Steel base metal life prediction method and system based on creep-fatigue interaction

Abstract

The invention discloses a method and system for predicting the service life of a steel base material based on creep-fatigue interaction, and relates to the technical field of data processing.The method comprises the steps that stress, strain and temperature data of an in-service steel base material are collected to construct a service parameter set, a creep, fatigue and composite loading test sequence is generated based on the parameter set, and the service life of the steel base material is predicted; and carrying out a creep-fatigue interaction test, extracting damage evolution data, carrying out multi-dimensional association on the damage evolution data and service parameters, establishing a creep-fatigue collaborative damage model, and realizing accurate prediction of the residual life of the steel base material through iterative calculation. The technical problems that in the prior art, the interaction effect of creep and fatigue under the complex service condition cannot be considered at the same time, so that the steel base metal service life prediction result is large in deviation, and reliability is insufficient are solved, and the aim of predicting the service life of the steel base metal by constructing the creep-fatigue collaborative damage model and combining real service parameters is achieved. And the service life prediction precision and reliability of the steel base metal under the complex working condition are improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and specifically to a method and system for predicting the life of steel base material based on creep-fatigue interaction. Background Technology

[0002] In modern industry, metallic materials are widely used in energy, aerospace, and machinery manufacturing. Key components of equipment in these fields, such as the steel base material of power plant units, are often subjected to complex conditions including high temperature, high pressure, and cyclic loading. Existing life prediction methods are mostly based on single creep or fatigue mechanisms, making it difficult to accurately assess the damage accumulation under the interaction of creep and fatigue, which is a key factor leading to early material failure. With the energy transition, operating modes such as deep peak shaving of power plant units are becoming more frequent, and the stress and temperature fluctuations experienced by the steel base material are more complex, increasing the difficulty of life prediction. Furthermore, existing methods struggle to accurately map real-world operating conditions and cannot effectively capture the entire process of damage occurrence and propagation. Summary of the Invention

[0003] This application provides a method and system for predicting the life of steel base material based on creep-fatigue interaction, which solves the technical problem that the existing technology cannot simultaneously consider the interaction between creep and fatigue under complex service conditions, resulting in large deviations and insufficient reliability in the prediction results of steel base material life.

[0004] The first aspect of this application provides a method for predicting the remaining service life of steel base material based on creep-fatigue interaction. The method includes: collecting real-time stress spectrum, strain history, and temperature fluctuation data of in-service steel base material to construct a service parameter set; dynamically generating a test loading sequence including a creep holding stage, a fatigue cyclic stage, and a combined loading stage based on the service parameter set; conducting creep-fatigue interaction tests according to the test loading sequence, simultaneously monitoring creep rate, cyclic stress-strain response, and microstructure evolution, and extracting damage evolution data corresponding to the service parameter set; multidimensionally correlating the damage evolution data with the service parameter set to construct a creep-fatigue synergistic damage model; and iteratively calculating the service parameter set based on the creep-fatigue synergistic damage model to output the remaining service life prediction result of the in-service steel base material.

[0005] A second aspect of this application provides a steel base material life prediction system based on creep-fatigue interaction. The system includes: a service parameter acquisition module for acquiring real-time stress spectrum, strain history, and temperature fluctuation data of in-service steel base materials to construct a service parameter set; a test loading sequence generation module for dynamically generating a test loading sequence including a creep holding stage, a fatigue cyclic stage, and a composite loading stage based on the service parameter set; a damage evolution data extraction module for conducting creep-fatigue interaction tests according to the test loading sequence, simultaneously monitoring creep rate, cyclic stress-strain response, and microstructure evolution, and extracting damage evolution data corresponding to the service parameter set; a collaborative damage model construction module for multidimensionally correlating the damage evolution data with the service parameter set to construct a creep-fatigue collaborative damage model; and a remaining life prediction module for iteratively calculating the service parameter set based on the creep-fatigue collaborative damage model and outputting the remaining life prediction result of the in-service steel base material.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] The method and system for predicting the life of steel base material based on creep-fatigue interaction provided in this application belong to the field of data processing technology. By collecting real working condition data of in-service steel base material, constructing a service parameter set, designing creep, fatigue and composite loading tests, extracting damage evolution characteristics, establishing a creep-fatigue synergistic damage model, and introducing environmental correction and dynamic optimization mechanisms, it can achieve high-precision prediction of the life of steel base material under complex service conditions. It solves the technical problem that the existing technology cannot simultaneously consider the interaction between creep and fatigue under complex service conditions, resulting in large deviations and insufficient reliability of the steel base material life prediction results. It realizes the technical effect of improving the accuracy and reliability of steel base material life prediction under complex conditions by constructing a creep-fatigue synergistic damage model and combining it with real service parameters. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 A schematic diagram of the process for predicting the life of steel base material based on creep-fatigue interaction, provided in an embodiment of this application;

[0010] Figure 2 This is a schematic diagram of the steel base material life prediction system based on creep-fatigue interaction provided in an embodiment of this application.

[0011] Figure labeling: Service parameter acquisition module 11, test loading sequence generation module 12, damage evolution data extraction module 13, collaborative damage model construction module 14, remaining life prediction module 15. Detailed Implementation

[0012] This application provides a method and system for predicting the life of steel base material based on creep-fatigue interaction, which solves the technical problem that the existing technology cannot simultaneously consider the interaction between creep and fatigue under complex service conditions, resulting in large deviations and insufficient reliability in the prediction results of steel base material life.

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0014] It should be noted that the terms "first," "second," etc., in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or devices.

[0015] Example 1, as Figure 1 As shown, this application provides a method for predicting the life of steel base material based on creep-fatigue interaction, the method comprising:

[0016] P10: Collect real-time stress spectrum, strain history and temperature fluctuation data of in-service steel base materials to construct a service parameter set.

[0017] Specifically, the first step is to systematically collect service condition information from the steel base material in actual operation of the target equipment, forming a service parameter set that accurately reflects the in-service conditions. This provides the basic input for subsequent loading sequence construction and life prediction models. The collected data includes real-time stress spectrum, strain history, and temperature fluctuation data.

