A color matching engineering efficiency improvement method and equipment based on large models

Abstract

The present application discloses a method and device for improving the efficiency of color matching engineering based on a large model, belonging to the technical field of vehicle color matching. The method includes: collecting color matching data; importing the vehicle data of the vehicle to be matched into the trained color matching model to obtain color matching suggestions; processing the vehicle model of the vehicle to be matched based on the color matching suggestions, vehicle data and color harmony algorithm to generate a second vehicle color matching model; processing the second vehicle color matching model based on the environment model to obtain the vehicle's exterior effect; processing the second vehicle color matching model based on light source data and in combination with a color physics algorithm to determine the vehicle's interior effect under different light source settings. The present application achieves the technical effect of improving the efficiency of vehicle color matching design through the above method.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle color matching, and in particular to a method and device for improving the efficiency of color matching engineering based on a large model. Background Art

[0002] In today's automotive industry, vehicle color design is not only a crucial tool for shaping brand image and attracting consumer attention, but also a key element in personalizing and differentiating vehicles. As consumers' pursuit of automotive aesthetics continues to rise and competition in the automotive market intensifies, the complexity and importance of vehicle color design are becoming increasingly prominent.

[0003] The traditional vehicle color design process often relies on the designer's personal experience and artistic sense, achieving a satisfactory color scheme through repeated manual testing and adjustments. However, this approach has many limitations. First, the lack of scientific data support and systematic design methods leads to a high degree of randomness and uncertainty in color design. Second, the traditional color design process is time-consuming and inefficient, making it difficult to meet the fast-paced and high-efficiency demands of modern automobile production. Especially in the current rapidly changing automotive market, consumer preferences for vehicle colors are constantly evolving. To keep pace with market trends and meet diverse consumer needs, automakers need to frequently introduce new color schemes. This requires a more efficient and flexible vehicle color design process that can quickly respond to market changes.

[0004] Therefore, how to improve the efficiency of vehicle color matching design has become a technical problem that needs to be solved urgently. Summary of the Invention

[0005] The embodiments of the present application provide a large-model-based color engineering efficiency improvement method and device to solve the following technical problem: how to improve the efficiency of vehicle color design.

[0006] In a first aspect, an embodiment of the present application provides a color engineering efficiency improvement method based on a large model, characterized in that the method includes: collecting color matching data; wherein the color matching data includes vehicle color matching data and color matching data, and the vehicle color matching data includes vehicle exterior color matching data and vehicle interior color matching data; importing vehicle data of a vehicle to be color matched into a trained color matching model to obtain color matching suggestions; wherein the vehicle data includes at least one of the following data: vehicle model, publicity, structure, and configuration; processing the vehicle model of the vehicle to be color matched based on the color matching suggestions to generate a first vehicle color matching model equipped with the color matching suggestions; adjusting the contrast, saturation, and brightness of the colors in the first vehicle color matching model based on the vehicle data and a preset color harmony algorithm to generate a second vehicle color matching model; processing the second vehicle color matching model based on a preset environmental model to obtain the exterior effects of the vehicle under different external environments; determining interior light source data based on the vehicle data; wherein the interior light source data includes light source position, light source brightness, light source structure, and light source setting; processing the second vehicle color matching model based on the light source data and in combination with a preset color physics algorithm to determine the interior effects of the vehicle under different light source settings.

[0007] In one implementation of the present application, before importing the vehicle data of the vehicle to be colored into the trained color matching model to obtain color matching suggestions, the method also includes: constructing a color matching model based on a preset convolutional neural network and a preset long short-term memory network; wherein the convolutional neural network is used to identify image features in the color matching data, and the long short-term memory network is used to process the time series information in the color matching data; cleaning and arranging the color matching data, removing outliers and noise data, to generate sample data; wherein the sample data is data in a unified format, and the sample data includes a training set, a test set, and a validation set; training and validating the color matching model based on the sample data, and evaluating the generalization ability of the color matching model based on a preset cross-validation strategy; optimizing the parameters of the color matching model based on a preset back-propagation algorithm; and generating a trained color matching model when the performance of the color matching model on the validation set reaches a preset accuracy threshold.

[0008] In one implementation of the present application, vehicle data of a vehicle to be color-matched is imported into a trained color-matching model to obtain color-matching suggestions, specifically including: preprocessing the vehicle data of the vehicle to be color-matched to generate input data; wherein the input data is in a format that can be recognized by the trained color-matching model; importing the input data into the trained color-matching model; and the color-matching model outputs color-matching suggestions corresponding to the input data based on feature information of the input data.

[0009] In one implementation of the present application, a vehicle model of a vehicle to be color-matched is processed based on color matching suggestions to generate a first vehicle color matching model equipped with color matching suggestions, specifically including: determining color matching suggestions; wherein the color matching suggestions include color suggestions and material suggestions for multiple components; establishing a three-dimensional model of the vehicle to be color-matched based on preset three-dimensional modeling software; wherein the three-dimensional model includes multiple components; matching the color matching suggestions and the three-dimensional model based on the components to generate a first vehicle color matching model; wherein some components in the first vehicle color matching model may not be colored.

[0010] In one implementation of the present application, the contrast, saturation and brightness of the colors in the first vehicle color matching model are adjusted based on vehicle data and a preset color harmony algorithm to generate a second vehicle color matching model, specifically including: processing the first vehicle color matching model and vehicle data to determine the design style and color matching tone of the vehicle to be matched; according to the color harmony algorithm, calculating the contrast, saturation and brightness relationship between the components in the first vehicle color matching model, and referring to the design style and color matching tone to identify the disharmonious factors in the first vehicle color matching model; wherein the disharmonious factors are colors that are inconsistent with the design style and color matching tone; removing the disharmonious factors to generate the vehicle color matching model to be filled; filling the vehicle color matching model to be filled according to the design style and color matching tone to generate the second vehicle color matching model.

[0011] In one implementation of the present application, a second vehicle color matching model is processed based on a preset environmental model to obtain the vehicle exterior effects under different external environments, specifically including: constructing a multi-environment simulation system; wherein the multi-environment simulation system includes multiple scenes and multiple weather conditions; importing the second vehicle color matching model into the multi-environment simulation system; processing the second vehicle color matching model based on a preset rendering algorithm to determine the vehicle exterior effects.

