In the present invention, the normalized feature data is input into a multimodal deep integration network, the features of different modalities are fused using a cross-modal self-attention mechanism, and fault classification data is generated through recursive analysis and adaptive classification. This process aims to integrate multiple feature data, extract the most representative fault features, and ultimately achieve high-precision classification and identification of faults in long-distance oil and gas pipelines.

[0104] The multimodal deep integration network is a deep learning model that combines multiple data features. In the present invention, it is used to fuse normalized feature data from different modalities. These feature data may include frequency features, phase features, time domain features, etc., which reflect the changes of the waveguide signal in different dimensions. The multimodal deep integration network uses a cross-modal self-attention mechanism to identify and select the most meaningful features in different modalities and perform weighted fusion on them.

[0105] The core principle of the cross-modal self-attention mechanism is to dynamically adjust the weight of each feature based on the similarity and difference between features. Specifically, the system first calculates the self-attention weight matrix of the normalized feature data, each element of which represents the similarity between features of different modalities. For features with high similarity, the system will assign larger weights to enhance the influence of these features during fusion; for features with low similarity, the system will appropriately reduce their weights to reduce interference with the classification results. This weight adjustment can be achieved through a dot product operation combined with normalization processing, so that the most discriminative features are highlighted during the fusion process.

[0106] Recursive analysis is the process of processing multimodal features layer by layer. The output features of each layer will be used as the input of the next layer, and higher-order features will be extracted layer by layer. Adaptive classification determines the fault type based on the recursive analysis results through the output layer of the deep neural network. The classification result depends on the final output of the multimodal deep integration network. These outputs represent the probability distribution of different types of faults, and the fault category is determined by comparing the probability values.

[0107] In the specific implementation, the normalized feature data is first input into the multimodal deep integration network. The first layer of the network performs basic feature fusion on the input data, calculates the cross-modal self-attention weight matrix, and uses the matrix to perform weighted summation of features of different modalities. This can preliminarily eliminate the interference of irrelevant features on classification. Next, the recursive analysis process extracts higher-order features layer by layer. For example, the first few layers focus on the changes in the time-frequency features of the signal, while the latter layers focus more on the correlation and nonlinear changes of the composite features.

[0108] In the classification stage, the output layer of the network uses an adaptive classification mechanism to generate fault classification data based on the features extracted by recursive analysis. These classification data include probability scores for each fault category, which represent the confidence level of the system in determining different types of faults. Based on these probability values, the network selects the category with the highest confidence level as the final classification result of the fault and generates the corresponding fault classification data.

[0109] Preferably, the multimodal deep integration network is recursively analyzed by the following recursive calculation formula:

[0110] F n+1 =f(W·F n +b)

[0111] Among them, F n+1 represents the feature vector of the n+1th layer; F n represents the feature vector of the nth layer; W is the weight matrix, which defines the linear combination relationship of the feature vector; b is the bias term, which is used to adjust the value range of the feature vector; f is the activation function, which acts on the result of the linear combination to introduce nonlinear features.

[0112] In the present invention, a multimodal deep integration network is used for recursive analysis of long-distance oil and gas pipeline fault warning and positioning. The core of the network is to extract the multi-scale features of the signal layer by layer, and gradually enhance the representation ability of the features through recursive calculation. The calculation process of the recursive analysis is shown above. This recursive analysis process enables the system to extract more complex feature information layer by layer from the multimodal feature data, providing a refined feature representation for fault classification and identification.

[0113] The core of recursive analysis is to gradually enhance the feature representation capability by extracting and processing features layer by layer. In the initial layer, the feature vector F0 comes from the normalized multimodal feature data, which may include frequency features, time domain change features, and phase change features. The first layer of recursive analysis linearly combines these input features with the weight matrix W, then adds the bias term b, and then introduces nonlinear transformation through the activation function f to generate a new feature vector F1. The activation function usually uses ReLU (corrected linear unit) or Sigmoid function to increase the nonlinear expression capability of the model. In this way, the features with significant nonlinear changes in the waveguide signal can be captured, thereby better identifying complex fault types.