[0018] Real-time stress spectrum refers to the continuous spectrum of stress changes over time caused by external loads on a material during service. It includes both the constant load characteristics during steady-state operation and the periodic load abrupt changes, stress peaks, and stress relaxation characteristics that occur during start-up and shutdown processes and deep peak-shaving operation. To obtain this information, stress sensing units or strain gauges can be deployed at representative stress locations on the base material, and high-frequency dynamic acquisition of stress over time can be achieved through inversion calculations and signal amplification.

[0019] The strain history refers to the deformation response record of a material throughout its service life under external stress, including instantaneous elastic deformation, creep deformation evolving over time, and cumulative plastic strain under thermal stress. This information can reveal the mechanical response characteristics of the base material under high-temperature and high-pressure service conditions and provide boundary conditions and load-response mapping relationships for subsequent fatigue life and damage accumulation calculations. Strain signal acquisition can be achieved through technologies such as installing high-temperature resistance strain gauges and distributed fiber optic strain sensors. The sampling frequency must be strictly synchronized with stress acquisition to facilitate subsequent time-series coupling analysis.

[0020] Temperature fluctuation data refers to the dynamic change curve of temperature over time in the service environment, reflecting the thermal load characteristics, thermal cycling frequency, and peak temperature difference of the base material at different operating stages. Temperature change is a significant factor leading to thermal fatigue and thermal creep, and it is highly correlated with stress and strain information. Continuous temperature recording can be achieved by deploying thermocouples or infrared temperature probes in key areas, combined with a high-precision data acquisition module, and the temperature curve can be segmented for statistical analysis and peak value identification.

[0021] After data acquisition, the aforementioned multi-source heterogeneous information needs to undergo time synchronization alignment, noise filtering, and anomaly removal. Methods such as moving average, FFT frequency domain analysis, or wavelet transform are used to extract characteristic load frequencies, strain amplitude distributions, and temperature fluctuation patterns. Subsequently, the characteristic information is normalized, load-leveled, and time-history segmented, ultimately forming a multi-dimensional service parameter set containing load spectrum characteristics, strain response characteristics, thermal cycling patterns, and time-series information. This parameter set not only possesses traceability and completeness but also has the ability to directly drive the automatic generation of subsequent creep-fatigue composite loading sequences, laying an executable data foundation for establishing damage evolution laws and remaining life prediction models.

[0022] P20: Based on the service parameter set, dynamically generate a test loading sequence that includes a creep load holding stage, a fatigue cycle stage, and a composite loading stage.

[0023] Furthermore, step P20 in this embodiment of the application also includes:

[0024] P21: Extract peak characteristics from the stress spectrum of the service parameter set to determine the stress loading conditions for the creep holding stage; P22: Analyze the amplitude-frequency characteristics based on the strain history of the service parameter set to set the strain loading parameters for the fatigue cycle stage; P23: Configure the time-series loading relationship for the composite loading stage by combining the temperature fluctuation data of the service parameter set; P24: Integrate the stress loading conditions for the creep holding stage, the strain loading parameters for the fatigue cycle stage, and the time-series loading relationship for the composite loading stage to generate the test loading sequence.

[0025] It should be understood that, based on the multidimensional feature information of the aforementioned service parameter set, a test loading sequence capable of realistically reproducing the service state of the steel base material is generated. This test loading sequence simultaneously includes three stages: a creep holding stage, a fatigue cycle stage, and a combined loading stage, and maintains consistency with actual operating conditions in terms of timing logic, loading amplitude, and environmental conditions. The purpose of this step is to quantify and trace the interaction between creep damage and fatigue damage through a repeatable and controllable loading method, thereby providing verifiable experimental evidence for subsequent damage modeling and life prediction.

[0026] First, peak characteristics are extracted from the stress spectrum of the service parameter set to determine the stress loading conditions during the creep hold-load stage. The stress spectrum records in detail the stress changes over time experienced by the steel base material during service, and the peak characteristics reflect the maximum stress level experienced by the steel base material under different operating conditions. By analyzing these peak characteristics, the stress magnitude, loading time, and hold-load time during the creep hold-load stage can be determined. The selection of these parameters should be based on the statistical characteristics of the stress peaks, such as the maximum stress value, the average stress value, and the frequency of stress peak occurrence. For example, if the service parameter set shows that the steel base material frequently experiences high stress peaks under a specific operating condition, then the stress loading conditions during the creep hold-load stage should be set to a correspondingly higher stress level, and the hold-load time should be determined based on the duration of the stress peak.

[0027] Next, the amplitude-frequency characteristics of the strain history from the service parameter set are analyzed to determine the strain loading parameters for the fatigue cyclic stage. The strain history records the change in strain over time in the steel base material during service, and its amplitude-frequency characteristics reflect the magnitude and frequency of strain variation. By analyzing these characteristics, the strain amplitude, cycle frequency, and number of cycles for the fatigue cyclic stage can be determined. The selection of these parameters should be based on the statistical characteristics of the strain history, such as the maximum strain amplitude, average strain amplitude, and frequency of strain cycles. For example, if the service parameter set shows that the steel base material is subjected to a high frequency of strain cycles under a specific working condition, then the strain loading parameters for the fatigue cyclic stage should be set accordingly to a higher cycle frequency, and the number of cycles should be determined based on the number of strain cycles.

[0028] Subsequently, by combining the temperature fluctuation data from the service parameter set, the temporal loading relationship for the composite loading stage is configured. During the composite loading stage, the effects of creep and fatigue interaction need to be considered simultaneously. To accurately simulate this complex condition, the switching time and sequence between the creep holding stage and the fatigue cycle stage need to be determined based on the statistical characteristics of temperature fluctuations, such as the frequency and amplitude of temperature changes, and the correlation between temperature changes and stress and strain changes. For example, if the service parameter set shows a significant temporal correlation between temperature fluctuations and stress and strain changes in the steel base material under a specific condition, then the temporal loading relationship for the composite loading stage should be set accordingly to match the actual service conditions to ensure that the experiment can realistically reflect the creep-fatigue interaction of the steel base material during actual service.

