Intelligent lighting effect regulation and control method and system for vehicle lamp module

Abstract

The invention provides an intelligent lighting effect regulation and control method and system for a vehicle lamp module, and relates to the technical field of vehicle lighting, and the method comprises the steps: building a lighting effect demand feature library through analyzing a lighting effect demand in an electric vehicle driving environment, carrying out the demand matching of the vehicle lamp module, and building mapping association; an illumination grid distribution diagram of the vehicle lamp modules is constructed, hardware constraint optimization and regulation and control parameter analysis search of the vehicle lamp modules are carried out based on mapping association, and optimized hardware configuration and dimming scene control strategies of the vehicle lamp modules are obtained; on the basis, a vehicle lamp module is arranged, and a man-machine interaction interface is constructed to match a dimming scene control strategy to carry out lighting effect adjustment control on vehicle lamp module hardware. The technical problem that in the prior art, due to the lack of comprehensive analysis on complex and changeable lighting effect requirements of the electric vehicle, vehicle lamp lighting effect adjustment cannot accurately adapt to driving conditions and scenes is solved, and the technical effect of remarkably improving the electric vehicle lamp lighting effect and driving safety is achieved.

Description

Technical Field

[0001] This application relates to the technical field of automotive lighting, and particularly to an intelligent light effect regulation method and system for a vehicle lamp module. Background Art

[0002] As an important part of an electric vehicle, the light effect regulation technology of vehicle lamps has become one of the key factors for improving driving safety and driving experience. At present, the light effect regulation of electric vehicle lamp modules mostly adopts an adjustment method based on a fixed lighting mode or a preset algorithm. These methods rely on simple light intensity adjustment or a fixed control mode set by the driver, and adjust the brightness or beam angle based on basic information such as an ambient light sensor, vehicle speed, and steering signal. They lack real-time analysis of the light effect requirements in a complex driving environment and cannot well meet the light effect requirements in variable scenarios such as suddenly entering a tunnel, bad weather, and special road conditions. At the same time, they do not fully consider the hardware performance and configuration limitations of the lamp module, lack intelligent optimization and dynamic adjustment for different hardware configurations, and thus it is difficult to achieve the optimal lighting effect and energy efficiency with limited hardware resources. Summary of the Invention

[0003] This application provides an intelligent light effect regulation method and system for a vehicle lamp module, which solves the technical problem that the existing technology cannot accurately adapt to variable driving conditions and scenarios due to the lack of a comprehensive analysis of the complex and variable light effect requirements in the electric vehicle driving environment, and achieves the technical effect of significantly improving the lighting effect and driving safety of electric vehicle lamps.

[0004] In view of the above problems, on the one hand, this application provides an intelligent light effect regulation method for a vehicle lamp module. The method includes: analyzing the light effect requirements in the electric vehicle driving environment and constructing a light effect requirement feature library; performing requirement matching on the vehicle lamp module based on the light effect requirement feature library and establishing a mapping association between the vehicle lamp module and the light effect requirement features; constructing a lighting grid distribution map of the vehicle lamp module, and performing hardware constraint optimization and regulation parameter analysis search on the vehicle lamp module based on the mapping association between the vehicle lamp module and the light effect requirement features to obtain the optimized hardware configuration of each vehicle lamp module and its dimming scene control strategy; according to the optimized hardware configuration and its dimming scene control strategy, arranging the vehicle lamp module and correspondingly constructing a human-machine interaction interface for interacting with dimming instructions to adjust and control the light effect of the vehicle lamp module hardware to match the dimming scene control strategy.

[0005] On the other hand, the present application also provides an intelligent light effect regulation system for a vehicle lamp module. The system includes: a light effect requirement analysis module for analyzing the light effect requirements in the driving environment of an electric vehicle and constructing a light effect requirement feature library; a requirement matching module for performing requirement matching on the vehicle lamp module based on the light effect requirement feature library and establishing a mapping association between the vehicle lamp module and the light effect requirement features; an optimization search module for constructing a light intensity grid distribution map of the vehicle lamp module and performing optimization of the hardware constraints of the vehicle lamp module and parsing and searching for regulation parameters based on the mapping association between the vehicle lamp module and the light effect requirement features to obtain the optimized hardware configuration of each vehicle lamp module and its dimming scene control strategy; and a light effect adjustment and layout module for arranging the vehicle lamp module according to the optimized hardware configuration and its dimming scene control strategy and correspondingly constructing a human-machine interaction interface for interacting with dimming commands to perform light effect adjustment control on the hardware of the vehicle lamp module to match the dimming scene control strategy.

[0006] One or more technical solutions provided in the present application have at least the following beneficial effects: By analyzing the light effect requirements in the driving environment of an electric vehicle and constructing a light effect requirement feature library, accurate environmental data support is provided for subsequent regulation, ensuring that the vehicle lamp light effect regulation can accurately adapt to different driving conditions. By performing requirement matching on the vehicle lamp module based on the light effect requirement feature library and establishing a mapping association between the vehicle lamp module and the light effect requirement features, intelligent requirement identification and matching are achieved, thus ensuring that the vehicle lamp light effect adjustment can be accurately docked with the requirements of the actual scenario. By constructing a light intensity grid distribution map of the vehicle lamp module and performing optimization of the hardware constraints of the vehicle lamp module and parsing and searching for regulation parameters based on the mapping association between the vehicle lamp module and the light effect requirement features to obtain the optimized hardware configuration of each vehicle lamp module and its dimming scene control strategy, it is ensured that the vehicle lamp module can perform optimally under actual hardware and environmental conditions. By arranging the vehicle lamp module according to the optimized hardware configuration and its dimming scene control strategy and correspondingly constructing a human-machine interaction interface for interacting with dimming commands to perform light effect adjustment control on the hardware of the vehicle lamp module to match the dimming scene control strategy, real-time interaction between the driver and the vehicle lamp system is realized, facilitating the driver to adjust the light effect according to specific situations, thereby further improving the driving experience and safety.

[0007] In summary, through the intelligent analysis of lighting effect requirements, requirement matching, hardware optimization, and the design of dynamic regulation strategies, this application has formed a comprehensive and accurate vehicle lighting effect regulation system for electric vehicles. First, by constructing a lighting effect requirement feature library and a requirement matching mechanism for the headlight module, the precise adaptation of vehicle lighting effect adjustment to complex driving environments is achieved; second, through the lighting grid distribution map and hardware optimization, the best lighting effect of the headlight module in different environments is ensured, improving energy efficiency and safety; finally, combined with the human-machine interaction interface, convenient dimming control is provided, enabling the driver to easily adjust the headlight system according to real-time needs. Overall, this application realizes the refined and personalized lighting effect regulation of the headlight module in different driving environments, effectively improving the lighting effect of electric vehicle headlights and driving safety.

