A method and system for analyzing the corrosion resistance of an aluminum pigment coating

Abstract

The application relates to the technical field of quality detection, and particularly discloses an aluminum pigment coating corrosion resistance analysis detection method and system, wherein the method comprises the following steps: acquiring surface geometric data; adjusting an optical probe based on the surface geometric data, and collecting optical data of a to-be-detected area by using the optical probe; collecting coating lower interface state data of the to-be-detected area based on a non-contact sensor; aligning the optical data and the coating lower interface state data; and generating a corrosion condition analysis result based on the aligned optical data and the coating lower interface state data; the method solves the problem that initial corrosion points on a large industrial storage tank arc-shaped complex surface are difficult to be nondestructively identified, and overcomes the challenge that multi-modal data is difficult to be effectively aligned and integrated in a traditional detection method, so that the comprehensiveness and accuracy of corrosion condition evaluation are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of quality detection, in particular to an aluminum pigment coating corrosion resistance analysis and detection method and system. BACKGROUND

[0002] Large industrial storage tanks are widely used in the field of oil and chemical storage. The internal aluminum pigment anticorrosive coating effectively isolates the contact between the corrosion medium and the steel substrate through physical barrier and electrochemical passivation, significantly prolongs the service life of the equipment and ensures the safety of operation. With the continuous development of non-destructive testing technology, automated detection methods have gradually become the mainstream trend of research and application, aiming to improve detection efficiency and reduce manual intervention to meet the needs of large-scale detection of industrial storage tanks.

[0003] However, there are several technical challenges to be solved in the actual detection process inside the storage tank: the inner wall of the storage tank is usually an arc surface and has complex structures such as welds and reinforcing ribs, making it difficult for the probe to maintain accurate and stable relative distance and incident angle during scanning, which in turn causes measurement signal fluctuations and poor data repeatability, affecting the reliability of the detection results. At the same time, the initial corrosion point under the coating is small and hidden, and the traditional non-destructive testing method is difficult to effectively capture the corrosion activity at the interface between the coating and the substrate, resulting in potential risks that cannot be discovered in time. In addition, the data collected by different detection technologies, such as optical information and coating state information, are difficult to effectively align and integrate, and there is a lack of a unified analysis framework, affecting the comprehensiveness and accuracy of corrosion condition assessment.

[0004] At present, there is no effective technical solution to the above problems. SUMMARY

[0005] The purpose of the present application is to provide an aluminum pigment coating corrosion resistance analysis and detection method and system to solve the problem of difficult non-destructive identification of initial corrosion points on the arc complex surface of large industrial storage tanks, and to overcome the challenge of difficult effective alignment and integration of multi-modal data in traditional detection methods.

[0006] In a first aspect, the present application provides an aluminum pigment coating corrosion resistance analysis and detection method for non-destructive identification of initial corrosion points under the aluminum pigment coating or at the interface between the coating and the substrate of a large industrial storage tank, the method comprising the following steps:

[0007] S1, acquiring surface geometry data of a to-be-detected area of a storage tank;

[0008] S2, adjusting the pose of an optical probe and the distance between the optical probe and the storage tank based on the surface geometry data, and collecting optical data of the to-be-detected area using the optical probe;

[0009] S3, collecting coating-sub-interface state data of the to-be-measured region based on a non-contact sensor;

[0010] S4, aligning the optical data and the coating-sub-interface state data;

[0011] S5, generating a corrosion condition analysis result based on the aligned optical data and coating-sub-interface state data.

[0012] The method of the present application can effectively solve the problem of difficult non-destructive identification of initial corrosion points on the complex arc surface of large industrial storage tanks, and improve the identification sensitivity of initial corrosion points under the coating or at the interface between the coating and the substrate. At the same time, the method of the present application overcomes the challenge of difficult effective alignment and integration of multi-modal data in traditional detection methods, improves the comprehensiveness and accuracy of corrosion condition evaluation through data fusion analysis, and thus ensures the safe operation of industrial storage tanks.

[0013] The aluminum pigment coating corrosion resistance analysis and detection method, wherein step S5 comprises:

[0014] S51, analyzing the aligned optical data to obtain spectral features of the to-be-measured region;

[0015] S52, analyzing the aligned coating-sub-interface state data to obtain physical defect features of the to-be-measured region;

[0016] S53, establishing associated features about the to-be-measured region according to the spectral features and the physical defect features;

[0017] S54, judging whether the to-be-measured region has initial corrosion points according to the associated features, and when the initial corrosion points exist, obtaining the position, type and corrosion degree of the initial corrosion points to constitute the corrosion condition analysis result.

[0018] The aluminum pigment coating corrosion resistance analysis and detection method, wherein the coating-sub-interface state data comprises pulse eddy current data and low-frequency ultrasonic guided wave data, and step S52 comprises:

[0019] S521, performing distance compensation on the pulse eddy current data according to the surface geometry data, and combining the preset electromagnetic characteristics of the coating material, performing coating influence correction on the compensated pulse eddy current data, and extracting eddy current signal features related to metal thinning according to the corrected pulse eddy current data;

[0020] S522, filtering the low-frequency ultrasonic guided wave data to filter out interference signals caused by surface medium residues or slight undulations, analyzing the propagation speed, attenuation characteristics and reflection mode of the guided wave according to the filtered low-frequency ultrasonic guided wave data to obtain the acoustic characteristic change information of the coating and substrate interface of the to-be-measured region, and extracting the guided wave propagation characteristics related to the interface peeling or interface delamination according to the acoustic characteristic change information;

[0021] S523, fusing the eddy current signal characteristics and the guided wave propagation characteristics to obtain the physical defect characteristics of the to-be-measured region.

[0022] The aluminum pigment coating corrosion resistance analysis and detection method, wherein step S54 comprises:

[0023] S541, analyzing the correlation characteristics based on a preset corrosion mode library to determine whether there is an initial corrosion point in the to-be-measured region, and if so, extracting the position and type of the initial corrosion point, and the corrosion mode library comprises a plurality of correlation characteristics corresponding to different types of initial corrosion points;

[0024] S542, quantifying the corrosion degree of the initial corrosion point according to the intensity of the correlation characteristics corresponding to the initial corrosion point;

[0025] S543, combining the position, type and corrosion degree of each initial corrosion point to form the corrosion condition analysis result.

