A method for adjusting a light environment of an intelligent automobile cabin and related equipment

Abstract

The application provides a kind of intelligent car cabin light environment adjusting method and related equipment, applied to data processing technical field.This application obtains training sample set and target intelligent cabin light environment data;The training sample set is preprocessed, and the training sample set with identification information is generated;Based on the preset processing rule, the training sample set with identification information is processed, and training set and test set are generated;Obtain the preset light environment evaluation index model matched with identification information;The preset light environment evaluation index model is trained based on the training set and test set, and the target light environment evaluation index model is generated;The target intelligent cabin light environment data is preprocessed, and the attribute information of target user is generated;The attribute information of target user is processed based on the target light environment evaluation index model, and the light environment evaluation index is generated;The light environment evaluation index is processed based on the target light environment evaluation index model, and the target illumination is generated.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and related equipment for adjusting the lighting environment of an intelligent car cabin. Background Technology

[0002] Light affects the human body in two ways: visual and non-visual. The visual effects of light, through the sensory cells and cone cells in the retina, primarily involve the perception of color, detail, and image formation. The non-visual effects, through the intrinsically photoreceptive retinal ganglion cells (ipRGCs) in the retina, mainly manifest in the regulation of circadian rhythms, alertness, work efficiency, and mood. Studying the physiological and psychological impacts of light within intelligent cockpits is crucial for improving human performance and creating a safe, comfortable, and healthy lighting environment in intelligent vehicle cabins.

[0003] With the rapid development of intelligent vehicles, the scenarios and needs within the cockpit are constantly changing, bringing new challenges to the creation of the cockpit lighting environment. Firstly, the types and states of work and activities of occupants will change, especially with the development of autonomous vehicles. Cockpit occupants will engage in more non-driving activities such as work, entertainment, and rest, requiring dynamic adjustments to the cockpit lighting environment design for different tasks. Secondly, the increasing intelligence of vehicles has led to an increase in the number and complexity of light sources such as display screens within the cockpit, making the lighting environment increasingly complex and increasing the possibility of light pollution that may harm human physiological and psychological health. Therefore, an evaluation of the overall lighting environment within the cockpit is necessary.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this application is to provide a method and related equipment for adjusting the lighting environment of an intelligent car cabin, which at least to some extent overcomes the problems existing in the prior art. By simulating three typical intelligent cabin lighting environments, physiological and psychological data of subjects were collected to obtain data on melatonin concentration, attention level, and comfort. Statistical analysis of the experimental data was performed to establish a lighting environment evaluation index model. An intelligent cabin healthy lighting environment evaluation index was defined, which takes into account both physiological and psychological health, with the working scene of the cabin occupants as the weight. Then, the illuminance at the optimal lighting environment evaluation index was obtained, which meets the scene requirements and is conducive to the physiological and psychological health of the cabin occupants.

[0006] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part by practice of the invention.

[0007] According to one aspect of this application, a method for adjusting the lighting environment of a smart car cabin is provided, comprising: acquiring a training sample set and target smart cabin lighting environment data, wherein the training sample set includes several different smart cabin lighting environment data and subjects in the smart cabin; preprocessing the training sample set to generate a training sample set with identification information, wherein the identification information is used to characterize abnormal physiological states of users; processing the training sample set with identification information based on preset processing rules to generate a training set and a test set; acquiring a preset lighting environment evaluation index model that matches the identification information; training the preset lighting environment evaluation index model based on the training set and the test set to generate a target lighting environment evaluation index model; preprocessing the target smart cabin lighting environment data to generate attribute information of a target user; processing the attribute information of the target user based on the target lighting environment evaluation index model to generate a lighting environment evaluation index; and processing the lighting environment evaluation index based on the target lighting environment evaluation index model to generate a target illuminance.

[0008] In one embodiment of this application, the step of processing the training sample set with identification information based on preset processing rules to generate a training set and a test set includes: extracting features from the training sample set to determine an original feature library; dividing the original feature library into various feature datasets to generate a training set and a test set; using a classifier to predict the test sets divided from the original feature library and determining the prediction results; training the test sets divided from the original feature library using a preset algorithm to obtain test set class prediction results; and generating training samples with identification information based on the prediction results and the test set class prediction results.

[0009] In one embodiment of this application, the step of extracting features from the training sample set to determine the original feature library includes: processing standard material information based on preset processing rules to generate key factors of non-visual influence of light, wherein the key factors of non-visual influence of light are features of the influence of the light environment in the cabin of an intelligent vehicle on human physiology; processing the standardized features based on preset feature screening and dimensionality reduction rules to generate original features; and generating an original feature library from several original features.

[0010] In one embodiment of this application, training the preset light environment evaluation index model based on the training set and the test set to generate a target light environment evaluation index model includes: extracting multiple sets of data from the training set, wherein each set of data contains a preset number of data samples, and at least one data sample includes identification information; training the initial light environment evaluation index model based on the data samples in the multiple sets of data to generate a trained light environment evaluation index model; processing the trained light environment evaluation index model based on the test set to generate test results; and if the data sample in the test results containing identification information indicates an abnormality in the user's physiological state, then the trained light environment evaluation index model is used as the target light environment evaluation index model.

[0011] In one embodiment of this application, the step of preprocessing the target smart cockpit lighting environment data to generate target user attribute information includes: preprocessing the target smart cockpit lighting environment data to generate the target user's attention factor, target user's melatonin factor, target user's comfort factor, target user's attention influence weight, target user's melatonin concentration influence weight, and target user's comfort influence weight; and using the target user's attention factor, target user's melatonin factor, target user's comfort factor, target user's attention influence weight, target user's melatonin concentration influence weight, and target user's comfort influence weight as the target user's attribute information.

