In-vehicle lamp strip design optimization method and device, vehicle and storage medium

Abstract

The invention provides an in-vehicle lamp strip design optimization method and device, a vehicle and a storage medium. The in-vehicle lamp strip design optimization method comprises the steps that in-vehicle space three-dimensional structure data and in-vehicle illumination data of a target vehicle are acquired; wherein the in-vehicle illumination data comprises illumination environment data when an in-vehicle lamp strip of the target vehicle is turned on; generating an in-vehicle lamp strip design optimization scheme of the target vehicle based on the in-vehicle space three-dimensional structure data and the in-vehicle illumination data; wherein the in-vehicle lamp strip design optimization scheme comprises an in-vehicle lamp strip installation optimization scheme and in-vehicle lamp strip control optimization parameters. The display effect of the light in the vehicle can be improved.

Description

Technical Field

[0001] This application relates to the technical field of vehicles, and particularly to a method, device, vehicle, and storage medium for optimizing the design of in-vehicle light strips. Background Art

[0002] Currently, the interior design of vehicles is constantly pursuing personalization and immersive experiences, and creating an in-vehicle atmosphere has become a key factor in enhancing the comfort and quality of driving and riding. Traditional in-vehicle lighting systems are mostly limited to basic lighting functions and are difficult to meet the growing aesthetic and emotional interaction needs of vehicle owners. With the rapid development of technology, in-vehicle light strips have emerged as a highly creative and potential interior lighting solution.

[0003] Traditional in-vehicle light strip design solutions often adopt a general design and do not conduct personalized design for vehicles of different models. Situations such as light occlusion, irradiation dead angles, or visual disharmony often occur, and the display effect of in-vehicle lights is poor. Summary of the Invention

[0004] This application provides a method, device, vehicle, and storage medium for optimizing the design of in-vehicle light strips to improve the display effect of in-vehicle lights.

[0005] According to the first aspect of the embodiments of this application, a method for optimizing the design of in-vehicle light strips is provided, including:

[0006] Obtain the three-dimensional structure data of the in-vehicle space and the in-vehicle lighting data of the target vehicle; wherein, the in-vehicle lighting data includes the lighting environment data when the in-vehicle light strips of the target vehicle are turned on.

[0007] Based on the three-dimensional structure data of the in-vehicle space and the in-vehicle lighting data, generate an optimization plan for the design of in-vehicle light strips of the target vehicle; wherein, the optimization plan for the design of in-vehicle light strips includes an optimization plan for the installation of in-vehicle light strips and optimization control parameters for in-vehicle light strips.

[0008] Optionally, the generating an optimization plan for the design of in-vehicle light strips of the target vehicle based on the three-dimensional structure data of the in-vehicle space and the in-vehicle lighting data includes:

[0009] Input the three-dimensional structure data of the in-vehicle space and the in-vehicle lighting data into an in-vehicle light strip design optimization model to obtain the probability corresponding to each candidate in-vehicle light strip design plan; wherein, the candidate in-vehicle light strip design plan includes a candidate in-vehicle light strip installation plan and candidate in-vehicle light strip control parameters.

[0010] Based on the probability corresponding to each candidate in-vehicle light strip design plan, determine the optimization plan for the design of in-vehicle light strips of the target vehicle from the various candidate in-vehicle light strip design plans.

[0011] Optionally, the in-vehicle lighting data includes in-vehicle lighting time series data;

[0012] Inputting the three-dimensional structure data of the in-vehicle space and the in-vehicle lighting data into the in-vehicle light strip design optimization model to obtain the probability corresponding to each candidate in-vehicle light strip design scheme includes:

[0013] Through the in-vehicle light strip design optimization model, based on the three-dimensional structure data of the in-vehicle space, identifying the layout rules of key interior components;

[0014] Through the in-vehicle light strip design optimization model, based on the in-vehicle lighting time series data, determining the change trend of the in-vehicle lighting data;

[0015] Through the in-vehicle light strip design optimization model, based on the layout rules of the key interior components and the change trend of the in-vehicle lighting data, determining the probability corresponding to each candidate in-vehicle light strip design scheme.

[0016] Optionally, the in-vehicle light strip design optimization model is trained based on sample data;

[0017] Wherein, the sample data includes the sample three-dimensional structure data of the in-vehicle space of vehicles of each sample vehicle model, the sample in-vehicle lighting data of vehicles of each sample vehicle model, each preset in-vehicle light strip design scheme, and the user feedback data corresponding to each of the preset in-vehicle light strip design schemes.

[0018] Optionally, the sample three-dimensional structure data of the in-vehicle space of vehicles of each sample vehicle model includes the sample three-dimensional structure data of the in-vehicle space obtained when each user group drives the vehicles of each sample vehicle model at various time periods and in various weather conditions;

[0019] The sample in-vehicle lighting data of vehicles of each sample vehicle model includes the sample in-vehicle lighting data obtained when each user group drives the vehicles of each sample vehicle model at various time periods and in various weather conditions.

[0020] Optionally, after generating the in-vehicle light strip design optimization scheme for the target vehicle based on the three-dimensional structure data of the in-vehicle space and the in-vehicle lighting data, the method further includes:

[0021] Based on the in-vehicle light strip design optimization scheme of the target vehicle, rendering the animation of the light strip installation effect in each driving scenario.

[0022] Optionally, after generating the in-vehicle light strip design optimization scheme for the target vehicle based on the three-dimensional structure data of the in-vehicle space and the in-vehicle lighting data, the method further includes:

[0023] Based on the optimized design solution of the in-vehicle light strip of the target vehicle, determine the target in-vehicle light strip design solution of the target vehicle; wherein, the target in-vehicle light strip design solution includes a target in-vehicle light strip installation solution and target in-vehicle light strip control parameters;

[0024] Based on the target in-vehicle light strip installation solution, generate an installation guide; wherein, the installation guide includes each key step in the process of installing the in-vehicle light strip.

[0025] According to a second aspect of the embodiments of the present application, there is provided an electronic device, including a memory and a processor;

[0026] The memory is connected to the processor and is used for storing programs;

[0027] The processor is used for implementing the in-vehicle light strip design optimization method as described in the first aspect by running the programs in the memory.

[0028] According to a third aspect of the embodiments of the present application, there is provided a vehicle, including the electronic device as described in the second aspect.

