A method and system for detecting damage to an automotive coating

Abstract

The embodiment of the application provides a kind of automobile coating damage resistance detection method and detection system, the method comprises: obtaining automobile coating factory parameter information, obtains safety index information based on automobile coating factory parameter information;Based on the setting of automobile coating factory parameter information, detection environment parameter is generated multiple detection condition information according to detection environment parameter;Based on multiple detection condition information, the sample to be detected is detected, and detection data is obtained;Detection data and safety index information are compared, and data difference information is obtained;Based on data difference information, data anomaly information is analyzed, based on data anomaly information, damage information of the sample to be detected is analyzed, and damage information is transmitted to terminal in real time;By analyzing the factory parameter of automobile coating, different detection environment parameters are set, so that the coating damage resistance detection of automobile coating in different use environments can be carried out, and the detection flexibility is improved.

Description

Technical Field

[0001] This application relates to the field of automotive coating analysis technology, and more specifically, to a method and system for detecting damage resistance of automotive coatings. Background Technology

[0002] The use of automobiles is growing rapidly every year, serving not only as a means of transportation but also as a symbol of status. As automobiles become high-end consumer goods and enter the public eye, the annual production of automobiles and their surface coatings is increasing by more than ten percent. Simultaneously, the appearance and lifespan of automobiles are receiving increasing attention. The purpose of automotive painting is to give the car body excellent aesthetics, weather resistance, comfort (livability), sealing, and corrosion resistance, thereby increasing its commercial value and extending its service life. Currently, with the development of the automotive industry, the requirements for automotive coating materials are becoming increasingly stringent. Depending on the environmental conditions in which the car is used, the coating must not only have excellent weather resistance and corrosion resistance but also excellent decorative properties. Existing methods for testing the damage resistance of automotive coatings make it difficult to set different testing environment parameters based on the factory parameters of the automotive coating, thus hindering comprehensive testing under various conditions and resulting in significant testing errors. Summary of the Invention

[0003] The purpose of this application is to provide a method and system for testing the damage resistance of automotive coatings. By analyzing the factory parameters of automotive coatings and setting different testing environment parameters, the damage resistance of automotive coatings under different usage environments can be tested, thereby improving the testing flexibility.

[0004] This application also provides a method for detecting damage resistance of automotive coatings, including:

[0005] Obtain automotive coating factory parameter information, and obtain safety indicator information based on automotive coating factory parameter information;

[0006] The testing environment parameters are set based on the automotive coating factory parameters, and multiple testing condition information is generated based on the testing environment parameters.

[0007] The sample to be tested is tested based on multiple testing conditions to obtain test data;

[0008] The detection data is compared with the safety indicator information to obtain data difference information, and data anomaly information is analyzed based on the data difference information.

[0009] Based on the analysis of data anomaly information, the damage information of the product to be inspected is analyzed and transmitted to the terminal in real time.

[0010] Optionally, in the automotive coating damage resistance testing method described in this application embodiment, obtaining automotive coating factory parameter information and obtaining safety indicator information based on the automotive coating factory parameter information specifically includes:

[0011] Obtain vehicle model and automotive parts production requirements; obtain automotive coating factory parameter information.

[0012] Analysis of the types, compositions, and thicknesses of automotive coatings based on factory parameters;

[0013] Based on the composition analysis of the automotive coating, the molecular structure and molecular composition of the automotive coating are analyzed, and based on the type of automotive coating, the component ratio of different components of the automotive coating is analyzed.

[0014] The physical and chemical properties of the automotive coating are obtained based on the type, composition ratio, and thickness of the coating. The physical properties include the friction properties, fracture properties, and hardness properties of the automotive coating, and the chemical properties include corrosion resistance, UV resistance, and high temperature resistance.

[0015] Based on the analysis of the physical and chemical properties of automotive coatings, this study provides information on safety indicators corresponding to different types of automotive coatings.

[0016] Optionally, in the automotive coating damage resistance testing method described in this application embodiment, testing environment parameters are set based on the automotive coating factory parameter information, and multiple testing condition information is generated according to the testing environment parameters, specifically including:

[0017] Obtain automotive coating factory parameter information and analyze the type, component ratio and thickness of automotive coatings;

[0018] The testing environment is constructed by setting the testing environment parameters based on the automotive coating factory parameter information, configuring the testing environment, obtaining the configuration information, and calculating the parameter matching degree based on the configuration information.

