Titanium equipment pitting corrosion early warning method and system based on electrochemical impedance spectroscopy analysis

Abstract

The application relates to the technical field of corrosion monitoring, and discloses a titanium equipment point corrosion early warning method and system based on electrochemical impedance spectrum analysis, which comprises the following steps: collecting an electrochemical impedance spectrum original response signal, and performing spectrum regularization on the electrochemical impedance spectrum original response signal to obtain an effective characteristic section; performing decoupling analysis on the real part and the imaginary part of the effective characteristic section to obtain decoupling component data, and performing phase correction on the decoupling component data to obtain calibrated components; taking a capacitive reactance characteristic in the calibrated components as the center and an impedance characteristic in the calibrated components as the edge, an impedance characteristic spectrum is constructed; performing precursor targeted identification on the impedance characteristic spectrum to obtain a point corrosion precursor characteristic; performing development gradient deduction on the point corrosion precursor characteristic to obtain point corrosion evolution data; performing hierarchical determination on the point corrosion evolution data to obtain a risk level, and generating early warning information according to the risk level; and the application can improve the efficiency of titanium equipment point corrosion early warning.

Description

Technical Field

[0001] This invention relates to the field of corrosion monitoring technology, and in particular to a method and system for early warning of pitting corrosion in titanium equipment based on electrochemical impedance spectroscopy analysis. Background Technology

[0002] In the operational monitoring of titanium equipment, the detection and analysis of pitting corrosion largely rely on traditional electrochemical impedance spectroscopy (EIS). This method lacks refined spectroscopic regularization techniques for processing the raw response signal, making it difficult to effectively extract valuable characteristic segments. Furthermore, the analysis of the real and imaginary parts of the impedance spectrum suffers from coupling interference, and the accuracy of phase correction is insufficient, failing to accurately capture the precursory features of pitting corrosion in titanium equipment. This results in low reliability of pitting corrosion-related feature identification. Current technologies lack a systematic method for extrapolating the evolution of pitting corrosion in titanium equipment, relying solely on single impedance characteristics for risk assessment without quantitative analysis of the development gradient of corrosion pits. The determination of risk levels lacks scientific quantitative indicators and matching logic, leading to a lag in the prediction of pitting corrosion risk in titanium equipment and limiting the reference value of early warning results.

[0003] Existing titanium equipment monitoring systems suffer from poor interoperability among their functional modules, failing to achieve integrated processing from electrochemical impedance spectroscopy signal acquisition to early warning information generation. The signal processing, feature analysis, and risk assessment processes are disconnected, reducing the overall efficiency of pitting corrosion early warning. Furthermore, early warning information generation relies solely on simple feature comparisons, lacking the integration of pitting corrosion evolution data to develop targeted treatment recommendations, rendering the warnings impractical and unhelpful. Therefore, improving the accuracy and intelligence of titanium equipment pitting corrosion early warning systems has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides a method and system for early warning of pitting corrosion in titanium equipment based on electrochemical impedance spectroscopy analysis, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, this invention provides a method for early warning of pitting corrosion in titanium equipment based on electrochemical impedance spectroscopy analysis, comprising:

[0006] Step a: Acquire the raw response signal of the electrochemical impedance spectroscopy of the titanium device, and perform spectroscopic regularization on the raw response signal of the electrochemical impedance spectroscopy to obtain the effective characteristic segment of the titanium device;

[0007] Step b: Decouple and analyze the real and imaginary parts of the effective feature segments to obtain the decoupled component data of the titanium device, and perform phase correction on the decoupled component data to obtain the calibrated component of the titanium device;

[0008] Step c: Construct the impedance characteristic spectrum of the titanium device with the capacitive reactance characteristic in the calibrated component as the center and the impedance characteristic in the calibrated component as the edge;

[0009] Step d: Perform precursor target identification on the impedance characteristic spectrum to obtain the pitting precursor characteristics of the titanium equipment;

[0010] Step e: Perform development gradient extrapolation on the pitting precursor features to obtain pitting evolution data of the titanium equipment;

[0011] Step f: Perform a hierarchical determination on the pitting evolution data to obtain the risk level of the titanium equipment, and generate early warning information for the titanium equipment based on the risk level.

[0012] In a preferred embodiment, the process of acquiring the raw electrochemical impedance spectroscopy response signal of the titanium device and performing spectroscopic normalization on the raw electrochemical impedance spectroscopy response signal to obtain the effective characteristic segment of the titanium device includes:

[0013] The raw response signal of the electrochemical impedance spectroscopy was time-sliced ​​to obtain the preprocessed impedance spectroscopy data of the titanium device.

[0014] The preprocessed impedance spectrum data is scanned for spectral energy distribution to obtain the candidate characteristic spectral range of the titanium device;

[0015] Based on the candidate feature spectrum range, the phase angle consistency of the preprocessed impedance spectrum data is checked to obtain the pure feature spectrum data of the titanium device;

[0016] The effective feature segments of the titanium device are obtained by performing feature localization on the pure feature spectrum data.

[0017] In a preferred embodiment, the step of decoupling and analyzing the real and imaginary parts of the effective feature segments to obtain decoupled component data of the titanium device, and performing phase correction on the decoupled component data to obtain the calibrated components of the titanium device, includes:

[0018] Orthogonal vector decomposition is performed on the real and imaginary data in the effective feature segment to obtain the initial resistive component data and the initial capacitive component data of the titanium device;

[0019] Cross-band correlation scanning is performed on the initial resistive component data and the initial capacitive component data to obtain the abnormal frequency points of the titanium device;

[0020] Based on the abnormal frequency points, the initial resistive component data and the initial capacitive component data are extracted in a targeted manner to obtain the resistive data and capacitive data to be corrected for the titanium device.

[0021] Based on the phase relationship between the adjacent normal frequency points of the abnormal frequency point, the resistive data and capacitive data to be corrected are compensated and corrected to obtain the corrected resistive data and corrected capacitive data of the titanium device.

[0022] The corrected resistive data, the corrected capacitive data, the initial resistive component data, and the initial capacitive component data are integrated to obtain the calibrated components of the titanium device.

[0023] In a preferred embodiment, the step of performing a cross-band correlation scan on the initial resistive component data and the initial capacitive component data to obtain the abnormal frequency points of the titanium device includes:

[0024] The initial resistive component data and the initial capacitive component data are reconstructed in time series to obtain the resistive trajectory sequence and the capacitive trajectory sequence of the titanium device;

[0025] The resistive trajectory sequence and the capacitive trajectory sequence are segmented and differencing to obtain the segmented resistive deviation sequence and the segmented capacitive deviation sequence of the titanium device;

[0026] Under the same coordinate system, the same sign count is obtained by performing same sign statistics on the segment resistive deviation sequence and the segment capacitive deviation sequence.

[0027] The center frequency point whose count of the same frequency point of the segment is lower than the count of the same frequency point of its adjacent segment is regarded as the abnormal frequency point of the titanium device.

[0028] In a preferred embodiment, constructing the impedance characteristic spectrum of the titanium device, centered on the capacitive reactance characteristic of the calibrated component and with the impedance characteristic of the calibrated component as its edges, includes:

[0029] Data points related to the double-layer capacitance of the electrode surface are extracted from the calibrated components and marked as capacitive reactance characteristic points. Data points related to the charge transfer resistance of the electrode reaction are extracted from the calibrated components and marked as impedance characteristic points.

