A corrosion intelligent steel wire fatigue life prediction method and system

Abstract

The present invention discloses a method and system for predicting the fatigue life of a corrosion-resistant smart steel wire. The method comprises: constructing a multi-parameter Weibull cumulative distribution function model for the fatigue life of the smart steel wire based on the Basquin equation; determining the mass loss rate based on corrosion testing, using the mass loss rate as the corrosion degree of the smart steel wire, and obtaining a cumulative distribution function for the fatigue life of the corrosion-resistant smart steel wire based on the corrosion degree and the multi-parameter Weibull cumulative distribution function model for the fatigue life of the smart steel wire; and obtaining the fatigue life of the smart steel wire based on the cumulative distribution function for the fatigue life of the smart steel wire and the guarantee rate of the smart steel wire. The present invention studies the influence of the corrosion degree on the fatigue life of the smart steel wire, establishes a fatigue life model for the corrosion-resistant smart steel wire, and solves the problem of predicting the fatigue life of the smart steel wire in a corrosive environment.

Description

Technical Field

[0001] The present invention relates to the technical field of civil engineering, and in particular to a corrosion intelligent steel wire fatigue life prediction method and system. Background Art

[0002] Smart steel wire refers to ordinary steel wire with a fiber Bragg grating (FBG) embedded in it. It has both sensing and force-bearing functions and is often used as a sensor in cables or hangers. During its service life, the smart steel wire will be subject to environmental corrosion, which will affect its fatigue life and sensing performance. Currently, in terms of the fatigue life of smart steel wire, the design must meet the requirement of 2 million cycles without breaking under the corresponding stress amplitude. There is no quantitative research on the impact of corrosion on the fatigue life of smart steel wire. In terms of the sensing performance of smart steel wire, the stress is monitored using an uncorroded smart steel wire sensing model during service, and the impact of corrosion on the sensing performance of the smart steel wire is not considered.

[0003] Therefore, the existing technology still needs to be improved and enhanced. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a corrosion intelligent steel wire fatigue life prediction method and system in response to the above-mentioned defects of the prior art, aiming to solve the problem that the prior art does not quantitatively study the impact of corrosion on the fatigue life of intelligent steel wire when analyzing the fatigue life of intelligent steel wire.

[0005] In order to solve the above technical problems, the technical solutions adopted by the present invention are as follows:

[0006] In a first aspect, the present invention provides a method for predicting fatigue life of a corrosion-resistant intelligent steel wire, wherein the method comprises:

[0007] A multi-parameter Weibul l cumulative distribution function model of smart steel wire fatigue life is constructed based on the Basquin equation;

[0008] Based on the corrosion test, the mass loss rate is determined, and the mass loss rate is used as the corrosion degree of the smart steel wire. Based on the multi-parameter Weibull cumulative distribution function model of the corrosion degree and the fatigue life of the smart steel wire, a cumulative distribution function of the fatigue life of the corroded smart steel wire is obtained;

[0009] Based on the cumulative distribution function of the fatigue life of the corroded smart steel wire and the guarantee rate of the smart steel wire, the fatigue life of the smart steel wire is obtained.

[0010] In one implementation, constructing a multi-parameter Weibull cumulative distribution function model of the fatigue life of the smart steel wire based on the Basquin equation includes:

[0011] Under a constant amplitude load, the relationship between the fatigue stress amplitude ΔS and the fatigue life N of the smart steel wire is determined as follows: lgN=C-mlgΔS, where C and m are material constants;

[0012] Determine the guarantee rate of the smart steel wire, and obtain the fatigue characteristic life N according to the relationship between the guarantee rate, fatigue stress amplitude ΔS and fatigue life N a , and based on the fatigue characteristic life N a The multi-parameter Weibull cumulative distribution function model of the fatigue life of the smart steel wire is obtained.

[0013] In one implementation, the fatigue characteristic life N is obtained according to the relationship between the guarantee rate, fatigue stress amplitude ΔS and fatigue life N. a ,include:

[0014] For a given guarantee rate P, the relationship between fatigue stress amplitude ΔS and fatigue life N is expressed as: lgN P =C P -mlgΔS; where N P is the fatigue life under the given guarantee rate P; C P is the model parameter for a given guarantee rate P;

[0015] Under the action of a specified stress amplitude ΔS, the fatigue life N of the smart steel wire obeys the Weibull distribution:

[0016]

[0017] Where b is the shape function of the Weibull distribution and has nothing to do with the stress amplitude ΔS; N a is the fatigue characteristic life of the smart steel wire under the specified stress amplitude ΔS.

