A light source driving module and a vehicle headlamp containing the same

Abstract

The present invention discloses a light source driving module and a vehicle lamp containing the same, which relates to the technical field of light source driving. The light source driving module includes a target analysis unit configured to monitor the current speed value of the vehicle, the current state parameter set of the vehicle lamp, and the current environmental state parameter set within a set area, and input them into a pre-trained target analysis model to analyze the target state parameter set of the vehicle lamp within the set area; a current analysis unit configured to analyze the target state parameter set of the vehicle lamp within the set area; and a driving unit configured to send a light source driving instruction to the vehicle lamp. By introducing the pre-trained target analysis model, the present invention realizes the real-time acquisition and data fusion of the current vehicle speed, the vehicle lamp state, and the regional environmental parameters, enabling the dynamic generation of the target states of the brightness, color temperature, and beam angle of the vehicle lamp. Thus, the driving module can automatically adjust the driving current set according to the real-time data, so as to accurately match the actual lighting requirements under various road and environmental conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of light source driving, and in particular to a light source driving module and a vehicle lamp containing the same. Background Art

[0002] In recent years, with the rapid development of the automobile industry and the increasing requirements for energy conservation and emission reduction, LED headlights, as the core components of automobile lighting systems, have received widespread attention. LED headlights have the advantages of small size, fast response speed, high energy efficiency, and long service life. They have gradually replaced traditional halogen lamps and xenon lamps and become an important configuration of modern automobiles. However, the performance of LED headlights not only depends on the LED device itself, but also the design and control strategy of its driver module have a key impact on the overall lighting effect and energy consumption performance.

[0003] Traditional headlight driving technology usually adopts a fixed current driving mode to drive the LED according to pre-set working parameters. It fails to fully consider the real-time changing environmental conditions, vehicle speed, and the working status of the headlight itself during driving. Due to the influence of road environment, weather changes and vehicle dynamic factors, the headlights may have problems such as insufficient brightness, unstable color temperature or misaligned beam angle during actual use, which directly affects the driver's visual safety and driving experience. In addition, the fixed driving mode also has certain limitations in energy efficiency, which can easily lead to energy waste, accelerate LED aging, and shorten service life.

[0004] Therefore, how to achieve dynamic control of the headlight driving current and perform intelligent analysis and adjustment based on real-time collection of vehicle status and environmental parameters has become an important issue that needs to be urgently solved in the current LED headlight driving technology. By introducing advanced target analysis models and adaptive control strategies, it is possible to not only optimize the brightness, color temperature and beam angle of the headlights, but also achieve energy saving and consumption reduction under different driving conditions, further improving the safety and economy of the automotive lighting system. This provides a new technological breakthrough for the development of future automotive intelligent and high-efficiency lighting systems.

[0005] The prior art, such as the light source driving device and the light source driving method disclosed in the invention patent application with the announcement number: CN106658886A, turns off each light-emitting unit in turn in each unit time period, wherein when one of the light-emitting units is turned off, the remaining light-emitting units are turned on to maintain the total brightness of the light-emitting units at a preset brightness. By using the light source driving device and the light source driving method of the present invention, the total brightness of the light-emitting units can be maintained at a preset brightness, so that the purpose of effectively saving power can be achieved without affecting the light quality.

[0006] Based on the above solutions, it is found that the limitations of the existing technologies at least include the following problems. First, the existing vehicle headlight drive systems are usually designed based on fixed working modes and environmental conditions, lacking the flexibility to adjust the light source output by real-time input of vehicle speed, headlight status, and environmental parameters. As a result, it is difficult to accurately adapt the brightness, color temperature, and beam angle of the headlights to different road conditions, and it is often difficult to fully optimize the lighting effect and energy efficiency. For example, in complex driving environments such as high-speed or low-speed driving, the brightness and color temperature of the headlights may not be dynamically optimized in a timely manner, resulting in a situation that affects the driver's vision and wastes energy. Second, in traditional technologies, the operating parameters of the headlights are usually fixed at the design stage to derive a formed system solution, lacking a response mechanism for real-time data feedback and environmental changes. This method not only fails to effectively adapt to the diverse needs of road and vehicle speed changes but may also cause additional energy waste due to improper light source matching, affecting the lifespan of the headlights during long-term driving and further affecting the overall vehicle energy efficiency and driving experience. Summary of the Invention

[0007] Aiming at the deficiencies of the existing technologies, the present invention provides a light source drive module and a vehicle headlight containing the same, which solves the problem of lacking real-time dynamic adjustment of the brightness, color temperature, and beam angle of the vehicle headlight in the existing technologies.

[0008] To achieve the above objectives, the present invention is realized through the following technical solutions: A light source drive module includes: a target analysis unit for monitoring the current vehicle speed value, the current status parameter set of the vehicle headlight, and the current environmental status parameter set within a set area, and inputting them into a pre-trained target analysis model for data analysis to obtain the target status parameter set of the vehicle headlight within the set area. The current status parameter set includes the current headlight brightness value, the current headlight color temperature value, and the current headlight beam angle value. The current environmental status parameter set includes the current environmental brightness value, the current environmental temperature value, and the current environmental humidity value. The target status parameter set includes the headlight target brightness value, the headlight target color temperature value, and the headlight target beam angle value; a current analysis unit for analyzing the drive current set of the vehicle headlight based on the target status parameter set of the vehicle headlight within the set area. The drive current set includes the brightness drive current value, the color temperature drive current value, and the beam angle drive current value; a drive unit for sending a light source drive instruction to the vehicle headlight based on the vehicle headlight drive current set.

