Laser cleaning method for railway steel bridge coating

Abstract

The invention discloses a railway steel bridge coating laser cleaning method which comprises the steps that moving parameters are set, a magnetic type cleaning platform is controlled to move along a railway steel bridge based on the moving parameters, and a steel bridge surface image is collected in real time; analyzing paint surface inferior condition information based on the steel bridge surface image, and obtaining a preset cleaning position according to the paint surface inferior condition information; the magnetic suction type cleaning platform is controlled to carry the laser cleaner to move to a preset cleaning position, and posture information of the laser cleaner is adjusted according to the preset cleaning position; performing hierarchical analysis and comparison on the surface image of the steel bridge based on a coating evaluation system, identifying different paint layer categories, and judging maintenance grades; setting cleaning parameters of the laser cleaner according to the maintenance grade, and controlling the laser cleaner to clean the paint layer according to the cleaning parameters; layered cleaning is conducted on the surface coatings of the railway steel bridge through the pulse laser cleaning device, different parameters are set for different coatings, layered paint removal is completed, and the cleaning effect is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of steel bridge cleaning, and particularly relates to a laser cleaning method for railway steel bridge coatings. Background Art

[0002] Currently, the surface cleaning of steel girders of large-span railway steel bridges mainly adopts the method of sandblasting. This cleaning method has problems such as environmental pollution, high energy consumption, and large labor input. The laser technology cleaning device for the surface of railway bridge steel structures can be used for cleaning the rust, aging, and powdered paint layers of railway steel bridge girders.

[0003] Most of the existing surface cleaning methods for railway bridges are through manual rust removal or chemical reagent rust removal, without using laser technology. It is impossible to improve the quality and efficiency of railway bridge maintenance and the environmental protection level. It is easy to cause dirt residues caused by similar dissolution in chemical cleaning, and it will also generate secondary pollution formed by paint removers, grinding dust, etc. The cleaning process quality is poor. Summary of the Invention

[0004] The purpose of the present invention is to provide a laser cleaning method for railway steel bridge coatings to solve the problems in the prior art.

[0005] To achieve the above purpose, the technical solution adopted by the present invention is: a laser cleaning method for railway steel bridge coatings, including:

[0006] S1, set moving parameters, control the magnetic adsorption cleaning platform to move along the railway steel bridge based on the moving parameters, and collect the surface image of the steel bridge in real time;

[0007] S2, analyze the information of the inferior paint surface based on the surface image of the steel bridge, and obtain the predetermined cleaning position according to the information of the inferior paint surface;

[0008] S3, control the magnetic adsorption cleaning platform to carry the laser cleaner to move to the predetermined cleaning position, and adjust the attitude information of the laser cleaner according to the predetermined cleaning position;

[0009] S4, perform hierarchical analysis and comparison on the surface image of the steel bridge based on the coating evaluation system, identify different paint layer categories, and determine the maintenance level;

[0010] S5, set the cleaning parameters of the laser cleaner according to the maintenance level, and control the laser cleaner to clean the paint layer according to the cleaning parameters.

[0011] Preferably, in step S1, setting the moving parameters, controlling the magnetic adsorption cleaning platform to move along the railway steel bridge based on the moving parameters, and collecting the surface image of the steel bridge in real time specifically includes:

[0012] S101, set the moving parameters, and the moving parameters include speed parameters, direction parameters, and distance parameters;

[0013] S102. Obtain the initial position of the magnetic adsorption cleaning platform, generate a moving path based on the direction parameter, and set the distance for collecting the steel bridge surface image each time according to the distance parameter;

[0014] S103. Drive the motor to drive the platform to move along the moving path at the set speed parameter, and set the distance of each movement according to the spacing set by the distance parameter;

[0015] S104. Collect a number of steel bridge surface images based on the set spacing.

[0016] Preferably, in one solution, the steel bridge surface images are collected in real time, which specifically includes:

[0017] Obtain the camera parameters, where the camera parameters include the light condition and the shooting angle;

[0018] Obtain the steel bridge surface image based on the camera parameters and analyze the resolution of the steel bridge surface image;

[0019] Judge whether the resolution meets the set resolution threshold;

[0020] If it meets, obtain the final steel bridge surface image;

[0021] If it does not meet, generate correction information and adjust the camera parameters based on the correction information.

