Titanium-copper composite board corrosion monitoring method and system based on wireless communication

Abstract

The invention discloses a titanium-copper composite board corrosion monitoring method and system based on wireless communication, and relates to the technical field of titanium-copper composite board corrosion monitoring. The system comprises a corrosion monitoring unit, a wireless communication module, an environmental parameter acquisition module and a central processing module, the multi-environment basic corrosion model is constructed, model adaptive adjustment is realized in combination with parameter similarity calculation, and the monitoring accuracy in a complex environment is improved. The system acquires corrosion and environmental data in real time, wirelessly transmits the corrosion and environmental data to the central processing module, and forms a visual report through scientific data preprocessing, rate calculation and trend prediction; the three-level early warning mechanism is accurately graded, and early warning information can be pushed in time. Closed-loop management from data acquisition to risk early warning is integrally realized, the system is adaptive to various environments, corrosion risks can be prevented and controlled in advance, the service life of the titanium-copper composite plate is prolonged, the maintenance cost is reduced, and safe and stable operation of equipment is guaranteed.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of corrosion monitoring of titanium-copper composite plates, in particular to a titanium-copper composite plate corrosion monitoring method and system based on wireless communication. BACKGROUND

[0002] Titanium-copper composite plates are widely used in the fields of marine engineering, water conservancy facilities, chemical equipment and soil pipelines due to their corrosion resistance of titanium and electrical conductivity and thermal conductivity of copper. However, the service environment of the titanium-copper composite plates is often complex and changeable, such as seawater, freshwater, acidic soil, alkaline industrial wastewater and the like, and there are corrosive media such as chloride ions, hydrogen ions and hydroxyl ions in the environment, and the parameters such as temperature, humidity and pH value fluctuate greatly, which can easily lead to corrosion of the composite plate. Corrosion not only reduces the mechanical properties and use performance of the titanium-copper composite plate, but also can cause safety accidents such as equipment leakage and structural failure, causing huge economic losses and safety hazards. Therefore, it is of great engineering significance and practical value to monitor the corrosion state of the titanium-copper composite plate in real time and accurately.

[0003] At present, the corrosion monitoring methods of the titanium-copper composite plate mainly include offline detection and online monitoring. The offline detection needs to be periodically sampled or stopped for detection, such as the weight loss method and metallographic analysis method, which can obtain relatively accurate corrosion data, but has the problems of long detection period, complicated operation and inability to reflect the corrosion dynamics in real time, and may affect the normal operation of the equipment. The online monitoring method mainly relies on wired transmission mode to collect corrosion-related data by arranging sensors, but the wired transmission has the defects of complex wiring, high maintenance cost and unsuitability for complex terrain or harsh environment, and is difficult to meet the needs of large-scale and distributed monitoring.

[0004] At the same time, the existing corrosion monitoring models are mainly designed for single environmental medium, and do not consider the dynamic changes and cross effects of the parameters in the actual environment. For example, the chlorine ion concentration and temperature fluctuation in seawater environment, and the coupling effect of humidity and pH value in acidic soil, all of which can cause the corrosion law to deviate from the prediction results of the basic model, resulting in a decrease in monitoring accuracy. In addition, the existing early warning mechanism is mainly based on a single corrosion rate threshold, lacks prediction and graded early warning of the corrosion trend, and is difficult to realize the early prevention and control of risks.

[0005] In order to solve the above problems, it is urgent to develop a corrosion monitoring technology which can adapt to various complex environments, realize real-time data transmission, has dynamic model adjustment capability and precise early warning function, so as to improve the timeliness, accuracy and reliability of the corrosion monitoring of the titanium-copper composite plate, and provide strong support for the safe operation and maintenance decision of the equipment. SUMMARY

[0006] (I) Technical problems solved

[0007] To address the shortcomings of existing technologies, this invention provides a method and system for monitoring corrosion of titanium-copper composite plates based on wireless communication, which solves the problems mentioned in the background section.

[0008] (II) Technical Solution

[0009] To achieve the above objectives, the present invention provides the following technical solution: a corrosion monitoring system for titanium-copper composite plates based on wireless communication, the system comprising:

[0010] The corrosion monitoring unit is used to collect corrosion-related data of the titanium-copper composite plate in real time.

[0011] The environmental parameter acquisition module is used to collect various parameters of the environment in which the titanium-copper composite plate is located, including the pH, temperature, humidity and chemical composition parameters of the environmental medium.

[0012] The wireless communication module is responsible for transmitting the data collected by the corrosion monitoring unit and the environmental parameter acquisition module to the central processing module in real time.

[0013] The central processing module is used to receive, store, process, and analyze the transmitted data, and generate corresponding monitoring reports and early warning information based on the analysis results.

[0014] As a further aspect of the present invention: each corrosion monitoring unit is equipped with multiple sensors, including corrosion potential sensors and corrosion current sensors;

[0015] The corrosion potential sensor is used to measure the potential difference between the surface of the titanium-copper composite plate and the surrounding environment. The measured value is represented by the symbol E.

[0016] The corrosion current sensor is used to measure the current generated during the corrosion process of titanium-copper composite plates, and its measured value is represented by the symbol L.

[0017] As a further aspect of the present invention: acidity and alkalinity are represented by pH value, which is usually between 0 and 14, with 7 being neutral, less than 7 being acidic, and greater than 7 being alkaline; temperature is represented by T; humidity is represented by H.

[0018] As a further aspect of the present invention: the environmental medium includes: seawater, freshwater, acidic soil, and alkaline industrial wastewater, wherein the chemical composition parameters of the environmental medium are:

[0019] For marine environments: collect the chloride ion concentration in seawater, in moles per liter (mol / L).

[0020] For freshwater environments: collect calcium ion concentration and bicarbonate ion concentration;

[0021] For acidic soil environments: collect the concentrations of sulfate ions and nitrate ions in the soil;

[0022] For alkaline industrial wastewater environments: the concentrations of chloride ions and hydroxide ions in the wastewater should be collected;

[0023] As a further aspect of the present invention, the central processing module pre-stores basic models for different typical environmental media.

[0024] A corrosion monitoring method for titanium-copper composite plates based on wireless communication is disclosed. This method is implemented using a wireless communication-based corrosion monitoring system for titanium-copper composite plates and includes the following steps:

[0025] Step 1: Establishing the basic model:

[0026] Basic corrosion monitoring models were established for the environmental media corresponding to seawater, freshwater, acidic soil, and alkaline industrial wastewater.

[0027] For marine environments, the basic model is represented as follows: ;

[0028] Among them, R corr,seawater The value represents the corrosion rate of the titanium-copper composite plate in a seawater environment; k1 is the corrosion rate constant in a seawater environment; C Cl T is the chloride ion concentration in seawater; T is the seawater temperature; pH is the acidity or alkalinity of seawater.

[0029] For freshwater environments, the basic model is represented as follows: ;

[0030] Among them, R corr,freshwater The value represents the corrosion rate of the titanium-copper composite plate in a freshwater environment; k2 is the corrosion rate constant in a freshwater environment; T is the freshwater temperature; pH is the acidity or alkalinity of the freshwater.

