Port steel member corrosion whole process analysis method and system based on image detection

Abstract

The invention discloses a port steel member corrosion whole process analysis method and system based on image detection. The method comprises the following steps: acquiring a corrosion image of a top view angle and a corrosion image of a section view angle of a to-be-detected object; a corrosion pit time-varying probability model used for describing the rule that the corrosion pit changes along with time is constructed; according to the method, local corrosion characteristics of the metal structure can be accurately described, prediction of the corrosion process can be realized according to the constructed metal corrosion pit model, and evaluation of metal service safety is completed.

Description

Technical Field

[0001] The invention relates to the technical field of metal structure corrosion detection, and in particular to a method and system for analyzing the entire corrosion process of port steel components based on image detection. Background Art

[0002] Metal structures are widely used in today's buildings, but their corrosion susceptibility will closely affect the service safety of metal structures. Especially for local corrosion of metals, there are still obvious local differences in the degree of corrosion of metal structures in the overall environment. And because of the differences in the load conditions of different structures, the impact of local uneven corrosion of metals will be magnified. Therefore, real-time monitoring of the local corrosion characteristics of metal structures, such as corrosion area, corrosion depth and other indicators, can provide real-time feedback on the safety status of metal structures in service.

[0003] Most of the corrosion characteristics assessments of metal structures in service currently assume uniform corrosion. This assumption has limitations for the study of the load response of metal structures and cannot accurately assess the impact of metal defects. In addition, metal corrosion characteristics are extremely complex, and the large amount of impurities present on the surface will affect the assessment results of metal corrosion characteristics, which cannot be generalized in one way. Furthermore, in view of the complex on-site service environment, the corrosion characteristics assessment technology of metal structures still remains in the form of manual detection, lacking real-time, efficient and convenient technical means.

[0004] Most existing corrosion detection methods rely on manual detection and cannot predict the development of corrosion. Therefore, a method that can reveal the laws of corrosion development is needed. Summary of the invention

[0005] In view of this, the purpose of the present invention is to provide a method and system for analyzing the whole process of corrosion of port steel components based on image detection. The method uses image processing to obtain the development law of rust pits so as to predict the development of corrosion.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] The present invention provides a method for analyzing the entire process of corrosion of port steel components based on image detection, comprising the following steps:

[0008] Acquire the corrosion image and detection time data of the object to be detected;

[0009] Performing image processing on the corrosion image to obtain the geometric features of the corrosion pits of the object to be detected; and establishing a corrosion time-varying feature library based on the geometric features of the corrosion pits; the corrosion time-varying feature library is used to store the corrosion features of the object to be detected and the corrosion time data of the corrosion pits;

[0010] A time-varying model of the geometric features of the rust pits is constructed according to the geometric features of the rust pits and the corresponding rust time data, wherein the time-varying model of the geometric features of the rust pits is used to describe the fitting relationship between the geometric features of the rust pits and the rust time;

[0011] According to the time-varying model of the geometric characteristics of the rust pits, the rust images collected on site are analyzed and processed to obtain the rust prediction results of the objects to be detected.

[0012] Furthermore, the time-varying model of the geometric characteristics of the rust pit is established according to the following steps:

[0013] Acquire a top-down corrosion image of the object to be inspected from a top-down perspective;

[0014] Performing image processing on the top-view rust image to obtain the top-view rust geometric features of the rust pit;

[0015] A top-view corrosion geometric feature time-varying model is constructed according to the top-view corrosion geometric feature of the corrosion pit and the corresponding corrosion time. The top-view corrosion geometric feature time-varying model is used to describe the law of the top-view corrosion geometric feature of the corrosion pit changing with the corrosion time.

[0016] Furthermore, the top-view rust geometric features include morphological features of rust pits; the morphological features include rust pit radius features or rust pit area features.

[0017] Furthermore, the time-varying model of the geometric characteristics of the rust pit is established according to the following steps:

[0018] Acquire a cross-sectional corrosion image of the object to be inspected from a cross-sectional perspective;

[0019] Performing image processing on the cross-sectional corrosion image to obtain cross-sectional corrosion geometric features of the corrosion pit; the cross-sectional corrosion geometric features include depth features of the corrosion pit;

[0020] A time-varying model of cross-sectional corrosion geometric features is constructed according to the cross-sectional corrosion geometric features of the corrosion pit and the corresponding corrosion time. The time-varying model of cross-sectional corrosion geometric features is used to describe the law of changes in the cross-sectional corrosion geometric features of the corrosion pit with corrosion time.

