Material corrosion-oriented surface microenvironment multi-parameter sensing method and system

Abstract

The invention discloses a material corrosion-oriented surface microenvironment multi-parameter sensing method and system, which realize real-time monitoring of a plurality of key physical and chemical parameters by arranging a micro sensing probe array on the surface of a material. And constructing a space-time parameter matrix by using the obtained data, calculating the time change rate of each parameter, and setting a disturbance threshold value to mark an abnormal point. A disturbance area is identified through spatial clustering of abnormal points, corrosion activity indexes are defined in combination with temperature, relative humidity, electrochemical driving force, local acidification degree and chloride ion concentration, and a continuous corrosion activity distribution diagram is generated. And tracking a spatial overlapping relation of the disturbance area to form an evolution chain, and predicting a future expansion trend. And finally generating a comprehensive report containing a corrosion activity space-time overview map, a key event timeline and a regional risk grading list. The accuracy and efficiency of corrosion monitoring are improved, and a scientific basis is provided for preventive maintenance.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of material monitoring, and particularly relates to a surface micro-environment multi-parameter sensing method and system for material corrosion. BACKGROUND

[0002] Material corrosion is one of the main reasons for the damage and failure of infrastructure, industrial equipment and various engineering structures. Traditionally, the method of monitoring material corrosion relies on periodic physical inspection or laboratory analysis, which is not only time-consuming and laborious, but also difficult to achieve real-time monitoring and prediction. In the prior art, there are also attempts to use sensing technology to monitor changes in specific environmental parameters (such as temperature, humidity, etc.) to indirectly assess the corrosion situation, but these methods often can only provide single-dimensional information, lacking the ability to comprehensively monitor key chemical parameters (such as pH value, chloride ion concentration, etc.) in the corrosion process.

[0003] In addition, the prior art has deficiencies in handling large amounts of sensor data, making it difficult to effectively identify the specific location of corrosion activity and its development trend. SUMMARY

[0004] The purpose of the present application is to provide a surface micro-environment multi-parameter sensing method and system for material corrosion, which realizes the simultaneous monitoring of multiple physical and chemical parameters such as potential, pH value, chloride ion concentration, humidity and temperature in the surface micro-environment of materials by integrating multiple sensing probes. To solve the problems raised in the background art.

[0005] To achieve the above purpose, the application adopts the following technical scheme: a surface micro-environment multi-parameter sensing method for material corrosion, comprising the following steps: Arranging a sensing probe array on the surface of the material to obtain raw signals of potential, pH value, chloride ion concentration, humidity and temperature; Converting the raw signals into physical and chemical parameter values, constructing a space-time parameter matrix containing spatial coordinates and time stamps, calculating the change rate of each parameter in the time dimension based on the space-time parameter matrix, setting a disturbance threshold and marking abnormal points; Spatially clustering the abnormal points to identify disturbance regions with spatial continuity, and combining temperature, relative humidity, electrochemical driving force, local acidification degree and chloride ion concentration in the disturbance region to define a corrosion activity index; Interpolating and calculating the corrosion activity index on the spatial grid to generate a continuous corrosion activity distribution map, and tracking the spatial overlap relationship of the disturbance region at different time points to form an evolution chain and calculate the motion vector and expansion rate; Based on the gradient direction and historical expansion rate in the corrosion activity distribution map, the future expansion trend is predicted, and a comprehensive report including the spatio-temporal overview of corrosion activity, the timeline of key events and the list of regional risk classification is generated.

[0006] Preferably, the acquisition of the original signal comprises: The probe head is integrated with a platinum microelectrode, a pH-sensitive film, a silver-doped tin oxide film, a porous silica coating, and a platinum resistor. The probes are then arranged in a two-dimensional grid on the material surface, and the electrical connection between the probes and the data acquisition unit is achieved through a flexible thin film circuit. The output values of each probe are recorded under standard environmental conditions to establish a mapping relationship between the original signal and the physicochemical parameters.

[0007] Preferably, the conversion of the original signal into physicochemical parameter values comprises: First, the potential difference, pH membrane potential, chloride ion potential, humidity capacitance, and resistance values of each probe during the scanning period are read. Then, the potential difference is converted into the potential value relative to the standard hydrogen electrode based on the mapping relationship. The pH membrane potential and chloride ion potential are then inversely calculated to obtain the local pH value and chloride ion concentration through electrochemical equations. Finally, the humidity capacitance and resistance values are converted into the relative humidity percentage and temperature value to form a complete set of physicochemical parameters.

[0008] Preferably, the calculation of the change rate of each parameter in the time dimension based on the spatio-temporal parameter matrix comprises: First, the physicochemical parameter values at consecutive time points at each spatial point are extracted, and the difference between the parameter values at adjacent time points is calculated. Then, the difference result is divided by the corresponding time interval to obtain the change amount per unit time. The change amount of each parameter is statistically analyzed to determine its normal fluctuation range.

[0009] Preferably, the setting of the disturbance threshold and the marking of the abnormal points comprises: Based on the normal fluctuation range, the upper and lower limits of the change rate of each parameter are set to determine whether the change rate of any parameter at each spatial point exceeds the corresponding threshold. The spatial points that exceed the threshold are marked as potential disturbance points, and the coordinates, time, and parameter type of all potential disturbance points are recorded to form an initial disturbance set.

[0010] Preferably, the spatial clustering of the abnormal points comprises: The minimum neighborhood radius is set to 100 μm, and the minimum number of points is set to 3. searching other points within the neighborhood of each potential perturbation point in the initial perturbation set; grouping points satisfying the density condition into the same cluster, calculating the geometric center, area and boundary contour of each cluster, and forming a perturbation region with spatial morphology.

[0011] Preferably, the temperature, relative humidity, electrochemical driving force, local acidification degree and chloride ion concentration in the perturbation region are combined to define a corrosion activity index, including: First, the temperature value, relative humidity value, potential offset relative to the initial value, pH value drop and chloride ion concentration increase in the perturbation region are extracted; then the offsets are normalized respectively; finally, the normalized values are weighted and summed to obtain the corrosion activity index.

[0012] Preferably, the spatial overlap relationship of the perturbation region at different time instants is tracked, including: extracting the spatial boundary of each perturbation region in adjacent time frames, and then calculating the spatial intersection area of the region in the previous frame and the region in the next frame; judging whether the intersection area is greater than 50% of the respective area, and if the condition is met, establishing a time link to form an evolution chain.

[0013] Preferably, the future expansion trend is predicted based on the gradient direction in the corrosion activity distribution map and the historical expansion rate, including: First, select the motion vectors at consecutive time points in the evolution chain; then calculate the average direction as the historical expansion direction; then select multiple sampling points on the front boundary of the perturbation region; finally, calculate the weighted average of the corrosion activity gradient direction at the sampling points, and combine the historical expansion direction to determine the prediction path.