[0029] According to a fourth aspect of the embodiments of the present application, there is provided a storage medium, on which a computer program is stored, and when the computer program is run by a processor, the in-vehicle light strip design optimization method as described in the first aspect is implemented.

[0030] In the present application, when the in-vehicle light strip of the target vehicle is already turned on, based on the three-dimensional structure data of the in-vehicle space and the in-vehicle lighting data, an optimized design solution of the in-vehicle light strip of the target vehicle is generated. Considering the differences in the internal structures of vehicles of different models, the three-dimensional structure data of the in-vehicle space of the target vehicle can reflect the in-vehicle space layout of the target vehicle. Furthermore, an optimized in-vehicle light strip installation solution and optimized in-vehicle light strip control parameters are obtained for the in-vehicle space layout of the target vehicle. The in-vehicle lighting data includes the lighting environment data when the in-vehicle light strip of the target vehicle is already turned on. According to the lighting environment data when the in-vehicle light strip of the target vehicle is already turned on, it can be known which areas in the vehicle have light occlusion, irradiation dead angles, or visual disharmony and other conditions. Furthermore, the existing in-vehicle light strip installation solution and in-vehicle light strip control parameters are optimized, and the optimized in-vehicle light strip installation solution and optimized in-vehicle light strip control parameters of the target vehicle are obtained more accurately to reduce the occurrence of conditions such as light occlusion and irradiation dead angles, improve the visual harmony of the in-vehicle lighting, and improve the display effect of the in-vehicle lighting. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only the embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on the provided drawings.

[0032] Figure 1 It is a schematic flowchart of a method for optimizing the design of in-vehicle light strips provided in an embodiment of the present application;

[0033] Figure 2 It is a schematic flowchart of step 102 provided in an embodiment of the present application;

[0034] Figure 3 It is a schematic flowchart of step 201 provided in an embodiment of the present application;

[0035] Figure 4 It is a schematic flowchart of a method for optimizing the design of in-vehicle light strips provided in an embodiment of the present application;

[0036] Figure 5 It is a schematic structural diagram of a device for optimizing the design of in-vehicle light strips provided in an embodiment of the present application;

[0037] Figure 6 It is a schematic structural diagram of an electronic device provided in an embodiment of the present application. Detailed implementation manners

[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0039] Exemplary implementation environment

[0040] The method for optimizing the design of in-vehicle light strips according to the embodiments of the present application can be executed by an electronic device such as a terminal device or a server. The terminal device can be a user device, a mobile device, a computing device, a wearable device, a vehicle-mounted device, etc. The server can be an independent physical server, a server cluster composed of multiple physical servers, or a cloud server capable of performing cloud computing. This method can be implemented by a processor calling computer-readable program instructions stored in a memory.

[0041] Exemplary method

[0042] Please refer to Figure 1, in an exemplary embodiment, an optimization method for the in-vehicle light strip design is provided. As Figure 1 shown, the process of the optimization method for the in-vehicle light strip design mainly includes:

[0043] Step 101, obtaining the three-dimensional structure data of the in-vehicle space and the in-vehicle lighting data of the target vehicle.

[0044] Among them, the in-vehicle lighting data includes the lighting environment data when the in-vehicle light strip of the target vehicle is turned on.

[0045] The optimization method for the in-vehicle light strip design provided in this application can be applied to the scenario of optimizing the existing in-vehicle light strip installation scheme and the in-vehicle light strip control parameters when the in-vehicle light strip of the target vehicle is turned on.

[0046] In an exemplary embodiment, by installing a high-definition depth camera in the target vehicle, it is ensured that the three-dimensional structure data of the in-vehicle space can be captured omnidirectionally and with high precision, with a resolution of at least 1280×720 and a frame rate of more than 30fps, so as to clearly obtain key part information such as seat contours, console details, and window positions. These key part information is used to better understand the in-vehicle space layout of the target vehicle, and then obtain the in-vehicle light strip installation optimization scheme and the in-vehicle light strip control optimization parameters for the in-vehicle space layout of the target vehicle, and can perform personalized optimization of the in-vehicle light strip design for vehicles of different models.

[0047] In an exemplary embodiment, by equipping the target vehicle with high-sensitivity light sensors, distributed at multiple positions such as the front and rear of the in-vehicle top, beside the windows, and above the instrument panel, the in-vehicle lighting data of different regions in the vehicle can be accurately monitored in real time. For example, the in-vehicle lighting data can include the light intensity (range 0-65535lux), color temperature (in the range of 2700K-6500K), and light direction, providing sufficient data for subsequent lighting environment analysis. The in-vehicle lighting data is a kind of lighting feedback data for the existing in-vehicle light strip installation scheme and the in-vehicle light strip control parameters. According to the lighting environment data when the in-vehicle light strip of the target vehicle is turned on, it can be known which regions in the vehicle have light occlusion, irradiation dead angles, or visual incoordination and other conditions, and then optimize the existing in-vehicle light strip installation scheme and the in-vehicle light strip control parameters, and more accurately obtain the in-vehicle light strip installation optimization scheme and the in-vehicle light strip control optimization parameters for the target vehicle to reduce the occurrence of conditions such as light occlusion and irradiation dead angles, improve the visual coordination of the in-vehicle lights, and improve the display effect of the in-vehicle lights.

[0048] In an exemplary embodiment, use in-vehicle devices or external high-performance data acquisition devices with a storage capacity of at least 1TB to ensure that the camera and sensor data can be continuously and stably recorded, and be properly connected to the vehicle power system to ensure uninterrupted operation during vehicle startup.

[0049] In an exemplary embodiment, the trigger condition for step 101 may be detecting that the vehicle owner has started the in-vehicle light strip optimization program. When the vehicle owner decides to optimize the installation of the light strip, the vehicle owner can start the in-vehicle light strip optimization program. When it is detected that the vehicle owner has started the in-vehicle light strip optimization program, the in-vehicle depth camera and the light sensor are immediately started and continuously collect data for a preset duration. For example, the preset duration can be 5-10 minutes to ensure that data under different working conditions such as the vehicle being stationary, starting, and driving to a stable speed are covered, and the data is quickly processed by the in-vehicle device.

[0050] Step 102, generate an optimized design plan for the in-vehicle light strip of the target vehicle based on the three-dimensional structure data of the in-vehicle space and the in-vehicle light data.