[0029] Finally, the stress loading conditions of the creep holding stage, the strain loading parameters of the fatigue cyclic stage, and the temporal loading relationships of the combined loading stage are integrated and arranged and serialized in chronological order. The loading information for each stage is normalized and converted into executable loading instruction files or control programs according to the control interface format of the test equipment. This process requires comprehensive consideration of the loading conditions and temporal relationships of each stage to ensure that the test loading sequence can comprehensively and accurately simulate the complex working conditions of the steel base material during actual service. The generated test loading sequence will serve as the basis for subsequent creep-fatigue interaction tests, providing crucial support for accurately assessing the damage evolution and life prediction of the steel base material.

[0030] P30: Conduct creep-fatigue interaction tests according to the test loading sequence, and simultaneously monitor creep rate, cyclic stress-strain response and microstructure evolution, and extract damage evolution data corresponding to the service parameter set.

[0031] Furthermore, step P30 in this embodiment of the application also includes:

[0032] P31: Implement constant stress loading according to the stress loading conditions of the creep holding stage, and monitor and obtain creep rate data; P32: Execute the strain loading parameters of the fatigue cycle stage, and record the cyclic stress-strain response data; P33: Under the time-series loading relationship of the composite loading stage of the test loading sequence, alternately apply creep and fatigue loads, and observe and obtain interaction effect data; P34: Compare and analyze the creep rate data, cyclic stress-strain response data, and interaction effect data with the service parameter set to form the damage evolution data.

[0033] Optionally, based on the generated test loading sequence, creep-fatigue interaction tests are conducted on the steel base material to realistically reproduce the mechanical response and damage accumulation behavior of the material under complex service conditions such as deep peak shaving. During the test, by precisely controlling stress loading, strain loading, and temperature fluctuations, continuous switching and stable operation of three states—creep holding, fatigue cycling, and combined loading—are achieved. The evolution characteristics of creep rate, cyclic stress-strain response, and material microstructure are monitored in real time, forming multi-dimensional damage evolution data that can be used for damage modeling and life prediction.

[0034] First, a constant stress is applied to the specimen according to the stress loading conditions set in the creep holding stage of the experimental loading sequence. During this process, the creep rate of the specimen is monitored in real time using high-precision strain measurement devices, such as electronic extensometers or fiber optic strain sensors. These devices can record the minute deformations of the specimen under constant stress with extremely high resolution, thereby obtaining accurate creep rate data. These data reflect the deformation behavior of the material under long-term constant stress and are important indicators for assessing the degree of creep damage.

[0035] Next, cyclic strain was applied to the specimen according to the strain loading parameters of the fatigue cyclic stage in the test loading sequence. During this stage, the magnitude of the strain and the cycle frequency were precisely controlled using a material testing machine, while the stress-strain response during the cycle was recorded using load cells and strain measurement devices. These data, including the stress peak and trough values ​​for each cycle, as well as the strain-stress relationship curve, reflect the hardening or softening behavior of the material under cyclic loading, and potential signs of fatigue crack initiation and propagation. These cyclic stress-strain response data are crucial for assessing fatigue damage accumulation.

[0036] In the composite loading phase of the experimental loading sequence, creep and fatigue loads were applied alternately according to a predetermined loading time relationship. This loading phase simulated the complex working conditions experienced by the steel base material during actual service, namely the interaction between creep and fatigue. By precisely controlling the loading time and load magnitude, and utilizing monitoring equipment such as in-situ scanning electron microscopy (SEM) or electron backscatter diffraction (EBSD), the microstructure evolution of the material under the interaction was observed. These microstructure evolution data, including grain size changes, dislocation density increases, phase transformations, and other phenomena, can reveal the mechanism by which the interaction between creep and fatigue affects material properties. Through high-frequency synchronous acquisition technology, stress relaxation, strain accumulation, changes in fatigue hysteresis curves, and the coupling effect of strain rate and temperature changes under alternating loading conditions were recorded, forming raw experimental data that truly reflects the characteristics of the interaction between creep and fatigue, generating interaction effect data, and providing rich information for in-depth analysis of material damage behavior.

[0037] Finally, the creep rate data obtained during the creep holding stage, the cyclic stress-strain response data recorded during the fatigue cyclic stage, and the interaction effect data observed during the combined loading stage were compared and analyzed in multiple dimensions with the original service parameter set. Through time history alignment, amplitude matching, and feature extraction, a mapping relationship between the damage response under experimental loading and the actual service conditions was established. Key parameters in the damage evolution law, such as creep damage rate, fatigue cyclic attenuation coefficient, and load coupling effect factor, were extracted, ultimately forming a structured damage evolution dataset. For example, by comparing the creep rate with the stress level under service conditions, the development trend of creep damage can be assessed; by analyzing the similarity between the cyclic stress-strain response curve and the service strain history, the degree of fatigue damage accumulation can be determined; and by studying the correlation between microstructure evolution and temperature fluctuation data, the intrinsic mechanism of material performance degradation under interaction can be revealed. The resulting damage evolution data will provide a solid foundation for establishing a creep-fatigue synergistic damage model, thereby enabling accurate prediction of the remaining service life of in-service steel base materials.

[0038] P40: The damage evolution data is correlated with the service parameter set in multiple dimensions to construct a creep-fatigue synergistic damage model.

[0039] Furthermore, step P40 in this embodiment of the application also includes:

[0040] P41: Analyze the mapping relationship between the creep rate data and the concentrated stress level of the service parameters to establish creep damage characteristics; P42: Analyze the correlation between the cyclic stress-strain response data and the concentrated strain history of the service parameters to extract fatigue damage characteristics; P43: Quantify the coupling degree between the interaction effect data and the concentrated temperature fluctuation of the service parameters to output interaction characteristics; P44: Based on the creep damage characteristics, fatigue damage characteristics, and interaction characteristics, construct a creep-fatigue synergistic damage model.

[0041] Specifically, the damage evolution data obtained through the creep-fatigue interaction test is systematically analyzed in a multidimensional correlation with the service parameter set to identify creep damage, fatigue damage, and the coupling characteristics of the two under temperature fluctuation conditions. Based on this, a creep-fatigue synergistic damage model is constructed to ensure that the model can accurately reflect the damage behavior of the material under actual service conditions.

