A vehicle-mounted display control method, system, medium and program product

Abstract

A method, system, medium and program product for controlling a vehicle-mounted display, which relates to the field of vehicle-mounted display, and includes: obtaining real-time traffic information of a navigation system and ambient light data collected by a light sensor; determining the type of light change based on the real-time traffic information and ambient light data; when the type of light change is an environmental factor type, determining the position of the light change based on the real-time traffic information; calculating the driving distance and estimated arrival time from the current vehicle position to the position of the light change, and generating a dynamic adjustment curve to gradually adjust the display parameters of the vehicle-mounted display; when the type of light change is a non-environmental factor type, determining the ambient light intensity and a preset fluctuation range at the current vehicle position based on the ambient light data; determining the tolerance interval of the display parameters according to the preset fluctuation range, and keeping the display parameters unchanged within the tolerance interval. The implementation of this application can optimize the vehicle-mounted display effect during nighttime driving and avoid affecting the service life of the display.

Description

Technical Field

[0001] The present application relates to the field of vehicle-mounted displays, and in particular to a vehicle-mounted display control method, system, medium and program product. Background Art

[0002] With the rapid development of automobile intelligence and networking, in-vehicle displays have become an indispensable human-computer interaction device in modern cars. In-vehicle displays need to provide drivers with clear and accurate information display, including navigation information, vehicle status, multimedia content, etc. in various complex driving environments.

[0003] In the related art, the in-vehicle display generally adopts the technical solution of LED backlight source and liquid crystal display panel. In order to improve the display effect, the related art adopts fixed brightness threshold and display parameter preset to switch different display modes under different lighting scenes.

[0004] However, when driving at night, although there are shrubs, green belts, fences and other dividing strips blocking the way, the oncoming high beam will still shine through the gaps in the dividing strips, forming a frequently flashing bright light source. The display mode in the related technology will be frequently adjusted in this scenario, resulting in poor display effects and affecting the service life of the display. Summary of the invention

[0005] The present application provides a display control method, system, medium and program product for a vehicle-mounted display, which are used to optimize the vehicle-mounted display effect during nighttime driving to avoid affecting the service life of the display.

[0006] In a first aspect, the present application provides a display control method for an on-board display, which is applied to an on-board display optimization system, the method comprising: obtaining real-time road condition information of a navigation system and ambient light data collected by a light sensor; determining a light change type based on the real-time road condition information and the ambient light data; the light change type comprises an environmental factor type and a non-environmental factor type, the environmental factor type being a light change at a fixed position, and the non-environmental factor type being a light change caused by a moving light source; when the light change type is an environmental factor type, determining a light change position based on the real-time road condition information; calculating a driving distance and an estimated arrival time from a current vehicle position to a light change position, and generating a dynamic adjustment curve to gradually adjust display parameters of the on-board display; when the light change type is a non-environmental factor type, determining an ambient light intensity and a preset fluctuation range at a current vehicle position based on the ambient light data; determining a tolerance interval for display parameters according to the preset fluctuation range, and keeping the display parameters unchanged within the tolerance interval.

[0007] In the above embodiment, the vehicle display optimization system obtains the navigation system and light sensor data in real time, intelligently distinguishes the types of light changes, and adopts corresponding display control strategies for different types. For environmental factor types, smooth transition is achieved by calculating and generating dynamic adjustment curves in advance; for non-environmental factor types, the display parameters are kept stable within a reasonable tolerance range, effectively avoiding the loss of display life caused by frequent adjustments, while ensuring a good visual experience for the driver.

[0008] In combination with some embodiments of the first aspect, in some embodiments, the step of determining the type of light change based on real-time traffic information and ambient light data specifically includes: identifying the light intensity change pattern in the ambient light data, and extracting the light intensity change characteristics including frequency characteristics and amplitude characteristics; matching the light intensity change characteristics with the environmental factor feature library, and identifying the fixed light source facilities within the preset driving range of the current vehicle position based on the geographic location data in the real-time traffic information; when the light intensity change characteristics match the environmental factor feature library and there are fixed light source facilities, determining that the light change type is an environmental factor type; when the light intensity change characteristics show periodic rapid fluctuations and there are no fixed light source facilities, determining that the light change type is a non-environmental factor type.

[0009] In the above embodiment, the vehicle display optimization system accurately distinguishes light changes caused by environmental factors and non-environmental factors by identifying the frequency and amplitude characteristics of the light intensity change pattern, combining environmental factor feature library matching and geographic location data analysis, so that the system can adopt the optimal display control strategy for different situations, thereby improving the adaptability and accuracy of the system.

[0010] In combination with some embodiments of the first aspect, in some embodiments, when the light change type is an environmental factor type, the step of determining the light change location based on real-time traffic information specifically includes: when the light change type is an environmental factor type, extracting the geographical environment data and infrastructure distribution information of the road ahead from the real-time traffic information; determining the fixed geographical location that causes the light change based on the geographical environment data and infrastructure distribution information; calculating the light impact index of multiple fixed geographical locations on the driving route based on the current driving direction of the vehicle; marking the fixed geographical location whose light impact index exceeds a preset impact threshold as the light change location.

[0011] In the above embodiment, the vehicle display optimization system analyzes the road geographical environment and infrastructure distribution, calculates the light impact index, and accurately locates the fixed light source position that causes display interference, so that the system can be prepared before the vehicle reaches the critical position and achieve more accurate display parameter adjustment.

[0012] In combination with some embodiments of the first aspect, in some embodiments, after calculating the driving distance and estimated arrival time from the current vehicle position to the light change position, generating a dynamic adjustment curve to gradually adjust the display parameters of the vehicle-mounted display, the method also includes: connecting to the vehicle interconnection server, obtaining an adjustment reference record of the vehicle in front including an ambient light record and a display adjustment record; based on the adjustment reference record, correcting the dynamic adjustment curve to a target adjustment curve; updating the estimated arrival time and the estimated arrival speed according to real-time road conditions and vehicle speed, and adjusting the target adjustment curve.

[0013] In the above embodiment, the vehicle display optimization system uses the Internet of Vehicles technology to obtain the ambient light records and display adjustment records of the preceding vehicle, corrects the dynamic adjustment curve by analyzing these data, and dynamically updates it according to real-time road conditions, thereby improving the accuracy and intelligence of the display control.

