High-temporal-spatial-resolution full-profile real sea precise corrosion test method and system

Abstract

The invention provides a high-temporal-spatial-resolution full-profile real-sea accurate corrosion test method and system, and the method comprises the steps: dividing the sea level into three layers of monitoring regions, namely an atmospheric near-sea-level layer, a sea-gas boundary layer and a seawater mixing layer, deploying corresponding sensor arrays in each monitoring region, and synchronously collecting the environmental parameters of each layer in real time; a standard corrosion coupon and a corrosion monitoring sensor for monitoring the local corrosion state of the sample are arranged in each monitoring area; performing space-time alignment on the acquired data to form an environment-corrosion strong association data set; an atmosphere-ocean coupling forecasting system is constructed, simulation and prediction of future environmental parameters are achieved, simulation results serve as input variables of a corrosion prediction model, and a material corrosion life prediction model is established in combination with historical test data. According to the invention, synchronous monitoring of multi-dimensional environmental parameters and material corrosion data within a range of 30 meters above and below the sea level can be realized, and an accurate basis is provided for material selection and corrosion prediction of marine equipment.

Description

Technical Field

[0001] This invention relates to the field of corrosion monitoring technology for marine engineering materials, specifically to a high spatiotemporal resolution full-profile real-sea precision corrosion test method and system, applicable to the study of corrosion behavior and life prediction of marine equipment materials in complex marine environments. Background Technology

[0002] The marine environment is characterized by multiphase coupling and highly variable parameters. From the near-sea level atmospheric layer above sea level to the sea-atmosphere boundary layer and seawater mixing layer below sea level, significant differences exist in parameters such as temperature, salinity, dissolved oxygen, wave energy, and salt spray deposition, creating complex corrosion microenvironments. For example, in tropical marine environments, high salinity (30–35‰), high humidity (annual average relative humidity > 85%), and strong ultraviolet radiation (annual total radiation > 5000 MJ / m²) are prevalent. 2 Frequent typhoons and high wave fields cause electrochemical corrosion, pitting, and stress corrosion cracking in metallic materials, peeling and aging of coatings, and failure risks such as fiber debonding and media penetration in composite materials. Statistics show that marine corrosion losses in my country exceed 100 billion yuan annually, accounting for about one-third of total corrosion losses. Due to the harsh environment, the corrosion rate of equipment in marine areas is 30% to 50% higher than in temperate sea areas, seriously affecting the service safety and lifespan of ships, offshore platforms, subsea pipelines, and other facilities.

[0003] Traditional material corrosion testing methods are insufficient to meet the needs of modern marine engineering. Early natural exposure tests (such as sun exposure of corrugated plates) can only cover a single environmental area (such as the atmospheric zone or the fully submerged zone), neglecting the complex corrosion mechanisms at the air-sea interface (such as the splash zone and tidal zone). In this area, due to wave splashing, alternating wet and dry conditions, localized salt concentrations and ample oxygen supply can result in corrosion rates that are 2 to 3 times higher than in the fully submerged zone, yet existing test stations have gaps in monitoring this area. While indoor accelerated simulation tests can control single environmental variables, they cannot reproduce the synergistic effects of multiple factors in the marine environment (temperature, salinity, microorganisms, and mechanical loads), leading to significant discrepancies between test results and actual sea service performance. Furthermore, existing corrosion monitoring technologies rely on offline detection (such as periodic retrieval and weighing of corrugated plates), with long data acquisition intervals (usually monthly or yearly), and a lack of spatiotemporal synchronization between environmental parameters (such as wind speed, salt spray deposition, and dissolved oxygen profile) and corrosion data (such as corrosion rate and product composition), making it difficult to construct accurate environment-corrosion coupling models. For example, the correlation analysis between corrosion rate and salinity in traditional databases is based only on annual average data and cannot capture the instantaneous acceleration effect of the sudden increase in salt spray deposition on corrosion during typhoons.

[0004] Despite the establishment of hundreds of environmental testing stations (fields) both domestically and internationally, key technological bottlenecks remain in the monitoring system for the full-profile microenvironment of the ocean. Internationally, institutions such as the Laque Corrosion Technology Research Center in the United States and the Choshi Test Site in Japan have accumulated long-term data in the fields of seawater corrosion and atmospheric exposure, but these primarily focus on temperate and nearshore environments, lacking stratified monitoring of tropical deep-sea environments. In China, of the existing 32 field scientific observation platforms for material environmental corrosion, only 7 are seawater stations, and most are concentrated in shallow sea areas, failing to form a gridded monitoring system spanning 30 meters above and below sea level. Specifically, existing technologies have three major shortcomings: First, the monitoring area is incomplete, failing to cover the salt spray deposition gradient in the near-sea level layer (0–10 meters), the wave energy distribution in the air-sea boundary layer (0–5 meters), and the temperature and salinity profile in the seawater mixing layer (5–30 meters), making it difficult to reveal the impact of material transport (such as oxygen, ions, and microorganisms) between different layers on corrosion. Second, the monitoring accuracy is insufficient; the spatial resolution of environmental parameters is generally greater than 5 meters, and the temporal resolution is greater than 1 hour, while the detection accuracy of corrosion parameters (such as metal thinning) is only at the micrometer level, making it impossible to capture the dynamic changes in the initial stage of corrosion (such as the formation of nanoscale oxide films). Third, the prediction technology is lagging behind; existing models rely on empirical formulas to fit historical data and do not integrate real-time monitoring data with regional climate model simulation results, resulting in insufficient predictive ability for extreme weather (such as salinity anomalies caused by typhoons and El Niño) and long-term environmental evolution (such as rising water temperatures due to global warming), thus failing to provide forward-looking guidance for equipment design.

[0005] As marine engineering equipment becomes more deep-sea and complex, higher demands are placed on the accurate assessment of material corrosion behavior. For example, deep-sea oil and gas platforms require materials to withstand high hydrostatic pressure, low dissolved oxygen (<200 μmol / L), and sulfate-reducing bacteria erosion at a water depth of 30 meters. Existing testing methods cannot simulate the synergistic effect of electrochemical corrosion and microbial fouling under such conditions. During service, marine engineering equipment must cope with high humidity and high salt spray atmospheric environments and high-speed current seawater environments. Traditional single-area corrosion data cannot support the reliability design throughout the equipment's entire life cycle. Furthermore, my country lacks a multi-dimensional performance evaluation platform under real-sea conditions in the research and development of new marine engineering materials (such as corrosion-resistant titanium alloys and titanium-steel composites), resulting in slow progress in the industrialization of these materials. Therefore, establishing a corrosion testing and monitoring system covering the entire marine micro-environment with high spatiotemporal resolution, enabling real-time synchronous acquisition, deep coupling analysis, and long-term prediction of environmental parameters and corrosion data, has become an urgent need to overcome existing technological bottlenecks and ensure the safe operation of marine equipment. This technology can not only provide material selection basis for marine resource development, but also promote the transformation of corrosion prediction models from experience-driven to data-mechanism fusion-driven, which is of great significance to the development of marine technology in my country. Summary of the Invention

[0006] To address the problems and shortcomings of existing technologies, this invention provides a high spatiotemporal resolution full-profile real-sea precision corrosion test method and system, which enables simultaneous monitoring of multi-dimensional environmental parameters and material corrosion data within a 30-meter range above and below sea level. By establishing a database with a strong correlation between environment and corrosion, it provides accurate basis for the selection of materials and corrosion prediction for marine equipment.