[0008] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features, and advantages of this application more obvious and understandable, the specific embodiments of this application are specifically given below. Brief Description of the Drawings

[0009] Figure 1 It is a schematic flowchart of the intelligent lighting effect regulation method for the headlight module provided by the embodiment of this application.

[0010] Figure 2 It is a schematic structural diagram of the intelligent lighting effect regulation system for the headlight module provided by the embodiment of this application.

[0011] Description of the reference numerals: Lighting effect requirement analysis module 10, requirement matching module 20, optimization search module 30, lighting effect adjustment layout module 40. Detailed Embodiments

[0012] The embodiment of this application provides an intelligent lighting effect regulation method and system for the headlight module, solving the technical problem that in the prior art, due to the lack of a comprehensive analysis of the complex and variable lighting effect requirements in the electric vehicle driving environment, the lighting effect adjustment of the vehicle headlights cannot accurately adapt to the changing driving conditions and scenarios. Through intelligent lighting effect requirement matching, hardware optimization configuration of the headlight module, and the generation of dimming scene control strategies, the accurate regulation of the headlight module in different environments is realized, achieving the technical effect of significantly improving the lighting effect of electric vehicle headlights and driving safety.

[0013] Embodiment 1, as Figure 1 shown, the embodiment of this application provides an intelligent lighting effect regulation method for the headlight module, and the method includes: Step S100: Analyze the lighting effect requirements in the electric vehicle driving environment and construct a lighting effect requirement feature library.

[0014] Specifically, the light efficiency demand feature library is a database that stores the light efficiency demand features under different driving environments, including the mapping relationship between multi-dimensional information such as road environment, driving scene, and driver's light perception and light efficiency demand. According to multiple dimensions such as road environment, driving scene, and driver's light perception characteristics, electric vehicle driving environment samples are collected to build a driving environment sample cluster. Light efficiency demand analysis is performed on the collected samples to distinguish between positive and negative sample light effects. Positive sample light effects refer to light effects that meet the needs of the driving environment, such as strong light suitable for night driving; negative sample light effects refer to light effects that do not meet the needs, such as excessive or insufficient light intensity, misaligned beam angles, etc. By establishing a corresponding relationship between positive and negative sample light effects and environmental characteristics, a light efficiency demand feature library is eventually formed to provide data support for subsequent light efficiency regulation.

[0015] This step builds an accurate light effect demand feature library by comprehensively collecting and analyzing the light effect requirements in different driving environments, providing a solid data foundation for subsequent light effect regulation, and ensuring that the light effect adjustment of the lights in different environments is more intelligent and accurate to meet the needs of various driving scenarios.

[0016] Step S200: performing demand matching on the vehicle light module based on the light effect demand feature library, and establishing a mapping association between the vehicle light module and the light effect demand feature.

[0017] Specifically, the headlight module refers to a lamp unit on the vehicle that can be controlled, dimmed or combined, including at least low beam (for short-distance lighting), high beam (for long-distance lighting), fog lamp (to penetrate rain and fog environment), and turn signal (to indicate driving direction). Each headlight has different light effect characteristics and adjustment capabilities. A comprehensive parametric analysis of the headlight module is carried out, including its installation location, hardware capabilities, light effect performance, etc. For each headlight of the electric vehicle, the relationship between the light effect performance of the headlight and the light effect requirements of the driving environment is analyzed by matching the data collected in the light effect requirement feature library. For example, by comparing the brightness, beam distribution, color temperature and other parameters of each headlight module under different driving environments, a mapping association between the headlight module and the light effect requirements is established, providing a theoretical basis for subsequent light effect adjustment. For example, when driving at high speed at night, the high beam needs to provide strong brightness and a large coverage angle, while in haze weather, the color temperature and brightness of the headlight need to be adjusted to improve road visibility and reduce glare. By mapping the headlight module with these light effect requirement characteristics, the precise adjustment of the headlight module can be achieved.

[0018] This step matches the lighting effect requirement feature library with the requirements of the headlight module, and can automatically select and optimize the appropriate headlight module and dimming strategy according to the lighting effect requirements of different driving environments, ensuring that the headlight module can be intelligently adjusted according to changes in the external environment.

[0019] Step S300: Construct a light intensity grid distribution map of the headlight module, and based on the mapping association between the headlight module and the light effect requirement characteristics, optimize the hardware constraints of the headlight module and analyze and search for control parameters, so as to obtain the optimized hardware configuration of each headlight module and its dimming scenario control strategy.

[0020] Specifically, the light intensity grid distribution map is a light coverage map obtained by three-dimensional modeling of the light effect of the headlight module, and is used to analyze the light effect performance of each headlight at different positions and angles. The dimming scenario control strategy refers to the headlight dimming strategy formulated according to the requirements of the driving environment.

[0021] According to the installation position of the headlight module and the structure of the electric vehicle, construct a three-dimensional light intensity grid distribution map, map the light effect performance of each type of headlight into the three-dimensional grid, and obtain the spatial distribution information of the headlight light effect. Then, based on the hardware capabilities and requirement characteristics of the headlight module, optimize the hardware constraints and analyze and search for control parameters, so as to obtain the optimized hardware configuration of each headlight module and its dimming scenario control strategy. If the actual hardware of the headlight module cannot meet the light effect requirements in some environments, adjustments and optimizations are carried out to improve the light effect performance by optimizing the light array or searching for the optimal dimming parameter strategy, so as to ensure that the headlight meets the light effect requirements.

[0022] This step can provide an accurate light effect adjustment scheme for the headlight module by constructing a light intensity grid distribution map and performing hardware optimization, ensuring that the headlight can provide the best lighting effect under different environmental conditions.

[0023] Step S400: According to the optimized hardware configuration and its dimming scenario control strategy, deploy the headlight module and correspondingly construct a human-machine interaction interface for interacting with dimming instructions to adjust and control the light effect of the headlight module hardware in accordance with the dimming scenario control strategy.

