A method for detecting and maintaining a corrosion protection coating of a hydraulic engineering work

Abstract

The application discloses a kind of water conservancy engineering anticorrosion coating detection maintenance methods, it is related to water conservancy engineering technical field, the present application is directed to single water conservancy engineering structure protection area, first using distributed detection equipment to collect four kinds of key parameters such as coating appearance, thickness, adhesion and corrosion resistance, then construct parameter-protection quality dynamic evaluation model and abnormal parameter influence degree evaluation model, calculate protection quality comprehensive index and determine whether coating protection quality is up to standard, screen the link needing optimization maintenance for non-standard area, identify defects and associated factors, finally retest after maintenance and still optimize process for non-standard link.The application uses distributed equipment to collect coating multidimensional parameters in succession and batch, constructs double-model evaluation system, establishes closed-loop control process, targets defects and optimizes process, improves coating protection effect and life, and is suitable for various water conservancy engineering structure coating detection maintenance.

Description

Technical Field

[0001] This invention relates to the field of water conservancy engineering technology, and specifically to a method for detecting and maintaining anti-corrosion coatings in water conservancy projects. Background Technology

[0002] Hydraulic engineering structures are exposed to complex environments of humidity and multi-media corrosion for extended periods. Anti-corrosion coatings are the core protective barrier ensuring their structural safety and service life. Current methods for inspecting and maintaining anti-corrosion coatings in hydraulic engineering projects commonly suffer from incomplete parameter collection, reliance on manual experience for quality assessment, and a lack of targeted handling of anomalies. Traditional methods struggle to monitor coating performance degradation in real time, easily leading to undetected localized coating failures, which in turn cause structural corrosion, increasing subsequent maintenance costs and safety risks.

[0003] Existing technology, such as the invention patent application with publication number CN118898618A, discloses a method for detecting and maintaining anti-corrosion coatings in water conservancy projects. This method includes acquiring an image of a pipe surface containing at least one crack; preprocessing the pipe surface image to obtain a grayscale image; determining the initial impact of each crack in the pipe surface image based on the grayscale gradient change and shape analysis of each connected region within the grayscale image; correcting each initial impact based on the crack's area on the pipe surface within the grayscale image to obtain the crack severity of each crack; and determining whether the pipe corresponding to the pipe surface image requires maintenance based on the crack severity. This invention assesses the initial impact of cracks by analyzing their shape, size, and grayscale values, and corrects them considering the cylindrical shape of the pipe. It allows for targeted treatment of each crack based on its severity, thereby effectively reducing maintenance costs.

[0004] As can be seen from the above solutions, existing technologies for the detection and maintenance of anti-corrosion coatings in water conservancy projects have many shortcomings: detection methods are traditional, relying on manual experience, highly subjective, prone to missed detections, and lacking quantitative assessment; there are no unified standards for key performance parameter testing, instrument calibration is non-standard, and data accuracy is low; maintenance lacks specificity, with a "one-size-fits-all" approach wasting resources and exhibiting poor compatibility; there is no scientific maintenance cycle, and a closed-loop management system is not formed. Existing technologies mostly focus on single defect identification, failing to achieve comprehensive detection and deviating from industry standards, making it difficult to meet the needs for high-precision, comprehensive, and low-cost management. Summary of the Invention

[0005] To address the aforementioned technical shortcomings, the present invention aims to provide a method for detecting and maintaining anti-corrosion coatings in water conservancy projects.

[0006] To solve the above technical problems, the present invention adopts the following technical solution: The present invention provides a method for detecting and maintaining anti-corrosion coatings in water conservancy projects, including the following steps: S1, for a single water conservancy project structure protection area, the key parameters are divided into coating appearance parameters, coating thickness parameters, coating adhesion parameters and coating corrosion resistance parameters according to the parameter type; and various key parameters of the area are collected in real time using distributed detection equipment.

[0007] S2. Construct a dual model based on the correlation between various key parameters and coating protection quality. The dual model includes a parameter-protection quality dynamic evaluation model and an abnormal parameter influence degree evaluation model. Calculate the comprehensive protection quality index of the protected area to determine whether the coating protection quality meets the standards.

[0008] S3. When the coating protection quality is substandard, extract the coating protection quality parameters and comprehensive protection quality index, screen out the protection links that need to be optimized and the corresponding coating protection quality parameters, analyze the deviation of the coating protection quality parameters, and identify potential coating defects and defect-related factors in the area.

[0009] S4. After maintenance, monitor the coating protection quality parameters again and determine whether the coating protection quality meets the standards. For substandard protection links and corresponding coating protection quality parameters, optimize the process.

