Intelligent recognition method for micro-morphology of magnesium oxide coating

Abstract

The present application belongs to the technical field of magnesium oxide coating intelligent monitoring, and particularly relates to a magnesium oxide coating microscopic morphology intelligent identification method. The method comprises: collecting a microscopic image of a target magnesium alloy component monitoring station and outputting a time sequence microscopic image sequence; based on the characteristic size difference between magnesium oxide hydration expansion hole filling and inner layer damage, the radial energy distribution curve curvature extreme point is taken as the optical cutoff frequency to segment the high and low frequency bands, and the outer layer and inner layer anisotropy degrees are calculated; the outer layer anisotropy degree is used to construct a confidence weight, the interference of the outer layer hydration product accumulation on the inner layer signal is corrected, the inner layer anisotropy degree correction value is calculated, and the self-repairing inflection point is locked; according to the difference in failure mechanism before and after the inflection point, the inner layer penetration time is solved in segments, the remaining effective protection time is output, and differential maintenance is performed. The present application solves the problems of outer layer self-repairing interference with inner layer damage observation and life prediction inaccuracy caused by sudden change in failure mechanism, and improves the service safety of magnesium alloy components.

Description

Technical Field

[0001] This invention belongs to the field of intelligent monitoring technology for magnesium oxide coatings, specifically relating to an intelligent identification method for the microstructure of magnesium oxide coatings. Background Technology

[0002] Magnesium alloys are widely used in aerospace, transportation, and electronic communications due to their lightweight and high specific strength. However, magnesium alloys are chemically reactive and prone to severe corrosion in humid, salt spray, and other corrosive environments, which limits their reliability in engineering applications. Magnesium oxide coatings, as an effective means of surface protection for magnesium alloys, achieve dual protection through the self-healing and pore-sealing properties of the outer porous region and the chemical inertness of the inner barrier layer.

[0003] Current methods for monitoring the condition of magnesium oxide coatings mainly rely on electrochemical impedance spectroscopy or accelerated salt spray experiments, assessing the degree of coating failure through changes in coating resistance or macroscopic weight loss. However, during actual service, the porous outer layer of the magnesium oxide coating undergoes a hydration reaction to generate magnesium hydroxide, and the volume expansion products fill the micropores, creating a self-healing effect. Simultaneously, the inner barrier layer experiences crack propagation and penetration under the coupled effects of stress and corrosion. Existing methods treat the outer layer's self-healing and the inner layer's damage separately, failing to establish a quantitative correlation between the two. This leads to discrepancies between the coating failure stage determination and remaining life prediction results and the actual physical processes.

[0004] The accumulation of hydration products in the outer layer can alter the frequency domain scattering characteristics of the coating, interfering with the effective extraction of damage signals from the inner layer. However, existing microscopic image analysis methods employ global statistical features and do not consider the physical interference of the outer layer's self-healing period on the inner layer signal, resulting in insufficient accuracy in identifying the inner layer's damage state. Regarding remaining lifetime prediction, existing methods primarily rely on empirical models or offline accelerated experiments, failing to incorporate real-time microscopic morphology evolution to dynamically correct prediction parameters, leading to delayed maintenance decisions or over-maintenance. Summary of the Invention

[0005] In order to overcome the defects and shortcomings of the existing technology, the present invention provides a method for intelligent identification of the microstructure of magnesium oxide coating.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for intelligent identification of the microstructure of magnesium oxide coatings, comprising the following steps: S1. During the monitoring period, microscopic images of the target magnesium alloy component monitoring station are collected according to the preset acquisition cycle and service parameters are recorded. The time-series microscopic image sequence is then output after registration. S2. Obtain the frequency domain power spectrum from the time-series microscopic image sequence; based on the characteristic size difference between magnesium oxide hydration expansion filling pores and inner layer damage, use the curvature extreme point of the radial energy distribution curve as the optical cutoff frequency to divide the high and low frequency bands, and calculate the anisotropy of the outer and inner layers of the magnesium oxide coating respectively. S3. Determine the outer layer state based on the degree of energy change in the high-frequency band; when the outer layer is in the self-healing period of magnesium hydroxide expansion filling the pores, construct confidence weights based on the anisotropy of the outer layer, correct the physical interference of the outer layer hydration product accumulation on the inner layer signal, and calculate the correction value of the anisotropy of the inner layer. S4. When the temporal evolution of the correction value conforms to the characteristics of the transition from the outer layer hydration self-healing period to the inner layer exposure, the corresponding service time is locked as the self-healing inflection point; based on the difference in the failure mechanism of the magnesium oxide coating before and after the self-healing inflection point, the inner layer penetration time is solved in segments, and the remaining effective protection time is output. S5. Perform differentiated maintenance on magnesium oxide components based on the remaining effective protection time.

[0007] According to the above technical solution, the steps of acquiring microscopic images of the target magnesium alloy component monitoring station according to a preset acquisition cycle within the monitoring period and recording service parameters, and then registering and outputting a time-series microscopic image sequence include: S110. Select a monitoring area on the surface of the target magnesium alloy component and establish a monitoring station. Prepare a feature mark of the inner barrier layer that does not penetrate the magnesium oxide coating as a space physical reference. S120. A microscopic imaging device is used to periodically acquire images of the monitoring station. The acquisition cycle is set according to the service environment of the component to obtain time-series microscopic images of the target magnesium alloy component monitoring station. The service duration of the target magnesium alloy component is recorded simultaneously at the moment of acquisition. S130. Perform two-stage registration on the time-series microscopic images. Use feature marks to eliminate rotation and large-scale displacement caused by changes in shooting conditions to perform geometric coarse registration. Use non-rigid fine registration to eliminate residual local deformation and output a time-series microscopic image sequence with spatial pixel-level alignment.

[0008] According to the above technical solution, the steps of obtaining the frequency domain power spectrum of the time-series microscopic image sequence; and calculating the anisotropy of the outer and inner layers of the magnesium oxide coating by dividing the high and low frequency bands based on the characteristic size difference between the pore filling and inner layer damage caused by magnesium oxide hydration expansion, using the curvature extreme point of the radial energy distribution curve as the optical cutoff frequency, respectively: S210. Perform a two-dimensional Fourier transform on each frame of the time-series microscopic image sequence that is spatially pixel-level aligned by S130 to obtain the frequency domain power spectrum and integrate with respect to the direction angle along the frequency radius to calculate the radial energy distribution curve. S220. Based on the characteristic size difference between magnesium oxide hydration expansion filling pores and inner layer damage, the curvature extreme point of the radial energy distribution curve is identified as the optical cutoff frequency; the frequency domain power spectrum is divided into high-frequency band and low-frequency band using the optical cutoff frequency as the boundary; the high-frequency band corresponds to the small-scale structural information of the micropore scale of the outer porous region of the magnesium oxide coating, and the low-frequency band corresponds to the large-scale structural information of the inner barrier layer peeling and through crack scale of the magnesium oxide coating. S230. Calculate the anisotropy of the outer layer in the high-frequency band, reflecting the directional differences of the outer layer microcracks and hydration filling; calculate the anisotropy of the inner layer in the low-frequency band, reflecting the directional differences of the inner layer peeling and penetration.

[0009] According to the above technical solution, the steps of determining the outer layer state based on the degree of high-frequency energy change, and when the outer layer is in the self-healing period of magnesium hydroxide expansion filling, constructing confidence weights based on the anisotropy of the outer layer, correcting the physical interference of the outer layer hydration product accumulation on the inner layer signal, and calculating the correction value of the inner layer anisotropy include: S310. Extract the total high-frequency energy of the time-series microscopic image sequence with spatial pixel-level alignment output by S130 frame by frame, calculate the ratio of the total high-frequency energy of each frame to the total high-frequency energy of the initial state, and obtain the high-frequency energy degradation coefficient; the high-frequency energy of the initial state is obtained by frequency domain transformation, radial energy integration and spectrum segmentation of the initial microscopic image acquired before the coating is put into service. S320. Determine the outer layer status based on the high-frequency energy degradation coefficient: If the high-frequency energy degradation coefficient indicates that the outer layer microporous structure maintains a stable self-repairing state, the outer layer is determined to be in the self-repairing period; if the high-frequency energy degradation coefficient indicates that the outer layer microporous structure undergoes disintegration and damage, the outer layer is determined to be completely detached, and the inner barrier layer is directly exposed to the corrosive environment. S330. When the outer layer is in the self-healing period, a confidence weight is constructed based on the anisotropy of the outer layer, and the anisotropy correction value of the inner layer is calculated based on the confidence weight to compensate for the signal interference of the inner layer caused by the filling of the porous region of the outer layer by the volume expansion products of the hydration reaction.