[0009] Further, the target analysis model is specifically a deep neural network model, which consists of an input layer, several hidden layers, and an output layer, where: the input layer is used to receive the current vehicle speed value, the current state parameter set of the vehicle lights, and the current environmental state parameter set within the set area, and perform preprocessing; the hidden layer is used to perform feature analysis and processing on the preprocessed current vehicle speed value, the current state parameter set of the vehicle lights, and the current environmental state parameter set within the set area; the output layer is used to convert the feature analysis result of the hidden layer into the target state parameter set of the vehicle lights within the set area.

[0010] Further, the specific steps for analyzing the brightness drive current value of the vehicle lights are as follows: Obtain the reference current value of the vehicle light brightness, the reference value of the vehicle speed, and the reference value of the environmental brightness; Obtain the brightness current response index, and perform comprehensive analysis in combination with the current vehicle speed value, the reference value of the vehicle speed, the reference current value of the vehicle light brightness, the current brightness value of the vehicle light, the target brightness value of the vehicle light within the set area, the current environmental brightness value within the set area of the vehicle light, and the reference value of the environmental brightness to obtain the brightness drive current value of the vehicle light.

[0011] Further, the specific steps for obtaining the brightness current response index are as follows: Pass several measurement currents into the light source, and record the brightness values of the light source under each measurement current respectively; Perform logarithmic linearization processing on the brightness values of the light source under each measurement current; After logarithmic linearization, perform data fitting analysis on the brightness values of the light source under each measurement current to obtain the brightness current response index.

[0012] Further, the specific formula for calculating the brightness drive current value of the vehicle light is as follows: ; where is the brightness drive current value of the vehicle light, is the reference current value of the vehicle light brightness, is the target brightness value of the vehicle light within the set area, is the current brightness value of the vehicle light, is the current vehicle speed value, is the reference value of the vehicle speed, is the vehicle speed brightness influence coefficient stored in the database, is the brightness current response index stored in the database, is the reference value of the environmental brightness, is the current environmental brightness value within the set area of the vehicle light, is the environmental brightness influence coefficient stored in the database.

[0013] Further, the specific steps for analyzing the color temperature drive current value of the vehicle lamp are as follows: Obtain the color temperature reference current value of the vehicle lamp, the color temperature adjustment amplitude value, and the environmental reference set, where the environmental reference set includes the environmental temperature reference value and the environmental humidity reference value; Read the current vehicle speed value and the vehicle speed reference value, and comprehensively analyze them in combination with the color temperature reference current value of the vehicle lamp, the current color temperature value of the vehicle lamp, the target color temperature value of the vehicle lamp in the set area, the color temperature adjustment amplitude value, the current environmental temperature value in the set area of the vehicle lamp, the current environmental humidity value, and the environmental reference set to obtain the color temperature drive current value of the vehicle lamp.

[0014] Further, the specific formula for calculating the color temperature drive current value of the vehicle lamp is as follows: ; where is the color temperature drive current value of the vehicle lamp, is the color temperature reference current value of the vehicle lamp, is the target color temperature value of the vehicle lamp in the set area, is the current color temperature value of the vehicle lamp, is the color temperature influence coefficient stored in the database, is the environmental temperature reference value, is the current environmental temperature value in the set area of the vehicle lamp, is the environmental humidity reference value, is the current environmental humidity value in the set area of the vehicle lamp, is the temperature-humidity combined influence coefficient stored in the database, is the vehicle speed reference value, is the current vehicle speed value, is the vehicle speed-color temperature influence coefficient stored in the database.

[0015] Further, the specific steps for analyzing the beam angle drive current value of the vehicle lamp are as follows: Obtain the beam angle reference current value of the vehicle lamp; Read the current vehicle speed value and the vehicle speed reference value, and comprehensively analyze them in combination with the color temperature reference current value of the vehicle lamp, the current beam angle value of the vehicle lamp, and the target beam angle value of the vehicle lamp in the set area to obtain the beam angle drive current value of the vehicle lamp.

[0016] Further, the specific formula for calculating the beam angle drive current value of the vehicle lamp is as follows: ; where is the beam angle drive current value of the vehicle lamp, is the beam angle reference current value of the vehicle lamp, is the target beam angle value of the vehicle lamp in the set area, is the current beam angle value of the vehicle lamp, is the beam angle attenuation coefficient stored in the database, is the beam angle adjustment coefficient stored in the database, is the current vehicle speed value, is the reference vehicle speed value, is the attenuation coefficient of the vehicle speed beam angle stored in the database, is the adjustment coefficient of the vehicle speed beam angle stored in the database.

[0017] A vehicle headlight, comprising: a data acquisition module for acquiring the current vehicle speed value, the current state parameter set of the headlight, and the current environmental state parameter set within a set area; a light source driving module for analyzing the light source driving current set based on the current vehicle speed value, the current state parameter set of the headlight, and the current environmental state parameter set within the set area, and sending a light source driving instruction to the headlight; and an execution module for receiving the light source driving instruction and performing a light source driving operation on the headlight.

[0018] The present invention has the following beneficial effects:

[0019] (1) By introducing a pre-trained target analysis model, the light source driving module realizes real-time acquisition and data fusion of the current vehicle speed, headlight state, and regional environmental parameters, enabling dynamic generation of the target states of the headlight brightness, color temperature, and beam angle. Thus, the driving module can automatically adjust the driving current set according to real-time data, accurately matching the actual lighting requirements under various road and environmental conditions, effectively improving the lighting effect and energy utilization efficiency, and avoiding the blind spots and resource waste problems caused by fixed parameter design.

