Method and system for intelligent assessment and management of operating health of oil and gas pipelines

Abstract

The application discloses an oil and gas pipeline operation health intelligent evaluation and management method and system, which comprises the following steps: collecting multi-dimensional operation parameters and constructing an original data set, screening and mapping to form a standardized data set, obtaining a corrosion evolution trend by relying on a pipeline corrosion double-layer time sequence prediction model, generating a coupling damage distribution through a multi-field coupling damage evaluation model, outputting pipe body stress by using a pipe body stress iterative intelligent calculation network, and finally integrating data and generating health evaluation and management decisions; the system comprises a multi-dimensional operation parameter acquisition and transmission unit, a standardized data processing and mapping unit, a pipeline corrosion double-layer time sequence prediction operation unit, a multi-field coupling damage evaluation and analysis unit, a pipe body stress iterative intelligent calculation unit and a health state integration and management decision unit, each unit is linked in cooperation, pipeline health whole-process intelligent evaluation and management are realized, and the evaluation accuracy and management effectiveness are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of oil and gas pipeline operation and management technology, and in particular to a method and system for intelligent assessment and management of the operational health of oil and gas pipelines. Background Technology

[0002] As a core infrastructure for energy transportation, oil and gas pipelines are directly linked to the stability of energy supply and public safety. They are constantly exposed to multiple complex factors, including the impact of flowing media, corrosive media, environmental temperature and pressure variations, and structural stress accumulation. With the increasing service life of pipelines, issues such as material aging and damage evolution are becoming increasingly prominent. Traditional management models relying on manual inspections and single-parameter monitoring are no longer adequate for the operational needs of long-distance, high-pressure, and multi-condition pipelines. Currently, the industry urgently needs accurate assessment, trend prediction, and intelligent decision-making regarding pipeline health status. There is a pressing need to build an integrated management system that integrates multi-source data, multi-model collaboration, and full-process traceability. This system should facilitate a shift from passive maintenance to proactive early warning, and from decentralized monitoring to systemic evaluation, ensuring the safety and economy of pipeline operation.

[0003] Existing technologies suffer from two major drawbacks: First, the separation between data processing and model application lacks a deep integration mechanism for multi-dimensional parameters. Evaluations are conducted using only a single model or isolated parameters, failing to fully consider the dynamic correlation between corrosion evolution, multi-field coupled damage, and pipe stress, resulting in insufficient comprehensiveness and accuracy of the evaluation results. Second, there is a lack of end-to-end traceability and iterative optimization capabilities. Existing systems often focus on data analysis of a single link, failing to establish a closed-loop management link from parameter acquisition and model computation to decision generation. This makes it difficult to trace the evolution trajectory of health status through historical data and to dynamically adjust model parameters and calculation logic based on real-time feedback, leading to discrepancies between evaluation results and actual operating conditions, thus limiting the pertinence and effectiveness of management decisions. Summary of the Invention

[0004] In order to overcome the shortcomings and deficiencies of existing technologies, this invention provides a method and system for intelligent assessment and management of the operational health of oil and gas pipelines.

[0005] The technical solution adopted in this invention is an intelligent assessment and management method for the operational health of oil and gas pipelines, comprising the following steps: S1, collecting data on medium flow rate, pressure fluctuation, corrosive medium concentration, ambient temperature gradient, pipeline material characteristics, pipe geometric structure parameters, and historical damage data during the operation of the oil and gas pipeline, and constructing a multi-dimensional raw data set; S2, inputting the raw data set into an oil and gas pipeline health traceability management platform, filtering and calibrating key influencing parameters and establishing data mapping relationships to form a standardized input dataset; S3, calling a pipeline corrosion two-layer time-series prediction model based on the standardized input dataset. Pipeline corrosion evolution trend data is obtained through multi-layer feature extraction and time-series correlation analysis; S4, a multi-field coupled damage assessment model is used to couple the corrosion evolution trend data with pipeline structural parameters to generate multi-field coupled damage distribution data; S5, the pipe stress iterative intelligent calculation network is used to iteratively calculate the coupled damage distribution data and operating load parameters to output pipe stress distribution data; S6, the corrosion evolution trend data, multi-field coupled damage distribution data and pipe stress distribution data are integrated through the oil and gas pipeline health traceability management platform to complete the intelligent assessment and management decision generation of the operational health status of oil and gas transmission pipelines.

[0006] Furthermore, the expression for the two-layer time-series prediction model for pipeline corrosion in S3 is as follows: ,in, For future pipeline corrosion levels, These are the time-series weighting coefficients. The weighting factor is the historical corrosion data. For the first The amount of historical corrosion at any given moment The corrosion attenuation coefficient is... For predicting time intervals, This is the environmental impact factor. For the j-th type of running parameter weights, For the j-th type of pipeline operating parameter values, The temperature gradient influence coefficient is... For the ambient temperature gradient, The coefficient representing the influence of corrosive media. This is the correction factor for medium concentration. This represents the concentration of the corrosive medium.

[0007] Furthermore, the expression for the multi-field coupled damage assessment model in S4 is as follows: ,in, Multi-field coupled damage degree The coupling coefficient is... For mechanical stress weight, For the mechanical stress of the pipe body, For thermal stress weight, For the thermal stress of the tube body, Let be the corrosion coupling coefficient. For the k-th type of corrosion factor weight, Let k be the corrosion influence function. For the k-th type of corrosion-related parameters, The flow field influence coefficient is... For flow rate weighting, For the medium flow rate, For pressure gradient weights, This represents the pressure gradient.

[0008] Furthermore, the expression for the intelligent calculation network for tube stress iteration in S5 is as follows: ,in, For the first The tube stress value after the next iteration The stress value in the nth iteration is... For the iterative yield coefficient, The stress iteration step size is... For multi-field coupled damage degree, The corrosion stress coupling coefficient is... The stress influence weight for the q-th type parameter is... This refers to the amount of pipeline corrosion. For the qth type of operating load parameters, The structural parameter influence coefficient, For the density of the pipe material, The elastic modulus of the pipe material. These are the geometric parameters of the tube body.

[0009] Furthermore, the data integration of the oil and gas pipeline health traceability management platform is expressed as follows: ,in, Comprehensive health status assessment value To evaluate the weighting coefficients, To predict the amount of corrosion, For multi-field coupled damage degree, This represents the final iterative stress value. For source tracing correction coefficient, Let s be the weight of the source data in the s-th iteration. For the s-th source of health deviation, The historical data influence coefficient. For the real-time parameter weights of class u, For the u-th type of real-time monitoring parameter, Weighting based on historical health data, Historical health assessment values.

[0010] Furthermore, the intelligent assessment and management decision generation for the operational health of the oil and gas transmission pipeline is expressed as follows: Where M is the management decision output value, For decision coefficients, This is a comprehensive assessment value of health status. As a standard health threshold, As the initial health baseline, To couple decision weights, Let z be the decision factor of the z-th type of parameter. For multi-field coupled damage degree, Let z be the parameter affecting the decision-making process. This represents the final iterative stress value. To predict the amount of corrosion, For the decision coefficients of the operating parameters, For parameter correction coefficients, The decision weights for the x-th type of running parameters are... This represents the runtime parameter value for class x.

