Operation method and system for anticorrosive coating on automobile surface

Abstract

The invention provides an operation method and system for an anti-corrosion coating on the surface of an automobile, and the method comprises the steps: obtaining the historical state data of the anti-corrosion coating, and marking each piece of historical state data; training according to the labeled historical state data to obtain a coating prediction model, and embedding a distributed sensor in the target anticorrosive coating to obtain current state data of the target anticorrosive coating every first preset time; inputting the current state data into a coating prediction model to obtain the current residual life and the current corrosion trend of the target anticorrosive coating; judging whether the current residual life is smaller than a first threshold value and whether the current corrosion trend is larger than a second threshold value; if the current remaining life is smaller than the first threshold value and the current corrosion trend is larger than the second threshold value, microcapsules arranged in the anti-corrosion coating are controlled to be broken, so that the microcapsules repair the anti-corrosion coating. According to the invention, accurate management and active repair of the whole life cycle of the anticorrosive coating can be realized.

Description

Technical Field

[0001] This invention relates to the field of anti-corrosion coating protection technology, and in particular to an operation method and system for an anti-corrosion coating used on automobile surfaces. Background Technology

[0002] With the rapid development of the automotive industry, anti-corrosion coating technology for automotive surfaces has become a key aspect of extending vehicle lifespan and reducing maintenance costs. Traditional anti-corrosion coatings mainly rely on physical barriers to inhibit the intrusion of corrosive media. However, under long-term exposure to complex environments (such as high temperature, high humidity, salt spray, and mechanical stress), the coatings are prone to failure behaviors such as microcrack propagation and decreased adhesion, leading to accelerated corrosion of the metal substrate. In existing technologies, passive protective coatings cannot detect changes in their own state in real time, let alone actively repair damage, resulting in a significant decline in anti-corrosion performance over time. Summary of the Invention

[0003] The purpose of this invention is to provide an operating method and system for an anti-corrosion coating on the surface of automobiles, which aims to solve the problem that traditional methods of protecting the surface of automobiles with anti-corrosion coatings cannot actively repair damage, resulting in a significant decline in anti-corrosion performance over time.

[0004] In a first aspect, the present invention provides a method for operating an anti-corrosion coating for automotive surfaces, the method comprising: The historical state data of the anti-corrosion coating is obtained, including strain, humidity temperature and corrosion potential, and each historical state data is labeled. The labeling result is the remaining life of the coating and the corrosion trend. A coating prediction model is trained based on labeled historical state data, and a distributed sensor is embedded inside the target anti-corrosion coating to acquire the current state data of the target anti-corrosion coating every first preset time interval. The current state data is input into the coating prediction model to obtain the current remaining life and current corrosion trend of the target anti-corrosion coating; Determine whether the current remaining lifetime is less than a first threshold and whether the current corrosion trend is greater than a second threshold; If the current remaining lifespan is less than a first threshold and the current corrosion trend is greater than a second threshold, then the microcapsules disposed in the anti-corrosion coating are controlled to rupture, so that the microcapsules can repair the anti-corrosion coating.

[0005] Furthermore, the step of training the coating prediction model based on the labeled historical state data includes: Extract static and dynamic features from the labeled historical state data; The static characteristics include the first average value of corrosion potential, the first standard deviation of corrosion potential, the maximum value of strain, the average temperature gradient, and the humidity fluctuation range. The dynamic characteristics include the rate of change of corrosion potential, the acceleration of strain change, the rate of change of temperature, and the acceleration of humidity change.

[0006] Furthermore, after the step of extracting static and dynamic features from the labeled historical state data, the method further includes: The coating prediction model includes a remaining life prediction model and a corrosion trend prediction model; Construct a remaining lifetime prediction model based on the following formula: ; in, For remaining lifespan, For the initial lifespan of the anti-corrosion coating, Let be the intrinsic aging rate constant of the anti-corrosion coating. For the service life of the anti-corrosion coating, This is a static decay term. These are the weighting coefficients. This is a dynamic acceleration item; The expression for the static decay term is: ; in, The first average value of the corrosion potential. The first standard deviation of the corrosion potential. For the maximum value of strain, The average temperature gradient, This refers to the range of humidity fluctuations. , , , , These are the baseline reference values ​​corresponding to the respective static features; The expression for the dynamic acceleration term is: ; in, The rate of change of corrosion potential. For the acceleration due to strain change, For the rate of temperature change, For the acceleration of humidity change, , , All are weighting coefficients.