[0051] Among them, the optimized design plan for the in-vehicle light strip includes an optimized installation plan for the in-vehicle light strip and optimized control parameters for the in-vehicle light strip.

[0052] In an exemplary embodiment, the optimized installation plan for the in-vehicle light strip may include optimized installation position parameters for the in-vehicle light strip. For example, the optimized installation position parameters for the in-vehicle light strip may include three-dimensional coordinates of each light in the in-vehicle light strip, light strip length, light strip angle, etc. The optimized installation position parameters for the in-vehicle light strip may also include the precise installation path of the light strip in the vehicle (presented in the form of a sequence of three-dimensional coordinate points with a precision of millimeters). This application does not limit this.

[0053] In an exemplary embodiment, the optimized control parameters for the in-vehicle light strip may include parameters such as the brightness and color mode of each light in the in-vehicle light strip, and may also include other control parameters of the in-vehicle light strip. This application does not limit this.

[0054] Currently, the installation positions of traditional in-vehicle light strips often adopt a general design without fully considering the unique internal structural differences of different vehicle models. From small hatchbacks to large SUVs (Sport Utility Vehicles), the in-vehicle seat layouts, the shapes of the center consoles, the arcs of the doors, and the interior headroom heights vary greatly, making it impossible for conventional in-vehicle light strip installation solutions to take all these into account. As a result, situations such as light blockage, irradiation dead angles, or visual disharmony often occur. In this application, when the in-vehicle light strip of the target vehicle is turned on, based on the three-dimensional structural data of the in-vehicle space and the in-vehicle lighting data, an optimized design solution for the in-vehicle light strip of the target vehicle is generated. Considering the internal structural differences of different vehicle models, the three-dimensional structural data of the in-vehicle space of the target vehicle can reflect the in-vehicle space layout of the target vehicle. Furthermore, an optimized installation solution for the in-vehicle light strip and optimized control parameters for the in-vehicle light strip are obtained for the in-vehicle space layout of the target vehicle. The in-vehicle lighting data includes the lighting environment data when the in-vehicle light strip of the target vehicle is turned on. According to the lighting environment data when the in-vehicle light strip of the target vehicle is turned on, it can be known which areas in the vehicle have situations such as light blockage, irradiation dead angles, or visual disharmony. Then, the existing in-vehicle light strip installation solution and the in-vehicle light strip control parameters are optimized to more accurately obtain the optimized installation solution for the in-vehicle light strip and the optimized control parameters for the in-vehicle light strip of the target vehicle, so as to reduce the occurrence of situations such as light blockage and irradiation dead angles, improve the visual harmony of the in-vehicle lighting, and enhance the display effect of the in-vehicle lighting.

[0055] In some embodiments, such as Figure 2 shown, step 102 includes:

[0056] Step 201, input the three-dimensional structural data of the in-vehicle space and the in-vehicle lighting data into the in-vehicle light strip design optimization model to obtain the probability corresponding to each candidate in-vehicle light strip design solution.

[0057] Among them, the candidate in-vehicle light strip design solutions include candidate in-vehicle light strip installation solutions and candidate in-vehicle light strip control parameters.

[0058] In some embodiments, the in-vehicle lighting data includes in-vehicle lighting time series data. The in-vehicle lighting time series data may refer to the in-vehicle lighting data corresponding to multiple time points within a preset time period. For example, the in-vehicle lighting time series data may refer to the in-vehicle lighting data corresponding to n time points t1, t2,..., tn, where n is an integer greater than 1.

[0059] In some other embodiments, the in-vehicle lighting data may also include the in-vehicle lighting data corresponding to a single time point. It is better that the in-vehicle lighting data includes the in-vehicle lighting time series data than that the in-vehicle lighting data includes the in-vehicle lighting data corresponding to a single time point. Because the in-vehicle light strip is generally used for lighting during the driving of the vehicle, and during the driving of the vehicle, due to the change of the external environment, the in-vehicle lighting data measured by the same in-vehicle light strip design scheme will also change. Using the in-vehicle lighting time series data is more in line with the actual vehicle driving scenario. Therefore, optimizing the existing in-vehicle light strip installation scheme and in-vehicle light strip control parameters according to the in-vehicle lighting time series data can more accurately obtain the optimized in-vehicle light strip installation scheme and in-vehicle light strip control optimization parameters of the target vehicle, so as to reduce the occurrence of situations such as light occlusion and irradiation dead angles, improve the visual coordination of the in-vehicle lighting, and improve the display effect of the in-vehicle lighting.

[0060] In an exemplary embodiment, before inputting the in-vehicle space three-dimensional structure data and the in-vehicle lighting data into the in-vehicle light strip design optimization model, the in-vehicle space three-dimensional structure data and the in-vehicle lighting data can be preprocessed first. For example, denoising and filtering the in-vehicle space three-dimensional structure data (for example, using median filtering to remove salt-and-pepper noise and Gaussian filtering to smooth the data), and coordinate normalization (scaling the in-vehicle space coordinates to the range of 0-1 for convenient model calculation); removing outliers from the in-vehicle lighting data (for example, removing data outside 3 times the standard deviation from the mean according to statistical methods) and standardizing the data (for example, making its mean 0 and standard deviation 1).

[0061] In some embodiments, the in-vehicle lighting data includes in-vehicle lighting time series data.

[0062] As Figure 3 shown, step 201 includes:

[0063] Step 301, through the in-vehicle light strip design optimization model, based on the in-vehicle space three-dimensional structure data, identify the layout rules of key interior components.

[0064] In an exemplary embodiment, the in-vehicle light strip design optimization model includes a convolutional neural network (Convolutional Neural Networks, CNN). Input the in-vehicle space three-dimensional structure data into the convolutional neural network, and extract the spatial features of the in-vehicle space three-dimensional structure data through the convolutional neural network to identify the layout rules of key interior components. For example, the convolutional layer of the convolutional neural network can be set with 3-5 layers, the first layer has a convolutional kernel of 3×3×3, and the subsequent layers are adjusted according to the data features, and the stride is 1 for all layers.

[0065] In an exemplary embodiment, the key interior components may include key interior components in the vehicle such as seats, center consoles, doors, windows, roofs, etc. The key interior components may also include other interior components, and the present application does not limit this.

[0066] Step 302: Through the in-vehicle light strip design optimization model, based on the in-vehicle light illumination time series data, determine the change trend of the in-vehicle light illumination data.

