Metal component fatigue damage assessment method and system and storage medium

Abstract

The invention relates to the technical field of metal component fatigue damage assessment, in particular to a metal component fatigue damage assessment method and system and a storage medium. The method comprises the following steps: evaluating the stability performance of a metal component by obtaining geometric structure data and operation log data of the metal component; based on the running log data, the nonlinear load increasing trend of the metal component is analyzed, the stress coupling increasing condition is detected, and then the dynamic fracture condition of the structure is determined; the corrosion condition of the metal component is evaluated in combination with operation environment data, and then the structural stability attenuation degree of the metal component is comprehensively judged; the force flow path disturbance condition of the metal component is tested, the fatigue damage of the metal component is accurately evaluated by utilizing force flow path disturbance information and the structural stability attenuation condition, and fatigue damage data is obtained; through metal component fatigue detection, metal component fatigue detection is more accurate and efficient.

Description

Technical Field

[0001] The present invention relates to the technical field of fatigue damage assessment of metal components, and in particular to a method, system and storage medium for fatigue damage assessment of metal components. Background Art

[0002] During long-term service, metal components are susceptible to the coupling effects of multiple factors such as cyclic loads, temperature fluctuations, and corrosive environments, which gradually produce microcracks and evolve into fatigue damage, leading to structural failure. Traditional fatigue life assessment methods mostly rely on qualitative or semi-quantitative analysis of material experimental data, which is difficult to cover the complex state changes of components in actual service environments. At the same time, most existing methods fail to fully combine the geometric characteristics of the component itself, the location of processing stress concentration, and the historical working load sequence, and lack accurate identification of fatigue starting points and damage extension paths, resulting in lag and error amplification in the assessment results. Although some methods introduce finite element simulation and fracture mechanics theory to improve assessment accuracy, they are still limited by the idealization of model boundary conditions and the abstraction of parameter settings, making it difficult to meet the needs of high-reliability assessment. However, traditional fatigue damage assessment of metal components has the problem of inaccurate assessment of metal component structural stability attenuation and inaccurate detection of dynamic fracture of metal component structures. Summary of the Invention

[0003] Based on this, it is necessary to provide a metal component fatigue damage assessment method, system and storage medium to solve at least one of the above technical problems.

[0004] To achieve the above object, a fatigue damage assessment method for a metal component comprises the following steps: Step S1: acquiring metal component data and metal component operation log data; collecting the metal component geometry according to the metal component data; and evaluating the stability performance of the metal component according to the metal component geometry and the metal component data; Step S2: evaluating the stability performance of the metal component based on the metal component operation log data and the nonlinear load growth trend of the metal component; detecting the stress coupling growth status of the metal component based on the nonlinear load growth trend of the metal component; and determining the dynamic fracture status of the metal component structure based on the stress coupling growth status of the metal component; Step S3: collecting metal component operating environment data based on the metal component operating log data; determining the metal component corrosion status based on the metal component stability performance based on the metal component operating environment data; and evaluating the metal component structural stability attenuation status based on the metal component corrosion status and the metal component structural dynamic fracture status; Step S4: testing the disturbance of the force flow path of the metal component based on the attenuation of the structural stability of the metal component; performing fatigue damage assessment on the metal component according to the disturbance of the force flow path of the metal component and the attenuation of the structural stability of the metal component, and obtaining fatigue damage data of the metal component.

[0005] The present invention achieves an initial assessment of stability performance and enhances the accuracy of preliminary judgments on structural integrity by integrating geometric structure analysis of metal components with analysis of operation log data. The stress coupling evolution path is deduced based on the nonlinear load growth trend, effectively enhancing the ability to capture abnormal load distribution inside components. The accuracy of identifying precursors to structural instability is improved by combining structural dynamic fracture data with the linkage processing of corrosive parameters in the operating environment. The corrosion and fracture states of metal components are cross-checked by multi-source data, making the structural stability attenuation assessment more reliable. On this basis, force flow path disturbance detection is introduced to enhance the real-time grasp of the continuous changes in the force of metal components, ensuring that the final judgment of fatigue damage is based on continuous mechanical disturbance analysis. The overall solution achieves systematic quantification of fatigue damage of metal components through multi-stage parameter recursion coupled with physical processes, and has high adaptability and data chain closure characteristics. Therefore, the present invention is an optimization treatment of the traditional fatigue damage assessment of metal components, which solves the problems of inaccurate assessment of the structural stability attenuation of metal components and inaccurate detection of dynamic fracture of metal component structures in the traditional fatigue damage assessment of metal components, and improves the accuracy of the structural stability attenuation assessment of metal components and the accuracy of dynamic fracture detection of metal component structures.

[0006] The present invention further provides a metal component fatigue damage assessment system for executing the metal component fatigue damage assessment method described above. The metal component fatigue damage assessment system comprises: A metal component stability performance evaluation module is used to obtain metal component data and metal component operation log data; collect metal component geometry based on the metal component data; and evaluate the stability performance of the metal component based on the metal component geometry and metal component data; The structural dynamic fracture determination module is used to evaluate the nonlinear load growth trend of the metal component based on the stability performance of the metal component based on the operation log data of the metal component; detect the stress coupling growth status of the metal component based on the nonlinear load growth trend of the metal component; and determine the dynamic fracture status of the metal component structure based on the stress coupling growth status of the metal component; The stability attenuation assessment module is used to collect the operating environment data of the metal component based on the operating log data of the metal component; determine the corrosion status of the metal component based on the stability performance of the metal component; and evaluate the stability attenuation status of the metal component structure based on the corrosion status of the metal component and the dynamic fracture status of the metal component structure; The fatigue damage assessment module is used to test the force flow path disturbance of metal components based on the structural stability attenuation of metal components; the fatigue damage assessment of metal components is performed according to the force flow path disturbance of metal components and the structural stability attenuation of metal components to obtain fatigue damage data of metal components.

[0007] The metal component fatigue damage assessment system of the present invention can implement the fatigue damage assessment method of any metal component of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the metal component fatigue damage assessment method. The internal modules of the system cooperate with each other, and the stability evolution analysis of the metal component based on the fusion of multi-dimensional structural data and environmental data can accurately identify the degree of fatigue damage.

[0008] A computer-readable storage medium stores a computer program, wherein the computer program is used to execute the metal component fatigue damage assessment method. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A schematic flow chart of the steps of a fatigue damage assessment method for metal components; Figure 2 for Figure 1 Detailed implementation steps of step S3 in FIG. Figure 3 for Figure 1 Detailed implementation steps of step S4 in FIG. The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0010] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0011] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0012] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0013] To achieve this, please refer to Figures 1 to 3 , a fatigue damage assessment method for a metal component, comprising the following steps: Step S1: acquiring metal component data and metal component operation log data; collecting the metal component geometry according to the metal component data; and evaluating the stability performance of the metal component according to the metal component geometry and the metal component data; In an embodiment of the present invention, a metal component fatigue damage assessment system uses a structural configuration acquisition system to acquire data from the metal component under test. The collected data includes the metal component's material number, heat treatment process number, batch number, production date, physical condition number, and the electronic tag information associated with the number. The metal component data is collected in parallel through multiple channels using RFID remote reading equipment and an industrial barcode scanning system. The data is then linked to the corresponding component's unique code and compared with the manufacturer's database to eliminate duplicate or inconsistent entries. Subsequently, a 3D laser scanner (such as the FARO Focus S350) performs a 360-degree multi-angle scan of the metal component's surface. This device is equipped with a 1mm resolution laser head and a scanning pitch controlled to within 0.3mm. A 3D point cloud image is generated based on the time difference of the echo signal and the reflection intensity. After scanning, Geomagic Design X software is used to reconstruct the metal component's geometric structure, including geometric parameters such as axial curvature, wall thickness distribution, edge profile, and connection node density. This geometric structure data is stored in OBJ format by the data center module for subsequent integration with operational data. Based on the acquired geometry, combined with collected metal component data such as material hardness (e.g., HRC value), elastic modulus, and yield strength, an initial stability performance index is calculated. This index uses the stability threshold formula defined in the "Metallic Materials Physical Property Evaluation Procedure" (GB / T 228.1-2021) for item-by-item comparison to determine the structural stability level of the metal component in its unstressed and unserviced state. For example, for a certain type of steel plate component, the scanned thickness consistency error is less than 0.1mm. Combined with its material elastic modulus of 210GPa and yield strength of 370MPa, its initial stability level is determined to be "Level 1 Steady State." This level serves as a baseline reference value for the subsequent fatigue damage assessment process. The key data output from this step includes a 3D geometric structure model, a list of key component structural dimensions, and a stability performance level table. All of this data is stored uniformly in the data association interface module.