[0030] The coordinates of the capacitive reactance feature points are identified to determine their coordinate positions.

[0031] Using the capacitive reactance feature point as the center, a circumferential search is performed on the impedance feature point to obtain the feature point pair of the titanium device;

[0032] By performing topology construction on the coordinate positions and the feature point pairs, the impedance characteristic spectrum of the titanium device is obtained.

[0033] In a preferred embodiment, the step of performing precursor targeting identification on the impedance characteristic spectrum to obtain the pitting precursor characteristics of the titanium device includes:

[0034] Based on the impedance feature map, the capacitive reactance feature points are extracted to obtain the surrounding impedance feature points of the capacitive reactance feature points.

[0035] By performing spatial distribution morphology analysis on the surrounding impedance characteristic points, the precursor response region of the titanium device is obtained.

[0036] Based on the precursor response region, the imaginary part values ​​of the surrounding impedance feature points are sequentially fitted to obtain the imaginary part change sequence of the titanium device.

[0037] Abrupt change identification is performed on the imaginary part change sequence to obtain the position of the sudden drop sequence of the imaginary part change sequence, and the frequency point of the sudden drop sequence position is used as the precursor characteristic frequency point of the titanium device;

[0038] Obtain the complex plane vector distance between the impedance characteristic point of the precursor characteristic frequency and the center node of the precursor response region, and use the complex plane vector distance as the precursor intensity value of the precursor characteristic frequency.

[0039] The precursor response region, the precursor characteristic frequency point, and the precursor intensity value are used as the pitting precursor characteristics of the titanium equipment.

[0040] In a preferred embodiment, the step of performing development gradient extrapolation on the pitting precursor features to obtain pitting evolution data of the titanium equipment includes:

[0041] Based on the precursor response zone of the titanium device, the precursor intensity values ​​are arranged in time sequence to obtain the precursor intensity time series of the precursor response zone.

[0042] Based on the precursor intensity time series, the precursor intensity values ​​are compared between adjacent time periods to obtain the intensity change gradient of the titanium device;

[0043] By performing a lateral comparison of the intensity change gradient, the main morphological region of the titanium device is obtained;

[0044] Spectral analysis is performed on the main evolution region to obtain the frequency migration path of the main evolution region;

[0045] By correlating and integrating the intensity change gradient with the frequency migration path, the pitting evolution data of the titanium device is obtained.

[0046] In a preferred embodiment, the step of performing hierarchical determination on the pitting corrosion evolution data to obtain the risk level of the titanium equipment, and generating early warning information for the titanium equipment based on the risk level, includes:

[0047] The intensity change gradient value and frequency migration path length are extracted from the pitting corrosion evolution data to obtain the evolution feature vector of the titanium device;

[0048] The evolution feature vector is compared with a preset baseline risk level template to obtain the risk level of the titanium device.

[0049] Based on the risk level, determine the warning color code and warning handling suggestion text for the titanium equipment;

[0050] The warning color code and the warning handling suggestion text are associated and bound together to obtain the warning information of the titanium device.

[0051] In a preferred embodiment, the step of comparing the evolutionary feature vector with a preset baseline risk level template to obtain the risk level of the titanium device includes:

[0052] Extract the intensity change gradient value and frequency point migration path length from the evolution feature vector, and use the intensity change gradient value as the first comparison index and the frequency point migration path length as the second comparison index.

[0053] Based on the intensity change gradient value and the frequency migration path length, the intensity change gradient value and the frequency migration path length in the preset benchmark risk level template are extracted to obtain the standard intensity change gradient value and standard frequency migration path length of the titanium equipment.

[0054] Based on the first comparison index, the second comparison index, the standard intensity change gradient value, and the standard frequency point migration path length, the matching degree between the evolutionary feature vector and the preset baseline risk level template is calculated. The formula for calculating the matching degree is as follows:

[0055] ;

[0056] in, The matching degree, The first comparison indicator is used. The standard intensity variation gradient value, The second comparison indicator is... The standard frequency migration path length is... These are the weighting coefficients for the intensity change gradient. The weighting coefficients for the frequency migration path length. It is a non-zero minimum constant.

[0057] To address the aforementioned problems, this invention also provides a pitting corrosion early warning system for titanium equipment based on electrochemical impedance spectroscopy analysis, the system comprising:

[0058] The acquisition and normalization module is used to acquire the original response signal of the electrochemical impedance spectroscopy of the titanium device and perform spectroscopic normalization on the original response signal of the electrochemical impedance spectroscopy to obtain the effective feature segment of the titanium device.

[0059] The phase calibration module is used to decouple and analyze the real and imaginary parts of the effective feature segment to obtain the decoupled component data of the titanium device, and to perform phase correction on the decoupled component data to obtain the calibrated component of the titanium device.

[0060] An impedance characteristic spectrum construction module is used to construct the impedance characteristic spectrum of the titanium device with the capacitive reactance characteristic in the calibrated component as the center and the impedance characteristic in the calibrated component as the edge.

[0061] A precursor targeting identification module is used to perform precursor targeting identification on the impedance characteristic spectrum to obtain the pitting precursor characteristics of the titanium equipment.

[0062] The evolution gradient extrapolation module is used to extrapolate the development gradient of the pitting precursor features to obtain the pitting evolution data of the titanium equipment.

[0063] The early warning generation module is used to perform hierarchical determination on the pitting evolution data to obtain the risk level of the titanium equipment, and generate early warning information for the titanium equipment based on the risk level.

[0064] Compared with the prior art, the present invention has the following beneficial effects:

[0065] 1. This invention performs spectral regularization, decoupling analysis of real and imaginary parts, and precise phase correction on the raw electrochemical impedance spectroscopy signals of titanium equipment. Based on capacitive reactance and impedance characteristics, it constructs a unique impedance feature spectrum. Then, through precursor targeted identification, pitting evolution gradient deduction, and scientific risk level determination, it forms a complete and refined pitting early warning technology system. This significantly improves the accuracy of identifying precursor features of pitting corrosion in titanium equipment, making the extraction and analysis of pitting corrosion-related features more targeted and effective. At the same time, it enhances the scientific nature of pitting evolution trend deduction, accurately captures the development law of pitting corrosion in titanium equipment, and achieves accurate assessment of pitting corrosion risk in titanium equipment.

[0066] 2. This invention establishes a corresponding pitting corrosion early warning system for titanium equipment. Through the coordinated operation of various functional modules, it deeply integrates electrochemical impedance spectroscopy analysis with pitting corrosion early warning, effectively improving the intelligence and automation level of pitting corrosion early warning for titanium equipment. It achieves integrated processing from signal acquisition to early warning information generation, significantly improving the efficiency of pitting corrosion early warning. Furthermore, based on quantified evolutionary feature vectors for risk level determination, the classification of pitting corrosion risk for titanium equipment becomes more objective and reasonable. The generated early warning information can provide accurate basis for the maintenance and management of titanium equipment, enhancing the safety assurance capability during the operation of titanium equipment. Attached Figure Description

[0067] Figure 1 This is a schematic flowchart of a method for early warning of pitting corrosion in titanium equipment based on electrochemical impedance spectroscopy analysis, provided in an embodiment of the present invention.

[0068] Figure 2 This is a functional block diagram of a titanium equipment pitting early warning system based on electrochemical impedance spectroscopy analysis, provided in an embodiment of the present invention.