[0012] In one implementation of the present application, the second vehicle color matching model is processed based on a preset rendering algorithm to generate a set of vehicle exterior effect images, specifically including: setting rendering parameters; wherein the rendering parameters include lighting conditions, shadow effects, reflectivity, refractive index and environment map; performing multi-angle rendering on the second vehicle color matching model to generate vehicle exterior effect images containing different perspectives; according to preset time periods and weather conditions, adjusting the lighting and weather effects in the rendering environment to generate vehicle exterior effect images under different time periods and weather conditions.

[0013] In one implementation of the present application, the second vehicle color matching model is processed based on light source data and in combination with a preset color physics algorithm to determine the interior effect of the vehicle under different light source settings, specifically including: establishing a light source simulation environment; wherein the light source simulation environment is configured according to the light source data; importing the second vehicle color matching model into the light source simulation environment; applying the color physics algorithm to perform lighting simulation on the second vehicle color matching model based on the spectral characteristics of the light source, the reflection and absorption characteristics of the material, and the visual characteristics of the human eye; calculating and generating the color rendering effects of various components of the vehicle interior under different light source settings; wherein the color rendering effects include color changes, shadow distribution and highlight effects; outputting the color rendering effects as a vehicle interior effect image set to generate a vehicle interior color matching effect.

[0014] In one implementation of the present application, the method also includes: establishing a color evaluation index; wherein the color evaluation index includes color harmony, visual comfort, spatial perception, and material texture performance; processing the vehicle interior effect and the vehicle exterior effect based on a preset image analysis algorithm to extract color feature parameters; wherein the color feature parameters include at least hue, brightness, and saturation; and calculating the color evaluation scores of the vehicle interior effect and the vehicle exterior effect based on the color evaluation index.

[0015] On the second aspect, the embodiment of the present application also provides a color engineering efficiency improvement device based on a large model, the device comprising: a data acquisition module for collecting color matching data; wherein the color matching data includes vehicle color matching data and color matching data; the vehicle color matching data includes vehicle external color matching data and vehicle internal color matching data; a color matching suggestion generation module for importing vehicle data of a vehicle to be matched into a trained color matching model to obtain color matching suggestions; wherein the vehicle data includes at least one of the following data: vehicle model, publicity, structure and configuration; a first vehicle model generation module for processing the vehicle model of the vehicle to be matched based on the color matching suggestion to generate a first vehicle color matching model equipped with the color matching suggestion; a second ... A module is provided for adjusting the contrast, saturation and brightness of the colors in the first vehicle color matching model based on the vehicle data and a preset color harmony algorithm to generate a second vehicle color matching model; a vehicle exterior color module is provided for processing the second vehicle color matching model based on a preset environment model to obtain the vehicle exterior effects under different external environments; a light source data determination module is provided for determining the vehicle interior light source data based on the vehicle data; wherein the vehicle interior light source data includes the light source position, light source brightness, light source structure and light source setting; a vehicle interior color module is provided for processing the second vehicle color matching model based on the light source data and in combination with a preset color physics algorithm to determine the vehicle interior effects under different light source settings.

[0016] The embodiments of the present application provide a large-model-based color matching engineering efficiency improvement method and device, which have at least the following technical effects:

[0017] By collecting comprehensive color matching data, including color matching data for the exterior and interior of the vehicle, as well as color matching data, rich and accurate samples are provided for the training of the color matching model. By establishing and training the color matching model, the present invention can intelligently output reasonable color matching suggestions based on input data such as the model, publicity, structure, and configuration of the vehicle to be matched, thereby greatly improving the efficiency and accuracy of the color matching design; based on the color matching suggestions, the present invention can further process the vehicle model of the vehicle to be matched and generate a first vehicle color matching model equipped with color matching suggestions; through a preset color harmony algorithm, the present invention can also adjust the contrast, saturation, and brightness of the colors in the first vehicle color matching model, thereby generating a more harmonious and beautiful second vehicle color matching model. In addition, the present invention also utilizes a preset environmental model and color physics algorithm to simulate the exterior effects of the vehicle under different external environments and the interior effects of the vehicle under different light source settings, thereby improving the efficiency of vehicle color matching design. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1 A flow chart of a color matching engineering efficiency improvement method based on a large model provided in an embodiment of the present application;

[0020] Figure 2 A schematic diagram of a large-model based color engineering efficiency improvement device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] To make the purpose, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the specific embodiments of this application and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0022] The embodiments of the present application provide a large-model-based color engineering efficiency improvement method and device to solve the following technical problem: how to improve the efficiency of vehicle color design.

[0023] The technical solutions proposed in the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0024] Figure 1 This is a flow chart of a color matching engineering efficiency improvement method based on a large model provided in the embodiment of this application. Figure 1 As shown, the embodiment of the present application provides a color matching engineering efficiency improvement method based on a large model, which specifically includes the following steps:

[0025] Step 1: Collect color matching data; the color matching data includes vehicle color matching data and color matching data. The vehicle color matching data includes vehicle exterior color matching data and vehicle interior color matching data.

[0026] This step aims to collect comprehensive color matching data to provide a foundation for subsequent color matching model training. Color matching data primarily includes vehicle color matching data and color matching data. Vehicle color matching data is further divided into vehicle exterior color matching data and vehicle interior color matching data, covering vehicle color schemes for various models, years, and market positioning, as well as interior trim color matching designs.

[0027] Color matching data: refers to all relevant data used for color scheme generation, optimization or analysis, including but not limited to color codes, color matching rules, color usage cases in actual application scenarios, etc.

[0028] Vehicle exterior color data: This data includes the color information of visible exterior components such as the vehicle's exterior shell, body, and windows. This data covers exterior color schemes for various types of vehicles. For example, it can record standard color schemes for different makes and models of vehicles, as well as user preferences and trends for customized color schemes.

[0029] Vehicle interior color data: color information of the vehicle's interior, such as seats, dashboards, door panels, etc.

[0030] Similar to collecting vehicle exterior color data, vehicle interior color data is collected by accessing the automaker's internal design materials, collaborating with interior suppliers, and analyzing user feedback. For example, the impact of different materials (such as leather, fabric, plastic, etc.) on color rendering is analyzed.

[0031] Color matching data: A dataset that shows how colors are combined to produce visual beauty or specific emotional responses in different application scenarios.