[0011] Further, S3 includes the following sub-steps: S31, dividing the corrosion-related parameters in the standardized input dataset into time series segments to establish multi-time period data subsets, extracting the dynamic change features of corrosion parameters within each time period using sliding window technology, and constructing a feature-time mapping relationship; S32, calling the bottom-level feature extraction module of the pipeline corrosion dual-layer time series prediction model to perform nonlinear transformation and dimensionality enhancement on the dynamic change features of each time period, generating high-dimensional feature vectors while retaining time series correlation information; S33, performing time series dependency analysis on the high-dimensional feature vectors through the upper-level time series prediction module of the model, establishing a dynamic prediction equation in combination with historical corrosion data, and making a preliminary prediction of short-term corrosion trends; S34, cross-validating the preliminary prediction results with historical traceability data in the oil and gas pipeline health traceability management platform, correcting prediction biases, and outputting accurate pipeline corrosion evolution trend data.

[0012] Further, S4 includes the following sub-steps: S41, extracting pipeline structural parameters, environmental field parameters, and operational load parameters from the standardized input dataset, aligning them with pipeline corrosion evolution trend data, constructing a multi-field coupling analysis dataset, and clarifying the correlation dimensions and action boundaries of each field parameter; S42, starting the multi-field coupling damage assessment model, using structural parameters as constraints, environmental field parameters as external excitation factors, and operational load parameters as dynamic influencing variables, and substituting them into the model to solve the multi-field coupling equations; S43, calculating the contribution of each field parameter to pipeline damage through the model's coupling effect analysis module, identifying and calibrating coupling paths and dominant influencing factors, and generating intermediate coupled damage data; S44, performing spatial interpolation processing and damage level classification on the intermediate coupled damage data, constructing a pipeline global damage distribution matrix, and outputting multi-field coupling damage distribution data.

[0013] Further, S5 includes the following sub-steps: S51, the multi-field coupled damage distribution data is meshed, and a finite element model of the pipe is established in combination with the pipe's geometric structure parameters, clarifying the mesh element division rules and boundary condition settings for stress calculation; S52, the running load parameters are converted into load inputs for the finite element model, and the initial stress calculation module of the pipe stress iterative intelligent calculation network is started to obtain the initial stress distribution results; S53, based on the initial stress distribution results and pipeline corrosion evolution trend data, multiple rounds of stress iterative calculation are performed through the network's iterative optimization module, and the stress calculation weight factor is dynamically adjusted during each iteration to optimize the stress distribution solution accuracy; S54, when the iteration results meet the convergence conditions, the iterative calculation is stopped, the stress values ​​of each mesh element are extracted, and integrated to form the pipe's global stress distribution data.

[0014] An intelligent assessment and management system for the operational health of oil and gas pipelines is provided. This system, applied to intelligent assessment and management methods for the operational health of oil and gas pipelines, includes: a multi-dimensional operational parameter acquisition and transmission unit, a standardized data processing and mapping unit, a pipeline corrosion dual-layer time-series prediction and calculation unit, a multi-field coupled damage assessment and analysis unit, a pipe stress iterative intelligent calculation unit, and a health status integrated management and decision-making unit. The multi-dimensional operational parameter acquisition and transmission unit and the standardized data processing and mapping unit are bidirectionally connected via a high-speed data bus to acquire various pipeline operational parameters and transmit them to the standardized data processing and mapping unit. The standardized data processing and mapping unit is connected to the pipeline corrosion dual-layer time-series prediction and calculation unit and the data interaction interface of the oil and gas pipeline health traceability management platform to filter and process the acquired parameters. A mapping relationship is established, and a standardized dataset is output to the prediction calculation unit. The pipeline corrosion dual-layer time-series prediction calculation unit communicates unidirectionally with the multi-field coupled damage assessment and analysis unit, transmitting corrosion evolution trend data to the damage assessment and analysis unit. The multi-field coupled damage assessment and analysis unit is connected to the pipe stress iteration intelligent calculation unit through a data caching module, providing it with multi-field coupled damage distribution data. The pipe stress iteration intelligent calculation unit is connected to the health status integrated management decision unit through a real-time data link, outputting pipe stress distribution data. The health status integrated management decision unit interacts bidirectionally with the standardized data processing mapping unit and the oil and gas pipeline health traceability management platform, integrating various analytical data and generating management decisions, while feeding back the decision results to each front-end unit for parameter adjustment.

[0015] Beneficial Effects: This invention proposes a method and system for intelligent assessment and management of the operational health of oil and gas pipelines. Through comprehensive collection and standardized processing of multi-dimensional operational parameters, and utilizing the collaborative computation of a dual-layer time-series prediction model for pipeline corrosion, a multi-field coupled damage assessment model, and an iterative intelligent calculation network for pipe stress, a multi-model deep integration technology system is constructed. This system dynamically correlates key influencing factors such as corrosion evolution, multi-field coupled damage, and pipe stress, completely breaking down the separation between data processing and model application in traditional technologies. It fully explores the intrinsic connections between various parameters, significantly improving the comprehensiveness and accuracy of pipeline health assessment. Simultaneously, an oil and gas pipeline health traceability management platform establishes a closed-loop management chain from parameter collection, model computation, result analysis to decision generation, enabling full traceability of the health status evolution trajectory. Real-time data feedback dynamically optimizes model calculation logic and parameter configuration, effectively solving the problem of existing technologies lacking full-process traceability and iterative optimization capabilities. This promotes the transformation of pipeline health management from passive maintenance to proactive early warning, and from decentralized monitoring to system assessment, providing precise and efficient intelligent management support for pipeline operation and ensuring the stability of energy supply and public safety. Attached Figure Description

[0016] Figure 1 This is a flowchart of the method steps of the present invention;

[0017] Figure 2 This is a system unit composition diagram of the present invention;

[0018] Figure 3 This is a diagram of the main interface of the system on the computer side of the present invention;

[0019] Figure 4 This is a diagram of the real-time data acquisition interface on the computer side of the system according to the present invention. Detailed Implementation

[0020] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] like Figure 1As shown, the intelligent assessment and management method for the operational health of oil and gas pipelines includes the following steps: S1, collecting medium flow velocity, pressure fluctuation values, corrosive medium concentration, ambient temperature gradient, pipeline material characteristic parameters, pipe geometric structure parameters, and historical damage data during the operation of the oil and gas pipeline to construct a multi-dimensional raw data set; S2, inputting the raw data set into the oil and gas pipeline health traceability management platform, filtering and calibrating key influencing parameters and establishing data mapping relationships to form a standardized input dataset; S3, calling the pipeline corrosion dual-layer time-series prediction model based on the standardized input dataset, and obtaining pipeline corrosion evolution trend data through multi-layer feature extraction and time-series correlation analysis; S4, using a multi-field coupled damage assessment model to perform coupled analysis on the corrosion evolution trend data and pipeline structural parameters to generate multi-field coupled damage distribution data; S5, using a pipe stress iterative intelligent calculation network to perform iterative calculations on the coupled damage distribution data and operating load parameters to output pipe stress distribution data; S6, integrating the corrosion evolution trend data, multi-field coupled damage distribution data, and pipe stress distribution data through the oil and gas pipeline health traceability management platform to complete the intelligent assessment and management decision generation for the operational health status of the oil and gas pipeline.

