Method and system for optimizing corrosion weathering resistance of electromagnetic shielding materials

Abstract

The application discloses an electromagnetic shielding material corrosion weather resistance optimization method and system, and relates to the electromagnetic shielding material field.The method comprises the following steps: obtaining the expected service life and working environment information of a target device, determining the target shielding material coating based on historical service data statistics; calculating the reference coating thickness of the material, predicting the corrosion weather resistance service life of the material through a corrosion simulation simulator; when the predicted life is insufficient, establishing a thickness interval combined with electromagnetic shielding requirements, calculating the life gap, and iteratively optimizing the reference thickness with the thickness interval as a constraint to obtain the optimal coating thickness that meets the life requirement and has the minimum thickness; and finally obtaining the optimization result based on the target material and the optimal thickness.The application solves the problems of blind material selection, inaccurate life prediction and lack of systematic optimization of thickness design in the traditional method under complex environment, and realizes the collaborative optimization of electromagnetic shielding performance and long-term weather resistance reliability.

Description

Technical Field

[0001] This invention relates to the field of electromagnetic shielding materials, and more specifically to a method and system for optimizing the corrosion and weather resistance of electromagnetic shielding materials. Background Technology

[0002] In practical applications of electromagnetic shielding materials, especially in harsh environments such as marine, chemical, and industrial settings, environmental factors such as high temperature, high humidity, salt spray, and acid and alkali media can continuously corrode electromagnetic shielding materials, leading to corrosion aging, decreased conductivity, and damage to the microstructure. This, in turn, causes a significant reduction in shielding effectiveness, ultimately resulting in the premature termination of equipment service due to electromagnetic interference protection failure.

[0003] However, existing technologies lack the ability to quantitatively analyze complex environmental factors and cannot accurately predict the actual service life of materials in target environments. At the same time, the selection of materials and the design of coating thickness often rely on engineering experience, lacking a systematic optimization mechanism that can simultaneously consider electromagnetic performance and long-term weather resistance. This leads to two extreme tendencies in design schemes: either being too conservative and wasting material costs, or being too aggressive and causing the risk of early failure. Summary of the Invention

[0004] This invention addresses the technical problems in existing technologies, such as insufficient quantitative analysis capabilities for complex environmental factors, inadequate accuracy in predicting material lifespan, and a lack of optimization mechanisms that balance electromagnetic performance and weather resistance. It provides a method and system for optimizing the corrosion and weather resistance of electromagnetic shielding materials.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: In a first aspect, the present invention provides a method for optimizing the corrosion and weather resistance of electromagnetic shielding materials, comprising: Obtain the expected service life and operating environment information of the target device, and determine the target shielding material coating from multiple candidate shielding material coatings based on the expected service life and operating environment information; Obtain the reference coating thickness of the target shielding material coating, and combine it with the working environment information to predict the corrosion and weather resistance service life of the target equipment; When the corrosion and weather resistance service life is less than the expected service life, the electromagnetic shielding requirement of the target equipment is obtained, and the reference coating thickness is optimized and corrected in combination with the corrosion and weather resistance service life and the expected service life to obtain the optimal coating thickness. Based on the target shielding material coating and the optimal coating thickness, the corrosion and weather resistance optimization results of the electromagnetic shielding material of the target device are obtained.

[0006] Secondly, the present invention provides a corrosion and weather resistance optimization system for electromagnetic shielding materials, comprising: The material selection module is used to obtain the expected service life and working environment information of the target equipment, and to determine the target shielding material coating from multiple candidate shielding material coatings based on the expected service life and the working environment information. The weathering prediction module is used to obtain the reference coating thickness of the target shielding material coating and, in combination with the working environment information, predict the corrosion and weathering resistance service life of the target equipment. The thickness optimization module is used to obtain the electromagnetic shielding requirements of the target equipment when the corrosion and weathering service life is less than the expected service life, and to optimize and correct the reference coating thickness in combination with the corrosion and weathering service life and the expected service life to obtain the optimal coating thickness. An optimized output module is used to prepare the electromagnetic shielding material coating based on the target shielding material coating and the optimal coating thickness, and output the optimized corrosion and weather resistance results of the electromagnetic shielding material of the target device.