[0032] Collect information through market research, automobile manufacturer databases, and online automobile sales platforms.

[0033] Color matching data can be collected by analyzing sources such as fashion magazines, design websites, and artwork. This data can include color matching rules (such as contrasting colors, adjacent colors, and complementary colors), color psychology applications (such as warm colors stimulate appetite and cool colors promote calmness), and color preferences in specific cultures or regions. Big data analysis technology can also be used to explore user preferences and trends in color matching from channels such as social media and e-commerce platforms.

[0034] In the embodiment of the present application, the automobile manufacturer collects color matching data to develop the color matching scheme for the next generation of models:

[0035] Standard color schemes for existing models, including color codes and color ratios, are extracted from automaker databases. Image recognition technology is used to extract additional color information from official vehicle images, such as color details for special paint jobs. Market research is also conducted to gather insights into user preferences and trends regarding vehicle exterior color schemes, such as the popularity of specific colors or color combinations among younger consumers.

[0036] Access automakers' internal design materials to understand their interior color palette design concepts and principles. Collaborate with interior suppliers to understand the impact of different materials on color rendering and collect actual samples for color analysis. Analyze user feedback to understand the impact of interior color palettes on driving experience and emotions, such as whether certain color combinations are more likely to cause driver fatigue.

[0037] Collect color matching rules and application examples from fashion magazines and design websites to understand current color trends and matching methods. Leverage big data analytics to uncover user preferences and trends regarding color matching on social media and e-commerce platforms, such as the popularity of specific color combinations in clothing, home furnishings, and other categories. Incorporate the principles of color psychology to analyze the impact of different color combinations on user emotional responses.

[0038] Before executing step 2, you need to build and train a color matching model, which includes the following steps:

[0039] Step A1: Construct a color matching model based on a preset convolutional neural network and a preset long short-term memory network; wherein the convolutional neural network is used to identify image features in the color matching data, and the long short-term memory network is used to process the time series information in the color matching data.

[0040] Convolutional Neural Network (CNN): A feed-forward neural network that is particularly well-suited for processing data with grid-like topologies, such as images. It uses a structure consisting of convolutional layers, pooling layers, and fully connected layers to automatically extract image features, such as edges and textures.

[0041] Long Short-Term Memory (LSTM): A special type of recurrent neural network (RNN) that can handle long-term dependencies in sequence data. By introducing memory cells and gating mechanisms, it can effectively retain and utilize key information in sequence data.

[0042] In the embodiments of this application, convolutional neural networks are used to identify image features in color matching data. For example, in vehicle color matching, CNNs can extract features such as color distribution and texture patterns in vehicle body images. Long short-term memory networks are used to process temporal information in color matching data. For example, LSTMs can provide additional contextual information such as market trends and user preferences over different time periods, helping the model generate color matching recommendations that better meet market needs.

[0043] By combining CNN and LSTM, we can build a color matching model that can process image features and consider temporal information.

[0044] Step A2: Clean and organize the color matching data, remove outliers and noise data, and generate sample data; wherein the sample data is data in a unified format, and the sample data includes a training set, a test set, and a validation set.

[0045] Outliers: Values ​​that deviate significantly from other data points in a data set, possibly due to measurement errors, data entry errors, or unusual events.

[0046] Noisy data: Random errors or fluctuations in a data set that interfere with useful information.

[0047] Sample data: After cleaning and organization, the data set used to train and test the model includes training set, test set and validation set.

[0048] In an embodiment of the present application: For the collected vehicle color matching data, data cleaning is first performed. By checking the color code, texture description and other information of each vehicle model, obvious outliers (such as wrong color codes, missing texture descriptions, etc.) are identified and removed. Then, the data is sorted. All image data are adjusted to the same resolution (such as 128x128 pixels) and color space (such as RGB color space), and all text data are converted to UTF-8 encoding format. Finally, the data set is divided into a training set (accounting for 70% of the total data), a test set (accounting for 20% of the total data) and a validation set (accounting for 10% of the total data) for subsequent training and testing.

[0049] Step A3: train and validate the color matching model based on the sample data, and evaluate the generalization ability of the color matching model based on a preset cross-validation strategy.

[0050] Model training: The process of learning a model using training set data, aiming to adjust the model's parameters and structure to minimize the loss function.

[0051] Model validation: The process of evaluating the trained model using validation set data to check the model's generalization ability and prevent overfitting.

[0052] Cross-validation: A statistical method for evaluating the generalization ability of a model. It divides the dataset into multiple subsets and uses each subset as a validation set to train the model in turn, thereby obtaining performance evaluation results of multiple models.

[0053] In an embodiment of the present application: For the vehicle color matching model, CNN and LSTM are jointly trained using the training set data. The cross entropy loss function is minimized by adjusting the parameters and structure of the model. During the training process, the model is regularly evaluated using the validation set data to check the generalization ability of the model and prevent overfitting. In order to more comprehensively evaluate the performance of the model, a 5-fold cross-validation strategy is adopted. The data set is divided into 5 subsets, and each subset is used as a validation set in turn to train the model. Then, the average value of indicators such as accuracy, recall rate, F1 score, etc. of each model is calculated as the final model performance evaluation result.

[0054] Step A4: Optimize the parameters of the color matching model based on a preset back propagation algorithm.

[0055] Backpropagation algorithm: An algorithm for training artificial neural networks that calculates the gradient of the loss function with respect to the model parameters and updates the parameters in the opposite direction of the gradient to minimize the loss function.

[0056] In this embodiment of the present application, a backpropagation algorithm is used to optimize the parameters of the CNN and LSTM models during training for the vehicle color matching model. First, the gradient of the cross-entropy loss function with respect to the model parameters is calculated. Then, the Adam optimizer is used to update the parameters in the opposite direction of the gradient to minimize the loss function. During the optimization process, the loss function is regularly monitored, and optimization parameters such as the learning rate are adjusted as needed to ensure model convergence and performance improvement.

[0057] Step A5: When the performance of the color matching model on the validation set reaches a preset accuracy threshold, a trained color matching model is generated.

[0058] Accuracy threshold: A performance indicator threshold set during the model training process. When the performance of the model on the validation set reaches this threshold, the model is considered to have been fully trained and training can be stopped.