[0018] In one implementation, the fatigue characteristic life N a The multi-parameter Weibull cumulative distribution function model of the fatigue life of the smart steel wire is obtained, including:

[0019] The fatigue life N of the smart steel wire is subjected to logarithmic transformation on both sides of the Weibul l distribution to obtain:

[0020] If lgN=C-mlgΔS and Take the same guarantee rate, according to the consistency condition requirements, fatigue characteristic life N a Expressed as:

[0021] N a =K(ΔS) -m ;

[0022] Na =K(ΔS) -m Substitution The multi-parameter Weibul l cumulative distribution function model of the fatigue life of the smart steel wire is obtained:

[0023] Among them, K is a parameter to be determined in the model.

[0024] In one implementation, the mass loss rate w is expressed as:

[0025]

[0026] Where l is the length of the corroded section of the steel wire, L is the total length of the steel wire, r is the radius of the steel wire, M is the mass of the smart steel wire, and m1 and m2 are the masses of the smart steel wire before and after corrosion, respectively.

[0027] In one implementation, the multi-parameter Weibull cumulative distribution function model based on the corrosion degree and the fatigue life of the smart steel wire obtains the cumulative distribution function of fatigue life of the corroded smart steel wire, including:

[0028] Under the same corrosion degree, the fatigue life N and fatigue stress amplitude ΔS of the smart steel wire are linearly related in the double logarithmic coordinate system. The slope of the SN curve and the mass loss of the smart steel wire are expressed as:

[0029] c and d are model unknowns, where cw+d<0, c>0 and d<0;

[0030] Under the same stress amplitude, the fatigue life N of the smart steel wire and the mass loss w are linearly related in the logarithmic coordinate system. The fatigue characteristic life N of the corroded smart steel wire is a The relationship between ' and mass loss rate w is expressed as:

[0031] N a '=N a exp(rw)=K(ΔS) -m exp(rw), where r is the unknown parameter of the model and r<0;

[0032] The obtained cumulative distribution function of fatigue life of corrosion intelligent steel wire is:

[0033]

[0034] In one implementation, the method further includes:

[0035] Acquiring the smart steel wire monitoring wavelength data, and determining a corrosion smart steel wire sensing model based on the smart steel wire monitoring wavelength data;

[0036] Based on the mass loss rate and the corrosion intelligent steel wire sensing model, an intelligent steel wire sensing performance model is constructed.

[0037] In a second aspect, an embodiment of the present invention further provides a corrosion intelligent steel wire fatigue life prediction device, wherein the device comprises:

[0038] Function model building module, used to build a multi-parameter Weibul l cumulative distribution function model of smart steel wire fatigue life based on the Basquin equation;

[0039] a distribution function establishment module for determining a mass loss rate based on a corrosion model and a corrosion test, and using the mass loss rate as the corrosion degree of the smart steel wire, and obtaining a cumulative distribution function of fatigue life of the corroded smart steel wire based on a multi-parameter Weibul l cumulative distribution function model of the corrosion degree and fatigue life of the smart steel wire;

[0040] The fatigue life determination module is used to obtain the fatigue life of the smart steel wire based on the cumulative distribution function of the fatigue life of the corroded smart steel wire and the guarantee rate of the smart steel wire.

[0041] In a third aspect, an embodiment of the present invention further provides a terminal device, wherein the terminal device is applied to a receiving end or a transmitting end, and includes a memory, a processor, and a corrosion intelligent steel wire fatigue life prediction program stored in the memory and runnable on the processor. When the processor executes the corrosion intelligent steel wire fatigue life prediction program, the steps of the corrosion intelligent steel wire fatigue life prediction method of any one of the above-mentioned schemes are implemented.

[0042] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a corrosion intelligent steel wire fatigue life prediction program is stored. When the corrosion intelligent steel wire fatigue life prediction program is executed by a processor, the steps of the corrosion intelligent steel wire fatigue life prediction method of any one of the above-mentioned schemes are implemented.