[0022] Preferably, in one solution, in step S2, analyze the information on the poor quality of the paint surface based on the steel bridge surface image, and obtain the predetermined cleaning position according to the information on the poor quality of the paint surface, which specifically includes:

[0023] S201. Obtain the steel bridge surface image and extract the image features;

[0024] S202. Compare the image features with a set of multiple feature intervals, judge the feature interval where the image features are located, and obtain the analysis result;

[0025] S203. Analyze the paint layer position and the information on the poor quality of the paint surface where the image features are located based on the analysis result;

[0026] S204. Set the cleaning area based on the information on the poor quality of the paint surface, analyze the position information of the cleaning area, and obtain the predetermined cleaning position.

[0027] Preferably, in one solution, in step S3, control the magnetic adsorption cleaning platform to carry the laser cleaner to move to the predetermined cleaning position, and adjust the attitude information of the laser cleaner according to the predetermined cleaning position, which specifically includes:

[0028] Obtain the position of the predetermined cleaning position and the laser cleaner, set the moving path of the magnetic adsorption cleaning platform, and control the magnetic adsorption cleaning platform to move along the moving path to the predetermined cleaning position;

[0029] Obtain the current attitude information of the laser cleaner, analyze the cleaning direction of the laser cleaner and the currently cleanable area;

[0030] Based on the cleaning direction, the currently cleanable area and the predetermined cleaning position for matching, obtain the cleaning deviation information;

[0031] Generate adjustment information based on the cleaning deviation information, and correct the attitude parameters of the laser cleaner based on the adjustment information to obtain new attitude information.

[0032] Preferably, in one solution, in step S4, the steel bridge surface image is analyzed and compared layer by layer based on the coating evaluation system to identify different paint layer categories and determine the maintenance level, specifically including:

[0033] Obtain a large number of steel bridge paint surface pictures, classify the corrosion of the steel bridge paint surface pictures to obtain paint surface pictures of multiple categories;

[0034] Input the paint surface pictures of multiple categories into the initial model for iterative training to obtain a deep learning model;

[0035] Obtain the current steel bridge paint surface image, and input the current paint surface image into the deep learning model to identify the paint surface state, the intermediate layer state and the bottom layer state of the cleaning area;

[0036] Analyze the corrosion information of different paint layers in the cleaning area based on the paint surface state, the intermediate layer state and the bottom layer state;

[0037] Establish a coating evaluation system, and establish cleaning thresholds for different layers according to the coating evaluation system;

[0038] Compare the corrosion information of different paint layers with the set cleaning thresholds to obtain the corresponding maintenance level, and generate cleaning parameters corresponding to the maintenance level.

[0039] Preferably, in one solution, the cleaning parameters of the laser cleaner in step S5 include laser power, pulse, frequency and scanning speed.

[0040] Due to the application of the above technical solutions, the beneficial effects of this application compared with the prior art are as follows:

[0041] A laser cleaning method for railway steel bridge coatings of the present application studies the layered removal of coatings (polyurethane topcoat, epoxy primer, elastic putty layer) on the surface of railway steel bridges by using a pulsed laser cleaner. By dynamically adjusting the influencing factors such as laser scanning speed, laser power, laser repetition frequency, and number of scans on coating removal, the controllable laser process parameters for paint removal are optimized. By obtaining the laser cleaning thresholds of the surface, intermediate, and bottom coating materials under different coating systems, and setting parameters for different materials of different cleaning layers in cooperation with the evaluation system, layered paint removal is completed. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art.

[0043] Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other

[0044] drawings can be obtained based on these drawings.

[0045] Figure 1 is a flowchart of the laser cleaning method for railway steel bridge coatings of the present invention;

[0046] Figure 2 is a flowchart of the steel bridge surface image acquisition of the laser cleaning method for railway steel bridge coatings of the present invention;

[0047] Figure 3 is a flowchart of the method for analyzing the predetermined cleaning position of the laser cleaning method for railway steel bridge coatings of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] In order to enable those skilled in the art of this technology to better understand the solution of this application, the following will clearly and completely describe the technical solutions in the embodiments of this application in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this application.

[0049] It should be noted that in the description of the present application, the claims, and the above-mentioned drawings, the terms "first", "second", etc. are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances, so as to implement the embodiments of the present application described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0050] In the present application, the orientation or positional relationship indicated by the terms "upper", "lower", "left", "right", "front", "rear", "top", "bottom", "inner", "outer", "middle", "vertical", "horizontal", "lateral", "longitudinal", etc. is based on the orientation or positional relationship shown in the drawings. These terms are mainly used to better describe the present invention and its embodiments, and are not used to limit that the indicated devices, elements, or components must have a specific orientation or be constructed and operated in a specific orientation.