[0069] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0070] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0071] This application provides a method for early warning of pitting corrosion in titanium equipment based on electrochemical impedance spectroscopy (EIS). The executing entity of this method includes, but is not limited to, at least one electronic device configured to execute the method provided in this application, such as a server or a terminal. In other words, the method can be executed by software or hardware installed on a terminal or server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cluster of cloud servers. The server can be an independent server or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0072] Reference Figure 1 The diagram shown is a flowchart illustrating a method for early warning of pitting corrosion in titanium equipment based on electrochemical impedance spectroscopy (EIS) according to an embodiment of the present invention. In this embodiment, the method for early warning of pitting corrosion in titanium equipment based on EIS includes:

[0073] Step a: Acquire the raw response signal of the electrochemical impedance spectroscopy of the titanium device, and perform spectroscopic regularization on the raw response signal of the electrochemical impedance spectroscopy to obtain the effective characteristic segment of the titanium device;

[0074] In this embodiment of the invention, the process of acquiring the raw electrochemical impedance spectroscopy response signal of the titanium device and performing spectroscopic normalization on the raw electrochemical impedance spectroscopy response signal to obtain the effective feature segment of the titanium device includes:

[0075] The raw response signal of the electrochemical impedance spectroscopy was time-sliced ​​to obtain the preprocessed impedance spectroscopy data of the titanium device.

[0076] The preprocessed impedance spectrum data is scanned for spectral energy distribution to obtain the candidate characteristic spectral range of the titanium device;

[0077] Based on the candidate feature spectrum range, the phase angle consistency of the preprocessed impedance spectrum data is checked to obtain the pure feature spectrum data of the titanium device;

[0078] The effective feature segments of the titanium device are obtained by performing feature localization on the pure feature spectrum data.

[0079] The raw response signal of electrochemical impedance spectroscopy is time-sliced ​​according to a preset fixed time length. The continuous raw response signal is divided into multiple independent time segments with clear boundaries. Each time segment contains complete electrochemical impedance amplitude and phase response information. At the same time, baseline alignment processing is performed on each time segment to eliminate the baseline drift effect generated during signal acquisition, thus obtaining the preprocessed impedance spectrum data of the titanium device.

[0080] The preprocessed impedance spectrum data is traversed through all covered frequency ranges. The cumulative energy value of the signal in each frequency range is calculated segment by segment with a fixed frequency step size. The frequency ranges whose cumulative energy value reaches the preset judgment threshold are marked as feature candidate ranges. All marked feature candidate ranges are summarized and the overlapping parts of the ranges are removed to obtain the candidate feature spectrum ranges of the titanium device.

[0081] Based on the candidate feature spectrum range, the phase angle of each sampling point in the corresponding frequency range in the preprocessed impedance spectrum data is compared point by point. Sampling points whose phase angle deviates from the preset consistency range are marked as interference signals and removed. Sampling points with stable and consistent phase angles are retained. At the same time, the retained sampling points are smoothed to eliminate small fluctuations, thus obtaining the pure feature spectrum data of the titanium device.

[0082] The peak and valley values ​​of the pure feature spectrum data are identified by traversing the spectrum to locate the extreme points of the feature signal. The extreme points are then used as a reference to expand to both sides to determine the complete start and end boundaries of the feature signal. The identified complete feature signal intervals are then defined as independent feature intervals. At the same time, the feature intervals are deduplicated and merged to obtain the effective feature segments of the titanium equipment.

[0083] The beneficial effects include: segmented and regularized preprocessing of the original signal by aligning time-series slices with the baseline, effectively eliminating baseline drift interference and providing a standardized data foundation for subsequent feature extraction; accurately locking potential feature intervals through spectral energy distribution scanning, avoiding redundant processing of invalid frequency intervals; eliminating noise interference and improving the purity of feature data through phase angle consistency verification and smoothing; and accurately delineating effective feature segments through extreme point location and boundary expansion. These effects comprehensively improve the integrity, accuracy, and anti-interference capability of electrochemical impedance spectroscopy feature extraction for titanium equipment, providing high-quality feature data support for subsequent pitting corrosion precursor identification and risk assessment.

[0084] Step b: Decouple and analyze the real and imaginary parts of the effective feature segments to obtain the decoupled component data of the titanium device, and perform phase correction on the decoupled component data to obtain the calibrated component of the titanium device;

[0085] In this embodiment of the invention, the step of decoupling and analyzing the real and imaginary parts of the effective feature segment to obtain the decoupled component data of the titanium device, and performing phase correction on the decoupled component data to obtain the calibrated component of the titanium device, includes:

[0086] Orthogonal vector decomposition is performed on the real and imaginary data in the effective feature segment to obtain the initial resistive component data and the initial capacitive component data of the titanium device;

[0087] Cross-band correlation scanning is performed on the initial resistive component data and the initial capacitive component data to obtain the abnormal frequency points of the titanium device;

[0088] Based on the abnormal frequency points, the initial resistive component data and the initial capacitive component data are extracted in a targeted manner to obtain the resistive data and capacitive data to be corrected for the titanium device.

[0089] Based on the phase relationship between the adjacent normal frequency points of the abnormal frequency point, the resistive data and capacitive data to be corrected are compensated and corrected to obtain the corrected resistive data and corrected capacitive data of the titanium device.

[0090] The corrected resistive data, the corrected capacitive data, the initial resistive component data, and the initial capacitive component data are integrated to obtain the calibrated components of the titanium device.

[0091] The step of performing a cross-band correlation scan on the initial resistive component data and the initial capacitive component data to obtain the abnormal frequency points of the titanium device includes:

[0092] The initial resistive component data and the initial capacitive component data are reconstructed in time series to obtain the resistive trajectory sequence and the capacitive trajectory sequence of the titanium device;

[0093] The resistive trajectory sequence and the capacitive trajectory sequence are segmented and differencing to obtain the segmented resistive deviation sequence and the segmented capacitive deviation sequence of the titanium device;

[0094] Under the same coordinate system, the same sign count is obtained by performing same sign statistics on the segment resistive deviation sequence and the segment capacitive deviation sequence.

[0095] The center frequency point whose count of the same frequency point of the segment is lower than the count of the same frequency point of its adjacent segment is regarded as the abnormal frequency point of the titanium device.

[0096] The real data is projected along a preset reference direction to obtain the initial resistive component data, and the imaginary data is projected along an orthogonal direction perpendicular to the reference direction to obtain the initial capacitive component data. During the projection process, the frequency correspondence of each data point is kept unchanged, thus obtaining the initial resistive component data and the initial capacitive component data of the titanium device.

[0097] The initial resistive component data are arranged sequentially from low to high frequency to form a continuous resistive trajectory sequence, and the initial capacitive component data are arranged sequentially according to the same frequency to form a continuous capacitive trajectory sequence. Each data point in the trajectory sequence corresponds to a unique frequency point, thus obtaining the resistive trajectory sequence and capacitive trajectory sequence of the titanium device.

[0098] The resistive trajectory sequence is divided into multiple equal-length frequency segments. The difference between adjacent data points in each segment is calculated to form a segment resistive deviation sequence. The capacitive trajectory sequence is divided into equal-length frequency segments according to the same segment division method. The difference between adjacent data points in each segment is calculated to form a segment capacitive deviation sequence. Each value in the deviation sequence represents the change amplitude of the component in the corresponding segment. Thus, the segment resistive deviation sequence and segment capacitive deviation sequence of the titanium device are obtained.

