On-line lossless real-time monitoring system for micro-strain of in-service natural gas pipeline

Abstract

The invention relates to the technical field of pipeline safety monitoring, and discloses an online lossless real-time monitoring system for micro-strain of an in-service natural gas pipeline. A micro-strain data acquisition unit of the system acquires a micro-strain data set on the surface of the in-service natural gas pipeline in real time. And the three-dimensional strain field reconstruction unit receives the data set and executes three-dimensional strain field reconstruction processing to generate strain distribution characteristics of the pipeline. And the life prediction model analysis unit calls a pre-trained life prediction model to carry out nonlinear analysis processing on the strain distribution characteristics, and outputs a residual life prediction value and a key risk area identifier of the pipeline. The environmental factor compensation unit performs environmental factor compensation correction processing on the residual life prediction value to generate a corrected residual life prediction value. And the maintenance strategy generation unit generates a pipeline maintenance strategy set according to the key risk area identifier. According to the invention, real-time and accurate evaluation and intelligent maintenance decision support of the health condition of the pipeline are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of pipeline safety monitoring, in particular to a micro-strain online non-destructive real-time monitoring system for in-service natural gas pipeline. BACKGROUND

[0002] Natural gas pipeline is an important infrastructure for energy transportation, and its long-term safe operation is of great significance to energy supply and public safety. During the long-term service, the pipeline is affected by internal pressure fluctuation, soil settlement, geological disasters and third-party construction, etc., and will produce cumulative damage, resulting in micro-strain on the pipe wall. These micro-strains are important indicators of the health status of the pipeline structure, and when they exceed a certain threshold, they may indicate potential pipeline failure risks such as crack propagation, fatigue damage or even leakage accidents. Therefore, timely and accurate micro-strain monitoring of in-service pipelines is a key technical means to prevent pipeline accidents.

[0003] Traditional pipeline detection methods mainly rely on periodic excavation inspection or internal detector for offline detection. These methods have obvious limitations: excavation inspection is costly and environmentally destructive, and can only obtain local information; internal detection requires shutdown for operation, affecting normal gas supply, and the detection period is long, which cannot realize real-time monitoring. In addition, these methods are difficult to capture the dynamic change process of the pipeline strain, and cannot accurately assess the remaining life of the pipeline. With the development of sensing technology, point measurement technologies such as fiber Bragg grating and resistance strain gauge have been applied to pipeline monitoring, but their distribution points are limited, which is difficult to fully reflect the overall strain field distribution of the pipeline, and the measurement accuracy and reliability need to be improved.

[0004] The analysis and life prediction of pipeline strain state is a complex system engineering. The pipeline in actual operation bears a multi-axial stress state, and its damage evolution has obvious nonlinear characteristics. The existing evaluation methods are mostly based on simplified mechanical models and empirical formulas, and do not fully consider the comprehensive influence of material performance degradation, complex load history and environmental factors, resulting in large deviation of the prediction results. At the same time, pipeline maintenance decisions often lack sufficient data support, and the development of maintenance strategies is subjective and blind, either leaving safety hazards due to insufficient maintenance, or causing resource waste due to excessive maintenance. Therefore, there is an urgent need for an online monitoring system that can realize real-time, full-range and accurate monitoring, and can make scientific life prediction and intelligent maintenance decisions. SUMMARY

[0005] The purpose of the present application is to provide a micro-strain online non-destructive real-time monitoring system for in-service natural gas pipeline to solve the problems raised in the background.

[0006] To achieve the above object, the application provides a micro-strain online nondestructive real-time monitoring system for in-service natural gas pipeline, which comprises the following units: a micro-strain data acquisition unit, a three-dimensional strain field reconstruction unit, a life prediction model analysis unit, an environmental factor compensation unit and a maintenance strategy generation unit; The micro-strain data acquisition unit acquires a micro-strain data set of the surface of the in-service natural gas pipeline in real time, the three-dimensional strain field reconstruction unit receives the micro-strain data set provided by the micro-strain data acquisition unit and performs three-dimensional strain field reconstruction processing to generate pipeline strain distribution characteristics including axial strain gradient, circumferential strain accumulation and radial strain fluctuation coefficient; the life prediction model analysis unit calls a pre-trained life prediction model to perform nonlinear analysis processing on the pipeline strain distribution characteristics output by the three-dimensional strain field reconstruction unit, and outputs a residual life prediction model analysis unit output value and a key risk area identifier of the pipeline; the environmental factor compensation unit performs environmental factor compensation correction processing on the residual life prediction value output by the life prediction model analysis unit to obtain a corrected residual life prediction value; and the maintenance strategy generation unit generates a pipeline maintenance strategy set according to the key risk area identifier and the corrected residual life prediction value of the life prediction model analysis unit.

[0007] Preferably, the micro-strain data acquisition unit is also used to acquire a pipeline internal pressure fluctuation sequence, an environmental temperature change sequence and pipeline surface deformation monitoring data. The pipeline internal pressure fluctuation sequence is generated by acquiring continuous data of pressure change over time through a pressure sensor; The environmental temperature change sequence records temperature fluctuations through a temperature sensor; The pipeline surface deformation monitoring data measures the micro-strain change of the pipeline by using a fiber Bragg grating sensor.

[0008] Preferably, the three-dimensional strain field reconstruction unit is also used to divide the micro-strain data set into a plurality of data sub-sequences according to time windows. Each data sub-sequence corresponds to a monitoring period, and a three-dimensional deformation topology structure of the pipeline is constructed for each data sub-sequence; The three-dimensional deformation topology structure includes spatial distribution data of axial strain field, circumferential strain field and radial strain field; The three-dimensional deformation topology structure is coupled and analyzed with the data sub-sequences to generate a strain field reconstruction result of the current time window; The strain field reconstruction results of consecutive time windows are accumulated and processed to calculate the axial strain gradient, the circumferential strain accumulation and the radial strain fluctuation coefficient.

[0009] Preferably, the life prediction model analysis unit is further configured to input the axial strain gradient into a first feature layer of the life prediction model to determine distribution coordinates and strain amplitude variation curves of high strain regions by a strain concentration factor calculation; input the hoop strain accumulation into a second feature layer of the life prediction model to perform contact fatigue damage, and perform cumulative calculation to generate a fatigue crack initiation probability and an extension rate prediction of the hoop contact surface; input the radial strain fluctuation coefficient into a third feature layer of the life prediction model to calculate material wear thickness and surface roughness variation data of the radial friction surface based on a surface wear model; fuse the strain amplitude variation curve, the fatigue crack initiation probability, and the material wear thickness to generate a comprehensive degradation index of the pipeline; determine a residual life prediction value according to a comparison of the comprehensive degradation index with a preset threshold value; based on spatial superposition of the distribution coordinates, the extension rate prediction, and the surface roughness variation data, identify geometric positions of strain concentration regions, crack propagation paths, and high-risk wear regions.

