Method and system for sensing catastrophe risk of active fault zone by integrating long-distance pipeline accompanying measurement, pipe measurement, storage and calculation

Abstract

The invention discloses a method and a system for sensing catastrophe risks of an active fault zone by integrating long-distance pipeline accompanying monitoring, pipe measurement, storage and calculation, and relates to the technical field of safety monitoring of long-distance pipelines. A polymer measuring pipe is laid along with a long-distance pipeline, the surface of the polymer measuring pipe is covered with a flexible conductive film, and distributed vibration optical cables are distributed in a guide groove to collect resistivity signals and acoustic vibration signals respectively. The distributed optical fiber acoustic sensing demodulation module is used for processing acoustic vibration signals, the resistivity dynamic data sensing demodulation module is used for processing resistivity signals, and the processed signals are uploaded through the wireless communication module. An RRAM storage and calculation integrated framework is adopted in the intelligent early warning module, acoustic vibration feature extraction and resistance-strain conversion calculation are completed, double signals are fused to monitor the displacement rate of the active fault in real time, and early warning is issued in the case of supercritical quantity. According to the invention, the displacement rate of the active fault is monitored and early warned in real time, and the early warning signal is issued when the displacement rate is supercritical, so that the safe operation of the long-distance pipeline is ensured.

Description

Technical Field

[0001] This invention relates to the field of safety monitoring technology for long-distance pipelines, specifically to a method that can effectively detect the potential catastrophic risks that active fault zones may pose to long-distance pipelines. Background Technology

[0002] As a country prone to earthquakes, my country has a wide distribution of high-risk earthquake zones. Long-distance oil and gas pipelines, as typical linear engineering projects, inevitably need to traverse active fault zones during their planning and construction. The surface dislocation effects generated by earthquakes along active fault zones can easily lead to pipeline failures such as tensile stress, buckling deformation, and even rupture, posing a serious threat to the safe operation of this vital energy artery. Currently, the academic community both domestically and internationally has conducted systematic research on the failure mechanisms of pipelines traversing active fault zones and the pipe-soil interaction, and has established corresponding theoretical analysis models. However, the field of health monitoring for oil and gas pipelines traversing active fault zones started relatively late and is currently still in the early stages of technological exploration and practical verification. In terms of monitoring and early warning technologies, developed countries have established relatively mature dynamic monitoring systems, achieving real-time perception of pipeline strain through technologies such as distributed fiber optic sensing and InSAR remote sensing. In comparison, my country started relatively late in the field of health monitoring for oil and gas pipelines traversing active fault zones and is currently still in the early stages of technological exploration and practical verification.

[0003] Chinese patent CN202410662627.7 discloses a method for earthquake disaster monitoring and early warning based on active fault hazard sources. This method obtains historical monitoring data of each active fault within the monitoring area, including the fault tensile stress offset ratio, relative slip rate, and groundwater level fluctuation frequency. After preprocessing, training and testing sets are generated, and a machine learning model is constructed. During actual monitoring, real-time monitoring data of active faults is collected, and the trained model is used to predict the real-time monitoring data. The deviations of the fault tensile stress offset ratio, relative slip rate, and groundwater level fluctuation frequency of different active faults are evaluated, and the risk level of earthquake disasters is predicted based on the evaluation results. This method deploys an earthquake monitoring network to monitor crustal movement areas using seismic waves. It is suitable for monitoring network-like active faults. However, seismic wave data has certain range and ambiguity, making it unsuitable for linear projects, such as monitoring active faults in long-distance pipelines. In specific engineering practice, some scholars have proposed using direct-access optical fiber (DAS fiber) for active fault monitoring. However, because optical fibers are not tensile-resistant, significant activity of the active fault can cause fiber breakage and signal loss. Summary of the Invention

[0004] The technical problem to be solved by this invention is to propose a method for sensing the catastrophic risk of active fault zones by integrating pipeline monitoring, storage and computing along with long-distance pipelines, which is applicable to situations where deformation and catastrophic events occur in active fault zones, thereby improving the effectiveness and reliability of early warning of catastrophic risks in active fault zones.