[0059] In an embodiment of the present application: For the vehicle color matching model, the validation set data is used regularly during the training process to evaluate the model, and performance indicators such as accuracy are calculated. When the accuracy of the model on the validation set reaches more than 90% for multiple consecutive times (such as 5 times), the model is considered to have been fully trained and training can be stopped. At this point, the trained model is saved for subsequent use. When saving the model, the parameters and structural information of the model and the relevant configuration information during the training process (such as the learning rate is set to 0.001, the Adam optimizer is used, etc.) are recorded. In this way, when designing color schemes for other models or brands in the future, this trained model can be used directly to generate high-quality color matching suggestions.

[0060] Step 2: Import the vehicle data of the vehicle to be color matched into the trained color matching model to obtain color matching suggestions; wherein the vehicle data includes at least one of the following data: vehicle model, promotion, structure and configuration.

[0061] Vehicle data: refers to information about the vehicle to be color matched, including but not limited to model, promotional information, structure, and configuration. This data is the basis for the color matching model to generate color matching suggestions.

[0062] Color suggestion: Based on vehicle data and the trained color matching model, the model outputs a color scheme or suggestion for a specific vehicle.

[0063] Step 21: pre-process the vehicle data of the vehicle to be color matched to generate input data; wherein the input data is in a format that can be recognized by the trained color matching model.

[0064] Data preprocessing: Before importing the data into the color matching model, the raw data is cleaned, converted, and formatted to generate input data that meets the model input requirements.

[0065] Input data: After preprocessing, formatted data that can be recognized by the trained color matching model.

[0066] Data preprocessing is a critical step in ensuring that the color matching model can accurately understand vehicle data and generate effective color matching recommendations. This preprocessing process may include data cleaning (e.g., removing duplicate data, correcting erroneous data), data conversion (e.g., converting text data to numerical data, resizing image data to a uniform resolution), and data formatting (e.g., organizing data into a specific data structure or format).

[0067] For vehicle data, the following preprocessing operations are required:

[0068] Vehicle model data: Convert vehicle model names into standardized codes so that the model can identify the differences between different models.

[0069] Promotional data: Extract key information from promotional materials (such as vehicle color, style description, etc.) and convert it into numerical or text vector form for model analysis and processing.

[0070] Structural data: Convert vehicle body structural information (such as the number of doors, seat layout, etc.) into numerical form and encode or normalize it.

[0071] Configuration data: Convert configuration information (such as interior light strips, displays, etc.) into numerical form and perform standardization or normalization.

[0072] In the embodiment of the present application, the following pre-processing operations are performed on the vehicle data of the SUV model:

[0073] Convert the vehicle model name into a unique identifier (such as "SUV_Model_A"). Extract the vehicle color and style description from the promotional materials and convert them into text vectors. Convert the vehicle body structure information (such as five doors and five seats, four-wheel drive system, etc.) into numerical coding. Convert the configuration information (such as interior light strips, displays, etc.) into numerical form and normalize it.

[0074] Step 22: Import the input data into the trained color matching model.

[0075] Continuing with the example of step 31: for SUV vehicle data, image data (such as vehicle promotional images) is resized to a uniform resolution and color space and fed into the model as one dimension of a tensor; text data (such as vehicle name, style description, etc.) is converted into a text vector and fed into the model as another dimension; structural data and configuration data are fed into the model as additional feature dimensions.

[0076] Step 23: The color matching model outputs color matching suggestions corresponding to the input data based on the feature information of the input data.

[0077] Feature information: Key information extracted from the input data to describe vehicle characteristics, such as vehicle model, color preference, body structure, etc.

[0078] Following the embodiment of step 22: After receiving the pre-processed input data, the color matching model begins to process and analyze it. The model first extracts feature information such as color distribution and texture pattern in the vehicle promotional image through CNN, and then processes other feature information such as model name, style description, body structure and configuration through LSTM. Based on these extracted feature information, the color matching model generates color matching suggestions for the SUV model. The suggestions include recommended color combinations (such as the combination of black body and silver wheels), color matching styles (such as sporty style or luxury style), and color matching proportions (such as the ratio between the main color and the auxiliary color of the body).

[0079] Step 3: Process the vehicle model of the vehicle to be color-matched based on the color matching suggestion to generate a first vehicle color matching model equipped with the color matching suggestion.

[0080] Step 31: Determine color matching suggestions; wherein the color matching suggestions include color suggestions and material suggestions for multiple components.

[0081] Color Suggestion: In this step, the color suggestion refers to a comprehensive recommendation generated by the color matching model based on the vehicle data of the vehicle to be matched. This recommendation includes color suggestions for multiple components and material suggestions. Color suggestions involve specific color codes, while material suggestions include various material types such as metal, plastic, and leather.

[0082] For example, the color matching model generates detailed color matching recommendations based on the car's model, promotional information, structure, and configuration. The color matching recommendations include dark blue metallic paint for the body, brown leather for the interior seats, and silver plastic trim for the instrument panel and door panels.

[0083] Step 32: Create a three-dimensional model of the vehicle to be color-matched based on a preset three-dimensional modeling software; wherein the three-dimensional model includes multiple components.

[0084] 3D modeling software: Professional software used to create and edit 3D models, such as 3ds Max, Maya, and Blender.

[0085] 3D model: A virtual vehicle model created using 3D modeling software, containing all the vehicle's components and details, such as the body, windows, wheels, and interior.

[0086] For example, designers can use 3ds Max software to create a 3D model of the vehicle to be color-matched, or directly obtain a 3D image of the vehicle with color matching from the vehicle R&D department.

[0087] Step 33: Generate a first vehicle color matching model based on the component matching color matching suggestions and the three-dimensional model; wherein, some components in the first vehicle color matching model may not be colored.

[0088] Component matching: The process of matching the color and material information in the color matching suggestion with each component in the 3D model.

[0089] First vehicle color matching model: A three-dimensional model containing color matching suggestions generated based on the component matching results. The model shows the color and material effects of each vehicle component.

[0090] In this example, when matching color suggestions with the 3D model, the vehicle's components, such as the body, windows, wheels, and interior, were first identified and categorized according to their function and location. The suggested deep blue metallic paint was then applied to the body, brown leather to the interior seats, and silver brushed metal trim to the instrument panel and door panels. However, some wheel hubs and detailing were not yet painted.