[0010] According to the above technical solution, when the temporal evolution of the correction value conforms to the characteristics of the transition from the outer layer hydration self-healing period to the inner layer exposure, the corresponding service time is locked as the self-healing inflection point; based on the difference in the failure mechanism of the magnesium oxide coating before and after the self-healing inflection point, the steps of solving the inner layer penetration time in segments and outputting the remaining effective protection time include: S410. Monitor the temporal evolution of the inner layer anisotropy correction value. When the temporal evolution of the inner layer anisotropy correction value conforms to the characteristics of the transition from the outer layer hydration self-healing period to the inner layer exposure, lock the corresponding service duration as the self-healing inflection point. When the temporal evolution of the inner layer anisotropy correction value does not conform to the characteristics of the transition from the outer layer hydration self-healing period to the inner layer exposure, it is determined that the current period is still in the outer layer hydration self-healing period, and the self-healing inflection point is not locked. S420. Before the self-healing inflection point, based on the hydration reaction and capillary diffusion mechanism of the outer porous region, the reaction diffusion equation is used to constrain the slow penetration and self-healing pore blockage of the outer porous region, and the delay time of the corrosion front reaching the inner barrier layer is deduced. S430. After the self-healing inflection point and when the outer layer falls off, based on the crack propagation and stress corrosion mechanism of the inner barrier layer, the stress corrosion crack propagation equation is used to map the rate of increase of the inner layer anisotropy correction value to the penetration rate of the corrosion front through the remaining thickness of the inner barrier layer, and to deduce the penetration time required for the corrosion front to penetrate the remaining thickness of the inner barrier layer. S440, combining the delay time of the corrosion front reaching the inner barrier layer and the penetration time required for the corrosion front to penetrate the remaining thickness of the inner barrier layer, yields the remaining effective protection time of the coating; combined with the inner layer anisotropy correction value, the coating protection failure stage is output.

[0011] According to the above technical solution, the step of performing differentiated maintenance on magnesium oxide components based on the remaining effective protection time includes: Differentiated maintenance responses are implemented for magnesium oxide components based on the stage of coating protection failure and the remaining effective protection time of the coating.

[0012] Secondly, this application provides an electronic device, including a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a method for intelligent recognition of the microstructure of magnesium oxide coating by calling the computer program stored in the memory.

[0013] Thirdly, this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a method for intelligent identification of the microstructure of magnesium oxide coatings.

[0014] Compared with the prior art, this application has the following advantages and beneficial effects: 1. By adaptively segmenting high and low frequency bands through optical cutoff frequency, the anisotropy of the outer and inner layers is calculated separately, thereby realizing the separation and quantitative characterization of damage characteristics of the magnesium oxide coating double-layer structure in the frequency domain and avoiding signal aliasing caused by the single-layer model. 2. The outer layer state is determined by the degree of energy change in the high-frequency band. The confidence weight is constructed based on the anisotropy of the outer layer to correct the anisotropy of the inner layer, compensate for the physical interference of the accumulation of hydration products on the inner layer signal, and improve the accuracy of inner layer damage state identification. 3. By locking the self-healing inflection point through the temporal evolution of the inner layer anisotropy correction value, the inner layer penetration time is solved in segments to realize the differential lifetime prediction before and after the coating failure mechanism transformation. 4. By performing differentiated maintenance based on the remaining effective protection time, we can avoid over-maintenance or delayed maintenance. Attached Figure Description

[0015] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a schematic diagram of the overall process of the intelligent identification method for the microstructure of magnesium oxide coatings of the present invention. Figure 2 This is a schematic diagram of the frequency domain double-layer signal separation and anisotropy calculation process of the intelligent identification method for the microstructure of magnesium oxide coatings of the present invention. Figure 3 This is a schematic diagram of the confidence weight correction, inflection point locking, and segmented lifetime extrapolation process of the intelligent identification method for the microstructure of magnesium oxide coatings of the present invention. Figure 4 This is a schematic diagram of the differentiated maintenance decision-making process for the intelligent identification method of magnesium oxide coating microstructure of the present invention. Detailed Implementation

[0016] The technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments and specific features in the embodiments are detailed descriptions of the technical solution of this application, rather than limitations thereof. In the absence of conflict, the embodiments and technical features in the embodiments can be combined with each other.

[0017] like Figure 1 As shown, this embodiment provides a method for intelligent identification of the microstructure of magnesium oxide coatings, which specifically includes the following steps: S1. During the monitoring period, microscopic images of the target magnesium alloy component monitoring station are collected according to the preset acquisition cycle and service parameters are recorded. The time-series microscopic image sequence is then output after registration. S2. Obtain the frequency domain power spectrum from the time-series microscopic image sequence; based on the characteristic size difference between magnesium oxide hydration expansion filling pores and inner layer damage, use the curvature extreme point of the radial energy distribution curve as the optical cutoff frequency to divide the high and low frequency bands, and calculate the anisotropy of the outer and inner layers of the magnesium oxide coating respectively. S3. Determine the outer layer state based on the degree of energy change in the high-frequency band; when the outer layer is in the self-healing period of magnesium hydroxide expansion filling the pores, construct confidence weights based on the anisotropy of the outer layer, correct the physical interference of the outer layer hydration product accumulation on the inner layer signal, and calculate the correction value of the anisotropy of the inner layer. S4. When the temporal evolution of the correction value conforms to the characteristics of the transition from the outer layer hydration self-healing period to the inner layer exposure, the corresponding service time is locked as the self-healing inflection point; based on the difference in the failure mechanism of the magnesium oxide coating before and after the self-healing inflection point, the inner layer penetration time is solved in segments, and the remaining effective protection time is output. S5. Perform differentiated maintenance on magnesium oxide components based on the remaining effective protection time.

[0018] In this embodiment, step S1 involves acquiring microscopic images of the target magnesium alloy component monitoring station according to a preset acquisition cycle within the monitoring period and recording service parameters. The resulting time-series microscopic image sequence is then registered and output. Specifically, this includes: S110. Select a monitoring area on the surface of the target magnesium alloy component and establish a monitoring station. Prepare a feature mark of the inner barrier layer that does not penetrate the magnesium oxide coating as a space physical reference. Monitoring stations were established in easily corroded areas selected on the surface of the target magnesium alloy components. Easily corroded areas are those where corrosive media easily accumulate or where the protective effectiveness of the coating is easily degraded in the service environment. They are determined by the following methods: statistical analysis of past service records of magnesium oxide components of the same model is performed, and the frequency of corrosion failure of magnesium oxide coatings at each location is ranked. The top 20% of locations with the highest failure frequency are selected as candidate areas. On-site inspections of the target magnesium oxide components are conducted, and areas where early signs of corrosion (such as discoloration, bulging, and microcracks) have been observed are recorded. The intersection of the candidate areas and the areas found during the inspection is used to finally determine the easily corroded areas.

[0019] It should be noted that in this embodiment, by statistically analyzing the failure frequency of past service records, monitoring resources are focused on the parts with the highest historical failure probability, avoiding data redundancy and wasted maintenance costs caused by densely deploying monitoring points across the entire surface; through empirical verification of early corrosion signs during on-site inspections, non-statistical weak areas caused by batch process fluctuations or local stress concentrations are excluded; the intersection of the two is taken as the final monitoring area, which takes into account both the statistical significance of failure prediction and the individual specificity of the current component, maximizing the representativeness of the samples and the efficiency of inflection point capture in subsequent microscopic image acquisition, and providing a high-confidence data source for differentiated maintenance decisions.