[0042] First, a mapping relationship analysis was conducted between creep rate data and stress levels in the service parameter set. During the creep holding stage, the creep rate curves under different stress levels exhibited distinct stage characteristics, including three typical stages: initial creep, steady-state creep, and accelerated creep. By performing nonlinear fitting on the creep rate variation over time, characteristic values ​​such as creep extrapolation parameters, steady-state creep rate, creep threshold stress, and creep sensitivity index were extracted. Combined with the actual stress spectrum in the service parameter set, a quantitative correlation between creep rate and stress level was established using regression analysis or multi-parameter mapping methods. This correlation not only characterizes the creep damage development trend of the material under different stress states but also provides input parameters for the basic expression of creep damage in the model.

[0043] Next, the correlation between cyclic stress-strain response data and the strain history of the service parameter set is analyzed to extract fatigue damage characteristics. Cyclic stress-strain response data records in detail the stress and strain changes of the material under cyclic loading, while the strain history of the service parameter set reflects the strain changes experienced by the material during actual service. By comparing and analyzing these two sets of data, the hardening or softening behavior of the material under cyclic loading, as well as the initiation and propagation characteristics of fatigue cracks, can be identified. For example, characteristic parameters of the cyclic stress-strain curve, such as the cyclic hardening index and cyclic strength coefficient, can be correlated with the strain history of the service parameter set to extract characteristic parameters that characterize fatigue damage accumulation.

[0044] Subsequently, the coupling degree between the interaction effect data and the temperature fluctuations in the service parameter set is quantified to output the interaction characteristics. The interaction effect data reflects the damage behavior of materials under alternating creep and fatigue loads, while the temperature fluctuations in the service parameter set reflect the temperature changes in the environment in which the material is located during actual service. Temperature fluctuations have a significant impact on the creep and fatigue properties of materials. Therefore, the influence mechanism of temperature fluctuations on the interaction between creep and fatigue can be analyzed by establishing a mathematical model between the temperature-time history and the interaction effect data. For example, a multiphysics coupling analysis method can be used to consider the impact of temperature changes on the mechanical properties of materials, as well as the interaction effects of creep and fatigue loads under different temperature conditions, thereby outputting quantitative indicators that can characterize the interaction features, including the temperature influence factor, loading synchronization coefficient, and damage acceleration rate. This quantitative result reflects the promoting effect of the interaction between thermal and mechanical cycles on the development of material damage, and can provide a basic support for the establishment of the interaction damage term in the model.

[0045] Finally, based on the established creep damage characteristics, fatigue damage characteristics, and interaction characteristics, a creep-fatigue synergistic damage model is constructed. The model can be constructed using various methods, such as theoretical modeling based on physical mechanisms, data-driven machine learning methods, or a combination of both. By integrating different types of damage characteristic parameters into a unified model framework, a comprehensive description and prediction of material damage behavior can be achieved. For example, the finite element method combined with damage mechanics theory can be used to establish a multi-field coupled model considering the interaction between creep and fatigue; or machine learning algorithms, such as neural networks or support vector machines, can be used to learn and model the damage characteristic parameters, thereby achieving accurate prediction of the material's remaining life. The constructed creep-fatigue synergistic damage model comprehensively considers the influence of creep, fatigue, and their interaction on material damage, and can more accurately reflect the damage behavior of materials under actual service conditions.

[0046] Furthermore, step P44 in this embodiment of the application also includes:

[0047] P44-1: Establish a creep damage accumulation model based on the creep damage characteristics; P44-2: Construct a fatigue damage development model based on the fatigue damage characteristics; P44-3: Couple the creep damage accumulation model and the fatigue damage development model through the interaction characteristics to form the creep-fatigue synergistic damage model.

[0048] In one possible embodiment of this application, creep damage and fatigue damage can be further modeled in stages based on the extracted damage features, and on this basis, coupling can be achieved through interaction features to form a complete creep-fatigue synergistic damage model.

[0049] First, a creep damage accumulation model is established based on creep damage characteristics. This model is based on the time-dependent deformation and damage accumulation laws of materials under constant stress at high temperatures. It divides the creep process into an initial stage, a steady-state stage, and an acceleration stage. By analyzing the time-history variation characteristics of the creep rate, the correspondence between the damage accumulation rate and duration under different stress levels is clarified. In the modeling process, the focus is on identifying the development path of creep damage, including the inflection point from reversible deformation to irreversible damage, and the law that the damage rate gradually accelerates with changes in stress level, temperature, and holding time, thus forming a creep damage accumulation expression suitable for long-term service conditions.

[0050] Secondly, a fatigue damage development model is constructed based on fatigue damage characteristics. This model focuses on characterizing the relationship between the cyclic response and fatigue life of materials under alternating strain loading. By analyzing the morphology of the cyclic hysteresis curve, the changing trend of the hysteresis area, and the regularity of the cyclic hardening and softening processes, the accumulation rate of fatigue damage and the evolution process of pre-failure characteristics are determined. The model also considers the influence of loading amplitude, loading frequency, and temperature on fatigue life, enabling it to realistically reflect the fatigue damage evolution process under actual working conditions. In this way, fatigue damage can not only be quantified during cyclic loading but also a direct mapping relationship can be established with the service strain history of the material.

[0051] Subsequently, the creep damage accumulation model and the fatigue damage development model are coupled based on interaction characteristics. This coupling process aims to comprehensively consider the impact of creep and fatigue interactions on material damage behavior. Specifically, by quantifying the coupling degree between interaction effect data and temperature fluctuations, the mutual influence mechanism of creep and fatigue loads under different temperature conditions can be determined. Using these mechanisms, the creep damage accumulation model and the fatigue damage development model are organically combined to form a unified creep-fatigue synergistic damage model. For example, the influence of the interaction can be characterized by introducing coupling coefficients or correction factors, thereby enabling the synergistic damage model to more accurately reflect the damage behavior of materials under actual service conditions. This model can not only calculate the damage increments caused by creep and fatigue separately, but also reflect their mutual amplification and acceleration effects under specific loading sequences and temperature environments, thus achieving a holistic description of the material damage evolution process.