[0051] Moreover, in addition to being able to represent the orientation or positional relationship, some of the above terms may also be used to represent other meanings. For example, the term "upper" may also be used to represent a certain attachment relationship or connection relationship in some cases. For those of ordinary skill in the art, the specific meanings of these terms in the present invention can be understood according to specific circumstances.

[0052] In addition, the terms "install", "set", "provided with", "connect", "connected", "socketed" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral structure; it can be a mechanical connection or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, or there can also be internal communication between two devices, elements, or components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0053] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The following will describe the present application in detail with reference to the drawings and in combination with the embodiments.

[0054] Embodiment 1

[0055] Figure 1 It is a flowchart of a method for laser cleaning the coating of a railway steel bridge according to the present invention. The method for laser cleaning the coating of a railway steel bridge includes:

[0056] S1. Set the movement parameters, control the magnetic adsorption cleaning platform to move along the railway steel bridge based on the movement parameters, and collect the images of the steel bridge surface in real time;

[0057] S2. Analyze the information on the inferior paint conditions on the steel bridge surface based on the steel bridge surface images, and obtain the predetermined cleaning positions according to the information on the inferior paint conditions;

[0058] S3. Control the magnetic adsorption cleaning platform to carry the laser cleaner to move to the predetermined cleaning positions, and adjust the attitude information of the laser cleaner according to the predetermined cleaning positions;

[0059] S4. Conduct hierarchical analysis and comparison on the steel bridge surface images based on the coating evaluation system, identify different paint layer categories, and determine the maintenance levels;

[0060] S5. Set the cleaning parameters of the laser cleaner according to the maintenance levels, and control the laser cleaner to perform cleaning treatment on the paint layers according to the cleaning parameters.

[0061] It should be noted that the paint layer is generally a multi-layer structure, and there are problems such as differences in physical and chemical properties, different thicknesses, and uneven distributions among the paint layers. In terms of laser paint removal, it is difficult to achieve an ideal paint removal effect with a single-mode laser, while the variable combination laser can more easily achieve layer-by-layer paint removal, improve the paint removal efficiency, and enhance the paint removal effect by adjusting the laser parameters. Among them, the laser paint removal threshold, as an important parameter for laser cleaning, has important reference value and practical significance for the research on layer-by-layer paint removal by the combination laser.

[0062] The laser paint removal technology uses the dry cleaning method, and directly irradiates the surface of the paint removal material with a pulsed laser of relatively high energy or power to overcome the bonding force between the paint layer and the substrate, so that the paint layer is completely removed. There are mainly two mechanisms for laser paint removal, the ablation effect and the vibration effect.

[0063] The ablation effect is that after the paint layer is irradiated by a laser with a high enough energy density, the paint layer burns, decomposes or vaporizes due to high temperature, and finally the paint layer is removed.

[0064] The vibration effect is to use pulsed laser radiation on the surface of the paint layer. The paint layer and the substrate respectively absorb part of the laser energy, generate thermal expansion due to heat in an extremely short time, form a vibration wave between the substrate and the paint layer, generate a strong detachment stress, and make the paint layer overcome the bonding force with the surface and detach from the substrate.

[0065] In the laser paint removal test, the important laser parameters affecting the paint removal effect include: laser energy density, pulse width, pulse frequency, and scanning speed. Therefore, it is necessary to select appropriate process parameters to make the paint layer completely fall off without damaging the substrate. When the laser power density is higher than a certain threshold, although the paint removal effect exists, the substrate surface has also been damaged, and this threshold is called the damage threshold; on the other hand, when the laser power density is lower than a certain threshold, even if the laser cleaning time is extended, there is no cleaning effect, and this threshold is the cleaning threshold. Therefore, when the laser power density is between the cleaning threshold and the damage threshold, increasing the laser power density can improve the cleaning effect.