[0014] On the other hand, the present application proposes a surface microenvironment multi-parameter sensing system for material corrosion, including: a sensor probe array arranged on the surface of the material for obtaining raw signals of potential, pH value, chloride ion concentration, humidity and temperature; a data acquisition unit connected to the probe for reading the raw signals and labeling them with time stamps and spatial coordinate labels; The processing unit is used to convert the raw signal into physicochemical parameter values, construct a spatiotemporal parameter matrix containing spatial coordinates and timestamps, calculate the rate of change of each parameter in the time dimension based on the spatiotemporal parameter matrix, set a perturbation threshold and mark outliers, perform spatial clustering on outliers to identify perturbation regions, define a corrosion activity index by combining temperature, relative humidity, electrochemical driving force, local acidification degree and chloride ion concentration in the perturbation region, interpolate the corrosion activity index on the spatial grid to generate a corrosion activity distribution map, track the spatial overlap relationship of the perturbation region at different times to form an evolution chain and calculate the motion vector and expansion rate, and predict the future expansion trend based on the gradient direction and historical expansion rate in the corrosion activity distribution map. Output device for generating a comprehensive report that includes a spatiotemporal overview of corrosion activity, a timeline of key events, and a list of regional risk classifications.

[0015] Technical effects and advantages of the present invention: The multi-parameter sensing method and system for surface microenvironment in response to material corrosion proposed in this invention have the following advantages compared with the prior art: This invention achieves real-time monitoring of multiple key physicochemical parameters by deploying a micro-sensor probe array on the material surface. A spatiotemporal parameter matrix is ​​constructed using the acquired data, and the time-varying rate of change of each parameter is calculated. Anomalies are then marked by setting disturbance thresholds. Disturbance regions are identified through spatial clustering of these anomalies, and a corrosion activity index is defined by combining temperature, relative humidity, electrochemical driving force, local acidification degree, and chloride ion concentration, generating a continuous corrosion activity distribution map. Furthermore, the spatial overlap of disturbance regions is traced to form an evolutionary chain, predicting future expansion trends. Finally, a comprehensive report is generated, including a spatiotemporal overview of corrosion activity, a timeline of key events, and a list of regional risk classifications. This technical solution not only improves the accuracy and efficiency of corrosion monitoring but also provides a scientific basis for preventative maintenance, thereby significantly reducing economic losses and safety hazards caused by corrosion. Attached Figure Description

[0016] Figure 1 This is a flowchart of the multi-parameter sensing method for surface microenvironment in response to material corrosion according to the present invention; Figure 2 This is a block diagram of the multi-parameter sensing system for surface microenvironment in response to material corrosion, as described in this invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] This invention provides, for example Figure 1 This paper presents a multi-parameter sensing method for the surface microenvironment of materials, aiming to achieve high spatial resolution monitoring of the corrosion process in micro-regions of material surfaces, thereby capturing the early initiation and spread of localized corrosion. This method integrates multi-physics sensing units and dynamic scanning imaging technology to construct a simultaneous sensing capability for multiple parameters such as electrochemistry, ion concentration, humidity, and temperature within the micro-scale region of the material surface. Combined with spatial reconstruction and gradient recognition techniques, it enables the localization of corrosion initiation points and the tracking of evolution paths. Specifically, it includes the following steps: A sensor probe array is deployed on the material surface to acquire raw signals of potential, pH value, chloride ion concentration, humidity, and temperature. Specifically, this involves integrating platinum microelectrodes, pH-sensitive membranes, silver-doped tin oxide films, porous silica coatings, and platinum resistance thermometers into the probe heads. The probes are then arranged in a two-dimensional grid on the material surface, and electrical connections between the probes and data acquisition units are established via flexible thin-film circuitry. The output values ​​of each probe are recorded under standard conditions, establishing a mapping relationship between the raw signals and physicochemical parameters. This approach enables synchronous, high spatial resolution sensing of multiple parameters in the micro-area environment of the material surface, ensuring stable deployment and signal integrity of the sensing units on complex surfaces, and providing a reliable data foundation for accurate identification and dynamic tracking of subsequent corrosion behavior.

[0019] The raw signals were converted into physicochemical parameter values, constructing a spatiotemporal parameter matrix containing spatial coordinates and timestamps. Specifically, this involved: first, reading the potential difference, pH membrane potential, chloride ion potential, humidity capacitance, and resistance values ​​of each probe during the scanning cycle; then, converting the potential difference into a potential value relative to a standard hydrogen electrode based on mapping relationships; next, using electrochemical equations to deduce the local pH value and chloride ion concentration from the pH membrane potential and chloride ion potential; and finally, converting the humidity capacitance and resistance values ​​into relative humidity percentages and temperature values, forming a complete set of physicochemical parameters. This unified conversion of raw electrical signals into environmental parameters with clear physicochemical significance achieves standardization and spatiotemporal alignment of multi-source heterogeneous data, providing fundamental data support for constructing continuous and analyzable dynamic maps of surface microenvironments.

[0020] The calculation of the rate of change of each parameter over time based on the spatiotemporal parameter matrix includes: first, extracting the physicochemical parameter values ​​at consecutive time points for each spatial point; then calculating the difference between parameter values ​​between adjacent time points; finally, dividing the difference result by the corresponding time interval to obtain the change per unit time; and then performing statistical analysis on the change of each parameter to determine its normal fluctuation range. This quantifies the dynamic evolution trend of environmental parameters, identifies abnormal changes deviating from the normal fluctuation range, and provides a sensitive criterion for early detection of micro-area environmental disturbances caused by corrosion.

[0021] Setting disturbance thresholds and marking anomalies involves: setting upper and lower limits for the rate of change of each parameter based on the normal fluctuation range; determining whether the rate of change of any parameter at each spatial point exceeds the corresponding threshold; marking spatial points exceeding the threshold as potential disturbance points; and recording the coordinates, time, and parameter type of all potential disturbance points to form an initial disturbance set. This enables automated identification and location of micro-area environmental anomalies, transforming continuous monitoring data into a traceable set of discrete events, providing clear input targets for subsequent spatial clustering and corrosion activity analysis.

[0022] Spatial clustering of anomalies identifies spatially continuous perturbation regions. A corrosion activity index is defined by combining temperature, relative humidity, electrochemical driving force, local acidification degree, and chloride ion concentration within these regions. Specifically, this involves: first, extracting temperature, relative humidity, potential offset from initial values, pH decrease, and chloride ion concentration increase within the perturbation region; then, normalizing each offset individually; and finally, weighted summing of the normalized values ​​to obtain the corrosion activity index. This approach integrates discrete anomalies into physically bounded corrosion perturbation regions and quantifies their corrosion tendency through multi-parameter fusion, thereby improving the assessment from single-parameter alarms to comprehensive localized corrosion risk assessment.

[0023] The spatial clustering of outliers specifically includes: setting a minimum neighborhood radius of 100 μm and a minimum number of points of 3; searching for other points within the neighborhood of each potential perturbation point in the initial perturbation set; grouping points that meet the density condition into the same cluster; calculating the geometric center, area, and boundary contour of each cluster to form a perturbation region with spatial morphology. This effectively distinguishes random noise from real corrosion perturbations, identifies micro-corrosion initiation regions with spatial clustering characteristics, and provides a morphological basis for subsequent corrosion evolution path tracing.

