Intelligent self-adaptive light adjusting method and device for AR glasses and electronic equipment

Abstract

The invention provides an intelligent adaptive light adjustment method and device for AR glasses and electronic equipment, and relates to the field of data processing. The method comprises the following steps: acquiring ambient light data, user light adaptability data and glasses brightness compensation data when a user wears AR glasses; processing the ambient light data by adopting an ambient light dynamic response model to obtain ambient light dynamic response adjustment data; processing the user light adaptability data through a user physiology and preference model to obtain user physiology and preference adjustment data; processing the glasses brightness compensation data by adopting a non-linear brightness compensation model to obtain non-linear brightness compensation adjustment data; and performing intelligent adaptive light adjustment on the AR glasses based on the ambient light dynamic response adjustment data, the user physiology and preference adjustment data and the nonlinear brightness compensation adjustment data. By implementing the technical scheme provided by the invention, the accuracy of light adjustment of the AR glasses is improved.

Description

Technical Field

[0001] The present application relates to the technical field of data processing, and in particular to an intelligent adaptive light adjustment method, device and electronic device for AR glasses. Background Art

[0002] As an emerging human-computer interaction device, AR glasses are widely used in navigation, entertainment, industry, and medical fields. During use, changes in ambient light have a significant impact on the user's visual experience and operating accuracy.

[0003] Current AR glasses usually have a light adjustment function. The basic principle is to monitor the ambient light intensity through sensors and then dynamically adjust the light transmittance of the lens. However, traditional light adjustment technology mainly relies on ambient light sensors to obtain light intensity data and make adjustments based on preset rules. This method lacks intelligent analysis and dynamic adaptation capabilities when dealing with the combined effects of multi-dimensional factors, resulting in an overly single adjustment method, making it difficult to accurately respond to users' actual needs, thereby reducing the accuracy of light adjustment.

[0004] Therefore, there is an urgent need for an intelligent adaptive light adjustment method, device and electronic equipment for AR glasses. Summary of the invention

[0005] The present application provides an intelligent adaptive light adjustment method, device and electronic device for AR glasses, which are convenient for improving the accuracy of light adjustment of AR glasses.

[0006] In a first aspect of the present application, a method for intelligent adaptive light adjustment of AR glasses is provided, the method comprising: obtaining ambient light data, user light adaptability data and glasses brightness compensation data when a user wears the AR glasses; processing the ambient light data using an ambient light dynamic response model to obtain ambient light dynamic response adjustment data; processing the user light adaptability data using a user physiological and preference model to obtain user physiological and preference adjustment data; processing the glasses brightness compensation data using a nonlinear brightness compensation model to obtain nonlinear brightness compensation adjustment data; and performing intelligent adaptive light adjustment on the AR glasses based on the ambient light dynamic response adjustment data, the user physiological and preference adjustment data and the nonlinear brightness compensation adjustment data.

[0007] By adopting the above technical solutions, by simultaneously collecting ambient light data, user light adaptability data and glasses brightness compensation data, and analyzing light adjustment needs from multiple angles, the actual scene and user needs can be more comprehensively reflected, and the accuracy of adjustment and user satisfaction can be significantly improved. The ambient light dynamic response model is used to process ambient light data, quickly adapt to light changes, enhance the real-time performance and smoothness of adjustment of the system, and reduce visual discomfort caused by delay or excessive adjustment. The user physiology and preference model takes into account personalized factors such as the user's pupil adaptability, visual fatigue state and light sensitivity, realizes targeted adjustment for different user needs, and improves wearing comfort and user experience. The nonlinear brightness compensation model effectively handles the compensation needs for virtual content display brightness, avoids the problem of too bright or too dark display content due to improper light adjustment, and enhances the visual fusion effect of virtual and real scenes. By combining ambient light dynamic response adjustment, user physiology and preference adjustment, and brightness compensation adjustment data, an intelligent comprehensive adjustment solution is realized, avoiding the limitations of a single adjustment mode, and ensuring the accuracy and adaptability of adjustment. The overall adjustment method focuses on user experience, dynamically adapts to different environments and user states, and effectively reduces visual fatigue caused by improper light adjustment, especially during long-term use. Therefore, it is easy to improve the accuracy of AR glasses light adjustment.

[0008] Optionally, the acquiring of ambient light data, user light adaptability data and glasses brightness compensation data during the process of the user wearing AR glasses specifically includes: receiving original ambient light data sent by an ambient light sensor located on the AR glasses, the original ambient light data including ambient light intensity data; receiving original user light adaptability data sent by an eye movement sensor located on the AR glasses, the original user light adaptability data including user pupil diameter and fatigue status data; receiving original glasses brightness compensation data sent by the AR glasses, the original glasses brightness compensation data including a virtual display brightness value; performing data processing on the original ambient light data, the original user light adaptability data and the original glasses brightness compensation data to obtain the ambient light data, the user light adaptability data and the glasses brightness compensation data, the data processing including denoising, filtering and normalization processing.

[0009] By adopting the above technical solutions, multi-dimensional data such as ambient light, user light adaptability, and virtual display brightness are collected through the ambient light sensor, eye movement sensor, and system interface of AR glasses, covering the main influencing factors of light adjustment, ensuring the diversity and comprehensiveness of data sources. The original data is denoised, filtered, and normalized to effectively remove noise interference in the collection process, smooth data fluctuations, and standardize data of different dimensions to enhance data comparability and processing efficiency, thereby improving the reliability and accuracy of the adjustment algorithm. Both the original data collection and processing can be carried out in real time, ensuring that the system can quickly adapt to changes in ambient light, user physiological status, and display content requirements, effectively improving the real-time and dynamic response performance of light adjustment. The introduction of user pupil diameter and fatigue status data can more accurately reflect the user's physiological adaptability and real-time comfort, provide strong data support for personalized light adjustment, and significantly improve user experience. Collecting virtual display brightness values ​​and incorporating them into adjustment data helps to achieve brightness coordination between virtual content and real light, avoid display content being too bright or too dark in strong or weak light environments, and optimize visual effects. The data collection and processing methods are highly versatile and can be adapted to different models of AR glasses and a variety of light adjustment scenarios, providing a basis for wide application. The denoising and filtering steps in the data processing link effectively reduce the impact of external interference factors, ensure data reliability and stability, and thus improve the performance of the system in complex light environments.