[0007] Furthermore, a corrosion trend prediction model is constructed based on the following formula: ; in, For corrosion trend, , All are weighting coefficients.

[0008] Furthermore, the step of controlling the rupture of microcapsules disposed in the anti-corrosion coating to allow the microcapsules to repair the anti-corrosion coating includes: Pressure is applied to the microcapsules using a piezoelectric ceramic actuator, with a pressure range of 10 MPa to 100 MPa and a loading rate ≥1 m / s.

[0009] Furthermore, the microcapsule has a dual cavity, with the first cavity encapsulating the repair monomer and the second cavity encapsulating the curing agent. The outer wall of the microcapsule is coated with a functionalized modified nanocellulose layer. The repair monomer is a low-viscosity epoxy monomer, and the curing agent is an amine curing agent.

[0010] Secondly, the present invention provides an operating system for an anti-corrosion coating on an automotive surface, the system comprising: The historical data acquisition module is used to acquire historical state data of the anti-corrosion coating. The historical state data includes strain, humidity temperature and corrosion potential. Each historical state data is labeled, and the labeling result is the remaining life of the coating and the corrosion trend. The model training module is used to train a coating prediction model based on labeled historical state data, and to embed distributed sensors inside the target anti-corrosion coating to acquire the current state data of the target anti-corrosion coating every first preset time interval. The prediction module is used to input the current state data into the coating prediction model to obtain the current remaining life and current corrosion trend of the target anti-corrosion coating. The detection module is used to determine whether the current remaining lifespan is less than a first threshold and whether the current corrosion trend is greater than a second threshold. The repair execution module is used to control the microcapsules disposed in the anti-corrosion coating to rupture if the current remaining lifespan is less than a first threshold and the current corrosion trend is greater than a second threshold, so that the microcapsules repair the anti-corrosion coating.

[0011] Thirdly, the present invention provides a storage medium storing one or more programs that, when executed by a processor, implement the above-described method for operating an anti-corrosion coating on an automotive surface.

[0012] Fourthly, the present invention provides an electronic device, the electronic device comprising a memory and a processor, wherein: The memory is used to store computer programs; When the processor executes the computer program stored in the memory, it implements the above-described method for operating the anti-corrosion coating on the surface of a car.

[0013] Compared with the prior art, the present invention has the following advantages: Based on the aforementioned operational method for anti-corrosion coatings on automotive surfaces, firstly, a coating prediction model trained on historical state data quantifies the basic damage state of the coating by extracting static features (first average value and first standard deviation of corrosion potential, maximum value of strain, average temperature gradient, and humidity fluctuation range). Combined with dynamic features (rate of change of corrosion potential, acceleration of strain change, rate of change of temperature, and acceleration of humidity change), the model captures the dynamic influence of environmental effects and load changes in real time. This reduces the remaining life prediction error from ≥30% to ≤5% using traditional methods, and improves the corrosion trend prediction accuracy to 92%, providing a reliable basis for preventative maintenance. Secondly, after distributed sensors collect real-time data on the current state of the target coating and input it into the model, the coating's health status can be dynamically assessed. When the remaining life and corrosion trend meet relevant conditions, the system automatically triggers the microcapsule repair mechanism, thereby achieving precise management and proactive repair throughout the entire lifecycle of the anti-corrosion coating. Attached Figure Description

[0014] Figure 1 This is a flowchart of an operation method for an anti-corrosion coating on an automobile surface according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the operating system for an anti-corrosion coating on an automobile surface according to an embodiment of the present invention.

[0015] The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, 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. Unless otherwise defined, the technical or scientific terms used herein should have the ordinary meaning understood by those skilled in the art. The terms "comprising" and similar expressions used herein mean that the element or object preceding the word covers the element or object listed after the word and its equivalents, but does not exclude other elements or objects.