[0024] The corrosion activity index is interpolated on the spatial grid to generate a continuous corrosion activity distribution map, and the spatial overlap relationship of the disturbed region at different times is tracked to form an evolution chain and calculate the motion vector and expansion rate. The process of tracing the spatial overlap of perturbation regions at different times includes: extracting the spatial boundaries of each perturbation region in adjacent time frames, then calculating the spatial intersection area between the regions in the previous frame and the regions in the next frame; determining whether the intersection area is greater than 50% of their respective areas; if the condition is met, a temporal link is established to form an evolutionary chain. This achieves a leap from static identification to dynamic tracking of corrosion micro-regions, establishing temporal continuity through spatial intersection analysis, accurately reconstructing the migration path and expansion behavior of corrosion regions, and revealing their evolutionary patterns.

[0025] Based on the gradient direction and historical expansion rate in the corrosion activity distribution map, future expansion trends are predicted, and a comprehensive report is generated, including a spatiotemporal overview of corrosion activity, a timeline of key events, and a regional risk classification list. This achieves accurate prediction of future expansion trends in corrosion activity, provides a spatiotemporal overview covering the entirety of corrosion behavior, clarifies the development trajectory of key events, and accurately assesses the corrosion risk levels of different regions through the regional risk classification list, providing a scientific basis for subsequent maintenance and protection strategies.

[0026] The prediction of future expansion trends based on the gradient direction and historical expansion rate in the corrosion activity distribution map includes: first, selecting motion vectors at multiple consecutive time points in the evolution chain; then calculating their average direction as the historical expansion direction; then selecting multiple sampling points on the leading edge boundary of the perturbation region; and finally calculating the weighted average of the corrosion activity gradient directions at the sampling points, and combining this with the historical expansion direction to determine the prediction path.

[0027] By quantitatively analyzing historical data and real-time corrosion activity, high-risk areas and their expansion trends can be effectively identified, providing clear guidance for preventative maintenance. Simultaneously, this technology enhances the understanding of complex corrosion behavior, contributing to the development of more scientific and rational resource allocation and risk management strategies. Ultimately, it enables early warning and precise control of corrosion damage, reducing the risk of structural failure and extending the service life of facilities.

[0028] On the other hand, this invention proposes a multi-parameter sensing system for the surface microenvironment oriented towards material corrosion, such as... Figure 2 As shown, it includes: A sensor probe array is arranged on the material surface to acquire raw signals of potential, pH value, chloride ion concentration, humidity and temperature; The data acquisition unit connects to the probe, reads the raw signal, and adds a timestamp and spatial coordinate label. The processing unit is used to convert the raw signal into physicochemical parameter values, construct a spatiotemporal parameter matrix containing spatial coordinates and timestamps, calculate the rate of change of each parameter in the time dimension based on the spatiotemporal parameter matrix, set a perturbation threshold and mark outliers, perform spatial clustering on outliers to identify perturbation regions, define a corrosion activity index by combining temperature, relative humidity, electrochemical driving force, local acidification degree and chloride ion concentration in the perturbation region, interpolate the corrosion activity index on the spatial grid to generate a corrosion activity distribution map, track the spatial overlap relationship of the perturbation region at different times to form an evolution chain and calculate the motion vector and expansion rate, and predict the future expansion trend based on the gradient direction and historical expansion rate in the corrosion activity distribution map. Output device for generating a comprehensive report that includes a spatiotemporal overview of corrosion activity, a timeline of key events, and a list of regional risk classifications.

[0029] In addition, the components of the above system are also used to implement other steps of the above-described method for multi-parameter sensing of surface microenvironment in response to material corrosion, as follows: Step 1: Constructing a multi-parameter sensing array for surface micro-regions This step aims to deploy a high spatial resolution sensing unit on the surface of the material under test to acquire key information such as electrochemical potential, local pH, chloride ion concentration, relative humidity, and temperature in the micro-environment. The design of this sensing array must meet the requirements of micron-level spatial resolution, low interference response, and long-term stability to ensure that even minor environmental disturbances can be detected in the early stages of corrosion.

[0030] Step 1.1: Design the sensing probe structure: First, based on the geometric features of the material surface and the size range of the expected monitoring area, a set of sensing probes was designed. The probe head integrates multiple independent sensing regions, each corresponding to the detection function of different physicochemical quantities. The probe substrate is made of insulating ceramic material, with micron-level trenches etched on its surface to fix each sensing element. The potential detection region uses a platinum microelectrode, with a size controlled within 10 μm to reduce disturbance to the in-situ electric field; the pH sensing region is coated with a silicon-based pH-sensitive membrane, which has selective response characteristics to hydrogen ions; the chloride ion detection region uses a silver-doped tin oxide film, utilizing the potential change generated by its reaction with chloride ions to produce silver chloride for quantitative measurement; the humidity sensing region uses a porous silica coating, whose dielectric constant changes with the amount of water molecules adsorbed; and the temperature sensing region embeds a miniature platinum resistor, utilizing the linear relationship between resistance and temperature for temperature measurement. Each sensing region is separated by a micron-level insulating strip to prevent signal crosstalk. This probe structure provides the physical basis for subsequent multi-parameter synchronous acquisition.

[0031] Step 1.2: Arrange the sensing probes in an array on the material surface: Based on the sensor probe pairs designed in step 1.1, they are arranged in a two-dimensional grid on the surface of the material to be tested, forming a sensor array. During the arrangement process, micro-nano fabrication technology is used to fix the bottom of the probes to the material surface with a low-stress adhesive, ensuring good electrical contact and thermal conduction between the probes and the material, while avoiding the introduction of additional stress concentration. The array spacing is set to 50 μm to achieve coverage of micron-level corrosion initiation regions. The position information of each probe is accurately recorded and mapped into a coordinate matrix for subsequent spatial registration of data. This array arrangement method enables the formation of a dense monitoring network on the material surface, which can spatially distinguish subtle differences in the local environment and provide data support for identifying corrosion initiation points.

[0032] Step 1.3: Establish a low-noise connection between the probe and the data acquisition unit: To ensure the signal acquired from the sensor array arranged in step 1.2 is not distorted, a stable, low-noise electrical connection path needs to be established. A flexible thin-film circuit is used as the connection medium, with its wire width controlled within 20 μm to reduce electromagnetic interference. Each wire is electrically connected to the sensing area of ​​its corresponding probe via gold wire ball bonding, and is coated with a parylene layer for environmental sealing and insulation protection. The data acquisition unit is housed in a control box away from corrosive environments and connected to the flexible circuit via shielded cables. A preamplifier is added to the signal transmission path to improve the signal-to-noise ratio of weak signals, especially for microvolt-level signals such as potential and ion activity. This connection structure ensures signal integrity from the probe to the acquisition end, making the data relied upon for subsequent analysis reliable.

[0033] Step 1.4: Complete the initial calibration and baseline setting of the sensor array: Before the sensor array is put into formal use, initial calibration is required to eliminate systematic deviations introduced during manufacturing and installation. The calibration process is conducted in a standard environmental chamber, with controlled temperatures of 25°C, relative humidity of 50%, electrolyte pH of 7.0, and chloride ion concentration of 10 mmol / L. Under these conditions, the output values ​​of each probe for each parameter are recorded as reference signals. Subsequently, response tests are performed under different known parameter conditions (e.g., pH 4-10, Cl⁻ concentration 1-100 mmol / L, etc.) to establish a mapping relationship between the output values ​​of each sensing area and the actual environmental parameters. This mapping relationship is stored in the data processing unit in the form of a lookup table for subsequent real-time environmental parameter inversion. This calibration process ensures that each probe can provide consistent and comparable measurement results after being put into use, laying the foundation for subsequent dynamic monitoring.