[0010] Preferably, the environmental factor compensation unit is further configured to extract extreme temperature values and temperature variation frequency in an environmental temperature variation sequence to calculate a dynamic adjustment amount of a material thermal expansion coefficient with temperature variation; perform thermal strain compensation calculation on the axial strain gradient according to the dynamic adjustment amount to generate a corrected axial strain gradient; based on a correlation between the temperature variation frequency and material creep characteristics, perform creep damage correction on the hoop strain accumulation to generate a corrected hoop strain accumulation; perform surface strength adaptive adjustment on the radial strain fluctuation coefficient according to material hardness variation at extreme temperatures to generate a corrected radial strain fluctuation coefficient; input the corrected axial strain gradient, the hoop strain accumulation, and the radial strain fluctuation coefficient into the life prediction model to recalculate a residual life prediction value after compensation of environmental factors.

[0011] Preferably, the maintenance strategy generation unit is further configured to calculate an optimal stress release path for the identification of the strain concentration region; the optimal stress release path is achieved by adjusting support conditions of adjacent pipe sections, and a surface strengthening treatment scheme is constructed according to the identification of the crack propagation path; the surface strengthening treatment scheme includes selection of a coating reinforcement region and configuration of a coating thickness parameter, and a lubricant coating strategy is generated based on the identification of the high-risk wear region; the lubricant coating strategy dynamically adjusts a coating frequency and a coating amount according to a wear rate prediction; The optimal stress release path, surface strengthening treatment scheme and lubricant coating strategy are prioritized to generate a maintenance strategy set containing execution timing and implementation parameters.

[0012] Preferably, the three-dimensional strain field reconstruction unit is further configured to, when performing the coupling analysis, establish an axial strain-load mapping equation based on a correspondence between the axial strain field and the axial load component in the data subsequence; A first distribution function of the axial strain component is obtained by solving the axial strain-load mapping equation, and a hoop contact strain calculation model is constructed according to the correlation between the hoop strain field and the internal pressure; The hoop contact strain calculation model contains parameters of the material Poisson's ratio and the elastic modulus; a radial friction strain iterative calculation process is established by combining the spatial variation rate of the radial strain field with the surface friction coefficient; The iterative calculation process contains a feedback mechanism of strain increment and radial stress increment; and the outputs of the first distribution function, the hoop contact strain calculation model and the radial friction strain iterative calculation process are spatially interpolated and fused to generate three-dimensional strain field distribution data containing axial, hoop and radial strain components.

[0013] Preferably, the environmental factor compensation unit is further configured to, when performing the thermal strain compensation calculation, obtain an initial thermal expansion coefficient of the pipe material at a reference temperature and a dynamic adjustment amount to establish a thermal expansion coefficient-temperature correlation function; An axial thermal strain increment is calculated according to the thermal expansion coefficient-temperature correlation function, the axial thermal strain increment being a product of a temperature change amount and a thermal expansion coefficient change amount; The axial thermal strain increment is superimposed into the calculation of the axial strain gradient to generate an axial strain gradient correction value containing the influence of thermal strain, and the axial strain gradient correction value is subjected to strain relaxation effect compensation; The strain relaxation effect compensation is realized based on a material strain relaxation curve and a factor of temperature holding time.

[0014] Preferably, the maintenance strategy generation unit is further configured to, when constructing the surface strengthening treatment scheme, extract geometric features of the crack propagation path to calculate a path curvature radius and an extension direction angle; The coating coverage density is selected according to the curvature radius, the coverage density being inversely related to the curvature radius; The coating application direction is adjusted based on the extension direction angle so that the coating direction forms a predetermined angle with the crack propagation direction; The coating thickness is dynamically adjusted according to the material surface hardness test data to ensure that the coating strength is within the material yield strength range, and a strengthening parameter configuration table containing the coverage density, the coating direction and the coating thickness is generated.

[0015] Preferably, the system is further configured to collect actual wear amount and crack propagation length of the pipe within a preset verification period. performing deviation analysis on the actual wear amount and the predicted wear thickness to generate a first error correction coefficient; performing time domain comparison on the crack propagation length and the predicted propagation rate to generate a second error correction coefficient; adjusting weight parameters of the life prediction model according to the first error correction coefficient and the second error correction coefficient to generate an optimized life prediction model; applying the optimized life prediction model to a subsequent pipeline monitoring task.

[0016] Compared with the prior art, the present application has the following beneficial effects: The present application realizes non-destructive, real-time and continuous monitoring of the strain state of the pipeline surface through the micro-strain data acquisition unit, overcoming the hysteresis of the traditional offline detection method. The system can continuously obtain response data of the pipeline under real operating load, providing a rich and dynamic information source for evaluating the structural health condition, making early detection of potential risks possible.

[0017] The three-dimensional strain field reconstruction unit converts discrete micro-strain data into continuous global strain distribution characteristics, realizing the upgrade of the monitoring perspective from "point" to "surface" and then to "volume". This helps to fully understand the overall stress state of the pipeline, accurately locate the stress concentration area and high-risk section, avoiding the problem that local high-risk points may be missed in traditional point measurement, greatly improving the comprehensiveness and accuracy of monitoring.

[0018] The life prediction model analysis unit adopts a nonlinear analysis method, fully considering the complexity of pipeline material damage evolution and the influence of historical load, making the residual life prediction more close to the engineering practice. Compared with the traditional linear cumulative damage model, the prediction result is more scientific and reliable, providing a key basis for the residual strength evaluation and risk control of the pipeline.

[0019] The introduction of the environmental factor compensation unit effectively reduces the interference of temperature and other environmental variables on the monitoring and prediction results, improves the stability of the data and the accuracy of the prediction results. This makes the system maintain reliable performance under different seasons and different climate conditions, enhancing its applicability in complex outdoor environments.