[0020] (2) The light source driving module adopts multi-dimensional parameter calibration and the influence coefficients stored in the database, and establishes targeted calculation logics for the driving currents of brightness, color temperature, and beam angle respectively, fully considering the comprehensive influence of LED non-linear response, environmental brightness, temperature and humidity, and vehicle speed. Through experimental calibration and data fitting, the brightness current response index, color temperature adjustment range, beam angle attenuation, and adjustment coefficient are obtained, enabling the driving module to finely control each optical parameter, effectively overcoming the problems of fixed parameters and inability to respond to environmental changes in real time in the prior art, thereby reducing energy waste and extending the life of LEDs.

[0021] (3) The light source driving module forms an intelligent light source driving system through three major modules: a target analysis unit, a current analysis unit, and a driving unit. It can not only generate the target state parameters of the headlight based on real-time data, but also extract and fuse data through multi-level and deep neural networks, thereby realizing precise control of the headlight brightness, color temperature, and beam angle. Compared with traditional technologies, the present solution can flexibly adapt to complex road and environmental changes, ensure stable and balanced operation of lighting, while significantly improving energy utilization efficiency and reducing system power consumption, providing stronger technical support for the overall energy efficiency and driving experience of the vehicle.

[0022] Of course, it is not necessary for any product implementing the present invention to achieve all of the above-mentioned advantages simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a block diagram of a light source driving module according to the present invention.

[0024] Figure 2 It is a specific step flowchart for analyzing the brightness driving current value of a vehicle lamp in a light source driving module according to the present invention.

[0025] Figure 3 It is a block diagram of a vehicle lamp according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] Please refer to Figure 1 , an embodiment of the present invention provides a technical solution: a light source driving module, including: a target analysis unit, configured to monitor the current vehicle speed value, the current state parameter set of the vehicle lamp, and the current environmental state parameter set in a set area, and input them into a pre-trained target analysis model for data analysis to obtain the target state parameter set of the vehicle lamp in the set area. The current state parameter set includes the current vehicle lamp brightness value, the current vehicle lamp color temperature value, and the current vehicle lamp beam angle value. The current environmental state parameter set includes the current environmental brightness value, the current environmental temperature value, and the current environmental humidity value. The target state parameter set includes the vehicle lamp target brightness value, the vehicle lamp target color temperature value, and the vehicle lamp target beam angle value; a current analysis unit, configured to analyze the driving current set of the vehicle lamp based on the target state parameter set of the vehicle lamp in the set area. The driving current set includes the brightness driving current value, the color temperature driving current value, and the beam angle driving current value; a driving unit, configured to send a light source driving instruction to the vehicle lamp based on the vehicle lamp driving current set.

[0027] Specifically, the target analysis model is specifically a deep neural network model, which consists of an input layer, several hidden layers, and an output layer, where:

[0028] The input layer is configured to receive the current vehicle speed value, the current state parameter set of the vehicle lamp, and the current environmental state parameter set in the set area, and perform preprocessing. Its function is to receive external data and transfer it to the next layer of the neural network. It is the interface between the model and the external information. Each node of the input layer corresponds to a feature, such as the vehicle speed, the brightness, color temperature, and beam angle of the current vehicle lamp, the temperature and humidity in the environment, etc. The data of the input layer will be transferred to the hidden layer through weights and biases for subsequent calculation and learning. The main purpose of the input layer is to convert the original external data into a form that can be used for model processing.

[0029] The hidden layer is used to perform feature analysis and processing on the preprocessed current vehicle speed value, the current state parameter set of the vehicle lights, and the current environmental state parameter set within the set area. Its role is to learn complex patterns and features from the input data and map them to the next layer. The hidden layer contains multiple neurons, each neuron is connected to the nodes of the previous layer, and after weighted summation, an activation function (such as ReLU) is applied to generate the output. The output of each layer is a further abstraction and feature extraction of the input data. Through multiple hidden layers, the model can capture the non-linear relationships in the data, learn higher-order features, and thus improve the prediction ability of the model. The hidden layer is the core part of the neural network, which determines the performance and complexity of the network.

[0030] The output layer is used to convert the feature analysis results of the hidden layer into the target state parameter set of the vehicle lights within the set area. Its role is to convert the features extracted by the hidden layer into the final prediction result. It maps the output of the hidden layer to the target output space through a linear combination of weights and biases. For example, in the scenario of vehicle light control, the output layer will predict the target brightness, color temperature, and beam angle of the vehicle lights. Each node of the output layer corresponds to a target parameter, and the final predicted value is the value of each target parameter given by the model. The main task of the output layer is to output a reasonable result according to the learning process of the model to meet specific application requirements.

[0031] In this embodiment, the input layer includes 7 nodes (corresponding to the current state parameters of the vehicle and the environment).

[0032] There are three hidden layers, among which:

[0033] Hidden layer 1 includes 128 neurons, activated by ReLU.

[0034] Hidden layer 2 includes 64 neurons, activated by ReLU.

[0035] Hidden layer 3 includes 32 neurons, activated by ReLU.

[0036] The output layer includes 3 nodes (target brightness, target color temperature, target beam angle), and predicts the values through a linear activation function.

[0037] Among them, the pre-training steps of the target analysis model are as follows:

[0038] Collect and prepare data. The data should include input features (vehicle and environmental states) and target parameters (target vehicle light states). These data need to be labeled and representative. Each sample should contain the vehicle state, environmental state, and the corresponding target values of the vehicle lights.

[0039] Input features (vehicle and environmental state parameters):

[0040] Vehicle speed (unit: km / h or m / s);

[0041] Current headlight brightness (unit: lumens);

[0042] Current headlight color temperature (unit: Kelvin);

[0043] Current headlight beam angle (unit: degrees);

[0044] Current ambient brightness (unit: lux);

[0045] Current ambient temperature (unit: °C);

[0046] Current ambient humidity (unit: %);

[0047] Target parameters (target state of the headlight):

[0048] Target brightness;

[0049] Target color temperature;

[0050] Target beam angle;

[0051] The target values (target brightness, target color temperature, and target beam angle) for each data sample are determined based on environmental factors and vehicle status. These target values can be obtained through simulation or actual testing. Data is generated under various different vehicle and environmental conditions to make the training set rich in diversity.