[0034] Step 2: Perform dynamic scanning micro-area environmental parameter acquisition After the sensor array is deployed and calibrated, the dynamic monitoring phase begins. This step involves periodically scanning each probe to acquire time-series data of the micro-environmental parameters on the material surface, capturing the evolution characteristics of the local environment during corrosion.

[0035] Step 2.1: Set the scan cycle and trigger conditions: Based on the calibration parameters established in step 1.4, the data acquisition scan cycle was set. The initial scan cycle was set to once every 10 seconds to balance data density and system load. Simultaneously, a dynamic triggering mechanism was set: when any probe detects a potential change rate exceeding ±5 μV / s, a pH change rate exceeding ±0.01 pH / s, or a chloride ion concentration change rate exceeding ±0.1 mmol / (L·s), the system automatically shortens the scan cycle to once every 1 second, entering a high-frequency monitoring mode. This triggering mechanism relies on the difference calculation between the previous and current cycle data to ensure timely response to rapid environmental changes. This setting allows the system to maintain long-term stable operation while improving monitoring sensitivity during periods of intensified corrosion.

[0036] Step 2.2: Read the multi-parameter signals of each probe point by point according to the set period: Within each scan cycle, the data acquisition unit sequentially accesses each probe in the sensor array according to a preset order, reading the raw output signals of its five sensing areas. The reading process employs time-division multiplexing, with the five signals from each probe acquired within 1 millisecond to ensure the temporal synchronization of multi-parameter data. The raw signals include potential difference (unit: μV), pH membrane potential (mV), chloride ion potential (mV), humidity capacitance (pF), and resistance (Ω). These signals are transmitted to the acquisition unit through the connection path established in step 1.3, and are labeled with precise timestamps and spatial coordinates. This point-by-point reading method ensures that all data possess clear spatial and temporal attributes, providing a foundation for subsequent spatial evolution analysis.

[0037] Step 2.3: Convert the original signal into physicochemical parameter values: After acquiring the raw signals in step 2.2, each signal is converted into its corresponding physicochemical parameter according to the calibration mapping relationship established in step 1.4. For example, the potential signal is converted into a potential value (E, V) relative to the standard hydrogen electrode after compensation by the reference electrode; the pH membrane potential is converted according to the Nernst equation. ; in, To measure the membrane potential, For standard electric potential, It is the gas constant (8.314 J / (mol·K)). It represents absolute temperature (K). This is the Faraday constant (96485 C / mol). From this formula, we can obtain... This allows us to deduce the current local pH value. Similarly, the chloride ion concentration... It can be calculated using the modified Nernst equation: ; Humidity values ​​were converted from capacitance changes to relative humidity percentages using an empirical formula, while temperature was obtained by looking up the resistance values ​​of the platinum resistance thermometer. This conversion process restored the original electrical signals to environmental parameters with clear physical meaning, allowing subsequent analysis to be performed at a real-world environmental scale.

[0038] Step 2.4: Construct and cache the spatiotemporal parameter matrix: The parameter values ​​obtained in step 2.3 are organized into a four-dimensional data matrix according to their spatial coordinates (x, y) and timestamp (t). ,in This matrix represents the parameter type (e.g., potential, pH, Cl⁻ concentration, etc.). It is stored in memory in a circular buffer, retaining data from the most recent 24 hours, with any data exceeding this limit automatically overwritten. Simultaneously, hourly data is packaged and stored in solid-state storage for long-term traceability. The construction of this spatiotemporal matrix allows for a complete recording of the environmental state of the material surface, providing a data foundation for subsequent corrosion behavior identification.

[0039] Step 3: Identify anomalous disturbance regions in micro-area environmental parameters After obtaining a continuous spatiotemporal parameter matrix, this step aims to identify local regions that deviate from the normal state, which may correspond to early germination points of corrosion.

[0040] Step 3.1: Calculate the rate of change of each parameter over time: Based on the spatiotemporal matrix constructed in step 2.4, for each spatial point Each parameter Calculate its rate of change over time. Taking pH as an example, its change at time... rate of change Defined as: ; The differential calculation is performed across all time points, generating a rate-of-change field for each parameter. Parameters such as potential and Cl⁻ concentration are calculated using the same method. The rate of change reflects the dynamic trend of environmental parameters and helps distinguish between slow drift and sudden disturbances. For example, early corrosion is often accompanied by localized acidification, manifested as a rapid negative change in pH, with a rate of change significantly lower than that of the surrounding area.

[0041] Step 3.2: Set the perturbation thresholds for each parameter and mark outliers: Based on step 3.1, a perturbation threshold is set for the rate of change of each parameter. These thresholds are derived from historical data statistics: under non-corrosive conditions, the standard deviation of the rate of change of each parameter is calculated, and the perturbation threshold is set to the mean plus or minus three times the standard deviation. For example, if the standard deviation of the pH change rate under normal conditions is 0.002 pH / s, then when... When the rate of change of any parameter exceeds its threshold, it is determined to be an abnormal decrease. For each spatial point, if the rate of change of any parameter exceeds its threshold, the point is marked as a "potential disturbance point," and its coordinates and time are recorded. This marking process realizes the transformation from continuous data to discrete events, narrowing the search scope for subsequent analysis.

[0042] Step 3.3: Perform spatial clustering of outliers to identify disturbed regions: Spatial clustering analysis was performed on all potential disturbance points marked in step 3.2. A density-based clustering method was used, with a minimum neighborhood radius of 100 μm and a minimum number of points of 3. If at least two other anomalous points exist within a 100 μm radius of a given point, it is grouped into the same cluster. Each cluster represents a possible environmental disturbance region. After clustering, the geometric center, area, and boundary profile of each cluster were calculated and assigned a unique identifier. This clustering process integrates discrete anomalous points into spatially continuous regions, which better reflects the morphological characteristics of actual corrosion germination.

[0043] Step 3.4: Eliminate spurious disturbances caused by environmental noise: In the perturbation regions identified in step 3.3, there may be spurious signals caused by fluctuations in the external environment (such as sudden temperature changes or humidity fluctuations). To eliminate such interference, a multi-parameter consistency test is introduced: a perturbation region is retained only if multiple parameters (at least two) show significant anomalies simultaneously. For example, if a region only experiences a decrease in pH while the potential and Cl⁻ concentration remain unchanged, it may be due to localized contamination rather than corrosion; however, if a decrease in pH, a negative shift in potential, and an increase in Cl⁻ concentration all occur simultaneously, it strongly suggests an anodic dissolution process. This test filters out isolated parameter anomalies, improving the reliability of perturbation identification.

[0044] Step 4: Reconstructing the distribution of the active field for micro-corrosion After identifying the actual disturbance area, this step constructs a spatial distribution map reflecting the intensity of corrosion activity by fusing information from multiple parameters.