[0031] For acidic soil environments, the basic model is represented as follows: ;

[0032] Among them, R corr,acidsoil The value represents the corrosion rate of titanium-copper composite plates in acidic soil environments; k3 is the corrosion rate constant in acidic soil environments; H is soil moisture; pH is the soil acidity / alkalinity.

[0033] For alkaline industrial wastewater environments, the basic model is represented as follows: ;

[0034] Among them, R corr,alkalinewastewater The value represents the corrosion rate of titanium-copper composite plates in an alkaline industrial wastewater environment; k4 is the corrosion rate constant in an alkaline industrial wastewater environment; T is the wastewater temperature; pH is the acidity or alkalinity of the wastewater.

[0035] Step 2: Adaptive adjustment of the basic model:

[0036] In a real-world environment, various environmental parameters are acquired through an environmental parameter acquisition module, including pH, temperature (T), humidity (H), and chemical composition. These parameters are then input into a central processing module and compared with the applicable conditions of various basic models. The method is as follows:

[0037] Calculate the similarity between the current environmental parameters and the applicable environmental parameters of each base model;

[0038] Taking pH as an example, the similarity S pH The calculation formula is: ;

[0039] Among them, pH current It refers to the acidity or alkalinity of the current environment, pH. model It refers to the acidity or alkalinity of the environment in which the basic model is applicable, specifically the pH value. max and pH min These refer to the historical maximum and minimum values ​​of the acidity / alkalinity parameter in the applicable environment of the base model corresponding to the current similarity calculation, and the applicable environment of the base model refers to seawater environment, freshwater environment, acidic soil environment or alkaline industrial wastewater environment.

[0040] Similarity S to pH pH The calculation logic is the same; for temperature, humidity and chemical composition parameters, their corresponding similarity is calculated respectively.

[0041] Then, based on the weighted average of the various similarities, the degree of matching between the current environment and each basic model is determined;

[0042] Adjust the base model based on the degree of matching:

[0043] If the current environment matches a certain base model closely, then based on that base model, the parameters in the model are fine-tuned according to the differences between the current environment parameters and the applicable environment parameters of the base model:

[0044] The parameter fine-tuning logic is the same in the models corresponding to various environmental media; the seawater environment is selected as an example.

[0045] If the current seawater temperature T current The temperature T is higher than the preset temperature applicable to the basic model. model The corrosion rate constant k1 is then adjusted according to the temperature difference. The adjusted corrosion rate constant k1 is... 1a Represented as: ;

[0046] Step 3: Processing and Analysis of Corrosion Monitoring Data

[0047] Step L1, Data Reception and Storage:

[0048] The central processing module receives data from various corrosion monitoring units and environmental parameter acquisition modules through the wireless communication module, and classifies and stores this data according to time sequence and monitoring location;

[0049] The stored data includes corrosion potential E, corrosion current I, environmental pH, temperature T, humidity H, and chemical composition parameters.

[0050] Step L2, Data Preprocessing:

[0051] The received data is preprocessed, including data cleaning and data transformation;

[0052] Data cleaning mainly involves removing outliers and noisy data.

[0053] Select corrosion current data. If the difference between a data point and its surrounding data points exceeds the corresponding preset threshold, the data point is considered an outlier and is removed.

[0054] Data conversion is the process of converting data from different units into a unified unit;

[0055] Step L3, Calculation of corrosion rate:

[0056] The corrosion rate of the titanium-copper composite plate was calculated based on the preprocessed data and the adaptively adjusted monitoring model.

[0057] For different environmental media, the corresponding adjusted models are used for calculation;

[0058] Step L4, Corrosion Trend Prediction:

[0059] Based on historical corrosion data and current corrosion rates, the future corrosion trend of titanium-copper composite panels is predicted.

[0060] Using a simple linear prediction method, when the corrosion rate remains stable over a certain period of time, the prediction formula is as follows: ;

[0061] Among them, R future (t) is the corrosion rate at future time t, R current is the current corrosion rate, and v is the rate of change of the corrosion rate, which is obtained through linear regression analysis of historical data.

[0062] As a further aspect of the present invention: if the current environment has a low degree of matching with all the basic models, then a new temporary model is constructed by combining the characteristics of multiple basic models;

[0063] Taking an environment between seawater and freshwater as an example, the new temporary model is constructed by integrating the basic models of seawater and freshwater to derive a new model:

[0064] ;

[0065] Wherein, γ is a preset weighting coefficient, determined based on the similarity between the current environment and the seawater and freshwater environments, and 0≤a≤1.

[0066] As a further aspect of the present invention, the monitoring report mainly includes the following:

[0067] Basic information about the monitoring system, including monitoring time, monitoring location, and monitoring unit number;

[0068] Real-time monitoring data of environmental parameters, such as pH, temperature (T), humidity (H), and chloride ion concentration (C). Cl ;

[0069] Corrosion-related data for titanium-copper composite plates, such as corrosion potential E, corrosion current I, and corrosion rate R. corr ;

[0070] The predicted corrosion trends include the predicted corrosion rate and the predicted degree of corrosion over a future period.

[0071] As a further aspect of the present invention, the monitoring report is formatted in the form of tables and charts.

[0072] As a further aspect of the present invention, the method for generating and disseminating early warning information is as follows:

[0073] When the monitoring system detects that the corrosion rate of the titanium-copper composite plate exceeds the preset threshold or that the corrosion trend is abnormal, it will automatically generate an early warning message.

[0074] As a further aspect of the present invention: the warning information is categorized into mild warning, moderate warning, and severe warning based on the severity of corrosion, as detailed below:

[0075] For mild warnings:

[0076] If the calculated current corrosion rate R corr Exceeding the preset threshold by 10%-30%;

[0077] If the rate of change of corrosion rate v exceeds the normal fluctuation range, but the predicted corrosion rate R for the next 3 months is... future (t) did not exceed the preset threshold;

[0078] Or if the corrosion rate R is predicted for the next month future (t) The difference from the preset threshold is within 0.05 mm / year, and the rate of change v shows signs of continuous increase but has not yet grown rapidly;

[0079] If any of the above conditions are met, it is determined to be a mild warning;

[0080] For a moderate alert:

[0081] If the current corrosion rate R corr Exceeding the preset threshold by 30%-60%;

[0082] If the rate of change of corrosion rate v exceeds the normal fluctuation range, and the predicted corrosion rate R for the next two months... future (t) exceeds the corresponding preset threshold;

[0083] Alternatively, if the current corrosion rate is within the normal fluctuation range, but the predicted corrosion rate R for the next month is... future (t) exceeds 20% of the preset threshold, and the rate of change v continues to increase;

[0084] If any of the above conditions are met, it is determined to be a moderate warning.

[0085] For severe warnings:

[0086] If the current corrosion rate R corr Exceeding the preset threshold by 60% or more;

[0087] If the corrosion rate R is predicted for the next month future (t) exceeds 50% or more of the preset threshold, and the corrosion rate change rate v rises sharply from the normal range to 0.2 mm / year2 or more in a short period of time.