[0014] In combination with some embodiments of the first aspect, in some embodiments, before connecting to the vehicle interconnection server and obtaining the adjustment reference record of the vehicle in front including the ambient light record and the display adjustment record, the method also includes: determining whether the vehicle interconnection server has collected the vehicle in front information; if not, filtering out similar scene records from local historical data according to the current time period, geographical location and weather conditions, and using the similar scene records as alternative reference records.

[0015] In the above embodiment, the vehicle display optimization system establishes a local historical database. When the preceding vehicle data cannot be obtained, similar scene records are filtered out as references based on time period, geographic location and weather conditions, thereby ensuring that the system can maintain good display effects in all situations.

[0016] In combination with some embodiments of the first aspect, in some embodiments, the step of correcting the dynamic adjustment curve to a target adjustment curve based on an adjustment reference record specifically includes: calculating, based on the ambient light record, the actual light intensity change trend when the vehicle in front passes the light change position; comparing the actual light intensity change trend with the current dynamic adjustment curve to determine the curve correction parameters; extracting the display parameter adjustment sequence of the vehicle in front and the corresponding driver feedback data based on the display adjustment record; determining the adjustment effect index based on the display parameter adjustment sequence and the driver feedback data; and correcting the dynamic adjustment curve based on the curve correction parameters and the adjustment effect index to generate a target adjustment curve.

[0017] In the above embodiment, the vehicle display optimization system calculates the adjustment effect index by analyzing the actual light intensity change trend of the preceding vehicle and the driver's feedback data, thereby optimizing the dynamic adjustment curve and ensuring that the display control strategy can be continuously optimized and improved.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after the step of determining the tolerance interval of the display parameters based on the preset fluctuation range and keeping the display parameters unchanged within the tolerance interval, the method also includes: receiving the brightness setting input by the user through the adjustment interface of the vehicle-mounted display, and constructing a display preference configuration; according to the display preference configuration, adjusting the baseline value of the preset fluctuation range.

[0019] In the above embodiment, the vehicle display optimization system receives the user's display preference settings and adjusts the reference value of the preset fluctuation range, thereby achieving personalized customization of display control and improving user-friendliness and adaptability.

[0020] In a second aspect, an embodiment of the present application provides an in-vehicle display optimization system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the in-vehicle display optimization system to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product comprising instructions. When the above-mentioned computer program product runs on an in-vehicle display optimization system, the above-mentioned in-vehicle display optimization system executes the method described in the first aspect and any possible implementation method of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium comprising instructions. When the instructions are executed on an in-vehicle display optimization system, the in-vehicle display optimization system executes the method described in the first aspect and any possible implementation method of the first aspect.

[0023] It is understandable that the vehicle display optimization system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiment of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, which will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0025] 1. Due to the adoption of a dual judgment mechanism based on real-time traffic information and ambient light data, and the use of dynamic adjustment and stable maintenance strategies for environmental factor types and non-environmental factor types respectively, the system can intelligently identify different types of light changes and adopt the optimal control solution, effectively solving the problem of frequent adjustment of displays in complex light environments in related technologies, and thus achieving a smooth transition and stable control of display parameters, which not only ensures the display effect but also extends the service life of the display.

[0026] 2. Due to the adoption of data sharing and adaptive optimization mechanism based on the Internet of Vehicles, combined with dynamic adjustment based on the preceding vehicle data and real-time road conditions, the system can continuously optimize the display control strategy using actual driving data, effectively solving the problem of single display control scheme and poor adaptability in related technologies, thereby realizing intelligent display control based on actual scenarios and improving the control accuracy and reliability of the system.

[0027] 3. Since an adaptive adjustment mechanism for display parameters based on user preferences is adopted and personalized customization is achieved by adjusting the baseline value of the preset fluctuation range, the system can flexibly adjust the display effect according to the visual needs of different users, effectively solving the problem of lack of personalization in display control solutions in related technologies, thereby optimizing the display control experience and improving the practicality of the system and user satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a flow chart of a method for controlling a vehicle-mounted display in an embodiment of the present application;

[0029] Figure 2 is another flowchart of the vehicle-mounted display control method in an embodiment of the present application;

[0030] Figure 3 It is a schematic diagram of the structure of a physical device of the vehicle display optimization system in an embodiment of the present application. DETAILED DESCRIPTION

[0031] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification of the present application, the singular expressions "one", "a kind of", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more of the listed items.

[0032] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0033] For ease of understanding, the application scenarios of the embodiments of the present application are introduced below.

[0034] The user drove a car along a mountain highway. This highway winds along the mountain, with multiple tunnels and viaducts along the route. When the vehicle exited the tunnel, strong sunlight suddenly shone into the cockpit, causing severe glare on the on-board display. Then, the vehicle drove into a mountain section, and the shadows of the trees cast a rapidly changing light and dark alternation on the display. Later, the vehicle drove into a viaduct, and the sunlight shone through the gaps in the bridge railings, forming a regular light and dark alternation on the display. These complex and changing light environments not only affect the driver's experience of viewing the navigation information and vehicle status data on the display, but also increase the driver's visual fatigue, posing potential safety hazards.

[0035] In the related art, the automatic adjustment of display parameters can be achieved by adopting a brightness adjustment method based on a fixed threshold. This method sets a predetermined light intensity threshold and triggers the adjustment of display parameters when a change in ambient light intensity is detected. However, due to the lack of prediction and scene perception capabilities, the adjustment is often delayed or over-adjusted. The following describes the scenario of using the display control method of the vehicle display in the related art.

[0036] The user is using a new energy vehicle equipped with a traditional display optimization system. When he drives through a section of an urban viaduct, the guardrail of the viaduct will form a rapid alternation of light and dark on the display screen. After the traditional system detects the light change, it immediately starts the preset display parameter adjustment scheme, and the brightness of the display screen increases rapidly. However, when the vehicle exits this section of the viaduct, the brightness of the display screen decreases rapidly. This sudden adjustment causes the driver to have obvious visual discomfort. Later, when the vehicle enters a longer tunnel, although the traditional system can reduce the brightness of the display screen, the adjustment process is not smooth enough, and it is impossible to predict the strong light environment at the exit of the tunnel, causing the driver to experience brief visual dizziness when exiting the tunnel. Not only does it fail to provide a good visual experience, it may also bring safety risks.

[0037] By using the vehicle display control method in the embodiment of the present application, the light changes are predicted in advance and the parameters are optimized, which not only improves the timeliness and accuracy of the display adjustment, but also achieves a smoother transition effect. The following describes the scenarios in which the vehicle display control method in the present application is used.