[0005] To solve the above technical problems, the present invention adopts the following technical solution:

[0006] First, this invention proposes a method for sensing the catastrophic risk of active fault zones through integrated pipeline monitoring, storage, and computation along long-distance pipelines, comprising the following steps:

[0007] S1. A polymer measuring tube is laid parallel to the long-distance pipeline. A flexible conductive film array is laid on the surface of the polymer measuring tube, and a vibrating optical cable is encapsulated in the guide groove of its four sides.

[0008] S2. Acquire the acoustic vibration signal of the vibrating optical cable and the dynamic resistivity signal of the flexible conductive film array;

[0009] S3. Transmit the acoustic vibration signal and resistivity dynamic signal to a memristor-based RRAM-based in-memory computing hardware unit; execute them in parallel within the RRAM hardware:

[0010] (a) Extract features from acoustic and vibration signals to identify the activation or cessation behavior of active faults;

[0011] (b) Convert the resistivity dynamic signal into strain value, and generate displacement rate and cumulative displacement through time integration;

[0012] S4. When the displacement rate or cumulative displacement exceeds the critical threshold, a disaster risk warning is triggered.

[0013] Furthermore, in step S1 of the method of the present invention, the polymer probe installation specifically includes:

[0014] The polymer measuring tube is provided with multiple monitoring sections, which are equidistantly distributed along the tube. A set of flexible conductive films is arranged on each monitoring section, and the flexible conductive films on each monitoring section together form a flexible conductive film array. The set of flexible conductive films consists of conductive films respectively arranged on the top and bottom surfaces of the polymer measuring tube, and conductive films respectively arranged on both sides of the polymer measuring tube. The top / bottom conductive films sense axial tension and displacement, and the side conductive films sense tangential tension and displacement.

[0015] Furthermore, in step S2 of the method of the present invention, the measured signals are fused based on spatial positioning, specifically including: after each of the four optical fiber channels is denoised by wavelet denoising, the measured acoustic vibration signals are fused by extended Kalman filtering to obtain the fused acoustic vibration data of the monitoring section; for any monitoring section, the resistivity signal data of each flexible conductive film are fused by extended Kalman filtering to obtain the fused conductive film data of the monitoring section.

[0016] Furthermore, in method step S3 of the present invention, the RRAM hardware execution step includes:

[0017] The acoustic vibration signal time segment and resistivity dynamic signal are mapped to the row / column input terminals of the RRAM matrix according to the preset encoding;

[0018] Multiply-accumulate operations are performed in parallel using RRAM:

[0019] (a) Short-time Fourier transform (STFT) spectrum analysis to extract dominant frequency, energy and critical slowing characteristics;

[0020] (b) Output strain values ​​based on the calibrated resistance change rate-strain relationship matrix, and perform time integration on the strain values ​​to generate displacement rate and cumulative displacement in real time.

[0021] Furthermore, in step S4 of the method of the present invention, the real-time extracted acoustic vibration features are compared with the pre-stored template library for similarity. When the matching degree exceeds the threshold, it is marked as a potential sliding event. Specifically:

[0022] When the characteristic matching degree of the acoustic vibration signal is greater than or equal to the threshold and the displacement rate continuously shows peak segments and the displacement amount is continuously greater than 0, the sliding is determined to start and an early warning is triggered. When the cumulative displacement change rate is less than or equal to the tolerance threshold and the displacement rate approaches 0 and continuously exceeds the set window, the sliding is determined to stop.

[0023] Furthermore, the method of the present invention also includes dynamically adjusting the signal acquisition frequency:

[0024] When the sliding is detected to have started, the acquisition frequency of the acoustic vibration signal and resistivity signal is increased;

[0025] When the sliding stops, the sampling frequency is reduced.

[0026] Furthermore, the flexible conductive film of the present invention is prepared by the following steps:

[0027] Modified graphene oxide was prepared by chemical reduction using aluminum foil as a reducing agent.

[0028] Modified graphene oxide and carbon nanotubes are dispersed in a silicone rubber matrix to form a carbon nanotube-based flexible conductive film.