[0020] Micron-level feature marks are prepared using femtosecond laser, diamond indenter, or electrochemical etching as a space physics reference. The depth of the feature marks does not exceed the thickness of the outer porous region of the magnesium oxide coating to ensure that they do not penetrate the inner barrier layer. Immediately after the marks are prepared, in-situ oxidation sealing treatment is performed to allow the surface of the marks to reform a magnesium oxide passivation layer with the same composition as the outer layer.

[0021] For example, in this embodiment, the groove depth is taken as 1 / 2 of the thickness of the outer porous region. This ensures the long-term recognizability of the groove geometry while maintaining sufficient depth margin to prevent penetration of the inner barrier layer due to thickness fluctuations or positioning errors during the preparation process. The groove linewidth is taken as 2μm, which matches the limit resolution of the integrated micro optical microscope. This ensures imaging clarity while avoiding significant disturbance of the local stress field of the coating due to excessively wide grooves. The thickness of the outer porous region accounts for 70% of the total coating thickness, corresponding to the thickness distribution ratio of the outer porous region and the inner barrier layer under a typical micro-arc oxidation preparation process.

[0022] In practical applications of this embodiment, the groove depth and geometry can be further adjusted flexibly in combination with the coating thickness and the severity of the service environment; for high corrosion rate environments, the groove depth can be increased or a grid-shaped groove can be used to improve registration stability.

[0023] It should be noted that the in-situ oxidation sealing treatment adopts a low-temperature oxidation process compatible with the coating preparation process. The treatment time is based on the formation of a uniform oxidation color on the scratch surface. The low-temperature oxidation process can re-oxidize the exposed surface of the scratch to form a passivation layer with the same chemical activity as the surrounding coating without damaging the original coating structure. This eliminates the electrochemical pairing effect between the scratch and the substrate and avoids accelerated local corrosion.

[0024] S120. A microscopic imaging device is used to periodically acquire images of the monitoring station. The acquisition cycle is set according to the service environment of the component to obtain time-series microscopic images of the target magnesium alloy component monitoring station. The service duration of the target magnesium alloy component is recorded simultaneously at the moment of acquisition. An integrated miniature optical microscope, coupled with a coaxial total internal reflection LED light source, is used to periodically acquire images of the monitoring station.

[0025] For example, in this embodiment, the integrated micro-optical microscope has a magnification of 1000×, corresponding to a field of view diameter of approximately 150μm, which can simultaneously cover representative micropore statistical samples of the outer porous region and local morphological features of the inner barrier layer; the numerical aperture is 0.8, ensuring an optical resolution limit of approximately 0.4μm, which is sufficient to distinguish typical outer micropore diameters (1μm to 5μm); the center wavelength of the coaxial total internal reflection LED light source is 550nm, which is in the middle of the visible light band, taking into account both the optical absorption characteristics of the magnesium oxide coating and the quantum efficiency peak of the CCD imaging sensor.

[0026] The acquisition cycle is set according to the service environment of the component, and the time-series microscopic images of the target magnesium alloy component monitoring station are output. For example, in this embodiment, the acquisition interval is 24 hours. The 24-hour interval can cover the key transformation nodes of the outer microstructure evolution under typical service environment, while taking into account the balance between data storage and computing load.

[0027] In practical applications of this embodiment, the acquisition interval can be further adjusted flexibly according to the service environment of the coating; for scenarios with severe corrosion, the acquisition interval can be shortened accordingly to improve the accuracy of inflection point capture; for mild environments, the acquisition interval can be extended accordingly to reduce operation and maintenance costs.

[0028] The service duration and ambient temperature of the target magnesium alloy component are recorded simultaneously at the moment of acquisition. The service duration is the cumulative time from when the component was put into service to the current acquisition time, which is obtained from the equipment operation log or maintenance record, with an accuracy of not less than 1 hour. The ambient temperature is obtained synchronously by a temperature sensor integrated near the monitoring station. The temperature sensor is integrated with an integrated miniature optical microscope and is used for temperature correction of the subsequent reaction diffusion equation and stress corrosion crack propagation equation.

[0029] It should be noted that the integrated design of the miniature optical microscope and the coaxial total internal reflection LED light source ensures in-situ monitoring capabilities in confined spaces and avoids secondary damage caused by disassembling components; the environmental adaptability setting of the acquisition cycle can balance monitoring accuracy and implementation cost. In practical applications of this embodiment, the acquisition interval can be further flexibly adjusted in conjunction with the coating failure stage; for the rapid degradation period after the self-healing inflection point is locked, the interval can be increased to 12 hours or 6 hours to improve the inflection point capture accuracy.

[0030] S130. Perform two-stage registration on the time-series microscopic images. Use feature marks to eliminate rotation and large-scale displacement caused by changes in shooting conditions to perform geometric coarse registration. Use non-rigid fine registration to eliminate residual local deformation and output a time-series microscopic image sequence with spatial pixel-level alignment.

[0031] Geometric coarse registration is performed on the time-series microscopic images of the target magnesium alloy component monitoring station using feature marks. The edge contours of the feature marks are extracted, and the iterative nearest neighbor algorithm is used to optimize the rigid transformation parameters until convergence by iteratively matching the nearest neighbor relationship of the feature point set, thus eliminating rotation and large-scale displacement caused by changes in shooting conditions.

[0032] To address the microscopic roughness of polycrystalline magnesium oxide in the background unaffected by corrosion, a non-rigid fine registration based on mutual information is employed to eliminate residual local deformation and output a time-series microscopic image sequence with spatial pixel-level alignment.

[0033] For example, in this embodiment, the convergence threshold of the iterative nearest point algorithm in the geometric coarse registration is set to 0.1 pixels to ensure sub-pixel level coarse registration accuracy and avoid aliasing interference from large-scale displacement on subsequent frequency domain analysis; the control point spacing of the non-rigid fine registration is set to 32 pixels, which corresponds to a spatial sampling density of about 5 times that of a typical micropore diameter, which is sufficient to capture local non-uniform deformation while controlling computational complexity.

[0034] It should be noted that the two-stage registration decouples rigid large-scale correction from non-rigid local correction, and the similarity metric based on mutual information is robust to illumination changes during service. In practical applications of this embodiment, the control point spacing can be further adjusted flexibly in conjunction with the image noise level; for high-noise images, the control point spacing can be increased accordingly to suppress registration artifacts.

[0035] In this embodiment, as Figure 2 As shown, in step S2, the frequency domain power spectrum of the time-series microscopic image sequence is obtained; based on the characteristic size difference between the pore-filling effect of magnesium oxide hydration expansion and the inner layer damage, the high and low frequency bands are divided using the curvature extremum of the radial energy distribution curve as the optical cutoff frequency, and the anisotropy of the outer and inner layers of the magnesium oxide coating is calculated respectively, specifically including: S210. Perform a two-dimensional Fourier transform on each frame of the time-series microscopic image sequence that is spatially pixel-level aligned by S130 to obtain the frequency domain power spectrum and integrate with respect to the direction angle along the frequency radius to calculate the radial energy distribution curve. Perform a two-dimensional Fourier transform on each frame of the time-series microscopic image sequence output by S130 to obtain the frequency domain power spectrum; transform the frequency domain power spectrum in Cartesian coordinates to polar coordinates to obtain the frequency domain power spectrum in polar coordinates. Taking the origin of the frequency domain as the center, the omnidirectional angle of each sampling point is integrated along the frequency radius to compress the two-dimensional frequency domain power spectrum into a one-dimensional energy variation curve with the frequency radius, i.e., the radial energy distribution curve. The two-dimensional Fourier transform and radial energy integration are both conventional methods for image frequency domain analysis, and their specific implementations are well known to those skilled in the art, and will not be described in detail here.

[0036] It should be noted that the two-dimensional Fourier transform converts the microscopic morphology in the spatial domain into frequency components in the frequency domain, and the radial energy integral compresses the two-dimensional spectrum into a one-dimensional curve, facilitating subsequent adaptive identification of the optical cutoff frequency. In practical applications of this embodiment, the frequency radius sampling interval can be further adjusted flexibly in combination with image size and computing resources; for large-size images or real-time processing requirements, the sampling interval can be increased accordingly to reduce the computational load.