[0067] In an exemplary embodiment, the in-vehicle light strip design optimization model includes a Recurrent Neural Network (RNN). For example, the recurrent neural network may be composed of Long Short-Term Memory (LSTM) units. Input the in-vehicle light illumination time series data into the recurrent neural network, and capture the change trend of the in-vehicle light illumination data through the memory units in the recurrent neural network to prevent the problem of gradient disappearance or explosion.

[0068] Step 303: Through the in-vehicle light strip design optimization model, based on the layout rules of the key interior components and the change trend of the in-vehicle light illumination data, determine the probabilities corresponding to each candidate in-vehicle light strip design scheme.

[0069] In an exemplary embodiment, the in-vehicle light strip design optimization model includes a convolutional neural network, a recurrent neural network, and a fully connected layer. Input the layout rules of the key interior components output by the convolutional neural network and the change trend of the in-vehicle light illumination data output by the recurrent neural network into the fully connected layer to obtain the probabilities corresponding to each candidate in-vehicle light strip design scheme.

[0070] In an exemplary embodiment, the number of neurons in the fully connected layer is flexibly set according to the light strip installation position parameters (such as X, Y, Z coordinates, light strip length, angle, etc., about 10 - 15 key parameters) and control parameters such as brightness and color mode (3 - 5 parameters). Output the probabilities corresponding to each candidate in-vehicle light strip design scheme through softmax (normalized exponential function) to achieve multi-classification prediction.

[0071] Through the in-vehicle light strip design optimization model, based on the three-dimensional structure data of the in-vehicle space, identify the layout rules of key interior components, based on the in-vehicle light time series data, determine the change trend of in-vehicle light data, and based on the layout rules of key interior components and the change trend of in-vehicle light data, determine the probability corresponding to each candidate in-vehicle light strip design scheme. The in-vehicle light strip design optimization model can fully learn the layout rules of key interior components and the change trend of in-vehicle light data, and then accurately obtain the optimized installation scheme of the in-vehicle light strip and the optimized control parameters of the in-vehicle light strip for the target vehicle, which can reduce the occurrence of situations such as light occlusion and irradiation dead angles, improve the visual coordination of in-vehicle lights, and not only consider the in-vehicle light data at one time point, but consider the change trend of in-vehicle light data over a period of time, which is more in line with the actual vehicle driving scenario and can better improve the display effect of in-vehicle lights. For example, it can accurately fit the curved door contour to ensure uniform light diffusion. It realizes the leap from batch rough installation to personalized customization, meeting the unique aesthetic and functional needs of each car owner.

[0072] In an exemplary embodiment, the in-vehicle light strip design optimization model can be built based on the TensorFlow (a machine learning framework) or PyTorch (a deep learning framework) framework. The training process of the in-vehicle light strip design optimization model is as follows: Using the Adam (Adaptive Moment Estimation) optimizer, the initial learning rate is set to 0.001, combined with a learning rate decay strategy (decaying by 0.1 times every 5 epochs (a complete traversal of the entire training set)) to prevent the model from overfitting; the loss function adopts a mixed form of cross-entropy and mean squared error, considering both the classification accuracy and the numerical deviation between the predicted parameters and the true values, prompting the model to converge accurately. Training is performed using the mini-batch gradient descent method, with the sample size per batch set to 32, and the training set is iteratively trained for multiple rounds (expected 50 - 100 rounds). Regularly evaluate the model performance on the test set, monitor indicators such as accuracy, recall rate, and mean squared error. When there is no obvious improvement in the indicators for 5 consecutive rounds or signs of overfitting (such as an increase in the loss value of the test set) appear, promptly adjust the model hyperparameters (such as increasing the regularization term L1 or L2 coefficient, fine-tuning the convolution kernel size, etc.) until the model performance is stable and the accuracy reaches more than 85%.

[0073] In some embodiments, the in-vehicle light strip design optimization model is trained based on sample data;

[0074] Among them, the sample data includes the sample in-vehicle space three-dimensional structure data of the vehicles of each sample vehicle model, the sample in-vehicle light data of the vehicles of each sample vehicle model, each preset in-vehicle light strip design scheme, and the user feedback data corresponding to each preset in-vehicle light strip design scheme.

[0075] In an exemplary embodiment, each preset in-vehicle light strip design scheme may include various preset in-vehicle light strip installation schemes that are commonly combined at different positions such as along the bottom of the seat, the door frame, the edge of the center console, and the roof contour. Each single preset in-vehicle light strip installation scheme also corresponds to multiple combination schemes of preset in-vehicle light strip control parameters.

[0076] In an exemplary embodiment, it can be that the user conducts a subjective experience evaluation on each preset in-vehicle light strip design scheme, gives real-time scores through the in-vehicle interaction interface (on a 0-10 scale, covering dimensions such as the atmosphere creation effect, visual comfort, and whether it interferes with the driver's line of sight), and at the same time verbally describes the feelings and expected improvement directions, which are synchronously recorded by the in-vehicle recording device. The user feedback data may include the scores corresponding to each preset in-vehicle light strip design scheme; the user feedback data may also include the scores and expected improvement directions corresponding to each preset in-vehicle light strip design scheme; the user feedback data may also include other data, and the present application does not limit this.

[0077] In an exemplary embodiment, the three-dimensional structure data of the sample in-vehicle space of the vehicles of each sample model, the sample in-vehicle lighting data of the vehicles of each sample model, each preset in-vehicle light strip design scheme, and the user feedback data corresponding to each preset in-vehicle light strip design scheme can be put into one-to-one correspondence and organized into a structured data set, which is divided into a training set and a test set according to an 8:2 ratio to ensure data diversity and balance. For example, ensure the reasonable distribution of the data of each model in the training set and the test set.

[0078] The in-vehicle light strip design optimization model is trained based on the sample data. Among them, the sample data includes the three-dimensional structure data of the sample in-vehicle space of the vehicles of each sample model, the sample in-vehicle lighting data of the vehicles of each sample model, each preset in-vehicle light strip design scheme, and the user feedback data corresponding to each preset in-vehicle light strip design scheme. It can train the in-vehicle light strip design optimization model for the data of vehicles of different sample models and the user feedback data corresponding to each preset in-vehicle light strip design scheme. The in-vehicle light strip design optimization model fully learns the data of vehicles of different sample models. The in-vehicle light strip design optimization model can accurately optimize the in-vehicle light strip installation scheme and the in-vehicle light strip control parameters for vehicles of different models, improving the display effect of the in-vehicle lights. Moreover, the process of training the in-vehicle light strip design optimization model fully considers the user feedback data, enabling the generated in-vehicle light strip design optimization scheme to better meet the user's needs. It can give a personalized layout scheme in combination with the internal structure characteristics of the vehicle and the driver's preferences, so as to achieve the best in-vehicle lighting setting and improve the user experience.