[0029] On the other hand, this invention proposes a system for sensing the catastrophic risk of active fault zones in long-distance pipelines, comprising:

[0030] Data acquisition module: Composed of polymer measuring tubes arranged parallel to the long-distance pipeline, with vibration optical cables encapsulated in the guide grooves on the four sides of the measuring tube. Multiple monitoring sections are set on the polymer measuring tube, which are equidistantly distributed along the measuring tube. A set of flexible conductive films is arranged on each monitoring section, and the flexible conductive films on all monitoring sections together form a flexible conductive film array. The set of flexible conductive films consists of conductive films arranged on the top and bottom surfaces of the polymer measuring tube, and conductive films arranged on both sides of the polymer measuring tube. The top / bottom conductive films sense axial tension and displacement, and the side conductive films sense tangential tension and displacement.

[0031] The data processing module includes:

[0032] A distributed fiber optic acoustic sensing demodulation module is used to acquire acoustic vibration signals from the optical fibers encapsulated in the guide grooves on the four sides of the measuring tube.

[0033] The resistivity dynamic data sensing and demodulation module is used to acquire the dynamic resistivity signal of the flexible conductive film on the surface of the measuring tube.

[0034] A wireless communication module is used to transmit the received acoustic vibration signal and the dynamic signal of resistance change rate to an intelligent early warning module;

[0035] Intelligent early warning module: It adopts an RRAM-based in-memory computing hardware unit for extracting acoustic and vibration signal features and calculating resistance-strain conversion. The acoustic and vibration signals are used to identify the activation or cessation of activation of active faults. The dynamic signal of resistance change rate is used to convert it into fault displacement rate and cumulative fault displacement. When the fault displacement rate or cumulative fault displacement exceeds the critical value, an early warning signal is automatically issued.

[0036] Furthermore, in the system proposed in this invention, the intelligent early warning module is used for real-time processing and collaborative analysis of acoustic vibration signals and resistivity dynamic signals, and is specifically configured to perform the following actions:

[0037] The time segments of acoustic vibration signals and corresponding resistivity dynamic signals output by the time-picking algorithm are mapped to the row / column input terminals of the RRAM matrix according to the preset encoding rules. The short-time Fourier transform spectrum analysis is directly completed through the analog multiply-accumulate characteristics of the RRAM to extract the dominant frequency, energy and critical slowing-down characteristics. At the same time, the transconductance calculation is performed according to the calibrated resistance change rate-strain relationship matrix to output the strain value, and the strain value is integrated over time to generate the displacement rate and cumulative displacement in real time.

[0038] A feature template library of active fault zone sliding events is pre-stored in the RRAM matrix. The real-time extracted acoustic and vibration signal features are compared with the template library in parallel for similarity. When the matching degree exceeds the set threshold, it is marked as a potential sliding event. The reliability of the event is verified by combining the displacement parameters calculated by the resistivity signal. Specifically, when the matching degree of acoustic and vibration signal features is ≥ the threshold and the displacement rate continuously shows peak segments and the displacement amount is continuously > 0, the sliding is determined to start and an early warning is triggered. When the cumulative displacement change rate is ≤ the tolerance threshold and the displacement rate approaches 0 and continuously exceeds the set window, the sliding is determined to stop and a risk assessment report is generated.

[0039] The polymer probe of this invention is a PP round tube with an outer diameter greater than 35mm and a probe length of not less than 2m; each flexible conductive film segment has a length greater than 50mm and a width greater than 15mm.

[0040] The present invention adopts the above technical solution and has the following beneficial effects compared with the prior art:

[0041] The method provided by this invention integrates the dynamic resistivity signal measured by a flexible conductive film with the acoustic vibration signal measured by optical fiber. This allows the intelligent early warning module to monitor and issue early warnings in real time about the displacement rate of active faults, thereby enabling early warning of catastrophic risks in active fault zones of long-distance pipelines. Specifically, the distributed fiber optic acoustic sensing demodulation module senses fault activity or cessation behavior through acoustic vibration signals to identify whether the fault is activated; the resistivity data from the flexible conductive film can effectively verify fault activation behavior; and the fusion of the dynamic resistivity signal and the optical fiber acoustic vibration signal improves the reliability of identifying catastrophic behavior of active faults. The intelligent early warning module employs a memory-based RAM-based in-memory computing hardware unit, simplifying the data transmission process and reducing overall power consumption by more than 60%. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the layout of the accompanying probes and the system structure in the system of the present invention.