[0037] S220. Based on the characteristic size difference between magnesium oxide hydration expansion filling pores and inner layer damage, the curvature extreme point of the radial energy distribution curve is identified as the optical cutoff frequency; the frequency domain power spectrum is divided into high-frequency band and low-frequency band using the optical cutoff frequency as the boundary; the high-frequency band corresponds to the small-scale structural information of the micropore scale of the outer porous region of the magnesium oxide coating, and the low-frequency band corresponds to the large-scale structural information of the inner barrier layer peeling and through crack scale of the magnesium oxide coating. A smoothing window is set to smooth the radial energy distribution curve obtained by S210, the curvature of each point on the curve is calculated, and the extreme point of curvature is identified as the optical cutoff frequency. The frequency domain power spectrum is divided into high-frequency band and low-frequency band using the optical cutoff frequency as the boundary.

[0038] For example, in this embodiment, the smoothing process uses a moving average filter with a window width of 5% of the total length of the frequency radius, which can suppress high-frequency noise while preserving the macroscopic shape of the energy valley; the curvature calculation uses the three-point difference method, which can provide a stable second derivative estimate under discrete sampling conditions and ensure the positioning accuracy of the curvature extremum point.

[0039] It should be noted that in this embodiment, the spatial frequency in the image frequency domain is inversely proportional to the physical scale: the micropores in the outer porous region have small diameters, which appear as fine textures in the microscopic image and occupy high-frequency information; the peeling and through cracks in the inner barrier layer have large scales, which appear as macroscopic morphological changes in the microscopic image and occupy low-frequency information; therefore, based on the characteristic size difference between magnesium oxide hydration expansion filling pores and inner layer damage, the signals of the outer and inner layers can be separated in the frequency domain by using the optical cutoff frequency.

[0040] Furthermore, in this embodiment, the curvature extremum point corresponds to the transition boundary between high-frequency energy attenuation and low-frequency energy dominance in the radial energy distribution curve, and the energy distribution at the curvature extremum point exhibits significant scale separation characteristics. In practical applications of this embodiment, the smoothing window can be further adjusted flexibly based on the coating's microstructure characteristics; for coatings with a wide micropore size distribution or ambiguous crack scale, the smoothing window can be increased accordingly to improve the stability of the cutoff frequency.

[0041] S230. Calculate the anisotropy of the outer layer in the high-frequency band, reflecting the directional differences of the outer layer microcracks and hydration filling; calculate the anisotropy of the inner layer in the low-frequency band, reflecting the directional differences of the inner layer peeling and penetration.

[0042] In this embodiment, the anisotropy degree is calculated as follows: within the corresponding frequency band of the power spectrum in the frequency domain, the directional angles are integrated along the frequency radius direction to calculate the directional energy distribution function, and the anisotropy degree is obtained based on the directional energy distribution function; the corresponding frequency band refers to the frequency band to which the calculation object belongs, that is, the high frequency band when calculating the anisotropy degree of the outer layer, and the low frequency band when calculating the anisotropy degree of the inner layer.

[0043] The formula for calculating the directional energy distribution function is: ; in, Direction angle The directional energy at a location is expressed in energy units (kN). ); The frequency domain power spectrum in polar coordinates is obtained by transforming the frequency domain power spectrum in Cartesian coordinates to polar coordinates using S210, with units of energy / frequency area. ); Frequency radius, unit: , , This represents the frequency radius boundary of the corresponding frequency band; when calculating the anisotropy of the outer layer, , The frequency radius boundary for the high-frequency band is determined by the optical cutoff frequency calculated by S220; when calculating the anisotropy of the inner layer, , The frequency radius boundary for the low-frequency band is determined by the optical cutoff frequency calculated by S220.

[0044] The formula for calculating the degree of anisotropy is: ; in, The anisotropy degree has a range of values. ; Directional energy distribution function exist The maximum value within the range corresponds to the direction where the energy is most concentrated. Directional energy distribution function exist The minimum value within the range corresponds to the direction in which energy is most dispersed, that is, energy is evenly distributed in all directions, corresponding to no obvious directional damage to the outer microporous structure or the inner barrier layer. =0 indicates complete isotropy. =1 indicates complete anisotropy, meaning that energy is concentrated in a single direction, corresponding to significant directional damage (such as directional cracks or penetrating peeling) in the outer microporous structure or inner barrier layer; the closer A is to 1, the more severe the directional damage to the coating microstructure.

[0045] It should be noted that the anisotropy degree defined in this embodiment is based on the range normalization of directional energy distribution. This definition is sensitive to the concentration of the principal direction of the frequency domain power spectrum and is suitable for characterizing the directional characteristics of the magnesium oxide coating microstructure. The outer layer anisotropy reflects the directional differences in microcracks and uneven local hydration filling in the outer porous region; the inner layer anisotropy reflects the directional differences in the peeling and penetration of the inner barrier layer. In practical applications of this embodiment, the frequency band boundaries can be further flexibly adjusted based on the coating microstructure characteristics. For coatings with a wide outer micropore size distribution, the high-frequency band boundaries can be narrowed accordingly to improve the stability of the outer layer anisotropy.

[0046] In this embodiment, as Figure 3 As shown, in step S3, the outer layer state is determined based on the degree of energy change in the high-frequency band; when the outer layer is in the self-healing period of magnesium hydroxide expansion filling the pores, a confidence weight is constructed based on the anisotropy of the outer layer, the physical interference of the outer layer hydration product accumulation on the inner layer signal is corrected, and the correction value of the inner layer anisotropy is calculated, specifically including: S310. Extract the total high-frequency energy of the time-series microscopic image sequence with spatial pixel-level alignment output by S130 frame by frame, calculate the ratio of the total high-frequency energy of each frame to the total high-frequency energy of the initial state, and obtain the high-frequency energy degradation coefficient; the high-frequency energy of the initial state is obtained by frequency domain transformation, radial energy integration and spectrum segmentation of the initial microscopic image acquired before the coating is put into service. For the frequency domain power spectrum of each frame of the time-series microscopic image sequence output by S130, the total energy of the high-frequency band is extracted frame by frame by integrating and summing along all directions (0° to 360°) and at all frequency positions within the high-frequency band range determined by S220.

[0047] The initial microscopic images acquired before the coating enters service are processed using the same procedures as S210 to S220: after frequency domain transformation, radial energy integration and spectrum segmentation, the total energy of the high-frequency band in the initial state is obtained; the ratio of the total energy of the high-frequency band in each frame to the total energy of the high-frequency band in the initial state is calculated to obtain the high-frequency energy degradation coefficient.

[0048] It should be noted that in this embodiment, the high-frequency energy degradation coefficient is normalized based on the initial state, eliminating the influence of differences in coating preparation processes on the absolute value of energy, and making the states of different samples and at different times comparable. In practical applications of this embodiment, the timing of initial state acquisition can be further flexibly adjusted in conjunction with the coating service environment; for components that have been in service for a period of time before monitoring begins, the current state can be used as a relative benchmark, and the initial state can be inferred from the subsequent degradation trend.

[0049] S320. Determine the outer layer status based on the high-frequency energy degradation coefficient: If the high-frequency energy degradation coefficient indicates that the outer layer microporous structure maintains a stable self-repairing state, the outer layer is determined to be in the self-repairing period; if the high-frequency energy degradation coefficient indicates that the outer layer microporous structure undergoes disintegration and damage, the outer layer is determined to be completely detached, and the inner barrier layer is directly exposed to the corrosive environment. Multiple microscopic images were continuously acquired during the initial service period of the coating to calculate the initial high-frequency energy degradation coefficient sequence. The mean of the initial high-frequency energy degradation coefficient sequence was taken as the baseline value, and the standard deviation of the initial high-frequency energy degradation coefficient sequence was taken as the fluctuation measure. For example, in this embodiment, the baseline value minus five times the standard deviation was taken as the structural integrity threshold. When the outer layer is in a stable self-healing period, the random fluctuation of the high-frequency energy degradation coefficient follows a normal distribution, and the probability of it falling below the baseline value minus five times the standard deviation is approximately (Corresponding to the left tail probability of a normal distribution), which is an extremely low probability event; in actual monitoring, when the high-frequency energy degradation coefficient is lower than this threshold, it indicates that the outer microporous structure has undergone disintegration damage rather than normal fluctuation, thus distinguishing between stable self-repair and disintegration damage states.

