Ship coating life prediction method, device, equipment and readable storage medium
By using a multiphysics-coupled degradation mechanism model and a benchmark-actual dual-track data architecture, the influencing factors of ship coating life are quantified, solving the problem of accurate prediction of coating life for new ship types, new routes, and new coatings, and achieving higher accuracy in prediction.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- WUXI ZHONGHUI TIANZE INTELLIGENT TECH CO LTD
- Filing Date
- 2026-03-05
- Publication Date
- 2026-06-16
AI Technical Summary
Existing technologies do not provide accurate predictions of the service life of ship coatings, especially for new ship types, new routes, and new coatings. Furthermore, laboratory simulation environments differ significantly from real marine environments.
A multiphysics-coupled degradation mechanism model is adopted, combined with a baseline-actual dual-track data architecture, to quantify the nonlinear interaction of various lifetime influencing factors. By determining the baseline degradation rate, influence function, and influence coefficient, the predicted degradation rate and lifetime are calculated.
It improves the accuracy of predicting the service life of ship coatings, is applicable to new ship types, new routes and new coatings, reduces the decoupling problem between laboratory simulation and field environment, and improves prediction accuracy.
Smart Images
Figure CN122220649A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of ship coating inspection technology, and more specifically, to a method, apparatus, equipment, and readable storage medium for predicting the lifespan of ship coatings. Background Technology
[0002] Coatings are a crucial line of defense protecting ship hull steel from seawater corrosion. Once the coating fails, corrosion occurs and spreads rapidly, especially in critical areas such as ballast tanks, cargo oil tanks, and the outer hull plating. The condition of the hull coating directly affects ship resistance; coating degradation or excessive marine organism adhesion significantly increases drag, leading to increased main engine load, higher fuel consumption, and even affecting ship maneuverability and speed. Therefore, accurate and real-time prediction of ship coating lifespan is essential, along with timely early warning and maintenance to ensure the ship's structural strength and performance.
[0003] Currently, the main methods for predicting ship coatings are as follows: (1) Estimate the average life of the coating based on the historical maintenance and inspection records of similar vessels (similar routes, operating modes, and vessel age). Although this method is simple and intuitive, it is too general and cannot take into account the individual circumstances of a single vessel, and lacks reference for new vessel types, new routes, and new coatings.
[0004] (2) The coated samples were placed in experimental environments that accelerate aging, such as salt spray, ultraviolet light, and cyclic immersion, to simulate years of degradation. Combined with test data and some theories (such as Fick's diffusion law for the simulation of water vapor penetration), a mathematical model of the coating performance decay over time was attempted to be established. Although this method is highly controllable, the laboratory environment cannot fully simulate the real, variable and interactive marine environment, and its predictive accuracy is questionable. Summary of the Invention
[0005] The main objective of this application is to provide a method, apparatus, device, and readable storage medium for predicting the lifespan of ship coatings, in order to solve the problems of low accuracy in predicting the lifespan of ship coatings in the prior art and its unsuitability for predicting the lifespan of coatings for new ship types, new routes, and new coatings. This application achieves the technical effect of improving the accuracy of predicting the lifespan of ship coatings and making it applicable to predicting the lifespan of coatings for new ship types, new routes, and new coatings.
[0006] To achieve the above objectives, the first aspect of this application proposes a method for predicting the lifespan of ship coatings, comprising: Determine the baseline degradation rate of the coating under test under the baseline data of each lifetime influencing factor; wherein, the lifetime influencing factor includes at least two of the following: seawater temperature, salinity, pH value, ultraviolet radiation intensity, pollutants, mechanical wear, biofouling, route area and effective thickness; The influence functions of each lifespan influencing factor are established based on the baseline data, actual data, and influence coefficients of each factor. Obtain actual data for each lifespan influencing factor, substitute the actual data into the influence function, and calculate the influence coefficient of each lifespan influencing factor respectively; wherein, for any of the lifespan influencing factors, the influence coefficient is used to indicate the acceleration or deceleration effect of the actual data relative to the baseline data; Substitute the influence coefficients of each lifespan influencing factor and the baseline degradation rate into the nonlinear product model to calculate the predicted degradation rate; The predicted service life of the coating under test is calculated based on the predicted degradation rate.
