A method and system for testing paint performance

Abstract

The present application relates to the technical field of paint performance testing, and in particular to a paint performance testing method and system, which is applied to a salt spray test chamber, a sensing unit for characterizing the corrosion strength of a test environment is deployed inside the salt spray test chamber, the method comprises the following steps: monitoring the sensing unit independently of a pH control system of the salt spray test chamber, acquiring corrosion response data of the sensing unit, and calculating the actual corrosion strength of the test environment based on the corrosion response data; acquiring nominal pH value data recorded by the pH control system of the salt spray test chamber; correlating and comparing the actual corrosion strength with the nominal pH value data to judge the effectiveness of the test environment and generate a judgment result; marking the effectiveness of the paint performance test result according to the judgment result; by introducing a sensing unit independent of the pH control system to monitor the actual corrosion strength, and correlating and comparing it with the nominal pH value recorded by the pH control system, the abnormality of the test environment can be effectively identified.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of paint performance testing, and particularly relates to a paint performance testing method and system. BACKGROUND

[0002] In the field of industrial production and material science, the performance of coatings is evaluated by professional equipment such as salt spray test chambers to simulate harsh environments and accelerate the degradation of coatings. To ensure consistency and efficiency of different platforms, most institutions implement standardized equipment maintenance procedures, and unify cleaning agents and operation steps to simplify the process and reduce differences. However, this mode has hidden risks: the universal procedure does not fully consider the material properties and unique operation requirements of individual equipment, which may cause a chain of problems.

[0003] Taking the standardized cleaning agent promoted by the laboratory as an example, when it is used for routine maintenance of the salt spray test chamber, it has an unexpected interaction with the special material probe of the pH sensor, such as special glass or high-performance polymer composite materials, which are selected for corrosion resistance. After multiple maintenance, the cleaning agent forms a microscopic insulating deposition film on the active surface of the probe that is difficult to distinguish with the naked eye and cannot be removed by conventional cleaning. This film does not cause static reading errors of the sensor, but severely hinders the transmission of hydrogen ions, greatly weakening the real-time response capability of the sensor to pH value changes. A healthy sensor can stabilize the reading within a few seconds, while a damaged sensor takes several minutes. Traditional static calibration only verifies the accuracy of fixed points and cannot detect this dynamic response lag problem, resulting in a "static normal, dynamic failure" sensor. The automatic pH control system of the salt spray test chamber needs to be based on the rapid feedback of the sensor to accurately add buffer to maintain the pH value within the preset range. Due to the response lag of the sensor, the system cannot timely receive the pH value drop signal when adjusting, misjudges the lack of buffer, and then over-adds acidic solution, forming a "measurement lag → over-dosing" cycle. In the long run, the actual average pH value of the salt spray solution in the chamber continues to be lower than the preset value, and the corrosion of the test environment is much higher than the standard requirement.

[0004] The core goal of salt spray testing is to evaluate the durability and corrosion resistance of coatings under controlled accelerated conditions. Excessive environmental acidity can accelerate the degradation of coatings, accelerate processes such as hydrolysis and polymer bond rupture, and cause coatings to fail prematurely, exposing and eroding the underlying metal substrate. Coating samples that would normally pass the standard test period may show signs of failure prematurely.

[0005] Modern salt spray test chambers are equipped with automated monitoring systems that can detect sample failure characteristics (such as rust spots, bubbles) through visual recognition or sensor arrays, and determine the results based on preset thresholds (such as adhesion level, rust spot density standards). When a sample reaches the failure threshold prematurely due to an abnormal environment, the system will correctly identify and determine it as "unqualified", automatically terminate the test, and generate a report. However, this result is not due to inherent defects in the coating formula, manufacturing or application, but is induced by an abnormal test environment, which is a false negative.

[0006] The above problems ultimately form a technical dilemma: the test system continuously outputs a "substandard" report of the coating, misleads the research and development direction, and causes resource mismatch to the coating component adjustment, process improvement, etc.; and because each maintenance will exacerbate the pH sensor problem, repeated testing will still obtain the same wrong result. More importantly, the routine diagnosis (such as temperature, humidity, and salt concentration verification) and static calibration of the salt spray test chamber cannot detect the dynamic response lag problem of the sensor, and the device appears to be running normally, but it is actually continuously generating false data. This not only leads to false product development decisions and unnecessary rework, but also seriously erodes the reliability of test data, forming a self-sustaining cycle of "false results → resource waste",

[0007] The existing technology needs to be improved in view of the above problems. SUMMARY

[0008] The purpose of the present application is to solve the problems existing in the prior art and provide a coating performance test method and system.

[0009] In a first aspect, the present application provides a coating performance test method applied in a salt spray test chamber, wherein the salt spray test chamber is internally arranged with a sensing unit for characterizing the corrosion intensity of a test environment, and the method comprises the following steps:

[0010] Monitoring the sensing unit independently of a pH control system of the salt spray test chamber, acquiring corrosion response data of the sensing unit, and calculating an actual corrosion intensity of the test environment based on the corrosion response data;

[0011] Acquiring nominal pH value data recorded by the pH control system of the salt spray test chamber;

[0012] Correlating and comparing the actual corrosion intensity with the nominal pH value data to judge the effectiveness of the test environment and generate a judgment result;

[0013] Marking the effectiveness of the coating performance test result according to the judgment result.

[0014] In a second aspect, a coating performance test system is provided, which comprises:

[0015] A monitoring module for monitoring the sensing unit independently of a pH control system of the salt spray test chamber, acquiring corrosion response data of the sensing unit, and calculating an actual corrosion intensity of the test environment based on the corrosion response data;

[0016] An acquisition module for acquiring nominal pH value data recorded by the pH control system of the salt spray test chamber;

[0017] The comparison module is used for comparing and correlating the actual corrosion intensity with the nominal pH value data to determine the effectiveness of the test environment and generate a determination result.

[0018] The test marking module is used for marking the effectiveness of the paint performance test result according to the determination result.