[0014] Step S2: evaluating the stability performance of the metal component based on the metal component operation log data and the nonlinear load growth trend of the metal component; detecting the stress coupling growth status of the metal component based on the nonlinear load growth trend of the metal component; and determining the dynamic fracture status of the metal component structure based on the stress coupling growth status of the metal component; In this embodiment of the present invention, based on the stability performance level table and geometric structure parameters generated in step S1, the operational log data of the metal component during actual use is retrieved for dynamic analysis. The operational log data is collected from real-time monitoring equipment such as strain gauges, accelerometers, and temperature sensors installed on the component surface and interfaces. The strain data frequency is 1kHz, the acceleration data frequency is 10kHz, the recording period is no less than 30 days, and the data format is CSV structured distributed log. This log data is imported into the component dynamic load analysis module. The actual load characteristics of the component are analyzed based on the loading frequency, loading amplitude, and loading waveform characteristics, and compared with the initial stability performance of the component. The nonlinear load growth trend is determined by comparing the fluctuation range between the maximum and minimum loads and the peak increment per unit time. For example, when the strain signal detected during the component operational load gradually deviates from the initial strain range, and the strain amplitude of the single-cycle loading increases from an initial 85με to continuously exceed 130με, the load growth is judged to exhibit nonlinear characteristics. After identifying the nonlinear load growth trend, multi-point strain data is used to analyze the structural coupling stress. Using finite region partitioning and multi-node interpolation, the stress interactions at different locations are compositely superimposed to deduce the stress coupling growth within the component. If three measuring points within a component's joint region experience continuous synchronous strain offsets within 5 hours, accompanied by reverse strain fluctuations (a stress crossover zone), this region is marked as a region of enhanced stress coupling. After identifying this region of enhanced stress coupling, the frequency of microcrack development and the intensity of the localized fracture acoustic signal (obtained by installed acoustic emission sensors) are further extracted to determine whether dynamic structural fracture has occurred. When the acoustic signal energy in a localized region consistently exceeds 20 dB and microcrack acoustic rebound signals recur within 20 minutes, the region is identified as a dynamic fracture zone, taking into account the load direction of the structural geometry. The output of the structural dynamic fracture information includes the fracture region number, fracture grade (based on area / crack length), and fracture duration.

[0015] Step S3: collecting metal component operating environment data based on the metal component operating log data; determining the metal component corrosion status based on the metal component stability performance based on the metal component operating environment data; and evaluating the metal component structural stability attenuation status based on the metal component corrosion status and the metal component structural dynamic fracture status; In this embodiment of the present invention, based on the dynamic fracture data output in step S2, environmental monitoring data embedded in the metal component's operation log is further utilized to extract key corrosion parameters of the metal component's environment, such as temperature and humidity, pH value, wind speed, particulate matter concentration, and salt spray concentration. This operating environment data is acquired via environmental collection nodes deployed at the component site, with a sampling period of 5 minutes. After standardization, the data is synchronously recorded in the component log channel to ensure spatiotemporal synchronization. This operating environment data is then imported into the metal corrosion assessment module, where a comprehensive comparison is performed based on the component's material properties (e.g., whether it is low-alloy steel, stainless steel, or aluminum alloy) and its anti-corrosion treatment information (e.g., surface spray coating thickness, coating type, and anodic protection potential). For example, for a mild steel component with a spray coating thickness of less than 70 μm, if its pH value remains between 4.5 and 5.0 for a long period, the salt spray concentration continuously exceeds 20 mg / m³, and the ambient relative humidity remains above 85%, it is determined to be moderately corroded. If the surface color difference index (obtained by visual image recognition) is greater than 10 and accompanied by the appearance of red rust spots, it is further confirmed that corrosion has begun to develop. This corrosion condition is then positionally matched with the structural dynamic fracture areas identified in step S2. If the overlap between the corrosion and fracture areas exceeds 30%, it indicates that corrosion directly induced fracture behavior. Based on this, the overall structural stability degradation is assessed based on the fracture area expansion rate, the number of corrosion points, and the number of metal grains lost (identified by electron microscopy or image recognition). Using the number of areas with a corrosion depth exceeding 200 μm as a weighting factor and combining it with the fracture length growth rate, a structural stability degradation curve is constructed, which then provides a stability degradation value. For example, a 45% degradation ratio is obtained for a transition from a first-level steady state to a third-level steady state. This data is used as input for force flow path disturbance detection in the next step.

[0016] Step S4: testing the disturbance of the force flow path of the metal component based on the attenuation of the structural stability of the metal component; performing fatigue damage assessment on the metal component according to the disturbance of the force flow path of the metal component and the attenuation of the structural stability of the metal component, and obtaining fatigue damage data of the metal component.

[0017] In this embodiment of the present invention, based on the structural stability degradation results obtained in step S3, the force flow path of the metal component under actual load is reconstructed using a six-axis force sensor (sampling frequency 1kHz) installed within the component and a strain grid array mounted on the component surface. This process dynamically loads the component by applying a standard constant-amplitude alternating load (loading frequency 3Hz, maximum load 70% of the component's yield strength), recording its strain response and the trajectory of the internal force flow. The collected strain data is input into the path analysis module, which determines whether there is a force flow path disturbance based on the strain vector superposition path, node force transmission sequence, and load redistribution in the coupling region. If the force transmission path in the central region of the component deviates by more than 15mm over three loading cycles, or if the node load reverses, and the loading lag time at the edge nodes exceeds 200ms, it is identified as a significant disturbance. After the force flow path disturbance areas are identified, their location and distribution within the structural stability degradation region are combined to construct a fatigue-sensitive region map. The cumulative strain energy analysis is performed on each sensitive area. Combined with its fracture number (obtained by acoustic emission detection) and corrosion level (derived by visual identification), the fixed integral method is used to calculate its fatigue damage factor. The fatigue damage data output includes the fatigue strength residual ratio (such as reduced to 62% of the original value), crack growth rate (such as 1.2mm / h), fatigue grade classification results (such as secondary fatigue), etc.

[0018] Preferably, step S1 includes the following steps: Step S11: setting the resolution of the industrial high-definition camera to 8 million pixels, the exposure time to 1 / 1000 second, and the image acquisition frequency to 10 frames per second; In this embodiment of the present invention, before capturing images of metal components, the capture parameters of an industrial HD camera are configured to ensure that the captured images are capable of accurately identifying details. The industrial HD camera model is a Basler ace acA4112-8gc, with a CMOS image sensor supporting a maximum resolution of 8 megapixels. The resolution is set to 3264×2464, ensuring that the spatial resolution of a single frame image meets the requirements for extracting detailed features such as microcracks and edge wear in the component. The exposure time is set to 1 / 1000 second to prevent image blur, adapting to the high-speed dynamic environment of mechanical components and ensuring image stability under strong light and vibration. The image acquisition frequency is set to 10 frames per second, covering key state changes during the component's operating cycle and preventing the loss of important state images. The camera and image acquisition controller are connected via Gigabit Ethernet, using the GigE Vision standard for data transmission. NI Vision Acquisition software controls acquisition task initiation, frequency synchronization, and image buffering, ensuring a stable acquisition frequency and 100% image frame integrity.

[0019] Step S12: Acquire metal component data and metal component operation log data; In an embodiment of the present invention, a basic dataset of metal component operation and physical status is constructed by collecting data related to metal components and operation log data. Metal component data includes component unique number, component type, dimensional parameters (length, thickness, aperture, etc.), manufacturing batch, heat treatment process parameters (such as quenching temperature and tempering time), material grade (such as 42CrMo, Q345B), and initial mechanical properties (tensile strength, yield strength, elongation, Brinell hardness). Operation log data is extracted through the factory-level data acquisition system SCADA and covers dynamic operating parameters such as component timestamps during operation, load size (in kN), number of operation cycles, axial / tangential force direction, temperature change (sampling frequency is 1 Hz), and vibration acceleration (sampling frequency is 100 Hz). The data is uniformly stored in a log storage unit issued by the PLC, exported in a CSV structured format, and imported into a database (such as PostgreSQL) used by the subsequent image analysis and stability assessment system.

[0020] Step S13: using an industrial high-definition camera to collect metal component images to obtain metal component image data; In an embodiment of the present invention, during the image acquisition phase, an industrial high-definition camera performs multi-angle image acquisition on the surface of a metal component based on the parameters and frequency configured in step S11. The component is placed on an automatic turntable (model THKTRT420), which rotates continuously in 15° increments, allowing the camera to obtain image data covering the component surface from all angles. The control logic of the turntable and the industrial camera is uniformly scheduled by a PLC master station, and image acquisition is synchronized with turntable rotation to avoid image blur or missed shots. After image acquisition, the image data is transmitted in real time to a back-end image processing terminal via a GigE interface. Within the image processing terminal, the image frame is extracted using the Mat class using the OpenCV library. The image format is uniformly converted to TIFF image format, ensuring the complete preservation of high-resolution and uncompressed images. A data set of metal component images from multiple angles, including front view, top view, and oblique view, is obtained. The image index table is named using a timestamp plus component number for subsequent geometric structure analysis.

[0021] Step S14: collecting the geometric structure of the metal component according to the metal component image data; In an embodiment of the present invention, based on the metal component image data obtained in step S13, an image edge extraction algorithm is used to obtain the geometric structure information of the metal component. The specific operations include: using the Sobel operator to extract the edge features of the image, enhancing the component contour line, and on this basis, using the Hough transform to identify the boundary straight line and circular arc contour. For irregular structure areas, the key edge nodes are extracted by the SURF feature point matching method, and a two-dimensional point cloud coordinate set is established in the CAD coordinate system. Subsequently, the closed contour is extracted by the findContours function in OpenCV, the contour is segmented and converted into actual size by a pixel-millimeter scale (image correction is performed by a known scale), and the dimensional parameters such as the length, diameter, wall thickness, and aperture of the metal component are accurately obtained to form a structured geometric data table, including the value of each key dimension, the corresponding boundary coordinate point, the error deviation range, etc., which is used for comparison with the original design drawing of the component and also serves as an input parameter for stability performance evaluation.

[0022] Step S15: testing the internal element composition information of the metal according to the metal component data; In this embodiment of the present invention, the internal elemental composition of metal components is determined by energy dispersive X-ray spectrometry (EDS). This testing is performed using a scanning electron microscope (JEOL JSM-IT510) equipped with an energy dispersive X-ray spectrometer (EDS). After mechanical polishing, ultrasonic cleaning with anhydrous ethanol, and gold plating, the component is placed in a vacuum chamber for elemental scanning. Chemical element distribution maps of the metal component's surface and internal cross-section at the micron scale are obtained at a scanning acceleration voltage of 20 kV and a magnification of 500x. The energy spectra output by the spectrometer are used to identify element types. Elements such as Fe, Cr, Ni, Mo, and C are identified by peak position, and the mass fraction (wt.%) of each element is obtained by integrating the peak area. Multi-point averaging (10 independent points per region) is used to eliminate local variations. The resulting elemental composition table, including component number, sampling location, element type, and element mass fraction, serves as essential data for assessing material phase transition risk and impurity interference during stability performance assessment.