[0024] Specifically, according to the optimized hardware configuration results, the headlight module is reasonably installed on the electric vehicle to ensure that parameters such as its position and angle meet the design requirements, so as to achieve the best lighting effect. For example, according to the optimization results, determine the specific installation positions and orientation angles of headlights such as low beam, high beam, and fog lights at the front of the vehicle. Design and develop a human-machine interaction interface, which can use the in-vehicle infotainment system or a dedicated headlight control display terminal. On the human-machine interaction interface, information such as the current dimming scene control strategy and headlight status can be displayed, and at the same time, manual adjustment options are provided, allowing the driver to customize the light effect when needed. After receiving the driver's dimming instruction, the human-machine interaction interface converts it into a control signal recognizable by the headlight module hardware, and adjusts and controls the light effect of the headlight module according to the dimming scene control strategy. For example, when the driver selects the "foggy day mode" through the human-machine interaction interface, the preset foggy day dimming scene control strategy is automatically matched to adjust the brightness and angle of the fog lights, and at the same time, appropriately adjust the light effect of the low beam to improve the lighting effect and driving safety in the foggy day environment.

[0025] In this step, by reasonably arranging the headlight module and constructing the human-machine interaction interface, the adjustment and control of the light effect of the headlight module are made more convenient and flexible, which can better meet the needs of the driver, improve the driving experience and driving safety. The introduction of the human-machine interaction interface enhances the user's control and participation in the headlight system, enabling the headlight module to more accurately adapt to different driving scenarios and personalized needs.

[0026] Furthermore, step S100 includes: Step S110: Collect samples in multiple dimensions of road environment, driving scenario, and driver light perception, and construct a driving environment sample cluster.

[0027] Step S120: Analyze the positive sample light effect and negative sample light effect according to the driving environment sample cluster, where the positive sample light effect is the light effect that meets the driving environment requirements, and the negative sample light effect is the light effect that does not meet the driving environment requirements.

[0028] Step S130: Establish the corresponding relationship between the positive sample light effect, negative sample light effect and driving environment characteristics, and construct the light effect requirement feature library, where the driving environment characteristics include road environment, driving scenario, and driver light perception characteristics.

[0029] Specifically, under different road environments, driving scenarios, and driver light perception conditions, a large number of driving environment samples are collected using a variety of devices: cameras record road conditions images and videos, millimeter-wave radars detect the distances and speeds of objects around the vehicle, ambient light sensors sense the ambient brightness, and driver light perception feedback is collected through questionnaires or user feedback, etc. A driving environment sample cluster is constructed based on the collected sample data. Each set of sample data in the driving environment sample cluster is labeled with a corresponding sample evaluation label, such as "satisfied" or "not satisfied", to indicate the quality of the vehicle's lighting effect configuration in that sample, which is derived from driver feedback, expert evaluation, etc.

[0030] Positive and negative samples are extracted according to the sample evaluation labels. Among them, positive samples are sample data where the vehicle's lighting effect can meet the driving requirements in a specific driving environment. Negative samples refer to sample data where the vehicle's lighting effect cannot meet the driving environment requirements, with problems such as too strong or too weak light intensity, unreasonable coverage angle, abnormal color temperature, etc. For positive samples, the lighting effect requirements in each dimension of the driving environment sample cluster are analyzed according to the road environment, driving scenario, and driver light perception type, and positive sample lighting effects including parameters such as headlight intensity, coverage angle distribution, and color temperature range are obtained; for negative samples, their abnormal lighting effect characteristics are analyzed according to the road environment, driving scenario, and driver light perception type, and negative sample lighting effects including parameters such as abnormal headlight intensity threshold, coverage angle missing range, and abnormal color temperature range are obtained.

[0031] The positive sample lighting effects, negative sample lighting effects are associated with the driving environment characteristics (i.e., road environment, driving scenario, driver light perception characteristics). For example, a corresponding relationship is established between the night urban road driving scenario and the lighting effect characteristics such as the light intensity range, coverage angle range, and color temperature range of the positive sample; a corresponding relationship is established between the rainy day driving scenario and the lighting effect characteristics such as the abnormal headlight intensity threshold, coverage angle missing range, and abnormal color temperature range of the negative sample. Then, a database management system is used to create a lighting effect requirement feature library, and the corresponding relationship data between the established positive sample lighting effects, negative sample lighting effects and driving environment characteristics are stored in the feature library. In the database, corresponding table structures can be designed to store different types of data, such as a driving environment characteristics table, a positive sample lighting effect table, a negative sample lighting effect table, etc., and mapping associations between these tables are established through relationships such as primary keys and foreign keys.

[0032] Through the above steps, the multi-dimensional requirements in the driving environment can be accurately captured and analyzed, and transformed into a structured lighting effect requirement feature library, providing a theoretical basis for subsequent vehicle lighting effect regulation.

[0033] Furthermore, step S120 includes: Step S121: Extract positive and negative samples according to the evaluation labels of the driving environment sample cluster.

[0034] Step S122: According to the positive samples, respectively analyze the light effect requirements in each dimension of the driving environment sample cluster according to the road environment, driving scenario, and driver light perception type, including headlight intensity, coverage angle distribution, and color temperature range, to obtain the positive sample light effect.

[0035] Step S123: According to the negative samples, respectively analyze the abnormal light effect characteristics according to the road environment, driving scenario, and driver light perception type, including abnormal headlight intensity threshold, coverage angle missing range, and abnormal color temperature range, to obtain the negative sample light effect.

[0036] Specifically, traverse each sample in the driving environment sample cluster, classify it as a positive sample or a negative sample according to its evaluation label, and store them in the corresponding data file or database table respectively.

[0037] For positive samples, according to different road environments (such as highways, tunnels), different driving scenarios (such as night-time passing, rainy days, foggy days), and different driver light perception types (such as sensitive to strong light, sensitive to weak light, etc.), extract the key parameters of the headlights from the positive sample set, including light intensity, coverage angle, and color temperature range, and organize these parameters into the positive sample light effect.

[0038] For negative samples, also according to different road environments (such as highways, tunnels), different driving scenarios (such as night-time passing, rainy days, foggy days), and different driver light perception types (such as sensitive to strong light, sensitive to weak light, etc.), extract the characteristics that cause uncomfortable light effects from the negative sample set, including abnormal headlight intensity threshold, coverage angle missing range, and abnormal color temperature range, and summarize the abnormal light effect parameter ranges under different driving scenarios to form the negative sample light effect. The whole process can be completed by means of statistical analysis and clustering algorithms (such as K-means for clustering and identifying light effects).

