A flaky zinc pigment conductive performance analysis detection method and system

Abstract

This invention discloses a method and system for analyzing and detecting the conductivity of flake zinc pigments, belonging to the field of conductivity analysis and detection technology. The method includes: acquiring first electrical signal data when a probe contacts the pigment, and denoising this data to obtain first clean electrical signal data; identifying defect signals, determining the number of defect signals and the conductive defect areas of the pigment, and extracting defect signal characteristic parameters; under external stimulus conditions, acquiring second electrical signal data for each conductive defect area when the probe contacts the conductive defect area, and denoising this data to obtain second clean electrical signal data; calculating a long-term stability prediction value based on preset rules and the second clean electrical signal data; and calculating a consistency risk level based on the characteristic parameters, the number of defect signals, and the long-term stability prediction value. This application enables comprehensive and detailed analysis of the conductivity of flake zinc pigments, solving the problems of disconnect between detection results and practical applications, and the inability to accurately predict pigment performance in existing technologies.

Description

Technical Field

[0001] This application relates to the field of electrical conductivity analysis and testing technology for flake zinc pigments, and more specifically, to a method and system for electrical conductivity analysis and testing of flake zinc pigments. Background Technology

[0002] In modern industrial production, flake zinc pigments are widely used in conductive coatings, composite materials, and electromagnetic shielding due to their unique electrical conductivity and corrosion resistance. To ensure the performance of the final product, accurate and reliable analysis and testing of the electrical conductivity of flake zinc pigments is crucial.

[0003] In routine laboratory and production line quality control processes, the probe tips used for testing the conductivity of flake zinc pigments inevitably experience wear and the accumulation of trace residues on their surfaces due to the need for frequent contact with different batches of pigment samples. This accumulated residue and wear marks result in a non-uniform distribution of the actual contact area and contact resistance between the probe tip and the flake zinc pigment sample.

[0004] Existing detection systems typically include "correction values ​​for poor electrode-sample contact" programs designed to compensate for variations in contact impedance. These programs are often designed based on ideal or uniform contact models. When complex, non-uniform local contact impedance fluctuations occur on the probe surface due to wear and residue, traditional correction programs struggle to accurately identify and compensate for them.

[0005] At the same time, some flake zinc pigments may contain a small number of incompletely dispersed micro-agglomerates. These micro-agglomerates can hinder the continuous formation of conductive pathways at the microscopic level.

[0006] When the non-uniform state of the probe tip interacts with micro-aggregates present in the sample, the wear and residue on the probe tip will cause an overall increase in the background noise level of the raw electrical signal acquired by the instrument, resulting in decreased signal stability. Because probe aging has raised the overall background noise, the program incorrectly identifies and filters out signals with genuine local resistance increases caused by micro-aggregates as part of the background noise.

[0007] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0008] This application discloses a method for analyzing and testing the conductivity of flake zinc pigments, aiming to solve the technical problems of existing methods for testing the conductivity of flake zinc pigments, such as difficulty in accurately predicting the conductivity of pigments in practical application systems, detection errors caused by probe wear and residue accumulation, and difficulty in capturing local conductivity defects caused by poor pigment dispersion.

[0009] The technical solution of this application is as follows:

[0010] In a first aspect, this application discloses a method for analyzing and detecting the conductivity of flake zinc pigment, comprising the following steps: when a probe with a preset high-frequency alternating current signal is in contact with a pigment sample, real-time acquisition of first electrical signal data between the probe and the pigment sample is performed; background denoising processing is performed on the first electrical signal data to obtain first clean electrical signal data of the pigment sample; defect signal identification is performed on the first clean electrical signal data to determine the number of defect signals and the corresponding conductive defect region in the pigment sample for each defect signal, and feature parameters of each defect signal are extracted; under the condition of applying a set external stimulus to the pigment sample, for each conductive defect region, when the probe with the input high-frequency alternating current signal is in contact with the conductive defect region, real-time acquisition of second electrical signal data between the probe and the conductive defect region is performed; background denoising processing is performed on the second electrical signal data to obtain second clean electrical signal data of the conductive defect region; according to preset rules and the second clean electrical signal data of each conductive defect region, a long-term stability prediction value of the pigment sample is calculated; and according to the feature parameters, the number of defect signals, and the long-term stability prediction value, the consistency risk level of the pigment sample is calculated.

[0011] This technical solution enables a comprehensive and detailed analysis of the conductivity of flake zinc pigments. It not only identifies conductive defect areas but also combines the response to external stimuli and long-term stability prediction with defect characteristic parameters to comprehensively assess the consistency risk level of the pigment. This effectively solves the problem of the disconnect between test results and actual applications and the inability to accurately predict pigment performance in existing technologies.

[0012] Further, the step of performing background denoising processing on the first electrical signal data to obtain the first pure electrical signal data of the pigment sample specifically includes: when the probe with the input high-frequency alternating current signal is in contact with the non-conductive area, collecting the background electrical signal data between the probe and the non-conductive area; and performing background denoising processing on the first electrical signal data based on the background electrical signal data to obtain the first pure electrical signal data of the pigment sample.

[0013] This technical solution enables the collection of background electrical signal data from non-conductive areas for noise reduction, effectively eliminating environmental noise and interference introduced by the instrument itself. This ensures the accuracy of the initial clean electrical signal data and provides a reliable foundation for subsequent defect identification.

[0014] Based on the above, this application further proposes a method for simultaneously recording the contact point coordinates of the probe on the pigment sample in real time while acquiring the first electrical signal data between the probe and the pigment sample; identifying defect signals in the first pure electrical signal data, determining the number of defect signals and the corresponding conductive defect region in the pigment sample for each defect signal, and extracting the characteristic parameters of each defect signal. Specifically, this includes: performing three-level discrete wavelet decomposition on the first pure electrical signal data to find the first peak-valley, where the amplitude change exceeds a preset threshold and the duration is less than a preset time threshold; each first peak-valley represents a defect signal; determining the number of defect signals based on the number of first peak-valleys, and determining the conductive defect region corresponding to each defect signal in the pigment sample based on the timestamp of each defect signal in the first electrical signal data and the contact point coordinates corresponding to that timestamp; and extracting the following characteristic parameters for each defect signal: peak intensity, peak width, and signal slope.

[0015] This technical solution enables fine analysis of signals using three-layer discrete wavelet decomposition, accurately identifying minute defect signals. By recording the coordinates of the contact points, it achieves precise location of the defect area and extracts multi-dimensional feature parameters, providing more comprehensive data support for subsequent risk assessment. This effectively solves the problem that traditional methods struggle to capture subtle local defects.

[0016] Furthermore, the steps for calculating the consistency risk level of pigment samples based on characteristic parameters, the number of defect signals, and long-term stability prediction values ​​specifically include: for each defect signal, calculating the initial defect score of each defect signal based on the following characteristic parameters extracted from the defect signal: peak intensity, peak width, signal slope, and the preset weight of each characteristic parameter; calculating the average defect score of the pigment sample based on the number of defect signals and the initial defect score of each defect signal; and calculating the consistency risk level of the pigment sample based on the number of defect signals, the average defect score, and the long-term stability prediction values.

[0017] This technical solution enables the calculation of a more objective initial defect score by weighting multiple characteristic parameters of the defect signal. By combining the number of defects, the average defect score, and the long-term stability prediction value, a more comprehensive and accurate consistency risk assessment model can be constructed, thereby improving the reliability and precision of risk assessment.

[0018] In some preferred embodiments, after the step of performing background denoising on the second electrical signal data to obtain the second pure electrical signal data of the conductive defect region, the method further includes: under the condition of applying a set external stimulus to the pigment sample, for each conductive defect region, when a probe with a preset low-frequency AC current signal is in contact with the conductive defect region, real-time acquisition of the third electrical signal data between the probe and the conductive defect region is performed; background denoising is performed on the third electrical signal data to obtain the third pure electrical signal data of the conductive defect region; for each conductive defect region, based on the corresponding second pure electrical signal data and the corresponding third pure electrical signal data, it is determined whether there is residual interference; for conductive defect regions with residual interference, the corresponding second pure electrical signal data is corrected to obtain the corresponding corrected second pure electrical signal data; the second pure electrical signal data used in the step of calculating the predicted value of the long-term stability of the pigment sample according to preset rules and the second pure electrical signal data of each conductive defect region is the corrected second pure electrical signal data.

[0019] This technical solution allows for the introduction of low-frequency alternating current signals for auxiliary detection. By comparing high-frequency and low-frequency signals, it can determine whether there is residual interference and correct the interfered second pure electrical signal data. This effectively eliminates the influence of residual matter on the probe surface on the measurement results and improves the accuracy of long-term stability prediction.

