A pipeline digital twin modeling and working condition deduction method and system, and a storage medium

Abstract

The present application relates to the technical field of data processing, and more particularly to a pipeline digital twin modeling and working condition deduction method and system and a storage medium. The method comprises the following steps: collecting temperature, pressure and corrosion data through a plurality of source sensors and performing fusion processing to obtain calibrated measurement values; comparing the calibrated measurement values with historical benchmark data to determine systematic error characteristics; if the error exceeds a preset threshold, generating error distribution samples using Monte Carlo simulation and calculating a cumulative bias sequence to obtain an error amplification coefficient; adjusting risk assessment model parameters based on the error amplification coefficient, constructing a digital twin model, generating a simulation scenario and evaluating a preliminary risk level; further analyzing the trend characteristics of risk quantification indicators, calculating potential failure probabilities using a prediction model, and generating a pipeline maintenance priority sequence; and optimizing the propagation path according to the priority sequence, reasonably allocating maintenance resources, and formulating an accurate maintenance scheduling scheme. The present application effectively improves the accuracy and efficiency of pipeline maintenance and reduces the risk of failure.

Description

Technical Field

[0001] This invention relates to the field of pipeline crack monitoring technology, and in particular to a pipeline digital twin modeling and operating condition simulation method, system and storage medium. Background Technology

[0002] Pipelines play a vital role in the transportation of energy resources such as oil and natural gas. However, pipelines frequently encounter corrosion and pressure fluctuations in extreme environments, leading to the formation and propagation of microcracks, posing a serious challenge to pipeline safety and reliability. Traditional pipeline maintenance methods often rely on periodic inspections and manual checks, which are not only time-consuming but also difficult to detect potential crack propagation risks in a timely manner. Currently, with advancements in sensing technology and data analysis methods, sensors can collect pipeline temperature, pressure, and corrosion data in real time, and advanced fusion algorithms can improve monitoring accuracy. Combining historical data with analysis, the identification and analysis of systematic errors becomes crucial for improving the accuracy of risk assessment. Especially in complex deep-sea environments, the amplification effect of errors and environmental changes have a more significant impact on pipeline monitoring. Dynamically capturing and correcting these effects has become one of the challenges in pipeline maintenance.

[0003] This invention proposes a method for pipeline digital twin modeling and operational condition simulation. It utilizes multi-source sensors to collect and fuse data in real time, accurately calibrating the comprehensive environmental state of areas with minor pipeline damage. Error distribution samples are generated through statistical analysis and Monte Carlo simulation, the cumulative deviation sequence is calculated, and the error amplification coefficient is quantified. This allows for adjustment of the risk assessment model, generating a digital twin model, which is then combined with real-time operational data for risk simulation. By analyzing risk trends and combining them with a prediction model to calculate potential failure probabilities, a maintenance priority sequence is generated, thereby optimizing pipeline maintenance scheduling. This ensures accurate and timely pipeline maintenance and repair, reducing failure risks and improving pipeline safety. Summary of the Invention

[0004] This invention provides a method, system, and storage medium for pipeline digital twin modeling and operational condition simulation. It is used to dynamically monitor the status of minor damage areas in pipelines in real time by combining multi-source sensors with digital twin modeling. By combining error analysis, risk assessment, and prediction models, it optimizes pipeline maintenance scheduling, ensuring accurate and timely pipeline maintenance and repair, and reducing the risk of failure.

[0005] In a first aspect, the present invention provides a method for pipeline digital twin modeling and operating condition simulation, comprising:

[0006] Step S1: Collect real-time data of the minor damage area of ​​the pipeline through multi-source sensors and perform fusion processing to obtain calibration measurement values. The real-time data includes temperature, pressure and corrosion data.

[0007] Step S2: Based on the comparison between the calibration measurement value and historical benchmark data, determine the systematic error characteristics; if the systematic error characteristics exceed the preset threshold, generate an error distribution sample using the Monte Carlo simulation method, calculate the cumulative effect in the dynamic propagation path based on the error distribution sample, and obtain the cumulative deviation sequence; quantify the cumulative deviation sequence through statistical analysis to obtain the error amplification coefficient;

[0008] Step S3: Adjust the parameters of the risk assessment model according to the error amplification factor, construct a digital twin model of the micro-damage area of ​​the pipeline based on the geometry of the physical pipeline and real-time data, generate a simulation scenario synchronously based on the digital twin model and real-time operating data, and obtain a preliminary risk level through the risk assessment model; match and fuse the preliminary risk level with the real-time operating data to determine the final risk quantification index.

[0009] Step S4: Analyze the trend sequence of the final risk quantification indicators, extract their periodicity and fluctuation characteristics, combine them with the predictive behavior response model, calculate the potential failure probability of the pipeline's minor damage area, and generate a pipeline maintenance priority sequence based on the failure probability.

[0010] Step S5: Optimize the dynamic propagation path and adjust the allocation of maintenance resources according to the pipeline maintenance priority sequence to obtain the maintenance schedule for the minor damage area.

[0011] As a preferred embodiment of the present invention, step S1 includes:

[0012] Temperature, pressure, and corrosion data of the pipeline's micro-damage area are collected in real time by multi-source sensors. Each data type is processed using Kalman filtering to obtain an intermediate measurement value for each data type. Based on the time series characteristics of the intermediate measurement values, a weighting coefficient for each data type is determined. The intermediate measurement values ​​are then weighted and fused using these weighting coefficients to obtain a calibration measurement value. This calibration measurement value represents the comprehensive environmental state of the pipeline's micro-damage area. The weighting coefficients are determined based on the historical stability and real-time noise level of the sensor data. Abnormal data points are removed during the fusion process.

[0013] As a preferred embodiment of the present invention, step S2, determining the systematic error characteristics based on the comparison between the calibration measurement value and historical benchmark data, includes:

[0014] Historical benchmark data is acquired. For each data dimension in the calibration measurement, the difference between the calibration measurement and the corresponding dimension of the historical benchmark data is calculated to obtain the deviation vector. Based on the characteristics of each component of the deviation vector, the time distribution characteristics of the systematic error are determined. The periodicity and trend of the deviation vector are analyzed through the time distribution characteristics to obtain the systematic error characteristics. The historical benchmark data includes reference values ​​of temperature, pressure and corrosion of the pipeline micro-damage area under different operating conditions. The systematic error characteristics characterize the influence of environmental interference on the data of the pipeline micro-damage area.