[0010] The beneficial effects of this invention are as follows: This invention provides a method for inspecting and maintaining anti-corrosion coatings in water conservancy projects. For a single protected area of ​​a water conservancy project structure, it first uses distributed detection equipment to collect four key parameters: coating appearance, thickness, adhesion, and corrosion resistance. Then, it constructs a parameter-protection quality dynamic evaluation model and an abnormal parameter influence degree evaluation model to calculate the comprehensive protection quality index and determine whether the coating protection quality meets the standards. For areas that do not meet the standards, it identifies the aspects requiring optimized maintenance, defects, and related factors. Finally, it retests after maintenance and optimizes the process for any remaining substandard aspects. This invention uses distributed equipment to collect multi-dimensional parameters of the coating continuously and in batches, constructs a dual-model evaluation system, establishes a closed-loop management process, targets defects, and optimizes the process, improving the coating protection effect and lifespan. It is applicable to the inspection and maintenance of coatings in various water conservancy project structures. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a schematic diagram of the implementation steps of the method of the present invention. Detailed Implementation

[0013] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] See Figure 1 As shown, a method for detecting and maintaining anti-corrosion coatings in water conservancy projects includes the following steps: S1. For a single water conservancy project structure protection area, the key parameters are divided into coating appearance parameters, coating thickness parameters, coating adhesion parameters, and coating corrosion resistance parameters according to the parameter type; various key parameters of the area are collected in real time using distributed detection equipment.

[0015] In a specific embodiment, the specific process of S1 is as follows: for the collection of coating appearance parameters, coating thickness parameters and coating adhesion parameters, continuous collection is carried out with the detection section as the collection unit, and the collection frequency is not less than once per hour; for the coating corrosion resistance parameters, a batch collection method is adopted, and the frequency of batch collection matches the corrosion environment monitoring cycle. During the collection process, the collection time and corresponding detection location of various key parameters are recorded simultaneously.

[0016] It should be noted that the detection section refers to the smallest collection unit, which is the area of ​​a single water conservancy project structure protected by the structure and the difference in the corrosive environment, in order to achieve accurate data collection.

[0017] Continuous acquisition: refers to a real-time data acquisition method that continuously acquires physical parameters at a fixed frequency, and records time and location information synchronously during the acquisition process.

[0018] Batch sampling: refers to a non-continuous sampling method that involves sampling and testing in batches according to a preset cycle for performance parameters that require laboratory testing.

[0019] Corrosion environment monitoring cycle: refers to the time interval for testing the corrosion resistance parameters of the coating, which is pre-set according to the characteristics of the corrosion environment in which the hydraulic engineering structure is located. The testing frequency should be positively correlated with the severity of the corrosion environment. For example, the monitoring cycle for seawater corrosion environment can be set to once every 3 months, and the monitoring cycle for freshwater corrosion environment can be set to once every 6 months.

[0020] Coating corrosion resistance parameters: These are indicators of the coating's corrosion resistance, determined through simulated corrosion environment tests. They include salt spray test corrosion resistance time, coating change during damp heat tests, and coating weight loss rate when immersed in corrosive media.

[0021] S2. Construct a dual model based on the correlation between various key parameters and coating protection quality. The dual model includes a parameter-protection quality dynamic evaluation model and an abnormal parameter influence degree evaluation model. Calculate the comprehensive protection quality index of the protected area to determine whether the coating protection quality meets the standards.

[0022] In a specific embodiment, the test preparation process for the construction parameter-protection quality dynamic evaluation model is as follows: a 1:1 standard test environment is constructed based on the actual corrosion environment of the hydraulic engineering structure, and safety thresholds for various key parameters such as coating appearance, coating thickness, coating adhesion and coating corrosion resistance are obtained from the design stage.

[0023] In a standard testing environment, a control group and four experimental groups were set up. In the control group, all types of key parameters were within the safe threshold range. In each experimental group, one type of key parameter was used as the independent variable and exceeded the safe threshold. The specific values ​​exceeding the safe threshold were set according to the gradient. The other types of key parameters were kept consistent with the control group. The control variables were used to complete multiple parallel tests in a gradient-increasing manner. The specific experimental groups for each gradient are as follows: Coating appearance parameter experimental groups: Experimental group 1: defect area ratio 8%; Experimental group 2: defect area ratio 15%; Experimental group 3: defect area ratio 25%, tested in order of defect area from low to high.

[0024] Coating thickness parameter experimental groups: Experimental group 1: 350μm or 950μm; Experimental group 2: 250μm or 1100μm; Experimental group 3: 150μm or 1300μm, and the coating thickness parameters were tested in order from low to high according to the deviation of the parameters from the safety threshold.

[0025] Coating adhesion parameters experimental groups: Experiment 1: 4MPa; Experiment 2: 3MPa; Experiment 3: 2MPa, tested in descending order of adhesion.

[0026] The coating corrosion resistance parameters were tested in the following groups: Group 1: 700h; Group 2: 500h; Group 3: 300h, in descending order of corrosion resistance time.

[0027] Both the control group and the experimental group were processed with the same basic coating specifications. The experimental group had only differences in the setting of key parameters before the testing process was carried out.

[0028] It should be noted that the standard test environment refers to a simulated test scenario that is consistent with the actual engineering environment in terms of temperature, humidity, equipment configuration, and operating procedures, based on the preset standard target parameters in the design stage of the anti-corrosion coating of water conservancy projects. It is used to conduct parallel tests between the control group and the experimental group.

[0029] Control group: refers to the benchmark test group that prepares coatings in a standard test environment using preset standard target parameters, and is used to provide reference data of "standard parameters - standard quality".

[0030] Experimental group: refers to the experimental group that adjusts only one key parameter as the independent variable in a standard test environment, while keeping the other parameters the same as the control group. It is used to analyze the impact of the change of this parameter on the coating protection quality.