[0012] In one embodiment of this application, the step of processing the attribute information of the target user based on the target light environment evaluation index model to generate a light environment evaluation index includes: the target light environment evaluation index model includes a calculation formula for obtaining the light environment evaluation index, the calculation formula being: I(x)=λ RT P RT x+λ MT P MT (x)+λ CF P CF (x); λ RT +λ MT +λ CF =1; where I(x) is the light environment evaluation index; λ RT , λ MT and λ CF These are the weights for the influence of attention, melatonin concentration, and comfort; P RT (x), P MT (x) and P CF (x) represent the attention factor, melatonin factor, and comfort factor as they vary with x, where x is the illuminance (lx).

[0013] In one embodiment of this application, the step of processing the light environment evaluation index based on the target light environment evaluation index model to generate a target illuminance includes: the target light environment evaluation index model includes a calculation formula for obtaining the target illuminance, the calculation formula being: Where x is the target illuminance; P i (x) represents the factor that varies with illuminance; I(x) represents the light environment evaluation index; λ i Let λ be the influence weight of the influence factor named after influence i. i The range is [0,1], and k is the total number of influencing factors.

[0014] Another aspect of this application discloses a device for adjusting the lighting environment of a smart car cabin, characterized in that it comprises: an acquisition module for acquiring a training sample set and target smart cabin lighting environment data, wherein the training sample set includes several different smart cabin lighting environment data and subjects within the smart cabin; acquiring a preset lighting environment evaluation index model matching identification information; and a processing module for preprocessing the training sample set to generate a training sample set with identification information, wherein the identification information is used to characterize abnormal physiological states of users; processing the training sample set with identification information based on preset processing rules to generate a training set and a test set; training the preset lighting environment evaluation index model based on the training set and the test set to generate a target lighting environment evaluation index model; preprocessing the target smart cabin lighting environment data to generate attribute information of the target user; processing the attribute information of the target user based on the target lighting environment evaluation index model to generate a lighting environment evaluation index; and processing the lighting environment evaluation index based on the target lighting environment evaluation index model to generate a target illuminance.

[0015] According to another aspect of this application, an electronic device is characterized by comprising: a first processor; and a memory for storing executable instructions of the first processor; wherein the first processor is configured to execute the above-described method for adjusting the lighting environment of a smart car cabin by executing the executable instructions.

[0016] According to another aspect of this application, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a second processor, implements the above-described method for adjusting the lighting environment of an intelligent vehicle cabin.

[0017] According to another aspect of this application, a computer program product is provided, comprising a computer program, characterized in that, when the computer program is executed by a third processor, it implements the above-described method for adjusting the lighting environment of an intelligent vehicle cabin.

[0018] This application provides a method and related equipment for adjusting the lighting environment of a smart car cabin. The method involves a server acquiring a training sample set and target smart cabin lighting environment data. The training sample set includes several different smart cabin lighting environment data sets and subjects within the smart cabin. The training sample set is preprocessed to generate a training sample set with labeled information, where the labeled information characterizes abnormal physiological states of the user. The labeled training sample set is then processed based on preset processing rules to generate a training set and a test set. A preset lighting environment evaluation index model matching the labeled information is obtained. This preset lighting environment evaluation index model is trained based on the training set and the test set to generate a target lighting environment evaluation index model. The target smart cabin lighting environment data is preprocessed to generate attribute information of the target user. The target user attribute information is processed based on the target lighting environment evaluation index model to generate lighting environment evaluation indices. Finally, the lighting environment evaluation indices are processed based on the target lighting environment evaluation index model to generate a target illuminance. By simulating three typical smart cockpit lighting environments, physiological and psychological data were collected from the subjects, including melatonin concentration, attention level, and comfort data. Statistical analysis of the experimental data led to the establishment of a lighting environment evaluation index model. A healthy lighting environment evaluation index for smart cockpits was defined, taking into account both physiological and psychological health, with the working environment of the occupants as the weight. The optimal illuminance for the lighting environment evaluation index was then obtained, which satisfies the requirements of the scenario while also benefiting the physiological and psychological health of the occupants.

[0019] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0020] Figure 1 A flowchart illustrating a method for adjusting the lighting environment of a smart car cockpit according to an embodiment of this application is shown.

[0021] Figure 2 A schematic diagram of the structure of an intelligent car cabin lighting environment adjustment device provided in an embodiment of this application is shown;

[0022] Figure 3 This illustration shows a schematic diagram of the structure of an electronic device according to an embodiment of this application;

[0023] Figure 4 A schematic diagram of a storage medium provided in one embodiment of this application is shown. Detailed Implementation

[0024] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0025] The following is combined Figure 1 This application describes a method for adjusting the lighting environment of a smart car cockpit according to exemplary embodiments. It should be noted that the following application scenarios are shown only to facilitate understanding of the spirit and principles of this application, and the embodiments of this application are not limited in any way. Rather, the embodiments of this application can be applied to any applicable scenario.

[0026] In one embodiment, this application also proposes a method and related equipment for adjusting the lighting environment of an intelligent vehicle cabin. Figure 1 A schematic flowchart illustrating a method for adjusting the lighting environment of a smart car cockpit according to an embodiment of this application is shown. Figure 1 As shown, this method is applied to a server and includes:

[0027] S101, acquire training sample set and target intelligent cockpit light environment data.

[0028] In one implementation, the training sample set includes data on several different smart cockpit lighting environments and subjects within those smart cockpits. The training sample set includes 23 healthy university students who underwent physiological and psychological tests at three different light intensities within a simulated car cockpit lighting environment. The experiment involved analyzing saliva samples to determine melatonin (MT) levels to reflect health status, using a psychomotor vigilance task (PVT) to test attention levels, and conducting psychological tests using a subjective comfort questionnaire. Statistical analysis of the experimental data led to the establishment of a mathematical model of the physiological and psychological impact of the car cockpit lighting environment on occupants. A task-weighted (attention, health, and comfort) lighting environment evaluation index model for smart cockpits, tailored to the task needs of occupants, was designed. A strategy for creating a healthy lighting environment was also developed, providing fundamental theoretical support for the design and optimization of smart cockpit lighting environments.

