A stress analysis method and system for high-temperature pressure pipelines

Abstract

The application belongs to the technical field of stress analysis, and relates to a stress analysis method and system for a high-temperature pressure pipeline. The method acquires temperature distribution data and vibration signal data of a pipeline surface, performs frequency domain analysis on the vibration signal data to obtain frequency spectrum characteristics, and constructs a preliminary distribution map of a thermal stress concentration area based on temperature gradient distribution, local high-temperature point positions and the frequency spectrum characteristics. A three-dimensional geometric simulation structure of the pipeline is constructed based on dynamic change characteristics to obtain stress distribution states, temperature abnormal interval and thermal conduction rate characteristics. When the temperature abnormal interval exceeds a first preset threshold and the thermal conduction rate characteristics abnormally fluctuate, a thermal load interaction influence scene is generated to determine a damage accumulation rate distribution curve, a cumulative damage peak value and a preliminary evaluation result of material degradation. A stress distribution mapping relationship is constructed and a deviation correction coefficient is determined to obtain a safety hidden danger boundary, a residual life estimation value and a microscopic damage evolution path. Reliable technical support is provided for pipeline risk prediction and structure optimization.

Description

Technical Field

[0001] This application belongs to the field of stress analysis technology, specifically relating to a stress analysis method and system for high-temperature pressure pipelines. Background Technology

[0002] High-temperature pressure pipelines play a crucial role in transporting high-temperature and high-pressure media in petrochemical, power, and metallurgical equipment systems. Their pipe walls are subjected to the combined effects of temperature gradients, internal pressure loads, support constraints, and operational vibrations over extended periods. Related technologies typically utilize intelligent sensors to collect temperature, vibration, or strain data, and then employ threshold judgment, finite element simulation, or material life curve analysis to examine the local state of the pipeline. Temperature monitoring can identify localized high-temperature areas, vibration monitoring can reflect the structural response during pipeline operation, and finite element analysis can calculate stress distribution under given boundary conditions.

[0003] However, existing methods typically process temperature anomalies, vibration responses, and simulated stress results separately, lacking a mechanism to uniformly correlate them according to sampling time and pipe surface location. Temperature gradients are related to uneven thermal expansion, and frequency spectrum characteristics are related to structural dynamic response. If these two cannot jointly participate in the identification of thermal stress concentration zones, the analysis results are prone to relying on a single temperature threshold or a single vibration index. On the other hand, finite element analysis often uses fixed thermal loads and fixed boundary conditions, making it difficult to use real-time monitoring errors to correct parameters in thermal stress concentration zones, resulting in discrepancies between simulation results and actual operating conditions. For material degradation and life analysis, existing methods mostly rely on single stress calculation results or empirical life curves for judgment, failing to form a continuous processing chain for temperature anomaly ranges, thermal conduction rate fluctuations, damage accumulation rates, monitoring errors, and parameter corrections. Therefore, it is difficult to stably determine the boundaries of safety hazards and the evolution path of microscopic damage. Summary of the Invention

[0004] To address the above problems, this application provides a stress analysis method and system for high-temperature pressure pipelines, which at least solves the problem of how to perform closed-loop analysis of thermal stress concentration, material degradation and micro-damage evolution and output safety hazard boundaries under the coupled conditions of abnormal temperature and vibration response in high-temperature pressure pipelines.

[0005] To achieve the above objectives, the technical solution adopted in this application is as follows: In a first aspect, this application provides a stress analysis method for high-temperature pressure pipelines, the method comprising: Temperature distribution data and vibration signal data of the pipe surface are obtained. Frequency domain analysis is performed on the vibration signal data to obtain frequency spectrum characteristics. Based on the temperature gradient distribution, local high temperature points and frequency spectrum characteristics in the temperature distribution data, a preliminary distribution map of the thermal stress concentration area is constructed to obtain dynamic change characteristics. A three-dimensional geometric simulation structure for the pipeline is constructed based on dynamic change characteristics. Temperature gradient distribution, local high temperature points and frequency spectrum characteristics are input into the three-dimensional geometric simulation structure of the pipeline to obtain stress distribution state, temperature anomaly range and heat conduction rate characteristics. When the temperature anomaly exceeds the first preset threshold and the heat conduction rate characteristics fluctuate abnormally, random simulation technology is used to generate multiple heat load interaction scenarios, and the damage accumulation rate distribution curve, cumulative damage peak value and preliminary material degradation assessment results are determined. Based on the damage accumulation rate distribution curve, cumulative damage peak, monitoring data time series, and monitoring error, a stress distribution mapping relationship is constructed, and a deviation correction coefficient is determined. The parameters of the thermal stress concentration zone are updated according to the deviation correction coefficient, and the safety hazard boundary and remaining life estimate are determined. The target thermal stress concentration area is determined based on the remaining lifetime estimate. Finite element analysis is performed on the target thermal stress concentration area to generate an optimized thermal load interaction scenario and an enhanced stress distribution mapping relationship, and the micro-damage evolution path is output.

[0006] Secondly, this application provides a stress analysis system for high-temperature pressure pipelines, used to implement a stress analysis method for high-temperature pressure pipelines. The system includes: The data acquisition module is used to acquire temperature distribution data and vibration signal data on the pipe surface, perform frequency domain analysis on the vibration signal data to obtain frequency spectrum characteristics, and construct a preliminary distribution map of thermal stress concentration area based on the temperature gradient distribution, local high temperature points and frequency spectrum characteristics in the temperature distribution data to obtain dynamic change characteristics; The simulation construction module is used to construct a three-dimensional geometric simulation structure of the pipeline based on dynamic change characteristics. The temperature gradient distribution, local high temperature points and frequency spectrum characteristics are input into the three-dimensional geometric simulation structure of the pipeline to obtain the stress distribution state, temperature anomaly range and heat conduction rate characteristics. The scenario generation module is used to generate various heat load interaction scenarios using stochastic simulation technology when the temperature exceeds the first preset threshold and the heat conduction rate characteristics fluctuate abnormally, and to determine the damage accumulation rate distribution curve, the cumulative damage peak, and the preliminary assessment results of material degradation. The mapping correction module is used to construct a stress distribution mapping relationship based on the damage accumulation rate distribution curve, the peak value of cumulative damage, the time series of monitoring data, and the monitoring error, and to determine the deviation correction coefficient; The life analysis module is used to update the parameters of the thermal stress concentration zone based on the deviation correction coefficient, and to determine the safety hazard boundary and the estimated remaining life. The path output module is used to determine the target thermal stress concentration area based on the remaining life estimate, perform finite element analysis on the target thermal stress concentration area, generate an optimized thermal load interaction scenario and an enhanced stress distribution mapping relationship, and output the micro-damage evolution path.

[0007] Compared with existing technologies, the advantages and beneficial effects of this application are as follows: By jointly acquiring temperature distribution data and vibration signal data on the surface of high-temperature pressure pipelines, and correlating temperature gradient distribution, local high-temperature points, and frequency spectrum characteristics, a unified organization of temperature anomaly information and structural dynamic response information at sampling time and pipeline surface location was achieved.

[0008] By constructing a three-dimensional geometric simulation structure for pipelines based on dynamic change characteristics, temperature gradient distribution, local high-temperature points, and frequency spectrum characteristics can be input into the same simulation structure, enabling stress distribution, temperature anomaly ranges, and heat conduction rate characteristics to be generated in the same processing chain.

[0009] By generating various heat load interaction scenarios when the temperature exceeds a first preset threshold and the heat conduction rate characteristics fluctuate abnormally, the stress changes under abnormal heat load conditions can be converted into damage accumulation rate distribution curves, cumulative damage peaks, and preliminary assessment results of material degradation.