[0021] Further, the corrosion area is established in the following manner:

[0022] The area characteristics of the rust pits from a top-down angle are obtained; the shape characteristics of the rust pits are obtained through image segmentation; the center and radius of the circle are determined according to the shape characteristics; the position and area of ​​each rust pit are determined according to the center and radius of the circle; and the effective superposition area is calculated by the superposition state of each circle.

[0023] Furthermore, the time-varying model of the geometric characteristics of the rust pit is a rust pit radius-time model, and the rust pit radius-time model is fitted according to the following formula:

[0024] R = a t ;

[0025] Where R is the radius of the rust pit and a is the fitting parameter.

[0026] Furthermore, the method also includes the following steps: fusing the rust pit morphology characteristics at a top-down angle with the rust pit depth characteristics at a cross-sectional angle to obtain a three-dimensional rust pit model.

[0027] Furthermore, the three-dimensional rust pit model is made according to the following steps: casting paraffin into a regular hexahedron as a basic mode; uniformly heating and softening one side of the paraffin mold and covering it on the surface of the on-site steel component, and maintaining the temperature for extrusion; until the paraffin fills the rust pit, a paraffin inverted mold result is obtained, that is, the convex surface of the paraffin is the concave surface of the rust pit.

[0028] The present invention provides a port steel component corrosion full process analysis system based on image detection, which includes a memory, a processor and a computer program stored in the memory and executable on the processor. The processor implements the above method when executing the program.

[0029] The beneficial effects of the present invention are:

[0030] The present invention provides a method for analyzing the entire process of corrosion of port steel components based on image detection. The method obtains the corrosion image of the top view and the corrosion image of the cross-sectional view of the object to be detected; thereby constructing a time-varying probability model of corrosion pits for describing the law of the change of corrosion pits over time; the method can not only accurately depict the local corrosion characteristics of metal structures, but also predict the corrosion process based on the constructed metal corrosion pit model, and complete the assessment of the service safety of metals. The time-varying probability model of corrosion pits established by the method can predict the life of the corrosion situation, that is, through the model, the morphology, corrosion characteristics, and corrosion area of ​​each stage of corrosion are described.

[0031] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to make the purpose, technical solution and beneficial effects of the present invention clearer, the present invention provides the following drawings for illustration:

[0033] Figure 1 This is a flow chart of the whole process analysis method of port steel structure corrosion based on image detection.

[0034] Figure 2Schematic diagram of the mathematical model of single pit expansion.

[0035] Figure 3 Schematic diagram of the mathematical model of multi-pit expansion.

[0036] Figure 4 Schematic diagram of the expansion process of the rust pit edge shape.

[0037] Figure 5 Schematic diagram of the development and fusion process of metal rust pits.

[0038] Figure 6 Schematic diagram of the fusion process of new and old pits.

[0039] Figure 7 Schematic diagram of the change of rust pit radius over time.

[0040] Figure 8 Schematic diagram of the principle of change of rust pit radius over time.

[0041] Fig. 9 Schematic diagram of the rust pit model based on the enlarged concentric circles of the spherical pit.

[0042] Fig.10 Schematic diagram of the entire process of metal corrosion development. DETAILED DESCRIPTION

[0043] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it, but the embodiments are not intended to limit the present invention.

[0044] Example 1

[0045] like Figure 1 As shown, the whole process analysis method of port steel component corrosion based on image detection provided in this embodiment is used to analyze the whole process of corrosion development, and then characterize the whole process of service development of metal components based on the reconstructed characteristic three-dimensional corrosion pit model; by analyzing the time factor, the whole process of metal corrosion development is characterized, and then the whole life cycle evaluation of the metal structure is obtained.

[0046] The whole process analysis method of port steel component corrosion based on image detection provided in this embodiment includes the following steps:

[0047] Acquire the corrosion image and detection time data of the object to be detected; in this embodiment, the detection time is recorded simultaneously when the image is acquired;

[0048] Performing image processing on the corrosion image to obtain the geometric features of the corrosion pits of the object to be detected; and establishing a corrosion time-varying feature library based on the geometric features of the corrosion pits; the corrosion time-varying feature library is used to store the corrosion features of the object to be detected and the corrosion time data of the corrosion pits;

[0049] A time-varying model of the geometric features of the rust pits is constructed according to the geometric features of the rust pits and the corresponding rust time data, wherein the time-varying model of the geometric features of the rust pits is used to describe the fitting relationship between the geometric features of the rust pits and the rust time;

[0050] According to the time-varying model of the geometric characteristics of the rust pits, the rust images collected on site are analyzed and processed to obtain the rust prediction results of the objects to be detected.