[0026] The aluminum pigment coating corrosion resistance analysis and detection method, wherein step S2 comprises:

[0027] S21, generating a reference detection path passing through a plurality of measurement points according to the to-be-measured region;

[0028] S22, calculating the target pose and target position of the optical probe at each measurement point in the reference detection path according to the reference detection path and the surface geometry data;

[0029] S23, driving the mechanical actuator carrying the optical probe to operate according to the reference detection path, the target pose and the target position to perform optical measurement on the to-be-measured region to obtain the optical data.

[0030] The aluminum pigment coating corrosion resistance analysis and detection method, wherein step S22 comprises:

[0031] S221, preprocessing the surface geometry data, and the preprocessing comprises removing abnormal data and completing data;

[0032] S222, acquiring local surface normal and local curvature information of each measuring point on the reference detection path according to the reference detection path and the preprocessed surface geometry information;

[0033] S223, calculating a target pose and a target position of the optical probe at each measuring point based on the local surface normal, the local curvature information and a preset measuring distance.

[0034] The aluminum pigment coating corrosion resistance analysis and detection method, wherein step S2 further comprises:

[0035] S24, in the process of optical measurement, synchronously acquiring environmental medium optical characteristic data and surface impurity spectral feature data of each measuring point on the reference detection path;

[0036] S25, correcting the collected optical data according to the environmental medium optical characteristic data and the surface impurity spectral feature data.

[0037] The aluminum pigment coating corrosion resistance analysis and detection method, wherein step S4 comprises:

[0038] S41, acquiring spatial position information corresponding to the optical data and spatial position information corresponding to the coating lower interface state data;

[0039] S42, establishing a spatial mapping relationship between the spatial position information of the optical data and the spatial position information of the coating lower interface state data according to the surface geometry data;

[0040] S43, based on the spatial mapping relationship, converting the optical data and the coating lower interface state data to a unified spatial coordinate system to realize data alignment.

[0041] The aluminum pigment coating corrosion resistance analysis and detection method, wherein the optical probe is a laser-induced breakdown spectroscopy probe or a Raman spectroscopy probe.

[0042] In a second aspect, the application further provides an aluminum pigment coating corrosion resistance analysis and detection system for non-destructive identification of initial corrosion points below an aluminum pigment coating or at an interface between the coating and a base material of a large industrial storage tank, the system comprising:

[0043] A first acquisition module is configured to acquire surface geometry data of a to-be-measured region of a storage tank;

[0044] A second acquisition module is configured to adjust a pose of an optical probe and a distance between the optical probe and the storage tank based on the surface geometry data, and collect optical data of the to-be-measured region by using the optical probe;

[0045] The third acquisition module is configured to acquire the under-coating interface state data of the to-be-measured area based on the non-contact sensor.

[0046] The data alignment module is configured to align the optical data and the under-coating interface state data.

[0047] The corrosion analysis module is configured to generate a corrosion condition analysis result based on the aligned optical data and under-coating interface state data.

[0048] As can be seen from the above, the application provides an aluminum pigment coating corrosion resistance analysis and detection method and system, wherein the aluminum pigment coating corrosion resistance analysis and detection method of the application solves the measurement stability problem on a complex curved surface by using surface geometric data to accurately adjust the attitude and working distance of an optical probe; meanwhile, by combining the optical data and under-coating interface state data of two different modal information and performing accurate alignment and fusion analysis, the problems of insufficient sensitivity of traditional methods and difficulty in integrating multi-modal data are solved, thereby improving the accuracy and reliability of identifying initial corrosion points of large industrial storage tanks. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 A flowchart of the aluminum pigment coating corrosion resistance analysis and detection method provided by the embodiments of the application.

[0050] Figure 2 A structural schematic diagram of the aluminum pigment coating corrosion resistance analysis and detection system provided by the embodiments of the application.

[0051] The drawings show that: 201, the first acquisition module; 202, the second acquisition module; 203, the third acquisition module; 204, the data alignment module; 205, the corrosion analysis module. DETAILED DESCRIPTION

[0052] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. The components of the embodiments of the application described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the application provided in the drawings is not intended to limit the scope of the claimed application, but only represents selected embodiments of the application. Based on the embodiments of the application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the application.

[0053] It should be noted that similar reference numerals and letters refer to like items throughout the drawings, and once an item is defined in one drawing, it is not necessary to further define and explain it in subsequent drawings. Also, in the description of the present application, the terms "first", "second", and the like are used only to distinguish descriptions, and cannot be understood as indicating or implying relative importance.

[0054] In a first aspect, referring to Figure 1 Some embodiments of the present application provide a method for analyzing the corrosion resistance of an aluminum pigment coating, which is used for non-destructive identification of initial corrosion points under the aluminum pigment coating or at the interface between the coating and the substrate of a large industrial storage tank. The method comprises the following steps:

[0055] S1, obtaining surface geometry data of a to-be-measured region of the storage tank;

[0056] S2, adjusting the pose of an optical probe and the distance between the optical probe and the storage tank based on the surface geometry data, and collecting optical data of the to-be-measured region using the optical probe;

[0057] S3, collecting coating-under-interface state data of the to-be-measured region based on a non-contact sensor;

[0058] S4, aligning the optical data and the coating-under-interface state data;

[0059] S5, generating a corrosion condition analysis result based on the aligned optical data and coating-under-interface state data.

[0060] Specifically, the surface geometry data can be obtained by scanning with a three-dimensional profile scanner, which refers to a device capable of obtaining three-dimensional shape information of an object surface. It can be implemented by technologies such as laser triangulation, structured light projection, or time-of-flight method, for example, a laser scanner or a structured light scanner. Its main purpose is to obtain accurate surface geometry information of the to-be-measured region, providing a basis for accurate control of the probe pose and distance in the subsequent process.

[0061] More specifically, the surface geometry data refers to the external shape and size information of the to-be-measured region, including curvature, undulation, and local normal direction, etc. Its main purpose is to characterize the complex structure of the inner wall of the storage tank, thereby guiding the accurate movement and data collection of the optical probe.

[0062] More specifically, the optical data refers to the information collected by the optical probe, which reflects the optical characteristics of the surface of the to-be-measured region, such as spectral intensity, wavelength, or characteristic peak position, etc. Its main purpose is to provide indicative information of surface chemical corrosion.

[0063] More specifically, the non-contact sensor refers to a sensor that can perform measurement without direct contact with the measured object, which can be implemented by using a pulsed eddy current sensor, a low-frequency ultrasonic guided wave sensor, or other electromagnetic or acoustic sensors, and is mainly used to obtain physical state information under the coating or at the interface between the coating and the base material to identify physical defects caused by incipient corrosion.

