Method and system for predicting fatigue state of alloy grounding material
By acquiring dynamic data of alloy grounding materials, extracting key features, and constructing a fatigue damage evolution model, the problem of inaccurate prediction caused by the simplification of soil environmental parameters was solved, and accurate prediction of the fatigue state of alloy grounding materials was achieved.
Patent Information
- Application Number
- CN202511326990.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-17
- Publication Date
- 2025-12-30
AI Technical Summary
In existing methods for predicting the fatigue state of alloy grounding materials, soil environmental parameters are simplified to constant values or static average values, leading to inaccurate predictions.
By acquiring dynamic data of alloy grounding materials, extracting the coupled characteristics of mechanics, electrochemistry, and environment, constructing a fatigue damage evolution model, and using dynamic damage gain terms to accurately capture environmental abrupt changes, updating the factors in the damage rate equation, and realizing dynamic damage prediction.
It enables accurate prediction of the fatigue state of alloy grounding materials, solves the problem of inaccurate life prediction under sudden environmental changes, and improves the accuracy of prediction.
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Figure CN121237279A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The application belongs to the technical field of alloy material state detection, and particularly relates to a fatigue state prediction method and system for alloy grounding material. BACKGROUND
[0002] Alloy grounding material (such as galvanized steel and copper-coated steel) is a core component of power system safety, and is subjected to the multi-field coupling of mechanical load (wind vibration, short-circuit electric force), electrochemical corrosion (soil Cl - , H + corrosion) and thermal cycle for a long time, which causes fatigue damage accumulation and leads to fracture accidents.
[0003] The current mainstream prediction method (such as life extrapolation method based on load spectrum) has a fundamental defect of lacking dynamic adaptability to the environment:
[0004] The existing model simplifies the soil environment parameters (Cl - concentration, humidity, temperature) into constant values or static average values, ignores the transient effects caused by rainfall, freezing and thawing and salt and alkali migration, and thus causes the fatigue state prediction of alloy grounding material to be inaccurate. SUMMARY
[0005] The application provides a fatigue state prediction method and system for alloy grounding material, and aims to solve the technical problem that the soil environment parameters (Cl- concentration, humidity, temperature) are simplified into constant values or static average values, causing the fatigue state prediction of alloy grounding material to be inaccurate.
[0006] In a first aspect, the application provides a fatigue state prediction method for alloy grounding material, comprising:
[0007] Obtaining dynamic data of the alloy grounding material, and extracting key features related to fatigue damage from the dynamic data, wherein the key features include mechanical features, electrochemical features and environment coupling features;
[0008] Constructing a fatigue damage evolution model of physical mechanism, and the expression of the fatigue damage evolution model is:
[0009] dD / dt=A(Δε_p)^α(f_cor)^β+Bexp(-Q / RT)(σ_h / σ_y)^γ,
[0010] D=1-(E_t / E_0),
[0011] In the formula, D is a damage variable representing the degree of stiffness degradation caused by internal micro-defects of the material, and has a value range of [0, 1]; E_t is a current effective elastic modulus, which is obtained in real time through in-situ ultrasonic measurement or dynamic load test; E_0 is an initial elastic modulus in a lossless state, which is determined by material factory detection or a standard tensile test; Δε_p is an equivalent plastic strain amplitude; A is a plastic damage coefficient, which is calibrated by a material fatigue test; alpha is a plastic strain index, and has a value range of 1.5-2.5; f_cor is an electrochemical corrosion factor, representing the acceleration effect of local corrosion on fatigue crack initiation; beta is a corrosion coupling index, and has a value range of 0.8-1.2; B is an environmental damage coefficient, describing stress corrosion sensitivity; Q is an activation energy, reflecting an atomic diffusion energy barrier under a corrosive medium; R is a gas constant; T is an absolute temperature; sigma_h is a hydrostatic stress; sigma_y is a material yield strength; and gamma is a stress state index.