[0052] Preprocess the collected data (a key step to ensure that the input data can be effectively processed by the neural network) Data preprocessing:

[0053] Normalization: Normalize each input feature (such as vehicle speed, current headlight brightness, ambient temperature, etc.), that is, convert its value to the form of zero mean and unit variance;

[0054] Missing value handling: If there are missing values in the dataset, usually fill them (such as using the mean, median, mode to fill), or delete the samples with missing data.

[0055] Data splitting: Split the dataset into a training set, a validation set, and a test set:

[0056] Training set: Used to train the neural network (usually accounting for 70% of the dataset).

[0057] Validation set: Used for model tuning to help select the optimal hyperparameters (usually accounting for 15%).

[0058] Test set: Used for the final evaluation of the model (usually accounting for 15%).

[0059] The mean squared error (MSE) is used as the loss function to measure the gap between the model output and the true target.

[0060] At the beginning of training, the weights and biases of all neural networks are randomly initialized. Xavier initialization or He initialization can be used to ensure proper weight initialization and avoid problems such as gradient explosion or gradient vanishing.

[0061] For each training sample, the network calculates the target headlight state through forward propagation:

[0062] The input data is passed through the input layer to the first hidden layer.

[0063] The hidden layer calculates the output by weighted summation and applying an activation function (ReLU).

[0064] The data passes through multiple hidden layers in sequence and finally generates a predicted value of the target headlight state through the output layer.

[0065] The loss function calculates the gap between the model prediction result and the actual target. The goal of the model is to minimize this loss value to improve the prediction accuracy.

[0066] The gradient of each weight is calculated through the backpropagation algorithm, and the weights are updated according to the gradient values. Common optimization methods include stochastic gradient descent (SGD) and the Adam optimizer (an adaptive learning rate optimization method that can automatically adjust the learning rate of each parameter to accelerate convergence and avoid local minima).

[0067] The training process includes multiple epochs, that is, traversing the entire training dataset multiple times. The weights are updated in each iteration, gradually reducing the value of the loss function. After each epoch is completed, the loss value on the validation set is calculated to verify whether the model is overfitting during training.

[0068] To avoid overfitting, the early stopping method is used: when the loss on the validation set no longer decreases, stop the training process.

[0069] The validation set is used to adjust hyperparameters (such as learning rate, number of neurons in the hidden layer, etc.). If the loss on the validation set starts to increase, it may indicate overfitting, and the training needs to be stopped or the model structure needs to be adjusted.

[0070] After training is completed, the test set is used to evaluate the generalization ability of the model. Calculate the loss on the test set to evaluate the model's prediction ability for unseen data.

[0071] Hyperparameter tuning: Use methods such as grid search or random search to tune hyperparameters (such as learning rate, batch size, number of hidden layers, etc.) to find the optimal configuration.

[0072] Regularization: Overfitting can be reduced by techniques such as L2 regularization or dropout.

[0073] In this implementation, by inputting multi-source data such as vehicle speed, headlight status, and environmental parameters into a deep neural network model, and through standardization, missing value processing, dataset partitioning, and multiple iterative trainings, the potential laws in the data can be fully explored and the target brightness, color temperature, and beam angle of the headlights can be predicted, thereby realizing intelligent control of the headlights. This method adopts multiple hidden layers in model design, can capture complex non-linear relationships, and through the cooperation of activation functions (such as ReLU) and mean squared error (MSE) loss functions, the training process has good generalization ability for various input features. In addition, using the validation set and test set for hyperparameter tuning and final performance evaluation, combined with early stopping method and regularization techniques, the risk of overfitting can be effectively reduced and the robustness of the model can be improved. Generally speaking, this set of training and validation processes can not only ensure accurate prediction of headlight target parameters in a multi-dimensional environment, but also flexibly adapt to different road conditions, providing an efficient, stable, and scalable intelligent control means for vehicle lighting.

[0074] Specifically, as Figure 2 shown, the specific steps for analyzing the brightness drive current value of the headlight are as follows: Obtain the headlight brightness reference current value, vehicle speed reference value, and ambient brightness reference value; Obtain the brightness current response index, and comprehensively analyze it in combination with the vehicle's current speed value, vehicle speed reference value, headlight brightness reference current value, current headlight brightness value of the headlight, headlight target brightness value within the set area of the headlight, current ambient brightness value within the set area of the headlight, and ambient brightness reference value to obtain the brightness drive current value of the headlight.

[0075] Among them, the headlight brightness reference current value can look up the typical drive current of the LED under standard working conditions in the LED manufacturer's specification sheet and select an intermediate value (for example, 1.5 A) as the reference current of the system in a normal and balanced state.

[0076] The vehicle speed reference value can be selected a typical reference speed (for example, 50 km / h) according to common road speed limits and driving habits.

[0077] The specific steps for obtaining the brightness current response index are as follows: Pass several measurement currents into the light source and record the brightness values of the light source under each measurement current respectively; Perform logarithmic linearization processing on the brightness values of the light source under each measurement current; After logarithmic linearization, perform data fitting analysis on the brightness values of the light source under each measurement current to obtain the brightness current response index. The specific embodiments are as follows:

[0078] Before starting the formal measurement, first apply a small or medium current (such as 0.8 A) to the LED and let it work for a period of time (such as 5 - 10 minutes) to bring the LED to a thermally stable state.

[0079] This can reduce the brightness fluctuation caused by the change in the LED junction temperature.