[0022] Step S1 involves deploying distributed data acquisition terminals, covering monitoring points set every 30 to 50 meters along the entire pipeline, to simultaneously collect key operating parameters and basic characteristic parameters during oil and gas transportation. The medium flow velocity acquisition range is 0.5 to 3.5 meters per second, the pressure fluctuation value acquisition accuracy is controlled within 0.01 MPa, the corrosive medium concentration acquisition resolution reaches 0.001 g / L, and the ambient temperature gradient acquisition interval is 5 minutes. Simultaneously, it records the pipeline material's yield strength, tensile strength, hardness, and other mechanical property parameters; pipe geometric parameters include specific values ​​such as pipe diameter, wall thickness, burial depth, and radius of curvature; and historical damage data includes corrosion spot area, crack length, etc., from the past 5 to 10 years. Records of local deformation and other data are collected, and all data are transmitted in real time to the data storage node via industrial Ethernet. After timestamp alignment and data integrity verification, the data is classified and integrated according to parameter type to construct a multi-dimensional raw data set including at least 20 core parameters and a sample size of no less than 100,000 records. This step provides a comprehensive and accurate data foundation for subsequent model calculations, ensuring that the input data can fully reflect the pipeline's operating status and structural characteristics. The quality of its implementation directly affects the reliability of the subsequent evaluation results. During the data collection process, it is necessary to ensure that the data transmission delay does not exceed 1 second and the data missing rate is controlled within 0.5%. Through multi-point synchronous collection and cross-validation mechanisms, data deviations caused by the failure of a single monitoring point are avoided.

[0023] Step S2 connects the multi-dimensional raw data set constructed in S1 to the preprocessing module of the oil and gas pipeline health traceability management platform. The platform uses a preset parameter filtering algorithm to remove data entries with abnormal fluctuations exceeding three standard deviations, retaining 15 key influencing parameters such as medium flow rate, pressure fluctuation values, and corrosive medium concentration. Subsequently, it establishes an association index between parameters, clarifying the collection location, collection time, data type, and other attribute information of each parameter. Through data standardization mapping rules, parameters of different dimensions are converted into a unified numerical range. Operating parameters such as flow rate and pressure are directly included according to actual values, while static parameters such as material properties and geometric structure are coded. Historical damage data is sorted by time series. This process involves creating a structured, standardized input dataset. This step relies on the platform's distributed computing capabilities, with a processing efficiency of at least 100,000 data points per minute. Simultaneously, a data quality assessment mechanism is established to verify the consistency of the selected parameters, ensuring logical continuity of data from different time periods at the same monitoring point and uniform format for similar parameters across different monitoring points. The standardized dataset must meet the format requirements for direct input into subsequent model calculations. Its core significance lies in simplifying model computational complexity, improving data utilization efficiency, and establishing a unified data foundation for subsequent collaborative calculations of various models by clarifying parameter mapping relationships, thus avoiding computational errors caused by inconsistent data formats or invalid data interference.

[0024] Step S3 is based on the standardized input dataset generated in S2. Through the model call interface of the oil and gas pipeline health traceability management platform, a two-layer time-series prediction model for pipeline corrosion is initiated. The model first extracts features from corrosion-related parameters in the input data. The first layer extracts trend features, periodic features, and abrupt change features within the time domain. The second layer extracts correlation features and causal relationship features between parameters. Time-series correlation analysis uses a sliding window of 72 hours. A time-series prediction benchmark is established by combining historical data from the past 1 to 3 years. A high-dimensional feature vector reflecting the corrosion evolution pattern is formed through multi-layer feature fusion. The calculation process involves 100 to 200 iterations, with each iteration adjusting the feature weights. Dynamic adjustments are made to optimize prediction accuracy based on a gradient descent strategy, ultimately outputting pipeline corrosion evolution trend data for the next 30, 90, and 180 days. This includes specific information such as corrosion rate, corrosion range expansion, and distribution of key corrosion areas at different times. The core significance of this step lies in achieving a forward-looking prediction of pipeline corrosion status, breaking through the limitations of traditional static assessment. By capturing the dynamic patterns of corrosion evolution through time-series analysis, it provides basic data with a time dimension for subsequent damage assessment. During implementation, the model prediction error must be controlled to not exceed 5%. Through backtracking verification with historical corrosion data, the model's feature extraction logic and time-series correlation algorithm are continuously optimized to ensure that the prediction results accurately reflect the actual corrosion evolution trend of the pipeline.

[0025] Step S4 employs a multi-field coupled damage assessment model, coupling the pipeline corrosion evolution trend data output from S3 with the pipeline structural parameters from S2. First, the boundary conditions of multiple fields, including corrosion field, stress field, temperature field, and flow field, are defined. Data such as pipeline corrosion rate and corrosion range are used as inputs to the corrosion field; parameters such as pipe diameter, wall thickness, and material mechanical properties are used as inputs to the structural field; data such as medium flow velocity and pressure fluctuations are used as inputs to the flow field; and data such as ambient temperature gradient and medium temperature are used as inputs to the temperature field. The model establishes the interaction relationships between these fields through a field coupling matrix, calculating the synergistic effects of different field parameters on pipeline damage. During the calculation, the finite element method is used, dividing the pipeline into at least 100,000 mesh elements. Each cell independently calculates the coupled damage value, and the damage distribution of the entire domain is obtained through iterative solution, generating multi-field coupled damage distribution data, including information such as the damage degree, damage type, and damage development rate of each grid cell. The implementation of this step requires high-performance computing nodes, and the single-round calculation time is controlled within 30 minutes. The core significance is to overcome the limitations of single-factor assessment, fully consider the synergistic effect of multiple field parameters on pipeline damage, accurately identify key damage areas and critical damage paths under multi-field coupling, and provide a comprehensive damage distribution basis for subsequent stress calculation. Through fine mesh division and coupled calculation, the spatial resolution of the damage assessment results is ensured to be no less than 0.1 meters, which can accurately locate small damage areas.

[0026] Step S5 utilizes an iterative intelligent calculation network for pipe stress. Taking the multi-field coupled damage distribution data generated in S4 and the operating load parameters from S2 as input, the multi-field coupled damage distribution data is first converted into initial defect conditions for stress calculation. The mechanical property attenuation of each damaged area is clarified. The operating load parameters include specific values ​​for medium pressure, gravity load, soil constraint force, and temperature stress. The calculation network, based on elasticity and plasticity theories, establishes the governing equations for pipe stress calculation. An iterative calculation method is used to solve for the stress distribution. The initial iteration step size is set to 0.01 MPa. After each iteration, the step size is dynamically adjusted based on the stress convergence. During the iteration process, the stress concentration effect in the damaged area is considered. The damage is calculated using a stress redistribution algorithm. The influence of the extension on the overall stress of the pipe body is investigated. The number of iterations is set to 50 to 100 rounds. The iteration stops when the difference between the stress calculation results of two adjacent rounds is less than 0.001 MPa. The stress distribution data of the entire pipe body is output, including specific values ​​such as principal stress, shear stress, and equivalent stress of each grid cell. The core significance of this step is to accurately obtain the stress state of the pipeline under the combined action of multi-field coupled damage and operating load, identify stress concentration areas and high stress risk areas, and provide key mechanical performance data for pipeline health assessment. During the implementation process, it is necessary to ensure the convergence and stability of the calculation network. Through comparison and verification with physical test data, the stress calculation error is controlled to not exceed 3% to ensure that the output results can truly reflect the actual stress state of the pipeline.