[0012] The key features are input into the fatigue damage evolution model, and the fatigue damage evolution model outputs a damage increment corresponding to the dynamic data.
[0013] In a second aspect, the present application provides a fatigue state prediction system for an alloy grounding material, comprising:
[0014] An extraction module configured to obtain dynamic data of the alloy grounding material, and extract key features related to fatigue damage from the dynamic data, wherein the key features include mechanical features, electrochemical features and environmental coupling features.
[0015] A construction module configured to construct a fatigue damage evolution model of a physical mechanism, and the fatigue damage evolution model has an expression as follows:
[0016] dD / dt=A(Δε_p)^alpha(f_cor)^beta+Bexp(-Q / RT)(sigma_h / sigma_y)^gamma,
[0017] D=1-(E_t / E_0),
[0018] In the formula, D is a damage variable representing the degree of stiffness degradation caused by internal micro-defects of the material, and has a value range of [0, 1]; E_t is a current effective elastic modulus, which is obtained in real time through in-situ ultrasonic measurement or dynamic load test; E_0 is an initial elastic modulus in a lossless state, which is determined by material factory detection or a standard tensile test; Δε_p is an equivalent plastic strain amplitude; A is a plastic damage coefficient, which is calibrated by a material fatigue test; α is a plastic strain index, and has a value range of 1.5-2.5; f_cor is an electrochemical corrosion factor representing the acceleration effect of local corrosion on fatigue crack initiation; β is a corrosion coupling index, and has a value range of 0.8-1.2; B is an environmental damage coefficient describing stress corrosion sensitivity; Q is an activation energy reflecting an atomic diffusion energy barrier under a corrosive medium; R is a gas constant; T is an absolute temperature; σ_h is a hydrostatic stress; σ_y is a material yield strength; and γ is a stress state index.
[0019] The output module is configured to input the key features into the fatigue damage evolution model, and the fatigue damage evolution model outputs a damage increment corresponding to the dynamic data.
[0020] In a third aspect, an electronic device is provided, which includes at least one processor and a memory connected to the at least one processor in communication, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the steps of the alloy grounding material fatigue state prediction method of any one of the embodiments.
[0021] In a fourth aspect, the present application further provides a computer readable storage medium having a computer program stored thereon, and the program instructions are executed by a processor to enable the processor to perform the steps of the alloy grounding material fatigue state prediction method of any one of the embodiments.
[0022] The alloy grounding material fatigue state prediction method and system of the present application can real-time analyze the transient gradient of soil Cl-concentration dC / dt (such as minute-level concentration fluctuation caused by rainstorm infiltration), drive the f_cor factor in the damage rate equation to be updated adaptively, combine the coefficient k calibrated by the electrochemical noise spectrum to accurately capture the acceleration effect of the pitting potential drift ΔE_pit on micro-crack initiation, and upgrade the traditional static “environmental correction coefficient” to a dynamic damage gain term driven by a differential equation, thereby solving the fundamental problem of life prediction inaccuracy under environmental mutation. BRIEF DESCRIPTION OF DRAWINGS
[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments description. Obviously, the drawings described below are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort based on these drawings.
[0024] Figure 1 A flow chart of a fatigue state prediction method of an alloy grounding material provided by an embodiment of the present application is shown in
[0025] Figure 2 A structural block diagram of a fatigue state prediction system of an alloy grounding material provided by an embodiment of the present application is shown in
[0026] Figure 3 A structural schematic diagram of an electronic device provided by an embodiment of the present application is shown in DETAILED DESCRIPTION
[0027] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the following will combine the drawings in the embodiments of the present application to make a clear and complete description of the technical solutions in the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present application.
[0028] Please refer to Figure 1 , which shows a flow chart of a fatigue state prediction method of an alloy grounding material.
[0029] As shown in Figure 1 , the fatigue state prediction method of the alloy grounding material specifically includes the following steps:
[0030] Step S101, acquiring dynamic data of the alloy grounding material, and extracting key features related to fatigue damage in the dynamic data, the key features including mechanical features, electrochemical features and environmental coupling features.