[0039] Exemplarily, a sample has a label of "well adapted", its environment is night-time highway driving, the driver's light perception is medium, the collected headlight intensity is 1500 lumens, the angle is 60° horizontal diffusion, and the color temperature is 4500K. It is included in the positive samples, and its light effect parameters are extracted and recorded as the reference configuration suitable for the high-speed night scenario. On the contrary, a sample causes frequent glare reactions from the driver due to the use of 6000K cold white light in rainy and foggy weather, so it is evaluated as a negative sample, and its abnormal color temperature value is recorded and classified into the abnormal color temperature range.

[0040] The above steps achieve the accurate extraction of multi-dimensional light effect parameters through hierarchical processing of the evaluation labels of driving environment samples and structured analysis of positive and negative samples, providing a basis for the subsequent construction of a light effect requirement feature library. The positive sample light effect provides an ideal light effect reference, while the negative sample light effect provides boundary conditions for avoiding uncomfortable parameters, thus greatly improving the intelligence, pertinence, and safety of the subsequent matching and optimization processes.

[0041] Further, step S200 includes: Step S210: Parametrize the installation positions and hardware capabilities of the headlight modules of the electric vehicle comprehensively, and analyze the light effect performance and regulation capabilities of various types of headlights.

[0042] Step S220: Perform parameter matching according to the required light effect characteristics in the light effect requirement characteristic library and the light effect performance and regulation capabilities of various types of headlights. Establish the mapping association between the headlight module and the light effect requirement characteristics according to the parameter matching relationship between the light effect requirement characteristics and various types of headlights.

[0043] Further, the headlight module includes at least: low beam, high beam, fog light, turn signal.

[0044] Specifically, first, parametric modeling is performed on all the headlight modules installed on the electric vehicle, including low beam, high beam, fog light, turn signal, etc. The installation positions of each type of headlight will be digitally described through a three-dimensional coordinate system. For example, the front headlights are installed on both sides of the vehicle head, 700 mm from the ground and 400 mm laterally offset, etc. At the same time, spatial information such as the lighting direction angle is recorded. In terms of hardware capabilities, key indicators such as the maximum brightness (e.g., 1500 lumens), dimming range (e.g., 5 - gear adjustment), color temperature adjustable range (e.g., 3000K to 6000K), irradiation angle (e.g., 70° horizontally and 20° vertically) of each headlight module are collected and organized into a unified parameter structure.

[0045] Subsequently, the target parameters extracted from the light effect requirement characteristic library are read, such as 1200 - lumen brightness, 40° horizontal diffusion angle, and 4500K color temperature required for a specific driving scenario. Then, comparison is made with the parameters of each headlight module, and the optimal pairing is achieved by using vector distance, weighted scoring, or fuzzy matching algorithms (such as Euclidean distance, TOPSIS evaluation). Finally, the mapping relationship between "scenario - light effect requirement" and "headlight type - capability parameter" is established, providing a data basis for subsequent intelligent regulation and light effect simulation.

[0046] The above steps achieve an efficient linkage between "actual hardware capabilities" and "dynamic scenario requirements" by performing refined parametric modeling on the installation positions and hardware capabilities of the headlight modules and matching them with the light effect requirement characteristic library, establishing a clearly structured mapping association, and providing a reference basis for subsequent rapid and adaptive dimming control under different environmental conditions.

[0047] Further, step S300 includes: Step S310: Establish a three - dimensional grid distribution map according to the installation positions of the headlight modules, where grid coordinate positioning is performed according to the structure of the electric vehicle and the installation coordinates of various types of headlights.

[0048] Step S320: Construct a three-dimensional light effect heat map according to the light effect performance and regulation ability of each type of vehicle lamp, and fit the three-dimensional light effect heat maps of each type of vehicle lamp to the three-dimensional grid distribution map according to the grid coordinates to obtain the illumination grid distribution map of the vehicle lamp module.

[0049] Specifically, based on the three-dimensional structural design drawing of the vehicle, a regular three-dimensional grid is constructed for the space around the vehicle body (such as 5 meters in front of the vehicle head × 3 meters horizontally × 2 meters in height), and each grid unit represents a spatial cube (such as 0.1 m³), forming a high-density three-dimensional space index system. The actual installation positions of each vehicle lamp module are accurately aligned through distance measurement, positioning sensors or model data, and their coordinate information is mapped to this three-dimensional grid coordinate system to complete the basic space construction and obtain the three-dimensional grid distribution map.

[0050] The regulation ability refers to the adjustable performance of the vehicle lamp, such as brightness adjustment, projection angle adjustment, color temperature adjustment, etc. According to the regulation ability and light effect performance of each vehicle lamp (including light intensity distribution, emission angle, color temperature diffusion, etc.), a three-dimensional light effect heat map of each type of vehicle lamp is constructed using a light illumination simulation algorithm (such as Monte Carlo ray tracing, light cone superposition simulation). Each point in each heat map corresponds to a spatial grid point and carries light intensity information, and intuitively presents the light intensity and light effect performance of the vehicle lamp at different positions in space through color gradient, with colors distinguished from blue (low light intensity) to red (high light intensity). Subsequently, the three-dimensional light effect heat maps of each type of vehicle lamp are aligned to the three-dimensional grid distribution map according to their actual installation positions to achieve accurate fitting in three-dimensional space, and finally a complete illumination grid distribution map of the vehicle lamp module is generated.

[0051] For example, the low beam lights of an electric vehicle are installed symmetrically on the left and right sides of the vehicle head, with a height of about 70 cm and a horizontal offset of 40 cm. This coordinate is accurately mapped to the three-dimensional space grid, such as point [7, 40, 70]. When simulating that the horizontal divergence angle of the low beam light is 60°, the vertical angle is 10°, the maximum irradiation distance is 20 m, and the illumination intensity decreases with the distance. The simulation system generates a three-dimensional intensity distribution of the illuminated area of the lamp through the heat map and covers it on the three-dimensional grid. Finally, a three-dimensional image or data table is obtained, showing the intensity, overlapping coverage, etc. of different areas illuminated by different vehicle lamps.

[0052] The above steps accurately fit the three-dimensional grid distribution map and the light effect heat maps of each type of vehicle lamp. On the one hand, it can realize the visualization of light illumination simulation, making the distribution effect of vehicle lamps and the illumination airspace clear at a glance; on the other hand, it also provides spatial data support for subsequent automatic dimming strategies, blind spot light compensation, overexposure suppression, etc., significantly improving the spatial analysis accuracy of the electric vehicle lighting system and facilitating the construction of a light effect regulation model based on spatial distribution in a dynamic environment.