[0088] If any of the above conditions are met, it is determined to be a severe warning.

[0089] As a further aspect of the present invention, the process of constructing the basic model in the environmental medium is as follows:

[0090] Step A1: Experimental Data Acquisition and Environmental Simulation

[0091] For four typical environments—seawater, freshwater, acidic soil, and alkaline industrial wastewater—multiple control experimental systems were built, and corrosion data of titanium-copper composite plates were obtained through long-term monitoring.

[0092] Step A1.1, Seawater Environment Simulation:

[0093] Artificial seawater was prepared, referencing international standard seawater formulations, and six groups of chloride ion concentration gradients, five groups of temperature gradients, and five groups of pH gradients were set up, with no fewer than three experimental samples in each group.

[0094] The titanium-copper composite plate sample was completely immersed in simulated seawater. The actual corrosion rate was calculated by the weight loss method. At the same time, the electrochemical parameters corresponding to the corrosion potential and corrosion current were recorded. The experiment lasted for 12 months.

[0095] Step A1.2, Freshwater Environment Simulation:

[0096] Distilled water was used as a base, with the addition of calcium ions and bicarbonate ions to simulate the composition of natural freshwater. Five temperature gradients and six pH gradients were set up. The same weight loss method was used to monitor the corrosion rate, and environmental parameters were collected simultaneously. The experimental period was nine months.

[0097] Step A1.3, Simulation of acidic soil environment:

[0098] Typical red soil was selected as the substrate, and the pH gradient was adjusted by adding sulfuric acid. Four sets of humidity gradients were set, based on the soil saturation moisture content.

[0099] The titanium-copper composite plate was buried in the soil at a depth of 50cm. It was taken out every 30 days to measure the corrosion weight loss. The experimental period was 18 months, during which the soil environmental parameters were kept stable.

[0100] Step A1.4, Environmental simulation of alkaline industrial wastewater:

[0101] To simulate actual pollution conditions, industrial wastewater containing sodium hydroxide and sodium carbonate was used, with pH and temperature gradients set up and a small amount of chloride ions (L) added.

[0102] A dynamic circulation device was used to keep the wastewater flowing, and the corrosion data of the samples were measured every 48 hours. The experimental period was 6 months.

[0103] Step A2, Theoretical Basis and Variable Selection:

[0104] Based on corrosion electrochemistry theory and materials science principles, the mechanism of environmental parameters on the corrosion of titanium-copper composite plates was analyzed, and key influencing variables were screened:

[0105] For marine environments:

[0106] Chloride ion C Cl The main factors leading to the damage of the passivation film in titanium-copper composite plates are: increasing temperature T will accelerate the electrochemical reaction rate, and the pH value deviating from the natural value of seawater will affect the stability of the passivation film.

[0107] For freshwater environments:

[0108] Temperature (T) is the dominant factor affecting the corrosion rate. When the pH value is below neutral (7.0), the depolarization effect of hydrogen ions is enhanced, accelerating the dissolution of the copper matrix.

[0109] For acidic soil environments:

[0110] Soil moisture (H) determines the continuity of the electrolyte solution; the higher the moisture, the better the conductivity of the corrosive medium; the lower the pH value, i.e., the stronger the acidity, the higher the hydrogen ion concentration, and the faster the corrosion rate.

[0111] For alkaline industrial wastewater environments:

[0112] High temperature (T) accelerates the dissolution of titanium layers by alkaline solutions, forming titanates. The corrosion rate increases exponentially when the pH value exceeds 10.0.

[0113] Step A3, Model Parameter Fitting and Expression Derivation:

[0114] The experimental data were fitted using multiple linear regression to determine the constant terms and coefficients in the model;

[0115] Step A3.1: Fitting parameters of the seawater environment model:

[0116] The experimental data from 12 months were normalized, with the corrosion rate R as the criterion. corr,seawater C is the dependent variable. Cl T and (pH-8) are the independent variables, and the results are obtained by least squares fitting:

[0117] ;

[0118] Step A3.2, Freshwater environment model parameter fitting:

[0119] Based on 9 months of monitoring data, the following results were obtained by fitting the data with T and (7-pH) as independent variables:

[0120] ;

[0121] Step A3.3: Fitting parameters for the acidic soil environment model:

[0122] After fitting the 18-month buried experimental data, the following results were obtained:

[0123] ;

[0124] Step A3.4, Fitting the environmental model for alkaline industrial wastewater:

[0125] Based on fitting of 6 months of dynamic experimental data, the following was obtained:

[0126] ;

[0127] Step A4, Model Validation and Error Correction:

[0128] Step A4.1: Verify the experimental design:

[0129] Ten additional parameter combinations, different from those in the modeling experiment, were set up for each environment, and the accuracy was verified by comparing the actual corrosion rate with the model prediction.

[0130] Step A4.2, Error Correction:

[0131] The relative errors of the verification experiments were calculated. The average relative errors of the seawater, freshwater, acidic soil, and alkaline industrial wastewater models were 4.2%, 5.8%, 6.5%, and 3.9%, respectively, all of which were below the acceptable threshold of 10%.

[0132] For data points where the error exceeds the threshold, a second fitting is performed to fine-tune the model coefficients, namely k1, k2, k3, and k4 in the model, ultimately ensuring that the deviation between the model's predicted value and the actual value is within the allowable range.

[0133] (III) Beneficial Effects

[0134] This invention provides a method and system for monitoring corrosion of titanium-copper composite plates based on wireless communication. Compared with existing technologies, it has the following advantages:

[0135] Basic corrosion monitoring models were established for four typical environmental media: seawater, freshwater, acidic soil, and alkaline industrial wastewater. The model parameters were derived from long-term control experimental data, with average relative errors all below 10%, providing a reliable basis for corrosion rate calculation. Furthermore, the basic models were adaptively adjusted by combining environmental parameter similarity calculations. This allows for parameter fine-tuning based on the degree of matching between the actual environment and the basic model, and even the construction of temporary models when the matching degree is low, further improving the accuracy of corrosion rate calculations under various complex environments.

[0136] The system can collect various environmental parameters, including pH, temperature, humidity, and chemical composition parameters specific to different environments (such as chloride ion concentration in seawater and calcium ion concentration in freshwater), and can flexibly adjust the model based on these parameters. Whether it is a typical environment or a complex environment between multiple typical environments, effective monitoring can be achieved through model adjustment or temporary model construction, breaking through the limitation of the limited applicability of a single model.

[0137] The data processing and analysis phase encompasses receiving and storing data, preprocessing, corrosion rate calculation, and trend prediction, with standardized procedures. Data cleaning removes outliers and noisy data, and data conversion ensures unit consistency, providing high-quality data for subsequent calculations. Corrosion rates are calculated based on preprocessed data and adjusted models, and a linear prediction method combined with historical data is used to predict trends, making corrosion status assessment more forward-looking.