[0080] Set a series of current points (for example, from 0.5 A to 2.5 A, with a measurement every 0.1 A or 0.2 A, and the specific step is determined by the experimental accuracy requirements).

[0081] For each current value, record the corresponding brightness.

[0082] If an integrating sphere or spectrometer is used, the luminous flux (lm) or luminous intensity can be directly read.

[0083] If an illuminance meter (lux meter) is used, the measured illuminance value needs to be converted or calibrated to a brightness value; or the relative magnitude of the illuminance value can be used instead of the brightness (as long as consistency is maintained during subsequent fitting).

[0084] After each change in current, wait for several seconds to dozens of seconds until the LED is thermally stable before making a measurement to reduce the transient thermal effect.

[0085] To improve the data reliability, multiple measurements can be made at the same current point, and the average value is taken and the standard deviation or error range is recorded.

[0086] Collect enough measurement points (at least 8 - 12 or more in this embodiment) for more accurate subsequent data fitting.

[0087] Perform logarithmic linearization on the light source brightness values at each measured current and use the linear regression method for fitting analysis to obtain the slope value and intercept value.

[0088] By checking the residuals or calculating the R 2 value, evaluate the goodness of fit.

[0089] If the deviation is too large, check the experimental error, temperature influence, or data distribution for reasonableness.

[0090] The specific formula for calculating the brightness drive current value of the vehicle lamp is as follows: ; where is the brightness drive current value of the vehicle lamp, is the reference current value of the vehicle lamp brightness, is the target brightness value of the vehicle lamp within the set area, is the current brightness value of the vehicle lamp, is the current vehicle speed value, is the reference vehicle speed value, is the speed - brightness influence coefficient stored in the database, is the brightness - current response index stored in the database, is the reference value of ambient brightness, is the current ambient brightness value within the set area of the vehicle lamp, is the ambient - brightness influence coefficient stored in the database.

[0091] It should be noted that the steps for specifically obtaining the speed - brightness influence coefficient and the ambient - brightness influence coefficient stored in the database are as follows: When the vehicle is driving at different speeds, common vehicle models and test environments (such as day and night, different vehicle speeds and ambient brightness) are selected respectively. The brightness changes of the vehicle lamp are measured on - site. Through this test, the changes in the brightness of the light source at different vehicle speeds can be recorded, and based on these data, the specific influence of vehicle speed on brightness can be calculated. The ambient - brightness influence coefficient is obtained by testing the brightness output of the vehicle lamp under different ambient light conditions (such as day, night, dim, etc.), and the correction factor of ambient brightness on the vehicle - lamp brightness is obtained. These data will be stored in the database so that during actual driving, according to the real - time collected vehicle - speed and ambient - brightness values, the driving current of the vehicle lamp can be dynamically adjusted to ensure the best lighting effect.

[0092] The specific implementation example of calculating the brightness driving - current value of the vehicle lamp is as follows. The following data are available:

[0093] The reference current value of the vehicle - lamp brightness is approximately: 1.572 A.

[0094] The target brightness value of the vehicle lamp within the set area is approximately: 780.234.

[0095] The current brightness value of the vehicle lamp is approximately: 620.456.

[0096] The current vehicle - speed value is approximately: 50.125.

[0097] The reference value of vehicle speed is approximately: 80.789.

[0098] The speed - brightness influence coefficient stored in the database is approximately: 0.785.

[0099] The brightness - current response index stored in the database is approximately: 1.23.

[0100] The reference value of ambient brightness is approximately: 950.345.

[0101] The current ambient brightness value within the set area of the vehicle lamp is approximately: 420.756.

[0102] The ambient - brightness influence coefficient stored in the database is approximately: 0.328.

[0103] Substitute the above data into the specific formula for calculating the brightness drive current value of the vehicle lamp respectively, and we get:

[0104] Brightness drive current value of the vehicle lamp = 1.572×((780.234 / 620.456) 1 / （1.23×0.785×（80.789 / 50.125）） ×exp(0.328×((950.345 - 420.756) / 950.345)) ≈ 2.186A.

[0105] In this implementation, by introducing a comprehensive analysis method of reference current, vehicle speed reference value, ambient brightness reference value and brightness current response index in the design and implementation, the brightness of the vehicle lamp can be accurately adjusted under different driving speeds and lighting conditions, significantly improving the intelligence and adaptability of vehicle lighting. Specifically, first obtain the brightness current response index through experimental measurement and logarithmic linearization fitting, and then combine database parameters such as vehicle speed brightness influence coefficient and ambient brightness influence coefficient to dynamically calculate the brightness drive current value of the vehicle lamp. This not only enables the vehicle lamp to maintain an ideal brightness when driving at high or low speeds, but also automatically compensates the light source output according to changes in daytime, night or dim environments, avoiding resource waste and insufficient lighting caused by fixed parameter design. At the same time, during the measurement and data fitting process, fully consider the differences in measurement methods such as LED thermal stability, illuminometer or integrating sphere, as well as the heat dissipation and optical characteristics of the vehicle at different speeds, making this solution more practical and robust. For the overall energy efficiency and driving safety of the vehicle, this method of optimizing the brightness of the vehicle lamp through real-time acquisition and dynamic adjustment can not only significantly extend the life of the LED and reduce energy consumption, but also maintain a stable lighting level under various complex road and weather conditions, thereby improving the clarity and reaction time of the driver's field of vision, which is of great significance for reducing traffic accidents and improving the driving experience.