[0038] The user drives a car equipped with the display optimization system of this solution on a city ring road. When the system predicts the presence of a tunnel ahead through navigation data, it analyzes the data and generates a smooth display parameter adjustment curve. When the vehicle approaches the tunnel entrance, the brightness of the display screen begins to slowly decrease, allowing the driver's visual system to adapt smoothly. While driving in the tunnel, the system continuously monitors light changes to maintain a stable display effect. When approaching the tunnel exit, the system makes progressive adjustments to the display parameters in advance to avoid glare problems when exiting the tunnel.

[0039] It can be seen that the display control method of the vehicle display in the embodiment of the present application can not only realize the automatic adjustment of display parameters, but also effectively solve the hysteresis and mutation problems existing in the traditional fixed threshold method, thereby realizing the optimization of the display effect.

[0040] For ease of understanding, the following describes the process of the method provided by this implementation in combination with the above scenario. Figure 1 , which is a flow chart of a method for controlling a vehicle-mounted display in an embodiment of the present application.

[0041] S101. Acquire real-time traffic information from a navigation system and ambient light data collected by a light sensor.

[0042] Among them, real-time road condition information refers to the comprehensive information data collected by the navigation system in real time, including road conditions, traffic conditions, weather conditions, etc.; ambient light data refers to the ambient light characteristic data collected by the vehicle-mounted light sensor, including parameters such as light intensity, light angle, and light wavelength; the navigation system refers to the combination of hardware equipment and software systems used to provide real-time positioning and navigation services for vehicles; the light sensor is used to represent a sensing device that can detect and measure the characteristics of ambient light.

[0043] The vehicle display optimization system continuously performs data collection during the driving process of the vehicle. Specifically, the vehicle display optimization system obtains the road environment information in the navigation system in real time through the vehicle network interface, including but not limited to the road type, road direction, distribution of surrounding buildings, location of traffic facilities, etc.; at the same time, it obtains the ambient light data collected by the light sensor through the data collection interface, including the light intensity change curve, light direction change characteristics, spectral distribution characteristics, etc., and pre-processes and stores these data.

[0044] S102: Determine the light change type based on the real-time traffic information and the ambient light data.

[0045] Among them, light change type refers to different lighting change modes that affect the display effect of the vehicle display, including environmental factor type and non-environmental factor type; the environmental factor type represents the light changes caused by light source facilities or geographical environment in a fixed position; the non-environmental factor type is used to represent the light changes caused by dynamic factors such as moving vehicles and mobile light sources.

[0046] After acquiring real-time data, the vehicle display optimization system determines the type of light change. Specifically, the vehicle display optimization system first performs time-frequency domain analysis on the ambient light data to extract the frequency, amplitude and direction characteristics of the light change; then, combined with the geographic location information and road environment information provided by the navigation system, the system uses a pattern recognition algorithm to determine the source and nature of the light change; finally, based on the preset discrimination rules, the light change is classified as an environmental factor type or a non-environmental factor type.

[0047] S103: When the light change type is an environmental factor type, determine the light change position based on real-time traffic information.

[0048] The light change position refers to a fixed geographical location point that will cause the display effect of the display to change.

[0049] The vehicle display optimization system performs a location determination operation after determining that the light change type is an environmental factor type. Specifically, the vehicle display optimization system first extracts the three-dimensional geographic model and infrastructure distribution map of the road ahead from the real-time traffic information, and analyzes the location of the fixed facilities that cause the light change; then, combined with the solar azimuth or the location information of the night lighting facilities, calculates the degree of light impact of these fixed facilities on the driving route; finally, the specific light change location point is determined through spatial geometric analysis and marked on the navigation map.

[0050] In some embodiments, the precise location of the light change position can be achieved in a variety of ways: Optionally, the vehicle display optimization system can establish a light propagation path model, combine the three-dimensional information and reflection characteristics of the building, calculate the impact range of light on the driving route at different times, and comprehensively determine the key change position; Optionally, the vehicle display optimization system can be based on historical data mining, by analyzing the light change feature points in the historical driving records, combined with the geographic information system to perform location clustering analysis, and establish a light change position prediction model. It is understandable that other methods can also be used to achieve the determination process of the light change position, which is not limited here.

[0051] S104, calculating the travel distance and estimated arrival time from the current vehicle position to the light change position, and generating a dynamic adjustment curve to gradually adjust the display parameters of the vehicle display.

[0052] Among them, the driving distance refers to the actual road distance from the current position to the predicted light change position; the estimated arrival time refers to the time point of arrival at the target position predicted based on the current vehicle speed and road conditions; the dynamic adjustment curve is used to represent the adjustment trajectory of the display parameters changing over time; the display parameters include adjustable display characteristics such as brightness, contrast, and color temperature.

[0053] The vehicle display optimization system will make parameter adjustment plans after determining the position of light changes. Specifically, the vehicle display optimization system first calculates the distance of the actual driving route based on the navigation data, and predicts the arrival time based on the current vehicle speed and road conditions; then, based on the light change prediction model, it builds a smooth transition curve from the current display parameters to the target display parameters; finally, according to the real-time status during driving, it dynamically adjusts the curve parameters to ensure the progressive optimization of the display effect.

[0054] It should be noted that when training the light change prediction model here, its input data includes historical light intensity data sequence (10 sampling points per second), geographic location information, time information, weather data and other multi-dimensional features. The training data comes from the actual driving records accumulated in the vehicle network, including light intensity change data in various typical scenarios. A supervised learning method is adopted, and the actual light intensity change curve is used as the label data. The training goal is to minimize the mean square error (MSE) between the predicted light intensity value and the actual light intensity value, while considering the penalty term of time correlation. The light change prediction model uses LSTM (Long Short-Term Memory Network) as the basic architecture, including 3 LSTM layers (128 units per layer) and 2 fully connected layers. The model integrates the temporal feature extraction module and the spatial feature extraction module, and fuses multi-source information through the attention mechanism. The temporal features are extracted by the LSTM layer, and the spatial features are processed by the convolution layer, and finally the light intensity change prediction sequence within the next 30 seconds is output. By inputting the current light intensity data stream (sampling rate 10Hz), vehicle position, driving speed and other information in real time, the light change prediction model can output the light intensity change prediction sequence within the next 30 seconds. The prediction results are used to guide the pre-adjustment of display parameters, and the prediction confidence is used as a weight factor for parameter adjustment.