[0007] The beneficial effects of this invention are: Compared to existing technologies, this invention firstly solves the problem of blind material selection in complex environments by statistical analysis of historical service data, thus improving the scientific nature of material selection; secondly, it uses a corrosion simulation simulator trained by machine learning to accurately predict the service life of materials under specific environments; thirdly, through a multi-objective optimization algorithm, it achieves the optimal design of coating thickness while ensuring electromagnetic shielding performance and service life, avoiding over-design or premature failure; finally, it integrates material selection, service life prediction, and thickness optimization into a unified framework, providing a solution that balances electromagnetic performance and weather resistance. Attached Figure Description

[0008] Figure 1 A flowchart illustrating the method for optimizing the corrosion and weather resistance of electromagnetic shielding materials provided by this invention; Figure 2 This is a schematic diagram of the structure of the corrosion and weather resistance optimization system for electromagnetic shielding materials provided by the present invention.

[0009] In the attached diagram, the components represented by each number are as follows: Material selection module 11, weathering prediction module 12, thickness optimization module 13, optimization output module 14. Detailed Implementation

[0010] 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.

[0011] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0012] In the description of this invention, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed herein.

[0013] Example 1, as Figure 1 As shown, this embodiment of the invention provides a method for optimizing the corrosion and weather resistance of electromagnetic shielding materials, including: S10: Obtain the expected service life and working environment information of the target device, and determine the target shielding material coating from multiple candidate shielding material coatings based on the expected service life and the working environment information; Obtain the expected service life and operating environment information of the target device, and determine the target shielding material coating from multiple candidate shielding material coatings based on the expected service life and operating environment information, including: Establish a working environment vector based on the working environment information of the target device; Based on the working environment vector, historical service data of each of the candidate shielding material coatings are retrieved to obtain multiple sample shielding material coating sets. The service compliance rate of each candidate shielding material coating is obtained by counting the number of samples whose shielding material coatings do not experience corrosion and weathering failure within the expected service life. The candidate shielding material coating with the highest service compliance rate is selected as the target shielding material coating.

[0014] In determining the implementation process of the target shielding material coating, it is first necessary to construct an operating environment vector based on the operating environment information of the target equipment. The target equipment refers to electronic and electrical devices or equipment that require electromagnetic shielding protection, such as offshore wind power converters, sensors in chemical environments, or industrial control cabinets. The operating environment information of the target equipment refers to environmental parameter data of the expected deployment location, including characteristic parameters such as temperature range, relative humidity, salt spray concentration, and the type and concentration of acidic and alkaline media. The operating environment vector is a multi-dimensional feature data structure that quantifies key environmental parameters such as temperature, humidity, and corrosive medium concentration to form a mathematical representation of environmental conditions, typically stored and processed in matrix form.

[0015] Specifically, a working environment vector is established based on the working environment information of the target device, including: Determine the pre-installation area of ​​the target device, and collect historical environmental data of the pre-installation area as the working environment information of the target device; Time-series analysis was performed on the historical environmental data to obtain temperature change curves, humidity change curves, and corrosive medium concentration change curves. The working environment vector is constructed based on the temperature change curve, the humidity change curve, and the corrosive medium concentration change curve.

[0016] First, it is necessary to determine the pre-installation area of ​​the target equipment and collect historical environmental data for that area as the basis for operational environment information. The pre-installation area refers to the specific geographical location or environmental type where the equipment is planned to be deployed, such as an offshore platform in a coastal area or a specific production workshop in a chemical plant. Historical environmental data includes environmental parameters such as temperature, humidity, salt spray concentration, and acid / alkali concentration obtained through long-term monitoring of the area.

[0017] Secondly, a time-series analysis was performed on the collected historical environmental data. Through statistical analysis, temperature change curves, humidity change curves, and corrosive medium concentration change curves were extracted to reflect the patterns and characteristics of environmental parameters changing over time, providing data support for subsequent vector construction.

[0018] Finally, based on the obtained temperature change curve, humidity change curve, and corrosive medium concentration change curve, a working environment vector is constructed. This working environment vector uses mathematical methods to fuse and quantify the characteristic parameters from multiple curves, forming a multi-dimensional data structure that can comprehensively characterize environmental features, providing accurate environmental feature input for subsequent material selection.

[0019] Furthermore, based on the constructed working environment vector, service data for each candidate shielding material coating in the historical database are retrieved to obtain multiple sample shielding material coating sets. This yields a set of sample shielding material coatings that match the current environmental conditions, providing a data foundation for subsequent analysis. Secondly, for each candidate material's corresponding sample set, the number of samples that did not experience corrosion or weathering failure within the expected service life is counted. By calculating the service compliance rate for each material, the reliability level of the material under specific environmental conditions is quantitatively evaluated. Service compliance rate = Number of samples that did not experience corrosion or weathering failure within the expected service life / Total number of samples in the sample set.