[0010] By constructing a stress distribution mapping relationship and determining the deviation correction coefficient, the monitoring data time series and monitoring errors can be used to correct the parameters of the thermal stress concentration area. Through finite element analysis, an optimized thermal load interaction scenario and an enhanced stress distribution mapping relationship are formed, enabling the micro-damage evolution path to maintain a processing correlation with the safety hazard boundary and the remaining life estimate. Attached Figure Description

[0011] Figure 1 This is a schematic flowchart of the stress analysis method for high-temperature pressure pipelines according to an embodiment of the present invention; Figure 2 This is a block diagram of the module combination of the stress analysis system for high-temperature pressure pipelines according to an embodiment of the present invention. Detailed Implementation

[0012] To enable those skilled in the art to better understand the technical solution, the present application will be described in detail below with reference to the embodiments. The description in this section is only exemplary and explanatory, and should not be used to limit the scope of protection of the present application in any way.

[0013] Stress analysis is a technical process for identifying and calculating the stress distribution, local concentration state, and damage evolution trend of pressure-bearing structures under the combined effects of thermal loads, pressure loads, vibration loads, and boundary constraints. For high-temperature pressure pipelines, the surface temperature distribution, local high-temperature points, changes in heat conduction, and operational vibration response all affect the stress state of the pipe wall. Relying solely on single temperature data or static structural parameters is insufficient to reflect the actual changes in thermal stress concentration during operation. Intelligent sensors can continuously collect temperature and vibration data from the pipeline surface, providing a monitoring basis with temporal continuity and spatial distribution characteristics for stress analysis. Based on this, by correlating temperature gradients, local high-temperature points, frequency spectrum characteristics, simulated stress distribution, and damage accumulation results, the stress analysis of high-temperature pressure pipelines can be transformed from a single-condition calculation into a continuous analysis process oriented towards changes in operating conditions. This application focuses on the identification of stress distribution, parameter correction, remaining life estimation, and micro-damage evolution path output of high-temperature pressure pipelines under the coupled effects of temperature anomalies and vibration responses.

[0014] like Figure 1 As shown, a stress analysis method for a high-temperature pressure pipeline includes the following processing steps.

[0015] The pipeline condition analysis platform receives temperature and vibration sampling sequences deployed on the surface of a high-temperature, high-pressure pipeline. The temperature sampling sequences originate from contact temperature sensors, infrared thermometers, or other acquisition devices capable of establishing the spatial temperature distribution on the pipeline surface. The vibration sampling sequences originate from accelerometers or acoustic vibration acquisition devices. The platform aligns the two types of sampling sequences according to sampling time and pipeline surface location, and performs frequency domain analysis on the vibration sampling sequences to generate frequency spectrum characteristics that characterize the local dynamic response of the pipeline. After completing time and location alignment, the platform extracts the temperature gradient distribution and local high-temperature points from the temperature distribution data, and correlates these with the frequency spectrum characteristics to form a preliminary distribution map of thermal stress concentration areas. This preliminary distribution map of thermal stress concentration areas is further converted into dynamic change characteristics and transferred to the subsequent three-dimensional geometric twin simulation structure construction stage of the pipeline.

[0016] In one embodiment, after receiving temperature distribution data, the pipeline condition analysis platform adds sampling time, pipeline surface location, and sensor channel identifier to each temperature sample value. The pipeline surface location can be calibrated based on the pipeline axial distance, circumferential angle, weld location, elbow location, or support location. The temperature gradient distribution is used to represent the degree of temperature change between adjacent sampling locations. The pipeline condition analysis platform determines the temperature gradient intensity based on the temperature difference between adjacent sampling locations and the sampling location spacing, and associates the temperature gradient intensity with the corresponding sampling time and pipeline surface location. The preset temperature range is used to identify local high-temperature points. Its value can be determined based on the allowable temperature of the high-temperature pressure pipeline material, design operating temperature, historical stable operating temperature range, and maintenance judgment criteria. When the temperature at a certain sampling location exceeds the preset temperature range, the pipeline condition analysis platform records the sampling location as a local high-temperature point and retains the duration of exceeding the preset temperature range and the spatially adjacent sampling locations.

[0017] When performing frequency domain analysis on vibration signal data, the pipeline condition analysis platform can divide the data into multiple frequency bands based on the sampling frequency and pipeline operating conditions. It can also statistically analyze the spectral peak positions, peak amplitudes, band energy, and frequency band energy variation trends within each frequency band. The frequency band division can be based on the inherent vibration range of the pipeline equipment, pump and valve start-up and shutdown conditions, fluid excitation frequency range, and historical abnormal vibration records. The frequency spectrum characteristics are used to characterize the changes in the local structural response of the pipeline under thermal load. They do not solely determine the areas of thermal stress concentration, but rather participate in area identification in conjunction with the temperature gradient distribution and local high-temperature points.

[0018] A high-risk thermal stress zone refers to a pipe surface area where temperature gradient distribution, local high-temperature points, and frequency spectrum characteristics are correlated across sampling time and pipe surface location. When identifying this zone, the pipe condition analysis platform uses sampling locations where the temperature gradient intensity exceeds the gradient judgment limit as candidate areas, local high-temperature points as temperature anomaly centers, and checks whether the frequency spectrum characteristics show increased band energy, peak shifts, or anomalous frequency components within the same sampling time or adjacent sampling periods. The gradient judgment limit can be jointly set by the statistical distribution of temperature gradients during historical stable operation, safe operating margins, and material thermal fatigue sensitivity. This limit restricts areas where only spatial temperature changes reach a set level from participating in high-risk thermal stress zone identification. If a candidate area simultaneously meets the spatial-temporal correlation requirements of temperature anomalies and vibration response anomalies, the pipe condition analysis platform identifies this candidate area as a high-risk thermal stress zone.

[0019] After completing region identification, the pipeline condition analysis platform correlates the location, extent, temperature gradient intensity determined by the temperature gradient distribution, and frequency spectrum characteristics of high-risk thermal stress zones according to sampling time and pipeline surface location, forming a preliminary distribution map of thermal stress concentration areas. Each region cell in the preliminary distribution map of thermal stress concentration areas carries temperature change information and vibration response information. Subsequent processing can directly determine the pipeline surface locations that require thermal boundary conditions, mesh refinement, or focused analysis based on these region cells. When there are short-term gaps in the temperature or vibration sampling sequence, the pipeline condition analysis platform can use data from adjacent sampling times and adjacent pipeline surface locations to fill in the gaps. When the same sensor channel has consecutive gaps exceeding the set sampling period, the region corresponding to that channel is not output as an independent high-risk thermal stress zone, but is only retained as a region to be reviewed. The set sampling period can be determined based on the data acquisition frequency, sensor stability, and the minimum continuous data length required for subsequent simulation, and is used to determine whether data gaps in sensor channels affect the preliminary identification of thermal stress concentration areas. After the preliminary distribution map of the thermal stress concentration area is completed, the pipeline condition analysis platform extracts the regional location, regional range, temperature gradient intensity change trend and frequency spectrum characteristic change trend from it, combines them to form dynamic change features, and outputs the dynamic change features to the pipeline three-dimensional geometric simulation structure construction stage to define the key loading area and key calculation area in subsequent simulations.