[0043] Beneficial effects: Compared with the prior art, the present invention provides a method for predicting the fatigue life of a corrosion-sensitive smart steel wire. The present invention first constructs a multi-parameter Weibul l cumulative distribution function model of the fatigue life of the smart steel wire based on the Basquin equation. Then, based on the corrosion test, the mass loss rate is determined, and the mass loss rate is used as the degree of corrosion of the smart steel wire, and based on the multi-parameter Weibul l cumulative distribution function model of the corrosion degree and the fatigue life of the smart steel wire, the cumulative distribution function of the fatigue life of the corrosion-sensitive smart steel wire is obtained. Finally, based on the cumulative distribution function of the fatigue life of the corrosion-sensitive smart steel wire and the guarantee rate of the smart steel wire, the fatigue life of the smart steel wire is obtained. The present invention studies the influence of the degree of corrosion on the fatigue life of the smart steel wire, establishes a fatigue life model of the corrosion-sensitive smart steel wire, and solves the problem of predicting the fatigue life of the smart steel wire in a corrosive environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a flowchart of a specific implementation of the corrosion intelligent steel wire fatigue life prediction method provided in an embodiment of the present invention.

[0045] Figure 2 This is a graph showing the relationship between the slope of the SN curve and the corrosion rate in the corrosion intelligent steel wire fatigue life prediction method provided by an embodiment of the present invention.

[0046] Figure 3 This is the wN curve diagram of the smart steel wire in the corrosion smart steel wire fatigue life prediction method provided by an embodiment of the present invention.

[0047] Figure 4 The corrosion intelligent steel wire wK in the fatigue life prediction method of the corrosion intelligent steel wire provided by the embodiment of the present invention σ curve chart.

[0048] Figure 5 This is a module schematic diagram of the corrosion intelligent steel wire fatigue life prediction device provided by an embodiment of the present invention.

[0049] Figure 6 A schematic diagram of a terminal device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and effect of the present invention clearer and more specific, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0051] The present embodiment provides a method for predicting the fatigue life of a corrosion-resistant smart steel wire. The present embodiment first constructs a multi-parameter Weibull cumulative distribution function model of the fatigue life of the smart steel wire based on the Basquin equation. Then, based on the corrosion test, the mass loss rate is determined, and the mass loss rate is used as the degree of corrosion of the smart steel wire, and based on the multi-parameter Weibull cumulative distribution function model of the corrosion degree and the fatigue life of the smart steel wire, the cumulative distribution function of the fatigue life of the corrosion-resistant smart steel wire is obtained. Finally, based on the cumulative distribution function of the fatigue life of the corrosion-resistant smart steel wire and the guarantee rate of the smart steel wire, the fatigue life of the smart steel wire is obtained. The present invention studies the influence of the degree of corrosion on the fatigue life of the smart steel wire, establishes a fatigue life model of the corrosion-resistant smart steel wire, and solves the problem of predicting the fatigue life of the smart steel wire in a corrosive environment.

[0052] Exemplary Methods

[0053] The corrosion intelligent steel wire fatigue life prediction method of this embodiment can be applied to terminal devices, which can be intelligent product devices such as computers, smart TVs and mobile phones. Specifically, Figure 1 As shown in , the corrosion intelligent steel wire fatigue life prediction method of this embodiment includes the following steps:

[0054] Step S100: constructing a multi-parameter Weibull cumulative distribution function model of the fatigue life of the smart steel wire based on the Basquin equation.

[0055] In specific applications, this embodiment determines the relationship between the fatigue stress amplitude ΔS and fatigue life N of the smart steel wire under constant amplitude load: lgN=C-mlgΔS(1)

[0056] Where C and m are material constants. Then determine the guarantee rate of the smart steel wire, and according to the relationship between the guarantee rate, fatigue stress amplitude ΔS and fatigue life N, obtain the fatigue characteristic life N a , and based on the fatigue characteristic life N a The multi-parameter Weibull cumulative distribution function model of the fatigue life of the smart steel wire is obtained.