[0051] This embodiment constructs a time-varying model based on the sampled images and the acquisition time scale data, that is, constructs a time-varying model of the geometric characteristics of the rust pit by data fitting;

[0052] The time-varying model of geometric features of rust pits in this embodiment is established according to the following steps:

[0053] Acquire a top-down corrosion image of the object to be inspected from a top-down perspective;

[0054] Performing image processing on the top-view corrosion image to obtain top-view corrosion geometric features of the corrosion pit; the top-view corrosion geometric features include morphological features of the corrosion pit;

[0055] A top-view corrosion geometric feature time-varying model is constructed according to the top-view corrosion geometric feature of the corrosion pit and the corresponding corrosion time, wherein the top-view corrosion geometric feature time-varying model is used to describe the law of the top-view corrosion geometric feature of the corrosion pit changing with the corrosion time;

[0056] The morphological features in this embodiment include rust pit radius features or rust pit area features;

[0057] The top view rust geometric feature in this embodiment adopts the rust pit area feature; the rust pit area feature is the image pixel area obtained by image processing of the top view rust image;

[0058] In this embodiment, a generalized processing is performed based on the obtained area characteristics of the rust pit to obtain a circular image of the rust pit, thereby obtaining the radius of the circular image of the rust pit, and then constructing a corresponding relationship between radius and time when the expansion time-varying law of the rust pit is obtained.

[0059] In this embodiment, the circular radius of the rust pit area feature at each moment is calculated through the image grid, and the image on the grid is generalized into a mathematical function expression.

[0060] The time-varying model of geometric features of rust pits in this embodiment is established according to the following steps:

[0061] Acquire a cross-sectional corrosion image of the object to be inspected from a cross-sectional perspective;

[0062] Performing image processing on the cross-sectional corrosion image to obtain cross-sectional corrosion geometric features of the corrosion pit; the cross-sectional corrosion geometric features include depth features of the corrosion pit;

[0063] A time-varying model of the cross-sectional corrosion geometric features is constructed according to the cross-sectional corrosion geometric features of the corrosion pit and the corresponding corrosion time, wherein the time-varying model of the cross-sectional corrosion geometric features is used to describe the law of the cross-sectional corrosion geometric features of the corrosion pit changing with the corrosion time;

[0064] The corrosion area in this embodiment is established in the following manner:

[0065] Obtain the area characteristics of the rust pits from a top-down angle; obtain the shape characteristics of the rust pits through image segmentation; determine the center and radius of the circle based on the shape characteristics; determine the position and area of ​​each rust pit based on the center and radius of the circle; and calculate the effective superposition area through the superposition state of each circle;

[0066] Through indoor research, it is found that the development of the corrosion area is closely related to time. In this embodiment, the radius of the corrosion pit is used as the corresponding function of the corrosion area, that is, the corresponding relationship between the radius of the corrosion pit and the corrosion time is obtained through the time-varying law of the expansion of the corrosion pit:

[0067] S i =πR(t) 2

[0068] Among them, R(t) represents the corresponding relationship between the radius of the rust pit and the rust time:

[0069] This embodiment also includes the following steps:

[0070] Acquire a rust image of the object to be inspected from a top-down perspective;

[0071] Acquire a corrosion image of the object to be inspected from a cross-sectional perspective;

[0072] The top view rust image is processed to obtain the morphological characteristics of the rust pit;

[0073] The corrosion image of the cross-sectional view is processed to obtain the depth characteristics of the corrosion pit;

[0074] A three-dimensional rust pit model is constructed, wherein the three-dimensional rust pit model is used to describe the three-dimensional morphological characteristics of the rust pit.

[0075] The three-dimensional rust pit model of the present embodiment is obtained by integrating the rust pit morphology characteristics at a top-down perspective and the rust pit depth characteristics at a cross-sectional perspective.

[0076] The top-view rust pit image and the cross-sectional rust pit image collected in this embodiment are used to obtain the relationship between the top-view circular radius and depth of the rust pit in the three-dimensional rust pit model through data fitting. Direct three-dimensional morphology data can also be obtained by on-site paraffin sampling as a sampling test to verify the accuracy of the three-dimensional model.

