Astigmatism correction and imaging optimization method of electrowetting cylindrical lens optical system

Abstract

The invention relates to the technical field of electrowetting cylindrical lens optical systems, and discloses an astigmatism correction and imaging optimization method for an electrowetting cylindrical lens optical system, and the method comprises the steps: obtaining the structure parameters and imaging environment parameters of the electrowetting cylindrical lens optical system, and determining a target imaging state; generating voltage driving signals including voltage amplitude, phase difference and driving frequency based on an astigmatism correction model, and performing real-time regulation and control on the electrowetting cylindrical lens; a self-adaptive optical compensation structure is adopted to adjust the system in real time, and axial and radial astigmatism errors are effectively corrected; the system monitors an imaging quality index in real time, and adjusts parameters of a voltage driving signal when detecting that the system deviates from a target state, such as adjusting voltage amplitude, phase difference or driving frequency, so as to realize adaptive astigmatism correction and imaging quality optimization. The astigmatism correction capability and the imaging stability of the electrowetting cylindrical lens optical system are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of electrowetting cylindrical lens optical systems, and more specifically, to a method for astigmatism correction and imaging optimization of electrowetting cylindrical lens optical systems. Background Technology

[0002] Existing electrowetting cylindrical lens optical systems typically need to address astigmatic errors caused by various factors during dynamic imaging, including target object morphology, changes in ambient lighting, temperature fluctuations, and system aging. These astigmatic errors vary considerably in their manifestations, including axial astigmatism, radial curvature deviation, and higher-order distortions caused by instability at the liquid interface. Furthermore, they differ in their causes, extent of influence, and rate of change. Due to the complexity of astigmatism's causes and its dynamic evolution over time, existing systems have significant limitations in real-time correction and image quality maintenance.

[0003] Currently, most mainstream astigmatism correction methods rely on fixed compensation optical elements or pre-calibrated voltage adjustment strategies, typically requiring the design of static correction schemes based on pre-measured aberration data. However, this approach is prone to issues such as response lag, insufficient adjustment accuracy, and poor environmental adaptability in dynamic imaging scenarios. When imaging conditions change rapidly or the system operates continuously for extended periods, the correction effect often deteriorates significantly, leading to reduced image resolution, loss of detail, and insufficient imaging reliability, thereby affecting detection accuracy and analysis results. Furthermore, existing correction methods lack real-time monitoring and feedback adjustment capabilities for image quality during operation, often requiring manual intervention or recalibration to restore system performance.

[0004] In summary, existing technologies are insufficient to effectively address the problems of poor real-time performance of astigmatism correction, weak environmental adaptability, and insufficient long-term stability in electrowetting cylindrical lenses during dynamic use, especially lacking the ability to online evaluate and adaptively optimize the imaging state. Therefore, there is an urgent need to propose a novel astigmatism correction and imaging optimization method to improve the performance of electrowetting cylindrical lens optical systems in terms of dynamic aberration suppression, real-time response, and imaging consistency, thereby meeting the requirements of high-precision imaging and real-time vision applications for image quality and stability. Summary of the Invention

[0005] In view of this, the present invention proposes an astigmatism correction and imaging optimization method for an electrowetting cylindrical lens optical system, aiming to solve the problems of poor real-time performance of astigmatism correction, weak adaptability to environmental changes, insufficient long-term working stability, and lack of online evaluation and adaptive optimization capabilities of imaging status in existing systems during dynamic imaging.

[0006] This invention proposes a method for astigmatism correction and imaging optimization of an electrowetting cylindrical lens optical system, comprising: The structural parameters and actual imaging environment parameters of the electrowetting cylindrical lens optical system are obtained, and the target imaging state of the system is determined based on the structural parameters and imaging environment parameters. The structural parameters include the initial curvature of the cylindrical lens, the thickness of the dielectric layer, the material properties of the hydrophobic layer, and the basic parameters of the liquid interface. An astigmatism correction model is constructed based on the target imaging state, and a corresponding voltage driving signal is generated according to the astigmatism correction model. The voltage driving signal includes parameters such as voltage amplitude, phase difference, and driving frequency. An adaptive optics compensation structure is used to adjust the electrowetting cylindrical lens optical system in real time to effectively correct astigmatism errors; In the actual imaging process, a voltage is applied to the electrowetting cylindrical lens based on the voltage driving signal, and the imaging quality-related indicators are monitored in real time. When an imaging quality index is detected to deviate from the target imaging state, at least one parameter of the voltage drive signal is adjusted in a timely manner to achieve reliable astigmatism correction and further optimize the overall imaging quality.