[0020] As a technical improvement, when acquiring the second electrical signal data between the probe and the conductive defect area in real time, the first phase angle relative to the high-frequency AC current signal is acquired simultaneously; when acquiring the third electrical signal data between the probe and the conductive defect area in real time, the second phase angle relative to the low-frequency AC current signal is acquired simultaneously; for each conductive defect area, the step of determining whether there is residual interference based on the corresponding second pure electrical signal data and the corresponding third pure electrical signal data specifically includes: for each conductive defect area, based on the corresponding second pure electrical signal data, calculating the high-frequency complex impedance Z(f) = R(f) + j1X(f) corresponding to the high-frequency AC current signal Z(f) = R(f) + j1X(f), where R(f) represents the high-frequency resistance component of the second pure electrical signal data, X(f) represents the high-frequency reactance component of the second pure electrical signal data, and j1 represents the imaginary unit of the second pure electrical signal data; based on the corresponding third pure electrical signal data, calculating the low-frequency complex impedance z(f) = r(f) + j1X(f) corresponding to the low-frequency AC current signal Z(f) = r(f) + j1X(f) j2x(f), where r(f) represents the low-frequency resistance component of the third pure electrical signal data, x(f) represents the low-frequency reactance component of the third pure electrical signal data, and j2 represents the imaginary unit of the third pure electrical signal data; calculate the high-frequency impedance strength based on the high-frequency complex impedance, calculate the low-frequency impedance strength based on the low-frequency complex impedance, and calculate the ratio P of the high-frequency impedance strength to the low-frequency impedance strength; calculate the difference D between the first phase angle and the second phase angle; based on the ratio P and the difference D, determine whether there is residual interference in the conductive defect region.

[0021] This technical solution enables the construction of a more sensitive residual interference detection mechanism by calculating the complex impedances and their intensity ratio P at high and low frequencies, as well as the phase angle difference D. By utilizing the differences in electrical parameters, it can more accurately identify whether there is residual interference between the probe and the sample, thereby improving the reliability of the detection.

[0022] To improve the solution, the steps for correcting the corresponding second pure electrical signal data in conductive defect areas with residual interference to obtain the corrected second pure electrical signal data specifically include: for conductive defect areas with residual interference, calculating the interference high-frequency reactance component introduced by the residue under high-frequency alternating current signals based on the ratio P of the high-frequency impedance intensity to the low-frequency impedance intensity corresponding to the conductive defect area, the difference D between the first phase angle and the second phase angle, and a preset empirical formula; correcting the high-frequency reactance component in the high-frequency complex impedance corresponding to the conductive defect area based on the interference high-frequency reactance component, obtaining the corrected high-frequency complex impedance of the conductive defect area, which is the corrected second pure electrical signal data.

[0023] This technical solution utilizes the ratio P of high-frequency impedance to low-frequency impedance, the phase angle difference D, and empirical formulas to accurately calculate and correct the high-frequency reactive components of interference introduced by residues. This results in a more realistic high-frequency complex impedance in the conductive defect region, effectively eliminating the systematic error of residues on the measurement results and improving the accuracy of the data.

[0024] As a further improvement, when the probe with a preset high-frequency alternating current signal comes into contact with the pigment sample, the following steps are also included: real-time acquisition of mechanical data and displacement data when the probe comes into contact with the pigment sample; background denoising processing of the first electrical signal data to obtain the first pure electrical signal data of the pigment sample specifically includes: extracting mechanical features from the mechanical data and extracting displacement features from the displacement data; searching for reference background electrical signal data that establishes a correlation with the mechanical feature and the displacement feature in a preset background interference correlation database; and performing background denoising processing on the first electrical signal data based on the found reference background electrical signal data to obtain the first pure electrical signal data of the pigment sample.

[0025] This technical solution enables the simultaneous acquisition of mechanical and displacement data, the extraction of features, and the integration with a background interference correlation database to achieve adaptive background denoising based on probe contact status. This effectively solves the problem of non-uniform local contact impedance fluctuations caused by probe wear and residue accumulation, and improves the accuracy of background denoising.

[0026] To improve the design, before the step of performing background noise reduction processing on the first electrical signal data to obtain the first pure electrical signal data of the pigment sample, the following steps are included: providing multiple first probes with different degrees of wear and / or different accumulation of trace residues; performing the following operations for each first probe: when the first probe with a preset high-frequency AC current signal is in contact with a non-conductive standard sample, collecting the reference background electrical signal data between the first probe and the non-conductive standard sample, and simultaneously collecting the reference mechanical data and reference displacement data between the first probe and the non-conductive standard sample; extracting the reference mechanical features from the reference mechanical data, and extracting the reference displacement features from the reference displacement data; establishing the correlation between the reference background electrical signal data and the reference mechanical features and the reference displacement features, and adding the correlation to the background interference correlation database.

[0027] This technical solution enables the construction of a comprehensive background interference correlation database by pre-establishing the correlation between the reference background electrical signal data and mechanical and displacement characteristics of probes under different wear levels and residue accumulation states. This provides a solid data foundation for subsequent adaptive background denoising and effectively solves the impact of probe aging on detection accuracy.

[0028] Secondly, this application also discloses a system for analyzing and detecting the conductivity of flake zinc pigment, comprising: a first acquisition module, used to acquire first electrical signal data between the probe and the pigment sample in real time when a probe with a preset high-frequency alternating current signal is in contact with the pigment sample, and to perform background denoising processing on the first electrical signal data to obtain first clean electrical signal data of the pigment sample; an identification module, used to identify defect signals in the first clean electrical signal data, determine the number of defect signals and the corresponding conductive defect area in the pigment sample for each defect signal, and extract feature parameters of each defect signal; a second acquisition module, used to acquire second electrical signal data between the probe and the conductive defect area in real time when the probe with the input high-frequency alternating current signal is in contact with the conductive defect area under the condition of applying a set external stimulus to the pigment sample; to perform background denoising processing on the second electrical signal data to obtain second clean electrical signal data of the conductive defect area; and a calculation module, used to calculate the predicted long-term stability value of the pigment sample according to preset rules and the second clean electrical signal data of each conductive defect area; and to calculate the consistency risk level of the pigment sample according to the feature parameters, the number of defect signals, and the predicted long-term stability value.

[0029] This technical solution provides a system for implementing the aforementioned detection methods. Through modular design, it integrates functions such as data acquisition, defect identification, secondary acquisition, and risk calculation, enabling automated and intelligent analysis and detection of the conductivity of flake zinc pigments, thereby improving detection efficiency and the reliability of results.

[0030] Beneficial Effects: The conductivity analysis and testing method for flake zinc pigments disclosed in this application obtains first clean electrical signal data of the pigment sample by acquiring first electrical signal data in real time when the probe contacts the pigment sample and performing background noise reduction processing. Based on this, defect signal identification is performed on the clean electrical signal data, determining not only the number of defect signals but also accurately identifying the corresponding conductive defect region in the pigment sample for each defect signal, and extracting the characteristic parameters of the defect signals. For these conductive defect regions, second electrical signal data is acquired again under external stimulation conditions and subjected to noise reduction processing. Finally, by combining preset rules, the second clean electrical signal data, defect characteristic parameters, and the number of defect signals, the long-term stability prediction value and consistency risk level of the pigment sample are calculated.

[0031] This method effectively addresses the limitations of existing technologies for detecting the conductivity of flake zinc pigments. First, by denoising the original electrical signal, interference from environmental noise and probe defects (such as wear and residue) is effectively eliminated, ensuring signal purity and overcoming the problems of increased background noise and incorrect filtering of true defect signals caused by probe aging in traditional detection systems. Second, through precise defect signal identification and defect region localization, tiny, localized conductive defects in pigment samples, such as incompletely dispersed micro-agglomerates, can be captured—something difficult to achieve with traditional macroscopic detection methods. Third, the introduction of secondary detection under external stimuli, combined with long-term stability prediction, allows the detection results to not only reflect the current conductivity of the pigment but also predict its long-term performance in practical applications, significantly improving the practicality and guiding significance of the results. Finally, by comprehensively considering the characteristic parameters and quantity of defect signals, as well as long-term stability prediction values, the consistency risk level of the pigment sample is calculated, providing a more comprehensive and accurate assessment basis for quality control and product optimization, avoiding potential product quality risks caused by the neglect of local defects in traditional methods.

[0032] In summary, the method of this application provides a more accurate, reliable, and comprehensive solution for analyzing and testing the conductivity of flake zinc pigments. It effectively solves the problems in the prior art, such as the disconnect between test results and actual applications, errors introduced by probe aging, and difficulty in identifying local defects. This significantly improves the quality control level and product performance prediction capability of flake zinc pigments. Attached Figure Description

[0033] Figure 1 This is a schematic flowchart of a method for analyzing and testing the conductivity of flake zinc pigment provided in this application.

[0034] Figure 2 This is a schematic diagram of a system for analyzing and testing the conductivity of flake zinc pigment provided in this application.