[0007] The present invention achieves the above objectives through the following technical solutions:

[0008] A high-spatiotemporal resolution, full-profile, precise marine corrosion testing method includes:

[0009] The area within 30 meters above and below sea level is divided into three monitoring zones: the near-sea level atmospheric layer, the air-sea boundary layer, and the seawater mixing layer. Corresponding sensor arrays are deployed in each monitoring zone to collect environmental parameters of each layer in real time.

[0010] Standard corrosion plates made of different materials and surface treatment processes are deployed in each monitoring area, along with corrosion monitoring sensors to monitor the local corrosion status of the samples. An online monitoring device is installed to record the corrosion morphology, thickness changes, and electrochemical parameters of the samples in real time. The environmental parameters and corrosion data collected in real time are transmitted to the data center via a wireless sensor network or submarine optical cable.

[0011] The collected environmental parameters at each layer and the corrosion data of the corresponding areas were spatiotemporally aligned to form a strongly correlated "environment-corrosion" dataset containing multiple environmental factors and multiple corrosion indicators. Based on the multi-factor coupling model, the synergistic effect of each environmental parameter on the corrosion process was analyzed, and a quantitative relationship model between environmental elements and corrosion rate was established.

[0012] A geophysical model was used to construct an atmosphere-ocean coupled forecasting system to simulate and predict future environmental parameters. The simulation results were used as input variables for a corrosion prediction model, and a material corrosion life prediction model was established by combining historical experimental data.

[0013] According to the present invention, a high spatiotemporal resolution full-profile real-sea precision corrosion test method is provided, wherein the monitoring parameters of the near-sea atmospheric plane layer include: temperature and humidity data collected in real time by a high-precision temperature and humidity sensor; salt spray deposition rate monitored by a salt spray deposition meter to quantify the salt content in the atmosphere; wind speed and wind direction information synchronously acquired by a wind speed and wind direction sensor; and atmospheric pollutant concentrations detected by an atmospheric pollutant analyzer, including at least key corrosive components such as chloride ions and sulfur dioxide.

[0014] The monitoring parameters of the air-sea boundary layer include: sea surface temperature, salinity, dissolved oxygen and pH value measured simultaneously by a multi-parameter water quality sensor; wave energy parameters measured by a wave buoy; and ocean current velocity vector obtained by an acoustic Doppler current profiler.

[0015] The monitoring parameters of the seawater mixing layer include: the stratified water temperature and salinity gradient continuously monitored by the CTD temperature, salinity and depth instrument system, the dissolved oxygen profile distribution measured by the dissolved oxygen probe, the water turbidity monitored by the turbidity meter, and the distribution characteristics of the microbial community analyzed by the microbial sampler.

[0016] According to the present invention, a high spatiotemporal resolution full-profile real-sea precision corrosion test method is provided. When dividing the monitoring area, the sea surface is used as the reference, and the above-water part is divided into multiple monitoring areas at preset height intervals, namely the near-sea level layer of the atmosphere. Steel platforms are set up on each layer using a meteorological gradient iron tower. Each platform is equipped with multiple sets of detachable sample racks and is fixed to the iron tower support through an insulating and anti-slip structure. The sample racks are equipped with standardized hanging plate installation holes and electrochemical insulation measures. The underwater part is divided into multiple monitoring areas at preset depth intervals, namely the sea-atmosphere boundary layer and the seawater mixing layer. Drawer-type sample racks are installed on the main cable of each layer using a buoy system. The sample racks are made of high-strength corrosion-resistant materials and are equipped with protective structures and rapid sampling devices.

[0017] According to the present invention, a high spatiotemporal resolution full-section marine precision corrosion test method is provided. When the sample rack is arranged in layers, each group of sample racks in the above-water section is equipped with no less than 72 standard-sized hanging plates, and the spacing between samples is no less than 20 mm to avoid electrochemical interference. In the underwater section, each group of sample racks is fixed with no less than 72 standard hanging plates, and the spacing between samples is no less than 50 mm. Both are designed in a modular manner to achieve periodic batch recovery. Samples are recovered from each layer of sample racks according to different cycles, and at least 3 parallel samples are retained in each group to ensure full coverage of the exposure cycle.

[0018] According to the present invention, a high spatiotemporal resolution full-profile real-sea precision corrosion test method is provided, wherein the construction of the "environment-corrosion" strongly correlated dataset includes:

[0019] The raw environmental parameter data and corrosion data collected by each layer of sensors are denoised, missing values ​​are imputed, and outliers are removed.

[0020] To address the nonlinear time offset between environmental parameters and corrosion data caused by differences in acquisition frequency, transmission delay, or equipment response time, the Dynamic Time Warping (DTW) algorithm is used to align the two types of data over time.

[0021] The aligned data is timestamped to ensure that environmental parameters and corrosion data strictly correspond in the time dimension.

[0022] By combining the spatial coordinate information of each layer of monitoring area, environmental parameters and corrosion data are mapped to a unified spatial coordinate system through Geographic Information System (GIS) technology, thereby achieving spatial dimension alignment.

[0023] The spatiotemporally aligned environmental parameters and corrosion data are stored in a structured database. Multivariate statistical analysis is used to mine the strong correlation between environmental factors and corrosion indicators, forming a "environment-corrosion" strongly correlated dataset containing multiple environmental factors and multiple corrosion indicators.

[0024] According to the present invention, a high spatiotemporal resolution full-profile marine corrosion testing method is provided, wherein the dynamic time warping (DTW) algorithm is used to align environmental parameters and corrosion data over time, specifically including the following steps:

[0025] Construct time series matrices E for environmental parameters and C for corrosion data, respectively, where E = [e1, e2, ..., e2]. n ] T C = [c1, c2, ..., c m ] T n and m are the number of sampling points for the two types of data, respectively;

[0026] Define the time window range W = [t s ,t e ], where t s To align the start time, t e To align the termination times, ensure that the time spans of the two types of data within the window are consistent;

[0027] Construct an n×m distance matrix D, where the element D(i,j) represents the i-th sampling point e in the environmental parameter sequence. i With the j-th sampling point c in the corrosion data sequence j The Euclidean distance is calculated using the following formula:

[0028]

[0029] Where k is the number of feature dimensions of the environmental parameters or corrosion data;

[0030] Initialize the cumulative distance matrix Γ, where Γ(i,j) represents the minimum cumulative distance from the starting point (1,1) to point (i,j), which is calculated recursively using dynamic programming and is expressed as the following formula:

[0031]

[0032] The boundary conditions are Γ(1,1)=D(1,1), Γ(i,1)=Γ(i-1,1)+D(i,1), Γ(1,j)=Γ(1,j-1)+D(1,j);

[0033] Backtracking from the endpoint (n,m) of the cumulative distance matrix to the starting point (1,1), extract the path P = [(i1,j1),(i2,j2),...,(i_p,j_p)] that minimizes the cumulative distance, where (i_p,j_p) = (n,m);

[0034] Path P is the optimal time matching path between the environmental parameter sequence and the corrosion data sequence, achieving non-linear alignment of the two types of data on the time axis;

[0035] The formula for calculating the average time offset Δt between the two types of data after alignment is:

[0036]

[0037] in, and These are the timestamps of the environmental parameters and corrosion data at the k-th matching point, respectively.

[0038] If Δt≤ε, the alignment result is considered valid; otherwise, the time window range W is adjusted and recalculated.

[0039] According to the present invention, a high spatiotemporal resolution full-profile marine precision corrosion test method is provided, and the establishment of a quantitative relationship model between environmental factors and corrosion rate includes:

[0040] We employ multivariate regression models or physicochemical coupling models based on machine learning, and introduce interaction terms between environmental parameters into the models based on spatiotemporally aligned environmental parameters and corrosion rate data, in order to characterize the nonlinear influence of the synergistic effect of multiple factors on the corrosion process.