[0020] The pipeline condition analysis platform receives the dynamic change characteristics output from the previous stage and uses the structural parameters, material parameters, and boundary constraint information of the high-temperature pressure pipeline to establish a three-dimensional geometric simulation structure. The dynamic change characteristics are used to identify the key areas requiring calculation in the preliminary distribution map of thermal stress concentration zones. Based on this, the pipeline condition analysis platform divides the area into mesh refinement regions and thermal boundary loading regions. The temperature gradient distribution is converted into thermal loads, local high-temperature points are converted into surface hotspot locations, and frequency spectrum characteristics are converted into dynamic response characteristics and incorporated into the three-dimensional geometric simulation structure of the pipeline. After the simulation is completed, the stress distribution state of the thermal stress concentration zone and surface hotspot region is formed, and the micro-evolution initiation region is identified from the stress concentration location. Temperature exceeding the limit range within this region forms a temperature anomaly zone, and the temperature change relationship between adjacent sampling locations forms the heat conduction rate characteristic. The temperature anomaly zone and heat conduction rate characteristic are then used for subsequent random simulation trigger judgments.

[0021] In one embodiment, when constructing a 3D geometric simulation structure of a pipeline, the pipeline condition analysis platform reads the outer diameter, inner diameter, wall thickness, axial length, elbow position, weld position, support constraint position, material elastic parameters, thermal expansion parameters, and thermal conductivity parameters of the high-temperature pressure pipeline. Structural parameters can be obtained from design drawings, 3D scanning data, or maintenance logs; material parameters can be obtained from material factory data, maintenance records, or material databases. The region location and region extent in the dynamic change characteristics are used to determine the key calculation areas in the 3D geometric simulation structure of the pipeline. When a region simultaneously has a large temperature gradient intensity, local high-temperature points, and abnormal frequency range distribution, the pipeline condition analysis platform divides this region into a mesh-refined region. The mesh-refined region is used to improve the accuracy of stress distribution calculation at that location. The mesh size can be determined based on the pipeline wall thickness, hot spot range, and sensor spacing, with the principle being to cover the local temperature change boundary without exceeding the stable processing capacity of the simulation equipment. The thermal boundary loading region is jointly determined by the local high-temperature points and temperature gradient distribution. Its boundary extent is not less than the spatial influence range of the local high-temperature points and extends to adjacent regions where the temperature gradient intensity continuously changes.

[0022] After the temperature gradient distribution is incorporated into the 3D geometric simulation structure of the pipeline, it is applied as a spatial thermal load to the thermal boundary loading region on the pipeline surface. Local high-temperature points are used as surface hotspot locations to define the center and extension range of the local high-temperature region. Frequency spectrum characteristics are used as dynamic response characteristics to define the additional load or boundary disturbance of the thermal stress concentration area under abnormal vibration response conditions. The pipeline condition analysis platform simulates and solves the thermal stress concentration area and surface hotspot region, outputting the stress distribution state of each mesh node or sampling location. The stress distribution state is used to indicate the local stress changes formed by temperature load, structural constraints, and dynamic response in the pipeline surface and wall thickness direction. The pipeline condition analysis platform defines the continuous region where the stress value exceeds the safety analysis benchmark or the stress change rate exceeds the historical stable range as the micro-evolution starting region. This region represents the initial range of subsequent material damage accumulation that needs to be analyzed in detail.

[0023] The temperature anomaly range is determined by the temperature exceedance range in the initial region of microscopic evolution. The criteria for determining the temperature exceedance range can be set based on the material's allowable temperature, design operating temperature, historical stable operating temperature range, and safety margin, used to constrain whether subsequent stochastic simulations enter the stage of generating thermal load interaction scenarios. The heat conduction rate characteristics can be determined based on the temperature change between adjacent sampling locations, the distance between adjacent sampling locations, and the sampling time interval. For adjacent sampling locations... and At the sampling time The characteristic value of the heat conduction rate can be expressed as: in, adjacent sampling positions and At sampling time The characteristic value of thermal conductivity rate, Sampling location At sampling time Temperature value, Sampling location At sampling time Temperature value, Sampling location The temperature value at the previous sampling time. Sampling location The temperature value at the previous sampling time. adjacent sampling positions and The distance between them This represents the time interval between two adjacent samples. This calculation characterizes how quickly the temperature difference between adjacent regions changes with spatial distance and time interval. If data for a combination of adjacent sampling locations is missing, the pipeline condition analysis platform prioritizes using adjacent sampling combinations within the same region to fill the gaps. If data is continuously missing for more than the set sampling period, the heat transfer rate characteristics of that region are marked as pending verification and are not included in the abnormal fluctuation judgment. After the temperature anomaly interval and heat transfer rate characteristics are calculated, they are passed to the subsequent heat load interaction scenario generation stage to jointly determine whether to initiate random simulation.

[0024] The pipeline condition analysis platform receives the temperature anomaly range, heat conduction rate characteristics, stress distribution status, and frequency spectrum characteristics generated in the previous stage. The temperature anomaly range is used to define the duration of the heat load anomaly, the heat conduction rate characteristics are used to characterize changes in heat diffusion, the frequency spectrum characteristics are used to characterize the dynamic response of the structure, and the stress distribution status is used to define the pipeline surface area where stress concentration has occurred. When the temperature anomaly range exceeds a first preset threshold and the heat conduction rate characteristics show abnormal fluctuations, the pipeline condition analysis platform initiates stochastic simulation calculations to generate multiple heat load interaction scenarios. Each heat load interaction scenario enters damage accumulation analysis, generating damage accumulation rate distribution curves at different sampling times and different pipeline surface locations. The pipeline condition analysis platform determines the cumulative damage peak value based on the damage accumulation rate distribution curves and generates preliminary material degradation assessment results. The preliminary material degradation assessment results and the damage accumulation rate distribution curves are then transferred to the subsequent stress distribution mapping relationship construction stage.

[0025] In one embodiment, the pipeline condition analysis platform uses an abnormal temperature range exceeding a first preset threshold as the temperature trigger condition for random simulation. The first preset threshold can be determined based on the allowable temperature of the high-temperature pressure pipeline material, the design operating temperature, the historical stable operating temperature range, and the safety margin, and is used to filter out non-critical temperature anomalies caused by short-term minor fluctuations. Abnormal fluctuations in the heat conduction rate characteristic can be determined by the statistical range of heat conduction rate during historical stable operating periods. When the heat conduction rate characteristic continuously exceeds this statistical range, or when the heat conduction rate characteristic undergoes a sudden change in direction within adjacent sampling periods, the pipeline condition analysis platform classifies it as an abnormal fluctuation. When both trigger conditions are met simultaneously, random simulation calculation is initiated; if only one condition is met, the pipeline condition analysis platform retains the current abnormal temperature range and heat conduction rate characteristic, treating them as continuously monitored objects, but does not include them in the damage accumulation rate calculation.

[0026] The heat load interaction scenario is generated jointly by temperature anomaly range, heat conduction rate characteristics, stress distribution state, and frequency spectrum characteristics. The temperature anomaly range defines the duration of the heat load and the temperature exceedance range within the scenario; the heat conduction rate characteristics define the rate of heat diffusion and its fluctuation direction; the stress distribution state defines the stress distribution in the thermal stress concentration area along the pipe surface and wall thickness; and the frequency spectrum characteristics define the impact of vibration response on local stress changes. The pipeline condition analysis platform sets random disturbances within the historical fluctuation range and safety evaluation boundary of each input variable to form various heat load interaction scenarios. The disturbance range does not exceed the effective range jointly defined by sensor measurement error, historical operating condition fluctuation range, and material safety boundary, to avoid random scenarios deviating from the actual operating state of the pipeline. If an input variable for a scenario is missing, the pipeline condition analysis platform uses data from the same pipe section, adjacent sampling time, or the same operating condition category to fill in the missing data; scenarios with consecutive missing data exceeding the set sampling period are marked as invalid scenarios and are not included in the generation of preliminary material degradation assessment results.