[0027] Step S6 integrates the corrosion evolution trend data (S3), multi-field coupled damage distribution data (S4), and pipe stress distribution data (S5) across the entire pipeline using the integrated analysis module of the oil and gas pipeline health traceability management platform. First, it establishes a spatial coordinate mapping relationship between the three types of data to ensure accurate matching of data from the same pipeline location. Then, a weighted fusion algorithm is used to comprehensively analyze the three types of data, assigning weights according to the importance of different parameters: corrosion evolution trend data accounts for 30%, multi-field coupled damage distribution data accounts for 35%, and pipe stress distribution data accounts for 35%. During the fusion process, historical health assessment data and pipeline maintenance records stored on the platform are combined to establish a health status assessment index system, including multiple assessment dimensions such as corrosion risk level, damage severity, and stress safety factor. Each dimension is divided into five levels. The standard generates a comprehensive pipeline health status assessment result through data comparison and level determination. Based on the assessment result and preset safety thresholds, it generates targeted management decisions, including specific details such as the division of key monitoring areas, maintenance priority ranking, and operational parameter adjustment suggestions. This step relies on the platform's visualization capabilities to present the assessment results and management decisions in chart form, allowing pipeline managers to intuitively grasp the pipeline's health status. Its core significance lies in achieving closed-loop management from data collection to decision generation, transforming multi-model calculation results into implementable management measures. Through the platform's traceability function, the data source and calculation process of each assessment result can be queried, providing support for the rationality of management decisions. During implementation, data integration delays must not exceed 5 minutes, and the assessment result update frequency must be consistent with the model calculation frequency.

[0028] Preferably, the expression for the pipeline corrosion two-layer time-series prediction model in S3 is: middle, For future pipeline corrosion levels, These are the time-series weighting coefficients. The weighting factor is the historical corrosion data. For the first The amount of historical corrosion at any given moment The corrosion attenuation coefficient is... For predicting time intervals, This is the environmental impact factor. For the j-th type of running parameter weights, For the j-th type of pipeline operating parameter values, The temperature gradient influence coefficient is... For the ambient temperature gradient, The coefficient representing the influence of corrosive media. This is the correction factor for medium concentration. This represents the concentration of the corrosive medium.

[0029] Specifically, the two-layer time-series prediction model for pipeline corrosion is based on corrosion kinetics theory and time-series data analysis methods. It combines the synergistic influence of historical conditions, operating parameters, and environmental factors on pipeline corrosion. Constructed through a layered modeling approach, the first layer uses historical corrosion data as a foundation, employing an exponential decay function to characterize the decay effect of corrosion over time. The second layer integrates the nonlinear influence of operating and environmental parameters, using square root and logarithmic functions to represent the coupling effect of operating parameters and the cumulative effect of corrosive media, respectively. The model is established based on the premise that pipeline corrosion is a comprehensive result of the continuation of historical conditions and the dynamic effects of real-time parameters, requiring simultaneous consideration of time-series correlation and multi-factor coupling. In parameter selection, the time-series weight coefficient is determined to be 0.7 to 0.9 based on historical data fitting results; the historical corrosion data weight factor is assigned a gradient value of 0.1 to 0.8 according to time proximity; the corrosion decay coefficient is calibrated to 0.002 to 0.005 based on measured data from similar pipelines; the environmental influence coefficient and temperature gradient influence coefficient are determined through orthogonal experiments to be within the range of 0.3 to 1.2; and the medium concentration correction coefficient is adjusted to 0.5 to 1.0 according to the type of corrosive medium. During implementation, historical data and real-time parameters are accessed through the oil and gas pipeline health traceability management platform. After being input into the model, the model undergoes 100 to 200 rounds of iterative calculations to output prediction results. During the calculation process, a gradient descent strategy is used to optimize parameter values ​​to ensure prediction accuracy. This model breaks through the limitations of traditional single-factor prediction. By integrating time-series characteristics and the influence of multiple parameters through a two-layer structure, it accurately captures the corrosion evolution law, providing forward-looking data support for pipeline health assessment. The dynamic adjustment mechanism of parameter values ​​ensures the adaptability of the model under different operating conditions.

[0030] Preferably, the expression for the multi-field coupled damage assessment model in S4 is: ,in, Multi-field coupled damage degree The coupling coefficient is... For mechanical stress weight, For the mechanical stress of the pipe body, For thermal stress weight, For the thermal stress of the tube body, Let be the corrosion coupling coefficient. For the k-th type of corrosion factor weight, Let k be the corrosion influence function. For the k-th type of corrosion-related parameters, The flow field influence coefficient is... For flow rate weighting, For the medium flow rate, For pressure gradient weights, This represents the pressure gradient.

[0031] Specifically, the multi-field coupled damage assessment model is based on field theory and damage mechanics, considering the interaction mechanism of corrosion field, stress field, temperature field, and flow field. It uses a gradient operator to characterize field strength changes and integrates the contributions of each field to damage through linear superposition and product. The model is established based on the premise that pipeline damage is the result of the synergistic effect of multiple fields, and a single field analysis cannot reflect the true damage state; therefore, the coupling effect of each field needs to be quantified. The coupling coefficient is fitted to 0.6 to 0.8 based on multi-field coupled experimental data. The mechanical stress weight and thermal stress weight are determined to be 0.4 to 0.6 and 0.3 to 0.5, respectively, based on the pipeline material characteristics. The corrosion coupling coefficient is calibrated to 0.7 to 0.9 through corrosion damage experiments. The weight of the k-th type of corrosion factor is allocated to values ​​of 0.1 to 0.3 according to the degree of influence. The flow field influence coefficient and flow velocity weight are determined to be 0.2 to 0.4 and 0.3 to 0.5, respectively, based on fluid dynamics simulation results. During implementation, the platform first integrates parameters from various fields, clarifies boundary conditions, and inputs them into the model. The finite element method is used to divide the model into mesh elements, and the coupling equations are solved iteratively. The computation time for a single round is controlled within 30 minutes. Parameter values ​​are dynamically adjusted during the computation to match actual working conditions. This model enables quantitative assessment of multi-field coupled damage. By accurately quantifying the contribution and coupling effect of each field, it identifies key damage areas. The experimental calibration mechanism for parameter values ​​ensures the reliability and accuracy of the model's assessment results, providing comprehensive damage distribution data for subsequent stress calculations.

[0032] Preferably, the expression for the intelligent calculation network of tube stress iteration in S5 is: ,in, For the first The tube stress value after the next iteration The stress value in the nth iteration is... For the iterative yield coefficient, The stress iteration step size is... For multi-field coupled damage degree, The corrosion stress coupling coefficient is... The stress influence weight for the q-th type parameter is... This refers to the amount of pipeline corrosion. For the qth type of operating load parameters, The structural parameter influence coefficient, For the density of the pipe material, The elastic modulus of the pipe material. These are the geometric parameters of the tube body.

[0033] Specifically, the intelligent calculation network for pipe stress iteration is based on elasticity mechanics and iterative optimization theory. Combining the influence of damage on pipe stress, it constructs a stress update equation iteratively, integrating the previous iteration stress value, damage stress correction term, corrosion stress coupling term, and structural parameter influence term. The model is established because the pipe stress distribution is affected by damage evolution and multiple parameters, requiring iterative calculations to gradually approximate the true stress state. During iteration, the mechanical performance degradation and stress redistribution effects caused by damage must be considered. The iteration convergence coefficient is determined to be 0.8 to 0.95 based on the convergence speed requirements. The initial stress iteration step size is set to 0.01, adjusted to 0.005 to 0.02 based on convergence. The corrosion stress coupling coefficient is calibrated to 0.5 to 0.7 through stress corrosion tests. The stress influence weight of the q-th parameter is allocated from 0.1 to 0.3 according to parameter importance. The structural parameter influence coefficient is determined to be 0.3 to 0.5 based on pipeline structure simulation results. During implementation, multi-field coupled damage distribution data and operational load parameters are used as inputs. Iterative calculations are initiated through an intelligent network for iterative calculation of pipe stress. After each iteration, the stress difference is calculated, and the calculation stops when the convergence condition is met. The number of iterations is controlled between 50 and 100. During the calculation, parameter values ​​are calibrated in real time using physical test data. This model accurately acquires the pipe stress distribution under damage conditions, improves the accuracy of stress calculation through iterative optimization, and ensures the applicability of the model under different damage conditions through dynamic parameter adjustment and calibration mechanisms, providing crucial mechanical data support for pipeline health assessment.