[0064] More specifically, the coating-sub-interface state data refers to information reflecting the physical condition under the coating or at the interface between the coating and the base material, such as changes in electrical conductivity, acoustic characteristics, or material thickness of the metal base material, and is used to provide indication information of physical damage caused by incipient corrosion.

[0065] Specifically, the method of the present application first scans the to-be-measured region of the storage tank by a three-dimensional profile scanner, thereby obtaining surface geometry data of the region, which contains information such as the exact shape, curvature, and undulations of the surface. Subsequently, using these surface geometry data, the system can intelligently adjust the pose of the optical probe and the distance between the optical probe and the measured surface. This dynamic adjustment ensures that the laser beam of the optical probe can be focused at any measurement point with a stable and perpendicular incidence angle to the surface, and the working distance between the probe and the surface remains unchanged, thereby improving the stability and repeatability of optical data acquisition. At the same time of optical data acquisition, a non-contact sensor is used to collect coating-sub-interface state data of the to-be-measured region, which can reveal physical defect information under the coating or at the interface between the coating and the base material. In order to fully utilize the data of the two different modalities, the optical data and the coating-sub-interface state data are accurately aligned and converted to a unified spatial coordinate system, ensuring the accurate correspondence of different types of data in spatial position. Finally, based on the aligned optical data and coating-sub-interface state data, comprehensive analysis is performed to establish the correspondence of different modal data in spatial position and the collaborative change in corrosion state, thereby comprehensively and accurately judging whether there is an incipient corrosion point and obtaining its position, type, and corrosion degree, thereby generating a corrosion condition analysis result.

[0066] Through the above processing, the method of the present application can effectively solve the problem of difficult non-destructive identification of incipient corrosion point on the arc-shaped complex surface of a large industrial storage tank, and improve the identification sensitivity of the incipient corrosion point under the coating or at the interface between the coating and the base material. At the same time, the method of the present application overcomes the challenge of difficult effective alignment and integration of multi-modal data in traditional detection methods, and improves the comprehensiveness and accuracy of corrosion condition evaluation through data fusion analysis, thereby ensuring the safe operation of the industrial storage tank.

[0067] The corrosion resistance analysis detection method of the aluminum pigment coating of the present application solves the measurement stability problem on a complex curved surface by using surface geometric data to accurately adjust the attitude and working distance of the optical probe; at the same time, by combining the information of optical data and coating lower interface state data of two different modalities and performing accurate alignment and fusion analysis, the problems of insufficient sensitivity of traditional methods and difficulty in integrating multi-modal data are solved, thereby improving the accuracy and reliability of identifying the initial corrosion point position of large industrial storage tanks.

[0068] In some preferred embodiments, step S5 comprises:

[0069] S51, analyzing the aligned optical data to obtain the spectral features of the to-be-measured region;

[0070] S52, analyzing the aligned coating lower interface state data to obtain the physical defect features of the to-be-measured region;

[0071] S53, establishing the associated features about the to-be-measured region according to the spectral features and the physical defect features;

[0072] S54, judging whether the to-be-measured region exists the initial corrosion point position according to the associated features, and when the initial corrosion point position exists, obtaining the position, type and corrosion degree of the initial corrosion point position to constitute the corrosion condition analysis result.

[0073] Specifically, the spectral features refer to the spectral information reflecting the chemical composition, structure or state of a substance obtained by analyzing the optical data, which can be characterized in the form of absorption spectrum, emission spectrum, Raman spectrum or fluorescence spectrum. The physical defect features refer to the features reflecting the physical structural integrity or damage condition of a material obtained by analyzing the coating lower interface state data, which can be characterized in the form of material thickness change, acoustic impedance change, electrical conductivity change or magnetic permeability change. The associated features refer to the corresponding of different modal data (such as spectral features and physical defect features) in spatial position, and reveal their comprehensive features of cooperative change in a specific state (such as corrosion state). The initial corrosion point position refers to the corrosion damage area appearing in the early stage of the corrosion process, which can be manifested in the form of pitting, crevice corrosion, interface peeling or local thinning. The position, type and corrosion degree refer to three attributes for quantitatively describing the initial corrosion point position, the position can be represented by spatial coordinates or region identifier; the type can be represented by corrosion morphology classification (such as pitting, crevice corrosion, interface peeling); the corrosion degree can be quantified by damage size, material loss amount or signal intensity attenuation.

[0074] Specifically, step S51 analyzes the aligned optical data to extract spectral features of the region of interest. The spectral features can reveal the chemical composition and structural information of the coating surface or corrosion products, thus providing a chemical indication of the presence of chemical changes or corrosion products associated with incipient corrosion. Meanwhile, step S52 analyzes the aligned coating-subinterface state data to obtain physical defect features of the region of interest. The physical defect features can penetrate the coating and detect physical damages below the coating or at the interface between the coating and the substrate, such as thinning of the metal substrate or interfacial delamination between the coating and the substrate, thus providing a physical damage indication of the physical structural changes associated with incipient corrosion. Subsequently, step S53 deeply fuses the obtained spectral features and physical defect features to establish associated features about the region of interest. The associated features can reveal the synergistic changes exhibited by these data under the corrosion state. For example, when a specific spectral feature (indicating chemical corrosion) and a specific physical defect feature (indicating physical damage) appear simultaneously at the same location, a stronger corrosion signal can be formed, thus overcoming the limitations that may exist in single modal data. In this way, the associated features integrate both chemical and physical corrosion information, enhancing the ability to identify incipient corrosion. Finally, step S54 determines whether there are incipient corrosion sites in the region of interest according to the established associated features. Since the associated features contain comprehensive information from multiple modalities, their ability to identify small and hidden incipient corrosion sites is enhanced. Once it is determined that there are incipient corrosion sites, the system can further accurately obtain the specific locations of these incipient corrosion sites, the types of corrosion (such as pitting corrosion, crevice corrosion, or corrosion caused by interfacial delamination), and the extent of corrosion according to the intensity and pattern of the associated features. These detailed and comprehensive information collectively constitute the final corrosion condition analysis results, providing accurate basis for subsequent maintenance and repair decisions. Through this multi-modal data fusion analysis strategy, the present scheme improves the accuracy and comprehensiveness of the identification of incipient corrosion sites, solving the problems of insufficient sensitivity and difficulty in integrating data when identifying small and hidden corrosion sites in traditional methods.