[0007] In one possible implementation, before determining the baseline degradation rate of the coating under test under the baseline data for each lifetime influence factor, the method further includes: Define the reference conditions for the coating to be tested; wherein, the reference conditions include the reference data; The determination of the baseline degradation rate of the coating under test under the baseline data of each lifetime influencing factor includes: Obtain historical performance data of the coating under test under the baseline conditions, perform regression analysis on the historical performance data, and obtain the baseline degradation rate; or... The performance data of the coating under test is obtained under standard laboratory test conditions, and the baseline degradation rate is calculated based on the performance data; wherein, the standard laboratory test conditions are the baseline conditions.
[0008] In one possible implementation, the formula for the nonlinear product model is: ; In the formula, The predicted degradation rate; The baseline degradation rate; The coefficient representing the influence of seawater temperature; The influence coefficient of salinity; The influence coefficient of pH value; The influence coefficient of ultraviolet radiation intensity; The impact coefficient of pollutants; The coefficient representing the influence of mechanical wear; The impact coefficient of biofouling; The influence coefficient for the air route area; The influence coefficient of effective thickness.
[0009] In one possible implementation, calculating the predicted lifetime of the coating under test based on the predicted degradation rate includes: Obtain the actual dry film thickness and the minimum designed dry film thickness of the coating to be tested; Substitute the actual dry film thickness, the minimum designed dry film thickness, and the predicted degradation rate into the first formula to calculate the predicted service life. The first formula is: ; In the formula, The predicted service life; The actual dry film thickness; The minimum designed dry film thickness; The predicted degradation rate is given.
[0010] In one possible implementation, the formula for the influence function of seawater temperature is: ; In the formula, The coefficient representing the influence of seawater temperature; The activation energy is the degradation reaction of the coating under test. It is the ideal gas constant; The baseline seawater temperature; The actual seawater temperature is e≈2.71828.
[0011] In one possible implementation, the formula for the effect function of pH value is: ; In the formula, The influence coefficient of pH value; pH reference value; This is the actual pH value; , These are the fitting parameters.
[0012] In one possible implementation, the formula for the influence function of mechanical wear is: ; In the formula, The coefficient representing the influence of mechanical wear; Average speed; Used as the base speed; For the proportion of sailing time; and / or, The formula for the effect function of biofouling is: ; In the formula, The impact coefficient of biofouling; The duration of stay in Hong Kong; These are the fitting parameters; This represents the proportion of sailing time.
[0013] A second aspect of this application provides a device for predicting the lifespan of a ship coating, comprising: A baseline rate determination module is used to determine the baseline degradation rate of the coating under test under baseline data of various lifetime influencing factors; wherein, the lifetime influencing factors include at least two of the following: seawater temperature, salinity, pH value, ultraviolet radiation intensity, pollutants, mechanical wear, biofouling, route area, and effective thickness; The influence function establishment module is used to establish the influence function of each lifespan influence factor based on the baseline data, actual data and influence coefficient of each lifespan influence factor; The influence coefficient calculation module is used to obtain the actual data of each lifespan influence factor, substitute the actual data into the influence function, and calculate the influence coefficient of each lifespan influence factor respectively; wherein, for any of the lifespan influence factors, the influence coefficient is used to indicate the acceleration or deceleration effect of the actual data relative to the baseline data. The prediction rate calculation module is used to substitute the influence coefficients of each lifespan influencing factor and the baseline degradation rate into the nonlinear product model to calculate the predicted degradation rate. The predicted lifetime calculation module is used to calculate the predicted lifetime of the coating under test based on the predicted degradation rate.