[0007] Furthermore, the acquisition of imaging environment parameters specifically includes: Acquire ambient light intensity, temperature changes, and external optical distortion parameters; The curvature threshold parameter in the astigmatism correction model is appropriately modified based on changes in ambient light and temperature.

[0008] Furthermore, the generation of the voltage drive signal specifically includes: In the initial astigmatism adjustment stage, a gradual phase difference voltage is used, wherein the phase difference change amplitude and the voltage holding time after each change are reasonably configured to enable the liquid interface to be smoothly adjusted to the target curvature and to effectively suppress optical distortion. Once the imaging quality reaches the target imaging state and enters a stable phase, it switches to a low-frequency sustaining signal with a frequency range of 10Hz to 50Hz.

[0009] Furthermore, the adaptive compensation structure includes: The first compensation layer, located at the front of the lens, is mainly used to absorb axial astigmatism error; A high-precision correction layer covering the first compensation layer is used to dynamically adjust radial curvature deviation; An integrated optical housing located at the rear of the system provides overall support and reliably interfaces with the imaging sensor.

[0010] Furthermore, the adaptive compensation structure also incorporates an optical compensation medium, including a low-refractive-index optical liquid or an adaptive polymer material, and the refractive index of the compensation medium is controlled within a range lower than a preset refractive index threshold.

[0011] Furthermore, the real-time monitoring imaging quality indicators specifically include: Obtain the actual distribution information of astigmatism error; And calculate the offset of the current imaging resolution based on the distribution information.

[0012] Furthermore, at least one parameter of the adjusted voltage drive signal specifically includes: The voltage amplitude of the voltage drive signal is adjusted, wherein the adjustment amount is determined based on the deviation between the real-time monitored imaging quality index and the target imaging state. Adjust the phase difference of the voltage drive signal to minimize astigmatism error while maintaining imaging stability; Adjust the driving frequency of the voltage drive signal to appropriately extend the system optimization time and reduce overall optical loss while meeting the imaging response speed requirements.

[0013] Furthermore, the astigmatism correction also includes: After the image quality reaches a stable state, switch to the compensation voltage mode; The compensation voltage is lower than the initial adjustment voltage set in the astigmatism correction model.

[0014] Furthermore, it also includes: The aging degree of the system is assessed based on the cumulative imaging time and the number of adjustments; And when the aging degree exceeds the preset threshold, the safe correction range in the astigmatism correction model is adjusted accordingly.

[0015] Furthermore, the astigmatism correction model is a neural network model trained based on historical imaging quality data and actual adjustment response data; Furthermore, the neural network model can adaptively optimize the combination of various parameters of the voltage driving signal based on the deviation between the real-time monitored imaging state and the target state.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: By acquiring the structural and imaging environment parameters of the electrowetting cylindrical lens optical system and determining the target imaging state based on these parameters, factors affecting astigmatism can be systematically identified. This significantly enhances the characterization of complex and dynamically changing imaging conditions, enabling the system to comprehensively consider both the inherent characteristics of the lens and environmental interference. This establishes a reliable benchmark for accurate correction, reduces reliance on pre-calibration data, and improves the comprehensiveness and accuracy of astigmatism analysis. Secondly, an astigmatism correction model is constructed based on the target imaging state, and a voltage driving signal containing amplitude, phase, and frequency is generated according to the model, achieving real-time, programmable control of the curvature of the liquid interface. The advantages of this technology lie in its ability to fully utilize the rapid response characteristics of the electrowetting effect, achieving smooth interface transition and distortion suppression through a gradual voltage signal. It effectively avoids optical oscillations during the initial adjustment phase and maintains the signal at a low frequency after steady state to reduce energy consumption, thereby improving the stability and efficiency of the correction process. Thirdly, an adaptive optical compensation structure containing multi-layer structures and compensation media is used to adjust the system in real time, directly absorbing and canceling residual astigmatism at the hardware level. Its beneficial effects are manifested in that the system can simultaneously correct axial and radial aberrations, and reduce the influence of stray light by introducing a low-refractive-index medium, thus maintaining high imaging quality and optical consistency even in complex astigmatic scenarios. Finally, by monitoring imaging quality indicators in real time and adaptively adjusting the parameters of the voltage drive signal when deviations from the target state are detected, the system can automatically complete the feedback control closed loop. The beneficial effect of this mechanism is that it enables the entire astigmatism correction process to have online optimization capabilities, and can adjust the voltage strategy and safe correction range in a timely manner when environmental conditions change, system performance drifts, or long-term aging occurs, thereby ensuring the long-term stability of imaging performance, environmental adaptability, and overall reliability. Attached Figure Description