[0041] The contribution of each environmental parameter and its interaction terms to the corrosion rate is quantified through model sensitivity analysis, and the dominant corrosion factors and key synergistic combinations are identified.

[0042] Principal component analysis (PCA) or partial least squares regression (PLSR) were used to reduce the dimensionality of high-dimensional environmental parameters, extract the set of main environmental factors affecting corrosion rate, and analyze their spatiotemporal distribution characteristics.

[0043] An explicit quantitative relationship model between environmental factors and corrosion rate is constructed based on the training dataset. The model may take the form of, but is not limited to, multinomial regression equations, neural network models, or support vector machine models. The output of the model is a predicted value of corrosion rate.

[0044] According to the present invention, a high spatiotemporal resolution full-profile real-sea precision corrosion test method is provided. When constructing an atmosphere-ocean coupled forecasting system, based on a geophysical model, it integrates atmospheric boundary layer dynamics, ocean turbulence mixing and air-sea interface flux exchange modules to construct a high-resolution atmosphere-ocean coupled numerical model covering the target sea area.

[0045] By integrating satellite remote sensing, buoy observation and meteorological gradient tower measured data through data assimilation technology, the initial field and boundary conditions of the model are optimized to improve the accuracy of future environmental parameter forecasts.

[0046] Output future environmental parameter prediction results, including temperature, humidity, wind speed, salt spray deposition rate, and air pollutant concentration in the atmosphere 0-30 meters above sea level, as well as the spatiotemporal distribution data of temperature, salinity, dissolved oxygen, pH value, wave energy, and ocean current speed in the seawater layer 0-30 meters below sea level.

[0047] The future environmental parameters output by the marine climate model, the long-term monitoring data stored in the historical environmental database, and the online data collected by real-time sensors are spatiotemporally aligned and unified to the same spatial grid and time step.

[0048] After standardizing the fused data, a corrosion prediction model is constructed.

[0049] According to the present invention, a high spatiotemporal resolution full-profile real-sea precision corrosion test method is provided, wherein the construction of the corrosion prediction model includes:

[0050] LSTM Neural Network Sub-model: Construct a two-layer LSTM network structure. The input layer receives historical environmental data and real-time monitoring data in time series form. The hidden layer captures the long-term dependence of environmental parameters through a gating mechanism. The output layer generates the time trend prediction component of corrosion rate.

[0051] Random Forest Sub-model: Using future environmental parameters and key environmental elements output by marine climate models as input features, the feature subset selection is optimized through out-of-bag error estimation of the random forest algorithm, and the nonlinear response component of corrosion rate is output.

[0052] Model fusion: The outputs of LSTM and random forest are fused using a weighted average method or a stacked ensemble method. The weight coefficients are determined by a Bayesian optimization algorithm, and finally a comprehensive corrosion rate prediction value is generated that integrates the spatiotemporal evolution characteristics and the nonlinear relationship between the environment and corrosion.

[0053] A high-spatiotemporal resolution full-profile real-sea precision corrosion testing system includes:

[0054] The marine microenvironment monitoring system includes a layered sensor array and a data acquisition module, supporting real-time synchronous monitoring of multiple parameters;

[0055] The corrosion test monitoring system includes corrosion-coated samples, in-situ monitoring equipment, and a remote transmission module, which is used to achieve multi-dimensional characterization of the corrosion process.

[0056] The data center integrates an environmental corrosion database and a data analysis platform, and has functions for data storage, spatiotemporal alignment, and coupled analysis.

[0057] The marine environment and climate simulation system is a numerical simulation platform based on Earth models, used to provide data for predicting future environmental parameters.

[0058] The corrosion prediction module is used to integrate real-sea test data and simulation results to establish an integrated corrosion prediction model for materials, components, and equipment.

[0059] Therefore, compared with existing technologies, the high spatiotemporal resolution full-profile real-sea precision corrosion testing method proposed in this invention has the following beneficial effects:

[0060] 1. Precise full-profile monitoring to reveal complex corrosion mechanisms: This invention utilizes full-profile layered monitoring up to 30 meters above and below sea level to cover key environmental parameters (such as salt spray deposition, wave energy, dissolved oxygen profile, etc.) of the near-sea level layer, air-sea boundary layer, and seawater mixing layer. It achieves synchronous data acquisition with spatial resolution ≤1 meter and temporal resolution ≤10 minutes, accurately capturing the differences in corrosion behavior under multiphase coupled environments (such as alternating wet and dry conditions, salinity gradients, and microbial distribution). This provides full-dimensional real-sea data support for revealing the corrosion failure mechanisms of marine engineering materials in different microenvironments, solving the problem of insufficient monitoring of key areas such as the air-sea interface and deep-sea mixing layer in traditional experiments.

[0061] 2. High spatiotemporal resolution data correlation supports precise material selection and design: This invention constructs a database with strong correlation between environment and corrosion, achieving spatiotemporal alignment of more than 10 environmental parameters with corrosion data (weight loss rate, corrosion rate, product composition, etc.), breaking through the bottleneck of traditional data "discretization and low correlation". Based on this database, it can provide precise matching basis for "environment-material-service performance" for equipment such as ships and marine platforms in typical marine environments such as high temperature, high salinity, and strong waves, significantly reducing the trial and error cost of new material research and development, shortening the equipment material selection cycle by more than 30%, and promoting the transformation of marine engineering materials from "experience-based selection" to "data-driven design".

[0062] 3. Integrating Predictive Technology to Enhance Equipment Service Safety and Maintenance Efficiency: This invention integrates real-world marine monitoring data with Earth climate model simulations (refined atmospheric-oceanic environmental forecasts for the next 100 years) to establish a corrosion prediction technology that couples "data-driven + mechanistic model." This technology can provide early warnings of corrosion risks caused by extreme environments (typhoons, salinity anomalies) 6-12 months in advance, with prediction accuracy more than 40% higher than traditional empirical formulas. Based on the prediction results, a dynamic maintenance strategy combining "regular + real-time" maintenance can be developed, reducing ineffective maintenance costs by more than 30%, avoiding sudden equipment failures due to corrosion, and significantly improving the service safety and reliability of naval equipment and marine engineering facilities.

[0063] 4. Multi-dimensional evaluation system to accelerate the industrialization of new materials: This invention achieves multi-scale characterization from nanoscale corrosion film growth to macroscopic corrosion morphology by layering standard samples and new materials (corrosion-resistant steel, titanium alloy, titanium-steel composite material, corrosion-resistant coating, etc.) and equipping them with in-situ electrochemical sensors and other equipment. This solves the problem of insufficient analysis of the synergistic effects of multiple factors such as microbial fouling and stress corrosion in existing technologies.

[0064] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments. Attached Figure Description

[0065] Figure 1 This is a flowchart of an embodiment of a high spatiotemporal resolution full-section real-sea precision corrosion test method according to the present invention.

[0066] Figure 2 This is a schematic diagram of a 30-meter meteorological gradient tower on water in an embodiment of a high spatiotemporal resolution full-section real-sea precision corrosion test method of the present invention.

[0067] Figure 3 This is a schematic diagram of a 30-meter underwater buoy in an embodiment of a high spatiotemporal resolution full-section real-sea precision corrosion testing system of the present invention.

[0068] Figure 4 This is a schematic diagram of the full-section, layered monitoring area 30 meters above and below sea level in an embodiment of the high spatiotemporal resolution full-section real-sea precision corrosion testing system of the present invention.