[0075] Through the above processing, the method of the present application can accurately identify small and hidden incipient corrosion sites and obtain their specific locations, types, and corrosion extents. This process integrates and analyzes multi-modal data, combining optical information (such as surface chemical changes) and coating-subinterface state information (such as physical defects), thus overcoming the problems of incomplete and inaccurate judgment in traditional methods, improving the reliability of corrosion condition evaluation, and enabling risks to be discovered in a timely manner.

[0076] In some preferred embodiments, the coating-subinterface state data includes pulsed eddy current data and low-frequency ultrasonic guided wave data, and step S52 includes:

[0077] S521, distance compensation is performed on the pulse eddy current data according to the surface geometry data, coating influence correction is performed on the compensated pulse eddy current data in combination with preset electromagnetic characteristics of the coating material, and eddy current signal features related to metal thinning are extracted according to the corrected pulse eddy current data;

[0078] S522, the low-frequency ultrasonic guided wave data are filtered to filter out interference signals caused by surface medium residues or slight undulations, the propagation speed, attenuation characteristics and reflection mode of the guided wave are analyzed according to the filtered low-frequency ultrasonic guided wave data, acoustic characteristic change information of the coating and substrate interface of the to-be-measured region is obtained, and guided wave propagation features related to interface peeling or interface delamination are extracted according to the acoustic characteristic change information;

[0079] S523, the eddy current signal features and the guided wave propagation features are fused to obtain physical defect features of the to-be-measured region.

[0080] Specifically, the pulse eddy current data refers to signal data collected by a pulse eddy current detection technology, which can be realized by emitting a transient pulse magnetic field to the measured material by using a transient eddy current probe, and receiving a secondary magnetic field signal generated by the induced eddy current in the material. The low-frequency ultrasonic guided wave data refers to the acoustic wave signal data collected by the low-frequency ultrasonic guided wave detection technology, which can be realized by using a piezoelectric transducer to excite and receive guided wave signals propagating along the interface in the material in the low-frequency range.

[0081] Specifically, in order to overcome the limitations of single detection technology and improve the comprehensiveness of detection, the coating-subinterface state data is explicitly composed of pulsed eddy current data and low-frequency ultrasonic guided wave data. After obtaining these data, the aligned coating-subinterface state data is further analyzed to obtain the physical defect characteristics of the region to be detected. This analysis process is refined into the following steps: for pulsed eddy current data, first, distance compensation is performed on the data according to the pre-acquired surface geometry data to eliminate the influence of the distance change between the probe and the surface on the signal. Subsequently, in combination with the preset electromagnetic characteristics of the coating material, the compensated pulsed eddy current data is corrected for coating influence to remove the interference of the coating itself on the eddy current signal, thereby obtaining information accurately reflecting the change in the electrical conductivity of the metal substrate. Thus, from the corrected pulsed eddy current data, eddy current signal features related to metal thinning are extracted, which directly indicate the loss of the metal substrate. At the same time, for low-frequency ultrasonic guided wave data, first, filtering is performed to filter out interference signals caused by surface medium residues or small undulations, ensuring the purity of the signal. Then, according to the filtered low-frequency ultrasonic guided wave data, the propagation speed, attenuation characteristics and reflection mode of the guided wave are analyzed, and changes in these parameters reflect changes in the acoustic characteristics of the coating-substrate interface. Thus, according to these acoustic characteristic change information, guided wave propagation features related to interface peeling or interface delamination are extracted, which are evidence of coating interface defects. Finally, the extracted eddy current signal features and guided wave propagation features are fused to obtain the physical defect characteristics of the region to be detected. This fusion takes full advantage of the sensitivity of pulsed eddy current technology to metal substrate loss and the sensitivity of low-frequency ultrasonic guided wave technology to interface defects, enabling the final physical defect characteristics to comprehensively characterize the metal substrate loss or coating interface defects caused by early-stage corrosion. Through accurate processing and fusion of multi-modal data, the method of the present application can comprehensively capture the physical damage caused by early-stage corrosion from different dimensions, overcoming the problem that single detection technology cannot comprehensively cover all corrosion forms, and improving the accuracy and comprehensiveness of identifying the physical defect characteristics of early-stage corrosion sites.

[0082] It should be noted that in the embodiment in which the coating-subinterface state data only includes pulsed eddy current data, the eddy current signal features obtained in step S521 can be regarded as the physical defect characteristics of the region to be detected, and similarly, in the embodiment in which the coating-subinterface state data only includes low-frequency ultrasonic guided wave data, the guided wave propagation features obtained in step S522 can be regarded as the physical defect characteristics of the region to be detected.

[0083] It should be noted that in cases where the detection accuracy requirement is not high, step S521 can be modified to directly extract the eddy current signal features from the pulsed eddy current data, and step S522 can be modified to directly extract the guided wave propagation features from the low-frequency ultrasonic guided wave data.

[0084] Through the above design, the method of the application can accurately identify the small initial corrosion points under the coating, which are manifested as the loss of the metal substrate or the defects of the coating interface. The present scheme overcomes the limitations of traditional single non-destructive testing techniques in capturing various physical defects, and improves the sensitivity and comprehensiveness of the detection method. At the same time, by performing distance compensation and coating influence correction on the pulse eddy current data, and filtering processing on the low-frequency ultrasonic guided wave data, the interference of factors such as probe distance change, coating material properties, surface medium residue or small ups and downs is effectively eliminated, so that the extracted physical defect features are more accurate and reliable.

[0085] In some preferred embodiments, step S53 comprises:

[0086] S531, obtaining chemical corrosion indication information of the to-be-tested region according to the spectral feature;

[0087] S532, obtaining physical damage indication information of the to-be-tested region according to the physical defect feature;

[0088] S533, spatially registering the chemical corrosion indication information and the physical damage indication information to construct associated features.

[0089] Specifically, the chemical corrosion indication information refers to the signal or data reflecting the material chemical composition or structural change of the to-be-tested region obtained by analyzing the spectral feature, which can be characterized by spectral absorption peak intensity, characteristic wavelength shift, or Raman scattering intensity of specific chemical bonds. The physical damage indication information refers to the signal or data reflecting the material structural integrity or geometric morphological change of the to-be-tested region obtained by analyzing the physical defect feature, which can be characterized by material thickness reduction, interface peeling area, or crack propagation length. The associated features refer to a comprehensive data representation formed by fusing or mapping the chemical corrosion indication information and the physical damage indication information after spatial registration, which can be constructed in the form of multi-dimensional feature vector, feature spectrum, or cooperative change index. The associated features are used to reflect the cooperative change of the to-be-tested region under the corrosion state.