[0019] Compared with the prior art, the present application has the following beneficial effects:

[0020] By deploying the sensing unit inside the salt spray test chamber to monitor the actual corrosion intensity of the test environment independently of the pH control system and comparing and correlating it with the nominal pH value data recorded by the pH control system, the effectiveness of the test environment is determined and the effectiveness of the paint performance test result is marked. This method effectively solves the problem of inaccurate paint performance test results caused by the actual pH value of the salt spray test chamber deviating from the preset range due to the response lag of the pH sensor in the prior art. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 The present application is a method flowchart.

[0022] Figure 2 The present application is a system structure schematic diagram.

[0023] In the figure: 201, monitoring module; 202, acquisition module; 203, comparison module; 204, test marking module. DETAILED DESCRIPTION

[0024] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference signs represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be understood as a limitation of the present application.

[0025] The terms "first", "second" are only used for description purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise specifically limited.

[0026] The present application can effectively identify the abnormality of the test environment, avoid misjudgment caused by the response lag of the pH sensor, and ensure the accuracy and reliability of the paint performance test results by monitoring the actual corrosion intensity independent of the pH control system and correlating it with the nominal pH value recorded by the pH control system. The paint performance test method described in the present application is applied in a salt spray test chamber. The salt spray test chamber is a test equipment that simulates a salt spray environment and is used to evaluate the performance of materials or coatings under salt spray corrosion conditions. The salt spray test chamber is equipped with a sensing unit for characterizing the corrosion intensity of the test environment. The sensing unit is a device that can sense and quantify the degree of corrosion in the test environment, and its corrosion response data can be used to calculate the actual corrosion intensity.

[0027] As shown in a paint performance test method applied in a salt spray test chamber, the salt spray test chamber is equipped with a sensing unit for characterizing the corrosion intensity of the test environment, the method comprises the following steps: Figure 1

[0028] S101, independent of the pH control system of the salt spray test chamber, monitoring the sensing unit, acquiring the corrosion response data of the sensing unit, and calculating the actual corrosion intensity of the test environment based on the corrosion response data;

[0029] It should be noted that this step is to obtain a corrosion intensity data independent of the pH control system of the salt spray test chamber, in order to avoid the influence of the possible deviation of the pH control system on the evaluation of the corrosion intensity. The monitoring of the sensing unit can be in various ways. For example, the output signal of the sensing unit, such as voltage, current or resistance value, can be read periodically by a data acquisition module. The corrosion response data of the sensing unit can be the original electrical signal or the physical quantity after preliminary processing.

[0030] Based on the corrosion response data, the actual corrosion intensity of the test environment can be calculated through a pre-established corrosion model or a calibration curve. For example, if the sensing unit is a resistance type corrosion sensor, the change of its resistance value is related to the degree of corrosion, then the actual corrosion intensity can be calculated through the corresponding relationship between the resistance change and the corrosion rate. Another way is that the sensing unit can be an electrochemical sensor, which reflects the corrosion intensity by measuring the corrosion current or potential, and then converts these electrochemical parameters into the actual corrosion intensity value.

[0031] S102, acquiring the nominal pH value data recorded by the pH control system of the salt spray test chamber;

[0032] ​It is noted that this step aims to obtain the pH value as perceived by the salt spray chamber's control system, i.e. its set value or the pH value reported by its sensor. Obtaining the nominal pH value data can be achieved in multiple ways. For example, the pH value history data can be directly exported from the salt spray chamber's control panel or its data logging system. It can also be achieved by communicating with the salt spray chamber's control system to read the internally recorded pH value in real time or periodically.

[0033] S103, correlate and compare the actual corrosion intensity with the nominal pH value data to judge the effectiveness of the test environment and generate a judgment result;

[0034] It is noted that this step is the core of the method, which evaluates the true state of the test environment by comparing two independent sources of data. The correlation and comparison methods can include but are not limited to: 1. Threshold comparison: a reasonable correlation range or threshold between actual corrosion intensity and nominal pH value is preset. If the relationship between actual corrosion intensity and nominal pH value deviates from the preset range, it is judged that the test environment is invalid. For example, at a certain nominal pH value, the actual corrosion intensity should be within a certain expected interval, and if it exceeds the interval, it is considered that the environment is abnormal. 2. Trend analysis comparison: analyze the trends of actual corrosion intensity and nominal pH value over time. If the trends are inconsistent, for example, the nominal pH value remains stable but the actual corrosion intensity continues to rise, it may indicate that the test environment is abnormal. 3. Statistical comparison: use statistical methods such as correlation analysis, regression analysis, etc. to evaluate the correlation between actual corrosion intensity and nominal pH value. If the correlation significantly deviates from the expected value, it is judged that the test environment is invalid.

[0035] Through the above comparison, the system will generate a judgment result indicating whether the test environment is valid or invalid.

[0036] S104, according to the judgment result, mark the effectiveness of the paint performance test result.

[0037] This step aims to mark the final paint performance test result according to the judgment of the effectiveness of the test environment, in order to improve the reliability of the test data. The marking methods can include: 1. Automatic marking: if the judgment result shows that the test environment is invalid, the system automatically adds "environment abnormal, result invalid" or similar mark in the paint performance test report. 2. Warning prompt: when the judgment result is invalid, the system can issue an alarm to remind the operator that the test environment has a problem, and suggest retesting or checking the equipment. 3. Data screening: in the data analysis stage, invalid test results can be automatically screened out according to this mark, ensuring that only test data obtained in a valid environment is used for evaluation and decision-making.