[0035] Figure 2 In the diagram: 1 is the first acquisition module, 2 is the recognition module, 3 is the second acquisition module, and 4 is the calculation module. Detailed Implementation

[0036] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0037] See Figure 1 This application proposes a method for analyzing and testing the conductivity of flake zinc pigment, comprising the following steps:

[0038] S10. When the probe with a preset high-frequency alternating current signal comes into contact with the pigment sample, the first electrical signal data between the probe and the pigment sample is collected in real time; the first electrical signal data is processed for background noise reduction to obtain the first clean electrical signal data of the pigment sample.

[0039] S20. Perform defect signal identification on the first pure electrical signal data, determine the number of defect signals and the corresponding conductive defect area of ​​each defect signal in the pigment sample, and extract the feature parameters of each defect signal.

[0040] S30. Under the condition of applying the set external stimulus to the pigment sample, for each conductive defect area, when the probe with the input high-frequency alternating current signal comes into contact with the conductive defect area, the second electrical signal data between the probe and the conductive defect area is collected in real time; the second electrical signal data is processed for background noise reduction to obtain the second clean electrical signal data of the conductive defect area.

[0041] S40. Calculate the predicted long-term stability value of the pigment sample based on the preset rules and the second pure electrical signal data of each conductive defect region; calculate the consistency risk level of the pigment sample based on the characteristic parameters, the number of defect signals, and the predicted long-term stability value.

[0042] The term "flaky zinc pigment" as used in this application refers to zinc-based pigments with a flaky structure, which play a role in conductivity and corrosion protection in conductive coatings, composite materials, and other fields. "Probe" refers to a conductive component used to contact the pigment sample. "High-frequency alternating current signal" refers to a high-frequency alternating current signal used to excite the electrical response of the pigment sample during detection, typically in the kHz to MHz range. "First electrical signal data" and "second electrical signal data" refer to the raw electrical data collected by the probe at different detection stages, which may include information such as voltage, current, or impedance. "Background denoising" refers to the process of using algorithms to eliminate signal fluctuations in the electrical signal data caused by environmental interference, instrument noise, and other non-sample characteristics, in order to obtain a more realistic sample response. "First pure electrical signal data" and "second pure electrical signal data" are electrical signal data after background denoising, more accurately reflecting the electrical characteristics of the pigment sample. "Defect signal identification" refers to identifying conductive discontinuities or abnormal regions in the pigment sample by analyzing abnormal fluctuations in the pure electrical signal data. "Conductive defect region" refers to a localized area in a pigment sample where the conductivity is below normal levels, possibly caused by agglomeration, voids, or impurities. "Characteristic parameters" are numerical values ​​used to quantify the characteristics of the defect signal, such as peak intensity, peak width, and signal slope. "External stimulus conditions" refer to the environmental conditions applied to the pigment sample during the testing process, such as temperature, humidity, mechanical stress, or chemical corrosion, designed to simulate real-world application scenarios and assess the long-term stability of the pigment. "Long-term stability prediction value" refers to a prediction of the pigment sample's ability to maintain conductivity during long-term use, based on test data and pre-defined rules. "Conformity risk level" refers to a classification that comprehensively considers the defect situation, defect characteristics, and long-term stability of the pigment sample to assess the consistency of its conductivity.

[0043] When a probe with a preset high-frequency alternating current signal comes into contact with a pigment sample, the initial electrical signal data between the probe and the pigment sample is acquired in real time. This step aims to obtain the electrical response of the pigment sample in its initial state. For example, the conductivity mode of atomic force microscopy (AFM) can be used, where the AFM probe is designed as a conductive probe and connected to a high-frequency alternating current source. When the probe scans the surface of the pigment sample with a preset contact force, the current or voltage signal between the probe and the sample is acquired in real time.

[0044] Background denoising was performed on the first electrical signal data to obtain the first clean electrical signal data of the pigment sample. This step aims to eliminate noise in the raw data to improve the accuracy of subsequent analyses.

[0045] Defect signal identification is performed on the first pure electrical signal data to determine the number of defect signals and the corresponding conductive defect region in the pigment sample for each defect signal, and to extract the characteristic parameters of each defect signal. This step is crucial for identifying potential conductive defects in the pigment sample. For example, threshold analysis can be used to identify signal fluctuations in the first pure electrical signal data that are below or above a certain preset threshold as defect signals. Once defect signals are identified, their number can be determined based on information such as duration and amplitude changes. Simultaneously, by combining the probe's scanning trajectory or contact point coordinates on the sample, the corresponding conductive defect region in the pigment sample for each defect signal can be accurately located. For each identified defect signal, its characteristic parameters can be extracted, such as peak intensity (maximum amplitude of signal fluctuation), peak width (signal duration), and signal slope (rate of signal rise or fall). These parameters help quantify the severity and type of the defect.

[0046] Under predetermined external stimuli applied to the pigment sample, for each conductive defect region, a second electrical signal data between the probe and the conductive defect region is acquired in real time when the probe, which is supplied with the high-frequency alternating current signal, comes into contact with the conductive defect region. This step aims to evaluate the performance changes of the defect region under simulated real-world application conditions. For example, the pigment sample can be placed in a constant temperature and humidity chamber to simulate a high-temperature and high-humidity environment as the external stimuli. Then, the probe is repositioned to each previously identified conductive defect region, and a high-frequency alternating current signal is input again to acquire the second electrical signal data between the probe and the defect region in real time.

[0047] Background denoising is performed on the second electrical signal data to obtain clean electrical signal data of the conductive defect region. Similar to the denoising of the first electrical signal data, this step aims to eliminate noise in the second electrical signal data acquired under external stimuli. For example, the same digital filter or wavelet denoising algorithm used for processing the first electrical signal data can be employed to process the second electrical signal data to obtain a more accurate electrical response of the defect region under external stimuli.

[0048] Based on preset rules and second-pure electrical signal data for each conductive defect region, the predicted long-term stability of the pigment sample is calculated. This step aims to assess the trend of conductivity changes in the pigment sample during long-term use. For example, the preset rules may include a mathematical model that correlates the second-pure electrical signal data (such as impedance change rate) of the defect region under external stimuli with the expected lifetime or performance degradation rate of the pigment. By inputting the second-pure electrical signal data for each defect region into this model, the predicted long-term stability of the pigment sample can be calculated.

[0049] The consistency risk level of pigment samples is calculated based on characteristic parameters, the number of defect signals, and long-term stability predictions. This step comprehensively considers the initial defect situation, defect characteristics, and stability performance under external stimuli to provide a comprehensive risk assessment. For example, a risk assessment model can be established that takes characteristic parameters such as peak intensity, peak width, and signal slope of defect signals, the total number of defect signals, and long-term stability predictions as inputs. A comprehensive risk score is calculated using methods such as weighted summation or multi-factor decision analysis, and the pigment samples are then classified into different risk levels, such as "low risk," "medium risk," or "high risk," based on this score.

[0050] The conductivity analysis method for flake zinc pigments in this application introduces background denoising processing on the first electrical signal data to obtain the first clean electrical signal data of the pigment sample. This effectively solves the problem of increased background noise caused by probe wear and residue accumulation in traditional detection methods. In traditional methods, wear and residue on the probe tip lead to an overall increase in the background noise level of the original electrical signal and a decrease in signal stability. This causes the real local resistance increase signal generated by tiny aggregates to be incorrectly identified as background noise and filtered out. This application, through background denoising processing, can more accurately identify and retain these key defect signals.

[0051] Furthermore, this application identifies defect signals from the first clean electrical signal data, determines the number of defect signals and the corresponding conductive defect region in the pigment sample for each defect signal, and extracts the characteristic parameters of each defect signal. This allows for precise location of conductive defect regions in the pigment sample and quantification of defect severity. This overcomes the limitation of traditional methods in capturing subtle, locally dispersed defects.

[0052] Furthermore, under the specified external stimulus conditions applied to the pigment sample, for each conductive defect region, the second electrical signal data between the probe and the conductive defect region is acquired in real time, and background noise reduction processing is performed to obtain the second clean electrical signal data of the conductive defect region. This step enables this application to evaluate the long-term stability of the pigment sample under simulated real-world application environments, thereby more accurately predicting its conductivity performance in actual use.

[0053] Finally, based on characteristic parameters, the number of defect signals, and long-term stability prediction values, the consistency risk level of the pigment samples is calculated, providing a comprehensive risk assessment for the quality control and application of flake zinc pigments. This comprehensive assessment method enables this application to more comprehensively and accurately evaluate the conductivity of flake zinc pigments, especially in the presence of local defects and external stimuli, thereby effectively overcoming the limitations of existing technologies and improving the accuracy and reliability of detection.

[0054] In some embodiments of this application, the step of performing background denoising processing on the first electrical signal data to obtain the first pure electrical signal data of the pigment sample may include the following operations:

[0055] When the probe with the input high-frequency alternating current signal comes into contact with the non-conductive area, background electrical signal data between the probe and the non-conductive area is collected.