[0045] Step 4.1: Define the corrosion activity index: Based on the disturbed areas identified in step 3.4, a comprehensive index—the corrosion activity index—is defined. This index is used to quantify the corrosion tendency of a localized area. It consists of five main factors: temperature... relative humidity Electrochemical driving force Local acidification degree Chloride ion concentration Its expression is: ; Where T is temperature, RH is relative humidity, This represents the offset of the local potential relative to its initial value. This represents the amount of pH decrease. This represents the increase in chloride ion concentration; Normalized reference values ​​(e.g., 25℃, 60%RH, 1V, 0.5pH, 0.1mol / L); These are weighting coefficients, pre-set according to the material type (e.g., for stainless steel). (It can be appropriately increased). This index compresses multidimensional environmental information into a single scalar, making it easier to visualize and compare.

[0046] Step 4.2: Interpolate and calculate the corrosion activity index field on the spatial grid: Since the probe spacing of the sensing array is 50μm, direct calculation... It exists only at discrete points. To obtain a continuous activity distribution map, inverse distance-weighted interpolation was used to reconstruct it on a fine 10μm × 10μm grid. Field. For any grid point Its activity value is a weighted average of the values ​​of the four nearest surrounding probes: ; in, For grid points to the th The distance between the probes A small constant (e.g., 1 μm²) is used to prevent the denominator from being zero. This interpolation process generates a high-resolution map of corrosion activity distribution, revealing the activity gradient and potential propagation direction.

[0047] Step 4.3: Identify the extreme points and gradient directions in the active field: In the active field generated in step 4.2, local maxima are identified. These points correspond to the regions with the strongest corrosion activity and may be the centers of corrosion pits. Simultaneously, the gradient vector field of the active field is calculated. ; Gradient direction indicates the direction of activity enhancement and may predict the path of corrosion propagation. Edge detection algorithms are used to extract boundary lines where activity values ​​abruptly change; these boundary lines may correspond to the leading edge of passivation film rupture. This analysis transforms the activity field into information with directional and structural characteristics, providing a basis for predicting evolution trends.

[0048] Step 4.4: Generate a dynamic corrosion activity map sequence: The analysis results from step 4.3 are organized chronologically to generate a series of dynamic corrosion activity maps. Each map includes contour lines, extreme point markers, gradient arrows, and boundary lines, visually representing the spatial distribution and evolution of corrosion activity. This map sequence can be played frame by frame, intuitively presenting the emergence of corrosion initiation points, the expansion and migration of active zones, providing a clear basis for manual interpretation and automatic identification.

[0049] Step 5: Tracing the evolution path of corroded micro-regions After reconstructing the distribution of the corrosion active field, this embodiment enters the stage of in-depth tracking of the dynamic behavior of corrosion micro-regions. This step aims to reveal the kinematic characteristics and topological evolution patterns of local corrosion from its initial germination to its continuous expansion by systematically analyzing the spatial evolution of perturbed regions in the time series. This process not only relies on the high-resolution activity map provided in the previous steps, but also needs to combine spatial geometric changes, regional correlations, and morphological evolution logic to achieve a continuous characterization of the corrosion behavior path. The entire tracking process is based on the principle of spatiotemporal consistency, ensuring that the identified evolution trajectory truly reflects the actual changes in the micro-regions on the material surface.

[0050] Step 5.1: Establish the temporal correlation of the perturbation region: Based on the dynamic corrosion activity map sequence generated in step four, the perturbation regions in each frame are first numbered and their spatial boundaries are extracted. Each perturbation region is represented by a set of closed contour lines, and its interior contains attributes such as geometric center coordinates, area size, average corrosion activity index, and boundary perimeter. In adjacent time frames... and Between, calculate each perturbation region in the current frame. With all regions in the next frame spatial intersection area and define the overlap rate. for: ; in Indicates the region The area. If Then it is believed and Continuations belonging to the same physical entity, and establishing temporal links. For newly emerging regions that cannot be matched (i.e., in...) Regions that appear in the middle but have no precursor are marked as "newly emerging disturbances"; for regions that disappear (in the middle)... Exists but (No match found) is marked as a "termination perturbation". This method connects independent perturbation regions at discrete time points into temporally continuous evolutionary chains, with each chain recording all state parameters within its lifecycle. This association mechanism ensures a coherent representation of the eroded region on the time axis, avoiding misjudgments caused by brief signal fluctuations and providing a stable data foundation for subsequent path analysis.

[0051] Step 5.2: Calculate the motion vector and expansion rate of the region: Based on the evolutionary chain constructed in step 5.1, the dynamic behavior of the eroded region is further quantified. This is done for three consecutive frames of data in each evolutionary chain. Extract its geometric center coordinates And calculate the displacement vectors within two adjacent time intervals: ; Take their average value as the motion vector at the current moment. To reduce the impact of instantaneous noise, the direction of this vector represents the overall movement trend of the eroded region, and its magnitude represents the movement speed. Simultaneously, the area change rate is calculated. , as a measure of lateral expansion rate. If and This indicates that the corrosion zone is both expanding and migrating, possibly driven by the electric field gradient or electrolyte flow; if but This is in-situ extension, commonly seen in the deepening process of pitting corrosion. Furthermore, the shape factor is introduced. (in To assess the regularity of a region (using its perimeter): when The time is round, The corrosion pattern is sometimes elongated or branched. The trend of this factor can help determine the corrosion mode (e.g., uniform propagation vs. crack propagation). These parameters together constitute the kinematic description system of the corrosion micro-region, allowing its behavior to be quantitatively compared and classified.

[0052] Step 5.3: Identify branch and merge events: During long-term monitoring, eroded regions may undergo complex topological changes, including a single region splitting into multiple sub-regions (branching) or multiple regions merging into a whole (merging). To accurately capture such events, it is necessary to establish a cross-temporal topological mapping between evolutionary chains.

[0053] Specifically, in time frames A disturbance area If in the next frame The middle corresponds to two or more non-overlapping regions. And the total area of ​​these sub-regions accounts for If the area exceeds 70%, it is considered a branching event. Conversely, if multiple areas... China is integrated into a single region ,and If the area of ​​a region is greater than 1.5 times that of any precursor region, it is considered a merging event. To enhance the robustness of the criterion, boundary curvature analysis is introduced: before branching occurs, the original region boundary often exhibits local bulges or necking, with its absolute curvature value significantly higher than the average level on a certain arc segment; before merging occurs, the boundaries of adjacent regions tend to be parallel and the distance between them is less than 20 μm, indicating a bridging trend. These geometric precursors can serve as auxiliary criteria to reduce the false recognition rate. The identified branching and merging events are marked in the evolutionary chain map and assigned specific symbols (such as "Y" shape for branching and "∩" shape for merging), thereby revealing the nonlinear development characteristics of the corrosion process.