[0114] As the number of recursive layers increases, each layer further linearly combines and nonlinearly activates the output features of the previous layer, gradually extracting higher-order features. These high-order features can reflect more complex signal patterns, such as the impact of cracks or corrosion in pipelines on guided wave propagation. The multi-layer processing of recursive analysis enables the system to extract not only shallow features (such as local frequency changes) but also deep features (such as complex relationships between features), providing a more comprehensive feature description for fault classification.

[0115] In the specific implementation, the system first preprocesses the normalized feature data and inputs it into the recursive analysis module of the multimodal deep integration network. The initial layer of the recursive analysis module will be based on the formula F1 = f(W·F n +b) Process the input features and extract preliminary nonlinear features. These preliminary features are then passed as input to the next layer, and similar calculations are repeated to generate the second layer features F2. This process will continue recursively until the last layer of the network extracts high-order features.

[0116] For example, in the monitoring application of an 80-kilometer-long oil and gas pipeline, the normalized feature data includes information such as the high and low changes in frequency components and the amplitude of phase changes. The initial layer of recursive analysis will prioritize the extraction of basic features of frequency changes to capture short-term changes in the signal, and as the number of recursive layers increases, the network will further extract the correlation information between multiple features, such as the mutual influence between frequency changes and phase changes, which is particularly important for identifying complex faults caused by cracks and corrosion.

[0117] The weight matrix W and the bias term b are adjusted layer by layer during the recursive analysis process and optimized through the training process to meet the specific fault feature classification requirements. For example, if the characteristics of a certain type of fault are mainly concentrated in high-frequency signals, the weight matrix W will be automatically adjusted so that the high-frequency components will receive higher weights during the recursive analysis process, thereby enhancing their influence on classification. Through this layer-by-layer recursive feature extraction method, the network can capture complex fault modes from multimodal feature data and generate fault classification data.

[0118] In a specific embodiment, the fault warning system of the oil and gas pipeline is deployed on a 100-kilometer-long pipeline, with a sensor node installed every 2 kilometers to collect multimodal feature data of the waveguide signal in real time. The initial normalized feature data is input into the recursive analysis module of the multimodal deep integration network. The basic features of the signal (such as frequency changes and basic trends of time domain signals) are first extracted through the first two layers of recursion, and then higher-order features (such as nonlinear relationships and mutual influences between multiple features) are gradually extracted in the next few layers of recursion. In the last few layers of recursive analysis, the system further identifies the significant features of the fault mode through complex nonlinear transformations and generates classification data.

[0119] Preferably, the cross-modal self-attention mechanism adjusts the attention weight according to the similarity and difference of different modal features. The calculation of the similarity and difference is based on the dot product operation and normalization processing of the feature vector to generate an attention weight matrix for guiding the weighted fusion of cross-modal features.

[0120] In the present invention, the cross-modal self-attention mechanism is used to fuse the features of multiple modalities to improve the accuracy of long-distance oil and gas pipeline fault warning and positioning. This mechanism calculates the similarity and difference between different modal features to generate an attention weight matrix for guiding feature weighted fusion, so that the system can adaptively adjust the fusion method according to the importance of each modal feature, thereby extracting more representative feature information.

[0121] The core principle of the cross-modal self-attention mechanism is to dynamically adjust the weights of each modal feature in the fusion process by calculating the similarities and differences between different modal features. Specifically, given the feature vectors of multiple modalities (such as frequency feature vectors, time domain feature vectors, and phase feature vectors), the system first measures the similarity between them through dot product operations between feature vectors. The result of the dot product operation reflects the angle between the two vectors in the vector space, thereby characterizing the degree of similarity between the features. Next, the results of these dot product operations are normalized to generate an attention weight matrix, whose elements represent the relative importance of each modal feature.

[0122] The attention weight matrix is ​​normalized to ensure that the sum of the weights of each modal feature is 1, thereby maintaining the consistency of the overall feature ratio during weighted fusion. Features with higher weights will have a greater impact on the fused results, while features with lower weights are appropriately suppressed. This weighting method enables the system to highlight those modal features that are closely related to the fault features during the fusion process, while suppressing the interference of noise or irrelevant information.