[0015] As a preferred embodiment of the present invention, in step S2, obtaining the error amplification factor includes:

[0016] If the magnitude of the systematic error characteristics exceeds a preset threshold, an error distribution sample is generated using the Monte Carlo simulation method. For the error distribution sample, the cumulative effect of the sample in the dynamic propagation path is calculated to obtain a cumulative deviation sequence. The preset threshold is determined based on the material properties and operating environment of the pipeline's micro-damage area. The cumulative deviation sequence characterizes the cumulative change law of the error during the propagation process in the pipeline's micro-damage area. The Monte Carlo simulation method simulates the dynamic propagation behavior of the error through multiple random samplings.

[0017] For each time point of the cumulative deviation sequence, the statistical characteristics of the sequence are calculated, including mean, variance, and skewness. Based on the statistical characteristics, the distribution pattern of the cumulative deviation sequence in the dynamic propagation path is determined. The amplification factor of the error in the propagation path is calculated through the distribution pattern to obtain the error amplification coefficient. The error amplification coefficient characterizes the gain effect of the error in the propagation process of the micro-damage area of ​​the pipeline. Statistical analysis uses a probability density function to fit the cumulative deviation sequence to quantify the error propagation characteristics.

[0018] As a preferred embodiment of the present invention, step S3, obtaining the preliminary risk level, includes:

[0019] The error amplification factor is obtained, and the weights of the parameters are adjusted to reflect the impact of the error amplification factor for the input parameters of the risk assessment model. A digital twin model of the pipeline micro-damage area is constructed through virtual entity mapping based on the geometry of the physical pipeline and real-time data. A simulation scenario of working condition is generated based on the synchronization of the digital twin model and real-time data. For the simulation scenario, the risk assessment model is run to obtain a preliminary risk level, wherein the preliminary risk level characterizes the potential failure risk of the pipeline micro-damage area under the current working condition.

[0020] As a preferred embodiment of the present invention, in step S3, the preliminary risk level is matched and fused with real-time operating data to determine the final risk quantification index, including:

[0021] A preliminary risk level and real-time operating condition data are obtained. A feedback loop mechanism is constructed for the environmental variables in the real-time operating condition data. The real-time operating condition data is matched and fused with the preliminary risk level through the feedback loop mechanism to obtain the fused risk feature vector. Based on the risk feature vector, the final risk quantification index is calculated. The final risk quantification index represents the comprehensive risk level of the pipeline's minor damage area under dynamic operating conditions. The feedback loop mechanism optimizes the fusion result by iteratively updating the weights of environmental variables.

[0022] As a preferred embodiment of the present invention, step S4, generating a pipeline maintenance priority sequence based on the failure probability, includes:

[0023] Based on the periodicity and fluctuation characteristics of the final risk quantification index, and combined with the predictive behavior response model, the potential failure probability of the pipeline's minor damage area is calculated. Through the potential failure probability and combined with the failure mode simulation results, a pipeline maintenance priority sequence is generated. The pipeline maintenance priority sequence represents the maintenance urgency of different pipeline areas. The time series analysis method includes a combination of autoregressive model and moving average model.

[0024] As a preferred embodiment of the present invention, step S5 includes:

[0025] A pipeline maintenance priority sequence is obtained. For each pipeline region in the priority sequence, the dynamic propagation path of error accumulation is tracked. The optimization variables in the dynamic propagation path are adjusted through a parameter iterative optimization method. The propagation rate of the error is calculated through the optimization variables. Based on the propagation rate and the priority sequence, a maintenance schedule for the minor damage region is generated. The maintenance schedule represents the maintenance time and resource allocation scheme for the minor damage region of the pipeline. The parameter iterative optimization method optimizes the error propagation path through the gradient descent algorithm.

[0026] Secondly, the present invention also provides a pipeline digital twin modeling and operating condition simulation system for implementing the above-mentioned method, the system comprising:

[0027] The data acquisition unit is used to collect real-time data of the minor damage area of ​​the pipeline through multi-source sensors and perform fusion processing to obtain calibration measurement values. The real-time data includes temperature, pressure and corrosion data.

[0028] The error analysis unit is used to determine the systematic error characteristics by comparing the calibration measurement values ​​with historical benchmark data. If the systematic error characteristics exceed a preset threshold, an error distribution sample is generated using the Monte Carlo simulation method. The cumulative effect in the dynamic propagation path is calculated based on the error distribution sample to obtain the cumulative deviation sequence. The cumulative deviation sequence is quantified through statistical analysis to obtain the error amplification factor.

[0029] The risk analysis unit is used to adjust the parameters of the risk assessment model according to the error amplification factor, construct a digital twin model of the pipeline micro-damage area, generate a simulation scenario synchronously based on the digital twin model and real-time operating data, and obtain a preliminary risk level; the preliminary risk level is matched and fused with the real-time operating data to determine the final risk quantification index.

[0030] The failure prediction unit is used to analyze the trend sequence of the final risk quantification indicators, extract their periodicity and fluctuation characteristics, combine them with the predictive behavior response model, calculate the potential failure probability of the pipeline's minor damage area, and generate a pipeline maintenance priority sequence based on the failure probability.

[0031] The maintenance scheduling unit is used to optimize the dynamic propagation path and adjust the allocation of maintenance resources according to the pipeline maintenance priority sequence to obtain the maintenance scheduling of minor damage areas.

[0032] Thirdly, the present invention also provides a computer-readable storage medium storing instructions that, when executed by a processor, implement the above-described method.

[0033] The beneficial effects of this invention are as follows:

[0034] This invention utilizes multi-source sensors to collect real-time environmental data such as temperature, pressure, and corrosion in areas of minor pipeline damage. Through fusion processing, it generates calibrated measurement values, ensuring the reliability and accuracy of different data types and effectively characterizing the pipeline's overall environmental condition. By comparing with historical benchmark data, it identifies and quantifies systematic errors, ensuring accurate identification of external environmental interference and reducing data noise. If the error exceeds a preset threshold, Monte Carlo simulation is used to generate an error distribution sample and calculate the cumulative deviation, thereby quantifying the error amplification factor and providing more accurate input for the risk assessment model. Furthermore, by adjusting the parameters of the risk assessment model and combining real-time operating data with simulated scenarios, it can accurately predict the pipeline's potential failure risks and, based on pre-defined parameters, accurately predict the pipeline's potential failure risks. The measured failure probability generates a pipeline maintenance priority sequence, effectively capturing and responding to changes in the pipeline environment, ensuring the real-time nature and accuracy of maintenance decisions. Combined with a feedback loop mechanism, the weights of environmental variables can be adjusted in real time, optimizing data fusion and risk assessment, thereby improving the precision of pipeline maintenance. Based on the simulation results of potential failure probabilities and failure modes, pipeline maintenance scheduling is optimized. By accurately allocating resources and adjusting maintenance paths, the risk of pipeline failure is effectively reduced, and high-risk areas are given priority maintenance. The synergy between these technical solutions not only improves the efficiency and reliability of pipeline maintenance but also accurately identifies the potential risk of crack propagation in complex environments such as water areas, providing strong decision support for pipeline maintenance. Attached Figure Description

[0035] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This is a flowchart of a pipeline digital twin modeling and operating condition simulation method in an embodiment of the present invention;

[0037] Figure 2 This is a diagram showing the error distribution of pipeline crack monitoring in an embodiment of the present invention.