[0031] Safety threshold: refers to the qualified values ​​or ranges of four key parameters of coating appearance, thickness, adhesion and corrosion resistance, which are determined in advance during the design stage of the protective area of ​​hydraulic engineering structures based on industry standards, coating material performance standards and actual corrosion environment characteristics. For example, coating thickness 500-800μm and adhesion ≥5MPa.

[0032] Gradient incremental test: For each type of key parameter, three experimental groups are tested in sequence according to the order of "parameter deviation from safety threshold from low to high" (or corresponding performance from good to bad), such as coating adhesion 4MPa→3MPa→2MPa, to realize the progressive verification of the relationship between parameter changes and protection quality.

[0033] Parallel testing: refers to conducting multiple repeated tests (usually ≥3 groups) on each experimental group (including the control group) under the same testing environment, the same experimental group specifications, and the same operating procedures, with the aim of reducing random errors.

[0034] Coating basic specifications: refers to the core physical properties and preparation process parameters of the coating experimental group, including but not limited to substrate material and size, coating material type, coating process (such as spraying method) and curing conditions (temperature or time), etc. The control group and all experimental groups have the same specifications, with only the target key parameters being set differently.

[0035] Deviation range: Specifically refers to the degree of deviation between the coating thickness parameter and the safety threshold (e.g., a thickness of 350μm relative to the lower threshold of 500μm has a deviation range of 30%; 950μm relative to the upper threshold of 800μm has a deviation range of 18.75%), which is the core basis for ranking the test order of the coating thickness experimental group.

[0036] Preferably, the specific process of the testing procedure is as follows: all experimental groups are placed in a standard testing environment, and an artificial accelerated corrosion test is conducted using a corrosive substance that matches the actual corrosive medium in the project. After standing for a preset time, the performance of the experimental groups is determined to be stable. Then, the protection quality indicators of each experimental group are tested to obtain the corresponding protection quality parameters. The protection quality parameters include coating integrity parameters, coating impermeability parameters, and coating aging degree parameters. The coating integrity parameters are obtained through image capture and recognition without additional destructive testing. Only the coating impermeability parameters and aging degree parameters, which cannot be directly collected in the actual project, are subjected to destructive testing to obtain the corresponding protection quality parameters.

[0037] After testing, each experimental group was rated for quality and divided into three levels: excellent protection, medium protection, and poor protection. The independent variable parameters of the experimental group with poor protection were the initial abnormal parameters. The parameter differences between each abnormal parameter and the standard parameters of the control group were recorded simultaneously to generate basic data on the coating protection quality corresponding to various key parameters, which served as the basic training data for model construction.

[0038] It should be noted that artificial accelerated corrosion testing refers to a testing method that artificially shortens the corrosion cycle by simulating the actual corrosion conditions of water conservancy projects (such as temperature, humidity and type of corrosive medium) in a 1:1 standard test environment and using corrosive substances consistent with those at the engineering site. The purpose is to quickly obtain performance degradation data of the coating under long-term corrosive environment and avoid the problem of excessively long natural corrosion test cycles.

[0039] Corrosive substances matched to the actual corrosive media of the project: These are substances selected based on the specific corrosive environment of the water conservancy project (such as fresh water, seawater, and industrial wastewater), which are consistent with or equivalent to the composition and concentration of the corrosive media on site (e.g., simulated seawater for seawater environment, and corrosive liquid containing specific chemical ions for industrial corrosive environment), to ensure that the test results can truly reflect the protective effect of the coating in the actual project.

[0040] Determining the stability of the experimental group after a preset settling time: After the coating experimental group is prepared, it is placed in a standard test environment (matched 1:1 with the actual corrosion environment of water conservancy projects) for a preset time (usually 24-72 hours). The hardness and adhesion of the coating are non-destructively tested using a pencil hardness tester and a pull-off adhesion tester. The surface condition is observed using an industrial camera. If there is no significant fluctuation in the deviation of the test indicators in two consecutive tests, the performance is determined to be stable, thus avoiding the distortion of test data due to the instability of the experimental group.

[0041] Coating integrity parameters: These are quantitative indicators (such as the percentage of defect area and the number of defects) that indicate the absence of defects such as cracking, peeling, and blistering in the coating. They are obtained through image capture and identification, without requiring damage to the experimental group, and belong to the "non-destructive testing" parameters. They can intuitively reflect the integrity of the physical structure of the coating.

[0042] Coating anti-permeability parameters: These are quantitative indicators (such as permeation rate and permeation depth) that represent the coating's ability to prevent the permeation of corrosive media (such as water and ions). Because they can only be tested by destroying the coating structure through methods such as pressurized permeation, they are considered "destructive testing" parameters and cannot be directly collected in actual engineering projects. They must be obtained through laboratory testing.

[0043] Coating aging parameters: These are quantitative indicators that represent the degree of performance degradation of a coating after exposure to a corrosive environment (such as gloss loss rate and elastic modulus change rate). They need to be obtained through artificial accelerated aging tests combined with destructive testing. They are difficult to collect directly in actual engineering projects and are key parameters for evaluating the long-term protective life of a coating.