[0057] Specifically, since the SN curves of metal materials are parallel to each other under different survival probabilities, for a given guarantee rate P, formula (1) can be expressed as:

[0058] lgN P =C P -mlgΔS (2)

[0059] Where: N P is the fatigue life under the given guarantee rate P; C Pis the model parameter for a given guarantee rate P. Under the action of a specified stress amplitude ΔS, the fatigue life N of the steel wire obeys the following two-parameter Weibull distribution:

[0060]

[0061] Where: b is the shape function of the Weibull distribution, which is independent of the stress amplitude ΔS; N a is the fatigue characteristic life parameter of the smart steel wire under the specified stress amplitude ΔS. Performing logarithmic transformation on both sides of formula (3) yields:

[0062]

[0063] When the same guarantee rate is taken for formula (2) and formula (5-4), comparing formula (2) and formula (4), according to the consistency condition requirements, the fatigue characteristic life N a It can be expressed by formula (5-5):

[0064] N a =K(ΔS) -m (5)

[0065] Substituting formula (5) into formula (3) yields:

[0066]

[0067] Formula (6) can be used as a multi-parameter Weibul l cumulative distribution function model to characterize the fatigue life of smart steel wire, where m, b and K are all parameters to be determined in the model.

[0068] Step S200: Based on the corrosion test, determine the mass loss rate, and use the mass loss rate as the corrosion degree of the smart steel wire. Then, based on the multi-parameter Weibul l cumulative distribution function model of the corrosion degree and the fatigue life of the smart steel wire, obtain the cumulative distribution function of the fatigue life of the corroded smart steel wire.

[0069] Specifically, the accelerated corrosion test of the smart steel wire in this embodiment adopts an acetic acid salt spray test, and the mass loss rate w is used to characterize the corrosion degree of the smart steel wire, as shown in formula (7):

[0070]

[0071] Where l is the length of the corroded section of the steel wire, L is the total length of the steel wire, r is the radius of the steel wire, M is the mass of the smart steel wire (excluding the optical fiber sensing line), and m1 and m2 are the masses of the smart steel wire (including the optical fiber sensing line) before and after corrosion, respectively. The mass loss rate of the corroded smart steel wire calculated using Equation (7) is shown in Table 1.

[0072]

[0073]

[0074] Table 1

[0075] After the corrosion test, the smart steel wire was subjected to fatigue testing. A fiber Bragg grating (FBG) interrogator was used to record the smart steel wire's wavelength data in real time, with a sampling frequency of 100 Hz. The fatigue test results are shown in Table 2.

[0076]

[0077]

[0078] Table 2

[0079] Figure 2 The relationship between the slope of the SN curve and the corrosion rate is given. At the same corrosion level, the fatigue life N and fatigue stress amplitude ΔS of the smart steel wire are linearly related in a double logarithmic coordinate system. Combined with formula (1), the slope of the SN curve and the corrosion rate of the smart steel wire can be expressed by formula (8):

[0080]

[0081] Where: c and d are model unknowns, where cw+d<0, c>0 and d<0.

[0082] Figure 3 The relationship curve between fatigue life N and mass loss rate w of corrosion smart steel wire is given. Under the same stress amplitude, fatigue life N and corrosion rate w of smart steel wire are linearly related in logarithmic coordinate system. a The relationship between ' and mass loss rate w can be expressed as:

[0083] N a '=N a exp(rw)=K(ΔS) -m exp(rw) (9)

[0084] Where r is the unknown parameter of the model, and r<0. Substituting (8) and (9) into (6) yields the cumulative distribution function of fatigue life of the corroded smart steel wire:

[0085]

[0086] This embodiment uses maximum likelihood estimation to estimate the unknown parameters in equation (10), as shown in Table 3.

[0087] parameter K r b c d Estimated value 2.4165×1011 -0.03317 15.734 0.00157 -0.40262

[0088] Table 3

[0089] Step S300: Obtain the fatigue life of the smart steel wire based on the cumulative distribution function of the fatigue life of the corroded smart steel wire and the guarantee rate of the smart steel wire.

[0090] Therefore, after obtaining the cumulative distribution function of the fatigue life of the corroded smart steel wire, this embodiment can calculate the fatigue life of the smart steel wire based on the cumulative distribution function of the fatigue life of the corroded smart steel wire and the guarantee rate of the smart steel wire. This embodiment quantitatively studies the impact of the corrosion level on the fatigue life of the smart steel wire based on experimental data and establishes a fatigue life model for the corroded smart steel wire. This quantitative analysis of the impact of corrosion on the fatigue life of the smart steel wire can predict the fatigue life of the smart steel wire under any corrosion level and stress amplitude.

