A quantitative assessment method for multi-source disturbance safety of underground pipelines

Abstract

This invention relates to the field of underground pipeline safety monitoring technology, specifically to a method for quantitative assessment of the safety of underground pipelines under multi-source disturbances. The method includes: acquiring distributed optical response (DAS) data generated by sensing optical fibers under external disturbances and establishing a correspondence between the monitoring channel and the actual spatial location; preprocessing, identifying events, and characterizing the DAS data to obtain at least one DAS response characterization quantity; establishing a correspondence between the DAS response characterization quantity and at least one pipeline mechanical state index based on the propagation and coupling characteristics of the multi-media environment in which the underground pipeline is located; inputting the measured DAS response characterization quantity to estimate the pipeline mechanical state index of the target pipe section, and outputting the underground pipeline safety impact judgment result and risk classification result. This invention improves the accuracy and interpretability of underground pipeline safety assessment, avoids misjudgment based on a single response amplitude, and is applicable to risk assessment under multi-source disturbances and damage conditions.

Description

Technical Field

[0001] This invention relates to the field of underground pipeline safety monitoring technology, specifically to a method for quantitative assessment of the safety of underground pipelines under multi-source disturbances. Background Technology

[0002] Underground pipelines play a crucial role in the transportation of water, drainage, gas, heat, and energy, serving as vital infrastructure for urban operations. External disturbances such as construction activities, traffic loads, and earthquakes are significant contributing factors to underground pipeline accidents. These include direct damage such as construction excavation, as well as indirect disturbances such as vibrations from nearby construction, repeated traffic loads, and low-intensity earthquakes. The former is characterized by its suddenness and severe consequences, while the latter is more insidious and cumulative, easily inducing pipeline fatigue, loosening of joints, and the expansion of existing damage.

[0003] Distributed fiber optic acoustic sensing technology utilizes optical fibers along pipelines to achieve continuous, long-distance, and high spatiotemporal resolution vibration observation, demonstrating advantages in event identification, classification, and localization. However, existing technologies often directly regard the responses measured along the optical fiber as representative of external disturbances or pipeline responses, primarily remaining at the level of abnormal event perception, and are difficult to directly serve for quantitative safety assessment of underground pipelines.

[0004] The root causes of the above problems include at least two aspects: First, when external disturbances propagate in a multi-medium system consisting of road surface, soil, backfill layer, pipeline and internal medium, they are affected by the combined effects of medium wave velocity, impedance difference, interface transmission, burial conditions and fiber coupling state. The resulting DAS response is not a simple mapping of disturbance information. Second, DAS directly measures the fiber phase change or the equivalent axial strain obtained by its conversion, while the safety assessment of underground pipelines is really concerned with mechanical indicators such as pipe wall stress, deformation, joint response and damage state. There is a lack of stable, clear and interpretable physical mapping relationship between the two. Summary of the Invention

[0005] The purpose of this invention is to provide a quantitative assessment method for the safety of underground pipelines under multi-source disturbances in order to solve the above-mentioned technical problems, thereby improving the accuracy and interpretability of underground pipeline safety assessments, avoiding misjudgment based on a single response amplitude, and applicable to risk assessment under multi-source disturbance and damage conditions.

[0006] The objective of this invention can be achieved through the following technical solutions: A method for quantitative safety assessment of multi-source disturbances in underground pipelines includes the following steps: S1. Acquire distributed optical response data generated by the sensing fiber under external disturbance, and establish the correspondence between the monitoring channel and the actual spatial location; S2. Preprocess, identify events, and characterize the distributed optical response data to obtain at least one DAS response characterization quantity; S3. Based on the propagation and coupling characteristics of the multi-media environment in which underground pipelines are located, establish the correspondence between DAS response characterization quantities and at least one pipeline mechanical state index. S4. Based on the correspondence, input the measured DAS response characterization quantity to estimate the pipeline mechanical state index of the target pipe section, and output the underground pipeline safety impact judgment result and risk classification result.

[0007] Furthermore, step S1 includes: S11. Obtain distributed optical response data generated by the sensing optical fiber under the action of external disturbance by the sensing optical fiber deployed along the pipeline body, and perform parameterized description of the external disturbance. S12. Acquire auxiliary response data by using at least one point sensor near the underground pipeline; S13. Based on the deployment location of the sensing optical fiber, establish the correspondence between the optical fiber monitoring channel and the actual spatial location, so that each optical fiber segment reflects the actual spatial location around the pipeline, and map the distributed optical response data with the actual pipeline location. S14. Perform data processing on the acquired distributed optical response data.