[0014] A third aspect of this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the ship coating life prediction method provided in the first aspect of this invention.
[0015] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the ship coating life prediction method provided in the first aspect of the present invention.
[0016] The technical solutions provided by the embodiments of this application may include the following beneficial effects: Compared to the fact that laboratories cannot fully simulate the real, variable and interactive marine environment, this application establishes a multiphysics field coupled degradation mechanism model, quantifies the nonlinear interaction of various lifespan influencing factors, and adopts a benchmark-actual dual-track data architecture to solve the problem of decoupling between laboratory acceleration and field correlation, improve the prediction accuracy of ship coating lifespan, and is more suitable for predicting the coating lifespan of new ship types, new routes and new coatings. Attached Figure Description
[0017] The accompanying drawings, which form part of this application, are used to provide a further understanding of the application and to make other features, objects, and advantages of the application more apparent. The illustrative embodiments and descriptions of this application are used to explain the application and do not constitute an undue limitation of the application. In the drawings: Figure 1 A flowchart of the method for predicting the lifespan of ship coatings provided in this application; Figure 2 A schematic diagram of the ship coating life prediction device provided in this application; Figure 3 A schematic diagram of the electronic device provided in this application. Detailed Implementation
[0018] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.
[0019] This invention provides a method for predicting the lifespan of ship coatings. This method can be executed by an electronic device, which can be a server or a terminal device. The server can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet computer, desktop computer, wearable device, etc., but is not limited to these.
[0020] Figure 1 This is a flowchart illustrating a method for predicting the lifespan of a ship coating provided in this embodiment. Figure 1 As shown, the main process of this method is described below (steps S101 to S105): Step S101: Determine the baseline degradation rate of the coating under test under the baseline data of each lifetime influencing factor. Step S102: Establish the influence function of each lifespan influencing factor based on the baseline data, actual data, and influence coefficient of each lifespan influencing factor; Step S103: Obtain the actual data of each lifespan influencing factor, substitute the actual data into the influence function, and calculate the influence coefficient of each lifespan influencing factor respectively. Step S104: Substitute the influence coefficients of each lifespan influencing factor and the baseline degradation rate into the nonlinear product model to calculate the predicted degradation rate. Step S105: Calculate the predicted service life of the coating under test based on the predicted degradation rate.
[0021] In this embodiment, the lifespan influencing factors include at least two of the following: seawater temperature, salinity, pH value, ultraviolet radiation intensity, pollutants, mechanical wear, biofouling, route area, and effective thickness.
[0022] Before determining the baseline degradation rate of the coating under test under the baseline data of each lifetime influencing factor, it is necessary to define the baseline conditions for the coating. These baseline conditions include the baseline data corresponding to each lifetime influencing factor, such as seawater temperature of 10°C, salinity of 3.5%, absence of pollutants, and absence of ultraviolet radiation. A large amount of historical performance data of the coating under test under or near-baseline conditions is obtained, and regression analysis is performed on this historical performance data to obtain the baseline degradation rate. For example, the least squares method is used to fit a linear / nonlinear model of the performance index changing over time; power-law models or exponential models are commonly used. The significance of the model is tested using analysis of variance (ANOVA), and the coefficient of determination R is calculated. 2 A value greater than 0.8 ensures goodness of fit, and confidence intervals are used to assess the uncertainty of the baseline degradation rate.
[0023] Of course, existing standard laboratory test conditions can be selected as benchmark conditions, such as the test conditions under the ISO 12944 standard. The performance data of each lifetime influencing factor of the coating under test under these standard laboratory test conditions can be monitored periodically, and a benchmark degradation rate can be calculated based on the performance data. For example, a performance degradation rate Δ can be established. P With exposure time t Functional relationship P ( t )= P 0 kt n The baseline degradation rate is obtained through linear regression or power-law fitting, where... P 0 represents the initial performance value of the coating under test. n It is a degradation kinetic index used to characterize the shape of the degradation curve. n =1 indicates linear degeneracy. n <1 indicates a gradual decline in quality, with a fast initial rate and a slow later rate. n >1 indicates accelerated degradation, determined by fitting experimental data using nonlinear regression. n and k value.