[0031] In this step, the mechanical features include non-proportionality and local strain gradient, wherein the expression for calculating the non-proportionality is:
[0032]
[0033] In the formula, is the strain gradient tensor, is the change rate of strain ε along the x direction (length direction), is the change rate of strain ε along the y direction (radial / circumferential direction), and i and j are unit vectors of a two-dimensional rectangular coordinate system.
[0034] The expression of the local strain gradient is calculated as:
[0035] F_np = ∫_0^T |ε_1·dε_2-ε_2·dε_1|dt / (4π·ε_a^2),
[0036] In the formula, F_np is a non-proportionality factor, ε_1 and ε_2 are both instantaneous principal strain components, ε_a is an equivalent strain amplitude, and T is a load cycle.
[0037] The electrochemical characteristics include impedance spectrum relaxation time constant, charge transfer resistance decay rate, and pitting sensitive index.
[0038] The environmental coupling characteristics include a corrosion-mechanical fatigue interaction factor η; wherein an electrochemical corrosion factor f_cor is calculated according to the corrosion-mechanical fatigue interaction factor η, and the expression is:
[0039] f_cor = 1 + ηt,
[0040]
[0041] In the formula, C is the Cl-concentration, k is calibrated by the shot noise parameter of the electrochemical noise spectrum, and ΔE_pit is the pitting potential drift.
[0042] In step S102, a fatigue damage evolution model of a physical mechanism is constructed.
[0043] In this step, the expression of the fatigue damage evolution model is:
[0044] dD / dt = A(Δε_p)^α(f_cor)^β+Bexp(-Q / RT)(σ_h / σ_y)^γ,
[0045] D = 1-(E_t / E_0),
[0046] In the formula, D is a damage variable, representing the stiffness degradation degree caused by internal micro-defects of the material, and the value range is [0, 1]; E_t is a current effective elastic modulus, which is obtained in real time by in-situ ultrasonic measurement or dynamic load test; E_0 is an initial undamaged state elastic modulus, which is determined by material factory detection or standard tensile test; Δε_p is an equivalent plastic strain amplitude; A is a plastic damage coefficient, which is calibrated by material fatigue test; α is a plastic strain index, and the value range is 1.5-2.5; f_cor is an electrochemical corrosion factor, representing the acceleration effect of local corrosion on fatigue crack initiation; β is a corrosion coupling index, and the value range is 0.8-1.2; B is an environmental damage coefficient, describing stress corrosion sensitivity; Q is an activation energy, reflecting the atomic diffusion energy barrier under the corrosion medium; R is a gas constant; T is an absolute temperature; σ_h is a hydrostatic stress; σ_y is a material yield strength; and γ is a stress state index.
[0047] Step S103, inputting the key features into the fatigue damage evolution model, and the fatigue damage evolution model outputs a damage increment corresponding to the dynamic data.
[0048] In summary, the method of the present application real-time analyzes the transient gradient of soil Cl- concentration dC / dt (such as the minute-level concentration fluctuation caused by rainstorm infiltration), drives the adaptive update of the f_cor factor in the damage rate equation; combines the electrochemical noise spectrum calibration coefficient k, and accurately captures the accelerated effect of the pit potential drift ΔE_pit on the micro-crack initiation; upgrades the traditional static "environmental correction coefficient" to a dynamic damage gain term driven by the differential equation, and solves the fundamental problem of life prediction inaccuracy under environmental mutation.
[0049] Please refer to Figure 2 which shows a structure block diagram of an alloy grounding material fatigue state prediction system of the present application.
[0050] As Figure 2 shown, the alloy grounding material fatigue state prediction system 200 includes an extraction module 210, a construction module 220, and an output module 230.