[0020] The maintenance strategy generation unit automatically generates a set of maintenance strategies based on the identification of key risk areas, directly linking advanced monitoring and prediction technology with specific maintenance management actions. This changes the previous mode of relying on experience to develop maintenance plans, realizes precise decision-making based on data, helps to optimize resource allocation, improves the pertinence and efficiency of maintenance work, and ultimately provides strong technical support for the safe and economic operation of natural gas pipelines. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1Pipeline three-dimensional strain field monitoring and risk assessment chart; Figure 2 Flow chart for multi-parameter acquisition of micro-strain data acquisition unit; Figure 3 Flow chart for feature processing and risk identification of life prediction model analysis unit; Figure 4 Environmental factor compensation effect analysis chart. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0023] Please refer to Figure 1 The present application provides a kind of micro-strain online nondestructive real-time monitoring system of in-service natural gas pipeline, the system includes multiple functional units integrated to realize the real-time monitoring and maintenance strategy generation of pipeline.Micro-strain data acquisition unit is responsible for real-time acquisition of the micro-strain data set of pipeline surface, these data include the strain change information of pipeline under different working conditions.Three-dimensional strain field reconstruction unit receives micro-strain data set and executes three-dimensional strain field reconstruction processing, generates the strain distribution characteristics of pipeline, which can reflect the strain state of pipeline in three-dimensional space.Life prediction model analysis unit calls pre-trained life prediction model to carry out nonlinear analysis processing to pipeline strain distribution characteristics, outputs the residual life prediction value and key risk area identification of pipeline, wherein nonlinear analysis processing considers material fatigue and damage accumulation effect.Environmental factor compensation unit implements environmental factor compensation correction processing to residual life prediction value, produces corrected residual life prediction value, and compensation processing is mainly aimed at the influence of temperature, pressure and other environmental variables.Maintenance strategy generation unit generates pipeline maintenance strategy set according to key risk area identification, and the strategy set includes specific maintenance measures and execution parameters.

[0024] Embodiment 1: refer to Figure 2The micro-strain data acquisition unit continuously captures the internal pressure fluctuation sequence of the pipeline by deploying pressure sensors at key nodes of the pipeline. The sensors use piezoelectric sensing principles to record the complete waveform of pressure changes over time at a sampling frequency of kilohertz. The pressure data includes both steady-state operating pressure and transient impact pressure components. A temperature sensor array arranged on the surface of the pipeline and the surrounding soil synchronously monitors the sequence of environmental temperature changes. Thermocouple sensors measure the contact point temperature, while infrared non-contact sensors scan the temperature distribution of the outer wall of the pipeline. The temperature sequence covers daily temperature difference cycles and seasonal slow temperature drift. The pipeline surface deformation monitoring data is obtained by a pre-embedded fiber Bragg grating sensor network. Each sensor node contains multiple grating measurement points forming a dense monitoring grid along the axial and circumferential directions of the pipeline. The wavelength shift of the optical signal is converted into micro-strain values by a demodulation device. The sensor network achieves high spatial resolution measurement under full pipeline coverage through time division multiplexing technology.

[0025] The three-dimensional strain field reconstruction unit receives the raw data stream from the acquisition unit and starts the preprocessing program. After filtering and denoising and removing outliers, the raw data is divided into preset time windows. The length of the time window is dynamically adjusted according to the operating conditions of the pipeline. During stable operation, a longer window is used to capture creep effects, and during severe operating condition changes, a short window is used to capture transient responses. Each data sub-sequence corresponds to a complete monitoring period. The system establishes an independent data buffer area for each sub-sequence and marks it with a time stamp. For the data sub-sequences in the buffer area, the reconstruction engine calls the finite element calculation kernel to build the three-dimensional deformation topology of the pipeline. The structure discretizes the pipeline entity into a million-level unit grid, and each grid node is assigned an initial coordinate and material attribute. The calculation process uses the discrete strain point data collected by the sensors as boundary conditions to solve the elastic mechanics control equation and obtain the continuously distributed axial strain field, circumferential strain field, and radial strain field. The field data is stored in a three-dimensional matrix form and associated with the pipeline geographic information system coordinates.

[0026] The coupling analysis module spatiotemporally correlates the three-dimensional deformation topology with the data sub-sequence of the corresponding time period, establishes a mapping relationship between the axial strain field data and the axial load component in the data sub-sequence, phase-matches the hoop strain field with the internal pressure fluctuation, and cross- verifies the radial strain field with the pipeline support reaction force data. The analysis process adopts an iterative optimization algorithm to make the reconstruction results meet the balance equation and the coordination condition, and finally generates the strain field reconstruction results of the current time window, which contains the full-field strain tensor of the pipeline surface and the internal points. The system performs time series accumulation processing on the strain field reconstruction results of the continuous time window, derives the axial strain gradient by calculating the strain difference between adjacent grid points, sums up the history hoop strain by using the numerical integration method for the hoop strain accumulation, and determines the radial strain fluctuation coefficient by calculating the ratio of the radial strain variance to the mean value in the time period. These derived parameters are packaged as structure data packets and transmitted in real time to the downstream life prediction unit. In the data acquisition link, pressure sensors are installed in stress concentration areas such as valves, bends and connecting flanges, sensor signals are transmitted to the data acquisition card through anti-interference shielded cables, the acquisition card supports multi-channel synchronous sampling and is used for automatic range switching function. The temperature sensor arrangement strategy considers factors such as solar radiation, underground burial depth and fluid heat exchange, and adopts a redundant arrangement method to eliminate the risk of single-point failure, and all temperature data are subjected to thermodynamic equilibrium correction. The fiber Bragg grating sensor network adopts a hybrid architecture of wavelength division multiplexing and space division multiplexing, the demodulation equipment analyzes the central wavelength shift of each grating in real time, converts the wavelength change into micro-strain value through the calibration curve, and the sensor data refresh frequency is synchronized with the pressure and temperature monitoring.