[0024] In this embodiment, the baseline degradation rate refers to the rate at which the coating under test thins or degrades in performance per unit time under baseline conditions. Its unit can be micrometers per year (thickness loss) or % performance loss per year.
[0025] Coating failure (such as rust, peeling, and blistering) is a process of cumulative damage over time. Each lifespan influencing factor accelerates or slows down this damage process to varying degrees. Therefore, it is necessary to construct a multi-factor degradation model to predict the service life of ship coatings through a more scientific mathematical model.
[0026] An influence coefficient is established for each lifespan influencing factor, representing its accelerating or decelerating effect relative to baseline conditions. An influence coefficient of 1 indicates that the lifespan influencing factor is no different from the baseline conditions; a coefficient greater than 1 indicates accelerated degradation; and a coefficient less than 1 indicates decelerated degradation. By combining physicochemical principles with data-driven methods, and using baseline data, actual data, and influence coefficients to construct the influence function for each lifespan influencing factor, a scientific and quantitative tool is provided for predicting the lifespan of ship coatings.
[0027] The influence functions of each lifespan influencing factor are explained in detail below.
[0028] (1) Seawater temperature The influence function is constructed based on the Arrhenius equation, which states that as seawater temperature increases, the chemical reaction rate (corrosion, coating aging) increases exponentially. The formula for the influence function of seawater temperature is: ; In the formula, The coefficient representing the influence of seawater temperature; The activation energy of the degradation reaction of the coating under test (provided by the coating supplier or determined experimentally); It is the ideal gas constant (a fundamental physical constant connecting the pressure, volume, temperature and amount of substance of an ideal gas; under standard conditions, 1 mole of an ideal gas does 8.314 joules of work for expansion when the temperature increases by 1 Kelvin). The reference seawater temperature (Kelvin scale); The actual seawater temperature (Kelvin scale); e≈2.71828.
[0029] It should be noted that the activation energy of a coating degradation reaction refers to the minimum energy barrier that the coating material must overcome to undergo chemical or physical degradation (such as oxidation, corrosion, hydrolysis, thermal decomposition, etc.). It reflects the stability of the coating against degradation reactions; the higher the activation energy, the more stable the coating and the slower the degradation rate; conversely, the lower the activation energy, the easier the degradation reaction occurs.
[0030] (2) Salinity Salinity and electrochemical corrosion are generally positively correlated; that is, the higher the salinity, the more severe the electrochemical corrosion. Therefore, the formula for the effect function of salinity is: ; In the formula, The influence coefficient of salinity; As the baseline salinity; This refers to the actual salinity. , These are the fitting parameters (the value is usually 1, which can be used as a preliminary approximation).
[0031] (3) pH value Strong acid or strong alkali environments accelerate coating failure and substrate corrosion, exhibiting a non-linear relationship. Therefore, the formula for the effect function of pH value is: ; In the formula, The influence coefficient of pH value; The pH reference value is typically 7 or 8.2 for marine environments. This is the actual pH value; , These are the fitting parameters.
[0032] (4) Ultraviolet radiation intensity Ultraviolet radiation causes polymer coating chain breakage and pulverization, showing a positive correlation. Therefore, the formula for the effect function of ultraviolet radiation intensity is: ; In the formula, The influence coefficient of ultraviolet radiation intensity; This serves as a reference value for ultraviolet radiation intensity. This represents the actual value of ultraviolet radiation intensity.