[0038] The coating performance test method proposed in the present application can effectively identify the abnormality of the test environment by introducing a sensing unit independent of the pH control system to monitor the actual corrosion intensity and correlating it with the nominal pH value recorded by the pH control system. In the traditional method, the pH sensor of the salt spray test chamber may not respond properly due to improper maintenance, resulting in excessive compensation by the pH control system, which makes the actual test environment more corrosive than expected, but this problem is difficult to discover through conventional calibration. The present application can detect this hidden environmental abnormality in a timely manner by independently monitoring the actual corrosion intensity and cross-verifying it with the nominal pH value. For example, when the nominal pH value is normal but the actual corrosion intensity is significantly increased, the system can determine that the test environment is invalid and mark the coating performance test results accordingly. This avoids misjudgment of coating performance caused by environmental abnormalities and ensures the authenticity and reliability of the test data, thereby effectively solving the problem of incorrect product development decisions and resource waste caused by hidden environmental abnormalities in the prior art. The core innovation of the present method is that it provides an independent and reliable means to verify the actual corrosion environment of the salt spray test chamber, which makes up for the blind spot of the traditional pH control system and significantly improves the accuracy and reliability of the coating performance test.

[0039] In some embodiments of the present application described above, the actual corrosion intensity is correlated with the nominal pH value data to determine the effectiveness of the test environment. However, in actual application, the corrosion response data obtained by the sensing unit may be affected by various factors, such as sensor drift, local environmental differences or contaminant deposition, which may cause potential fluctuations in its accuracy or reliability, thereby affecting the accuracy of the judgment of the effectiveness of the test environment. If the data reliability problem of the sensing unit is not solved, it may cause deviation in the effectiveness marking of the coating performance test results, thereby affecting the accuracy of the coating performance evaluation. To this end, the present application further proposes an optimization scheme, in which the degradation state of the coating sample itself is monitored to calibrate the corrosion intensity data of the sensing unit or determine its data reliability in the step of determining the effectiveness of the test environment.

[0040] As an embodiment of the present application, the step of correlating the actual corrosion intensity with the nominal pH value data to determine the effectiveness of the test environment further comprises:

[0041] continuously monitoring the optical characteristics of the surface of the coating sample to be tested;

[0042] It is to be noted that the above step refers to obtaining the visual or spectral information of the coating sample surface in the corrosion environment in real time or quasi-real time through non-contact or in-situ monitoring technology. For example, a high-resolution digital image acquisition system, a spectrometer or a gloss meter can be used to record the color change, gloss attenuation, cracks, blistering, peeling and other macro or micro features of the coating sample surface periodically or continuously. The changes in these optical features are a direct manifestation of the degradation of the coating in the corrosion environment.

[0043] Based on the optical features, the degradation rate of the coating sample is calculated;

[0044] It is to be noted that the above step can be understood as obtaining the rate of change of the performance of the coating sample over time by quantitatively analyzing the obtained optical feature data. For example, the rate of change of color difference (such as ΔE value in CIE L*a*b* color space), the rate of loss of gloss, or the growth rate of surface defects (such as crack density, blister area) quantified by image processing technology can be calculated. These rate values directly reflect the corrosion resistance of the coating in a particular corrosion environment.

[0045] The degradation rate of the coating sample is compared with the corrosion intensity of the sensing unit;

[0046] It is to be noted that the above step refers to establishing a correlation between the degradation of the coating sample itself and the corrosion intensity measured by the sensing unit. For example, it can be expected that in a stable corrosion environment, the degradation rate of the coating should show a certain positive correlation with the corrosion intensity of the sensing unit. The comparison can be made by statistical methods, such as calculating the correlation coefficient, performing regression analysis, or setting predefined thresholds and rules for logical judgment.

[0047] According to the comparison result, the corrosion intensity data of the sensing unit is calibrated or the data reliability of the sensing unit is judged.

[0048] It is to be noted that if the comparison result between the degradation rate of the coating sample and the corrosion intensity of the sensing unit shows significant consistency or conforms to the pre-set correlation pattern, it can be considered that the data of the sensing unit is reliable. Conversely, if the comparison result deviates significantly, for example, the sensing unit shows high corrosion intensity but the coating sample degrades slowly, or the sensing unit shows low corrosion intensity but the coating sample degrades rapidly, it may indicate that the data of the sensing unit is biased or the sensing unit itself is malfunctioning. In this case, the corrosion intensity data of the sensing unit can be calibrated according to the degree of deviation, for example, a correction factor is applied, or it is directly judged that the data of the sensing unit is unreliable, and a corresponding maintenance or replacement alarm is triggered.

[0049] The scheme of the present application provides an independent and direct verification reference for the corrosion intensity data obtained by the sensing unit by introducing the monitoring of the degradation state of the paint sample itself. Traditionally, the corrosion intensity data of the sensing unit is the main basis for judging the effectiveness of the test environment, but it may have drift or error itself. By comparing the corrosion intensity data of the sensing unit with the actual degradation rate of the paint sample to be tested, a closed-loop verification mechanism can be formed. When there is a significant inconsistency between the two, it indicates that the data of the sensing unit may no longer accurately reflect the true corrosion environment intensity, so that potential problems of the sensing unit can be found and corrected in time. This double verification mechanism ensures the accuracy and reliability of the corrosion intensity data used to judge the effectiveness of the test environment, avoiding misjudgment due to the deviation of a single data source.

[0050] Through the above technical scheme, the present application can significantly improve the accuracy and reliability of the judgment of the effectiveness of the test environment in the salt spray test chamber. Specifically, by introducing the cross-verification of the sensing unit corrosion intensity and the degradation rate of the paint sample itself, the possible deviation or unreliability of the sensing unit data can be effectively identified and corrected, so as to ensure that the actual corrosion intensity calculated is more realistic to reflect the corrosion ability of the test environment. Thus, it can avoid the false labeling of the paint performance test results caused by the inaccurate sensing unit data, improve the accuracy and reliability of the paint performance evaluation, and provide more reliable data support for the research, production and quality control of the paint.