[0091] It is understandable that the uncolored portion may be a component that is extra on the vehicle to be colored compared to other vehicles, such as a display frame on the roof.

[0092] Step 4: Adjust the contrast, saturation, and brightness of the colors in the first vehicle color matching model based on the vehicle data and a preset color harmony algorithm to generate a second vehicle color matching model.

[0093] Color harmony algorithm: A mathematical or computational model used to adjust and optimize color matching. This algorithm precisely controls color matching by calculating parameters such as contrast, saturation, and brightness between colors.

[0094] Contrast: refers to the difference in brightness between two or more colors. It is one of the important factors affecting the visual effect in color matching.

[0095] Saturation: refers to the purity or vividness of color. The higher the saturation, the brighter the color; the lower the saturation, the duller the color.

[0096] Brightness: refers to the lightness or darkness of a color. The higher the brightness, the brighter the color; the lower the brightness, the darker the color.

[0097] Step 41: Process the first vehicle color matching model and vehicle data to determine the design style and color matching tone of the vehicle to be color matched.

[0098] Design style: refers to the artistic style and characteristics presented by the overall design of the vehicle, such as luxury, sportiness, simplicity, etc.

[0099] Color matching tone: refers to the combination of main color and auxiliary color used in vehicle color matching.

[0100] When determining the design style and color palette for a vehicle, multiple factors must be considered, including vehicle data, market demand, and brand positioning. First, a detailed analysis of the vehicle's data is required, including model, size, purpose, and target consumer group. Then, combining market demand and brand positioning, the vehicle's overall design style is determined. For example, a sports sedan targeted at younger consumers might adopt a dynamic, vibrant design.

[0101] For example, taking a luxury car for young consumers as an example, after determining the overall design style of the model as a combination of luxury and sportiness, the color matching tone is further determined. The color matching tone is the main color and auxiliary color of the first vehicle color matching model, such as main red and auxiliary silver.

[0102] Step 42: Calculate the contrast, saturation, and brightness relationships between components in the first vehicle color matching model based on a color harmony algorithm, and identify disharmonious factors in the first vehicle color matching model with reference to the design style and color matching tone; wherein the disharmonious factors are colors that are inconsistent with the design style and color matching tone.

[0103] Disharmonious factors: refers to the colors or color matching methods that are inconsistent with the design style and color matching tone in the first vehicle color matching model.

[0104] In the first vehicle color matching model of the luxury sedan described above, calculations and analysis using a color harmony algorithm revealed that the roof antenna's color had a significant contrast with the red color of the vehicle's primary body color, and its saturation was too high, making it appear too abrupt. This was inconsistent with the vehicle's overall design, which combined luxury and sportiness, and was therefore identified as a discordant factor.

[0105] Step 43: Remove the inharmonious factors to generate a color matching model of the vehicle to be filled.

[0106] In the embodiment of the present application, in the above-mentioned luxury car example, the antenna color is deleted, and the adjusted model is the vehicle color matching model to be filled.

[0107] Step 44 : Fill the vehicle color matching model to be filled according to the design style and color matching tone to generate a second vehicle color matching model.

[0108] In the embodiment of the present application, in the luxury car example, based on the design style combining luxury and sportiness and the color matching tone of red as the main color and silver as the auxiliary color, the color matching model of the vehicle to be filled is filled. Red and silver tones are selected to complement the antenna color.

[0109] Step 5: Process the second vehicle color matching model based on the preset environment model to obtain the vehicle exterior effects under different external environments.

[0110] Step 51: Construct a multi-environment simulation system; wherein the multi-environment simulation system includes multiple scenes and multiple weather conditions.

[0111] Multi-Environment Simulation System: This system integrates a variety of scenarios and weather conditions into a virtual environment, simulating vehicle performance in different external environments. This system allows for comprehensive evaluation of the visual effects of vehicle colors under different conditions, providing strong support for optimizing color schemes.

[0112] First, build a variety of scenes, such as city roads, highways, country roads, and mountain highways. Each scene includes detailed elements such as roads, buildings, and vegetation to simulate a realistic external environment. In addition to scene construction, set up a variety of weather conditions, such as sunny, cloudy, rainy, and snowy days. Each weather condition includes corresponding lighting, shadows, reflections, and other parameters to accurately simulate the visual effects under different weather conditions. To more accurately simulate the visual effects in different environments, users can fine-tune the relevant parameters of the scene and weather conditions. For example, users can adjust the intensity and direction of the lighting, the softness and depth of shadows, the intensity and color of reflections, and so on.

[0113] In this example, a color scheme is being designed for an SUV. First, a multi-environment simulation system is constructed, encompassing both urban and highway scenarios. Next, three weather conditions are set: sunny, cloudy, and rainy. Parameters such as lighting, shadows, and reflections are adjusted.

[0114] Step 52: Import the second vehicle color matching model into the multi-environment simulation system.

[0115] Before importing, the second vehicle color matching model needs to be converted into a format suitable for the simulation system. After data preparation is complete, the vehicle color matching model is imported into the multi-environment simulation system. After importing, the vehicle color matching model is verified.

[0116] Step 53: Process the second vehicle color matching model based on a preset rendering algorithm to determine the exterior effect of the vehicle.

[0117] Rendering algorithms are computational methods used to generate high-quality images. In vehicle color design, rendering algorithms are used to simulate the vehicle's exterior appearance in various environments. These algorithms can generate images of the vehicle's exterior under varying perspectives, lighting conditions, and weather effects, providing intuitive support for optimizing color schemes.

[0118] Step 531: Set rendering parameters; wherein the rendering parameters include lighting conditions, shadow effects, reflectivity, refractive index, and environment map.

[0119] Lighting conditions: Lighting conditions are one of the most important parameters in the rendering process. They determine the brightness and color of objects in the scene. You can set different lighting conditions based on your needs, such as natural light and artificial light.

[0120] Shadow effect: Shadow effect is one of the important factors to enhance the realism of the scene. By adjusting the softness and depth of the shadow, you can simulate a more realistic shadow effect.

[0121] Reflectivity: Reflectivity determines the intensity of light reflected from an object's surface. By adjusting the reflectivity parameter, you can simulate the reflection effects of different material surfaces.