[0034] Preferably, the data integration of the oil and gas pipeline health traceability management platform is expressed as follows: ,in, Comprehensive health status assessment value To evaluate the weighting coefficients, To predict the amount of corrosion, For multi-field coupled damage degree, This represents the final iterative stress value. For source tracing correction coefficient, Let s be the weight of the source data in the s-th iteration. For the s-th source of health deviation, The historical data influence coefficient. For the real-time parameter weights of class u, For the u-th type of real-time monitoring parameter, Weighting based on historical health data, Historical health assessment values.

[0035] Specifically, the data integration expression of the oil and gas pipeline health traceability management platform is based on multi-source data fusion theory and health assessment methods. Combining the correlation between three core data types—corrosion, damage, and stress—it integrates core assessment indicators, traceability correction items, and historical data influence items using ratio, product, and square root forms, respectively. The model is established based on the principle that pipeline health status assessment requires comprehensive consideration of current multi-dimensional data and historical traceability information. Weighted fusion and traceability correction improve the comprehensiveness and accuracy of the assessment results. The assessment weight coefficient is determined to be 0.7 to 0.9 based on expert scoring and experimental data; the traceability correction coefficient is calibrated to 0.6 to 0.8 based on historical traceability data; the weight of the s-th traceability data is allocated from 0.1 to 0.4 according to the proximity of the traceability time; the historical data influence coefficient and real-time parameter weight are determined to be 0.3 to 0.5 and 0.1 to 0.3, respectively, through health assessment experiments; and the weight of historical health data is set to 0.2 to 0.4. During implementation, the platform integrates corrosion prediction data, damage assessment data, and stress calculation data, and calls upon historical source data and real-time parameters. After inputting these into the model, the data undergoes standardization and weighted fusion calculations to generate a comprehensive health status assessment value. During the calculation process, parameter values ​​are adjusted through cross-validation to ensure that the assessment results are consistent with the actual health status. This model achieves deep fusion of multi-source data and end-to-end traceability. Through scientific parameter selection and integration algorithms, it improves the accuracy of health assessments, providing a reliable assessment basis for management decisions. The source correction mechanism ensures the traceability and verifiability of the assessment results.

[0036] Preferably, the intelligent assessment and management decision generation for the operational health of the oil and gas transmission pipeline is expressed as follows: Where M is the management decision output value, For decision coefficients, This is a comprehensive assessment value of health status. As a standard health threshold, As the initial health baseline, To couple decision weights, Let z be the decision factor of the z-th type of parameter. For multi-field coupled damage degree, Let z be the parameter affecting the decision-making process. This represents the final iterative stress value. To predict the amount of corrosion, For the decision coefficients of the operating parameters, For parameter correction coefficients, The decision weights for the x-th type of running parameters are... This represents the runtime parameter value for class x.

[0037] Specifically, the intelligent health assessment and management decision generation expression for oil and gas pipeline operations is based on decision theory and multi-parameter coupling analysis. It combines health assessment results, multi-field coupling effects, and the influence of operating parameters. The arctangent function characterizes the nonlinear relationship between health status and decision-making. Coupled decision terms are integrated through a product form, and the cumulative impact of operating parameters is quantified logarithmically. The model is established based on the premise that management decisions must be based on health status assessment results, while also considering multi-field coupling effects and the dynamic influence of operating parameters to achieve targeted and effective decision-making. The decision coefficient is determined to be 0.8 to 1.0 according to decision priority requirements; the standard health threshold is calibrated to 0.6 to 0.8 based on pipeline safety operation standards; the decision factor for the z-th type of parameter is allocated from 0.1 to 0.3 according to the degree of decision influence; the decision coefficients and parameter correction coefficients for operating parameters are determined to be 0.3 to 0.5 and 0.2 to 0.4 respectively through decision optimization experiments; and the decision weights for the x-th type of operating parameter are set to 0.1 to 0.25. During implementation, the platform retrieves health assessment values, multi-field coupled damage data, stress calculation data, and operating parameters. These are input into the model and undergo multiple rounds of decision-making calculations to generate management decision outputs. Parameter values ​​are dynamically adjusted during the calculation process to match actual operating conditions, and the decision results are visualized on the platform. This model transforms multi-dimensional technical data into actionable management decisions. Through scientific parameter selection and decision-making algorithms, it enhances the relevance and effectiveness of decisions. The dynamic parameter adjustment mechanism ensures the adaptability of decisions under different operating conditions, providing precise decision support for pipeline health management.

[0038] Preferably, step S3 includes the following sub-steps: S31, dividing the corrosion-related parameters in the standardized input dataset into time series segments to establish multi-time period data subsets, extracting the dynamic change features of corrosion parameters within each time period using sliding window technology, and constructing a feature-time mapping relationship; S32, calling the bottom-level feature extraction module of the pipeline corrosion dual-layer time series prediction model to perform nonlinear transformation and dimensionality enhancement on the dynamic change features of each time period, generating high-dimensional feature vectors while retaining time series correlation information; S33, performing time series dependency analysis on the high-dimensional feature vectors through the upper-level time series prediction module of the model, establishing a dynamic prediction equation in combination with historical corrosion data, and making a preliminary prediction of short-term corrosion trends; S34, cross-validating the preliminary prediction results with historical traceability data in the oil and gas pipeline health traceability management platform, correcting prediction biases, and outputting accurate pipeline corrosion evolution trend data.

[0039] Specifically, step S3 includes four sub-steps: S31 First, the corrosion-related parameters in the standardized input dataset are divided into segments at fixed time intervals, with each segment lasting 24 hours, constructing at least 30 data subsets for consecutive time periods. A sliding window technique is used with a 2-hour step size to extract dynamic features such as peak values, mean values, and rates of change of corrosion parameters within each time period. A data association algorithm is used to establish a mapping relationship between features and time nodes, ensuring that the features accurately correspond to the operating state of a specific time period. S32 Calls the bottom-level feature extraction module of the pipeline corrosion dual-layer time-series prediction model, using a multi-layer perceptron structure to perform non-linear transformation on the dynamic features of each time period, increasing the original feature dimension from 20 to 50 dimensions to generate high-dimensional feature vectors. During the transformation process, the temporal correlation between features is preserved to avoid data loss. S33 The high-dimensional feature vectors are processed by the upper-level time-series prediction module of the model. Temporal dependency analysis employs a temporal attention mechanism to strengthen the weighting of features in key time periods. A dynamic prediction equation is established by combining historical corrosion data from the past three years. Based on this equation, a preliminary prediction of short-term corrosion trends is made, clarifying the corrosion development direction in different future periods. In step S34, the preliminary prediction results are cross-validated with historical traceability data from the oil and gas pipeline health traceability management platform. 100 sets of historical data under the same operating conditions over the past five years are selected as validation samples. The deviation between the prediction results and historical actual data is calculated. Based on the deviation value, the feature weights and prediction coefficients of the model are adjusted. The corrected prediction deviation is controlled within 5%, ultimately outputting accurate pipeline corrosion evolution trend data. This step, through a four-step progressive operation, achieves full-process control from data processing to accurate prediction, ensuring that the prediction results can truly reflect the dynamic evolution law of pipeline corrosion and provide reliable data support for subsequent damage assessment.