[0099] Under the same coordinate system, the resistive deviation sequence and the capacitive deviation sequence of the segment are counted for the same sign. All deviation data points in each frequency segment are traversed, and the number of data points with the same sign for resistive and capacitive deviations is counted. This number is recorded as the segment same-sign frequency point count. During the statistical process, the division boundary of the frequency segment remains unchanged, and the segment same-sign frequency point count of the titanium device is obtained.

[0100] The center frequency point of the segment whose same frequency point count is lower than that of its adjacent segment is taken as the abnormal frequency point of the titanium device. The same frequency point count values ​​of the current segment are compared with those of the left and right adjacent segments. If the count value of the current segment is lower than the count values ​​of the adjacent segments on both sides, the center frequency point of the segment is marked as an abnormal frequency point. The frequency position information of the abnormal frequency point is retained during the marking process to obtain the abnormal frequency point of the titanium device.

[0101] Locate the corresponding position of the abnormal frequency point in the initial resistive component data and the initial capacitive component data, extract the resistive and capacitive data corresponding to the abnormal frequency point and the preset number of frequency points before and after it, keep the frequency order of the data unchanged during the extraction process, and obtain the resistive data and capacitive data to be corrected for the titanium equipment.

[0102] Using the phase of the normal frequency points on both sides of the abnormal frequency point as a reference, the expected phase change trend of the data in the abnormal frequency point area is calculated. According to this trend, the resistive data and capacitive data to be corrected corresponding to the abnormal frequency point are numerically adjusted so that the phase of the adjusted data remains continuous and smooth with the phase of the adjacent normal frequency point, thus obtaining the corrected resistive data and corrected capacitive data of the titanium equipment.

[0103] The corrected resistive data, corrected capacitive data, initial resistive component data, and initial capacitive component data are integrated collaboratively. The data in the corresponding abnormal frequency region of the initial resistive component data are replaced with the corrected resistive data, and the data in the corresponding abnormal frequency region of the initial capacitive component data are replaced with the corrected capacitive data. After integration, the frequency correspondence and sequence integrity of all data points remain unchanged, and the calibrated components of the titanium device are obtained.

[0104] The beneficial effects are as follows: the real and imaginary parts of the data are initially decoupled through orthogonal vector decomposition, the core features of resistive and capacitive components are accurately separated, the deviation sequence is constructed through time-series reconstruction and piecewise difference to intuitively reflect the local anomalies of component changes, the abnormal frequency points are accurately located through same-sign statistics and neighborhood comparison to avoid misjudgment and omission, the abnormal interference is eliminated through targeted extraction and phase compensation correction to improve the accuracy and stability of component data, and the integrity and continuity of the data sequence are maintained through collaborative integration, providing high-quality decoupled calibration data for subsequent pitting precursor feature extraction.

[0105] Step c: Construct the impedance characteristic spectrum of the titanium device with the capacitive reactance characteristic in the calibrated component as the center and the impedance characteristic in the calibrated component as the edge;

[0106] In this embodiment of the invention, constructing the impedance characteristic spectrum of the titanium device, centered on the capacitive reactance characteristic of the calibrated component and with the impedance characteristic of the calibrated component as the edges, includes:

[0107] Data points related to the double-layer capacitance of the electrode surface are extracted from the calibrated components and marked as capacitive reactance characteristic points. Data points related to the charge transfer resistance of the electrode reaction are extracted from the calibrated components and marked as impedance characteristic points.

[0108] The coordinates of the capacitive reactance feature points are identified to determine their coordinate positions.

[0109] Using the capacitive reactance feature point as the center, a circumferential search is performed on the impedance feature point to obtain the feature point pair of the titanium device;

[0110] By performing topology construction on the coordinate positions and the feature point pairs, the impedance characteristic spectrum of the titanium device is obtained.

[0111] Data points related to the double-layer capacitance of the electrode surface are extracted from the calibrated components and marked as capacitive reactance feature points. Data points related to the charge transfer resistance of the electrode reaction are extracted from the calibrated components and marked as impedance feature points. During the extraction process, based on the physical characteristics of the electrochemical impedance spectroscopy, all data points directly related to the double-layer capacitance of the electrode surface are accurately screened, and a unique identifier for capacitive reactance feature points is added to each selected data point. At the same time, based on the physical characteristics of the electrochemical impedance spectroscopy, all data points directly related to the charge transfer resistance of the electrode reaction are accurately screened, and a unique identifier for impedance feature points is added to each selected data point. This ensures that the extraction of capacitive reactance feature points and impedance feature points is complete and without misselection, and retains the frequency and numerical information corresponding to all data points.

[0112] Based on the complex plane analysis system of electrochemical impedance spectroscopy, the real part of the capacitive reactance feature point is used as the abscissa and the imaginary part is used as the ordinate. The abscissa and ordinate values ​​of each capacitive reactance feature point are read and calibrated in turn, and the specific values ​​of the abscissa and ordinate corresponding to each capacitive reactance feature point are recorded to form a dataset containing the coordinate information of all capacitive reactance feature points, so as to accurately determine the unique coordinate position of each capacitive reactance feature point in the complex plane.

[0113] For each capacitive reactance feature point, a fixed search radius is set with its coordinate position in the complex plane as the center. A global search is performed on all impedance feature points within the search radius in the complex plane. The retrieved impedance feature points are paired one by one with the corresponding capacitive reactance feature points at the center of the circle to form feature point pairs consisting of one capacitive reactance feature point and several impedance feature points. During the search process, the coordinates and numerical information of each data point in the feature point pair are retained to ensure that the pairing relationship of the feature point pairs is clear and traceable.

[0114] Based on the determined coordinates of the capacitive reactance feature points, the points of all capacitive reactance feature points are first drawn in the complex plane. Then, according to the pairing relationship of feature point pairs, each capacitive reactance feature point is connected to all its paired impedance feature points. During the connection process, the relationship between the lines and the corresponding feature point pairs is maintained. At the same time, the physical characteristic attributes and key numerical information corresponding to each feature point are marked in the graph. A complex plane topology structure with capacitive reactance feature points as the core, impedance feature points as extensions, and feature point pair connections as the framework is constructed, ultimately forming a complete impedance feature graph of titanium equipment.

[0115] The beneficial effects are as follows: by accurately extracting and marking capacitive and impedance feature points, the core elements of spectrum construction are clarified, ensuring the precise correspondence between feature points and the electrochemical and physical characteristics of titanium devices. The complex plane position of capacitive feature points is determined by coordinate identification, laying a precise spatial foundation for spectrum construction. Feature point pairs are formed by surrounding retrieval, establishing the correlation between capacitive and impedance feature points. Impedance feature spectrum is formed by topology construction, transforming abstract electrochemical impedance data into intuitive and visual spectrum, clearly presenting the correlation law of electrochemical features on the electrode surface of titanium devices. This provides an intuitive and accurate analytical carrier for subsequent targeted identification of pitting corrosion precursors, improving the efficiency and accuracy of subsequent feature identification.

[0116] Step d: Perform precursor target identification on the impedance characteristic spectrum to obtain the pitting precursor characteristics of the titanium equipment;

[0117] In this embodiment of the invention, the step of performing precursor targeting identification on the impedance characteristic spectrum to obtain the pitting precursor characteristics of the titanium equipment includes:

[0118] Based on the impedance feature map, the capacitive reactance feature points are extracted to obtain the surrounding impedance feature points of the capacitive reactance feature points.