[0091] In another implementation, this embodiment further obtains the smart steel wire monitoring wavelength data and determines a corrosion smart steel wire sensing model based on the smart steel wire monitoring wavelength data. A smart steel wire sensing performance model is then constructed based on the mass loss rate and the corrosion smart steel wire sensing model. In other words, this embodiment considers the impact of corrosion on the sensing performance of the smart steel wire and constructs a sensing performance model for the smart steel wire during service.

[0092] Specifically, the sensing performance of the smart steel wire in this embodiment is determined by the built-in fiber Bragg grating (FBG) sensor. Without considering the temperature effect, the wavelength difference of the FBG can be expressed as:

[0093] Δλ=K ε ε g (11)

[0094] Where: ε g is the axial strain of FBG; K ε is the FBG strain sensitivity.

[0095] FBG strain ε g With smart wire strain ε m They are related by the strain transfer rate β, which is:

[0096] Δλ=K ε βε m =K ε 'ε m (12)

[0097] Where: K ε ' is the strain sensitivity of FBG monitoring. The strain ε of the smart steel wire m The relationship with stress σ is:

[0098]

[0099] Where: E is the elastic modulus of the smart steel wire. Substituting equation (13) into equation (12), the relationship between Δλ and σ can be obtained:

[0100]

[0101] Where: K σ is the stress sensitivity coefficient of FBG monitoring.

[0102] Furthermore, based on formula (14) and the wavelength data of the corroded smart steel wire under various stresses in Table 2, the stress sensitivity coefficient K of the smart steel wire under various corrosion degrees can be obtained. σ . Figure 4 The inverse of the corrosion smart wire force sensitivity coefficient is given and mass loss rate w.

[0103] Then, the least square method was used to fit the data, and the inverse of the stress sensitivity coefficient The relationship expression with mass loss rate w is:

[0104]

[0105] Substituting (15) into (14) yields the corrosion intelligent steel wire sensing performance model:

[0106]

[0107] As can be seen, this example quantitatively studies the impact of corrosion on the fatigue life and sensing performance of the smart steel wire based on experimental data. It also establishes a fatigue life model and a sensing model for the corroded smart steel wire. This quantitatively analyzes the impact of corrosion on the fatigue life of the smart steel wire and can predict the fatigue life of the smart steel wire under any corrosion level and stress amplitude. Furthermore, the impact of corrosion on the sensing performance of the smart steel wire is considered, and a sensing performance model for the smart steel wire during service is constructed.

[0108] Exemplary devices

[0109] Based on the above embodiments, the present invention also provides a corrosion intelligent steel wire fatigue life prediction device, such as Figure 5As shown, the device includes: a function model building module 10, a distribution function building module 20, and a distribution function building module 30. Specifically, the function model building module 10 is used to build a multi-parameter Weibul l cumulative distribution function model of the fatigue life of the smart steel wire based on the Basquin equation. The distribution function building module 20 is used to determine the mass loss rate based on the corrosion model based on the corrosion test, and use the mass loss rate as the corrosion degree of the smart steel wire, and obtain the cumulative distribution function of the fatigue life of the corroded smart steel wire based on the corrosion degree and the multi-parameter Weibul l cumulative distribution function model of the fatigue life of the smart steel wire. The fatigue life determination module 30 is used to obtain the fatigue life of the smart steel wire based on the cumulative distribution function of the fatigue life of the corroded smart steel wire and the guarantee rate of the smart steel wire.

[0110] The working principle of the corrosion intelligent steel wire fatigue life prediction device in this embodiment is the same as the execution process of each step in the above method embodiment, and will not be repeated here.

[0111] Based on the above embodiment, the present invention further provides a terminal device, and the principle block diagram of the terminal device may be shown in Figure 6. The terminal device of this embodiment may include one or more processors 100 ( Figure 6 Only one is shown in the figure), memory 101, and a computer program 102 stored in memory 101 and executable on one or more processors 100, for example, a program for predicting the fatigue life of a corrosion-sensitive steel wire. When one or more processors 100 execute computer program 102, each step of an embodiment of a method for predicting the fatigue life of a corrosion-sensitive steel wire can be implemented. Alternatively, when one or more processors 100 execute computer program 102, each module / unit in an embodiment of an apparatus for predicting the fatigue life of a corrosion-sensitive steel wire can be implemented, without limitation herein.