[0055] S105: When the light change type is a non-environmental factor type, determine the ambient light intensity and a preset fluctuation range at the current vehicle position based on the ambient light data.

[0056] Among them, ambient light intensity refers to the real-time light intensity value at the current location; the preset fluctuation range indicates the allowable ambient light intensity change range; the non-environmental factor type here specifically refers to the short-term light changes caused by dynamic factors such as oncoming vehicles and moving light sources.

[0057] The vehicle display optimization system performs parameter stability control after determining that it is a non-environmental factor type. Specifically, the vehicle display optimization system first obtains real-time ambient light intensity data through the light sensor, performs noise reduction and smoothing on the data; then determines a reasonable fluctuation range threshold based on historical data analysis, and establishes a model for the change of ambient light intensity; finally, combined with the current driving scene, dynamically adjusts the upper and lower limits of the preset fluctuation range to ensure the system's adaptability to short-term light fluctuations.

[0058] In some embodiments, the determination of ambient light intensity and fluctuation range can be achieved in a variety of ways: Optionally, the vehicle display optimization system can construct an adaptive filter to perform real-time filtering processing on ambient light data, extract a stable light intensity signal, and determine a reasonable fluctuation range through statistical analysis; Optionally, the vehicle display optimization system can dynamically adjust the fluctuation range parameters based on the scene recognition algorithm by analyzing the current driving environment characteristics to achieve intelligent adaptation to different scenes. It is understandable that other methods can also be used to achieve the analysis and processing of ambient light characteristics, which are not limited here.

[0059] S106: Determine a tolerance interval of the display parameter according to a preset fluctuation range, and keep the display parameter unchanged within the tolerance interval.

[0060] The tolerance range refers to the allowable fluctuation range of the display parameters; the display parameters here specifically refer to the display characteristic parameters that need to be kept stable; and the fluctuation range represents the parameter adjustment limit range determined according to the change of ambient light.

[0061] The vehicle display optimization system performs parameter stability control after determining the preset fluctuation range. Specifically, the vehicle display optimization system first calculates the reasonable tolerance interval of the display parameters according to the preset fluctuation range and establishes a parameter stability control model; then, by real-time monitoring of the change trend of the display parameters, it determines whether it is within the tolerance interval; finally, a parameter locking mechanism is used to suppress the frequent adjustment of the display parameters within the tolerance interval to ensure the stability of the display effect.

[0062] In some embodiments, stable control of display parameters can be achieved in a variety of ways: Optionally, the vehicle display optimization system can analyze the change trend of display parameters by establishing a multi-level judgment mechanism, and trigger adjustment only when the parameters exceed the tolerance interval and last for a certain period of time; Optionally, the vehicle display optimization system can achieve flexible control of display parameters by establishing a parameter stability evaluation model based on a fuzzy control algorithm to avoid frequent fluctuations in parameters. It is understandable that other methods can also be used to achieve the stable control process of display parameters, which are not limited here.

[0063] In the above embodiment, the basic prediction and adjustment process is mainly described. In practical applications, the prediction model parameters and optimization strategies can be flexibly adjusted according to the characteristics of different scenarios and user needs to further improve the adaptability of the system. The following is a supplement to the scenario of this embodiment.

[0064] The user uses a smart car equipped with an optimized version of the display system. The system learns that the user usually likes a higher display brightness, so when generating the adjustment curve, the baseline brightness value is appropriately increased while ensuring safety. When passing this section of road, the display not only copes with the complex light environment, but also provides a comfortable experience for the driver. The system also uploads the adjustment effect to the vehicle interconnection server to provide reference data for other vehicles.

[0065] After combining the above scenarios, the following is a more detailed description of the process of the method provided by this implementation. Figure 2 , is another flow chart of the vehicle display control method in an embodiment of the present application.

[0066] S201. Acquire real-time traffic information from a navigation system and ambient light data collected by a light sensor.

[0067] Referring to step S101 , the vehicle display optimization system collects real-time road condition information and ambient light data.

[0068] S202: Determine the light change type based on the real-time traffic information and the ambient light data.

[0069] Referring to step S102 , the vehicle display optimization system determines the type of light change.

[0070] In some embodiments, the vehicle-mounted display optimization system identifies the light intensity change pattern in the ambient light data, extracts the light intensity change characteristics including frequency characteristics and amplitude characteristics; matches the light intensity change characteristics with the environmental factor feature library, and identifies the fixed light source facilities within the preset driving range of the current vehicle position based on the geographic location data in the real-time road condition information; when the light intensity change characteristics match the environmental factor feature library and there are fixed light source facilities, determines that the light change type is an environmental factor type; when the light intensity change characteristics show periodic rapid fluctuations and there are no fixed light source facilities, determines that the light change type is a non-environmental factor type.

[0071] Among them, the light intensity change pattern refers to the regular characteristics of the ambient light intensity changing over time; the frequency characteristics represent the periodic indicators of light intensity changes; the amplitude characteristics are used to represent the intensity range of light intensity changes; the environmental factor feature library refers to a database that stores standard light intensity change characteristics caused by various fixed environmental factors; the preset driving range represents the area within a specific distance in front of the vehicle.

[0072] The vehicle display optimization system performs an accurate identification process of the light change type. Specifically, the vehicle display optimization system first processes the data collected by the light sensor to extract the frequency and amplitude characteristics of the light intensity change; then the extracted features are matched with the standard patterns in the feature library for similarity, and the information of the fixed light source facilities around the current location is retrieved; finally, according to the matching results and the existence of the facilities, the specific light change type is determined through judgment logic.

[0073] S203: When the light change type is an environmental factor type, extract geographical environment data and infrastructure distribution information of the road ahead from the real-time traffic information.

[0074] Referring to step S103, the vehicle display optimization system extracts geographic related parameters

[0075] S204: Determine the fixed geographical location causing the light change based on the geographical environment data and infrastructure distribution information.

[0076] Among them, geographic environment data refers to a data set that describes the natural and artificial environmental characteristics around roads; infrastructure distribution information represents the spatial location information of fixed facilities such as road lighting facilities and traffic facilities; and fixed geographic locations are used to represent specific spatial points that may cause changes in light.

[0077] The vehicle display optimization system performs location analysis after acquiring environmental data. Specifically, the vehicle display optimization system first performs spatial analysis on the geographic environment data to identify the terrain features that may cause light changes; then combines the infrastructure distribution information to establish a three-dimensional spatial model of the road environment; and finally, through the light propagation model analysis, it determines the fixed geographical location points that have light influence.