[0019] Determine whether the parameter matching degree is greater than or equal to the set matching degree threshold;

[0020] If it is greater than or equal to, multiple detection condition information is generated according to the configuration information. The detection environment parameter information includes corrosion resistance detection environment parameters, high temperature resistance detection environment parameters and ultraviolet resistance detection environment parameters. The detection condition information includes corrosion resistance parameter level, high temperature resistance parameter level and ultraviolet resistance parameter level.

[0021] If it is less than the specified value, then adjust the configuration information of the detection environment parameters.

[0022] Optionally, in the automotive coating damage resistance testing method described in this application embodiment, the sample to be tested is tested based on multiple testing condition information to obtain testing data, specifically including:

[0023] Obtain environmental parameters for corrosion resistance testing, high temperature resistance testing, and ultraviolet resistance testing;

[0024] Multiple levels of corrosion resistance parameters are set according to the environmental parameters for corrosion resistance testing. The levels of corrosion resistance parameters are adjusted sequentially to test the corrosion resistance of the automotive coating and obtain the corrosion data of the automotive coating.

[0025] Multiple levels of high-temperature resistance parameters are set according to the high-temperature resistance testing environment parameters. The levels of high-temperature resistance parameters are adjusted sequentially to test the high-temperature resistance of the automotive coating and obtain the high-temperature impact data of the automotive coating.

[0026] Multiple levels of UV resistance parameters are set according to the UV resistance testing environment parameters. The UV resistance levels are adjusted sequentially to test the UV resistance performance of the automotive coating and obtain the UV impact data of the automotive coating.

[0027] Based on corrosion data, high-temperature impact data, and ultraviolet radiation impact data of automotive coatings, detection data corresponding to multiple detection conditions were obtained.

[0028] Optionally, in the automotive coating damage resistance detection method described in this application embodiment, analyzing data anomaly information based on data difference information specifically includes:

[0029] Obtain data discrepancy information, compare the data discrepancy information with the set discrepancy information, and obtain the data deviation rate;

[0030] The data deviation rate is compared with a set data deviation rate threshold, wherein the set data deviation rate threshold includes a first data deviation rate threshold and a second data deviation rate threshold, and the first data deviation rate threshold is less than the second data deviation rate threshold.

[0031] If the data deviation rate is less than or equal to the first data deviation rate threshold, then the data difference information is deemed to meet the requirements.

[0032] If the data deviation rate is greater than the first data deviation rate threshold and less than the second data deviation rate threshold, then the first data anomaly information is generated.

[0033] If the data deviation rate is greater than or equal to the second data deviation rate threshold, then a second data anomaly message is generated.

[0034] Optionally, in the automotive coating damage resistance testing method described in this application embodiment, analyzing the damage information of the test sample based on data anomaly information specifically includes:

[0035] Historical damage analysis data is obtained based on big data, and training and testing sets are established based on the historical damage analysis data.

[0036] The initial model is iteratively trained based on the training set to obtain the training results;

[0037] Determine whether the training results have converged;

[0038] If convergence is achieved, a damage analysis model is obtained. Based on the damage analysis model, abnormal information in the data is analyzed, and damage information of the product to be inspected is output.

[0039] If convergence is not achieved, the hyperparameters of the damage analysis model are dynamically adjusted based on the test set.

[0040] Secondly, embodiments of this application provide an automotive coating damage resistance detection system. The system includes a memory and a processor. The memory includes a program for an automotive coating damage resistance detection method. When the program for the automotive coating damage resistance detection method is executed by the processor, it performs the following steps:

[0041] Obtain automotive coating factory parameter information, and obtain safety indicator information based on automotive coating factory parameter information;

[0042] The testing environment parameters are set based on the automotive coating factory parameters, and multiple testing condition information is generated based on the testing environment parameters.

[0043] The sample to be tested is tested based on multiple testing conditions to obtain test data;

[0044] The detection data is compared with the safety indicator information to obtain data difference information, and data anomaly information is analyzed based on the data difference information.

[0045] Based on the analysis of data anomaly information, the damage information of the product to be inspected is analyzed and transmitted to the terminal in real time.

[0046] Optionally, in the automotive coating damage resistance testing system described in this application embodiment, obtaining automotive coating factory parameter information and obtaining safety indicator information based on the automotive coating factory parameter information specifically includes:

[0047] Obtain vehicle model and automotive parts production requirements; obtain automotive coating factory parameter information.