[0056] The first electrical signal data is denoised based on the background electrical signal data to obtain the first clean electrical signal data of the pigment sample.

[0057] Specifically, before or during conductivity analysis of flake zinc pigment samples, a probe with a preset high-frequency alternating current signal can be brought into contact with a non-conductive region. During this contact, electrical signal data between the probe and the non-conductive region is acquired in real time; this data is considered background electrical signal data. The background electrical signal data primarily reflects noise and interference introduced by the probe itself, the measurement environment, and the non-conductive material. Subsequently, the acquired background electrical signal data is used to perform background denoising processing on the first electrical signal data acquired from the entire pigment sample or a specific region. This denoising process can be achieved through various signal processing techniques, such as subtraction denoising, filtering denoising, or statistical model-based denoising. The aim is to effectively remove background noise components from the original first electrical signal data, thereby obtaining more accurate and pure first clean electrical signal data for the pigment sample.

[0058] The proposed solution acquires background electrical signal data when the probe contacts a non-conductive region, effectively obtaining inherent noise characteristics related to the measurement environment, the probe itself, and the non-conductive substrate. Since this background noise typically exhibits a certain degree of stability and repeatability throughout the measurement process, removing this background electrical signal data from the actual measured first electrical signal data allows for the precise extraction of signals generated by the conductive properties of the pigment sample itself, thereby avoiding interference from background noise in subsequent defect identification and performance analysis.

[0059] The above technical solution provides a more accurate and reliable background noise reduction method. By directly measuring and utilizing the background noise of non-conductive regions, the influence of environmental interference and inherent equipment noise on the measurement results can be effectively eliminated, significantly improving the signal-to-noise ratio and accuracy of the first clean electrical signal data. This provides high-quality input data for subsequent steps such as defect signal identification, feature parameter extraction, and long-term stability prediction, thereby improving the overall accuracy and reliability of the conductivity analysis and detection of flake zinc pigments.

[0060] In some embodiments described above in this application, defect signal identification is performed on the first pure electrical signal data to determine the number of defect signals and the corresponding conductive defect region in the pigment sample for each defect signal, and feature parameters of each defect signal are extracted. To more accurately identify and quantify these defects, this application further proposes specific identification and parameter extraction methods.

[0061] In response, the above-mentioned method for analyzing and detecting the conductivity of sheet zinc pigment simultaneously records the coordinates of the contact point of the probe on the pigment sample in real time while acquiring the first electrical signal data between the probe and the pigment sample in real time.

[0062] The steps of identifying defect signals from the first pure electrical signal data, determining the number of defect signals and the corresponding conductive defect region in the pigment sample for each defect signal, and extracting the feature parameters of each defect signal specifically include:

[0063] The first clean electrical signal data is subjected to three-level discrete wavelet decomposition to find the first peak and valley. The first peak and valley satisfy the condition that the amplitude change exceeds a preset threshold and the duration is less than a preset time threshold; a first peak and valley is a defect signal.

[0064] The number of defect signals is determined based on the number of the first peak and valley, and the conductive defect region corresponding to each defect signal is determined based on the timestamp of each defect signal in the first electrical signal data and the contact point coordinates corresponding to the timestamp.

[0065] Extract the following characteristic parameters for each defect signal: peak intensity, peak width, and signal slope.

[0066] Specifically, when the probe contacts the pigment sample and collects the first electrical signal data, the coordinates of the contact point of the probe on the pigment sample are recorded simultaneously in real time. The purpose is to provide spatial location information for subsequent defect signal identification, so that each identified defect signal can correspond to a specific physical location on the pigment sample.

[0067] The process of performing three-level discrete wavelet decomposition on the first clean electrical signal data can be understood as decomposing the original signal into sub-signals with different frequency components, thereby effectively separating noise and transient defect signals. Through multi-level decomposition, subtle changes in the signal can be captured more precisely, laying the foundation for accurate identification of defect signals.

[0068] Furthermore, identifying the first peak / valley refers to recognizing signal features with significant amplitude changes and short durations within the wavelet decomposition signal. The first peak / valley is defined as a signal event whose amplitude change exceeds a preset threshold and whose duration is less than a preset time threshold. Its purpose is to filter out instantaneous electrical signal anomalies that truly represent conductive defects, eliminating background noise or slowly changing signal interference. A first peak / valley that meets these conditions is identified as a defect signal.

[0069] In practical applications, the number of defect signals is determined based on the number of the first peaks and valleys identified. Each defect signal corresponds to a timestamp in the first electrical signal data. By combining this with the synchronously recorded contact point coordinates corresponding to that timestamp, the conductive defect region on the pigment sample corresponding to each defect signal can be accurately determined. This makes the defect localization more accurate and helps with subsequent further analysis of specific areas.

[0070] Furthermore, characteristic parameters for each defect signal were extracted, including peak intensity, peak width, and signal slope. Peak intensity reflects the instantaneous maximum amplitude of the defect signal and can be used to assess the severity of the defect; peak width represents the duration of the defect signal and can be used to assess the size or persistence of the defect; signal slope describes the rate at which the defect signal rises or falls, and can be used to characterize the abruptness of the defect formation or disappearance process. The extraction of these characteristic parameters aims to provide quantitative, multi-dimensional input information for subsequent calculations of the pigment sample consistency risk level.

[0071] The proposed solution provides precise spatial location information for defect signals by simultaneously recording the probe's contact point coordinates during the acquisition of the first electrical signal data. By performing three-level discrete wavelet decomposition on the first clean electrical signal data, noise and potential defect signals in the signal can be effectively separated, and multi-scale analysis of the signal can be performed, thereby improving the detection sensitivity of defect signals. Furthermore, by setting preset thresholds for amplitude variation and duration to find the first peak and valley, transient defect signals with abnormal conductivity characteristics can be accurately identified and defined as defect signals. Thus, by combining timestamps and contact point coordinates, the specific conductive defect region on the pigment sample for each defect signal can be accurately determined. Simultaneously, by extracting characteristic parameters such as peak intensity, peak width, and signal slope, each defect signal can be quantitatively described, providing comprehensive and detailed data support for subsequent long-term stability prediction and consistency risk level calculation.

[0072] Through the above technical solution, this application can achieve accurate identification and localization of conductive defects in flake zinc pigments. Specifically, three-layer discrete wavelet decomposition effectively improves the signal-to-noise ratio, enabling the detection of weak defect signals; the identification criterion of the first peak and valley ensures the validity and representativeness of the identified defects; the combination of synchronously recorded contact point coordinates and timestamps allows each defect to be accurately mapped to the physical location of the pigment sample, providing a basis for subsequent local analysis. Furthermore, the extraction of multi-dimensional feature parameters makes the quantitative assessment of defects more comprehensive and in-depth, thereby significantly improving the accuracy and reliability of the conductivity performance analysis of pigment samples, and providing more refined guidance for product quality control and performance optimization.

[0073] This application further proposes the following steps for calculating the consistency risk level of pigment samples based on characteristic parameters, the number of defect signals, and long-term stability prediction values:

[0074] For each defect signal, the initial defect score is calculated based on the following feature parameters extracted from the defect signal: peak intensity, peak width, signal slope, and the preset weight of each feature parameter.

[0075] The average defect score of the pigment sample is calculated based on the number of defect signals and the initial defect score of each defect signal.

[0076] The consistency risk level of pigment samples is calculated based on the number of defect signals, the average defect score, and the long-term stability prediction value.

[0077] Specifically, when calculating the consistency risk level of pigment samples, an initial defect score is first calculated for each identified defect signal based on its characteristic parameters such as peak intensity, peak width, and signal slope, combined with pre-set weights for these parameters. Peak intensity can be understood as the amplitude of the defect signal, reflecting the severity of the defect; peak width can be understood as the duration or spatial range of the defect signal, reflecting the size of the defect; and signal slope can be understood as the rate of change of the defect signal, reflecting the nature of the defect. By setting pre-set weights for these characteristic parameters, the importance of different characteristic parameters in assessing defect severity can be adjusted according to actual application needs or empirical knowledge, thereby enabling the initial defect score to more accurately quantify the potential impact of individual defects.

[0078] Furthermore, after obtaining the initial defect scores for all defect signals, these initial defect scores are combined with the number of defect signals to calculate the average defect score of the pigment sample. This average defect score provides a comprehensive indicator for measuring the overall defect level of the pigment sample. For example, the average defect score can be obtained simply by summing all initial defect scores and dividing by the number of defect signals, or it can be calculated using other statistical methods to better reflect the overall distribution and severity of defects.

[0079] Ultimately, the consistency risk level of the pigment samples is calculated based on three key indicators: the number of defect signals, the average defect score, and the long-term stability prediction value. This calculation method ensures that the risk assessment not only considers the number of defects but also analyzes the severity of individual defects in depth (reflected by the initial defect score and the average defect score), and combines the long-term performance of the pigment samples under external stimuli (reflected by the long-term stability prediction value).