[0051] In some preferred embodiments, the following is illustrated by a specific example. Assume that in a salt spray test chamber, the sensing unit is a pure zinc probe, whose corrosion intensity is characterized by monitoring its resistance value change. Meanwhile, a coating sample to be tested is placed inside the test chamber, and a CCD camera mounted on the top of the test chamber is used to continuously capture high-resolution images of the coating sample surface. Every 24 hours, the captured images are analyzed to quantify the color change (e.g., calculate the ΔΕ value in the L*a*b* color space) and gloss loss of the coating sample surface by image processing algorithms. Based on the changes in these optical characteristics, the average degradation rate of the coating sample in the past 24 hours is calculated. Then, this degradation rate is compared with the average corrosion intensity measured by the pure zinc probe during the same period. For example, a preset linear regression model can be established, which describes the expected relationship between the coating degradation rate and the pure zinc probe corrosion intensity under standard conditions. If the actual comparison result deviates from the model prediction by more than a preset threshold (e.g., more than 10%) within a certain monitoring period, the system will determine that the data of the pure zinc probe may be biased. At this time, the calibration program can be automatically triggered, for example, to adjust the calibration factor of the pure zinc probe corrosion intensity data according to the amount of deviation, so that its output value is more consistent with the actual degradation of the coating sample; or if the deviation persists and exceeds a larger threshold, the system will issue a warning that the sensing unit may be damaged or needs maintenance, thus determining that its data is unreliable. In this way, even if the sensing unit itself has a slight drift or the local environment has a slight change, it can be verified and corrected by the response of the coating sample itself, ensuring the accuracy of the test environment effectiveness judgment.

[0052] As an embodiment of the present application, in the step of calibrating the corrosion intensity data of the sensing unit or determining the data reliability of the sensing unit according to the comparison result, the step of calibrating the corrosion intensity data of the sensing unit includes:

[0053] quantifying the influence of the deposited thin film on the optical characteristics of the coating sample surface to obtain an influence quantity;

[0054] It should be noted that the above step refers to evaluating and numerically the degree of change in the inherent optical properties of the coating sample (e.g., reflectivity, transmissivity, color or gloss) caused by the thin film deposited on the sample surface through specific optical measurement and analysis techniques. The influence quantity can be represented as a specific numerical value, a functional relationship or a mathematical model, the core purpose of which is to accurately characterize how the presence of the thin film interferes with or changes the observation of the optical characteristics of the coating sample itself, so as to be able to distinguish the measurement error caused by the thin film from the changes caused by the actual degradation of the coating sample.

[0055] According to the comparison result and the influence quantity, a correction rule between the thin film influence quantity and the corrosion intensity of the sensing unit and the degradation rate of the paint sample is established;

[0056] It should be noted that the above step can be understood as constructing a systematic mathematical model or a set of logical judgment mechanisms. The correction rule aims to describe a more realistic and accurate correspondence between the corrosion intensity data of the sensing unit and the degradation rate of the paint sample, taking into full consideration of the influence of the deposited thin film. For example, the correction rule can be a multivariate regression model, where the input variables can include the corrosion intensity of the sensing unit, the degradation rate of the paint sample, and the thin film influence quantity, and the output is the corrected corrosion intensity data or the calibration factor. The purpose is to provide a comprehensive method to systematically consider various factors to accurately correct the corrosion intensity data.

[0057] According to the correction rule and the current influence quantity, the calibration factor of the corrosion intensity data of the sensing unit is adjusted;

[0058] It should be noted that the above step specifically refers to dynamically calculating or looking up a calibration factor suitable for the current test conditions according to the pre-established correction rule and the current real-time monitored thin film influence quantity. The calibration factor is usually a multiplier or an adder used to adjust the original corrosion response data of the sensing unit to effectively eliminate or weaken the measurement error caused by the thin film influence quantity. For example, if the correction rule indicates that there is a systematic bias in the readings of the sensing unit under a certain thin film influence quantity, the calibration factor will be adjusted accordingly to ensure that the output data of the sensing unit accurately reflects the actual corrosion intensity, even in the case of continuous deposition of thin film.

[0059] The adjusted calibration factor is applied to correct the corrosion intensity data of the sensing unit.

[0060] It should be noted that the above step refers to applying the calibration factor calculated or adjusted above to the original corrosion response data of the sensing unit, thereby obtaining more accurate actual corrosion intensity data corrected for the influence of the thin film. For example, if the original corrosion response data is X and the adjusted calibration factor is C, the corrected corrosion intensity data can be represented as X * C (multiplication calibration) or X + C (addition calibration), depending on the definition and actual needs of the correction rule. The purpose is to provide a reliable corrosion intensity index corrected for environmental factors such as thin film deposition, providing a solid data foundation for subsequent test environment effectiveness judgment and paint performance evaluation.

[0061] The scheme of the present application effectively solves the problem of inaccurate measurement of the optical characteristics of the paint sample caused by thin film deposition in the traditional method, which in turn affects the calibration accuracy of the sensing unit, by introducing a quantitative measure of the optical impact of the deposited thin film and incorporating it as a key parameter in the correction rule for the calibration of the corrosion intensity data of the sensing unit. Specifically, by quantifying the thin film impact, the system can identify and distinguish between optical changes caused by the degradation of the paint itself and those caused by the deposition of the thin film. Thus, when establishing the correlation between the corrosion intensity of the sensing unit and the degradation rate of the paint sample, the original comparison result can be corrected using the impact quantity, thereby establishing a more accurate correction rule. Based on this correction rule and in combination with the real-time thin film impact, the calibration factor of the corrosion intensity data of the sensing unit can be dynamically adjusted to ensure that even in an environment with continuous deposition of thin films, the corrosion intensity data measured by the sensing unit can be accurately calibrated to reflect the actual corrosion intensity of the test environment.

[0062] Through the above technical scheme, the present application can significantly improve the accuracy and reliability of the calibration of the corrosion intensity data of the sensing unit in the salt spray test chamber. Especially in long-term or high-corrosion tests, the influence of the deposited thin film on the optical characteristics cannot be ignored, and the present scheme can effectively eliminate or weaken the measurement errors caused thereby, ensuring that the actual corrosion intensity data obtained is closer to the true value. This makes the judgment of the effectiveness of the test environment more accurate, and ultimately improves the effectiveness and reliability of the test results of the paint performance, providing more reliable data support for the research, production and quality control of the paint.