[0038] Figure 3 This is a structural diagram of a pipeline digital twin modeling and operating condition simulation system according to an embodiment of the present invention. Detailed Implementation

[0039] This invention provides a method, system, and storage medium for pipeline digital twin modeling and operational condition simulation. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings 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 described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0040] For ease of understanding, the specific process of the embodiments of the present invention will be described below, such as... Figure 1 As shown, one embodiment of a pipeline digital twin modeling and operating condition simulation method according to the present invention includes:

[0041] Step S1: Real-time data of the minor damage area of ​​the pipeline is collected by multi-source sensors and fused to obtain calibration measurement values. The real-time data includes temperature, pressure, and corrosion data. Specifically, this includes: collecting temperature, pressure, and corrosion data of the minor damage area of ​​the pipeline in real time by multi-source sensors; processing each data type of the collected data using Kalman filtering to obtain intermediate measurement values ​​for each data type; determining the weighting coefficients of each data type based on the time series characteristics of the intermediate measurement values; and weighting and fusing the intermediate measurement values ​​using the weighting coefficients to obtain calibration measurement values. The calibration measurement values ​​characterize the comprehensive environmental state of the minor damage area of ​​the pipeline. The weighting coefficients are determined based on the historical stability and real-time noise level of the sensor data. Abnormal data points are removed during the fusion process.

[0042] Specifically, in this embodiment, temperature, pressure, and corrosion data of the minor damage area in the pipeline are collected in real time using multi-source sensors. This real-time data is collected by various sensors at different locations and under different environmental conditions within the minor damage area to ensure data coverage of multiple points around the crack. The sensors collect raw data at a set frequency to ensure accurate real-time environmental change information. For each type of data, a Kalman filtering algorithm is used. This algorithm is a recursive estimation method used to estimate the true system state from noisy data. Specifically, the Kalman filtering algorithm initializes the state vector and covariance matrix, calculates a priori estimates through a prediction step, and then combines newly acquired measurements (i.e., real-time data) with an update step to correct the estimation results. In this process, corresponding process models are used, including heat conduction and pressure fluctuation models. The update step adjusts the calibrated data based on the measured noise covariance. To ensure the accuracy and reliability of the data, after obtaining intermediate measurements for each data type, the stability and volatility of these intermediate measurements are analyzed based on their time-series characteristics to determine the weighting coefficients for each data type. These weighting coefficients reflect the reliability and stability of different data types. For example, if the historical data of one sensor shows high stability, while the data of another sensor is significantly affected by real-time noise, the former will be given a higher weight, and the latter a lower weight. The intermediate measurements are then weighted and fused using these weighting coefficients to obtain the final calibration measurement. This calibration measurement integrates the processing results of various data types and can more accurately characterize the overall environmental state of the micro-damage area in the pipeline. During the fusion process, outlier data points are also removed. These outlier data points are usually caused by environmental interference or sensor malfunctions. Removing these data points helps improve the accuracy of the calibration measurement and ensures that it can truly reflect the changes in the crack area.

[0043] Furthermore, the determination of weighting coefficients is also based on the historical stability and real-time noise level of sensor data. For example, sensor data with a smaller standard deviation over a period of time will have a higher weight, and vice versa. For data with a large real-time noise level, if its variance exceeds a certain threshold, its weight will be reduced. The above technical solution, through the combination of Kalman filtering algorithm and weighted fusion processing, ensures that the contribution of each sensor data in the final result is reasonable and stable. Especially under complex environmental changes, it can effectively reduce the impact of external interference on the data, thereby improving the accuracy of subsequent risk assessment and maintenance decisions. The final calibration measurement value can accurately reflect the environmental change status of the pipeline's minor damage area, ensuring that the entire pipeline monitoring system can operate effectively and identify potential pipeline failure risks in a timely manner.

[0044] Step S2: Based on the comparison between the calibration measurement value and historical benchmark data, determine the systematic error characteristics; if the systematic error characteristics exceed the preset threshold, generate an error distribution sample using the Monte Carlo simulation method, calculate the cumulative effect in the dynamic propagation path based on the error distribution sample, and obtain the cumulative deviation sequence; quantify the cumulative deviation sequence through statistical analysis to obtain the error amplification coefficient;

[0045] In step S2, the systematic error characteristics are determined by comparing the calibration measurement values ​​with historical benchmark data, including:

[0046] Historical benchmark data is acquired. For each data dimension in the calibration measurement, the difference between the calibration measurement and the corresponding dimension of the historical benchmark data is calculated to obtain the deviation vector. Based on the characteristics of each component of the deviation vector, the time distribution characteristics of the systematic error are determined. The periodicity and trend of the deviation vector are analyzed through the time distribution characteristics to obtain the systematic error characteristics. The historical benchmark data includes reference values ​​of temperature, pressure and corrosion of the pipeline micro-damage area under different operating conditions. The systematic error characteristics characterize the influence of environmental interference on the data of the pipeline micro-damage area.

[0047] Specifically, historical benchmark data is acquired, including reference values ​​for temperature, pressure, and corrosion in pipeline micro-damage areas under different operating conditions. For each data dimension in the calibration measurements, such as temperature, pressure, and corrosion data, the difference between the calibration measurements and the corresponding dimension of the historical benchmark data is calculated, thus obtaining a deviation vector. Each component of the deviation vector corresponds to a data type difference, representing the degree of deviation in temperature, pressure, or corrosion, respectively. Based on the characteristics of each component of the deviation vector, the temporal distribution characteristics of systematic errors are analyzed, i.e., the periodicity and trend of the deviation vector are analyzed. First, the statistical characteristics of each component in the deviation vector, such as mean and variance, are extracted to form a time distribution curve. These characteristics characterize the distribution of errors at different time points; for example, the changes in the deviation values ​​of temperature, pressure, and corrosion at different time periods. To reveal the impact of changes in the external environment or operating conditions on pipeline status, the analysis of time distribution characteristics can identify the regularity of errors in periodic changes, such as periodic disturbances caused by tidal changes or seasonal climate changes, or trend changes caused by equipment aging or corrosion during long-term operation. Fourier transform is used to perform periodic analysis on the time distribution curve to identify possible periodic fluctuations, such as periodic changes every 24 hours, which may be caused by tidal fluctuations in the marine environment. Furthermore, linear regression is used to analyze the trend of the deviation vector, fitting the trend slope of each component to reveal the changes in temperature, pressure, or corrosion over time, such as a continuous increase in temperature or a gradual increase in pressure. These trend changes help predict the long-term risks of pipelines.