[0054] Step 5.4: Construct the corrosion evolution path diagram: After calculating motion parameters and identifying topological events, all information is integrated to generate a corrosion evolution path map. This map is based on a two-dimensional plane and overlays multiple layers of information: the bottom layer is a simplified diagram of the material surface morphology, the middle layer is the trajectory lines of each evolution chain, and the top layer is event markers and parameter labels. Each trajectory line consists of a series of connected points; the color intensity of the points indicates the corrosion activity index at the corresponding time point, the line width dynamically adjusts with the area size, and the arrow direction indicates the motion trend. For branching events, the trajectory line forks into two or more sub-paths at a specific node; for merging events, multiple paths converge at a point and then continue as a single line segment. Furthermore, key parameters are marked along the path: for example, the expansion rate is marked every 5 time steps. With shape factor The numerical values ​​facilitate observation of its evolution. This path diagram not only presents the spatial movement trajectory of the corrosion micro-regions but also integrates morphological evolution information over time, forming a four-dimensional (x, y, t, morphology) visualization. Through this diagram, the origin point, main expansion direction, active cycle, and potential connection paths of corrosion can be intuitively identified, providing a direct basis for assessing the local stability of materials. Furthermore, this path diagram can be exported as structured data for use by the subsequent trend prediction module.

[0055] Step Six: Predicting Corrosion Expansion Trend After obtaining the historical evolution path of the corrosion micro-regions, this embodiment further extrapolates future expansion trends. This step is not a simple extrapolation, but rather a prediction model with physical basis constructed based on a coupled analysis of environmental driving factors and historical behavior patterns. The prediction results include not only possible future expansion directions and ranges, but also risk level assessments and key time node estimates. The entire prediction process is divided into four progressive stages, gradually transitioning from static field analysis to dynamic scenario simulation.

[0056] Step 6.1: Analyze the consistency of historical path directions: Based on the evolution path diagram generated in step 5.4, the stability of the movement direction of each corrosion region is first evaluated. Data from five consecutive time steps in each evolution chain are selected to calculate the sequence of angles between their movement vectors. ,in For the first With the The angle between vectors. Define the directional consistency index. for: ; in Let be the standard deviation of the angle sequence. When the direction of motion is highly stable, linear extrapolation is suitable; when The presence of such paths indicates that the paths are tortuous and variable, requiring consideration of the effects of environmental disturbances or material heterogeneity. For highly consistent paths, their future trajectory can be predicted by fitting a straight line using the least squares method; for low-consistency paths, corrections based on local environmental gradients are necessary. This analysis provides a basis for selecting subsequent prediction methods, avoiding the forced simplification of complex behaviors.

[0057] Step 6.2: Predict the propagation direction based on the gradient field: Based on the directional consistency analysis, the corrosion activity gradient field reconstructed in step four is introduced. As a proxy variable for physical driving forces, assuming that the corrosion front tends to advance towards more active regions, i.e., following an "uphill diffusion" mechanism, the predicted direction of expansion is then determined. It should be consistent with the direction of the local gradient. Specifically, it should be selected on the leading edge boundary of the current corrosion region. Calculate the value at each of the equidistant sampling points. Vectors, and take their weighted average: ; Weight Using the corrosion activity index at this point allows the highly active region to contribute more to the direction prediction. This formula reflects the "strongest driving force priority" principle, meaning the boundary segment with the strongest activity is more likely to become the expansion breakthrough point. Simultaneously, the influence of material anisotropy is considered: if the existence of grain boundaries or rolling directions is known, a direction preference factor can be superimposed on the gradient vector, for example, assigning a weight of 1.2 times along the grain boundary direction. The final result... This will be the main direction of expansion in the next stage, used to guide the delineation of the forecast range.

[0058] Step 6.3: Estimate the spread distance and time scale: After determining the expansion direction, its spatial extent and temporal progression need to be quantified. The expansion distance is estimated based on historical expansion rates. Correlation with local environmental intensity. Define local driving intensity. The average value within a 10μm ring outside the current region boundary value: ; in These are sampling points on the ring. Based on empirical relationships... Estimate in time Extended distance within ,coefficient Certification is based on material type and environmental conditions (e.g., stainless steel in a seawater environment). This relationship assumes that the expansion rate is proportional to the environmental driving force, which aligns with the fundamental principles of electrochemical dissolution. The timescale prediction incorporates corrosion depth information: if the system integrates micro-area morphology measurements (such as confocal microscopy), a correlation model between area expansion and depth growth can be established, thereby predicting the time point at which the critical depth (e.g., 10% of wall thickness) is reached. For cases without depth data, a conservative estimate is used, setting an upper limit for the maximum expansion rate (e.g., 5 μm / h) to prevent over-extrapolation.

[0059] Step 6.4: Generate the predicted heatmap: Integrate the results from steps 6.2 and 6.3 to generate the future. A heatmap predicting corrosion risk over a given time period. This map uses the current material surface as a base and plots along the prediction direction. Draw a conical region whose angular size is determined by the oscillation range of the historical path (e.g., ±15°). Divide the conical region into multiple concentric rings based on their distance from each other. The color intensity of each ring represents the probability of reaching that region. Calculation, where The distance to the current boundary. To predict the extended distance, For the uncertainty parameter (initially set to) At the same time, key information is annotated next to the heat map: the expected location of the expansion front, the time interval for reaching that location, and the risk level (low / medium / high, depending on...). and (Comprehensive judgment). This heat map provides intuitive support for maintenance decisions, identifying areas that require key monitoring or early intervention.

[0060] Step 7: Output a corrosion status report with multi-parameter fusion After tracking the evolution path of corrosion micro-regions and predicting their future expansion trends, this embodiment enters the final stage of information integration and decision support. This step involves multi-level, multi-dimensional information reconstruction, deeply fusing the generated heterogeneous data streams (including original sensor signals, environmental parameter fields, disturbance region identification results, activity index distribution, evolution chain trajectories, and predicted heatmaps) to form a comprehensive report with spatiotemporal continuity, logical traceability, and operational guidance. This report aims to provide direct evidence for material service status assessment, maintenance strategy formulation, and lifespan prediction.

[0061] Step 7.1: Generate a spatiotemporal overview of the corrosion activity: Based on the predicted heatmap generated in step 6.4, corrosion-related data from all time points in steps two through six are further integrated to construct a high-dimensional spatiotemporal overview of corrosion activity. This map uses a three-dimensional coordinate system: the x-axis and y-axis represent the spatial location of the material surface, and the z-axis represents the monitoring time axis, starting from the initial moment. Extending to the present moment In this space, each identified perturbation region is represented as a two-dimensional contour on the corresponding time layer, and the color of its contour line is based on the corrosion activity index of that region at the corresponding time. A mapping process is performed—darker colors (such as deep red) indicate higher activity, while lighter colors (such as yellow) indicate lower activity. Simultaneously, each outline is filled with a semi-transparent color block, the transparency of which is inversely proportional to the area, ensuring that overlapping areas remain identifiable.

[0062] For continuous regions in the evolutionary chain, thin lines are used to connect their geometric centers at different time levels, forming "erosion trajectory pillars." The diameter of the trajectory pillars varies with the expansion rate. Dynamic changes: the greater the rate, the thicker the line; if a branching event occurs, the trajectory bar splits into two or more sub-bars on the z-axis; if a merging event occurs, multiple trajectory bars merge into one. In addition, one or more environmental background curves, such as the overall average pH value, average chloride ion concentration, and ambient temperature, are superimposed along the z-axis. These curves are plotted as gray lines on the side of the graph, facilitating the observation of the temporal correlation between changes in external conditions and corrosion activity. For example, if multiple new disturbance points appear within 30 minutes after a humidity surge, this response delay can be visually identified on the graph.