[0048] Analysis of the types, compositions, and thicknesses of automotive coatings based on factory parameters;

[0049] Based on the composition analysis of the automotive coating, the molecular structure and molecular composition of the automotive coating are analyzed, and based on the type of automotive coating, the component ratio of different components of the automotive coating is analyzed.

[0050] The physical and chemical properties of the automotive coating are obtained based on the type, composition ratio, and thickness of the coating. The physical properties include the friction properties, fracture properties, and hardness properties of the automotive coating, and the chemical properties include corrosion resistance, UV resistance, and high temperature resistance.

[0051] Based on the analysis of the physical and chemical properties of automotive coatings, this study provides information on safety indicators corresponding to different types of automotive coatings.

[0052] Optionally, in the automotive coating damage resistance testing system described in this application embodiment, testing environment parameters are set based on the automotive coating factory parameter information, and multiple testing condition information is generated according to the testing environment parameters, specifically including:

[0053] Obtain automotive coating factory parameter information and analyze the type, component ratio and thickness of automotive coatings;

[0054] The testing environment is constructed by setting the testing environment parameters based on the automotive coating factory parameter information, configuring the testing environment, obtaining the configuration information, and calculating the parameter matching degree based on the configuration information.

[0055] Determine whether the parameter matching degree is greater than or equal to the set matching degree threshold;

[0056] If it is greater than or equal to, multiple detection condition information is generated according to the configuration information. The detection environment parameter information includes corrosion resistance detection environment parameters, high temperature resistance detection environment parameters and ultraviolet resistance detection environment parameters. The detection condition information includes corrosion resistance parameter level, high temperature resistance parameter level and ultraviolet resistance parameter level.

[0057] If it is less than the specified value, then adjust the configuration information of the detection environment parameters.

[0058] Thirdly, embodiments of this application also provide a computer-readable storage medium, which includes a program for detecting damage resistance of automotive coatings. When the program for detecting damage resistance of automotive coatings is executed by a processor, it implements the steps of the method for detecting damage resistance of automotive coatings as described in any of the preceding claims.

[0059] As can be seen from the above, the automotive coating damage resistance testing method and system provided in this application obtains automotive coating factory parameter information and safety index information based on the automotive coating factory parameter information; sets testing environment parameters based on the automotive coating factory parameter information and generates multiple testing condition information based on the testing environment parameters; tests the sample to be tested based on the multiple testing condition information to obtain testing data; compares the testing data with the safety index information to obtain data difference information; analyzes data anomaly information based on the data difference information; analyzes the damage information of the sample to be tested based on the data anomaly information; and transmits the damage information to the terminal in real time. By analyzing the automotive coating factory parameters and setting different testing environment parameters, the damage resistance testing of automotive coatings under different usage environments can be performed, thereby improving testing flexibility. Attached Figure Description

[0060] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1 A flowchart of the method for detecting damage resistance of automotive coatings provided in the embodiments of this application;

[0062] Figure 2 A flowchart illustrating the method for obtaining safety index information in the automotive coating damage resistance testing method provided in this application embodiment;

[0063] Figure 3 A flowchart illustrating the detection condition information analysis method for the automotive coating damage resistance detection method provided in this application embodiment. Detailed Implementation

[0064] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0065] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0066] Please refer to Figure 1 , Figure 1 This is a flowchart of a method for detecting the damage resistance of an automotive coating, as described in some embodiments of this application. The method is used in a terminal device and includes the following steps:

[0067] S101, Obtain automotive coating factory parameter information, and obtain safety indicator information based on automotive coating factory parameter information;

[0068] S102, set the testing environment parameters based on the automotive coating factory parameter information, and generate multiple testing condition information according to the testing environment parameters;

[0069] S103, based on multiple detection condition information, the sample to be tested is tested to obtain detection data;

[0070] S104, compare the detection data with the safety indicator information to obtain data difference information, and analyze data anomaly information based on the data difference information;

[0071] S105 analyzes the damage information of the product under inspection based on data anomaly information and transmits the damage information to the terminal in real time.

[0072] It should be noted that by analyzing the factory parameters of the automotive coating and setting the corresponding testing environment parameters, it is possible to obtain the test data of the sample under multiple testing conditions, thereby achieving accurate analysis and identification of automotive coating damage data and improving testing accuracy.