[0048] By combining the analysis results of periodicity and trend, a systematic error feature vector is generated to comprehensively characterize the impact of environmental disturbances on the data of minor damage areas in pipelines. The aforementioned systematic error feature vector includes the periodicity of periodic errors, the rate of change of trend errors, and the corresponding sources of disturbance, providing a basis for subsequent error correction and risk assessment. The above technical solution can effectively identify and quantify error patterns caused by environmental changes, help accurately predict the potential risks of minor damage areas in pipelines, and optimize pipeline maintenance scheduling.

[0049] Further, in step S2, the error amplification factor is obtained, including:

[0050] If the magnitude of the systematic error characteristics exceeds a preset threshold, an error distribution sample is generated using the Monte Carlo simulation method. For the error distribution sample, the cumulative effect of the sample in the dynamic propagation path is calculated to obtain a cumulative deviation sequence. The preset threshold is determined based on the material properties and operating environment of the pipeline micro-damage area. The cumulative deviation sequence characterizes the cumulative change law of the error during the propagation process in the pipeline micro-damage area. The Monte Carlo simulation method simulates the dynamic propagation behavior of the error through multiple random samplings.

[0051] For each time point of the cumulative deviation sequence, the statistical characteristics of the sequence are calculated, including mean, variance, and skewness. Based on the statistical characteristics, the distribution pattern of the cumulative deviation sequence in the dynamic propagation path is determined. The amplification factor of the error in the propagation path is calculated through the distribution pattern to obtain the error amplification coefficient. The error amplification coefficient characterizes the gain effect of the error in the propagation process of the micro-damage area of ​​the pipeline. Statistical analysis uses a probability density function to fit the cumulative deviation sequence to quantify the error propagation characteristics.

[0052] Specifically, in this embodiment, if the amplitude of the systematic error characteristics exceeds a preset threshold, an error distribution sample is generated using a Monte Carlo simulation method. First, a preset threshold is set based on the material properties and operating environment of the minor damage area of ​​the pipeline. This ensures that the threshold can adapt to the environmental interference and sensitivity of the specific area. The preset threshold is determined based on factors such as the yield strength, corrosion rate, temperature range, and water pressure of the pipeline steel, ensuring that it can effectively filter out insignificant errors and highlight significant errors that may cause potential risks. When the amplitude of the systematic error characteristics exceeds this threshold, it indicates that the error change exceeds the normal range. An error distribution sample is generated using a Monte Carlo simulation method, that is, by simulating the dynamic propagation behavior of the error through multiple random samplings. The random sampling simulates different increments of the systematic error, with each sampling generating an error increment to simulate the uncertainty propagation of the error. Through inverse... Repeated sampling and summarization form an error distribution sample, which can more realistically reflect the impact of environmental factors on the error propagation in the pipeline's micro-damage area. Especially in high-pressure, high-temperature, or corrosive environments, it has strong robustness and effectively avoids the limitations of a single model. After obtaining the error distribution sample, the cumulative effect of the sample in the dynamic propagation path is calculated, thus obtaining a cumulative deviation sequence. The above sequence characterizes the cumulative change law of error in the pipeline's micro-damage area along the propagation path. Here, the propagation path is first defined as multiple nodes along the pipeline axis from the crack initiation point, with each node representing a location in the crack area. The error distribution sample is accumulated node by node to form a cumulative deviation sequence. The above cumulative deviation sequence reveals that the error changes continuously with the increase of path nodes. Especially in deep-sea environments, due to factors such as high pressure, high temperature, or corrosion, the error may show an exponential growth trend.

[0053] For the cumulative deviation sequence at each time point, statistical characteristics are calculated, including mean, variance, and skewness, to analyze the distribution pattern of the sequence. These statistical characteristics quantify the error propagation characteristics and identify the dynamic changes of the error during propagation. Specifically, based on the distribution pattern of the cumulative deviation sequence, the amplification factor of the error in the propagation path is calculated, yielding the error amplification coefficient. This coefficient reflects the gain effect of the error in the micro-damage area of ​​the pipeline, indicating the degree of amplification during propagation. Analyzing the distribution pattern and path evolution provides a precise basis for subsequent risk assessment and maintenance decisions. To further optimize the description of error propagation, a probability density function is used to fit the cumulative deviation sequence, quantifying the error propagation characteristics. This fitting process involves selecting an appropriate distribution model, such as a Gaussian or log-normal distribution, and applying maximum likelihood estimation to fit the parameters of the sequence data. The goodness of fit is evaluated, and error characteristics, such as peak and tail behavior, are quantified. This effectively improves the accuracy of error quantification and allows for precise identification and quantification of error changes caused by environmental disturbances, improving monitoring accuracy and helping to identify potential pipeline failure risks early. Figure 2 As shown, in oil pipeline crack monitoring, assuming the cumulative deviation sequence is [0.1, 0.15, 0.22, 0.35], the statistical characteristics are calculated to obtain a mean of 0.205, a variance of 0.009, and a skewness of 0.8. Based on these, the distribution is determined to be right-skewed. Then, the amplification factor is calculated to be 3.5, resulting in an error amplification coefficient of 3.5, which characterizes the gain effect. After fitting with a probability density function, the quantitative characteristics show that the error propagation rate increases by 15%, which is beneficial for prioritizing the maintenance of high-risk areas.

[0054] Step S3: Adjust the parameters of the risk assessment model according to the error amplification factor, construct a digital twin model of the micro-damage area of ​​the pipeline based on the geometry of the physical pipeline and real-time data, generate a simulation scenario synchronously based on the digital twin model and real-time operating data, and obtain a preliminary risk level through the risk assessment model; match and fuse the preliminary risk level with the real-time operating data to determine the final risk quantification index.

[0055] In step S3, the preliminary risk level is obtained, including:

[0056] The error amplification factor is obtained, and the weights of the parameters are adjusted to reflect the impact of the error amplification factor for the input parameters of the risk assessment model. A digital twin model of the pipeline micro-damage area is constructed through virtual entity mapping based on the geometry of the physical pipeline and real-time data. A simulation scenario of working condition is generated based on the synchronization of the digital twin model and real-time data. For the simulation scenario, the risk assessment model is run to obtain a preliminary risk level, wherein the preliminary risk level characterizes the potential failure risk of the pipeline micro-damage area under the current working condition.