[0027] For any heat load interaction scenario, the pipeline condition analysis platform calculates the damage accumulation rate of the thermal stress concentration zone based on sampling time and pipeline surface location. The damage accumulation rate represents the rate of material damage growth per unit sampling time, and its value is constrained by the stress distribution state, temperature anomaly duration, heat conduction rate characteristics, and frequency spectrum characteristics within the scenario. The pipeline condition analysis platform sequentially connects the damage accumulation rates of the same pipeline surface location over consecutive sampling times to form a damage accumulation rate distribution curve. For the pipeline surface location... At sampling time The cumulative damage value can be determined according to the following formula: in, Position of the pipe surface At sampling time The cumulative damage value, Position of the pipe surface In the Damage accumulation rate within each sampling interval For the first The time length of each sampling interval This represents the number of sampling intervals involved in the cumulative calculation. This relationship is used to convert the damage growth rate into a cumulative amount that can be compared with the material degradation state.

[0028] After calculating the cumulative damage value at each pipe surface location, the pipe condition analysis platform selects the maximum value from all cumulative damage values ​​within the same heat load interaction scenario, determining it as the cumulative damage peak value. The cumulative damage peak value characterizes the location of the most concentrated material degradation and its damage upper limit under the current scenario. The preliminary material degradation assessment results are determined jointly based on the damage accumulation rate distribution curve and the cumulative damage peak value. The damage accumulation rate distribution curve is used to identify the persistence of damage growth, while the cumulative damage peak value is used to identify the degree of local degradation concentration. The assessment results can be divided into different degradation intervals according to the material degradation judgment threshold, which can be determined by material fatigue test data, historical maintenance records, similar pipe failure samples, and safety evaluation standards. After the preliminary material degradation assessment results are generated, an index relationship is established with the corresponding heat load interaction scenario, damage accumulation rate distribution curve, and cumulative damage peak value, and this is transferred to the subsequent stress distribution mapping relationship construction stage to form a basis for deviation correction together with the monitoring data time series and monitoring errors.

[0029] The pipeline condition analysis platform receives the damage accumulation rate distribution curve, the cumulative damage peak value, the monitoring data time series, and the monitoring error. The damage accumulation rate distribution curve reflects the damage growth trend of the thermal stress concentration zone at different sampling times and different pipeline surface locations. The cumulative damage peak value identifies the upper limit of the damage concentration. The monitoring data time series comes from continuous monitoring records generated during temperature sampling, vibration sampling, and simulation verification. The monitoring error comes from sensor calibration errors, sampling fluctuations, and simulation prediction deviations. The pipeline condition analysis platform extracts key points from the damage accumulation rate distribution curve and aligns the key points, monitoring data time series, and monitoring errors according to the sampling time and pipeline surface location to form an aligned error distribution. After alignment, the pipeline condition analysis platform constructs a stress distribution mapping relationship and determines the deviation correction coefficient accordingly. The deviation correction coefficient is transferred to the thermal stress concentration zone parameter update stage to correct local parameters in subsequent 3D geometric simulations.

[0030] In one embodiment, key points are sampling points with correction value extracted from the damage accumulation rate distribution curve. These points are used to compress key changes in the continuous curve, avoiding data redundancy caused by directly introducing all sampling points into the stress distribution mapping relationship. When extracting key points, the pipeline condition analysis platform detects curve slope changes and peak positions. Slope changes indicate sampling points where the damage growth trend changes from stable to rapid or from rapid to gradual. Peak positions indicate sampling points where the damage accumulation rate reaches a local maximum at the same pipeline surface location or within the same sampling time range. The threshold for determining slope changes can be determined based on the damage accumulation rate fluctuation range during historical stable operation, the sampling period, and the simulation resolution. This ensures that only trend changes exceeding the normal fluctuation range are included in the key point set. Peak position determination can be set by combining the rate magnitude and duration of adjacent sampling points to prevent a single noisy sampling point from being misidentified as a peak position.

[0031] After extracting key points, the pipeline condition analysis platform establishes an index relationship between each key point and the sampling time, pipeline surface location, damage accumulation rate, and peak cumulative damage. Monitoring data time series are retrieved according to the same sampling time and pipeline surface location, with monitoring errors synchronously linked to the corresponding index locations. If the sampling time of a key point is not completely consistent with the monitoring data time series, the pipeline condition analysis platform selects monitoring records from adjacent sampling periods for time series alignment; if there is a shift in the pipeline surface location, the platform performs location matching based on the spatial distance between adjacent sampling locations. The result of time series alignment is an aligned error distribution, which represents the deviation between the predicted stress distribution value and the monitoring data time series at the same sampling time and pipeline surface location, as well as the impact of monitoring errors on the deviation.

[0032] The stress distribution mapping relationship is used to describe the correlation between key points, cumulative damage peak value, and aligned error distribution. The pipeline condition analysis platform uses the pipeline surface location of key points as a spatial index and the sampling time as a temporal index to organize the damage accumulation rate, cumulative damage peak value, monitoring error, and stress distribution prediction values ​​into mapping records. The stress distribution prediction value is the stress estimation result output by the stress distribution mapping relationship at the corresponding sampling time and pipeline surface location, which is subsequently used for comparison with the measured or verified stress records in the monitoring data time series. The deviation correction coefficient can be determined according to the following formula: in, This is the deviation correction factor. The number of key points involved in the calibration. The key point number is... For the first Stress records at key locations in the time series of monitoring data. For the first The predicted stress distribution values ​​at key points are output from the stress distribution mapping relationship. For the first Monitoring errors at key locations, To prevent the denominator from being too small, the lower stress limit can be determined based on sensor resolution, simulation accuracy, and material safety analysis benchmarks. This calculation incorporates both prediction bias and monitoring error into the correction, and the resulting bias correction coefficient is used for subsequent updates to the thermal stress concentration zone parameters. If the number of key points involved in the correction is lower than the lower limit, or if the monitoring data sequence is missing within a continuous sampling period, the pipeline condition analysis platform does not output new bias correction coefficients. Instead, it uses the bias correction coefficients formed in the previous stable sampling period and marks the areas to be verified. The lower limit for the number of key points can be determined based on the number of sampling locations on the pipeline surface, the range of the thermal stress concentration zone, and the minimum number of samples required for bias correction calculations, limiting the output of correction coefficients when samples are insufficient. After determining the bias correction coefficients, the pipeline condition analysis platform transfers the bias correction coefficients, stress distribution mapping relationship, and aligned error distribution to the thermal stress concentration zone parameter update stage to correct the location, range, and temperature load amplitude of the thermal stress concentration zone.

[0033] The pipeline condition analysis platform receives deviation correction coefficients, the pipeline's 3D geometric simulation structure, temperature data from local high-temperature points, stress distribution mapping relationships, preliminary material degradation assessment results, and data acquisition frequency. Deviation correction coefficients are used to correct the location, extent, temperature load amplitude, and influence range of local high-temperature points in the thermal stress concentration zone, resulting in updated thermal stress concentration zone parameters. The platform then uses these updated parameters to recalculate the surface hotspot area and temperature fluctuation frequency influence parameters, generating an updated local stress distribution. This updated local stress distribution, along with the temperature data from local high-temperature points, is used to identify potential safety hazard areas and determine safety hazard boundaries from continuous areas that meet safety criteria. These safety hazard boundaries are then used in the remaining life estimation stage to select key high-temperature points, safety hazard areas, and thermal stress concentration zone evolution scenarios.