[0052] Furthermore, the creep-fatigue synergistic damage model incorporates an environmental correction coefficient. The calculation of this environmental correction coefficient, in step P40 of this embodiment, further includes:

[0053] P45-1: Extract the peak temperature and average temperature from the temperature fluctuation data of the service parameter set; P45-2: Calculate the time accumulation effect based on the stress holding time and material property parameters of the service parameter set; P45-3: Combine the difference between the peak temperature and the average temperature with the time accumulation effect to generate the environmental correction coefficient.

[0054] Optionally, to further improve the accuracy and applicability of the creep-fatigue synergistic damage model, an environmental correction coefficient can be introduced. By introducing this correction coefficient, the amplification effect of the external environment on the creep-fatigue coupling effect under different operating conditions can be effectively reflected, thereby improving the model's adaptability to actual service conditions and its prediction accuracy.

[0055] Specifically, to calculate the environmental correction factor, the peak temperature and average temperature are first extracted from the temperature fluctuation data in the service parameter set. The peak temperature represents the highest temperature the material experiences during service, while the average temperature reflects the average temperature level of the material over its service life. These two temperature parameters are key indicators for evaluating the impact of temperature fluctuations on material performance. By analyzing the temperature data in the service parameter set, these two temperature values ​​can be accurately determined. For example, statistical analysis of the temperature data can be performed to identify the highest temperature value as the peak temperature, while the arithmetic mean of all temperature data can be calculated as the average temperature.

[0056] Next, based on the stress holding time and material property parameters in the service parameter set, the time accumulation effect is calculated. The stress holding time refers to the length of time a material is subjected to constant stress during service; this parameter directly affects the degree of creep damage. Material property parameters include the creep coefficient and activation energy, which determine the creep behavior of the material under different temperature and stress conditions. By combining the stress holding time with the material property parameters, the impact of the time accumulation effect on material damage can be calculated. For example, the Arrhenius equation can be used to describe the combined effect of temperature and time on creep damage, and the degree of creep damage accumulation at a specific temperature and holding time can be calculated using this equation.

[0057] Subsequently, the difference between the peak and average temperatures is combined with the time-cumulative effect to generate an environmental correction coefficient. A larger temperature difference indicates stronger thermal stress and thermal cycling effects, resulting in a more significant amplification of the coupling effect between creep and fatigue processes; a longer holding time further promotes the effect of creep damage on fatigue damage. By combining the temperature difference with the time-cumulative effect, the influence of temperature fluctuations on material damage behavior can be quantified. For example, a correction function can be established, taking the temperature difference and the time-cumulative effect as input parameters and outputting an environmental correction coefficient. This correction coefficient will be used to adjust relevant parameters in the creep-fatigue co-damage model to reflect the impact of temperature fluctuations in actual service environments.

[0058] Furthermore, in the embodiment of this application, step P40 further includes dynamically optimizing the parameter configuration of the creep-fatigue synergistic damage model:

[0059] P46: Adjust the stress sensitivity coefficient of the creep damage accumulation model according to the dynamic change characteristics of the service parameter set; P47: Correct the strain weight parameters of the fatigue damage development model based on the strain history change trend of the service parameter set; P48: Update the temperature influence factor of the environmental correction coefficient in combination with the temperature fluctuation law of the service parameter set.

[0060] It should be understood that, to further improve the accuracy and adaptability of the creep-fatigue synergistic damage model, a dynamic optimization mechanism can be introduced. This mechanism can adjust the model's parameter configuration in real time according to the dynamic changes in the service parameter set, ensuring that the model can always accurately reflect the damage behavior of materials under actual service conditions.

[0061] First, the stress sensitivity coefficient of the creep damage accumulation model is adjusted based on the dynamic characteristics of the service parameter set. The stress sensitivity coefficient is a key parameter describing the rate of creep damage accumulation in materials under different stress levels. The dynamic characteristics of stress data in the service parameter set, such as fluctuations in stress levels and the frequency of stress peak changes, directly affect the creep behavior of the material. By monitoring the stress data in the service parameter set in real time and analyzing its dynamic characteristics, the stress sensitivity coefficient in the creep damage accumulation model can be dynamically adjusted. For example, if a significant increase in stress peaks is detected during service, the stress sensitivity coefficient can be increased accordingly to reflect accelerated creep damage at higher stress levels.

[0062] Next, based on the strain history variation trends of the service parameter set, the strain weight parameters of the fatigue damage development model are modified. The strain weight parameters are important indicators characterizing the degree of fatigue damage accumulation in materials under different strain amplitudes. The variation trends of the strain history in the service parameter set, such as increases in strain amplitude and changes in strain cycle frequency, have a significant impact on the fatigue damage of materials. By analyzing the strain history data in the service parameter set, the direction and magnitude of the correction to the strain weight parameters can be determined. For example, if higher amplitude strain cycles are found in the strain history, the strain weight parameters can be increased accordingly to more accurately reflect the fatigue damage accumulation of materials under high strain conditions.

[0063] Finally, the temperature influence factor of the environmental correction coefficient is updated based on the temperature fluctuation patterns in the service parameter set. The temperature influence factor is a key parameter in the environmental correction coefficient used to quantify the impact of temperature fluctuations on material damage behavior. Temperature fluctuation patterns in the service parameter set, such as the amplitude, frequency, and duration of temperature changes, have a significant impact on the creep and fatigue properties of materials. By analyzing the temperature data in the service parameter set, an update strategy for the temperature influence factor can be determined. For example, if an increase in the amplitude or frequency of temperature fluctuations is detected during service, the temperature influence factor can be adjusted accordingly to reflect the enhanced impact of temperature fluctuations on material damage behavior. Through this dynamic optimization strategy, the model can adaptively reflect the gradual and abrupt changes in stress, strain, and temperature environment, continuously maintaining a high-fidelity characterization of creep, fatigue, and their coupling effects.

[0064] P50: Based on the creep-fatigue synergistic damage model, iterative calculations are performed on the service parameter set to output the remaining service life prediction results of the in-service steel base material.

[0065] Furthermore, step P50 in this embodiment of the application also includes:

[0066] P51: Divide the service parameter set into multiple load time blocks; P52: For each load time block, apply the creep-fatigue co-damage model to calculate the damage increment; P53: Accumulate the damage increment of each load time block until the failure threshold is reached, and summarize the time block duration as the remaining service life prediction result.