[0027] The time window division algorithm adopts an adaptive mechanism, and the window length is automatically adjusted according to the pipeline pressure change rate and temperature gradient. When the pressure fluctuation exceeds the threshold or the temperature changes suddenly, the system automatically switches to the minute-level short window mode. In the stable operation stage, the system adopts the hour-level long window to reduce the calculation load. Each data sub-sequence contains complete time series data and spatial coordinate information, and the system establishes metadata index for subsequent traceability analysis. The three-dimensional deformation topology construction process adopts parameterized modeling technology. The pipeline geometric model includes different material partitions such as base material area, weld area and anticorrosion layer. The grid division density is adaptively encrypted according to the sensor distribution density, and the grid size of the key area reaches millimeter level. The strain field reconstruction calculation adopts the inverse problem solving method based on the variational principle. The sparse sensor measurement value is taken as the known quantity, and the full-field strain distribution is solved by minimizing the residual error between the reconstructed field and the measured value. The axial strain field reconstruction considers the comprehensive effect of pipeline self-weight, internal pressure expansion and temperature expansion. The circumferential strain field calculation introduces a thin-walled cylinder theory correction term. The radial strain field combines the contact mechanics model to process the interaction between the pipeline and the soil. In the coupling analysis stage, the data assimilation technology is adopted to fuse the real-time monitoring data and the physical model prediction value, and the Kalman filter algorithm is used to continuously correct the strain field distribution. The cumulative processing module adopts the sliding window integration method. The axial strain gradient calculation includes the first-order gradient and the second-order curvature term. The circumferential strain cumulative amount introduces a time decay factor to distinguish the contribution degree of new and old damage. The radial strain fluctuation coefficient adopts wavelet analysis to extract the fluctuation characteristics of different frequency bands.

[0028] Example 2: see Figure 3The implementation process of the life prediction model analysis unit involves comprehensive operations of multi-level feature processing and nonlinear analysis. The unit receives structured data such as axial strain gradient, hoop strain accumulation, and radial strain fluctuation coefficient from the three-dimensional strain field reconstruction unit output. The axial strain gradient is introduced into the first feature layer of the life prediction model for in-depth analysis. The feature layer has a strain concentration factor calculation algorithm built-in. The algorithm accurately calculates the spatial distribution coordinates of high-strain regions and their strain amplitude curves over time by identifying the stress distribution characteristics of geometric discontinuities in the pipeline. The moving window technique is used to analyze the time domain of the axial strain gradient during the calculation process, capturing the dynamic evolution of strain concentration regions. Each identified high-strain region is labeled with its three-dimensional coordinates, strain peak value, and change trend attributes. The hoop strain accumulation is sent to the second feature layer for contact fatigue damage analysis. The calculation engine of this layer builds a contact fatigue model based on the cumulative damage theory, considering the coupling effects of material microstructure evolution and cyclic loading. The calculation process performs rainflow counting on the hoop strain time series data to identify effective strain cycles. Combined with the material S-N curve and the Miner linear cumulative damage rule, the probability of fatigue crack initiation on the hoop contact surface is calculated. Meanwhile, based on the strain intensity factor range and material fracture toughness parameters, the crack propagation rate at different stages is predicted through a crack propagation dynamics model, generating complete prediction data including crack initiation time and propagation path.

[0029] The radial strain fluctuation coefficient enters the third feature layer to perform surface wear evaluation. This layer integrates a surface wear model based on the Archard wear theory. The model calculates the material wear thickness evolution curve of the radial friction surface by analyzing the fluctuation frequency and amplitude of the radial strain, combined with material hardness and surface roughness parameters. The calculation process considers the interaction of sliding distance, contact pressure, and material wear resistance, and predicts the change trend of surface roughness through a surface topography analysis algorithm, generating a wear depth distribution map and surface quality degradation index. The model also introduces temperature and lubrication condition correction factors to make the wear prediction more realistic. The feature fusion module processes the output data from the three feature layers, using a weighted fusion algorithm to convert heterogeneous data such as strain amplitude change curve, fatigue crack initiation probability, and material wear thickness into a unified comprehensive degradation index. The fusion process considers the coupling effects between different damage mechanisms, such as high strain amplitude accelerating fatigue damage and surface wear changing stress distribution. The comprehensive degradation index is compared with the preset safety threshold in real time. When the index exceeds the threshold, the warning mechanism is triggered. The remaining life prediction value is determined by extrapolating the intersection of the degradation curve and the critical state. The prediction result is output in time units.

[0030] The risk area identification system performs spatial overlay analysis based on multi-source data. The high-strain area distribution coordinates output by the first feature layer, the crack propagation path prediction generated by the second feature layer, and the wear high-risk area coordinates obtained by the third feature layer are uniformly mapped into the three-dimensional digital twin model of the pipeline. The overlay process uses a spatial interpolation algorithm to process data of different resolutions, and realizes the visualization fusion of multiple risk factors through color coding and transparency adjustment. The system automatically identifies the geometric positional relationship between the strain concentration area, the potential crack propagation path, and the wear high-risk area, and labels the special attention area of multi-risk overlay. During the model running process, the strain concentration factor calculation of the first feature layer adopts a progressive refinement strategy. First, the potential high-risk area is located through a fast algorithm, and then the grid encryption fine calculation is performed on these areas. The generation of strain amplitude change curve combines statistical analysis and signal processing technology to extract mean value, amplitude, and overload times, etc. The fatigue analysis of the second feature layer introduces a probability damage model, considers the discreteness of material performance, and generates a confidence interval of crack initiation probability through Monte Carlo simulation. The crack propagation rate prediction adopts a segmented calculation strategy to handle different mechanical behaviors in the subcritical and critical propagation stages.

[0031] The wear calculation of the third feature layer introduces a real-time correction mechanism to dynamically adjust the model parameters according to the online monitoring wear data. Surface roughness prediction describes the evolution law of surface morphology through fractal theory and establishes a correlation model between roughness and wear rate. The weighting coefficients in the feature fusion stage are dynamically adjusted according to the importance of different sections of the pipeline, for example, the pipeline section passing through densely populated areas will be given a higher weight coefficient. The risk area identification system also includes an automatic clustering function, which combines risk points with similar spatial positions into comprehensive risk areas and calculates the risk level index of each area. The entire life prediction process adopts an iterative optimization mechanism. The model will re-run the calculation process every time it obtains new monitoring data, realizing the rolling update of the prediction results. All intermediate calculation results and final prediction values are stored in the historical database for model parameter self-learning and optimization. The system also has a result verification interface that can compare the predicted risk area with the actual detected defects to continuously improve the accuracy of the prediction model. The final generated remaining life prediction value and risk area identification information are transmitted to the maintenance strategy generation unit through a standard data interface to form a complete monitoring-prediction-decision closed loop.

[0032] Example 3: Environmental factor compensation unit implementation process begins with in-depth analysis of the environmental temperature variation sequence transmitted by the micro-strain data acquisition unit, which extracts continuous monitoring data streams from the temperature sensor network, identifies extreme temperature values such as historical maximum and minimum temperatures in the sequence, and calculates the temperature variation frequency by counting the number of temperature fluctuations per unit time. Extreme temperature values are used to assess material behavior under critical conditions, and temperature variation frequency is related to the cumulative effect of thermal cycles. The analysis algorithm uses a sliding window technique to update these parameters in real time, and the calculation of dynamic adjustment quantities is based on the thermal expansion coefficient temperature dependence model in the materials science database. The model establishes a functional relationship between the thermal expansion coefficient and temperature by fitting experimental data, and introduces a temperature gradient correction factor to reflect the temperature differences at different positions in the pipeline during calculation.

