Method and system for synergistic optimization of surface roughness and light transmittance of smart lenses

Abstract

The application provides a method and system for synergistic optimization of surface roughness and light transmittance of intelligent lenses, applied to the field of intelligent optical technology, by acquiring external parameters of an environmental sensing unit and determining its abnormal state, if abnormal, controlling the adjustment layer to perform optical fine tuning of a preset amplitude, simultaneously collecting the response of the environmental sensing unit and the physiological characteristics of the user's eyes to the fine tuning, and then calculating the calibrated environmental target parameters according to the correlation between the preset amplitude and the two response characteristics, and finally synchronously adjusting the light transmittance and surface roughness of the optical assembly. The problems of evaluation function self-adaptation, sensor data deviation lag, algorithm instability and heat accumulation are solved, and the optical performance and user visual experience of intelligent lenses in complex environments are significantly improved.

Description

Technical Field

[0001] This application relates to the field of intelligent optical technology, and in particular to a method and system for synergistic optimization of surface roughness and light transmittance of intelligent lenses. Background Technology

[0002] In the field of intelligent optics, especially for display lenses in augmented reality (AR) and virtual reality (VR) devices, optimizing their optical performance has always been a core technological challenge. Existing technologies typically employ fixed-weight multi-objective evaluation functions to optimize the surface roughness and transmittance of intelligent lenses, aiming to meet basic visual experience requirements in standard indoor AR / VR scenarios.

[0003] However, as high-end devices expand into dynamic outdoor environments, fixed weights cannot adaptively adjust priorities based on real-time lighting changes. Therefore, the system introduces ambient light sensors and eye-tracking sensors to enhance adaptability. However, under strong light, sensors are prone to saturation effects and response delays, leading to biases and lags in environmental data. This causes the optimization algorithm to receive inaccurate feedback during iteration, resulting in oscillations between transmittance and roughness or getting stuck in local optima, making stable equilibrium difficult. Furthermore, frequent state switching causes localized heat accumulation in the electroactive material layer within the lens. Uneven heat dissipation creates minute temperature differences, generating a thermal lensing effect, ultimately introducing localized image distortion or blurring in high-resolution displays.

[0004] This shows that the existing methods have difficulty in accurately maintaining the dynamic balance of optical performance in complex outdoor scenarios, resulting in a decrease in image clarity and comfort, which restricts the outdoor application of high-end AR / VR devices.

[0005] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0006] In view of the shortcomings of the prior art, this application provides a method and system for the coordinated optimization of surface roughness and light transmittance of smart lenses. This method can effectively solve the problems of smart lenses in dynamic outdoor environments, such as the inability of multi-objective evaluation functions to adaptively adjust, bias and lag in sensor data, instability of optimization algorithms, and internal heat accumulation. This method can significantly improve the optical performance of smart lenses in complex environments and enhance the user's visual experience.

[0007] In a first aspect, a method for synergistically optimizing the surface roughness and light transmittance of a smart lens is provided for controlling a smart lens having optical components and an environmental sensing unit, wherein the optical components have an adjustment layer, and the method includes the following steps:

[0008] S1: Obtain the external environmental parameters collected by the environmental sensing unit;

[0009] S2: Determine whether the environmental sensing unit is in an abnormal sensing state based on the numerical or fluctuation characteristics of the external environmental parameters.

[0010] S3: If the environmental sensing unit is in an abnormal sensing state, control the adjustment layer to perform optical state fine-tuning of a preset amplitude;

[0011] S4: Obtain the first response feature of the environmental perception unit in response to the optical state fine-tuning, and obtain the second response feature of the user's eye physiological characteristics in response to the optical state fine-tuning;

[0012] S5: Perform correlation calculations based on the preset amplitude, the first response feature, and the second response feature to determine the calibrated environmental target parameters;

[0013] S6: Adjust the transmittance and surface roughness of the optical component according to the environmental target parameters.

[0014] Furthermore, step S2 includes:

[0015] S21: When the value of the external environmental parameter reaches a preset saturation threshold, it is determined that the environmental sensing unit is in a saturation abnormal state.

[0016] S22: When the fluctuation range of the external environmental parameter within a preset time period is lower than a preset change threshold, and the deviation between the current value and the historical value of the external environmental parameter is greater than a preset deviation threshold, the environmental sensing unit is determined to be in a response lag abnormal state; the abnormal sensing state includes a saturation abnormal state and a response lag abnormal state.

[0017] Furthermore, step S3 includes:

[0018] S31: If the environmental sensing unit is in an abnormal sensing state, a transient voltage pulse is applied to the adjustment layer to cause the transmittance of the optical component to fluctuate transiently at a preset amplitude, wherein the preset amplitude is lower than the brightness change threshold perceived by the human eye.

[0019] Furthermore, in step S31, applying a transient voltage pulse to the regulating layer includes the following steps:

[0020] S311: Control the duration of the transient voltage pulse within a preset time interval, control the change in transmittance within a preset ratio range, and restore the transmittance to its initial state after the transient voltage pulse ends, wherein the preset ratio range is 2% to 5%.

[0021] Furthermore, step S4 includes:

[0022] S41: Collect the amplitude change and / or response phase of the output signal of the environmental sensing unit during the fluctuation period, as the first response feature;

[0023] S42: The rate of change of the user's pupil contraction or expansion during the fluctuation is collected by an eye-tracking device as the second response feature.