[0033] (5) Pollutants This is a complex factor that needs to be quantified. It can be a concentration index of petroleum hydrocarbons, heavy metals, organic matter, etc. The formula for the pollutant's influence function is: ; In the formula, C represents the impact coefficient of pollutants; C represents the measured concentration of pollutant. This is the lowest observed value; This is the highest observed value; These are the fitting parameters.
[0034] (6) Mechanical wear The formula for the effect function of mechanical wear is: ; In the formula, The coefficient representing the influence of mechanical wear; Average speed; Used as the base speed; This refers to the sailing time ratio (the ratio of sailing time to total operating time).
[0035] (7) Biofouling The formula for the effect function of biofouling is: ; In the formula, The impact coefficient of biofouling; The length of stay in port (longer berthing can lead to more severe biofouling); These are the fitting parameters; This refers to the sailing time ratio (the ratio of sailing time to total operating time).
[0036] (8) Route area This is a macro-geographic factor that integrates the typical environments (temperature, salinity, biological activity) of different sea areas, and can be regarded as a correction multiplier.
[0037] Influence coefficient of the route area You can obtain the relevant sea area correction factor table for EEDI (Energy Efficiency Design Index) by consulting the table. This table reflects the impact of actual sea conditions on the reduction of ship speed and is used to more realistically assess the energy efficiency performance of ships in actual operation. For example, the value is 0.9 for the Baltic Sea, 1.5 for tropical seas, and 0.7 for Arctic seas.
[0038] (9) Effective thickness Effective thickness is related to coating type, dry film thickness, and application quality. Therefore, the formula for the influence function of effective thickness is: ; In the formula, The influence coefficient of effective thickness; This refers to the actual dry film thickness. This represents the minimum design dry film thickness of the coating to be tested. The construction quality coefficient (0.8~1.2) is determined by a third-party inspection report (for example, 1.2 represents excellent construction, 1.0 represents standard construction, and 0.8 represents defective construction).
[0039] It should be noted that, , , , , The values of these fitting parameters are determined by fitting experimental data to ensure that the model predictions best match the measured data. In this embodiment, the predicted degradation rate refers to the rate at which the coating under test thins or degrades in performance per unit time under current operating conditions. Its unit can be micrometers per year (thickness loss) or % performance loss per year.
[0040] In some optional embodiments, a nonlinear product model will be used to integrate the various lifetime-affecting factors in order to better reflect the synergistic effect among the factors. The formula for the nonlinear product model is as follows: ; In the formula, To predict the rate of degradation; The baseline degradation rate; The coefficient representing the influence of seawater temperature; The influence coefficient of salinity; The influence coefficient of pH value; The influence coefficient of ultraviolet radiation intensity; The impact coefficient of pollutants; The coefficient representing the influence of mechanical wear; The impact coefficient of biofouling; The influence coefficient for the air route area; The influence coefficient of effective thickness.
[0041] The service life L of a coating (in years) is not a fixed value, but rather the reciprocal of its degradation rate R. The degradation rate R itself is a function of multiple lifespan influencing factors (environmental factors and operational factors), and is the sum of the degradation rates caused by all lifespan influencing factors. Its basic formula is: .
[0042] In some optional embodiments, after calculating the predicted degradation rate of the coating to be tested, the actual dry film thickness and the minimum designed dry film thickness of the coating to be tested are obtained; the actual dry film thickness, the minimum designed dry film thickness, and the predicted degradation rate are substituted into the first formula to calculate the predicted service life.
[0043] The first formula is: ; In the formula, The predicted service life is in years; This refers to the actual dry film thickness. Minimum design dry film thickness; The thickness of the coating that can be consumed is the reserve of thickness that can be watched to degrade without the risk of immediate failure; it is measured in micrometers. To predict the degradation rate.
[0044] Furthermore, if the calculated remaining thickness is less than the preset lifespan, such as 2 years, then the next inspection or maintenance needs to be planned to prevent equipment corrosion due to coating failure. Compared to traditional scheduled or reactive maintenance, this method significantly saves costs and improves safety. The preset lifespan can be set based on factors such as port coordination time, approval time, and bidding processes.