[0008] Furthermore, external disturbances include construction activities, traffic loads, ground motion, impact loads, backfill disturbances, or combinations thereof.

[0009] Furthermore, the preprocessing includes at least one of the following: phase-strain conversion, denoising, filtering, synchronization, channel-position calibration, event window partitioning, time-frequency analysis, envelope extraction, peak extraction, root mean square calculation, band energy calculation, and propagation feature extraction.

[0010] Furthermore, the distributed optical response data includes phase change data, and the method for characterizing the distributed optical response data includes converting the phase change data into the DAS equivalent axial strain response ε(x,t): Where λ is the laser wavelength, n is the fiber refractive index, and ξ is the photoelastic parameter. This is the system gauge length.

[0011] Furthermore, the distributed optical response data includes field-measured response data. The methods for characterizing the distributed optical response data include: estimating the noise level using the resting period before the event, the background period, and the reference period, and performing noise floor correction, background compensation, and scale normalization on the measured response.

[0012] Furthermore, step S3 includes: S31. Construct a multi-media digital twin model that includes two of the following: road structure layer, soil layer, backfill layer, pipeline body, internal medium and interface coupling relationship. Set up a virtual sensing path in the multi-media digital twin model that is consistent with the actual sensing fiber optic position along the pipeline. S32. Set the input for the multi-media digital twin model, including physical parameters related to the piping system and external disturbance characteristics; S33. Use auxiliary response data to calibrate the multi-media digital twin model. The calibration target includes at least one of the following: response magnitude, spatial distribution law, peak position, attenuation trend, and frequency band characteristics. S34. Extract the simulation response corresponding to the virtual sensing path. The simulation response is used to estimate the pipeline mechanical state index of the target pipe section.

[0013] Furthermore, the mechanical state indicators of pipelines include at least one of the following: pipe wall stress, equivalent stress, axial stress, circumferential stress, displacement, deformation, relative displacement of joints, joint opening, local stiffness degradation index, damage index, and fatigue-related index.

[0014] Furthermore, the method for establishing the correspondence includes: establishing the conversion relationship between the pipeline mechanical state and the DAS response characterization quantity for different working conditions and different structural constraints. The conversion relationship is expressed as follows: in, The equivalent stress requirement at position x, This is the corrected response characterization. These are the conversion parameters for the corresponding operating condition category.

[0015] Furthermore, step S4 includes: S41. Introducing a damage correction factor and threshold correction amount Output the mechanical inversion results and risk threshold under the damage state: in, For the equivalent stress requirement after damage correction, The risk threshold in a healthy state. This is the risk threshold after damage correction; S42, Introducing Disturbance Intensity Repeat action factor Inversion mechanical indices and existing damage factors Construct the risk indicator R: in to As preset weights, and + + + = 1; S43. Based on the risk indicator R and the revised risk threshold The corresponding relationship is used to output the risk level.

[0016] Furthermore, the risk classification is constructed by considering at least two of the following types of information: external disturbance attributes, pipeline mechanical state indicators, existing damage correction information, repeated action information, and safety thresholds; and outputs risk results of at least three levels.

[0017] Furthermore, a quantitative safety assessment system for multi-source disturbances in underground pipelines includes: The sensing and acquisition module is used to acquire distributed optical response data generated by the sensing fiber under external disturbances; The spatial calibration module is used to establish the correspondence between the monitoring channel and the actual spatial location; The signal processing module is used to preprocess, identify events, and characterize the distributed optical response data to obtain at least one DAS response characterization quantity. The relationship establishment module is used to establish the correspondence between DAS response characterization quantities and at least one pipeline mechanical state index based on the propagation and coupling characteristics of the multi-media environment in which the underground pipeline is located. The state estimation module is used to estimate the pipe mechanical state indicators of the target pipe section based on the corresponding relationship; The risk assessment module is used to output the safety impact assessment results and risk classification results of underground pipelines based on the pipeline mechanical state indicators.