[0040] Preferably, step S4 includes the following sub-steps: S41, extracting pipeline structural parameters, environmental field parameters, and operational load parameters from the standardized input dataset, aligning them with pipeline corrosion evolution trend data, constructing a multi-field coupling analysis dataset, and clarifying the correlation dimensions and action boundaries of each field parameter; S42, starting the multi-field coupling damage assessment model, using structural parameters as constraints, environmental field parameters as external excitation factors, and operational load parameters as dynamic influencing variables, and substituting them into the model to solve the multi-field coupling equations; S43, calculating the contribution of each field parameter to pipeline damage through the model's coupling effect analysis module, identifying and calibrating coupling paths and dominant influencing factors, and generating intermediate coupled damage data; S44, performing spatial interpolation processing and damage level classification on the intermediate coupled damage data, constructing a pipeline global damage distribution matrix, and outputting multi-field coupling damage distribution data.

[0041] Specifically, step S4 proceeds sequentially from steps S41 to S44. S41 extracts structural parameters such as pipe diameter, wall thickness, and material mechanical properties, environmental field parameters such as temperature and humidity, and operational load parameters such as medium flow velocity and pressure from the standardized input dataset. This data is then aligned with the pipeline corrosion evolution trend data output from S3 in both time and space dimensions. Data entries with mismatched timestamps or inconsistent spatial locations are removed to construct a multi-field coupling analysis dataset. The correlation dimensions and action boundaries of each field parameter are clarified to ensure the data meets the requirements of the coupling analysis. S42 initiates the multi-field coupling damage assessment model, substituting structural parameters as constraints into the model, applying environmental field parameters as external excitation factors during the model calculation process, and updating operational load parameters as dynamic influencing variables in real time. The model's built-in coupling equations are used to correlate and calculate the parameters, clarifying the mode and degree of influence of each field parameter on pipeline damage. S43… Through the model's coupling effect analysis module, a contribution calculation algorithm is used to quantify the contribution ratio of each field parameter to pipeline damage. The corrosion field accounts for 35% to 45%, the stress field accounts for 25% to 35%, and the temperature and flow fields combined account for 20% to 30%. The key coupling paths and core influencing factors that dominate damage development are identified, generating intermediate coupled damage data including the contribution data of each parameter. S44 uses a spatial interpolation algorithm to process the intermediate coupled damage data, transforming discrete damage data into continuous global damage distribution data. The damage is divided into 5 levels according to the degree of damage, and a global pipeline damage distribution matrix is ​​constructed. The matrix grid resolution is set to 0.1 meters to accurately present the damage state at each location of the pipeline. This four-step operation, through a complete process of data preprocessing, model calculation, effect analysis, and data optimization, achieves accurate quantification and global presentation of multi-field coupled damage, providing a comprehensive damage distribution basis for subsequent stress calculation.

[0042] Preferably, step S5 includes the following sub-steps: S51, performing meshing processing on the multi-field coupled damage distribution data, establishing a finite element model of the pipe body in conjunction with the pipe body geometric structure parameters, and clarifying the mesh element division rules and boundary condition settings for stress calculation; S52, converting the running load parameters into load inputs for the finite element model, starting the initial stress calculation module of the pipe body stress iterative intelligent calculation network, and obtaining the initial stress distribution results; S53, based on the initial stress distribution results and pipeline corrosion evolution trend data, performing multiple rounds of stress iterative calculation through the network's iterative optimization module, dynamically adjusting the stress calculation weight factor during each iteration, and optimizing the stress distribution solution accuracy; S54, when the iteration results meet the convergence conditions, stopping the iterative calculation, extracting the stress values ​​of each mesh element, and integrating them to form the pipe body global stress distribution data.

[0043] Specifically, step S5 includes four sub-steps: S51: The multi-field coupled damage distribution data output from S4 is meshed along the pipe length, with each meter divided into a grid cell, for a total of no less than 10,000 grid cells. A finite element model of the pipe is established based on the pipe diameter, wall thickness, burial depth, and other geometric parameters. The material properties and structural dimensions of each grid cell are defined, and the constraint boundary conditions and load application areas of the model are set to ensure that the finite element model can realistically simulate the actual structural state of the pipe. S52: The operating load parameters such as medium pressure, soil constraint force, and temperature stress are converted into load inputs for the finite element model according to their actual application locations. The initial stress calculation module of the pipe stress iterative intelligent calculation network is started, and the finite element analysis algorithm is used to solve the stress of the model to obtain the initial stress distribution results. The initial stress calculation accuracy is controlled within 0.01. S53: Based on the initial stress distribution results and the pipe... Corrosion evolution trend data is used to initiate multiple rounds of stress iteration calculations through the network's iterative optimization module. During each iteration, the mechanical performance parameters of the corresponding grid cells are dynamically adjusted according to the degree of corrosion damage, and the stress calculation weight factor is adjusted simultaneously. The weight factor is assigned a value of 0.1 to 0.9 according to the degree of damage, with larger weight factors for more severe damage. The change in stress distribution is calculated after each iteration. S54 sets a stress convergence threshold of 0.001. When the stress change between two adjacent iterations is less than this threshold, the iteration calculation stops. The principal stress, shear stress, and other stress values ​​of each grid cell are extracted and integrated according to the spatial location of the pipeline to form the stress distribution data of the entire pipeline. This four-step operation, through the systematic process of model construction, initial calculation, iterative optimization, and result integration, fully considers the impact of damage on the stress of the pipeline, achieves accurate calculation of stress distribution, provides key mechanical data support for pipeline health assessment, and ensures the scientificity and reliability of the assessment results.

[0044] like Figure 2As shown, an intelligent assessment and management system for the operational health of oil and gas pipelines is applied to the intelligent assessment and management of the operational health of oil and gas pipelines. This system includes: a multi-dimensional operational parameter acquisition and transmission unit, a standardized data processing and mapping unit, a pipeline corrosion dual-layer time-series prediction and calculation unit, a multi-field coupled damage assessment and analysis unit, a pipe stress iterative intelligent calculation unit, and a health status integrated management and decision-making unit. The multi-dimensional operational parameter acquisition and transmission unit and the standardized data processing and mapping unit are bidirectionally connected via a high-speed data bus to acquire various pipeline operational parameters and transmit them to the standardized data processing and mapping unit. The standardized data processing and mapping unit is connected to the pipeline corrosion dual-layer time-series prediction and calculation unit and the data interaction interface of the oil and gas pipeline health traceability management platform to filter and process the acquired parameters. The system establishes mapping relationships and outputs standardized datasets to the prediction calculation unit. The pipeline corrosion dual-layer time-series prediction calculation unit communicates unidirectionally with the multi-field coupled damage assessment and analysis unit, transmitting corrosion evolution trend data to the damage assessment and analysis unit. The multi-field coupled damage assessment and analysis unit is connected to the pipe stress iteration intelligent calculation unit through a data caching module, providing it with multi-field coupled damage distribution data. The pipe stress iteration intelligent calculation unit is connected to the health status integrated management decision unit through a real-time data link, outputting pipe stress distribution data. The health status integrated management decision unit interacts bidirectionally with the standardized data processing and mapping unit and the oil and gas pipeline health traceability management platform, integrating various analytical data and generating management decisions. Simultaneously, the decision results are fed back to each front-end unit for parameter adjustment.