[0045] Based on the same inventive concept, embodiments of the present invention provide a device for predicting the lifespan of ship coatings. Figure 2 This is a structural block diagram of a ship coating life prediction device 200 provided in an embodiment of the present invention. Figure 2 As shown, the ship coating life prediction device 200 mainly includes: The baseline rate determination module 201 is used to determine the baseline degradation rate of the coating under test under the baseline data of various lifetime influencing factors; wherein, the lifetime influencing factors include at least two of the following: seawater temperature, salinity, pH value, ultraviolet radiation intensity, pollutants, mechanical wear, biofouling, route area and effective thickness. The influence function establishment module 202 is used to establish the influence function of each lifespan influence factor based on the baseline data, actual data and influence coefficient of each lifespan influence factor; The influence coefficient calculation module 203 is used to obtain the actual data of each lifespan influence factor, substitute the actual data into the influence function, and calculate the influence coefficient of each lifespan influence factor respectively; wherein, for any lifespan influence factor, the influence coefficient is used to indicate the acceleration or deceleration effect of the actual data relative to the baseline data. The prediction rate calculation module 204 is used to substitute the influence coefficients of each lifespan influencing factor and the baseline degradation rate into the nonlinear product model to calculate the predicted degradation rate. The predicted lifetime calculation module 205 is used to calculate the predicted lifetime of the coating under test based on the predicted degradation rate.
[0046] In some optional embodiments, the ship coating life prediction device 200 further includes: The baseline condition definition module is used to define the baseline conditions of the coating under test before determining the baseline degradation rate of the coating under test under the baseline data of each lifetime influencing factor; wherein, the baseline conditions include the baseline data; The reference rate determination module 201 is specifically used to acquire historical performance measurement data of the coating under test under reference conditions, perform regression analysis on the historical performance measurement data to obtain the reference degradation rate; or, acquire performance data of the coating under test under laboratory standard test conditions, and calculate the reference degradation rate based on the performance data; wherein, the laboratory standard test conditions are the reference conditions.
[0047] In some optional embodiments, the formula for the nonlinear product model is: ; In the formula, To predict the rate of degradation; The baseline degradation rate; The coefficient representing the influence of seawater temperature; The influence coefficient of salinity; The influence coefficient of pH value; The influence coefficient of ultraviolet radiation intensity; The impact coefficient of pollutants; The coefficient representing the influence of mechanical wear; The impact coefficient of biofouling; The influence coefficient for the air route area; The influence coefficient of effective thickness.
[0048] In some optional embodiments, the predicted lifetime calculation module 205 is specifically used to obtain the actual dry film thickness and the minimum designed dry film thickness of the coating to be tested; and to calculate the predicted lifetime by substituting the actual dry film thickness, the minimum designed dry film thickness, and the predicted degradation rate into the first formula. The first formula is: ; In the formula, To predict service life; This refers to the actual dry film thickness. Minimum design dry film thickness; To predict the degradation rate.
[0049] In some optional embodiments, the formula for the influence function of seawater temperature is as follows: ; In the formula, The coefficient representing the influence of seawater temperature; The activation energy is the degradation reaction of the coating under test. It is the ideal gas constant; The baseline seawater temperature; The actual seawater temperature is e≈2.71828.
[0050] In some optional embodiments, the formula for the effect function of pH value is as follows: ; In the formula, The influence coefficient of pH value; pH reference value; This is the actual pH value; , These are the fitting parameters.
[0051] In some optional embodiments, the formula for the influence function of mechanical wear is: ; In the formula, The coefficient representing the influence of mechanical wear; Average speed; Used as the base speed; For the proportion of sailing time; and / or, The formula for the effect function of biofouling is: ; In the formula, The impact coefficient of biofouling; The duration of stay in Hong Kong; These are the fitting parameters; This represents the proportion of sailing time.