[0024] Furthermore, step S5 includes:

[0025] S51: Establish an incentive-feedback correlation model, wherein the incentive is the preset amplitude, and the feedback includes the first response feature and the second response feature;

[0026] S52: Cross-compare the first response feature with the second response feature to identify the nonlinear distortion deviation of the environment perception unit;

[0027] S53: The external environmental parameters are compensated using the nonlinear distortion deviation to obtain the target environmental parameters.

[0028] Furthermore, step S52 includes:

[0029] S521: Perform time-series analysis on the first response feature to extract the response amplitude and response phase of the output signal of the environmental sensing unit during the fluctuation period;

[0030] S522: Perform time-series analysis on the second response feature to extract the physiological response amplitude and physiological response phase of the user's pupil diameter change during the fluctuation period;

[0031] S523: Compare the amplitude of the response with the amplitude of the physiological response, calculate the amplitude deviation rate, and compare the phase of the response with the phase of the physiological response, calculate the phase delay difference;

[0032] S524: Based on the amplitude deviation rate and the phase delay difference, construct the nonlinear distortion deviation parameter of the environmental sensing unit under the current environmental state. The nonlinear distortion deviation parameter includes at least the gain nonlinearity coefficient and the response hysteresis coefficient.

[0033] Furthermore, step S53 includes:

[0034] S531: Obtain the original external environmental parameters collected by the environmental sensing unit, and obtain the gain nonlinearity coefficient and response hysteresis coefficient in the nonlinear distortion deviation;

[0035] S532: Perform time-axis compensation on the original external environment parameters based on the response hysteresis coefficient to obtain the first intermediate parameter;

[0036] S533: Perform amplitude domain nonlinear correction on the first intermediate parameter based on the gain nonlinearity coefficient to obtain the second intermediate parameter;

[0037] S534: Compare and verify the second intermediate parameter with the preset standard environmental reference value. If the verification passes, the second intermediate parameter is determined as the environmental target parameter.

[0038] Furthermore, step S6 includes:

[0039] S61: Determine the target optical state based on the environmental target parameters, and calculate the difference between the current optical state and the target optical state;

[0040] S62: When the difference is greater than the preset adjustment threshold, the transmittance and surface roughness of the optical component are simultaneously adjusted to the target optical state to reduce the state switching frequency of the optical component and suppress local heat accumulation.

[0041] Secondly, a system for synergistic optimization of surface roughness and light transmittance of a smart lens, the system being used to implement the steps of any of the methods described above, the system comprising:

[0042] The environmental parameter acquisition module is used to acquire external environmental parameters collected by the environmental sensing unit.

[0043] An anomaly detection module is used to determine whether the environmental sensing unit is in an abnormal sensing state based on the numerical or fluctuation characteristics of the external environmental parameters.

[0044] The fine-tuning control module is used to control the adjustment layer to perform optical state fine-tuning of a preset amplitude if the environmental sensing unit is in an abnormal sensing state.

[0045] The response feature acquisition module is used to acquire the first response feature of the environmental perception unit in response to the optical state fine-tuning, and to acquire the second response feature of the user's eye physiological features in response to the optical state fine-tuning.

[0046] The calibration calculation module is used to perform correlation calculations based on the preset amplitude, the first response feature, and the second response feature to determine the calibrated environmental target parameters.

[0047] An optical adjustment module is used to adjust the transmittance and surface roughness of the optical components according to the environmental target parameters.

[0048] Beneficial Effects: The method and system for synergistic optimization of surface roughness and transmittance of smart lenses proposed in this application acquire external environmental parameters collected by an environmental sensing unit and determine whether the environmental sensing unit is in an abnormal sensing state based on its characteristics. If it is in an abnormal state, the control adjustment layer performs optical state fine-tuning of a preset amplitude, while simultaneously acquiring the response characteristics of the environmental sensing unit and the user's eye to the fine-tuning. Then, based on the preset amplitude and the two response characteristics, correlation calculations are performed to determine the calibrated environmental target parameters. Finally, based on the calibrated environmental target parameters, the transmittance and surface roughness of the optical components are adjusted synchronously. This method, by introducing optical state fine-tuning and correlation calculations of dual response characteristics, can accurately identify and compensate for the nonlinear distortion deviation of the environmental sensing unit, thereby obtaining more accurate environmental target parameters. Based on this, the simultaneous adjustment of transmittance and surface roughness can not only reduce the state switching frequency of optical components, but also effectively suppress local heat accumulation. This solves the problems of existing smart lenses in dynamic outdoor environments, such as the inability of multi-objective evaluation functions to adaptively adjust, deviations and lags in sensor data, instability of optimization algorithms, and internal heat accumulation. It has the advantage of significantly improving the optical performance of smart lenses in complex environments and enhancing the user's visual experience. Attached Figure Description

[0049] Figure 1 This is a flowchart of a method for synergistic optimization of surface roughness and light transmittance of a smart lens proposed in this application.

[0050] Figure 2 This is a structural diagram of a system for synergistic optimization of surface roughness and light transmittance of a smart lens proposed in this application.

[0051] Figure 3 This is a schematic diagram of a system for synergistic optimization of surface roughness and light transmittance of a smart lens proposed in this application.