[0119] By performing spatial distribution morphology analysis on the surrounding impedance characteristic points, the precursor response region of the titanium device is obtained.

[0120] Based on the precursor response region, the imaginary part values ​​of the surrounding impedance feature points are sequentially fitted to obtain the imaginary part change sequence of the titanium device.

[0121] Abrupt change identification is performed on the imaginary part change sequence to obtain the position of the sudden drop sequence of the imaginary part change sequence, and the frequency point of the sudden drop sequence position is used as the precursor characteristic frequency point of the titanium device;

[0122] Obtain the complex plane vector distance between the impedance characteristic point of the precursor characteristic frequency and the center node of the precursor response region, and use the complex plane vector distance as the precursor intensity value of the precursor characteristic frequency.

[0123] The precursor response region, the precursor characteristic frequency point, and the precursor intensity value are used as the pitting precursor characteristics of the titanium equipment.

[0124] Taking each capacitive reactance feature point in the impedance feature spectrum as the core, all impedance feature points that are connected to the capacitive reactance feature point are extracted in the whole domain according to the range of the connection between feature point pairs in the spectrum. During the extraction process, the coordinate position, imaginary part value and corresponding frequency information of each impedance feature point are retained. At the same time, impedance feature points that are not directly related to the capacitive reactance feature point are removed to ensure that all extracted impedance feature points are the surrounding impedance feature points of the capacitive reactance feature point.

[0125] Within the complex plane of the impedance characteristic spectrum, cluster analysis of the spatial location of all extracted surrounding impedance characteristic points is performed. Based on the coordinate distribution density of each impedance characteristic point, a continuous distribution area is defined. The area where the distribution density reaches a preset threshold and the spatial location of the surrounding impedance characteristic points is continuous is defined as an independent analysis area. At the same time, the boundary of this area is calibrated to clarify the range of the horizontal and vertical coordinates of the area. The calibrated area is determined as the precursor response area of ​​the titanium device.

[0126] First, extract the imaginary part values ​​of all surrounding impedance characteristic points within the precursor response region. Then, arrange the imaginary part values ​​sequentially according to the frequency corresponding to each impedance characteristic point from low to high. Perform continuous curve fitting based on the changing trend of the arranged imaginary part values ​​so that the fitted curve can fully reflect the law of change of imaginary part values ​​with frequency. The sequence of imaginary part values ​​sorted by frequency and fitted is determined as the imaginary part change sequence of the titanium device.

[0127] Abrupt changes are identified in the imaginary part variation sequence to obtain the position of the sudden drop sequence. The frequency point of the sudden drop sequence position is used as the precursor characteristic frequency point of the titanium device. All numerical nodes in the imaginary part variation sequence are traversed, and the change difference between adjacent numerical nodes is calculated. Numerical nodes with negative differences and change amplitudes reaching a preset threshold are identified as sudden drop nodes. The specific arrangement position of the sudden drop node in the imaginary part variation sequence is determined. This position is the sudden drop sequence position of the imaginary part variation sequence. Then, the frequency point corresponding to the sudden drop sequence position is matched, and this frequency point is directly determined as the precursor characteristic frequency point of the titanium device.

[0128] Obtain the complex plane vector distance between the impedance characteristic point of the precursor characteristic frequency and the center node of the precursor response region, and use the complex plane vector distance as the precursor intensity value of the precursor characteristic frequency. First, determine the coordinates of the center node of the precursor response region in the complex plane, and then extract the complex plane coordinates of the impedance characteristic point corresponding to the precursor characteristic frequency. Taking the coordinates of the center node as the starting point and the coordinates of the impedance characteristic point corresponding to the precursor characteristic frequency as the ending point, calculate the complex plane vector distance between the two points. During the calculation, retain the magnitude value of the vector and directly use the magnitude value as the precursor intensity value of the precursor characteristic frequency.

[0129] The precursor response zone, precursor characteristic frequency point, and precursor intensity value are used as the precursor features of pitting corrosion in titanium equipment. The boundary coordinates and spatial range information of the precursor response zone, the specific frequency value of the precursor characteristic frequency point, and the specific modulus value of the precursor intensity value are integrated and bound together to form a complete set of feature information. This set of information is the precursor feature of pitting corrosion in titanium equipment. At the same time, the original data and corresponding relationships of each type of information are retained to ensure the integrity and traceability of the feature information.

[0130] The beneficial effects are as follows: by precisely locking the impedance feature points around the capacitive reactance feature points through surround extraction, a clear analysis range is defined for the identification of pitting precursors; by delineating the precursor response zone through spatial distribution morphology analysis, the regionalized and precise localization of pitting precursor features is achieved; by obtaining the imaginary part change sequence through sequence fitting, the variation law of impedance features with frequency is clearly presented, providing a regular data analysis carrier for abrupt change identification; by accurately capturing the precursor feature frequency points through abrupt change identification, the key frequency of pitting precursors is located; by calculating the vector distance of the complex plane, the precursor intensity value is obtained, realizing the quantitative characterization of the degree of pitting precursors; and finally, the integrated pitting precursor features contain multi-dimensional information of region, frequency, and intensity, providing a comprehensive, accurate, and quantitative feature basis for subsequent pitting evolution gradient deduction, greatly improving the pertinence and effectiveness of pitting precursor identification.

[0131] Step e: Perform development gradient extrapolation on the pitting precursor features to obtain pitting evolution data of the titanium equipment;

[0132] In this embodiment of the invention, the step of performing development gradient extrapolation on the pitting precursor features to obtain pitting evolution data of the titanium equipment includes:

[0133] Based on the precursor response zone of the titanium device, the precursor intensity values ​​are arranged in time sequence to obtain the precursor intensity time series of the precursor response zone.

[0134] Based on the precursor intensity time series, the precursor intensity values ​​are compared between adjacent time periods to obtain the intensity change gradient of the titanium device;

[0135] By performing a lateral comparison of the intensity change gradient, the main morphological region of the titanium device is obtained;

[0136] Spectral analysis is performed on the main evolution region to obtain the frequency migration path of the main evolution region;

[0137] By correlating and integrating the intensity change gradient with the frequency migration path, the pitting evolution data of the titanium device is obtained.

[0138] Based on the spatial range and characteristic identifiers of the precursor response area, all precursor intensity values ​​obtained at different acquisition times within this area are extracted. All precursor intensity values ​​are arranged sequentially according to the acquisition order from earliest to latest time. During the arrangement process, the acquisition time, precursor characteristic frequency points, and precursor response area association information corresponding to each precursor intensity value are retained, forming a numerical sequence that is ordered by the time dimension. This sequence is the precursor intensity time series of the precursor response area.

[0139] Traverse all precursor intensity values ​​sorted by time in the precursor intensity time series, select the precursor intensity values ​​corresponding to two adjacent acquisition times as a set of comparison data, calculate the difference between the precursor intensity value at the next time point and the precursor intensity value at the previous time point, and then divide the difference by the time interval between the two acquisition times to obtain the rate of change of the precursor intensity value in each time interval. Arrange the rate of change corresponding to all time intervals in chronological order, and the resulting set of values ​​is the intensity change gradient of the titanium device.