[0029] In addition, the subjects were 23 college students with a mean age of 21.91 years (standard deviation 1.90 years), including 15 males and 8 females. The males had a mean height of 176 cm (standard deviation 6 cm) and a mean weight of 74.57 kg (standard deviation 8.44 kg), while the females had a mean height of 161 cm (standard deviation 4.81 cm) and a mean weight of 51 kg (standard deviation 4 kg). All subjects were in good health, had normal color vision, and were free from oral diseases, injuries, or inflammation, as well as mental illnesses such as depression, anxiety, or mania. Because the 23 subjects were of similar age, had similar height and weight among members of the same sex, and exhibited similar overall (including between males and females) circadian rhythms, individual and gender differences among the subjects were ignored in this invention.

[0030] To simulate a cockpit environment, a lighting environment simulator with an internal space of 3000mm×2150mm×2800mm was constructed and placed in an indoor laboratory environment. The top, left, right, and front surfaces of the simulator can be individually adjusted for illuminance and color temperature to simulate different lighting conditions in a cockpit. The bottom of the lighting environment simulator is directly in contact with the ground, and the rear is the entrance passage. During testing, the rear was covered with an opaque black cloth, and the bottom and rear surfaces could not be adjusted for lighting.

[0031] Based on the International Commission on Illumination (CIE)'s "Comparison Table of Illuminance and Environmental Standards" and "Common Natural Light Color Temperatures," this study analyzes the spatiotemporal environmental characteristics of major traffic accidents in China from 2014 to 2018 and the temporal distribution patterns of road traffic accidents. Combining this with the light environment adjustment capabilities of the cockpit lighting simulator, two representative lighting environments prone to major traffic accidents at the same color temperature level (5000K) were ultimately determined: Light1 and Light3 were used to simulate the car cabin lighting environment at midday on a sunny day and at sunset, respectively, along with a general weather lighting environment (Light2) with illuminance between the two. This was used to obtain more data for the subsequent establishment of mathematical models. Since this invention primarily focuses on the impact of illuminance on occupants, the measured light data in the actual experimental lighting environment are shown in Table 1. The color temperatures of the three lighting environments did not show significant differences, therefore the influence of color temperature was ignored.

[0032] Table 1. Selected light environment parameters and corresponding subject numbers in the experiment.

[0033]

[0034] S102, preprocess the training sample set to generate a training sample set with identification information.

[0035] In one implementation, experiments were conducted for one day each under three different light environments. Two tests were performed each day, once in the morning (10:00-11:30) and once in the afternoon (14:00-15:30), for a total of six experiments over three days. Twenty-three subjects were randomly divided into three groups to receive tests under the three different light environments. Three to four subjects participated in each experiment, and seven to eight subjects participated in each light environment. To further control for the impact of gender differences on the results, the male-to-female ratio in each group was referenced to the overall gender ratio. Due to the small number of participants, one to two female subjects were included in each group. The entire experiment was completed over six days. Each subject was tested under only one light environment, with each test lasting 90 minutes, including a 30-minute preparation phase and a 60-minute testing phase. Subjects were required to arrive at the laboratory 10 minutes before the test for pre-test preparation. The ambient temperature during both the preparation and testing phases was controlled at 20-25°C.

[0036] Through experiments, melatonin concentration and average PVT response time were measured every 20 minutes during 60 minutes of exposure under three different light environments, starting from the moment of entry into the experimental environment. Subjective comfort and overall scores were also assessed after each group of experiments. Therefore, the experimental data includes human salivary melatonin concentration, average PVT response time, and subjective comfort data over a 60-minute experimental period under different light environments (illuminance). The information is used to characterize any abnormalities in the user's physiological state.

[0037] Based on the data analysis of melatonin concentration, PVT response time, and subjective comfort evaluation in this experiment, the following findings can be obtained for the population represented by the subjects under the experimental conditions and environment, within a specific light environment exposure duration of 60 minutes:

[0038] 1) Melatonin concentration, PVT response time, and subjective comfort evaluation are all related to light intensity, but not to exposure duration.

[0039] 2) Drastic changes in light intensity can cause drastic changes in melatonin concentration, PVT response time, and comfort.

[0040] S103, the training sample set with identification information is processed based on preset processing rules to generate a training set and a test set.

[0041] In one implementation, features are extracted from the training sample set to determine the original feature library; the original feature library is used to divide the data into various feature datasets, generating training and test sets; a classifier is used to predict the test sets divided from the original feature library, determining the prediction results; a preset algorithm is used to train the training sets divided from the original feature library, obtaining the prediction results for the test set classes; and training samples with labeling information are generated based on the prediction results and the prediction results for the test set classes. Feature extraction includes four types of features: original features, statistical features, frequency domain features, and time domain features. Commonly extracted features are described below: Original features: Original features utilize all information from each sample data matrix after preprocessing the collected data for model training. To avoid losing sample information, each curve of sample Xi is directly expanded and stretched into a row vector. Statistical features: Statistical features consider the changing trends of all data points on the curve. By extracting statistical features, the dimensionality of the data samples can be reduced, facilitating the analysis of gene data and accelerating the convergence speed during model training. The extracted statistical features include maximum value, minimum value, mean, variance, and standard deviation. Frequency domain features: Wavelet transform is applied to the dataset, and the coefficients obtained from the second-order wavelet transform are used as new features to form frequency domain features. Time domain features: Time domain features primarily focus on the time perspective to discover the changing patterns of signals and systems. Time domain features can reflect information about the curve data over time. This process mainly extracts the first-order forward difference of the dataset to form first-order difference time domain features, and uses the exponential moving average feature processing method.

[0042] S104, Obtain a preset light environment evaluation index model that matches the identification information.

[0043] In one implementation, the light environment evaluation index model is set based on the actual design changes of the smart cockpit received within a preset time period. This is because, with the rapid development of intelligent vehicles, the scenes and needs within the smart cockpit are constantly changing, bringing new challenges to the creation of the light environment within the cockpit. First, the types and states of work of people in the cockpit will change. Especially with the development of autonomous vehicles, people in the cockpit will engage in more activities unrelated to driving, such as work, entertainment, and rest. The light environment design of the cockpit also needs to be dynamically adjusted for different activities and tasks. In addition, the improvement of vehicle intelligence has led to an increase in the number and complexity of light sources such as display screens in the car cockpit, making the light environment in the smart cockpit increasingly complex and increasing the possibility of light pollution that is harmful to human physiological and psychological health. Therefore, it is necessary to evaluate the overall light environment status within the cockpit.