[0010] Optionally, the ambient light data is processed using an ambient light dynamic response model to obtain ambient light dynamic response adjustment data, and the ambient light dynamic response adjustment data is specifically calculated using the following formula: ; Among them, A is the ambient light dynamic response adjustment data, k1 is the ambient light response coefficient, τ is the time response smoothing coefficient, L e (t) is the ambient light intensity at time point t in the ambient light data, L e (t-1) is the ambient light intensity at the previous time point of time point t.

[0011] By adopting the above technical solution, by introducing the ambient light intensity at time points t and t-1, the model can capture the dynamic trend of light changes, quickly respond to real-time fluctuations in ambient light, and improve the timeliness and flexibility of adjustment. The introduction of the time response smoothing coefficient makes the adjustment process smoother, avoiding the abrupt brightness adjustment caused by sudden changes in ambient light intensity, thereby improving the user's visual comfort. The ambient light response coefficient allows adjustment optimization according to specific scenarios, improving the adaptability and accuracy of the model under different light conditions. The formula calculation process is efficient and suitable for the real-time processing requirements of embedded devices, and can achieve good dynamic light adjustment effects under limited resources. By processing ambient light data in real time and generating dynamic response adjustment data, the model provides a basis for intelligent light adjustment, ensuring that the adjustment results are more in line with the actual environment and user needs.

[0012] Optionally, the user light adaptability data is processed by a user physiology and preference model to obtain user physiology and preference adjustment data, and the user physiology and preference adjustment data is specifically calculated using the following formula: ; Among them, B is the user's physiological and preference adjustment data, k2 is the user's physiological adjustment coefficient, d p (t) is the user light adaptation data at time point t, d max is the maximum pupil diameter of the user, β is the nonlinear magnification parameter, T pref is the user's light preference parameter, μ is the fatigue attenuation coefficient, t is the current recording time of the user's preference, and t0 is the first recording time of the user's preference.

[0013] By adopting the above technical solutions, the formula fully considers the user's physiological adaptability and subjective preferences by introducing the user's pupil diameter and light preference parameters, and can achieve personalized light adjustment to meet the unique needs of different users. The user preference recording time point and fatigue attenuation coefficient are introduced to dynamically capture the user's physiological state changes, especially the light adaptation ability under the influence of fatigue, and provide real-time support for adjustment. The relationship between pupil diameter and adjustment effect is amplified or suppressed using nonlinear amplification parameters, so that the adjustment result can adapt to complex physiological response patterns and provide a more natural visual experience. The user's physiological adjustment coefficient, maximum pupil diameter, light preference and fatigue attenuation and other multiple parameters work together to enable the model to comprehensively consider the user's physiological and behavioral characteristics and improve the accuracy and adaptability of adjustment. By introducing the comparison between the initial time of preference and the current time, the model can track the user's long-term use behavior, optimize the adaptation to user habits and preferences, and provide a smarter adjustment strategy. The model pays attention to the user's fatigue state and light preference, which can effectively reduce the visual discomfort caused by excessive or insufficient adjustment, and significantly improve the comfort and user satisfaction of long-term wearing.

[0014] Optionally, the nonlinear brightness compensation model is used to process the glasses brightness compensation data to obtain nonlinear brightness compensation adjustment data, and the nonlinear brightness compensation adjustment data is specifically calculated using the following formula: ; Among them, C is the nonlinear brightness compensation adjustment data, γ is the brightness compensation nonlinear adjustment coefficient, α is the adjustment sensitivity parameter, L e (t) is the ambient light intensity at time point t in the ambient light data, L thr is the brightness compensation threshold, δ is the brightness compensation smoothing coefficient, Y current (t) is the glasses brightness compensation data at time point t, Y max is the maximum brightness value of AR glasses.

[0015] By adopting the above technical solution, through the nonlinear adjustment coefficient of brightness compensation, the model can make nonlinear adjustments according to the ambient light and device brightness characteristics, optimize the brightness compensation effect, avoid over-compensation or under-compensation, and improve the user's visual experience in complex light environments. The introduction of the adjustment sensitivity parameter enables the model to flexibly adjust the sensitivity to light changes according to the specific usage scenario, which improves the applicability and accuracy of the model. By combining the ambient light intensity and the brightness compensation threshold, the model can dynamically capture and respond to changes in ambient light, thereby achieving real-time brightness optimization and ensuring that good display effects can be maintained in scenes with frequent light changes. The addition of the brightness compensation smoothing coefficient makes the adjustment process smoother, avoiding the abruptness or lag caused by too fast or too slow brightness compensation, thereby improving the user's visual comfort. The model ensures that the compensation result runs within the hardware capability of AR glasses through the coordination of the maximum brightness value and the current brightness value, avoiding over-brightness or over-darkness, and improving the device performance utilization and display effect. Comprehensively considering the ambient light, device brightness and user needs, intelligent compensation is achieved through the nonlinear association between parameters, so that the brightness adjustment is more in line with actual needs and provides an optimized display and operation experience. Through reasonable brightness compensation and smooth adjustment, the model effectively reduces the user's visual fatigue in complex lighting environments, providing a more comfortable and natural user experience, especially suitable for long-term wearing scenarios.

[0016] Optionally, the intelligent adaptive light adjustment of the AR glasses based on the ambient light dynamic response adjustment data, the user physiological and preference adjustment data, and the nonlinear brightness compensation adjustment data specifically includes: generating a target transmittance of the AR glasses according to the ambient light dynamic response adjustment data, the user physiological and preference adjustment data, and the nonlinear brightness compensation adjustment data; sending a light adjustment instruction to the AR glasses to control the AR glasses to perform intelligent adaptive light adjustment according to the target transmittance, the light adjustment instruction including the target transmittance.