[0140] The rate of change of each time interval in the intensity change gradient is compared with the preset evolution judgment threshold one by one. Gradient data with a rate of change exceeding the threshold are selected, and the specific location and feature information of the precursor response zone corresponding to the gradient data are located. At the same time, the spatial range of the region corresponding to the gradient data in the precursor response zone is accurately delineated. The delineated precursor response zone sub-region with an intensity change rate exceeding the threshold is determined as the main evolution zone of the titanium device. During the delineation process, the boundary coordinates, spatial range and gradient data association information of the main evolution zone are retained.

[0141] Extract all precursor feature frequency points corresponding to different acquisition times within the main evolution region, record the specific frequency value of each precursor feature frequency point and the corresponding acquisition time, arrange the frequency values ​​of all precursor feature frequency points in chronological order, and mark the corresponding position of each frequency point in the frequency-time coordinate system. Connect the frequency point positions of adjacent time points to form a frequency point change trajectory extending along the time dimension, which is the frequency point migration path of the main evolution region. The trajectory retains the frequency, time and spatial location association information of all frequency points.

[0142] Using acquisition time as a unified correlation dimension, the rate of change of each time interval in the intensity change gradient is matched one by one with the frequency position and frequency change information of the corresponding time point in the frequency migration path. At the same time, information such as the spatial range and boundary coordinates of the evolution zone is integrated. The matched intensity change data, frequency migration data and the spatial information of the evolution zone are then integrated to form a multi-dimensional data set containing time dimension, intensity change dimension, frequency migration dimension and spatial dimension. This data set is the pitting evolution data of titanium equipment. During the integration process, the correspondence between the data of each dimension and the integrity of the original information are maintained.

[0143] The beneficial effects are as follows: by constructing a time series through the temporal arrangement of precursor intensity values, a quantitative analysis of the temporal dimension of pitting erosion precursor intensity is achieved, laying an orderly data foundation for gradient calculation. By comparing adjacent time periods, the intensity change gradient is obtained, accurately characterizing the rate of change of pitting erosion precursor intensity over time, and achieving preliminary quantification of evolution trend. By delineating the main evolution zone through horizontal comparison, the core area of ​​pitting erosion evolution is accurately located, avoiding redundant analysis of invalid areas and improving the pertinence of evolution inference. By obtaining the frequency migration path through spectrum analysis, the spatiotemporal variation law of precursor characteristic frequency points in the core evolution area is clearly presented. By integrating multi-dimensional data to form pitting erosion evolution data, the fusion characterization of multi-dimensional evolution information in time, intensity, frequency, and space is achieved, providing a comprehensive, systematic, and quantitative evolutionary basis for subsequent pitting erosion risk level determination, and significantly improving the scientificity and accuracy of pitting erosion evolution trend inference.

[0144] Step f: Perform a hierarchical determination on the pitting evolution data to obtain the risk level of the titanium equipment, and generate early warning information for the titanium equipment based on the risk level.

[0145] In this embodiment of the invention, the step of performing hierarchical determination on the pitting corrosion evolution data to obtain the risk level of the titanium equipment, and generating early warning information for the titanium equipment based on the risk level, includes:

[0146] The intensity change gradient value and frequency migration path length are extracted from the pitting corrosion evolution data to obtain the evolution feature vector of the titanium device;

[0147] The evolution feature vector is compared with a preset baseline risk level template to obtain the risk level of the titanium device.

[0148] Based on the risk level, determine the warning color code and warning handling suggestion text for the titanium equipment;

[0149] The warning color code and the warning handling suggestion text are associated and bound together to obtain the warning information of the titanium device.

[0150] The step of comparing the evolutionary feature vector with a preset baseline risk level template to obtain the risk level of the titanium device includes:

[0151] Extract the intensity change gradient value and frequency point migration path length from the evolution feature vector, and use the intensity change gradient value as the first comparison index and the frequency point migration path length as the second comparison index.

[0152] Based on the intensity change gradient value and the frequency migration path length, the intensity change gradient value and the frequency migration path length in the preset benchmark risk level template are extracted to obtain the standard intensity change gradient value and standard frequency migration path length of the titanium equipment.

[0153] Based on the first comparison index, the second comparison index, the standard intensity change gradient value, and the standard frequency point migration path length, the matching degree between the evolutionary feature vector and the preset baseline risk level template is calculated. The formula for calculating the matching degree is as follows:

[0154] ;

[0155] in, The matching degree, The first comparison indicator is used. The standard intensity variation gradient value, The second comparison indicator is... The standard frequency migration path length is... These are the weighting coefficients for the intensity change gradient. The weighting coefficients for the frequency migration path length. It is a non-zero minimum constant.

[0156] The extracted intensity change gradient values ​​are summarized across the entire domain and the core characterization values ​​are determined. The frequency migration path length is fully extracted and confirmed. The determined intensity change gradient values ​​and frequency migration path length are combined in a fixed dimensional order to form a feature data set consisting of two feature values. This data set is the evolution feature vector of the titanium device. During the combination process, the original dimensions and physical characterization meaning of the two values ​​are preserved to ensure that the feature vector has clear dimensions and accurate values.

[0157] The intensity change gradient value and frequency migration path length are extracted from the evolutionary feature vector. The intensity change gradient value is used as the first comparison index, and the frequency migration path length is used as the second comparison index. The specific value of the intensity change gradient value is directly read from the evolutionary feature vector, and this value is separately designated as the first comparison index and marked. At the same time, the specific value of the frequency migration path length is read from the evolutionary feature vector, and this value is separately designated as the second comparison index and marked. During the marking process, the original correlation between the two indices and the evolutionary feature vector is maintained to ensure that the index values ​​are unbiased and without omission.

[0158] Based on the physical characterization attributes of the first and second comparison indicators, standard numerical items corresponding to the two indicators are retrieved from the preset benchmark risk level template. The standard numerical values ​​of the corresponding intensity change gradient in the template are accurately extracted and determined as the standard intensity change gradient value. At the same time, the standard numerical values ​​of the corresponding frequency point migration path length in the template are extracted and determined as the standard frequency point migration path length. During the extraction process, it is ensured that the physical meaning and dimensions of the standard numerical values ​​are completely consistent with those of the comparison indicators.

[0159] Using the first comparison index and the standard intensity change gradient value as one set of calculation data, and the second comparison index and the standard frequency point migration path length as another set of calculation data, the numerical deviation between the two sets of data is calculated and normalized. Then, the normalized results of the two sets are assigned corresponding weight coefficients and weighted summed. Through this calculation method, a value that can comprehensively reflect the degree of fit between the evolution feature vector and the benchmark risk level template is obtained. This value is the matching degree between the two. The principle of uniformity of dimensions is followed in the calculation process to ensure the accuracy and rationality of the calculation results.

[0160] The matching degree is obtained by weighting the intensity change gradient and the frequency migration path length. The first comparison index is derived from the intensity change gradient calculation of the object to be analyzed, and the standard intensity change gradient value is derived from the preset standard intensity change gradient benchmark. The second comparison index is derived from the frequency migration path length calculation of the object to be analyzed, and the standard frequency migration path length is derived from the preset standard frequency migration path benchmark. The weight coefficients of the intensity change gradient and the weight coefficients of the frequency migration path length are derived from the preset weight allocation rules. The non-zero minimum constant is derived from the preset minimum value to avoid the denominator being zero.

[0161] The matching degree is used to quantify the degree of fit between the intensity change gradient and frequency migration path length of the object under analysis and the corresponding standard benchmark. By normalizing and weighting the two indicators, a value that can comprehensively reflect the degree of matching between the object under analysis and the standard benchmark is obtained. This value can be directly used in subsequent judgment and analysis.