[0069] Figure 5 This is a technical roadmap for the environmental-corrosion data coupling analysis and prediction model in an embodiment of a high spatiotemporal resolution full-profile real-sea precision corrosion testing system of the present invention. Detailed Implementation

[0070] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0071] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0072] See Figure 1 This embodiment provides a high spatiotemporal resolution full-section real-sea precision corrosion test method, including:

[0073] Step S1: Divide the area within 30 meters above and below the sea level into three monitoring zones: the near-sea level atmospheric layer, the air-sea boundary layer, and the seawater mixing layer. Deploy corresponding sensor arrays in each monitoring zone to collect environmental parameters of each layer in real time.

[0074] Step S2 involves deploying standard corrosion plates (such as metals, coatings, and composite materials) made of different materials and surface treatment processes in each monitoring area, as well as corrosion monitoring sensors (such as electrochemical impedance sensors and hydrogen flux sensors) for monitoring the local corrosion state of the samples. Online monitoring devices (such as high-definition cameras and electrochemical monitoring sensors) are then installed to record the corrosion morphology, thickness changes, and electrochemical parameters of the samples in real time. The real-time collected environmental parameters and corrosion data are transmitted to a data center via a wireless sensor network or submarine optical cable, supporting networked access and real-time curve display. Specifically, the corrosion rate is calculated by monitoring sample thickness changes using the resistance method in the above-water portion, while the corrosion rate is monitored using a weak polarization method with a three-electrode system in the underwater portion.

[0075] Step S3: The collected environmental parameters (temporal resolution ≤ 10 minutes, spatial resolution ≤ 1 meter) and the corrosion data (including weight loss rate, corrosion rate, product composition, etc.) of the corresponding areas are spatiotemporally aligned to form a strongly correlated "environment-corrosion" dataset containing multiple environmental factors and multiple corrosion indicators.

[0076] Step S4: Based on a multi-factor coupling model (such as response surface methodology or artificial neural network), analyze the synergistic effect of various environmental parameters on the corrosion process and establish a quantitative relationship model between environmental factors and corrosion rate.

[0077] Step S5: An atmospheric-ocean coupled forecasting system is constructed using geophysical models such as atmospheric models, ocean models, and wave models to simulate and predict environmental parameters (such as extreme temperatures, typhoon wave fields, and salinity anomalies) for the future (e.g., 100 years). The simulation results are used as input variables for the corrosion prediction model, and a material corrosion life prediction model considering long-term environmental evolution is established by combining historical experimental data.

[0078] Among them, the monitoring parameters for the near-sea level atmosphere (0-10 meters above sea level) include temperature, humidity, salt spray deposition rate, wind speed, and atmospheric pollutants (Cl). - Monitoring parameters for the ocean-atmosphere boundary layer (0-5 meters below sea level) include sea surface temperature, salinity, dissolved oxygen, pH, wave energy, and ocean current velocity; monitoring parameters for the mixed layer (5-30 meters below sea level) include stratified water temperature, salinity gradient, dissolved oxygen profile, turbidity, and microbial distribution.

[0079] Specifically, the monitoring parameters for the near-sea level atmosphere include: real-time temperature and humidity data collected by high-precision temperature and humidity sensors, with measurement ranges covering -80℃ to 60℃ and 0% to 100% RH respectively, and accuracies reaching ±0.226℃ and ±1% RH; monitoring of salt spray deposition rate using a salt spray deposition meter to quantify the salt content in the atmosphere; wind speed and direction information synchronously acquired by anemometers, with a wind speed measurement range of 0–100 m / s and an accuracy of ±0.3 m / s, and a wind direction accuracy of ±3°; and atmospheric pollutant concentrations detected by an atmospheric pollutant analyzer, including at least chloride ions (Cl). - Key corrosive components include sulfur dioxide (SO2); monitoring parameters for the sea-air boundary layer include: sea surface temperature, salinity, dissolved oxygen, and pH value measured simultaneously by multi-parameter water quality sensors, with dissolved oxygen measurement range of 0–500 μM and accuracy ±5%; wave energy parameters measured using wave buoys; and ocean current velocity vectors obtained using acoustic Doppler current profilers; monitoring parameters for the seawater mixing layer include: stratified water temperature and salinity gradients continuously monitored by a CTD temperature, salinity, and depth instrument system; dissolved oxygen profile distribution measured using a dissolved oxygen probe; water turbidity monitored using a turbidity meter; and microbial community distribution characteristics analyzed using a microbial sampler.

[0080] In step S1 above, when dividing the monitoring area, the sea surface is used as the reference, and the above-water part is divided into multiple monitoring areas at preset height intervals, namely the near-sea level layer of the atmosphere. Steel platforms are set up on each layer based on the meteorological gradient tower. Each platform is equipped with multiple sets of detachable sample racks and is fixed to the tower support through an insulating and anti-slip structure. The sample racks are equipped with standardized hanging plate installation holes and electrochemical insulation measures. The underwater part is divided into multiple monitoring areas at preset depth intervals, namely the sea-air boundary layer and the seawater mixing layer. Drawer-type sample racks are installed on the main cable of each layer based on the buoy system. The sample racks are made of high-strength corrosion-resistant materials and are equipped with protective structures and rapid sampling devices.

[0081] When the sample racks are arranged in layers, each sample rack in the above-water section is equipped with no less than 72 standard-sized hanging plates, and the spacing between samples is no less than 20 mm to avoid electrochemical interference. Each sample rack in the underwater section is fixed with no less than 72 standard hanging plates, and the spacing between samples is no less than 50 mm. Both are designed in a modular manner to achieve periodic batch recovery. Samples are recovered from each layer of sample racks according to different cycles, and at least 3 parallel samples are retained in each group to ensure full coverage of the exposure cycle.

[0082] In step S3 above, the construction of the "environment-corrosion" strongly correlated dataset includes:

[0083] The raw environmental parameter data (including temperature, humidity, salt spray deposition rate, wind speed, atmospheric pollutant concentration, sea surface temperature, salinity, dissolved oxygen, pH value, wave energy, ocean current speed, stratified water temperature, salinity gradient, dissolved oxygen profile, turbidity, and microbial distribution) and corrosion data (including corrosion morphology images, thickness changes, and electrochemical parameters) collected by each layer sensor are denoised, missing values ​​are imputed, and outliers are removed.

[0084] To address the nonlinear time offset between environmental parameters and corrosion data caused by differences in acquisition frequency, transmission delay, or equipment response time, the Dynamic Time Warping (DTW) algorithm is used to align the two types of data over time.

[0085] The aligned data is timestamped to ensure that environmental parameters and corrosion data strictly correspond in the time dimension.

[0086] By combining the spatial coordinate information of each layer of monitoring area (such as water height and underwater depth), environmental parameters and corrosion data are mapped to a unified spatial coordinate system through geographic information system (GIS) technology to achieve spatial dimension alignment.

[0087] The spatiotemporally aligned environmental parameters and corrosion data are stored in a structured database. Multivariate statistical analysis methods (such as principal component analysis and correlation analysis) are used to explore the strong correlation between environmental factors and corrosion indicators, forming a "environment-corrosion" strongly correlated dataset containing multiple environmental factors and multiple corrosion indicators.