[0067] Specifically, using the aforementioned creep-fatigue synergistic damage model, iterative calculations are performed on the service parameter set to output the remaining service life prediction results of the in-service steel base material. By using a time-segmented, stage-by-stage, and progressively accumulated damage calculation method, a refined characterization of the material's service life consumption process under complex service conditions can be achieved, thereby providing a quantitative basis for maintenance decisions and risk control.

[0068] First, the service parameter set is divided into multiple load time blocks, which decomposes the complex service history into several time periods with similar characteristics to more accurately analyze the damage accumulation within each time period. Load time blocks are basic calculation units defined based on the load and temperature characteristics changes under actual material operating conditions, typically segmented according to stress level change points, temperature fluctuation periods, and strain amplitude distribution characteristics. This segmentation avoids the accuracy loss caused by treating the entire service process as a single loading state, allowing the model to consider the impact of stress, strain, and temperature on damage evolution at different operating stages during calculation. Simultaneously, this time segmentation provides a basis for iterative calculations, giving the prediction process flexible adjustment capabilities and adaptability.

[0069] Next, for each load time block, the creep-fatigue co-damage model is applied to calculate the damage increment. Within each time block, the load conditions on the material are relatively stable; therefore, the creep-fatigue co-damage model, combined with the stress, strain, and temperature parameters within that time block, can be used to calculate the corresponding damage increment. First, the creep damage increase is calculated based on the stress level and holding time corresponding to the time block. Then, the fatigue damage increment is calculated based on information such as strain amplitude, cycle frequency, and loading waveform. Finally, the coupled damage effect of creep and fatigue is evaluated by combining the temperature fluctuation characteristics and environmental correction factor of the time block. By superimposing these three types of damage contributions, the total damage increment corresponding to that time block can be obtained. This process is equivalent to decomposing the complex service process into a series of computable physical units and accurately tracking the damage evolution within each unit. In this way, the damage increment within each load time block can be accurately calculated, providing basic data for subsequent cumulative analysis.

[0070] Finally, the damage increments of each load time block are accumulated until the total damage reaches the failure threshold set by the model. The failure threshold can be determined based on material test results, engineering experience, or safety specifications, and represents the critical state at which the material loses its reliable load-bearing capacity. In actual service, the accumulation of material damage is a continuous process, therefore, the damage increments within each load time block need to be accumulated until the accumulated damage reaches the material's failure threshold. When the accumulated damage reaches this threshold, it means that the material is about to fail. By summarizing the duration of all load time blocks before reaching the failure threshold, the remaining service life prediction result of the in-service steel base material can be obtained. This result not only provides a quantitative remaining service life but also reflects the rate of damage accumulation at different service stages, providing a reliable basis for early identification of high-risk operating ranges and reasonable scheduling of maintenance cycles.

[0071] Furthermore, step P50 in this embodiment of the application also includes:

[0072] P54: Dynamically adjust the time block duration based on the stress change rate of the service parameter set; P55: Correct the time block division interval based on the temperature fluctuation gradient of the service parameter set.

[0073] Optionally, to further improve the accuracy and adaptability of life prediction, a dynamic adjustment mechanism is introduced into the division of time blocks during the iterative calculation of the creep-fatigue synergistic damage model. This makes the length and interval of the time blocks no longer fixed, but adaptively adjusted according to the dynamic characteristics of the concentrated stress and temperature changes in service parameters. This makes the division of time blocks more reasonable and the prediction results more accurate.

[0074] In actual service, the stress level experienced by the steel base material is not constant but changes dynamically over time. The stress change rate, or the rate at which stress changes over time, reflects the stress fluctuations experienced by the material in different time periods. Therefore, by analyzing stress data from the service parameter set, the stress change rate can be calculated, and the duration of the load time block can be dynamically adjusted accordingly. Specifically, firstly, time series analysis is performed on the stress data from the service parameter set, and the stress change rate is calculated using methods such as the finite difference method or the moving average method. Subsequently, the duration of each load time block is dynamically adjusted based on the magnitude of the stress change rate. If the stress change rate is high, it indicates that the material experiences large stress fluctuations within that time period. In this case, the duration of the time block should be appropriately shortened to more precisely capture the damage accumulation of the material under high stress fluctuation conditions. Conversely, if the stress change rate is low, it indicates that the stress experienced by the material is relatively stable within that time period. The duration of the time block can be appropriately extended to reduce the computational load and improve computational efficiency.

[0075] Besides the rate of stress change, temperature fluctuations also significantly affect the damage behavior of materials. The temperature fluctuation gradient, i.e., the rate of temperature change over time, reflects the fluctuations in the ambient temperature of the material over different time periods. By analyzing temperature data in the service parameter set, the temperature fluctuation gradient can be calculated, and the interval between load time blocks can be adjusted accordingly. For example, firstly, time series analysis is performed on the temperature data in the service parameter set, using methods such as the finite difference method or the moving average method to calculate the temperature fluctuation gradient. Then, the interval between each load time block is adjusted based on the magnitude of the temperature fluctuation gradient. If the temperature fluctuation gradient is high, it indicates that the ambient temperature fluctuates greatly during that time period, requiring more frequent time block division to more precisely capture the damage accumulation of the material under high temperature fluctuation conditions. Conversely, if the temperature fluctuation gradient is low, it indicates that the ambient temperature is relatively stable during that time period, allowing for a more appropriate increase in the time block interval to reduce computational load and improve computational efficiency.

[0076] By dynamically adjusting the time block duration and partitioning interval, the model's temporal resolution is closely matched to environmental change characteristics, avoiding the limitations of traditional fixed-time-step methods in effectively capturing rapid damage evolution. Especially under deep peak-shaving conditions, rapid fluctuations in stress and temperature often correspond to abrupt changes in damage accumulation rates. This dynamic partitioning mechanism effectively improves the model's accuracy in identifying key damage stages, making life prediction results closer to the material's actual service condition. Simultaneously, this method retains the long-block calculation method for stable intervals, reducing unnecessary computational overhead, balancing accuracy and efficiency, and possessing good engineering applicability and scalability.