[0044] S105, the preset light environment evaluation index model is trained based on the training set and the test set to generate the target light environment evaluation index model.

[0045] In one implementation, multiple sets of data are extracted from the training set, each set containing a preset number of data samples, wherein at least one data sample includes identification information; an initial light environment evaluation index model is trained based on the data samples in the multiple sets of data to generate a trained light environment evaluation index model; the trained light environment evaluation index model is processed based on the test set to generate test results; if the data sample containing identification information in the test results indicates an abnormality in the user's physiological state, then the trained light environment evaluation index model is used as the target light environment evaluation index model.

[0046] Because the training data for the light environment evaluation index model may be insufficient, and the proportion of different periods in the training data is uneven, a Stable Diffusion model is used to generate the training data. The training set is divided based on the identification information, generating a certain number of class samples; any minority class sample with a smaller number of samples is obtained from the training set; neighboring samples are generated based on the distance between any minority class sample and other minority class samples of the same class, wherein the neighboring samples include a predetermined number of samples from any minority class; these neighboring samples are sampled to generate a predetermined number of sampled samples; the sampling ratio is determined based on the number of samples from each class in the training set; the sampling ratio is then determined; the neighboring samples are sampled based on the sampling ratio to generate a predetermined number of sampled samples; and multiple data sets are generated based on any minority class sample and each sampled sample.

[0047] S106, preprocess the target smart cockpit light environment data to generate the target user's attribute information.

[0048] In one implementation, the target smart cockpit light environment data is preprocessed to generate the target user's attention factor, melatonin factor, comfort factor, attention influence weight, melatonin concentration influence weight, and comfort influence weight. These factors are then used as the target user's attribute information.

[0049] Key factors affecting light's non-visual aspects include light intensity, spectral distribution, lighting time, lighting duration, lighting direction, and lighting history.

[0050] Light intensity can rapidly affect non-visual effects on the human body. Experimental data shows that increased light intensity has a stronger inhibitory effect on melatonin secretion. Light intensity has a significant impact on sleep, mental state, work efficiency, and alertness. Prolonged exposure to light that is inconsistent with the circadian rhythm affects melatonin secretion, causing circadian rhythm disruption and reducing alertness and cognitive ability. Therefore, light intensity is one of the important non-visual influencing factors in this invention.

[0051] Because non-visual effects are delayed, a certain duration of light exposure is a necessary condition for their occurrence. Based on the "2023 Annual Commute Monitoring Report of Major Chinese Cities," which states that approximately 12% of people have a one-way commute time exceeding 60 minutes, it can be concluded that in major Chinese cities, the dwell time of people inside smart cockpits mostly does not exceed 60 minutes. Furthermore, in academic inventions, light exposure time is generally controlled within the range of 45 minutes to 6 hours. Therefore, the duration of light exposure in this invention is set at 60 minutes.

[0052] The timing of light exposure is related to the human circadian rhythm. In this experiment, subjects in each group were tested at the same time of day, assuming that their circadian rhythm levels were basically consistent. Therefore, the influence of the timing of light exposure on the individual circadian rhythms of the subjects was ignored in the experiment. Meanwhile, the spectral distribution within the cabin of a smart car can be approximated as being basically uniform due to the small space. Furthermore, the orientation of most seats in the cabin is consistent with the direction of travel and remains relatively fixed during travel; therefore, the influence of the direction of light exposure was ignored in the experiment.

[0053] In current academic inventions on non-visual phenomena of light, the isolation time before illumination is usually controlled within the range of 15 minutes to 1 hour. In the experiment of this invention, the isolation time before illumination is 30 minutes, which can be considered as basically eliminating the influence of the history of illumination.

[0054] In summary, this invention uses light intensity and duration (60 min) as variables affecting the non-visual effects of light in the smart cockpit on occupants. The experimental design, which involves 30 min of isolation before the experiment to eliminate the influence of historical light exposure and ignores the effects of spectral distribution and duration of light exposure, is reasonable.

[0055] The focus of this invention is the non-visual impact of the intelligent cockpit lighting environment on the physiological and psychological state of occupants. It explores the effect of the lighting environment on the subjective feelings of alertness, attention level, and comfort of occupants. Details will be provided later and will not be elaborated on here.

[0056] S107, Based on the target light environment evaluation index model, process the attribute information of the target user to generate a light environment evaluation index.

[0057] In one embodiment, the target light environment evaluation index model includes a calculation formula for obtaining the light environment evaluation index, the specific calculation formula of which is shown below:

[0058] I(x)=λ RT P RT (x)+λ MT P MT (x)+λ CF P CF (x);

[0059] λ RT +λ MT +λ CF =1;

[0060] Where I(x) is the light environment evaluation index; λ RT , λ MT and λ CF These are the weights for the influence of attention, melatonin concentration, and comfort; P RT (x), P MT (x) and P CF (x) represent the attention factor, melatonin factor, and comfort factor as they vary with x, where x is the illuminance (1x).

[0061] Through observation and analysis of the experimental data of this invention, it can be found that during daytime, for the population represented by the subjects, the melatonin concentration (pg / mL), PVT correct reaction time (s), and overall comfort (dimensionless, [0-20]) did not change significantly with the exposure time within 60 minutes of exposure to a certain light environment, but changed significantly with the light intensity. Therefore, within the scope of this invention, a healthy light environment can be regarded as a combination of three factors (attention factor, melatonin factor, and comfort factor) that change with light intensity. A light environment evaluation index system composed of attention, melatonin concentration, and comfort factors with different weights is established. According to different needs and scenarios, different factor weights are combined to calculate the optimal light intensity, forming a light environment that is beneficial to both the physiological and psychological health of the human body in the environment.