[0088] Specifically, the Dynamic Time Warping (DTW) algorithm is used to align environmental parameters and corrosion data over time, including the following steps:

[0089] Construct time series matrices E for environmental parameters and C for corrosion data, respectively, where E = [e1, e2, ..., e2]. n ] T C = [c1, c2, ..., c m ] T n and m are the number of sampling points for the two types of data, respectively;

[0090] Define the time window range W = [t s ,t e ], where t s To align the start time, t e To align the termination times, ensure that the time spans of the two types of data within the window are consistent;

[0091] Construct an n×m distance matrix D, where the element D(i,j) represents the i-th sampling point e in the environmental parameter sequence. i With the j-th sampling point c in the corrosion data sequence j The Euclidean distance is calculated using the following formula:

[0092]

[0093] Where k is the number of feature dimensions of the environmental parameters or corrosion data;

[0094] Initialize the cumulative distance matrix Γ, where Γ(i,j) represents the minimum cumulative distance from the starting point (1,1) to point (i,j), which is calculated recursively using dynamic programming and is expressed as the following formula:

[0095]

[0096] The boundary conditions are Γ(1,1)=D(1,1), Γ(i,1)=Γ(i-1,1)+D(i,1), Γ(1,j)=Γ(1,j-1)+D(1,j);

[0097] Backtracking from the endpoint (n,m) of the cumulative distance matrix to the starting point (1,1), extract the path P = [(i1,j1),(i2,j2),...,(i_p,j_p)] that minimizes the cumulative distance, where (i_p,j_p) = (n,m);

[0098] Path P is the optimal time matching path between the environmental parameter sequence and the corrosion data sequence, achieving non-linear alignment of the two types of data on the time axis;

[0099] The formula for calculating the average time offset Δt between the two types of data after alignment is:

[0100]

[0101] in, and These are the timestamps of the environmental parameters and corrosion data at the k-th matching point, respectively.

[0102] If Δt ≤ ε, the alignment result is considered valid; otherwise, the time window range W is adjusted and recalculated. In step S4 above, the establishment of the quantitative relationship model between environmental factors and corrosion rate includes:

[0103] A multivariate regression model or a physicochemical coupling model based on machine learning is used. Based on spatiotemporally aligned environmental parameters (including temperature, humidity, salt spray deposition rate, wind speed, atmospheric pollutant concentration, sea surface temperature, salinity, dissolved oxygen, pH value, wave energy, ocean current velocity, stratified water temperature, salinity gradient, dissolved oxygen profile, turbidity, and microbial distribution) and corrosion rate data (including corrosion thickness loss per unit time, mass loss rate, or electrochemical impedance spectroscopy characteristic parameters), interaction terms between environmental parameters (such as temperature-salinity interaction and dissolved oxygen-microbial interaction) are introduced into the model to characterize the nonlinear influence of the synergistic effect of multiple factors on the corrosion process.

[0104] The contribution of each environmental parameter and its interaction terms to the corrosion rate is quantified by model sensitivity analysis (such as Sobol index method and Morris screening method), and the dominant corrosion factors and key synergistic combinations are identified.

[0105] Principal component analysis (PCA) or partial least squares regression (PLSR) were used to reduce the dimensionality of high-dimensional environmental parameters, extract the set of main environmental factors affecting corrosion rate, and analyze their spatiotemporal distribution characteristics.

[0106] An explicit quantitative relationship model between environmental factors and corrosion rate is constructed based on the training dataset. The model may take the form of, but is not limited to, multinomial regression equations, neural network models, or support vector machine models. The output of the model is a predicted value of corrosion rate.

[0107] The model was validated using an independent test dataset, and the coefficient of determination (R²) was used to evaluate its performance. 2 The accuracy of model predictions is evaluated using metrics such as root mean square error (RMSE) to ensure R... 2 The prediction error range is ≥0.85 and RMSE≤10%; the model is dynamically corrected by combining long-term marine monitoring data, and the model parameters are updated through online learning algorithms to adapt to the corrosion process of environmental parameters evolving over time.

[0108] Develop an interactive visualization platform to display the synergistic effect strength between environmental parameters and the comprehensive influence path on corrosion rate in the form of heat maps, 3D surface maps or network diagrams, providing intuitive decision-making basis for corrosion protection strategy formulation.

[0109] In step S5 above, when constructing the atmosphere-ocean coupled forecasting system, based on geophysical models (such as the WRF-ROMS coupled model or the HYCOM ocean circulation model), the atmospheric boundary layer dynamics, ocean turbulent mixing and air-sea interface flux exchange modules are integrated to construct a high-resolution atmosphere-ocean coupled numerical model covering the target sea area.

[0110] By fusing satellite remote sensing, buoy observation and meteorological gradient tower measured data through data assimilation techniques (such as three-dimensional variational assimilation or ensemble Kalman filtering), the initial field and boundary conditions of the model are optimized, thereby improving the accuracy of environmental parameter forecasts for the next 7-30 days.

[0111] Output future environmental parameter prediction results, including temperature, humidity, wind speed, salt spray deposition rate, and air pollutant concentration in the atmosphere 0-30 meters above sea level, as well as the spatiotemporal distribution data of temperature, salinity, dissolved oxygen, pH value, wave energy, and ocean current speed in the seawater layer 0-30 meters below sea level.

[0112] The future environmental parameters output by the marine climate model, the long-term monitoring data (≥5 years) stored in the historical environmental database, and the online data collected by real-time sensors (sampling frequency ≥1 time / minute) are spatiotemporally aligned and unified to the same spatial grid (resolution ≤1km×1km) and time step (≤1 hour);

[0113] After standardizing the fused data (such as Z-score standardization or Min-Max normalization) to eliminate the impact of dimensional differences on model training, a corrosion prediction model is constructed.

[0114] In this embodiment, the construction of the corrosion prediction model includes:

[0115] LSTM Neural Network Sub-model: Construct a two-layer LSTM network structure. The input layer receives historical environmental data and real-time monitoring data in time series form. The hidden layer captures the long-term dependence of environmental parameters through a gating mechanism. The output layer generates the time trend prediction component of corrosion rate.

[0116] Random Forest Sub-model: Taking future environmental parameters and key environmental elements (such as temperature-salinity joint distribution and dissolved oxygen-pH synergistic index) output by the marine climate model as input features, the feature subset selection is optimized by estimating the out-of-bag error of the random forest algorithm, and the nonlinear response component of corrosion rate is output.

[0117] Model fusion: The outputs of LSTM and random forest are fused using a weighted average method or a stacked ensemble method. The weight coefficients are determined by a Bayesian optimization algorithm, and finally a comprehensive corrosion rate prediction value is generated that integrates spatiotemporal evolution characteristics and the nonlinear relationship between environment and corrosion.

[0118] The model's predictive performance was validated using an independent test set (including data from real-sea exposure experiments). Evaluation metrics included mean absolute error (MAE) ≤ 0.02 mm / a, root mean square error (RMSE) ≤ 0.03 mm / a, and coefficient of determination (R²). 2 ≥0.90;

[0119] An online learning mechanism for the model was established, which automatically updates the LSTM network weights and the random forest decision tree structure every 72 hours to adapt to the impact of seasonal and interannual variations in marine environmental parameters on the corrosion process.

[0120] An Example of a High Spatiotemporal Resolution Full-Profile Precision Corrosion Testing System in the Sea

[0121] A high-spatiotemporal resolution full-profile real-sea precision corrosion testing system includes:

[0122] The marine microenvironment monitoring system includes a layered sensor array and a data acquisition module, supporting real-time synchronous monitoring of multiple parameters;

[0123] The corrosion test monitoring system includes corrosion-coated samples, in-situ monitoring equipment, and a remote transmission module, which is used to achieve multi-dimensional characterization of the corrosion process.

[0124] The data center integrates an environmental corrosion database and a data analysis platform, and has functions for data storage, spatiotemporal alignment, and coupled analysis.

[0125] The marine environment and climate simulation system is a numerical simulation platform based on Earth models, used to provide data for predicting future environmental parameters.