[0050] The outer layer status is determined based on the high-frequency energy degradation coefficient and structural integrity threshold obtained by S310: when the high-frequency energy degradation coefficient is not lower than the structural integrity threshold, the outer layer is determined to be in the self-repair period; when the high-frequency energy degradation coefficient is lower than the structural integrity threshold in a single acquisition cycle, the outer layer microporous structure is determined to have disintegrated and damaged, the outer layer is completely detached, and the inner barrier layer is directly exposed to the corrosive environment.

[0051] In practical applications of this embodiment, the value of the structural integrity threshold can be further adjusted flexibly in combination with the service environment of the coating; for high corrosion rate environments, the standard deviation can be reduced by a factor of four to improve response sensitivity, or an adaptive threshold can be adopted to dynamically update according to real-time fluctuations.

[0052] S330. When the outer layer is in the self-healing period, a confidence weight is constructed based on the anisotropy of the outer layer, and the anisotropy correction value of the inner layer is calculated based on the confidence weight to compensate for the signal interference of the inner layer caused by the filling of the porous region of the outer layer by the volume expansion products of the hydration reaction.

[0053] When the outer layer is in the self-healing phase, the confidence weight is calculated based on the anisotropy of the outer layer; when the outer layer has completely detached, there is no need to correct the anisotropy of the inner layer; the formula for calculating the confidence weight is: ; in, As confidence weights, γ represents the outer-layer anisotropy obtained from S230, and γ is the outer-layer interference coupling coefficient.

[0054] The outer layer interference coupling coefficient γ was determined through the following calibration experiment: A set of magnesium oxide coating calibration samples with different degrees of outer layer hydration were prepared, with no fewer than 30 samples covering the entire range from fresh coating to fully hydrated coating; for each calibration sample, microscopic images of the complete coating state were obtained by microscopic imaging, and the outer layer anisotropy was calculated after frequency domain transformation. Observations of anisotropy of inner layers with outer interferometry Subsequently, a focused ion beam (FIB) was used to locally peel off the outer porous region layer by layer within the same field of view. Imaging was performed immediately after each layer was peeled off until the inner barrier layer was completely exposed and the surface morphology no longer changed. At this point, the true value of the anisotropy of the inner layer was obtained. Based on this, the inner layer signal interference is calculated. Establish inner layer signal interference With outer layer anisotropy The corresponding dataset was fitted using the least squares method. The outer layer interference coupling coefficient γ is obtained. This calibration experiment is only performed once during the coating development stage, and the parameters are applicable to the same batch of coating products after curing. The least squares method is a conventional mathematical optimization method, and its specific implementation is known to those skilled in the art, so it will not be described in detail here.

[0055] It should be noted that the confidence weight formula in this embodiment adopts the inverse square form of the anisotropy of the outer layer, which characterizes the spatial random scattering effect of the outer layer microcracks and hydration filling: the more uniform the outer layer microstructure, the more the scattering in each direction cancels each other out, the weaker the physical interference with the inner layer signal, and the higher the confidence weight, which approaches 1; the more chaotic the outer layer microstructure, the more incoherent the scattering in each direction is superimposed, the stronger the physical interference with the inner layer signal, and the lower the confidence weight.

[0056] In practical applications of this embodiment, the outer layer interference coupling coefficient γ can be flexibly adjusted in combination with the microstructure characteristics of the coating; for environments with severe hydration reactions, γ can be increased accordingly to enhance the attenuation intensity, or an adaptive coupling coefficient can be used to dynamically update according to the real-time outer layer state.

[0057] The formula for calculating the inner layer anisotropy correction value based on confidence weights is as follows: ; in, This is the correction value for the anisotropy of the inner layer. The inner-layer anisotropy degree obtained for S230.

[0058] When the outer layer completely detaches, the outer layer signal interference disappears, and the anisotropy of the inner layer obtained by S230 is directly used as the correction value, i.e. .

[0059] It should be noted that in this embodiment, the inner layer anisotropy correction value characterizes the linear attenuation of the outer layer signal interference on the inner layer observation. When the confidence weight... When the value approaches 1, the outer layer interference can be ignored, and the correction value is... Approaching the original inner layer anisotropy That is, the inner layer signal truly reflects the inner layer state; when the confidence weight When reduced, the correction value Corresponding attenuation is used to compensate for the signal interference in the inner layer caused by the accumulation of hydration products in the outer layer, and to avoid misjudging the chaotic state of the outer layer as damage in the inner layer.

[0060] In this embodiment, as Figure 3 As shown, in step S4, when the temporal evolution of the correction value conforms to the characteristics of the transition from the outer layer hydration self-healing period to the inner layer exposure, the corresponding service time is locked as the self-healing inflection point; based on the difference in the failure mechanism of the magnesium oxide coating before and after the self-healing inflection point, the inner layer penetration time is solved in segments, and the remaining effective protection time is output, specifically including: S410. Monitor the temporal evolution of the inner layer anisotropy correction value. When the temporal evolution of the inner layer anisotropy correction value conforms to the characteristics of the transition from the outer layer hydration self-healing period to the inner layer exposure, lock the corresponding service duration as the self-healing inflection point. When the temporal evolution of the inner layer anisotropy correction value does not conform to the characteristics of the transition from the outer layer hydration self-healing period to the inner layer exposure, it is determined that the current period is still in the outer layer hydration self-healing period, and the self-healing inflection point is not locked. In the outer dimension, the slope of the linear fitting within the high-frequency energy degradation coefficient sequence acquired by S310 is calculated using a preset width of the moving time window. For example, the width of the moving time window is 5 acquisition cycles, and the slope is calculated using the least squares linear fitting method. When the slopes of three consecutive moving windows are all negative, and the absolute value of the slope of each window is monotonically increasing, it indicates that the self-repairing ability of the outer layer is nearly exhausted and the filling of the micropores by the outer layer hydration products has become saturated.

[0061] In the inner layer dimension, the second derivative is calculated for the sequence of inner layer anisotropy correction values. For example, the three-point difference method is used for numerical approximation. During the self-healing period, the inner layer is not substantially damaged, the inner layer anisotropy correction value remains stable, and the second derivative fluctuates randomly around zero. As the outer layer's self-healing ability is exhausted and the corrosive medium penetrates to the inner layer barrier layer, inner layer damage begins to occur and accelerates. The inner layer anisotropy correction value changes from stable to continuously rising, and the second derivative correspondingly changes from fluctuating around zero to a positive value. Therefore, when the second derivative changes from non-positive to positive and remains positive continuously, it indicates that the inner layer barrier layer begins to generate cracks and accelerates their propagation. When the slope of the high-frequency energy degradation coefficient sequence is negative and its absolute value increases monotonically in three consecutive moving windows, and the second derivative of the inner layer anisotropy correction value sequence changes from non-positive to positive and remains positive continuously, it is determined that the transition from the outer layer hydration self-healing period to the inner layer exposure is met, and the service duration corresponding to the current frame is locked as the self-healing inflection point. If the outer layer dimension condition and the inner layer dimension condition are not simultaneously met, it is determined that the current period is still in the outer layer hydration self-healing period, and the self-healing inflection point is not locked. For example, the time window is taken as 5 consecutive acquisition cycles. In practical applications of this embodiment, the width of the time window can be further flexibly adjusted according to the coating service environment. For environments with severe corrosion rates, the time window can be shortened accordingly to improve the inflection point response sensitivity. For environments with mild corrosion rates or high monitoring noise, the time window can be extended accordingly to reduce occasional noise interference.

[0062] It should be noted that this embodiment adopts a two-dimensional coupling judgment. The outer dimension judgment reflects from the perspective of frequency domain energy that the outer porous region tends to saturate or disintegrate and the self-healing incremental benefit tends to zero. The inner dimension judgment reflects from the perspective of directional texture that the inner barrier layer has generated directional cracks or peeling. Both are satisfied simultaneously within a continuous time window, which corresponds exactly to the physical critical zone of the corrosion front transitioning from the outer layer to the inner layer. Moreover, the hierarchical continuous periodic constraint suppresses occasional noise to the statistical extreme tail, ensuring the reliability of the self-healing inflection point locking.