[0062] In another embodiment, according to the experiment conducted according to the present invention, the algorithm for evaluating the light environment index is simulated and tested. First, based on the changes in melatonin concentration, attention, and comfort with illuminance in the experiment, the data relationship between illuminance and these three influencing factors is established using the above formula. By performing a second-order polynomial fitting on the experimental data, the following can be obtained:

[0063] RT(x) = -3 × 10 -7 x 2 +0.0009x+2.624;

[0064] MT(x) = -4 × 10-7 x 2 +0.0007x+2.3179;

[0065] CF(x) = -5 × 10 -6 x 2 +0.0093x+12.092.

[0066] To establish a light environment evaluation index, it is first necessary to process the data of these three different factors so that they can be calculated in the same coordinate system. This invention uses Z-score normalization to normalize and shift the data of the above three factors to positive values. After fitting the experimental data with a second-order polynomial, the following mathematical formula with illuminance as the variable can be obtained:

[0067] P RT (x)=-2×10 -6 x 2 +0.0046x+98.3168;

[0068] P MT (x)=-2×10 -6 x 2 +0.0029x+100.0708;

[0069] P CF (x)=-2×10 -6 x 2 +0.0032x+100.0126.

[0070] In the intelligent cockpit light environment simulation of the autonomous driving scenario of this invention, the occupants in the cockpit need to perform (non-driving) work, which requires a relatively high level of attention. Considering both health and comfort, three factors with weights of λ can be initially considered. RT ∶λ MT ∶λ CF = 6∶2∶2, and the formula for the light environment evaluation index is derived from this:

[0071] I(x) = -2 × 10 -6 x 2 +0.0040x+99.0068.

[0072] S108, Based on the target light environment evaluation index model, the light environment evaluation index is processed to generate the target illuminance.

[0073] In one embodiment, the target light environment evaluation index model includes a calculation formula for obtaining the target illuminance, the specific calculation formula being as follows:

[0074]

[0075]

[0076]

[0077] Where x is the target illuminance; P i (x) represents the factor that varies with illuminance; I(x) represents the light environment evaluation index; λ i Let λ be the influence weight of the influence factor named after influence i. i The range is [0, 1], and k is the total number of influencing factors.

[0078] With the advent of self-driving cars, the design of the lighting environment in smart cockpits will increasingly consider scenarios unrelated to driving, such as office work, entertainment, and rest. The weights of the influencing factors on the lighting environment will change. First, based on the different scenario requirements within the smart cockpit, the weights of each influencing factor in the formula for the lighting environment are comprehensively adjusted to determine the weight of each factor. Then, these weights are substituted into the formula. Since each factor is approximately fitted to a quadratic function with illuminance as the variable, there exists a (relatively) optimal solution for the lighting environment evaluation index. Finally, by differentiating the formula and setting the derivative to zero, the illuminance at which the lighting environment evaluation index is optimal can be obtained. This illuminance is the lighting intensity of the smart cockpit lighting environment that satisfies both the scenario requirements and is beneficial to the physiological and psychological well-being of the occupants.

[0079]

[0080] Let the above formula When x equals 0, we can obtain the illuminance value x corresponding to the maximum light environment evaluation index. Here, x = 1000 lx. Substituting x into the other formulas above, we can obtain the PVT reaction time (the reciprocal of attention), melatonin concentration, and comfort level of a person under the optimal healthy light environment. In other words, we can obtain the physiological and psychological indicators of the human body under the optimal healthy light environment within the scope of this invention.

[0081] Please refer to Table 2 for details:

[0082] Table 2. Strategy for Creating a Healthy Light Environment within the Illumination Range of the Invention

[0083]

[0084] Comparing the attention level and comfort data in Table 3 with the data from the experiment, it can be seen that the attention level, melatonin concentration, and comfort under the illuminance in the table are all within the optimal range of the overall data. That is, while meeting the requirements of high performance, physiological indicators are also taken into account, and comfort is improved. This shows that this strategy for creating a healthy light environment is feasible within the scope of this invention.

[0085] Therefore, even without changing the vehicle's shape and structure, by modifying the light transmission of car windows and intelligent cabin lighting systems, the cabin's light environment can be effectively adjusted to enhance the alertness of occupants and improve the driving experience, providing them with an efficient and more comfortable overall light environment that is conducive to their physical and mental health.

[0086] This invention simulates three typical smart cockpit lighting environments and collects physiological and psychological data from 23 eligible subjects, obtaining data on melatonin concentration, attention level, and comfort. Statistical analysis of the experimental data establishes mathematical models of how human melatonin concentration, attention level, and comfort change with illuminance. A healthy lighting environment evaluation index for smart cockpits, taking into account both physiological and psychological health, is defined, with the working environment of the in-cabin personnel as a weight. Strategies for creating a healthy lighting environment in smart cockpits are also provided. The main findings and conclusions of this invention are as follows:

[0087] 1) Melatonin concentration, attention level, and comfort are sensitive to drastic changes in light intensity;

[0088] 2) Melatonin concentration, attention level, and comfort did not change significantly with exposure duration within 60 minutes. That is, under the conditions of this invention, melatonin concentration, attention, and comfort can be considered to be independent of exposure duration.

[0089] 3) Melatonin concentration, attention level, and comfort are all correlated with light intensity; that is, the stronger the light intensity, the lower the melatonin concentration, the higher the attention level, and the lower the comfort level. However, excessively low light intensity can also lead to decreased comfort.

[0090] 4) Melatonin concentration has a strong positive correlation with comfort level, and melatonin concentration is correlated with attention level;

[0091] 5) Attention level is correlated with comfort level;

[0092] 6) With a weighting ratio of 6:2:2 for attention, melatonin, and comfort, a light environment with an illuminance of 1000 lx is the healthiest. This illuminance provides high performance for the subjects, ensures that melatonin concentration does not decrease, and also provides high comfort.