[0020] Furthermore, by comparing and analyzing the service compliance rates of all candidate materials, the shielding material coating with the highest compliance rate was selected as the target material. This ensures that the selected material has the best historical reliability performance under the target environmental conditions, laying the material foundation for subsequent thickness optimization.

[0021] S20: Obtain the reference coating thickness of the target shielding material coating, and predict the corrosion and weather resistance service life of the target equipment based on the working environment information; Specifically, obtaining the reference coating thickness of the target shielding material coating, and combining it with the working environment information to predict the corrosion and weather resistance service life of the target equipment, includes: Based on the target shielding material coating, obtain the corresponding sample shielding material coating set from multiple sample shielding material coating sets to obtain the target sample shielding material coating set; The average coating thickness of the shielding material coating in the target sample shielding material coating set is calculated to obtain the reference coating thickness; The corrosion simulation simulator is invoked, which includes an environmental parameter unit and a material parameter unit. Configure the environmental parameter unit based on the working environment information, configure the material parameter unit based on the reference coating thickness, start and run the corrosion simulation simulator, and obtain the corrosion and weather resistance service time of the target equipment under the reference coating thickness.

[0022] First, based on the selected target shielding material coating, a corresponding sample set is selected from multiple sample shielding material coating sets to obtain the target sample shielding material coating set. The sample shielding material coating set is a structured dataset that records the service performance data of a specific shielding material coating under different environmental conditions, including coating thickness, environmental parameters, service duration, and failure status, providing a historical performance reference for the material under real-world conditions. Sample records that are completely identical to the target shielding material coating type are then selected to obtain the target sample shielding material coating set, which contains relevant data records of the target shielding material coating in historical applications.

[0023] Secondly, the arithmetic mean of the coating thickness of all samples in the target shielding material coating set is calculated, and this mean is determined as the baseline coating thickness. The baseline coating thickness represents the typical coating thickness value of the target shielding material in conventional applications. Next, a pre-built corrosion simulation simulator is retrieved. The corrosion simulation simulator is a machine learning-based predictive model used to simulate the corrosion aging process of materials under specific environmental conditions and predict their service life. It contains two core components: an environmental parameter unit and a material parameter unit, used to input environmental condition parameters and material property parameters, respectively.

[0024] Specifically, the construction steps of the corrosion simulation simulator include: Multiple historical samples were collected based on the target shielding material coating. Each historical sample includes sample working environment information, sample coating thickness, and sample corrosion and weather resistance service time. The architecture for constructing a corrosion simulation simulator includes the environmental parameter unit and the material parameter unit; The environmental parameter unit is configured based on the sample working environment information in the multiple historical samples, and the material parameter unit is configured based on the sample coating thickness; The corrosion simulation simulator is trained by supervised learning using the corrosion and weathering service time of the multiple historical samples until the prediction accuracy meets the preset requirements, thus completing the construction of the corrosion simulation simulator.

[0025] First, multiple historical sample data points need to be collected based on the target shielding material coating. Each historical sample contains three key data dimensions: sample operating environment information, which records the environmental parameters during the material's service; sample coating thickness, which accurately records the actual thickness value of the material coating; and sample corrosion and weather resistance service time, which provides the effective service life of the material in the actual environment. Together, these constitute the data foundation for training the corrosion simulation simulator.

[0026] Secondly, the basic architecture of the corrosion simulation simulator is constructed, which includes two core functional units: the environmental parameter unit is responsible for receiving and processing environmental characteristic data; and the material parameter unit is specifically for processing material property parameters.

[0027] Specifically, when constructing a corrosion simulation simulator based on machine learning methods, it is necessary to select an algorithm model that can effectively handle the complex relationship between environmental parameters and material lifespan. Since the corrosion process involves the cumulative impact of temporal changes in environmental parameters (temperature, humidity, corrosive medium concentration, etc.) on material properties, exhibiting significant time dependence and nonlinear characteristics, a model that excels at processing time-series data and can capture long-term dependencies should be selected. For example, a Long Short-Term Memory (LSTM) network can be chosen as the core prediction model.

[0028] Long Short-Term Memory (LSTM) networks are a special type of recurrent neural network specifically designed to process long-sequence data, effectively capturing long-term dependencies in time series. Their core components include memory cells and gating mechanisms. Memory cells act as information transmission channels, storing long-term pattern information from environmental parameter sequences. The gating unit regulates information flow through three key gate structures: the forget gate determines which historical environmental information needs to be discarded, the input gate controls which new environmental features need to be stored in the memory cell, and the output gate determines which information to output to the next time step based on the current cell state.