[0162] The smaller the difference between the intensity change gradient of the object under analysis and the standard intensity change gradient value, and the smaller the difference between the frequency migration path length and the standard frequency migration path length, the higher the matching degree value. Conversely, the larger the difference between the intensity change gradient of the object under analysis and the standard intensity change gradient value, and the larger the difference between the frequency migration path length and the standard frequency migration path length, the lower the matching degree value. The value of the weighting coefficient will directly affect the contribution ratio of the two indicators to the matching degree value.

[0163] The calculated matching degree value is compared one by one with the matching degree threshold intervals corresponding to each risk level in the preset benchmark risk level template. The threshold interval to which the matching degree value belongs is determined, and the risk level corresponding to the threshold interval to which the matching degree value belongs is directly determined as the risk level of the titanium equipment. The comparison process is carried out in accordance with the preset threshold interval judgment rules to ensure the uniqueness and accuracy of the risk level judgment results.

[0164] Based on the preset rules for the correspondence between risk levels and warning color codes, the exclusive color code corresponding to the identified risk level is retrieved and designated as the warning color code for the titanium equipment. At the same time, according to the preset association specifications between risk levels and handling suggestions, the standardized handling suggestion text matching the risk level is retrieved. This text contains targeted operational requirements for equipment inspection, maintenance, and control. The retrieved text is designated as the warning handling suggestion text for the titanium equipment. During the determination process, it is ensured that both the color code and the handling suggestion text are highly matched with the risk level.

[0165] The established warning color codes are matched one-to-one with the warning handling suggestion text, and a unified equipment identifier and risk level identifier are added to both. This makes the warning color code a visual identifier for the warning handling suggestion text, while the warning handling suggestion text serves as a textual description of the warning color code. The warning color code and the warning handling suggestion text, after being linked and bound, are integrated into a complete warning information unit. This unit is the warning information for titanium equipment. During the integration process, all identification information and original content are retained to ensure the integrity and interpretability of the warning information.

[0166] The beneficial effects are as follows: by extracting intensity change gradient values ​​and frequency migration path lengths to construct evolutionary feature vectors, the core features of pitting corrosion evolution data are extracted, providing accurate quantitative basis for risk assessment; by dividing comparison indicators and extracting corresponding standard values, the reference benchmark for risk assessment is clarified, ensuring the pertinence and standardization of the comparison process; by scientifically calculating the matching degree to achieve similarity comparison between evolutionary feature vectors and benchmark templates, the risk level assessment has quantitative support, improving the objectivity and accuracy of the assessment results; by matching risk levels with corresponding warning color codes and handling suggestion texts, the visualization and standardization of risk warnings are achieved; and by associating and binding to form complete warning information, the warning results are both intuitive and instructive, providing clear and explicit basis for timely handling of pitting corrosion risks in titanium equipment, effectively improving the practicality and operability of pitting corrosion warnings for titanium equipment, and ensuring the safe and stable operation of titanium equipment.

[0167] like Figure 2 The diagram shown is a functional block diagram of a titanium equipment pitting early warning system based on electrochemical impedance spectroscopy analysis provided in an embodiment of the present invention.

[0168] The titanium equipment pitting corrosion early warning system 100 based on electrochemical impedance spectroscopy analysis described in this invention can be installed in electronic devices. Depending on the functions implemented, the titanium equipment pitting corrosion early warning system 100 may include an acquisition and normalization module 101, a phase calibration module 102, an impedance characteristic spectrum construction module 103, a precursor target identification module 104, an evolution gradient inference module 105, and an early warning generation module 106. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0169] In this embodiment, the functions of each module / unit are as follows:

[0170] The acquisition and normalization module 101 is used to acquire the original response signal of the electrochemical impedance spectroscopy of the titanium device and perform spectroscopic normalization on the original response signal of the electrochemical impedance spectroscopy to obtain the effective feature segment of the titanium device.

[0171] The phase calibration module 102 is used to decouple and analyze the real and imaginary parts of the effective feature segment to obtain the decoupled component data of the titanium device, and to perform phase correction on the decoupled component data to obtain the calibrated component of the titanium device.

[0172] The impedance characteristic spectrum construction module 103 is used to construct the impedance characteristic spectrum of the titanium device with the capacitive reactance characteristic in the calibrated component as the center and the impedance characteristic in the calibrated component as the edge.

[0173] The precursor target identification module 104 is used to perform precursor target identification on the impedance characteristic spectrum to obtain the pitting precursor characteristics of the titanium equipment.

[0174] The evolution gradient extrapolation module 105 is used to perform development gradient extrapolation on the pitting precursor features to obtain pitting evolution data of the titanium equipment.

[0175] The early warning generation module 106 is used to perform hierarchical determination on the pitting evolution data to obtain the risk level of the titanium equipment, and generate early warning information for the titanium equipment based on the risk level.

[0176] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0177] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0178] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0179] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0180] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0181] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for early warning of pitting corrosion in titanium equipment based on electrochemical impedance spectroscopy analysis, characterized in that, The method includes: Step a: Acquire the raw response signal of the electrochemical impedance spectroscopy of the titanium device, and perform spectroscopic regularization on the raw response signal of the electrochemical impedance spectroscopy to obtain the effective characteristic segment of the titanium device; Step b: Decouple and analyze the real and imaginary parts of the effective feature segments to obtain the decoupled component data of the titanium device, and perform phase correction on the decoupled component data to obtain the calibrated component of the titanium device; Step c: Construct the impedance characteristic spectrum of the titanium device with the capacitive reactance characteristic in the calibrated component as the center and the impedance characteristic in the calibrated component as the edge; Step d: Perform precursor targeting identification on the impedance characteristic spectrum to obtain the pitting precursor characteristics of the titanium equipment, including: Based on the impedance feature map, the capacitive reactance feature points are extracted to obtain the surrounding impedance feature points of the capacitive reactance feature points. By performing spatial distribution morphology analysis on the surrounding impedance characteristic points, the precursor response region of the titanium device is obtained. Based on the precursor response region, the imaginary part values ​​of the surrounding impedance feature points are sequentially fitted to obtain the imaginary part change sequence of the titanium device. Abrupt change identification is performed on the imaginary part change sequence to obtain the position of the sudden drop sequence of the imaginary part change sequence, and the frequency point of the sudden drop sequence position is used as the precursor characteristic frequency point of the titanium device; Obtain the complex plane vector distance between the impedance characteristic point of the precursor characteristic frequency and the center node of the precursor response region, and use the complex plane vector distance as the precursor intensity value of the precursor characteristic frequency. The precursor response region, the precursor characteristic frequency point, and the precursor intensity value are used as the pitting precursor characteristics of the titanium equipment. Step e: Perform development gradient extrapolation on the pitting precursor features to obtain pitting evolution data of the titanium equipment; Step f: Perform a hierarchical determination on the pitting evolution data to obtain the risk level of the titanium equipment, and generate early warning information for the titanium equipment based on the risk level.