[0043] Figure 2 This is a schematic diagram of the three-dimensional structure of the flexible conductive film and optical fiber arrangement in the system of the present invention.

[0044] Figure 3 This is a schematic diagram of the cross-sectional structure of the flexible conductive film and optical fiber arrangement in the system of the present invention.

[0045] Figure 4 This is a schematic diagram of the composition of each module in the system of the present invention.

[0046] Figure 5 This is the data curve measured by the distributed acoustic sensor in the system of this invention.

[0047] Figure 6 This is the resistance change rate-strain value calibration curve of the flexible conductive film used in the system of this invention.

[0048] Figure 7 This is a flowchart of the data processing and early warning process of the intelligent early warning module in the system of this invention.

[0049] Figure 8 This is a flowchart of the event picking algorithm in the system of this invention.

[0050] Figure 9 This is a flowchart of the extended Kalman filter algorithm in the system of this invention. Detailed Implementation

[0051] The invention will now be further explained with reference to the accompanying drawings.

[0052] This invention encompasses all alternatives, modifications, and equivalent methods and solutions within its core ideas and scope. To provide the public with a more comprehensive understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these detailed descriptions. Furthermore, the accompanying drawings are illustrative and not drawn to scale; this is hereby noted.

[0053] Example 1: A method for sensing the catastrophic risk of active fault zones through integrated pipeline monitoring and data storage in long-distance pipelines. The specific application scenario and the structure of the intelligent early warning and forecasting system adopted in this invention are as follows: Figure 1 As shown, near the long-distance pipeline that needs monitoring, polymer probes are laid parallel to the pipeline, with the probe body parallel to the ground. When the fault is activated, the soil itself moves, causing the probe to deform. Friction generates acoustic vibration signals. The fiber optic acoustic modulation module senses these signals and analyzes the fault activation or cessation behavior. The resistivity dynamic data sensing demodulation module senses the strain value of the active fault.

[0054] like Figure 2 , Figure 3 As shown, guide grooves are formed on both sides of the central axis of the polymer probe 1, and an optical fiber 3 is embedded in each groove. The optical fiber 3 is a common single-mode communication optical fiber. Multiple monitoring sections are set on the polymer probe 1, with each section evenly spaced. A set of flexible conductive films is arranged on each monitoring section. Each set of flexible conductive films consists of conductive films 4 coated axially on the top and bottom surfaces of the polymer probe 1, and conductive films 2 coated on both sides of the polymer probe 1. When the probe undergoes bending deformation or stretching, the resistance change rate of the flexible conductive films and the acoustic vibration signal of the optical fiber change. The conductive films are used to sense the tensile and compressive strain of the probe, characterizing the time and location of the onset of fault activity. The polymer probe 1, flexible conductive films 2 and 4, and optical fiber 3 are end-side devices in this system.

[0055] like Figure 4As shown, this invention includes a data acquisition module, a data processing module, and an intelligent early warning module. The distributed fiber optic acoustic sensing demodulation module, based on phase-sensitive optical time-domain reflectometry (Φ-OTDR) technology, demodulates the backscattered Rayleigh signal from fiber 3. With a sampling frequency set to 10kHz and a spatial resolution of 1m, it can locate the position of micro-vibrations caused by fault slippage. Figure 5 The image shows the time-frequency diagram of the acoustic vibration signal. The resistivity dynamic data fusion module uses a high-precision ADC chip to acquire the resistivity change rate signal of the flexible conductive film. The signal conditioning circuit filters and amplifies the raw data. The electrodes of each conductive film are connected to the module via shielded twisted-pair cables to ensure anti-interference capability. The relationship between resistivity data and strain is calibrated through pre-experiments, such as... Figure 6 As shown. The wireless communication module adopts LoRa and 4G dual-mode transmission. Data from the edge side is aggregated to the gateway via LoRa and then uploaded to the cloud via 4G, ensuring communication reliability in the field.