[0029] In its specific construction, the environmental parameter unit uses an LSTM network to process time-series environmental data. This unit receives historical sequences of environmental parameters such as temperature, humidity, and corrosive medium concentration as input. The network consists of a two-layer LSTM structure, with 64 neurons in each layer. It captures the temporal dependencies and long-term variation patterns of environmental parameters through a gating mechanism. The environmental parameter unit outputs a 64-dimensional environmental feature vector, representing the cumulative effect of environmental conditions on the material corrosion process.

[0030] The material parameter unit uses an LSTM network to process material property data. This unit receives time-series data on parameters such as coating thickness, conductivity, and substrate type. The network employs a single-layer LSTM structure containing 32 neurons, specifically designed to learn the correlation between material properties and corrosion rate. The material parameter unit outputs a 32-dimensional material feature vector, characterizing the evolution of the material's corrosion resistance.

[0031] The fusion prediction unit concatenates the feature vectors output by the two units to form a 96-dimensional fusion feature vector. Feature integration is performed through two fully connected layers, and the final output layer uses a linear activation function to predict the corrosion and weather resistance service life of the material.

[0032] In the specific training process, a uniform learning rate of 0.001, 100 training epochs, and a batch size of 64 were set. Environmental parameter sequences and material parameter sequences were standardized and then input into the corresponding units. Mean squared error was used as the loss function during training, and the weight parameters of both units were updated simultaneously using the backpropagation algorithm. After each training cycle, the model performance was evaluated using a validation set. Training was terminated when the prediction accuracy met preset requirements, such as reaching 90%. The trained corrosion simulation simulator can simultaneously process environmental time-series data and material property data, accurately predicting the service life of materials under specific environmental conditions and coating thicknesses, providing reliable data support for the life assessment and thickness optimization of electromagnetic shielding materials.

[0033] Finally, based on the working environment information of the target equipment, the environmental parameter unit is configured, and the material parameter unit is configured according to the calculated reference coating thickness. The corrosion simulation simulator is then started to obtain the corrosion and weather resistance service life of the target equipment under the reference coating thickness condition.

[0034] S30: When the corrosion and weathering service life is less than the expected service life, obtain the electromagnetic shielding requirements of the target equipment, and optimize and correct the reference coating thickness in combination with the corrosion and weathering service life and the expected service life to obtain the optimal coating thickness; The electromagnetic shielding requirements of the target device are obtained, and the baseline coating thickness is optimized and corrected based on the corrosion and weather resistance service life and the expected service life to obtain the optimal coating thickness, including: Obtain the electromagnetic shielding requirements of the target device, and establish the coating thickness range of the target shielding material coating based on the electromagnetic shielding requirements; The difference between the corrosion and weathering service life and the expected service life is calculated to obtain the corrosion and weathering gap duration; Using the coating thickness range as a constraint, the baseline coating thickness is iteratively optimized and adjusted based on the corrosion and weathering notch duration to obtain the optimal coating thickness.

[0035] If the corrosion and weather resistance service life obtained above is greater than or equal to the expected service life, it indicates that the current material at the baseline coating thickness can meet the equipment's lifespan requirements, and thickness optimization is unnecessary. In this case, the baseline coating thickness can be directly adopted as the final solution, ensuring equipment reliability while avoiding material waste and increased costs due to over-design.

[0036] When the predicted corrosion and weathering resistance service life is less than the expected service life, it indicates that the currently selected material, at the baseline coating thickness, cannot meet the long-term reliability requirements of the equipment, posing a risk of equipment failure due to premature material failure. Therefore, it is necessary to obtain the electromagnetic shielding requirements of the target equipment, optimize and correct the baseline coating thickness, and improve the corrosion resistance of the material by increasing the coating thickness, thereby extending its service life and ensuring that the equipment maintains effective electromagnetic shielding function throughout its expected service life.

[0037] Specifically, firstly, it is necessary to obtain the electromagnetic shielding requirements of the target equipment, and then establish the coating thickness range of the target shielding material coating based on these requirements. Electromagnetic shielding requirements refer to the electromagnetic interference protection capability level that the equipment needs to achieve, usually expressed in decibels (dB) to indicate the shielding effectiveness requirement within a specific frequency band. Based on the correspondence between electromagnetic shielding effectiveness and coating thickness, the minimum thickness (ensuring minimum shielding effectiveness) and the maximum allowable thickness (considering factors such as process feasibility, cost, and weight) that meet the shielding requirements can be determined, thus forming the coating thickness range of the target shielding material coating and providing a feasible selection range for subsequent thickness optimization.