[0052] Labeling Explanation: 201, Environmental Parameter Acquisition Module; 202, Anomaly Detection Module; 203, Fine-tuning Control Module; 204, Response Feature Acquisition Module; 205, Calibration Calculation Module; 206, Optical Adjustment Module. Detailed Implementation

[0053] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. The components of the embodiments of this application described and marked in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0054] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0055] In the field of intelligent optics, particularly for display lenses in augmented reality (AR) and virtual reality (VR) devices, optimizing their optical performance has always been a core technological challenge. Traditional intelligent lens optimization methods typically employ fixed-weight multi-objective evaluation functions to optimize the surface roughness and transmittance of the intelligent lens, aiming to meet basic visual experience requirements in standard indoor AR / VR scenarios. However, this fixed-weight optimization method struggles to adapt to dynamically changing external environments, especially in complex scenarios such as outdoor strong light, where its performance is poor. Furthermore, existing systems' ambient light sensors and eye-tracking sensors are prone to saturation effects and response delays in strong light environments, leading to biases and lags in the collected environmental data. This inaccurate environmental data feeds back to the optimization algorithm, causing it to oscillate between transmittance and roughness or get trapped in local optima, making it difficult to stably balance the two. This results in frequent state switching of the intelligent lens, causing localized heat accumulation and thermal lensing effects, ultimately introducing localized image distortion or blurring in the display, severely impacting the user experience.

[0056] Please refer to Figure 1 A method for synergistic optimization of surface roughness and light transmittance of a smart lens, used to control a smart lens having optical components and an environmental sensing unit, wherein the optical components have an adjustment layer, the method comprising the steps of:

[0057] S1: Acquire external environmental parameters collected by the environmental sensing unit;

[0058] S2: Determine whether the environmental sensing unit is in an abnormal sensing state based on the numerical or fluctuation characteristics of the external environmental parameters.

[0059] S3: If the environmental sensing unit is in an abnormal sensing state, the control adjustment layer will perform optical state fine-tuning of a preset amplitude.

[0060] S4: Obtain the first response features of the environmental perception unit in response to the optical state fine-tuning, and obtain the second response features of the user's eye physiological features in response to the optical state fine-tuning;

[0061] S5: Perform correlation calculations based on the preset amplitude, first response characteristics, and second response characteristics to determine the calibrated environmental target parameters;

[0062] S6: Adjust the transmittance and surface roughness of the optical components according to the target environmental parameters.

[0063] This application achieves dynamic and precise optimization of the optical performance of smart lenses by introducing an abnormal state judgment and calibration mechanism of the environmental perception unit and combining it with the user's eye physiological response. This method can effectively solve the problem of poor adaptability of traditional fixed-weight optimization methods in complex dynamic environments, and overcome the problems of inaccurate optimization and frequent state switching caused by sensor saturation and response lag in strong light environments, thereby improving the visual experience and stability of smart lenses in various environments.

[0064] In a specific application scenario, the smart lens integrates a complete microcontroller system. The environmental sensing unit can be a high-sensitivity photodiode array, such as the BH1750FVI ambient light sensor module, connected to the main control unit via the I2C communication protocol. The main control unit, such as an STM32F4 series microcontroller, continuously reads the digital signal output by the sensor at a sampling frequency of 100 times per second. This signal directly reflects the light intensity of the external environment, i.e., the external environmental parameters. The optical components include at least an adjustment layer made of electrochromic material, used to change the transmittance under voltage drive; the optical components also include a surface microstructure layer for adjusting the roughness of the lens surface.

[0065] When a sensor saturates or experiences response delays under strong outdoor light, its output data will exhibit specific patterns. By capturing these patterns, we can determine if the sensor is in an abnormal sensing state. Once an anomaly is identified, it means that the information provided by the sensor can no longer be fully trusted, and a more advanced sensing mechanism needs to be activated.

[0066] The external environmental parameters mainly refer to the ambient light intensity captured by the environmental sensing unit, quantified in lux. Numerical characteristics include the raw digital signal value output by the environmental sensing unit at the current sampling time, and the statistical average of this signal value over a sliding window period. Fluctuation characteristics are defined by calculating the standard deviation or coefficient of variation of the raw signal sequence, reflecting the degree of drastic change in ambient light. A jump in the signal value output by the environmental sensing unit (e.g., the absolute value of its first derivative) within a very short time characterizes the instantaneous rate of change in light intensity.

[0067] The optical components include an adjustment layer, which can be an electrochromic thin film material whose transmittance can be changed by applying different voltages. While performing optical fine-tuning, the main control unit activates a dual-channel synchronous monitoring mode. Firstly, it captures changes in the output signal of the environmental sensing unit during lens transmittance fine-tuning at a higher sampling frequency, such as 200 times per second. Even when the sensor is saturated, the last bit of its internal analog signal or digital output may still respond to these minute changes in light; this response constitutes the first response feature. Secondly, a high-speed eye-tracking camera is integrated into the smart lens, which is activated synchronously. This camera continuously captures images of the user's eyes at 200 frames per second. Image processing algorithms within the main control unit, such as commonly used edge detection algorithms, accurately identify the pupil boundary from the image sequence in real time and calculate its diameter. The rate of change in pupil diameter during lens fine-tuning, resulting from the pupil's contraction and expansion in response to light changes, constitutes an independent and highly reliable second response feature.

[0068] Subsequently, the first response feature from the electronic sensor and the second response feature from the user are cross-referenced and fused using a built-in excitation feedback correlation model. This excitation feedback correlation model identifies the nonlinear distortion bias of the environmental sensing unit by cross-comparing the first and second response features. This nonlinear distortion bias is then used to compensate for the original external environmental parameters, resulting in calibrated environmental target parameters. This calibration process effectively eliminates errors caused by sensor anomalies, allowing the environmental target parameters to more accurately reflect the real environmental conditions.