[0033] The calculation of thermal strain compensation quantity uses the following expression:

[0034] Where: represents the thermal strain compensation quantity, is the material thermal response coefficient at temperature , with units of per degree Celsius; is the temperature distribution weight function, is the initial temperature, is the current temperature, both in degrees Celsius; the integral variable represents the temperature, with units of degrees Celsius.

[0035] The calculation of axial thermal strain increment is achieved by multiplying the temperature variation quantity by the thermal expansion coefficient variation quantity. The temperature variation quantity is obtained by subtracting the reference value from the instantaneous value of the environmental temperature sequence, and the thermal expansion coefficient variation quantity is derived from the correlation function. The calculation process considers the non-uniformity of temperature distribution in the axial direction of the pipeline and uses the integral method to accumulate thermal strain contributions along the pipeline length. The incremental value includes linear expansion and volume expansion components. In the thermal strain compensation stage, the axial thermal strain increment is added to the original calculation value of the axial strain gradient. The addition operation is performed at the strain tensor level, and vector addition is used to ensure directional consistency. The compensated axial strain gradient eliminates the false strain signals caused by temperature.

[0036] The strain relaxation effect compensation compensates for the stress release behavior of the material under continuous high temperature. The compensation mechanism is based on the strain relaxation curve of the material, which is obtained through a creep experiment and stored as a database query table. The compensation calculation introduces a temperature holding time factor, which represents the influence of the duration of high temperature on the relaxation rate. The relaxation strain value corresponding to the time is extracted from the curve by an interpolation algorithm, and this value is subtracted from the compensated axial strain gradient to generate the final corrected axial strain gradient. This step eliminates the strain decay error caused by long-term heat exposure, making the strain data more accurately reflect the mechanical load. The creep damage correction of the hoop strain accumulation uses a correlation model between temperature change frequency and material creep properties. The model converts the temperature fluctuation frequency into an equivalent number of thermal cycles, and calculates the cumulative creep strain by combining the material creep law. The correction process uses the time hardening theory and introduces a creep damage factor to scale the hoop strain accumulation. The factor value increases with the increase of temperature frequency. The corrected hoop strain accumulation includes the additional damage contribution caused by creep. The surface strength adaptive adjustment of the radial strain fluctuation coefficient is based on the material hardness change data at extreme temperatures. The hardness data is obtained from the temperature-hardness relationship curve in the material library. The adjustment algorithm multiplies the radial strain fluctuation coefficient by the strength correction coefficient, which is inversely proportional to the hardness, thereby reducing the calculated value of the strain fluctuation amplitude at high temperatures and avoiding overestimating the wear risk.

[0037] The environmental factor compensation unit re-enters the corrected axial strain gradient, hoop strain accumulation, and radial strain fluctuation coefficient into the life prediction model. The model runs the same as the initial prediction but uses the compensated parameters to re-calculate and generate the remaining life prediction value after compensating for environmental factors. The entire compensation process is iteratively executed, and the compensation amount is updated each time new temperature data is obtained to ensure that the prediction value reflects environmental changes in real time. The compensation algorithm uses a numerical iteration method to ensure convergence, and all intermediate results are stored in the cache for subsequent analysis and verification. In the analysis of temperature change sequences, the system uses a multi-scale analysis method to process the original data. Short-term fluctuations are extracted through high-pass filtering, long-term trends are captured through low-pass filtering, extreme temperature values are identified using a peak detection algorithm, and temperature change frequency is calculated through zero-crossing detection or Fourier transform. When calculating the dynamic adjustment amount, the thermal expansion coefficient temperature-dependent model considers the material phase transition point, and uses a piecewise function near the critical temperature to improve accuracy. The thermal response coefficient The weight function is determined through material thermophysical property testing. According to the temperature distribution characteristics of the pipe cross section.

[0038] The axial thermal strain increment calculation involves spatial integration, the pipeline is discretized into finite elements, each element is assigned a local temperature value, the thermal strain increment is calculated at the element level and aggregated into a global value, the superposition of thermal strain compensation uses tensor transformation to ensure the correctness of strain components in the global coordinate system, in the strain relaxation effect compensation, the relaxation curve database contains data at multiple stress levels and temperatures, accurate values are obtained through bilinear interpolation during compensation, the temperature holding time factor is accumulated from the operation history, the factor calculation considers the weighted effect of high temperature duration and temperature amplitude. The creep damage correction model integrates the classical creep equation, maps the temperature frequency to the equivalent creep time, and updates the hardness data in real time when the surface strength is adapted, automatically queries the latest hardness value when a temperature jump is detected, introduces a safety margin in the adjustment coefficient calculation to avoid underestimation risk, recalculates the remaining life, and uses the compensated parameters to run all feature layers again, outputs the corrected prediction value and generates a compensation report to record the impact of each environmental factor. The entire environmental factor compensation unit adopts a modular design, each compensation step is independently configurable, allowing parameters to be adjusted according to the specific conditions of the pipeline, the unit operation frequency is synchronized with data acquisition to ensure real-time performance, all compensation algorithms have been verified for numerical stability to avoid divergence or oscillation, the compensation results are compared with the original prediction values for analysis, used for system performance evaluation and model optimization, and finally output reliable remaining life prediction to support maintenance decisions.

[0039] Referring to Figure 4 , it demonstrates the key role of the environmental factor compensation mechanism in pipeline strain monitoring. The chart clearly presents the complex effects of temperature changes on pipeline strain and the corresponding compensation effects through an up-down layout. The upper subgraph focuses on the dynamic characteristics of environmental temperature changes and the resulting thermal strain response, revealing the internal relationship between temperature fluctuations and material thermal expansion behavior. The lower subgraph visually presents the strain data changes before and after environmental compensation through comparative analysis, clearly distinguishing the combined effects of mechanical load and environmental temperature in the original measurement values. The compensation mechanism effectively identifies and eliminates false strain signals caused by temperature, making the monitoring data more accurately reflect the actual stress state of the pipeline. The entire chart presents the whole process compensation strategy from temperature change analysis to thermal strain calculation and creep effect correction, reflecting the important value of the environmental factor compensation unit in improving monitoring accuracy and providing a reliable data foundation for pipeline safety assessment.