[0038] Secondly, the difference between the corrosion and weathering resistance service life and the expected service life is calculated to obtain the corrosion and weathering resistance gap duration. Corrosion and weathering resistance gap duration = expected service life - corrosion and weathering resistance service life, which is used to quantify the gap between material performance and life requirements at the current coating thickness, providing a quantitative basis for thickness optimization.

[0039] Furthermore, using a defined coating thickness range as a constraint, the baseline coating thickness is iteratively optimized and adjusted based on the corrosion and weathering notch duration. Specifically, using the coating thickness range as a constraint, the baseline coating thickness is iteratively optimized and adjusted based on the corrosion and weathering notch duration to obtain the optimal coating thickness, including: Using the reference coating thickness as an initial value, multiple candidate coating thicknesses are generated within the coating thickness range; The corrosion simulation simulator is invoked to predict the candidate corrosion and weather resistance service time corresponding to each candidate coating thickness. The difference between the candidate corrosion and weathering resistance service time and the expected service time for each candidate coating thickness is calculated to obtain multiple candidate corrosion and weathering resistance gap durations; Based on the analysis of the duration of multiple candidate corrosion and weathering notches, the influence trend of coating thickness on corrosion and weathering performance is determined, and the direction of thickness optimization adjustment is determined. Based on the thickness optimization adjustment direction, the coating thickness is adjusted within the coating thickness range to generate multiple new candidate coating thicknesses; The thickness adjustment and prediction process is iteratively executed until the corrosion and weathering notch duration meets the preset convergence condition or reaches the preset number of iterations. From all candidate coating thicknesses, select the qualified coating thicknesses whose corrosion and weather resistance service life is greater than or equal to the expected service life; The coating thickness with the smallest thickness among the qualified coating thicknesses is selected as the optimal coating thickness.

[0040] Specifically, firstly, using a baseline coating thickness as the initial starting point, multiple candidate coating thicknesses are generated within the coating thickness range using uniform sampling or intelligent generation algorithms. The candidate coating thicknesses should be uniformly distributed within the feasible thickness domain to ensure comprehensiveness of the search. Secondly, a pre-trained corrosion simulation simulator is invoked, and each candidate coating thickness and its corresponding operating environment parameters are input sequentially to predict the corrosion and weathering resistance service life of the material under each candidate thickness condition. By simulating the aging process of different thickness schemes under specific environments, accurate service life prediction values ​​are obtained.

[0041] Then, the difference between the candidate corrosion and weathering resistance service life and the expected service life corresponding to each candidate coating thickness is calculated to obtain multiple sets of candidate corrosion and weathering resistance gap durations. Among them, a positive candidate corrosion and weathering resistance gap duration indicates insufficient service life, a negative value indicates surplus service life, and a zero value indicates that the requirements are just met.

[0042] Based on the obtained candidate corrosion and weathering notch durations, the influence trend of coating thickness variation on corrosion and weathering performance is analyzed to clarify the direction of thickness optimization, i.e., increasing or decreasing the thickness. Specifically, by plotting the relationship curve between candidate coating thickness and corresponding corrosion and weathering notch duration, the variation law between the two can be intuitively analyzed. If the curve shows a negative correlation trend, that is, as the coating thickness increases, the corrosion and weathering notch duration gradually decreases (or turns from positive to negative), it indicates that increasing the coating thickness can effectively extend the service life of the material, and the optimization direction should be to increase the coating thickness. Conversely, if the curve shows a positive correlation trend, it indicates that the current thickness has exceeded the optimal value, and the coating thickness should be reduced.

[0043] Furthermore, based on the determined thickness optimization adjustment direction, the coating thickness is adjusted within the coating thickness range to generate multiple new candidate coating thicknesses. Among them, the newly generated candidate coating thicknesses are closer to the ideal solution region. The thickness adjustment and lifetime prediction process is iteratively executed until one of the following termination conditions is met: the corrosion weathering notch duration meets the preset convergence condition, such as the absolute value of the corrosion weathering notch duration being less than or equal to 30 days, or the maximum preset number of iterations is reached, such as 50 times, indicating that the iterative process is gradually approaching the optimal solution.

[0044] Finally, from all candidate coating thicknesses generated during the iteration process, qualified coating thicknesses with a corrosion and weather resistance service life greater than or equal to the expected service life are selected. All qualified coating thicknesses meet the equipment life requirements. The smallest qualified coating thickness is chosen as the optimal coating thickness. This optimal coating thickness minimizes material usage while ensuring lifespan compliance, achieving an optimal balance between economy and reliability.

[0045] S40: Based on the target shielding material coating and the optimal coating thickness, the corrosion and weather resistance optimization results of the electromagnetic shielding material of the target device are obtained.