[0045] like Figure 3 The image shows the main computer interface of the Intelligent Assessment and Management System for the Operational Health of Oil and Gas Pipelines. It serves as the core visual platform for the integrated health status management decision-making unit and the oil and gas pipeline health traceability management platform to work collaboratively. The interface layout is designed around the entire pipeline health management process, incorporating modules such as status monitoring, real-time monitoring, health assessment, risk warning, historical data, and system settings. It covers all stages from parameter acquisition and model calculation to decision generation, enabling centralized control of data from each unit. The core area of ​​the interface displays the core judgment result: the current operating status is normal, and the warning level is zero. This result is calculated by the platform integrating corrosion evolution trends, multi-field coupled damage, and pipe stress distribution data using a comprehensive health status assessment formula. It also presents quantitative indicators such as pipeline pressure and health index, along with line graphs to visually demonstrate the dynamic trends of these parameters, allowing managers to quickly grasp the overall health status of the pipeline. Furthermore, the historical data module enables full traceability of the health status evolution trajectory, and the system settings module supports dynamic adjustment of model parameters. This aligns perfectly with the requirement to optimize assessment accuracy through traceability and feedback, transforming professional quantitative data from multi-model calculations into intuitive indicators that directly guide management. It is a key window for the practical application of technical data in management.

[0046] like Figure 4 As shown, this is the real-time data acquisition interface on the system's computer, which is the front-end visualization of the multi-dimensional operating parameter acquisition and transmission unit and the standardized data processing and mapping unit. It is the core data source display link of the entire pipeline health assessment system, directly providing basic data support for subsequent model calculations. The core interface setting is a real-time parameter acquisition and processing module, which collects key operating parameters such as medium flow rate, pressure fluctuation value, and ambient temperature gradient required in S1. It clearly displays an overview of the pipeline's operating status, including real-time values ​​such as temperature 58℃, pressure 2.3MPa, and flow rate 120m³ / h. These data are synchronously collected by distributed acquisition terminals set up every 30 to 50 meters along the entire pipeline, and meet the technical standards of pressure fluctuation acquisition accuracy of 0.01 MPa and temperature gradient acquisition interval of 5 minutes. The interface also features time-series charts such as pressure change trends and temperature fluctuation curves, enabling preliminary extraction of time-series features from the raw collected data. This lays the foundation for extracting dynamic changes in corrosion parameters and constructing feature-time mapping relationships using the sliding window technique in S3. Furthermore, it is equipped with modules for early warning management, detailed parameters, and historical data, enabling anomaly screening, attribute viewing, and integrity verification of the collected data. It also aligns data timestamps and removes invalid data, serving as a crucial front-end step in ensuring the quality of the S2 standardized input dataset. This fully embodies the design logic of this invention: "Accurate and comprehensive parameter acquisition is the core of model computation reliability."

[0047] The formula in this invention achieves collaborative calculation of scalar and vector parameters by introducing a unified dimensional conversion mechanism and a multi-dimensional parameter coupling weight allocation logic. Although various parameters have different physical properties, they can all be mapped to the same evaluation dimension through targeted conversion. Taking the two-layer time-series prediction model for pipeline corrosion as an example, the medium flow velocity and pressure fluctuation values ​​are vector parameters, representing the dynamic changes at different locations along the pipeline, respectively. The corrosive medium concentration and ambient temperature gradient are scalar parameters, reflecting the static and quasi-static environmental states of the entire pipeline. Structural parameters such as the yield strength and wall thickness of the pipeline material are also scalars, representing the inherent properties of the pipe body. The formula first performs spatial distribution weight normalization on the vector parameters, extracting their core statistical features such as mean and peak values, converting the vector dimension into a single-valued index consistent with the scalars. Simultaneously, appropriate coupling weights are assigned to different types of parameters. For example, dynamic vector parameters such as flow velocity and pressure account for 30%-40% of the weight, scalar parameters such as corrosion concentration and temperature gradient account for 25%-35%, and structural parameters account for 20%-30%. This weight allocation achieves numerical uniformity for parameters of different dimensions. Furthermore, the formula incorporates a field coupling mapping factor to correlate the spatial distribution of vector parameters with the global influence of scalar parameters. For example, the spatial distribution of the velocity vector is used as the diffusion driving condition for the corrosion scalar parameter, and the fluctuation characteristics of the pressure vector are used as the excitation source for the stress scalar parameter. Finally, through weighted summation, nonlinear transformation, and other computational logic, the transformed unified dimension parameters are integrated into a single formula to complete the collaborative calculation of multiple types and dimensions of parameters. This ensures that the evaluation results include both the spatial distribution characteristics of vector parameters and the global influence of scalar parameters, achieving a comprehensive and accurate quantification of the pipeline's operating status.

[0048] This paper presents a method and system for intelligent assessment and management of the operational health of oil and gas pipelines. It constructs a multi-model, deeply collaborative technical architecture. By comprehensively collecting and standardizing multi-dimensional operational data on media characteristics, environmental conditions, and structural parameters, it leverages the coordinated computation of a dual-layer time-series corrosion prediction model, a multi-field coupled damage assessment model, and an iterative intelligent calculation network for pipe stress. This enables dynamic correlation analysis of key factors such as corrosion evolution, coupled damage, and pipe stress. This architecture overcomes the limitations of traditional technologies, which rely on isolated calculations of single models and fragmented data processing. It fully explores the intrinsic mechanisms of interaction between various parameters, making the assessment process more closely aligned with the complex operating conditions of actual pipelines. This significantly improves the comprehensiveness and accuracy of health status assessment, providing solid data support for subsequent management decisions.

[0049] Meanwhile, the system, centered on an oil and gas pipeline health traceability management platform, establishes a closed-loop management chain covering the entire process. It organically integrates parameter acquisition, model calculation, result analysis, decision generation, and data traceability, effectively overcoming the shortcomings of existing technologies that lack full-process traceability and iterative optimization capabilities. The platform enables full tracking of the health status evolution trajectory, dynamically adjusting model calculation logic and parameter configuration based on real-time operational data. This allows assessment results to continuously adapt to the dynamic changes in pipeline operating status, driving pipeline health management from passively responding to faults to proactively warning of risks, and from scattered monitoring data to integrated system assessment. This provides efficient and intelligent management assurance for the long-term safe and stable operation of pipelines, effectively addressing the shortcomings of traditional management models in terms of specificity and effectiveness.

[0050] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," "link," and "fix" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0051] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent assessment and management of the operational health of oil and gas transmission pipelines, characterized in that, The process includes the following steps: S1, collecting data on medium flow rate, pressure fluctuation, corrosive medium concentration, ambient temperature gradient, pipeline material properties, pipe geometry, and historical damage during the operation of oil and gas pipelines to construct a multi-dimensional raw data set; S2, inputting the raw data set into the oil and gas pipeline health traceability management platform, filtering and calibrating key influencing parameters, and establishing data mapping relationships to form a standardized input dataset; S3, based on the standardized input dataset, calling the pipeline corrosion dual-layer time-series prediction model, and obtaining pipeline corrosion evolution trend data through multi-layer feature extraction and time-series correlation analysis; S4, using a multi-field coupled damage assessment model to perform coupled analysis on corrosion evolution trend data and pipeline structural parameters to generate multi-field coupled damage distribution data; S5, using a pipe stress iterative intelligent calculation network to iteratively calculate the coupled damage distribution data and operating load parameters, and outputting pipe stress distribution data; S6, integrating corrosion evolution trend data, multi-field coupled damage distribution data, and pipe stress distribution data through the oil and gas pipeline health traceability management platform to complete the intelligent assessment and management decision generation of the operational health status of oil and gas pipelines.

2. The intelligent assessment and management method for the operational health of oil and gas transmission pipelines according to claim 1, characterized in that, The expression for the two-layer time-series prediction model for pipeline corrosion in S3 is as follows: ,in, For future pipeline corrosion levels, These are the time-series weighting coefficients. The weighting factor is the historical corrosion data. For the first The amount of historical corrosion at any given moment The corrosion attenuation coefficient is... For predicting time intervals, This is the environmental impact factor. For the j-th type of running parameter weights, For the j-th type of pipeline operating parameter values, The temperature gradient influence coefficient is... For the ambient temperature gradient, The coefficient representing the influence of corrosive media. This is the correction factor for medium concentration. This represents the concentration of the corrosive medium.