[0063] As an embodiment of the present application, the step of quantifying the impact of the deposited thin film on the optical characteristics of the surface of the paint sample to obtain the impact quantity comprises:

[0064] Before and after each in-situ micro-cleaning operation, multiple image acquisitions are performed on the pre-set reference points to obtain multiple sets of optical characteristic data of the reference points before and after cleaning;

[0065] It should be noted that the in-situ micro-cleaning operation refers to a local and slight cleaning of the surface of the paint sample or sensing unit without removing it, so as to temporarily remove or reduce the influence of the deposited film. This operation can be realized in various ways, such as by directional air flow purging, micro-solvent spraying or mechanical scraping, etc., and the purpose is to provide an optical reference benchmark in a clean state. The preset reference point can be understood as a region with stable optical characteristics pre-marked or selected on or near the surface of the paint sample, and the optical characteristics of the region should remain relatively constant without the interference of the film. Multiple image acquisition refers to capturing image or spectral data of the reference point at multiple angles and time points before and after the cleaning operation by using a high-resolution camera or optical sensor, so as to obtain sufficient data samples to characterize the optical characteristics. The optical characteristic data can include but are not limited to image brightness, color, texture, reflectivity, transmissivity or spectral absorption characteristics, etc.

[0066] Based on the multiple sets of optical characteristic data, the multiple sets of preliminary film interference amounts of the reference point before and after cleaning are calculated;

[0067] It should be noted that the preliminary film interference amount can be understood as the optical change amount caused by the deposited film calculated by comparing the optical characteristic data of the reference point before and after cleaning. For example, the image pixel brightness difference, color space distance or spectral intensity change before and after cleaning can be calculated to obtain the preliminary film interference amount.

[0068] The multiple sets of preliminary film interference amounts are statistically processed to obtain a stable film interference amount;

[0069] It should be noted that the multiple sets of preliminary film interference amounts are statistically processed, and the purpose is to eliminate the influence of random noise and local outliers, so as to obtain a more representative and stable film interference amount. The statistical processing method can include but is not limited to average value calculation, median filtering, Gaussian filtering or outlier rejection, etc. The stable film interference amount obtained thereby can more accurately reflect the overall influence of the deposited film on the optical characteristics at the current time.

[0070] The stable film interference amount is subjected to time series analysis to track the dynamic change trend of the optical influence of the deposited film, and the dynamic change trend is taken as the influence amount.

[0071] It is noted that the purpose of time series analysis on the stable film interference quantity is to capture the law and trend of the optical influence of the deposited film changing over time. The time series analysis method can include but is not limited to moving average, exponential smoothing, regression analysis or more complex machine learning models to identify the dynamic pattern of film growth, peeling or property change. Taking the dynamic change trend as the influence quantity means not only focusing on the film interference at a certain moment, but also considering the overall effect of its accumulation and change over time, which is crucial for establishing accurate correction rules.

[0072] The scheme of the present application can effectively obtain optical characteristic data under different film deposition states by introducing in-situ micro-cleaning operation and multiple image acquisition before and after cleaning the reference point. By comparing the optical characteristics before and after cleaning, the preliminary optical interference caused by the deposited film can be accurately quantified. Further, statistical processing of multiple sets of preliminary film interference quantities can effectively filter out measurement noise and local non-uniformity, thereby obtaining more stable and reliable film interference quantities. On this basis, by performing time series analysis on the stable film interference quantity, the present application can track and identify the dynamic change trend of the optical influence of the deposited film, rather than relying only on the measurement value at a single time point. This dynamic tracking mechanism enables the system to adapt to the complexity and nonlinear change of film deposition in a salt spray environment, thereby providing a more accurate and representative influence quantity for subsequent calibration of the corrosion intensity data of the sensing unit.

[0073] Through the above technical scheme, the present application can overcome the limitation of the traditional method that it is difficult to accurately quantify the optical influence of the deposited film in a complex corrosion environment. By introducing in-situ micro-cleaning and multiple-point, multi-time image acquisition and statistical analysis, the accuracy and stability of the film interference quantity measurement are significantly improved. In addition, by tracking the dynamic change trend of the film influence through time series analysis, the obtained influence quantity can more truly reflect the actual working condition, thereby providing a more reliable input for subsequent establishment of the correction rule between the film influence and the corrosion intensity and the paint degradation rate. As a result, the calibration accuracy of the corrosion intensity data of the sensing unit is greatly improved, ultimately ensuring the effectiveness and reliability of the paint performance test results, providing a more accurate basis for the research and quality control of the paint.

[0074] As an embodiment of the present application, the step of performing time series analysis on the stable film interference quantity to track the dynamic change trend of the optical influence of the deposited film includes:

[0075] The stable film interference quantity data is divided into data segments;

[0076] It should be noted that the above step refers to grouping the continuously collected stable film interference data according to the preset time interval or the number of data points. For example, every hour or every 100 data points collected can be taken as a data segment.

[0077] Calculate the change rate of the film interference in each data segment.

[0078] It should be noted that the above step can obtain the slope by performing linear regression analysis on the data in the data segment, or calculate the average change rate of the film interference in the data segment.

[0079] Monitor the difference in the change rate between adjacent data segments.

[0080] It should be noted that the above step refers to comparing the change rate of the current data segment with the change rate of the previous one or more data segments to evaluate the continuity of the change trend.

[0081] When the difference exceeds the threshold, identify the nonlinear change or mutation of the optical effect of the deposited film.

[0082] It should be noted that when the difference exceeds the preset threshold, it indicates that the optical effect of the film may have undergone significant nonlinear change or mutation, for example, from slow growth to rapid growth, or sudden decline.