[0046] Based on the target shielding material coating type and optimal coating thickness value determined in the previous optimization process, the corresponding electromagnetic shielding material is prepared. This yields the optimized corrosion and weather resistance of the electromagnetic shielding material for the target equipment, which combines reliable electromagnetic shielding performance with long-term environmental adaptability. This not only meets the electromagnetic shielding requirements of the target equipment in the expected service environment, but also ensures the corrosion and weather resistance reliability of the electromagnetic shielding material throughout its entire life cycle through precise thickness design, avoiding problems caused by premature failure or over-design.

[0047] In summary, the embodiments of this application have at least the following technical effects: Compared with existing technologies, this application firstly solves the problem of blind material selection in complex environments by establishing a material selection mechanism based on statistical analysis of historical service data, and significantly improves the scientificity and applicability of material selection; secondly, it uses a corrosion simulation simulator trained by supervised learning to predict service life, and realizes accurate evaluation of the corrosion and weather resistance performance of materials under specific environmental parameters, overcoming the technical defects of existing technologies that cannot accurately predict the actual service life of materials. Furthermore, by establishing an iterative optimization algorithm constrained by electromagnetic shielding requirements and aiming at achieving the required lifespan and minimizing thickness, a systematic optimization design for coating thickness was achieved. This effectively solved the dilemma in traditional design methods where either conservative approaches lead to cost waste or aggressive approaches result in premature failure. Finally, by integrating material selection, lifespan prediction, and thickness optimization into a unified system framework, a comprehensive solution was provided that can simultaneously meet electromagnetic performance requirements and long-term weather resistance indicators, providing reliable electromagnetic shielding protection for electronic equipment operating in harsh environments.

[0048] Example 2, as Figure 2 As shown, based on the same inventive concept as the corrosion and weather resistance optimization method for electromagnetic shielding materials provided in Embodiment 1, this embodiment of the invention also provides a corrosion and weather resistance optimization system for electromagnetic shielding materials, comprising: The material selection module 11 is used to obtain the expected service life and working environment information of the target equipment, and to determine the target shielding material coating from a plurality of candidate shielding material coatings based on the expected service life and the working environment information. The weathering prediction module 12 is used to obtain the reference coating thickness of the target shielding material coating and predict the corrosion and weathering resistance service life of the target equipment in combination with the working environment information. The thickness optimization module 13 is used to obtain the electromagnetic shielding requirements of the target equipment when the corrosion and weathering service time is less than the expected service time, and to optimize and correct the reference coating thickness in combination with the corrosion and weathering service time and the expected service time to obtain the optimal coating thickness. The optimized output module 14 is used to prepare the electromagnetic shielding material coating based on the target shielding material coating and the optimal coating thickness, and output the corrosion and weather resistance optimization results of the electromagnetic shielding material of the target device.

[0049] Specifically, the material selection module 11 is used for: Obtain the expected service life and operating environment information of the target device, and determine the target shielding material coating from multiple candidate shielding material coatings based on the expected service life and operating environment information, including: Establish a working environment vector based on the working environment information of the target device; Based on the working environment vector, historical service data of each of the candidate shielding material coatings are retrieved to obtain multiple sample shielding material coating sets. The service compliance rate of each candidate shielding material coating is obtained by counting the number of samples whose shielding material coatings do not experience corrosion and weathering failure within the expected service life. The candidate shielding material coating with the highest service compliance rate is selected as the target shielding material coating.

[0050] Specifically, a working environment vector is established based on the working environment information of the target device, including: Determine the pre-installation area of ​​the target device, and collect historical environmental data of the pre-installation area as the working environment information of the target device; Time-series analysis was performed on the historical environmental data to obtain temperature change curves, humidity change curves, and corrosive medium concentration change curves. The working environment vector is constructed based on the temperature change curve, the humidity change curve, and the corrosive medium concentration change curve.

[0051] Specifically, the weathering prediction module 12 is used for: Obtaining the baseline coating thickness of the target shielding material coating, and combining this with the working environment information to predict the corrosion and weather resistance service life of the target equipment, includes: Based on the target shielding material coating, obtain the corresponding sample shielding material coating set from multiple sample shielding material coating sets to obtain the target sample shielding material coating set; The average coating thickness of the shielding material coating in the target sample shielding material coating set is calculated to obtain the reference coating thickness; The corrosion simulation simulator is invoked, which includes an environmental parameter unit and a material parameter unit. Configure the environmental parameter unit based on the working environment information, configure the material parameter unit based on the reference coating thickness, start and run the corrosion simulation simulator, and obtain the corrosion and weather resistance service time of the target equipment under the reference coating thickness.