[0017] By adopting the above technical solution, the dynamic response adjustment data of ambient light, the adjustment data of user physiology and preference, and the adjustment data of nonlinear brightness compensation are combined to comprehensively integrate the influence of different factors, so that the generated target transmittance is more in line with the actual environment and user needs, and the adjustment effect is more accurate. By generating the target transmittance and sending the light adjustment instruction, the system realizes full-process automatic adjustment without user intervention, provides an intelligent and convenient light adjustment experience, and greatly improves user satisfaction. It can respond to different ambient light changes, user physiological state and virtual display requirements in real time, adapt to various usage scenarios, and enhance the versatility and adaptability of the system. By directly generating the target transmittance and passing it to the AR glasses in the form of instructions, complex intermediate calculation steps are avoided, the system process is simplified, and the efficiency and accuracy of the transmittance adjustment are ensured. The system can continuously adjust the target transmittance according to the real-time changes of multi-factor data to ensure that the light adjustment effect is consistent with the current environment and user needs, thereby providing a stable visual experience. Adjustment based on user physiology and preference data can effectively reduce the visual burden of users in strong or weak light environments, reduce eye fatigue, and is particularly suitable for long-term wear scenarios.

[0018] Optionally, the target transmittance of the AR glasses is generated according to the ambient light dynamic response adjustment data, the user physiological and preference adjustment data, and the nonlinear brightness compensation adjustment data. The target transmittance of the AR glasses is specifically calculated using the following formula: ; Among them, T is the target transmittance of AR glasses, w1 is the weight coefficient corresponding to the ambient light dynamic response adjustment data A, w2 is the weight coefficient corresponding to the user physiological and preference adjustment data B, and w3 is the weight coefficient corresponding to the nonlinear brightness compensation adjustment data C.

[0019] By adopting the above technical solutions, the multi-faceted influence of light adjustment is comprehensively considered by integrating the dynamic response adjustment data of ambient light, the physiological and preference adjustment data of users, and the nonlinear brightness compensation adjustment data, so that the target light transmittance is more in line with the actual needs. The weight coefficients corresponding to different data can be adjusted according to the actual scene or user needs, so as to give priority to specific factors and enhance the flexibility and adaptability of the model. The formula structure is clear and suitable for real-time processing and resource constraints of embedded devices, ensuring that the adjustment process responds quickly and meets the requirements of high efficiency. The target light transmittance is generated by real-time data and can quickly adapt to changes in ambient light, user physiological status and device brightness requirements, ensuring that users can get an ideal visual experience in various scenarios. The introduction of user physiological and preference adjustment data enables the target light transmittance to be dynamically optimized according to the user's physiological characteristics and preferences, reducing the deviation between light adjustment and user needs, and significantly improving comfort and satisfaction.

[0020] In a second aspect of the present application, an intelligent adaptive light adjustment device for AR glasses is provided, and the intelligent adaptive light adjustment device includes an acquisition module and a processing module, wherein the acquisition module is used to acquire ambient light data, user light adaptability data and glasses brightness compensation data when a user wears the AR glasses; the processing module is used to process the ambient light data using an ambient light dynamic response model to obtain ambient light dynamic response adjustment data; the processing module is also used to process the user light adaptability data through a user physiological and preference model to obtain user physiological and preference adjustment data; the processing module is also used to process the glasses brightness compensation data using a nonlinear brightness compensation model to obtain nonlinear brightness compensation adjustment data; the processing module is also used to perform intelligent adaptive light adjustment on the AR glasses based on the ambient light dynamic response adjustment data, the user physiological and preference adjustment data and the nonlinear brightness compensation adjustment data.

[0021] In the third aspect of the present application, an electronic device is provided, which includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device performs the method described above.

[0022] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, the method described above is executed.

[0023] In summary, one or more technical solutions provided in this application have at least the following technical effects or advantages: By simultaneously collecting ambient light data, user light adaptability data, and glasses brightness compensation data, the light adjustment needs can be analyzed from multiple angles, which can more comprehensively reflect the actual scene and user needs, and significantly improve the accuracy of adjustment and user satisfaction. The ambient light dynamic response model is used to process ambient light data, quickly adapt to light changes, enhance the real-time performance and smoothness of adjustment of the system, and reduce visual discomfort caused by delay or excessive adjustment. The user physiology and preference model takes into account personalized factors such as the user's pupil adaptability, visual fatigue status, and light sensitivity, and realizes targeted adjustment for different user needs, improving wearing comfort and user experience. The nonlinear brightness compensation model effectively handles the compensation needs for virtual content display brightness, avoids the problem of too bright or too dark display content due to improper light adjustment, and enhances the visual fusion effect of virtual and real scenes. By combining ambient light dynamic response adjustment, user physiology and preference adjustment, and brightness compensation adjustment data, an intelligent comprehensive adjustment solution is realized, avoiding the limitations of a single adjustment mode, and ensuring the accuracy and adaptability of adjustment. The overall adjustment method takes user experience as the core, dynamically adapts to different environments and user states, and effectively reduces visual fatigue caused by improper light adjustment, especially in long-term use. Therefore, it is convenient to improve the accuracy of light adjustment of AR glasses. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of a flow chart of an intelligent adaptive light adjustment method for AR glasses provided in an embodiment of the present application; Figure 2 Another schematic diagram of a flow chart of an intelligent adaptive light adjustment method for AR glasses provided in an embodiment of the present application; Figure 3 A schematic diagram of a module of an intelligent adaptive light adjustment device for AR glasses provided in an embodiment of the present application; Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0025] Explanation of the reference numerals: 31, acquisition module; 32, processing module; 41, processor; 42, communication bus; 43, user interface; 44, network interface; 45, memory. DETAILED DESCRIPTION

[0026] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0027] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0028] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0029] As an emerging human-computer interaction device, AR glasses have been widely used in navigation, entertainment, industry, and medical fields. In actual use, changes in ambient light have a significant impact on the user's visual experience and operation accuracy.

[0030] Currently, most AR glasses are equipped with a light adjustment function, which usually works by using sensors to monitor the ambient light intensity and dynamically adjusting the light transmittance of the lens based on the monitoring results. However, traditional light adjustment technology mainly relies on ambient light sensors to collect single light intensity data and adjust it according to preset rules. This method lacks the ability to intelligently analyze and dynamically adapt to multi-dimensional factors. The adjustment process is too simple and it is difficult to flexibly respond to the actual needs of users, thus affecting the accuracy of light adjustment and user experience.