[0023] Step S16: Evaluate the stability performance of the metal component based on the geometric structure of the metal component and the internal element composition information of the metal.

[0024] In an embodiment of the present invention, the geometric structure of the metal component extracted in step S14 and the elemental composition information obtained in step S15 are used as joint inputs to conduct a stability performance assessment of the metal component. The cross-sectional continuity, thickness distribution uniformity, and the location of structural stress concentration factors in key areas (such as hole edges and weld areas) of the component are determined based on the geometric structure data. Image analysis is used to extract dimensional features such as edge sharpness and corner radius. The critical stability thickness of the standard component is compared with the current measured thickness using theoretical formulas to assess whether there are mechanically weak areas. Secondly, the content of impurity elements (such as S and P) in the elemental composition information is restricted and judged. If the content exceeds the standard limit (such as S>0.03%), the area is marked as a potential crack source. At the same time, by analyzing the proportion of stabilizing and strengthening elements such as Cr and Ni, combined with the operating temperature conditions of the component, it is assessed whether the thermal fatigue resistance requirements are met. The current stability state of the component is graded and evaluated based on the geometric distribution characteristics, element uniformity and crack resistance. The evaluation results are output as "structural continuity index", "material homogeneity index" and "potential fatigue source area marking table". After matching with the operation log data, they are passed to the subsequent nonlinear load trend analysis stage.

[0025] Preferably, step S16 includes the following steps: Step S161: Calculating the activity level of the metal inside the component based on the internal element composition information of the metal; In an embodiment of the present invention, a statistical analysis of the main metal elements and their contents in a metal component is performed based on the internal elemental composition information of the metal. The elemental composition of a metal component sample is measured using an energy spectrometer (such as an X-ray spectrometer) to obtain the mass percentage and molar percentage data of each element. Based on this elemental composition information, the activity of the metal inside the component is calculated using the standard electrode potential value in the electrochemical activity series, combined with the element content weight. The specific operation includes multiplying the electrode potential of each element by its corresponding mass percentage, performing a weighted average, and obtaining a numerical value for the metal activity of the entire component. The output data of this step is a quantitative indicator of the metal activity inside the metal component, which serves as the basic input parameter for subsequent reaction status calculations.

[0026] Step S162: Calculating the metal reaction status inside the component based on the metal activity inside the component; In this embodiment of the present invention, based on the metal reactivity within the component obtained in step S161, the reaction status of the metal within the component is calculated using electrochemical reaction theory. Electrochemical kinetics formulas are used to combine the metal reactivity value and environmental factors (such as humidity and electrolyte concentration) to derive the redox reaction rate of the metal. This reaction rate is calculated by calculating the electrochemical reaction current density to quantify the metal's reaction status. The specific method involves measuring the change in sample surface potential, combining the reactivity of the contained elements, and determining the current response curve using an electrochemical tester (such as a potentiostat) to obtain a reaction rate curve. The reaction status data obtained in this step represents the level of electrochemical reaction activity within the metal within the component.

[0027] Step S163: predicting the galvanic cell reaction phenomenon of the metal component based on the metal reaction status and the activity level of the metal inside the component; In this embodiment of the present invention, the galvanic cell reaction phenomenon of the metal component is predicted based on the metal reaction status within the component obtained in step S162 and the metal activity level in step S161. By combining the electrode potential difference of the metal elements and analyzing the electrochemical potential differences between the different elements, the location and intensity of the galvanic cell reaction are estimated. By calculating the potential gradient between the different metal elements within the component and combining it with the reaction rate, the local electrochemical corrosion trend is assessed. This prediction process relies on a comprehensive analysis of element distribution data, activity level, and reaction status to output a spatial distribution map of the galvanic cell reaction and an intensity index, providing a reference for subsequent structural strength assessment.

[0028] Step S164: Evaluate the metal hardness data of the metal component based on the internal element composition information of the metal; In this embodiment of the present invention, the metal hardness data of a metal component is evaluated based on the internal elemental composition of the metal. Standard material mechanics testing methods are used to perform Brinell hardness testing (HRB) on the sample. A hardness tester is used to measure the hardness values ​​of different areas, and a weighted analysis is performed based on the influence of elemental composition on hardness. Specifically, the microhardness tester is used to mark multiple test points on the surface of the metal sample, and the hardness values ​​are measured at each point. The hardness is then corrected and calibrated based on the chemical composition data to obtain representative metal hardness data that reflects the overall hardness performance of the component material.

[0029] Step S165: determining the size ratio of the metal components according to the geometric structure of the metal components; In this embodiment of the present invention, the dimensional ratio relationships of metal components are determined based on their geometric structural data. Using the high-precision image data acquired in step S14, image processing software is used to perform edge detection and measurement of component boundaries and key geometric features, extracting the dimensional parameters of each component. A dimensional ratio relationship matrix is ​​constructed by calculating the length, width, height, and dimensional ratios of key components. This process includes an image calibration step to ensure the accuracy of dimensional measurement and outputs a numerical matrix of dimensional ratio relationships, providing geometric foundational data for subsequent structural strength analysis.

[0030] Step S166: determining the connection relationship between the various parts of the metal component according to the geometric structure of the metal component; In this embodiment of the present invention, the connection relationships between various parts of a metal component are determined based on the geometric structure data of the component. Structural image analysis technology is used to identify the connection nodes and connection methods (such as welding, riveting, and bolting) of each component, and the connection locations are classified and numbered. Three-dimensional reconstruction technology is then used to combine the connection features detected in the image to generate a connection relationship topology map. This topology map uses nodes to represent component parts and edges to represent connection relationships, forming a network structure. The output is a connection relationship data set, providing descriptive information about the connection status for structural strength assessment.

[0031] Step S167: Evaluate the structural strength data of the metal component based on the connection relationship between the various parts of the metal component, the size ratio of the metal component, and the metal hardness data of the metal component exceeding 75.3 HRB; In this embodiment of the present invention, based on the connection relationship data from step S166, the dimensional ratio relationship from step S165, and the metal hardness data from step S164, the structural strength data of the metal component is evaluated when the metal hardness data exceeds 75.3 HRB. Utilizing the principles of material mechanics, combined with the mechanical properties of the connection method and the dimensional ratios, the component's load-bearing capacity and deformation resistance are calculated. Finite element analysis (FEA) software is used for structural simulation. By inputting geometric dimensional ratios, connection relationships, and hardness data, the stress distribution and deformation of the component under different loads are calculated to obtain a structural strength value. This step outputs structural strength data, which reflects the mechanical stability of the component.

[0032] Step S168: Evaluate the stability performance of the metal component based on the metal component structural strength data exceeding 0.78 and the metal component galvanic cell reaction phenomenon.

[0033] In this embodiment of the present invention, based on the structural strength data obtained in step S167, when the structural strength data exceeds 0.78, combined with the galvanic reaction phenomenon predicted in step S163, a comprehensive assessment of the stability of the metal component is performed. Correlation analysis is performed between the structural strength data and the galvanic reaction intensity index to determine the impact of electrochemical corrosion on component stability under the existing strength conditions. This comprehensive assessment utilizes multi-index fusion technology to output a stability performance evaluation index, which is used to determine the component's fatigue damage risk and safety status, providing data support for subsequent maintenance and monitoring.

[0034] Preferably, the evaluation of the nonlinear load growth trend of the metal component in step S2 includes: Statistics on the changes in the operation cycle of metal components based on the metal component operation log data; In this embodiment of the present invention, detailed log data generated during the operation of a metal component is collected. The log data includes the load value, load direction, number of cycles, and the operation timestamp for each operation cycle. Data analysis tools are used to organize the log data into time series, sorting the load cycles in chronological order, extracting the peak and valley load values ​​for each operation cycle, and calculating the number of cycles and cycle amplitude variations. By statistically analyzing periodic load variations, a complete dataset of the metal component's cyclical operation is generated, and a series of periodic load amplitude values ​​and cycle number statistical parameters are output, providing a data foundation for subsequent high-intensity cyclic analysis.

[0035] Evaluate the high-intensity cycle operation status of metal components based on the changes in the operation cycle of metal components; In an embodiment of the present invention, based on the obtained load peak value and cycle number data, a detailed amplitude and frequency analysis of the load time series is performed to clarify the characteristics of the load fluctuation. The specific operation includes using digital signal processing technology to filter the load signal, remove noise interference, and ensure the accuracy of the load fluctuation data. Subsequently, the peak detection algorithm is used to identify the peak points in the load curve, and the amplitude of each load peak and its corresponding time position are extracted. For the load amplitude within each cycle, a pre-set load intensity threshold is used for comparative analysis to clearly distinguish the load cycles that exceed the threshold and determine them as high-intensity cycles. Using statistical analysis methods, the identified high-intensity cycle data is summarized, the proportion of high-intensity cycles in the total cycles is calculated, and the trend of the proportion over time is analyzed to reflect the dynamic change characteristics of the high-intensity cycle. At the same time, the key parameters of the high-intensity cycle are extracted, including the number of high-intensity cycles, the average load amplitude, and the duration of each high-intensity cycle, to form a complete high-intensity cycle parameter set. During the entire process, threshold comparison technology is used to accurately identify high-intensity cycle time periods, and time series data is combined to clarify the intensity level corresponding to each high-intensity cycle. The high-intensity cycle parameter set output by this step includes key indicators such as the number, amplitude statistics and duration of high-intensity cycles, providing a solid data foundation and technical support for subsequent detailed inspections and analysis based on multi-directional loads, ensuring a comprehensive grasp of the operating load characteristics of metal components.