[0052] Specifically, the construction steps of the corrosion simulation simulator include: Multiple historical samples were collected based on the target shielding material coating. Each historical sample includes sample working environment information, sample coating thickness, and sample corrosion and weather resistance service time. The architecture for constructing a corrosion simulation simulator includes the environmental parameter unit and the material parameter unit; The environmental parameter unit is configured based on the sample working environment information in the multiple historical samples, and the material parameter unit is configured based on the sample coating thickness; The corrosion simulation simulator is trained by supervised learning using the corrosion and weathering service time of the multiple historical samples until the prediction accuracy meets the preset requirements, thus completing the construction of the corrosion simulation simulator.

[0053] Specifically, the thickness optimization module 13 is used for: The electromagnetic shielding requirements of the target device are obtained, and the baseline coating thickness is optimized and corrected based on the corrosion and weather resistance service life and the expected service life to obtain the optimal coating thickness, including: Obtain the electromagnetic shielding requirements of the target device, and establish the coating thickness range of the target shielding material coating based on the electromagnetic shielding requirements; The difference between the corrosion and weathering service life and the expected service life is calculated to obtain the corrosion and weathering gap duration; Using the coating thickness range as a constraint, the baseline coating thickness is iteratively optimized and adjusted based on the corrosion and weathering notch duration to obtain the optimal coating thickness.

[0054] Specifically, constrained by the coating thickness range, the baseline coating thickness is iteratively optimized and adjusted based on the corrosion and weathering notch duration to obtain the optimal coating thickness, including: Using the reference coating thickness as an initial value, multiple candidate coating thicknesses are generated within the coating thickness range; The corrosion simulation simulator is invoked to predict the candidate corrosion and weather resistance service time corresponding to each candidate coating thickness. The difference between the candidate corrosion and weathering resistance service time and the expected service time for each candidate coating thickness is calculated to obtain multiple candidate corrosion and weathering resistance gap durations; Based on the analysis of the duration of multiple candidate corrosion and weathering notches, the influence trend of coating thickness on corrosion and weathering performance is determined, and the direction of thickness optimization adjustment is determined. Based on the thickness optimization adjustment direction, the coating thickness is adjusted within the coating thickness range to generate multiple new candidate coating thicknesses; The thickness adjustment and prediction process is iteratively executed until the corrosion and weathering notch duration meets the preset convergence condition or reaches the preset number of iterations. From all candidate coating thicknesses, select the qualified coating thicknesses whose corrosion and weather resistance service life is greater than or equal to the expected service life; The coating thickness with the smallest thickness among the qualified coating thicknesses is selected as the optimal coating thickness.

[0055] The optimized output module 14 is specifically used for: Based on the target shielding material coating and the optimal coating thickness, the corrosion and weather resistance optimization results of the electromagnetic shielding material of the target device are obtained.

[0056] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0057] The above description is only a preferred embodiment of this application and is not intended to limit this application. 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.

[0058] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for optimizing the corrosion and weather resistance of electromagnetic shielding materials, characterized in that, The method includes: Obtain the expected service life and operating environment information of the target device, and determine the target shielding material coating from multiple candidate shielding material coatings based on the expected service life and operating environment information; Obtain the reference coating thickness of the target shielding material coating, and predict the corrosion and weather resistance service life of the target equipment based on the working environment information; When the corrosion and weather resistance service life is less than the expected service life, the electromagnetic shielding requirement of the target equipment is obtained, and the reference coating thickness is optimized and corrected in combination with the corrosion and weather resistance service life and the expected service life to obtain the optimal coating thickness. Based on the target shielding material coating and the optimal coating thickness, the corrosion and weather resistance optimization results of the electromagnetic shielding material of the target device are obtained.

2. The method according to claim 1, characterized in that, Obtain the expected service life and operating environment information of the target device, and determine the target shielding material coating from multiple candidate shielding material coatings based on the expected service life and operating environment information, including: Establish a working environment vector based on the working environment information of the target device; Based on the working environment vector, historical service data of each of the candidate shielding material coatings are retrieved to obtain multiple sample shielding material coating sets. The service compliance rate of each candidate shielding material coating is obtained by counting the number of samples whose shielding material coatings do not experience corrosion and weathering failure within the expected service life. The candidate shielding material coating with the highest service compliance rate is selected as the target shielding material coating.