[0056] The wireless communication module uploads data to the intelligent early warning module for processing and analysis, such as... Figure 7 As shown, for fiber optic acoustic vibration signals, preprocessing (de-trendification and de-meaning) and wavelet denoising are performed first. Then, a time-picking algorithm (STA / LTA algorithm) is used to pick up the fault activity time window, and the dominant frequency, energy, and other feature values ​​are extracted through STFT. These are compared with pre-stored feature templates to confirm the slip event. Simultaneously, the resistance dynamic signal within the STA / LTA picking time period is fused by Kalman filtering to calculate the resistance change rate, which is then converted into strain values ​​according to the calibration relationship. The intelligent early warning module adopts an in-memory computing architecture based on RRAM, which directly performs acoustic vibration signal feature extraction and resistance-strain conversion calculation in parallel within the RRAM hardware. Displacement parameters are output in real time through simulated multiplication and addition operations. When the feature matches and the displacement rate is consistently >0, a slip warning is triggered; when the displacement rate approaches 0, the slip is determined to stop. This system utilizes the non-volatile storage and analog computing characteristics of RRAM to reduce overall power consumption by more than 60%.

[0057] The flowchart of the time-picking algorithm (STA / LTA algorithm) is as follows: Figure 8As shown, its workflow first initializes key parameters such as the short-time window (STA) length N, the long-time window (LTA) length M, and the trigger threshold Th. Then, it inputs the fiber optic acoustic vibration signal data stream in real time and performs sliding window reading. The system synchronously calculates the current STA value (the average signal energy within the short-time window N) and LTA value (the average signal energy within the long-time window M), and obtains the instantaneous ratio R = STA / LTA. When R exceeds the preset threshold Th, the algorithm immediately marks it as a potential fault slip event and records the timestamp. At the same time, it triggers the early warning module to increase the sampling frequency to enhance monitoring accuracy. Signals that are not triggered are dynamically updated with LTA values ​​to adapt to changes in environmental noise. Finally, the algorithm outputs the fault activity time window and extracts the dynamic resistivity data within the time window, providing a highly timely acoustic vibration event criterion for subsequent fusion analysis.

[0058] The flowchart of the extended Kalman filter algorithm is as follows: Figure 9 As shown, the system first initializes the state estimation vector X0, the error covariance matrix P0, and the process noise Q and observation noise parameters R. Then, the system enters a prediction-update iterative loop: In the prediction phase, the algorithm performs a priori estimation of the current state based on the nonlinear state transition equation, while simultaneously linearizing the model using the Jacobian matrix and updating the error covariance to quantify prediction uncertainty. In the update phase, after acquiring actual observations, the system calculates the Jacobian matrix of the observation model, then fuses the prediction and observation information to obtain the Kalman gain. This gain is used to optimally correct the prior state and covariance, forming the posterior estimate, and outputting X_k and P_k. The high-confidence state estimate output in each iteration serves as the initial input for the next time step, forming a closed-loop feedback. The dynamically adjusted covariance matrix continuously reflects the system's adaptive ability to model uncertainty, ultimately providing real-time optimal state tracking for the nonlinear dynamic system.

[0059] When the data analysis system indicates that fault slip has occurred, the early warning system is triggered. The early warning system notifies designated personnel of the slip time and fault slip displacement via email or SMS, achieving the goal of detecting catastrophic risks in active fault zones along long-distance pipelines.

[0060] Example 2: This example proposes a sensing system for the catastrophic risk of active fracture zones in long-distance pipelines, including: a data acquisition module, a data processing module, and an intelligent early warning module. The data acquisition module mainly consists of a traveling probe, composed of a polymer probe, a flexible conductive film, and a vibrating optical cable; the data processing module consists of a distributed fiber optic acoustic sensing demodulation module, a resistivity dynamic data sensing demodulation module, and a wireless communication module; the intelligent early warning module adopts a memristor-based in-memory computing architecture.