[0073] Please refer to Figure 2 , Figure 2 This is a flowchart illustrating a method for obtaining safety indicator information in an automotive coating damage resistance testing method according to some embodiments of this application. According to embodiments of the present invention, obtaining automotive coating factory parameter information and obtaining safety indicator information based on the automotive coating factory parameter information specifically includes:

[0074] S201, Obtain vehicle model and automotive parts production requirements to obtain automotive coating factory parameter information;

[0075] S202, based on the automotive coating factory parameter information, analyze the type, composition and thickness of automotive coatings;

[0076] S203, Based on the compositional analysis of automotive coatings, the molecular structure and molecular composition of automotive coatings are analyzed, and based on the types of automotive coatings, the component ratios of different components in automotive coatings are analyzed.

[0077] S204, the physical and chemical properties of automotive coatings are obtained based on the type, composition ratio and thickness of the automotive coating. The physical properties include the friction properties, fracture properties and hardness properties of the automotive coating, and the chemical properties include corrosion resistance, UV resistance and high temperature resistance.

[0078] S205, based on the analysis of the physical and chemical properties of automotive coatings, provides safety index information corresponding to different types of automotive coatings.

[0079] It should be noted that the physical and chemical properties of automotive coatings are analyzed based on their composition ratios and types. This allows for separate analysis of the physical and chemical properties of automotive coatings, ensuring accurate safety index information for different automotive coatings and facilitating subsequent damage analysis of the coatings.

[0080] Please refer to Figure 3 , Figure 3 This is a flowchart illustrating the detection condition information analysis method for an automotive coating damage resistance testing method according to some embodiments of this application. According to embodiments of the present invention, detection environment parameters are set based on the automotive coating factory parameter information, and multiple detection condition information is generated based on the detection environment parameters, specifically including:

[0081] S301, obtain automotive coating factory parameter information and analyze the type, composition ratio and thickness of automotive coating;

[0082] S302, Construct the testing environment, set the testing environment parameters based on the automotive coating factory parameter information, configure the testing environment, obtain the configuration information, and calculate the parameter matching degree based on the configuration information;

[0083] S303, Determine whether the parameter matching degree is greater than or equal to the set matching degree threshold;

[0084] S304, if greater than or equal to, then multiple test condition information is generated according to the configuration information. The test environment parameter information includes corrosion resistance test environment parameters, high temperature resistance test environment parameters and ultraviolet resistance test environment parameters. The test condition information includes corrosion resistance parameter level, high temperature resistance parameter level and ultraviolet resistance parameter level.

[0085] If S305 is less than 5, then adjust the configuration information of the detection environment parameters.

[0086] It should be noted that by configuring the testing environment parameters and analyzing the matching degree between the testing environment parameters and the test sample, the configuration information can be dynamically adjusted to improve the flexibility of the testing environment configuration.

[0087] According to an embodiment of the present invention, the sample to be tested is tested based on multiple detection condition information to obtain detection data, specifically including:

[0088] Obtain environmental parameters for corrosion resistance testing, high temperature resistance testing, and ultraviolet resistance testing;

[0089] Multiple levels of corrosion resistance parameters are set according to the environmental parameters for corrosion resistance testing. The levels of corrosion resistance parameters are adjusted sequentially to test the corrosion resistance of the automotive coating and obtain the corrosion data of the automotive coating.

[0090] Multiple levels of high-temperature resistance parameters are set according to the high-temperature resistance testing environment parameters. The levels of high-temperature resistance parameters are adjusted sequentially to test the high-temperature resistance of the automotive coating and obtain the high-temperature impact data of the automotive coating.

[0091] Multiple levels of UV resistance parameters are set according to the UV resistance testing environment parameters. The UV resistance levels are adjusted sequentially to test the UV resistance performance of the automotive coating and obtain the UV impact data of the automotive coating.

[0092] Based on corrosion data, high-temperature impact data, and ultraviolet radiation impact data of automotive coatings, detection data corresponding to multiple detection conditions were obtained.

[0093] It should be noted that different detection levels are set under different testing conditions, so that the detection levels can be continuously adjusted to analyze the tolerance of the automotive coating and improve the accuracy of automotive coating damage analysis.

[0094] According to an embodiment of the present invention, analyzing data anomaly information based on data difference information specifically includes:

[0095] Obtain data discrepancy information, compare the data discrepancy information with the set discrepancy information, and obtain the data deviation rate;

[0096] The data deviation rate is compared with a set data deviation rate threshold, which includes a first data deviation rate threshold and a second data deviation rate threshold, and the first data deviation rate threshold is less than the second data deviation rate threshold.