[0036] Detect the multi-directional load growth of metal components based on the high-intensity cyclic operation of metal components; In this embodiment of the present invention, the identified high-intensity cyclic time periods, combined with component load vector data, are used to decompose the load into three spatial components using vector decomposition techniques. The load amplitude and cycle count in different directions are statistically analyzed, and the load growth rate and its trend in each direction are calculated. Time series analysis tools are used to detect whether there is overlapping or alternating growth in load directions, generating spatiotemporal distribution data for multi-directional load growth. The output is a numerical curve showing the load intensity in different directions and a growth rate indicator.

[0037] Determine the change of the principal stress axis of the metal component based on the multi-directional load growth of the metal component and the high-intensity cycle operation of the metal component; In an embodiment of the present invention, based on the load vector decomposition data and the load time series information, a stress tensor calculation method is used to comprehensively analyze the loads borne by the metal component at each time point. The specific operation steps include decomposing the load vector into components in three directions according to the spatial coordinate system, and calculating the stress state of the component at each time node by constructing a three-dimensional stress tensor matrix. Subsequently, the stress tensor is solved using the eigenvalue decomposition technology to extract the principal stress value and the corresponding principal stress axis direction, and to clarify the magnitude and direction of the maximum, intermediate and minimum stresses borne by the component. The principal stress axis direction in the entire load time series is continuously tracked and counted, and the rotation angle and spatial offset of the principal stress axis at different time points are calculated to quantify the dynamic change trend of the principal stress axis. Combined with the high-intensity cycle time period information in the component operation log, the change data of the principal stress axis is matched with the high-intensity cycle event, and the response characteristics and change rate of the principal stress axis during the load change process are analyzed. By constructing a time curve of the principal stress axis change and counting its characteristic parameters such as offset amplitude, rotation frequency, and change rate, a complete database of dynamic changes in the principal stress axis is formed. This step outputs the principal stress axis direction change data in the time series, the rotation angle statistics, and the corresponding principal stress values, providing precise input parameters and reference basis for subsequent multi-axis stress cross analysis, ensuring accurate characterization and in-depth analysis of the complex load response of metal components.

[0038] Estimate the multi-axial stress cross-state of metal components based on the change of principal stress axes and the multi-directional load growth of metal components; In an embodiment of the present invention, the stress axis direction data of the metal component and the multi-directional load growth information are combined to systematically collect and classify the stress components of the component in various directions, and clarify the stress amplitude and direction changes of each principal stress axis under different loads. Based on the classical stress superposition principle, the principal stresses in different directions are gradually vectorized and superimposed, and the superposition effect between multiple principal stress axes is accurately calculated. The specific operation uses finite element analysis tools or mechanical calculation software to perform high-precision meshing on the stress field of the component surface and internal area, and constructs a three-dimensional stress distribution model based on the principal stress value at each node. Through this model, the stress distribution overlapping areas between different principal stress axes are systematically analyzed, focusing on identifying the intersections where stress superposition is significantly enhanced, quantifying the stress intensity of the intersections, and obtaining a numerical index of the multi-axial stress cross intensity. The stress path analysis method is used to track the path of the stress vector changing with time and load, dynamically identify the stress concentration area and its evolution process, and reveal the stress accumulation trend of the local area of ​​the component under complex multi-axial load conditions. Throughout the entire process, component geometry data and material mechanical properties are combined to ensure the accuracy and reliability of stress calculation results. Output includes a three-dimensional spatial distribution diagram of multi-axial stress intersection, a graphic representation of stress concentration locations and intensity values, and a detailed stress superposition value table, providing complete and high-precision basic data support for subsequent elastic-plastic cycle superposition trend detection.

[0039] The multi-axial stress cross condition of metal components is used to detect the elastic-plastic cycle superposition trend of metal components; In an embodiment of the present invention, based on the acquired multi-axial stress cross data, the stress-strain response characteristics of the metal component material under multi-axial stress are systematically analyzed according to the elastic-plastic cycle theory in material mechanics. In the specific operation, the multi-axial stress data is decomposed into the main stress components, and combined with the yield strength data of the material, the stress amplitude in each load cycle is gradually compared with the elastic limit and yield point of the material to clarify the starting time of plastic deformation and the corresponding number of cycles. The stress-strain curve analysis method is used to identify the switching node between the elastic deformation stage and the plastic deformation stage, and the elastic-plastic deformation energy accumulation of the material in each cycle is quantified by monitoring the area and shape changes of the strain hysteresis loop. The continuous load cycles are tracked in time series to calculate the cumulative effect of the elastic-plastic cycle, including the total amount of plastic deformation, stress relaxation and hardening / softening trend. Combined with the Fatemi-Socie or fatigue damage accumulation theory, the fatigue limit, fracture toughness and other parameters of the material are used, and the Miner linear damage accumulation method or nonlinear damage evolution model is used to estimate the actual impact of the elastic-plastic cycle on the fatigue life of the material. During the process, the stress amplitude, number of cycles and corresponding material response of each load cycle are recorded one by one to form detailed cumulative effect data. A curve graph describing the evolution of elastic-plastic cycle superposition over time is output to clarify the relationship between the cumulative amount of plastic deformation and the number of cycles. At the same time, a numerical index of the cumulative deformation is provided, which is used for subsequent quantitative evaluation of fatigue damage and prediction of the remaining life of the component.

[0040] According to the elastic-plastic cycle superposition trend of metal components and the multi-axial stress cross condition of metal components, the stability performance of metal components is tested to detect abnormal mechanical response of metal components; In this embodiment of the present invention, the mechanical response characteristics of a metal component under different load cycles are comprehensively tested by combining the accumulated data from elastic-plastic cycles with the multiaxial stress distribution information obtained in step five. High-precision vibration analyzers are deployed at key locations on the component to collect the vibration frequency, amplitude, and spectral characteristics of the component surface in real time. Simultaneously, adhesive strain gauges are distributed along key strain-sensitive areas on the component surface to monitor the component's subtle deformation and strain change signals under load in real time. During the acquisition process, vibration and strain signals are continuously recorded at a high sampling rate using a synchronous sampling system to ensure that subtle and rapidly changing dynamic responses are captured. Subsequently, the monitored vibration and strain data are combined with load cycle time series data based on the accumulated information from the elastic-plastic cycles for multi-dimensional time series comparative analysis. Signal processing techniques are used to extract the baseline characteristics of the component's mechanical response under normal load cycles, including the vibration amplitude range, main frequency components, and strain variation patterns. By comparing the response signals collected during actual operation, abnormal fluctuations and response distortions that exceed the normal baseline range are identified, with particular emphasis on identifying abnormal vibration peaks, frequency drifts, and strain mutation intervals. Combined with multi-axial stress distribution data, the specific load state and stress direction corresponding to the abnormal response interval are analyzed to determine the correlation between the abnormal response and the load combination. Based on this analysis, abnormal mechanical response indicators are extracted, including parameters such as abnormal vibration amplitude peak value, frequency offset, strain mutation amplitude and duration. The specific time period when the abnormal response occurs is also marked, and structured abnormal mechanical response data and its time index are output. This provides an accurate diagnostic basis for the subsequent comprehensive assessment of the stability performance of metal components and supports fatigue damage identification and early warning decision-making.

[0041] The nonlinear load growth trend of metal components is evaluated based on the abnormal mechanical response of metal components and the elastic-plastic cycle superposition trend of metal components.

[0042] In this embodiment of the present invention, the collected high-frequency time series data is preprocessed to remove noise and abnormal fluctuations from the signal, ensuring data continuity and accuracy. Subsequently, the processed mechanical response anomaly data is time-synchronously integrated with the accumulated information from the elastic-plastic cycle to form a complete load response time series data sequence. Based on this time series data, advanced time series data analysis techniques are employed to extract the instantaneous velocity and acceleration of the load change through numerical difference calculations, capturing the dynamic characteristics of the load growth process. To accurately quantify the nonlinear characteristics of load growth, an appropriate nonlinear function model, such as an exponential growth model, a power function model, or a logistic function model, is selected and fitted using the least squares method to ensure that the fitted curve accurately reflects the load variation trend over time. During the fitting process, the fit is verified using goodness-of-fit metrics such as the coefficient of determination (R²) and residual analysis to ensure an accurate description of the nonlinear trend. Based on the fitted curve, the load growth rate parameter (i.e., the first-order derivative of the fitting function, which reflects the load change rate over time) and the acceleration parameter (i.e., the second-order derivative of the fitting function, which reveals the acceleration or deceleration trend of the load change) are further calculated. The mathematical expression for nonlinear load growth and its key numerical parameters obtained through the above calculations constitute a quantitative description of the load growth trend, which can effectively reflect the nonlinear mechanical response characteristics of metal components due to complex loads during actual operation. The mathematical description and numerical indicators of the nonlinear load growth trend output by this step serve as important input data for fatigue damage assessment of metal components, supporting subsequent fatigue life prediction and structural stability analysis, ensuring the scientific and accurate assessment results.

[0043] Preferably, the detection of stress coupling growth status of the metal component in step S2 includes: Identify the sudden increase of metal components based on the nonlinear load growth trend of metal components; In an embodiment of the present invention, load time series data is used in combination with a nonlinear load trend curve, and a time series anomaly detection algorithm is applied to perform a point-by-point analysis of the curve slope and the load increase change rate. By calculating the first-order and second-order differential values ​​of the load increase, the load increase mutation point in the curve is detected, that is, at certain time nodes, the load value shows a significant jump-like increase. To achieve this detection, the load growth trend data is smoothed and filtered to reduce the influence of signal noise, and then a mutation threshold is set. The threshold is obtained based on historical load change statistics. If the load increase difference exceeds the threshold, it is regarded as a mutation increase. The detection results form a set of time points and corresponding mutation amplitude data, which serve as input for subsequent load-bearing force growth condition detection.