[0077] In this embodiment, a paraffin wax casting method can be used to construct a three-dimensional model of the rust pit on site:

[0078] Use paraffin wax to mold the on-site rust pit, that is, use soft paraffin wax to completely depict the three-dimensional shape of the on-site rust pit. The first step is to cast the paraffin wax into a 5*5*2cm regular hexahedron as the basic mode; the second step is to evenly heat one side of the paraffin mold until it is slightly softened, and then the wide side can be directly covered on the surface of the on-site steel component, and the temperature is maintained and the extrusion is continued; the third step is to carefully remove the paraffin wax until the thickness is less than 1cm, and then the mold result can be obtained, that is, the convex surface of the paraffin wax is the concave surface of the rust pit.

[0079] According to the bubble theory of rust, the rust pits in the rust pit image can be regarded as spheres, and the area of ​​the rust pits at a top-down angle can be determined according to their depth and radius.

[0080] In this embodiment, a quantitative relationship between time and the top view and the cross-sectional view can be established respectively, and the quantitative relationship is established through indoor experiments; wherein, the area time-varying model and the depth time-varying model can be established based on the two-dimensional graph, and the volume time-varying model can be established based on the three-dimensional graph.

[0081] The quantitative relationship between the corrosion area and time in this embodiment is established according to the following steps:

[0082] (1) Single rust pit situation, such as Figure 2 As shown, Figure 2 It is a schematic diagram of the mathematical model of single pit expansion;

[0083] Based on the established bubble model assumption, it is believed that the shape of the rust pit starts from a circle and gradually expands and merges concentrically. Therefore, for the case where a single rust pit has not yet merged, the mathematical model of concentric circle expansion can be used to characterize it. Its time-varying relationship is as follows:

[0084] Its expansion rate can be expressed by an exponential function or by other functions:

[0085] R=R(t)

[0086] Example: R=a t ;

[0087] Where R is the radius of the rust pit, a is a fitting parameter, which is related to environmental variables and will affect the metal polarization in the rust mechanism. It is usually taken as 1.1 to 1.4, and t is the rust cycle.

[0088] (2) Multiple rust pits, such as Figure 3 As shown, Figure 3 It is a schematic diagram of the mathematical model of multi-pit concentric circle expansion;

[0089] After the circular rust pit develops to a certain extent, it will merge with the nearby rust pits and continue to develop. The boundaries of the fused rust pits will tend to be uniform and smooth during the continuous development.

[0090] Time T2 is the initial fusion state, and the effective superposition circle area can be calculated by fusion;

[0091] The T3 moment is the fusion development state, the overlapped part increases, and the left and right circles expand concentrically, which can be calculated by the fusion effective superposition method;

[0092] During the continuous development of fusion at T4, the overlap increases, the edges begin to appear smooth, and the sharp parts disappear;

[0093] At time T5, the edge is completely smooth and can be generalized as a tangent state. At this time, the rust pit will expand evenly along the edge shape;

[0094] In summary, if Figure 4 As shown, Figure 4 Schematic diagram of the expansion process of the edge shape of the rust pit. In this embodiment, the rust pit time-varying probability model adopts a rust pit radius time-varying model, that is, the relationship between the rust pit radius and time is established.

[0095] The quantitative relationship between corrosion depth and time described in this embodiment is established according to the following steps:

[0096] The corrosion depth distribution function can be based on a parabolic function, a hemispherical function or a catenary function.

[0097]

[0098] Among them, h is the point corrosion depth at the corresponding radius, parameters a and b are time-related variables, and their corresponding relationship is affected by the corrosion type and corrosion environment. Parameter a affects the shape of the corrosion pit, and parameter b affects the absolute depth of the corrosion pit, which can be written as:

[0099]

[0100] According to the development and fusion of rust pits, the pitting development is expanded outward in concentric circles, and the germination of new pitting pits and the fusion of old pitting pits are considered at the same time. Figure 5 As shown, Figure 5Schematic diagram of the development and fusion process of metal rust pits.

[0101] In this embodiment, after obtaining the three-dimensional model of the rust pit, in order to characterize the development law of the rust pit, the time-varying law of the rust area and the time-varying law of the depth can be obtained respectively, that is, the variation law of the rust radius and the rust depth over time is established.