[0097] If the data deviation rate is less than or equal to the first data deviation rate threshold, then the data difference information is deemed to meet the requirements.

[0098] If the data deviation rate is greater than the first data deviation rate threshold and less than the second data deviation rate threshold, then the first data anomaly information is generated.

[0099] If the data deviation rate is greater than or equal to the second data deviation rate threshold, then a second data anomaly message is generated.

[0100] It should be noted that by analyzing data discrepancies, different data deviation rates yield different data anomaly information, thereby classifying data anomalies into levels and enabling segmented analysis of automotive coating damage. This allows for precise analysis of the damage condition and extent.

[0101] According to an embodiment of the present invention, analyzing the damage information of the sample under inspection based on data anomaly information specifically includes:

[0102] Historical damage analysis data is obtained based on big data, and training and testing sets are established based on the historical damage analysis data.

[0103] The initial model is iteratively trained based on the training set to obtain the training results;

[0104] Determine whether the training results have converged;

[0105] If convergence is achieved, a damage analysis model is obtained. Based on the damage analysis model, abnormal information in the data is analyzed, and damage information of the product to be inspected is output.

[0106] If convergence is not achieved, the hyperparameters of the damage analysis model are dynamically adjusted based on the test set.

[0107] It should be noted that by continuously training the model with historical data, the output results of the damage analysis model can be made closer to the actual results, thereby improving the output accuracy of the damage analysis model and reducing damage analysis errors.

[0108] Secondly, embodiments of this application provide an automotive coating damage resistance testing system. The system includes a memory and a processor. The memory includes a program for an automotive coating damage resistance testing method. When the program for the automotive coating damage resistance testing method is executed by the processor, it performs the following steps:

[0109] Obtain automotive coating factory parameter information, and obtain safety indicator information based on automotive coating factory parameter information;

[0110] The testing environment parameters are set based on the automotive coating factory parameters, and multiple testing condition information is generated based on the testing environment parameters.

[0111] The sample to be tested is tested based on multiple testing conditions to obtain test data;

[0112] The detection data is compared with the safety indicator information to obtain data difference information, and data anomaly information is analyzed based on the data difference information.

[0113] Based on the analysis of data anomaly information, the damage information of the product to be inspected is analyzed and transmitted to the terminal in real time.

[0114] It should be noted that by analyzing the factory parameters of the automotive coating and setting the corresponding testing environment parameters, it is possible to obtain the test data of the sample under multiple testing conditions, thereby achieving accurate analysis and identification of automotive coating damage data and improving testing accuracy.

[0115] According to an embodiment of the present invention, obtaining automotive coating factory parameter information and obtaining safety indicator information based on the automotive coating factory parameter information specifically includes:

[0116] Obtain vehicle model and automotive parts production requirements; obtain automotive coating factory parameter information.

[0117] Analysis of the types, compositions, and thicknesses of automotive coatings based on factory parameters;

[0118] Analysis of the molecular structure and composition of automotive coatings based on component composition, and analysis of the component ratios of different components in automotive coatings based on the types of automotive coatings.

[0119] The physical and chemical properties of automotive coatings are obtained based on their type, composition ratio, and thickness. Physical properties include the friction properties, fracture properties, and hardness properties of the automotive coating, while chemical properties include corrosion resistance, UV resistance, and high temperature resistance.

[0120] Based on the analysis of the physical and chemical properties of automotive coatings, this study provides information on safety indicators corresponding to different types of automotive coatings.

[0121] It should be noted that the physical and chemical properties of automotive coatings are analyzed based on their composition ratios and types. This allows for separate analysis of the physical and chemical properties of automotive coatings, ensuring accurate safety index information for different automotive coatings and facilitating subsequent damage analysis of the coatings.

[0122] According to an embodiment of the present invention, testing environment parameters are set based on the automotive coating factory parameter information, and multiple testing condition information is generated based on the testing environment parameters, specifically including:

[0123] Obtain automotive coating factory parameter information and analyze the type, component ratio and thickness of automotive coatings;

[0124] The testing environment is constructed by setting the testing environment parameters based on the automotive coating factory parameter information, configuring the testing environment, obtaining the configuration information, and calculating the parameter matching degree based on the configuration information.