[0044] Quality rating: refers to the classification of the protective performance of the experimental group into grades (excellent protection, medium protection, and poor protection) based on the test results of three protective quality parameters: coating integrity, coating impermeability, and coating aging degree. The classification is based on the comprehensive quality difference rate range (0-0.3 corresponds to excellent protection, 0.3-0.7 corresponds to medium protection, and 0.7-1.0 corresponds to poor protection), which is the core basis for identifying initial abnormal parameters.

[0045] Initial abnormal parameters: These refer to the independent variable parameters of the experimental group corresponding to the "poor protection" quality rating (such as defect area ratio of 25% and adhesion of 2MPa, etc.), which are the key parameters that cause the coating's protective performance to fail.

[0046] Coating protection quality basic data: refers to the data set corresponding to "key parameter values ​​- protection quality index values ​​- quality rating" obtained through testing. It includes the difference between each abnormal parameter and the standard parameter of the control group, as well as the specific values ​​of the three types of protection quality parameters. It is the core training data for constructing the parameter-protection quality dynamic evaluation model.

[0047] Preferably, the specific process of constructing the parameter-protection quality dynamic evaluation model is as follows: calculate the parameter difference rate of abnormal parameters in each experimental group relative to the standard parameters of the control group, and the calculation formula is: parameter difference rate = |actual parameter value - standard parameter value| ÷ standard parameter value.

[0048] The quality difference rate of each gradient experimental group relative to the corresponding protection quality parameters of the control group was calculated. The difference rate of coating integrity parameter was calculated based on image recognition results, while the difference rates of coating permeability parameter and coating aging parameter were calculated based on test data. The single-index quality difference rate = |actual quality parameter value - standard quality parameter value| ÷ standard quality parameter value. Weights were assigned to each quality parameter based on their importance, and a weighted average was calculated to obtain the overall quality difference rate = coating integrity parameter difference rate. 0.3 + Coating anti-permeability parameter difference rate × 0.4 + Coating aging degree parameter difference rate × 0.3.

[0049] A mapping table between parameter difference rate and overall quality difference rate was established. The overall quality difference rate was divided into three levels: excellent protection, medium protection, and poor protection, corresponding to the ranges of 0-0.3, 0.3-0.7, and 0.7-1.0, respectively. The threshold values ​​of the comprehensive protection quality index corresponding to each level were set: excellent protection ≥ 0.8, medium protection 0.5-0.8, and poor protection < 0.5, forming a parameter-protection quality dynamic evaluation model.

[0050] It should be noted that the parameter difference rate is a quantitative indicator of the degree of deviation between the actual value of the abnormal parameter in the experimental group and the standard parameter value in the control group. The calculation formula is parameter difference rate = |actual parameter value - standard parameter value| ÷ standard parameter value, which is used to measure the deviation of a single key parameter.

[0051] Single-index quality difference rate: refers to the quantitative index of the deviation of a certain protective quality parameter of the experimental group coating from the corresponding quality parameter of the control group. The calculation formula is: Single-index quality difference rate = |actual quality parameter value - standard quality parameter value| ÷ standard quality parameter value. It is the basis for calculating the overall quality difference rate.

[0052] Overall quality difference rate: refers to the overall quality deviation index of the coating obtained by weighting the importance of each protection quality parameter. In this scheme, the weights are assigned as follows: coating integrity 0.3, impermeability 0.4, and aging degree 0.3.

[0053] Mapping table: This refers to the table that correlates parameter difference rate with overall quality difference rate, and clarifies the range of coating quality deviation corresponding to different parameter deviation magnitudes. It is the core basis for constructing a parameter-protection quality dynamic evaluation model.

[0054] Comprehensive Protection Quality Index: This is a core indicator used to quantify the overall protection quality level of the coating, calculated using a weighted summation formula. The calculation relies solely on the "Comprehensive Quality Difference Rate" and "Impact Level" output by the dual models, without directly using the actual values ​​of the parameters. It can intuitively reflect the overall compliance status of the protected area.

[0055] The comprehensive protection quality index threshold is a critical value used to classify the coating protection quality level. In this scheme, protection quality is set to excellent ≥ 0.8, protection quality to medium 0.5-0.8 and protection quality to poor < 0.5, which is used to quickly determine whether the coating protection quality meets the standard.

[0056] Preferably, the specific process of constructing the abnormal parameter influence assessment model is as follows: the 0-1 interval of parameter difference rate is divided into 5 continuous intervals, namely 0-0.2, 0.2-0.4, 0.4-0.6, 0.6-0.8 and 0.8-1.0, corresponding to influence levels 1-5. The value of the influence level is positively correlated with the degree of influence of abnormal parameters on coating protection quality.

[0057] Based on the parameter difference rate and overall quality difference rate data obtained during the construction of the parameter-protection quality dynamic assessment model, a correspondence table of parameter difference rate, impact level and overall quality difference rate is established to clarify the range of change of overall quality difference rate of various key parameters in each impact level interval, and an assessment model of the degree of influence of abnormal parameters is formed.

[0058] It should be noted that the impact level refers to the output result of the abnormal parameter impact assessment model. It is divided according to the range of parameter difference rate. The level value is positively correlated with the impact of abnormal parameters on coating protection quality and is used to quantify the priority of different abnormal parameters.