[0069] Finally, based on the calibrated environmental target parameters, a pre-defined optimization strategy library is consulted to determine the ideal combination of transmittance and surface roughness under the current environment. For example, if the actual light intensity is inferred to be extremely high, the adjustment layer is instructed to significantly reduce the transmittance, while another microstructure array driven by microelectromechanical systems (MEMS) is instructed to increase the microscopic roughness of the lens surface, effectively suppressing glare by enhancing light scattering. Due to the accuracy of the decision-making basis, the optical state adjustment of the lens will be a one-step, stable, and consistent process, thus avoiding unnecessary oscillations and eliminating the resulting local heat accumulation and image distortion problems.

[0070] Furthermore, step S2 includes:

[0071] S21: When the value of the external environmental parameter reaches the preset saturation threshold, the environmental sensing unit is determined to be in an abnormal saturation state.

[0072] S22: When the fluctuation range of external environmental parameters within a preset time period is lower than the preset change threshold, and the deviation between the current value and the historical value of the external environmental parameters is greater than the preset deviation threshold, the environmental sensing unit is determined to be in a response lag abnormal state; the abnormal sensing state includes the saturation abnormal state and the response lag abnormal state.

[0073] The preset saturation threshold is set to the maximum range value of the analog-to-digital converter built into the environmental sensing unit. If the environmental sensing unit is a 10-bit converter, the saturation threshold is 1023. When the output signal remains locked at this value, it is considered an abnormal saturation state. The preset variation threshold measures the signal activity. If the standard deviation of the signal fluctuation is lower than this threshold within 100 milliseconds, it indicates that the signal is in a pseudo-stationary state. The preset deviation threshold measures the degree of deviation between the current sampled value and the historical average value one second ago. If the fluctuation amplitude is extremely low but the deviation from the historical average exceeds the preset deviation threshold, it indicates that the ambient light has changed significantly, but the sensor has failed to track it in real time, thus indicating an abnormal response lag state.

[0074] Furthermore, step S3 includes:

[0075] S31: If the environmental sensing unit is in an abnormal sensing state, a transient voltage pulse is applied to the adjustment layer to cause the transmittance of the optical component to fluctuate transiently at a preset amplitude, wherein the preset amplitude is lower than the brightness change threshold perceived by the human eye.

[0076] The main control unit generates a precisely controlled voltage pulse signal through its digital-to-analog converter interface. This signal is applied to both ends of the electrochromic material that constitutes the adjustment layer. The molecular structure of the electrochromic material responds to the electric field, thereby altering its absorption characteristics of visible light, which macroscopically manifests as a change in transmittance. The voltage pulse is designed to be transient, meaning its duration is extremely short, and it can be designed as a single cycle of a sine wave, square wave, or triangular wave, thus causing periodic, minute fluctuations in transmittance.

[0077] The amplitude of this fluctuation, i.e., the change in transmittance, is strictly controlled at an extremely low level, below the threshold of brightness change that the human eye can perceive. The threshold of brightness change perceived by the human eye under typical ambient lighting is approximately 5% to 10% transmittance change. This application presets the amplitude to between 2% and 5% to ensure that the user is unaware of it. The human visual system is insensitive to slow or minute changes in brightness. Utilizing this physiological characteristic, it can be ensured that the entire active detection process is completely transparent and imperceptible to the user, without causing any interference to the user's visual experience.

[0078] The preset amplitude refers to the percentage change in transmittance of the adjustment layer after receiving a pulse command. This amplitude is typically controlled between 2% and 5%. The brightness change threshold perceived by the human eye refers to the minimum brightness change that can be detected by the human eye under specific ambient background brightness. According to Weber's Law, in brighter environments, the human eye's sensitivity to small changes in brightness decreases. By setting the fine-tuning amplitude below 5% and combining it with transient switching within 50 milliseconds, it is possible to ensure that perturbations in transmittance are within the blind spot of the human visual system, achieving covert detection.

[0079] Furthermore, in step S31, applying a transient voltage pulse to the regulating layer includes the following steps:

[0080] S311: Control the duration of the transient voltage pulse within a preset time interval, control the change in transmittance within a preset ratio range, and restore the transmittance to its initial state after the transient voltage pulse ends, wherein the preset ratio range is 2% to 5%.

[0081] The preset ratio range is 2% to 5%. For example, the main control unit can generate a square wave pulse with a duration of 50 milliseconds. The voltage amplitude of this pulse is pre-calibrated to precisely reduce the total light transmittance of the lens from its unadjusted state, such as 80%, to 77.6% briefly, with a change of 3%, falling within the preferred range of 2% to 5%.

[0082] This range represents a balance point achieved through extensive experimental verification: too small a change might fail to effectively stimulate the sensor and pupil response; too large a change risks being detected by the user. After the 50-millisecond pulse duration ends, the main control unit immediately cancels the voltage or applies a reverse voltage, causing the electrochromic material to rapidly return to its initial molecular state, thus precisely restoring the lens transmittance to 80% of its pre-adjustment level. This precise and recoverable control ensures the instantaneous and seamless nature of the active detection process.

[0083] Furthermore, step S4 includes:

[0084] S41: Collect the amplitude change and / or response phase of the output signal of the environmental sensing unit during the fluctuation period as the first response feature;

[0085] S42: The rate of change of the user's pupil contraction or expansion during fluctuations is collected by an eye-tracking device as a second response feature.