[0080] This application refines the calculation of the consistency risk level of pigment samples by introducing the concepts of "initial defect score" and "average defect score." Specifically, firstly, for each identified defect signal, an initial defect score is calculated by comprehensively considering its characteristic parameters such as peak intensity, peak width, and signal slope, and assigning these parameters preset weights. This weighted calculation method allows for more precise quantification of different types of defects or different manifestations of the same defect, thus reflecting their potential impact on the conductivity of the pigment sample. Secondly, by summarizing the initial defect scores of all defect signals and combining them with the number of defect signals, an average defect score is calculated for the pigment sample. This average defect score provides a macroscopic, holistic indicator of defect severity, avoiding the one-sidedness of judging solely based on the number of defects or a single characteristic parameter. Finally, the consistency risk level of the pigment sample is calculated by comprehensively considering three key indicators: the number of defect signals, the average defect score, and the long-term stability prediction value. This hierarchical, weighted, and comprehensive calculation method makes risk assessment more comprehensive, objective, and accurate, and can more effectively identify pigment samples with potential conductivity risks.

[0081] Through the above technical solution, this application provides a more refined and comprehensive method for risk assessment of the conductivity consistency of flake zinc pigments. Compared to basic methods that rely solely on the number of defect signals and long-term stability predictions for rough judgments, this application introduces the quantification of the severity of individual defects (initial defect score) and a comprehensive assessment of the overall defect level (average defect score). This makes the calculation of risk levels no longer a simple accumulation of parameters, but rather a more accurate reflection of the true impact of internal defects in pigment samples through in-depth analysis and weighted processing of defect characteristics. Therefore, it effectively avoids misjudgments caused by ignoring the severity of individual defects or the complexity of defect distribution, significantly improving the accuracy and reliability of pigment sample consistency risk assessment, and providing more solid data support for the quality control and application of flake zinc pigments.

[0082] This application further proposes that after performing background denoising processing on the second electrical signal data to obtain the second clean electrical signal data of the conductive defect region, the following steps are also included:

[0083] Under the condition of applying a set external stimulus to the pigment sample, for each conductive defect area, when the probe with a preset low-frequency alternating current signal comes into contact with the conductive defect area, the third electrical signal data between the probe and the conductive defect area is collected in real time, and the third electrical signal data is processed by background denoising to obtain the third clean electrical signal data of the conductive defect area.

[0084] For each conductive defect area, the presence of residual interference is determined based on the corresponding second and third pure electrical signal data.

[0085] For conductive defect areas with residual interference, the corresponding second pure electrical signal data is corrected to obtain the corrected second pure electrical signal data.

[0086] The second pure electrical signal data used in the step of calculating the predicted value of the long-term stability of the pigment sample according to the preset rules and the second pure electrical signal data of each conductive defect region is the corrected second pure electrical signal data.

[0087] Specifically, after obtaining the second clean electrical signal data of the conductive defect region, under the condition of applying a set external stimulus to the pigment sample, a preset low-frequency alternating current signal is also input when the probe contacts the conductive defect region. The low-frequency alternating current signal typically refers to an alternating current signal with a frequency much lower than that of a high-frequency alternating current signal. For example, the frequency of a high-frequency alternating current signal can be set at the megahertz (MHz) level, while the frequency of a low-frequency alternating current signal can be set at the kilohertz (kHz) or hertz (Hz) level. The introduction of the low-frequency alternating current signal aims to utilize the difference in current response between the residue and the conductive material at different frequencies. When the probe contacts the conductive defect region, the third electrical signal data between the probe and the conductive defect region is acquired in real time. This third electrical signal data also needs to undergo background denoising processing to eliminate the influence of environmental noise and equipment noise, thereby obtaining the third clean electrical signal data of the conductive defect region. Background denoising processing can employ methods similar to those used for processing the first or second electrical signal data, such as denoising techniques based on wavelet transform, Fourier transform, or machine learning algorithms.

[0088] Determining the presence of residual interference involves comparing and analyzing electrical signal data collected at different frequencies (high and low) to identify whether residual substances that may affect conductivity are present on the surface of conductive defect areas. Residual substances typically exhibit significant differences in their response to high-frequency signals compared to low-frequency signals; for example, they may show greater capacitive reactance or dielectric loss at high frequencies. By extracting and comparing features from second- and third-clean electrical signal data—such as calculating parameters like impedance and phase angle—and based on a pre-defined judgment model or threshold, the presence of residual interference can be determined.

[0089] In practical applications, for conductive defect areas with residual interference, the corresponding second-pure electrical signal data needs to be corrected. The purpose of correction is to eliminate or reduce the interference components introduced by the residue under high-frequency AC current signals, so that the corrected second-pure electrical signal data can more accurately reflect the conductivity of the conductive defect area itself. Correction methods can be based on physical models, empirical formulas, or data-driven models. For example, by establishing electrical response models of the residue at high and low frequencies, the interference components can be calculated and subtracted from the original second-pure electrical signal data. The corrected second-pure electrical signal data will be used for subsequent calculations of long-term stability prediction values, thereby improving the accuracy of the predictions.

[0090] This application's solution, by introducing a low-frequency alternating current signal and acquiring a third electrical signal data, can effectively distinguish the conductivity characteristics of the conductive defect region itself from the interference introduced by surface residues. Specifically, high-frequency alternating current signals are more sensitive to surface effects and dielectric losses. Residues (such as oxide layers, adsorbates, etc.) may exhibit significant capacitance or dielectric loss characteristics at high frequencies, thus introducing a large reactance component into the high-frequency complex impedance. In contrast, low-frequency alternating current signals mainly reflect the ohmic resistance characteristics of the material and are less sensitive to the dielectric effects of surface residues. Therefore, by comparing electrical signal data (e.g., impedance strength and phase angle) at high and low frequencies, the additional reactance or resistance component caused by residues can be identified. Once residue interference is identified, a correction model can be established based on its response differences at high and low frequencies to correct the second pure electrical signal data under the high-frequency alternating current signal, thereby eliminating the influence of residues and obtaining more realistic electrical response data of the conductive defect region.

[0091] Through the above technical solution, this application can effectively identify and quantify the interference from residual substances that may exist on the surface of the conductive defect area of ​​flake zinc pigment. By correcting the second pure electrical signal data, the influence of residues on high-frequency electrical signal measurements is eliminated, making the subsequent calculations of the long-term stability prediction values ​​of pigment samples more accurate and reliable. This significantly improves the accuracy and reliability of the conductivity analysis and detection of flake zinc pigment, avoids misjudgments caused by residue interference, and thus enables a more accurate assessment of the consistency risk level of pigment samples, providing a more solid data foundation for pigment quality control and application.

[0092] This application further proposes to simultaneously acquire a first phase angle relative to a high-frequency alternating current signal when acquiring the second electrical signal data between the probe and the conductive defect region in real time; and to simultaneously acquire a second phase angle relative to a low-frequency alternating current signal when acquiring the third electrical signal data between the probe and the conductive defect region in real time.

[0093] For each conductive defect region, the specific steps for determining whether residual interference exists based on the corresponding second and third pure electrical signal data include:

[0094] For each conductive defect region, based on the corresponding second pure electrical signal data, the high-frequency complex impedance Z(f) = R(f) + j1X(f) corresponding to the high-frequency AC current signal is calculated, where R(f) represents the high-frequency resistance component of the second pure electrical signal data, X(f) represents the high-frequency reactance component of the second pure electrical signal data, and j1 represents the imaginary unit of the second pure electrical signal data.

[0095] Based on the corresponding third pure electrical signal data, calculate the low-frequency complex impedance z(f) = r(f) + j2x(f) corresponding to the low-frequency AC current signal, where r(f) represents the low-frequency resistance component of the third pure electrical signal data, x(f) represents the low-frequency reactance component of the third pure electrical signal data, and j2 represents the imaginary unit of the third pure electrical signal data.

[0096] Calculate the high-frequency impedance strength based on the high-frequency complex impedance, calculate the low-frequency impedance strength based on the low-frequency complex impedance, and calculate the ratio P between the high-frequency impedance strength and the low-frequency impedance strength.

[0097] Calculate the difference D between the first phase angle and the second phase angle;

[0098] Based on the ratio P and the difference D, it is determined whether there is residual interference in the conductive defect region.

[0099] Specifically, while acquiring the second electrical signal data, a first phase angle is simultaneously acquired. This first phase angle reflects the phase difference between the high-frequency AC current signal and the response electrical signal between the probe and the conductive defect region. Similarly, while acquiring the third electrical signal data, a second phase angle is simultaneously acquired. This second phase angle reflects the phase difference between the low-frequency AC current signal and the response electrical signal between the probe and the conductive defect region. These phase angles provide additional information about the electrical characteristics of the conductive defect region, especially considering that the phase response changes at different frequencies in the presence of residual interference.