[0031] In order to solve the above technical problems, the present application provides an intelligent adaptive light adjustment method for AR glasses. Figure 1 , Figure 1 A flowchart of an intelligent adaptive light adjustment method for AR glasses provided in an embodiment of the present application. The intelligent adaptive light adjustment method is applied to a server, and includes steps S110 to S150, which are as follows: S110, obtaining ambient light data, user light adaptability data, and glasses brightness compensation data when the user wears the AR glasses.

[0032] Specifically, the server undertakes the core task of data processing and analysis in the system. By communicating with the sensor module of the AR glasses, the required multiple data are obtained in real time, analyzed and processed, and adjustment instructions are generated. This is the external light intensity information collected by the ambient light sensor on the AR glasses, which reflects the lighting conditions of the user's surrounding environment. For example, the ambient light intensity under strong light outdoors on a sunny day may be very high, while the light intensity indoors or on a cloudy day is lower. The user's light adaptability data is collected by the biosensors on the glasses (such as eye movement sensors), mainly including the user's physiological state and preferences, such as: pupil diameter: when the environment becomes dark, the pupil will dilate; under strong light, the pupil will shrink. The server uses this data to judge the user's adaptability to the current light. By monitoring eye movements or blinking frequency, the server can infer the user's visual fatigue and optimize the light adjustment strategy. This is data generated by the display system of the AR glasses, describing the brightness level of the current virtual display. For example, when the virtual content is bright, the transmittance of the lens may need to be reduced to avoid visual fatigue. By obtaining the above data, the server comprehensively analyzes the user's current environment and physiological state, generates a targeted light adjustment strategy, and thus realizes intelligent dynamic adaptation.

[0033] In a possible implementation, ambient light data, user light adaptability data, and glasses brightness compensation data are obtained when the user wears AR glasses, specifically including: receiving original ambient light data sent by an ambient light sensor located on the AR glasses, the original ambient light data including ambient light intensity data; receiving original user light adaptability data sent by an eye movement sensor located on the AR glasses, the original user light adaptability data including user pupil diameter and fatigue status data; receiving original glasses brightness compensation data sent by the AR glasses, the original glasses brightness compensation data including a virtual display brightness value; performing data processing on the original ambient light data, the original user light adaptability data, and the original glasses brightness compensation data to obtain ambient light data, user light adaptability data, and glasses brightness compensation data, the data processing including denoising, filtering, and normalization.

[0034] Specifically, ambient light data is collected by ambient light sensors to reflect the lighting conditions of the surrounding environment. The data includes the intensity and possible direction information of the light, which is used to judge the light environment of the current scene (such as strong light, weak light or changing light). User light adaptability data is collected by eye movement sensors to capture the user's physiological state, mainly including the following two aspects: pupil diameter, the size of the pupil changes with the ambient light, which is an important indicator for judging the user's adaptability, and fatigue state, which infers the user's fatigue level through parameters such as the user's blinking frequency and eye movement trajectory. Glasses brightness compensation data is provided by the display module of the AR glasses themselves, describing the brightness of the virtual display content. This is to ensure that the display is balanced with the external light to avoid visual conflict or fatigue. The acquired raw data usually contains noise or outliers, so it needs to go through the following processing steps: Denoising is used to eliminate data noise caused by sensor errors or environmental interference to ensure data reliability. Data is smoothed by filtering algorithms (such as low-pass filters) to remove excessive fluctuations or instantaneous changes. Data from different sources are then mapped to a unified range or dimension (such as between 0 and 1) for subsequent analysis and fusion. After the final output is processed, the standardized results of the three types of data are obtained.

[0035] For example, scenario 1: the user walks from indoors to an outdoor bright light environment. The ambient light sensor detects that the ambient light intensity rises rapidly from 300 lux (indoor low light) to 10,000 lux (outdoor strong light). The eye movement sensor detects that the pupil shrinks rapidly from 5 mm (adapting to low light) to 2 mm (coping with strong light), and at the same time detects that the blinking frequency is normal and the user is in a relaxed state. At this time, the virtual display brightness in the AR glasses brightness compensation data is set to medium (50 nits). Secondly, denoising eliminates outliers caused by instantaneous light intensity fluctuations (such as rapidly changing reflected light). Smooth the light intensity data so that it can smoothly reflect the transition from indoor to outdoor. Map the light intensity data between 0 and 1 to unify the calculations under different lighting conditions.

[0036] Further, scenario 2: the user uses AR glasses in a low-light environment at night. The ambient light sensor detects that the ambient light intensity is 50 lux (low light). The eye movement sensor detects that the pupil is dilated to 6 mm. The blinking frequency is low, and there may be mild fatigue. The virtual display brightness in the AR glasses brightness compensation data is set to low brightness (20 nits). Filter out abnormal values ​​from sensor errors, such as sudden short-term strong light. Smoothing light data is to avoid excessive adjustments caused by slight changes in low light. Map low-light data to a uniform interval to make the adjustment more accurate. The final output ambient light data is to indicate low light conditions and the lens transmittance needs to be increased. The user light adaptability data is to indicate that the user has good adaptability in low light, but there is mild fatigue. The current brightness is suitable for night use and does not need to be adjusted. Through this process, the system can perceive the user's environment, visual state and device brightness requirements in real time, and combine the pre-processed data to provide smarter and more accurate light adjustment, significantly improving the user's visual experience and comfort.

[0037] S120: Processing the ambient light data using an ambient light dynamic response model to obtain ambient light dynamic response adjustment data.

[0038] Specifically, the main function of the ambient light dynamic response model is to generate dynamic adjustment data suitable for the user's visual needs based on the changing trend and current state of the ambient light. Its core is to smoothly and dynamically track the changes in ambient light to avoid discomfort to the user caused by too fast or too slow adjustment. The model outputs the ambient light dynamic response adjustment data, which is used to indicate how to adjust the lens transmittance to optimize the user's visual experience. Among them, the ambient light dynamic response model is pre-built.

[0039] In a possible implementation, the ambient light data is processed using an ambient light dynamic response model to obtain ambient light dynamic response adjustment data, and the ambient light dynamic response adjustment data is specifically calculated using the following formula: ; Among them, A is the ambient light dynamic response adjustment data, k1 is the ambient light response coefficient, τ is the time response smoothing coefficient, L e (t) is the ambient light intensity at time point t in the ambient light data, L e (t-1) is the ambient light intensity at the previous time point of time point t.