[0017] like Figure 1As shown, an embodiment of the present invention proposes an operation method for an anti-corrosion coating on an automotive surface, the method comprising steps S101 to S105, wherein: Step S101: Obtain the historical state data of the anti-corrosion coating. The historical state data includes strain, humidity temperature and corrosion potential. Each historical state data is labeled, and the labeling result is the remaining life and corrosion trend of the coating. It should be noted that the performance degradation of anti-corrosion coatings is the result of both static damage (such as material aging and stress concentration) and dynamic environmental effects (such as temperature and humidity fluctuations and corrosive media erosion). By collecting historical data such as strain, humidity, temperature, and corrosion potential, the static characteristics (such as the maximum strain value reflecting local stress concentration) and dynamic characteristics (such as the rate of temperature and humidity change reflecting the risk of accelerated environmental corrosion) of coating damage can be comprehensively covered, providing a complete dataset for subsequent model training.

[0018] Step S102: Train a coating prediction model based on the labeled historical state data, and embed a distributed sensor inside the target anti-corrosion coating to acquire the current state data of the target anti-corrosion coating every first preset time interval; It should be noted that, in some embodiments, static and dynamic features are extracted from the labeled historical state data; the static features include the first average value of corrosion potential, the first standard deviation of corrosion potential, the maximum value of strain, the average temperature gradient, and the humidity fluctuation range; the dynamic features include the rate of change of corrosion potential, the acceleration of strain change, the rate of change of temperature, and the acceleration of humidity change.

[0019] It is important to note that static features (such as average corrosion potential and maximum strain) provide a "static baseline" for coating performance degradation by quantifying the cumulative degree of basic coating damage (e.g., average potential reflects the degree of long-term corrosion, and maximum strain locates local stress concentration areas). Dynamic features (such as corrosion potential change rate and temperature and humidity acceleration) supplement the model with "dynamic correction parameters" by capturing the instantaneous effects of environmental factors and load changes (e.g., potential change rate reveals the risk of sudden corrosion rate changes, and temperature and humidity acceleration reflects the accelerating trend of environmental corrosion). Both are indispensable—relying solely on static features will ignore the real-time risks of environmental factors (e.g., a sudden surge in corrosion rate caused by salt spray), while relying solely on dynamic features will lack the basis for the cumulative damage to the basic coating (e.g., the propagation of microcracks caused by long-term stress). By establishing a "basic profile" of coating damage through static features and providing "real-time correction" for environmental factors through dynamic features, the model can achieve a leap from "summarizing historical patterns" to "predicting future conditions," enabling accurate prediction of remaining life and corrosion trends.

[0020] Furthermore, in some embodiments, the coating prediction model includes a remaining life prediction model and a corrosion trend prediction model; Specifically, the remaining lifetime prediction model is constructed based on the following formula: ; in, For remaining lifespan, For the initial lifespan of the anti-corrosion coating, Let be the intrinsic aging rate constant of the anti-corrosion coating. For the service life of the anti-corrosion coating, This is a static decay term. These are the weighting coefficients. This is a dynamic acceleration item; The expression for the static decay term is: ; in, The first average value of the corrosion potential. The first standard deviation of the corrosion potential. For the maximum value of strain, The average temperature gradient, This refers to the range of humidity fluctuations. , , , , These are the baseline reference values ​​corresponding to the respective static features; The expression for the dynamic acceleration term is: ; in, The rate of change of corrosion potential. For the acceleration due to strain change, For the rate of temperature change, For the acceleration of humidity change, , , All are weighting coefficients.

[0021] In summary, the remaining life prediction model constructed in this abstract significantly improves the accuracy and practicality of anti-corrosion coating life prediction by integrating static characteristics and dynamic environmental factors. Specifically, the static degradation term quantifies the intrinsic degradation of the coating under constant conditions (such as corrosion potential and strain characteristics), providing a basic damage assessment for the model; the dynamic acceleration term captures real-time environmental effects such as temperature and humidity fluctuations and potential / strain change rates, reflecting the accelerated failure mechanism of the coating under complex working conditions. The weighted fusion of the two terms allows the model to summarize degradation patterns based on historical data and dynamically correct for risks brought about by sudden environmental changes (such as salt spray, high humidity, or sudden temperature changes), which helps reduce the remaining life prediction error and is far superior to traditional static models. In addition, the model flexibly adapts to different coating materials and environmental conditions through weight coefficients, providing a reliable basis for the precise triggering of microcapsule repair strategies (such as the coordinated control of crack width and dynamic release timing), ultimately realizing the leap from passive maintenance to active intelligent repair of anti-corrosion coatings.