[0086] Simultaneously, as the main control unit sends out voltage pulses, its internal analog-to-digital converter performs intensive sampling of the ambient light sensor's output at a much higher frequency than usual, such as two hundred times per second. This yields high-resolution time-series data, completely recording every reading change of the sensor during fluctuations in lens transmittance. This time-series data itself, or key statistics extracted from it, such as amplitude changes or response phase, constitute the first response characteristic.

[0087] Secondly, in sync with sensor sampling, an eye-tracking camera integrated into the inner frame of the smart lens continuously captures high-definition infrared images of the user's eyes at a rate of 200 frames per second. Each frame is transmitted to the main control unit in real time. The main control unit runs a pupil recognition algorithm. This algorithm first preprocesses the image, such as grayscale conversion and binarization, and then uses an edge detection algorithm to accurately locate the circular boundary of the pupil in the image and calculate its pixel diameter. By combining the pixel diameter with pre-calibrated camera parameters, the physical diameter of the pupil, in millimeters, can be calculated. This yields a second time-series data point, synchronized with the sensor data, recording the pupil diameter changes over time. By taking the first derivative of this time-series data, the instantaneous rate of change of the pupil diameter, i.e., the speed of contraction or expansion, can be obtained; this rate of change constitutes the second response feature.

[0088] After acquiring these two sets of parallel response feature data, step S5 further includes:

[0089] S51: Establish an incentive-feedback correlation model, wherein the incentive is a preset amplitude, and the feedback includes a first response feature and a second response feature;

[0090] S52: Cross-compare the first response feature with the second response feature to identify the nonlinear distortion bias of the environmental perception unit;

[0091] S53: Compensate for external environmental parameters using nonlinear distortion bias to obtain environmental target parameters.

[0092] The excitation is a preset amplitude, and the feedback includes first and second response characteristics. In the algorithmic logic of the main control unit, this model is a mathematical framework used to describe the relationship between input and output. Within this framework, the known transmittance change amplitude actively applied by the main control unit, for example, 3%, is defined as the system's excitation signal. The synchronously acquired sensor output signal change trend and the pupil constriction or dilation rate are defined as two parallel feedback signals of the system.

[0093] The excitation-feedback correlation model is a multi-input, multi-output mathematical mapping function. The excitation input consists of known voltage pulse parameters and the expected transmittance variation. The feedback input includes the digital signal variation trend of the environmental sensing unit and the pupil diameter variation curve captured by the eye-tracking device.

[0094] Specifically, the internal processing of the excitation-feedback correlation model is as follows: First, the two sets of feedback signals are filtered, denoised, and normalized to eliminate high-frequency interference and individual differences. Then, a sliding window Fourier transform or wavelet transform is used to extract the response component with the same excitation frequency, thus obtaining the sensor response amplitude. With response phase Pupil physiological response amplitude Phase with physiological response .

[0095] By directly comparing the amplitude and phase of the sensor response with the pupil response, the amplitude deviation rate can be obtained. Where k is the pre-calibrated ideal linear response coefficient; phase delay difference Therefore, the nonlinear distortion bias parameter is constructed as follows:

[0096] Gain nonlinear coefficient ,in For gain nonlinearity coefficients, The original illumination intensity value collected by the current environmental sensing unit; the function f is a nonlinear mapping function pre-calibrated through experiments, used to determine the gain nonlinear coefficient based on the current illumination operating point and response deviation.

[0097] Response hysteresis coefficient in The response hysteresis coefficient; The frequency of the excitation signal is angular frequency.

[0098] The model outputs the nonlinear distortion deviation parameter set of the current environmental sensing unit at a specific operating point. This parameter set is used by the subsequent compensation calculation module. It is dynamically updated over time to reflect the real-time status of the sensor under different environmental conditions.

[0099] Once the sensor's distortion level is quantified, an inverse compensation function can be established. The raw external environmental parameters collected by the sensor are input into this compensation function, which corrects for the distortion deviation and outputs a calibrated environmental target parameter that more closely approximates the actual physical world's light intensity. The specific formula for calculating the environmental target parameter is as follows:

[0100] , where S(t) represents the original external environmental parameters collected by the environmental sensing unit at time t; The hysteresis coefficient represents the delay time of the sensor's response relative to the real environment. The gain nonlinearity coefficient (dimensionless) represents the degree of amplitude distortion of the sensor at the current operating point, and the function H is the inverse compensation function. This indicates a forward shift of the time axis, i.e., delay compensation for the original data. (Function) This represents the amplitude domain nonlinearity correction function, used to eliminate sensor gain nonlinearity.

[0101] Specifically, step S52 includes:

[0102] S521: Perform time-series analysis on the first response characteristics to extract the response amplitude and response phase of the output signal of the environmental sensing unit during the fluctuation period;

[0103] S522: Perform time-series analysis on the second response features to extract the physiological response amplitude and physiological response phase of the user's pupil diameter change during the fluctuation period;

[0104] S523: Compare the response amplitude with the physiological response amplitude to calculate the amplitude deviation rate, and compare the response phase with the physiological response phase to calculate the phase delay difference;

[0105] S524: Based on the amplitude deviation rate and phase delay difference, construct the nonlinear distortion deviation parameters of the environmental sensing unit under the current environmental state. The nonlinear distortion deviation parameters include at least the gain nonlinearity coefficient and the response hysteresis coefficient.