[0059] The three-way correspondence table refers to a multi-dimensional correlation comparison table established by integrating three sets of data: the parameter difference rate range, the impact level, and the range of changes in the overall quality difference rate. It is the core basis for constructing an assessment model of the degree of impact of abnormal parameters.

[0060] Abnormal Parameter Impact Assessment Model: This model takes the parameter difference rate as input and outputs the corresponding abnormal parameter impact level and quality impact degree, which is used to accurately locate core quality defect factors.

[0061] Preferably, the specific process for calculating the comprehensive protection quality index of the protected area is as follows: the actual values ​​of the four types of key parameters collected in real time are compared one by one with the anomaly judgment threshold preset in the design stage, and the abnormal parameters that exceed the anomaly judgment threshold are screened out.

[0062] For each abnormal parameter, calculate the parameter difference rate with the corresponding abnormal judgment threshold, input them into the dual models respectively, and obtain the corresponding overall quality difference rate and impact level.

[0063] Based on the overall quality difference rate and impact level obtained from the dual models for each anomaly parameter, the comprehensive protection quality index is calculated using the following formula: The comprehensive protection quality index is obtained, where... This indicates the comprehensive index of protection quality. Indicates the first The overall quality difference rate obtained from the parameter-protection quality dynamic assessment model is one of the abnormal parameters. Indicates the first The impact level of each outlier parameter is obtained from the outlier parameter impact assessment model. The number of abnormal parameters in this scheme. ≤4, and It is a positive integer.

[0064] It should be noted that the anomaly judgment threshold is a critical value used to determine whether a single protection quality parameter is abnormal. It is set by technical personnel according to the judgment requirements. In this solution, the anomaly judgment threshold is set to 0.8.

[0065] The parameter-protection quality dynamic assessment model has a built-in mapping table: This table, established during the model construction phase based on parallel test data from the control and experimental groups, defines a one-to-one correlation between "parameter difference rate" and "overall quality difference rate." It clarifies the overall coating quality deviation results corresponding to abnormal parameters with different deviation amplitudes. The overall quality difference rate can be directly obtained by querying the parameter difference rate without repeated calculation.

[0066] Preferably, the specific process for determining whether the coating protection quality meets the standard is as follows: obtain the calculated comprehensive protection quality index of the protected area and compare it with the preset comprehensive protection quality index threshold; If the comprehensive protection quality index is ≥0.8, the coating protection quality is directly judged to meet the standard; if the comprehensive protection quality index is between 0.5 and 0.8 or <0.5, the coating protection quality is judged to be substandard. Extract all the filtered abnormal parameters, combine the parameter difference rate of each abnormal parameter, sort them from high to low according to the impact level, and filter out the abnormal parameters with an impact level ≥ 3 to determine the core abnormal parameters. The parameter difference rate of each core abnormal parameter is input again into the abnormal parameter impact assessment model to review and confirm its corresponding impact level and quality impact degree. The system outputs a conclusion on whether the coating protection quality is substandard, and simultaneously outputs the type of core abnormal parameters, the parameter difference rate of each core abnormal parameter, the impact level, and the corresponding comprehensive quality difference rate.

[0067] It should be noted that the core abnormal parameters refer to the key parameters that have the greatest impact on the coating protection quality, selected from all abnormal parameters. The selection criteria are the parameter difference rate and the impact level output by the abnormal parameter impact assessment model.

[0068] Judgment conclusion: Based on the comparison results of the comprehensive protection quality index and the threshold, combined with the analysis of core abnormal parameters, the final conclusion is whether the coating protection quality meets the standard, and the attribution of the core problems when it does not meet the standard.

[0069] S3. When the coating protection quality is substandard, extract the coating protection quality parameters and comprehensive protection quality index, screen out the protection links that need to be optimized and the corresponding coating protection quality parameters, analyze the deviation of the coating protection quality parameters, and identify potential coating defects and defect-related factors in the area.

[0070] In a specific embodiment, the specific process of S3 is as follows: when the coating protection quality is substandard, the coating protection quality parameters and the comprehensive protection quality index are extracted. The influence level of the coating protection quality parameters is determined from high to low according to the influence level. The influence level of 5 is the highest influence level. The protection links corresponding to the abnormal parameters with the highest influence level are selected first. The deviation of the coating protection quality parameters is analyzed, and potential coating defects and defect-related factors in the area are identified.

[0071] For the selected maintenance and protection links that need optimization, potential coating defects and defect-related factors are identified by combining abnormal parameter types. Corresponding maintenance measures are then implemented for coating defects and defect-related factors: abnormal coating appearance parameters are related to defects in surface pretreatment and coating construction; abnormal coating thickness parameters are related to defects in coating equipment operation and coating process parameter setting; abnormal coating adhesion parameters are related to defects in substrate treatment and coating curing; and abnormal coating corrosion resistance parameters are related to defects in coating material ratio and protection system design.

[0072] It should be noted that priority is given to screening the protective links corresponding to the abnormal parameters with the highest impact level: extract all abnormal parameters and match the corresponding protective links and coating protection quality parameters according to type. First, screen the protective links corresponding to the core abnormal parameters of level 5, 4 and 3 according to the impact level from high to low. Then, screen the protective links corresponding to the general abnormal parameters of level 2 and 1. Combine the actual deviation of the coating protection quality parameters (including deviation value, deviation magnitude and deviation direction) for cross-verification to lock the protective links and corresponding coating protection quality parameters that need to be optimized and maintained.