[0044] Detect the load growth status of metal components based on the nonlinear load growth trend and sudden increase of metal components; In an embodiment of the present invention, based on the sudden increase condition identified in the first step, combined with the complete nonlinear load growth trend data, the load-bearing force growth condition of the metal component is detected, and the original load data recorded by the digital load sensor or strain gauge is used to perform segmented statistics on the time evolution of the load, and a difference analysis is performed on the load values ​​and change trends before and after the mutation point. The average load, maximum load and increase rate in each time period are calculated to form a detailed force growth curve. Combined with the sudden increase position and amplitude, the force level and growth rate of the component at each stage are determined. This step uses statistical regression analysis technology to output numerical indicators and growth curves of the load-bearing force growth, providing a data basis for the subsequent determination of the degree of structural deformation.

[0045] Determine the degree of structural deformation of metal components based on the load-bearing growth of metal components; In an embodiment of the present invention, the degree of structural deformation of a metal component is determined based on the load-bearing stress growth condition. This step utilizes a laser displacement sensor and three-dimensional digital image correlation (DIC) technology to monitor component surface deformation in real time. Laser scanning equipment is installed to periodically scan the component to obtain its three-dimensional contour data. Subsequently, digital image correlation technology is used to compare the surface images of the component at different stress stages to calculate the deformation field distribution. Combined with the stress growth data, a numerical integration method is used to calculate the local and global deformation variables of the component, including elastic and plastic deformation variables. The severity and distribution characteristics of the deformation are determined through curvature change analysis. Structural deformation degree parameters are output, including maximum deformation, average deformation rate, and deformation distribution diagram, to provide support for subsequent estimation of abnormal stress concentration conditions.

[0046] Estimate the abnormal stress concentration of metal components based on the degree of structural deformation of the metal components; In an embodiment of the present invention, based on the obtained degree of structural deformation, the abnormal stress concentration of the metal component is estimated. Finite element analysis (FEA) software is used in combination with the actual measured deformation data to calculate the stress distribution within the component. The three-dimensional deformation data is imported into the finite element model, and the material constitutive relationship and boundary conditions are applied to calculate the stress field of the component under the current stress. The stress concentration area corresponding to the deformation area is analyzed in detail. By extracting the maximum principal stress value and its distribution position, the specific range of the abnormal stress concentration is determined. The stress gradient calculation is used to identify the stress concentration peak and the surrounding change trend. The abnormal stress concentration index is output, including the maximum stress value, the stress concentration coefficient, and the spatial distribution of the abnormal area, providing a data basis for subsequent judgment of the force balance destruction trend.

[0047] Determine the force balance destruction trend of metal components based on the abnormal stress concentration of metal components and the load growth of metal components; In an embodiment of the present invention, the force balance destruction trend of the metal component is determined based on the estimated abnormal stress concentration condition and the load-bearing force growth condition. The specific operation is to quantify the mismatch between the local and overall stress states of the component by analyzing the ratio of the stress change rate in the stress concentration area to the overall force growth rate. The dynamic response analysis technology is used in combination with the time series stress data to calculate the evolution trend curve of the force balance destruction of the component. By defining the force balance destruction index, the degree of deviation of the abnormal local stress growth relative to the overall load change is reflected. The index is calculated based on the ratio of the stress concentration peak to the load growth rate. The higher the value, the more serious the force balance destruction. The force balance destruction trend data is output, including the destruction index time series and its change rate, which provides an important reference for stability attenuation analysis.

[0048] Determine the attenuation of the mechanical stability of metal components based on the force balance destruction trend of metal components; In an embodiment of the present invention, the mechanical stability attenuation of the metal component is determined based on the output force balance destruction trend. The vibration response signal of the component at different load stages is collected by dynamic mechanical analysis equipment, and the changes in the stiffness and damping characteristics of the component are identified by using spectrum analysis and modal analysis techniques in combination with the force balance destruction index. The specific operation includes comparing the natural frequency and vibration mode of the component in the initial state and the current state, and calculating the stiffness attenuation ratio and damping change rate. Combining the load and force balance destruction data, the data fusion method is applied to generate a stability attenuation curve to reflect the degradation trend of mechanical properties with time and load changes. The mechanical stability attenuation parameters are output, including the stiffness loss rate, the damping change amplitude and the stability attenuation index, to provide a stability quantitative basis for subsequent stress coupling growth detection.

[0049] The stress coupling growth of metal components is detected based on the attenuation of the mechanical stability of metal components and the abnormal stress concentration of metal components.

[0050] In an embodiment of the present invention, the stress coupling growth status of the metal component is detected based on the mechanical stability attenuation and the abnormal stress concentration status in step four, and the stress coupling effect is quantified by multivariate statistical analysis methods in combination with the multidimensional stress data and mechanical performance indicators collected in the structural health monitoring system. Specifically, correlation analysis and principal component analysis methods are used to reveal the coupling relationship between stress concentration areas and mechanical performance changes. The time-series coupling curve is used to describe the spatial and temporal characteristics of stress coupling growth, forming a comprehensive evaluation index of stress coupling growth. This index includes the stress coupling intensity coefficient and the coupling growth rate, which reflect the complex coupling changes of the component's stress state. The stress coupling growth status parameters are output to provide key input for fatigue damage assessment of metal components.

[0051] Preferably, determining the dynamic fracture condition of the metal component structure in step S2 includes: Estimate the degree of structural deviation of metal components based on the stress coupling growth status of metal components; In an embodiment of the present invention, a high-precision three-dimensional laser scanner is used to perform multi-point scanning on the current structure of the metal component to obtain the spatial coordinate data of the component surface. The three-dimensional point cloud data is compared point by point with the standard geometric model of the initial state of the component, and the spatial offset vector of the corresponding point is calculated to obtain the overall and local offset distribution of the component. The offset vector is decomposed into translation and rotation components using coordinate transformation and rigid body motion decomposition methods to quantify the specific value of the structural offset. Combined with the aforementioned stress coupling growth data, the correspondence between the structural offset and the stress coupling peak moment is verified through statistical analysis. The structural offset degree parameters are output, including the maximum offset, average offset and offset direction distribution, to provide a quantitative basis for subsequent functional offset judgment.

[0052] Determine the functional deviation of metal components based on the degree of structural deviation of the metal components; In an embodiment of the present invention, the functional offset status of the metal component is determined based on the degree of structural offset of the metal component. The specific operation adopts a functional status monitoring system based on a sensor array to dynamically monitor the key moving parts of the component. The sensor array includes a displacement sensor, an accelerometer and an angular velocity sensor to collect motion parameters in real time. Combined with the structural offset data, through time-space synchronization analysis, the functional offset is identified as a deviation in the motion path, an abnormality in the motion angle or a change in the motion rate. Based on the motion trajectory reconstruction technology, the actual motion trajectory of the component is reconstructed and compared with the design trajectory to quantify the degree and range of the functional offset. The functional offset indicators are output, including the offset angle, trajectory error and motion rate change, to provide input for the motion abnormality interference assessment.

[0053] Determine the abnormal interference condition of the metal component movement based on the degree of structural deviation of the metal component and the functional deviation condition of the metal component; In an embodiment of the present invention, a three-dimensional motion simulation environment is established by combining component assembly drawings and motion space parameters. Numerical simulation and collision detection algorithms are used to analyze the overlap and interference risks of the motion spaces of various component parts. A dynamic capture system is used to monitor the relative positions and gap changes between components during actual operation, identifying abnormal intersections and collision areas in the motion trajectory. Historical motion data is compared to calculate the frequency and intensity of abnormal interference events. Abnormal motion interference parameters are output, including interference frequency, interference intensity, and abnormal interference duration, providing key data for mechanical impact estimation.

[0054] Estimate the mechanical impact of metal component movement based on the abnormal interference of metal component movement; In an embodiment of the present invention, a high-sensitivity vibration sensor and an acoustic emission sensor are used to monitor the operating status of the component in real time, capturing the transient vibration and acoustic wave signals generated by the impact. The vibration signals collected by the sensor are analyzed in the time domain and frequency domain to extract the characteristic parameters of the impact event, including the impact energy, impact frequency, duration, and impact peak. Combined with the motion abnormal interference parameters, the specific time period and component part where the impact occurred are located. Through frequency matching and signal feature comparison, the corresponding relationship between mechanical impact and abnormal interference is identified. The mechanical impact condition indicators are output, covering the number of impacts, impact energy distribution, and impact duration, laying a data foundation for the detection of microcrack growth trends.

[0055] Detect the growth trend of micro cracks in metal components based on the mechanical impact of metal component movement and the stress coupling growth of metal components; In an embodiment of the present invention, ultrasonic nondestructive testing technology is used to periodically scan the surface and interior of metal components to capture the acoustic reflection signals of microcracks. Ultrasonic phased array technology is used to measure the starting position, extension length, and growth rate of microcracks. Combined with stress-coupled growth data, the time series and spatial distribution characteristics of crack extension are analyzed to evaluate the impact of mechanical impact on accelerated crack growth. By calculating the crack length growth rate and crack density changes, a microcrack growth trend curve is generated. Microcrack growth parameters are output, including the number of cracks, average length, extension rate, and spatial distribution, providing a quantitative basis for dynamic fracture assessment.

[0056] The dynamic fracture condition of the metal component structure is determined based on the growth trend of micro cracks in the metal component caused by mechanical impact during its movement.