[0079] In some embodiments, the three-dimensional structure data of the sample interior space of the vehicles of each sample vehicle model includes the three-dimensional structure data of the sample interior space obtained when each user group drives the vehicles of each sample vehicle model at each time period and under each weather condition;

[0080] The sample interior lighting data of the vehicles of each sample vehicle model includes the sample interior lighting data obtained when each user group drives the vehicles of each sample vehicle model at each time period and under each weather condition.

[0081] In an exemplary embodiment, each time period may include time periods such as early morning, noon, evening, night, etc.

[0082] In an exemplary embodiment, each weather condition may include weather conditions such as sunny, cloudy, rainy, snowy, etc.

[0083] In an exemplary embodiment, each user group may include a large number of users (at least 500 people) of different ages, genders, and occupations.

[0084] In an exemplary embodiment, the sample data can be sorted into a structured data set and divided into a training set and a test set according to an 8:2 ratio to ensure data diversity and balance. For example, ensure that the data of each vehicle model, each time period, each type of weather condition, and different user groups are reasonably distributed in the training set and the test set.

[0085] The data of the vehicles of each sample vehicle model, including the data obtained when each user group drives the vehicles of each sample vehicle model at each time period and under each weather condition, can improve the diversity of the samples, can improve the learning ability of the in-vehicle light strip design optimization model for various samples, and the in-vehicle light strip design optimization model can accurately optimize the in-vehicle light strip installation scheme and the in-vehicle light strip control parameters, improving the display effect of the in-vehicle lights.

[0086] Step 202, based on the probabilities corresponding to each candidate in-vehicle light strip design scheme, determine the optimized in-vehicle light strip design scheme for the target vehicle from each candidate in-vehicle light strip design scheme.

[0087] In an exemplary embodiment, step 202 may be implemented inside the in-vehicle light strip design optimization model or outside the in-vehicle light strip design optimization model, and this application does not limit this.

[0088] In an exemplary embodiment, step 202 may include: determining a candidate in-vehicle light strip design solution corresponding to a probability greater than a probability threshold as the in-vehicle light strip design optimization solution for the target vehicle. Step 202 may also include: sorting the probabilities corresponding to the respective candidate in-vehicle light strip design solutions from largest to smallest to obtain a sorting result; determining the candidate in-vehicle light strip design solutions corresponding to the top N probabilities in the sorting result as the in-vehicle light strip design optimization solution for the target vehicle; where N is a positive integer.

[0089] In an exemplary embodiment, the in-vehicle light strip design optimization solution for the target vehicle may include only one in-vehicle light strip design optimization solution, or may include at least two in-vehicle light strip design optimization solutions, and the present application does not limit this.

[0090] Inputting the three-dimensional structure data of the vehicle interior space and the in-vehicle lighting data into the in-vehicle light strip design optimization model can deeply analyze the three-dimensional structure data of the vehicle interior space and the in-vehicle lighting data through the in-vehicle light strip design optimization model, and then accurately predict the probabilities corresponding to the respective candidate in-vehicle light strip design solutions through the in-vehicle light strip design optimization model, and further accurately determine the in-vehicle light strip design optimization solution for the target vehicle from the respective candidate in-vehicle light strip design solutions, improving the display effect of the in-vehicle lighting.

[0091] In an exemplary embodiment, after receiving the three-dimensional structure data of the vehicle interior space and the in-vehicle lighting data, the in-vehicle light strip design optimization model generates the in-vehicle light strip design optimization solution for the target vehicle within a few seconds (less than 3 seconds in an ideal state). For example, the in-vehicle light strip design optimization solution may include the precise installation path of the light strip in the vehicle (presented in the form of a three-dimensional coordinate point sequence with a precision of up to millimeters), the optimized brightness level (in the numerical range of 0 - 255), and the appropriate color ratio (such as the RGB (a color model) three-color ratio value).

[0092] In some embodiments, after step 102, the in-vehicle light strip design optimization method further includes: rendering an animation of the light strip installation effect in each driving scenario based on the in-vehicle light strip design optimization solution for the target vehicle.

[0093] In an exemplary embodiment, each driving scenario includes, but is not limited to, driving scenarios such as urban congestion, highway cruising, and night parking.

[0094] In an exemplary embodiment, it may be that the in-vehicle display uses a graphics engine such as OpenGL or Unity to render an animation of the light strip installation effect in each driving scenario based on the in-vehicle light strip design optimization solution for the target vehicle. The vehicle owner can view it omnidirectionally by touching and rotating the screen perspective, and intuitively feel the effect of creating the in-vehicle atmosphere. It is also possible to switch between different in-vehicle light strip design optimization solutions for comparison, freely adjust the detailed parameters (such as brightness fine-tuning, color fade speed) and preview immediately.

[0095] Based on the design optimization plan of the interior light strip of the target vehicle, the light strip installation effect animation under various driving scenarios is rendered to facilitate the owner to intuitively feel the effect of creating the interior atmosphere and enhance the owner's experience.

[0096] In some embodiments, Figure 4 As shown, after step 102, the method for optimizing the design of the interior light strip further includes:

[0097] Step 401, based on the interior light strip design optimization scheme of the target vehicle, determine a target interior light strip design scheme of the target vehicle, wherein the target interior light strip design scheme includes a target interior light strip installation scheme and a target interior light strip control parameter.

[0098] In an exemplary embodiment, the interior light strip design optimization scheme of the target vehicle can be directly determined as the target interior light strip design scheme of the target vehicle; or a target interior light strip design scheme can be selected from multiple interior light strip design optimization schemes of the target vehicle; or the interior light strip design optimization scheme of the target vehicle can be adjusted to obtain the target interior light strip design scheme of the target vehicle; this application is not limited to this.

[0099] Step 402: Generate an installation guide based on the target in-vehicle light strip installation solution.

[0100] Among them, the installation guide includes the key steps in the installation process of the interior light strip.