[0073] Protection steps requiring optimization and maintenance: These refer to specific construction or maintenance procedures with defects identified when the coating protection quality is substandard, based on the type and level of abnormal parameters. Examples include surface pretreatment, coating equipment operation, and substrate treatment.

[0074] Coating protection quality parameter deviation: refers to the degree of deviation between the actual detected value of abnormal parameters and the safety threshold (or abnormal judgment threshold) preset in the design stage, including the deviation value and the deviation direction, such as insufficient or excessive thickness and adhesion below the standard.

[0075] Potential coating defects: These are quality problems that occur during the construction or use of the coating, which are derived from abnormal parameters. Examples include surface cracking, uneven thickness, insufficient adhesion, and poor corrosion resistance.

[0076] Defect-related factors: These refer to the root causes of potential coating defects, which directly correspond to operational or setting deviations in specific protection steps, such as inadequate surface pretreatment, incorrect coating process parameters, and incomplete substrate treatment.

[0077] S4. After maintenance, monitor the coating protection quality parameters again and determine whether the coating protection quality meets the standards. For substandard protection links and corresponding coating protection quality parameters, optimize the process.

[0078] In a specific embodiment, the specific process of S4 is as follows: After maintenance is completed, the key monitoring data and the overall monitoring data are synchronously input into the dual model. Combining the comprehensive protection quality index threshold judgment standard and the requirement that the coating parameter difference rate between the repaired area and the non-repaired area is ≤0.1, the overall protection quality of the coating is comprehensively judged to determine whether it meets the standard. For the substandard protection links and the corresponding coating protection quality parameters, the process is optimized.

[0079] After the maintenance measures are completed, the coating protection quality parameters of the protected area after the optimized maintenance are collected again in real time. The comprehensive protection quality index is recalculated and it is determined whether the coating protection quality meets the standards.

[0080] If the coating protection quality meets the standard and the difference rate of coating parameters between the repaired area and the non-repaired area is ≤0.1, then the maintenance of this step ends; if the coating protection quality still does not meet the standard, or the difference in parameters between the new and old areas is >0.1, then the protection process of this protection step is systematically optimized, the process parameters are adjusted, and the above collection, analysis and maintenance steps are repeated until the coating protection quality meets the standard and there is no significant difference between the new and old areas.

[0081] It should be noted that process optimization refers to the systematic improvement of the corresponding protection process, equipment parameters, and operating procedures when the quality of the coating still does not meet the standards after maintenance. It is different from a single parameter adjustment and focuses on solving quality problems from the root.

[0082] The coating parameter difference rate between the repaired area and the non-repaired area is the core indicator used to quantitatively determine the consistency of coating performance between the repaired area and the surrounding normal non-repaired area of ​​the anti-corrosion coating in water conservancy projects. It is the core indicator to judge whether the repair construction effect matches the original coating. In this method, the standard for the consistency of repair performance is set to ≤0.1. If the standard is not met, the repair process needs to be further optimized. Specific calculation method: (1) Single parameter difference rate: The four key parameters are calculated separately. The formula is single parameter difference rate = | - |÷ ( To repair the measured average value of a certain parameter in the region, (1) The average value of the same type of parameters in the non-repair area); (2) Comprehensive parameter difference rate: Based on the weight allocation of parameter importance, the weighted calculation is obtained. The formula is: Comprehensive parameter difference rate = Coating appearance parameter difference rate × 0.2 + Coating thickness parameter difference rate × 0.25 + Coating adhesion parameter difference rate × 0.25 + Coating corrosion resistance parameter difference rate × 0.3, of which the coating corrosion resistance has the highest weight, which is in line with the core requirements of corrosion resistance of coatings in water conservancy projects.

[0083] Process parameters: These refer to the core technical indicators that need to be strictly controlled during the execution of the protective process. They are the specific adjustment objects for process optimization, such as coating temperature, curing time, and material ratio.

[0084] The examples described in this invention are not limited to the specific embodiments listed above. The examples are merely illustrative to facilitate understanding of the invention and do not constitute a limitation on the scope of protection of this invention. Any modifications, equivalent substitutions, etc., made within the spirit and principles of this invention should be included within the scope of protection.

[0085] The above description is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the protection scope of the present invention.

Claims

1. A method for detecting and maintaining anti-corrosion coatings in water conservancy projects, characterized in that, Includes the following steps: S1. For the protection area of ​​a single water conservancy project structure, the key parameters are divided into coating appearance parameters, coating thickness parameters, coating adhesion parameters and coating corrosion resistance parameters according to the parameter type. The distributed detection equipment was used to collect various key parameters of the area in real time. S2. Based on the correlation between various key parameters and coating protection quality, a dual model is constructed. The dual model includes a parameter-protection quality dynamic evaluation model and an abnormal parameter influence degree evaluation model. The comprehensive protection quality index of the protected area is calculated to determine whether the coating protection quality meets the standards. S3. When the coating protection quality is substandard, extract the coating protection quality parameters and the comprehensive protection quality index, screen out the protection links that need to be optimized and the corresponding coating protection quality parameters, analyze the deviation of the coating protection quality parameters, and identify potential coating defects and defect-related factors in the area. S4. After maintenance, monitor the coating protection quality parameters again and determine whether the coating protection quality meets the standards. For substandard protection links and corresponding coating protection quality parameters, optimize the process.