[0034] In one embodiment, the pipeline condition analysis platform uses a deviation correction coefficient as a simulation parameter correction factor when updating the parameters of the thermal stress concentration zone. The location of the thermal stress concentration zone is a spatial parameter, and its correction direction is determined by the deviation direction between the predicted stress distribution and the monitoring data time series in the stress distribution mapping relationship. The range of the thermal stress concentration zone, the temperature load amplitude, and the influence range of local high-temperature points are amplitude or scale parameters, and their correction magnitude is controlled by the deviation correction coefficient. When the deviation correction coefficient is greater than the upper limit of the stable correction interval, it indicates that the original simulation structure underestimated the local stress level, and the pipeline condition analysis platform expands the range of the thermal stress concentration zone and increases the temperature load amplitude. When the deviation correction coefficient is less than the lower limit of the stable correction interval, it indicates that the original simulation structure overestimated the local stress level, and the pipeline condition analysis platform shrinks the range of the thermal stress concentration zone and reduces the temperature load amplitude. When the deviation correction coefficient is within the stable correction interval, the original thermal stress concentration zone parameters are retained, and only the state markers in subsequent calculations are updated. The stable correction interval can be jointly determined based on the sensor's allowable error, the finite element calculation error, and the historical calibration error, to avoid excessive parameter correction caused by single sampling fluctuations.

[0035] After the updated parameters for the thermal stress concentration area are incorporated into the 3D geometric simulation structure of the pipeline, the pipeline condition analysis platform recalculates the surface hotspot region and the influence parameters of temperature fluctuation frequency. The surface hotspot region represents a continuous temperature anomaly area formed on the pipeline surface by local high-temperature points, and its boundary is jointly determined by the temperature data of the local high-temperature points, the temperature changes at adjacent sampling locations, and the updated influence range. The influence parameter of temperature fluctuation frequency represents the impact of the frequency characteristics of the temperature change at local high-temperature points over sampling time on the local stress distribution, derived from the periodic changes in the temperature sampling sequence and the dynamic response information in the frequency spectrum characteristics. The pipeline condition analysis platform inputs the recalculated surface hotspot region and temperature fluctuation frequency influence parameters into the local stress calculation process, forming an updated local stress distribution. The updated local stress distribution replaces the previous uncorrected local stress distribution and serves as a direct basis for identifying potential safety hazard areas.

[0036] Potential safety hazard areas are determined by the updated local stress distribution and temperature data of local high-temperature points. The pipeline condition analysis platform marks pipeline surface areas where stress levels exceed material safety analysis benchmarks, temperature data exceeds the material's allowable temperature range, or stress changes show a continuous expanding trend as potential safety hazard areas. Preset safety judgment conditions can be determined based on material allowable stress, material allowable temperature, historical failure samples, maintenance judgment criteria, and safety margins, used to determine whether potential safety hazard areas have reached the safety boundary output conditions. If adjacent sampling locations within a potential safety hazard area continuously meet the preset safety judgment conditions, the pipeline condition analysis platform determines the outer boundary of the continuous area as the safety hazard boundary; if only isolated sampling points within a potential safety hazard area meet the preset safety judgment conditions, these isolated sampling points are retained as points to be reviewed and do not participate in the formation of the safety hazard boundary. After the safety hazard boundary is formed, an index relationship is established with the updated thermal stress concentration area parameters, the updated local stress distribution, and the temperature data of local high-temperature points, and this relationship is then transferred to the remaining life estimation stage.

[0037] In one embodiment, when determining the estimated remaining life, the pipeline condition analysis platform uses the safety hazard boundary as a spatial screening criterion, selecting key high-temperature points and safety hazard areas within the safety hazard boundary. Key high-temperature points are sampling locations within the safety hazard boundary where temperature data remains consistently high and coincides with locations of local stress concentration. Safety hazard areas are pipeline surface regions formed by multiple adjacent key high-temperature points and continuous stress concentration areas. Based on the stress distribution mapping relationship, the pipeline condition analysis platform performs finite element analysis on the key high-temperature points and safety hazard areas to form the range of thermal stress concentration zones and stress intensity distribution. The stress intensity distribution is used to represent the stress concentration level at different locations within the safety hazard area, and is subsequently used to generate thermal stress concentration zone evolution scenarios and determine the stress accumulation state.

[0038] The data acquisition frequency is derived from the acquisition configurations of temperature sampling equipment, vibration sampling equipment, and the pipeline condition analysis platform. The pipeline condition analysis platform determines the iteration time interval for the thermal stress concentration zone evolution scenario based on the data acquisition frequency. When the data acquisition frequency is high, the iteration time interval can be set shorter to reflect fine-grained changes in high-temperature points and stress concentration zones; when the data acquisition frequency is low, the iteration time interval is consistent with the actual sampling cycle to avoid generating evolution states lacking monitoring data support. Based on the iteration time interval, the range of the thermal stress concentration zone, and the stress intensity distribution, the pipeline condition analysis platform generates various thermal stress concentration zone evolution scenarios. Each thermal stress concentration zone evolution scenario includes the spatial range within the safety hazard boundary, the stress intensity distribution at different iteration times, the temperature change trend, and preliminary material degradation assessment results, used to describe the stress accumulation state of the safety hazard area during subsequent operation.

[0039] The remaining lifetime estimate is determined using a Support Vector Machine (SVM) algorithm. The input features for the SVM algorithm can be selected from the stress accumulation state, safety hazard boundary range, temperature duration at key high-temperature points, preliminary material degradation assessment results, peak stress intensity distribution, and data acquisition frequency within the thermal stress concentration zone evolution scenario. These input features are chosen because the stress accumulation state reflects the continuous load, the safety hazard boundary range reflects the size of the affected area, the temperature duration at key high-temperature points reflects thermal aging, the preliminary material degradation assessment results reflect the existing damage level, the peak stress intensity distribution reflects the upper limit of local stress concentration, and the data acquisition frequency constrains the temporal resolution of the evolution scenario. Training samples can come from historical operation records, maintenance records, similar pipeline failure samples, and verified simulation samples. Before entering the SVM algorithm, the pipeline condition analysis platform normalizes each input feature and removes sample points corresponding to abnormal sensor channels. The SVM algorithm outputs estimation results that match different remaining lifetime intervals. The pipeline condition analysis platform determines the lifetime interval with the highest matching degree to the current safety hazard boundary and stress accumulation state as the remaining lifetime estimate. If the proportion of missing input features exceeds the upper limit, the pipeline condition analysis platform will not output a new remaining lifetime estimate. Instead, it will use the estimate from the previous stable assessment cycle and mark the corresponding safety hazard area as an area requiring supplementary monitoring. The upper limit for the missing proportion can be determined based on the completeness requirements of effective features in the training samples and the distribution of missing proportions in historical stable assessment samples. This limit is used to restrict the remaining lifetime estimate output when input features are insufficient. After the remaining lifetime estimate is generated, it is transferred to the finite element analysis verification stage along with the safety hazard boundary, the evolution scenario of the thermal stress concentration zone, and the preliminary assessment results of material degradation.

[0040] The pipeline condition analysis platform receives the remaining life estimate, safety hazard boundary, thermal stress concentration zone parameters, and stress distribution mapping relationship. The remaining life estimate is used to determine the target thermal stress concentration zone that needs to be verified by finite element analysis. The target thermal stress concentration zone originates from the area within the safety hazard boundary where the remaining life is low and the stress accumulation state is relatively concentrated. The pipeline condition analysis platform performs finite element calculations on the target thermal stress concentration zone to obtain the verification stress distribution, and compares the verification stress distribution with the stress distribution mapping relationship to form the parameter deviation of the thermal stress concentration zone parameters. When the parameter deviation exceeds a preset parameter deviation threshold, the pipeline condition analysis platform adjusts the heat load distribution of the target thermal stress concentration zone to form an optimized heat load interaction scenario. The optimized heat load interaction scenario enters the frequency spectrum feature update and enhanced stress distribution mapping relationship construction stage, and finally outputs the micro-damage evolution path.