[0017] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart illustrating an astigmatism correction and imaging optimization method for an electrowetting cylindrical lens optical system provided in an embodiment of the present invention; Figure 2 This is a flowchart illustrating a method for astigmatism correction and imaging optimization of an electrowetting cylindrical lens optical system provided in an embodiment of the present invention. Detailed Implementation

[0018] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0019] like Figures 1-2 As shown in some embodiments of this application, this embodiment provides a method for astigmatism correction and imaging optimization of an electrowetting cylindrical lens optical system, including: Step S100: Obtain the structural parameters of the electrowetting cylindrical lens optical system and the actual imaging environment parameters, and determine the target imaging state.

[0020] Specifically, step S100 includes: acquiring the inherent structural parameters and real-time imaging environment parameters of the electrowetting cylindrical lens optical system through an interactive module, internal storage unit of the device, or external sensor interface. The structural parameters mainly include the initial radius of curvature of the cylindrical lens, the thickness and dielectric constant of the dielectric layer, the surface material properties of the hydrophobic layer (such as the contact angle and surface energy parameters of the fluoropolymer coating), the interfacial tension coefficients of the two immiscible liquids, the geometric dimensions of the liquid cavity, and the layout of the electrode array. Based on this, the system comprehensively analyzes the structural parameters and imaging environment parameters to dynamically determine the target imaging state of the system. The target state specifically includes key indicators such as preset astigmatism error tolerance thresholds (such as axial astigmatism less than 0.05λ and radial astigmatism less than 0.1mm), target resolution requirements, overall optical distortion tolerance, and imaging contrast lower limit.

[0021] In specific implementation, preferably, the system first reads the inherent structural parameters of the cylindrical lens from the device's factory calibration database or non-volatile memory, such as an initial radius of curvature of 8mm to 12mm, a dielectric layer thickness of 1μm to 3μm, a hydrophobic layer made of a highly stable fluoropolymer material, and an initial contact angle greater than 110°. Simultaneously, the system collects actual imaging environment parameters in real time through integrated high-precision environmental sensors, including but not limited to ambient light intensity (unit: lux), ambient temperature variation range (within ±10℃), humidity fluctuations, external vibration interference, and external optical distortion parameters caused by changes in the distance between the imaging target and the lens. Furthermore, based on the influence of temperature on liquid viscosity and interfacial tension, the contribution of light intensity to the noise level of the imaging sensor, and the superposition effect of external distortion on overall aberrations, the system dynamically corrects the key curvature threshold parameters in the astigmatism correction model in real time. For example, the curvature threshold is increased by 0.2% to 0.5% for every 1℃ increase in temperature to ensure that the target imaging state is always highly adapted to the current actual working conditions and to avoid correction deviations caused by environmental drift.

[0022] Understandably, compared to traditional methods that rely solely on fixed calibration parameters or offline calibration data for astigmatism compensation, this step, by acquiring structural and environmental parameters in real time and dynamically determining the target imaging state, can more comprehensively and accurately reflect the changes in the optical characteristics of the electrowetting cylindrical lens during actual dynamic use. For example, when the ambient temperature rises, causing a decrease in liquid interfacial tension and resulting in contact angle drift, or when the hydrophobic layer undergoes slight aging due to prolonged use, leading to a decrease in surface energy, this method can directly reflect these changes in the target state through real-time parameter correction. This provides a reliable data foundation for the accurate generation of subsequent voltage-driven signals, significantly improving the robustness and environmental adaptability of astigmatism correction. Simultaneously, this step effectively reduces reliance on tedious manual pre-calibration, enabling rapid self-calibration of the system after power-on, significantly improving deployment efficiency in practical applications.