[0053] Further, step S320 includes: Step S321: Obtain the light effect range characteristics of the three-dimensional space according to the light effect performance of various vehicle lights, and construct a three-dimensional light effect heat map according to the three-dimensional light effect distribution characteristics.

[0054] Step S322: Establish a three-dimensional light effect heat map of each regulation ability parameter according to the regulation ability, and perform heat map hierarchical superposition according to the regulation gradient of each regulation ability parameter to obtain the three-dimensional light effect heat map.

[0055] Specifically, the light effect range characteristics refer to the boundaries (spatial shapes and sizes) that each type of vehicle light can actually illuminate in space, such as illumination distance, lateral divergence angle, vertical projection depth, etc. When constructing the three-dimensional light effect heat map of each type of vehicle light, input the lighting parameters of each type of vehicle light into the simulation engine, and extract information such as light intensity attenuation and illumination coverage range at different spatial coordinate points. For example, the high beam can reach a distance of 30 meters and the projection angle is about 20°; the fog lamp has a wider projection angle but a smaller vertical depth. Define these lighting boundary conditions as light effect range characteristics. On this basis, generate a three-dimensional lighting data matrix for all illuminated areas, with each spatial point containing a light intensity value, and then encode it in the form of a heat map. The higher the intensity, the redder the color of the area, and the lower the intensity, the bluer the area, thus forming a complete three-dimensional light effect heat map. Exemplarily, the maximum illumination depth of the low beam is 20m, and the divergence angle is 40°. Within the illumination cone, 0 - 5m is the red area (high light intensity), 5 - 15m is the orange to green area (medium light intensity), and 15 - 20m is the blue area (low light intensity). These information are mapped to the grid in front of the vehicle head to form the three-dimensional light effect heat map of the low beam.

[0056] For each type of vehicle light, not only the lighting effect in the default state needs to be simulated, but also the adjustable lightness of the vehicle light needs to be considered. Taking brightness adjustment as an example, divide the light intensity adjustment from 20% to 100% into several gradient levels (such as 5 levels), generate a heat map again at each level, and record its three-dimensional distribution. Subsequently, superimpose these hierarchical heat maps (using weighted, transparent fusion or maximum value superposition methods) to generate a composite three-dimensional heat map, which is used to reflect all the light effect ability performances of the light in the adjustment space. Exemplarily, generate heat maps A, B, and C for the fog lamp at three gradients of 30%, 60%, and 90% brightness respectively: Map A represents the low brightness coverage range (only within 3m); Map B represents the medium brightness coverage (up to 5m); Map C represents the maximum brightness coverage (up to 7m); By superimposing, a gradually changing and continuous light effect distribution volume map is formed to realize the effect modeling of visualizing the influence of adjustment on the light effect.

[0057] By layering and superimposing three-dimensional light effect heat maps with different regulation ability parameters, the comprehensive impact of each regulation parameter on the light effect of vehicle lights can be comprehensively evaluated, which helps to formulate more effective light effect regulation strategies. Based on the superimposed three-dimensional light effect heat map, the light effect distribution under different combinations of regulation parameters can be intuitively seen, providing a basis for optimizing the light effect regulation strategy and improving the lighting effect and adaptability of the headlight module in different driving scenarios.

[0058] Further, step S300 further includes: Step S330: Construct a demand light effect heat map distribution according to the light effect demand characteristics.

[0059] Step S340: According to the mapping relationship between the headlight module and the light effect demand characteristics, add the demand light effect heat map distribution to the light grid distribution map of the headlight module for heat distribution matching, and obtain the matching headlight type, installation position, and regulation ability parameters.

[0060] Step S350: Perform matching degree analysis according to the matching relationship. When the matching degree reaches the threshold, obtain the hardware configuration and dimming scene control strategy according to the matching relationship.

[0061] Step S360: When the matching degree does not reach the threshold, perform light array adjustment and optimization based on the array optimization parameters and constraints of the headlight hardware or perform optimization of the regulation parameter strategy of the headlight module. To meet the goal of maximizing the light effect demand characteristics and minimizing the headlight energy consumption, perform balanced optimization of the combination of headlight hardware and regulation parameters, and obtain the optimized hardware configuration and its dimming scene control strategy.

[0062] Specifically, the demand light effect heat map distribution refers to the light intensity target distribution map generated in three-dimensional space based on the ideal light distribution demand in the target driving environment, which is used as the lighting performance target value. Based on the previously constructed light effect demand characteristic library, extract the lighting requirements of the target driving scenario, including brightness distribution, coverage angle, color temperature range, etc., and generate the expected spatial lighting distribution map, that is, the demand light effect heat map distribution. This demand light effect heat map distribution adopts the same grid space structure as the three-dimensional light effect heat map of the headlight, which is convenient for subsequent differential matching calculations. Taking the night mountain curve scene as an example, the called light effect requirements include: brightness > 500 lumens, color temperature between 4000K and 4500K, and the lateral coverage angle needs to reach 180 degrees. According to this light effect demand characteristic, a grid model is established in front of the vehicle, and the size of each grid is 0.1m³. Convert these light effect demand values into heat map data. For example, the color of the inner area of the curve is red (indicating high-brightness supplementary light), the color of the central area of the road is yellow (medium intensity), and the edge is blue (low intensity), and generate a demand heat map distribution.

[0063] Perform a matching operation on the three-dimensional demand heat map and the three-dimensional light effect heat map. The matching method uses image processing methods such as spatial overlap rate analysis, mean square error calculation, or structural similarity measurement to compare the ideal heat value and the actual heat value grid by grid. The comparison dimensions include: the light intensity error range (such as ±10%), the coverage angle overlap degree (such as more than 90%), and the color temperature matching level. According to the existing mapping relationship between the headlight module and the light effect demand characteristics, select the headlight combination with the closest shape to the current demand light effect heat distribution map from all candidate light groups, and output a set of the most matching headlight module parameter sets, including: the type of headlight used, the grid coordinates of the installation position, and the regulation ability parameters.

[0064] After the matching is completed, perform a quantitative analysis on the heat map matching result. By calculating the overall matching degree, if the matching degree reaches the set threshold (for example, 90%), it means that the current headlight configuration and regulation ability parameters can better meet the driving environment requirements, and then automatically generate a dimming control strategy based on the matching parameters. This strategy adjusts the headlight output in real time according to environmental changes. For example, it automatically switches the color temperature when passing through a tunnel, automatically increases the intensity and activates the fog lights when entering a rain or fog scene, or enhances the high beam coverage in a high-speed scene. The control strategy can be dynamically generated based on a rule engine or a machine learning model (such as a decision tree, a policy network) to achieve the adaptive adjustment of the lighting system to the driving situation.