[0057] In an embodiment of the present invention, the dynamic fracture condition of the metal component structure is determined based on the influence of the mechanical impact on the microcrack growth trend. The fracture risk index is calculated by comprehensively analyzing the correlation between the microcrack growth trend and the mechanical impact intensity and frequency, combined with the material fracture mechanics parameters. The critical fracture state is judged by the critical crack extension length and load threshold, and the time period when dynamic fracture occurs is clarified. The spatiotemporal correspondence between the fracture risk and the actual structural deviation and functional abnormality is verified by multi-sensor data fusion technology. The dynamic fracture parameters of the structure are output, including the fracture risk index, critical crack length and fracture time period, to achieve accurate judgment of the fracture state of the component.

[0058] Preferably, step S3 includes the following steps: Step S31: collecting metal component operating environment data based on metal component operating log data; In this embodiment of the present invention, raw data entries containing environmental information are exported from the metal component operation log database. These data items include, but are not limited to, temperature, humidity, air pressure, salt spray concentration, pH value, concentrations of harmful gases in the air (such as sulfur dioxide and hydrogen chloride), and dust particle size and concentration. Dedicated data parsing software is used to format the log data, removing abnormal or missing data points. Time series interpolation is used to fill in gaps in the data to form a complete time series of environmental parameters. Through weighted averaging and statistical analysis, the environmental data are used to calculate characteristic parameters of the environmental conditions during the metal component's operation, including maximum, minimum, mean, and fluctuation range, providing an accurate environmental basis for subsequent corrosion assessments. The output metal component operation environment dataset is stored in a structured file format.

[0059] Step S32: evaluating the external corrosion of the metal component based on the stability performance of the metal component according to the operating environment data of the metal component to obtain the external corrosion status of the metal component; In an embodiment of the present invention, an electrochemical corrosion monitoring method is used to quantitatively calculate the corrosion rate of metal materials in combination with environmental parameters. Based on the environmental data such as temperature, humidity, pH, and harmful gas concentration output in step S31, the surface of the component is periodically detected using electrochemical impedance spectroscopy (EIS) measurement technology to obtain the electrode reaction impedance value of the metal surface. The corrosion rate is obtained by using a formula to calculate the impedance change trend. The environmental parameters entered in the formula correct the electrochemical reaction activity factor to ensure the accuracy of the corrosion rate calculation. By performing ultrasonic testing on the integrity of the component surface coating and the thickness of the corrosion products of the metal matrix, supplemented by scanning electron microscopy (SEM) analysis of the corrosion morphology, the corrosion range, corrosion depth, and corrosion rate are comprehensively obtained to form a report on the external corrosion status of the metal component. The report contains a corrosion rate curve, a corrosion depth distribution diagram, and a corrosion morphology photograph.

[0060] Step S33: evaluating the weakening of the external load-bearing section of the metal component based on the external corrosion condition of the metal component; In an embodiment of the present invention, the current geometric data of the surface of the metal component is obtained by three-dimensional laser scanning, and the load-bearing section of the component is corrected in combination with the corrosion depth distribution map in step S32. The finite element analysis software is used to simulate the cross-sectional mechanical properties of the area affected by corrosion, and the reduction in effective cross-sectional area and the change in cross-sectional modulus caused by corrosion are calculated. The specific operation includes a cross-sectional splitting method, which divides the cross section of the component into several units, adjusts the effective thickness of each unit according to the corrosion depth data, and then calculates the changes in the bending and shearing capacity of the overall section. The results are expressed as a cross-sectional weakening rate, and the specific parameters include the weakened cross-sectional area, the change in cross-sectional moment of inertia, and the attenuation ratio of the cross-sectional bending modulus. The data forms a report on the weakening of the external load-bearing section of the metal component, providing accurate cross-sectional mechanical property input for structural stability attenuation assessment.

[0061] Step S34: Evaluate the stability attenuation of the metal component structure based on the weakening of the external load-bearing section of the metal component and the dynamic fracture of the metal component structure.

[0062] In an embodiment of the present invention, a comprehensive structural mechanics evaluation method is used to fuse the external load-bearing section weakening parameters in step S33 with the structural dynamic fracture parameters obtained in the aforementioned step S2. The specific operation includes importing the geometric parameters of the component after the cross-section weakening into the structural finite element model, combining the local material performance degradation data induced by dynamic fracture, and recalculating the overall stiffness, strength and fatigue life of the component. Through stress-strain analysis under static and dynamic load conditions, the stability indicators of the component in the current environment and fatigue state are obtained, such as critical buckling load, fatigue limit and fracture critical load. The safety factor calculation method is used to quantify the degree of structural stability attenuation and output a stability attenuation index. The index reflects the deviation between the bearing capacity of the component and the design requirements, and forms a structural stability attenuation status report for the overall fatigue damage assessment system to call.

[0063] It is particularly important that step S32 includes the following steps: Step S321: detecting the operating environment humidity according to the metal component operating environment data; In an embodiment of the present invention, given the long-term service life of metal components in highly corrosive environments such as high humidity and high temperature, it is necessary to obtain real-time information on the humidity level of their operating environment. In actual operation, four precision industrial-grade humidity sensors (Sensirion SHT85, with a measurement accuracy of ±1.5% RH) are deployed at different locations around the metal component. Each sensor is connected to an environmental data acquisition controller via the Modbus RTU protocol. To ensure representative data, each sensor collects relative humidity values ​​once per minute and continuously records them for 72 hours. A humidity time series matrix is ​​formed using an integrated data buffer module. After acquisition, a third-order median filter algorithm is used to remove abnormal peaks from the humidity sequence output by each sensor, and its local time-weighted average is calculated to obtain the effective humidity environment curve for the metal component during actual operation. This humidity data will serve as input for subsequent moisture adsorption analysis.

[0064] Step S322: analyzing the moisture adsorption of the metal components based on the operating environment humidity to obtain moisture adsorption data of the metal components; In this embodiment of the present invention, the moisture adsorption capacity of a metal component is analyzed based on the humidity curve data obtained in step S321. This analysis combines the metal component's surface roughness (Ra value, measured in μm using an atomic force microscope (AFM)) and surface area (measured using the BET method). Assuming the material is Q345 structural steel, the water physical adsorption rate per unit surface area at different humidity levels is calculated. To obtain a quantitative relationship between adsorption performance, a constant temperature and humidity test chamber (controlled at 25±1°C and four humidity levels of 40%, 60%, 80%, and 95%) was constructed. Multiple standard specimens were placed in the chamber, each held at a humidity level for 24 hours. The specimens were weighed every four hours, and the mass change due to moisture adsorption was recorded. A linear fitting method was used to extract the adsorption rate curve corresponding to each humidity level. This was further combined with the actual humidity data from step S321 to convert the water adsorption per unit area (in mg / cm²) of the target metal component under the current operating environment into a moisture adsorption data set.

[0065] Step S323: performing a metal galvanic cell reaction aggravation evaluation on the stability performance of the metal component based on the metal component moisture adsorption data to obtain metal galvanic cell reaction aggravation data; In an embodiment of the present invention, based on the moisture adsorption data of the previous step, the aggravation of the primary cell reaction caused by moisture absorption on the surface of the metal component is judged. In this step, a local electrochemical test module is arranged in the area where the moisture adsorption exceeds 10 mg / cm², including a reference electrode, a working electrode and an auxiliary electrode (all using the Ag / AgCl system), and an open circuit potential scan and microcurrent measurement are performed through an electrochemical workstation (CHI660E). When a small potential difference is formed between the electrodes, the stable current density is recorded, and a current density map is generated in units of μA / cm². In order to quantify the aggravation of the reaction, the trend of the current density change within 24 hours is analyzed, and the maximum current density change per unit time is calculated. The result is named the primary cell reaction aggravation index (for example, ΔI=8μA / cm², Δt=6h, and K=1.33μA / cm²·h -1 ) The K value is associated with the spatial position of the measuring point to form the galvanic cell reaction intensification distribution data, which serves as the input for the next oxidation evaluation.

[0066] Step S324: determining the degree of oxidation of the metal component based on the metal galvanic cell reaction intensification data; In the embodiment of the present invention, the oxidation degree analysis is carried out for the high-risk areas of the primary cell reaction identified in step S323. An X-ray photoelectron spectrometer (model: Thermo Scientific K-Alpha+) is used to perform high-resolution XPS scanning on the surface of the component to record the chemical state transition of the metal elements, especially focusing on the Fe 2p, O 1s, and Zn 2p spectral lines. For the Fe element, the Fe 2+ / Fe 3+ The ratio of FeO, 、Fe(OH)3 and other oxidation products. Further, a surface profiler (such as Bruker Contour GT-K) is used to measure the thickness of the surface oxide layer of each oxidation zone. The thickness distribution range (for example, 1.5μm~6μm) is obtained through multi-point statistics. At the same time, combined with the galvanic cell reaction intensification coefficient K value, the section with a strong oxidation growth trend is marked. The component oxidation degree data is output through statistical processing, including the oxide layer thickness of each area, the density of oxidation products (unit: mg / cm²), Fe 3+ / Fe 2+ The relative intensity ratio, etc.

[0067] Step S325: Based on the oxidation degree of the metal component and the metal galvanic cell reaction intensification data, the external corrosion status of the metal component is evaluated to obtain the external corrosion status of the metal component.

[0068] In this embodiment of the present invention, the oxidation degree data from step S324 and the galvanic cell reaction intensification index from step S323 are combined to quantitatively assess the external corrosion condition of metal components. A 3D laser scanner (e.g., the KEYENCE LJ-V7200) is used to measure the three-dimensional morphology of corrosion pits on the component's exterior surface. This generates a corrosion depth distribution map, extracting the maximum corrosion depth (in μm) and the average corrosion value. Next, image recognition software (based on OpenCV's edge detection and color distribution clustering algorithm) is used to count the area of ​​the corrosion spots and calculate the corrosion area ratio (corrosion area / total monitored area, in %). The corrosion rate is assessed using the historical oxidation thickness data from step S324 and the service life, using a linear derivation method to obtain the corrosion growth per unit time (μm / h). Spatial correlation mapping is performed between the corrosion depth, area ratio, and corrosion rate to generate a corrosion distribution map for the metal component. This output is structured corrosion data, including the numerical distribution of each parameter and region identifiers, for input into the subsequent fatigue damage analysis process.