[0122] Refractive Index: The refractive index is a physical quantity that describes the refraction of light on an object's surface. During the rendering process, you can adjust the refractive index parameter to simulate the visual effects of transparent or translucent objects.

[0123] Environment Map: Environment Map is a technology used to simulate the background of a scene. By adding an environment map, the system can provide richer background details and color performance for the scene.

[0124] In this example, when rendering an SUV, natural light was used as the lighting condition, and parameters such as shadow softness and depth were adjusted to enhance the scene's realism. The reflectivity and refractive index parameters of different material surfaces were also set to simulate realistic material effects. Finally, an environment map containing the sky and distant mountains was added to provide richer background details.

[0125] Step 532: Perform multi-angle rendering on the second vehicle color matching model to generate vehicle exterior effect images containing different perspectives.

[0126] Multi-angle rendering: Multi-angle rendering is a technique used to generate images of an object from different perspectives. In vehicle color design, multi-angle rendering can help users more comprehensively evaluate the performance of vehicle colors from different perspectives.

[0127] In this application example, when rendering an SUV, three perspectives were selected: front, side, and top. A high rendering resolution and appropriate anti-aliasing parameters were set to ensure high-quality images. After rendering, the resulting renderings included exterior renderings of the vehicle from different perspectives.

[0128] Step 533: Adjust the lighting and weather effects in the rendering environment according to the preset time period and weather conditions to generate vehicle exterior effect images under different time periods and weather conditions.

[0129] Time period and weather conditions: In vehicle color matching design, time period and weather conditions are one of the important factors affecting the vehicle's exterior effect.

[0130] Referring to step 532, in this embodiment of the present application, to simulate the exterior effects of an SUV model under different time periods and weather conditions, three time periods were set: morning, noon, and evening, and the corresponding lighting conditions and color rendering were adjusted. Two weather types, sunny and rainy, were also set, and corresponding effects such as shadows and reflections were added. By simulating the lighting and weather effects of different time periods and weather conditions, images of the vehicle's exterior effects were generated, encompassing a variety of scenarios.

[0131] Step 6: Determine the in-vehicle light source data based on the vehicle data; wherein the in-vehicle light source data includes the light source position, light source brightness, light source structure, and light source setting.

[0132] Interior lighting data: This data refers to the specific information about the vehicle's interior lighting, including but not limited to its location, brightness, structure, and configuration. This data significantly impacts the vehicle's interior color scheme, as varying light source data can lead to variations in color performance. Accurately acquiring and configuring this data ensures the stability and aesthetics of the vehicle's interior color scheme under varying lighting conditions.

[0133] Light source position: Light source position refers to the location of interior light sources within the vehicle. Different light source positions can significantly affect the lighting distribution and color performance within the vehicle. For example, different placements of the dome light and reading light can result in different lighting intensity and color saturation in different areas of the vehicle.

[0134] Light source brightness: Light source brightness refers to the intensity of the light emitted by a light source. In vehicle interior color matching, light source brightness directly affects the brightness and contrast of the colors. Properly setting light source brightness ensures the stability and readability of vehicle interior colors under varying lighting conditions.

[0135] Light source brightness: Light source structure refers to the physical form and components of the light source. Light sources with different functions emit different amounts of light, for example, reading lights and ambient lights.

[0136] Step 7: Process the second vehicle color matching model based on the light source data and in combination with a preset color physics algorithm to determine the vehicle interior effects under different light source settings.

[0137] Step 71: Establish a light source simulation environment; wherein the light source simulation environment is configured according to the light source data.

[0138] Lighting simulation environment: A virtual environment used to simulate the lighting conditions of a vehicle's interior under different lighting settings. This environment is configured based on light source data (including light source position, brightness, structure, etc.) to reflect the actual lighting conditions inside the vehicle.

[0139] Step 72: Import the second vehicle color matching model into the light source simulation environment.

[0140] Same as step 52, which will not be described here.

[0141] Step 73: Apply a color physics algorithm to perform illumination simulation on the second vehicle color matching model based on the spectral characteristics of the light source, the reflection and absorption characteristics of the material, and the visual characteristics of the human eye.

[0142] Spectral characteristics: The wavelength distribution of light emitted by a light source determines the color of the light.

[0143] Material reflection and absorption characteristics: The ability of the surface materials of various components inside the vehicle to reflect and absorb light affects the color performance.

[0144] Human visual characteristics: the human eye's ability to perceive color, including color brightness, saturation, and contrast.

[0145] Step 74: Calculate and generate color rendering effects of various components inside the vehicle under different light source settings; wherein the color rendering effects include color changes, shadow distribution, and highlight effects.

[0146] Step 75: Output the color rendering effect as a vehicle interior effect image set to generate a vehicle interior color matching effect.

[0147] In an embodiment of the present application, taking a family sedan as an example, a light source simulation environment for the vehicle's interior is first established based on light source data. The light source data includes parameters such as the position, brightness, and color of light sources such as roof lights, reading lights, and ambient lights. Next, a second vehicle color matching model is imported into the light source simulation environment, and the model is rendered to preliminarily demonstrate the color matching effect. Then, a color physics algorithm is applied to perform lighting simulation on the model. During the simulation process, the spectral characteristics of the light source, the reflection and absorption characteristics of the materials of various components inside the vehicle, and the visual characteristics of the human eye are taken into account. By calculating the reflection, refraction, and absorption of light on the surface of the component, the color performance of the component surface is adjusted to reflect the actual lighting effect. Next, color rendering effect images of various components inside the vehicle under different light source settings are calculated and generated. These images show the color changes, shadow distribution, and highlight effects of the components under different lighting conditions. Finally, the generated color rendering effect images are organized into a set of vehicle interior effect images and output.

[0148] After the vehicle interior and exterior effects are output, the vehicle interior and exterior effects can also be evaluated. The evaluation methods include:

[0149] B1. Establish color evaluation indicators; color evaluation indicators include color harmony, visual comfort, spatial perception, and material texture.

[0150] Color Evaluation Metrics: This is a standardized system for quantitatively evaluating vehicle color matching, encompassing multiple dimensions such as color harmony, visual comfort, spatial perception, and material texture. These metrics are designed to objectively reflect the overall performance of a color scheme in terms of aesthetics, ergonomics, and material application.