[0065] If the heat map matching degree is lower than the set threshold, start an optimization process to improve the matching degree. First, perform an array optimization analysis at the hardware layer. According to the spatial constraints of the vehicle body structure and the light dead angle area, re-simulate the headlight layout method, generate a new headlight layout plan through heuristic search methods such as genetic algorithms, and simulate the fitting degree of the light heat map and the demand heat map in real time. If the optimization space of the hardware position is limited or has reached the optimum, transfer to the regulation parameter optimization stage. On the premise that the existing light group remains unchanged, perform multiple rounds of optimization on control parameters such as brightness, angle, and color temperature, and perform constraint solving through the objective function (maximizing the matching degree, minimizing the energy consumption), and finally output a new dimming control strategy.

[0066] By optimizing the hardware configuration and regulation parameters, the headlight module can better adapt to various complex driving scenarios. Even in the case of a low initial matching degree, it can find a suitable light effect regulation plan through optimization and adjustment, enhancing the flexibility and adaptability of the headlight module, and further improving driving safety and comfort.

[0067] Furthermore, the method includes: Step S510: Collect electric vehicle driving environment parameters through a multi-modal sensor, including brightness, illumination, weather, and surrounding obstacles.

[0068] Step S520: Match according to the electric vehicle driving environment parameters and the dimming scene characteristics, and identify the dimming scene control strategy.

[0069] Step S530: Perform adaptive regulation of the vehicle headlight module according to the dimming scenario control strategy.

[0070] Specifically, through a multi-modal sensor array installed around and in front of and behind the electric vehicle body, including visual sensors (cameras), infrared sensors, radar sensors (such as millimeter-wave radars, lidars), environmental sensors (light, temperature and humidity, rain and snow detectors, etc.), etc., environmental parameter information in the current driving scenario is collected in real time. The camera obtains visual images and extracts the overall brightness and obstacle contours; the infrared sensor detects the long-distance temperature difference to discover low visibility targets; the lidar or millimeter-wave radar captures the spatial positions and dynamic behaviors of surrounding obstacles; the environmental sensor synchronously detects weather data such as environmental light intensity, visibility level, humidity, and whether there is rain or snow. After synchronously collecting these multi-source information, through timestamp alignment and spatial coordinate unification, a structured set of driving environment parameters is formed as the input for subsequent scenario recognition and dimming strategies.

[0071] Match the electric vehicle driving environment parameters with the built-in dimming scenario feature library. A large number of dimming scenario features are stored in the feature library. For example, when the environmental brightness is lower than 200 lumens and the detected humidity is higher than 80% and the temperature difference fluctuates greatly, it can be recognized as a "night rain and fog scenario"; if the brightness changes violently (such as entering a tunnel from daytime), it is recognized as a "transition dark field scenario". The matching judgment can be carried out through a rule engine. The recognition result is a specific dimming scenario label, and the corresponding dimming scenario control strategy is associated as the basis for the next control execution.

[0072] After the dimming scenario control strategy is selected, the vehicle-mounted control system will instruct the corresponding lighting module to perform parameter adaptive adjustment according to the strategy content. First, the main control unit sends control instructions to each lighting module controller, and then adjusts according to parameters such as the brightness level, light-emitting angle, and color temperature adjustment value specified in the strategy. For example, adjust the PWM duty cycle of the LED lamp to control the brightness, fine-tune the lamp head angle through a stepper motor, and switch the color temperature control path of the light-emitting diode through an electronic control chip. At the same time, it can be linked with the camera to fine-tune in real time according to the road conditions ahead; in foggy and rainy days, the anti-glare mode can be activated or the fog lamp can be automatically enabled for auxiliary lighting. The entire regulation process is a closed-loop structure, and with continuous environmental perception, it ensures that the light output always matches the actual environment.

[0073] Through the above steps, the accurate conversion of the dimming strategy into an execution action is realized, enabling the vehicle headlight module to achieve real-time, fine-grained, and adaptive lighting control in different driving environments, comprehensively improving driving safety, energy consumption rationality, and the user's driving experience.

[0074] In summary, the intelligent light effect control method for a vehicle lamp module provided in the embodiment of the present application has the following beneficial effects: By analyzing the light efficiency requirements in the driving environment of electric vehicles, a light efficiency requirement feature library is constructed to provide accurate environmental data support for subsequent regulation and control, ensuring that the light efficiency regulation of the car lights can accurately adapt to different driving conditions. By matching the requirements of the light module based on the light efficiency requirement feature library and establishing the mapping association between the light module and the light efficiency requirement feature, intelligent demand identification and matching are realized, thereby ensuring that the light efficiency regulation of the car lights can accurately meet the requirements of the actual scene. By constructing the illumination grid distribution map of the light module, the hardware constraint optimization and control parameter analysis search of the light module are carried out based on the mapping association between the light module and the light efficiency requirement feature, and the optimized hardware configuration of each light module and its dimming scene control strategy are obtained, ensuring that the light module can play the best performance under actual hardware and environmental conditions. By laying out the light module according to the optimized hardware configuration and its dimming scene control strategy, and correspondingly constructing a human-computer interaction interface for interactive dimming instructions, the light module hardware is adjusted and controlled to match the dimming scene control strategy, and the real-time interaction between the driver and the light system is realized, which facilitates the driver to adjust the light effect according to the specific situation, thereby further improving the driving experience and safety.

[0075] In summary, the embodiments of the present application form a comprehensive and accurate light effect control system for electric vehicles through the design of intelligent light effect demand analysis, demand matching, hardware optimization and dynamic control strategies. First, by constructing a light effect demand feature library and a demand matching mechanism for the light module, the light effect adjustment can be accurately adapted to complex driving environments; secondly, through the illumination grid distribution map and hardware optimization, the optimal lighting effect of the light module in different environments is ensured, and energy efficiency and safety are improved; finally, combined with the human-computer interaction interface, convenient dimming control is provided, allowing the driver to easily adjust the light system according to real-time needs. On the whole, the embodiments of the present application realize the refined and personalized light effect control of the light module in different driving environments, effectively improving the lighting effect and driving safety of electric vehicle lights.