[0078] In some embodiments, the determination of a fixed geographic location can be achieved in a variety of ways: Optionally, the vehicle display optimization system can use a spatial geometric analysis method to establish a light reflection and occlusion model, combined with road curvature and slope information, to accurately calculate the spatial coordinates of the light impact point; Optionally, the vehicle display optimization system can be based on image processing technology, by analyzing environmental features in high-precision maps, identifying key areas where light changes may occur, and establishing a location prediction model. It is understandable that other methods can also be used to achieve the determination process of a fixed geographic location, which is not limited here.

[0079] S205. Calculate light impact indexes of multiple fixed geographical locations on the driving route according to the current driving direction of the vehicle.

[0080] Among them, the light impact index refers to a numerical value that quantifies the degree of influence of a fixed geographical location on the vehicle display system; the driving direction indicates the real-time movement direction of the vehicle; and the fixed geographical location here specifically refers to a determined spatial point that may cause light interference.

[0081] The vehicle display optimization system performs impact assessment after determining the fixed geographic location. Specifically, the vehicle display optimization system first establishes a dynamic coordinate system based on the vehicle's current location and driving direction; then performs light impact analysis on each fixed geographic location, taking into account factors such as distance attenuation, incident angle, and degree of occlusion; and finally obtains the light impact index of each location through comprehensive calculation.

[0082] In some embodiments, the calculation of the light impact index can be implemented in a variety of ways: Optionally, the vehicle display optimization system can convert parameters such as distance, angle, and light intensity into standardized indicators by establishing a multi-factor weighted model, and obtain a comprehensive impact index by weighted summation; Optionally, the vehicle display optimization system can accurately evaluate the degree of light impact by calculating the light propagation path and energy attenuation based on an optical physics model, combined with the atmospheric scattering effect. It is understandable that other methods can also be used to implement the light impact evaluation process, which is not limited here.

[0083] S206: Mark a fixed geographical location where the light impact index exceeds a preset impact threshold as a light change location.

[0084] Among them, the preset impact threshold refers to the system-predefined light impact judgment standard; the light impact index represents the calculated quantitative value of the impact degree; and the light change position is used to represent the key geographical location where display parameters need to be adjusted.

[0085] The vehicle display optimization system performs threshold screening after calculating the impact index. Specifically, the vehicle display optimization system first sets a reasonable impact threshold based on historical data and test results; then compares the light impact index of each fixed geographical location with the preset threshold; and finally marks the location points that exceed the threshold as light change locations that require special attention.

[0086] In some embodiments, the marking of the light change position can be achieved in a variety of ways: Optionally, the vehicle display optimization system can use a hierarchical clustering algorithm to perform spatial clustering analysis on the position points that exceed the threshold, identify the main light change areas, and prioritize them; Optionally, the vehicle display optimization system can dynamically adjust the impact threshold based on spatiotemporal sequence analysis, taking into account the vehicle's driving trajectory and time factors, to achieve more accurate position marking. It is understandable that other methods can also be used to achieve the marking process of the light change position, which is not limited here.

[0087] S207, calculating the travel distance and estimated arrival time from the current vehicle position to the light change position, and generating a dynamic adjustment curve to gradually adjust the display parameters of the vehicle display.

[0088] Referring to step S104 , the vehicle display optimization system will gradually adjust the display parameters of the vehicle display.

[0089] S208: Connecting to the vehicle interconnection server to obtain adjustment reference records of the vehicle ahead including ambient light records and display adjustment records.

[0090] Among them, the vehicle interconnection server refers to a network service system used for data exchange and information sharing between vehicles; the ambient light record represents the lighting environment change data collected by the vehicle in front; the display adjustment record is used to represent the display parameter adjustment data made by the vehicle in front in response to light changes; the adjustment reference record refers to a comprehensive data set that includes ambient light records and display adjustment records.

[0091] The in-vehicle display optimization system performs data acquisition when planning the adjustment strategy. Specifically, the in-vehicle display optimization system first establishes a secure connection with the vehicle interconnection server to confirm data access rights; then queries the historical passing records of the vehicles ahead on the driving route, extracts relevant ambient light data and display parameter adjustment data; finally, the acquired data is formatted and processed to build a complete adjustment reference record.

[0092] In some embodiments, the acquisition and processing of adjustment reference records can be achieved in a variety of ways: Optionally, the vehicle display optimization system can establish a real-time data stream processing mechanism to perform online analysis and screening of the preceding vehicle data, extract the most valuable adjustment records, and perform data quality assessment; Optionally, the vehicle display optimization system can integrate environmental perception data of different vehicles through a multi-source data fusion algorithm based on distributed database technology to form a more comprehensive reference basis. It is understandable that other methods can also be used to achieve the acquisition process of adjustment reference records, which are not limited here.

[0093] In some embodiments, the vehicle display optimization system will first determine whether the vehicle interconnection server has collected information about the vehicle in front; if not, it will filter out similar scene records from local historical data based on the current time period, geographic location and weather conditions, and use the similar scene records as alternative reference records.

[0094] Among them, the vehicle information ahead refers to the environmental data collected by other vehicles through the vehicle Internet network; the similar scene record represents the historical data that is similar to the current situation in terms of time, location and weather conditions; the alternative reference record is used to represent the reference data as an alternative when the real-time data cannot be obtained.

[0095] The in-vehicle display optimization system adopts alternative solutions when performing reference data acquisition. Specifically, the in-vehicle display optimization system first checks the connection status with the vehicle interconnection server to verify the feasibility of data acquisition; then, when real-time data cannot be obtained, it retrieves similar scenes from the local database based on the current environmental conditions; finally, the filtered historical records are scored for similarity and the most suitable data is selected as a reference.

[0096] In some embodiments, the recognition of similar scenes can be achieved in a variety of ways: Optionally, the vehicle display optimization system can use a multi-dimensional feature matching algorithm to calculate the similarity with historical records by constructing a feature vector containing factors such as time, location, and weather; Optionally, the vehicle display optimization system can achieve intelligent matching and screening of scenes based on scene clustering analysis by establishing a fuzzy reasoning system. It is understandable that other methods can also be used to achieve the recognition process of similar scenes, which are not limited here.

[0097] S209: Based on the adjustment reference record, correct the dynamic adjustment curve to be the target adjustment curve.