[0063] To enhance spatial positioning capabilities, reference markers (such as weld lines, hole edges, and notch locations) are embedded in the material surface in the overview map. These markers are optically aligned with the sensor array coordinate system to ensure that all corrosion events can be mapped to actual structural features. This overview map not only shows the spatial distribution density and temporal activity cycle of corrosion events but also reveals their evolution patterns (such as concentrated outbreaks, slow diffusion, and periodic recurrence), providing a global perspective for subsequent analysis.

[0064] Step 7.2: Extract the timeline of key events: Based on the spatiotemporal overview map generated in step 7.1, key corrosion events with engineering significance are further screened and extracted to construct a timeline of events arranged chronologically. This timeline is not a simple list, but rather automatically identifies turning points that pose a potential threat to material integrity by setting quantitative thresholds and logical rules. Specifically, the criteria for determining key events include the following five categories: The first category is initial disturbance detection: that is, the system identifies the disturbance region that meets the multi-parameter consistency test for the first time and marks it as the starting point of corrosion initiation; The second category is extreme events: including records of the maximum corrosion activity index (such as...). ), fastest expansion rate (e.g.) ), and the rate of change of the maximum shape factor (e.g. These events reflect the severity of the corrosion process; The third category is topological abrupt changes: that is, the first occurrence of branching or merging events. These events usually indicate that corrosion is transforming from local pitting corrosion to a more complex morphology (such as crack networks). The fourth category is the predicted risk leap: This event is triggered when the risk level of a certain area in the predicted heatmap rises from orange to red, or when it is expected that the expansion front will reach the critical structural boundary within 24 hours; The fifth category is environmental coupling response: when corrosion activity is significantly enhanced within a certain time window (e.g., within 1 hour) after a specific environmental disturbance (e.g., a sudden temperature change of ±5°C or a sudden increase in humidity of more than 20%), and the effect is statistically significant. At that time, it was identified as an environmentally induced event.

[0065] Each key event is represented on the timeline by a labeled vertical line. The label includes the event type, occurrence time (accurate to the second), spatial location (coordinates), relevant parameter values, and a brief description. For example: "t=142h23min, (320,180)μm, Merged Event: Regions A and B merged, resulting in a 68% increase in area and a 41% increase in activity index." A zoom slider at the bottom of the timeline allows users to focus on specific time periods for detailed viewing. This timeline not only records milestones in corrosion development but can also be used for retrospective analysis to identify combinations of factors that accelerate corrosion (such as the triple effect of "high temperature + high humidity + chloride ion enrichment").

[0066] Step 7.3: Compile a regional risk classification list: Based on step 7.2, the material surface is divided into several functional zones, and risk classification is performed according to their corrosion status, generating a structured inventory. Zone division is based on the material's structural characteristics and service function, such as: weld zone (including heat-affected zone), base metal zone, edge transition zone, stress concentration zone (e.g., around holes, corners), coating damage zone, etc. Each zone is assessed independently, and the assessment dimensions include: Current status: Does an active disturbance region exist, and what is its maximum value? Value, area growth rate Speed ​​of movement ; Historical behavior: Frequency of new disturbance points in the past 24 hours, whether branching / merging has occurred, and cumulative expansion area; Forecast trend: Predict the proportion of the area covered by high risk (orange or red) in the heat map and the estimated time to reach the critical size (e.g., diameter > 100 μm); Spatial proximity: the shortest distance from other high-risk areas, whether it is near critical load-bearing structures or sealing interfaces.

[0067] Based on the above dimensions, a weighted scoring method is used to calculate the comprehensive risk value for each region. : ; in This is the current activity normalized value (0-1). This is a frequency index of historical events. To predict the risk level (0-1). The weighting coefficient represents the proximity threat level (inversely proportional to the distance to the nearest high-risk area). Set according to the importance of the equipment and safety margin (e.g., pressure-bearing components). Take the higher value). According to The values ​​are divided into four levels: green( No obvious corrosion activity was observed; routine monitoring should be maintained. yellow( There is a potential risk; it is recommended to increase the scanning frequency to once every 5 seconds and pay closer attention. orange color( Corrosion is active and an expansion trend has been confirmed. It is recommended to arrange an inspection in the near future and prepare an intervention plan. red( : An urgent threat has arisen, with corrosion spreading close to a critical size or rate, requiring immediate shutdown for inspection or protective measures.

[0068] The checklist is presented in tabular form, with each row corresponding to a functional area. Columns include the area name, coordinate range, risk level, main basis, monitoring recommendations, and intervention recommendations. For example, a weld area is rated red due to a recent fusion event and a predicted front-end reaching the fusion line within 18 hours, with a recommendation to "immediately conduct ultrasonic testing and assess the feasibility of local repair welding." This checklist provides clear operational guidance for maintenance personnel, enabling a direct transformation from data to decision-making.

[0069] Step 7.4: Output a structured report file: After completing the three information organization tasks described above, this embodiment proceeds to the final report generation and output stage. The goal of this stage is to encapsulate all analysis results into a standardized, exchangeable, and traceable file format to ensure consistency and usability of information across different systems and users.

[0070] First, generate a main report file: in portable document (PDF) format, containing the following sections: 1. Cover page: Project name, material number, monitoring start and end dates, report generation date, responsible unit; 2. Summary page: Summarize the core findings of this monitoring in no more than 300 words, such as "A total of 7 disturbance areas were identified, of which 2 have entered the active expansion stage. It is predicted that area 1 will reach the critical size within 20 hours and it is recommended to prioritize its handling"; 3. Spatiotemporal overview diagram: Embedded with the 3D corrosion activity diagram generated in step 7.1, with legend and coordinate explanation; 4. Key Event Timeline: Displays the event sequence extracted in step 7.2, marking important turning points; 5. Regional Risk Classification List: The assessment results for each functional area are listed in tabular form, highlighting high-risk items; 6. Appendix: Includes technical details such as parameter definition table, basis for weight coefficient values, and coordinate alignment method.

[0071] This PDF report is designed with a two-column layout, combining text and images, and highlighting or bolding key data for easy and quick reading.

[0072] Simultaneously, a data exchange file is generated in Extensible Markup Language (XML) format, following a custom corrosion monitoring data architecture.

[0073] Finally, the report is transmitted to the central monitoring platform via industrial Ethernet, and two copies are stored on local solid-state storage: one full version and one abridged version (containing only PDF and key data summaries). An encrypted channel is used during transmission to ensure data security. Once the report is generated, the system automatically sends a notification to the designated maintenance terminal, indicating "Corrosion status report is ready, please check."

[0074] Through this output mechanism, this embodiment realizes a complete information chain from microscopic sensing signals to macroscopic decision support, ensuring that corrosion monitoring not only stays at the "sensing" level, but also serves actual engineering maintenance, improving the safety and reliability of materials in service.

[0075] In summary, this invention achieves real-time monitoring of multiple key physicochemical parameters by deploying a micro-sensor probe array on the material surface. A spatiotemporal parameter matrix is ​​constructed using the acquired data, and the time-varying rate of change of each parameter is calculated. Anomalies are then marked by setting a perturbation threshold. Perturbation regions are identified through spatial clustering of these anomalies, and a corrosion activity index is defined by combining electrochemical driving force, local acidification degree, and chloride ion concentration, generating a continuous corrosion activity distribution map.