[0151] Color harmony: Evaluate whether the color matching inside and outside the vehicle is visually harmonious and unified, and whether it follows the basic principles of color matching, such as whether the use of contrasting colors, adjacent colors, complementary colors, etc. is appropriate.

[0152] Visual comfort: Inspect whether the color scheme is likely to cause visual fatigue and whether it can maintain visual pleasure after long-term viewing.

[0153] Spatial Perception: Evaluates the impact of color matching on the sense of space inside the vehicle. For example, light colors may make the space appear more spacious, while dark colors may create a sense of compactness.

[0154] Material texture: Analyze how the color scheme combines with the texture of the vehicle's interior and exterior materials to highlight the characteristics of the materials, such as the coolness of metal and the luxury of leather.

[0155] Based on the principles of color science and vehicle design experience, specific standards and scoring criteria for color evaluation indicators are formulated. The rationality and practicality of the indicator system are verified and adjusted through expert review and user surveys.

[0156] B2. Process the vehicle interior and exterior images based on a preset image analysis algorithm to extract color feature parameters; wherein the color feature parameters include at least hue, brightness, and saturation.

[0157] Image analysis algorithm: It is an algorithm that can automatically analyze the color information in an image and accurately extract color characteristic parameters such as hue, brightness, and saturation.

[0158] B3. Calculate the color evaluation scores of the vehicle interior and exterior based on the color evaluation index.

[0159] Color evaluation score: It is a quantitative score derived from color evaluation indicators and color characteristic parameters through certain calculation rules, and is used to intuitively reflect the effect of the vehicle color scheme.

[0160] Color evaluation scores include color harmony score, visual comfort score, spatial perception score and material texture performance score.

[0161] Color Harmony Score: This score is based on factors such as the uniformity of hue distribution and the appropriateness of color contrast. For example, the entropy of the hue distribution can be calculated to assess uniformity, while the number and intensity of contrasting colors can be used to assess the appropriateness of contrast. The final score is calculated through a weighted summation, with weights determined based on expert opinion and user research.

[0162] Visual Comfort Score: This factor takes into account factors such as color softness (e.g., by calculating how close a color is to a neutral color) and brightness contrast (e.g., by calculating the average brightness difference within the image). The final score is also calculated using a weighted summation method.

[0163] Spatial Perception Score: This score is calculated by analyzing the effect of color on the sense of depth and spaciousness. For example, bright colors generally increase the sense of space, while dark tones may decrease it. The specific score is calculated based on the degree of match between color characteristic parameters and the spatial perception model.

[0164] Material Texture Performance Score: This score is determined by comparing the degree of match between color and material texture and gloss. A machine learning model can be used to learn the relationship between color and texture by training on a large amount of sample data, thereby generating a score.

[0165] The above is an embodiment of the method proposed in this application. Based on the same inventive concept, this application embodiment also provides a color matching engineering efficiency improvement device based on a large model, the structure of which is as follows: Figure 2 shown.

[0166] Figure 2 This is a schematic diagram of a color matching engineering efficiency improvement device based on a large model provided in the embodiment of this application. Figure 2 As shown, the equipment includes:

[0167] The data acquisition module is used to collect color matching data; wherein the color matching data includes vehicle color matching data and color matching data. The vehicle color matching data includes vehicle exterior color matching data and vehicle interior color matching data;

[0168] A color suggestion generation module is used to import vehicle data of the vehicle to be color matched into the trained color matching model to obtain color matching suggestions; wherein the vehicle data includes at least one of the following data: vehicle model, promotion, structure and configuration;

[0169] A first vehicle model generation module, configured to process a vehicle model of a vehicle to be color matched based on the color matching suggestion to generate a first vehicle color matching model equipped with the color matching suggestion;

[0170] a second vehicle model generation module, configured to adjust the contrast, saturation, and brightness of the colors in the first vehicle color matching model based on the vehicle data and a preset color harmony algorithm to generate a second vehicle color matching model;

[0171] A vehicle exterior color module, configured to process a second vehicle color matching model based on a preset environment model to obtain vehicle exterior effects under different external environments;

[0172] A light source data determination module is used to determine the in-vehicle light source data based on the vehicle data; wherein the in-vehicle light source data includes the light source position, light source brightness, light source structure and light source setting;

[0173] The vehicle interior color module is used to process the second vehicle color matching model based on the light source data and in combination with a preset color physics algorithm to determine the vehicle interior effect under different light source settings.

[0174] The various embodiments in this application are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other. Each embodiment focuses on the differences between the other embodiments. In particular, the IoT device and media embodiments are described briefly because they are generally similar to the method embodiments. For relevant portions, refer to the description of the method embodiments.

[0175] The devices and methods provided in the embodiments of the present application correspond one to one, and therefore, the devices also have similar beneficial technical effects as the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the system and the medium will not be repeated here.

[0176] Those skilled in the art will appreciate that the embodiments of the present application may provide methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0177] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0178] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0179] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0180] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0181] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0182] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0183] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0184] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included within the scope of the claims of the present application.

Claims

1. A color matching engineering efficiency improvement method based on a large model, characterized in that: The method comprises: Collecting color matching data; wherein the color matching data includes vehicle color matching data and color matching data, and the vehicle color matching data includes vehicle exterior color matching data and vehicle interior color matching data; Importing vehicle data of a vehicle to be color matched into the trained color matching model to obtain color matching suggestions; wherein the vehicle data includes at least one of the following data: vehicle model, promotion, structure, and configuration; Processing the vehicle model of the vehicle to be color-matched based on the color matching suggestion to generate a first vehicle color matching model equipped with the color matching suggestion; Adjusting and filling the contrast, saturation, and brightness of the colors in the first vehicle color matching model based on the vehicle data and a preset color harmony algorithm to generate a second vehicle color matching model; processing the second vehicle color matching model based on a preset environment model to obtain vehicle exterior effects under different external environments; Determining in-vehicle light source data based on the vehicle data; wherein the in-vehicle light source data includes light source position, light source brightness, light source structure, and light source setting; The second vehicle color matching model is processed based on the light source data and in combination with a preset color physics algorithm to determine the vehicle interior effect under different light source settings.