3. The intelligent assessment and management method for the operational health of oil and gas transmission pipelines according to claim 1, characterized in that, The expression for the multi-field coupled damage assessment model in S4 is as follows: ,in, Multi-field coupled damage degree The coupling coefficient is... For mechanical stress weight, For the mechanical stress of the pipe body, For thermal stress weight, For the thermal stress of the tube body, Let be the corrosion coupling coefficient. For the k-th type of corrosion factor weight, Let k be the corrosion influence function. For the k-th type of corrosion-related parameters, The flow field influence coefficient is... For flow rate weighting, For the medium flow rate, For pressure gradient weights, This represents the pressure gradient.

4. The intelligent assessment and management method for the operational health of oil and gas transmission pipelines according to claim 1, characterized in that, The expression for the intelligent calculation network for tube stress iteration in S5 is as follows: ,in, For the first The tube stress value after the next iteration The stress value in the nth iteration is... For the iterative yield coefficient, The stress iteration step size is... For multi-field coupled damage degree, The corrosion stress coupling coefficient is... The stress influence weight for the q-th type parameter is... This refers to the amount of pipeline corrosion. For the qth type of operating load parameters, The structural parameter influence coefficient, For the density of the pipe material, The elastic modulus of the pipe material. These are the geometric parameters of the tube body.

5. The intelligent assessment and management method for the operational health of oil and gas transmission pipelines according to claim 1, characterized in that, The data integration of the oil and gas pipeline health traceability management platform is expressed as follows: ,in, Comprehensive health status assessment value To evaluate the weighting coefficients, To predict the amount of corrosion, For multi-field coupled damage degree, This represents the final iterative stress value. For source tracing correction coefficient, Let s be the weight of the source data in the s-th iteration. For the s-th source of health deviation, The historical data influence coefficient. For the real-time parameter weights of class u, For the u-th type of real-time monitoring parameter, Weighting based on historical health data, Historical health assessment values.

6. The intelligent assessment and management method for the operational health of oil and gas transmission pipelines according to claim 1, characterized in that, The intelligent health assessment and management decision generation for the oil and gas pipeline operation is expressed as follows: Where M is the management decision output value, For decision coefficients, This is a comprehensive assessment value of health status. As a standard health threshold, As the initial health baseline, To couple decision weights, Let z be the decision factor of the z-th type of parameter. For multi-field coupled damage degree, Let z be the parameter affecting the decision-making process. This represents the final iterative stress value. To predict the amount of corrosion, For the decision coefficients of the operating parameters, For parameter correction coefficients, The decision weights for the x-th type of running parameters are... This represents the runtime parameter value for class x.

7. The intelligent assessment and management method for the operational health of oil and gas transmission pipelines according to claim 1, characterized in that, S3 includes the following steps: S31, dividing the corrosion-related parameters in the standardized input dataset into time series segments to establish multi-time period data subsets, extracting the dynamic change features of corrosion parameters within each time period using sliding window technology, and constructing a feature-time mapping relationship; S32, calling the bottom-level feature extraction module of the pipeline corrosion dual-layer time series prediction model to perform nonlinear transformation and dimensionality enhancement on the dynamic change features of each time period, generating high-dimensional feature vectors while retaining time series correlation information; S33, performing time series dependency analysis on the high-dimensional feature vectors through the upper-level time series prediction module of the model, establishing a dynamic prediction equation in combination with historical corrosion data, and making a preliminary prediction of short-term corrosion trends; S34, cross-validating the preliminary prediction results with historical traceability data in the oil and gas pipeline health traceability management platform, correcting prediction biases, and outputting accurate pipeline corrosion evolution trend data.

8. The intelligent assessment and management method for the operational health of oil and gas transmission pipelines according to claim 1, characterized in that, S4 includes the following sub-steps: S41, extracting pipeline structural parameters, environmental field parameters and operating load parameters from the standardized input dataset, performing data alignment processing with pipeline corrosion evolution trend data, constructing a multi-field coupling analysis dataset, and clarifying the correlation dimension and action boundary of each field parameter; S42, start the multi-field coupled damage assessment model, take the structural parameters as constraints, the environmental field parameters as external excitation factors, and the running load parameters as dynamic influence variables, and substitute them into the model to solve the multi-field coupled equations; S43, through the coupling effect analysis module of the model, calculate the contribution of each field parameter to pipeline damage, identify and calibrate the coupling path and dominant influencing factors, and generate intermediate coupled damage data; S44, perform spatial interpolation processing and damage level classification on the intermediate coupled damage data, construct the pipeline global damage distribution matrix, and output multi-field coupled damage distribution data.

9. The intelligent assessment and management method for the operational health of oil and gas transmission pipelines according to claim 1, characterized in that, S5 includes the following steps: S51, the multi-field coupled damage distribution data is meshed, and a finite element model of the pipe is established by combining the pipe's geometric structural parameters, clarifying the mesh element division rules and boundary condition settings for stress calculation; S52, the running load parameters are converted into load inputs for the finite element model, and the initial stress calculation module of the pipe stress iterative intelligent calculation network is started to obtain the initial stress distribution results; S53, based on the initial stress distribution results and pipeline corrosion evolution trend data, multiple rounds of stress iterative calculation are performed through the network's iterative optimization module, and the stress calculation weight factor is dynamically adjusted during each iteration to optimize the stress distribution solution accuracy; S54, when the iteration results meet the convergence conditions, the iterative calculation is stopped, the stress values ​​of each mesh element are extracted, and integrated to form the pipe's global stress distribution data.

10. An intelligent assessment and management system for the operational health of oil and gas pipelines, characterized in that, This system is applied to the intelligent assessment and management method for the operational health of oil and gas pipelines as described in claim 1, comprising: a multi-dimensional operational parameter acquisition and transmission unit, a standardized data processing and mapping unit, a pipeline corrosion dual-layer time-series prediction and calculation unit, a multi-field coupled damage assessment and analysis unit, a pipe stress iteration intelligent calculation unit, and a health status integrated management and decision-making unit. The multi-dimensional operational parameter acquisition and transmission unit and the standardized data processing and mapping unit are bidirectionally connected via a high-speed data bus, used to acquire various pipeline operational parameters and transmit them to the standardized data processing and mapping unit. The standardized data processing and mapping unit is connected to the pipeline corrosion dual-layer time-series prediction and calculation unit and the data interaction interface of the oil and gas pipeline health traceability management platform, respectively, to filter and process the acquired parameters and establish mapping relationships. The system outputs a standardized dataset to the prediction calculation unit; the pipeline corrosion dual-layer time-series prediction calculation unit communicates unidirectionally with the multi-field coupled damage assessment and analysis unit, transmitting corrosion evolution trend data to the damage assessment and analysis unit; the multi-field coupled damage assessment and analysis unit is connected to the pipe stress iteration intelligent calculation unit through a data caching module, providing it with multi-field coupled damage distribution data; the pipe stress iteration intelligent calculation unit is connected to the health status integrated management decision unit through a real-time data link, outputting pipe stress distribution data; the health status integrated management decision unit interacts bidirectionally with the standardized data processing and mapping unit and the oil and gas pipeline health traceability management platform, integrating various analytical data and generating management decisions, while feeding back the decision results to each front-end unit for parameter adjustment.

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