[0022] Furthermore, in some embodiments, a corrosion trend prediction model is constructed based on the following formula: ; in, For corrosion trend, , All are weighting coefficients.

[0023] It should be noted that the corrosion trend prediction model constructed by the above formula achieves high-precision prediction of the corrosion process of anti-corrosion coatings by innovatively integrating the potential change rate, strain acceleration, and dynamic derivatives of temperature and humidity. Specifically, the model uses the product of the square of the potential change rate and the strain acceleration to characterize the nonlinear characteristics of corrosion dynamics (such as the coupling effect of a sudden drop in potential and a sudden increase in strain), and dynamically quantifies the environmental acceleration effect (such as the synergistic effect of humidity fluctuations and temperature gradients on the corrosion rate) through a weighted combination of the first and second-order change rates of temperature and humidity. The combination of these two factors can accurately capture the evolution law of the coating from uniform corrosion to localized deterioration.

[0024] Step S103: Input the current state data into the coating prediction model to obtain the current remaining life and current corrosion trend of the target anti-corrosion coating; Step S104: Determine whether the current remaining lifetime is less than a first threshold and whether the current corrosion trend is greater than a second threshold; Step S105: If the current remaining lifespan is less than a first threshold and the current corrosion trend is greater than a second threshold, then control the microcapsules disposed in the anti-corrosion coating to rupture, so that the microcapsules repair the anti-corrosion coating.

[0025] It should be noted that by inputting current state data into the prediction model and then extracting its static and dynamic features, the health status of the coating can be dynamically assessed. Relying solely on static features (such as using only the average corrosion potential) will ignore the dynamic influence of environmental factors (such as high temperature and humidity accelerating corrosion), while relying solely on dynamic features (such as using only the rate of change of temperature and humidity) will ignore the underlying damage to the coating. By fusing static and dynamic features, the model can more accurately predict the coating condition. For example, in a low-temperature environment (static feature: small temperature gradient), even if the dynamic features (large rate of change of humidity) indicate a corrosion risk, the model will adjust the remaining lifetime prediction based on the static features.

[0026] Furthermore, setting a first threshold and a second threshold is crucial for balancing "premature repair" and "late repair." Relying solely on the remaining lifetime threshold may overlook the risk of accelerated short-term corrosion (such as sudden corrosion in salt spray environments); relying solely on the corrosion trend threshold may overlook static damage where the coating is nearing failure. Dual threshold determination ensures that the repair mechanism is triggered only when the coating exhibits both basic damage (static characteristics) and faces accelerated environmental corrosion (dynamic characteristics).

[0027] Furthermore, in some embodiments, if the current remaining lifetime is greater than or equal to a first threshold, and / or the current corrosion trend is less than or equal to a second threshold, it indicates that the anti-corrosion coating is in good condition and requires no repair.

[0028] In addition, in some embodiments, when the anti-corrosion coating needs to be repaired, pressure is applied to the microcapsules by a piezoelectric ceramic actuator to cause the microcapsules to rupture, with the pressure range being 10 MPa to 100 MPa and the loading rate being ≥1 m / s.

[0029] Furthermore, in some embodiments, the microcapsules have a dual-cavity structure, with the first cavity encapsulating the repair monomer and the second cavity encapsulating the curing agent. The outer wall of the microcapsule is coated with a functionalized modified nanocellulose layer, and a three-dimensional interconnected network formed by functionalized nanocellulose is constructed in the coating matrix to enhance mechanical properties and guide the transport of the repair monomer. The microcapsule wall material is a controllable brittle polymer that can break under mechanical stress or corrosive media. The repair monomer is a low-viscosity epoxy monomer, and the curing agent is an amine curing agent.

[0030] In addition, the distributed optical fiber sensor is a fiber Bragg grating (FBG) array with an array spacing of less than 5 cm.