[0126] The corrosion prediction module is used to integrate real-sea test data and simulation results to establish an integrated corrosion prediction model for materials, components, and equipment.

[0127] Furthermore, the layered sensor array consists of distributed sensor nodes deployed in the atmosphere (0-30m above sea level) and the seawater layer (0-30m below sea level). Each node integrates data on temperature, humidity, salinity, dissolved oxygen, pH, wave energy, ocean current velocity, and atmospheric pollutant concentrations (such as SO2 and Cl). - The monitoring unit for microbial distribution parameters has a sampling frequency of ≥1 time / minute.

[0128] Furthermore, the data acquisition module adopts an underwater / atmospheric data acquisition terminal with corrosion-resistant packaging, supports multi-protocol (RS485 / Modbus / LoRa) data transmission, has self-powered (solar + lithium battery) and local data storage (≥30 days) functions, and synchronizes real-time data to the data center through fiber optic / 5G dual links;

[0129] Furthermore, the corrosion-resistant sample is a standardized sample made of the same material as the target equipment (size ≥50mm×50mm×3mm), and the surface is sandblasted to Sa2.5 level. It is fixed at different depths (sea surface, underwater 5m, 10m, 20m) of the live sea exposure test station by a detachable bracket.

[0130] Furthermore, the in-situ monitoring equipment integrates an electrochemical impedance spectroscopy (EIS), a linear polarization resistance meter (LPR), an ultrasonic thickness gauge, and a 3D laser profile scanner to achieve real-time monitoring of corrosion rate, pitting depth, and surface morphology, with monitoring accuracies of ±0.1μm / a, ±0.01mm, and ±1μm, respectively.

[0131] Furthermore, the remote transmission module uses an industrial-grade router (supporting IP68 protection) and an edge computing gateway to perform protocol conversion and compression on multi-source monitoring data, and then uploads it to the data center through a dual-channel system of Beidou short message + satellite communication.

[0132] Furthermore, the environment-corrosion database is built on the Hadoop distributed file system, storing structured data (raw sensor data, monitoring equipment data) and unstructured data (corrosion morphology images, spectral analysis reports), and supports efficient retrieval of petabyte-level data (response time ≤ 1s).

[0133] Furthermore, the data analysis platform integrates a dynamic time warping (DTW) algorithm module, a multi-factor coupling analysis engine, and a visual interactive interface to achieve spatiotemporal alignment of environmental parameters and corrosion data (time synchronization error ≤ 0.1h), principal component analysis (PCA) dimensionality reduction, and output of heat maps / 3D surface maps.

[0134] Furthermore, the numerical simulation platform is built based on the WRF-ROMS coupled model, with an atmospheric module resolution of ≤3km and an ocean module with ≥50 vertical layers. Through data assimilation technology, ECMWF reanalysis data and ocean buoy observation data are fused to output a predicted field of environmental parameters (temperature, salinity, dissolved oxygen, wave energy) for the next 7-30 days.

[0135] Furthermore, simulation results are published using OGC standard services (WMS / WFS), supporting real-time calls from data centers via RESTful APIs, with data updates occurring twice daily.

[0136] Furthermore, a stacked ensemble model combining LSTM neural network (to handle temporal dependencies) and random forest algorithm (to capture nonlinear relationships) is adopted. The input layer receives historical environmental data (≥5 years), real-time monitoring data and simulated prediction data, and the output layer generates predicted values ​​of the three-level corrosion rate of materials-components-equipment (unit: mm / a).

[0137] Furthermore, the model's hyperparameters are automatically adjusted every 72 hours using a Bayesian optimization algorithm, and online incremental learning is performed using newly added real-world data (≥100 sets) to ensure the model's prediction accuracy (R²). 2 ≥0.90, RMSE≤0.03mm / a).

[0138] Specifically, the marine microenvironment monitoring system and corrosion test monitoring system collect data in real time and upload it to the data center → the data center completes data cleaning, spatiotemporal alignment and coupling analysis → the marine environment and climate simulation system generates future environmental parameter prediction data and pushes it to the data center → the corrosion prediction module integrates multi-source data to generate corrosion prediction results → the results are displayed through a visualization platform and fed back to the equipment protection strategy formulation module.

[0139] In practical applications, this embodiment provides a high spatiotemporal resolution full-profile precision corrosion testing method and system for marine environments. It achieves precise monitoring and corrosion testing of the full-profile microenvironment through the coordinated operation of an integrated system of offshore gradient iron towers, buoys, and a data management platform. In the full-profile layered sampling and sample rack installation phase, using the sea surface as a reference, the 30-meter water surface (near sea level) is divided into six layers, each spaced 5 meters apart. Steel platforms are installed at heights of 5m, 10m, 15m, 20m, 25m, and 30m, respectively, using a 40-meter meteorological gradient iron tower. Figure 2 As shown, each platform is equipped with 6 sets of detachable sample racks, which are bolted to the pre-installed supports on the tower. Rubber washers are installed between the bolts and the supports for insulation and anti-slip purposes. Each sample rack can install 72 standard-sized hanging plates. The samples are fixed to the reserved mounting holes with bolts, and polytetrafluoroethylene gaskets are used for insulation. The spacing between samples is greater than 20mm to avoid electrochemical interference. The underwater 30-meter depth (sea-air boundary layer and seawater mixing layer) is also divided into 6 layers at 5-meter intervals, with depths of 0m, 5m, 10m, 15m, 20m, and 25m respectively. This is achieved using a buoy system, such as... Figure 3As shown, a drawer-type sample rack is installed on the main cable. The sample rack is welded from stainless steel and has a load-bearing capacity of no less than 6 tons. It is equipped with two protective rings on the outside and 72 standard hanging plates are fixed inside by nylon blocks with slots. The spacing between samples is greater than 50mm. The independent metal frame can be removed as a whole by pulling out the fixing pins, which is convenient for underwater robots or divers to collect samples periodically. Each sample rack is divided into 6 groups, and samples are collected at 2-month, 4-month, 6-month, 8-month, 10-month and 12-month cycles. At least 3 parallel samples are retained in each group to ensure that the sample exposure period covers 12 months.

[0140] like Figure 4 and Figure 5 As shown, in the multi-dimensional environmental parameter monitoring system, each platform in the near-sea level of the atmosphere is equipped with wind speed and direction sensors (wind speed measurement range 0–100 m / s, accuracy ±0.3 m / s; wind direction accuracy ±3°), temperature and humidity sensors (temperature measurement range -80–60℃, accuracy ±0.226℃ (-80℃–+20℃); relative humidity accuracy ±1%RH (15℃–25℃, 0%RH–90%RH)), and a four-component net radiation sensor (spectral range shortwave 305–2800 nm, longwave 4500–50000 nm, measurement range 0–2000 W / m). 2 The system includes a barometric pressure sensor (measuring range 600–1100 hPa, accuracy ±0.3 hPa (+15℃–+25℃)) to collect parameters such as wind speed, wind direction, temperature, humidity, radiation, and air pressure in real time at a frequency of 1 Hz; and a dissolved oxygen sensor (range 0–500 μM, accuracy ±5%), a conductivity sensor (inductive measurement, range 0–75 mS / cm, accuracy ±0.018 mS / cm), a pressure sensor (range 0–60 MPa, accuracy ±0.02% FSO), and a Doppler current profiler (600 kHz frequency, layer thickness 0.5–5 m, current velocity accuracy 0.3 cm / s) to synchronously monitor parameters such as salinity, dissolved oxygen, pressure, and ocean current velocity at 10-minute intervals in the sea-air coupling layer and the seawater mixing layer. All sensors are connected to the data acquisition unit via armored communication cables (underwater) or wireless sensor networks (above water) for nanosecond-level time synchronization, ensuring that the spatiotemporal alignment accuracy of environmental parameters and corrosion data reaches ≤2 minutes in time and ≤1 meter in space. The data is transmitted back to the shore station server in real time via Beidou communication module and satellite communication.