[0018] Furthermore, an electronic device includes a processor and a memory, the memory storing a computer program that, when executed by the processor, implements the above-described method.

[0019] Furthermore, a computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the above-described method.

[0020] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention no longer simply equates the DAS response with the disturbance signal or the degree of pipeline hazard. Instead, it introduces multi-media propagation constraints and relationship establishment processes, making the physical meaning of the DAS response clearer and the evaluation results more interpretable.

[0021] 2. This invention establishes a conversion link from DAS response characterization quantities to pipeline mechanical state indicators, which can transform distributed optical response data, which is traditionally only used for event identification, into indicators such as stress, deformation, joint response and damage indicators that can be used for engineering safety assessment.

[0022] 3. This invention can distinguish the different pipeline mechanical risks corresponding to the same DAS response under different pipe materials and different structural constraints, avoid misjudgment based solely on the DAS response amplitude, and thus improve the accuracy of underground pipeline safety assessment.

[0023] 4. This invention takes into account multiple disturbance scenarios such as construction activities, traffic loads, and earthquakes, and can consider existing damage and repeated action factors, making it suitable for online deployment of projects and risk classification along the route. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the structure of the underground pipeline multi-source disturbance safety quantitative assessment system in an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the underground pipeline sensing unit, electronic equipment, and computer-readable storage medium in an embodiment of the present invention. Figure 3 This is a schematic diagram of a multi-media digital twin model of "road-soil-pipe-flow" in an embodiment of the present invention. Detailed Implementation

[0025] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0026] This implementation addresses the general scenario of multi-source disturbance safety assessment for underground pipelines. First, it acquires distributed optical response (DAS) data generated by sensing fibers deployed along the underground pipeline, and can combine this with auxiliary response data obtained from point sensors placed near the pipeline. After calibrating the correspondence between the fiber optic monitoring channel and the actual spatial location, it performs synchronization, noise reduction, event window segmentation, and response characterization on the data, obtaining at least one DAS response characterization quantity such as peak value, root mean square (RMS), bandwidth energy, propagation attenuation rate, arrival time difference, and envelope. Subsequently, considering the multi-media propagation and coupling characteristics of the underground pipeline environment, it establishes or invokes a correspondence between the DAS response characterization quantities and the pipeline's mechanical state indicators. Based on this correspondence, it estimates the stress, deformation, joint response, or damage indicators of the target pipe section, ultimately forming a safety impact assessment result and a risk classification result.

[0027] In this general technical process, the correspondence is not limited to a single form. It can originate from a digital twin model under structural dynamics and wave propagation constraints, an empirical transfer model calibrated from measured data, or a hybrid mapping model combining physical priors and data-driven approaches. In other words, the core of this invention is not limited to a specific modeling software, a specific solver, or a specific mathematical expression, but rather to transforming the DAS response characterization quantity into a pipeline mechanical state index that can be used for engineering safety assessment, and outputting risk results based on this state index.

[0028] like Figures 1 to 3 The method for quantitative safety assessment of multi-source disturbances in underground pipelines, as shown, includes the following steps: S1. Acquire distributed optical response data generated by the sensing fiber under external disturbance, and establish the correspondence between the monitoring channel and the actual spatial location; further, step S1 includes: S11. Distributed optical response data generated by sensing optical fibers under external disturbances is acquired through fibers deployed along the pipeline body to provide a parameterized description of the external disturbances. The sensing optical fibers are deployed along the underground pipeline body to ensure that they can sense external disturbances and generate distributed optical response data. External disturbances include construction activities, traffic loads, seismic motion, impact loads, backfilling disturbances, or combinations thereof. Through the analysis of external disturbances, different types of disturbances can be parameterized for subsequent analysis and processing.

[0029] S12. Auxiliary response data is acquired by at least one point sensor near the underground pipeline. In addition to the optical response data acquired by the sensing fiber, auxiliary response data is also acquired by point sensors deployed near the underground pipeline, such as accelerometers, displacement sensors, or strain sensors. The point sensors are used to provide local reference response data, which can be used to calibrate and verify the signal of the main fiber sensor, thereby improving the reliability and accuracy of the data.