[0057] Specifically, based on the error amplification factor and the input parameters of the risk assessment model, the weights of the parameters are adjusted to accurately reflect the impact of the error amplification factor. This is achieved by using the error amplification factor as a weighting factor, multiplying it by the original weights of the temperature, pressure, and corrosion data (i.e., the input parameters), and thus updating the model parameters. This enhances the model's sensitivity to systematic errors and ensures that the risk assessment model fully considers the impact of errors when processing real-world operating data, thereby improving the accuracy and reliability of predictions. Furthermore, virtual entity mapping technologies, such as BIM, laser scanning, or oblique photography, are used to construct digital twin models of the pipeline's micro-damage areas. This ensures that the model can reflect the state of the cracked areas in real time. These digital twin models are three-dimensional geometric models, and the digital twin models are constructed by physical... The pipeline's geometry and real-time data, such as temperature, pressure, corrosion data, and flow velocity, are synchronously mapped into a virtual space. This reflects the pipeline's environmental information and mechanical behavior, such as stress and fluid dynamics, as well as a data model that can receive and dynamically update real-time sensor data. This allows for the simulation of changes under conditions such as crack propagation. The Kalman filter algorithm is used to fuse the above data to obtain calibrated measurements, which are then compared with historical benchmark data to calculate a deviation vector to identify systematic error characteristics. Finally, the error amplification factor is derived. The construction of this digital twin model helps to accurately predict the behavior of minor damage areas in the pipeline, especially in the monitoring scenario of offshore oil pipelines. The digital twin model can effectively identify potential failure risks and provide strong support for subsequent decision-making.

[0058] After completing the construction of the digital twin model, simulation scenarios for working condition extrapolation are generated based on the synchronization of the model and real-time data. These simulation scenarios ensure data consistency and effective state prediction by matching the real-time data and the virtual model's synchronous updates through timestamp matching. Numerical simulation methods are also used to predict crack propagation rates and simulate crack propagation paths under different working conditions, such as high pressure or intensified corrosion. By extracting environmental variables from real-time data, such as environmental pressure and temperature, the simulation scenarios can comprehensively reflect the impact of changes in working conditions on micro-damaged areas of the pipeline. For example, by setting an instantaneous rise or fall in the fluid pressure inside the pipe, such as an increase of 10%–20%, the resulting stress concentration distribution is calculated. The corrosion rate parameter is increased in local areas, such as from a first value to a second value, to simulate the impact of long-term corrosion on wall thickness reduction and stress redistribution. Furthermore, a limited number of parameters are invoked in each scenario. The meta-numerical calculation module and the fluid-structure interaction model dynamically simulate the stress-strain field, crack tip stress intensity factor, plastic zone range, and strain energy density of the pipeline under different disturbance conditions. This provides more refined simulation input for risk assessment. For the generated simulation scenario, the risk assessment model is run to obtain a preliminary risk level. This risk assessment model is based on probabilistic calculation methods, such as using Bayesian networks to calculate the failure probability of the crack region. Combined with adjusted model parameters and the simulation scenario, a preliminary assessment of potential failure risk is obtained. This preliminary risk level characterizes the potential failure risk of the pipeline's minor damage area under current operating conditions and provides a decision-making basis for subsequent maintenance priority setting. This technical solution can dynamically adjust pipeline maintenance strategies based on real-time data and simulation scenarios, ensuring effective reduction of the risks caused by potential failures in actual operation.

[0059] Furthermore, in step S3, the preliminary risk level is matched and integrated with real-time operating condition data to determine the final risk quantification indicators, including:

[0060] A preliminary risk level and real-time operating condition data are obtained. A feedback loop mechanism is constructed for the environmental variables in the real-time operating condition data. The real-time operating condition data is matched and fused with the preliminary risk level through the feedback loop mechanism to obtain the fused risk feature vector. Based on the risk feature vector, the final risk quantification index is calculated. The final risk quantification index represents the comprehensive risk level of the pipeline's minor damage area under dynamic operating conditions. The feedback loop mechanism optimizes the fusion result by iteratively updating the weights of environmental variables.

[0061] Specifically, in this embodiment, after obtaining the preliminary risk level and real-time operating condition data, a feedback loop mechanism is constructed for the environmental variables in the real-time operating condition data to optimize the risk assessment process. First, real-time data from multiple sensors is collected and fused using a Kalman filtering algorithm to obtain calibrated measurement values. By comparing these values ​​with historical benchmark data, a deviation vector is calculated, and a preliminary risk level is generated. Simultaneously, real-time operating condition data, such as temperature, pressure, and corrosion indicators, are also collected. Based on environmental variables in the real-time operating condition data, such as temperature fluctuations, pressure changes, and corrosion rates, a feedback loop mechanism is constructed. Specifically, environmental variables are identified by real-time data collection from sensors and preliminary data classification. Based on historical data statistics, an initial weight matrix is ​​established to ensure that the initial weights reasonably reflect the relative importance of each environmental variable to the risk assessment. For example… Temperature, pressure, and corrosion each have initial weights set to specific values. By constructing iterative update rules and based on a feedback loop mechanism, the impact of each environmental variable on risk deviation is calculated in each iteration, and the weights are gradually adjusted to optimize the fusion result. This allows for dynamic adaptation to the changing conditions in the deep-sea environment, significantly improving the accuracy and reliability of risk assessment. Considering the differences under different operating conditions, the above feedback mechanism can be dynamically adjusted under specific environments. For example, when pressure changes drastically, the initial weight matrix prioritizes increasing the weight of the pressure variable, and the weight is gradually optimized through iterative update rules. In corrosion-dominated scenarios, a higher initial weight is assigned to the corrosion rate to ensure a rapid response to changes in environmental variables. This ensures that even under different operating conditions and environmental influences, risk assessment can quickly and accurately reflect the actual risk status of the pipeline's minor damage areas.