[0063] S420. Before the self-healing inflection point, based on the hydration reaction and capillary diffusion mechanism of the outer porous region, the reaction diffusion equation is used to constrain the slow penetration and self-healing pore blockage of the outer porous region, and the delay time of the corrosion front reaching the inner barrier layer is deduced. Input the thickness of the outer porous region, the hydration reaction consumption rate constant, the initial diffusion coefficient of the new coating, the pore-blocking factor of magnesium hydroxide solid phase volume expansion, the arched pore connectivity index, the time series data of outer anisotropy, the surface concentration of the corrosive medium, and the service time. Construct a dynamic attenuation constraint relationship between the time-varying effective diffusion coefficient coupled with the volume expansion of hydration products and the anisotropy of the outer layer, and calculate the delay time for the corrosion front to penetrate the outer porous region and reach the inner barrier layer.

[0064] The thickness of the outer porous region is determined by the coating preparation process parameters; the hydration reaction consumption rate constant and the initial diffusion coefficient of the new coating are also considered. The results were determined by isothermal and humidity accelerated testing and steady-state diffusion experiments. For example, the isothermal and humidity accelerated testing was conducted at a constant temperature of 35°C and a constant relative humidity of 95%. Periodic sampling was performed to determine the amount or mass increment of magnesium hydroxide generated in the magnesium oxide coating. The hydration reaction consumption rate constant was obtained by fitting a first-order reaction kinetic model. The steady-state diffusion experiment used a two-chamber diffusion cell device, with a fresh magnesium oxide coating sample as the diaphragm, to measure the steady-state permeation flux of the corrosive medium driven by a concentration gradient. The initial diffusion coefficient of the new coating was calculated according to Fick's first law. The isothermal and humidity accelerated testing and steady-state diffusion experiments are routine testing methods in the field of material corrosion and mass transfer, and are well known to those skilled in the art; therefore, they will not be elaborated further here.

[0065] Magnesium hydroxide solid phase volume expansion pore-blocking factor Connectivity index of arched pores Offline calibration experiments were conducted to establish a standard coating sample with different hydration and filling degrees, covering the possible filling state range that magnesium oxide coatings may reach during their service life. The effective diffusion coefficient of each sample was measured using mercury intrusion porosimetry or gas adsorption. Pore structure parameters were extracted by combining scanning electron microscopy image analysis. A nonlinear regression was performed with the hydration product filling ratio as the independent variable and the effective diffusion coefficient as the dependent variable to obtain the desired results. and For example, in this embodiment, no fewer than 15 sets of standard coating samples were prepared, with the hydration product filling ratio distributed at equal intervals with a gradient of 5%, covering the range of 0% to 75%. The effective diffusion coefficient was measured using steady-state permeation experiments or electrochemical impedance spectroscopy, and nonlinear regression was performed with the hydration product filling ratio as the independent variable and the effective diffusion coefficient as the dependent variable. and These are the parameters to be fitted.

[0066] The temporal data of the outer layer anisotropy is obtained by calculating frame by frame from the S230; service duration Recorded synchronously by S120.

[0067] The dynamic decay constraint relationship of the time-varying effective diffusion coefficient is as follows: ; in, The effective diffusion coefficient is time-varying, and its unit is 1000 kJ / m². h This is used to describe the real-time permeability of corrosive media in the outer porous region; The initial diffusion coefficient of the new coating is given in units of . h ; is the solid phase volume expansion and pore-blocking factor of magnesium hydroxide, which is dimensionless; The dimensionless index represents the connectivity of arched pores. This is a normalized solid-phase volume accumulation function, dimensionless, with input parameters including the hydration reaction consumption rate constant, outer layer anisotropy, and service life. The solution was obtained by numerically solving the reaction-diffusion equation coupled with the hydration reaction kinetics. Characterized by the filling ratio of the solid phase volume of hydration products to the initial pore volume of the outer porous region from the start of service to the present moment, the value range is: t represents service duration, in units of h The data is recorded synchronously by S120; the numerical solution method for the reaction diffusion equation coupled with the hydration reaction kinetics is existing technology and will not be described in detail here.

[0068] It should be noted that in this embodiment, the time-varying effective diffusion coefficient dynamically decreases with the accumulation of hydration products and the evolution of the outer microstructure, reflecting the dynamic balance of self-repair in the outer porous region from penetration to reaction to blockage; when the interface concentration reaches the critical concentration threshold, it marks the loss of the physical barrier effect of the outer layer and the corrosion front reaching the inner barrier layer. This time point is the delay time before the self-repair inflection point; the critical concentration threshold is the minimum concentration of corrosive medium that induces chemical erosion of the inner barrier layer, which is obtained by electrochemical corrosion experiments or standard corrosion tests of the coating material.

[0069] Since the outer porous region involves a coupled process of corrosive medium penetration, magnesium oxide hydration reaction, and magnesium hydroxide volume expansion filling the pores, a reaction-diffusion equation is used to describe this dynamic equilibrium: a reaction-diffusion control equation is established to control the transmission of the corrosive medium concentration field in the depth direction of the outer porous region; a dynamic attenuation constraint relationship is constructed for the time-varying effective diffusion coefficient coupled with the volume expansion of hydration products and the anisotropy of the outer layer; the equation is solved numerically using the finite difference method or the finite element method; the evolution of the medium concentration at the interface is tracked; and the time point when the interface concentration first reaches the critical concentration threshold that induces chemical erosion of the inner barrier layer is determined as the delay time. The establishment of the reaction-diffusion equation and the numerical solution using the finite difference method or the finite element method are well known to those skilled in the art and will not be elaborated here.

[0070] S430. After the self-healing inflection point and when the outer layer falls off, based on the crack propagation and stress corrosion mechanism of the inner barrier layer, the stress corrosion crack propagation equation is used to map the rate of increase of the inner layer anisotropy correction value to the penetration rate of the corrosion front through the remaining thickness of the inner barrier layer, and to deduce the penetration time required for the corrosion front to penetrate the remaining thickness of the inner barrier layer. Input the remaining thickness of the inner barrier layer, the stress corrosion crack propagation rate constant, the time series data of the inner layer anisotropy correction value, the inner layer anisotropy correction value corresponding to the self-healing inflection point, and the service environment temperature, and calculate the penetration time required for the corrosion front to penetrate the remaining thickness of the inner barrier layer.

[0071] The remaining thickness of the inner barrier layer is obtained by subtracting the thickness of the outer porous region from the total thickness calibrated by the coating preparation process parameters; the stress corrosion crack propagation rate constant is obtained by fitting a constant load accelerated corrosion experiment; for example, this embodiment uses a standard sample with a pre-cracked inner barrier layer, applies a constant bending load to keep the stress intensity factor at the crack tip constant, and continuously exposes it in a 3.5 wt% NaCl solution at 35°C. The crack propagation length is monitored in real time by the DC potential method, and the stress corrosion crack propagation rate constant is obtained by fitting an exponential stress corrosion crack propagation model; the constant load accelerated corrosion experiment is a conventional accelerated testing method in the field of material corrosion and is well known to those skilled in the art, so it will not be described in detail here; the time series data of the inner layer anisotropy correction value is obtained by calculating the inner layer anisotropy correction value frame by frame by S330; the inner layer anisotropy correction value corresponding to the self-healing inflection point is calculated by the self-healing inflection point time locked by S410; the service environment temperature is recorded synchronously by S120.

[0072] The formula for calculating the penetration rate of a corrosion front through the remaining thickness of the inner barrier layer is: ; in, The corrosion front penetration rate is expressed in units of... It characterizes the rate at which the corrosion front penetrates along the thickness direction of the inner barrier layer; The reference penetration rate is expressed in units of... Characterizing the inner layer anisotropy correction value at the reference rate of change. The corresponding feature penetration rate; The reference rate of change for the inner layer anisotropy correction value, in units of This is used to normalize the rate of increase of the inner layer anisotropy correction value, making the reference penetration rate... The dimension is constant as length / time and does not change with the damage acceleration exponent n, ensuring the penetration rate. The calculation formula is dimensionally consistent; The rate of increase of the inner layer anisotropy correction value is given by... The inner layer anisotropy correction value is obtained by time series data difference calculation, and the unit is . This reflects the real-time trend of inner layer damage expansion; The damage acceleration index is dimensionless and characterizes the nonlinearity of the transition from stable to accelerated crack propagation.