[0100] The high-frequency complex impedance Z(f) = R(f) + j1X(f) is obtained by performing Fourier transform or impedance spectrum analysis on the second clean electrical signal data. R(f) represents the high-frequency resistance component, indicating the pure resistive characteristics of the material, while X(f) represents the high-frequency reactance component, indicating the capacitance or inductance characteristics of the material. Similarly, the low-frequency complex impedance z(f) = r(f) + j2x(f) is obtained by performing the same processing on the third clean electrical signal data. r(f) represents the low-frequency resistance component, and x(f) represents the low-frequency reactance component.

[0101] High-frequency impedance and low-frequency impedance refer to the magnitudes of high-frequency complex impedance and low-frequency complex impedance, respectively. They quantify the overall impedance of the conductive defect region to current at different frequencies. The ratio P is the ratio of the high-frequency impedance to the low-frequency impedance, reflecting the differences in the conductivity or insulation properties of the residue at different frequencies. The difference D is the difference between the first and second phase angles, revealing the influence of the residue on the phase response of signals at different frequencies. For example, some residues may exhibit a stronger capacitive effect at high frequencies, leading to significant changes in the phase angle. By comprehensively analyzing the ratio P and the difference D, a more comprehensive and accurate determination can be made of the presence and nature of residue interference in the conductive defect region.

[0102] The solution proposed in this application, by introducing the calculation and analysis of the phase angle and complex impedance corresponding to high-frequency AC current signals and low-frequency AC current signals, can more precisely identify whether there is residual interference in the conductive defect region.

[0103] Specifically, the calculation of high-frequency complex impedance Z(f) and low-frequency complex impedance z(f) allows for characterizing the electrical behavior of conductive defect regions from both resistive and reactive dimensions. The presence of residues alters these components; for example, insulating residues increase resistance and reactance, while conductive residues may decrease resistance. By calculating the ratio P of high-frequency impedance to low-frequency impedance, the relative impact of residues on signal impedance at different frequencies can be quantified. For instance, if the residue exhibits stronger insulation at high frequencies, the high-frequency impedance will be relatively high, leading to an increase in the ratio P.

[0104] Meanwhile, the difference D between the first and second phase angles provides crucial information about the capacitive or inductive characteristics of the residue. Capacitive residues (such as insulating films) typically cause phase lead, while inductive residues (such as certain metal oxides) may cause phase lag. The difference D in phase angles at different frequencies can more sensitively capture these frequency-related changes in electrical characteristics. By comprehensively utilizing the impedance ratio P and the phase angle difference D, two complementary parameters, the solution in this application can more accurately distinguish between regions with normal conductive defects and regions with residue interference, and make a preliminary judgment on the nature of the residue, thereby overcoming the limitations of relying solely on amplitude information for judgment.

[0105] Through the above technical solution, this application can significantly improve the accuracy and reliability of detecting residue interference in the conductive defect region of flake zinc pigment. By introducing phase angle information under high-frequency and low-frequency AC current signals and combining it with the calculation of complex impedance, the electrical characteristics of the conductive defect region can be comprehensively analyzed from multiple dimensions such as resistance, reactance, and phase response. This multi-parameter comprehensive judgment method effectively avoids misjudgment or omission that may be caused by single-parameter judgment, especially when facing complex and diverse residue types, it can more accurately identify the presence of residues and their impact on conductivity. Therefore, it provides a more solid foundation for subsequent correction of the second pure electrical signal data, thereby ensuring the accuracy of the long-term stability prediction value of the pigment sample, and ultimately improving the overall reliability and effectiveness of the method for analyzing and detecting the conductivity of flake zinc pigment.

[0106] This application further proposes a step for correcting the corresponding second pure electrical signal data in conductive defect regions where residual interference exists, to obtain the corrected second pure electrical signal data. The specific steps include:

[0107] For conductive defect areas with residual interference, the high-frequency reactance component of the interference introduced by the residual under high-frequency alternating current signal is calculated based on the ratio P of the high-frequency impedance intensity to the low-frequency impedance intensity corresponding to the conductive defect area, the difference D between the first phase angle and the second phase angle, and the preset empirical formula.

[0108] Based on the high-frequency electrical components of the interference, the high-frequency reactance component in the high-frequency complex impedance corresponding to the conductive defect region is corrected to obtain the corrected high-frequency complex impedance of the conductive defect region. The corrected high-frequency complex impedance is the corrected second pure electrical signal data.

[0109] Specifically, the "interference high-frequency reactance component" refers to the additional reactance effect generated by residues present in conductive defect regions under the action of high-frequency alternating current signals. This reactance effect is not an inherent conductivity characteristic of the pigment sample itself, but rather a response exhibited by the physical or chemical properties of the residues (such as dielectric constant, conductivity, etc.) in a high-frequency field. Its calculation aims to separate the reactance contribution of this non-pigment sample bulk from the total high-frequency reactance. The "preset empirical formula" can be understood as a mathematical relationship established through experimental data, simulation models, or theoretical derivation. This formula takes the ratio P of high-frequency impedance to low-frequency impedance and the difference D between the first and second phase angles as input parameters, and outputs the interference high-frequency reactance component introduced by residues under high-frequency alternating current signals. For example, this empirical formula can be a polynomial function, an exponential function, or a neural network-based model, whose parameters are obtained by measuring and fitting standard samples with known residue types and contents. Its purpose is to provide an effective means of quantifying the interference effect of residues. In practical applications, correcting the high-frequency reactance component in the high-frequency complex impedance corresponding to the conductive defect region involves subtracting or otherwise adjusting the calculated interference high-frequency reactance component from the high-frequency reactance component X(f) in the original high-frequency complex impedance Z(f) = R(f) + j1X(f). The resulting high-frequency complex impedance is the corrected high-frequency complex impedance, which more accurately reflects the true conductivity characteristics of the pigment sample after removing residual interference. The corrected high-frequency complex impedance is then used as the corrected second clean electrical signal data for subsequent calculations of long-term stability prediction values.

[0110] This application's solution utilizes the ratio P of high-frequency impedance to low-frequency impedance and the difference D between the first and second phase angles, combined with a pre-defined empirical formula, to accurately calculate the high-frequency reactive component of interference introduced by residues under high-frequency alternating current signals. High-frequency and low-frequency signals exhibit different response characteristics to residues; the ratio P and difference D capture this difference, thus serving as key indicators characterizing the degree of residue interference. These indicators are converted into specific interference reactive values ​​using empirical formulas, allowing the high-frequency reactive component in the original high-frequency complex impedance to be specifically corrected. It is precisely this precise quantification and correction mechanism that effectively removes the influence of residues from the second pure electrical signal data, thereby obtaining corrected second pure electrical signal data that more closely approximates the true conductivity of the pigment sample.

[0111] Through the above technical solution, this application eliminates the interference of residues on high-frequency electrical signals by precisely correcting the second pure electrical signal data, making the subsequent prediction values ​​of long-term stability of pigment samples calculated based on this data more accurate and reliable. This correction mechanism significantly improves the accuracy and reliability of conductivity performance analysis and detection, providing a more solid data foundation for the quality control and performance evaluation of flake zinc pigments, thereby enabling a more accurate assessment of the consistency risk level of pigment samples.

[0112] This application further proposes a method for optimizing background denoising. When a probe with a preset high-frequency alternating current signal comes into contact with a pigment sample, in addition to real-time acquisition of the first electrical signal data between the probe and the pigment sample, mechanical data and displacement data of the probe in contact with the pigment sample are also acquired simultaneously in real time.

[0113] Specifically, the steps for performing background denoising on the first electrical signal data to obtain the first clean electrical signal data of the pigment sample include:

[0114] Extract mechanical features from mechanical data, and extract displacement features from displacement data;

[0115] Search for reference background electrical signal data that are associated with the mechanical features and displacement features in a preset background interference association database;

[0116] Based on the found baseline background electrical signal data, the first electrical signal data is subjected to background denoising processing to obtain the first pure electrical signal data of the pigment sample.

[0117] Mechanical data refers to the information on the mechanical forces experienced by the probe during contact with the pigment sample, such as contact pressure, friction, or vibration. Displacement data refers to the information on the positional changes of the probe as it moves or presses into the pigment sample surface, such as probe depth, scanning speed, or path. This data can be acquired in real time using force sensors, displacement sensors, and other devices integrated into the probe or detection system. Mechanical and displacement characteristics are key parameters extracted from the aforementioned mechanical and displacement data, used to characterize the physical state of the probe-sample contact. For example, mechanical characteristics may include average contact force and force fluctuation amplitude; displacement characteristics may include the probe's instantaneous velocity, acceleration, or contact depth. The background interference correlation database is a pre-established database that stores reference background electrical signal data collected under different mechanical and displacement characteristic conditions and their corresponding correlations. The establishment of this database aims to provide an accurate reference for subsequent background denoising processing. Reference background electrical signal data refers to the pure background noise signal collected under specific physical contact conditions on a known defect-free non-conductive region or standard sample.