[0093] In this application, a server acquires a training sample set and target smart cockpit lighting environment data. The training sample set includes lighting environment data from several different smart cockpits and subjects within those smart cockpits. The training sample set is preprocessed to generate a training sample set with labeled information, which characterizes abnormal physiological states of users. Based on preset processing rules, the training sample set with labeled information is processed to generate a training set and a test set. A preset lighting environment evaluation index model matching the labeled information is obtained. The preset lighting environment evaluation index model is trained based on the training set and the test set to generate a target lighting environment evaluation index model. The target smart cockpit lighting environment data is preprocessed to generate target user attribute information. Based on the target lighting environment evaluation index model, the target user attribute information is processed to generate lighting environment evaluation indices. Based on the target lighting environment evaluation index model, the lighting environment evaluation indices are processed to generate target illuminance. By simulating three typical smart cockpit lighting environments, physiological and psychological data were collected from the subjects, including melatonin concentration, attention level, and comfort data. Statistical analysis of the experimental data led to the establishment of a lighting environment evaluation index model. A healthy lighting environment evaluation index for smart cockpits was defined, taking into account both physiological and psychological health, with the working environment of the occupants as the weight. The optimal illuminance for the lighting environment evaluation index was then obtained, which satisfies the requirements of the scenario while also benefiting the physiological and psychological health of the occupants.

[0094] Optionally, in another embodiment based on the method described above in this application, the step of extracting features from the training sample set to determine the original feature library includes:

[0095] Based on preset processing rules, standard material information is processed to generate key factors of non-visual influence of light. These key factors of non-visual influence of light are the characteristics of the effect of the light environment in the cabin of an intelligent car on human physiology.

[0096] The standardized features are processed based on preset feature filtering and dimensionality reduction rules to generate original features;

[0097] Generate an original feature library from several original features.

[0098] In one implementation, the experiment selected objective alertness and attention levels as the main evaluation indicators, and visual perception (comfort) as an auxiliary evaluation indicator, as shown in Table 3.

[0099] Table 3. Human body evaluation indicators and evaluation methods selected in this invention.

[0100]

[0101] Attention levels were assessed using the Psychomotor Vigilance Task (PVT), a performance test for evaluating visual performance / sustained attention. Evaluation dimensions mainly include reaction accuracy, attention neglect rate, reaction miss rate, and average correct reaction time. Since the PVT is a simple response test to obvious visual signals, it does not require prior learning or adaptation and is not affected by individual differences in ability.

[0102] Alertness is evaluated using melatonin (MT) levels. MT is a biochemical indicator used to measure alertness; measuring the level of melatonin (MT) in the human body reflects circadian rhythms and alertness. This invention uses an enzyme-linked immunosorbent assay (ELISA) to measure the hormone content in human body fluid samples, thereby determining the level of melatonin (MT) in the human body.

[0103] Comfort was assessed using a comfort questionnaire, which provided a comprehensive subjective evaluation of the testing process after completion. Participants rated their overall comfort level and comfort levels at each stage on a subjective scale, ranging from -10 (very uncomfortable) to 10 (very comfortable), reflecting the impact and changes in comfort under different lighting conditions.

[0104] By applying the above technical solutions, the server acquires training sample sets and target smart cockpit light environment data. The training sample set includes light environment data from several different smart cockpits and subjects within those smart cockpits. The training sample set is preprocessed to generate a training sample set with labeled information, which characterizes abnormalities in the user's physiological state. Standard material information is processed based on preset processing rules to generate key factors of non-visual light effects, which are the characteristics of the impact of the light environment within the smart car cockpit on human physiology. Standardized features are processed based on preset feature selection and dimensionality reduction rules to generate original features. Several original features are used to generate an original feature library. The original feature library is divided into various feature datasets to generate training and test sets. A classifier is used to predict the results of each test set within the original feature library. A preset algorithm is used to train the test sets within the original feature library to obtain prediction results for the test set classes. Based on the prediction results and the test set class prediction results, training samples with labeled information are generated.

[0105] Obtain a preset light environment evaluation index model that matches the identification information; extract multiple sets of data from the training set, where each set contains a preset number of data samples, and at least one data sample includes identification information; train the initial light environment evaluation index model based on the data samples in the multiple sets of data to generate a trained light environment evaluation index model; process the trained light environment evaluation index model based on the test set to generate test results; if the data sample containing identification information in the test results indicates an abnormality in the user's physiological state, then the trained light environment evaluation index model is used as the target light environment evaluation index model. The target intelligent cockpit lighting environment data is preprocessed to generate the target user's attention factor, melatonin factor, comfort factor, attention influence weight, melatonin concentration influence weight, and comfort influence weight. These factors are then used as the target user's attribute information. Finally, the target user's attribute information is processed based on the target lighting environment evaluation index model to generate lighting environment evaluation indices.

[0106] The target light environment evaluation index model includes the calculation formula for obtaining the light environment evaluation index. The calculation formula is: I(x)=λ RT P RT (x)+λ MT P MT (x)+λ CF P CF (x); λ RT +λ MT +λ CF =1; where I(x) is the light environment evaluation index; λ RT , λ MT and λ CF These are the weights for the influence of attention, melatonin concentration, and comfort; P RT (x), P MT (x) and P CF (x) represent the attention factor, melatonin factor, and comfort factor as they vary with x, where x is the illuminance (1x). The illuminance is generated by processing the illuminance evaluation index based on the target illuminance evaluation index model. The target illuminance evaluation index model includes the calculation formula for obtaining the target illuminance, which is: Where x is the target illuminance; P i (x) represents the factor that varies with illuminance; I(x) represents the light environment evaluation index; λ i Let λ be the influence weight of the influence factor named after influence i.i The range is [0, 1], and k is the total number of influencing factors. Physiological and psychological data were collected from subjects by simulating three typical smart cockpit lighting environments, obtaining data on melatonin concentration, attention level, and comfort. Statistical analysis of the experimental data led to the establishment of a lighting environment evaluation index model. A smart cockpit healthy lighting environment evaluation index was defined, taking into account both physiological and psychological health, with the working environment of the personnel inside the cockpit as the weight. The optimal illuminance for the lighting environment evaluation index was then obtained, which satisfies both the scene requirements and is beneficial to the physiological and psychological health of the personnel inside the cockpit.