[0066] As Figure 2 shown, according to an embodiment of the present invention, in step S1, movement parameters are set, and based on the movement parameters, the magnetic adsorption cleaning platform is controlled to move along the railway steel bridge, and the surface image of the steel bridge is collected in real time, specifically including:

[0067] S101, set movement parameters, and the movement parameters include speed parameters, direction parameters, and distance parameters;

[0068] S102, obtain the initial position of the magnetic adsorption cleaning platform, generate a movement path based on the direction parameters, and set the distance for collecting the surface image of the steel bridge each time according to the distance parameters;

[0069] S103, drive the motor to drive the platform to move along the movement path according to the set speed parameters, and the distance of each movement is set according to the interval set by the distance parameters;

[0070] S104, collect a plurality of surface images of the steel bridge based on the set interval.

[0071] According to an embodiment of the present invention, collecting the surface image of the steel bridge in real time specifically includes:

[0072] Obtain camera parameters, and the camera parameters include light conditions and shooting angles;

[0073] Obtain the surface image of the steel bridge based on the camera parameters, and analyze the resolution of the surface image of the steel bridge;

[0074] Judge whether the resolution meets the set resolution threshold;

[0075] If it meets, obtain the final surface image of the steel bridge;

[0076] If it does not meet, generate correction information, and adjust the camera parameters based on the correction information.

[0077] As Figure 3 shown, according to an embodiment of the present invention, in step S2, analyze the information on the inferior quality of the paint surface based on the surface image of the steel bridge, and obtain the predetermined cleaning position according to the information on the inferior quality of the paint surface, specifically including:

[0078] S201, Obtain the image of the steel bridge surface and extract the image features;

[0079] S202, Compare the image features with multiple set feature intervals, determine the feature interval where the image features are located, and obtain the analysis result;

[0080] S203, Analyze the paint layer position and paint surface inferiority information where the image features are located based on the analysis result;

[0081] S204, Set the cleaning area based on the paint surface inferiority information, analyze the position information of the cleaning area, and obtain the predetermined cleaning position.

[0082] According to the embodiment of the present invention, in step S3, control the magnetic adsorption type cleaning platform to carry the laser cleaner to move to the predetermined cleaning position, and adjust the attitude information of the laser cleaner according to the predetermined cleaning position, specifically including:

[0083] Obtain the position of the predetermined cleaning position and the laser cleaner, set the moving path of the magnetic adsorption type cleaning platform, and control the magnetic adsorption type cleaning platform to move to the predetermined cleaning position along the moving path;

[0084] Obtain the current attitude information of the laser cleaner, analyze the cleaning direction and the current cleanable area of the laser cleaner;

[0085] Match based on the cleaning direction, the current cleanable area and the predetermined cleaning position to obtain the cleaning deviation information;

[0086] Generate adjustment information based on the cleaning deviation information, and correct the attitude parameters of the laser cleaner based on the adjustment information to obtain the new attitude information.

[0087] According to the embodiment of the present invention, in step S4, perform hierarchical analysis and comparison on the steel bridge surface image based on the coating evaluation system, identify different paint layer categories, and determine the maintenance level, specifically including:

[0088] Obtain a large number of steel bridge paint surface pictures, classify the rust of the steel bridge paint surface pictures to obtain paint surface pictures of multiple categories;

[0089] Input the paint surface pictures of multiple categories into the initial model for iterative training to obtain the deep learning model;

[0090] Obtain the current steel bridge paint surface image, and input the current paint surface image into the deep learning model to identify the paint surface state, the intermediate layer state and the bottom layer state of the cleaning area;

[0091] Analyze the rust information of different paint layers in the cleaning area based on the paint surface state, the intermediate layer state and the bottom layer state;

[0092] Establish a coating evaluation system, and establish cleaning thresholds for different layers according to the coating evaluation system;

[0093] Compare the rust information of different paint layers with the set cleaning threshold to obtain the corresponding repair level and generate the cleaning parameters corresponding to the repair level.

[0094] In a specific embodiment of the present invention, the cleaning parameters of the laser cleaner include laser power, pulse, frequency and scanning speed. When the fixed laser process parameters are f = 200 kHz, V = 1500 mm / s, pulse width is 200 ns, scanning times is 1 time, and laser power is 10W - 40W, the paint layer stripping effect is better, and the paint layer stripping amount increases with the increase of laser power; keeping other laser process parameters unchanged, when the laser power increases to 40W - 90W, the paint layer stripping effect decreases, and at the same time, a large amount of black carbonized particles and powders appear on the paint layer, and the higher laser power does not significantly improve the paint removal effect. When removing paint with laser process parameters P = 40W, P = 24W, scanning times is 2 times, V = 1200 mm / s, f = 110 kHz, the topcoat is stripped thoroughly and enters the primer layer; when V = 1000 mm / s, f = 150 kHz, the primer layer is stripped thoroughly and the elastic putty layer is revealed; continue to increase the pulse frequency, when V = 700 mm / s, f = 200 kHz, the elastic putty layer is stripped thoroughly and the CFRP fiber is revealed, and the resin remains. That is, by selecting the appropriate laser power and scanning times, and increasing the pulse frequency in cooperation with the scanning speed, the topcoat, primer and putty layer can be effectively stripped to meet the requirement of controllable paint removal and delamination.