[0076] Furthermore, by tracing the spatial overlap of disturbed areas to form an evolutionary chain, future expansion trends can be predicted. Ultimately, a comprehensive report is generated, including a spatiotemporal overview of corrosion activity, a timeline of key events, and a list of regional risk classifications. This technical solution not only improves the accuracy and efficiency of corrosion monitoring but also provides a scientific basis for preventative maintenance, thereby significantly reducing economic losses and safety hazards caused by corrosion.

[0077] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A method for sensing multiple parameters of the surface microenvironment for material corrosion, characterized in that, Includes the following steps: An array of sensing probes is arranged on the surface of the material to acquire raw signals of potential, pH value, chloride ion concentration, humidity and temperature; The original signal is converted into physicochemical parameter values, a spatiotemporal parameter matrix containing spatial coordinates and timestamps is constructed, the rate of change of each parameter in the time dimension is calculated based on the spatiotemporal parameter matrix, a perturbation threshold is set and outliers are marked; Spatial clustering of anomalies is performed to identify perturbation regions with spatial continuity. Based on the temperature, relative humidity, electrochemical driving force, local acidification degree and chloride ion concentration in the perturbation region, a corrosion activity index is defined. The corrosion activity index is interpolated on the spatial grid to generate a continuous corrosion activity distribution map, and the spatial overlap relationship of the disturbed region at different times is tracked to form an evolution chain and calculate the motion vector and expansion rate. Based on the gradient direction and historical expansion rate in the corrosion activity distribution map, the future expansion trend is predicted, and a comprehensive report is generated that includes a spatiotemporal overview map of corrosion activity, a timeline of key events, and a list of regional risk classifications.

2. The multi-parameter sensing method for surface microenvironment in response to material corrosion according to claim 1, characterized in that, The acquisition of the original signal includes: The probe tip integrates a platinum microelectrode, a pH-sensitive membrane, a silver-doped tin oxide film, a porous silica coating, and a platinum resistance thermometer. The probes are then arranged on the material surface in a two-dimensional grid pattern, and the electrical connection between the probes and the data acquisition unit is achieved through a flexible thin-film circuit. The output values ​​of each probe were recorded under standard conditions to establish a mapping relationship between the original signal and physicochemical parameters.

3. The multi-parameter sensing method for surface microenvironment in response to material corrosion according to claim 1, characterized in that, The process of converting the original signal into physicochemical parameter values ​​includes: First, read the potential difference, pH membrane potential, chloride ion potential, humidity capacitance and resistance values ​​of each probe during the scan cycle; Then, based on the mapping relationship, the potential difference is converted into a potential value relative to the standard hydrogen electrode; Then, the local pH value and chloride ion concentration are derived from the pH membrane potential and chloride ion potential through electrochemical equations, respectively. Finally, the humidity capacitance and resistance values ​​are converted into relative humidity percentages and temperature values ​​to form a complete set of physicochemical parameters.

4. The multi-parameter sensing method for surface microenvironment in response to material corrosion according to claim 1, characterized in that, The calculation of the rate of change of each parameter in the time dimension based on the spatiotemporal parameter matrix includes: First, extract the physicochemical parameter values ​​at consecutive time points at each spatial point, and then calculate the difference between parameter values ​​between adjacent time points; Then divide the difference result by the corresponding time interval to obtain the change per unit time. Statistical analysis was performed on the changes in each parameter to determine its normal fluctuation range.

5. The multi-parameter sensing method for surface microenvironment in response to material corrosion according to claim 1, characterized in that, The setting of the perturbation threshold and marking of outliers includes: Based on the normal fluctuation range, set the upper and lower limits of the rate of change of each parameter, and determine whether the rate of change of any parameter at each spatial point exceeds the corresponding threshold. Spatial points exceeding the threshold are marked as potential disturbance points. The coordinates, time, and parameter type of all potential disturbance points are recorded to form an initial disturbance set.

6. The multi-parameter sensing method for surface microenvironment in response to material corrosion according to claim 1, characterized in that, The spatial clustering of outliers includes: The minimum neighborhood radius is set to 100μm, and the minimum number of points is 3. For each potential perturbation point in the initial perturbation set, search for other points within its neighborhood; Points that meet the density condition are grouped into the same cluster, and the geometric center, area and boundary contour of each cluster are calculated to form a perturbation region with spatial morphology.

7. The multi-parameter sensing method for surface microenvironment in response to material corrosion according to claim 1, characterized in that, The corrosion activity index is defined by combining temperature, relative humidity, electrochemical driving force, local acidification degree, and chloride ion concentration in the disturbed region, including: First, extract the temperature, relative humidity, potential offset from the initial value, pH decrease, and chloride ion concentration increment within the disturbed area; then, normalize each offset individually; finally, sum the normalized values ​​by weight to obtain the corrosion activity index.

8. The multi-parameter sensing method for surface microenvironment in response to material corrosion according to claim 1, characterized in that, The spatial overlap relationship of the tracking disturbance region at different times includes: Extract the spatial boundary of each perturbation region in adjacent time frames, and then calculate the spatial intersection area between the regions in the previous frame and the regions in the next frame. Determine if the intersection area is greater than 50% of the respective areas. If the condition is met, establish a time link to form an evolutionary chain.

9. A multi-parameter sensing method for surface microenvironment in response to material corrosion according to claim 1, characterized in that, The prediction of future expansion trends based on the gradient direction and historical expansion rate in the corrosion activity distribution map includes: First, select motion vectors at multiple consecutive time points in the evolutionary chain; then calculate their average direction as the historical expansion direction; next, select multiple sampling points on the leading edge boundary of the perturbation region; finally, calculate the weighted average of the corrosion activity gradient directions at the sampling points, and combine it with the historical expansion direction to determine the prediction path.

10. A multi-parameter sensing system for surface microenvironment oriented towards material corrosion for implementing the method as described in any one of claims 1-9, characterized in that, include: A sensor probe array is arranged on the material surface to acquire raw signals of potential, pH value, chloride ion concentration, humidity and temperature; The data acquisition unit connects to the probe, reads the raw signal, and adds a timestamp and spatial coordinate label. The processing unit is used to convert the raw signal into physicochemical parameter values, construct a spatiotemporal parameter matrix containing spatial coordinates and timestamps, calculate the rate of change of each parameter in the time dimension based on the spatiotemporal parameter matrix, set a perturbation threshold and mark outliers, perform spatial clustering on outliers to identify perturbation regions, define a corrosion activity index by combining temperature, relative humidity, electrochemical driving force, local acidification degree and chloride ion concentration in the perturbation region, interpolate the corrosion activity index on the spatial grid to generate a corrosion activity distribution map, track the spatial overlap relationship of the perturbation region at different times to form an evolution chain and calculate the motion vector and expansion rate, and predict the future expansion trend based on the gradient direction and historical expansion rate in the corrosion activity distribution map. Output device for generating a comprehensive report that includes a spatiotemporal overview of corrosion activity, a timeline of key events, and a list of regional risk classifications.

Images