[0098] Among them, the target adjustment curve refers to the display parameter adjustment trajectory after optimization and correction; the dynamic adjustment curve represents the parameter adjustment scheme initially planned by the system; and the correction process is used to represent the operation of optimizing the adjustment curve based on reference data.

[0099] The vehicle display optimization system performs curve correction after obtaining the reference record. Specifically, the vehicle display optimization system first analyzes the ambient light change trend and display parameter adjustment effect in the reference record; then compares and analyzes these actual data with the currently planned dynamic adjustment curve to identify the key points that need to be optimized; finally, the original curve is corrected through an adaptive algorithm to generate a better target adjustment curve.

[0100] In some embodiments, the vehicle display optimization system can be based on model predictive control, by establishing a dynamic optimization model, combining historical effect evaluation, dynamically adjusting curve parameters, and improving control accuracy. The input data of the display parameter optimization model during training includes the predicted light intensity change sequence, historical display parameter adjustment records, and driver feedback data (including active adjustment behavior and satisfaction score). The training data comes from the adjustment effect evaluation data collected in the actual driving scene. The training goal is to maximize the comprehensive evaluation index, which considers multiple dimensions such as display effect stability, visual comfort, and driver satisfaction. The display parameter optimization model adopts a reinforcement learning framework, and the state space includes the current light intensity, the predicted light intensity sequence, the display parameters, etc.; the action space is the adjustment amount of the display parameters; the reward function is constructed based on driver feedback and objective evaluation indicators. The double Q-learning algorithm is used, which includes two parts: the policy network and the evaluation network. By receiving the light intensity prediction sequence as input and combining the current display state, the display parameter optimization model can output the optimal display parameter adjustment strategy. Feedback is continuously received during the execution of the strategy, and the adjustment effect is continuously optimized through the online learning mechanism.

[0101] In some embodiments, the vehicle-mounted display optimization system calculates the actual light intensity change trend when the vehicle in front passes the light change position based on the ambient light record; compares the actual light intensity change trend with the current dynamic adjustment curve to determine the curve correction parameters; extracts the display parameter adjustment sequence of the vehicle in front and the corresponding driver feedback data based on the display adjustment record; determines the adjustment effect index based on the display parameter adjustment sequence and the driver feedback data; corrects the dynamic adjustment curve according to the curve correction parameters and the adjustment effect index to generate a target adjustment curve.

[0102] Among them, the actual light intensity change trend refers to the real light intensity data recorded when the front vehicle passes the light change position; the curve correction parameter represents the specific parameters that need to be adjusted for the current adjustment curve; the display parameter adjustment sequence refers to the display parameter change process actually executed by the front vehicle; the adjustment effect index is an evaluation indicator used to quantify the display parameter adjustment effect.

[0103] The vehicle display optimization system performs curve optimization after obtaining reference data. Specifically, the vehicle display optimization system first analyzes the light intensity change data recorded by the preceding vehicle and extracts the actual change trend; then compares and analyzes the trend with the planned adjustment curve and calculates the parameters that need to be adjusted; then evaluates the display parameter adjustment effect of the preceding vehicle and comprehensively considers the driver's feedback information; finally, based on the analysis results, the adjustment curve is optimized to generate a more suitable target curve.

[0104] In some embodiments, the optimization of the adjustment curve can be achieved in a variety of ways: Optionally, the vehicle display optimization system can use an adaptive control algorithm to dynamically adjust the curve parameters by establishing an error compensation model and combining effect evaluation; Optionally, the vehicle display optimization system can use the reinforcement learning method to adjust the display parameters as a decision-making problem and continuously improve the adjustment strategy through experience accumulation. It is understandable that other methods can also be used to achieve the optimization process of the adjustment curve, which is not limited here.

[0105] S210: Update the estimated arrival time and the estimated arrival speed according to the real-time traffic information and the vehicle speed, and adjust the target adjustment curve.

[0106] Among them, the estimated arrival time refers to the time point when the light changes based on the current road conditions; the estimated arrival speed refers to the expected driving speed of the vehicle when it reaches the target location; and the target adjustment curve adjustment refers to the process of dynamically optimizing the curve according to the real-time status.

[0107] The vehicle display optimization system continuously updates its status during the adjustment process. Specifically, the vehicle display optimization system first determines the road conditions ahead based on real-time traffic information, and calculates the new estimated arrival time based on the current vehicle speed; then, based on the updated time and speed parameters, it adjusts the time scale of the target adjustment curve; finally, through the feedback control mechanism, it ensures that the display parameter adjustment is synchronized with the actual driving status.

[0108] S211. When the light change type is a non-environmental factor type, determine the ambient light intensity and a preset fluctuation range at the current vehicle position based on the ambient light data.

[0109] Referring to step S105 , the vehicle display optimization system determines the ambient light intensity and the preset fluctuation range.

[0110] S212: Determine a tolerance interval of the display parameter according to a preset fluctuation range, and keep the display parameter unchanged within the tolerance interval.

[0111] Referring to step S106, the vehicle display optimization system will keep the display parameters unchanged.

[0112] Specifically, when the system detects that the type of light change is not an environmental factor type, it first needs to analyze the light intensity data in real time. The system uses a sliding window method to process the light intensity data. The window size is usually set to 1 second and the sliding step is 100 milliseconds. In each window, the standard deviation σ and mean μ of the light intensity value are calculated to evaluate the degree of fluctuation. In order to eliminate occasional interference, the system uses the exponentially weighted moving average (EWMA) method to smooth the calculation results. The smoothing factor α is usually set to 0.3. The mathematical expression is:

[0113] EWMA_t = α × current_value + (1-α) × EWMA_{t-1}. For example, when it is detected that the standard deviation of a piece of light intensity data within 1 second exceeds the set threshold (such as 20 lux), the system will start the fluctuation suppression mechanism.

[0114] In addition, the system can dynamically adjust the tolerance threshold of light intensity fluctuations through adaptive methods. First, a regression model based on time series is established to predict the trend of light intensity changes in the short term.

[0115] The model adopts an AR (p) autoregressive structure, where the order of p is determined by the AIC criterion. Based on the prediction results, the system calculates the adaptive threshold: θ_t = μ_t ± k × σ_t, where k is an adjustable parameter with an initial value of 2. The system also considers the seasonal and cyclical characteristics in historical data, extracts these characteristics through Fourier transform analysis and incorporates them into the threshold calculation. For example, at noon on a sunny day, due to the high base value of light intensity, the system will increase the tolerance threshold accordingly.