[0030] S13. Based on the deployment location of the sensing optical fibers, establish a correspondence between the optical fiber monitoring channels and the actual spatial locations, ensuring that each optical fiber segment reflects the actual spatial location around the pipeline, and mapping the distributed optical response data to the actual pipeline location; ensuring that the data collected from the optical fibers accurately reflects the situation around the pipeline. This mapping relationship can be calibrated through engineering surveying, on-site positioning technology, and other means to improve the spatial positioning accuracy of the data.

[0031] S14. Perform data processing on the acquired distributed optical response data.

[0032] S2. Preprocess, identify events, and characterize the distributed optical response data to obtain at least one DAS response characterization quantity. Preprocessing includes at least one of the following: phase-strain conversion, denoising, filtering, synchronization, channel-position calibration, event window division, time-frequency analysis, envelope extraction, peak extraction, root mean square calculation, bandwidth energy calculation, and propagation feature extraction. Specifically, phase-strain conversion converts the phase change data acquired from the optical fiber into corresponding strain data. Based on the distributed optical fiber sensing principle, using a known phase-strain conversion relationship, the phase change of the optical fiber is converted into a strain response along the fiber, and the phase change data is converted into the DAS equivalent axial strain response ε(x,t). Where λ is the laser wavelength, n is the fiber refractive index, and ξ is the photoelastic parameter. The system gauge length is defined as follows: Denoising is used to remove noise caused by environmental interference, equipment errors, or other irrelevant signal sources; in the filtering step, high-frequency or low-frequency noise in the signal is removed through band-pass filtering, low-pass filtering, or high-pass filtering; the synchronization step ensures that data collected by multiple sensors are consistent in time; channel-position calibration is the process of ensuring accurate mapping between the sensing fiber optic monitoring channel and the actual spatial location; event window partitioning is used to divide the response data into multiple time periods based on the characteristics of external disturbance events. The data within each event window represents an independent disturbance event, and event window partitioning allows each event to be analyzed individually, thereby obtaining more accurate response characteristics; the time-frequency analysis step is used to extract the spectral characteristics of the signal and analyze the frequency changes of the signal in different time periods; envelope extraction is used to perform envelope analysis on the signal to extract the signal envelope; peak extraction is used to extract significant extreme points from the processed signal; root mean square (RMS) is used to calculate and evaluate the overall strength or energy of the signal; the propagation feature extraction step is used to analyze the characteristics of the signal propagation process, such as signal attenuation and propagation speed. Furthermore, distributed optical response data includes field-measured response data. Methods for characterizing distributed optical response data include: estimating noise levels using resting periods, background periods, and reference periods before the event, and performing noise floor correction, background compensation, and scale normalization on the measured response. Field-measured data can come from raw signals acquired by sensing fibers or from other sensors. The noise level in the signal is estimated using resting periods, background periods, or reference periods before the event. These periods are typically time windows before the disturbance event occurs, or periods with minimal external interference, representing the normal operating state of the system. Within these periods, a baseline level of background noise can be calculated. Common methods for noise estimation include averaging, standard deviation calculation, and wavelet denoising. A resting period refers to a stable period without disturbance before the event; a background period refers to a period with slight interference in the signal but no significant disturbance; and a reference period is a specific time period before a known disturbance occurs, used as a noise baseline. Noise floor correction aims to remove low-frequency noise from the signal, highlighting the target response. The background compensation step is used to further adjust for interference signals caused by environmental factors or equipment errors in the data. By comparing the normal signals during resting and background periods, a compensation coefficient can be calculated and applied to the measured response data to remove errors caused by background noise. Since different sensors and monitoring environments may cause amplitude differences in the signals, normalization helps eliminate these differences, enabling effective comparison of response data from different time periods and pipeline locations.

[0033] S3. Based on the propagation and coupling characteristics of the multi-media environment in which underground pipelines are located, establish the correspondence between DAS response characterization quantities and at least one pipeline mechanical state index; further, step S3 includes: S31. Construct a multi-media digital twin model including two of the following: pavement structure layer, soil layer, backfill layer, pipeline body, internal medium, and interface coupling relationship. Set a virtual sensing path in the multi-media digital twin model that matches the actual location of the sensing optical fibers deployed along the pipeline. The pavement structure layer characterizes the conduction characteristics of the upper soil and pavement structure to disturbances; the soil layer characterizes the mechanical properties, density, and response to external disturbances of the underground soil; the backfill layer characterizes the mechanical properties of the backfill soil surrounding the pipeline, especially the potential impacts during construction; the pipeline body characterizes the mechanical parameters such as pipeline material, thickness, and strength; the internal medium characterizes the properties of the fluid inside the pipeline, such as flow velocity and fluid type; and the interface coupling relationship characterizes the interaction and coupling relationship between the pipeline and the surrounding soil and backfill layer. The virtual sensing path matches the actual optical fiber deployment location to match the responses of each media layer in the model with the actual response data of the sensing optical fibers.