[0101] In an exemplary embodiment, the installation guide may use pictures and texts to show key steps such as the fixing position of the light strip, the wiring direction, and the connection method.

[0102] In an exemplary embodiment, after the installation guide is generated, the interior light strip can be installed according to the installation guide. For example, if the vehicle is equipped with an intelligent auxiliary installation robot (such as a simple mechanical arm device), it can assist the owner to accurately locate the light strip according to the planned path; if not, the owner follows the installation guide to manually install the light strip, and the on-board camera monitors the entire process, using image recognition technology to compare the actual installation with the planned path deviation in real time, and voice prompts correction.

[0103] In an exemplary embodiment, after the interior light strip is installed according to the installation guide, the parameters of the interior light strip may be adjusted based on the target interior light strip control parameters.

[0104] Based on the design optimization plan of the interior light strip of the target vehicle, the target interior light strip design plan of the target vehicle is determined, wherein the target interior light strip design plan includes the target interior light strip installation plan and the target interior light strip control parameters; based on the target interior light strip installation plan, an installation guide is generated to facilitate the car owner to install the interior light strip according to the installation guide.

[0105] In some embodiments, after the in-vehicle light strip is installed and enabled, the in-vehicle device continuously collects the subsequent usage feedback of the vehicle owner. By regularly popping up questionnaires (covering issues such as atmosphere satisfaction, whether it affects the line of sight, and expected improvement points), analyzing driving behaviors (such as the stability of the vehicle speed and the change in operation convenience when the light strip is turned on), etc., the feedback data is anonymized and regularly transmitted back to the cloud server for regular model update and optimization, continuously adapting to the new needs of different users and the new scenarios of vehicle use.

[0106] In summary, in this application, when the in-vehicle light strip of the target vehicle is turned on, based on the three-dimensional structure data of the in-vehicle space and the in-vehicle lighting data, an optimization scheme for the in-vehicle light strip design of the target vehicle is generated. Considering the differences in the internal structures of different vehicle models, the three-dimensional structure data of the in-vehicle space of the target vehicle can reflect the layout of the in-vehicle space of the target vehicle. Furthermore, an optimized installation scheme for the in-vehicle light strip and optimized control parameters for the in-vehicle light strip are obtained for the layout of the in-vehicle space of the target vehicle. The in-vehicle lighting data includes the lighting environment data when the in-vehicle light strip of the target vehicle is turned on. According to the lighting environment data when the in-vehicle light strip of the target vehicle is turned on, it can be known which areas in the vehicle have light blockage, irradiation dead angles, or visual disharmony, etc. Then, the existing installation scheme for the in-vehicle light strip and the control parameters of the in-vehicle light strip are optimized, and an optimized installation scheme for the in-vehicle light strip and optimized control parameters for the in-vehicle light strip of the target vehicle are obtained more accurately to reduce the occurrence of situations such as light blockage and irradiation dead angles, improve the visual harmony of the in-vehicle lighting, and enhance the display effect of the in-vehicle lighting.

[0107] Exemplary device

[0108] Correspondingly, an embodiment of this application also provides an in-vehicle light strip design optimization device, as Figure 5 shown. The in-vehicle light strip design optimization device includes:

[0109] An acquisition unit 501, configured to acquire the three-dimensional structure data of the in-vehicle space and the in-vehicle lighting data of the target vehicle; wherein, the in-vehicle lighting data includes the lighting environment data when the in-vehicle light strip of the target vehicle is turned on;

[0110] A generation unit 502, configured to generate an optimization scheme for the in-vehicle light strip design of the target vehicle based on the three-dimensional structure data of the in-vehicle space and the in-vehicle lighting data; wherein, the optimization scheme for the in-vehicle light strip design includes an optimized installation scheme for the in-vehicle light strip and optimized control parameters for the in-vehicle light strip.

[0111] Optionally, the generation unit 502 includes:

[0112] The first processing sub-unit is configured to input the three-dimensional structure data of the vehicle interior space and the vehicle interior lighting data into the vehicle interior light strip design optimization model to obtain the probability corresponding to each candidate vehicle interior light strip design scheme; wherein, the candidate vehicle interior light strip design scheme includes a candidate vehicle interior light strip installation scheme and candidate vehicle interior light strip control parameters;

[0113] The second processing sub-unit is configured to determine the vehicle interior light strip design optimization scheme of the target vehicle from the various candidate vehicle interior light strip design schemes based on the probability corresponding to each candidate vehicle interior light strip design scheme.

[0114] Optionally, the vehicle interior lighting data includes vehicle interior lighting time series data;

[0115] The first processing sub-unit is specifically configured to:

[0116] Based on the three-dimensional structure data of the vehicle interior space through the vehicle interior light strip design optimization model, identify the layout rules of key interior components;

[0117] Based on the vehicle interior lighting time series data through the vehicle interior light strip design optimization model, determine the change trend of the vehicle interior lighting data;

[0118] Based on the layout rules of the key interior components and the change trend of the vehicle interior lighting data through the vehicle interior light strip design optimization model, determine the probability corresponding to each candidate vehicle interior light strip design scheme.

[0119] Optionally, the vehicle interior light strip design optimization model is trained based on sample data;

[0120] Wherein, the sample data includes the sample three-dimensional structure data of the vehicle interior space of each sample vehicle model, the sample vehicle interior lighting data of each sample vehicle model, each preset vehicle interior light strip design scheme, and the user feedback data corresponding to each preset vehicle interior light strip design scheme.

[0121] Optionally, the sample three-dimensional structure data of the vehicle interior space of each sample vehicle model includes the sample three-dimensional structure data of the vehicle interior space obtained by each user group driving each sample vehicle model at various time periods and in various weather conditions;

[0122] The sample vehicle interior lighting data of each sample vehicle model includes the sample vehicle interior lighting data obtained by each user group driving each sample vehicle model at various time periods and in various weather conditions.

[0123] Optionally, the vehicle interior light strip design optimization device further includes:

[0124] A rendering unit, configured to render an animation of the installation effect of the interior light strip in each driving scenario based on the optimized design solution of the interior light strip of the target vehicle.

[0125] Optionally, the interior light strip design optimization device further includes:

[0126] A first processing unit, configured to determine the target interior light strip design solution of the target vehicle based on the optimized design solution of the interior light strip of the target vehicle; wherein, the target interior light strip design solution includes a target interior light strip installation solution and target interior light strip control parameters;

[0127] A second processing unit, configured to generate an installation guide based on the target interior light strip installation solution; wherein, the installation guide includes each key step in the installation process of the interior light strip.