[0112] In one embodiment, the processor 100 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0113] In one embodiment, the memory 101 may be an internal storage unit of an electronic device, such as a hard disk or memory of the electronic device. The memory 101 may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device. Furthermore, the memory 101 may also include both an internal storage unit of the electronic device and an external storage device. The memory 101 is used to store computer programs and other programs and data required by the terminal device. The memory 101 may also be used to temporarily store data that has been output or is about to be output.

[0114] Those skilled in the art will understand that Figure 6 The principle block diagram shown in the figure is only a block diagram of a partial structure related to the solution of the present invention, and does not constitute a limitation on the terminal device to which the solution of the present invention is applied. The specific terminal device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0115] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-described method embodiments. Among them, any reference to memory, storage, operation database or other media used in the various embodiments provided by the present invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAM bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0116] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A corrosion intelligent steel wire fatigue life prediction method, characterized in that: The method comprises: A multi-parameter Weibull cumulative distribution function model of the fatigue life of smart steel wire is constructed based on the Basquin equation; Based on the corrosion test, the mass loss rate is determined, and the mass loss rate is used as the corrosion degree of the smart steel wire. Based on the multi-parameter Weibull cumulative distribution function model of the corrosion degree and the fatigue life of the smart steel wire, a cumulative distribution function of the fatigue life of the corroded smart steel wire is obtained; Based on the cumulative distribution function of fatigue life of the corroded smart steel wire and the guarantee rate of the smart steel wire, the fatigue life of the smart steel wire is obtained; Acquiring the smart steel wire monitoring wavelength data, and determining the smart steel wire sensing model based on the smart steel wire monitoring wavelength data; Constructing a corrosion intelligent steel wire sensing performance model based on the mass loss rate and the intelligent steel wire sensing model; Acquiring the smart steel wire monitoring wavelength data, determining a smart steel wire sensing model based on the smart steel wire monitoring wavelength data, and constructing a corrosion smart steel wire sensing performance model based on the mass loss rate and the smart steel wire sensing model, including: The wavelength difference of the fiber Bragg grating (FBG) is obtained by the built-in fiber Bragg grating (FBG) sensor of the smart steel wire, which is expressed as: Δλ=K ε e g Where: ε g is the axial strain of FBG; K ε is the FBG strain sensitivity; Correlation between FBG axial strain ε and strain transmissibility β g With smart wire strain ε m ,get: Δλ=K ε be m =K ε hey m Where: K ε ' is the strain sensitivity of FBG monitoring, smart wire strain ε m The relationship with stress σ is expressed as: Where: E is the elastic modulus of the smart steel wire; Substituting the relationship between the strain and stress of the smart steel wire into the wavelength difference expression of the FBG, the relationship between Δλ and σ is obtained, which is expressed as: Where: K σ is the stress sensitivity coefficient of FBG monitoring; Based on the relationship between Δλ and σ and the wavelength data of the smart steel wire under various stresses obtained from the test, the stress sensitivity coefficient K of the smart steel wire is obtained. σ ; Based on the stress sensitivity coefficient K of the corrosion smart steel wire at each corrosion degree obtained by test σ and mass loss rate w, the data were fitted using the least squares method to obtain the inverse of the stress sensitivity coefficient A linear relationship between the stress sensitivity coefficient and the mass loss rate w; The relationship between Δλ and mass loss rate w is substituted into the relationship expression between Δλ and σ to obtain the corrosion intelligent steel wire sensing performance model.

2. The corrosion intelligent steel wire fatigue life prediction method according to claim 1 is characterized in that: A multi-parameter Weibul l cumulative distribution function model of the fatigue life of smart steel wire is constructed based on the Basquin equation, including: Under a constant amplitude load, the relationship between the fatigue stress amplitude ΔS and the fatigue life N of the smart steel wire is determined as follows: lgN=C-mlgΔS, where C and m are material constants; Determine the guarantee rate of the smart steel wire, and obtain the fatigue characteristic life N according to the relationship between the guarantee rate, fatigue stress amplitude ΔS and fatigue life N a , and based on the fatigue characteristic life N a The multi-parameter Weibull cumulative distribution function model of the fatigue life of the smart steel wire is obtained.