[0102] The fusion process of the new and old pits in this embodiment is as follows: Figure 6 As shown, Figure 6 Schematic diagram of the fusion process of new and old pitting pits; the expansion of new pitting pits follows the single rust pit development mode, and the expansion of old pitting pits follows the multiple rust pit development mode.

[0103] Through indoor research, it is found that the development of the corrosion area is closely related to time. In this embodiment, the radius of the corrosion pit is used as the corresponding function of the corrosion area, that is, the corresponding relationship between the radius of the corrosion pit and the corrosion time is obtained through the time-varying law of the expansion of the corrosion pit:

[0104] S i =πR(t) 2

[0105] Among them, R(t) represents the corresponding relationship between the rust pit radius and the rust time.

[0106] Through indoor tests, such as Figure 7 As shown, Figure 7 Schematic diagram of the change of rust pit radius over time. Figure 8 The following is a schematic diagram of the principle of the change of the radius of the rust pit over time. The corresponding relationship between the radius of the rust pit and time can be fitted by measuring the change of the radius of a large number of small rust pits. The quantitative relationship between the rust depth and time is established according to the following steps:

[0107] Get the depth characteristics of the rust pits from a cross-sectional perspective;

[0108] The development of the depth of the rust pit at the cross-sectional angle is more complicated than that at the top-down angle. According to different rust types under different rust environments, two mathematical models of the development of rust depth are constructed; in this embodiment, a catenary or a parabola can be used for fitting;

[0109] The rust pit model based on the enlarged concentric circles of the spherical pit, such as Fig. 9 As shown, Fig. 9 This is a schematic diagram of the rust pit model based on the enlarged concentric circles of the spherical pit. Fig.10 Schematic diagram of the whole process of metal corrosion development. This embodiment takes the corrosion development of a certain test specimen as an example to illustrate the whole process of rust pit initiation. This model is suitable for the generalization of rust pits covered by rust product enrichment. This model assumes that the depth development of the rust pit is a time relationship that is linearly related to the top projection radius of the rust pit, and is calculated according to the following formula:

[0110]

[0111] Wherein, h represents the point corrosion depth; x, y represent the coordinates of the corrosion point; R' represents the radius of the generalized space sphere;

[0112] In the formula, the corrosion depth h can be calculated by the following formula:

[0113]

[0114] Where R is the radius of a single rust pit in a top-down view; α represents the angle between the radii of the large ball and the small ball in a spatial cross-section.

[0115] In this embodiment, the distribution of the pitting positions of the metal structure in the same area is randomly distributed from a macroscopic perspective. Therefore, three variables in the rust pit initiation process are considered as key factors for pitting initiation, namely, the initial size of the pit, the appearance of the pit, and the number of pits. The three variables are all dependent variables of the time variable. The specific formula is as follows:

[0116]

[0117] Among them, N um Indicates the number of pits;

[0118] According to the indoor tests at the current stage, it can be known that in actual situations, the initial size of new pitting pits is 1mm to 3mm in diameter, and their distribution is mainly concentrated near the original rust pits, that is, the closer to the original rust point, the denser it is. However, within a small range, it can be considered as an average probability function. The relationship between the number of pitting pits and time is relatively obvious, showing a slow increase at first, then a sharp increase, and a rapid decrease after reaching a threshold until it stabilizes near a value. The threshold is affected by the rust type and rust product.

[0119] That is, R can be expressed as follows

[0120] R = rand([1,3])

[0121] (x,y) can be expressed as follows

[0122]

[0123] In the formula, x, y are the coordinates of the rust initiation point, x0, y0 are the center of the original rust point, and R0 is the radius of the original rust pit;

[0124] N um It can be expressed by the following formula and follow the following rules.

[0125] N um =rand([3,10])

[0126]

[0127] In the formula, t is time, a is a parameter to be determined, and N is allowed um There is a fluctuation of 3 to 10 around the value of the regular function, so an additional random function needs to be formulated.

[0128] The three-dimensional rust pit model of this embodiment can adopt a rust pit model based on the expansion of a parabolic pit. This model is suitable for generalizing rust pits of small hole corrosion in local corrosion types. This model takes into account that the development direction of small hole corrosion is mainly concentrated at the bottom of the rust pit, and the development rate of the longitudinal depth far exceeds the edge expansion rate. Therefore, the rust pit can be regarded as a rotating parabola shape.