[0090] Specifically, step S531 extracts the chemical corrosion indication information of the to-be-tested region from the spectrum features analyzed from the aligned optical data. This process focuses on identifying signals related to material chemical changes, such as the formation of corrosion products or the chemical fingerprint of coating degradation. Subsequently, step S532 extracts the physical damage indication information of the to-be-tested region from the physical defect features analyzed from the aligned coating-subinterface state data. This process focuses on quantifying structural damage caused by corrosion, such as the thinning of the metal substrate or the peeling of the coating-substrate interface. Step S533 spatially registers the acquired chemical corrosion indication information and physical damage indication information. This registration step is important as it ensures the spatial consistency of chemical and physical information from different modalities, allowing subsequent fusion analysis to reflect the corrosion status of the same region. After completing the spatial registration, the associated features are constructed. This associated feature is not simply a combination of chemical and physical information, but rather reflects the synergistic development of chemical changes and physical damage of the to-be-tested region in the corrosion state through their spatial correspondence.

[0091] Through the above scheme, the method of the present application can realize the extraction and fusion of corrosion status indication information from different modalities of data, ensure the spatial correspondence and synergistic change of chemical corrosion and physical damage, and make the constructed associated feature reflect the complex characteristics of initial corrosion, thereby improving the integrity and reliability of corrosion status evaluation.

[0092] In some preferred embodiments, step S54 comprises:

[0093] S541, analyzing the associated features based on a preset corrosion mode library to determine whether there is an initial corrosion point in the to-be-tested region, and if so, extracting the position and type of the initial corrosion point, the corrosion mode library comprising a plurality of associated features corresponding to different types of initial corrosion points;

[0094] S542, quantifying the corrosion degree of the initial corrosion point according to the intensity of the associated feature corresponding to the initial corrosion point;

[0095] S543, combining the position, type and corrosion degree of each initial corrosion point to form a corrosion status analysis result.

[0096] Specifically, the corrosion mode library refers to a pre-established data set containing a plurality of known corrosion types and their corresponding associated features.

[0097] Specifically, in step S541, the correlation features are input into a pre-set corrosion pattern library for analysis. The corrosion pattern library contains a variety of correlation features corresponding to different types of initial corrosion sites, which are fused from optical data and coating-subinterface state data, reflecting the synergistic changes of different modal data under corrosion state. By comparing the correlation features of the to-be-measured region with the known patterns in the corrosion pattern library, the automatic and standardized identification and classification of the initial corrosion sites can be realized, overcoming the problem of insufficient sensitivity in identifying small and hidden corrosion sites in traditional methods, and ensuring the objectivity and consistency of corrosion type judgment. In step S542, once the initial corrosion site is identified, the intensity of the corresponding correlation feature is quantified. The intensity of the correlation feature directly reflects the severity of the material change or defect caused by corrosion, for example, more significant spectral changes or more obvious physical defect signals will correspond to stronger correlation features. By quantifying this intensity, a continuous, data-driven corrosion degree index can be provided for the initial corrosion site, rather than a simple binary judgment, making the corrosion evaluation more precise and accurate, and providing a more sufficient basis for subsequent maintenance decisions. Finally, in step S543, the position, type and corrosion degree of each initial corrosion site are combined to form the final corrosion condition analysis result.

[0098] Through the above design, the method of the present application can provide a specific, standardized and quantitative mechanism to accurately analyze complex correlation features, achieving precise identification, classification and quantitative evaluation of initial corrosion sites.

[0099] In some preferred embodiments, step S2 comprises:

[0100] S21, generating a reference detection path passing through a plurality of measurement points according to the to-be-measured region;

[0101] S22, calculating the target pose and target position of the optical probe at each measurement point in the reference detection path according to the reference detection path and the surface geometry data;

[0102] S23, driving the mechanical execution mechanism carrying the optical probe to run according to the reference detection path, the target pose and the target position to perform optical measurement on the to-be-measured region, so as to obtain the optical data.

[0103] Specifically, the reference detection path refers to a series of ordered spatial point sets planned in advance for the optical probe to measure the target area, which constitutes the detection trajectory of the optical probe. It can adopt various geometric shapes of paths to ensure coverage and systematic measurement of the target area. The target pose refers to the spatial direction and angle that the optical probe should maintain at a specific measurement point. It can be represented by Euler angles or quaternions to ensure that the laser beam can be focused at the expected incident angle. The target position refers to the spatial coordinates that the optical probe should be at a specific measurement point. The mechanical actuator refers to an automated device used to drive and position the optical probe for movement measurement, which can take the form of a multi-axis robot or a mobile platform.

[0104] Specifically, step S21 generates a reference detection path containing multiple measurement points according to the target area. The pre-planning of this path lays the foundation for subsequent optical measurement, ensuring the systematicness and coverage of the measurement process, avoiding missing critical areas, and providing clear trajectory guidance for the precise motion of the probe. Secondly, step S22 calculates the target pose and target position of the optical probe at each measurement point in the path according to the reference detection path and the surface geometry data of the target area. This step combines the pre-set detection path and the actual surface geometry information of the target area, enabling the system to accurately calculate the spatial pose and position of the optical probe at each measurement point on the path. This means that the probe is no longer blindly adjusted, but purposefully and accurately knows where and how to measure, providing a theoretical basis for subsequent stable focusing and perpendicular incidence. Finally, step S23 drives the mechanical actuator carrying the optical probe to operate according to the calculated reference detection path, target pose, and target position to perform optical measurement on the target area. By inputting accurate target pose and position information into the mechanical actuator, the mechanical arm or robot can accurately guide the optical probe to move along the pre-set path and adjust the pose and distance of the probe at each measurement point, ensuring that the laser beam can always be focused at a stable and perpendicular angle to the surface, and the working distance between the probe and the measured surface remains constant, greatly improving the quality, stability, and reliability of the collected optical data. These steps directly integrate surface geometry data into path planning and probe control, overcoming the challenge of maintaining stable measurement on non-planar surfaces, achieving automated and high-precision optical data acquisition, and significantly improving the accuracy and reliability of initial corrosion point identification.