[0083] According to the identified nonlinear change or mutation, adjust the tracking strategy of the dynamic change trend to track the dynamic change trend of the optical effect of the deposited film based on the adjusted tracking strategy.

[0084] It should be noted that adjusting the tracking strategy of the dynamic change trend according to the identified nonlinear change or mutation means dynamically selecting or modifying the model or algorithm used to predict or fit the trend of the film interference according to the type of change detected.

[0085] The scheme of the present application can monitor the dynamic evolution of the optical effect of the deposited film in real time by segmenting the stable film interference data and calculating and comparing the change rate in each data segment. When a significant difference in the change rate is detected, i.e., exceeding the preset threshold, the system can timely identify the nonlinear change or mutation of the optical effect of the film. Thus, the tracking strategy can be dynamically adjusted according to the type of change identified, for example, switching to a model more suitable for handling nonlinear or mutation, or adjusting the parameters of the existing model, so as to ensure that the tracking of the optical effect of the deposited film always maintains high precision and high adaptability, and avoids tracking deviation caused by fixed strategy.

[0086] By the technical scheme, the complexity and dynamics of the optical influence of the deposited film in the salt spray test environment can be effectively coped with, especially when nonlinear changes or mutations occur, the tracking strategy can be timely adjusted, so that the influence of the deposited film on the optical characteristics of the coating sample surface can be more accurately quantified. This makes the subsequent sensing unit corrosion intensity data calibration more accurate, significantly improves the reliability and effectiveness of the coating performance test results, and provides more solid data support for the research and quality control of coatings.

[0087] As an embodiment of the present application, the step of adjusting the tracking strategy of the dynamic change trend according to the identified nonlinear change or mutation comprises:

[0088] According to the type of the identified nonlinear change or mutation, a tracking model is selected from a plurality of preset tracking models, or the parameters of the current tracking model are adjusted.

[0089] Specifically, the "type of the identified nonlinear change or mutation" refers to the specific change pattern presented when the difference in the change rate of the film interference quantity detected by time series analysis of the stable film interference quantity exceeds a threshold value. For example, this type can be sudden acceleration, deceleration, plateau, periodic fluctuation or instantaneous jump of the film growth rate. Each type may correspond to different physical or chemical processes and require different mathematical models for accurate description. Among them, the "plurality of preset tracking models" can be understood as a set of predefined mathematical or algorithmic models, each of which is good at handling a specific type of dynamic change trend. For example, it can include linear regression model, polynomial regression model, exponential growth / decay model, piecewise linear model, Kalman filter model, neural network model, etc. These models are configured at system initialization and stored in the system for calling when needed. In practical applications, "selecting a tracking model" means that the system intelligently selects the model most suitable for the current change pattern from the above-mentioned preset model library according to the specific type of the currently identified nonlinear change or mutation. For example, if the film interference quantity is identified to present exponential growth, the exponential growth model is selected; if a sudden jump is identified, a piecewise linear model or a model with mutation detection capability may be selected. The purpose is to ensure that the selected model can best fit and predict subsequent film optical influence data. In addition, "adjusting the parameters of the current tracking model" means optimizing the internal parameters of the tracking model currently in use without changing the model type. For example, for a linear regression model, the slope and intercept can be adjusted; for a Kalman filter model, the process noise and measurement noise covariance can be adjusted. This adjustment aims to make the model better adapt to the local characteristics of the current data and improve the accuracy and robustness of tracking.

[0090] The scheme of the present application solves the limitations of single or fixed tracking strategies in dealing with complex dynamic changes in thin film optical effects by dynamically selecting or adjusting the tracking model according to the specific type of identified nonlinear changes or mutations. When the optical effects of the deposited thin film exhibit nonlinear changes or mutations, traditional single models may not accurately capture their inherent regularities, resulting in prediction bias. By introducing multiple preset tracking models and intelligently selecting them according to the change type, the system can match the most suitable mathematical description for each specific change pattern. For example, for exponentially growing thin films, an exponential model can more accurately fit its growth curve; for sudden changes, an adaptive model that can quickly respond can timely adjust the tracking path. In addition, even without changing the model, by adjusting the parameters of the current model, it can better adapt to the local characteristics of the current data. For example, by adjusting the gain of the filter or the regression coefficient, the response speed and fitting accuracy of the model to the latest data can be improved. Thus, it ensures that the tracking of the dynamic change trend of the optical effects of the deposited thin film remains highly accurate and reliable under various complex working conditions.

[0091] As an embodiment of the present application, the step of selecting a tracking model from a plurality of preset tracking models comprises:

[0092] running a plurality of preset tracking models in parallel;

[0093] It should be noted that the above step refers to simultaneously starting and executing multiple different tracking algorithms or models when tracking the dynamic change trend of the optical effects of the deposited thin film. For example, these models can include but are not limited to linear regression models, exponential smoothing models, Kalman filter models, support vector machine models, or neural network-based models, etc. Each model independently processes and predicts the current and historical stable thin film interference quantity data to generate its own tracking results.

[0094] evaluating the prediction bias or fitting degree of each tracking model for the latest thin film interference quantity data;

[0095] It should be noted that the above step can be understood as comparing the prediction results output by each parallel running tracking model with the latest stable thin film interference quantity data actually observed. Specifically, statistical indicators such as root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R 2 ) or goodness of fit can be used to quantify the prediction accuracy or data fitting ability of each model. For example, for prediction bias, the residual between the model predicted value and the actual value can be calculated and statistically analyzed; for fitting degree, the closeness between the model curve and the actual data points can be evaluated.

[0096] According to the evaluation result, the tracking model with the best performance is selected.

[0097] It should be noted that the above step is specifically performed after the evaluation of all parallel running models is completed. The system selects the model with the best performance from all models according to the preset optimization standard or weight. For example, if the evaluation index is the root mean square error, the model with the smallest root mean square error is selected. If multiple indexes are considered comprehensively, a weighted score or ranking method can be used to determine the optimal model. The purpose is to ensure that the selected model can most accurately and robustly reflect the dynamic change trend of the optical influence of the deposited film.