[0116] In order to avoid frequent jumps in display parameters, the system implements a smooth transition mechanism based on a state machine. Define the target state S_target and current state S_current of the display parameters, and calculate the transition path through the nonlinear control function f(x):

[0117] S_t=S_current+f(t)×(S_target-S_current). Where f(t) is in the form of a sigmoid function: f(t)=1 / (1+e^(-αt)), and α is a parameter that controls the transition rate. The system dynamically adjusts the α value according to the severity of the light intensity change to achieve a smooth or fast transition effect. For example, when a rapid fluctuation in light intensity is detected, the α value will be reduced to achieve a smoother transition.

[0118] The system uses a prediction-compensation mechanism to optimize the response characteristics of display parameter adjustment. By establishing a Kalman filter model of light intensity change, the light intensity change trend in the short term in the future is predicted. The state equation is described by a second-order system:

[0119] x_k=Ax_{k-1}+Bu_k+w_k, where x represents the light intensity state, u is the control input, and w is the process noise. Based on the prediction results, the system calculates the required display parameter adjustment in advance and eliminates the control delay through the compensation link. In practical applications, the system can complete parameter pre-adjustment within 100ms after detecting the start of light intensity change.

[0120] The system can also ensure the stability of the display effect by building a multi-layer evaluation model. First, an evaluation system based on fuzzy reasoning is established. The input variables include the amplitude, frequency and duration of light intensity fluctuations, and the output variable is the stability score. The fuzzy rules use the Mamdani reasoning method, and the membership function is obtained through historical data training. The system also introduces a time weighting factor to give higher weight to recent unstable events: Score_t=Σ(w_i×s_i), where w_i represents the time weight and s_i is the single evaluation score. For example, if the system detects frequent display parameter adjustments in the last 5 minutes, the current stability score will be reduced.

[0121] In some embodiments, the vehicle display optimization system also receives brightness settings input by the user through the adjustment interface of the vehicle display, constructs a display preference configuration, and adjusts the reference value of the preset fluctuation range according to the display preference configuration.

[0122] Among them, the adjustment interface refers to the interactive interface for users to set display parameters; the display preference configuration represents the user-defined display parameter settings; the reference value refers to the central reference value of the preset fluctuation range; and the brightness setting is used to represent the display brightness level expected by the user.

[0123] The in-vehicle display optimization system performs personalized adjustments after receiving user input. Specifically, the in-vehicle display optimization system first collects the user's display parameter preferences through the adjustment interface; then converts these preference settings into standardized configuration parameters; and finally adjusts the baseline value of the preset fluctuation range according to the configuration parameters to achieve personalized optimization of the display effect.

[0124] In some embodiments, personalized adjustment of display parameters can be achieved in a variety of ways: Optionally, the in-vehicle display optimization system can adopt a user preference learning model to automatically optimize the fluctuation range parameters by recording and analyzing the user's adjustment habits. Specifically, the in-vehicle display optimization system describes the driver's display parameter preferences by constructing a multi-dimensional user preference feature vector. The feature vector contains four core dimensions: basic brightness preference B, contrast sensitivity C, adjustment rate preference R, and environmental adaptability A. The system uses a time series analysis method to convert the user's historical adjustment behavior into a standardized feature value. For the features of each dimension, the time-weighted average is calculated using the exponential smoothing method: V_t=α×X_t+(1-α)×V_{t-1}, where α is the smoothing coefficient (default 0.3), X_t is the current observation value, and V_t is the smoothed feature value. For example, when the system observes that the user frequently increases the display brightness at night, the basic brightness preference value will be increased accordingly.

[0125] The in-vehicle display optimization system also has a dynamic update mechanism for user preferences based on incremental learning. By building a Bayesian probability model, the system can learn and update preference parameters from each user's adjustment behavior. The model uses multivariate Gaussian distribution to describe the uncertainty of user preferences and uses the expectation maximization (EM) algorithm for parameter estimation. The optimization objective function is defined as:

[0126] L(θ)=ΣlogP(x_i|θ)+λR(θ), where θ is the model parameter and R(θ) is the regularization term. When the user adjusts the display parameters in a specific scenario, the system will update the preference distribution parameters in the corresponding scenario.

[0127] The in-vehicle display optimization system can also establish a scene-aware preference mapping network to convert the user's general preferences into specific parameter settings in a specific scene. The mapping network adopts a feedforward neural network structure. The input layer receives the scene feature vector S and the user preference vector P, and generates a scene-adaptive display parameter vector D through multi-layer nonlinear transformation. The network training adopts the back-propagation algorithm, and the loss function contains the parameter fitting error term and the smoothness constraint term. In practical applications, for example, after the system detects the user's specific preference in the tunnel scene, it will adjust the display parameter mapping relationship in the scene accordingly.

[0128] In addition, for scenarios where multiple people share a vehicle, the system implements a multi-user preference coordination mechanism based on fuzzy logic. The system maintains an independent preference model for each user and automatically loads the corresponding user configuration through identity recognition technology (such as facial recognition or fingerprint recognition). When a new user is detected, the system initializes the preference model of the new user based on the preference data of similar user groups through a collaborative filtering algorithm. The optimization objective is defined as minimizing the average dissatisfaction of all users: min(Σwi×D(Pi,Pactual)), where wi is the user weight and D() is the preference difference measurement function.

[0129] The in-vehicle display optimization system also has a mechanism for tracking the long-term evolution of user preferences. By constructing a time series feature extraction network, the system can identify the gradual trends and mutation points of user preferences. Feature extraction uses a bidirectional LSTM network, with the input being the user adjustment sequence within the time window T and the output being the preference change feature vector. The system uses a variational inference method to estimate the probability distribution of preference transitions: P(Z_t|X_{1:t})≈q_φ(Z_t), where Z_t represents the potential preference state at time t and φ is a variational parameter. When the system detects a significant preference change trend, it adjusts the basic preference model parameters accordingly. For example, when the system finds that users have gradually tended to prefer higher display contrast in the near future, it will smoothly adjust the contrast-related baseline parameters.

[0130] The in-vehicle display optimization system can also ensure the reliability and consistency of preference settings by establishing a multi-layer verification mechanism. First, a confidence-based preference evaluation model is constructed, and the preference credibility is evaluated by calculating the consistency index CI of user behavior: CI=1-σ(P_t) / μ(P_t), where σ(P_t) and μ(P_t) represent the standard deviation and mean of the preference sequence, respectively. The system also implements an anomaly detection algorithm, using the isolation forest method to identify anomalies in the preference data and triggering a manual confirmation mechanism when an anomaly is detected. For example, when the system detects that a user's adjustment behavior is seriously inconsistent with historical preferences, it will temporarily maintain the original settings and wait for user confirmation.