[0076] Embodiment 2, as Figure 2 As shown, based on the same inventive concept as the above-mentioned embodiment 1, the embodiment of the present application provides an intelligent light effect control system for a vehicle lamp module, the system comprising: The light efficiency requirement analysis module 10 is used to analyze the light efficiency requirement in the driving environment of the electric vehicle and build a light efficiency requirement feature library.

[0077] The demand matching module 20 is used to match the demand of the vehicle light module based on the light effect demand feature library, and establish a mapping association between the vehicle light module and the light effect demand feature.

[0078] An optimized search module 30 is used to construct a light distribution grid map of a vehicle headlight module, and based on the mapping association between the vehicle headlight module and the light effect demand characteristics, optimize the hardware constraints of the vehicle headlight module and parse and search for control parameters, so as to obtain the optimized hardware configuration of each vehicle headlight module and its dimming scene control strategy.

[0079] A light effect adjustment layout module 40 is used to layout the vehicle headlight module according to the optimized hardware configuration and its dimming scene control strategy, and correspondingly construct a human-computer interaction interface for interacting with dimming instructions to match the dimming scene control strategy to control the light effect of the vehicle headlight module hardware.

[0080] Furthermore, the light effect demand analysis module 10 in the embodiment of the present application is further used to execute the following steps: Collect samples in multiple dimensions according to the road environment, driving scene, and driver's light perception to construct a driving environment sample cluster; analyze the positive sample light effect and negative sample light effect according to the driving environment sample cluster, where the positive sample light effect is the light effect that meets the driving environment requirements, and the negative sample light effect is the light effect that does not meet the driving environment requirements; establish the corresponding relationship between the positive sample light effect, negative sample light effect and driving environment characteristics, and construct the light effect demand feature library, where the driving environment characteristics include road environment, driving scene, and driver's light perception characteristics.

[0081] Furthermore, the light effect demand analysis module 10 in the embodiment of the present application is further used to execute the following steps: Extract positive samples and negative samples according to the evaluation labels of the driving environment sample cluster; according to the positive samples, respectively analyze the light effect requirements of the driving environment sample cluster in each dimension according to the road environment, driving scene, and driver's light perception type, including headlight intensity, coverage angle distribution, and color temperature range, to obtain the positive sample light effect; according to the negative samples, respectively analyze the abnormal light effect characteristics according to the road environment, driving scene, and driver's light perception type, including headlight abnormal intensity threshold, coverage angle missing range, and color temperature abnormal range, to obtain the negative sample light effect.

[0082] Furthermore, the demand matching module 20 in the embodiment of the present application is further used to execute the following steps: Fully parameterize the installation position and hardware capabilities of the vehicle headlight module of the electric vehicle, and analyze the light effect performance and regulation capabilities of various vehicle headlights; perform parameter matching according to the required light effect characteristics in the light effect demand feature library and the light effect performance and regulation capabilities of various vehicle headlights, and establish the mapping association between the vehicle headlight module and the light effect demand characteristics according to the parameter matching relationship between the light effect demand characteristics and various vehicle headlights.

[0083] Furthermore, the vehicle headlight module at least includes: low beam, high beam, fog lamp, and turn signal.

[0084] Further, the optimization search module 30 in the embodiments of the present application is further configured to perform the following steps: According to the installation position of the vehicle lamp module, establish a three-dimensional grid distribution map, where the grid coordinates are located according to the structure of the electric vehicle and the installation coordinates of various vehicle lamps; according to the light effect performance and regulation ability of various vehicle lamps, construct a three-dimensional light effect heat map, and fit the three-dimensional light effect heat maps of various vehicle lamps to the three-dimensional grid distribution map according to the grid coordinates to obtain the illumination grid distribution map of the vehicle lamp module.

[0085] Further, the optimization search module 30 in the embodiments of the present application is further configured to perform the following steps: According to the light effect performance of various vehicle lamps, obtain the light effect range characteristics of the three-dimensional space, and construct a three-dimensional light effect heat map according to the three-dimensional light effect distribution characteristics of the three-dimensional space; according to the regulation ability, establish a three-dimensional light effect heat map of each regulation ability parameter, and perform heat map layer-by-layer superposition according to the regulation gradient of each regulation ability parameter to obtain the three-dimensional light effect heat map.

[0086] Further, the optimization search module 30 in the embodiments of the present application is further configured to perform the following steps: According to the light effect demand characteristics, construct a demand light effect heat distribution map; according to the mapping relationship between the vehicle lamp module and the light effect demand characteristics, add the demand light effect heat distribution map to the illumination grid distribution map of the vehicle lamp module for heat distribution matching to obtain the matching vehicle lamp type, installation position, and regulation ability parameters; perform matching degree analysis according to the matching relationship. When the matching degree reaches the threshold, according to the matching relationship, obtain the hardware configuration and dimming scene control strategy; when the matching degree does not reach the threshold, perform light array adjustment and optimization based on the array optimization parameters and constraint conditions of the vehicle lamp hardware or perform optimization of the regulation parameter strategy of the vehicle lamp module, and perform balanced optimization of the combination of the vehicle lamp hardware and regulation parameters to meet the goals of maximizing the light effect demand characteristics and minimizing the vehicle lamp energy consumption, so as to obtain the optimized hardware configuration and its dimming scene control strategy.

[0087] Further, the system in the embodiments of the present application is further configured to perform the following steps: Collect electric vehicle driving environment parameters through a multi-modal sensor, including brightness, illumination, weather, and surrounding obstacles; identify the dimming scene control strategy according to the matching between the electric vehicle driving environment parameters and the dimming scene characteristics; perform adaptive regulation of the vehicle lamp module according to the dimming scene control strategy.

[0088] Through the foregoing detailed description of the intelligent light effect regulation method for a vehicle lamp module, those skilled in the art can clearly know the intelligent light effect regulation system for a vehicle lamp module in this embodiment. For the system disclosed in Embodiment 2, since it corresponds to the method disclosed in Embodiment 1, it has corresponding functional modules and beneficial effects. For the relevant parts, reference can be made to the description in the method part.