[0098] The scheme of the present application overcomes the limitations of a single model in complex dynamic change tracking by running multiple preset tracking models in parallel and evaluating their performance in real time. Because different models have different sensitivity and adaptability to different types of data change patterns, running multiple models in parallel can provide a comprehensive performance view. By quantitatively evaluating the prediction deviation or fitting degree of the latest data, the system can dynamically identify the model that best describes the dynamic change trend of the optical influence of the deposited film. This dynamic selection mechanism ensures the adaptability of the tracking strategy, allowing the system to flexibly switch or adjust the tracking model used according to the changes in the actual data characteristics, thereby improving the accuracy and robustness of the tracking.

[0099] Through the above technical scheme, the accuracy and adaptability of tracking the dynamic change trend of the optical influence of the deposited film can be significantly improved. Compared with simply selecting a preset model, the present scheme can better cope with possible nonlinear changes or mutations of the deposited film during the salt spray test by parallel evaluation and dynamic selection of the optimal model, thereby ensuring that the quantified influence quantity is more accurate. Thus, when adjusting the calibration factor of the corrosion intensity data of the sensing unit according to the correction rule and the current influence quantity, a more reliable calibration factor can be obtained, and the corrected corrosion intensity data of the sensing unit can more accurately reflect the actual corrosion intensity, thereby ultimately improving the effectiveness and reliability of the paint performance test results.

[0100] As an embodiment of the present application, the sensing unit includes a pure zinc probe and a microcontroller for monitoring the resistance value of the pure zinc probe.

[0101] Specifically, the pure zinc probe is a corrosion-sensitive element, and the material thereof is pure zinc. This is because pure zinc has predictable and stable corrosion behavior in a salt spray environment, and the corrosion rate thereof is directly related to the corrosion intensity of the environment. When the pure zinc probe is exposed to a salt spray environment, electrochemical corrosion occurs on the surface of the pure zinc probe, causing the resistance value of the pure zinc probe to change. The microcontroller is used to monitor the resistance value of the pure zinc probe in real time. The microcontroller can be configured to convert the resistance change of the pure zinc probe into an electrical signal by Ohm's law or other resistance measurement principles, and further process the signal to obtain corrosion response data. For example, the microcontroller can periodically apply a known current or voltage to the pure zinc probe and measure the corresponding voltage or current response to calculate the real-time resistance value of the probe. The microcontroller can also integrate data acquisition, storage and transmission functions to transmit the monitored corrosion response data to a subsequent processing module for calculation of the actual corrosion intensity.

[0102] The scheme of the present application uses the resistance change characteristics of the pure zinc probe in a corrosive environment to characterize the corrosion intensity by taking the pure zinc probe as the core of the sensing unit. The resistance value of the pure zinc probe changes quantitatively with the increase of the corrosion degree, and this change can directly reflect the corrosion activity of the internal environment of the salt spray test chamber. The microcontroller is responsible for accurately capturing and quantifying these small resistance changes and converting them into corrosion response data for analysis. Thus, the sensing unit can provide a physical quantity that directly reflects the corrosion intensity of the environment independent of the pH control system, thereby providing reliable raw data for subsequent calculation of the actual corrosion intensity. This resistance change-based monitoring method can effectively avoid the interference of pH value fluctuations in the traditional method on the corrosion intensity evaluation, ensuring the objectivity and accuracy of the corrosion intensity data.

[0103] As an embodiment of the present application, the microcontroller is an ESP32 microcontroller or an STM32 microcontroller.

[0104] It should be noted that the microcontroller is an integrated circuit for processing and controlling electronic signals. In this application, its main function is to accurately monitor the resistance value of the pure zinc probe. The ESP32 microcontroller and the STM32 microcontroller are widely used microcontroller series in the industry, and they have strong processing capability, rich on-chip peripherals (such as analog-to-digital converters ADC, general-purpose input-output GPIO, etc.) and flexible communication interfaces. Among them, the ESP32 microcontroller usually integrates Wi-Fi and Bluetooth functions, which is convenient for wireless data transmission and remote monitoring; the STM32 microcontroller is known for its high performance, low power consumption and wide product line, and is suitable for various embedded applications. As a preferred embodiment, either of the two microcontrollers can meet the demand for high-precision and real-time monitoring of the resistance value of the pure zinc probe.

[0105] The solution presented in this application, employing an ESP32 or STM32 microcontroller, effectively enables precise monitoring of the resistance value of the pure zinc probe. Specifically, the integrated analog-to-digital converter (ADC) of the microcontroller converts the resistance change of the pure zinc probe into a digital signal, which is then processed and analyzed in real time by its built-in processing unit. For example, the microcontroller can periodically read the resistance value of the pure zinc probe and calculate corrosion response data according to a preset algorithm. Furthermore, the microcontroller can transmit the processed corrosion response data to a host computer or data storage device via its communication interface (e.g., UART, SPI, I2C, or a wireless communication module), thereby achieving continuous monitoring and recording of the actual corrosion intensity in the test environment. This configuration ensures the accuracy and real-time nature of data acquisition, providing a reliable foundation for subsequent corrosion intensity calculations and environmental effectiveness assessments.

[0106] The above technical solution, employing an ESP32 or STM32 microcontroller as the microcontroller for the sensing unit, provides high-precision and high-reliability resistance monitoring capabilities. These microcontrollers possess excellent computing performance and rich peripheral interfaces, enabling efficient and accurate acquisition and processing of the corrosion response data from the pure zinc probe. Furthermore, they typically feature low power consumption, helping to extend the sensing unit's runtime and reduce maintenance costs. Simultaneously, their mature ecosystem and development toolchain simplify system development and deployment, improving the overall practicality and operability of the testing method. Therefore, the actual corrosion intensity of the test environment can be obtained more stably and accurately, thereby improving the accuracy of the validity judgment of coating performance test results.