[0023] In a specific embodiment of this application, the above steps are implemented as follows: For an electrowetting cylindrical lens optical system used in high-precision industrial visual inspection, before the system starts or the imaging task begins, the interactive module first automatically reads the structural parameters stored inside the device, including the initial curvature of the cylindrical lens being 10mm, the dielectric layer thickness being 2.5μm, the hydrophobic layer being a Teflon-based fluoropolymer material with an initial contact angle of 115°, and the liquid interfacial tension coefficient being 0.035N / m. Simultaneously, the environmental sensor detects in real time that the current workshop temperature is 27.5℃, the ambient light intensity is approximately 4500 lux, and the humidity is 55%. The distance sensor also identifies external distortion caused by fluctuations in the distance between the inspected object and the lens within the range of 150mm to 200mm. Based on this, the system comprehensively corrects the curvature threshold of the astigmatism correction model, increasing it by approximately 4.8%, and finally sets the target imaging state as follows: axial astigmatism error not exceeding 0.04λ, radial curvature deviation not exceeding 0.08mm, system resolution not less than 99%, and overall optical distortion less than 0.5%. Through the visual interface, the operator can intuitively see the degree of matching between the target state and the real-time environmental parameters, and finally obtain a highly personalized target imaging state, providing an accurate and reliable benchmark reference for subsequent voltage regulation and astigmatism correction.

[0024] The above scenarios are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0025] Step S200: Construct an astigmatism correction model based on the target imaging state and generate a corresponding voltage driving signal.

[0026] Specifically, step S200 includes: constructing a dedicated astigmatism correction model based on the target imaging state determined in step S100. This model can be a deep neural network model trained based on a large amount of historical imaging quality data, voltage regulation response data, and physical electrowetting effect equations, or it can be a hybrid physical-data driven model that combines finite element simulation and empirical formulas. Subsequently, multi-parameter voltage driving signals are generated in real time based on the astigmatism correction model, mainly including voltage amplitude, phase difference, driving frequency, and necessary waveform modulation methods.

[0027] In practical implementation, the system preferably adopts a deep learning model based on convolutional neural network or recurrent neural network architecture as the core astigmatism correction model. This model is fully trained with thousands to tens of thousands of historical adjustment data accumulated before leaving the factory or during operation, and can accurately predict the optimal combination of voltage parameters required to achieve the target imaging state under given structural parameters, environmental parameters and current astigmatism state. During the initial astigmatism adjustment phase, the model preferentially generates a gradually changing multi-segment phase difference voltage signal, with the phase difference variation amplitude strictly controlled within the range of 5° to 20°. After each change, a reasonable voltage maintenance time (e.g., 0.3 to 3 seconds) is set to ensure that the two liquid interfaces can be smoothly and uniformly adjusted to the target radius of curvature, while effectively suppressing transient optical oscillations and higher-order distortions caused by voltage abrupt changes. After real-time monitoring of imaging quality indicators confirms that the target imaging state has been reached or is close to it and enters the stable maintenance phase, the system automatically switches to a low-frequency periodic maintenance signal with a frequency between 10Hz and 50Hz, while appropriately reducing the voltage amplitude (usually to 70% to 85% of the initial adjustment amplitude) to significantly reduce drive power consumption, reduce hydrophobic layer electrowetting fatigue, and extend the overall system life.

[0028] Understandably, the voltage-driven strategy combining gradual initial adjustment with low-frequency steady-state maintenance can fully leverage the rapid response advantage of the electrowetting effect (response time is typically in the millisecond range) while minimizing problems such as liquid interface oscillation, bubble formation, or contact angle hysteresis that are easily caused by traditional abrupt voltage-driven methods. On the one hand, gradual phase difference modulation helps to achieve a quasi-static deformation process at the liquid interface, ensuring the continuity of curvature changes and optical consistency; on the other hand, the low-frequency maintenance signal not only significantly reduces the overall system energy consumption (by 30% to 50%) but also effectively reduces the long-term electrochemical damage of the hydrophobic layer material to the alternating electric field, providing a solid guarantee for the stable operation of the system in long-term continuous imaging tasks. In addition, the model also supports an online learning mechanism, which can continuously incorporate new adjustment data during actual use, further improving long-term prediction accuracy.