[0034] S32, such as Figure 3 As shown, the inputs of the multi-media digital twin model are set, including physical parameters related to the pipeline system and external disturbance characteristics; the inputs include disturbance parameter sets, as well as pipe material, geometric dimensions, burial depth, backfill medium parameters, damping parameters, internal medium state and coupling boundary conditions, etc.

[0035] S33. Use auxiliary response data to calibrate the multi-media digital twin model. The calibration target includes at least one of the following: response magnitude, spatial distribution law, peak position, attenuation trend, and frequency band characteristics. S34. Extract the simulation response corresponding to the virtual sensing path. The simulation response is used to estimate the pipeline mechanical state indicators of the target pipe section. According to the setting of the virtual sensing path, the model calculates and outputs the simulation response at the corresponding location. Among them, the pipeline mechanical state indicators include at least one of the following: pipe wall stress, equivalent stress, axial stress, circumferential stress, displacement, deformation, joint relative displacement, joint opening, local stiffness degradation index, damage index, and fatigue-related index. In addition to the digital twin model, the correspondence can also be established using an experience transfer model or a hybrid mapping model. This implementation method constructs a multi-media digital twin model and combines it with auxiliary response data collected on-site to accurately simulate and estimate the mechanical state of underground pipelines. From model establishment, input setting, calibration to simulation response extraction, it is ensured that the final mechanical state indicators can accurately reflect the real situation of the pipeline. Specifically, the method for establishing the correspondence includes: establishing the conversion relationship between the pipeline mechanical state and DAS response characterization quantities for different working conditions and different structural constraints. The conversion relationship is expressed as: in, The equivalent stress requirement at position x, This is the corrected response characterization. These are the conversion parameters for the corresponding operating condition categories. The DAS response characteristics are converted between the pipeline's mechanical state indices and other parameters. Operating conditions typically include the pipeline's operational state and the intensity of external disturbances. Under each operating condition, the pipeline's response characteristics differ; therefore, conversion relationships need to be established for each condition. The pipeline's geometry, support method, burial depth, and other structural parameters affect its response to external disturbances. Structural conditions lead to different pipeline response characteristics; therefore, conversion relationships are also needed for each different structural constraint condition. The establishment of these conversion relationships relies on a large amount of experimental data or numerical simulation results. Regression analysis, least squares methods, or machine learning methods can be used to determine the conversion parameters. .

[0036] S4. Based on the correspondence, input the measured DAS response characterization quantity to estimate the pipeline mechanical state index of the target pipe section, and output the underground pipeline safety impact judgment result and risk classification result.

[0037] Furthermore, step S4 includes: S41. Introducing a damage correction factor and threshold correction amount Output the mechanical inversion results and risk threshold under the damage state: in, For the equivalent stress requirement after damage correction, The risk threshold in a healthy state. The risk threshold after damage correction; damage correction factor This reflects the degree of degradation in the pipeline's mechanical properties, such as localized corrosion, loose joints, or crack propagation. The damage correction factor, determined through experimental data or engineering experience, can adjust the mechanical calculation results according to different damage types and degrees. A threshold correction is introduced. This is used to adjust the risk threshold from a healthy state to a damaged state. The threshold correction amount is determined based on the type, location, and extent of pipeline damage, making the risk assessment results more accurate.

[0038] S42, Introducing Disturbance Intensity Repeat action factor Inversion mechanical indices and existing damage factors Construct the risk indicator R: in to As preset weights, and + + + = 1; Disturbance intensity Used to describe the intensity of the impact of external disturbances on pipelines, such as construction loads and traffic loads. Disturbance intensity reflects the instantaneous stress or deformation caused to the pipeline by external events; repetitive action factor. Used to assess potential fatigue damage to pipelines under repeated disturbances; inversion mechanical indices Used to reflect the immediate response of the pipeline; existing damage factors This describes the impact of existing damage to a pipeline on its mechanical behavior. By adjusting the weights of each factor, the impact of different risk factors on the overall risk of the pipeline is assessed.