[0041] In one embodiment, when determining the target thermal stress concentration area, the pipeline condition analysis platform uses the remaining life estimate as the basis for region selection, and sorts them in conjunction with the safety hazard boundary and the preliminary material degradation assessment results. Regions with lower remaining life estimates indicate that they are closer to the maintenance decision time under the current stress accumulation and material degradation states. The safety hazard boundary is used to define the spatial range, and the preliminary material degradation assessment results are used to identify areas where damage accumulation has occurred. The target thermal stress concentration area can consist of a single continuous safety hazard area or can be formed by merging multiple adjacent critical high-temperature points. During merging, the pipeline condition analysis platform checks whether the distance, temperature change trend, and stress intensity distribution between adjacent critical high-temperature points are continuous. Only when the continuity meets the region merging condition are multiple critical high-temperature points included as the same target thermal stress concentration area in the finite element calculation. The region merging condition can be determined based on the sensor deployment spacing, pipeline wall thickness, and the scale of historical failure areas to prevent spatially independent thermal anomaly regions from being incorrectly merged.

[0042] Once the target thermal stress concentration zone is determined, the pipeline condition analysis platform inputs its thermal stress concentration zone parameters into the finite element method (FEM) calculation process. These parameters include the region's location, extent, temperature load amplitude, the influence range of local high-temperature points, and the influence parameters of temperature fluctuation frequency. The FEM calculation process reads the mesh, material properties, and boundary constraints related to the target thermal stress concentration zone from the pipeline's 3D geometric simulation structure, applies the temperature load and thermal load distribution to the corresponding locations, and calculates the verification stress distribution. The verification stress distribution represents the stress level at each mesh node or sampling location within the target thermal stress concentration zone under the current thermal stress concentration zone parameters.

[0043] The pipeline condition analysis platform aligns the predicted stress distribution output from the verification stress distribution and stress distribution mapping relationship by both position and time, obtaining the parameter deviation of the thermal stress concentration area parameters. The parameter deviation represents the degree of inconsistency between the finite element calculation results and the stress distribution mapping relationship. Its setting method can be determined jointly based on the relative difference between the verified stress distribution and the predicted stress distribution, monitoring errors, and finite element calculation errors. A preset parameter deviation threshold is used to determine whether the heat load distribution needs adjustment. The value can be derived from historical calibration errors, allowable deviations in material safety evaluation, and the calculation accuracy of the simulation equipment. When the parameter deviation does not exceed the preset parameter deviation threshold, the pipeline condition analysis platform retains the current heat load distribution and transfers the verification stress distribution to the enhanced stress distribution mapping relationship construction stage. When the parameter deviation exceeds the preset parameter deviation threshold, the pipeline condition analysis platform adjusts the heat load distribution of the target thermal stress concentration area according to the deviation direction. The adjustment includes the heat load range, local heat load amplitude, and hot spot boundary expansion range. The adjusted target thermal stress concentration area forms an optimized heat load interaction scenario and establishes an index relationship with the corresponding parameter deviation, verification stress distribution, and remaining life estimate for subsequent frequency spectrum feature updates. The optimized heat load interaction scenario is also associated with the vibration response data of the target thermal stress concentration area at the corresponding sampling time and pipe surface location. The vibration response data can come from the original vibration sampling sequence, the frequency spectrum characteristics corresponding to the target thermal stress concentration area, or the dynamic response records retained in the finite element verification stage, which are used for subsequent hierarchical decomposition and to form updated frequency spectrum characteristics.

[0044] In one embodiment, the pipeline condition analysis platform extracts vibration response data from the optimized heat load interaction scenario and performs hierarchical decomposition on the vibration response data. Hierarchical decomposition divides the vibration response data into multiple feature levels according to frequency range, sampling time, and the location of the target thermal stress concentration area. The low-frequency level reflects the overall structural response, the mid-frequency level reflects operational disturbances, and the high-frequency level reflects abnormal responses related to local impacts or crack propagation. The frequency range can be determined based on historical vibration records, pipeline support conditions, pump and valve operating frequencies, and sensor sampling frequencies. After completing the hierarchical decomposition, the pipeline condition analysis platform extracts the spectral peak position, frequency band energy, frequency band energy variation trend, and abnormal frequency components from each feature level to form an updated frequency spectrum feature. The updated frequency spectrum feature reflects the latest dynamic response of the target thermal stress concentration area under the optimized heat load interaction scenario and serves as one of the inputs to the enhanced stress distribution mapping relationship.

[0045] The enhanced stress distribution mapping relationship is established based on the original stress distribution mapping relationship. The pipeline condition analysis platform associates the updated frequency spectrum characteristics, the verification stress distribution in the optimized heat load interaction scenario, the heat load distribution, the target thermal stress concentration area parameters, and the original stress distribution mapping relationship to form a new mapping record. The new mapping record uses sampling time and pipeline surface location as indexes to record the stress distribution results, frequency spectrum characteristic changes, and parameter deviations of the target thermal stress concentration area under different heat load distributions. Preset deviation conditions are used to filter stress distribution results that can be used for path generation, and the values ​​can be determined based on finite element calculation errors, monitoring errors, and historical stable calibration intervals. When the stress distribution result output by the enhanced stress distribution mapping relationship meets the preset deviation conditions, the pipeline condition analysis platform uses this stress distribution result as the basis for extracting the location of micro-damage points; when the preset deviation conditions are not met, the pipeline condition analysis platform re-examines the target thermal stress concentration area parameters and the updated frequency spectrum characteristics, and does not directly generate a micro-damage evolution path.

[0046] The location of micro-damage points is determined by stress distribution results that meet preset deviation conditions. The pipeline condition analysis platform marks grid nodes or sampling locations where the stress concentration level exceeds the material safety analysis benchmark and coincides with the high damage accumulation area in the preliminary material degradation assessment results as micro-damage point locations. The distribution density of micro-damage point locations is used to determine whether a continuous evolution path can be formed. The distribution density can be determined based on the number of micro-damage point locations per unit pipeline surface area, the distance between adjacent micro-damage point locations, and the spacing of sampling locations. Path construction conditions can be set based on the pipeline surface sampling density, grid scale, and material crack propagation judgment scale to restrict only micro-damage point locations with continuous spatial distribution to participate in path construction. When the distribution density meets the path construction conditions, the pipeline condition analysis platform connects the micro-damage point locations according to the sampling time sequence and spatial adjacency relationship to generate an evolution path map. Isolated points, short-term anomalies, and points generated by a single anomaly channel in the evolution path map are marked and removed. The remaining paths are smoothed to form the micro-damage evolution path. The micro-damage evolution path, the optimized heat load interaction scenario, the enhanced stress distribution mapping relationship, and the remaining life estimate are output together as the basis for subsequent inspection, maintenance sequencing, and monitoring parameter updates of high-temperature pressure pipelines.

[0047] like Figure 2 As shown, a stress analysis system for a high-temperature pressure pipeline includes the following modules.