[0069] It is particularly important that step S34 includes the following steps: Step S341: detecting changes in the structural resonance frequency based on the weakening of the external load-bearing section of the metal component and the dynamic fracture of the metal component structure; In this embodiment of the present invention, a multi-point excitation frequency response test (FRS) technique is used to examine the changes in the resonant frequency of structural metal components under load, targeting the weakening of the load-bearing cross-section caused by external corrosion during long-term service and the dynamic fracture caused by microcrack propagation under fatigue. Quantitative analysis is performed on the surface corrosion images of the metal components obtained in the previous step to extract the effective load-bearing cross-sectional dimensions of the components and calculate the corrosion depth, area ratio, and cross-sectional weakening rate. Subsequently, a frequency sweep excitation is applied to the components at multiple locations using an excitation hammer or a swept-frequency vibration table. High-sensitivity excitation accelerometers are placed at key component nodes to precisely sample the response frequencies. Experimental data is acquired using a 16-channel data acquisition system with a sampling rate of 10 kHz to ensure high-precision acquisition of the frequency response characteristics. A Fourier transform is used to obtain a frequency domain response spectrum, and the first and second-order resonant frequencies are extracted based on the amplitude-frequency relationship between the component's excitation frequency and the response acceleration. A baseline resonant frequency data is recorded in the component's original state, free of corrosion damage, and then compared to the current frequency change to determine the frequency offset Δf. The frequency offset is matched with the cross-sectional weakening rate and dynamic fracture growth rate obtained in the previous step to establish a multi-factor response matrix of "resonance frequency change-corrosion cross-section-structural fracture", and then the resonant frequency change of the current structure is obtained as the basic data for subsequent microstructure evaluation.

[0070] Step S342: Identifying changes in the microstructure of the metal component based on changes in the structural resonance frequency; In an embodiment of the present invention, based on the changes in the resonant frequency obtained above, the changes in the internal microstructure of the component are further identified, and the ultrasonic attenuation spectrum analysis method is combined with the scanning acoustic microscopy technology to complete a precise assessment of the microstructural state of the component. The acoustic coupling agent is evenly coated on the surface of the component to ensure the signal integrity of the sound wave transmission process. Subsequently, an ultrasonic probe with a center frequency of 25MHz is used for local scanning. By measuring the acoustic impedance response of the reflected wave and the transmitted wave, abnormal microstructural features such as microcracks, twin boundary dislocations, and pore expansion are captured. The attenuation spectrum of the ultrasonic sampling signal is calculated to extract the local elastic modulus change ΔE of the component material and the acoustic wave scattering density index ρs in the signal propagation path. The two together indicate the level of microstructural integrity. Combined with the previous frequency change data, the resonance frequency shift Δf and the elastic modulus change ΔE are analyzed accordingly. The response function is used to construct the coupling relationship between the component's organizational substructure changes and frequency shift under fatigue. It is determined whether the material has structural anomalies such as plastic zone expansion, microlattice distortion, stress concentration or carbonization precipitation. The component microstructure change status data is output in a regional distribution manner, including a microcrack density distribution map, an elastic modulus reduction rate distribution map and an acoustic wave scattering intensity map, which serve as input data for load yield prediction.

[0071] Step S343: predicting the load yield condition of the metal component based on the change in the metal component's microstructure and the weakening of the metal component's external load-bearing section; In this embodiment of the present invention, based on data on microstructural changes and load-bearing section weakening, a load-yield deduction process based on the stress-strain response regression method is used to predict and analyze component yield performance. The corrosion section weakening rate δS and the decrease in microstructural elastic modulus ΔE are input as influencing factors. Strain gauges are placed at key locations on the component, and a gradually increasing static load (in 5 MPa increments) is applied. The microstrain response εr on the component surface is recorded. The strain gauge signals are transmitted in real time at a sampling frequency of 100 Hz to a data analysis module, which constructs a graph showing the relationship between load σ and strain εr. In the elastic stage, the stress-strain relationship is linear. Upon entering the microplastic stage, the strain response exhibits a distinct nonlinear inflection point. Based on this inflection point, the local yield onset point σy1 of the component is extracted. By comparing the component elastic modulus decrease with the historical benchmark yield point σy0, the load-yield decrease degree Δσy = σy0 - σy1 is calculated. After sampling multiple yield starting points across the entire component, a surface plot of the metal component's overall load-yield response is generated, and the current load-yield status data for the metal component is output in the form of equivalent stress. This data is used in the next step of structural stability assessment.

[0072] Step S344: Evaluate the structural stability attenuation condition of the metal component based on the load yield condition of the metal component and the microstructure change condition of the metal component.

[0073] In this embodiment of the present invention, the current load-yield condition data and microstructural change condition data of the metal component are used as evaluation input parameters, and the critical stability threshold method is used to evaluate the structural stability attenuation of the metal component. The microstructural anomaly distribution map (including the microcrack density map and the elastic modulus drop map) and the load-yield starting point distribution map are extracted and image coincidence mapping is performed. The stress concentration area within the structural cross section is selected as the high-risk unit, and the equivalent yield stress σy value of this area is compared with the original yield strength σy0. The regional stability drop coefficient Rs = σy / σy0 is calculated. Subsequently, the finite element static analysis system is used to divide the component into several structural analysis units, and each unit is assigned the material mechanical parameters (including local elastic modulus, yield stress, microcrack density, etc.) obtained by actual testing. Apply standard working load, analyze the displacement response, stiffness attenuation curve and ultimate deformation degree of the overall structure, and output the structural stability degradation index Ks=K0-Kc, where K0 is the standard stiffness and Kc is the current structural stiffness. A structural stability degradation status assessment report is formed, including the stability degradation coefficient Rs, the overall structural stiffness loss Ks, the heat map distribution of the displacement concentration area, the positioning coordinates of the low-stability unit, etc., which serves as the main reference for the fatigue damage level assessment of metal components.

[0074] Preferably, step S4 includes the following steps: Step S41: estimating the looseness of the metal component connection according to the attenuation of the metal component structure stability; In an embodiment of the present invention, the structural stability attenuation index data obtained in step S3 is called, and the index is used as a basic reference for determining the tightness of the connection. High-frequency vibration response test technology is used to collect signals from the connection parts of metal components, and acceleration sensors are arranged around the connection points to record vibration spectrum data. The vibration spectrum data is converted into frequency domain information through Fourier transform, and the characteristic frequency and amplitude changes are analyzed. Loose connections can cause vibration characteristic frequency offset and amplitude abnormalities. Combined with the stability attenuation index, the connection looseness index is calculated using spectrum differences. This indicator quantitatively reflects the degree of decrease in the tightness of the connector, and the value range is from 0 to 1, where 0 means completely tightened and 1 means severely loose. The test results form a connection looseness status report for use in step S42. Through vibration testing, the status of the connector can be obtained non-destructively, and real-time online monitoring can be achieved.

[0075] Step S42: testing the disturbance of the force flow path of the metal component based on the loose connection condition of the metal component and the attenuation condition of the structural stability of the metal component; In an embodiment of the present invention, this step relies on the connection looseness index in step S41 and the structural stability attenuation data in step S3, and uses strain gauge arrangement technology to perform force flow path detection. According to the structural design drawings of the component and the force analysis, the key force paths and force node positions are determined, and high-sensitivity strain gauges are installed at key locations to record the strain data of the component under different working conditions. The collected strain data is analyzed in the time domain and mapped with spatial distribution to draw a stress transfer map of the force flow path. The force flow path disturbance caused by loose connection and structural stability attenuation is manifested as an abnormal strain concentration area and a path deviation from the original design trajectory. Based on these deviations, the force flow path disturbance coefficient is calculated to quantify the reduction in force flow transmission efficiency and the degree of path change. The force flow path disturbance coefficient is used as a key parameter for evaluating the mechanical integrity of the component. The result forms a force flow path disturbance analysis report, which provides a basis for performance attenuation judgment.

[0076] Step S43: determining the performance degradation of the metal component based on the disturbance of the force flow path of the metal component and the loose connection of the metal component; In an embodiment of the present invention, this step combines the force flow path disturbance coefficient in step S42 and the connection looseness index in step S41, and adopts a multi-parameter fusion method to generate a comprehensive performance attenuation index. The specific operation includes normalizing the two indicators to eliminate the dimensional difference, and then calculating the performance attenuation index by weighted superposition. The weighting coefficient is determined based on the structural characteristics of the component and historical fatigue failure data to ensure that the attenuation index can accurately reflect the actual performance degradation. The performance attenuation index value ranges from 0 to 1, 0 indicates good performance and 1 indicates severe performance degradation. During the index calculation process, statistical analysis tools are used to aggregate data under different working conditions, eliminate outliers, and ensure the stability and accuracy of the index. The performance attenuation status results form a performance attenuation report, which provides accurate input data for fatigue damage assessment and realizes an effective correlation between performance and structural status.

[0077] Step S44: performing fatigue damage assessment on the metal component according to the disturbance of the force flow path of the metal component and the performance attenuation of the metal component to obtain fatigue damage data of the metal component.