[0128] The interior light strip design optimization device provided in this embodiment belongs to the same inventive concept as the interior light strip design optimization method provided in the above embodiments of the present application, and can execute the interior light strip design optimization method provided in any of the above embodiments of the present application, and has the corresponding functional modules and beneficial effects for executing the interior light strip design optimization method. For the technical details not described in detail in this embodiment, reference may be made to the specific processing content of the interior light strip design optimization method provided in the above embodiments of the present application, which will not be elaborated herein.

[0129] The functions implemented by the above-mentioned acquisition unit 501 and generation unit 502 can be implemented by the same or different processors respectively, which is not limited in the embodiments of the present application.

[0130] It should be understood that the units in the above device can be implemented in the form of a processor calling software. For example, the device includes a processor, which is connected to a memory. Instructions are stored in the memory, and the processor calls the instructions stored in the memory to implement any of the above methods or the functions of each unit of the device. The processor can be a general-purpose processor, such as a CPU or a microprocessor, etc., and the memory can be a memory inside the device or a memory outside the device. Alternatively, the units in the device can be implemented in the form of a hardware circuit. By designing the hardware circuit, the functions of some or all of the units can be realized. The hardware circuit can be understood as one or more processors. For example, in one implementation, the hardware circuit is an ASIC, and by designing the logical relationship of the components in the circuit, the functions of some or all of the above units are realized. Another example is that in another implementation, the hardware circuit can be implemented by a PLD. Taking an FPGA as an example, it can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured through a configuration file, so as to realize the functions of some or all of the above units. All units of the above device can be all implemented in the form of a processor calling software, or all implemented in the form of a hardware circuit, or some implemented in the form of a processor calling software, and the remaining part implemented in the form of a hardware circuit.

[0131] In the embodiments of the present application, a processor is a circuit with the ability to process signals. In one implementation, the processor can be a circuit with the ability to read and execute instructions, such as a CPU, a microprocessor, a GPU, or a DSP, etc. In another implementation, the processor can realize certain functions through the logical relationship of a hardware circuit, and the logical relationship of the hardware circuit is fixed or can be reconstructed. For example, the processor is a hardware circuit implemented by an ASIC or a PLD, such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document to implement the configuration of the hardware circuit can be understood as the process of the processor loading instructions to realize the functions of some or all of the above units. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as a type of ASIC, such as an NPU, a TPU, a DPU, etc.

[0132] It can be seen that each unit in the above device can be one or more processors (or processing circuits) configured to implement the above method, such as: a CPU, a GPU, an NPU, a TPU, a DPU, a microprocessor, a DSP, an ASIC, an FPGA, or a combination of at least two of these processor forms.

[0133] In addition, each unit in the above device can be integrated in whole or in part, or can be implemented independently. In one implementation, these units are integrated together and implemented in the form of an SOC. The SOC can include at least one processor for implementing any of the above methods or implementing the functions of each unit of the device. The types of the at least one processor can be different. For example, it includes a CPU and an FPGA, a CPU and an artificial intelligence processor, a CPU and a GPU, etc.

[0134] Exemplary electronic device

[0135] An embodiment of the present application proposes an electronic device. Refer to Figure 6 As shown, the device includes:

[0136] A memory 200 and a processor 210;

[0137] Wherein, the memory 200 is connected to the processor 210 and is used for storing programs;

[0138] The processor 210 is used for implementing the in-vehicle light strip design optimization method disclosed in any of the above embodiments by running the programs stored in the memory 200.

[0139] Specifically, the above electronic device may further include: a bus, a communication interface 220, an input device 230, and an output device 240.

[0140] The processor 210, the memory 200, the communication interface 220, the input device 230, and the output device 240 are interconnected through the bus. Among them:

[0141] The bus may include a path for transmitting information between various components of the computer system.

[0142] The processor 210 can be a general-purpose processor, such as a general-purpose central processing unit (CPU), a microprocessor, etc., or can be an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the program of the solution of the present invention. It can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0143] The processor 210 may include a main processor, and may also include a baseband chip, a modem, etc.

[0144] The program for implementing the technical solution of the present invention is stored in the memory 200, and the operating system and other key services can also be stored. Specifically, the program may include program codes, and the program codes include computer operation instructions. More specifically, the memory 200 may include a read-only memory (ROM), other types of static storage devices that can store static information and instructions, a random access memory (RAM), other types of dynamic storage devices that can store information and instructions, a disk memory, a flash memory, and so on.

[0145] The input device 230 may include devices for receiving data and information input by a user, such as a keyboard, a mouse, a camera, a scanner, a light pen, a voice input device, a touch screen, a pedometer, or a gravity sensor, etc.

[0146] The output device 240 may include devices for allowing information to be output to a user, such as a display screen, a printer, a speaker, etc.

[0147] The communication interface 220 may include devices of any transceiver type for communicating with other devices or communication networks, such as an Ethernet, a radio access network (RAN), a wireless local area network (WLAN), etc.

[0148] The processor 210 executes the program stored in the memory 200 and calls other devices, and can be used to implement each step of any one of the in-vehicle light strip design optimization methods provided in the above embodiments of the present application.

[0149] Exemplary vehicle

[0150] An embodiment of the present application provides a vehicle, including the electronic device provided in the above embodiments of the present application.

[0151] For technical details not described in detail in this embodiment, reference may be made to the specific processing content of the electronic device provided in the above embodiments of the present application, and details will not be elaborated here.

[0152] Exemplary computer program product and storage medium

[0153] In addition to the above methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions. When the computer program instructions are run by a processor, the processor is caused to execute the steps in the in-vehicle light strip design optimization method according to various embodiments of the present application described in any of the above embodiments of this specification.

[0154] The computer program product can be written in any combination of one or more programming languages for executing the program code of the operations of the embodiments of the present application. The programming languages include object-oriented programming languages such as Java, C++, etc., and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, executed as an independent software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0155] In addition, an embodiment of the present application can also be a storage medium on which a computer program is stored. The computer program is executed by a processor to perform the steps in the method for optimizing the design of the in-vehicle light strip according to various embodiments of the present application described in any of the above embodiments of this specification, and specifically can implement the following steps:

[0156] Step 101, obtain the three-dimensional structure data of the in-vehicle space and the in-vehicle lighting data of the target vehicle.