[0061] The polymer measuring tube has guide grooves on its four sides along its central axis. Optical fibers are embedded in these grooves and then encapsulated. Flexible conductive films are applied to the surface of the polymer measuring tube. The polymer measuring tube is laid parallel to the long-distance pipeline. Vibration optical cables are connected to a distributed fiber optic acoustic sensing demodulation module via jumpers. Each flexible conductive film is connected to a resistivity dynamic data sensing demodulation module via wires. The polymer measuring tube possesses both rigidity and flexibility, maintaining good monitoring performance even when subjected to significant tensile deformation due to active breakage.

[0062] The distributed fiber acoustic sensing demodulation module is used to collect the acoustic vibration signals of the optical fibers encapsulated in the guide grooves on the four sides of the test tube, and the resistivity dynamic data sensing demodulation module is used to collect the resistivity dynamic signals of the flexible conductive film on the surface of the test tube.

[0063] The wireless communication module is used to transmit the received acoustic vibration signal and the dynamic signal of resistance change rate to the intelligent early warning module.

[0064] Furthermore, the intelligent early warning module adopts a memory-computing integrated architecture based on memristors (RRAM). Acoustic and vibration signal feature extraction and resistance-strain conversion calculation are directly completed in parallel in the RRAM hardware. Acoustic and vibration signals are used to identify the activation or cessation of activation behavior of active faults, and the dynamic signal of resistance change rate is used to convert it into fault displacement rate and cumulative fault displacement. When the fault displacement rate or cumulative fault displacement exceeds the critical amount, an early warning signal is automatically issued.

[0065] Furthermore, the polymer measuring tube is provided with multiple monitoring sections, each section being equally spaced. A set of flexible conductive films is arranged on each monitoring section, and the flexible conductive films on each monitoring section together form the flexible conductive film array. Each set of flexible conductive films consists of conductive films respectively arranged on the top and bottom surfaces of the polymer measuring tube, and conductive films respectively arranged on both sides of the polymer measuring tube. The conductive films on the top and bottom surfaces are used to sense the axial stretching and displacement of the measuring tube, while the conductive films on both sides are used to sense the tangential stretching and displacement of the measuring tube. Each electrode in each set of flexible conductive films is connected to a resistivity dynamic data sensing and demodulation module via wires.

[0066] Furthermore, the intelligent early warning module integrates a memory-based RAM (RRAM) integrated hardware unit for real-time processing and collaborative analysis of acoustic vibration signals (Data 1) and resistivity dynamic signals (Data 2). Specifically, the acoustic vibration signal time segments and corresponding resistivity dynamic signals output by the STA / LTA algorithm are mapped to the row / column inputs of the RRAM matrix according to a preset encoding rule. Short-time Fourier transform (STFT) spectrum analysis is directly performed using the analog multiply-accumulate characteristics of the RRAM to extract the dominant frequency, energy, and critical slowing-down characteristics. Simultaneously, transconductance calculations are performed based on the calibrated resistance change rate-strain relationship matrix to output strain values, and the strain values ​​are integrated over time to generate displacement rate and cumulative displacement in real time. The RRAM matrix... The system pre-stores a feature template library for active fault zone sliding events. It performs parallel similarity comparisons between the real-time extracted acoustic and vibration signal features and the template library. When the matching degree exceeds a set threshold, it is marked as a potential sliding event, and the reliability of the event is verified by combining the displacement parameters calculated by the resistivity signal. When the matching degree of acoustic and vibration signal features is greater than or equal to the threshold and the displacement rate shows continuous peak segments and the displacement amount is continuously greater than 0, the sliding is determined to have started and an early warning is triggered. When the cumulative displacement change rate is less than or equal to the tolerance threshold and the displacement rate approaches 0 and continuously exceeds the set window, the sliding is determined to have stopped and a risk assessment report is generated. The system retains the feature template library by utilizing the non-volatile storage characteristics of RRAM to reduce external access, uses analog domain calculation to avoid redundant data transfer, simplifies the data transmission process, and reduces the overall power consumption by more than 60%.

[0067] Furthermore, the data fusion module performs fusion based on spatial positioning, specifically including: denoising the acoustic vibration signals of the four fiber optic channels measured by the distributed fiber acoustic sensing demodulation module using wavelet denoising, and then fusing them through extended Kalman filtering to obtain the fused acoustic vibration data of the monitoring section, i.e., Data 1; for any monitoring section, fusing the resistivity signal data of each flexible conductive film through extended Kalman filtering to obtain the fused conductive film data of the monitoring section, i.e., Data 2.