[0095] After laser delamination paint removal, the topcoat layer is peeled off relatively completely, and its characteristic elements of Fe and Ni gradually decrease and disappear. At the same time, the primer layer is revealed. The paint removal effect meets one of the requirements of controllable laser paint removal delamination, that is, the topcoat layer is peeled off while the primer layer and the elastic putty layer are retained. After laser delamination paint removal, the primer layer is peeled off relatively completely, and its characteristic elements of Ti and Zn decrease and disappear. The elastic putty layer is revealed. The paint removal effect meets one of the requirements of controllable laser paint removal delamination, that is, the topcoat layer and the primer layer are peeled off while the elastic putty layer is retained. After laser delamination paint removal, the surface coating of CFRP fibers is completely peeled off. At the same time, the resin layer is vaporized, and the damage and fracture of the surface fibers are acceptable. The cleanliness of the CFRP fiber surface after paint removal is good. The paint removal effect meets one of the requirements of controllable laser paint removal delamination, that is, all the coatings are peeled off while minimizing the damage to the CFRP matrix fibers. During the research on the mechanism of delamination paint removal, the pulsed laser mainly acts on the coating material through the photo-thermal effect, that is, the ablation and thermal vibration effects. The coating material ablates, vaporizes (above 1200 °C), and decomposes and disappears under the irradiation of the pulsed laser with high energy density. At the same time, due to the physical and chemical property differences of the organic polymers in different paint layers, a temperature gradient is formed when irradiated, and obvious thermoelastic vibrations occur. When the thermal stress between the CFRP fiber matrix layer and the putty layer is greater than (5.9×10~8 Pa), the matrix layer and the putty layer are peeled off due to thermoelastic vibrations; when the thermal stress between the paint layer and the putty layer is greater than (4.2×10~8 Pa), the paint layer and the putty layer are peeled off from each other. On the basis of the photo-thermal effect, during the paint removal process, a photo-mechanical effect of instantaneous high-pressure impact (2.21×10~9 Pa) occurs when the short-pulse laser acts on the coating material. The high laser energy density acting on the material surface will induce the laser plasma effect. During the research process, when the pulsed laser energy density is 7.22 J / cm~2 and 10.35 J / cm~2, the laser plasma effect is obvious. The high pressure generated by the laser plasma causes changes in the melting point of the particles, and the paint layer particles are damaged under the interaction of the thermal effect and the mechanical effect. Specifically, it is manifested as the formation of a large area of residual paint layer particles. The spherical small particles formed in the molten state will increase the difficulty of the paint layer removal process.

[0096] In summary, a laser cleaning method for railway steel bridge coatings in this application studies the delamination removal of the surface coatings (polyurethane topcoat, epoxy primer, elastic putty layer) of railway steel bridges by using a pulsed laser cleaner. By dynamically adjusting the influence of factors such as laser scanning speed, laser power, laser repetition frequency, and scanning times on coating removal, the laser process parameters for controllable paint removal delamination are optimized. By obtaining the laser cleaning thresholds of the surface, intermediate, and bottom coating materials under different coating systems, and setting parameters for different materials of different cleaning layers in cooperation with the evaluation system, delamination paint removal is completed.

[0097] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A laser cleaning method for railway steel bridge coatings, characterized in that , including the following steps: S1. Set the movement parameters, control the magnetic adsorption cleaning platform to move along the railway steel bridge based on the movement parameters, and collect the surface images of the steel bridge in real time; S2. Analyze the information on the inferior paint condition based on the surface images of the steel bridge, and obtain the predetermined cleaning positions according to the information on the inferior paint condition; S3. Control the magnetic adsorption cleaning platform to move the laser cleaner to the predetermined cleaning positions, and adjust the attitude information of the laser cleaner according to the predetermined cleaning positions; S4. Conduct hierarchical analysis and comparison on the surface images of the steel bridge based on the coating evaluation system, identify different paint layer categories, and determine the maintenance level; S5. Set the cleaning parameters of the laser cleaner according to the maintenance level, and control the laser cleaner to clean the paint layer according to the cleaning parameters.