[0051] The extraction module 210 is configured to obtain dynamic data of the alloy grounding material, and extract key features related to fatigue damage from the dynamic data, the key features including mechanical features, electrochemical features, and environmental coupling features;
[0052] The construction module 220 is configured to construct a fatigue damage evolution model of physical mechanism, and the expression of the fatigue damage evolution model is:
[0053] dD / dt=A(Δε_p)^α(f_cor)^β+Bexp(-Q / RT)(σ_h / σ_y)^γ,
[0054] D=1-(E_t / E_0),
[0055] In the formula, D is a damage variable representing the degree of stiffness degradation caused by internal micro-defects of the material, and has a value range of [0, 1]; E_t is a current effective elastic modulus, which is obtained in real time through in-situ ultrasonic measurement or dynamic load test; E_0 is an initial elastic modulus in a lossless state, which is determined by material factory detection or a standard tensile test; Δε_p is an equivalent plastic strain amplitude; A is a plastic damage coefficient, which is calibrated by a material fatigue test; α is a plastic strain index, and has a value range of 1.5-2.5; f_cor is an electrochemical corrosion factor representing the acceleration effect of local corrosion on fatigue crack initiation; β is a corrosion coupling index, and has a value range of 0.8-1.2; B is an environmental damage coefficient describing stress corrosion sensitivity; Q is an activation energy, reflecting an atomic diffusion energy barrier under a corrosive medium; R is a gas constant; T is an absolute temperature; σ_h is a hydrostatic stress; σ_y is a material yield strength; and γ is a stress state index.
[0056] The output module 230 is configured to input the key features into the fatigue damage evolution model, and the fatigue damage evolution model outputs a damage increment corresponding to the dynamic data.
[0057] It should be understood that, Figure 2 The modules described in the above Figure 1 Correspond to the steps in the methods described in the above Figure 2 The operations and features described above for the methods also apply to the modules in the above
[0058] In some other embodiments, the present application also provides a computer readable storage medium having stored thereon a computer program, wherein the program instructs a processor to execute the alloy grounding material fatigue state prediction method in any of the above method embodiments when the program is executed by the processor.
[0059] As an implementation form, the computer readable storage medium of the present application stores computer executable instructions, and the computer executable instructions are configured to:
[0060] Obtaining dynamic data of the alloy grounding material, and extracting key features related to fatigue damage from the dynamic data, wherein the key features include mechanical features, electrochemical features and environmental coupling features;
[0061] Constructing a fatigue damage evolution model of a physical mechanism, and an expression of the fatigue damage evolution model is:
[0062] dD / dt=A(Δε_p)^α(f_cor)^β+Bexp(-Q / RT)(σ_h / σ_y)^γ,
[0063] D=1-(E_t / E_0),
[0064] In the formula, D is the damage variable, which characterizes the degree of stiffness degradation caused by micro-defects inside the material, and its value ranges from [0,1]. E_t is the current effective elastic modulus, which is obtained in real time through in-situ ultrasonic measurement or dynamic load test. E_0 is the initial undamaged state elastic modulus, which is determined by the material factory test or standard tensile test. Δε_p is the equivalent plastic strain amplitude. A is the plastic damage coefficient, which is calibrated by the material fatigue test. α is the plastic strain exponent, with a value range of 1.5 to 2.5. f_cor is the electrochemical corrosion factor, which characterizes the accelerating effect of local corrosion on the initiation of fatigue cracks. β is the corrosion coupling index, with a value range of 0.8 to 1.2. B is the environmental damage coefficient, which describes the stress corrosion sensitivity. Q is the activation energy, which reflects the atomic diffusion energy barrier under corrosive medium. R is the gas constant. T is the absolute temperature. σ_h is the hydrostatic stress. σ_y is the material yield strength. γ is the stress state index.
[0065] The key features are input into the fatigue damage evolution model, and the fatigue damage evolution model outputs the damage increment corresponding to the dynamic data.