[0105] Through the above design, the method of the application solves the problem that the optical probe is difficult to accurately and stably maintain the relative distance with the measured surface and the laser beam incidence angle in the complex curved surface environment such as the inner wall of a large industrial storage tank. The scheme avoids the situation of measurement signal fluctuation and poor data repeatability, improves the reliability of the collected optical data and the accuracy of the subsequent corrosion condition analysis. At the same time, it ensures that the laser beam of the optical probe can be focused at a stable and perpendicular to the surface incidence angle at any measurement point, and ensures that the working distance between the optical probe and the measured surface is unchanged, providing high-quality input for subsequent corrosion condition analysis.

[0106] In some preferred embodiments, step S22 comprises:

[0107] S221, pre-processing the surface geometry data, the pre-processing comprising removing abnormal data and completing data;

[0108] S222, obtaining local surface normal and local curvature information of each measurement point on the reference detection path according to the reference detection path and the pre-processed surface geometry information;

[0109] S223, calculating the target pose and target position of the optical probe at each measurement point based on the local surface normal, the local curvature information and the preset measurement distance.

[0110] Specifically, the surface geometry data preferably comprises surface curvature and undulation.

[0111] More specifically, the pre-processing aims to improve data quality and applicability, and various data processing techniques can be used to achieve it, for example, removing abnormal data can use statistical methods (such as Z-score, IQR) or model-based methods (such as RANSAC) to identify and eliminate outliers; completing data can use interpolation methods (such as linear interpolation, spline interpolation), regression analysis or machine learning-based methods (such as K-neighbor filling) to fill in missing values.

[0112] Specifically, step S221 preprocesses these raw surface geometry data, addressing possible abnormalities, incompleteness, and other issues in the raw data. By removing abnormal data, the influence of measurement noise or erroneous points can be eliminated; by completing the data, the blind spots or data loss that may occur during scanning can be made up. After preprocessing, high-quality, accurate, and complete surface geometry information is obtained, which lays the foundation for subsequent calculations and avoids calculation errors caused by data quality problems. Subsequently, in step S222, based on the preprocessed surface geometry information and the preset reference detection path, the system can obtain the local surface normal and local curvature information of each measurement point on the reference detection path. The local surface normal directly indicates the vertical direction of the surface at that point and is the basis for ensuring that the laser beam of the optical probe is perpendicular to the surface. The local curvature information describes the degree of curvature of the surface at that point, which is crucial for maintaining a constant working distance on a curved surface. The acquisition of these local geometric features enables the probe to make local adjustments to the complex curved surface and structure of the inner wall of the storage tank. Finally, in step S223, based on these acquired local surface normal, local curvature information, and preset measurement distance, the system calculates the target pose and target position of the optical probe at each measurement point. Through the local surface normal, the pose that the probe should maintain can be determined to ensure stable and perpendicular incidence of the laser beam. At the same time, combined with the local curvature information and the preset measurement distance, the system can calculate the target position of the probe in space, thereby maintaining a constant working distance between the optical probe and the measured surface throughout the detection path. The synergistic effect of this series of steps enables the optical probe to overcome the challenges posed by the complex structure of the inner wall of the large industrial storage tank, adjust its pose and position, and thus ensure stable and perpendicular incidence of the laser beam and constant working distance during the optical data acquisition process. This probe control capability directly improves the accuracy and reliability of the optical data acquisition in step S2, thereby providing high-quality input for the data alignment in step S4 and the corrosion condition analysis in step S5, enhancing the accuracy and reliability of the entire anti-corrosion analysis and detection method.

[0113] In some preferred embodiments, step S2 further comprises:

[0114] S24, during the optical measurement process, the environmental medium optical property data and the surface impurity spectral feature data of each measurement point on the reference detection path are synchronously acquired;

[0115] S25, based on the environmental medium optical property data and the surface impurity spectral feature data, the collected optical data is corrected.

[0116] Specifically, the environmental medium optical property data refers to the physical quantitative information of the influence of air or other gases, liquid media in the measurement path on the propagation of the optical signal, which can be obtained in real time by using a humidity sensor, a temperature sensor, a gas composition analyzer or a particulate matter concentration sensor, etc., and is used to characterize the absorption, scattering, refraction or attenuation effect of the medium on the light. The surface impurity spectral feature data refers to the light absorption, reflection or emission characteristics of non-target substances attached to the measured surface in a specific wavelength range, which can be synchronously collected by using an auxiliary spectrometer, a hyperspectral imager or a special impurity detection sensor, etc., and is used to identify and quantify the interference of the impurities on the target spectral signal. The correction processing refers to correcting the originally collected optical data based on a pre-established model or algorithm to eliminate or compensate for the errors or interferences introduced by the environmental medium and the surface impurities, which can be realized by using spectral deconvolution, baseline correction, scattering compensation algorithm or machine learning model, etc.

[0117] Specifically, step S24 synchronously acquires the environmental medium optical property data and the surface impurity spectral feature data of each measurement point on the reference detection path when step S23 is performed. This synchronous acquisition ensures a high matching of the interference data with the actual measurement conditions, and can accurately capture and quantify the influence of external factors on the optical signal. Subsequently, step S25 corrects the originally collected optical data according to the synchronously acquired environmental medium optical property data and surface impurity spectral feature data. By analyzing the absorption, scattering or refraction effect of the environmental medium on the optical signal, and the superposition or covering of the surface impurities on the target spectral information, these interferences can be eliminated, so that the corrected optical data which is more pure and more truly reflects the state of the coating and the substrate can be obtained.

[0118] Through the above design, the method of the present application can effectively eliminate the interference of the environmental medium and the surface impurities on the optical data, and significantly improve the accuracy and reliability of the collected optical data. Thus, more accurate input is provided for the subsequent corrosion condition analysis based on the optical data, thereby improving the accuracy of the initial corrosion point identification and the corrosion degree evaluation, and ensuring the reliability of the detection results.

[0119] In some preferred embodiments, step S4 comprises:

[0120] S41, acquiring spatial position information corresponding to the optical data and spatial position information corresponding to the coating lower interface state data;

[0121] S42, establishing a spatial mapping relationship between the spatial position information of the optical data and the spatial position information of the coating lower interface state data according to the surface geometry data;

[0122] S43, based on the spatial mapping relationship, converting the optical data and the coating-sub-interface state data to a unified spatial coordinate system to realize data alignment.