2. A color matching engineering efficiency improvement method based on a large model according to claim 1, characterized in that: Before importing the vehicle data of the vehicle to be color matched into the trained color matching model to obtain color matching suggestions, the method further includes: Constructing a color matching model based on a preset convolutional neural network and a preset long short-term memory network; wherein the convolutional neural network is used to identify image features in the color matching data, and the long short-term memory network is used to process time series information in the color matching data; Cleaning and arranging the color matching data, removing outliers and noise data, to generate sample data; wherein the sample data is data in a unified format, and the sample data includes a training set, a test set, and a validation set; Training and validating the color matching model based on the sample data, and evaluating the generalization ability of the color matching model based on a preset cross-validation strategy; Optimizing the parameters of the color matching model based on a preset back propagation algorithm; When the performance of the color matching model on the validation set reaches a preset accuracy threshold, a trained color matching model is generated.

3. The color matching engineering efficiency improvement method based on a large model according to claim 1 is characterized in that: Importing the vehicle data of the vehicle to be color matched into the trained color matching model to obtain color matching suggestions, specifically including: Preprocessing the vehicle data of the vehicle to be color matched to generate input data; wherein the input data is in a format that can be recognized by the trained color matching model; Importing the input data into the trained color matching model; The color matching model outputs a color matching suggestion corresponding to the input data based on feature information of the input data.

4. The color matching engineering efficiency improvement method based on a large model according to claim 1 is characterized in that: Processing the vehicle model of the vehicle to be color-matched based on the color matching suggestion to generate a first vehicle color matching model equipped with the color matching suggestion specifically includes: Determining the color matching suggestion; wherein the color matching suggestion includes color suggestions and material suggestions for multiple components; Building a three-dimensional model of the vehicle to be color-matched based on preset three-dimensional modeling software; wherein the three-dimensional model includes a plurality of components; The color matching suggestion is matched with the three-dimensional model based on the component to generate a first vehicle color matching model; wherein, some components in the first vehicle color matching model may not be colored.

5. The color matching engineering efficiency improvement method based on a large model according to claim 4 is characterized in that: Adjusting the contrast, saturation, and brightness of the colors in the first vehicle color matching model based on the vehicle data and a preset color harmony algorithm to generate a second vehicle color matching model specifically includes: Processing the first vehicle color matching model and vehicle data to determine the design style and color matching tone of the vehicle to be color matched; Calculating, based on the color harmony algorithm, the contrast, saturation, and brightness relationships between components in the first vehicle color matching model, and identifying discordant factors in the first vehicle color matching model with reference to the design style and color matching tone; wherein the discordant factors are colors that are inconsistent with the design style and color matching tone; Removing the inharmonious factors to generate a color matching model of a vehicle to be filled; The vehicle color matching model to be filled is filled according to the design style and color matching tone to generate a second vehicle color matching model.

6. The color matching engineering efficiency improvement method based on a large model according to claim 1 is characterized in that: Processing the second vehicle color matching model based on a preset environment model to obtain vehicle exterior effects under different external environments specifically includes: Construct a multi-environment simulation system; wherein the multi-environment simulation system includes multiple scenes and multiple weather conditions; importing the second vehicle color matching model into the multi-environment simulation system; The second vehicle color matching model is processed based on a preset rendering algorithm to determine the vehicle exterior effect.

7. The color matching engineering efficiency improvement method based on a large model according to claim 6 is characterized in that Processing the second vehicle color matching model based on a preset rendering algorithm to generate a vehicle exterior effect image set specifically includes: Setting rendering parameters; wherein the rendering parameters include lighting conditions, shadow effects, reflectivity, refractive index and environment map; Performing multi-angle rendering on the second vehicle color matching model to generate vehicle exterior effect images including different perspectives; According to the preset time period and weather conditions, the lighting and weather effects in the rendering environment are adjusted to generate vehicle exterior effect images under different time periods and weather conditions.

8. The color matching engineering efficiency improvement method based on a large model according to claim 1 is characterized in that: Processing the second vehicle color matching model based on the light source data and in combination with a preset color physics algorithm to determine the vehicle interior effects under different light source settings, specifically including: Establishing a light source simulation environment; wherein the light source simulation environment is configured according to the light source data; Importing the second vehicle color matching model into the light source simulation environment; Applying a color physics algorithm to perform lighting simulation on the second vehicle color matching model based on the spectral characteristics of the light source, the reflection and absorption characteristics of the material, and the visual characteristics of the human eye; Calculate and generate the color rendering effects of various components inside the vehicle under different light source settings; wherein the color rendering effects include color changes, shadow distribution and highlight effects; The color rendering effect is output as a vehicle interior effect image set to generate a vehicle interior color matching effect.

9. The color matching engineering efficiency improvement method based on a large model according to claim 8 is characterized in that: The method further comprises: Establishing color evaluation indicators; wherein the color evaluation indicators include color harmony, visual comfort, spatial perception, and material texture performance; Processing the vehicle interior and exterior effects based on a preset image analysis algorithm to extract color feature parameters; wherein the color feature parameters include at least hue, brightness, and saturation; Color evaluation scores of the vehicle interior effect and the vehicle exterior effect are calculated according to the color evaluation index.

10. A color matching engineering efficiency improvement device based on a large model, characterized in that: The device comprises: A data acquisition module is used to acquire color matching data; wherein the color matching data includes vehicle color matching data and color matching data; the vehicle color matching data includes vehicle exterior color matching data and vehicle interior color matching data; A color suggestion generation module is used to import vehicle data of a vehicle to be color matched into a trained color matching model to obtain color matching suggestions; wherein the vehicle data includes at least one of the following data: vehicle model, promotion, structure, and configuration; a first vehicle model generating module, configured to process the vehicle model of the vehicle to be color-matched based on the color matching suggestion to generate a first vehicle color matching model equipped with the color matching suggestion; a second vehicle model generating module, configured to adjust the contrast, saturation, and brightness of the colors in the first vehicle color matching model based on the vehicle data and a preset color harmony algorithm to generate a second vehicle color matching model; a vehicle exterior color module, configured to process the second vehicle color matching model based on a preset environment model to obtain vehicle exterior effects under different external environments; A light source data determination module, configured to determine in-vehicle light source data based on the vehicle data; wherein the in-vehicle light source data includes light source position, light source brightness, light source structure, and light source settings; A vehicle interior color module is used to process the second vehicle color matching model based on the light source data and in combination with a preset color physics algorithm to determine the vehicle interior effect under different light source settings.

Images