[0089] The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An intelligent light effect control method for a vehicle light module, characterized in that: include: Analyze the lighting efficiency requirements in the driving environment of electric vehicles and build a lighting efficiency requirement feature library; Based on the light effect demand feature library, the vehicle light module is matched with demand, and a mapping association between the vehicle light module and the light effect demand feature is established; Constructing a lighting grid distribution map of the headlight module, optimizing the hardware constraints of the headlight module and performing analytical search for control parameters based on the association between the headlight module and the light efficiency requirement feature mapping, and obtaining the optimized hardware configuration of each headlight module and its dimming scene control strategy; According to the optimized hardware configuration and its dimming scene control strategy, the car light module is arranged, and a human-computer interaction interface is constructed accordingly for interactive dimming instructions to match the dimming scene control strategy to adjust the light effect of the car light module hardware.

2. The intelligent light effect control method for a vehicle light module according to claim 1, characterized in that: Analyze the lighting efficiency requirements in the electric vehicle driving environment and build a lighting efficiency requirement feature library, including: Collect samples based on road environment, driving scenario, and driver's light perception to build a driving environment sample cluster; Analyzing positive sample light effects and negative sample light effects according to the driving environment sample cluster, wherein the positive sample light effects are light effects that meet the driving environment requirements, and the negative sample light effects are light effects that do not meet the driving environment requirements; A correspondence between the positive sample light effect, the negative sample light effect and the driving environment characteristics is established, and the light effect demand characteristic library is constructed, wherein the driving environment characteristics include road environment, driving scene, and driver light perception characteristics.

3. The intelligent light effect control method for a vehicle light module according to claim 2, characterized in that: Analyzing the positive sample light effect and the negative sample light effect according to the driving environment sample cluster includes: Extracting positive samples and negative samples according to the evaluation labels of the driving environment sample cluster; According to the positive samples, respectively, according to the road environment, driving scene, and driver's light perception type, the driving environment sample cluster is analyzed for light efficiency requirements in various dimensions, including headlight intensity, coverage angle distribution, and color temperature range, to obtain positive sample light efficiency; According to the negative samples, the abnormal light effect characteristics are analyzed according to the road environment, driving scene, and driver's light perception type, including abnormal light intensity threshold, coverage angle missing range, and color temperature abnormal range, to obtain negative sample light effects.

4. The intelligent light effect control method for a vehicle light module according to claim 3, characterized in that: The requirements of the vehicle light module are matched based on the light effect requirement feature library, and a mapping association between the vehicle light module and the light effect requirement feature is established, including: Comprehensively parameterize the installation position and hardware capabilities of the electric vehicle’s headlight modules, and analyze the light performance and control capabilities of various types of headlights; Parameter matching is performed according to the required light effect characteristics in the light effect requirement characteristic library and the light effect performance and control capability of the various types of vehicle lamps, and according to the parameter matching relationship between the light effect requirement characteristics and the various types of vehicle lamps, a mapping association between the vehicle lamp module and the light effect requirement characteristics is established.

5. The intelligent light effect control method for a vehicle light module according to claim 4, characterized in that: The vehicle light module includes at least: low beam, high beam, fog light and turn signal.

6. The intelligent light effect control method for a vehicle light module according to claim 4, characterized in that: Construct the lighting grid distribution map of the headlight module, including: According to the installation position of the headlight module, a three-dimensional grid distribution map is established, wherein the grid coordinates are located according to the structure of the electric vehicle and the installation coordinates of various headlights; According to the lighting performance and controllability of the various types of headlights, a three-dimensional lighting effect heat map is constructed, and the three-dimensional lighting effect heat maps of the various types of headlights are fitted into the three-dimensional grid distribution map according to grid coordinate positioning to obtain the lighting grid distribution map of the headlight module.

7. The intelligent light effect control method for a vehicle light module according to claim 6, characterized in that: According to the light performance and control capabilities of various types of headlights, a three-dimensional light effect heat map is constructed, including: According to the light effect performance of various types of car lights, the light effect range characteristics of the three-dimensional space are obtained, and the three-dimensional light effect heat map is constructed according to the light effect distribution characteristics of the three-dimensional space; According to the regulation capability, a three-dimensional light efficiency heat map of each regulation capability parameter is established, and the heat map is layered and superimposed according to the regulation gradient of each regulation capability parameter to obtain the three-dimensional light efficiency heat map.

8. The intelligent light effect control method for a vehicle light module according to claim 7, characterized in that: Based on the association between the vehicle light module and the light effect requirement feature mapping, hardware constraint optimization and control parameter analysis and search of the vehicle light module are performed, including: According to the light efficiency demand characteristics, construct a required light efficiency thermal distribution map; According to the mapping relationship between the headlight module and the light effect requirement characteristics, the required light effect thermal distribution map is added to the illumination grid distribution map of the headlight module for thermal distribution matching to obtain the matching headlight type, installation position, and control capability parameters; Perform matching degree analysis according to the matching relationship, and when the matching degree reaches a threshold, obtain hardware configuration and dimming scene control strategy according to the matching relationship; When the matching degree does not reach the threshold, the light array is adjusted and optimized based on the array optimization parameters and constraints of the headlight hardware, or the control parameter strategy of the headlight module is optimized to achieve the goal of maximizing the lighting efficiency requirements and minimizing the energy consumption of the headlights. The headlight hardware and control parameter combination are balanced and optimized to obtain the optimized hardware configuration and its dimming scene control strategy.

9. The intelligent light effect control method for a vehicle light module according to claim 1, characterized in that: Also includes: Collect electric vehicle driving environment parameters through multimodal sensors, including brightness, lighting, weather, and surrounding obstacles; According to the matching of the electric vehicle driving environment parameters with the dimming scene characteristics, a dimming scene control strategy is identified; The vehicle light module is adaptively controlled according to the dimming scene control strategy.

10. An intelligent light effect control system for a vehicle light module, characterized in that: The system is used to execute the intelligent light effect control method for a vehicle light module according to any one of claims 1 to 9, comprising: Lighting efficiency requirement analysis module, used to analyze the lighting efficiency requirements in the electric vehicle driving environment and build a lighting efficiency requirement feature library; A demand matching module, used to match the demand of the vehicle light module based on the light effect demand feature library, and establish a mapping association between the vehicle light module and the light effect demand feature; An optimization search module is used to construct a light grid distribution map of the headlight module, and to optimize the hardware constraints of the headlight module and perform a search for control parameters based on the association between the headlight module and the light effect requirement feature mapping, so as to obtain the optimized hardware configuration of each headlight module and its dimming scene control strategy; The light effect adjustment and layout module is used to layout the car light module according to the optimized hardware configuration and its dimming scene control strategy, and to construct a corresponding human-computer interaction interface for interactive dimming instructions to match the dimming scene control strategy to adjust the light effect of the car light module hardware.

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