[0062] Furthermore, a feedback loop mechanism is used to match and fuse real-time operating data with the preliminary risk level, resulting in a fused risk feature vector. The real-time operating data is weighted and matched against the deviation vector of the preliminary risk level using a weighted matrix, and the weighted average is calculated to fuse the data. If the fusion error exceeds a preset threshold, the weights are adjusted through the feedback loop, and the matching is repeated. This process iterates continuously, optimizing the weights to ultimately output the fused risk feature vector. This risk feature vector integrates data from dimensions such as temperature, pressure, and corrosion, representing the comprehensive risk characteristics of the pipeline's minor damage areas. Based on the fused risk feature vector, the final risk quantification index is calculated. Among them, the risk quantification index characterizes the comprehensive risk level of the pipeline's minor damage area under dynamic operating conditions. This index, through iterative optimization of the feedback loop mechanism, can accurately reflect the risk evolution process. Specifically, by applying statistical quantification formulas to the risk feature vector, such as calculating the Euclidean norm of the vector, the final risk quantification index is generated. The above technical solution, through the weights optimized by the feedback loop, ensures that the index can accurately characterize the potential failure risk of the crack area under dynamic operating conditions, effectively improving the risk assessment accuracy of the pipeline's minor damage area. Especially in the monitoring and maintenance of waterway oil pipelines, it can accurately identify and predict potential failure risks, optimize maintenance decisions, and reduce the probability of pipeline failure.

[0063] Step S4: Analyze the trend sequence of the final risk quantification indicators, extract their periodicity and fluctuation characteristics, combine them with the predictive behavior response model, calculate the potential failure probability of the pipeline's minor damage area, and generate a pipeline maintenance priority sequence based on the failure probability; specifically including:

[0064] Based on the periodicity and fluctuation characteristics of the final risk quantification index, and combined with the predictive behavior response model, the potential failure probability of the pipeline's minor damage area is calculated. Through the potential failure probability and combined with the failure mode simulation results, a pipeline maintenance priority sequence is generated. The pipeline maintenance priority sequence represents the maintenance urgency of different pipeline areas. The time series analysis method includes a combination of autoregressive model and moving average model.

[0065] Specifically, in this embodiment, the trend sequence of the final risk quantification index is first obtained. This trend sequence is obtained by matching and fusing the preliminary risk level with real-time operating condition data, combined with environmental variables and a feedback loop mechanism. The final risk quantification index is calculated based on the deviation vector calculated by comparing the calibrated measured values ​​with historical benchmark data, combined with the risk assessment model output adjusted by the error amplification factor. This extracts the time-varying sequence data of the final risk quantification index, forming a trend sequence. For the extracted trend sequence, time series analysis methods are used to extract its periodicity and volatility characteristics. Specifically, an autoregressive model is applied to calculate the linear dependence between the current value and past values ​​in the trend sequence, thereby capturing the trend component in the sequence. The autoregressive model estimates parameters using the least squares method to characterize the pattern of trend changes. A moving average model is also used to handle random fluctuations in the sequence. The moving average model smooths the sequence by weighted averaging of historical error terms, thereby extracting volatility characteristics. Combining with a moving average model can effectively fit trend sequences, extract periodic features such as seasonal repetition patterns, and fluctuation features such as variance changes, thereby quantifying the dynamic behavior of the sequence. This combined model can effectively handle noise in pipeline data and improve the accuracy of feature extraction. After obtaining periodic and fluctuation features, the predictive behavior response model is combined to calculate the potential failure probability of the pipeline's minor damage area and the future crack state changes of the pipeline. Specifically, the extracted periodic features are first input into the predictive behavior response model to simulate the response of the crack area to environmental changes, such as how temperature and pressure fluctuations lead to crack propagation, thus affecting the overall safety of the pipeline. Fluctuation features are also incorporated to calculate the probability distribution in the response model and obtain the potential failure probability. The above process further infers the impact of the cumulative deviation sequence on crack propagation through error distribution samples generated by Monte Carlo simulation, thereby accurately capturing the long-term impact of environmental disturbances on the pipeline's minor damage area and helping to predict potential failure risks.

[0066] Based on the calculated potential failure probability and combined with the failure mode simulation results, a pipeline maintenance priority sequence is further generated. The failure mode simulation simulates crack propagation paths, outputs the probability distribution of failure scenarios, and integrates real-time operating data and potential failure probabilities to obtain a comprehensive evaluation result. By combining this comprehensive evaluation result with different operating conditions in different pipeline areas, pipelines in different areas can be ranked to generate a priority sequence, indicating which areas require more urgent maintenance. By tracking the accumulated error in the priority sequence, the maintenance scheduling strategy is further optimized to ensure efficient resource allocation, reduce the risk of potential failures, and optimize maintenance work under different operating conditions and risk levels. This technical solution further quantifies the potential failure probability by combining the extraction of the periodicity and fluctuation characteristics of the trend sequence with a predictive behavior response model. By integrating this with the failure mode simulation results, a pipeline maintenance priority sequence is finally generated. This sequence can accurately quantify and predict the risk level of minor pipeline damage areas under dynamic operating conditions, effectively improving the accuracy of risk assessment and providing optimized maintenance solutions for minor pipeline damage areas under different environmental and operating conditions, ensuring the safe operation of the pipeline.

[0067] Step S5: Based on the pipeline maintenance priority sequence, optimize the dynamic propagation path and adjust the allocation of maintenance resources to obtain the maintenance schedule for the minor damage area; specifically including:

[0068] A pipeline maintenance priority sequence is obtained. For each pipeline region in the priority sequence, the dynamic propagation path of error accumulation is tracked. The optimization variables in the dynamic propagation path are adjusted through a parameter iterative optimization method. The propagation rate of the error is calculated through the optimization variables. Based on the propagation rate and the priority sequence, a maintenance schedule for the minor damage region is generated. The maintenance schedule represents the maintenance time and resource allocation scheme for the minor damage region of the pipeline. The parameter iterative optimization method optimizes the error propagation path through the gradient descent algorithm.

[0069] Specifically, by acquiring a pipeline maintenance priority sequence, the dynamic propagation path of error accumulation is tracked for each pipeline region in the sequence. This involves extracting deviation vectors associated with each region and mapping the cumulative effect of each node along the propagation path using these vectors. Quantifying this dynamic propagation path effectively identifies how errors propagate from initial sensor data deviations to other pipeline regions, further impacting the pipeline's structure and condition. Furthermore, an iterative parameter optimization method is employed to adjust the optimization variables in the dynamic propagation path, ensuring that the optimization process minimizes the impact of error propagation. Specifically, optimization variables, such as the weights of path nodes and deviation thresholds, are initialized, and these variables are iteratively optimized using a gradient descent algorithm. In each iteration, the gradient descent algorithm calculates the gradient of the loss function and adjusts the optimization variables based on the partial derivatives until the error converges to a minimum. This approach can handle nonlinear error propagation in pipeline data and adapt to complex environmental changes. Particularly in monitoring scenarios involving water and oil pipelines, gradient descent optimization can effectively address the impact of different environmental variables on errors.