[0077] In summary, the embodiments of this application have at least the following technical effects:

[0078] This application introduces a creep-fatigue synergistic damage model, comprehensively considering the interactive effects of stress, strain, and temperature fluctuations, which can significantly improve the accuracy of life prediction for in-service steel base materials. It constructs a loading sequence based on actually collected service parameters, ensuring a high degree of matching between the test and operating environment, and guaranteeing consistency between the prediction results and the actual service condition. Through an adaptive adjustment mechanism for model parameters, it can respond to changes in operating conditions in real time, ensuring the continuity and reliability of life assessment results. It can simultaneously characterize the damage evolution law under the coupling of multiple factors such as creep, fatigue, and temperature, breaking through the limitations of traditional single damage models. Employing an iterative, modular life assessment method, it can be directly embedded into equipment operation and maintenance management systems, providing technical support for safety assessment and maintenance strategy optimization.

[0079] The technical effect of improving the accuracy and reliability of steel base material life prediction under complex working conditions by constructing a creep-fatigue synergistic damage model and combining it with real service parameters has been achieved.

[0080] Example 2, based on the same inventive concept as the steel base material life prediction method based on creep-fatigue interaction in the previous examples, such as... Figure 2 As shown, this application provides a steel base material life prediction system based on creep-fatigue interaction. The system and method embodiments in this application are based on the same inventive concept. The system includes:

[0081] The service parameter acquisition module 11 is used to acquire real-time stress spectrum, strain history and temperature fluctuation data of in-service steel base material to construct a service parameter set.

[0082] The test loading sequence generation module 12 is used to dynamically generate a test loading sequence that includes a creep load holding stage, a fatigue cycle stage, and a composite loading stage based on the service parameter set.

[0083] The damage evolution data extraction module 13 is used to conduct creep-fatigue interaction tests according to the test loading sequence, simultaneously monitor creep rate, cyclic stress-strain response and microstructure evolution, and extract damage evolution data corresponding to the service parameter set.

[0084] The collaborative damage model construction module 14 is used to perform multidimensional correlation between the damage evolution data and the service parameter set to construct a creep-fatigue collaborative damage model.

[0085] The remaining service life prediction module 15 is used to perform iterative calculations on the service parameter set based on the creep-fatigue synergistic damage model and output the remaining service life prediction results of the in-service steel base material.

[0086] Furthermore, the test loading sequence generation module 12 is also used to perform the following steps:

[0087] Peak characteristics are extracted from the stress spectrum of the service parameter set to determine the stress loading conditions for the creep holding stage; amplitude-frequency characteristics are analyzed based on the strain history of the service parameter set to set the strain loading parameters for the fatigue cycle stage; the time-series loading relationship for the composite loading stage is configured by combining the temperature fluctuation data of the service parameter set; the stress loading conditions for the creep holding stage, the strain loading parameters for the fatigue cycle stage, and the time-series loading relationship for the composite loading stage are integrated to generate the test loading sequence.

[0088] Furthermore, the damage evolution data extraction module 13 is also used to perform the following steps:

[0089] Constant stress loading is applied according to the stress loading conditions of the creep holding stage, and creep rate data is monitored and obtained; strain loading parameters of the fatigue cycle stage are executed, and cyclic stress-strain response data are recorded; under the time-series loading relationship of the composite loading stage of the test loading sequence, creep and fatigue loads are applied alternately, and interaction effect data are observed and obtained; the creep rate data, cyclic stress-strain response data, and interaction effect data are compared and analyzed with the service parameter set to form the damage evolution data.

[0090] Furthermore, the collaborative damage model construction module 14 is also used to perform the following steps:

[0091] The mapping relationship between the creep rate data and the concentrated stress level of the service parameters is analyzed to establish creep damage characteristics; the correlation between the cyclic stress-strain response data and the concentrated strain history of the service parameters is analyzed to extract fatigue damage characteristics; the coupling degree between the interaction effect data and the concentrated temperature fluctuation of the service parameters is quantified to output interaction characteristics; based on the creep damage characteristics, fatigue damage characteristics and interaction characteristics, a creep-fatigue synergistic damage model is constructed.

[0092] Furthermore, the collaborative damage model construction module 14 is also used to perform the following steps:

[0093] Based on the creep damage characteristics, a creep damage accumulation model is established; based on the fatigue damage characteristics, a fatigue damage development model is constructed; and the creep damage accumulation model and the fatigue damage development model are coupled through the interaction characteristics to form the creep-fatigue synergistic damage model.

[0094] Furthermore, the collaborative damage model construction module 14 is also used to perform the following steps:

[0095] Peak temperature and average temperature are extracted from the temperature fluctuation data of the service parameter set; the time accumulation effect is calculated based on the stress holding time and material property parameters of the service parameter set; the difference between the peak temperature and the average temperature is combined with the time accumulation effect to generate the environmental correction coefficient.

[0096] Furthermore, the collaborative damage model construction module 14 is also used to perform the following steps:

[0097] Based on the dynamic changes of the service parameter set, the stress sensitivity coefficient of the creep damage accumulation model is adjusted; based on the strain history change trend of the service parameter set, the strain weight parameters of the fatigue damage development model are corrected; and combined with the temperature fluctuation law of the service parameter set, the temperature influence factor of the environmental correction coefficient is updated.

[0098] Furthermore, the remaining lifetime prediction module 15 is also used to perform the following steps:

[0099] The service parameter set is divided into multiple load time blocks; for each load time block, the creep-fatigue co-damage model is applied to calculate the damage increment; the damage increment of each load time block is accumulated until the failure threshold is reached, and the duration of the time blocks is summarized as the remaining service life prediction result.

[0100] Furthermore, the remaining lifetime prediction module 15 is also used to perform the following steps:

[0101] The duration of the time block is dynamically adjusted based on the stress change rate of the service parameter set; the interval of the time block division is corrected in conjunction with the temperature fluctuation gradient of the service parameter set.