[0138] The early warning system categorizes corrosion severity into three levels: mild, moderate, and severe. Each level has clearly defined criteria for assessment, accurately reflecting corrosion risk. When the corrosion rate exceeds a threshold or exhibits an abnormal trend, an early warning is automatically generated and disseminated through various channels, facilitating timely countermeasures by relevant personnel to prevent equipment damage or safety accidents caused by corrosion.

[0139] The monitoring report is comprehensive, including basic system information, environmental parameters, corrosion-related data, and trend predictions, presented intuitively in tables and charts, providing a reliable basis for equipment maintenance. The system's wireless communication function enables real-time data transmission and remote monitoring. Combined with a robust early warning and reporting mechanism, it helps extend the service life of titanium-copper composite panels, reduce maintenance costs, and ensure the safe and stable operation of related equipment. It has significant practical value in fields such as marine engineering, water conservancy facilities, and industrial wastewater treatment. Attached Figure Description

[0140] Figure 1 This is a system block diagram of a titanium-copper composite plate corrosion monitoring system based on wireless communication according to the present invention.

[0141] Figure 2 This is a schematic flowchart of a corrosion monitoring method for titanium-copper composite plates based on wireless communication according to the present invention.

[0142] Figure 3 This is a system block diagram of an electronic device in a titanium-copper composite plate corrosion monitoring system based on wireless communication according to the present invention. Detailed Implementation

[0143] 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. 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.

[0144] Please see Figure 1 and Figure 2 As shown, the embodiments of the present invention provide the following technical solutions:

[0145] As an embodiment of the present invention:

[0146] This invention relates to a corrosion monitoring system for titanium-copper composite plates based on wireless communication, comprising:

[0147] The corrosion monitoring system for titanium-copper composite plates based on wireless communication consists of multiple corrosion monitoring units, a wireless communication module, an environmental parameter acquisition module, and a central processing module.

[0148] The corrosion monitoring unit is used to collect corrosion-related data of the titanium-copper composite plate in real time.

[0149] Each corrosion monitoring unit is equipped with multiple sensors, including corrosion potential sensors and corrosion current sensors.

[0150] The corrosion potential sensor is used to measure the potential difference between the surface of the titanium-copper composite plate and the surrounding environment. The measured value is represented by the symbol E and the unit is volt (V).

[0151] The corrosion current sensor is used to measure the current generated during the corrosion process of titanium-copper composite plates. Its measured value is represented by the symbol L and the unit is ampere (A).

[0152] The environmental parameter acquisition module is used to collect various parameters of the environment in which the titanium-copper composite plate is located, including the pH, temperature, humidity and chemical composition parameters of the environmental medium.

[0153] in:

[0154] Acidity and alkalinity are expressed by pH value, which usually ranges from 0 to 14. 7 is neutral, less than 7 is acidic, and greater than 7 is alkaline.

[0155] Temperature is represented by T, and the unit is degrees Celsius (°C).

[0156] Humidity is expressed in H and is measured as a percentage (%).

[0157] The environmental media include: seawater, freshwater, acidic soil, and alkaline industrial wastewater;

[0158] Among the chemical composition parameters of the environmental medium:

[0159] For marine environments: collect the chloride ion concentration in seawater, in moles per liter (mol / L).

[0160] For freshwater environments: collect calcium ion concentration and bicarbonate ion concentration;

[0161] For acidic soil environments: collect the concentrations of sulfate ions and nitrate ions in the soil;

[0162] For alkaline industrial wastewater environments: the concentrations of chloride ions and hydroxide ions in the wastewater should be collected;

[0163] The collection of these chemical composition parameters can more comprehensively reflect the characteristics of different environmental media, provide richer data support for the construction and adjustment of adaptive monitoring models, and improve the accuracy of corrosion monitoring of titanium-copper composite plates.

[0164] The wireless communication module is responsible for transmitting the data collected by the corrosion monitoring unit and the environmental parameter acquisition module to the central processing module in real time.

[0165] In this embodiment, the wireless communication module adopts a common wireless communication protocol to ensure the stability and timeliness of data transmission;

[0166] The central processing module is used to receive, store, process, and analyze the transmitted data.

[0167] A corrosion monitoring method for titanium-copper composite plates based on wireless communication is disclosed. This method is implemented using a wireless communication-based corrosion monitoring system for titanium-copper composite plates and includes the following steps:

[0168] Step 1: Establishing the basic model:

[0169] The central processing module pre-stores basic models for different typical environmental media;

[0170] The basic model is established as follows:

[0171] Basic corrosion monitoring models were established for several common typical environmental media, such as seawater, freshwater, acidic soil, and alkaline industrial wastewater.

[0172] These basic models, based on long-term experimental data and theoretical analysis, describe the corrosion patterns of titanium-copper composite plates under specific environmental conditions.

[0173] For marine environments, the basic model is represented as follows:

[0174] ;

[0175] Among them, R corr,seawater The corrosion rate of the titanium-copper composite plate in seawater is expressed in millimeters per year (mm / year); k1 is the corrosion rate constant in seawater, which is obtained by fitting a large amount of experimental data; C Cl T is the chloride ion concentration in seawater; T is the seawater temperature; pH is the acidity or alkalinity of seawater.

[0176] For freshwater environments, the basic model is represented as follows:

[0177] ;

[0178] Among them, R corr,freshwater The value represents the corrosion rate of the titanium-copper composite plate in a freshwater environment; k2 is the corrosion rate constant in a freshwater environment; T is the freshwater temperature; pH is the acidity or alkalinity of the freshwater.

[0179] For acidic soil environments, the basic model is represented as follows:

[0180] ;

[0181] Among them, R corr,acidsoil The value represents the corrosion rate of titanium-copper composite plates in acidic soil environments; k3 is the corrosion rate constant in acidic soil environments; H is soil moisture; pH is the soil acidity / alkalinity.

[0182] For alkaline industrial wastewater environments, the basic model is represented as follows:

[0183] ;

[0184] Among them, R corr,alkalinewastewater The value represents the corrosion rate of titanium-copper composite plates in an alkaline industrial wastewater environment; k4 is the corrosion rate constant in an alkaline industrial wastewater environment; T is the wastewater temperature; pH is the acidity or alkalinity of the wastewater.

[0185] Step 2: Adaptive adjustment of the basic model:

[0186] In a real-world environment, various environmental parameters are acquired through an environmental parameter acquisition module, including pH, temperature (T), humidity (H), and chemical composition. These parameters are then input into a central processing module and compared with the applicable conditions of various basic models. The method is as follows:

[0187] Calculate the similarity between the current environmental parameters and the applicable environmental parameters of each base model;

[0188] Taking pH as an example, the similarity S pH The calculation formula is: ;

[0189] Among them, pH current It refers to the acidity or alkalinity of the current environment, pH. model It refers to the acidity or alkalinity of the environment in which the basic model is applicable, specifically the pH value. max and pH min These refer to the historical maximum and minimum values ​​of the acidity / alkalinity parameter in the applicable environment of the base model corresponding to the current similarity calculation, and the applicable environment of the base model refers to seawater environment, freshwater environment, acidic soil environment or alkaline industrial wastewater environment.