[0048] The data acquisition module consists of a temperature sensor array, a vibration sensor array, a signal conditioning circuit, an analog-to-digital converter circuit, and an edge processor. The temperature sensor array is positioned on the surface of the high-temperature, high-pressure pipeline to collect temperature signals from different sampling locations; the vibration sensor array collects vibration signals during pipeline operation; the signal conditioning circuit amplifies, filters, and performs anti-interference processing on the collected signals; the analog-to-digital converter circuit converts analog signals into digital signals; and the edge processor performs frequency domain analysis on the vibration signals and, combined with temperature gradient distribution, local high-temperature points, and frequency spectrum characteristics, generates a preliminary distribution map and dynamic variation characteristics of the thermal stress concentration area.

[0049] The simulation module consists of an industrial computer, a memory, a graphics computing unit, and a structural parameter storage unit. The industrial computer retrieves pipeline structural parameters, material parameters, and boundary constraint information; the memory stores dynamic variation characteristics, temperature gradient distribution, local high-temperature points, and frequency spectrum features; the graphics computing unit performs mesh generation and simulation solving for the pipeline's 3D geometric structure; and the structural parameter storage unit stores the pipeline's outer diameter, inner diameter, wall thickness, elbow locations, weld locations, and support constraint locations. Through the coordinated use of these hardware components, the simulation module generates stress distribution patterns, temperature anomaly zones, and heat conduction rate characteristics.

[0050] The scenario generation module consists of a simulation computing processor, a random simulation operation unit, a condition parameter memory, and a cache unit. The simulation computing processor receives temperature anomaly ranges, heat conduction rate characteristics, stress distribution states, and frequency spectrum characteristics; the random simulation operation unit generates various heat load interaction scenarios under the triggering conditions of temperature anomalies and abnormal fluctuations in heat conduction rate; the condition parameter memory stores historical operating temperatures, vibration responses, material parameters, and safety evaluation parameters; and the cache unit temporarily stores intermediate calculation data under different heat load interaction scenarios and outputs damage accumulation rate distribution curves, cumulative damage peak values, and preliminary material degradation assessment results.

[0051] The mapping correction module consists of a data alignment processor, an error calculation unit, a mapping relationship memory, and a correction parameter output unit. The data alignment processor aligns the damage accumulation rate distribution curve, monitoring data sequence, and monitoring error according to the sampling time and pipe surface location. The error calculation unit calculates the deviation between the predicted stress distribution value and the monitoring data sequence. The mapping relationship memory stores the correlation records between key points, cumulative damage peak values, and the aligned error distribution. The correction parameter output unit outputs deviation correction coefficients based on the stress distribution mapping relationship and transmits these coefficients to the parameter update process for the thermal stress concentration area.

[0052] The life analysis module consists of a parameter update processor, a finite element calculation interface, a life estimation processor, and a safety boundary memory. The parameter update processor corrects the location and extent of thermal stress concentration zones, temperature load amplitudes, and the influence range of local high-temperature points based on deviation correction coefficients. The finite element calculation interface calls up the calculation results of local stress distribution. The life estimation processor determines the estimated remaining life based on safety hazard boundaries, stress distribution mapping relationships, data acquisition frequency, and preliminary material degradation assessment results. The safety boundary memory stores safety hazard boundaries, key high-temperature points, and safety hazard areas, and transfers the relevant data to subsequent finite element analysis verification processes.

[0053] The path output module consists of a finite element verification processor, a frequency spectrum update unit, a path generation processor, and a result output interface. The finite element verification processor verifies the parameters of the thermal stress concentration zone based on the remaining life estimate and generates an optimized thermal load interaction scenario. The frequency spectrum update unit performs hierarchical decomposition on the vibration response data in the optimized thermal load interaction scenario to obtain updated frequency spectrum features. The path generation processor extracts the location of micro-damage points based on the enhanced stress distribution mapping relationship and constructs an evolution path diagram. The result output interface outputs the micro-damage evolution path and transmits the micro-damage evolution path to pipeline operation status assessment, inspection sequencing, or monitoring parameter update processes.

[0054] It should be noted that, in this document, the terms "comprising," "including," and any other variations are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Specific examples have been used in this document to illustrate the principles and implementation methods of the technical solutions of this application. The above examples are only for the purpose of helping to understand the methods and core ideas of this application. The above descriptions are merely preferred embodiments of this application. It should be pointed out that, due to the limitations of written expression and the objective existence of infinite specific structures, those skilled in the art can make several improvements, modifications, or changes without departing from the principles of this application, and can also combine the above technical features in an appropriate manner; these improvements, modifications, changes, or combinations, or the direct application of the concept and technical solutions of this application to other situations without modification, should all be considered within the scope of protection of this application.

Claims

1. A stress analysis method for high-temperature pressure pipelines, characterized in that, include: Temperature distribution data and vibration signal data of the pipe surface are acquired. Frequency domain analysis is performed on the vibration signal data to obtain frequency spectrum characteristics. Based on the temperature gradient distribution, local high temperature points and frequency spectrum characteristics in the temperature distribution data, a preliminary distribution map of thermal stress concentration area is constructed to obtain dynamic change characteristics. Based on the dynamic change characteristics, a three-dimensional geometric simulation structure of the pipeline is constructed. The temperature gradient distribution, the local high temperature points and the frequency spectrum characteristics are input into the three-dimensional geometric simulation structure of the pipeline to obtain the stress distribution state, temperature anomaly range and heat conduction rate characteristics. When the temperature anomaly range exceeds the first preset threshold and the heat conduction rate characteristics fluctuate abnormally, a variety of heat load interaction scenarios are generated using stochastic simulation technology, and the damage accumulation rate distribution curve, cumulative damage peak value, and preliminary material degradation assessment results are determined. Based on the damage accumulation rate distribution curve, the cumulative damage peak value, the monitoring data time series, and the monitoring error, a stress distribution mapping relationship is constructed, and a deviation correction coefficient is determined; the parameters of the thermal stress concentration zone are updated according to the deviation correction coefficient, and the safety hazard boundary and the remaining life estimate are determined. Based on the estimated remaining lifetime, the target thermal stress concentration area is determined. Finite element analysis is performed on the target thermal stress concentration area to generate an optimized thermal load interaction scenario and an enhanced stress distribution mapping relationship, and the micro-damage evolution path is output.

2. The method according to claim 1, characterized in that, The preliminary distribution map of the thermal stress concentration area is constructed based on the temperature gradient distribution, local high-temperature points, and frequency spectrum characteristics in the temperature distribution data, and the dynamic change characteristics are obtained, including: Based on the temperature distribution data, the temperature gradient distribution between each sampling location on the pipe surface and the local high temperature points where the temperature exceeds the preset temperature range are determined; The distribution of the vibration signal data in different frequency bands is determined based on the frequency spectrum characteristics. Based on the correlation between the temperature gradient distribution, the local high-temperature points, and the frequency spectrum characteristics at the sampling time and the pipe surface location, high-risk thermal stress zones are identified. The location and extent of the high-risk thermal stress zone, the intensity of the temperature gradient determined by the temperature gradient distribution, and the frequency spectrum characteristics are correlated according to the sampling time and the location on the pipe surface to construct a preliminary distribution map of the thermal stress concentration zone, and the dynamic change characteristics are obtained based on the preliminary distribution map of the thermal stress concentration zone.