3. The method according to claim 2, characterized in that, A working environment vector is established based on the working environment information of the target device, including: Determine the pre-installation area of ​​the target device, and collect historical environmental data of the pre-installation area as the working environment information of the target device; Time-series analysis was performed on the historical environmental data to obtain temperature change curves, humidity change curves, and corrosive medium concentration change curves. The working environment vector is constructed based on the temperature change curve, the humidity change curve, and the corrosive medium concentration change curve.

4. The method according to claim 2, characterized in that, Obtaining the baseline coating thickness of the target shielding material coating, and combining this with the working environment information to predict the corrosion and weather resistance service life of the target equipment, includes: Based on the target shielding material coating, obtain the corresponding sample shielding material coating set from multiple sample shielding material coating sets to obtain the target sample shielding material coating set; The average coating thickness of the shielding material coating in the target sample shielding material coating set is calculated to obtain the reference coating thickness; The corrosion simulation simulator is invoked, which includes an environmental parameter unit and a material parameter unit. Configure the environmental parameter unit based on the working environment information, configure the material parameter unit based on the reference coating thickness, start and run the corrosion simulation simulator, and obtain the corrosion and weather resistance service time of the target equipment under the reference coating thickness.

5. The method according to claim 4, characterized in that, The construction steps of the corrosion simulation simulator include: Multiple historical samples were collected based on the target shielding material coating. Each historical sample includes sample working environment information, sample coating thickness, and sample corrosion and weather resistance service time. The architecture for constructing a corrosion simulation simulator includes the environmental parameter unit and the material parameter unit; The environmental parameter unit is configured based on the sample working environment information in the multiple historical samples, and the material parameter unit is configured based on the sample coating thickness; The corrosion simulation simulator is trained by supervised learning using the corrosion and weathering service time of the multiple historical samples until the prediction accuracy meets the preset requirements, thus completing the construction of the corrosion simulation simulator.

6. The method according to claim 5, characterized in that, The electromagnetic shielding requirements of the target device are obtained, and the baseline coating thickness is optimized and corrected based on the corrosion and weather resistance service life and the expected service life to obtain the optimal coating thickness, including: Obtain the electromagnetic shielding requirements of the target device, and establish the coating thickness range of the target shielding material coating based on the electromagnetic shielding requirements; The difference between the corrosion and weathering service life and the expected service life is calculated to obtain the corrosion and weathering gap duration; Using the coating thickness range as a constraint, the baseline coating thickness is iteratively optimized and adjusted based on the corrosion and weathering notch duration to obtain the optimal coating thickness.

7. The method according to claim 6, characterized in that, Using the coating thickness range as a constraint, the baseline coating thickness is iteratively optimized and adjusted based on the corrosion and weathering notch duration to obtain the optimal coating thickness, including: Using the reference coating thickness as an initial value, multiple candidate coating thicknesses are generated within the coating thickness range; The corrosion simulation simulator is invoked to predict the candidate corrosion and weather resistance service time corresponding to each candidate coating thickness. The difference between the candidate corrosion and weathering resistance service time and the expected service time for each candidate coating thickness is calculated to obtain multiple candidate corrosion and weathering resistance gap durations; Based on the analysis of the duration of multiple candidate corrosion and weathering notches, the influence trend of coating thickness on corrosion and weathering performance is determined, and the direction of thickness optimization adjustment is determined. Based on the thickness optimization adjustment direction, the coating thickness is adjusted within the coating thickness range to generate multiple new candidate coating thicknesses; The thickness adjustment and prediction process is iteratively executed until the corrosion and weathering notch duration meets the preset convergence condition or reaches the preset number of iterations. From all candidate coating thicknesses, select the qualified coating thicknesses whose corrosion and weather resistance service life is greater than or equal to the expected service life; The coating thickness with the smallest thickness among the qualified coating thicknesses is selected as the optimal coating thickness.

8. A corrosion and weather resistance optimization system for electromagnetic shielding materials, characterized in that, For performing the method according to any one of claims 1-7, comprising: The material selection module is used to obtain the expected service life and working environment information of the target equipment, and to determine the target shielding material coating from multiple candidate shielding material coatings based on the expected service life and the working environment information. The weathering prediction module is used to obtain the reference coating thickness of the target shielding material coating and, in combination with the working environment information, predict the corrosion and weathering resistance service life of the target equipment. The thickness optimization module is used to obtain the electromagnetic shielding requirements of the target equipment when the corrosion and weathering service life is less than the expected service life, and to optimize and correct the baseline coating thickness in combination with the corrosion and weathering service life and the expected service life to obtain the optimal coating thickness. An optimized output module is used to prepare the electromagnetic shielding material coating based on the target shielding material coating and the optimal coating thickness, and output the optimized corrosion and weather resistance results of the electromagnetic shielding material of the target device.

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