[0040] Example 4: The implementation process of the maintenance strategy generation unit is illustrated with an example of a section of in-service natural gas pipeline crossing a highway, which is numbered PL-2024-7B, with a length of about 1.2 kilometers, a pipe diameter of 812 millimeters, and a wall thickness of 12.7 millimeters. The system outputs the identification of key risk areas of this pipe section through the life prediction model analysis unit, including the hoop strain concentration area at stake number K23+450, the axial crack propagation path in the K23+500 to K23+600 section, and the local wear high-risk area at stake number K23+550. After receiving these identification data, the maintenance strategy generation unit starts the multi-dimensional analysis process, calculates the optimal stress release path for the strain concentration area, and the system calls the finite element analysis module to simulate the influence of different support condition adjustment schemes on stress distribution. The simulation results show that the addition of hydraulic support bases at stake numbers K23+430 and K23+470 can reduce the maximum equivalent stress by about 28%, and the support base installation angle is set to be 15 degrees with the pipe axis, and the support reaction force is used for an intelligent hydraulic system that can automatically adjust with internal pressure fluctuations.

[0041] According to the geometric characteristics of the crack propagation path, a surface strengthening treatment scheme is constructed, and the system extracts the curvature radius distribution and extension direction angle data of the crack path through three-dimensional scanning data. At stake number K23+520, a bending point with a minimum curvature radius of 85 millimeters is detected, and a high coverage density coating scheme is automatically matched in this area. The coating material is selected as a polyurethane-ceramic composite system, and the coverage density is set to 12 coating points per square centimeter. The extension direction angle analysis shows that the crack mainly deflects 7 degrees along the pipe axis and extends, and the coating application direction is adjusted to 83 degrees with the axis for cross-laminating. The system integrates the material surface hardness test data to dynamically adjust the coating thickness in the range of 0.8-1.2 millimeters, ensuring that the coating strength matches the substrate yield strength. Finally, a strengthening parameter configuration table containing coverage density, coating direction, and coating thickness is generated, which is directly linked to the control system of the automated spraying equipment.

[0042] Referring to Table 1, the lubricant coating strategy is generated based on the identification of high-risk wear areas, and the system outputs dynamic coating parameters according to the wear rate prediction model. A peak in wear rate is detected at stake number K23+550, and this area is configured with a high-frequency coating scheme, with a coating frequency set to automatically execute every 72 hours, and a coating volume calculated according to the pipe diameter curve as 0.35 liters per meter. The lubricant is selected as a graphene-modified composite grease, with its viscosity index automatically adjusted according to the operating temperature of the pipeline. The system establishes a coating effect feedback mechanism to dynamically optimize the coating parameters by monitoring the changes in wear amount in real time, and automatically extends the coating interval to 120 hours when the wear rate is monitored to decrease. The maintenance strategy generation unit integrates the above analysis results into a systematic maintenance scheme, and prioritizes each measure through a multi-objective decision-making algorithm. The factors considered in the prioritization include risk level, construction difficulty, cost-effectiveness, and window period restrictions, generating the following maintenance strategy set.

[0043] Table 1: PL-2024-7B pipe segment maintenance strategy priority configuration

[0044] During the stress release path optimization implementation process, the system plans the installation process of the support base in detail. The base is designed in a modular manner to facilitate quick installation, and the hydraulic system is configured with pressure sensors to monitor the support reaction force in real time, and is linked with the pipeline internal pressure monitoring system. When a sudden increase in internal pressure is detected, the hydraulic system automatically increases the support reaction force to the preset upper limit to prevent excessive deformation of the pipeline. The depth of the concrete foundation of the support base is determined through geotechnical mechanics calculation to ensure sufficient bearing stability in soft soil sections. The implementation of the surface strengthening treatment scheme includes three sub-processes: surface pretreatment, coating spraying, and curing maintenance. The pretreatment stage uses a sandblasting process to achieve Sa2.5 cleanliness, the coating spraying uses a six-axis mechanical arm to ensure construction precision, and the curing process uses an infrared heating system to control the temperature rise curve. The scheme specially designs a coating overlap scheme, which increases the coating overlap amount by 20% at the crack path turning point to prevent edge effects from causing coating failure. All construction parameters are virtually verified through a digital twin model, and after optimization, they can be transmitted to the field construction equipment.

[0045] The implementation of the lubricant coating strategy is configured with a special coating device that integrates a liquid storage tank, a metering pump, and a rotating spray head, which can uniformly coat the lubricant along the circumference of the pipeline. The coating frequency is dynamically adjusted according to real-time monitoring data. When the system detects an increase in the friction coefficient through the optical fiber sensor, it automatically triggers the early coating program. The lubricant formula considers environmental compatibility to avoid affecting the surrounding soil ecosystem. The maintenance strategy generation unit also establishes an effect evaluation mechanism. After each maintenance operation is completed, the system collects pipeline surface topography data, strain distribution changes, and wear monitoring values, which are compared and analyzed with the prediction model. The evaluation results are used to optimize the generation parameters of subsequent strategies, forming a continuous improvement closed-loop management system. All maintenance records are stored in the database, providing reference cases for maintenance decisions for similar pipelines.

[0046] In Example 5, a section of an in-service natural gas pipeline that passes through a geologically active area is taken as an example. The pipe section is numbered PL-2024-9C, with a length of 3.2 kilometers, a diameter of 1016 millimeters, a wall thickness of 15.9 millimeters, and a pipe material of X80 steel. The three-dimensional strain field reconstruction unit receives micro-strain data subsequences from the K45+200 to K45+500 section, which includes 30 consecutive days of monitoring data with a sampling interval of 10 minutes. The unit establishes a correspondence between the axial strain field and the axial load component in the data subsequence, which is obtained by stress sensors installed at both ends of the pipeline. The correspondence is described by a multivariate linear regression model, and the model coefficients are determined by least squares fitting. Based on this correspondence, an axial strain-load mapping equation is constructed, which is a fifth-order polynomial that accurately reflects the nonlinear characteristics of axial strain and complex load conditions. The first distribution function of the axial strain component is obtained by solving the mapping equation numerically. The function image shows that there is a strain peak region at the stake number K45+320, which corresponds to the active area of the pipeline passing through the fault zone.