[0052] The functional modules in the embodiments of this invention can be integrated together to form an independent unit, such as integrated into a processing unit, or each module can exist physically separately, or two or more modules can be integrated to form an independent unit. The integrated unit can be implemented in hardware or as a software functional unit. If the function is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.
[0053] Various variations and specific examples of the methods provided in the embodiments of the present invention are also applicable to the ship coating life prediction device provided in this embodiment. Through the foregoing detailed description of the ship coating life prediction method, those skilled in the art can clearly understand the implementation method of the ship coating life prediction device in this embodiment. For the sake of brevity, it will not be described in detail here.
[0054] Figure 3 This is a structural block diagram of an electronic device 300 provided in an embodiment of the present invention. Figure 3 As shown, the electronic device 300 includes a memory 301, a processor 302, and a communication bus 303; the memory 301 and the processor 302 are connected through the communication bus 303.
[0055] The memory 301 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 301 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the ship coating life prediction method provided in the above embodiments, etc. The data storage area may store data involved in the ship coating life prediction method provided in the above embodiments, etc.
[0056] Processor 302 may include one or more processing cores. Processor 302 executes instructions, programs, code sets, or instruction sets stored in memory 301, and calls data stored in memory 301 to perform various functions and process data as described in this application. Processor 302 may be at least one of the following: Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), Central Processing Unit (CPU), controller, microcontroller, and microprocessor. It is understood that for different devices, the electronic devices used to implement the functions of processor 302 may also be other types, and this embodiment of the invention does not specifically limit the specific devices used.
[0057] The communication bus 303 may include a path for transmitting information between the aforementioned components. The communication bus 303 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 303 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 The symbol is represented by only one double arrow, but this does not indicate that there is only one bus or one type of bus. Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.
[0058] This invention also provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as described in the above embodiments for predicting the life of ship coatings.
[0059] In this embodiment, the computer-readable storage medium can be a tangible device that holds and stores instructions used by an instruction execution device. The computer-readable storage medium can be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. Specifically, the computer-readable storage medium can be a portable computer disk, a hard disk, a USB flash drive, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), staging random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory stick, floppy disk, optical disk, magnetic disk, mechanical encoding device, or any combination thereof.
[0060] The computer program in this embodiment includes functions for executing... Figure 1 The program code for the method shown may include instructions corresponding to the execution of the method steps provided in the above embodiments. The computer program may be downloaded from a computer-readable storage medium to various computing / processing devices, or downloaded via a network (e.g., the Internet, local area network, wide area network, and / or wireless network) to an external computer or external storage device. The computer program may be executed entirely on the user's computer as a standalone software package.
[0061] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.
[0062] Obviously, those skilled in the art should understand that the various units or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device, or fabricating them separately as individual integrated circuit modules, or fabricating multiple modules or steps into a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.
[0063] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0064] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.
Claims
1. A method for predicting the lifespan of a ship coating, characterized in that, include: Determine the baseline degradation rate of the coating under test under the baseline data of each lifetime influencing factor; wherein, the lifetime influencing factor includes at least two of the following: seawater temperature, salinity, pH value, ultraviolet radiation intensity, pollutants, mechanical wear, biofouling, route area and effective thickness; The influence functions of each lifespan influencing factor are established based on the baseline data, actual data, and influence coefficients of each factor. Obtain actual data for each lifespan influencing factor, substitute the actual data into the influence function, and calculate the influence coefficient of each lifespan influencing factor respectively; wherein, for any of the lifespan influencing factors, the influence coefficient is used to indicate the acceleration or deceleration effect of the actual data relative to the baseline data; Substitute the influence coefficients of each lifespan influencing factor and the baseline degradation rate into the nonlinear product model to calculate the predicted degradation rate; The predicted service life of the coating under test is calculated based on the predicted degradation rate.