[0106] Specifically, the specific steps for analyzing the color temperature drive current value of the vehicle lamp are as follows: Obtain the color temperature reference current value of the vehicle lamp, the color temperature adjustment amplitude value, and the ambient reference set. The ambient reference set includes the ambient temperature reference value and the ambient humidity reference value; Read the current vehicle speed value and the vehicle speed reference value, and conduct a comprehensive analysis in combination with the color temperature reference current value of the vehicle lamp, the current color temperature value of the vehicle lamp, the target color temperature value of the vehicle lamp in the set area, the color temperature adjustment amplitude value, the current ambient temperature value and the current ambient humidity value in the set area of the vehicle lamp, and the ambient reference set to obtain the color temperature drive current value of the vehicle lamp.

[0107] Among them, the color temperature reference current value of the vehicle lamp is the reference current for the LED to work under default or standard color temperature conditions (such as the working state recommended by the LED manufacturer). It represents the drive current required for the LED to achieve an ideal spectral output under "normal" color temperature.

[0108] The color temperature adjustment amplitude value represents the maximum increment or variation range of the allowed current during the color temperature adjustment process, that is, the amount of current that the LED is allowed to increase or decrease within a safe range to achieve the target color temperature. The steps to obtain it are as follows:

[0109] By gradually changing the driving current, record the variation range of the LED color temperature until the LED enters the saturation state or exceeds the safe operating area.

[0110] Analyze the data to find out the maximum current increment that the LED can safely adjust on the premise of meeting the spectral adjustment requirements.

[0111] Combined with the thermal characteristics and life requirements of the LED, determine an amplitude value that can not only meet the color temperature adjustment requirements but also not cause excessive thermal load to the LED. This amplitude value can be used as the upper limit during system design (for example, 0.8A or other appropriate values).

[0112] The ambient temperature reference value refers to a reference temperature selected in the experiment or design of LED color temperature adjustment as the ideal or standard ambient temperature. For example, the LED spectral data measured under a certain standard temperature condition indoors or outdoors (such as 25°C, that is, 298K) can be used as a reference.

[0113] The ambient humidity reference value refers to the humidity reference used by the LED color temperature adjustment system under standard conditions (such as indoor standard humidity). It is used to compare with the current ambient humidity to correct the influence of humidity change on the LED color temperature output.

[0114] The specific formula for calculating the color temperature driving current value of the vehicle headlight is as follows: ; where is the color temperature driving current value of the vehicle headlight, is the color temperature reference current value of the vehicle headlight, is the target color temperature value of the vehicle headlight within the set area, is the current color temperature value of the vehicle headlight, is the color temperature influence coefficient stored in the database, is the ambient temperature reference value, is the current ambient temperature value within the set area of the vehicle headlight, is the ambient humidity reference value, is the current ambient humidity value within the set area of the vehicle headlight, is the temperature-humidity combined influence coefficient stored in the database, is the vehicle speed reference value, is the current vehicle speed value, is the vehicle speed color temperature influence coefficient stored in the database.

[0115] It should be explained that the color temperature influence coefficient stored in the database The specific acquisition steps are as follows: In laboratory or field tests, adjust the color temperature output of the vehicle lamp and record the color temperature change data under different target color temperatures, current color temperatures, and multiple sets of comparison environments (such as standard temperature, humidity, or typical driving speed). Subsequently, perform regression or machine learning analysis on these data to extract the key parameters of the color temperature response varying with different environments and vehicle lamp states. Solidify these parameters into the color temperature influence coefficients in the database for real-time calculation of the color temperature drive current of the vehicle lamp.

[0116] The temperature and humidity combined influence coefficient stored in the database , and the vehicle speed color temperature influence coefficient The specific acquisition steps are as follows: In multi-condition tests, collect the color temperature change of the vehicle lamp and the corresponding current and environmental feedback data under different temperature, humidity, and vehicle speed conditions. Use regression or model fitting methods to quantitatively analyze the coupled influence of temperature and humidity on the color temperature and the dynamic influence of vehicle speed on the color temperature. Finally, store the obtained temperature and humidity combined influence coefficient and vehicle speed color temperature influence coefficient in the database to provide real-time reference for the color temperature adjustment formula.

[0117] In this implementation plan, through laboratory and field tests, using regression and machine learning methods, extract the color temperature influence coefficient, temperature and humidity combined influence coefficient, and vehicle speed color temperature influence coefficient from a large amount of LED color temperature adjustment data, and solidify these key parameters into the database, providing a reliable basis for real-time calculation of the color temperature drive current of the vehicle lamp. This method can, according to the difference between the target color temperature and the current color temperature, comprehensively consider the dynamic influences of environmental temperature, humidity, and vehicle speed at the same time, achieve precise adjustment of the LED drive current, ensure that the vehicle lamp can output the expected color temperature under various working conditions, thus effectively avoiding the problems of insufficient adjustment or over-adjustment caused by fixed parameter design. This plan fully considers the thermal characteristics and working life of LEDs, optimizes the energy utilization rate, reduces energy waste, and greatly improves the adaptability and safety of the vehicle lamp under complex road and environmental changes through a real-time feedback mechanism, providing solid technical support for intelligent driving and energy conservation and environmental protection.

[0118] Specifically, the specific steps for analyzing the beam angle drive current value of the vehicle lamp are as follows: Obtain the reference current value of the vehicle lamp beam angle; Read the current vehicle speed value and the vehicle speed reference value of the vehicle, and comprehensively analyze them in combination with the reference current value of the vehicle lamp color temperature, the current beam angle value of the vehicle lamp, and the target beam angle value of the vehicle lamp in the set area to obtain the beam angle drive current value of the vehicle lamp.

[0119] Among them, the reference current value of the vehicle lamp beam angle is the reference current for the LED to work under the default or standard beam angle (such as the working state recommended by the LED manufacturer). It represents the drive current required for the LED to achieve an ideal spectral output under the "normal" beam angle.