[0106] The main control unit applies signal processing techniques, such as Fourier analysis or wavelet analysis, to the time-series data output by the sensor. Since the feedback signal contains components with the same frequency as the excitation signal, the response amplitude of the sensor output signal can be obtained by analyzing the amplitude of this frequency component. The response phase can be obtained by analyzing its delay relative to the starting point of the excitation signal. Similarly, the main control unit performs the same time-series analysis on the time-series data of pupil diameter changes to extract the physiological response amplitude and physiological response phase of pupil diameter changes.

[0107] Dividing the sensor's response amplitude by the pupil's physiological response amplitude yields a ratio. Comparing this ratio to the ratio under ideal conditions, the difference is the amplitude deviation rate. Similarly, subtracting the pupil's physiological response phase from the sensor's response phase gives the phase delay difference.

[0108] Finally, based on the amplitude deviation rate and phase delay difference, nonlinear distortion deviation parameters of the environmental sensing unit under the current environmental state are constructed. These parameters include at least the gain nonlinearity coefficient and the response hysteresis coefficient. The amplitude deviation rate directly reflects whether the sensor's gain characteristics deviate from the linear region at the current operating point, and therefore can be used to calculate the gain nonlinearity coefficient. The phase delay difference directly quantifies the time lag of the sensor's response and can be used to calculate the response hysteresis coefficient. These two coefficients together constitute a set of key parameters that can accurately describe the current nonlinear distortion state of the sensor.

[0109] After constructing the nonlinear distortion deviation parameters, the specific steps for calculating compensation for external environmental parameters using these parameters are as follows. Further, step S53 includes:

[0110] S531: Obtain the raw external environmental parameters collected by the environmental sensing unit, and obtain the gain nonlinearity coefficient and response hysteresis coefficient in the nonlinear distortion deviation;

[0111] S532: Time axis compensation is performed on the original external environment parameters based on the response hysteresis coefficient to obtain the first intermediate parameter;

[0112] S533: Perform amplitude domain nonlinear correction on the first intermediate parameter based on the gain nonlinearity coefficient to obtain the second intermediate parameter;

[0113] S534: Compare and verify the second intermediate parameter with the preset standard environmental reference value. If the verification passes, the second intermediate parameter is determined as the environmental target parameter.

[0114] The main control unit retrieves the raw, unprocessed sensor readings from its memory, along with the gain nonlinearity coefficient and response hysteresis coefficient calculated via cross-comparison. A response hysteresis coefficient, for example, calculated to be 20 milliseconds, means the sensor readings lag behind the actual environmental changes by 20 milliseconds. The compensation algorithm shifts the entire raw external environment parameter time series data forward by 20 milliseconds on the time axis. This can be achieved through digital signal processing methods such as interpolation. After this step, the resulting time series data, i.e., the first intermediate parameter, is now aligned with the actual environmental changes in time.

[0115] The gain nonlinearity coefficient describes the nonlinear relationship between the sensor output value and the true light intensity. For example, the coefficient might indicate that the sensor output value is compressed by 30% in the current high-light area. The correction algorithm applies a correction function that is the opposite of this nonlinear relationship, stretching or compressing the amplitude of each data point in the first intermediate parameter sequence to restore it to the value it should have under a linear relationship. The resulting sequence, the second intermediate parameter, is also closer to the true light intensity in amplitude.

[0116] The second intermediate parameter is compared and verified with a preset standard environmental reference value. If the verification passes, the second intermediate parameter is determined as the environmental target parameter. To increase robustness, the main control unit also performs a final rationality verification. The preset standard environmental reference value is a standard light intensity value pre-stored in the system, matching the current geographical location, time period, or typical lighting conditions, used to verify the rationality of the correction results. For example, the corrected light intensity value is compared with data from the inertial measurement unit integrated on the smart lens. If the inertial measurement unit indicates that the user is moving rapidly, then high-frequency fluctuations in light intensity are reasonable. If the verification passes, this second intermediate parameter, which has undergone time compensation and amplitude correction, is finally confirmed as a reliable environmental target parameter for subsequent lens adjustment.

[0117] Furthermore, if the verification of the second intermediate parameter fails, the historical valid parameters or default security parameters in the storage unit are retrieved as the environmental target parameters.

[0118] Further, step S6 includes:

[0119] S61: Determine the target optical state based on the environmental target parameters, and calculate the difference between the current optical state and the target optical state;

[0120] S62: When the difference is greater than the preset adjustment threshold, the transmittance and surface roughness of the optical components are adjusted synchronously to the target optical state in order to reduce the state switching frequency of the optical components and suppress local heat accumulation.

[0121] The target's optical state can be converted from environmental target parameters into corresponding optical state parameters through a pre-established mapping relationship. The difference refers to the deviation between the target's optical state and the current optical state, such as the difference in transmittance or the difference in surface roughness.

[0122] Furthermore, the preset adjustment threshold can be empirically set or determined through experimental optimization based on the application scenario of the smart lens, the user's requirements for visual comfort, and the hardware's response characteristics. Synchronous adjustment refers to maintaining a coordinated and consistent change in transmittance and surface roughness during the adjustment process to ensure overall optimization of optical performance.