[0190] Similarity S to pH pH The calculation logic is the same; for temperature, humidity and chemical composition parameters, their corresponding similarity is calculated respectively.

[0191] Then, based on the weighted average of the various similarities, the degree of matching between the current environment and each basic model is determined;

[0192] Adjust the base model based on the degree of matching:

[0193] If the current environment matches a certain base model closely, then based on that base model, the parameters in the model are fine-tuned according to the differences between the current environment parameters and the applicable environment parameters of the base model:

[0194] Taking the marine environment as an example: if the current temperature of the marine environment is T current Temperature T higher than that applicable to the basic model model The corrosion rate constant k1 is then adjusted according to the temperature difference. The adjusted corrosion rate constant k1 is...1a Represented as: ;

[0195] If the current environment has a low degree of matching with all the basic models, then a new temporary model is constructed by combining the characteristics of multiple basic models.

[0196] Taking an environment between seawater and freshwater as an example, the new temporary model is constructed by integrating the basic models of seawater and freshwater to derive a new model:

[0197] ;

[0198] Wherein, γ is a preset weighting coefficient, which is determined based on the similarity between the current environment and the seawater and freshwater environments, and 0≤a≤1;

[0199] Step 3: Processing and Analysis of Corrosion Monitoring Data

[0200] Step L1, Data Reception and Storage:

[0201] The central processing module receives data from various corrosion monitoring units and environmental parameter acquisition modules through the wireless communication module, and classifies and stores this data according to time sequence and monitoring location;

[0202] The stored data includes corrosion potential E, corrosion current I, environmental pH, temperature T, humidity H, and chemical composition parameters.

[0203] Step L2, Data Preprocessing:

[0204] The received data is preprocessed, including data cleaning and data transformation;

[0205] Data cleaning mainly involves removing outliers and noisy data.

[0206] Taking corrosion current data as an example, if the difference between a data point and its surrounding data points exceeds the corresponding preset threshold, the data point is considered an outlier and is removed.

[0207] Data transformation is the process of converting data from different units into a unified unit to facilitate subsequent analysis and calculation.

[0208] Step L3, Calculation of corrosion rate:

[0209] The corrosion rate of the titanium-copper composite plate was calculated based on the preprocessed data and the adaptively adjusted monitoring model.

[0210] For different environmental media, the corresponding adjusted models are used for calculation;

[0211] For example, in a seawater environment, the corrosion rate R is calculated using an adjusted seawater corrosion model. corr :

[0212] ;

[0213] Where, k 1a It is the adjusted corrosion rate constant, C Cl This is the current chloride ion concentration in seawater, T current It is the current seawater temperature and pH. current It is the current pH level of the seawater;

[0214] Step L4, Corrosion Trend Prediction:

[0215] Based on historical corrosion data and current corrosion rates, the future corrosion trend of titanium-copper composite panels is predicted.

[0216] Using a simple linear prediction method, when the corrosion rate remains stable over a certain period of time, the prediction formula is as follows:

[0217] ;

[0218] Among them, R future (t) is the corrosion rate at future time t, R current is the current corrosion rate, and v is the rate of change of the corrosion rate, which is obtained through linear regression analysis of historical data.

[0219] The corrosion monitoring system and method for titanium-copper composite plates based on wireless communication provided in Example 1 achieves real-time acquisition and transmission of corrosion data and environmental parameters through modular design. The corrosion monitoring unit and environmental parameter acquisition module specifically collect parameters such as corrosion potential, current, pH, temperature, and specific chemical components, providing comprehensive data support for monitoring. The wireless communication module ensures real-time data transmission, and the central processing module performs centralized data processing. Simultaneously, basic corrosion models are established for four typical environments, and adaptive adjustments are achieved by combining environmental parameter similarity calculations. This ensures the accuracy of corrosion rate calculations under typical environments while allowing for flexible fine-tuning to address environmental differences, effectively improving the system's applicability and monitoring accuracy in different scenarios, and providing a reliable technical framework for corrosion monitoring of titanium-copper composite plates.

[0220] As a second embodiment of the present invention:

[0221] In specific implementation of this application, compared with Embodiment 1, the technical solution of this embodiment differs from that of Embodiment 1 only in that in this embodiment, the establishment of the basic model is the prerequisite for the entire corrosion monitoring system to achieve accurate monitoring. It is necessary to combine experimental data accumulation, theoretical mechanism analysis and parameter fitting optimization to form a quantifiable and verifiable mathematical expression.

[0222] The following details the construction process of a basic model in a typical environmental medium through four key steps:

[0223] Step A1: Experimental Data Acquisition and Environmental Simulation

[0224] For four typical environments—seawater, freshwater, acidic soil, and alkaline industrial wastewater—multiple control experimental systems were built, and corrosion data of titanium-copper composite plates were obtained through long-term monitoring.

[0225] Step A1.1, Seawater Environment Simulation:

[0226] Artificial seawater was prepared according to international standard seawater formulations, with 6 groups of chloride ion concentration gradients ranging from 0.1 to 0.6 mol / L, 5 groups of temperature gradients ranging from 5 to 35℃, and 5 groups of pH gradients ranging from 7.5 to 8.5. Each group of experiments had no fewer than 3 samples.

[0227] The titanium-copper composite plate sample was completely immersed in simulated seawater. In this embodiment, the titanium-copper composite plate sample size was 100mm×50mm×2mm. The actual corrosion rate was calculated by the weight loss method, that is, the sample mass change was measured every 72 hours, and the corresponding electrochemical parameters of corrosion potential and corrosion current were recorded. The experimental period lasted for 12 months.

[0228] Step A1.2, Freshwater Environment Simulation:

[0229] Based on distilled water, calcium ions (range 0.01-0.05 mol / L) and bicarbonate ions (range 0.005-0.02 mol / L) are added to simulate the composition of natural freshwater. Five temperature gradients (range 10-40℃) and six pH gradients (range 6.0-8.0) are also set.

[0230] The same weightlessness method was used to monitor the corrosion rate, and environmental parameters were collected simultaneously. The experimental period was 9 months.

[0231] Step A1.3, Simulation of acidic soil environment:

[0232] Typical red soil was selected as the substrate, with a pH range of 4.0-5.5. The pH gradient was adjusted by adding sulfuric acid, with a range of 3.0-6.0. Four sets of humidity gradients were set, with a range of 20%-60%, based on the soil saturation moisture content.

[0233] The titanium-copper composite plate was buried in the soil at a depth of 50cm. It was taken out every 30 days to measure the corrosion weight loss. The experimental period was 18 months, during which the soil environmental parameters were kept stable.

[0234] Step A1.4, Environmental simulation of alkaline industrial wastewater:

[0235] To simulate industrial wastewater containing sodium hydroxide and sodium carbonate, a pH gradient ranging from 9.0 to 12.0 and a temperature gradient ranging from 25 to 60°C were set, and a small amount of chloride ions ranging from 0.001 to 0.01 mol / L were added to simulate actual pollution conditions.