3. The corrosion intelligent steel wire fatigue life prediction method according to claim 2 is characterized in that: According to the relationship between the guarantee rate, fatigue stress amplitude ΔS and fatigue life N, the fatigue characteristic life N is obtained. a ,include: For a given guarantee rate P, the relationship between fatigue stress amplitude ΔS and fatigue life N is expressed as: lgN P =C P -mlgΔS; where N P is the fatigue life under the given guarantee rate P; C P is the model parameter for a given guarantee rate P; Under the action of a specified stress amplitude ΔS, the fatigue life N of the smart steel wire obeys the Weibull distribution: Where b is the shape function of the Weibull distribution and has nothing to do with the stress amplitude ΔS; N a is the fatigue characteristic life of the smart steel wire under the specified stress amplitude ΔS.

4. The corrosion intelligent steel wire fatigue life prediction method according to claim 3 is characterized in that: Based on the fatigue characteristic life N a The multi-parameter Weibull cumulative distribution function model of the fatigue life of the smart steel wire is obtained, including: The fatigue life N of the smart steel wire is subjected to logarithmic transformation on both sides of the Weibull distribution, and the obtained result is: If lgN=C-mlgΔS and Take the same guarantee rate, according to the consistency condition requirements, fatigue characteristic life N a Expressed as: N a =K(ΔS) -m ; N a =K(ΔS) -m Substitution The multi-parameter Weibull cumulative distribution function model of the fatigue life of the smart steel wire is obtained: Among them, K is a parameter to be determined in the model.

5. The corrosion intelligent steel wire fatigue life prediction method according to claim 4 is characterized in that: The mass loss rate w is expressed as: Where l is the length of the corroded section of the steel wire, L is the total length of the steel wire, M is the mass of the smart steel wire, and m1 and m2 are the masses of the smart steel wire before and after corrosion, respectively.

6. The corrosion intelligent steel wire fatigue life prediction method according to claim 5 is characterized in that: The multi-parameter Weibull cumulative distribution function model based on the corrosion degree and the fatigue life of the smart steel wire obtains the cumulative distribution function of the fatigue life of the smart steel wire, including: Under the same corrosion degree, the fatigue life N and fatigue stress amplitude ΔS of the smart steel wire are linearly related in the double logarithmic coordinate system. The slope of the SN curve and the mass loss rate of the smart steel wire are expressed as: c and d are model unknowns, where cw+d<0, c>0 and d<0; Under the same stress amplitude, the fatigue life N of the smart steel wire and the mass loss rate w are linearly related in the logarithmic coordinate system. The fatigue characteristic life N of the corroded smart steel wire is a The relationship between ' and mass loss rate w is expressed as: N a '=N a exp(rw)=K(ΔS) -m exp(rw), where r is the unknown parameter of the model and r<0; The cumulative distribution function of fatigue life of the smart steel wire is obtained as follows:

7. A corrosion intelligent steel wire fatigue life prediction device, characterized in that: The device is applied to implement the steps of the corrosion intelligent steel wire fatigue life prediction method according to any one of claims 1 to 6, comprising: Function model building module, used to build a multi-parameter Weibul l cumulative distribution function model of smart steel wire fatigue life based on the Basquin equation; a distribution function establishment module for determining a mass loss rate based on a corrosion model and a corrosion test, and using the mass loss rate as the corrosion degree of the smart steel wire, and obtaining a cumulative distribution function of fatigue life of the corroded smart steel wire based on a multi-parameter Weibul l cumulative distribution function model of the corrosion degree and fatigue life of the smart steel wire; The fatigue life determination module is used to obtain the fatigue life of the smart steel wire based on the cumulative distribution function of the fatigue life of the corroded smart steel wire and the guarantee rate of the smart steel wire.

8. A terminal device, characterized in that: The terminal device is applied to a receiving end or a transmitting end, and includes a memory, a processor, and a corrosion intelligent steel wire fatigue life prediction program stored in the memory and runnable on the processor. When the processor executes the corrosion intelligent steel wire fatigue life prediction program, the steps of the corrosion intelligent steel wire fatigue life prediction method as described in any one of claims 1-6 are implemented.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a corrosion intelligent steel wire fatigue life prediction program. When the corrosion intelligent steel wire fatigue life prediction program is executed by the processor, the steps of the corrosion intelligent steel wire fatigue life prediction method according to any one of claims 1 to 6 are implemented.

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