[0029] In a specific embodiment of this application, the above steps are implemented as follows: For the aforementioned industrial vision inspection system, after determining the target imaging state in step S100, the system immediately calls the pre-trained neural network astigmatism correction model. This model is trained using a hybrid approach based on over 5000 historical adjustment data points and the electrowetting physical equation, resulting in extremely high parameter prediction accuracy. The model quickly outputs the voltage drive scheme for the initial adjustment phase: the initial voltage amplitude is 42V, the phase difference gradually increases from 0° to 16° in 8° increments, each step is maintained for 1.2 seconds, and the initial curvature adjustment is completed in 5 segments. Subsequently, the real-time monitoring module confirms that both axial and radial astigmatism have decreased below the target threshold. The system then automatically switches to a low-frequency sustaining signal with a frequency of 28Hz, and the voltage amplitude synchronously decreases to 36V. The waveform is slightly sinusoidally modulated to further suppress residual micro-vibrations. The entire initial adjustment process takes less than 8 seconds, and the liquid interface curvature smoothly transitions from the initial 10mm to the target 7.2mm without any obvious optical oscillations or distortions. The imaging quality quickly reaches over 99.5%.

[0030] Similarly, the above scenarios are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0031] Step S300: The system is adjusted in real time using an adaptive optics compensation structure to correct astigmatism error.

[0032] Specifically, step S300 includes: performing real-time dynamic hardware-level adjustment of the electrowetting cylindrical lens based on a pre-designed adaptive optical compensation structure integrated into the optical system. The compensation structure mainly includes a first compensation layer located at the front end of the lens (mainly responsible for absorbing and offsetting axial astigmatism error), a high-precision correction layer covering the first compensation layer (dedicated to dynamically adjusting radial curvature deviation), and an integrated optical housing located at the rear end of the system (providing overall mechanical support, heat dissipation management, and ensuring precise optical axis alignment with the imaging sensor). At the same time, a low-refractive-index optical liquid or adaptive polymer material is introduced between the compensation layers as an auxiliary compensation medium.

[0033] In practical implementation, the first compensation layer preferably employs a flexible optical thin film or microstructure array design, which can effectively absorb axial astigmatism errors caused by temperature, voltage fluctuations, or inhomogeneous liquid interfaces through minute deformations or changes in refractive index gradients. The high-precision correction layer integrates piezoelectric or electromagnetic microactuators, enabling sub-micron-level radial curvature fine-tuning, thereby precisely offsetting residual deviations in the radial direction. The integrated optical housing not only provides robust mechanical protection but also incorporates a temperature control module to maintain system thermal stability. The refractive index of the compensation medium is strictly controlled within the range of 1.3 to 1.4 to minimize stray light caused by interface reflections, while enhancing the synergistic compensation effect for axial and radial aberrations. This adaptive compensation structure, based on voltage-driven software control, can further absorb and offset errors caused by residual astigmatism, environmental disturbances, and system aging at the hardware level, achieving higher precision astigmatism suppression.

[0034] Understandably, the organic combination of adaptive optics compensation structure and voltage-driven regulation forms a hardware-software co-operation, two-layer closed-loop astigmatism correction mechanism, which can maintain excellent imaging quality in a wider dynamic range and more complex astigmatic scenarios. On the one hand, voltage driving is mainly responsible for the active curvature shaping of the liquid interface; on the other hand, the compensation structure provides passive / active supplementary correction for residual errors or sudden environmental disturbances that are difficult to completely eliminate by voltage regulation, thereby significantly improving the overall robustness and long-term stability of the system. In addition, this structural design fully considers the constraints of size, weight and power consumption, making it particularly suitable for embedded or portable imaging devices.

[0035] In a specific embodiment of this application, the above steps are implemented as follows: After the voltage-driven liquid interface initially approaches the target curvature in step S200, the system synchronously activates the adaptive optical compensation structure. The first compensation layer rapidly absorbs approximately 0.035λ axial astigmatism caused by slight fluctuations in workshop temperature (±0.5℃). The high-precision correction layer performs a 0.06mm curvature fine-tuning in the radial direction via a piezoelectric actuator. Simultaneously, the low-refractive-index optical liquid medium further reduces stray light reflected from the interface by approximately 15%. Through the synergistic effect of the compensation structure, the overall astigmatism error of the system is stably controlled within 0.018λ, the resolution is improved to 99.8%, and the clarity of imaging details is significantly enhanced.