[0118] This application's solution, by introducing mechanical and displacement data and utilizing a background interference correlation database for background denoising, can more accurately identify and remove background noise. Specifically, since the mechanical state and displacement of the probe when in contact with the pigment sample directly affect the electrical contact characteristics between the probe and the sample, as well as background noise such as mechanical vibration and electromagnetic interference that may be introduced, real-time acquisition of these physical parameters can more comprehensively characterize the current detection environment. After extracting mechanical features from the mechanical data and displacement features from the displacement data, the system can search for the benchmark background electrical signal data that best matches the current detection conditions (i.e., mechanical features and displacement features) in a preset background interference correlation database. Because this benchmark background electrical signal data is obtained under physical conditions similar to the current detection environment, it can more accurately reflect the background noise components present in the current detection process. By subtracting the found benchmark background electrical signal data from the real-time acquired first electrical signal data or processing it through other denoising algorithms, background interference related to the physical contact state can be effectively removed, thereby obtaining a purer first clean electrical signal data.

[0119] Through the above technical solution, this application can significantly improve the accuracy of background noise removal. Because background noise removal is more precise, the first clean electrical signal data of the pigment sample will more accurately reflect the conductivity of the pigment sample itself, reducing errors introduced by external physical interference. Therefore, subsequent defect signal identification will be more accurate, enabling more reliable determination of the number of defect signals and conductive defect areas, and extraction of more representative feature parameters. This provides more reliable basic data for subsequent long-term stability prediction and consistency risk level calculation, thereby improving the overall accuracy and reliability of the entire method for analyzing and detecting the conductivity of flake zinc pigments.

[0120] This application further proposes an optimized scheme, which includes, before the step of performing background denoising processing on the first electrical signal data to obtain the first clean electrical signal data of the pigment sample, the following:

[0121] Provide multiple first probes with different degrees of wear and / or different accumulation of trace residues;

[0122] Perform the following operations for each first probe:

[0123] When the first probe, which is input with a preset high-frequency alternating current signal, comes into contact with a non-conductive standard sample, the reference background electrical signal data between the first probe and the non-conductive standard sample is collected, as well as the reference mechanical data and reference displacement data between the first probe and the non-conductive standard sample are collected simultaneously.

[0124] Extract the benchmark mechanical features from the benchmark mechanical data, and extract the benchmark displacement features from the benchmark displacement data;

[0125] Establish the correlation between the reference background electrical signal data, the reference mechanical characteristics, and the reference displacement characteristics, and add the correlation to the background interference correlation database.

[0126] Specifically, multiple first probes with varying degrees of wear and / or trace residue accumulation are provided to cover various states that probes may encounter in practical use. The first probe can be understood as a sensor used to contact pigment samples or non-conductive standard samples to acquire electrical signals, mechanical data, and displacement data. Different degrees of wear refer to the differences in physical wear on the probe tip due to long-term use, while different amounts of trace residue accumulation refer to the differences in the type and quantity of non-conductive or semi-conductive substances adhering to the probe surface. These differences all affect the contact characteristics of the probe and the acquisition of electrical signals.

[0127] For each first probe, when the probe, with a preset high-frequency alternating current signal input, comes into contact with a non-conductive standard sample, reference background electrical signal data between the first probe and the non-conductive standard sample is collected, along with reference mechanical data and reference displacement data. The non-conductive standard sample refers to a reference sample known to be non-conductive; its function is to ensure that the collected electrical signal data primarily originates from background interference from the probe itself, rather than the conductivity of the sample. The input of the high-frequency alternating current signal helps simulate actual detection conditions and excites the electrical response at the probe-sample interface. The reference background electrical signal data, reference mechanical data, and reference displacement data are raw data used to characterize the electrical and mechanical behavior of the probe in contact with the non-conductive standard sample under specific conditions.

[0128] The purpose of extracting reference mechanical features from reference mechanical data and reference displacement features from reference displacement data is to extract key parameters that represent the probe's condition from the raw data. Reference mechanical features may include contact force and friction, while reference displacement features may include probe indentation depth and lateral sliding distance. These features can quantify the degree of probe wear and residue accumulation.

[0129] A correlation is established between the reference background electrical signal data and the reference mechanical and displacement features, and this correlation is added to the background interference correlation database. This correlation can take the form of a multidimensional lookup table, regression model, or machine learning model, etc., with the aim of mapping the physical state of the probe (characterized by mechanical and displacement features) to its corresponding background electrical signal interference pattern. By storing these correlations in the background interference correlation database, the best-matching reference background electrical signal data can be quickly and accurately found in subsequent actual detection based on the real-time acquired mechanical and displacement data, thereby achieving accurate background denoising.

[0130] This application addresses the problem of insufficient accuracy in background denoising when probe states change by establishing a comprehensive background interference correlation database before performing background denoising on the first electrical signal data. Specifically, since the wear level and trace residue accumulation of the probe affect its mechanical and electrical response when in contact with the sample, this application provides multiple first probes with different wear levels and / or residue accumulations, and systematically collects reference background electrical signal data, reference mechanical data, and reference displacement data under various probe states by contacting them with non-conductive standard samples. Subsequently, features are extracted from these reference data, and correlations are established between the reference background electrical signal data and the reference mechanical and displacement features, storing these relationships in the background interference correlation database. When actually testing pigment samples, the system can collect mechanical and displacement data when the probe contacts the pigment sample in real time, and search for the most matching reference background electrical signal data in the pre-established database based on these real-time features. Because this database covers the background interference patterns of the probe under different states, the most accurate background signal can be selected for subtraction based on the actual state of the probe in subsequent background denoising processing, thereby obtaining purer first electrical signal data.

[0131] Through the above technical solution, this application can significantly improve the accuracy and robustness of background denoising in the method for analyzing and detecting the conductivity of flake zinc pigments. Since the background interference correlation database is pre-established based on various probe states (including different degrees of wear and residue accumulation), even if the probe is worn or has residue on its surface during actual testing, the system can match the benchmark background electrical signal data closest to the current probe state from the database based on the real-time acquired mechanical and displacement characteristics, thereby achieving more accurate background interference subtraction. This effectively avoids the problem of inaccurate background denoising caused by changes in probe state, ensures the authenticity and reliability of the first clean electrical signal data, and thus improves the accuracy of defect signal identification and the precision of pigment sample consistency risk level assessment.

[0132] See Figure 2 The specific embodiments of this application also disclose a system for analyzing and detecting the conductivity of flake zinc pigment, including: a first acquisition module 1, an identification module 2, a second acquisition module 3, and a calculation module 4.

[0133] The first acquisition module 1 is used to acquire the first electrical signal data between the probe and the pigment sample in real time when the probe with a preset high-frequency alternating current signal is in contact with the pigment sample, and to perform background noise reduction processing on the first electrical signal data to obtain the first pure electrical signal data of the pigment sample.

[0134] The identification module 2 is used to identify defect signals in the first pure electrical signal data, determine the number of defect signals and the corresponding conductive defect area in the pigment sample for each defect signal, and extract the feature parameters of each defect signal.

[0135] The second acquisition module 3 is used to acquire, in real time, second electrical signal data between the probe and the conductive defect region when the probe with the input high-frequency alternating current signal comes into contact with the conductive defect region under the condition of applying a set external stimulus to the pigment sample; and to perform background noise reduction processing on the second electrical signal data to obtain the second clean electrical signal data of the conductive defect region.

[0136] The calculation module 4 is used to calculate the predicted long-term stability value of the pigment sample according to preset rules and the second pure electrical signal data of each conductive defect region; and to calculate the consistency risk level of the pigment sample according to the characteristic parameters, the number of defect signals and the predicted long-term stability value.

[0137] The specific steps and principles of the method for analyzing and testing the conductivity of flake zinc pigments have been described in the above embodiments, and will not be repeated here. It should be emphasized that the analysis and testing system proposed in this application provides an operable and deployable solution by instantiating these method steps into specific hardware or software modules.

[0138] This application proposes a system for analyzing and detecting the conductivity of flake zinc pigments. The system acquires and processes the initial electrical signal through a first acquisition module 1, effectively removing background noise and ensuring data purity. The identification module 2 accurately identifies and quantifies conductive defects in the pigment sample, overcoming the limitations of traditional methods in capturing subtle local defects. The second acquisition module 3 performs secondary detection on the defect area under external stimuli. Combined with the comprehensive assessment of long-term stability and consistency risks by the calculation module 4, this system can comprehensively and accurately predict the performance of the pigment in practical applications, thereby significantly improving the accuracy and reliability of the detection.