[0123] In practical applications, the feature vectors of multiple modes (such as frequency features, time domain features, and phase features) are first input into the cross-modal self-attention mechanism. The system uses the dot product between the feature vectors to calculate the similarity matrix and generates the attention weight matrix through normalization. Subsequently, these attention weights are used to weight the features of each modality to achieve weighted fusion. For example, on an 80-kilometer-long oil and gas pipeline, the collected frequency feature vectors and time domain feature vectors show high similarity in certain areas. The system will assign higher attention weights to these features to highlight their importance in fault detection. For features with lower similarity (such as some low-frequency noise features), the attention weights are relatively small, thereby reducing the impact of these features on the final fault identification results.

[0124] In a specific embodiment, for a fault warning system for a long-distance oil and gas pipeline, multiple sensor nodes are arranged along the pipeline, and the multimodal feature data collected by each node include changes in frequency, phase, and time domain. The system first calculates the similarity of these multimodal feature data. Assuming that in a certain section of the pipeline, the dot product calculation results of the frequency feature and the phase feature show a high similarity (for example, their change trends in the frequency domain are similar), this means that these modal features may have a strong correlation with the fault. On the contrary, if the change of a time domain feature has a low similarity with other features, the feature may be a noise signal or information unrelated to the fault. Based on these similarity results, the system generates an attention weight matrix and assigns larger weights to features with higher similarity to enhance their influence in the fusion process.

[0125] Next, guided by the attention weight matrix, the multimodal features will be weighted and summed to achieve fusion. The weighted fusion features can better retain the key information related to the fault while reducing the interference of irrelevant information. This method is very effective in fault warning and location of long-distance oil and gas pipelines because it can adapt to the feature changes of different regions and fault types, adaptively adjust the feature fusion strategy, and enhance the detection robustness in complex fault environments.

[0126] On an 80-kilometer-long oil and gas pipeline, the cross-modal self-attention mechanism can identify frequency feature mutations and abnormal changes in phase features in different areas of the pipeline, and assign higher attention weights to these features to prioritize fault signals related to these features. Through this adaptive weighted fusion method, the system can more accurately detect fault types such as cracks and corrosion, thereby improving the accuracy and timeliness of early warnings.

[0127] like Figure 4 As shown, by using the fault classification data, iterative error correction and multi-modal feature constraint optimization are performed through inverse wave propagation calculation to obtain optimized fault location data and realize accurate positioning of the fault position;

[0128] Preferably, the iterative error correction method uses the following optimization formula for error correction:

[0129]

[0130] Among them, L i+1 is the fault location result of the i+1th iteration; L i is the fault location result of the i-th iteration, indicating the current fault location estimate; α is the learning rate, which determines the step size of each iteration; is the gradient of the fault location error function, which is used to adjust the offset of the fault location estimation.

[0131] In the present invention, by using fault classification data, the system performs reverse wave propagation calculations, and adopts iterative error correction and multi-modal feature constraint optimization to gradually improve the accuracy of fault location. This method combines the physical characteristics of reverse wave propagation and iterative optimization algorithms, continuously reduces errors from multiple estimates, and finally obtains optimized fault location data, realizing accurate positioning of fault locations in long-distance oil and gas pipelines.

[0132] Reverse wave propagation is a method of locating faults by using the reverse characteristics of waveguide signals propagating in oil and gas pipelines. On long-distance oil and gas pipelines, the propagation path and speed of waveguide signals are affected by pipeline characteristics, structural changes, and fault locations. Through fault classification data, the possible location of the fault can be preliminarily estimated. Then, using the reverse wave propagation method, reverse tracking is performed based on the signal changes on the propagation path, and the positioning results are gradually corrected.

[0133] Iterative error correction is the core step in the positioning process. The basic idea is to use the current fault location estimate L i , calculate the positioning error function E(L i ) The calculation expression is shown above.

[0134] Update the fault location estimate L for the next iterationi+1 , where α is the learning rate, which is used to control the step size of each iteration. This formula indicates that the system adjusts according to the direction of the error gradient to reduce the fault location error and gradually approach the true fault location. Through multiple iterations, the fault location result will gradually converge to the area with smaller error, achieving accurate fault location.