2. The laser cleaning method for railway steel bridge coatings according to claim 1, characterized in that: In step S1, setting the movement parameters, controlling the magnetic adsorption cleaning platform to move along the railway steel bridge based on the movement parameters, and collecting the surface images of the steel bridge in real time specifically includes: S101. Set the movement parameters, where the movement parameters include speed parameters, direction parameters, and distance parameters; S102. Obtain the initial position of the magnetic adsorption cleaning platform, generate a movement path based on the direction parameters, and set the distance for collecting the surface images of the steel bridge each time according to the distance parameters; S103. Drive the motor to drive the platform to move along the movement path according to the set speed parameters, and set the distance of each movement according to the interval set by the distance parameters; S104. Collect a number of surface images of the steel bridge based on the set interval.

3. The laser cleaning method for the coating of a railway steel bridge according to claim 2, wherein: Collecting the surface images of the steel bridge in real time specifically includes: Obtain the camera parameters, where the camera parameters include light conditions and shooting angles; Obtain the surface images of the steel bridge based on the camera parameters, and analyze the resolution of the surface images of the steel bridge; Judge whether the resolution meets the set resolution threshold; If it meets the requirement, obtain the final surface images of the steel bridge; If it does not meet the requirement, generate correction information, and adjust the camera parameters based on the correction information.

4. The laser cleaning method for the coating of a railway steel bridge according to claim 3, wherein: In step S2, analyzing the information on the inferior paint condition based on the surface images of the steel bridge, and obtaining the predetermined cleaning positions according to the information on the inferior paint condition specifically includes: S201. Obtain the surface images of the steel bridge and extract the image features; S202. Compare the image features with a set of multiple feature intervals, judge the feature interval where the image features are located, and obtain the analysis result; S203. Analyze the paint layer position and the information on the inferior paint condition where the image features are located based on the analysis result; S204. Set the cleaning area based on the information on the inferior paint condition, analyze the position information of the cleaning area, and obtain the predetermined cleaning positions.

5. The laser cleaning method for railway steel bridge coatings according to claim 4, characterized in that: In step S3, controlling the magnetic adsorption cleaning platform to move the laser cleaner to the predetermined cleaning positions, and adjusting the attitude information of the laser cleaner according to the predetermined cleaning positions specifically includes: Obtain the predetermined cleaning positions and the position of the laser cleaner, set the movement path of the magnetic adsorption cleaning platform, and control the magnetic adsorption cleaning platform to move to the predetermined cleaning positions along the movement path; Obtain the current attitude information of the laser cleaner, and analyze the cleaning direction and the current cleanable area of the laser cleaner; Match based on the cleaning direction, the current cleanable area, and the predetermined cleaning positions to obtain the cleaning deviation information; Generate adjustment information based on the cleaning deviation information, and correct the attitude parameters of the laser cleaner based on the adjustment information to obtain new attitude information.

6. The laser cleaning method for the coating of a railway steel bridge according to claim 5, wherein: In step S4, conduct hierarchical analysis and comparison on the steel bridge surface image based on the coating evaluation system, identify different paint layer categories, and determine the maintenance level, specifically including: Obtain a large number of steel bridge paint surface pictures, classify the rust of the steel bridge paint surface pictures to obtain paint surface pictures of multiple categories; Input the paint surface pictures of multiple categories into the initial model for iterative training to obtain a deep learning model; Obtain the current steel bridge paint surface image, and input the current paint surface image into the deep learning model to identify the paint surface state, intermediate layer state, and bottom layer state of the cleaning area; Analyze the rust information of different paint layers in the cleaning area based on the paint surface state, intermediate layer state, and bottom layer state; Establish a coating evaluation system, and establish cleaning thresholds for different layers according to the coating evaluation system; Compare the rust information of different paint layers with the set cleaning thresholds to obtain the corresponding maintenance level, and generate cleaning parameters corresponding to the maintenance level.

7. The laser cleaning method for the coating of railway steel bridges according to claim 1, characterized in that: The cleaning parameters of the laser cleaner in step S5 include laser power, pulse, frequency, and scanning speed.

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