2. The method for detecting and maintaining anti-corrosion coatings in water conservancy projects according to claim 1, characterized in that, The specific process of S1 is as follows: For the collection of coating appearance parameters, coating thickness parameters, and coating adhesion parameters, continuous collection is carried out with the detection section as the collection unit, and the collection frequency is no less than once per hour; For parameters related to coating corrosion resistance, a batch sampling method was adopted, and the frequency of batch sampling matched the corrosion environment monitoring cycle. During the sampling process, the sampling time and corresponding detection location of various key parameters were recorded simultaneously.

3. The method for testing and maintaining anti-corrosion coatings in water conservancy projects according to claim 1, characterized in that, The specific process for setting up the test preparation for the construction parameter-protection quality dynamic evaluation model is as follows: Based on the actual corrosive environment of the hydraulic engineering structure, a 1:1 standard test environment was constructed to obtain the safety thresholds of various key parameters such as coating appearance, coating thickness, coating adhesion and coating corrosion resistance from the design stage. In a standard testing environment, a control group and four experimental groups were set up. In the control group, all types of key parameters were within the safety threshold range. In each experimental group, one type of key parameter was used as the independent variable and exceeded the safety threshold. The specific values ​​exceeding the safety threshold were set according to a gradient. The other types of key parameters remained consistent with the control group. Multiple parallel tests were performed sequentially with the control variables increasing in a gradient manner. The specific experimental groups for each gradient are as follows: Coating appearance parameter test groups: Test group 1: defect area ratio 8%; Test group 2: defect area ratio 15%; Test group 3: defect area ratio 25%, tested sequentially from low to high defect area; Coating thickness parameter experimental groups: Experimental group 1: 350μm or 950μm; Experimental group 2: 250μm or 1100μm; Experimental group 3: 150μm or 1300μm, and the coating thickness parameters were tested in order from low to high according to the deviation of the parameters from the safety threshold. Coating adhesion parameters experimental groups: Experimental group 1: 4MPa; Experimental group 2: 3MPa; Experimental group 3: 2MPa, tested in descending order of adhesion; The coating corrosion resistance parameters were tested in the following groups: Group 1: 700h; Group 2: 500h; Group 3: 300h, in descending order of corrosion resistance time.

4. Both the control group and the experimental group were processed with the same basic coating specifications. The experimental group had only differences in the setting of key parameters before the testing process was carried out.

5. The method for detecting and maintaining anti-corrosion coatings in water conservancy projects according to claim 3, characterized in that, The specific process of the detection procedure is as follows: All experimental groups were placed in a standard testing environment and subjected to artificial accelerated corrosion tests using corrosive substances matched to the actual corrosive media in the engineering project. After a preset set time, the performance of the experimental groups was determined to be stable. Then, the protective quality indicators of each experimental group were tested to obtain the corresponding protective quality parameters. The protective quality parameters include coating integrity parameters, coating permeability parameters, and coating aging degree parameters. The coating integrity parameters were obtained through image capture and recognition, the coating permeability parameters were obtained through a pressure permeameter, and the coating aging degree parameters were obtained through an artificial accelerated aging test chamber. After testing, each experimental group was rated for quality and divided into three levels: excellent protection, medium protection, and poor protection. The independent variable parameters of the experimental group with poor protection were the initial abnormal parameters. The parameter differences between each abnormal parameter and the standard parameters of the control group were recorded simultaneously to generate basic data on the coating protection quality corresponding to various key parameters, which served as the basic training data for model construction.

6. The method for detecting and maintaining anti-corrosion coatings in water conservancy projects according to claim 4, characterized in that, The specific process for constructing the parameter-protection quality dynamic evaluation model is as follows: Calculate the parameter difference rate of abnormal parameters in each experimental group relative to the standard parameters of the control group. The calculation formula is: Parameter difference rate = |actual parameter value - standard parameter value| ÷ standard parameter value; The quality difference rate of each gradient experimental group relative to the corresponding protection quality parameters of the control group was calculated. The difference rate of coating integrity parameter was calculated based on image recognition results, while the difference rates of coating permeability parameter and coating aging parameter were calculated based on test data. The single-index quality difference rate = |actual quality parameter value - standard quality parameter value| ÷ standard quality parameter value. Weights were assigned to each quality parameter based on their importance, and a weighted average was calculated to obtain the overall quality difference rate = coating integrity parameter difference rate. 0.3 + Coating impermeability parameter difference rate × 0.4 + Coating aging degree parameter difference rate × 0.3; A mapping table between parameter difference rate and overall quality difference rate was established. The overall quality difference rate was divided into three levels: excellent protection, medium protection, and poor protection, corresponding to the ranges of 0-0.3, 0.3-0.7, and 0.7-1.0, respectively. The threshold values ​​of the comprehensive protection quality index corresponding to each level were set: excellent protection ≥ 0.8, medium protection 0.5-0.8, and poor protection < 0.5, forming a parameter-protection quality dynamic evaluation model.