[0039] S43. Based on the risk indicator R and the revised risk threshold The risk classification is constructed by considering at least two of the following types of information: external disturbance attributes, pipeline mechanical state indicators, existing damage correction information, repeated action information, and safety thresholds; and outputs at least three levels of risk results. The pipeline risk is divided into different levels. For example, low risk, medium risk, high risk, or different classifications such as safe, warning, and high risk. These risk levels represent the safety of the pipeline under current operating conditions and damage states. Risk thresholds. This is a corrected value based on the pipeline's health and damage status. When R exceeds the threshold, the pipeline is considered to be in a high-risk state; when R is below the threshold, the pipeline is in a low-risk state.

[0040] In a set of demonstrative implementations, an underground pipeline test trench approximately 30 m long, 1 m wide, and 1.3 m deep can be constructed. The pipeline is buried at the bottom of the trench, and the backfill layer includes a lower backfill sand layer encasing the pipeline and an upper restored soil layer. Comparative pipe sections with significantly different stiffness are set along the pipeline, such as a high-stiffness metal section, a low-stiffness polymer section, and a pipeless reference section, to obtain response comparisons under different constraint conditions. The sensing path can be formed by a single single-mode optical fiber deployed along the pipeline, and the correspondence between the monitoring channel and the actual location is established through spatial calibration. Auxiliary point sensors can be set near the optical fiber for reference verification. The DAS host can use time-gated digital optical frequency domain reflectometry (DOL) technology to achieve distributed dynamic response acquisition, with a spatial sampling interval on the order of 0.2 m, a gauge length on the order of meters, and a sampling frequency on the order of kHz; the auxiliary accelerometer sampling frequency can be below the order of kHz. The disturbance scenarios can include crushing conditions, mechanical traffic conditions, and mechanical excavation conditions. Among them, the mechanical excavation condition can be used as a high-energy, strongly non-stationary representative scenario to establish the response-mechanical mapping relationship along the line.

[0041] In the demonstrative implementation, comparisons between actual measurements and simulations revealed a clear hierarchical relationship in the DAS response along the pipeline: the response was lowest in high-stiffness pipe sections, followed by low-stiffness pipe sections, with the highest response in the pipeless reference section. Further analysis of the pipe wall stress extracted using a multi-media model showed that the DAS response and actual stress requirements do not exhibit a simple co-increase relationship; material-related conversion parameters can differ significantly among different pipe materials. This demonstrates that the "response characterization—relationship establishment—state estimation—risk assessment" technical process employed in this invention can effectively avoid errors caused by directly judging the pipeline's hazard level based solely on the DAS response amplitude.

[0042] Specifically, a quantitative safety assessment system for multi-source disturbances in underground pipelines includes: The sensing and acquisition module is used to acquire distributed optical response data generated by the sensing fiber under external disturbances; The spatial calibration module is used to establish the correspondence between the monitoring channel and the actual spatial location; The signal processing module is used to preprocess, identify events, and characterize the distributed optical response data to obtain at least one DAS response characterization quantity. The relationship establishment module is used to establish the correspondence between DAS response characterization quantities and at least one pipeline mechanical state index based on the propagation and coupling characteristics of the multi-media environment in which the underground pipeline is located. The state estimation module is used to estimate the pipe mechanical state indicators of the target pipe section based on the corresponding relationship; The risk assessment module is used to output the safety impact assessment results and risk classification results of underground pipelines based on the pipeline mechanical state indicators.

[0043] Specifically, an electronic device includes a processor and a memory, the memory storing a computer program that, when executed by the processor, implements the method described above.

[0044] Specifically, a computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the above-described method.

[0045] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0046] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in sequences other than those illustrated or described herein.

[0047] The present invention has been further described above with reference to specific embodiments. However, it should be understood that the specific description herein should not be construed as limiting the nature and scope of the present invention. Various modifications made to the above embodiments by those skilled in the art after reading this specification are all within the scope of protection of the present invention.