[0120] The specific formula for calculating the beam angle drive current value of the vehicle lamp is as follows: ; where is the beam angle drive current value of the vehicle lamp, is the reference current value of the vehicle lamp beam angle, is the target beam angle value of the vehicle lamp within the set area, is the current beam angle value of the vehicle lamp, is the beam angle attenuation coefficient stored in the database, is the beam angle adjustment coefficient stored in the database, is the current vehicle speed value, is the reference vehicle speed value, is the vehicle speed beam angle attenuation coefficient stored in the database, is the vehicle speed beam angle adjustment coefficient stored in the database.

[0121] Among them, the sigmoid function is: sigmoid(x) = 1 / (1 + e -x ).

[0122] It should be noted that the specific steps for obtaining the beam angle attenuation coefficient and the beam angle adjustment coefficient stored in the database are as follows: In the experimental environment, by changing the beam angle of the vehicle lamp and recording its optical output, energy consumption, and lighting effect at different target angles and current angles, and cooperating with measuring information such as the LED chip temperature and power, after obtaining multiple groups of data, use regression or machine learning methods to fit the angle deviation and the corresponding current response, and then refine the beam angle attenuation coefficient and adjustment coefficient, and solidify these parameters into the database for real-time call by the formula.

[0123] The specific steps for obtaining the vehicle speed beam angle attenuation coefficient and the vehicle speed beam angle adjustment coefficient stored in the database are as follows: Similarly, conduct dynamic adjustment tests on the beam angle of the vehicle lamp under different vehicle speed conditions, record the deviation between the target angle and the current angle and the corresponding data such as power, temperature, and lighting quality, combine the vehicle speed information, find out the influence law of vehicle speed change on beam angle attenuation and adjustment through regression or modeling analysis, and store the fitted vehicle speed beam angle attenuation coefficient and adjustment coefficient in the database for real-time control.

[0124] In this embodiment, by dynamically adjusting and testing the beam angle of vehicle lights in an experimental environment, data such as optical output, energy consumption, LED chip temperature, and power at different target angles and current angles are systematically recorded. Regression or machine learning methods are used to fit the angle deviation and the corresponding current response, so as to extract the beam angle attenuation coefficient and adjustment coefficient and store them in the database. At the same time, under different vehicle speed conditions, by testing the deviation between the target angle and the actual angle, as well as relevant power consumption and lighting quality data, the influence law of vehicle speed on beam angle adjustment is analyzed by modeling, and the vehicle speed beam angle attenuation coefficient and adjustment coefficient are determined and stored. By calling these database parameters in real time, the system can accurately calculate the required beam angle drive current in combination with the current angle of the vehicle light, the target angle, and the vehicle speed, realizing the intelligent control of the vehicle light under different driving conditions, thus ensuring that the lighting effect always reaches the optimal state, reducing energy waste, prolonging the service life of the LED, and significantly improving the safety and energy efficiency of the entire vehicle light system.

[0125] Please refer to Figure 3 , a vehicle light, comprising: a data acquisition module for acquiring the current vehicle speed value, the current state parameter set of the vehicle light, and the current environmental state parameter set within a set area; a light source driving module for analyzing the light source driving current set based on the current vehicle speed value, the current state parameter set of the vehicle light, and the current environmental state parameter set within the set area, and sending a light source driving instruction to the vehicle light; an execution module for receiving the light source driving instruction and performing a light source driving operation on the vehicle light.

[0126] In summary, this application has at least the following effects:

[0127] By introducing a pre-trained target analysis model, the real-time acquisition and data fusion of the current vehicle speed, vehicle light state, and regional environmental parameters are realized, enabling the dynamic generation of the target states of the brightness, color temperature, and beam angle of the vehicle light. Therefore, the driving module can automatically adjust the driving current set according to the real-time data, so as to accurately match the actual lighting requirements under various road and environmental conditions, effectively improving the lighting effect and energy utilization efficiency, and avoiding the blind spots and resource waste problems caused by fixed parameter design.

[0128] The influence coefficients of multi-dimensional parameter calibration and database storage are adopted, and targeted calculation logics are established for the driving currents of brightness, color temperature, and beam angle respectively. The comprehensive influences of LED non-linear response, environmental brightness, temperature and humidity, and vehicle speed are fully considered. Through experimental calibration and data fitting, the brightness current response index, color temperature adjustment range, beam angle attenuation, and adjustment coefficient are obtained, enabling the driving module to finely control each optical parameter, effectively overcoming the problems of fixed parameters and inability to respond to environmental changes in real time in the prior art, thereby reducing energy waste and prolonging the life of the LED.

[0129] Through three major modules: the target analysis unit, the current analysis unit, and the drive unit, an intelligent light source drive system is formed. It can not only generate the target state parameters of vehicle lights based on real-time data, but also extract and fuse features from the data through multi-level and deep neural networks, so as to achieve precise control of the brightness, color temperature, and beam angle of vehicle lights. Compared with traditional technologies, this solution can flexibly adapt to complex road and environmental changes, ensure stable and balanced operation of lighting, and at the same time greatly improve energy utilization efficiency and reduce system power consumption, providing stronger technical support for the overall energy efficiency of the vehicle and the driving experience.