[0157] Among them, the in-vehicle lighting data includes the lighting environment data when the in-vehicle light strip of the target vehicle is turned on.

[0158] Step 102, generate an optimized design plan for the in-vehicle light strip of the target vehicle based on the three-dimensional structure data of the in-vehicle space and the in-vehicle lighting data.

[0159] Among them, the optimized design plan for the in-vehicle light strip includes an optimized installation plan for the in-vehicle light strip and optimized control parameters for the in-vehicle light strip.

[0160] For the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present application.

[0161] It should be noted that the embodiments in this specification are all described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other. For the device embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0162] The steps in the methods of the embodiments of the present application can be adjusted, combined, and deleted according to actual needs, and the technical features recorded in each embodiment can be replaced or combined.

[0163] In each embodiment of the present application, the modules and sub-modules in the device and the terminal can be combined, divided, and deleted according to actual needs.

[0164] In several embodiments provided by the present application, it should be understood that the disclosed terminal, device, and method can be implemented in other ways. For example, the terminal embodiments described above are merely illustrative. For example, the division of modules or sub-modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple sub-modules or modules can be combined or integrated into another module, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the device or module can be in electrical, mechanical, or other forms.

[0165] The modules or sub-modules described as separate components may or may not be physically separated. The components as modules or sub-modules may or may not be physical modules or sub-modules, that is, they can be located in one place, or they can be distributed to multiple network modules or sub-modules. Some or all of the modules or sub-modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0166] In addition, in each embodiment of the present application, each functional module or sub-module can be integrated in a processing module, or each module or sub-module can exist physically alone, or two or more modules or sub-modules can be integrated in one module. The above-mentioned integrated modules or sub-modules can be implemented in the form of hardware or in the form of software functional modules or sub-modules.

[0167] Those skilled in the art can further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0168] The steps of the methods or algorithms described in connection with the embodiments disclosed herein may be implemented directly in hardware, in a software unit executed by a processor, or in a combination thereof. The software unit may be placed in a random access memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium well known in the art.

[0169] Finally, it should also be noted that in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover a non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0170] The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing the design of an interior light strip, characterized in that: include: Acquire the three-dimensional structure data of the interior space and the interior lighting data of the target vehicle; wherein the interior lighting data includes the lighting environment data when the interior light strip of the target vehicle is turned on; Based on the in-vehicle space three-dimensional structure data and the in-vehicle lighting data, an in-vehicle light strip design optimization scheme for the target vehicle is generated; wherein the in-vehicle light strip design optimization scheme includes an in-vehicle light strip installation optimization scheme and in-vehicle light strip control optimization parameters.

2. The method for optimizing the design of interior light strips according to claim 1, characterized in that: The generating of the interior light strip design optimization scheme of the target vehicle based on the interior space three-dimensional structure data and the interior lighting data includes: Inputting the in-vehicle space three-dimensional structure data and the in-vehicle lighting data into an in-vehicle light strip design optimization model to obtain the probability corresponding to each candidate in-vehicle light strip design scheme; wherein the candidate in-vehicle light strip design scheme includes a candidate in-vehicle light strip installation scheme and a candidate in-vehicle light strip control parameter; Based on the probabilities corresponding to the candidate interior light strip design solutions, an optimized design solution of the interior light strip of the target vehicle is determined from the candidate interior light strip design solutions.

3. The method for optimizing the design of interior light strips according to claim 2, characterized in that: The in-vehicle illumination data includes in-vehicle illumination time series data; The step of inputting the in-vehicle space three-dimensional structure data and the in-vehicle illumination data into an in-vehicle light strip design optimization model to obtain the probability corresponding to each candidate in-vehicle light strip design scheme includes: By using the interior light strip design optimization model, based on the interior space three-dimensional structure data, the layout rules of key interior components are identified; Determine the change trend of the in-vehicle lighting data based on the in-vehicle lighting time series data by using the in-vehicle light strip design optimization model; The in-vehicle light strip design optimization model is used to determine the probability corresponding to each candidate in-vehicle light strip design scheme based on the layout rules of the key interior components and the change trend of the in-vehicle lighting data.

4. The method for optimizing the design of interior light strips according to claim 2, characterized in that: The interior light strip design optimization model is obtained based on sample data training; Among them, the sample data includes sample interior space three-dimensional structure data of each sample vehicle model, sample interior lighting data of each sample vehicle model, each preset interior light strip design scheme, and user feedback data corresponding to each preset interior light strip design scheme.

5. The method for optimizing the design of the interior light strip according to claim 4, characterized in that: Sample in-vehicle space three-dimensional structure data of each sample vehicle model, including sample in-vehicle space three-dimensional structure data obtained by each user group driving each sample vehicle model in each time period and under each weather condition; The sample interior lighting data of each of the sample vehicle models includes the sample interior lighting data obtained when each user group drives each of the sample vehicle models in each time period and each weather condition.

6. The method for optimizing the design of interior light strips according to claim 1, characterized in that: After generating the interior light strip design optimization scheme of the target vehicle based on the interior space three-dimensional structure data and the interior lighting data, the method further includes: Based on the interior light strip design optimization scheme of the target vehicle, the light strip installation effect animation under various driving scenarios is rendered.

7. The method for optimizing the design of interior light strips according to claim 1, characterized in that: After generating the interior light strip design optimization scheme of the target vehicle based on the interior space three-dimensional structure data and the interior lighting data, the method further includes: Based on the interior light strip design optimization scheme of the target vehicle, determine the target interior light strip design scheme of the target vehicle; wherein the target interior light strip design scheme includes the target interior light strip installation scheme and the target interior light strip control parameters; Based on the target in-vehicle light strip installation solution, an installation guide is generated; wherein the installation guide includes various key steps in the in-vehicle light strip installation process.

8. An electronic device, characterized in that: including memory and processor; The memory is connected to the processor and is used to store programs; The processor is used to implement the in-vehicle light strip design optimization method as described in any one of claims 1 to 7 by running the program in the memory.

9. A vehicle, characterized in that: Comprising the electronic device as claimed in claim 8.

10. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the method for optimizing the design of the interior light strip of a vehicle as claimed in any one of claims 1 to 7 is implemented.

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