[0123] Specifically, step S41 provides necessary data points for subsequent spatial correlation and conversion by assigning each modal measurement data with its precise coordinates in three-dimensional space. These spatial position information can be provided by the sensor's own positioning system or obtained through an external tracking system. Secondly, step S42 establishes a spatial mapping relationship between the optical data spatial position information and the coating-sub-interface state data spatial position information using the pre-acquired surface geometry data. Since large industrial storage tanks usually have complex curved surfaces and structures, there may be differences in relative position and attitude when different sensors collect data. Surface geometry data as a common spatial reference can accurately understand and compensate for these differences, thereby establishing a mapping model that can reflect the corresponding relationship in the real physical space. Finally, step S43 converts the optical data and the coating-sub-interface state data to a unified spatial coordinate system based on the established spatial mapping relationship to realize data alignment. This conversion eliminates spatial deviations caused by different sensor types, measurement methods, or local coordinate systems, allowing different data points to be directly compared and fused.

[0124] More specifically, after completing data alignment, step S43 can also perform spatial interpolation processing on the aligned data to address the inconsistency in sampling density or resolution that may exist in different data sources, ensuring that accurate optical and coating-sub-interface state data can be found in any area of interest.

[0125] Through the above design, the method of the present application can effectively solve the problem that the complex surface geometry of the inner wall of a large industrial storage tank makes it difficult to effectively and accurately spatially align heterogeneous data collected by different detection technologies. The present scheme acquires spatial position information of different modal data and establishes an accurate spatial mapping relationship using surface geometry data, and finally converts heterogeneous data to a unified spatial coordinate system, thereby ensuring the accurate correspondence of optical data and coating-sub-interface state data in space, significantly improving the comprehensiveness and accuracy of subsequent corrosion condition evaluation, and making the identification of initial corrosion points more reliable.

[0126] In some preferred embodiments, the optical probe is a laser-induced breakdown spectroscopy probe or a Raman spectroscopy probe.

[0127] Specifically, when a laser-induced breakdown spectroscopy probe is employed, the optical probe is capable of exciting plasma on the surface to be measured by high-energy laser pulses and analyzing the spectrum emitted by the plasma. This method can achieve rapid, in-situ analysis of the elemental composition of the coating material and the underlying substrate. For aluminum pigment coatings, the laser-induced breakdown spectroscopy probe can detect the migration of metal elements or the appearance of specific elements in corrosion products during the corrosion process, thereby directly obtaining information about the elemental composition changes related to chemical corrosion. This elemental-level analysis is crucial for identifying the initial corrosion site, as initial corrosion is often accompanied by trace changes in elemental composition, which are difficult for traditional optical probes to capture.

[0128] More specifically, when a Raman spectroscopy probe is employed, the optical probe utilizes the Raman scattering effect to obtain molecular vibration information of the material by analyzing the frequency shift of scattered light, and then identifies its chemical bonds and molecular structure. In corrosion detection, the Raman spectroscopy probe can identify the molecular fingerprints of specific corrosion products (such as different forms of iron oxides and hydroxides), as well as the chemical structure changes of the coating material under the action of corrosion. This molecular-level analysis provides more detailed and specific chemical corrosion indication information, which can distinguish different types of corrosion products, and even in the initial stage of corrosion, when macroscopic physical defects are not yet apparent, the occurrence of corrosion can be judged through changes in chemical structure.

[0129] More specifically, by selecting a laser-induced breakdown spectroscopy probe or a Raman spectroscopy probe, the present scheme ensures that the optical data collected in method step S2 is not just visual information on the surface, but high-quality data rich in spectral features and chemical corrosion indication information. These high-quality optical data can be effectively analyzed in subsequent step S51 to obtain the spectral features of the area to be measured, and further obtain chemical corrosion indication information in step S531.

[0130] In a second aspect, referring to Figure 2 Some embodiments of the present application also provide an aluminum pigment coating corrosion resistance analysis and detection system for non-destructive identification of initial corrosion sites under the aluminum pigment coating or at the interface between the coating and the substrate of a large industrial storage tank. The system comprises:

[0131] A first acquisition module for acquiring surface geometry data of the area to be measured of the storage tank;

[0132] A second acquisition module for adjusting the pose of the optical probe and the distance between the optical probe and the storage tank based on the surface geometry data, and collecting optical data of the area to be measured using the optical probe;

[0133] A third acquisition module for collecting coating lower interface state data of the area to be measured based on a non-contact sensor;

[0134] a data alignment module configured to align the optical data and the coating-sub-interface state data;

[0135] a corrosion analysis module configured to generate a corrosion condition analysis result based on the aligned optical data and the coating-sub-interface state data.

[0136] The aluminum pigment coating corrosion resistance analysis detection system of the present application solves the measurement stability problem on a complex curved surface by using surface geometry data to precisely adjust the attitude and working distance of an optical probe; at the same time, by combining the optical data and the coating-sub-interface state data of two different modalities of information and performing precise alignment and fusion analysis, the problem of insufficient sensitivity of traditional systems and difficulty in integrating multi-modal data is solved, thereby improving the accuracy and reliability of the identification of initial corrosion points on large industrial storage tanks.

[0137] In addition, the units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., they can be located in one place or distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the present embodiment.

[0138] Furthermore, the functional modules in each embodiment of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0139] In this document, relational terms such as first and second and the like can only be used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that these entities or operations exist in any such actual relationship or order.

[0140] The above only describes the embodiments of the present application and is not used to limit the protection scope of the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for analyzing the corrosion resistance of an aluminum pigment coating, for non-destructively identifying the initial corrosion point under the aluminum pigment coating or at the interface between the coating and the base material of a large industrial storage tank, characterized in that, The method comprises the following steps: S1, acquiring surface geometry data of a to-be-measured region of a storage tank; S2, adjusting the pose of an optical probe and the distance between the optical probe and the storage tank based on the surface geometry data, and collecting optical data of the to-be-measured region by using the optical probe; S3, collecting coating-sub-interface state data of the to-be-measured region based on a non-contact sensor; S4, aligning the optical data and the coating-sub-interface state data; S5, generating a corrosion condition analysis result based on the aligned optical data and coating-sub-interface state data; Step S5 comprises: S51, analyzing the aligned optical data to obtain spectral features of the to-be-measured region; S52, analyzing the aligned coating-sub-interface state data to obtain physical defect features of the to-be-measured region; S53, establishing associated features about the to-be-measured region according to the spectral features and the physical defect features; S54, judging whether there is an initial corrosion point in the to-be-measured region according to the associated features, and when there is the initial corrosion point, obtaining the position, type and corrosion degree of the initial corrosion point to constitute the corrosion condition analysis result; The coating-sub-interface state data comprises pulse eddy current data and low-frequency ultrasonic guided wave data, and step S52 comprises: S521, performing distance compensation on the pulse eddy current data according to the surface geometry data, and performing coating influence correction on the compensated pulse eddy current data in combination with preset electromagnetic characteristics of the coating material, and extracting eddy current signal features related to metal thinning according to the corrected pulse eddy current data; S522, filtering the low-frequency ultrasonic guided wave data to filter out interference signals caused by surface medium residues or slight undulations, analyzing the propagation speed, attenuation characteristics and reflection mode of the guided wave according to the filtered low-frequency ultrasonic guided wave data to obtain acoustic characteristic change information of the coating and substrate interface of the to-be-measured region, and extracting guided wave propagation features related to interface peeling or interface delamination according to the acoustic characteristic change information; S523, fusing the eddy current signal features and the guided wave propagation features to obtain the physical defect features of the to-be-measured region; Step S53 comprises: S531, obtaining chemical corrosion indication information of the to-be-measured region according to the spectral features; S532, obtaining physical damage indication information of the to-be-measured region according to the physical defect features; S533, spatially registering the chemical corrosion indication information and the physical damage indication information to construct the associated features.