[0070] The propagation rate of the error is calculated by optimizing the variables, and then the propagation rate of each node on the path is obtained. The propagation rate can characterize the speed and impact of the error spreading along the path. Combined with the priority sequence, corresponding maintenance resources can be allocated to each area. The combination of propagation rate and priority generates a maintenance scheduling scheme for areas with minor damage. The maintenance scheduling scheme clarifies the maintenance time and resource allocation for each area, such as the number of personnel and equipment allocated, and the priority timing of maintenance. The above scheduling scheme, through the fusion of priority ranking and error propagation rate, ensures that pipeline maintenance work can be responded to in a timely manner at critical moments, reducing the risk of potential failure. In specific applications, for different pipeline areas, the optimization process can allocate more resources to high-risk areas according to the propagation rate and priority of each area, and arrange maintenance work in advance. Especially in aquatic environments, when the propagation rate of a certain crack area is high, the scheduling scheme will prioritize the allocation of more resources, such as remotely operated vehicles and detection instruments, to ensure that maintenance can be carried out in advance and prevent greater risks caused by crack propagation. It can not only be used to guide manual scheduling, but also be considered for integration with control systems in the future to achieve automated adjustment or early warning, thereby forming a closed loop of "perception-analysis-decision-execution". Simultaneously, the correlation between the model and data from the pipeline design and construction phases can be considered to reflect the concept of full life-cycle management. Through iterative updates of the aforementioned optimization algorithms and resource allocation, the above technical solution can achieve more efficient pipeline maintenance under dynamic operating conditions and reduce safety hazards caused by pipeline failures.

[0071] This invention also provides a pipeline digital twin modeling and operating condition simulation system for implementing the above-mentioned methods, such as... Figure 3As shown, the system includes:

[0072] The data acquisition unit is used to collect real-time data of the minor damage area of ​​the pipeline through multi-source sensors and perform fusion processing to obtain calibration measurement values. The real-time data includes temperature, pressure and corrosion data.

[0073] The error analysis unit is used to determine the systematic error characteristics by comparing the calibration measurement values ​​with historical benchmark data. If the systematic error characteristics exceed a preset threshold, an error distribution sample is generated using the Monte Carlo simulation method. The cumulative effect in the dynamic propagation path is calculated based on the error distribution sample to obtain the cumulative deviation sequence. The cumulative deviation sequence is quantified through statistical analysis to obtain the error amplification factor.

[0074] The risk analysis unit is used to adjust the parameters of the risk assessment model according to the error amplification factor, construct a digital twin model of the pipeline micro-damage area, generate a simulation scenario synchronously based on the digital twin model and real-time operating data, and obtain a preliminary risk level; the preliminary risk level is matched and fused with the real-time operating data to determine the final risk quantification index.

[0075] The failure prediction unit is used to analyze the trend sequence of the final risk quantification indicators, extract their periodicity and fluctuation characteristics, combine them with the predictive behavior response model, calculate the potential failure probability of the pipeline's minor damage area, and generate a pipeline maintenance priority sequence based on the failure probability.

[0076] The maintenance scheduling unit is used to optimize the dynamic propagation path and adjust the allocation of maintenance resources according to the pipeline maintenance priority sequence to obtain the maintenance scheduling of minor damage areas.

[0077] The present invention also provides a computer-readable storage medium storing instructions that, when executed by a processor, implement the above-described method.

[0078] In summary, this invention uses multi-source sensors to collect environmental data such as temperature, pressure, and corrosion in areas of minor pipeline damage in real time, and generates calibration measurements through fusion processing, ensuring the reliability and accuracy of different types of data, thereby effectively characterizing the comprehensive environmental state of the pipeline. By comparing with historical benchmark data, systematic errors are identified and quantified, ensuring that external environmental interference is accurately identified and reducing the impact of data noise. If the error exceeds a preset threshold, Monte Carlo simulation is used to generate an error distribution sample and calculate the cumulative deviation, thereby quantifying the error amplification factor and providing more accurate input for the risk assessment model. Furthermore, by adjusting the parameters of the risk assessment model and combining real-time operating data and simulated scenarios, the potential failure risk of the pipeline can be accurately predicted, and the results can be applied accordingly. The predicted failure probability generates a pipeline maintenance priority sequence, effectively capturing and responding to changes in the pipeline environment to ensure the real-time nature and accuracy of maintenance decisions. Combined with a feedback loop mechanism, the weights of environmental variables can be adjusted in real time, optimizing data fusion and risk assessment, thereby improving the precision of pipeline maintenance. Based on simulation results of potential failure probabilities and failure modes, pipeline maintenance scheduling is optimized. By precisely allocating resources and adjusting maintenance paths, the risk of pipeline failure is effectively reduced, and high-risk areas are prioritized for maintenance. The synergy between these technical solutions not only improves the efficiency and reliability of pipeline maintenance but also accurately identifies potential crack propagation risks in complex environments such as water bodies, providing strong decision support for pipeline maintenance.

[0079] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0080] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0081] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for digital twin modeling and operational condition simulation of pipelines, characterized in that, The method includes: Step S1: Collect real-time data of the minor damage area of ​​the pipeline through multi-source sensors and perform fusion processing to obtain calibration measurement values. The real-time data includes temperature, pressure and corrosion data. Step S2: Based on the comparison between the calibration measurement value and historical benchmark data, determine the systematic error characteristics; if the systematic error characteristics exceed the preset threshold, generate an error distribution sample using the Monte Carlo simulation method, calculate the cumulative effect in the dynamic propagation path based on the error distribution sample, and obtain the cumulative deviation sequence; quantify the cumulative deviation sequence through statistical analysis to obtain the error amplification coefficient; Step S3: Adjust the parameters of the risk assessment model according to the error amplification factor, construct a digital twin model of the micro-damage area of ​​the pipeline based on the geometry of the physical pipeline and real-time data, generate a simulation scenario synchronously based on the digital twin model and real-time operating data, and obtain a preliminary risk level through the risk assessment model; match and fuse the preliminary risk level with the real-time operating data to determine the final risk quantification index. Step S4: Analyze the trend sequence of the final risk quantification indicators, extract their periodicity and fluctuation characteristics, combine them with the predictive behavior response model, calculate the potential failure probability of the pipeline's minor damage area, and generate a pipeline maintenance priority sequence based on the failure probability. Step S5: Optimize the dynamic propagation path and adjust the allocation of maintenance resources according to the pipeline maintenance priority sequence to obtain the maintenance schedule for the minor damage area.