[0078] In an embodiment of the present invention, relying on the force flow path disturbance coefficient in step S42 and the performance attenuation index in step S43, combined with the component service life and load history data, the cumulative damage theory is used to calculate the fatigue damage amount, and the local stress concentration coefficient is determined based on the degree of force flow path disturbance. This coefficient corrects the stress amplitude of each cycle in the load history. Secondly, the performance attenuation index is used as a correction factor for the fatigue performance degradation of the material to adjust the fatigue limit and fracture toughness parameters. The cumulative damage calculation is completed by the Palmgren-Miner linear damage rule, and the damage amount corresponding to each load cycle is accumulated to obtain the overall fatigue damage degree. The calculation result is output in the form of a fatigue damage factor with a numerical range of 0 to 1, reflecting the critical degree of fatigue failure of the component. The fatigue damage data is integrated into the structural health monitoring system database for subsequent maintenance decisions and life prediction. After completing step S4, the entire fatigue damage assessment process forms a closed loop, and the loose connection, force flow disturbance, performance attenuation and fatigue damage amount data are mapped to each other to ensure the accuracy and completeness of the assessment results.

[0079] The present invention further provides a metal component fatigue damage assessment system for executing the metal component fatigue damage assessment method described above. The metal component fatigue damage assessment system comprises: A metal component stability performance evaluation module is used to obtain metal component data and metal component operation log data; collect metal component geometry based on the metal component data; and evaluate the stability performance of the metal component based on the metal component geometry and metal component data; The structural dynamic fracture determination module is used to evaluate the nonlinear load growth trend of the metal component based on the stability performance of the metal component based on the operation log data of the metal component; detect the stress coupling growth status of the metal component based on the nonlinear load growth trend of the metal component; and determine the dynamic fracture status of the metal component structure based on the stress coupling growth status of the metal component; The stability attenuation assessment module is used to collect the operating environment data of the metal component based on the operating log data of the metal component; determine the corrosion status of the metal component based on the stability performance of the metal component; and evaluate the stability attenuation status of the metal component structure based on the corrosion status of the metal component and the dynamic fracture status of the metal component structure; The fatigue damage assessment module is used to test the force flow path disturbance of metal components based on the structural stability attenuation of metal components; the fatigue damage assessment of metal components is performed according to the force flow path disturbance of metal components and the structural stability attenuation of metal components to obtain fatigue damage data of metal components.

[0080] A computer-readable storage medium stores a computer program, wherein the computer program is used to execute the metal component fatigue damage assessment method.

[0081] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for assessing fatigue damage of a metal component, characterized in that: The following steps are involved: Step S1: Acquire metal component data and metal component operation log data; Collecting metal component geometry based on metal component data; Evaluate the stability performance of metal components based on their geometry and data; Step S2: evaluating the nonlinear load growth trend of the metal component based on the stability performance of the metal component according to the metal component operation log data; Detect the stress coupling growth status of metal components according to the nonlinear load growth trend of metal components; Determine the dynamic fracture condition of the metal component structure based on the stress coupling growth condition of the metal component; Step S3: Collecting metal component operation environment data based on metal component operation log data; Determine the corrosion status of metal components based on the stability performance of metal components according to the operating environment data of metal components; Evaluate the stability degradation of metal components based on their corrosion status and dynamic fracture conditions; Step S4: testing the disturbance of the force flow path of the metal component based on the attenuation condition of the metal component structure stability; The fatigue damage of metal components is assessed based on the disturbance of the force flow path of the metal components and the attenuation of the structural stability of the metal components to obtain the fatigue damage data of the metal components.

2. The metal component fatigue damage assessment method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: setting the resolution of the industrial high-definition camera to 8 million pixels, the exposure time to 1 / 1000 second, and the image acquisition frequency to 10 frames per second; Step S12: Acquire metal component data and metal component operation log data; Step S13: using an industrial high-definition camera to collect metal component images to obtain metal component image data; Step S14: collecting the geometric structure of the metal component according to the metal component image data; Step S15: testing the internal element composition information of the metal according to the metal component data; Step S16: Evaluate the stability performance of the metal component based on the geometric structure of the metal component and the internal element composition information of the metal.

3. The metal component fatigue damage assessment method according to claim 2, characterized in that: Step S16 includes the following steps: Step S161: Calculating the activity level of the metal inside the component based on the internal element composition information of the metal; Step S162: Calculating the metal reaction status inside the component based on the metal activity inside the component; Step S163: predicting the galvanic cell reaction phenomenon of the metal component based on the metal reaction status and the activity level of the metal inside the component; Step S164: Evaluate the metal hardness data of the metal component based on the internal element composition information of the metal; Step S165: determining the size ratio of the metal components according to the geometric structure of the metal components; Step S166: determining the connection relationship between the various parts of the metal component according to the geometric structure of the metal component; Step S167: Evaluate the structural strength data of the metal component based on the connection relationship between the various parts of the metal component, the size ratio of the metal component, and the metal hardness data of the metal component exceeding 75.3 HRB; Step S168: Evaluate the stability performance of the metal component based on the metal component structural strength data exceeding 0.78 and the metal component galvanic cell reaction phenomenon.

4. The metal component fatigue damage assessment method according to claim 1, characterized in that: The evaluation of the nonlinear load growth trend of the metal component in step S2 includes: Statistics on the changes in the operation cycle of metal components based on the metal component operation log data; Evaluate the high-intensity cycle operation status of metal components based on the changes in the operation cycle of metal components; Detect the multi-directional load growth of metal components based on the high-intensity cyclic operation of metal components; Determine the change of the principal stress axis of the metal component based on the multi-directional load growth of the metal component and the high-intensity cycle operation of the metal component; Estimate the multi-axial stress cross-state of metal components based on the change of principal stress axes and the multi-directional load growth of metal components; The multi-axial stress cross condition of metal components is used to detect the elastic-plastic cycle superposition trend of metal components; According to the elastic-plastic cycle superposition trend of metal components and the multi-axial stress cross condition of metal components, the stability performance of metal components is tested to detect abnormal mechanical response of metal components; The nonlinear load growth trend of metal components is evaluated based on the abnormal mechanical response of metal components and the elastic-plastic cycle superposition trend of metal components.

5. The metal component fatigue damage assessment method according to claim 1, characterized in that: The detection of the stress coupling growth condition of the metal component in step S2 includes: Identify the sudden increase of metal components based on the nonlinear load growth trend of metal components; Detect the load growth status of metal components based on the nonlinear load growth trend and sudden increase of metal components; Determine the degree of structural deformation of metal components based on the load-bearing growth of metal components; Estimate the abnormal stress concentration of metal components based on the degree of structural deformation of the metal components; Determine the force balance destruction trend of metal components based on the abnormal stress concentration of metal components and the load growth of metal components; Determine the attenuation of the mechanical stability of metal components based on the force balance destruction trend of metal components; The stress coupling growth of metal components is detected based on the attenuation of the mechanical stability of metal components and the abnormal stress concentration of metal components.

6. The metal component fatigue damage assessment method according to claim 1, characterized in that: Determining the dynamic fracture condition of the metal component structure in step S2 includes: Estimate the degree of structural deviation of metal components based on the stress coupling growth status of metal components; Determine the functional deviation of metal components based on the degree of structural deviation of the metal components; Determine the abnormal interference condition of the metal component movement based on the degree of structural deviation of the metal component and the functional deviation condition of the metal component; Estimate the mechanical impact of metal component movement based on the abnormal interference of metal component movement; Detect the growth trend of micro cracks in metal components based on the mechanical impact of metal component movement and the stress coupling growth of metal components; The dynamic fracture condition of the metal component structure is determined based on the growth trend of micro cracks in the metal component caused by mechanical impact during its movement.

7. The metal component fatigue damage assessment method according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: collecting metal component operating environment data based on metal component operating log data; Step S32: evaluating the external corrosion of the metal component based on the stability performance of the metal component according to the operating environment data of the metal component to obtain the external corrosion status of the metal component; Step S33: evaluating the weakening of the external load-bearing section of the metal component based on the external corrosion condition of the metal component; Step S34: Evaluate the stability attenuation of the metal component structure based on the weakening of the external load-bearing section of the metal component and the dynamic fracture of the metal component structure.

8. The metal component fatigue damage assessment method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: estimating the looseness of the metal component connection according to the attenuation of the metal component structure stability; Step S42: testing the disturbance of the force flow path of the metal component based on the loose connection condition of the metal component and the attenuation condition of the structural stability of the metal component; Step S43: determining the performance degradation of the metal component based on the disturbance of the force flow path of the metal component and the loose connection of the metal component; Step S44: performing fatigue damage assessment on the metal component according to the disturbance of the force flow path of the metal component and the performance attenuation of the metal component to obtain fatigue damage data of the metal component.

9. A metal component fatigue damage assessment system, characterized in that: For executing the metal component fatigue damage assessment method according to claim 1, the metal component fatigue damage assessment system comprises: A metal component stability performance evaluation module is used to obtain metal component data and metal component operation log data; collect metal component geometry based on the metal component data; and evaluate the stability performance of the metal component based on the metal component geometry and metal component data; The structural dynamic fracture determination module is used to evaluate the nonlinear load growth trend of the metal component based on the stability performance of the metal component based on the operation log data of the metal component; detect the stress coupling growth status of the metal component based on the nonlinear load growth trend of the metal component; and determine the dynamic fracture status of the metal component structure based on the stress coupling growth status of the metal component; The stability attenuation assessment module is used to collect the operating environment data of the metal component based on the operating log data of the metal component; determine the corrosion status of the metal component based on the stability performance of the metal component; and evaluate the stability attenuation status of the metal component structure based on the corrosion status of the metal component and the dynamic fracture status of the metal component structure; The fatigue damage assessment module is used to test the force flow path disturbance of metal components based on the structural stability attenuation of metal components; the fatigue damage assessment of metal components is performed according to the force flow path disturbance of metal components and the structural stability attenuation of metal components to obtain fatigue damage data of metal components.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed, the metal component fatigue damage assessment method according to any one of claims 1 to 8 is implemented.

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