[0040] Specifically, the ambient light intensity at the current moment t is usually obtained through an ambient light sensor in lux. The ambient light intensity at the previous moment is used to calculate the light change trend. The ambient light response coefficient indicates the system's sensitivity to changes in ambient light. The higher the value, the greater the adjustment. The time response smoothing coefficient controls the smoothness of the system's response to changes in light intensity. When the value is close to 1, the model gives a higher weight to the current light intensity; when the value is close to 0, it pays more attention to historical light intensity. The ambient light dynamic response adjustment data is a smoothed and dynamically adjusted value used to guide AR glasses on how to adjust the transmittance.

[0041] For example, suppose a user enters the outdoors (strong light) from a place with low ambient light intensity (dark room). The current ambient light intensity is 12000 lux. The ambient light intensity at the previous moment was 300 lux. The time response smoothing coefficient is 0.8. The ambient light response coefficient is 1.2. Substituting the formula into the generated ambient light dynamic response adjustment data is 11592, which is much higher than the light intensity at the previous moment, reflecting the significant increase in light. Specifically, this change is due to the fact that the current light intensity is much higher than the low light intensity at the previous moment, and the model parameter design responds quickly to the current changes. The data finally generated intuitively shows that the ambient light in the user's environment is rapidly increasing. It means that the light is increasing rapidly, and the system needs to quickly reduce the transmittance (for example, the lens darkens rapidly) to protect the user's eyes from strong light stimulation.

[0042] S130 , processing the user light adaptability data through the user physiological and preference model to obtain user physiological and preference adjustment data.

[0043] Specifically, pupil diameter data is used to measure the physiological response of the eyes to changes in light. For example, the pupil will shrink in strong light and dilate in weak light. Fatigue status data judges the user's fatigue level based on eye movement characteristics (such as blinking frequency and eye closure time), because sensitivity to light may decrease when tired. Preference data is the user's historical usage data or set light comfort parameters. For example, users who prefer darker environments may prefer lower screen brightness. Users who prefer bright environments may accept higher brightness. The server uses physiological data (pupil diameter, fatigue status) and preference parameters to analyze the user's adaptability through a model and generate adjustment data. The adjustment data reflects the user's current ability to adapt to light and the required brightness adjustment. Finally, a quantitative data is generated to guide AR glasses to adjust the transmittance or screen brightness.

[0044] In a possible implementation, the user light adaptability data is processed by the user physiological and preference model to obtain the user physiological and preference adjustment data, and the user physiological and preference adjustment data is specifically calculated using the following formula: ; Among them, B is the user's physiological and preference adjustment data, k2 is the user's physiological adjustment coefficient, d p (t) is the user light adaptation data at time point t, d max is the maximum pupil diameter of the user, β is the nonlinear magnification parameter, T pref is the user's light preference parameter, μ is the fatigue attenuation coefficient, t is the current recording time of the user's preference, and t0 is the first recording time of the user's preference.

[0045] Specifically, the user's physiological and preference adjustment data represents the final generated adjustment data, which is used to guide the device to adjust the light brightness. The user's physiological adjustment coefficient represents the weight of the physiological data on the adjustment. It affects the contribution of the pupil diameter to the result. The pupil diameter at time point t reflects the user's ability to adapt to the current light. The maximum diameter of the user's pupil is used to normalize the pupil data to unify its range. The nonlinear magnification parameter is used to amplify the pupil diameter to adapt to a larger adjustment range. The user's light preference parameters, such as the user's preference for low brightness values ​​(dark environment). The fatigue attenuation coefficient represents the attenuation effect of fatigue on the preferred light. The more severe the fatigue, the smaller the impact of the user's preference value. The time difference between the current recording time point and the initial recording time point is used to calculate the accumulation of fatigue effects.

[0046] For example, assume that the scenario is that the user uses AR glasses for a long time outdoors in strong light, where the user's pupil diameter is 2.0 mm (pupil contraction under strong light, typical value), and the maximum pupil diameter is 7.0 mm. The nonlinear magnification parameter is 1.5 (magnifying the pupil adjustment effect), and the light preference is 300 lux. The fatigue attenuation coefficient is 0.2 (the user is slightly fatigued), the time difference is 2 hours (using AR glasses for a long time), and the physiological adjustment coefficient is 0.8. After substituting into the formula, the user's physiological and preference adjustment data is approximately equal to 201.43 lux, and the user's current optimal adjustment data is 201.43 lux, indicating that under strong light and fatigue conditions, the user's suitable brightness is low, and the glasses need to dim the virtual display brightness and reduce light transmission.

[0047] S140: Process the glasses brightness compensation data using a nonlinear brightness compensation model to obtain nonlinear brightness compensation adjustment data.

[0048] Specifically, the server processes the glasses brightness compensation data through a nonlinear brightness compensation model to generate nonlinear brightness compensation adjustment data, thereby optimizing the adaptability of the glasses display brightness. The nonlinear brightness compensation model smoothly adjusts the compensation results by introducing nonlinear adjustment parameters, combining the ambient light intensity and the current brightness data of the glasses, to adapt to the visual needs of users in complex lighting scenes. The nonlinear brightness compensation model is used to process the brightness adjustment data of the glasses to ensure that the brightness adjustment is smooth and adaptable under different lighting conditions. The nonlinear function is used to make the adjustment process closer to the characteristics of human eye perception rather than a simple linear change. The glasses brightness compensation data includes information such as the current ambient light intensity and the virtual display brightness value, reflecting the compensation requirements under the current lighting conditions. The nonlinear brightness compensation adjustment data is the final output of the model calculation, which is used to guide the glasses on how to adjust the brightness to provide a more comfortable visual experience.

[0049] In a possible implementation, a nonlinear brightness compensation model is used to process the glasses brightness compensation data to obtain nonlinear brightness compensation adjustment data, and the nonlinear brightness compensation adjustment data is specifically calculated using the following formula: ; Among them, C is the nonlinear brightness compensation adjustment data, γ is the brightness compensation nonlinear adjustment coefficient, α is the adjustment sensitivity parameter, L e (t) is the ambient light intensity at time point t in the ambient light data, L thr is the brightness compensation threshold, δ is the brightness compensation smoothing coefficient, Y current (t) is the glasses brightness compensation data at time point t, Y max is the maximum brightness value of AR glasses.