[0135] Multimodal feature constraint optimization combines information from different modal features to further correct the positioning results. For example, the changing trends of frequency features and phase features may be different for certain fault types. By combining the constraints of these multimodal features, the uncertainty caused by a single mode can be better eliminated and the positioning error can be reduced.

[0136] In specific applications, the fault classification data is first used to obtain a preliminary fault location estimate L0, and this is used as the starting point to start the iterative error correction process. The reverse wave propagation calculation will track back based on the preliminary estimated position, simulate the propagation of the guided wave signal on the path, and compare the error between the actual measured signal and the theoretical model. The system will calculate the error function gradient based on the calculated error function. Update the estimated value of the fault location. During the iteration process, the selection of the learning rate α is crucial, and its size needs to be adjusted according to the length of the pipeline and the type of fault to ensure the convergence speed and accuracy of the positioning result.

[0137] In one embodiment, for an oil and gas pipeline with a length of 100 kilometers, the system first estimates that the fault may be located at the 30th kilometer based on the fault classification results of the waveguide signal. Then, through the reverse wave propagation calculation, it is detected that the frequency characteristics of the location are not completely consistent with the changes in the time domain signal. The system uses the iterative error correction method to correct the estimated value and adjust the direction to the opposite direction of the error gradient. After 5 iterations, the fault location gradually converged from the initial 30 kilometers to around 30.5 kilometers. At the same time, multimodal feature constraint optimization combined with the changing trends of frequency and phase further reduces the error, so that the positioning accuracy is within ±100 meters.

[0138] This positioning method combining reverse wave propagation, iterative error correction and multimodal feature constraints can significantly improve the accuracy of fault location. Since reverse wave propagation can reflect the propagation characteristics of guided waves in pipelines, it can better adapt to structural changes and different fault types in pipelines. Through iterative error correction, the error can be continuously reduced and the accuracy of positioning can be improved. Multimodal feature constraint optimization further uses information from different modes for correction, reduces the uncertainty caused by a single feature, and makes the system more robust to fault location in complex environments.

[0139] Based on the optimized fault location data and fault classification data, a fault trend prediction model is constructed to generate fault trend prediction data. The fault warning is triggered by dynamically adjusting the warning threshold to generate fault warning data, and the fault warning data is fed back and updated to optimize the warning strategy.

[0140] Preferably, the multimodal feature constraint optimization is based on the joint constraints of time delay features, phase features and spectrum features, and the accuracy of the finally optimized fault location data is improved by performing multiple fine-tuning on the fault location data after iterative error correction.

[0141] In the present invention, based on the optimized fault location data and fault classification data, a fault trend prediction model is constructed, and fault warning is triggered by predicting the possible fault trends in the future and dynamically adjusting the warning threshold, generating fault warning data, and continuously optimizing the warning strategy. This process can provide more accurate warning information for the monitoring of long-distance oil and gas pipelines, and improve the timeliness and accuracy of fault detection.

[0142] The construction of the fault trend prediction model is based on the comprehensive analysis of fault location data and fault classification data. First, the system uses the optimized fault location data to determine the specific location and nature of the fault, including information such as the frequency of fault occurrence and time change trend. At the same time, the fault classification data provides the type of fault (such as cracks, corrosion, etc.) and the judgment of the severity. This information is input into the fault trend prediction model. Through machine learning algorithms or statistical analysis methods, the model can analyze the time series characteristics and spatial distribution characteristics of the fault development process, and then predict possible future fault trends.

[0143] In the process of fault trend prediction, the joint constraints of multimodal features can improve the accuracy of prediction. Time delay features, phase features, and spectrum features show different sensitivities and change patterns in pipeline fault detection. Combined with the joint constraints of these features, the model can more accurately capture subtle changes in the fault development process. For example, spectrum features can reflect the propagation speed of cracks, while phase features may be more suitable for judging the trend of pipeline deformation. By jointly analyzing these features, the system can obtain a more comprehensive fault trend prediction data.