[0047] The circumferential contact strain calculation model was constructed considering the coupling effect of internal pressure fluctuations and material properties within the pipeline. The model input parameters included real-time internal pressure data, a Poisson's ratio of 0.28, and an elastic modulus of 210 GPa. The model used thick-walled cylinder theory to derive the analytical relationship between circumferential strain and internal pressure, while also incorporating a correction term for soil lateral constraints to calculate the spatial distribution of the circumferential strain field. Results showed that in the fault activity influence zone, the circumferential strain exhibited a significant gradient change, with the maximum strain value occurring on the uplift surface at the intersection of the pipeline and the fault. The radial friction strain iterative calculation process was established based on the interaction mechanism between the pipeline and the surrounding soil. The surface friction coefficient was set to 0.4 during process initialization, a value calibrated through field direct shear tests. The iterative calculation employed a prediction-correction algorithm, with each step including bidirectional feedback of strain increment and radial stress increment. Convergence was determined when the strain change in consecutive iterations was less than the tolerance threshold. Calculations revealed that the radial strain fluctuation was most significant at station K45+320, with an amplitude 2.3 times that of other areas, which corroborated the soil settlement monitoring data at this location. The spatial interpolation fusion process integrates the outputs of the first distribution function, the circumferential contact strain calculation model, and the radial friction strain iterative calculation process. It then uses the Kriging interpolation method to generate three-dimensional strain field distribution data containing axial, circumferential, and radial strain components. An anisotropic variability function is set during the interpolation process to adapt to the strain variation characteristics of the pipeline in different directions. The final generated three-dimensional strain field data volume contains complete strain tensor information for over 500,000 grid nodes.

[0048] The system starts the data collection and model optimization process within the preset verification period, which is set to be performed every 90 days. In the last verification period, the actual wear of the pipeline was measured by the ultrasonic thickness gauge carried by the crawling robot, with a measurement point spacing of 2 meters, and a total of 124 valid data points were obtained. At the same time, a magnetic flux leakage detection device was used to scan the surface of the pipeline to accurately measure the extension length of the existing cracks, with a minimum detectable crack length of 0.1 millimeters. The actual wear data showed that there was a maximum wear depth at stake number K45+320, with a measured value of 0.82 millimeters, while the predicted wear thickness at this location was 0.79 millimeters, with a deviation of 3.7%. The crack extension length monitoring showed that there was an axial crack at stake number K45+280, with an actual extension length of 12.3 millimeters and a predicted extension rate of 0.9 millimeters per month, which was highly consistent with the actual observed extension speed of 0.87 millimeters per month. The deviation analysis module compares the actual wear with the predicted wear thickness, calculates the relative error of each measurement point, and generates the first error correction coefficient. This coefficient is calculated using the exponential weighted moving average method, with recent data given a higher weight, and the final coefficient value is 1.03, indicating that the model prediction is slightly conservative. The time domain comparison module performs correlation analysis on the crack extension length sequence and the predicted extension rate curve, evaluates the prediction accuracy by calculating the determination coefficient, and generates the second error correction coefficient 0.96, reflecting that the model has high accuracy in crack extension prediction.

[0049] The weight parameter adjustment process is based on the first error correction coefficient 1.03 and the second error correction coefficient 0.96, and the contribution weights of different features in the life prediction model are redistributed. The weight of the wear prediction part is reduced by 3%, the weight of the crack extension prediction part is increased by 2%, and the weight of the strain concentration prediction part remains unchanged. Adjustment uses the gradient descent algorithm to find the optimal weight combination to ensure that the corrected model performs well on both the training set and the validation set. The optimized life prediction model shows significant improvement in prediction accuracy in the next 30-day trial run on the monitoring data of the new section from stake number K45+600 to K45+800, with the wear thickness prediction error reduced from an average of 5.2% to 3.1%, and the crack extension rate prediction error improved from 7.8% to 4.5%. A forgetting factor mechanism is also introduced during the model optimization process, with the weights of earlier historical data gradually decaying, allowing the model to better adapt to changes in pipeline conditions. All optimization records and parameter adjustment trajectories are stored in the model version library for subsequent tracing and analysis. The optimized life prediction model is formally deployed to the full-line monitoring system and begins a new round of monitoring tasks on the entire pipeline. The system sets up an automatic verification trigger mechanism that automatically starts a shortened verification process when strain mutations or drastic changes in environmental conditions are detected, ensuring that the model always maintains optimal prediction performance.

[0050] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and adaptions can be effected without departing from the spirit and scope of the present application, which is not limited to the exact construction and arrangement described. It is intended, therefore, to cover all modifications and adaptions that fall within the scope of the claims and their equivalents.

Claims

1. A micro-strain online non-destructive real-time monitoring system for in-service natural gas pipelines, characterized in that, The system comprises a micro-strain data acquisition unit, a three-dimensional strain field reconstruction unit, a life prediction model analysis unit, an environmental factor compensation unit and a maintenance strategy generation unit, wherein: The micro-strain data acquisition unit acquires micro-strain data sets of the surface of the in-service natural gas pipeline in real time; The three-dimensional strain field reconstruction unit receives the micro-strain data sets provided by the micro-strain data acquisition unit and performs three-dimensional strain field reconstruction processing to generate pipeline strain distribution characteristics including axial strain gradient, ring strain accumulation and radial strain fluctuation coefficient; The life prediction model analysis unit calls a pre-trained life prediction model to perform nonlinear analysis processing on the pipeline strain distribution characteristics output by the three-dimensional strain field reconstruction unit, and outputs the residual life prediction value of the pipeline and the key risk area identification; The environmental factor compensation unit performs environmental factor compensation correction processing on the residual life prediction value output by the life prediction model analysis unit to obtain a corrected residual life prediction value; The maintenance strategy generation unit generates a set of pipeline maintenance strategies according to the key risk area identification and the corrected residual life prediction value of the life prediction model analysis unit.

2. The micro-strain online non-destructive real-time monitoring system for in-service natural gas pipeline of claim 1, wherein, The micro-strain data acquisition unit is also used to acquire pipeline internal pressure fluctuation sequences, environmental temperature change sequences and pipeline surface deformation monitoring data; The pipeline internal pressure fluctuation sequences are generated by a pressure sensor acquiring continuous data of pressure change over time; The environmental temperature change sequences are recorded by a temperature sensor; The pipeline surface deformation monitoring data are measured by a fiber Bragg grating sensor.