[0050] Specifically, the server uses a nonlinear brightness compensation model to process the glasses brightness compensation data, and gives a specific calculation formula. Through the formula, the server can generate nonlinear brightness compensation adjustment data based on the ambient light intensity, the current brightness of the device and the system parameters, thereby guiding the brightness adjustment of the AR glasses. The nonlinear brightness compensation adjustment data represents the brightness adjustment result after the model calculation, which will directly affect the display brightness of the AR glasses. The brightness compensation nonlinear adjustment coefficient determines the overall amplitude of the compensation, and the adjustment sensitivity parameter is used to control the sensitivity of the compensation to changes in ambient light. The brightness compensation smoothing coefficient is used to slow down the amplitude of brightness changes and avoid overly abrupt adjustments. The brightness compensation threshold is used to determine whether the glasses need stronger compensation or weaker compensation. The maximum brightness value of the device is used to normalize the current brightness.

[0051] S150, based on the ambient light dynamic response adjustment data, the user's physiological and preference adjustment data, and the nonlinear brightness compensation adjustment data, the AR glasses are intelligently and adaptively adjusted in light.

[0052] Specifically, by monitoring the ambient light intensity and responding dynamically, the model adjusts the light in real time according to environmental changes. This helps to cope with differences in environments such as outdoor sunlight and indoor lighting. Adjust the light settings based on the user's physiological responses (such as pupil diameter and fatigue level) and the user's personal light preferences. For example, some users may prefer to work in low-light environments, while other users may prefer higher light brightness. By calculating a nonlinear compensation model, the corresponding brightness adjustment is calculated based on the ambient light intensity and the current brightness of the glasses. This compensation ensures that the display brightness of AR glasses is always in the most suitable range in different light environments.

[0053] In one possible implementation, refer to Figure 2 , Figure 2 Another flow chart of an intelligent adaptive light adjustment method for AR glasses provided in an embodiment of the present application. Based on the ambient light dynamic response adjustment data, the user's physiological and preference adjustment data, and the nonlinear brightness compensation adjustment data, the AR glasses are intelligently and adaptively adjusted to light, specifically including steps S210 to S220, the above steps are as follows: S210, generating the target transmittance of the AR glasses according to the ambient light dynamic response adjustment data, the user's physiological and preference adjustment data, and the nonlinear brightness compensation adjustment data; S220, sending a light adjustment instruction to the AR glasses to control the AR glasses to perform intelligent adaptive light adjustment according to the target transmittance, the light adjustment instruction including the target transmittance.

[0054] Specifically, the server calculates a target transmittance based on the above three adjustment data. This target transmittance is the transmittance level that the glasses lenses should have to ensure the best visual effect. Once the target transmittance is calculated, the server controls the transmittance adjustment of the lenses by sending light adjustment instructions to the AR glasses. This instruction includes the target transmittance value to ensure that the glasses are automatically adjusted according to the environment and user needs. The AR glasses adjust the transmittance through the received light adjustment instructions, so as to achieve dynamic and intelligent light adjustment according to changes in the environment and users.

[0055] Therefore, based on the dynamic response adjustment data of ambient light, the adjustment data of user physiology and preference, and the nonlinear brightness compensation adjustment data, the server can calculate a target transmittance, and automatically optimize the transmittance of AR glasses through intelligent light adjustment instructions to ensure the best visual effect in different environments. This adaptive adjustment method not only responds to environmental changes, but also makes corresponding adjustments according to the needs of individual users, greatly improving the user experience.

[0056] In a possible implementation, the target transmittance of the AR glasses is generated according to the ambient light dynamic response adjustment data, the user physiological and preference adjustment data, and the nonlinear brightness compensation adjustment data. The target transmittance of the AR glasses is specifically calculated using the following formula: ; Among them, T is the target transmittance of AR glasses, w1 is the weight coefficient corresponding to the ambient light dynamic response adjustment data A, w2 is the weight coefficient corresponding to the user physiological and preference adjustment data B, and w3 is the weight coefficient corresponding to the nonlinear brightness compensation adjustment data C.

[0057] Specifically, the target transmittance of AR glasses is used to adjust the transmittance of the lenses to complete light adjustment. w1, w2, w3 are weight coefficients corresponding to different adjustment data. They are used to determine the degree of influence of each adjustment data on the final target transmittance. The larger the weight coefficient, the greater the influence of the corresponding data on the target transmittance. The ambient light dynamic response adjustment data reflects how changes in external illumination affect the adjustment of transmittance. The user physiological and preference adjustment data reflects how the user's physiological state and preferences affect the adjustment of transmittance. The nonlinear brightness compensation adjustment data reflects how the brightness compensation algorithm affects the adjustment of transmittance. For example, if the ambient light dynamic response is critical to the current scene, the value of w1 will be relatively large, thereby increasing the influence of the ambient light data.

[0058] Therefore, the server simultaneously collects ambient light data, user light adaptability data, and glasses brightness compensation data, and analyzes light adjustment requirements from multiple angles, which can more comprehensively reflect actual scenes and user needs, and significantly improve the accuracy of adjustment and user satisfaction. The server processes ambient light data through the ambient light dynamic response model, quickly adapts to light changes, enhances the real-time performance and smoothness of adjustment of AR glasses, and reduces visual discomfort caused by delays or excessive adjustments. The user physiology and preference model takes into account personalized factors such as the user's pupil adaptability, visual fatigue status, and light sensitivity, and realizes targeted adjustment for different user needs, improving wearing comfort and user experience. The nonlinear brightness compensation model effectively handles the compensation requirements for virtual content display brightness, avoids the problem of too bright or too dark display content due to improper light adjustment, and enhances the visual fusion effect of virtual and real scenes. The server realizes an intelligent comprehensive adjustment solution by combining ambient light dynamic response adjustment, user physiology and preference adjustment, and brightness compensation adjustment data, avoiding the limitations of a single adjustment mode and ensuring the accuracy and adaptability of adjustment. The overall adjustment method focuses on user experience, dynamically adapts to different environments and user states, and effectively reduces visual fatigue caused by improper light adjustment, especially during long-term use. Therefore, it is easy to improve the accuracy of AR glasses light adjustment.