[0131] In the embodiment of the present application, due to the adoption of an intelligent prediction mechanism based on real-time road condition information and ambient light data, combined with the collaborative perception capability of the vehicle Internet network, a dynamic adaptive display parameter adjustment system is established, so that the optimal display parameter adjustment strategy can be planned in advance before the vehicle reaches the light change position, effectively solving the problems of delayed response, sudden adjustment, and poor adaptability of the traditional display system, thereby achieving multiple optimization effects such as smooth transition, accurate prediction, and personalized adjustment of display parameters. By introducing innovative technologies such as multi-source data fusion, collaborative perception sharing, and adaptive algorithm optimization, this solution not only improves the driver's visual experience and operating comfort, but also provides new technical ideas and solutions for the optimization of the display system of intelligent networked vehicles.

[0132] The following describes the vehicle display optimization system in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , which is a schematic diagram of a physical device structure of the vehicle display optimization system in an embodiment of the present application.

[0133] It should be noted that Figure 3 The structure of the vehicle display optimization system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0134] like Figure 3As shown, the vehicle display optimization system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method described in the above embodiment. In RAM 303, various programs and data required for system operation are also stored. CPU 301, ROM 302 and RAM 303 are connected to each other through bus 304. Input / output (I / O) interface 305 is also connected to bus 304.

[0135] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a button switch, etc.; an output section 307 including a liquid crystal display (LCD) and an audio output device, an indicator light, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0136] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0137] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0138] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings.

[0139] Specifically, the vehicle display optimization system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the vehicle display control method provided in the above embodiment is implemented.

[0140] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the vehicle display optimization system described in the above embodiment; or may exist independently without being assembled into the vehicle display optimization system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of the vehicle display optimization system, the vehicle display optimization system implements the vehicle display control method provided in the above embodiment.

[0141] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0142] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0143] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A method for controlling a vehicle-mounted display, characterized in that: Applied to a vehicle display optimization system, the method comprises: Obtain real-time traffic information from the navigation system and ambient light data collected by the light sensor; Based on the real-time traffic information and the ambient light data, determining the type of light change; the light change type includes an environmental factor type and a non-environmental factor type, the environmental factor type is a light change at a fixed position, and the non-environmental factor type is a light change caused by a moving light source; When the light change type is an environmental factor type, determining the light change position based on the real-time traffic information; Calculating the travel distance and estimated arrival time from the current vehicle position to the light change position, and generating a dynamic adjustment curve to gradually adjust the display parameters of the vehicle display; Connecting to the vehicle interconnection server to obtain adjustment reference records including ambient light records and display adjustment records of the vehicle ahead; Based on the adjustment reference record, modifying the dynamic adjustment curve to be a target adjustment curve; updating the estimated time of arrival and estimated speed of arrival according to the real-time traffic information and the vehicle speed, and adjusting the target adjustment curve; When the light change type is a non-environmental factor type, determining the ambient light intensity and a preset fluctuation range at the current vehicle position based on the ambient light data; A tolerance interval of the display parameter is determined according to the preset fluctuation range, and the display parameter is kept unchanged within the tolerance interval.

2. The method according to claim 1, characterized in that The step of determining the light change type based on the real-time traffic information and the ambient light data specifically includes: Identify the light intensity variation pattern in the ambient light data, and extract light intensity variation features including frequency features and amplitude features; Matching the light intensity variation characteristics with the environmental factor characteristic library, and identifying the fixed light source facilities within the preset driving range of the current vehicle position based on the geographical location data in the real-time traffic information; When the light intensity variation feature matches the environmental factor feature library and there is a fixed light source facility, determining the light intensity variation type as an environmental factor type; When the light intensity variation characteristic presents periodic rapid fluctuations and there is no fixed light source facility, it is determined that the light intensity variation type is a non-environmental factor type.

3. The method according to claim 1, characterized in that When the light change type is an environmental factor type, the step of determining the light change position based on the real-time traffic information specifically includes: When the light change type is an environmental factor type, extracting geographical environment data and infrastructure distribution information of the road ahead from the real-time traffic information; Determine the fixed geographical location causing the light change based on the geographical environment data and the infrastructure distribution information; Calculating the light impact index of the plurality of fixed geographical locations on the driving route according to the current driving direction of the vehicle; The fixed geographical location where the light impact index exceeds a preset impact threshold is marked as a light change location.

4. The method according to claim 1, characterized in that Before the step of connecting to the vehicle interconnection server and acquiring the adjustment reference record of the vehicle ahead including the ambient light record and the display adjustment record, the method further includes: Determine whether the vehicle interconnection server has collected the information of the vehicle ahead; If not, similar scene records are screened out from local historical data according to the current time period, geographical location and weather conditions, and the similar scene records are used as alternative reference records.

5. The method according to claim 1, characterized in that The step of correcting the dynamic adjustment curve to a target adjustment curve based on the adjustment reference record specifically includes: Based on the ambient light record, calculating the actual light intensity change trend when the front vehicle passes the light change position; Comparing the actual light intensity variation trend with the current dynamic adjustment curve to determine the curve correction parameters; extracting a display parameter adjustment sequence of a preceding vehicle and corresponding driver feedback data according to the display adjustment record; determining an adjustment effect index based on the display parameter adjustment sequence and the driver feedback data; The dynamic adjustment curve is corrected according to the curve correction parameter and the adjustment effect index to generate a target adjustment curve.

6. The method according to claim 1, characterized in that After the step of determining the tolerance interval of the display parameter according to the preset fluctuation range and keeping the display parameter unchanged within the tolerance interval, the method further includes: Receiving brightness settings input by the user through the adjustment interface of the vehicle display, and constructing a display preference configuration; According to the display preference configuration, the reference value of the preset fluctuation range is adjusted.

7. A vehicle display optimization system, characterized in that: The in-vehicle display optimization system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the in-vehicle display optimization system to execute the method described in any one of claims 1-6.

8. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on the in-vehicle display optimization system, the in-vehicle display optimization system executes the method as described in any one of claims 1-6.

9. A computer program product, characterized in that When the computer program product runs on an in-vehicle display optimization system, the in-vehicle display optimization system is enabled to execute the method according to any one of claims 1 to 6.

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