Claims

1. A method for quantitatively assessing the safety of underground pipelines under multi-source disturbance, characterized in that, Includes the following steps: S1. Acquire distributed optical response data generated by the sensing fiber under external disturbance, and establish the correspondence between the monitoring channel and the actual spatial location; S2. Preprocess the distributed optical response data, identify events, and characterize the response to obtain at least one DAS response characterization quantity; S3. Based on the propagation and coupling characteristics of the multi-media environment in which the underground pipeline is located, establish the correspondence between the DAS response characterization quantity and at least one pipeline mechanical state index. S4. Based on the aforementioned correspondence, input the measured DAS response characterization quantity to estimate the pipeline mechanical state index of the target pipe section, and output the underground pipeline safety impact judgment result and risk classification result.

2. The method according to claim 1, characterized in that, Step S1 includes: S11. Obtain distributed optical response data generated by the sensing optical fiber under the action of external disturbance through the sensing optical fiber deployed along the pipeline body, and perform parameterized description of the external disturbance. S12. Acquire auxiliary response data by using at least one point sensor near the underground pipeline; S13. Based on the deployment location of the sensing optical fiber, establish a correspondence between the optical fiber monitoring channel and the actual spatial location, so that each segment of optical fiber reflects the actual spatial location around the pipeline, and map the distributed optical response data with the actual pipeline location. S14. Perform data processing on the acquired distributed optical response data.

3. The method according to claim 1, characterized in that: The external disturbances include construction activities, traffic loads, ground motion, impact loads, backfill disturbances, or combinations thereof.

4. The method according to claim 1, characterized in that: The preprocessing includes at least one of the following: phase-strain conversion, denoising, filtering, synchronization, channel-position calibration, event window division, time-frequency analysis, envelope extraction, peak extraction, root mean square calculation, frequency band energy calculation, and propagation feature extraction.

5. The method according to claim 1, characterized in that: The distributed optical response data includes phase change data, and the method for characterizing the distributed optical response data includes: converting the phase change data into a DAS equivalent axial strain response ε(x,t): Where λ is the laser wavelength, n is the fiber refractive index, and ξ is the photoelastic parameter. This is the system gauge length.

6. The method according to claim 1, characterized in that: The distributed optical response data includes field-measured response data. The method for characterizing the distributed optical response data includes: estimating the noise level using the resting period before the event, the background period, and the reference period, and performing noise floor correction, background compensation, and scale normalization on the measured response.

7. The method according to claim 2, characterized in that: Step S3 includes: S31. Construct a multi-media digital twin model that includes two of the following: road structure layer, soil layer, backfill layer, pipeline body, internal medium and interface coupling relationship; and set a virtual sensing path in the multi-media digital twin model that is consistent with the actual sensing fiber optic position laid along the pipeline. S32. Set the input of the multi-media digital twin model, the input including physical parameters related to the pipeline system and external disturbance characteristics; S33. The multi-media digital twin model is calibrated using the auxiliary response data. The calibration target includes at least one of the following: response magnitude, spatial distribution pattern, peak position, attenuation trend, and frequency band characteristics. S34. Extract the simulation response corresponding to the virtual sensing path, and use the simulation response to estimate the pipeline mechanical state index of the target pipe section.

8. The method according to claim 7, characterized in that: The mechanical state indicators of the pipeline include at least one of the following: pipe wall stress, equivalent stress, axial stress, circumferential stress, displacement, deformation, relative displacement of joints, joint opening, local stiffness degradation index, damage index, and fatigue-related index.

9. The method according to claim 7, characterized in that: The method for establishing the correspondence includes: establishing a conversion relationship between the pipeline mechanical state and the DAS response characterization quantity for different working conditions and different structural constraints, wherein the conversion relationship is expressed as: in, The equivalent stress requirement at position x, This is the corrected response characterization. These are the conversion parameters for the corresponding operating condition category.

10. The method according to claim 9, characterized in that: Step S4 includes: S41. Introducing a damage correction factor and threshold correction amount Output the mechanical inversion results and risk threshold under the damage state: in, For the equivalent stress requirement after damage correction, The risk threshold in a healthy state. This is the risk threshold after damage correction; S42, Introducing Disturbance Intensity Repeat action factor Inversion mechanical indices and existing damage factors Construct the risk indicator R: in to As preset weights, and + + + = 1; S43. Based on the risk indicator R and the corrected risk threshold The corresponding relationship is used to output the risk level.

Images