[0059] The present application also provides an intelligent adaptive light adjustment device for AR glasses, referring to Figure 3 , Figure 3 A module schematic diagram of an intelligent adaptive light adjustment device for AR glasses provided in an embodiment of the present application. The intelligent adaptive light adjustment device is a server, and the server includes an acquisition module 31 and a processing module 32, wherein the acquisition module 31 acquires ambient light data, user light adaptability data, and glasses brightness compensation data during the user wearing the AR glasses; the processing module 32 uses an ambient light dynamic response model to process the ambient light data to obtain ambient light dynamic response adjustment data; the processing module 32 processes the user light adaptability data through a user physiological and preference model to obtain user physiological and preference adjustment data; the processing module 32 uses a nonlinear brightness compensation model to process the glasses brightness compensation data to obtain nonlinear brightness compensation adjustment data; the processing module 32 performs intelligent adaptive light adjustment on the AR glasses based on the ambient light dynamic response adjustment data, the user physiological and preference adjustment data, and the nonlinear brightness compensation adjustment data.

[0060] In a possible implementation, the acquisition module 31 acquires ambient light data, user light adaptability data, and glasses brightness compensation data when the user wears AR glasses, specifically including: the acquisition module 31 receives original ambient light data sent by the ambient light sensor located on the AR glasses, and the original ambient light data includes ambient light intensity data; the acquisition module 31 receives original user light adaptability data sent by the eye movement sensor located on the AR glasses, and the original user light adaptability data includes user pupil diameter and fatigue status data; the acquisition module 31 receives original glasses brightness compensation data sent by the AR glasses, and the original glasses brightness compensation data includes a virtual display brightness value; the processing module 32 processes the original ambient light data, the original user light adaptability data, and the original glasses brightness compensation data to obtain ambient light data, user light adaptability data, and glasses brightness compensation data, and the data processing includes denoising, filtering, and normalization.

[0061] In a possible implementation, the processing module 32 processes the ambient light data using an ambient light dynamic response model to obtain ambient light dynamic response adjustment data, and the ambient light dynamic response adjustment data is specifically calculated using the following formula: ; Among them, A is the ambient light dynamic response adjustment data, k1 is the ambient light response coefficient, τ is the time response smoothing coefficient, L e (t) is the ambient light intensity at time point t in the ambient light data, L e (t-1) is the ambient light intensity at the previous time point of time point t.

[0062] In a possible implementation, the processing module 32 processes the user light adaptability data through the user physiological and preference model to obtain the user physiological and preference adjustment data, and the user physiological and preference adjustment data is specifically calculated using the following formula: ; Among them, B is the user's physiological and preference adjustment data, k2 is the user's physiological adjustment coefficient, d p (t) is the user light adaptation data at time point t, d max is the maximum pupil diameter of the user, β is the nonlinear magnification parameter, T pref is the user's light preference parameter, μ is the fatigue attenuation coefficient, t is the current recording time of the user's preference, and t0 is the first recording time of the user's preference.

[0063] In a possible implementation, the processing module 32 processes the glasses brightness compensation data using a nonlinear brightness compensation model to obtain nonlinear brightness compensation adjustment data, and the nonlinear brightness compensation adjustment data is specifically calculated using the following formula: ; Among them, C is the nonlinear brightness compensation adjustment data, γ is the brightness compensation nonlinear adjustment coefficient, α is the adjustment sensitivity parameter, L e (t) is the ambient light intensity at time point t in the ambient light data, L thr is the brightness compensation threshold, δ is the brightness compensation smoothing coefficient, Y current (t) is the glasses brightness compensation data at time point t, Y max is the maximum brightness value of AR glasses.

[0064] In a possible implementation, the processing module 32 performs intelligent adaptive light adjustment on the AR glasses based on the ambient light dynamic response adjustment data, the user physiological and preference adjustment data, and the nonlinear brightness compensation adjustment data. Specifically, the processing module 32 generates a target transmittance of the AR glasses according to the ambient light dynamic response adjustment data, the user physiological and preference adjustment data, and the nonlinear brightness compensation adjustment data; the processing module 32 sends a light adjustment instruction to the AR glasses to control the AR glasses to perform intelligent adaptive light adjustment according to the target transmittance, and the light adjustment instruction includes the target transmittance.

[0065] In a possible implementation, the processing module 32 generates a target transmittance of the AR glasses according to the ambient light dynamic response adjustment data, the user physiological and preference adjustment data, and the nonlinear brightness compensation adjustment data. The target transmittance of the AR glasses is specifically calculated using the following formula: ; Among them, T is the target transmittance of AR glasses, w1 is the weight coefficient corresponding to the ambient light dynamic response adjustment data A, w2 is the weight coefficient corresponding to the user physiological and preference adjustment data B, and w3 is the weight coefficient corresponding to the nonlinear brightness compensation adjustment data C.

[0066] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0067] The present application also provides an electronic device, referring to Figure 4 , Figure 4 The electronic device may include: at least one processor 41 , at least one network interface 44 , a user interface 43 , a memory 45 , and at least one communication bus 42 .

[0068] The communication bus 42 is used to realize the connection and communication between these components.

[0069] The user interface 43 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 43 may also include a standard wired interface and a wireless interface.

[0070] The network interface 44 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0071] Among them, the processor 41 may include one or more processing cores. The processor 41 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 45, and calling data stored in the memory 45. Optionally, the processor 41 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 41 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 41, and it can be implemented separately through a chip.

[0072] Among them, the memory 45 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 45 includes a non-transitory computer-readable storage medium. The memory 45 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 45 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments, etc. The memory 45 may also be optionally at least one storage device located away from the aforementioned processor 41. As Figure 4 As shown, the memory 45 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program of an intelligent adaptive light adjustment method for AR glasses.

[0073] exist Figure 4In the electronic device shown, the user interface 43 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 41 can be used to call an application program stored in the memory 45 for an intelligent adaptive light adjustment method for AR glasses. When executed by one or more processors, the electronic device executes one or more methods in the above-mentioned embodiments.