[0139] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for analyzing and testing the electrical conductivity of flake zinc pigment, characterized in that, Includes the following steps: When a probe with a preset high-frequency alternating current signal comes into contact with a pigment sample, the first electrical signal data between the probe and the pigment sample is collected in real time. The first electrical signal data is subjected to background denoising processing to obtain the first clean electrical signal data of the pigment sample. Defect signal identification is performed on the first pure electrical signal data to determine the number of defect signals and the corresponding conductive defect region in the pigment sample for each defect signal, and the feature parameters of each defect signal are extracted. Under the condition of applying a set external stimulus to the pigment sample, for each conductive defect area, when the probe with the input high-frequency alternating current signal comes into contact with the conductive defect area, the second electrical signal data between the probe and the conductive defect area is collected in real time. Background denoising processing is performed on the second electrical signal data to obtain the second clean electrical signal data of the conductive defect region; Based on preset rules and the second pure electrical signal data of each conductive defect region, the predicted value of the long-term stability of the pigment sample is calculated. The consistency risk level of pigment samples is calculated based on characteristic parameters, the number of defect signals, and long-term stability prediction values. When the probe, which has been given a preset high-frequency alternating current signal, comes into contact with the pigment sample, the following steps are also included: Real-time acquisition of mechanical and displacement data when the probe comes into contact with the pigment sample; The specific steps for performing background denoising on the first electrical signal data to obtain the first clean electrical signal data of the pigment sample include: Extract mechanical features from mechanical data, and extract displacement features from displacement data; Search for reference background electrical signal data that are associated with the mechanical features and displacement features in a preset background interference association database; Based on the found baseline background electrical signal data, the first electrical signal data is subjected to background denoising processing to obtain the first pure electrical signal data of the pigment sample. Before the step of performing background denoising on the first electrical signal data to obtain the first clean electrical signal data of the pigment sample, the following steps are also included: Provide multiple first probes with different degrees of wear and / or different accumulation of trace residues; Perform the following operations for each first probe: When the first probe, which is input with a preset high-frequency alternating current signal, comes into contact with a non-conductive standard sample, the reference background electrical signal data between the first probe and the non-conductive standard sample is collected, as well as the reference mechanical data and reference displacement data between the first probe and the non-conductive standard sample are collected simultaneously. Extract the benchmark mechanical features from the benchmark mechanical data, and extract the benchmark displacement features from the benchmark displacement data; Establish the correlation between the reference background electrical signal data, the reference mechanical characteristics, and the reference displacement characteristics, and add the correlation to the background interference correlation database.

2. The method for analyzing and testing the conductivity of flake zinc pigment according to claim 1, characterized in that, The specific steps for performing background denoising on the first electrical signal data to obtain the first clean electrical signal data of the pigment sample include: When the probe with the input high-frequency alternating current signal comes into contact with the non-conductive area, background electrical signal data between the probe and the non-conductive area is collected. The first electrical signal data is denoised based on the background electrical signal data to obtain the first clean electrical signal data of the pigment sample.

3. The method for analyzing and testing the conductivity of flake zinc pigment according to claim 1, characterized in that, While acquiring the first electrical signal data between the probe and the pigment sample in real time, the coordinates of the contact point of the probe on the pigment sample are recorded synchronously in real time. The steps of identifying defect signals from the first pure electrical signal data, determining the number of defect signals and the corresponding conductive defect region in the pigment sample for each defect signal, and extracting the feature parameters of each defect signal specifically include: The first clean electrical signal data is subjected to three-level discrete wavelet decomposition to find the first peak and valley. The first peak and valley satisfy the condition that the amplitude change exceeds a preset threshold and the duration is less than a preset time threshold; a first peak and valley is a defect signal. The number of defect signals is determined based on the number of the first peak and valley, and the conductive defect region corresponding to each defect signal is determined based on the timestamp of each defect signal in the first electrical signal data and the contact point coordinates corresponding to the timestamp. Extract the following characteristic parameters for each defect signal: peak intensity, peak width, and signal slope.

4. The method for analyzing and testing the conductivity of flake zinc pigment according to claim 3, characterized in that, The steps for calculating the consistency risk level of pigment samples based on characteristic parameters, the number of defect signals, and long-term stability prediction values ​​specifically include: For each defect signal, the initial defect score is calculated based on the following feature parameters extracted from the defect signal: peak intensity, peak width, signal slope, and the preset weight of each feature parameter. The average defect score of the pigment sample is calculated based on the number of defect signals and the initial defect score of each defect signal. The consistency risk level of pigment samples is calculated based on the number of defect signals, the average defect score, and the long-term stability prediction value.

5. The method for analyzing and testing the conductivity of flake zinc pigment according to claim 1, characterized in that, After performing background denoising on the second electrical signal data to obtain the second clean electrical signal data for the conductive defect region, the process further includes: Under the condition of applying a set external stimulus to the pigment sample, for each conductive defect area, when the probe with a preset low-frequency alternating current signal comes into contact with the conductive defect area, the third electrical signal data between the probe and the conductive defect area is collected in real time, and the third electrical signal data is processed by background denoising to obtain the third clean electrical signal data of the conductive defect area. For each conductive defect area, the presence of residual interference is determined based on the corresponding second and third pure electrical signal data. For conductive defect areas with residual interference, the corresponding second pure electrical signal data is corrected to obtain the corrected second pure electrical signal data. The second pure electrical signal data used in the step of calculating the predicted value of the long-term stability of the pigment sample according to the preset rules and the second pure electrical signal data of each conductive defect region is the corrected second pure electrical signal data.

6. The method for analyzing and testing the conductivity of flake zinc pigment according to claim 5, characterized in that, When acquiring the second electrical signal data between the probe and the conductive defect region in real time, the first phase angle relative to the high-frequency AC current signal is acquired simultaneously; when acquiring the third electrical signal data between the probe and the conductive defect region in real time, the second phase angle relative to the low-frequency AC current signal is acquired simultaneously. For each conductive defect region, the specific steps for determining whether residual interference exists based on the corresponding second and third pure electrical signal data include: For each conductive defect region, based on the corresponding second pure electrical signal data, the high-frequency complex impedance Z(f) = R(f) + j1X(f) corresponding to the high-frequency AC current signal is calculated, where R(f) represents the high-frequency resistance component of the second pure electrical signal data, X(f) represents the high-frequency reactance component of the second pure electrical signal data, and j1 represents the imaginary unit of the second pure electrical signal data. Based on the corresponding third pure electrical signal data, calculate the low-frequency complex impedance z(f) = r(f) + j2x(f) corresponding to the low-frequency AC current signal, where r(f) represents the low-frequency resistance component of the third pure electrical signal data, x(f) represents the low-frequency reactance component of the third pure electrical signal data, and j2 represents the imaginary unit of the third pure electrical signal data. Calculate the high-frequency impedance strength based on the high-frequency complex impedance, calculate the low-frequency impedance strength based on the low-frequency complex impedance, and calculate the ratio P between the high-frequency impedance strength and the low-frequency impedance strength. Calculate the difference D between the first phase angle and the second phase angle; Based on the ratio P and the difference D, it is determined whether there is residual interference in the conductive defect region.

7. The method for analyzing and testing the conductivity of flake zinc pigment according to claim 6, characterized in that, The specific steps for correcting the corresponding second pure electrical signal data in conductive defect areas with residual interference to obtain the corrected second pure electrical signal data include: For conductive defect areas with residual interference, the high-frequency reactance component of the interference introduced by the residual under high-frequency alternating current signal is calculated based on the ratio P of the high-frequency impedance intensity to the low-frequency impedance intensity corresponding to the conductive defect area, the difference D between the first phase angle and the second phase angle, and the preset empirical formula. Based on the high-frequency reactance component of the interference, the high-frequency reactance component in the high-frequency complex impedance corresponding to the conductive defect region is corrected to obtain the corrected high-frequency complex impedance of the conductive defect region. The corrected high-frequency complex impedance is the corrected second pure electrical signal data.

8. A system for analyzing and testing the conductivity of flake zinc pigment, used to perform the method for analyzing and testing the conductivity of flake zinc pigment as described in any one of claims 1-7, characterized in that, include: The first acquisition module is used to acquire the first electrical signal data between the probe and the pigment sample in real time when the probe with a preset high-frequency alternating current signal is in contact with the pigment sample, and to perform background noise reduction processing on the first electrical signal data to obtain the first pure electrical signal data of the pigment sample. The identification module is used to identify defect signals in the first pure electrical signal data, determine the number of defect signals and the corresponding conductive defect area of ​​each defect signal in the pigment sample, and extract the feature parameters of each defect signal. The second acquisition module is used to acquire, in real time, the second electrical signal data between the probe and the conductive defect area when the probe with the input high-frequency alternating current signal comes into contact with the conductive defect area under the condition of applying a set external stimulus to the pigment sample. Background denoising processing is performed on the second electrical signal data to obtain the second clean electrical signal data of the conductive defect region; The calculation module is used to calculate the predicted value of the long-term stability of the pigment sample based on preset rules and the second pure electrical signal data of each conductive defect region. The consistency risk level of pigment samples is calculated based on characteristic parameters, the number of defect signals, and long-term stability prediction values.

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