[0144] After the prediction is completed, the system will dynamically adjust the warning threshold according to the changes in the fault trend. The setting of the warning threshold directly determines when the system triggers the fault warning. For example, when the prediction model shows that the fault development will accelerate in the future, the system can lower the warning threshold to issue a warning signal earlier. On the contrary, if the trend is relatively stable, the system can appropriately increase the threshold to avoid too many false alarms. The mechanism of dynamically adjusting the warning threshold can flexibly adapt to changes in trend forecasts, thereby improving the accuracy of warnings.

[0145] In practical applications, the optimized fault location data (such as the specific crack location in the pipeline) and fault classification data (such as the width and depth of the crack) are first input into the fault trend prediction model. Based on historical data and currently detected features, the model uses regression analysis or machine learning methods to predict the direction of fault development and possible impact range. In this process, the system combines the joint constraints of delay features, phase features, and spectrum features to make the prediction results more accurate. For example, if the delay feature shows that the signal propagates slower in a certain area, and the spectrum feature shows that a specific frequency component is enhanced, this may indicate that there is a trend of crack expansion in that area.

[0146] Once the trend prediction is completed, the system will dynamically adjust the warning threshold according to the predicted data. If the model shows that the crack propagation rate is gradually increasing, the system will lower the warning threshold, trigger the fault warning in advance, and generate fault warning data. At the same time, the warning data will be fed back to the system to optimize the warning strategy. For example, the system can update the parameters of the warning strategy based on past warning records and current warning data to improve the reliability of future warnings.

[0147] In a specific embodiment, for a 100-kilometer oil pipeline, the system detected initial signs of cracks at the 30th kilometer, and optimized the fault location data through iterative error correction to determine the crack location at 30.5 kilometers. The fault classification data showed that the crack width was 3 mm and the depth was 1 mm. Through the joint constraint analysis of the time delay change, phase change and spectral components of the crack characteristics, the fault trend prediction model predicted that the crack may expand to a width of 5 mm in the next two weeks. To this end, the system lowered the warning threshold and set the warning range to trigger a warning when the crack width reaches 4 mm.

[0148] In the following monitoring, the system continued to track the crack characteristics at the 30.5 km mark and continuously updated the fault trend prediction data. The early warning strategy is optimized based on the actual monitoring data. For example, if the monitoring data shows that the crack propagates faster than predicted, the system will further lower the threshold to issue an early warning. Through this dynamic adjustment and feedback mechanism, the system can not only accurately locate the fault, but also effectively predict the development trend of the fault, ensuring the safe operation of long-distance pipelines.

[0149] By using optimized fault location data and fault classification data, combined with a fault trend prediction model and dynamic warning threshold adjustment, the present invention can significantly improve the fault detection accuracy and timeliness of warning for long-distance oil and gas pipelines. The joint constraints of multimodal features play an important role in capturing subtle changes in fault development, enabling the system to issue alarms in the early stages of common faults such as cracks and corrosion, effectively preventing fault deterioration. At the same time, the dynamic warning threshold adjustment mechanism enhances the system's adaptability to different fault types, reduces false alarm rates and missed alarm rates, and improves the robustness of the fault warning strategy.

[0150] like Figure 5 As shown, a system for implementing the long-distance oil and gas pipeline fault early warning and positioning method includes the following modules:

[0151] The magneto-acoustic coupling excitation module is used to generate an adaptively modulated waveguide signal; The magneto-acoustic coupling excitation module is used to generate an adaptively modulated waveguide signal to propagate the waveguide along the pipeline. By adjusting the strength and frequency of the magnetic field, the module can generate a waveguide signal that adapts to different operating conditions. During the propagation process, the waveguide signal will change when it encounters a fault area, providing the basis for fault information.

[0152] Fiber Bragg grating sensor modules are placed on oil and gas pipelines to collect real-time data on strain changes caused by guided wave propagation. Fiber Bragg grating sensors are placed on oil and gas pipelines to collect real-time data on strain changes caused by guided wave propagation. The sensor can detect the propagation characteristics of guided waves in the pipeline, such as changes in reflection and transmission, and these data will be used for subsequent fault location and feature analysis.