2. The method according to claim 1, characterized in that, Before determining the baseline degradation rate of the coating under test under the baseline data for each lifetime influence factor, the method further includes: Define the reference conditions for the coating to be tested; wherein, the reference conditions include the reference data; The determination of the baseline degradation rate of the coating under test under the baseline data of each lifetime influencing factor includes: Obtain historical performance data of the coating under test under the baseline conditions, perform regression analysis on the historical performance data, and obtain the baseline degradation rate; or... The performance data of the coating under test is obtained under standard laboratory test conditions, and the baseline degradation rate is calculated based on the performance data; wherein, the standard laboratory test conditions are the baseline conditions.
3. The method according to claim 1, characterized in that, The formula for the nonlinear product model is: ； In the formula, The predicted degradation rate; The baseline degradation rate; The coefficient representing the influence of seawater temperature; This is the influence coefficient of salinity; The influence coefficient of pH value; The influence coefficient of ultraviolet radiation intensity; The impact coefficient of pollutants; The coefficient representing the influence of mechanical wear; The impact coefficient of biofouling; The influence coefficient for the air route area; The influence coefficient of effective thickness.
4. The method according to any one of claims 1 to 3, characterized in that, The calculation of the predicted lifetime of the coating under test based on the predicted degradation rate includes: Obtain the actual dry film thickness and the minimum designed dry film thickness of the coating to be tested; Substitute the actual dry film thickness, the minimum designed dry film thickness, and the predicted degradation rate into the first formula to calculate the predicted service life. The first formula is: ； In the formula, The predicted service life; The actual dry film thickness; The minimum designed dry film thickness; The predicted degradation rate is given.
5. The method according to any one of claims 1 to 3, characterized in that, The formula for the function influencing seawater temperature is: ； In the formula, The coefficient representing the influence of seawater temperature; The activation energy is the degradation reaction of the coating under test. It is the ideal gas constant; The baseline seawater temperature; The actual seawater temperature is e≈2.71828.
6. The method according to any one of claims 1 to 3, characterized in that, The formula for the effect function of pH value is: ； In the formula, The influence coefficient of pH value; pH reference value; This is the actual pH value; , These are the fitting parameters.
7. The method according to any one of claims 1 to 3, characterized in that, The formula for the influence function of mechanical wear is: ； In the formula, The coefficient representing the influence of mechanical wear; Average speed; Used as the base speed; For the proportion of sailing time; and / or, The formula for the effect function of biofouling is: ； In the formula, The impact coefficient of biofouling; The duration of stay in Hong Kong; These are the fitting parameters; This represents the proportion of sailing time.
8. A device for predicting the lifespan of a ship coating, characterized in that, include: A baseline rate determination module is used to determine the baseline degradation rate of the coating under test under baseline data of various lifetime influencing factors; wherein, the lifetime influencing factors include at least two of the following: seawater temperature, salinity, pH value, ultraviolet radiation intensity, pollutants, mechanical wear, biofouling, route area, and effective thickness; The influence function establishment module is used to establish the influence function of each lifespan influence factor based on the baseline data, actual data and influence coefficient of each lifespan influence factor; The influence coefficient calculation module is used to obtain the actual data of each lifespan influence factor, substitute the actual data into the influence function, and calculate the influence coefficient of each lifespan influence factor respectively; wherein, for any of the lifespan influence factors, the influence coefficient is used to indicate the acceleration or deceleration effect of the actual data relative to the baseline data. The prediction rate calculation module is used to substitute the influence coefficients of each lifespan influencing factor and the baseline degradation rate into the nonlinear product model to calculate the predicted degradation rate. The predicted lifetime calculation module is used to calculate the predicted lifetime of the coating under test based on the predicted degradation rate.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the ship coating life prediction method as described in any one of claims 1 to 7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the ship coating life prediction method according to any one of claims 1 to 7.