[0123] Please refer to Figure 2 , Figure 3 A system for synergistic optimization of surface roughness and light transmittance of a smart lens, the system being used to implement the steps of any of the above methods, the system comprising:

[0124] The environmental parameter acquisition module 201 is used to acquire external environmental parameters collected by the environmental sensing unit;

[0125] The anomaly determination module 202 is used to determine whether the environmental sensing unit is in an abnormal sensing state based on the numerical or fluctuation characteristics of the external environmental parameters.

[0126] The fine-tuning control module 203 is used to control the adjustment layer to perform optical state fine-tuning of a preset amplitude if the environmental sensing unit is in an abnormal sensing state.

[0127] The response feature acquisition module 204 is used to acquire the first response feature of the environmental perception unit in response to the optical state fine-tuning, and to acquire the second response feature of the user's eye physiological features in response to the optical state fine-tuning.

[0128] The calibration calculation module 205 is used to perform correlation calculations based on the preset amplitude, the first response characteristic, and the second response characteristic to determine the calibrated environmental target parameters.

[0129] The optical adjustment module 206 is used to adjust the transmittance and surface roughness of the optical components according to environmental target parameters.

[0130] Specifically, the environmental parameter acquisition 201 can be a data acquisition interface, for example, including an analog-to-digital converter (ADC) and corresponding signal conditioning circuitry, used to convert the analog electrical signals output by the environmental sensing unit (such as a photosensitive sensor, a spectral sensor, etc.) into digital data and perform preliminary data preprocessing for use by subsequent modules.

[0131] The anomaly detection module 202 can be implemented by an embedded processor or microcontroller, which internally presets and stores a saturation threshold, a preset duration, a preset change threshold, and a preset deviation threshold. This module identifies and outputs the saturation anomaly or response lag anomaly of the environmental sensing unit by real-time monitoring and analysis of environmental parameter data streams. For example, by comparing the current environmental parameter value with the saturation threshold, or calculating the fluctuation amplitude of the environmental parameter within a specific time window with the preset change threshold, and the deviation between the current value and historical values ​​with the preset deviation threshold.

[0132] The fine-tuning control module 203 can be a precision voltage or current generator configured to apply a transient electrical signal, such as a voltage pulse, to the adjustment layer based on the output of the anomaly detection module. This module ensures that the applied electrical signal causes the transmittance of the optical component to fluctuate periodically at a preset amplitude, and that this preset amplitude is strictly controlled below the brightness change threshold perceived by the human eye, so as to avoid visual interference to the user.

[0133] The response feature acquisition module 204 may include two independent sub-modules: one for real-time acquisition and analysis of the output signal change trend of the environmental perception unit during optical state fine-tuning to extract the first response feature; the other for communication with the eye-tracking device to acquire and analyze the rate of change of the user's pupil contraction or dilation during the same fine-tuning, thereby extracting the second response feature.

[0134] The calibration calculation module 205 can be implemented by a central processing unit (CPU) or a digital signal processor (DSP), which internally runs a complex excitation feedback correlation model. This module receives the preset amplitude provided by the fine-tuning control module and the first and second response features provided by the response feature acquisition module as inputs, and performs cross-comparison, nonlinear distortion deviation identification, and compensation calculation. Finally, it outputs calibrated environmental target parameters that can more accurately reflect the real environmental conditions.

[0135] The optical adjustment module 206 can be a multi-channel drive circuit configured to generate and apply precise control voltage or current to the adjustment layer based on environmental target parameters output by the calibration calculation module, so as to synchronously and collaboratively adjust the transmittance and surface roughness of the optical components. Furthermore, this module is designed to calculate the difference between the current optical state and the target optical state, and trigger the adjustment action only when the difference is greater than a preset adjustment threshold, thereby optimizing the adjustment frequency, reducing energy consumption, and extending device lifespan.

[0136] The system of this application forms a closed-loop intelligent calibration mechanism by introducing an anomaly detection module 202, a fine-tuning control module 203, a response feature acquisition module 204, and a calibration calculation module 205. This mechanism can actively identify abnormal states of the environmental sensing unit and accurately calibrate the nonlinear distortion of the environmental sensing unit through dual feedback of optical state fine-tuning and human eye physiological response, thereby obtaining highly accurate environmental target parameters.

[0137] Therefore, the optical adjustment module 206 can perform precise and coordinated adjustments to transmittance and surface roughness based on these calibrated environmental target parameters. This intelligent system design not only solves the problem of inaccurate data in traditional systems under complex environments, but also effectively reduces the state switching frequency of optical components by optimizing the adjustment strategy, suppressing local heat accumulation, avoiding image distortion caused by thermal lensing effects, and significantly improving the visual experience and stability of smart lenses in various application scenarios. Therefore, the system of this application provides solid technical support for the outdoor application of high-end AR / VR devices.

[0138] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for synergistic optimization of surface roughness and light transmittance of a smart lens, used to control a smart lens having optical components and an environmental sensing unit, wherein the optical components have an adjustment layer, characterized in that, The method includes the following steps: S1: Obtain the external environmental parameters collected by the environmental sensing unit; S2: Determine whether the environmental sensing unit is in an abnormal sensing state based on the numerical or fluctuation characteristics of the external environmental parameters. S3: If the environmental sensing unit is in an abnormal sensing state, control the adjustment layer to perform optical state fine-tuning of a preset amplitude; S4: Obtain the first response feature of the environmental perception unit in response to the optical state fine-tuning, and obtain the second response feature of the user's eye physiological characteristics in response to the optical state fine-tuning; S5: Perform correlation calculations based on the preset amplitude, the first response feature, and the second response feature to determine the calibrated environmental target parameters; S6: Adjust the transmittance and surface roughness of the optical component according to the environmental target parameters.