[0107] In one implementation, such as Figure 2 As shown, this application also provides a device for adjusting the lighting environment of a smart car cabin, comprising:

[0108] The acquisition module 201 is used to acquire training sample sets and target smart cockpit light environment data, wherein the training sample set includes light environment data of several different smart cockpits and subjects in the smart cockpit; and to acquire a preset light environment evaluation index model that matches the identification information.

[0109] The processing module 202 is used to preprocess the training sample set to generate a training sample set with identification information, wherein the identification information is used to characterize the abnormality of the user's physiological state; process the training sample set with identification information based on preset processing rules to generate a training set and a test set; train the preset light environment evaluation index model based on the training set and the test set to generate a target light environment evaluation index model; preprocess the target smart cockpit light environment data to generate target user attribute information; process the target user attribute information based on the target light environment evaluation index model to generate a light environment evaluation index; and process the light environment evaluation index based on the target light environment evaluation index model to generate a target illuminance.

[0110] In another embodiment of this application, the processing module 202 is configured to preprocess the training sample set to generate a training sample set with identification information, including:

[0111] Feature extraction is performed on the training sample set to determine the original feature library;

[0112] Based on the original feature library, divide each feature dataset to generate training and test sets;

[0113] The original feature library is divided into various test sets using a classifier for prediction, and the prediction results are determined.

[0114] The original feature library is divided into training sets for training using a preset algorithm, and the test set class prediction results are obtained.

[0115] Based on the prediction results and the test set class prediction results, training samples with labeling information are generated.

[0116] In another embodiment of this application, the processing module 202 is configured to extract features from the training sample set to determine the original feature library, including:

[0117] Based on preset processing rules, standard material information is processed to generate key factors of non-visual influence of light. These key factors of non-visual influence of light are the characteristics of the effect of the light environment in the cabin of an intelligent car on human physiology.

[0118] The standardized features are processed based on preset feature filtering and dimensionality reduction rules to generate original features;

[0119] Generate an original feature library from several original features.

[0120] In another embodiment of this application, the processing module 202 is configured to train the preset light environment evaluation index model based on the training set and the test set to generate a target light environment evaluation index model, including:

[0121] Multiple sets of data are extracted from the training set, wherein each set of data contains a preset number of data samples, and at least one data sample includes identification information.

[0122] The initial light environment evaluation index model is trained based on data samples from multiple sets of data groups to generate a trained light environment evaluation index model.

[0123] The trained light environment evaluation index model is processed based on the test set to generate test results;

[0124] If the data sample containing the identification information in the test results indicates an abnormality in the user's physiological state, then the trained light environment evaluation index model will be used as the target light environment evaluation index model.

[0125] In another embodiment of this application, the processing module 202 is configured to preprocess the target smart cockpit light environment data to generate target user attribute information, including:

[0126] The target smart cockpit light environment data is preprocessed to generate the target user's attention factor, target user's melatonin factor, target user's comfort factor, target user's attention influence weight, target user's melatonin concentration influence weight, and target user's comfort influence weight.

[0127] The target user's attribute information is based on the target user's attention factor, melatonin factor, comfort factor, attention influence weight, melatonin concentration influence weight, and comfort influence weight.

[0128] In another embodiment of this application, the processing module 202 is configured to process the attribute information of the target user based on the target light environment evaluation index model to generate a light environment evaluation index, including:

[0129] The target light environment evaluation index model includes a calculation formula for obtaining the light environment evaluation index, and the calculation formula is as follows:

[0130] I(x)=λ RT P RT (x)+λ MT P MT (x)+λ CF P CF (x);

[0131] λ RT +λ MT +λ CF =1;

[0132] Where I(x) is the light environment evaluation index; λ RT , λ MT and λ CF These are the weights for the influence of attention, melatonin concentration, and comfort; P RT (x), P MT (x) and P CF (x) represent the attention factor, melatonin factor, and comfort factor as they vary with x, where x is the illuminance (lx).

[0133] In another embodiment of this application, the processing module 202 is configured to process the light environment evaluation index based on the target light environment evaluation index model to generate a target illuminance, including:

[0134] The target light environment evaluation index model includes a calculation formula for obtaining the target illuminance, and the calculation formula is as follows:

[0135]

[0136]

[0137]

[0138] Where x is the target illuminance; P i x is a factor that varies with illuminance; I(x) is a light environment evaluation index; λ i Let λ be the influence weight of the influence factor named after influence i. i The range is [0,1], and k is the total number of influencing factors.

[0139] This application provides an electronic device, such as... Figure 3 As shown, the electronic device 3 includes a first processor 300, a memory 301, a bus 302, and a communication interface 303. The first processor 300, the communication interface 303, and the memory 301 are connected via the bus 302. The memory 301 stores a computer program that can run on the first processor 300. When the first processor 300 runs the computer program, it executes the method for adjusting the intelligent car cabin light environment provided in any of the foregoing embodiments of this application.

[0140] The memory 301 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 303 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc.

[0141] Bus 302 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory 301 is used to store programs. After receiving an execution instruction, the first processor 300 executes the program. The method for adjusting the intelligent vehicle cockpit lighting environment disclosed in any of the foregoing embodiments of this application can be applied to the first processor 300, or implemented by the first processor 300.

[0142] The first processor 300 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the first processor 300 or by instructions in software form. The first processor 300 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application may be executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules may reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 301. The first processor 300 reads the information in memory 301 and, in conjunction with its hardware, completes the steps of the above method.

[0143] The electronic devices provided in the above embodiments of this application and the method for adjusting the lighting environment of the intelligent car cockpit provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the applications stored therein.

[0144] This application provides a computer-readable storage medium, such as... Figure 4 As shown, the computer-readable storage medium 401 stores a computer program, which is read and executed by the second processor 402 to implement the aforementioned method for adjusting the lighting environment of the intelligent vehicle cabin.