3. The micro-strain online non-destructive real-time monitoring system for in-service natural gas pipeline of claim 1, wherein, The three-dimensional strain field reconstruction unit is also used to divide the micro-strain data sets into multiple data subsequences according to time windows; Each data subsequence corresponds to a monitoring period, and a three-dimensional deformation topology structure of the pipeline is constructed for each data subsequence; The three-dimensional deformation topology structure and the data subsequence are coupled and analyzed to generate a strain field reconstruction result for the current time window; The strain field reconstruction results of consecutive time windows are accumulated and processed to calculate the axial strain gradient, the ring strain accumulation and the radial strain fluctuation coefficient.

4. The micro-strain online non-destructive real-time monitoring system for in-service natural gas pipeline of claim 1, wherein, The life prediction model analysis unit is also used to input the axial strain gradient into a first feature layer of the life prediction model to calculate the distribution coordinates and strain amplitude variation curve of the high-strain area through a strain concentration factor; The ring strain accumulation is input into a second feature layer of the life prediction model to perform contact fatigue damage and cumulative calculation to generate the fatigue crack initiation probability and propagation rate prediction of the ring contact surface; The radial strain fluctuation coefficient is input into a third feature layer of the life prediction model to calculate the material wear thickness and surface roughness change data of the radial friction surface based on a surface wear model; The strain amplitude variation curve, the fatigue crack initiation probability and the material wear thickness are fused to generate a comprehensive degradation index of the pipeline; The residual life prediction value is determined according to the comparison between the comprehensive degradation index and a preset threshold value; The spatial superposition of the distribution coordinates, the propagation rate prediction and the surface roughness change data identifies the geometric positions of the strain concentration area, the crack propagation path and the wear high-risk area.

5. The micro-strain online non-destructive real-time monitoring system for in-service natural gas pipeline of claim 1, wherein, The environmental factor compensation unit is further configured to extract extreme temperature values in the environmental temperature change sequence and a temperature change frequency to calculate a dynamic adjustment amount of the material thermal expansion coefficient changing with temperature; The axial strain gradient is compensated for thermal strain according to the dynamic adjustment amount, and a corrected axial strain gradient is calculated and generated; The ring-shaped strain accumulation amount is creep damage corrected based on the correlation between the temperature change frequency and the material creep characteristics to generate a corrected ring-shaped strain accumulation amount; The radial strain fluctuation coefficient is surface strength adapted according to the material hardness change at the extreme temperature to adjust and generate a corrected radial strain fluctuation coefficient; The corrected axial strain gradient, the ring-shaped strain accumulation amount and the radial strain fluctuation coefficient are input into a life prediction model to recalculate a residual life prediction value after the environmental factors are compensated.

6. The micro-strain online non-destructive real-time monitoring system for in-service natural gas pipelines of claim 1, wherein, The maintenance strategy generation unit is further configured to calculate an optimal stress release path for the identification of the strain concentrated region; The optimal stress release path is realized by adjusting the support conditions of adjacent pipe segments, and a surface strengthening treatment scheme is constructed according to the identification of the crack propagation path; The surface strengthening treatment scheme includes the selection of the coating reinforcement region and the configuration of the coating thickness parameter, and a lubricant coating strategy is generated based on the identification of the high-wear-risk region; The lubricant coating strategy dynamically adjusts the coating frequency and the coating amount according to the wear rate prediction; The optimal stress release path, the surface strengthening treatment scheme and the lubricant coating strategy are prioritized to generate a maintenance strategy set including execution timing and implementation parameters.

7. The micro-strain online non-destructive real-time monitoring system for in-service natural gas pipeline of claim 3, wherein, The three-dimensional strain field reconstruction unit is further configured to establish an axial strain-load mapping equation based on the corresponding relationship between the axial strain field and the axial load component in the data sub-sequence when performing coupling analysis; A first distribution function of the axial strain component is obtained by solving the axial strain-load mapping equation, and a ring-shaped contact strain calculation model is constructed according to the correlation characteristics of the ring-shaped strain field and the internal pressure; The ring-shaped contact strain calculation model includes parameters of the material Poisson's ratio and the elastic modulus, and an iterative calculation process of the radial friction strain is established based on the spatial variation rate of the radial strain field and the surface friction coefficient; The iterative calculation process includes a feedback mechanism of strain increment and radial stress increment, and the output of the first distribution function, the ring-shaped contact strain calculation model and the radial friction strain iterative calculation process is spatially interpolated and fused to generate three-dimensional strain field distribution data including axial, ring-shaped and radial strain components.

8. The micro-strain online non-destructive real-time monitoring system for in-service natural gas pipeline of claim 5, wherein, The environmental factor compensation unit is further configured to obtain the initial thermal expansion coefficient of the pipeline material at the reference temperature and the dynamic adjustment amount to establish a thermal expansion coefficient-temperature correlation function when performing thermal strain compensation calculation; An axial thermal strain increment is calculated according to the thermal expansion coefficient-temperature correlation function, and the axial thermal strain increment is the product of the temperature change amount and the thermal expansion coefficient change amount; The axial thermal strain increment is added to the calculation of the axial strain gradient to generate an axial strain gradient correction value including the influence of thermal strain, and the axial strain gradient correction value is compensated for strain relaxation effect; The strain relaxation effect compensation is realized based on the material strain relaxation curve and the factor of the temperature holding time.

9. The micro-strain online non-destructive real-time monitoring system for in-service natural gas pipeline of claim 6, wherein, The maintenance strategy generation unit is further configured to extract the geometric characteristics of the crack propagation path, calculate the path curvature radius and the propagation direction angle when constructing the surface reinforcement treatment scheme; The coating coverage density is selected according to the curvature radius, and the coverage density is inversely related to the curvature radius; The coating application direction is adjusted based on the propagation direction angle, so that the coating direction forms a predetermined angle with the crack propagation direction; The coating thickness is dynamically adjusted according to the material surface hardness test data to ensure that the coating strength is within the material yield strength range, and a reinforcement parameter configuration table containing the coverage density, the coating direction and the coating thickness is generated.

10. The micro-strain online non-destructive real-time monitoring system for in-service natural gas pipelines of claim 1, wherein, The system is also configured to collect the actual wear amount and the crack propagation length of the pipeline within a preset verification period; The actual wear amount is subjected to deviation analysis with the predicted wear thickness to generate a first error correction coefficient; The crack propagation length is subjected to time domain comparison with the predicted propagation rate to generate a second error correction coefficient; The weight parameters of the life prediction model are adjusted according to the first error correction coefficient and the second error correction coefficient to generate an optimized life prediction model; The optimized life prediction model is applied to subsequent pipeline monitoring tasks.

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