2. The method for synergistic optimization of surface roughness and light transmittance of a smart lens according to claim 1, characterized in that, Step S2 includes: S21: When the value of the external environmental parameter reaches a preset saturation threshold, it is determined that the environmental sensing unit is in a saturation abnormal state. S22: When the fluctuation range of the external environmental parameter within a preset time period is lower than a preset change threshold, and the deviation between the current value and the historical value of the external environmental parameter is greater than a preset deviation threshold, the environmental sensing unit is determined to be in a response lag abnormal state; the abnormal sensing state includes a saturation abnormal state and a response lag abnormal state.

3. The method for synergistic optimization of surface roughness and light transmittance of a smart lens according to claim 1, characterized in that, Step S3 includes: S31: If the environmental sensing unit is in an abnormal sensing state, a transient voltage pulse is applied to the adjustment layer to cause the transmittance of the optical component to fluctuate transiently at a preset amplitude, wherein the preset amplitude is lower than the brightness change threshold perceived by the human eye.

4. The method for synergistic optimization of surface roughness and light transmittance of a smart lens according to claim 3, characterized in that, In step S31, applying a transient voltage pulse to the regulating layer includes the following steps: S311: Control the duration of the transient voltage pulse within a preset time interval, control the change in transmittance within a preset ratio range, and restore the transmittance to its initial state after the transient voltage pulse ends, wherein the preset ratio range is 2% to 5%.

5. The method for synergistic optimization of surface roughness and light transmittance of a smart lens according to claim 3, characterized in that, Step S4 includes: S41: Collect the amplitude change and / or response phase of the output signal of the environmental sensing unit during the fluctuation period, as the first response feature; S42: The rate of change of the user's pupil contraction or expansion during the fluctuation is collected by an eye-tracking device as the second response feature.

6. The method for synergistic optimization of surface roughness and light transmittance of a smart lens according to claim 1, characterized in that, Step S5 includes: S51: Establish an incentive-feedback correlation model, wherein the incentive is the preset amplitude, and the feedback includes the first response feature and the second response feature; S52: Cross-compare the first response feature with the second response feature to identify the nonlinear distortion deviation of the environment perception unit; S53: The external environmental parameters are compensated using the nonlinear distortion deviation to obtain the target environmental parameters.

7. The method for synergistic optimization of surface roughness and light transmittance of a smart lens according to claim 6, characterized in that, Step S52 includes: S521: Perform time-series analysis on the first response feature to extract the response amplitude and response phase of the output signal of the environmental sensing unit during the fluctuation period; S522: Perform time-series analysis on the second response feature to extract the physiological response amplitude and physiological response phase of the user's pupil diameter change during the fluctuation period; S523: Compare the amplitude of the response with the amplitude of the physiological response, calculate the amplitude deviation rate, and compare the phase of the response with the phase of the physiological response, calculate the phase delay difference; S524: Based on the amplitude deviation rate and the phase delay difference, construct the nonlinear distortion deviation parameter of the environmental sensing unit under the current environmental state. The nonlinear distortion deviation parameter includes at least the gain nonlinearity coefficient and the response hysteresis coefficient.

8. The method for synergistic optimization of surface roughness and light transmittance of a smart lens according to claim 7, characterized in that, Step S53 includes: S531: Obtain the original external environmental parameters collected by the environmental sensing unit, and obtain the gain nonlinearity coefficient and response hysteresis coefficient in the nonlinear distortion deviation; S532: Perform time-axis compensation on the original external environment parameters based on the response hysteresis coefficient to obtain the first intermediate parameter; S533: Perform amplitude domain nonlinear correction on the first intermediate parameter based on the gain nonlinearity coefficient to obtain the second intermediate parameter; S534: Compare and verify the second intermediate parameter with the preset standard environmental reference value. If the verification passes, the second intermediate parameter is determined as the environmental target parameter.

9. The method for synergistic optimization of surface roughness and light transmittance of a smart lens according to claim 1, characterized in that, Step S6 includes: S61: Determine the target optical state based on the environmental target parameters, and calculate the difference between the current optical state and the target optical state; S62: When the difference is greater than the preset adjustment threshold, the transmittance and surface roughness of the optical component are simultaneously adjusted to the target optical state to reduce the state switching frequency of the optical component and suppress local heat accumulation.

10. A system for synergistic optimization of surface roughness and light transmittance of a smart lens, characterized in that, The system is used to implement the steps of the method according to any one of claims 1-9, the system comprising: The environmental parameter acquisition module is used to acquire external environmental parameters collected by the environmental sensing unit. An anomaly detection module is used to determine whether the environmental sensing unit is in an abnormal sensing state based on the numerical or fluctuation characteristics of the external environmental parameters. The fine-tuning control module is used to control the adjustment layer to perform optical state fine-tuning of a preset amplitude if the environmental sensing unit is in an abnormal sensing state. The response feature acquisition module is used to acquire the first response feature of the environmental perception unit in response to the optical state fine-tuning, and to acquire the second response feature of the user's eye physiological features in response to the optical state fine-tuning. The calibration calculation module is used to perform correlation calculations based on the preset amplitude, the first response feature, and the second response feature to determine the calibrated environmental target parameters. An optical adjustment module is used to adjust the transmittance and surface roughness of the optical components according to the environmental target parameters.

Images