[0068] Furthermore, when the intelligent early warning module determines that the active fracture zone has started to slide, it controls the resistivity dynamic data fusion module and the distributed fiber acoustic sensor demodulation module to increase the signal acquisition frequency; when the intelligent early warning module determines that the active fracture zone has stopped sliding, it controls the resistivity dynamic data fusion module and the distributed fiber acoustic sensor demodulation module to decrease the signal acquisition frequency.

[0069] Furthermore, the polymer probe is a PP round tube with an outer diameter greater than 35mm and a probe length of not less than 2m; each segment of the flexible conductive film has a length greater than 50mm and a width greater than 15mm.

[0070] Furthermore, the flexible conductive film is a carbon nanotube-based flexible conductive film. Aluminum foil is used as a reducing agent to prepare modified graphene oxide by chemical reduction method, and then the modified graphene oxide is dispersed together with carbon nanotubes in a silicone rubber matrix to obtain the carbon nanotube-based flexible conductive film.

[0071] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A method for sensing the catastrophic risk of active fault zones through integrated pipeline monitoring, storage, and computation in long-distance pipelines, characterized in that... Includes the following steps: S1. A polymer measuring tube is laid parallel to the long-distance pipeline. A flexible conductive film array is laid on the surface of the polymer measuring tube, and a vibrating optical cable is encapsulated in the guide groove of its four sides. S2. Acquire the acoustic vibration signal of the vibrating optical cable and the dynamic resistivity signal of the flexible conductive film array; S3. Transmit the acoustic vibration signal and resistivity dynamic signal to a memristor-based RRAM-based in-memory computing hardware unit; execute them in parallel within the RRAM hardware: (a) Extract features from acoustic and vibration signals to identify the activation or cessation behavior of active faults; (b) Convert the resistivity dynamic signal into strain value, and generate displacement rate and cumulative displacement through time integration; S4. When the displacement rate or cumulative displacement exceeds the critical threshold, a disaster risk warning is triggered.

2. The method according to claim 1, characterized in that, In step S1, the installation of the polymer probes specifically includes: The polymer measuring tube is provided with multiple monitoring sections, which are equidistantly distributed along the tube. A set of flexible conductive films is arranged on each monitoring section, and the flexible conductive films on each monitoring section together form a flexible conductive film array. The set of flexible conductive films consists of conductive films respectively arranged on the top and bottom surfaces of the polymer measuring tube, and conductive films respectively arranged on both sides of the polymer measuring tube. The top / bottom conductive films sense axial tension and displacement, and the side conductive films sense tangential tension and displacement.

3. The method according to claim 1, characterized in that, In step S2, the measured signals are fused based on spatial positioning. Specifically, this includes: denoising the acoustic vibration signals of the four fiber optic channels by wavelet denoising, and then fusing them by extended Kalman filtering to obtain the fused acoustic vibration data of the monitoring section; for any monitoring section, fusing the resistivity signal data of each flexible conductive film by extended Kalman filtering to obtain the fused conductive film data of the monitoring section.

4. The method according to claim 1, characterized in that, In step S3, the RRAM hardware execution steps include: The acoustic vibration signal time segment and resistivity dynamic signal are mapped to the row / column input terminals of the RRAM matrix according to the preset encoding; Multiply-accumulate operations are performed in parallel using RRAM: (a) Short-time Fourier transform (STFT) spectrum analysis to extract dominant frequency, energy and critical slowing characteristics; (b) Output strain values ​​based on the calibrated resistance change rate-strain relationship matrix, and perform time integration on the strain values ​​to generate displacement rate and cumulative displacement in real time.

5. The method according to claim 1, characterized in that, In step S4, the real-time extracted acoustic and vibration features are compared with the pre-stored template library for similarity. When the matching degree exceeds the threshold, it is marked as a potential sliding event. Specifically: When the characteristic matching degree of the acoustic vibration signal is greater than or equal to the threshold and the displacement rate continuously shows peak segments and the displacement amount is continuously greater than 0, the sliding is determined to start and an early warning is triggered. When the cumulative displacement change rate is less than or equal to the tolerance threshold and the displacement rate approaches 0 and continuously exceeds the set window, the sliding is determined to stop.