[0102] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0103] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0104] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A method for predicting the life of steel base material based on creep-fatigue interaction, characterized in that, The method includes: Real-time stress spectrum, strain history and temperature fluctuation data of in-service steel base materials are collected to construct a service parameter set; Based on the service parameter set, a test loading sequence including creep load holding stage, fatigue cycle stage and composite loading stage is dynamically generated; Creep-fatigue interaction tests were conducted according to the test loading sequence, and creep rate, cyclic stress-strain response and microstructure evolution were monitored simultaneously. Damage evolution data corresponding to the service parameter set were extracted. The damage evolution data is correlated with the service parameter set in multiple dimensions to construct a creep-fatigue synergistic damage model; The service parameter set is iteratively calculated based on the creep-fatigue synergistic damage model to output the remaining service life prediction results of the in-service steel base material.

2. The method for predicting the life of steel base material based on creep-fatigue interaction as described in claim 1, characterized in that, Based on the service parameter set, a test loading sequence is dynamically generated, including a creep holding phase, a fatigue cycle phase, and a combined loading phase, including: Peak features are extracted from the stress spectrum of the service parameter set to determine the stress loading conditions of the creep maintenance stage; Based on the strain history analysis of the service parameter set, the amplitude-frequency characteristics are determined, and the strain loading parameters for the fatigue cycle stage are set. Based on the temperature fluctuation data of the service parameter set, configure the timing loading relationship of the composite loading stage; The test loading sequence is generated by integrating the stress loading conditions of the creep holding stage, the strain loading parameters of the fatigue cycle stage, and the time-series loading relationship of the composite loading stage.

3. The method for predicting the life of steel base material based on creep-fatigue interaction as described in claim 2, characterized in that, Creep-fatigue interaction tests were conducted according to the test loading sequence, and creep rate, cyclic stress-strain response, and microstructure evolution were monitored simultaneously. Damage evolution data corresponding to the service parameter set were extracted, including: Constant stress loading was applied according to the stress loading conditions of the creep holding stage, and creep rate data was obtained by monitoring. The strain loading parameters for the fatigue cycle stage are applied, and the cyclic stress-strain response data are recorded. Under the temporal loading relationship of the composite loading stage of the test loading sequence, creep and fatigue loads were applied alternately, and data on the interaction effect were observed. The creep rate data, cyclic stress-strain response data, and interaction effect data are compared and analyzed with the service parameter set to form the damage evolution data.

4. The method for predicting the life of steel base material based on creep-fatigue interaction as described in claim 3, characterized in that, The damage evolution data is correlated with the service parameter set in multiple dimensions to construct a creep-fatigue co-damage model, including: Analyze the mapping relationship between the creep rate data and the concentrated stress level of the service parameters to establish creep damage characteristics; The correlation between the cyclic stress-strain response data and the concentrated strain history of the service parameters was analyzed to extract fatigue damage characteristics; The degree of coupling between the interaction effect data and the temperature fluctuations of the service parameter set is quantified, and the interaction characteristics are output. Based on the creep damage characteristics, fatigue damage characteristics, and interaction characteristics, a creep-fatigue synergistic damage model is constructed.

5. The method for predicting the life of steel base material based on creep-fatigue interaction as described in claim 4, characterized in that, Based on the aforementioned creep damage characteristics, fatigue damage characteristics, and interaction characteristics, a creep-fatigue synergistic damage model is constructed, including: Based on the creep damage characteristics, a creep damage accumulation model is established; Based on the aforementioned fatigue damage characteristics, a fatigue damage development model is constructed. The creep-fatigue synergistic damage model is formed by coupling the creep damage accumulation model and the fatigue damage development model through the interaction characteristics.

6. The method for predicting the life of steel base material based on creep-fatigue interaction as described in claim 5, characterized in that, The creep-fatigue synergistic damage model incorporates an environmental correction coefficient, which is calculated using the following methods: Extract the peak temperature and average temperature from the temperature fluctuation data of the service parameter set; The time accumulation effect is calculated based on the stress holding time and material property parameters of the service parameter set. The environmental correction coefficient is generated by combining the difference between the peak temperature and the average temperature with the time accumulation effect.

7. The method for predicting the life of steel base material based on creep-fatigue interaction as described in claim 6, characterized in that, Dynamically optimizing the parameter configuration of the creep-fatigue synergistic damage model includes: The stress sensitivity coefficient of the creep damage accumulation model is adjusted according to the dynamic change characteristics of the service parameter set. Based on the strain history variation trend of the service parameter set, the strain weight parameters of the fatigue damage development model are corrected. Based on the temperature fluctuation patterns of the service parameter set, the temperature influence factor of the environmental correction coefficient is updated.

8. The method for predicting the life of steel base material based on creep-fatigue interaction as described in claim 1, characterized in that, The service parameter set is iteratively calculated based on the creep-fatigue synergistic damage model to output the remaining service prediction results of the in-service steel base material, including: The service parameter set is divided into multiple load time blocks; For each load time block, the creep-fatigue synergistic damage model is applied to calculate the damage increment; The damage increment of each load time block is accumulated until the failure threshold is reached, and the sum of the time block durations is used as the remaining lifetime prediction result.

9. The method for predicting the life of steel base material based on creep-fatigue interaction as described in claim 8, characterized in that, The method further includes: The duration of the time block is dynamically adjusted based on the stress change rate of the service parameter set. The time block division interval is determined by combining the temperature fluctuation gradient correction of the service parameter set.

10. A life prediction system for steel base material based on creep-fatigue interaction, characterized in that, The system includes: The service parameter acquisition module is used to collect real-time stress spectrum, strain history and temperature fluctuation data of in-service steel base material to construct a service parameter set; The test loading sequence generation module is used to dynamically generate a test loading sequence that includes a creep load holding stage, a fatigue cycle stage, and a composite loading stage based on the service parameter set. The damage evolution data extraction module is used to conduct creep-fatigue interaction tests according to the test loading sequence, simultaneously monitor creep rate, cyclic stress-strain response and microstructure evolution, and extract damage evolution data corresponding to the service parameter set. The collaborative damage model construction module is used to correlate the damage evolution data with the service parameter set in multiple dimensions to construct a creep-fatigue collaborative damage model. The remaining service life prediction module is used to perform iterative calculations on the service parameter set based on the creep-fatigue synergistic damage model and output the remaining service life prediction results of the in-service steel base material.

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