[0125] Determine whether the parameter matching degree is greater than or equal to the set matching degree threshold;

[0126] If it is greater than or equal to, multiple detection condition information will be generated based on the configuration information. The detection environment parameter information includes corrosion resistance detection environment parameters, high temperature resistance detection environment parameters and ultraviolet resistance detection environment parameters. The detection condition information includes corrosion resistance parameter level, high temperature resistance parameter level and ultraviolet resistance parameter level.

[0127] If it is less than the specified value, then adjust the configuration information of the detection environment parameters.

[0128] It should be noted that by configuring the testing environment parameters and analyzing the matching degree between the testing environment parameters and the test sample, the configuration information can be dynamically adjusted to improve the flexibility of the testing environment configuration.

[0129] According to an embodiment of the present invention, the sample to be tested is tested based on multiple detection condition information to obtain detection data, specifically including:

[0130] Obtain environmental parameters for corrosion resistance testing, high temperature resistance testing, and ultraviolet resistance testing;

[0131] Multiple levels of corrosion resistance parameters are set according to the environmental parameters for corrosion resistance testing. The levels of corrosion resistance parameters are adjusted sequentially to test the corrosion resistance of the automotive coating and obtain the corrosion data of the automotive coating.

[0132] Multiple levels of high-temperature resistance parameters are set according to the high-temperature resistance testing environment parameters. The levels of high-temperature resistance parameters are adjusted sequentially to test the high-temperature resistance of the automotive coating and obtain the high-temperature impact data of the automotive coating.

[0133] Multiple levels of UV resistance parameters are set according to the UV resistance testing environment parameters. The UV resistance levels are adjusted sequentially to test the UV resistance performance of the automotive coating and obtain the UV impact data of the automotive coating.

[0134] Based on corrosion data, high-temperature impact data, and ultraviolet radiation impact data of automotive coatings, detection data corresponding to multiple detection conditions were obtained.

[0135] It should be noted that different detection levels are set under different testing conditions, so that the detection levels can be continuously adjusted to analyze the tolerance of the automotive coating and improve the accuracy of automotive coating damage analysis.

[0136] According to an embodiment of the present invention, analyzing data anomaly information based on data difference information specifically includes:

[0137] Obtain data discrepancy information, compare the data discrepancy information with the set discrepancy information, and obtain the data deviation rate;

[0138] The data deviation rate is compared with a set data deviation rate threshold, which includes a first data deviation rate threshold and a second data deviation rate threshold, and the first data deviation rate threshold is less than the second data deviation rate threshold.

[0139] If the data deviation rate is less than or equal to the first data deviation rate threshold, then the data difference information is deemed to meet the requirements.

[0140] If the data deviation rate is greater than the first data deviation rate threshold and less than the second data deviation rate threshold, then the first data anomaly information is generated.

[0141] If the data deviation rate is greater than or equal to the second data deviation rate threshold, then a second data anomaly message is generated.

[0142] It should be noted that by analyzing data discrepancies, different data deviation rates yield different data anomaly information, thereby classifying data anomalies into levels and enabling segmented analysis of automotive coating damage. This allows for precise analysis of the damage condition and extent.

[0143] According to an embodiment of the present invention, analyzing the damage information of the sample under inspection based on data anomaly information specifically includes:

[0144] Historical damage analysis data is obtained based on big data, and training and testing sets are established based on the historical damage analysis data.

[0145] The initial model is iteratively trained based on the training set to obtain the training results; then it is determined whether the training results have converged.

[0146] If convergence is achieved, a damage analysis model is obtained. Based on the damage analysis model, abnormal information in the data is analyzed, and damage information of the product to be inspected is output.

[0147] If convergence is not achieved, the hyperparameters of the damage analysis model are dynamically adjusted based on the test set.

[0148] It should be noted that by continuously training the model with historical data, the output results of the damage analysis model can be made closer to the actual results, thereby improving the output accuracy of the damage analysis model and reducing damage analysis errors.

[0149] A third aspect of the present invention provides a computer-readable storage medium including a program for detecting damage resistance of automotive coatings, wherein when the program is executed by a processor, it implements the steps of the method for detecting damage resistance of automotive coatings as described above.