[0066] Computer-readable storage media may include a stored program area and a stored data area, wherein the stored program area may store an operating system and an application program required for at least one function; the stored data area may store data created based on the use of the alloy grounding material fatigue state prediction system, etc. Furthermore, the computer-readable storage medium may include high-speed random access memory, and may also include memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include memory remotely located relative to a processor, which can be connected to the alloy grounding material fatigue state prediction system via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.
[0067] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present invention, such as... Figure 3 As shown, the device includes a processor 310 and a memory 320. The electronic device may also include an input device 330 and an output device 340. The processor 310, memory 320, input device 330, and output device 340 can be connected via a bus or other means. Figure 3The bus is taken as an example. The memory 320 is the computer readable storage medium described above. The processor 310 performs various functions of the server and data processing by running the non-volatile software programs, instructions and modules stored in the memory 320, that is, the alloy grounding material fatigue state prediction method described above is implemented. The input device 330 can receive input digital or character information and generate key signal input related to user settings and function control of the alloy grounding material fatigue state prediction system. The output device 340 can include a display device such as a display screen.
[0068] The electronic device described above can execute the method provided by the embodiments of the present application, and has the corresponding function modules and beneficial effects of the execution method. Technical details not described in detail in the present embodiment can be referred to the method provided by the embodiments of the present application.
[0069] As an implementation, the electronic device described above is applied to the alloy grounding material fatigue state prediction system, and is used for a client, and includes: at least one processor; and a memory in communication connection with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to:
[0070] Obtain dynamic data of the alloy grounding material, and extract key features related to fatigue damage from the dynamic data, the key features including mechanical features, electrochemical features and environmental coupling features;
[0071] Construct a fatigue damage evolution model of a physical mechanism, and an expression of the fatigue damage evolution model is:
[0072] dD / dt=A(Δε_p)^α(f_cor)^β+Bexp(-Q / RT)(σ_h / σ_y)^γ,
[0073] D=1-(E_t / E_0),
[0074] In the formula, D is the damage variable, which characterizes the degree of stiffness degradation caused by micro-defects inside the material, and its value ranges from [0,1]. E_t is the current effective elastic modulus, which is obtained in real time through in-situ ultrasonic measurement or dynamic load test. E_0 is the initial undamaged state elastic modulus, which is determined by the material factory test or standard tensile test. Δε_p is the equivalent plastic strain amplitude. A is the plastic damage coefficient, which is calibrated by the material fatigue test. α is the plastic strain exponent, with a value range of 1.5 to 2.5. f_cor is the electrochemical corrosion factor, which characterizes the accelerating effect of local corrosion on the initiation of fatigue cracks. β is the corrosion coupling index, with a value range of 0.8 to 1.2. D is the environmental damage coefficient, which describes the stress corrosion sensitivity. Q is the activation energy, which reflects the atomic diffusion energy barrier under the corrosive medium. R is the gas constant. T is the absolute temperature. σ_h is the hydrostatic stress. σ_y is the material yield strength. γ is the stress state index.
[0075] The key features are input into the fatigue damage evolution model, and the fatigue damage evolution model outputs the damage increment corresponding to the dynamic data.
[0076] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.
[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A method of predicting a fatigue state of an alloy grounding material, characterized by, The method comprises the following steps: acquiring dynamic data of an alloy grounding material, and extracting key features related to fatigue damage from the dynamic data, wherein the key features comprise mechanical features, electrochemical features, and environmental coupling features; constructing a fatigue damage evolution model of a physical mechanism, wherein an expression of the fatigue damage evolution model is: dD / dt=A(Δε_p)^α(f_cor)^β+Bexp(-Q / RT)(σ_h / σ_y)^γ, D=1-(E_t / E_0), wherein D is a damage variable, representing a stiffness degradation degree caused by internal micro-defects of the material, and the value range of D is [0, 1], E_t is a current effective elastic modulus, which is acquired in real time through in-situ ultrasonic measurement or dynamic load test, E_0 is an initial elastic modulus in a lossless state, which is determined by material factory detection or a standard tensile test, Δε_p is an equivalent plastic strain amplitude, A is a plastic damage coefficient, which is calibrated by material fatigue test, α is a plastic strain index, and the value range of α is 1.5-2.5, f_cor is an electrochemical corrosion factor, representing an acceleration effect of local corrosion on fatigue crack initiation, β is a corrosion coupling index, and the value range of β is 0.8-1.2, B is an environmental damage coefficient, describing stress corrosion sensitivity, Q is an activation energy, reflecting an atomic diffusion energy barrier under a corrosion medium, R is a gas constant, T is an absolute temperature, σ_h is a hydrostatic stress, σ_y is a material yield strength, and γ is a stress state index; inputting the key features into the fatigue damage evolution model, and obtaining a damage increment corresponding to the dynamic data through the fatigue damage evolution model.