[0141] During corrosion testing and in-situ monitoring, the periodically retrieved sample plates were first cleaned with a soft brush to remove surface corrosion products and contaminants. After dehydration and drying with ethanol, the weight loss was measured using a 0.01% electronic balance to calculate the corrosion rate. Subsequently, the corrosion morphology was observed using a scanning electron microscope (SEM), and the elemental composition of the products was analyzed using an X-ray energy dispersive spectroscopy (EDS). The in-situ monitoring equipment included an atmospheric corrosion monitor mounted on a 5m high platform, based on thin-film resistance grid technology, which measures the metal thinning amount in real time through constant current excitation and a high-precision bridge principle, with a resolution of 10nm and support for adjustable measurement intervals from 1 to 24 hours. The electrochemical impedance sensors integrated into the underwater sample holders used a three-electrode system (working electrode, reference electrode, and auxiliary electrode) to measure the AC impedance spectrum by applying a high-frequency sinusoidal signal (10μV~100mV, 10mHz~100kHz), calculating the corrosion current density and polarization resistance, with a measurement range covering 1×10 -4 ~10mm / a, with an electromagnetic interference resistance of ≥80dB. Monitoring data is transmitted via armored cable to a data acquisition unit inside the buoy, and after encoding, is sent to a data management cloud service platform via satellite communication. Users can view parameter curves such as corrosion rate, self-corrosion potential, and polarization resistance in real time through a web interface, and data export and offline analysis are supported.

[0142] In terms of data center and predictive model construction, a three-layer database architecture is established. The bottom layer stores raw monitoring data (environmental parameters, corrosion data, and sample information). The middle layer achieves data matching through a spatiotemporal alignment algorithm (based on Dynamic Time Warping (DTW) technology) (timestamp error ≤ 1 minute, depth error ≤ 0.5 meters). The top layer constructs multi-dimensional analysis tables, supporting SQL queries and data visualization. The expected storage capacity is 50TB, capable of accommodating more than 500,000 sets of monitoring data. The corrosion prediction model is based on the fusion of LSTM neural network and random forest algorithm. Input parameters include historical environmental data (salinity, temperature, dissolved oxygen, and ocean current velocity over the past 12 months), real-time monitoring data (current 10-minute average), and future environmental parameters output from marine climate models (WRF atmospheric module and FVCOM ocean module) (extreme temperature fluctuations of ±2℃, salinity anomalies of ±1.5‰, and typhoon wave field frequency changes over the next 100 years). The output is the predicted service life of materials and the corrosion risk level. Validated by actual sea data, the prediction accuracy is 40% higher than traditional empirical formulas, and the early warning lead time can reach 6–12 months.

[0143] System reliability assurance measures include: the power supply system uses 4800W solar panels (single panel efficiency ≥22%) and 450Ah lithium iron phosphate battery packs (cycle life ≥5000 times), equipped with a charging controller, supporting operation in ambient temperatures of -20℃ to 60℃, and maintaining normal power supply even under continuous 90 days of cloudy and rainy weather; the anchor system adopts a "main anchor + three-way inclined anchor rope" structure, the main anchor is a 2-ton Hall anchor, the inclined anchor rope is a φ30mm anti-torsion rope (breaking force ≥150 tons), each anchor rope is equipped with a 5kg buoyancy buoy (for easy recovery), the anchor rope length is 55m to adapt to a 5m tidal range, and the buoy positioning accuracy is ≤15m (GPS + Beidou dual-mode); during deployment, the main anchor point is first accurately anchored by a dynamic positioning vessel (DP2 class), and an ROV underwater robot (operating depth 300m) is used to assist in the installation of the underwater sample frame, ensuring that the horizontal inclination angle of each layer of the sample frame is 45° (simulating the actual service attitude), and the deployment error is ≤0.3 meters. This scheme achieves standardized operation throughout the entire process from sample installation, environmental monitoring, corrosion characterization to data application. It is especially suitable for extreme marine environments with high salinity (30-35‰), high temperature (annual average 28℃), and strong waves (30% of days with effective wave height ≥2m). It provides an engineerable and precise test platform for the study of corrosion mechanisms of marine engineering materials, the selection of high-performance materials, and the development of corrosion protection technologies.

[0144] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0145] The above embodiments are merely preferred embodiments of the present invention and should not be construed as limiting the scope of protection of the present invention. Any non-substantial changes and substitutions made by those skilled in the art based on the present invention shall fall within the scope of protection claimed by the present invention.

Claims

1. A high-spatiotemporal resolution, full-profile, precise marine corrosion testing method, characterized in that, include: The area within 30 meters above and below sea level is divided into three monitoring zones: the near-sea level atmospheric layer, the air-sea boundary layer, and the seawater mixing layer. Corresponding sensor arrays are deployed in each monitoring zone to collect environmental parameters of each layer in real time. Standard corrosion plates made of different materials and surface treatment processes are deployed in each monitoring area, along with corrosion monitoring sensors for monitoring the local corrosion state of the samples; online monitoring devices are installed to record the corrosion morphology, thickness changes, and electrochemical parameters of the samples in real time; and the real-time collected environmental parameters and corrosion data are transmitted to the data center via a wireless sensor network or submarine optical cable. The collected environmental parameters at each layer and the corrosion data of the corresponding areas are spatiotemporally aligned to form a strongly correlated "environment-corrosion" dataset containing multiple environmental factors and multiple corrosion indicators. Based on the multi-factor coupling model, the synergistic effect of each environmental parameter on the corrosion process is analyzed, and a quantitative relationship model between environmental elements and corrosion rate is established. A geophysical model was used to construct an atmosphere-ocean coupled forecasting system to simulate and predict future environmental parameters. The simulation results were used as input variables for a corrosion prediction model, and a material corrosion life prediction model was established by combining historical experimental data.

2. The method according to claim 1, characterized in that: The monitoring parameters for the near-sea level layer of the atmosphere include: temperature and humidity data collected in real time by high-precision temperature and humidity sensors; salt spray deposition rate monitored by salt spray deposition meter to quantify the salt content in the atmosphere; wind speed and wind direction information obtained synchronously by wind speed and wind direction sensors; and atmospheric pollutant concentrations detected by atmospheric pollutant analyzer, including at least key corrosive components such as chloride ions and sulfur dioxide. The monitoring parameters of the air-sea boundary layer include: sea surface temperature, salinity, dissolved oxygen and pH value measured simultaneously by a multi-parameter water quality sensor; wave energy parameters measured by a wave buoy; and ocean current velocity vector obtained by an acoustic Doppler current profiler. The monitoring parameters of the seawater mixing layer include: the stratified water temperature and salinity gradient continuously monitored by the CTD temperature, salinity and depth instrument system, the dissolved oxygen profile distribution measured by the dissolved oxygen probe, the water turbidity monitored by the turbidity meter, and the distribution characteristics of the microbial community analyzed by the microbial sampler.

3. The method according to claim 1, characterized in that: When dividing the monitoring area, the sea surface is used as the reference, and the above-water part is divided into multiple monitoring areas at preset height intervals, namely the near-sea level layer of the atmosphere. Steel platforms are set up on each layer using a meteorological gradient tower. Each platform is equipped with multiple sets of detachable sample racks and is fixed to the tower support through an insulated and anti-slip structure. The sample racks are equipped with standardized hanging plate mounting holes and electrochemical insulation measures. The underwater part is divided into multiple monitoring areas at preset depth intervals, namely the air-sea boundary layer and the seawater mixing layer. Drawer-type sample racks are installed on the main cable of each layer using a buoy system. The sample racks are made of high-strength corrosion-resistant materials and are equipped with protective structures and rapid sampling devices.