[0130] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they learn the basic creative concept. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0131] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A light source driving module, characterized in that, Including: A target analysis unit, configured to monitor the current vehicle speed value, the current state parameter set of the vehicle lights, and the current environmental state parameter set within a set area, and input them into a pre-trained target analysis model for data analysis to obtain the target state parameter set of the vehicle lights within the set area. The current state parameter set includes the current vehicle light brightness value, the current vehicle light color temperature value, and the current vehicle light beam angle value. The current environmental state parameter set includes the current environmental brightness value, the current environmental temperature value, and the current environmental humidity value. The target state parameter set includes the target vehicle light brightness value, the target vehicle light color temperature value, and the target vehicle light beam angle value; A current analysis unit, configured to analyze the drive current set of the vehicle lights based on the target state parameter set of the vehicle lights within the set area. The drive current set includes the brightness drive current value, the color temperature drive current value, and the beam angle drive current value; A drive unit, configured to send a light source drive instruction to the vehicle lights based on the vehicle light drive current set; The specific steps for analyzing the brightness drive current value of the vehicle lights are as follows: Obtain the vehicle light brightness reference current value, the vehicle speed reference value, and the environmental brightness reference value; Obtain the brightness current response index, and perform comprehensive analysis in combination with the current vehicle speed value, the vehicle speed reference value, the vehicle light brightness reference current value, the current vehicle light brightness value of the vehicle lights, the target vehicle light brightness value of the vehicle lights within the set area, the current environmental brightness value within the set area of the vehicle lights, and the environmental brightness reference value to obtain the brightness drive current value of the vehicle lights; The specific formula for calculating the brightness drive current value of the vehicle lights is as follows: ； Wherein, is the brightness drive current value of the vehicle lamp, is the reference current value of the vehicle lamp brightness, is the target brightness value of the vehicle lamp within the set area, is the current brightness value of the vehicle lamp, 、 are the current vehicle speed value and the reference vehicle speed value in sequence, 、 are the vehicle speed brightness influence coefficient and the brightness current response index stored in the database in sequence, is the reference environmental brightness value, is the current environmental brightness value within the set area of the vehicle lamp, is the environmental brightness influence coefficient stored in the database.

2. The light source driving module according to claim 1, wherein The target analysis model is specifically a deep neural network model, which consists of an input layer, several hidden layers, and an output layer. Among them: The input layer is configured to receive the current vehicle speed value, the current state parameter set of the vehicle lights, and the current environmental state parameter set within the set area, and perform preprocessing; The hidden layer is configured to perform feature analysis processing on the preprocessed current vehicle speed value, the current state parameter set of the vehicle lights, and the current environmental state parameter set within the set area; The output layer is configured to convert the feature analysis result of the hidden layer into the target state parameter set of the vehicle lights within the set area.

3. The light source driving module according to claim 1, wherein The specific steps for obtaining the brightness current response index are as follows: Apply several measurement currents to the light source, and record the light source brightness values under each measurement current respectively; Perform logarithmic linearization processing on the light source brightness values under each measurement current; After logarithmic linearization, perform data fitting analysis on the light source brightness values under each measurement current to obtain the brightness current response index.

4. The light source driving module according to claim 1, wherein The specific steps for analyzing the color temperature drive current value of the vehicle lights are as follows: Obtain the vehicle light color temperature reference current value, the color temperature adjustment amplitude value, and the environmental reference set. The environmental reference set includes the environmental temperature reference value and the environmental humidity reference value; Read the current vehicle speed value and the vehicle speed reference value, and perform comprehensive analysis in combination with the vehicle light color temperature reference current value, the current vehicle light color temperature value of the vehicle lights, the target vehicle light color temperature value of the vehicle lights within the set area, the color temperature adjustment amplitude value, the current environmental temperature value within the set area of the vehicle lights, the current environmental humidity value, and the environmental reference set to obtain the color temperature drive current value of the vehicle lights.

5. The light source driving module according to claim 4, characterized in that, The specific formula for calculating the color temperature drive current value of the vehicle lights is as follows: ； Among them, is the color temperature drive current value of the vehicle lamp, is the reference current value of the vehicle lamp color temperature, is the target color temperature value of the vehicle lamp within the set area, is the current color temperature value of the vehicle lamp, is the color temperature influence coefficient stored in the database, is the reference value of the ambient temperature, is the current ambient temperature value within the set area of the vehicle lamp, is the reference value of the ambient humidity, is the current ambient humidity value within the set area of the vehicle lamp, is the combined temperature and humidity influence coefficient stored in the database, 、 are the reference value of the vehicle speed and the current vehicle speed value in sequence, is the vehicle speed color temperature influence coefficient stored in the database.

6. The light source driving module according to claim 1, wherein, The specific steps for analyzing the drive current value of the headlight beam angle are as follows: Obtain the reference current value of the headlight beam angle; Read the current vehicle speed value and the vehicle speed reference value, and comprehensively analyze them in combination with the reference current value of the headlight color temperature, the current headlight beam angle value of the headlight, and the target headlight beam angle value of the headlight in the set area to obtain the drive current value of the headlight beam angle.

7. The light source driving module according to claim 6, wherein The specific formula for calculating the drive current value of the headlight beam angle is as follows: ； Among them, is the beam angle drive current value of the vehicle lamp, is the reference current value of the vehicle lamp beam angle, is the target beam angle value of the vehicle lamp within the set area, is the current beam angle value of the vehicle lamp, 、 are the beam angle attenuation coefficient and the beam angle adjustment coefficient stored in the database in sequence, 、 are the current vehicle speed value and the vehicle speed reference value in sequence, 、 are the vehicle speed beam angle attenuation coefficient and the vehicle speed beam angle adjustment coefficient stored in the database in sequence.

8. A vehicle lamp, applying the light source driving module according to any one of claims 1-7, characterized in that Including: A data acquisition module for acquiring the current vehicle speed value, the current state parameter set of the headlight, and the current environmental state parameter set in the set area; A light source drive module for analyzing the headlight drive current set based on the current vehicle speed value, the current state parameter set of the headlight, and the current environmental state parameter set in the set area, and sending a light source drive instruction to the headlight; An execution module for receiving the light source drive instruction and performing a light source drive operation on the headlight.

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