[0129] The above-described embodiments are only preferred embodiments for fully illustrating the present invention, and the protection scope of the present invention is not limited thereto. Equivalent substitutions or changes made by those skilled in the art based on the present invention are within the protection scope of the present invention. The protection scope of the present invention shall be subject to the claims.

Claims

1. A method for analyzing the whole process of corrosion of port steel components based on image detection, characterized by: The following steps are involved: Acquire the corrosion image and detection time data of the object to be detected; Performing image processing on the rust image to obtain the geometric features of the rust pits of the object to be detected; A corrosion time-varying feature library is established according to the geometric features of the corrosion pits; the corrosion time-varying feature library is used to store the corrosion features of the object to be detected and the corrosion time data of the corrosion pits; A time-varying model of the geometric features of the rust pits is constructed according to the geometric features of the rust pits and the corresponding rust time data, wherein the time-varying model of the geometric features of the rust pits is used to describe the fitting relationship between the geometric features of the rust pits and the rust time; According to the time-varying model of the geometric characteristics of the rust pits, the rust images collected on site are analyzed and processed to obtain the rust prediction results of the objects to be detected.

2. The method for analyzing the whole process of corrosion of port steel components based on image detection as claimed in claim 1 is characterized by: The time-varying model of the geometric characteristics of the rust pit is established according to the following steps: Acquire a top-down corrosion image of the object to be inspected from a top-down perspective; Performing image processing on the top-view rust image to obtain the top-view rust geometric features of the rust pit; A top-view corrosion geometric feature time-varying model is constructed according to the top-view corrosion geometric feature of the corrosion pit and the corresponding corrosion time. The top-view corrosion geometric feature time-varying model is used to describe the law of the top-view corrosion geometric feature of the corrosion pit changing with the corrosion time.

3. The method for analyzing the whole process of corrosion of port steel components based on image detection as claimed in claim 2 is characterized by: The top view rust geometric features include the morphological features of the rust pits; the morphological features include the rust pit radius features or the rust pit area features.

4. The method for analyzing the whole process of corrosion of port steel components based on image detection as claimed in claim 1 is characterized by: The time-varying model of the geometric characteristics of the rust pit is established according to the following steps: Acquire a cross-sectional corrosion image of the object to be inspected from a cross-sectional perspective; Performing image processing on the cross-sectional corrosion image to obtain cross-sectional corrosion geometric features of the corrosion pit; the cross-sectional corrosion geometric features include depth features of the corrosion pit; A time-varying model of cross-sectional corrosion geometric features is constructed according to the cross-sectional corrosion geometric features of the corrosion pit and the corresponding corrosion time. The time-varying model of cross-sectional corrosion geometric features is used to describe the law of changes in the cross-sectional corrosion geometric features of the corrosion pit with corrosion time.

5. The method for analyzing the whole process of corrosion of port steel components based on image detection as claimed in claim 1, characterized in that: The corrosion area is established in the following manner: The area characteristics of the rust pits from a top-down angle are obtained; the shape characteristics of the rust pits are obtained through image segmentation; the center and radius of the circle are determined according to the shape characteristics; the position and area of ​​each rust pit are determined according to the center and radius of the circle; and the effective superposition area is calculated by the superposition state of each circle.

6. The method for analyzing the whole process of corrosion of port steel components based on image detection as claimed in claim 4 is characterized by: The time-varying model of the geometric characteristics of the rust pit is a rust pit radius-time model, and the rust pit radius-time model is fitted according to the following formula: R=a t ; Where R is the radius of the rust pit and a is the fitting parameter.

7. The method for analyzing the whole process of corrosion of port steel components based on image detection as claimed in claim 1, characterized in that: The following steps are also included: The 3D rust pit model is obtained by fusing the morphological features of the rust pit at a top-down perspective with the depth features of the rust pit at a cross-sectional perspective.

8. The method for analyzing the whole process of corrosion of port steel components based on image detection as claimed in claim 7, characterized in that: The three-dimensional rust pit model is carried out according to the following steps: The paraffin is cast into a regular hexahedron as the basic mode; one side of the paraffin mold is evenly heated and softened, and then covered on the surface of the on-site steel component, and the temperature is maintained for extrusion; until the paraffin fills the rust pit, the paraffin inverted mold result is obtained, that is, the convex surface of the paraffin is the concave surface of the rust pit.

9. A full-process analysis system for corrosion of port steel components based on image detection, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 8 is implemented.

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