3. The method according to claim 1, characterized in that, The process involves constructing a three-dimensional geometric simulation structure for the pipeline based on the dynamic change characteristics. The temperature gradient distribution, local high-temperature points, and frequency spectrum characteristics are input into the three-dimensional geometric simulation structure to obtain stress distribution, temperature anomaly ranges, and heat conduction rate characteristics, including: Based on the dynamic change characteristics, the mesh refinement region and thermal boundary loading region in the three-dimensional geometric simulation structure of the pipeline are determined; The temperature gradient distribution is used as the thermal load input, the local high temperature point is used as the surface hot spot location input, and the frequency spectrum feature is used as the dynamic response feature input to the three-dimensional geometric simulation structure of the pipeline to obtain the stress distribution state of the thermal stress concentration area and the surface hot spot area. The micro-evolution initiation region is determined based on the stress distribution state, and the temperature anomaly range is determined based on the temperature exceedance range in the micro-evolution initiation region. The thermal conduction rate characteristics are determined based on the temperature changes at adjacent sampling locations in the micro-evolution initiation region, the distance between adjacent sampling locations, and the sampling time interval.

4. The method according to claim 3, characterized in that, The method employs stochastic simulation technology to generate various heat load interaction scenarios and determines the damage accumulation rate distribution curve, cumulative damage peak value, and preliminary material degradation assessment results, including: Using the temperature anomaly range, the heat conduction rate characteristics, the stress distribution state, and the frequency spectrum characteristics as random simulation inputs, the various heat load interaction scenarios are generated. For any heat load interaction scenario, the damage accumulation rate of the thermal stress concentration area is calculated at different sampling times and different pipe surface locations to obtain the damage accumulation rate distribution curve. The cumulative damage value is determined based on the cumulative result of the damage accumulation rate distribution curve over the sampling time, and the maximum value among the cumulative damage values ​​is determined as the cumulative damage peak value. The preliminary assessment results of material degradation are determined based on the damage accumulation rate distribution curve and the cumulative damage peak value.

5. The method according to claim 4, characterized in that, The process of constructing a stress distribution mapping relationship based on the damage accumulation rate distribution curve, the cumulative damage peak value, the monitoring data time series, and the monitoring error, and determining the deviation correction coefficient, includes: Key points are obtained by extracting the locations of curve slope changes and peak locations from the damage accumulation rate distribution curve. Based on the sampling time and pipe surface location of the key points, the key points, the monitoring data time series, and the monitoring error are time-series aligned to obtain the aligned error distribution; The stress distribution mapping relationship is constructed based on the key points, the cumulative damage peak value, and the aligned error distribution. The deviation correction coefficient is determined based on the deviation between the predicted stress distribution value output by the stress distribution mapping relationship and the time series of the monitoring data, as well as the monitoring error.

6. The method according to claim 5, characterized in that, The step of updating the thermal stress concentration zone parameters based on the deviation correction coefficient and determining the safety hazard boundary includes: Based on the deviation correction coefficient, the location, range, temperature load amplitude, and influence range of the local high temperature point in the three-dimensional geometric simulation structure of the pipeline are updated to obtain the updated thermal stress concentration zone parameters. Based on the updated thermal stress concentration zone parameters, the surface hot spot region and temperature fluctuation frequency influence parameters are recalculated to obtain the updated local stress distribution. Based on the updated local stress distribution and the temperature data of the local high-temperature points, potential safety hazard areas are identified. The boundary of a continuous area in the potential safety hazard area that meets the preset safety judgment conditions is defined as the safety hazard boundary.

7. The method according to claim 1, characterized in that, The determination of the safety hazard boundary and the remaining life estimate includes: Based on the aforementioned safety hazard boundaries, key high-temperature locations and safety hazard areas are identified; Based on the stress distribution mapping relationship, the finite element analysis method is used to simulate stress concentration at the key high-temperature points and the safety hazard areas to obtain the range of thermal stress concentration areas and stress intensity distribution. The data acquisition frequency is obtained, the iteration time interval of the thermal stress concentration zone evolution scenario is determined based on the data acquisition frequency, and multiple thermal stress concentration zone evolution scenarios are generated based on the iteration time interval, the range of the thermal stress concentration zone, and the stress intensity distribution. Based on the stress accumulation state in the various thermal stress concentration zone evolution scenarios, the safety hazard boundary, and the preliminary assessment results of material degradation, the remaining lifetime estimate is determined using the support vector machine algorithm.

8. The method according to claim 7, characterized in that, The process of determining the target thermal stress concentration area based on the remaining lifetime estimate, performing finite element analysis on the target thermal stress concentration area, and generating an optimized thermal load interaction scenario includes: The target thermal stress concentration zone for finite element analysis verification is determined based on the estimated remaining life. The parameters of the thermal stress concentration area of ​​the target thermal stress concentration area were simulated and calculated using the finite element analysis method to obtain the verification stress distribution; Based on the deviation between the verified stress distribution and the stress distribution mapping relationship, the parameter deviation of the thermal stress concentration zone parameters is determined; If the parameter deviation exceeds a preset parameter deviation threshold, the heat load distribution of the target thermal stress concentration area is adjusted to generate the optimized heat load interaction scenario. If the parameter deviation does not exceed the preset parameter deviation threshold, the verification scenario corresponding to the current heat load distribution will be used as the optimized heat load interaction impact scenario. The optimized thermal load interaction scenario is associated with vibration response data corresponding to the target thermal stress concentration area.

9. The method according to claim 8, characterized in that, The process of generating an enhanced stress distribution mapping relationship based on the optimized thermal load interaction scenario and outputting a micro-damage evolution path includes: The vibration response data in the optimized thermal load interaction scenario is decomposed into hierarchical features to obtain the updated frequency spectrum characteristics. Based on the updated frequency spectrum characteristics, the optimized heat load interaction scenario, and the stress distribution mapping relationship, the enhanced stress distribution mapping relationship is constructed. The stress distribution result that meets the preset deviation condition is obtained based on the enhanced stress distribution mapping relationship; The locations of micro-damage points are extracted from the stress distribution results. If the distribution density of the micro-damage points satisfies the path construction conditions, an evolution path graph is constructed based on the locations of the micro-damage points. The evolution path graph is then smoothed, and the micro-damage evolution path is output.

10. A stress analysis system for high-temperature pressure pipelines, used to implement the stress analysis method for high-temperature pressure pipelines according to any one of claims 1 to 9, characterized in that, The system includes: The data acquisition module is used to acquire temperature distribution data and vibration signal data on the pipe surface, perform frequency domain analysis on the vibration signal data to obtain frequency spectrum characteristics, and construct a preliminary distribution map of thermal stress concentration area based on the temperature gradient distribution, local high temperature points and the frequency spectrum characteristics in the temperature distribution data to obtain dynamic change characteristics. The simulation construction module is used to construct a three-dimensional geometric simulation structure of the pipeline based on the dynamic change characteristics. The temperature gradient distribution, the local high temperature points and the frequency spectrum characteristics are input into the three-dimensional geometric simulation structure of the pipeline to obtain the stress distribution state, temperature anomaly range and heat conduction rate characteristics. The scenario generation module is used to generate multiple heat load interaction scenarios using stochastic simulation technology when the temperature anomaly range exceeds a first preset threshold and the heat conduction rate characteristics fluctuate abnormally, and to determine the damage accumulation rate distribution curve, the cumulative damage peak value, and the preliminary assessment results of material degradation. The mapping correction module is used to construct a stress distribution mapping relationship based on the damage accumulation rate distribution curve, the cumulative damage peak value, the monitoring data time series and the monitoring error, and to determine the deviation correction coefficient; The life analysis module is used to update the parameters of the thermal stress concentration zone based on the deviation correction coefficient, and to determine the safety hazard boundary and the estimated remaining life. The path output module is used to determine the target thermal stress concentration area based on the remaining lifetime estimate, perform finite element analysis on the target thermal stress concentration area, generate an optimized thermal load interaction scenario and an enhanced stress distribution mapping relationship, and output the micro-damage evolution path.

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