[0031] In some embodiments, the microcapsule preparation process is as follows: ① Preparation of dual-cavity microcapsules: The repair monomer and curing agent are first encapsulated in two independent droplets, and a dual-cavity structure is formed through stepwise emulsification and polymerization. The outer capsule wall is made of a controllable brittle polymer to ensure reliable rupture and release of contents when the coating is damaged. ② Surface functionalization treatment: The prepared dual-cavity microcapsules are contacted with functionalized nanocellulose in a dispersion to uniformly coat the outer surface of the capsule. This layer can improve the dispersion stability of the capsule in the coating and assist in material transport during the repair process. ③ Preparation of functionalized nanocellulose: Nanocellulose is obtained by mechanical dissociation of natural cellulose, and then active groups are introduced through chemical modification so that it can form a stable network with the coating matrix and produce good compatibility with the repair material. ④ Premixing of the coating matrix: The functionalized nanocellulose dispersion is mixed with the coating matrix resin, and then the dual-cavity microcapsules are slowly added and stirred at low speed to avoid damaging the capsule structure. Finally, necessary additives are added to adjust the workability.

[0032] In summary, based on the aforementioned operational method for anti-corrosion coatings on automotive surfaces, firstly, a coating prediction model trained on historical state data quantifies the basic damage state of the coating by extracting static features (first average value and first standard deviation of corrosion potential, maximum value of strain, average temperature gradient, and humidity fluctuation range). Combined with dynamic features (rate of change of corrosion potential, acceleration of strain change, rate of change of temperature, and acceleration of humidity change), the model captures the dynamic influence of environmental effects and load changes in real time. This reduces the remaining life prediction error from ≥30% to ≤5% using traditional methods, and improves the corrosion trend prediction accuracy to 92%, providing a reliable basis for preventative maintenance. Secondly, after distributed sensors collect real-time data on the current state of the target coating and input it into the model, the coating's health status can be dynamically assessed. When the remaining life and corrosion trend meet relevant conditions, the system automatically triggers the microcapsule repair mechanism, thereby achieving precise management and proactive repair throughout the entire lifecycle of the anti-corrosion coating.

[0033] like Figure 2 As shown, one embodiment of the present invention also proposes an operating system for an anti-corrosion coating on an automotive surface, the system comprising: The historical data acquisition module 10 is used to acquire the historical state data of the anti-corrosion coating. The historical state data includes strain, humidity temperature and corrosion potential. Each historical state data is labeled, and the labeling result is the remaining life of the coating and the corrosion trend. The model training module 20 is used to train a coating prediction model based on labeled historical state data, and to embed distributed sensors inside the target anti-corrosion coating to acquire the current state data of the target anti-corrosion coating every first preset time. The prediction module 30 is used to input the current state data into the coating prediction model to obtain the current remaining life and current corrosion trend of the target anti-corrosion coating; Detection module 40 is used to determine whether the current remaining lifespan is less than a first threshold and whether the current corrosion trend is greater than a second threshold; The repair execution module 50 is used to control the microcapsules disposed in the anti-corrosion coating to rupture if the current remaining lifespan is less than a first threshold and the current corrosion trend is greater than a second threshold, so that the microcapsules repair the anti-corrosion coating.

[0034] In another aspect, the present invention also proposes a storage medium having stored one or more programs thereon, which, when executed by a processor, implement the above-described method for operating an anti-corrosion coating on an automotive surface.

[0035] In another aspect, the present invention provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to realize the above-described method for operating the anti-corrosion coating on the surface of an automobile.

[0036] Those skilled in the art will understand that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain stored, communicated, propagated, or transmitted programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0037] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0038] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0039] While embodiments of the present invention have been described in detail above, it will be apparent to those skilled in the art that various modifications and variations can be made to these embodiments. However, it should be understood that such modifications and variations fall within the scope and spirit of the invention as set forth in the claims. Furthermore, the invention described herein may have other embodiments and can be implemented or carried out in various ways.

Claims

1. A method for operating an anti-corrosion coating for automotive surfaces, characterized in that, The method includes: The historical state data of the anti-corrosion coating is obtained, including strain, humidity temperature and corrosion potential, and each historical state data is labeled. The labeling result is the remaining life of the coating and the corrosion trend. A coating prediction model is trained based on labeled historical state data, and a distributed sensor is embedded inside the target anti-corrosion coating to acquire the current state data of the target anti-corrosion coating every first preset time interval. The current state data is input into the coating prediction model to obtain the current remaining life and current corrosion trend of the target anti-corrosion coating; Determine whether the current remaining lifetime is less than a first threshold and whether the current corrosion trend is greater than a second threshold; If the current remaining lifespan is less than a first threshold and the current corrosion trend is greater than a second threshold, then the microcapsules disposed in the anti-corrosion coating are controlled to rupture, so that the microcapsules can repair the anti-corrosion coating.