2. The method for early warning of pitting corrosion in titanium equipment based on electrochemical impedance spectroscopy as described in claim 1, characterized in that, The raw electrochemical impedance spectroscopy response signal of the acquired titanium device is then subjected to spectroscopic normalization to obtain the effective characteristic segment of the titanium device, including: The raw response signal of the electrochemical impedance spectroscopy was time-sliced ​​to obtain the preprocessed impedance spectroscopy data of the titanium device. The preprocessed impedance spectrum data is scanned for spectral energy distribution to obtain the candidate characteristic spectral range of the titanium device; Based on the candidate feature spectrum range, the phase angle consistency of the preprocessed impedance spectrum data is checked to obtain the pure feature spectrum data of the titanium device; The effective feature segments of the titanium device are obtained by performing feature localization on the pure feature spectrum data.

3. The method for early warning of pitting corrosion in titanium equipment based on electrochemical impedance spectroscopy as described in claim 1, characterized in that, The process of decoupling and analyzing the real and imaginary parts of the effective feature segments to obtain decoupled component data of the titanium device, and then performing phase correction on the decoupled component data to obtain the calibrated components of the titanium device, includes: Orthogonal vector decomposition is performed on the real and imaginary data in the effective feature segment to obtain the initial resistive component data and the initial capacitive component data of the titanium device; Cross-band correlation scanning is performed on the initial resistive component data and the initial capacitive component data to obtain the abnormal frequency points of the titanium device; Based on the abnormal frequency points, the initial resistive component data and the initial capacitive component data are extracted in a targeted manner to obtain the resistive data and capacitive data to be corrected for the titanium device. Based on the phase relationship between the adjacent normal frequency points of the abnormal frequency point, the resistive data and capacitive data to be corrected are compensated and corrected to obtain the corrected resistive data and corrected capacitive data of the titanium device. The corrected resistive data, the corrected capacitive data, the initial resistive component data, and the initial capacitive component data are integrated to obtain the calibrated components of the titanium device.

4. The method for early warning of pitting corrosion in titanium equipment based on electrochemical impedance spectroscopy analysis as described in claim 3, characterized in that, The step of performing a cross-band correlation scan on the initial resistive component data and the initial capacitive component data to obtain the abnormal frequency points of the titanium device includes: The initial resistive component data and the initial capacitive component data are reconstructed in time series to obtain the resistive trajectory sequence and the capacitive trajectory sequence of the titanium device; The resistive trajectory sequence and the capacitive trajectory sequence are segmented and differencing to obtain the segmented resistive deviation sequence and the segmented capacitive deviation sequence of the titanium device; Under the same coordinate system, the same sign count is obtained by performing same sign statistics on the segment resistive deviation sequence and the segment capacitive deviation sequence. The center frequency point whose count of the same frequency point of the segment is lower than the count of the same frequency point of its adjacent segment is regarded as the abnormal frequency point of the titanium device.

5. The method for early warning of pitting corrosion in titanium equipment based on electrochemical impedance spectroscopy as described in claim 1, characterized in that, The process of constructing the impedance characteristic spectrum of the titanium device, centered on the capacitive reactance characteristic of the calibrated component and with the impedance characteristic of the calibrated component as the edges, includes: Data points related to the double-layer capacitance of the electrode surface are extracted from the calibrated components and marked as capacitive reactance characteristic points. Data points related to the charge transfer resistance of the electrode reaction are extracted from the calibrated components and marked as impedance characteristic points. The coordinates of the capacitive reactance feature points are identified to determine their coordinate positions. Using the capacitive reactance feature point as the center, a circumferential search is performed on the impedance feature point to obtain the feature point pair of the titanium device; By performing topology construction on the coordinate positions and the feature point pairs, the impedance characteristic spectrum of the titanium device is obtained.

6. The method for early warning of pitting corrosion in titanium equipment based on electrochemical impedance spectroscopy as described in claim 1, characterized in that, The process of extrapolating the development gradient of the pitting precursor features to obtain the pitting evolution data of the titanium equipment includes: Based on the precursor response zone of the titanium device, the precursor intensity values ​​are arranged in time sequence to obtain the precursor intensity time series of the precursor response zone. Based on the precursor intensity time series, the precursor intensity values ​​are compared between adjacent time periods to obtain the intensity change gradient of the titanium device; By performing a lateral comparison of the intensity change gradient, the main morphological region of the titanium device is obtained; Spectral analysis is performed on the main evolution region to obtain the frequency migration path of the main evolution region; By correlating and integrating the intensity change gradient with the frequency migration path, the pitting evolution data of the titanium device is obtained.

7. The method for early warning of pitting corrosion in titanium equipment based on electrochemical impedance spectroscopy as described in claim 1, characterized in that, The step of performing a hierarchical determination on the pitting corrosion evolution data to obtain the risk level of the titanium equipment, and generating early warning information for the titanium equipment based on the risk level, includes: The intensity change gradient value and frequency migration path length are extracted from the pitting corrosion evolution data to obtain the evolution feature vector of the titanium device; The evolution feature vector is compared with a preset baseline risk level template to obtain the risk level of the titanium device. Based on the risk level, determine the warning color code and warning handling suggestion text for the titanium equipment; The warning color code and the warning handling suggestion text are associated and bound together to obtain the warning information of the titanium device.

8. The method for early warning of pitting corrosion in titanium equipment based on electrochemical impedance spectroscopy analysis as described in claim 7, characterized in that, The step of comparing the evolutionary feature vector with a preset baseline risk level template to obtain the risk level of the titanium device includes: Extract the intensity change gradient value and frequency point migration path length from the evolution feature vector, and use the intensity change gradient value as the first comparison index and the frequency point migration path length as the second comparison index. Based on the intensity change gradient value and the frequency migration path length, the intensity change gradient value and the frequency migration path length in the preset benchmark risk level template are extracted to obtain the standard intensity change gradient value and standard frequency migration path length of the titanium equipment. Based on the first comparison index, the second comparison index, the standard intensity change gradient value, and the standard frequency point migration path length, the matching degree between the evolutionary feature vector and the preset baseline risk level template is calculated. The formula for calculating the matching degree is as follows: ； in, The matching degree, The first comparison indicator is used. The standard intensity variation gradient value, The second comparison indicator is... The standard frequency migration path length is... These are the weighting coefficients for the intensity change gradient. The weighting coefficients for the frequency migration path length. It is a non-zero minimum constant.

9. A pitting corrosion early warning system for titanium equipment based on electrochemical impedance spectroscopy analysis, characterized in that, For implementing the method for early warning of pitting corrosion in titanium equipment based on electrochemical impedance spectroscopy analysis according to any one of claims 1-8, the system comprises: The acquisition and normalization module is used to acquire the original response signal of the electrochemical impedance spectroscopy of the titanium device and perform spectroscopic normalization on the original response signal of the electrochemical impedance spectroscopy to obtain the effective feature segment of the titanium device. The phase calibration module is used to decouple and analyze the real and imaginary parts of the effective feature segment to obtain the decoupled component data of the titanium device, and to perform phase correction on the decoupled component data to obtain the calibrated component of the titanium device. An impedance characteristic spectrum construction module is used to construct the impedance characteristic spectrum of the titanium device with the capacitive reactance characteristic in the calibrated component as the center and the impedance characteristic in the calibrated component as the edge. A precursor targeting identification module is used to perform precursor targeting identification on the impedance characteristic spectrum to obtain the pitting precursor characteristics of the titanium equipment. The evolution gradient extrapolation module is used to extrapolate the development gradient of the pitting precursor features to obtain the pitting evolution data of the titanium equipment. The early warning generation module is used to perform hierarchical determination on the pitting evolution data to obtain the risk level of the titanium equipment, and generate early warning information for the titanium equipment based on the risk level.

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