[0150] This invention discloses a method and system for testing the damage resistance of automotive coatings. The method involves acquiring factory parameters of the automotive coating and then obtaining safety indicators based on these parameters. Based on these factory parameters, testing environment parameters are set, and multiple testing conditions are generated. The sample under test is then tested using these multiple testing conditions to obtain test data. This test data is compared with the safety indicators to obtain data difference information. Data anomaly information is analyzed based on the data difference information, and damage information of the sample under test is further analyzed based on the data anomaly information. This damage information is then transmitted to a terminal in real time. By analyzing the factory parameters of the automotive coating and setting different testing environment parameters, the method enables damage testing of the automotive coating under different usage environments, thus improving testing flexibility.

[0151] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0152] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0153] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0154] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0155] Alternatively, if the integrated units of the present invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A method for detecting damage resistance of automotive coatings, characterized in that, include: Obtain automotive coating factory parameter information, and obtain safety indicator information based on automotive coating factory parameter information; The testing environment parameters are set based on the automotive coating factory parameters, and multiple testing condition information is generated based on the testing environment parameters. The sample to be tested is tested based on multiple testing conditions to obtain test data; The detection data is compared with the safety indicator information to obtain data difference information, and data anomaly information is analyzed based on the data difference information. Based on the analysis of data anomaly information, the damage information of the product to be inspected is analyzed and transmitted to the terminal in real time. Obtain automotive coating factory parameter information, and based on this information, obtain safety indicator information, specifically including: Obtain vehicle model and automotive parts production requirements; obtain automotive coating factory parameter information. Analysis of the types, compositions, and thicknesses of automotive coatings based on factory parameters; Based on the composition analysis of the automotive coating, the molecular structure and molecular composition of the automotive coating are analyzed, and based on the type of automotive coating, the component ratio of different components of the automotive coating is analyzed. The physical and chemical properties of the automotive coating are obtained based on the type, composition ratio, and thickness of the coating. The physical properties include the friction properties, fracture properties, and hardness properties of the automotive coating, and the chemical properties include corrosion resistance, UV resistance, and high temperature resistance. Analysis of safety indicators for different types of automotive coatings based on their physical and chemical properties; The testing environment parameters are set based on the automotive coating's factory parameters, and multiple testing condition information is generated based on these parameters, including: Obtain automotive coating factory parameter information and analyze the type, component ratio and thickness of automotive coatings; The testing environment is constructed by setting the testing environment parameters based on the automotive coating factory parameter information, configuring the testing environment, obtaining the configuration information, and calculating the parameter matching degree based on the configuration information. Determine whether the parameter matching degree is greater than or equal to the set matching degree threshold; If it is greater than or equal to, multiple detection condition information is generated according to the configuration information. The detection environment parameter information includes corrosion resistance detection environment parameters, high temperature resistance detection environment parameters and ultraviolet resistance detection environment parameters. The detection condition information includes corrosion resistance parameter level, high temperature resistance parameter level and ultraviolet resistance parameter level. If it is less than, then adjust the configuration information of the detection environment parameters; The sample to be tested is analyzed based on multiple detection conditions to obtain detection data, which specifically includes: Obtain environmental parameters for corrosion resistance testing, high temperature resistance testing, and ultraviolet resistance testing; Multiple levels of corrosion resistance parameters are set according to the environmental parameters for corrosion resistance testing. The levels of corrosion resistance parameters are adjusted sequentially to test the corrosion resistance of the automotive coating and obtain the corrosion data of the automotive coating. Multiple levels of high-temperature resistance parameters are set according to the high-temperature resistance testing environment parameters. The levels of high-temperature resistance parameters are adjusted sequentially to test the high-temperature resistance of the automotive coating and obtain the high-temperature impact data of the automotive coating. Multiple levels of UV resistance parameters are set according to the UV resistance testing environment parameters. The UV resistance levels are adjusted sequentially to test the UV resistance performance of the automotive coating and obtain the UV impact data of the automotive coating. Based on corrosion data, high-temperature impact data, and ultraviolet radiation impact data of automotive coatings, detection data corresponding to multiple detection conditions were obtained.

2. The method for detecting damage resistance of automotive coatings according to claim 1, characterized in that, Analyzing data anomalies based on data discrepancy information specifically includes: Obtain data discrepancy information, compare the data discrepancy information with the set discrepancy information, and obtain the data deviation rate; The data deviation rate is compared with a set data deviation rate threshold, wherein the set data deviation rate threshold includes a first data deviation rate threshold and a second data deviation rate threshold, and the first data deviation rate threshold is less than the second data deviation rate threshold. If the data deviation rate is less than or equal to the first data deviation rate threshold, then the data difference information is deemed to meet the requirements. If the data deviation rate is greater than the first data deviation rate threshold and less than the second data deviation rate threshold, then the first data anomaly information is generated. If the data deviation rate is greater than or equal to the second data deviation rate threshold, then a second data anomaly message is generated.