[0236] A dynamic circulation device was used to keep the wastewater flowing. In this embodiment, the flow rate was 0.5 m / s, and the corrosion data of the sample was measured every 48 hours. The experimental cycle was 6 months.

[0237] Step A2, Theoretical Basis and Variable Selection:

[0238] Based on corrosion electrochemistry theory and materials science principles, the mechanism of environmental parameters on the corrosion of titanium-copper composite plates was analyzed, and key influencing variables were screened:

[0239] For marine environments:

[0240] Chloride ion C Cl The main factors leading to the damage of the passivation film in the titanium-copper composite plate are: increasing temperature T will accelerate the electrochemical reaction rate; and when the pH value deviates from the natural value of seawater, it will affect the stability of the passivation film. In this embodiment, the natural value of seawater is about 8.0.

[0241] Therefore, these three parameters were selected as the core variables of the model, and their significant correlation with the corrosion rate was verified by correlation analysis, with all correlation coefficients > 0.85.

[0242] For freshwater environments:

[0243] Temperature (T) is the dominant factor affecting the corrosion rate. When the pH value is below neutral (7.0), the depolarization effect of hydrogen ions is enhanced, accelerating the dissolution of the copper matrix.

[0244] Experimental data show that calcium ion concentration has a weak effect on corrosion rate, with a correlation coefficient of <0.3. Therefore, only temperature and pH value are retained as variables in the model.

[0245] For acidic soil environments:

[0246] Soil moisture (H) determines the continuity of the electrolyte solution; the higher the moisture, the better the conductivity of the corrosive medium; the lower the pH value, i.e., the stronger the acidity, the higher the hydrogen ion concentration, and the faster the corrosion rate.

[0247] Principal component analysis determined that these two parameters could explain 92% of the corrosion rate variation, and therefore they were used as core variables in the model.

[0248] For alkaline industrial wastewater environments:

[0249] High temperature (T) accelerates the dissolution of titanium layers by alkaline solutions, forming titanates. The corrosion rate increases exponentially when the pH value exceeds 10.0.

[0250] Experiments have shown that chloride ions have a relatively small effect at low concentrations, therefore the model mainly includes temperature and pH variables.

[0251] Step A3, Model Parameter Fitting and Expression Derivation:

[0252] The experimental data were fitted using multiple linear regression to determine the constant terms and coefficients in the model;

[0253] Step A3.1: Fitting parameters of the seawater environment model:

[0254] The experimental data from 12 months were normalized, with the corrosion rate R as the criterion. corr,seawater C is the dependent variable. Cl T and (pH-8) are the independent variables, and the results are obtained by least squares fitting:

[0255] ;

[0256] In this embodiment, the constant k1 is determined by averaging 150 sets of valid experimental data;

[0257] Step A3.2, Freshwater environment model parameter fitting:

[0258] Based on 9 months of monitoring data, the following results were obtained by fitting the data with T and (7-pH) as independent variables:

[0259] ;

[0260] In this embodiment, k2 is calculated using 120 sets of valid data;

[0261] Step A3.3: Fitting parameters for the acidic soil environment model:

[0262] After fitting the 18-month buried experimental data, the following results were obtained:

[0263] ;

[0264] In this embodiment, k3 is determined by 180 sets of valid data, and the humidity H in the model is calculated as a percentage value.

[0265] Step A3.4, Fitting the environmental model for alkaline industrial wastewater:

[0266] Based on fitting of 6 months of dynamic experimental data, the following was obtained:

[0267] ;

[0268] In this embodiment, k4 is determined using 90 sets of valid data;

[0269] Step A4, Model Validation and Error Correction:

[0270] Step A4.1: Verify the experimental design:

[0271] Ten additional parameter combinations, different from those in the modeling experiment, were set up for each environment, and the accuracy was verified by comparing the actual corrosion rate with the model prediction.

[0272] Step A4.2, Error Correction:

[0273] The relative errors of the verification experiments were calculated. The average relative errors of the seawater, freshwater, acidic soil, and alkaline industrial wastewater models were 4.2%, 5.8%, 6.5%, and 3.9%, respectively, all of which were below the acceptable threshold of 10%.

[0274] For data points where the error exceeds the threshold, a second fitting is performed to fine-tune the model coefficients, namely k1, k2, k3, and k4 in the model, ultimately ensuring that the deviation between the model's predicted value and the actual value is within the allowable range.

[0275] The basic model established through the above steps is based on solid experimental data and conforms to the laws of corrosion theory, providing a reliable initial framework for subsequent adaptive adjustments.

[0276] Example 2 focuses on optimizing the construction process of the basic model, improving its scientific rigor and reliability through standardized experimental design and data accumulation. It establishes multiple control experimental systems to simulate seawater, freshwater, acidic soil, and alkaline industrial wastewater environments, sets gradient parameters and conducts long-term monitoring, and combines corrosion electrochemistry theory to screen key influencing variables. Then, through multiple linear regression fitting and error correction, a quantifiable and verifiable mathematical model is formed. This process ensures that the model is based on solid experimental data and conforms to corrosion theory, providing a precise initial framework for subsequent adaptive adjustments. It fundamentally enhances the theoretical basis and data support for corrosion rate calculation, making the core algorithm of the monitoring system more convincing and practical.

[0277] As an embodiment of the present invention:

[0278] In specific implementation, compared with Embodiment 1 and Embodiment 2, the technical solution of this embodiment is to combine the solutions of Embodiment 1 and Embodiment 2. The difference between the technical solution of this embodiment and Embodiment 1 and Embodiment 2 is only in this embodiment; the central processing module also generates corresponding monitoring reports and early warning information based on the analysis results;

[0279] The monitoring report mainly includes the following:

[0280] Basic information about the monitoring system, including monitoring time, monitoring location, and monitoring unit number;

[0281] Real-time monitoring data of environmental parameters, such as pH, temperature (T), humidity (H), and chloride ion concentration (C). Cl ;

[0282] Corrosion-related data for titanium-copper composite plates, such as corrosion potential E, corrosion current I, and corrosion rate R. corr ;

[0283] The prediction results of corrosion trends include the predicted corrosion rate and corrosion degree over a future period of time;

[0284] The monitoring report is formatted in a clear and concise manner using tables and charts, making it easy for users to understand and use.

[0285] The methods for generating and issuing early warning information are as follows:

[0286] When the monitoring system detects that the corrosion rate of the titanium-copper composite plate exceeds the preset threshold or that the corrosion trend is abnormal, it will automatically generate an early warning message.

[0287] The warning information is categorized into three levels based on the severity of corrosion: mild, moderate, and severe.