2. The method according to claim 1, characterized in that, Step S1 includes: Temperature, pressure, and corrosion data of the pipeline's micro-damage area are collected in real time by multi-source sensors. Each data type is processed using Kalman filtering to obtain an intermediate measurement value for each data type. Based on the time series characteristics of the intermediate measurement values, a weighting coefficient for each data type is determined. The intermediate measurement values ​​are then weighted and fused using these weighting coefficients to obtain a calibration measurement value. This calibration measurement value represents the comprehensive environmental state of the pipeline's micro-damage area. The weighting coefficients are determined based on the historical stability and real-time noise level of the sensor data. Abnormal data points are removed during the fusion process.

3. The method according to claim 1, characterized in that, In step S2, the systematic error characteristics are determined by comparing the calibration measurements with historical benchmark data, including: Historical benchmark data is acquired. For each data dimension in the calibration measurement, the difference between the calibration measurement and the corresponding dimension of the historical benchmark data is calculated to obtain the deviation vector. Based on the characteristics of each component of the deviation vector, the time distribution characteristics of the systematic error are determined. The periodicity and trend of the deviation vector are analyzed through the time distribution characteristics to obtain the systematic error characteristics. The historical benchmark data includes reference values ​​of temperature, pressure and corrosion of the pipeline micro-damage area under different operating conditions. The systematic error characteristics characterize the influence of environmental interference on the data of the pipeline micro-damage area.

4. The method as described in claim 3, characterized in that, In step S2, the cumulative effect in the dynamic propagation path is calculated based on the error distribution sample to obtain the cumulative deviation sequence, including: If the magnitude of the systematic error characteristics exceeds a preset threshold, an error distribution sample is generated using the Monte Carlo simulation method. For the error distribution sample, the cumulative effect of the sample in the dynamic propagation path is calculated to obtain a cumulative deviation sequence. The preset threshold is determined based on the material properties and operating environment of the pipeline's micro-damage area. The cumulative deviation sequence characterizes the cumulative change law of the error during the propagation process in the pipeline's micro-damage area. The Monte Carlo simulation method simulates the dynamic propagation behavior of the error through multiple random samplings. For each time point of the cumulative deviation sequence, the statistical characteristics of the sequence are calculated, including the mean, variance, and skewness. The distribution law of the cumulative deviation sequence in the dynamic propagation path is determined based on the statistical characteristics. The amplification factor of the error in the propagation path is calculated based on the distribution law to obtain the error amplification coefficient. The error amplification coefficient characterizes the gain effect of the error during the propagation process in the pipeline's micro-damage area. Statistical analysis uses a probability density function to fit the cumulative deviation sequence to quantify the error propagation characteristics.

5. The method as described in claim 1, characterized in that, In step S3, a preliminary risk level is obtained, including: The error amplification factor is obtained, and the weights of the parameters are adjusted to reflect the impact of the error amplification factor for the input parameters of the risk assessment model. A digital twin model of the pipeline micro-damage area is constructed through virtual entity mapping based on the geometry of the physical pipeline and real-time data. A simulation scenario of working condition is generated based on the synchronization of the digital twin model and real-time data. For the simulation scenario, the risk assessment model is run to obtain a preliminary risk level, wherein the preliminary risk level characterizes the potential failure risk of the pipeline micro-damage area under the current working condition.

6. The method as described in claim 5, characterized in that, In step S3, the preliminary risk level is matched and integrated with real-time operating condition data to determine the final risk quantification indicators, including: A preliminary risk level and real-time operating condition data are obtained. A feedback loop mechanism is constructed for the environmental variables in the real-time operating condition data. The real-time operating condition data is matched and fused with the preliminary risk level through the feedback loop mechanism to obtain the fused risk feature vector. Based on the risk feature vector, the final risk quantification index is calculated. The final risk quantification index represents the comprehensive risk level of the pipeline's minor damage area under dynamic operating conditions. The feedback loop mechanism optimizes the fusion result by iteratively updating the weights of environmental variables.

7. The method as described in claim 1, characterized in that, In step S4, a pipeline maintenance priority sequence is generated based on the failure probability, including: Based on the periodicity and fluctuation characteristics of the final risk quantification index, and combined with the predictive behavior response model, the potential failure probability of the pipeline's minor damage area is calculated. Through the potential failure probability and combined with the failure mode simulation results, a pipeline maintenance priority sequence is generated. The pipeline maintenance priority sequence represents the maintenance urgency of different pipeline areas. The time series analysis method includes a combination of autoregressive model and moving average model.

8. The method as described in claim 1, characterized in that, Step S5 includes: A pipeline maintenance priority sequence is obtained. For each pipeline region in the priority sequence, the dynamic propagation path of error accumulation is tracked. The optimization variables in the dynamic propagation path are adjusted through a parameter iterative optimization method. The propagation rate of the error is calculated through the optimization variables. Based on the propagation rate and the priority sequence, a maintenance schedule for the minor damage region is generated. The maintenance schedule represents the maintenance time and resource allocation scheme for the minor damage region of the pipeline. The parameter iterative optimization method optimizes the error propagation path through the gradient descent algorithm.

9. A pipeline digital twin modeling and operating condition simulation system, used to implement the method as described in any one of claims 1-8, characterized in that, The system includes: The data acquisition unit is used to collect real-time data of the minor damage area of ​​the pipeline through multi-source sensors and perform fusion processing to obtain calibration measurement values. The real-time data includes temperature, pressure and corrosion data. The error analysis unit is used to determine the systematic error characteristics by comparing the calibration measurement values ​​with historical benchmark data. If the systematic error characteristics exceed a preset threshold, an error distribution sample is generated using the Monte Carlo simulation method. The cumulative effect in the dynamic propagation path is calculated based on the error distribution sample to obtain the cumulative deviation sequence. The cumulative deviation sequence is quantified through statistical analysis to obtain the error amplification factor. The risk analysis unit is used to adjust the parameters of the risk assessment model according to the error amplification factor, construct a digital twin model of the pipeline micro-damage area, generate a simulation scenario synchronously based on the digital twin model and real-time operating data, and obtain a preliminary risk level; the preliminary risk level is matched and fused with the real-time operating data to determine the final risk quantification index. The failure prediction unit is used to analyze the trend sequence of the final risk quantification indicators, extract their periodicity and fluctuation characteristics, combine them with the predictive behavior response model, calculate the potential failure probability of the pipeline's minor damage area, and generate a pipeline maintenance priority sequence based on the failure probability. The maintenance scheduling unit is used to optimize the dynamic propagation path and adjust the allocation of maintenance resources according to the pipeline maintenance priority sequence to obtain the maintenance scheduling of minor damage areas.

10. A computer-readable storage medium storing instructions thereon, characterized in that, When the instructions are executed by the processor, they implement the method as described in any one of claims 1-8.

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