[0074] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0075] The present application also provides a computer-readable storage medium, which stores instructions. When executed by one or more processors, the electronic device executes one or more of the methods described in the above embodiments.

[0076] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0077] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0078] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0079] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0080] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0081] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, it will be easy for those skilled in the art to think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field that are not recorded in the present disclosure. The description and examples are regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. An intelligent adaptive light adjustment method for AR glasses, characterized in that: The method comprises: Obtaining ambient light data, user light adaptability data, and glasses brightness compensation data when the user wears AR glasses; The ambient light data is processed using an ambient light dynamic response model to obtain ambient light dynamic response adjustment data; Processing the user light adaptability data through a user physiological and preference model to obtain user physiological and preference adjustment data; Using a nonlinear brightness compensation model to process the glasses brightness compensation data to obtain nonlinear brightness compensation adjustment data; Based on the ambient light dynamic response adjustment data, the user physiological and preference adjustment data, and the nonlinear brightness compensation adjustment data, the AR glasses are intelligently and adaptively adjusted in light.

2. The intelligent adaptive light adjustment method for AR glasses according to claim 1, characterized in that: The obtaining of the ambient light data, the user light adaptability data, and the glasses brightness compensation data during the user wearing the AR glasses specifically includes: Receiving raw ambient light data sent by an ambient light sensor located on the AR glasses, wherein the raw ambient light data includes ambient light intensity data; Receiving original user light adaptability data sent by an eye movement sensor located on the AR glasses, wherein the original user light adaptability data includes user pupil diameter and fatigue status data; Receiving original glasses brightness compensation data sent by the AR glasses, where the original glasses brightness compensation data includes a virtual display brightness value; The original ambient light data, the original user light adaptability data and the original glasses brightness compensation data are processed to obtain the ambient light data, the user light adaptability data and the glasses brightness compensation data, wherein the data processing includes denoising, filtering and normalization processing.

3. The intelligent adaptive light adjustment method for AR glasses according to claim 1, characterized in that: The ambient light data is processed by using the ambient light dynamic response model to obtain ambient light dynamic response adjustment data, and the ambient light dynamic response adjustment data is specifically calculated using the following formula: ； Among them, A is the ambient light dynamic response adjustment data, k1 is the ambient light response coefficient, τ is the time response smoothing coefficient, L e (t) is the ambient light intensity at time point t in the ambient light data, L e (t-1) is the ambient light intensity at the previous time point of time point t.

4. The intelligent adaptive light adjustment method for AR glasses according to claim 1, characterized in that: The user light adaptability data is processed by the user physiological and preference model to obtain the user physiological and preference adjustment data, and the user physiological and preference adjustment data is specifically calculated using the following formula: ； Among them, B is the user's physiological and preference adjustment data, k2 is the user's physiological adjustment coefficient, d p (t) is the user light adaptation data at time point t, d max is the maximum pupil diameter of the user, β is the nonlinear magnification parameter, T pref is the user's light preference parameter, μ is the fatigue attenuation coefficient, t is the current recording time of the user's preference, and t0 is the first recording time of the user's preference.

5. The intelligent adaptive light adjustment method for AR glasses according to claim 1, characterized in that: The nonlinear brightness compensation model is used to process the glasses brightness compensation data to obtain nonlinear brightness compensation adjustment data, and the nonlinear brightness compensation adjustment data is specifically calculated using the following formula: ； Among them, C is the nonlinear brightness compensation adjustment data, γ is the brightness compensation nonlinear adjustment coefficient, α is the adjustment sensitivity parameter, L e (t) is the ambient light intensity at time point t in the ambient light data, L thr is the brightness compensation threshold, δ is the brightness compensation smoothing coefficient, Y current (t) is the glasses brightness compensation data at time point t, Y max is the maximum brightness value of AR glasses.

6. The intelligent adaptive light adjustment method for AR glasses according to claim 1, characterized in that: The performing intelligent adaptive light adjustment on the AR glasses based on the ambient light dynamic response adjustment data, the user physiological and preference adjustment data, and the nonlinear brightness compensation adjustment data specifically includes: Generate a target light transmittance of the AR glasses according to the ambient light dynamic response adjustment data, the user physiological and preference adjustment data, and the nonlinear brightness compensation adjustment data; A light adjustment instruction is sent to the AR glasses to control the AR glasses to perform intelligent adaptive light adjustment according to the target transmittance, wherein the light adjustment instruction includes the target transmittance.

7. The intelligent adaptive light adjustment method for AR glasses according to claim 6, characterized in that: The target light transmittance of the AR glasses is generated according to the ambient light dynamic response adjustment data, the user physiological and preference adjustment data, and the nonlinear brightness compensation adjustment data. The target light transmittance of the AR glasses is specifically calculated using the following formula: ； Among them, T is the target transmittance of AR glasses, w1 is the weight coefficient corresponding to the ambient light dynamic response adjustment data A, w2 is the weight coefficient corresponding to the user physiological and preference adjustment data B, and w3 is the weight coefficient corresponding to the nonlinear brightness compensation adjustment data C.

8. An intelligent adaptive light adjustment device for AR glasses, characterized in that: The intelligent adaptive light adjustment device comprises an acquisition module (31) and a processing module (32), wherein: The acquisition module (31) is used to acquire ambient light data, user light adaptability data, and glasses brightness compensation data when the user wears the AR glasses; The processing module (32) is used to process the ambient light data using an ambient light dynamic response model to obtain ambient light dynamic response adjustment data; The processing module (32) is further used to process the user light adaptability data through a user physiological and preference model to obtain user physiological and preference adjustment data; The processing module (32) is further used to process the glasses brightness compensation data using a nonlinear brightness compensation model to obtain nonlinear brightness compensation adjustment data; The processing module (32) is further used to perform intelligent adaptive light adjustment on the AR glasses based on the ambient light dynamic response adjustment data, the user physiological and preference adjustment data, and the nonlinear brightness compensation adjustment data.

9. An electronic device, characterized in that: The electronic device comprises a processor (41), a memory (45), a user interface (43) and a network interface (44), wherein the memory (45) is used to store instructions, the user interface (43) and the network interface (44) are both used to communicate with other devices, and the processor (41) is used to execute the instructions stored in the memory (45) so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is performed.

Images