[0288] Specifically as follows:

[0289] For mild warnings:

[0290] If the calculated current corrosion rate R corr Exceeding the preset threshold by 10%-30%;

[0291] If the rate of change of corrosion rate v exceeds the normal fluctuation range, but the predicted corrosion rate R for the next 3 months is... future (t) did not exceed the preset threshold;

[0292] Or if the corrosion rate R is predicted for the next month future (t) The difference from the preset threshold is within 0.05 mm / year, and the rate of change v shows signs of continuous increase but has not yet grown rapidly;

[0293] If any of the above conditions are met, it is determined to be a mild warning;

[0294] For a moderate alert:

[0295] If the current corrosion rate R corr Exceeding the preset threshold by 30%-60%;

[0296] If the rate of change of corrosion rate v exceeds the normal fluctuation range, and the predicted corrosion rate R for the next two months... future(t) exceeds the corresponding preset threshold;

[0297] Alternatively, if the current corrosion rate is within the normal fluctuation range, but the predicted corrosion rate R for the next month is... future (t) exceeds 20% of the preset threshold, and the rate of change v continues to increase;

[0298] If any of the above conditions are met, it is determined to be a moderate warning.

[0299] For severe warnings:

[0300] If the current corrosion rate R corr Exceeding the preset threshold by 60% or more;

[0301] If the corrosion rate R is predicted for the next month future (t) exceeds 50% or more of the preset threshold, and the corrosion rate change rate v rises sharply from the normal range to 0.2 mm / year2 or more in a short period of time.

[0302] If any of the above conditions are met, it is determined to be a severe warning.

[0303] After the early warning information is generated, it is promptly released to relevant users, such as equipment maintenance personnel and engineering management personnel, through the wireless communication module;

[0304] Users receive warning information through mobile phones, computers and other terminal devices, and take corresponding measures based on the warning information.

[0305] Example 3, building upon previous data processing and model calculations, adds monitoring report generation and tiered early warning functions, enhancing the system's practical value and decision support capabilities. The monitoring reports are comprehensive, covering system information, environmental parameters, corrosion data, and trend predictions, presented intuitively in tables and charts for easy user understanding of corrosion status. The early warning mechanism categorizes corrosion severity into three levels: mild, moderate, and severe, with clear and specific criteria to accurately reflect different risk levels. Furthermore, early warning information is promptly pushed to relevant personnel via wireless communication, enabling them to take targeted measures based on the warning level. This design achieves closed-loop management from data monitoring to risk early warning, effectively preventing the escalation of corrosion problems and providing timely and accurate guidance for the maintenance of titanium-copper composite panels.

[0306] As an embodiment of the present invention:

[0307] In specific implementation, compared with Embodiment 1, Embodiment 2 and Embodiment 3, the technical solution of this embodiment is to combine the solutions of Embodiment 1, Embodiment 2 and Embodiment 3.

[0308] Example 4 integrates the technical solutions of Examples 1, 2, and 3 to form a complete corrosion monitoring closed-loop system, possessing comprehensive data acquisition, accurate model calculation, intuitive report presentation, and timely early warning mechanism. From real-time acquisition of environmental parameters and corrosion data, to corrosion rate calculation and trend prediction based on the optimized model, and then to the generation of detailed reports and tiered early warnings, each link in the system is seamlessly connected, realizing full-process monitoring and management of the corrosion status of titanium-copper composite panels. This integrated solution retains the technical advantages of each module while improving overall performance through synergy. It can meet the diverse needs of corrosion monitoring in different scenarios, significantly reduce the risk of equipment failure due to corrosion, and extend the service life of titanium-copper composite panels, demonstrating extremely high application value in engineering practice.

[0309] It should be stated that all user data collected in this application was collected with the user's consent and authorization, and the use of user data is legal and compliant, and the use and processing of user data comply with the relevant laws, regulations and standards of the relevant regions.

[0310] Furthermore, any content not described in detail in this specification is existing technology known to those skilled in the art.

[0311] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0312] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

[0313] Corresponding to the aforementioned application function implementation method embodiments, this application also provides an electronic device and corresponding embodiments.

[0314] See Figure 3 Electronic devices include memory and processors.

[0315] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0316] Memory can include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage devices. ROM can store static data or instructions required by the processor or other modules of the computer. Permanent storage devices can be read-write storage devices. Permanent storage devices can be non-volatile storage devices that retain stored instructions and data even when the computer is powered off. In some embodiments, permanent storage devices use mass storage devices (e.g., magnetic or optical disks, flash memory) as permanent storage devices. In other embodiments, permanent storage devices can be removable storage devices (e.g., floppy disks, optical drives). System memory can be a read-write storage device or a volatile read-write storage device, such as dynamic random access memory. System memory can store some or all of the instructions and data required by the processor during operation. Furthermore, memory can include any combination of computer-readable storage media, including various types of semiconductor memory chips (DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), and disks and / or optical disks can also be used. In some implementations, the memory may include removable storage devices that are readable and / or writable, such as laser discs (CDs), read-only digital versatile optical discs (e.g., DVD-ROMs, dual-layer DVD-ROMs), read-only Blu-ray discs, ultra-density optical discs, flash memory cards (e.g., SD cards, minSD cards, Micro-SD cards, etc.), magnetic floppy disks, etc. Computer-readable storage media do not contain carrier waves or transient electronic signals transmitted wirelessly or via wired connections.

[0317] The memory stores executable code, which, when processed by the processor, can cause the processor to execute some or all of the methods described above.

[0318] The solution of this application has been described in detail above with reference to the accompanying drawings. In the above embodiments, the descriptions of each embodiment have different focuses; for parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. Those skilled in the art should also understand that the actions and modules involved in the specification are not necessarily essential to this application. Furthermore, it is understood that the steps in the method of this application embodiment can be adjusted, combined, and deleted according to actual needs, and the modules in the device of this application embodiment can be combined, divided, and deleted according to actual needs.

[0319] Furthermore, the method according to this application can also be implemented as a computer program or computer program product, which includes computer program code instructions for performing some or all of the steps in the method described above.

[0320] Alternatively, this application may be implemented as a non-transitory machine-readable storage medium (or computer-readable storage medium, or machine-readable storage medium) storing executable code (or computer program, or computer instruction code) thereon, which, when executed by a processor of an electronic device (or electronic device, server, etc.), causes the processor to perform part or all of the steps of the methods described above according to this application.

[0321] Those skilled in the art will also understand that the various exemplary logic blocks, modules, circuits, and algorithm steps described in connection with the present application can be implemented as electronic hardware, computer software, or a combination of both.

[0322] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems and methods according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that indicated in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved.