2. The method for operating the anti-corrosion coating for automotive surfaces according to claim 1, characterized in that, The step of training the coating prediction model based on labeled historical state data includes: Extract static and dynamic features from the labeled historical state data; The static characteristics include the first average value of corrosion potential, the first standard deviation of corrosion potential, the maximum value of strain, the average temperature gradient, and the humidity fluctuation range. The dynamic characteristics include the rate of change of corrosion potential, the acceleration of strain change, the rate of change of temperature, and the acceleration of humidity change.

3. The method for operating the anti-corrosion coating for automotive surfaces according to claim 2, characterized in that, Following the step of extracting static and dynamic features from the labeled historical state data, the method further includes: The coating prediction model includes a remaining life prediction model and a corrosion trend prediction model; Construct a remaining lifetime prediction model based on the following formula: ； in, For remaining lifespan, For the initial lifespan of the anti-corrosion coating, Let be the intrinsic aging rate constant of the anti-corrosion coating. For the service life of the anti-corrosion coating, This is a static decay term. These are the weighting coefficients. This is a dynamic acceleration item; The expression for the static decay term is: ； in, The first average value of the corrosion potential. The first standard deviation of the corrosion potential. For the maximum value of strain, The average temperature gradient, This refers to the range of humidity fluctuations. , , , , These are the baseline reference values ​​corresponding to the respective static features; The expression for the dynamic acceleration term is: ； in, The rate of change of corrosion potential. For the acceleration due to strain change, For the rate of temperature change, For the acceleration of humidity change, , , All are weighting coefficients.

4. The method for operating the anti-corrosion coating for automotive surfaces according to claim 3, characterized in that, A corrosion trend prediction model is constructed based on the following formula: ； in, For corrosion trend, , All are weighting coefficients.

5. The method of operating the anti-corrosion coating for automobile surfaces according to claim 4, characterized in that, The step of controlling the rupture of microcapsules disposed in the anti-corrosion coating to allow the microcapsules to repair the anti-corrosion coating includes: Pressure is applied to the microcapsules using a piezoelectric ceramic actuator, with a pressure range of 10 MPa to 100 MPa and a loading rate ≥1 m / s.

6. The method for operating the anti-corrosion coating for automotive surfaces according to claim 1, characterized in that, The microcapsule has a dual cavity: the first cavity encapsulates the repair monomer, and the second cavity encapsulates the curing agent. The outer wall of the microcapsule is coated with a functionalized modified nanocellulose layer. The repair monomer is a low-viscosity epoxy monomer, and the curing agent is an amine curing agent.

7. An operating system for an anti-corrosion coating on an automotive surface, characterized in that, The system includes: The historical data acquisition module is used to acquire historical state data of the anti-corrosion coating. The historical state data includes strain, humidity temperature and corrosion potential. Each historical state data is labeled, and the labeling result is the remaining life of the coating and the corrosion trend. The model training module is used to train a coating prediction model based on labeled historical state data, and to embed distributed sensors inside the target anti-corrosion coating to acquire the current state data of the target anti-corrosion coating every first preset time interval. The prediction module is used to input the current state data into the coating prediction model to obtain the current remaining life and current corrosion trend of the target anti-corrosion coating. The detection module is used to determine whether the current remaining lifespan is less than a first threshold and whether the current corrosion trend is greater than a second threshold. The repair execution module is used to control the microcapsules disposed in the anti-corrosion coating to rupture if the current remaining lifespan is less than a first threshold and the current corrosion trend is greater than a second threshold, so that the microcapsules repair the anti-corrosion coating.

8. A storage medium, characterized in that, The storage medium stores one or more programs that, when executed by a processor, implement the method of operating an anti-corrosion coating for an automotive surface as described in any one of claims 1-6.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, wherein: The memory is used to store computer programs; When the processor executes the computer program stored in the memory, it implements the method of operating the anti-corrosion coating for the surface of an automobile as described in any one of claims 1-6.

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