[0145] The technical solutions of this application embodiment, in essence, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an electronic device (which may be an air conditioner, refrigeration unit, personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the method described in the embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0146] The computer-readable storage medium provided in the above embodiments of this application and the method for adjusting the light environment of a smart car cockpit provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the applications stored therein.

[0147] This application provides a computer program product, including a computer program, which is executed by a third processor to implement the method described above.

[0148] The computer program product provided in the above embodiments of this application and the method for adjusting the lighting environment of the intelligent car cockpit provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application stored therein.

[0149] It should be noted that in this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0150] The various embodiments in this application are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of the method, electronic device, electronic device, and readable storage medium for evaluating the adjustment of the lighting environment in a smart car cockpit are basically similar to the embodiments of the method for adjusting the lighting environment in a smart car cockpit described above, and therefore are described relatively simply. Relevant parts can be referred to the descriptions of the embodiments of the method for adjusting the lighting environment in a smart car cockpit described above.

[0151] While this application discloses the above information, it is not limited thereto. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of this application; therefore, the scope of protection of this application shall be determined by the scope defined in the claims.

Claims

1. A method for adjusting the lighting environment of an intelligent car cabin, characterized in that, include: Acquire training sample set and target smart cockpit light environment data, wherein the training sample set includes light environment data of several different smart cockpits and subjects in the smart cockpit; The training sample set is preprocessed to generate a training sample set with identification information, wherein the identification information is used to characterize the abnormality of the user's physiological state. The training sample set with the identification information is processed according to the preset processing rules to generate a training set and a test set; Obtain a preset light environment evaluation index model that matches the identification information; The preset light environment evaluation index model is trained based on the training set and the test set to generate the target light environment evaluation index model. The target smart cockpit's light environment data is preprocessed to generate the target user's attribute information; The target user's attribute information is processed based on the target light environment evaluation index model to generate a light environment evaluation index. Based on the target light environment evaluation index model, the light environment evaluation index is processed to generate the target illuminance. The target light environment evaluation index model includes a calculation formula for obtaining the target illuminance, and the calculation formula is as follows: ; ; 0; where, Target illuminance; is a factor that varies with illuminance; I(x) is a light environment evaluation index; For The influence weights of factors affecting naming The range is [0,1]. It represents the total number of influencing factors; the target illuminance is obtained based on the calculation formula for target illuminance.

2. The method as described in claim 1, characterized in that, The step of processing the training sample set with identification information based on preset processing rules to generate a training set and a test set includes: Feature extraction is performed on the training sample set to determine the original feature library; Based on the original feature library, divide each feature dataset to generate training and test sets; The original feature library is divided into various test sets using a classifier for prediction, and the prediction results are determined. The original feature library is divided into training sets for training using a preset algorithm, and the test set class prediction results are obtained. Based on the prediction results and the test set class prediction results, training samples with labeling information are generated.

3. The method as described in claim 2, characterized in that, The step of extracting features from the training sample set to determine the original feature library includes: Based on preset processing rules, standard material information is processed to generate key factors of non-visual influence of light. These key factors of non-visual influence of light are the characteristics of the effect of the light environment in the cabin of an intelligent car on human physiology. Based on preset feature selection and dimensionality reduction rules, standardized features are processed to generate original features; An original feature library is generated using several original features.

4. The method as described in claim 1, characterized in that, The step of training the preset light environment evaluation index model based on the training set and the test set to generate the target light environment evaluation index model includes: Multiple sets of data are extracted from the training set, wherein each set of data contains a preset number of data samples, and at least one data sample includes identification information. The initial light environment evaluation index model is trained based on data samples from multiple sets of data groups to generate a trained light environment evaluation index model. The trained light environment evaluation index model is processed based on the test set to generate test results; If the data sample containing the identification information in the test results indicates an abnormality in the user's physiological state, then the trained light environment evaluation index model will be used as the target light environment evaluation index model.

5. The method as described in claim 1, characterized in that, The preprocessing of the target smart cockpit's light environment data to generate the target user's attribute information includes: The target smart cockpit light environment data is preprocessed to generate the target user's attention factor, target user's melatonin factor, target user's comfort factor, target user's attention influence weight, target user's melatonin concentration influence weight, and target user's comfort influence weight. The target user's attribute information is obtained based on the target user's attention factor, melatonin factor, comfort factor, attention influence weight, melatonin concentration influence weight, and comfort influence weight.

6. The method as described in claim 5, characterized in that, The step of processing the attribute information of the target user based on the target light environment evaluation index model to generate a light environment evaluation index includes: The target light environment evaluation index model includes a calculation formula for obtaining the light environment evaluation index, and the calculation formula is as follows: ； ； in, As an indicator for evaluating the light environment; , and These are the weights of attention, melatonin concentration, and comfort. , and They are respectively The changing attention factor, melatonin factor, and comfort factor Illuminance (lx).

7. A device for adjusting the lighting environment of an intelligent car cabin, characterized in that, The apparatus for implementing the method of claim 1 includes: The acquisition module is used to acquire training sample sets and target smart cockpit light environment data, wherein the training sample set includes light environment data of several different smart cockpits and subjects in the smart cockpit; and to acquire a preset light environment evaluation index model that matches the identification information. The processing module is used to preprocess the training sample set to generate a training sample set with identification information, wherein the identification information is used to characterize the abnormality of the user's physiological state; process the training sample set with identification information based on preset processing rules to generate a training set and a test set; train a preset light environment evaluation index model based on the training set and the test set to generate a target light environment evaluation index model; preprocess the target smart cockpit light environment data to generate target user attribute information; process the target user attribute information based on the target light environment evaluation index model to generate light environment evaluation indexes; and process the light environment evaluation indexes based on the target light environment evaluation index model to generate target illuminance.

8. An electronic device, characterized in that, include: First processor; and memory for storing executable instructions of the first processor; The first processor is configured to execute the method for adjusting the lighting environment of an intelligent vehicle cabin according to any one of claims 1 to 6 by executing the executable instructions.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the second processor, it implements the method for adjusting the lighting environment of the intelligent vehicle cabin as described in any one of claims 1 to 6.

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