2. The method of claim 1, wherein The mechanical features comprise a non-proportionality degree and a local strain gradient, wherein an expression for calculating the non-proportionality degree is: In the formula, is a strain gradient tensor, is a rate of change of strain ε along the x direction (length direction), is a rate of change of strain ε along the y direction (radial / circumferential direction), i and j are unit vectors of a two-dimensional rectangular coordinate system; an expression for calculating the local strain gradient is: F_np=∫_0∧T|ε_1·dε_2-ε_2·dε_1|dt / (4π·ε_a^2), wherein F_np is a non-proportionality degree factor, ε_1 and ε_2 are both instantaneous principal strain components, ε_a is an equivalent strain amplitude, and T is a load cycle.
3. The method of claim 1, wherein The electrochemical features comprise an impedance spectrum relaxation time constant, a charge transfer resistance decay rate, and a pitting corrosion sensitivity index.
4. The method of claim 1, wherein The environmental coupling features comprise a corrosion-mechanical fatigue interaction factor η, wherein the electrochemical corrosion factor f_cor is calculated according to the corrosion-mechanical fatigue interaction factor η, and an expression is: f_cor=1+ηt, wherein C is Cl - The concentration, k, is calibrated by the parameter of the shot noise spectrum of the electrochemical noise, and ΔE pit is the potential shift of the pitting corrosion.
5. An alloy ground material fatigue state prediction system characterized by comprising: The method comprises the following steps: a extracting module is configured to acquire dynamic data of an alloy grounding material, and extract key features related to fatigue damage from the dynamic data, wherein the key features comprise mechanical features, electrochemical features, and environmental coupling features; a constructing module is configured to construct a fatigue damage evolution model of a physical mechanism, wherein an expression of the fatigue damage evolution model is: dD / dt=A(Δε_p)^α(f_cor)^β+Bexp(-Q / RT)(σ_h / σ_y)^γ, D=1-(E_t / E_0), In the formula, D is a damage variable representing the degree of stiffness degradation caused by internal micro-defects of the material, and has a value range of [0, 1]; E_t is a current effective elastic modulus, which is obtained in real time through in-situ ultrasonic measurement or dynamic load testing; E_0 is an initial undamaged state elastic modulus, which is determined by material factory detection or standard tensile testing; Δε_p is an equivalent plastic strain amplitude; A is a plastic damage coefficient, which is calibrated by material fatigue testing; α is a plastic strain index, and has a value range of 1.5-2.5; f_cor is an electrochemical corrosion factor representing the acceleration effect of local corrosion on fatigue crack initiation; β is a corrosion coupling index, and has a value range of 0.8-1.2; B is an environmental damage coefficient describing stress corrosion sensitivity; Q is an activation energy reflecting an atomic diffusion energy barrier under a corrosive medium; R is a gas constant; T is an absolute temperature; σ_h is a hydrostatic stress; σ_y is a material yield strength; and γ is a stress state index. An output module is configured to input the key features into the fatigue damage evolution model, and the fatigue damage evolution model outputs a damage increment corresponding to the dynamic data.
6. An electronic device, comprising: The method comprises the following steps: At least one processor and a memory connected in communication with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 4.
7. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the method of any one of claims 1 to 4.