4. The method according to claim 3, characterized in that: When the sample racks are arranged in layers, each sample rack in the above-water section is equipped with no less than 72 standard-sized hanging plates, and the spacing between samples is no less than 20 mm to avoid electrochemical interference. Each sample rack in the underwater section is fixed with no less than 72 standard hanging plates, and the spacing between samples is no less than 50 mm. Both are designed in a modular manner to achieve periodic batch recovery. Samples are recovered from each layer of sample racks according to different cycles, and at least 3 parallel samples are retained in each group to ensure full coverage of the exposure cycle.

5. The method according to claim 1, characterized in that, The construction of the "environment-corrosion" strongly correlated dataset includes: The raw environmental parameter data and corrosion data collected by each layer of sensors are denoised, missing values ​​are imputed, and outliers are removed. To address the nonlinear time offset between environmental parameters and corrosion data caused by differences in acquisition frequency, transmission delay, or equipment response time, the Dynamic Time Warping (DTW) algorithm is used to align the two types of data over time. The aligned data is timestamped to ensure that environmental parameters and corrosion data strictly correspond in the time dimension. By combining the spatial coordinate information of each layer of monitoring area, environmental parameters and corrosion data are mapped to a unified spatial coordinate system through Geographic Information System (GIS) technology, thereby achieving spatial dimension alignment. The spatiotemporally aligned environmental parameters and corrosion data are stored in a structured database. Multivariate statistical analysis is used to mine the strong correlation between environmental factors and corrosion indicators, forming a "environment-corrosion" strongly correlated dataset containing multiple environmental factors and multiple corrosion indicators.

6. The method according to claim 5, characterized in that, The step of using the Dynamic Time Warping (DTW) algorithm to align environmental parameters and corrosion data over time includes the following steps: Construct time series matrices E for environmental parameters and C for corrosion data, respectively, where E = [e1, e2, ..., e2]. n ] T C = [c1, c2, ..., c m ] T n and m are the number of sampling points for the two types of data, respectively; Define the time window range W = [t s ,t e ], where t s To align the start time, t e To align the termination times, ensure that the time spans of the two types of data within the window are consistent; Construct an n×m distance matrix D, where the element D(i,j) represents the i-th sampling point e in the environmental parameter sequence. i With the j-th sampling point c in the corrosion data sequence j The Euclidean distance is calculated using the following formula: Where k is the number of feature dimensions of the environmental parameters or corrosion data; Initialize the cumulative distance matrix Γ, where Γ(i,j) represents the minimum cumulative distance from the starting point (1,1) to point (i,j), which is calculated recursively using dynamic programming and is expressed as the following formula: The boundary conditions are Γ(1,1)=D(1,1), Γ(i,1)=Γ(i-1,1)+D(i,1), Γ(1,j)=Γ(1,j-1)+D(1,j); Backtracking from the endpoint (n,m) of the cumulative distance matrix to the starting point (1,1), extract the path P = [(i1,j1),(i2,j2),...,(i_p,j_p)] that minimizes the cumulative distance, where (i_p,j_p) = (n,m); Path P is the optimal time matching path between the environmental parameter sequence and the corrosion data sequence, achieving non-linear alignment of the two types of data on the time axis; The formula for calculating the average time offset Δt between the two types of data after alignment is: in, and These are the timestamps of the environmental parameters and corrosion data at the k-th matching point, respectively. If Δt≤ε, the alignment result is considered valid; otherwise, the time window range W is adjusted and recalculated.

7. The method according to any one of claims 1 to 6, characterized in that, The establishment of a quantitative model of the relationship between environmental factors and corrosion rate includes: We employ multivariate regression models or physicochemical coupling models based on machine learning, and introduce interaction terms between environmental parameters into the models based on spatiotemporally aligned environmental parameters and corrosion rate data, in order to characterize the nonlinear influence of the synergistic effect of multiple factors on the corrosion process. The contribution of each environmental parameter and its interaction terms to the corrosion rate is quantified through model sensitivity analysis, and the dominant corrosion factors and key synergistic combinations are identified. Principal component analysis (PCA) or partial least squares regression (PLSR) were used to reduce the dimensionality of high-dimensional environmental parameters, extract the set of main environmental factors affecting corrosion rate, and analyze their spatiotemporal distribution characteristics. An explicit quantitative relationship model between environmental factors and corrosion rate is constructed based on the training dataset. The model may take the form of, but is not limited to, multinomial regression equations, neural network models, or support vector machine models. The output of the model is a predicted value of corrosion rate.

8. The method according to any one of claims 1 to 6, characterized in that: When constructing the atmosphere-ocean coupled forecasting system, based on the geophysical model, modules for atmospheric boundary layer dynamics, ocean turbulent mixing, and air-sea interface flux exchange are integrated to construct a high-resolution atmosphere-ocean coupled numerical model covering the target sea area. By integrating satellite remote sensing, buoy observation and meteorological gradient tower measured data through data assimilation technology, the initial field and boundary conditions of the model are optimized to improve the accuracy of future environmental parameter forecasts. Output future environmental parameter prediction results, including temperature, humidity, wind speed, salt spray deposition rate, and air pollutant concentration in the atmosphere 0-30 meters above sea level, as well as the spatiotemporal distribution data of temperature, salinity, dissolved oxygen, pH value, wave energy, and ocean current speed in the seawater layer 0-30 meters below sea level. The future environmental parameters output by the marine climate model, the long-term monitoring data stored in the historical environmental database, and the online data collected by real-time sensors are spatiotemporally aligned and unified to the same spatial grid and time step. After standardizing the fused data, a corrosion prediction model is constructed.

9. The method according to claim 8, characterized in that, The construction of the corrosion prediction model includes: LSTM Neural Network Sub-model: Construct a two-layer LSTM network structure. The input layer receives historical environmental data and real-time monitoring data in time series form. The hidden layer captures the long-term dependence of environmental parameters through a gating mechanism. The output layer generates the time trend prediction component of corrosion rate. Random Forest Sub-model: Using future environmental parameters and key environmental elements output by marine climate models as input features, the feature subset selection is optimized through out-of-bag error estimation of the random forest algorithm, and the nonlinear response component of corrosion rate is output. Model fusion: The outputs of LSTM and random forest are fused using a weighted average method or a stacked ensemble method. The weight coefficients are determined by a Bayesian optimization algorithm, and finally a comprehensive corrosion rate prediction value is generated that integrates the spatiotemporal evolution characteristics and the nonlinear relationship between the environment and corrosion.

10. A high-spatiotemporal resolution full-profile real-sea precision corrosion testing system, characterized in that, include: The marine microenvironment monitoring system includes a layered sensor array and a data acquisition module, supporting real-time synchronous monitoring of multiple parameters; The corrosion test monitoring system includes corrosion-coated samples, in-situ monitoring equipment, and a remote transmission module, which is used to achieve multi-dimensional characterization of the corrosion process. The data center integrates an environmental corrosion database and a data analysis platform, and has functions for data storage, spatiotemporal alignment, and coupled analysis. The marine environment and climate simulation system is a numerical simulation platform based on Earth models, used to provide data for predicting future environmental parameters. The corrosion prediction module is used to integrate real-sea test data and simulation results to establish an integrated corrosion prediction model for materials, components, and equipment.

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