[0036] The above scenarios are merely preferred embodiments of the present invention and are not intended to limit the present invention.

[0037] Step S400: Monitor imaging quality indicators in real time, and adjust voltage drive signal parameters when deviations are detected to achieve adaptive astigmatism correction and imaging optimization.

[0038] Specifically, step S400 includes: during the entire actual imaging process, monitoring multiple key imaging quality indicators in real time using a high frame rate imaging sensor or a dedicated wavefront sensor, including the actual spatial distribution information of astigmatism error, the current resolution offset, the contrast reduction, the distortion coefficient change, and higher-order aberration components; when the monitoring module detects that any one or more indicators deviate from the target imaging state set in step S100 by more than a preset threshold, the system immediately triggers an adaptive adjustment process to optimize and iterate at least one parameter of the voltage driving signal in a timely manner, including increasing or decreasing the voltage amplitude (the adjustment amount is proportional to the deviation amplitude), fine-tuning the phase difference (aiming to minimize residual astigmatism), and increasing or decreasing the driving frequency (taking into account both response speed and power consumption), and automatically switching to a lower compensation voltage mode after the imaging quality stabilizes again to maintain long-term low-power operation.

[0039] In the specific implementation process, the monitoring module continuously acquires images or wavefront data at a sampling frequency of no less than 30Hz, and extracts astigmatic distribution features in real time through Fast Fourier Transform or Zernike polynomial fitting algorithms. If the deviation exceeds the threshold (e.g., astigmatism increases by 0.02λ or resolution decreases by 1%), the neural network astigmatism correction model or the built-in multi-objective optimization algorithm will complete parameter iteration calculations within milliseconds to seconds and output a new combination of voltage parameters. At the same time, the system will also accumulate the total imaging time, the number of voltage adjustments, and the degree of hydrophobic layer response hysteresis to comprehensively evaluate the system aging level. When the aging level exceeds the preset threshold (e.g., the cumulative number of adjustments > 10,000 or the aging index > 15%), the system will automatically tighten the safe correction range of the astigmatism correction model (e.g., reduce the upper limit of the allowable voltage amplitude by 5% to 10%) to prevent potential dielectric layer breakdown or permanent failure of the hydrophobic layer.

[0040] Understandably, this real-time monitoring and adaptive parameter adjustment mechanism constitutes a complete feedback control closed loop, enabling the system to possess powerful online self-learning and self-correction capabilities. This effectively addresses sudden environmental changes (such as rapid temperature changes and light flicker), slow system performance drift, and aging effects caused by prolonged continuous operation, ensuring that imaging quality remains highly consistent throughout its entire lifecycle. This closed-loop design not only significantly improves the system's environmental adaptability and reliability but also substantially reduces the frequency of manual intervention, making it particularly suitable for unattended or harsh industrial scenarios.

[0041] In a specific embodiment of this application, the above steps are implemented as follows: After the aforementioned industrial vision inspection system has been working continuously for approximately 3.5 hours, the workshop temperature suddenly rises by 2°C, causing a decrease in the liquid interfacial tension. The monitoring module quickly detects an increase in axial astigmatism error of 0.045λ and a resolution shift of approximately 2.8%. The neural network model immediately initiates adaptive adjustment, increasing the voltage amplitude from 36V to 41V, fine-tuning the phase difference to 15.5°, and briefly increasing the drive frequency to 35Hz to accelerate the response. After adjustment, it only takes 12 seconds for the astigmatism error to drop back below 0.02λ, and the resolution is restored to 99.7%. Subsequently, the system automatically switches to a 34V low-power compensation voltage mode to maintain stability. At the same time, the system assesses that the current cumulative imaging time has reached 280 minutes, the number of adjustments has increased to 312, and the aging degree is approximately 9.8%. Therefore, the upper limit of the voltage in the safe correction range is tightened to 96% of the original value to reserve a greater safety margin for subsequent long-term operation.

[0042] The above scenarios fully demonstrate that by combining high-frequency real-time monitoring with rapid adaptive parameter adjustment, this method can achieve accurate and reliable suppression of astigmatism in complex dynamic environments, significantly improving overall imaging consistency and system reliability.