[0073] Reference rate of change of inner layer anisotropy correction value The initial calibration experiments determined that: time-series microscopic images were synchronously acquired according to the acquisition cycle set in S120 on at least three sets of calibration samples with known inner layer damage evolution trajectories. After processing in S210 to S330 (frequency domain power spectrum segmentation, anisotropy calculation, outer layer state determination, and confidence weight correction), time-series data of the inner layer anisotropy correction values ​​for each calibration sample were obtained. For each calibration sample, the rate of change of the inner layer anisotropy correction value within adjacent acquisition cycles was calculated frame by frame. The arithmetic mean of the rate of change at the self-healing inflection point for each calibration sample was taken as the... If the rate of change of the self-healing inflection point of a certain calibration sample is negative or zero, then the sample is removed and the calculation is repeated.

[0074] Reference penetration rate With damage acceleration index The initial calibration experiments determined that, on calibration samples with known inner layer damage evolution histories, the rise rate of the inner layer anisotropy correction value and the actual corrosion front penetration depth were simultaneously measured to establish a corresponding dataset of rise rate and penetration rate. This dataset was then fitted using the least squares method to obtain... and The least squares method is a conventional mathematical optimization technique, and its specific implementation is well known to those skilled in the art, so it will not be described in detail here.

[0075] In practical applications of this embodiment, the reference penetration rate can be further adjusted flexibly based on the coating's service environment. With damage acceleration index For environments with severe stress corrosion, the benchmark penetration rate can be increased accordingly. To match the actual crack propagation rate, or to use an adaptive damage acceleration index. To reflect the gradual evolution of the crack propagation stage.

[0076] Since the inner barrier layer exhibits a coupled process of stress corrosion crack propagation and chemical penetration, the stress corrosion crack propagation equation is used to describe this dynamic equilibrium. The rate of increase of the inner layer anisotropy correction value is mapped to the penetration rate of the corrosion front through the remaining thickness of the inner barrier layer. The penetration time is solved by integral with the remaining thickness of the inner barrier layer as the penetration target. The establishment and numerical solution of the stress corrosion crack propagation equation are well known to those skilled in the art and will not be elaborated here.

[0077] S440, combining the delay time of the corrosion front reaching the inner barrier layer and the penetration time required for the corrosion front to penetrate the remaining thickness of the inner barrier layer, yields the remaining effective protection time of the coating; combined with the inner layer anisotropy correction value, the coating protection failure stage is output.

[0078] Input the delay time for the corrosion front to penetrate the outer porous region and reach the inner barrier layer, the penetration time required for the corrosion front to penetrate the remaining thickness of the inner barrier layer, the current service duration, and the current anisotropy correction value of the inner layer, calculate the remaining effective protection time of the coating, and determine the coating protection failure stage.

[0079] The total effective protection time of the coating is the sum of the delay time for the corrosion front to penetrate the outer porous region and reach the inner barrier layer and the penetration time required for the corrosion front to penetrate the remaining thickness of the inner barrier layer; the remaining part after deducting the current service time from the total effective protection time is the remaining effective protection time of the coating.

[0080] The rules for determining the stage of coating protection failure are as follows: when the current anisotropy correction value of the inner layer is lower than the anisotropy correction value of the inner layer corresponding to the self-healing inflection point, it is determined to be the outer layer self-healing period; when the current anisotropy correction value of the inner layer is higher than the anisotropy correction value of the inner layer corresponding to the self-healing inflection point and the remaining effective protection time of the coating is greater than zero, it is determined to be the inner layer penetration period; when the remaining effective protection time of the coating is less than or equal to zero, it is determined to be the complete failure of the coating.

[0081] In practical applications of this embodiment, the failure stage determination boundary can be further adjusted flexibly in combination with the service environment of the coating; for components with high reliability requirements, a safety margin can be reserved accordingly, and the failure warning period can be determined in advance.

[0082] In this embodiment, as Figure 4 As shown, in step S5, differentiated maintenance is performed on the magnesium oxide components based on the remaining effective protection time, specifically including: Differentiated maintenance responses are implemented for magnesium oxide components based on the stage of coating protection failure and the remaining effective protection time of the coating; Input the coating protection failure stage and the remaining effective protection time of the coating to execute differentiated maintenance response routing.

[0083] When the current coating protection failure stage is the outer layer self-healing period, the coating's self-healing potential still exists. Regular monitoring should be maintained, and maintenance commands should not be triggered.

[0084] When the current coating protection failure stage is the inner layer penetration period, the recommended maintenance deadline is calculated based on the remaining effective protection time of the coating: after deducting the safety margin from the remaining effective protection time of the coating, the current service time is added to obtain the recommended maintenance deadline; a maintenance time window is generated with the current time as the lower bound and the recommended maintenance deadline as the upper bound; the maintenance time window is compared with the regular maintenance cycle. If the maintenance time window falls within the next regular maintenance cycle, a maintenance prompt adapted to the regular maintenance cycle is generated; if the maintenance time window is shorter than the regular maintenance cycle, an expedited maintenance prompt is generated; the prompt content includes the recommended maintenance deadline, the current coating status assessment, and key monitoring areas.

[0085] It should be noted that the routine maintenance cycle is a standardized inspection and maintenance interval pre-defined for the target magnesium alloy component, determined by equipment management procedures or industry maintenance standards. The safety margin is a buffer time reserved from the remaining effective protection time of the coating, used to cover prediction model errors, service environment fluctuations, and maintenance preparation cycles, ensuring that maintenance operations are completed before the corrosion front penetrates the inner barrier layer and severe chemical pitting occurs in the substrate. For example, the safety margin is determined based on the component's risk level: 50% of the remaining effective protection time for high-risk components and 20% for medium- and low-risk components. In practical applications of this embodiment, the safety margin value can be further flexibly adjusted based on the component's risk level, the severity of the service environment, and the accessibility of maintenance resources. For critical load-bearing components or environments with drastic fluctuations in corrosive medium concentration, the safety margin ratio can be increased accordingly to enhance protective redundancy; for mild environments with rapid maintenance response and sufficient spare parts, the safety margin ratio can be decreased accordingly to improve coating utilization.

[0086] When the current coating protection failure stage is when the coating is completely failed, it is determined that the coating's self-healing potential has been exhausted and the internal stress has begun to damage the double-layer structure. A high-priority maintenance instruction is immediately issued, and cross-process scheduling and replacement are forcibly initiated.

[0087] In practical applications of this embodiment, maintenance strategies can be further adjusted flexibly based on the severity of component failure consequences, detection accessibility, and the severity of the service environment; for critical load-bearing components, maintenance time windows can be tightened or response levels can be increased to enhance safety assurance.

[0088] Embodiments of the present invention also provide an electronic device, including a memory, a processor, and a communication bus; the memory and the processor are connected via the communication bus. The memory stores a method for intelligent identification of the microstructure of magnesium oxide coatings as provided in the above embodiments, which can be loaded and executed by the processor.

[0089] The memory can be used to store instructions, programs, code, code sets, or instruction sets. The memory may include a program storage area and a data storage area, wherein the program storage area stores instructions for implementing the intelligent identification method for the microstructure of magnesium oxide coatings provided in the above embodiments; and the data storage area stores data involved in the intelligent identification method for the microstructure of magnesium oxide coatings provided in the above embodiments.

[0090] A processor may include one or more processing cores. The processor executes instructions, programs, code sets, or instruction sets stored in memory, and calls data stored in memory to perform various functions and process data as described in this application. The processor may be at least one of a specific application-specific integrated circuit, a digital signal processor, a digital signal processing device, a programmable logic device, a field-programmable gate array, a central processing unit, a controller, a microcontroller, and a microprocessor. It is understood that, for different devices, the electronic devices used to implement the above-described processor functions may also be other types, and the embodiments of this application do not specifically limit the specific implementation.