3. The method for detecting damage resistance of automotive coatings according to claim 2, characterized in that, Based on the analysis of data anomaly information, the damage information of the sample to be inspected is analyzed, specifically including: Historical damage analysis data is obtained based on big data, and training and testing sets are established based on the historical damage analysis data. The initial model is iteratively trained based on the training set to obtain the training results; Determine whether the training results have converged; If convergence is achieved, a damage analysis model is obtained. Based on the damage analysis model, abnormal information in the data is analyzed, and damage information of the product to be inspected is output. If convergence is not achieved, the hyperparameters of the damage analysis model are dynamically adjusted based on the test set.

4. A damage detection system for automotive coatings, characterized in that, The system includes a memory and a processor. The memory contains a program for a method of detecting damage to automotive coatings. When the program for detecting damage to automotive coatings is executed by the processor, it performs the following steps: Obtain automotive coating factory parameter information, and obtain safety indicator information based on automotive coating factory parameter information; The testing environment parameters are set based on the automotive coating factory parameters, and multiple testing condition information is generated based on the testing environment parameters. The sample to be tested is tested based on multiple testing conditions to obtain test data; The detection data is compared with the safety indicator information to obtain data difference information, and data anomaly information is analyzed based on the data difference information. Based on the analysis of data anomaly information, the damage information of the product to be inspected is analyzed and transmitted to the terminal in real time. Obtain automotive coating factory parameter information, and based on this information, obtain safety indicator information, specifically including: Obtain vehicle model and automotive parts production requirements; obtain automotive coating factory parameter information. Analysis of the types, compositions, and thicknesses of automotive coatings based on factory parameters; Based on the composition analysis of the automotive coating, the molecular structure and molecular composition of the automotive coating are analyzed, and based on the type of automotive coating, the component ratio of different components of the automotive coating is analyzed. The physical and chemical properties of the automotive coating are obtained based on the type, composition ratio, and thickness of the coating. The physical properties include the friction properties, fracture properties, and hardness properties of the automotive coating, and the chemical properties include corrosion resistance, UV resistance, and high temperature resistance. Analysis of safety indicators for different types of automotive coatings based on their physical and chemical properties; The testing environment parameters are set based on the automotive coating's factory parameters, and multiple testing condition information is generated based on these parameters, including: Obtain automotive coating factory parameter information and analyze the type, component ratio and thickness of automotive coatings; The testing environment is constructed by setting the testing environment parameters based on the automotive coating factory parameter information, configuring the testing environment, obtaining the configuration information, and calculating the parameter matching degree based on the configuration information. Determine whether the parameter matching degree is greater than or equal to the set matching degree threshold; If it is greater than or equal to, multiple detection condition information is generated according to the configuration information. The detection environment parameter information includes corrosion resistance detection environment parameters, high temperature resistance detection environment parameters and ultraviolet resistance detection environment parameters. The detection condition information includes corrosion resistance parameter level, high temperature resistance parameter level and ultraviolet resistance parameter level. If it is less than, then adjust the configuration information of the detection environment parameters; The sample to be tested is analyzed based on multiple detection conditions to obtain detection data, which specifically includes: Obtain environmental parameters for corrosion resistance testing, high temperature resistance testing, and ultraviolet resistance testing; Multiple levels of corrosion resistance parameters are set according to the environmental parameters for corrosion resistance testing. The levels of corrosion resistance parameters are adjusted sequentially to test the corrosion resistance of the automotive coating and obtain the corrosion data of the automotive coating. Multiple levels of high-temperature resistance parameters are set according to the high-temperature resistance testing environment parameters. The levels of high-temperature resistance parameters are adjusted sequentially to test the high-temperature resistance of the automotive coating and obtain the high-temperature impact data of the automotive coating. Multiple levels of UV resistance parameters are set according to the UV resistance testing environment parameters. The UV resistance levels are adjusted sequentially to test the UV resistance performance of the automotive coating and obtain the UV impact data of the automotive coating. Based on corrosion data, high-temperature impact data, and ultraviolet radiation impact data of automotive coatings, detection data corresponding to multiple detection conditions were obtained.

Images