[0043] Step S500: Deploy the optimized voltage drive signal and compensation structural parameters to the electrowetting cylindrical lens optical system to achieve closed-loop adaptive imaging optimization.

[0044] Specifically, step S500 includes: based on the final voltage drive parameters obtained through iterative optimization in step S400 and the optimal working state of the adaptive compensation structure, sending them completely to the core drive and control system of the electrowetting cylindrical lens; in this system, through the close collaborative work between the main control module, voltage drive module, imaging quality monitoring module, adaptive compensation actuator, environmental sensor module and interaction module, continuous, high-precision, safe and controllable adaptive astigmatism correction and overall imaging quality optimization are achieved.

[0045] In its implementation, the main control module, acting as the core brain of the system, receives user commands from the interaction module, feedback data from the real-time monitoring module, and optimized outputs from the astigmatism correction model. It parses the comprehensively optimized voltage amplitude, phase difference, frequency, waveform type, and compensation structure fine-tuning commands into directly executable digital control signals, which are then precisely transmitted to the voltage drive module and compensation actuator via high-speed communication interfaces (such as SPI, I2C, or CAN bus). The voltage drive module employs a high-precision digital-to-analog converter and power amplifier circuit, enabling it to output multi-channel complex voltage waveforms with microvolt-level resolution, ensuring nanometer-level control accuracy of the liquid interface curvature. The imaging quality monitoring module continuously provides feedback on multiple indicators at a high frame rate, forming a multi-level closed-loop control. The adaptive compensation actuator drives piezoelectric or electromagnetic components in real-time according to the main control commands to complete micro-deformation adjustments at the hardware level. The environmental sensor module provides real-time environmental context for the entire closed loop, further improving prediction accuracy.

[0046] Understandably, through the aforementioned modular and closed-loop system architecture design, this method achieves a complete technical closed-loop process from "structural and environmental parameter perception—target state determination—model prediction and voltage generation—real-time quality monitoring—adaptive parameter and structural adjustment—optimized parameter deployment and execution." Compared with traditional fixed voltage or open-loop correction methods, this closed-loop system can not only accurately predict the optimal parameters through the model before intervention, but also continuously fine-tune parameters and compensate for structures based on real-time feedback during actual operation. This significantly improves the dynamic response speed, environmental adaptability, long-term stability, and repeatability of overall imaging performance of astigmatism correction, making it particularly suitable for scenarios with extremely high imaging quality requirements, such as industrial inspection, medical endoscopes, augmented reality (AR / VR) optical modules, and high-end mobile phone zoom lenses.

[0047] In a specific embodiment of this application, the above steps are implemented as follows: For the aforementioned industrial vision inspection system, after completing a series of adaptive optimizations in step S400, the main control module packages and sends out the final determined voltage drive parameters (amplitude 40.5V, phase difference 14.8°, drive frequency 26Hz, slight sinusoidal modulation) and compensation structure status (pre-deformation of the first compensation layer 0.012mm, radial offset of the high-precision correction layer 0.05mm). The voltage drive module immediately outputs the multi-parameter composite signal with the highest precision, and the adaptive compensation actuator synchronously completes hardware fine-tuning. The monitoring module feeds back the imaging quality index once per second. During the subsequent 12-hour continuous working period, the system automatically completes 42 small adaptive adjustments, with the astigmatism error consistently remaining within 0.025λ and the resolution fluctuation less than 0.3%. This successfully achieves error-free visual inspection of thousands of high-precision parts, fully verifying the excellent stability and practical value of this method.

[0048] In another preferred embodiment, the present invention also provides an electrowetting cylindrical lens optical system applying the above-described method. This system includes a main control module, a high-precision voltage drive module, an adaptive optical compensation structure, a real-time imaging quality monitoring module, an environmental sensor module, a power management module, and a human-computer interaction module. The electrical connections, signal flows, and functional configurations between these modules completely correspond to the detailed descriptions in steps S100 to S500 of the aforementioned method. By organically and deeply integrating high-precision perception of structural and environmental parameters, deep learning-driven astigmatism correction model prediction, precise driving of multi-parameter voltages, high-frequency real-time monitoring of imaging quality, and hardware coordination of the adaptive compensation structure, this system achieves rapid generation, precise deployment, and long-term stable implementation of adaptive astigmatism correction and imaging quality optimization schemes for various high-precision, dynamic imaging tasks. This invention demonstrates outstanding practical value, broad market prospects, and significant industrialization potential in multiple fields, including industrial automation visual inspection, precision medical imaging, consumer-grade variable-focus optical modules, augmented / virtual reality head-mounted displays, and research-grade optical experimental platforms.