6. The method according to claim 1, characterized in that, It also includes dynamically adjusting the signal acquisition frequency: When the sliding is detected to have started, the acquisition frequency of the acoustic vibration signal and resistivity signal is increased; When the sliding stops, the sampling frequency is reduced.

7. The method according to claim 1, characterized in that, The flexible conductive film is prepared by the following steps: Modified graphene oxide was prepared by chemical reduction using aluminum foil as a reducing agent. Modified graphene oxide and carbon nanotubes are dispersed in a silicone rubber matrix to form a carbon nanotube-based flexible conductive film.

8. A sensing system for the catastrophic risk of active fault zones in long-distance pipelines, characterized in that, include: Data acquisition module: Composed of polymer measuring tubes arranged parallel to the long-distance pipeline, with vibration optical cables encapsulated in the guide grooves on the four sides of the measuring tube. Multiple monitoring sections are set on the polymer measuring tube, which are equidistantly distributed along the measuring tube. A set of flexible conductive films is arranged on each monitoring section, and the flexible conductive films on all monitoring sections together form a flexible conductive film array. The set of flexible conductive films consists of conductive films arranged on the top and bottom surfaces of the polymer measuring tube, and conductive films arranged on both sides of the polymer measuring tube. The top / bottom conductive films sense axial tension and displacement, and the side conductive films sense tangential tension and displacement. The data processing module includes: A distributed fiber optic acoustic sensing demodulation module is used to acquire acoustic vibration signals from the optical fibers encapsulated in the guide grooves on the four sides of the measuring tube. The resistivity dynamic data sensing and demodulation module is used to acquire the dynamic resistivity signal of the flexible conductive film on the surface of the measuring tube. A wireless communication module is used to transmit the received acoustic vibration signal and the dynamic signal of resistance change rate to an intelligent early warning module; Intelligent early warning module: It adopts an RRAM-based in-memory computing hardware unit for extracting acoustic and vibration signal features and calculating resistance-strain conversion. The acoustic and vibration signals are used to identify the activation or cessation of activation of active faults. The dynamic signal of resistance change rate is used to convert it into fault displacement rate and cumulative fault displacement. When the fault displacement rate or cumulative fault displacement exceeds the critical value, an early warning signal is automatically issued.

9. The system according to claim 8, characterized in that, The intelligent early warning module is used for real-time processing and collaborative analysis of acoustic vibration signals and resistivity dynamic signals, and is specifically configured to perform the following actions: The time segments of acoustic vibration signals and corresponding resistivity dynamic signals output by the time-picking algorithm are mapped to the row / column input terminals of the RRAM matrix according to the preset encoding rules. The short-time Fourier transform spectrum analysis is directly completed through the analog multiply-accumulate characteristics of the RRAM to extract the dominant frequency, energy and critical slowing-down characteristics. At the same time, the transconductance calculation is performed according to the calibrated resistance change rate-strain relationship matrix to output the strain value, and the strain value is integrated over time to generate the displacement rate and cumulative displacement in real time. A feature template library of active fault zone sliding events is pre-stored in the RRAM matrix. The real-time extracted acoustic and vibration signal features are compared with the template library in parallel for similarity. When the matching degree exceeds the set threshold, it is marked as a potential sliding event. The reliability of the event is verified by combining the displacement parameters calculated by the resistivity signal. Specifically, when the matching degree of acoustic and vibration signal features is ≥ the threshold and the displacement rate continuously shows peak segments and the displacement amount is continuously > 0, the sliding is determined to start and an early warning is triggered. When the cumulative displacement change rate is ≤ the tolerance threshold and the displacement rate approaches 0 and continuously exceeds the set window, the sliding is determined to stop and a risk assessment report is generated.

10. The system according to claim 8, characterized in that, The polymer probe is a PP round tube with an outer diameter greater than 35mm and a length of not less than 2m; each flexible conductive film segment has a length greater than 50mm and a width greater than 15mm.

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