[0153] Metamaterial coating modules are applied at specific locations on oil and gas pipelines to enhance the reflected wave field of specific frequencies; metamaterial coatings are applied at specific locations on pipelines to enhance the reflection of waveguides of specific frequencies. The characteristics of metamaterials can significantly increase the reflectivity of waveguide signals and reduce signal attenuation, which is particularly helpful in maintaining signal strength and improving detection sensitivity on long-distance pipelines.

[0154] The data fusion processing module is used to perform weighted complementary correlation matrix fusion processing on the collected synchronized preliminary processing data, extract and enhance multi-scale features, and obtain normalized feature data through adaptive feature screening and normalization processing; this module is responsible for performing weighted complementary correlation matrix fusion processing on the collected preliminary processing data to extract and enhance multi-scale features. Adaptive feature screening and normalization processing can eliminate noise and redundant information, generate high-quality normalized feature data, and ensure the accuracy of subsequent data analysis.

[0155] The multimodal deep integration network module is used to receive normalized feature data, fuse multimodal features using a cross-modal self-attention mechanism, and generate fault classification data through recursive analysis and adaptive classification. The received data is input into the multimodal deep integration network, which fuses data features from different modalities through a cross-modal self-attention mechanism. The network comprehensively processes these multimodal data through recursive analysis and adaptive classification to generate fault classification data and reveal the type and severity of the fault.

[0156] The iterative reverse wave propagation positioning module is used to receive fault classification data, perform iterative error correction and multi-modal feature constraint optimization through reverse wave propagation calculation, generate optimized fault location data, and achieve accurate positioning of the fault position; according to the fault classification data, the system performs iterative error correction through reverse wave propagation calculation, and combines multi-modal feature constraint optimization to gradually improve the fault location accuracy. The iterative error correction process fine-tunes the fault position through the optimization algorithm to achieve accurate positioning of the fault.

[0157] The fault trend prediction and early warning module is used to build a fault trend prediction model based on the optimized fault location data and fault classification data, generate fault trend prediction data, trigger fault early warning by dynamically adjusting the early warning threshold, generate fault early warning data, and feedback and update the fault early warning data to optimize the early warning strategy. This module builds a fault trend prediction model based on the optimized fault location data and fault classification data. The prediction model generates fault trend data, and the system dynamically adjusts the early warning threshold according to the changes in real-time data, triggers fault early warning, and feeds back the generated early warning data to the system to optimize the early warning strategy and improve the efficiency of future fault detection and response.

[0158] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A long-distance oil and gas pipeline fault early warning and positioning method, characterized in that: The following steps are involved: The adaptively modulated waveguide signal is generated by the magnetoacoustic coupling exciter, and the fiber Bragg grating sensor arranged on the oil and gas pipeline is used to collect the strain change data caused by the waveguide propagation in real time. The metamaterial coating is applied at a specific location to enhance the reflected wave field of a specific frequency, and the preliminary processed data after synchronization is obtained. The synchronized preliminary processed data is subjected to weighted complementary correlation matrix fusion processing to extract and enhance multi-scale features, and normalized feature data is obtained through adaptive feature screening and normalization processing; The normalized feature data is input into the multimodal deep integration network, the multimodal features are fused using the cross-modal self-attention mechanism, and fault classification data is generated through recursive analysis and adaptive classification; By using the fault classification data, iterative error correction and multi-modal feature constraint optimization are performed through inverse wave propagation calculation to obtain optimized fault location data and achieve accurate positioning of the fault position. Based on the optimized fault location data and fault classification data, a fault trend prediction model is constructed to generate fault trend prediction data. The fault warning is triggered by dynamically adjusting the warning threshold to generate fault warning data, and the fault warning data is fed back and updated to optimize the warning strategy.

2. The long-distance oil and gas pipeline fault early warning and positioning method according to claim 1 is characterized in that: The frequency and amplitude of the waveguide signal generated by the magnetoacoustic coupling exciter are dynamically adjusted according to the real-time operating status of the oil and gas pipeline by adjusting the magnetic field strength and frequency of the magnetoacoustic coupling exciter, so that the waveguide signal generated by the magnetoacoustic coupling exciter can adapt to the changing propagation path and material properties under different environmental conditions.