[0091] A communication bus may include a pathway for transmitting information between the aforementioned components. The communication bus can be a PCI bus or an EISA bus, etc. Communication buses can be categorized into address buses, data buses, control buses, etc.

[0092] This application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as described in the above embodiments for intelligent identification of the microstructure of magnesium oxide coatings.

[0093] In this embodiment, a computer-readable storage medium can be a tangible device that holds and stores instructions used by an instruction execution device. A computer-readable storage medium can be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. Specifically, a computer-readable storage medium can be a portable computer disk, a hard disk, a USB flash drive, a random access memory, a read-only memory, an erasable programmable read-only memory, a static random access memory, a portable compressed disk read-only memory, a digital multifunction disk, a memory stick, a floppy disk, an optical disk, a magnetic disk, a mechanical encoding device, or any combination thereof.

[0094] The terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0095] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing application concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. A method for intelligent identification of the microstructure of magnesium oxide coatings, characterized in that, The method includes the following steps: S1. During the monitoring period, microscopic images of the target magnesium alloy component monitoring station are collected according to the preset acquisition cycle and service parameters are recorded. The time-series microscopic image sequence is then output after registration. S2. Obtain the frequency domain power spectrum from the time-series microscopic image sequence; based on the characteristic size difference between magnesium oxide hydration expansion filling pores and inner layer damage, use the curvature extreme point of the radial energy distribution curve as the optical cutoff frequency to divide the high and low frequency bands, and calculate the anisotropy of the outer and inner layers of the magnesium oxide coating respectively. S3. Determine the outer layer state based on the degree of high-frequency energy change; when the outer layer is in the self-healing period of magnesium hydroxide expansion filling pores, construct confidence weights based on the anisotropy of the outer layer, correct the physical interference of the outer layer hydration product accumulation on the inner layer signal, and calculate the correction value of the inner layer anisotropy; specifically, this includes: extracting the total high-frequency energy of the time-series microscopic image sequence frame by frame, calculating the ratio of the total high-frequency energy of each frame to the total high-frequency energy of the initial state, and obtaining the high-frequency energy degradation coefficient; the initial state high-frequency energy is obtained from the initial microscopic image acquired before the coating service through frequency domain transformation and radial energy product. After spectral segmentation, the outer layer state is determined based on the high-frequency energy degradation coefficient: if the high-frequency energy degradation coefficient indicates that the outer layer microporous structure is in a stable self-healing state, the outer layer is determined to be in the self-healing period; if the high-frequency energy degradation coefficient indicates that the outer layer microporous structure has undergone disintegration and damage, the outer layer is determined to be completely detached, and the inner barrier layer is directly exposed to the corrosive environment; when the outer layer is in the self-healing period, a confidence weight is constructed based on the anisotropy of the outer layer, and the anisotropy correction value of the inner layer is calculated based on the confidence weight to compensate for the signal interference of the inner layer caused by the hydration reaction volume expansion products filling the porous region of the outer layer; S4. When the temporal evolution of the correction value conforms to the characteristics of the transition from the outer layer hydration self-healing period to the inner layer exposure, the corresponding service duration is locked as the self-healing inflection point; based on the difference in the failure mechanism of the magnesium oxide coating before and after the self-healing inflection point, the inner layer penetration time is solved in segments, and the remaining effective protection time is output; specifically, this includes: monitoring the temporal evolution of the inner layer anisotropy correction value; when the temporal evolution of the inner layer anisotropy correction value conforms to the characteristics of the transition from the outer layer hydration self-healing period to the inner layer exposure, the corresponding service duration is locked as the self-healing inflection point; when the temporal evolution of the inner layer anisotropy correction value does not conform to the characteristics of the transition from the outer layer hydration self-healing period to the inner layer exposure, it is determined that the current period is still in the outer layer hydration self-healing period, and the self-healing inflection point is not locked; before the self-healing inflection point, based on the outer layer... The hydration reaction and capillary diffusion mechanism in the porous region are investigated. A reaction-diffusion equation is used to constrain the slow penetration and self-healing pore-blocking of the outer porous region, estimating the delay time for the corrosion front to reach the inner barrier layer. After the self-healing inflection point and during outer layer detachment, based on the crack propagation and stress corrosion mechanism of the inner barrier layer, a stress corrosion crack propagation equation is used to map the rate of increase of the inner layer anisotropy correction value to the penetration rate of the corrosion front through the remaining thickness of the inner barrier layer, estimating the penetration time required for the corrosion front to penetrate the remaining thickness of the inner barrier layer. Combining the delay time for the corrosion front to reach the inner barrier layer and the penetration time required for the corrosion front to penetrate the remaining thickness of the inner barrier layer, the remaining effective protection time of the coating is obtained. Finally, combined with the inner layer anisotropy correction value, the coating protection failure stage is output. S5. Perform differentiated maintenance on magnesium oxide components based on the remaining effective protection time.

2. The intelligent identification method for the microstructure of magnesium oxide coatings according to claim 1, characterized in that, During the monitoring period, microscopic images of the target magnesium alloy component at the monitoring station are acquired according to the preset acquisition cycle, and service parameters are recorded. After registration, a time-series microscopic image sequence is output, including the following steps: S110. Select a monitoring area on the surface of the target magnesium alloy component and establish a monitoring station. Prepare a feature mark of the inner barrier layer that does not penetrate the magnesium oxide coating as a space physical reference. S120. A microscopic imaging device is used to periodically acquire images of the monitoring station. The acquisition cycle is set according to the service environment of the component to obtain time-series microscopic images of the target magnesium alloy component monitoring station. The service duration of the target magnesium alloy component is recorded simultaneously at the moment of acquisition. S130. Perform two-stage registration on the time-series microscopic images. Use feature marks to eliminate rotation and large-scale displacement caused by changes in shooting conditions to perform geometric coarse registration. Use non-rigid fine registration to eliminate residual local deformation and output a time-series microscopic image sequence with spatial pixel-level alignment.

3. The intelligent identification method for the microstructure of magnesium oxide coatings according to claim 2, characterized in that, The frequency domain power spectrum of the time-series microscopic image sequence is obtained; based on the characteristic size difference between the pore-filling effect of magnesium oxide hydration expansion and the inner layer damage, the high and low frequency bands are divided by the curvature extreme points of the radial energy distribution curve as the optical cutoff frequency, and the anisotropy of the outer and inner layers of the magnesium oxide coating is calculated respectively, including the following steps: S210. Perform a two-dimensional Fourier transform on each frame of the time-series microscopic image sequence that is spatially pixel-level aligned by S130 to obtain the frequency domain power spectrum and integrate with respect to the direction angle along the frequency radius to calculate the radial energy distribution curve. S220. Based on the characteristic size difference between magnesium oxide hydration expansion filling pores and inner layer damage, the curvature extreme point of the radial energy distribution curve is identified as the optical cutoff frequency; the frequency domain power spectrum is divided into high-frequency band and low-frequency band using the optical cutoff frequency as the boundary; the high-frequency band corresponds to the small-scale structural information of the micropore scale of the outer porous region of the magnesium oxide coating, and the low-frequency band corresponds to the large-scale structural information of the inner barrier layer peeling and through crack scale of the magnesium oxide coating. S230. Calculate the anisotropy of the outer layer in the high-frequency band, reflecting the directional differences of the outer layer microcracks and hydration filling; calculate the anisotropy of the inner layer in the low-frequency band, reflecting the directional differences of the inner layer peeling and penetration.

4. The intelligent identification method for the microstructure of magnesium oxide coatings according to claim 3, characterized in that, Based on the remaining effective protection time, perform differentiated maintenance on magnesium oxide components, including the following steps: Differentiated maintenance responses are implemented for magnesium oxide components based on the stage of coating protection failure and the remaining effective protection time of the coating.

5. An electronic device comprising a memory, a processor, and a communication bus, wherein the memory and the processor are connected via the communication bus, characterized in that, The memory stores a computer program that can be loaded by the processor and executed as described in any one of claims 1 to 4, for intelligent identification of the microstructure of magnesium oxide coatings.

6. A computer-readable storage medium, characterized in that, The computer program is stored and can be loaded by a processor and executed as described in any one of claims 1 to 4 for intelligent identification of the microstructure of magnesium oxide coatings.

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