2. The method for analyzing and testing the corrosion resistance of aluminum pigment coatings according to claim 1, characterized in that, Step S54 comprises: S541, analyzing the associated features based on a preset corrosion mode library to judge whether there is an initial corrosion point in the to-be-measured region, and if so, extracting the position and type of the initial corrosion point, wherein the corrosion mode library comprises a plurality of associated features corresponding to initial corrosion points of different types; S542, quantifying the corrosion degree of the initial corrosion point according to the intensity of the associated features corresponding to the initial corrosion point; S543, combining the position, type and corrosion degree of each initial corrosion point to constitute the corrosion condition analysis result.

3. The method for analyzing and testing the corrosion resistance of aluminum pigment coatings according to claim 1, characterized in that, Step S2 comprises: S21, generating a reference detection path passing through a plurality of measurement points according to the to-be-measured region; S22, calculating a target pose and a target position of the optical probe at each measurement point in the reference detection path according to the reference detection path and the surface geometry data; S23, driving a mechanical execution mechanism carrying the optical probe to operate optical measurement on the to-be-measured region according to the reference detection path, the target pose and the target position, so as to obtain the optical data.

4. The method for analyzing and testing the corrosion resistance of aluminum pigment coatings according to claim 3, characterized in that, Step S22 comprises: S221, preprocessing the surface geometry data, the preprocessing comprising removing abnormal data and completing data; S222, obtaining local surface normal and local curvature information of each measurement point on the reference detection path according to the reference detection path and the preprocessed surface geometry information; S223, calculating the target pose and the target position of the optical probe at each measurement point based on the local surface normal, the local curvature information and a preset measurement distance.

5. The method for analyzing and testing the corrosion resistance of aluminum pigment coatings according to claim 3, characterized in that, Step S2 further comprises: S24, synchronously obtaining environmental medium optical characteristic data and surface impurity spectral feature data of each measurement point on the reference detection path in the process of optical measurement; S25, correcting the collected optical data according to the environmental medium optical characteristic data and the surface impurity spectral feature data.

6. The method of claim 1, wherein the aluminum pigment coating is applied to the surface of the metal product by a method selected from the group consisting of: anodizing, electrolytic coloring, and chemical coloring. Step S4 comprises: S41, obtaining spatial position information corresponding to the optical data and spatial position information corresponding to the coating-sub-interface state data; S42, establishing a spatial mapping relationship between the optical data spatial position information and the coating-sub-interface state data spatial position information according to the surface geometry data; S43, converting the optical data and the coating-sub-interface state data to a unified spatial coordinate system based on the spatial mapping relationship to realize data alignment.

7. The method of claim 1, wherein the aluminum pigment coating is applied to the surface of the metal product by a method selected from the group consisting of: anodizing, thermal spraying, and powder coating. The optical probe is a laser-induced breakdown spectroscopy probe or a Raman spectroscopy probe.

8. An aluminum pigment coating anticorrosion analysis detection system for nondestructive identification of incipient corrosion sites under the aluminum pigment coating or at the interface of the coating and the substrate of a large industrial storage tank, characterized in that, The system comprises: A first acquisition module configured to acquire surface geometry data of a to-be-measured region of a storage tank; A second acquisition module configured to adjust a pose of an optical probe and a distance between the optical probe and the storage tank based on the surface geometry data, and collect optical data of the to-be-measured region by using the optical probe; A third acquisition module configured to collect coating-sub-interface state data of the to-be-measured region based on a non-contact sensor; A data alignment module configured to align the optical data and the coating-sub-interface state data; A corrosion analysis module configured to generate a corrosion condition analysis result based on the aligned optical data and the coating-sub-interface state data; The step of generating the corrosion condition analysis result based on the aligned optical data and the coating-sub-interface state data comprises: S51, analyzing the aligned optical data to obtain spectral features of the to-be-measured region; S52, analyzing the aligned coating-sub-interface state data to obtain physical defect features of the to-be-measured region; S53, establishing associated features about the to-be-measured region according to the spectral features and the physical defect features; S54, judging whether the initial corrosion point exists in the to-be-tested region according to the correlation feature, and obtaining the position, type and corrosion degree of the initial corrosion point when the initial corrosion point exists, to form the corrosion condition analysis result; The coating lower interface state data includes pulse eddy current data and low-frequency ultrasonic guided wave data, and step S52 includes: S521, distance compensation is performed on the pulse eddy current data according to the surface geometry data, coating influence correction is performed on the compensated pulse eddy current data in combination with preset electromagnetic characteristics of the coating material, and eddy current signal features related to metal thinning are extracted according to the corrected pulse eddy current data; S522, the low-frequency ultrasonic guided wave data is filtered to filter out interference signals caused by surface medium residues or slight undulations, and the propagation speed, attenuation characteristics and reflection mode of the guided wave are analyzed according to the filtered low-frequency ultrasonic guided wave data to obtain acoustic characteristic change information of the coating and substrate interface of the to-be-tested region, and guided wave propagation features related to interface peeling or interface delamination are extracted according to the acoustic characteristic change information; S523, the eddy current signal features and the guided wave propagation features are fused to obtain physical defect features of the to-be-tested region; Step S53 includes: S531, obtaining chemical corrosion indication information of the to-be-tested region according to the spectral feature; S532, obtaining physical damage indication information of the to-be-tested region according to the physical defect feature; S533, spatially registering the chemical corrosion indication information and the physical damage indication information to construct the correlation feature.

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