7. The method for detecting and maintaining anti-corrosion coatings in water conservancy projects according to claim 5, characterized in that, The specific process for constructing the model for assessing the impact of abnormal parameters is as follows: The parameter difference rate range of 0-1 was divided into 5 continuous ranges, namely 0-0.2, 0.2-0.4, 0.4-0.6, 0.6-0.8 and 0.8-1.0, corresponding to influence levels 1-5. The value of the influence level is positively correlated with the degree of influence of abnormal parameters on the coating protection quality. Based on the parameter difference rate and overall quality difference rate data obtained during the construction of the parameter-protection quality dynamic assessment model, a correspondence table of parameter difference rate, impact level and overall quality difference rate is established to clarify the range of change of overall quality difference rate of various key parameters in each impact level interval, and an assessment model of the degree of influence of abnormal parameters is formed.

8. The method for detecting and maintaining anti-corrosion coatings in water conservancy projects according to claim 6, characterized in that, The specific process for calculating the comprehensive protection quality index of the protected area is as follows: The actual values ​​of the four key parameters collected in real time are compared one by one with the anomaly judgment thresholds preset in the design stage, and the abnormal parameters that exceed the anomaly judgment thresholds are filtered out. For each abnormal parameter, calculate the parameter difference rate with the corresponding abnormal judgment threshold, input them into the dual models respectively, and obtain the corresponding overall quality difference rate and impact level; Based on the overall quality difference rate and impact level obtained from the dual models for each anomaly parameter, the comprehensive protection quality index is calculated using the following formula: The comprehensive protection quality index is obtained, where... This indicates the comprehensive index of protection quality. Indicates the first The overall quality difference rate obtained from the parameter-protection quality dynamic assessment model is one of the abnormal parameters. Indicates the first The impact level of each outlier parameter is obtained from the outlier parameter impact assessment model. The number of abnormal parameters in this scheme. ≤4, and It is a positive integer.

9. A method for detecting and maintaining anti-corrosion coatings in water conservancy projects according to claim 1, characterized in that, The specific process for determining whether the coating protection quality meets the standards is as follows: Obtain the calculated comprehensive protection quality index of the protected area and compare it with the preset comprehensive protection quality index threshold. If the comprehensive protection quality index is ≥0.8, the coating protection quality is directly judged to meet the standard; if the comprehensive protection quality index is between 0.5 and 0.8 or <0.5, the coating protection quality is judged to be substandard. Extract all the filtered abnormal parameters, combine the parameter difference rate of each abnormal parameter, sort them from high to low according to the impact level, and filter out the abnormal parameters with an impact level ≥ 3 to determine the core abnormal parameters. The parameter difference rate of each core abnormal parameter is input again into the abnormal parameter impact assessment model to review and confirm its corresponding impact level and quality impact degree. The system outputs a conclusion on whether the coating protection quality is substandard, and simultaneously outputs the type of core abnormal parameters, the parameter difference rate of each core abnormal parameter, the impact level, and the corresponding comprehensive quality difference rate.

10. A method for detecting and maintaining anti-corrosion coatings in water conservancy projects according to claim 1, characterized in that, The specific process of S3 is as follows: When the coating protection quality is substandard, the coating protection quality parameters and comprehensive protection quality index are extracted. The influence level of the coating protection quality parameters is determined from high to low according to the influence level. Level 5 is the highest influence level. The protection links corresponding to the abnormal parameters with the highest influence level are selected first. The deviation of the coating protection quality parameters is analyzed, and potential coating defects and defect-related factors in the area are identified. For the selected maintenance and protection links that need optimization, potential coating defects and defect-related factors are identified by combining abnormal parameter types, and corresponding maintenance measures are taken for coating defects and defect-related factors: abnormal coating appearance parameters are related to defects in surface pretreatment and coating construction; abnormal coating thickness parameters are related to defects in coating equipment operation and coating process parameter setting. Abnormal coating adhesion parameters are associated with defects in substrate treatment and coating curing processes. Abnormal corrosion resistance parameters of the coating are related to defects in the coating material ratio and the design of the protection system; A method for detecting and maintaining anti-corrosion coatings in water conservancy projects according to claim 1, characterized in that, The specific process of S4 is as follows: After maintenance, key monitoring data and overall monitoring data are simultaneously input into the dual model. Combining the comprehensive protection quality index threshold judgment standard and the requirement that the coating parameter difference rate between the repaired area and the non-repaired area is ≤0.1, the overall protection quality of the coating is comprehensively judged to determine whether it meets the standard. For the substandard protection links and corresponding coating protection quality parameters, the process is optimized. After the maintenance measures are completed, the coating protection quality parameters of the optimized maintenance process in the protected area are collected again in real time, the comprehensive protection quality index is recalculated, and it is determined whether the coating protection quality meets the standards. If the coating protection quality meets the standards, and the difference rate of coating parameters between the repaired area and the non-repaired area is ≤0.1, then this maintenance step is completed. If the coating protection quality still does not meet the standard, or the difference in parameters between the old and new areas is greater than 0.1, the protection process for this protection link should be systematically optimized. After adjusting the process parameters, repeat the above collection, analysis and maintenance steps until the coating protection quality meets the standard and there is no significant difference between the old and new areas.

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