3. The long-distance oil and gas pipeline fault early warning and positioning method according to claim 1 is characterized in that: The strain change data collected by the fiber Bragg grating sensor is filtered out through a multi-stage filter, which includes a low-pass filter, a high-pass filter and a band-pass filter to remove low-frequency background noise, high-frequency sharp noise and interference signals in non-target frequency bands, respectively, thereby generating preliminary processed data with optimized signal-to-noise ratio.

4. The long-distance oil and gas pipeline fault early warning and positioning method according to claim 1 is characterized in that: Weighted coefficient w for weighted complementary correlation matrix fusion processing i Calculated by the following formula: Among them, w i represents the weight coefficient of the i-th data source; C i represents the relevance value of the i-th data source; n represents the total number of data sources; j represents the index of each data source traversed during the weighted summation process.

5. The long-distance oil and gas pipeline fault early warning and positioning method according to claim 4 is characterized in that: Multi-scale feature extraction is achieved through discrete wavelet transform, which decomposes the synchronized preliminary processed data f(t) into sub-band signals of multiple scales. The transformation formula is: Where f(t) is the original signal; ψ j，k (t) represents discrete wavelet basis function; c j，k represents the corresponding decomposition coefficient; j represents the scale parameter; k represents the translation parameter.

6. The long-distance oil and gas pipeline fault early warning and positioning method according to claim 1 is characterized in that: The multimodal deep integration network is recursively analyzed using the following recursive calculation formula: F n+1 =f(W·F n +b) Among them, F n+1 represents the feature vector of the n+1th layer; F n represents the feature vector of the nth layer; W is the weight matrix, which defines the linear combination relationship of the feature vector; b is the bias term, which is used to adjust the value range of the feature vector; f is the activation function, which acts on the result of the linear combination to introduce nonlinear features.

7. The long-distance oil and gas pipeline fault early warning and positioning method according to claim 6 is characterized in that: The cross-modal self-attention mechanism adjusts the attention weight according to the similarity and difference of different modal features. The calculation of the similarity and difference is based on the dot product operation and normalization processing of the feature vector to generate an attention weight matrix for guiding the weighted fusion of cross-modal features.

8. The long-distance oil and gas pipeline fault early warning and positioning method according to claim 1 is characterized in that: The iterative error correction method uses the following optimization formula for error correction: Among them, L i+1 is the fault location result of the i+1th iteration; L i is the fault location result of the i-th iteration, indicating the current fault location estimate; α is the learning rate, which determines the step size of each iteration; is the gradient of the fault location error function, which is used to adjust the offset of the fault location estimation.

9. The long-distance oil and gas pipeline fault early warning and positioning method according to claim 1 is characterized in that: Multimodal feature constrained optimization is based on the joint constraints of time delay features, phase features and spectrum features. By repeatedly fine-tuning the fault location data after iterative error correction, the accuracy of the final optimized fault location data is improved.

10. A system for implementing the long-distance oil and gas pipeline fault early warning and positioning method according to any one of claims 1 to 9, characterized in that: Includes the following modules: A magneto-acoustic coupling excitation module for generating an adaptively modulated waveguide signal; Fiber Bragg grating sensor module, arranged on oil and gas pipelines, is used to collect real-time data on strain changes caused by guided wave propagation; Metamaterial coating modules are applied at specific locations on oil and gas pipelines to enhance the reflected wave fields of specific frequencies; The data fusion processing module is used to perform weighted complementary correlation matrix fusion processing on the collected synchronized preliminary processed data, extract and enhance multi-scale features, and obtain normalized feature data through adaptive feature screening and normalization processing; A multimodal deep integration network module, which receives normalized feature data, fuses multimodal features using a cross-modal self-attention mechanism, and generates fault classification data through recursive analysis and adaptive classification; Iterative reverse wave propagation positioning module, which is used to receive fault classification data, perform iterative error correction and multi-modal feature constraint optimization through reverse wave propagation calculation, generate optimized fault location data, and realize accurate positioning of the fault position; The fault trend prediction and warning module is used to build a fault trend prediction model based on the optimized fault location data and fault classification data, generate fault trend prediction data, trigger fault warnings by dynamically adjusting the warning threshold, generate fault warning data, and feedback and update the fault warning data to optimize the warning strategy.

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