[0323] It should also be noted that each block in a block diagram and / or flowchart, as well as combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0324] The various embodiments of this application have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A wireless communication-based corrosion monitoring method for titanium-copper clad plate, characterized in that: The method comprises the following steps: First step, basic model establishment: for the corresponding environmental medium of seawater, freshwater, acidic soil and alkaline industrial wastewater, a basic corrosion monitoring model is established respectively; Second step, self-adaptive adjustment of the basic model: in the actual environment, the parameters of the current environment, i.e. pH, T, H and chemical composition parameters, are obtained through the environmental parameter acquisition module, and then the parameters are input into the central processing module to compare with the applicable conditions of each basic model, so as to determine the adjusted corrosion monitoring model; Third step, processing and analysis of corrosion monitoring data: the central processing module receives data from the corrosion monitoring unit and the environmental parameter acquisition module through the wireless communication module, and then stores the data in chronological order and according to the monitoring position; then the data is cleaned and converted, and then the corrosion rate is calculated; based on the pretreated data and the self-adaptive adjusted monitoring model, the corrosion rate of the titanium-copper composite plate is calculated by using the corresponding adjusted model for different environmental media; finally, the corrosion trend is predicted based on the historical corrosion data and the current corrosion rate. 2.The method according to claim 1, wherein: The corrosion monitoring model adjustment method is as follows: For seawater environment, the base model is expressed as: ; wherein R corr,seawater represents the corrosion rate of the titanium-copper clad plate in seawater environment; k1 is the corrosion rate constant in seawater environment; C Cl is the chloride ion concentration in seawater; T is the seawater temperature; and pH is the acidity and alkalinity of seawater. For fresh water environment, the base model is expressed as: ; wherein R corr,freshwater represents the corrosion rate of the titanium-copper clad plate in a fresh water environment; k2 is the corrosion rate constant in the fresh water environment; T is the fresh water temperature; and pH is the acidity of the fresh water. For acidic soil environment, the base model is expressed as: ; wherein R corr,acidsoil represents the corrosion rate of the titanium-copper composite plate in an acidic soil environment; k3 is the corrosion rate constant in the acidic soil environment; H is the soil humidity; pH is the acidity of the soil; For alkaline industrial wastewater environment, the basic model is expressed as: ; wherein R corr,alkalinewastewater represents the corrosion rate of the titanium-copper composite plate in the alkaline industrial wastewater environment; k4 is the corrosion rate constant in the alkaline industrial wastewater environment; T is the wastewater temperature; and pH is the acidity of the wastewater. 3.The method according to claim 2, wherein: The similarity of the current environmental parameters and the applicable environmental parameters of each basic model is calculated; The calculation logic of the similarity of temperature, humidity and chemical composition parameters and pH is the same, and the corresponding similarity is calculated respectively; Selected pH, similarity S pH The calculation formula is: ; wherein pH current is the pH of the current environment, pH model is the pH of the base model applicable environment, pH max and pH min respectively refer to the historical measured maximum and minimum values of the pH parameter in the base model applicable environment corresponding to the current calculation of the similarity, and the base model applicable environment refers to a seawater environment, a freshwater environment, an acidic soil environment, or an alkaline industrial wastewater environment. Then, the matching degree of the current environment and each basic model is determined according to the weighted average of each similarity; According to the matching degree, the basic model is adjusted: If the matching degree of the current environment and a certain basic model is high, the basic model is used as the basis, and the parameters in the model are fine-tuned according to the difference between the current environmental parameters and the applicable environmental parameters of the basic model: The parameter fine-tuning logic in the model corresponding to various environmental media is the same, and the seawater environment is selected: The specific method of processing and analysis of corrosion monitoring data is as follows: If the temperature T current of the current seawater environment is higher than the preset temperature T model applicable to the basic model, the corrosion rate constant k1 is adjusted according to the temperature difference, and the adjusted corrosion rate constant k 1a is expressed as: .

4. The method according to claim 3, wherein the method is characterized by: Step L1, data receiving and storage: The central processing module receives data from each corrosion monitoring unit and the environmental parameter acquisition module through the wireless communication module, and stores the data in chronological order and according to the monitoring position; The stored data includes corrosion potential E, corrosion current I, environmental pH, temperature T, humidity H and chemical composition parameters; Step L2, data preprocessing: The received data is preprocessed, including data cleaning and data conversion; Data cleaning mainly removes outliers and noise data; Select the corrosion current data, if the difference between a data point and the surrounding data points exceeds the corresponding preset threshold, the data point is considered as an outlier and is removed; Data conversion is to convert data in different units to a unified unit; Step L3, corrosion rate calculation: According to the pretreated data and the self-adaptive adjusted monitoring model, the corrosion rate of the titanium-copper composite plate is calculated; For different environmental media, the adjusted corresponding model is used for calculation; Step L4, corrosion trend prediction: ​ Based on historical corrosion data and current corrosion rate, predict the future corrosion trend of titanium-copper composite plate; When the corrosion rate is stable in a certain time, the prediction formula is: ; where R future (t) is the corrosion rate at future time t, R current is the current corrosion rate, and v is the rate of change of the corrosion rate, which is obtained by linear regression analysis of historical data.

5. The method according to claim 3, wherein the method is characterized by: If the current environment is not matched with all the basic models, a new temporary model is constructed by integrating the characteristics of multiple basic models; if the current environment is between seawater and freshwater, the new temporary model is constructed by integrating the basic models of seawater and freshwater: ; Wherein, γ is a preset weight coefficient, determined according to the similarity between the current environment and seawater and freshwater environment, and 0≤a≤1.

6. A wireless communication-based corrosion monitoring system for titanium-copper clad plate, the system being configured to perform the wireless communication-based corrosion monitoring method of any one of claims 1-5. The system comprises: Corrosion monitoring unit, for real-time acquisition of corrosion-related data of titanium-copper composite plate; Environment parameter acquisition module, for acquiring various parameters of the environment in which the titanium-copper composite plate is located, including the pH value, temperature, humidity of the environment, and the chemical composition parameters of the environmental medium; Wireless communication module, responsible for real-time transmission of data collected by the corrosion monitoring unit and the environment parameter acquisition module to the central processing module; Central processing module, for receiving, storing, processing and analyzing the transmitted data, and generating corresponding monitoring reports and warning information according to the analysis results.

7. The corrosion monitoring system for titanium-copper clad plate based on wireless communication according to claim 6, characterized in that: Each corrosion monitoring unit is provided with a plurality of sensors, including corrosion potential sensors and corrosion current sensors; the corrosion potential sensor is used to measure the potential difference between the surface of the titanium-copper composite plate and the surrounding environment, and the corrosion current sensor is used to measure the current generated during the corrosion process of the titanium-copper composite plate. 8.The corrosion monitoring system based on wireless communication for titanium-copper composite plate according to claim 6, characterized in that: The central processing module pre-stores the basic model for different typical environmental media. 9.The corrosion monitoring system based on wireless communication for titanium-copper composite plate according to claim 6, characterized in that: The monitoring report mainly includes the following contents: Basic information of the monitoring system, including monitoring time, monitoring location, monitoring unit number; Real-time monitoring data of environmental parameters, such as pH, T, H, C Cl ; Corrosion-related data of titanium-copper composite plate, such as corrosion potential E, corrosion current I, and corrosion rate R corr ; The prediction result of the corrosion trend, including the corrosion rate prediction value and the corrosion degree prediction in the future period of time. 10.The corrosion monitoring system based on wireless communication for titanium-copper composite plate according to claim 6, characterized in that: The generation mode of the warning information is: when the monitoring system detects that the corrosion rate of the titanium-copper composite plate exceeds the preset threshold or the corrosion trend is abnormal, the warning information is automatically generated.

Images