[0107] like Figure 2 The system shown is a coating performance testing system, which includes:

[0108] The monitoring module 201, independent of the pH control system of the salt spray test chamber, is used to monitor the sensing unit, acquire the corrosion response data of the sensing unit, and calculate the actual corrosion intensity of the test environment based on the corrosion response data.

[0109] The acquisition module 202 is used to acquire the nominal pH value data recorded by the pH control system of the salt spray test chamber;

[0110] The comparison module 203 is used to correlate and compare the actual corrosion intensity with the nominal pH value data to determine the effectiveness of the test environment and generate a judgment result.

[0111] The test marking module 204 is used to mark the validity of the coating performance test results based on the judgment result.

[0112] The foregoing is considered as illustrative only of the principles of the application. Further, since numerous modifications and changes will readily occur to those skilled in the art, it is not desired to limit the application to the exact construction and practice described. Accordingly, all such variations and modifications are intended to be included within the scope of the application as defined in the following claims.

Claims

1. A method for testing the performance of a coating applied to a salt spray test chamber, characterised in that, The salt spray test chamber is internally disposed with a sensing unit for characterizing the corrosion intensity of a test environment, and the method comprises the following steps: Independently of a pH control system of the salt spray test chamber, monitoring the sensing unit, acquiring corrosion response data of the sensing unit, and calculating an actual corrosion intensity of the test environment based on the corrosion response data; Acquiring nominal pH value data recorded by the pH control system of the salt spray test chamber; Correlating and comparing the actual corrosion intensity with the nominal pH value data to determine the effectiveness of the test environment and generate a determination result, and the correlation and comparison method comprises one or more of threshold comparison, trend analysis comparison, and statistical comparison; According to the determination result, marking the effectiveness of a paint performance test result; The step of correlating and comparing the actual corrosion intensity with the nominal pH value data to determine the effectiveness of the test environment further comprises: Continuously monitoring the optical characteristics of the surface of a paint sample to be tested; Based on the optical characteristics, calculating the degradation rate of the paint sample; Comparing the degradation rate of the paint sample with the corrosion intensity of the sensing unit; According to the comparison result, calibrating the corrosion intensity data of the sensing unit or determining the data reliability of the sensing unit.

2. A method of testing the performance of a coating according to claim 1, characterised in that, In the step of calibrating the corrosion intensity data of the sensing unit according to the comparison result or determining the data reliability of the sensing unit, the step of calibrating the corrosion intensity data of the sensing unit comprises: Quantifying the influence of the deposited film on the optical characteristics of the surface of the paint sample to obtain an influence quantity; According to the comparison result and the influence quantity, establishing a correction rule of the correlation between the film influence quantity and the corrosion intensity of the sensing unit and the degradation rate of the paint sample; According to the correction rule and the current influence quantity, adjusting the calibration factor of the corrosion intensity data of the sensing unit; Applying the adjusted calibration factor to correct the corrosion intensity data of the sensing unit.

3. A method of testing the performance of a coating according to claim 2, characterised in that, The step of quantifying the influence of the deposited film on the optical characteristics of the surface of the paint sample to obtain an influence quantity comprises: Before and after each in-situ micro-cleaning operation, multiple image acquisitions are performed on a preset reference point to obtain multiple sets of optical characteristic data of the reference point before and after cleaning; Based on the multiple sets of optical characteristic data, multiple sets of preliminary film interference quantities of the reference point before and after cleaning are calculated; Statistical processing is performed on the multiple sets of preliminary film interference quantities to obtain a stable film interference quantity; Time series analysis is performed on the stable film interference quantity to track the dynamic change trend of the optical influence of the deposited film, and the dynamic change trend is taken as the influence quantity.

4. A method of testing the performance of a coating according to claim 3, characterised in that, The step of performing time series analysis on the stable film interference quantity to track the dynamic change trend of the optical influence of the deposited film comprises: Dividing the stable film interference quantity data into data segments; Calculating the change rate of the film interference quantity in each data segment; Monitoring the difference in the change rate between adjacent data segments; When the difference exceeds a threshold value, identifying a nonlinear change or mutation of the optical influence of the deposited film; According to the identified nonlinear change or mutation, adjusting the tracking strategy of the dynamic change trend to track the dynamic change trend of the optical influence of the deposited film based on the adjusted tracking strategy.

5. A method of testing the performance of a coating according to claim 4, characterised in that, The step of adjusting the tracking strategy of the dynamic change trend according to the identified nonlinear change or mutation comprises: According to the type of the identified nonlinear change or mutation, a tracking model is selected from a plurality of preset tracking models, or the parameters of the current tracking model are adjusted.

6. A method of testing the performance of a coating according to claim 5, characterised in that, The step of selecting a tracking model from a plurality of preset tracking models comprises: Running a plurality of preset tracking models in parallel; Evaluating the prediction deviation or fitting degree of each tracking model for the latest film interference quantity data; According to the evaluation result, the tracking model with the best performance is selected.

7. The method of claim 1, wherein the coating is a paint. The sensing unit comprises a pure zinc probe and a microcontroller for monitoring the resistance value of the pure zinc probe.

8. The method of claim 7, wherein the coating is a paint. The microcontroller is an ESP32 microcontroller or an STM32 microcontroller.

9. A paint performance test system for performing a paint performance test method as claimed in any one of claims 1 to 8, characterised in that, The system comprises: A monitoring module independent of the pH control system of the salt spray test chamber, for monitoring the sensing unit, acquiring corrosion response data of the sensing unit, and calculating the actual corrosion intensity of the test environment based on the corrosion response data; An acquisition module for acquiring nominal pH value data recorded by the pH control system of the salt spray test chamber; A comparison module for correlating and comparing the actual corrosion intensity with the nominal pH value data to determine the effectiveness of the test environment and generate a determination result; A test marking module for marking the effectiveness of the paint performance test result according to the determination result.

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