[0049] It is understood that the astigmatism correction and imaging optimization methods and systems of the electrowetting cylindrical lens optical system in the above embodiments have the same or highly similar technical advantages and beneficial effects. The main differences between the aforementioned embodiments are reflected in the specific parameter value range, hardware implementation form, application scenario selection and optimization algorithm details. Reasonable adjustments, functional equivalent substitutions or detail improvements made by those skilled in the art without departing from the core spirit and substantive technical solution of this invention should be considered to fall within the protection scope of the claims of this invention.

[0050] It is understood that the astigmatism correction and imaging optimization methods for an electrowetting cylindrical lens optical system in the above embodiments have the same beneficial effects, and will not be described in detail here.

[0051] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program goods. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program goods embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0052] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program goods according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0053] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0054] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0055] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for astigmatism correction and imaging optimization of an electrowetting cylindrical lens optical system, characterized in that, Includes the following steps: The structural parameters and actual imaging environment parameters of the electrowetting cylindrical lens optical system are obtained, and the target imaging state of the system is determined based on the structural parameters and imaging environment parameters. The structural parameters include the initial curvature of the cylindrical lens, the thickness of the dielectric layer, the material properties of the hydrophobic layer, and the liquid interface parameters. An astigmatism correction model is constructed based on the target imaging state, and a corresponding voltage driving signal is generated according to the astigmatism correction model. The voltage driving signal includes voltage amplitude, phase difference, and driving frequency. An adaptive optics compensation structure is used to adjust the electrowetting cylindrical lens optical system in real time to effectively correct astigmatism errors; In the actual imaging process, a voltage is applied to the electrowetting cylindrical lens based on the voltage driving signal, and the imaging quality-related indicators are monitored in real time. When an imaging quality index is detected to deviate from the target imaging state, at least one parameter of the voltage drive signal is adjusted in a timely manner to achieve reliable astigmatism correction and further optimize the overall imaging quality.

2. The astigmatism correction and imaging optimization method for an electrowetting cylindrical lens optical system as described in claim 1, characterized in that, The acquisition of imaging environment parameters includes: Acquire ambient light intensity, temperature changes, and external optical distortion parameters; The curvature threshold parameter in the astigmatism correction model is modified according to changes in ambient light and temperature.

3. The astigmatism correction and imaging optimization method for an electrowetting cylindrical lens optical system as described in claim 1, characterized in that, The generation of the voltage drive signal includes: In the initial astigmatism adjustment stage, a gradual phase difference voltage is used, wherein the phase difference change amplitude and the voltage holding time after each change are reasonably configured to enable the liquid interface to be smoothly adjusted to the target curvature and to effectively suppress optical distortion. Once the imaging quality reaches the target imaging state and enters a stable phase, it switches to a low-frequency sustaining signal with a frequency range of 10Hz to 50Hz.

4. The astigmatism correction and imaging optimization method for an electrowetting cylindrical lens optical system as described in claim 1, characterized in that, The adaptive compensation structure includes: The first compensation layer, located at the front of the lens, is used to absorb axial astigmatism error; A high-precision correction layer covering the first compensation layer is used to dynamically adjust radial curvature deviation; An integrated optical housing located at the rear of the system provides overall support and reliably interfaces with the imaging sensor.

5. The astigmatism correction and imaging optimization method for an electrowetting cylindrical lens optical system as described in claim 1, characterized in that, The adaptive compensation structure also incorporates an optical compensation medium, including a low-refractive-index optical liquid and an adaptive polymer material, and the refractive index of the compensation medium is controlled to be below a preset refractive index threshold.

6. The astigmatism correction and imaging optimization method for an electrowetting cylindrical lens optical system as described in claim 1, characterized in that, The real-time monitoring imaging quality indicators include: Obtain the actual distribution information of astigmatism error; And calculate the offset of the current imaging resolution based on the distribution information.

7. The astigmatism correction and imaging optimization method for an electrowetting cylindrical lens optical system as described in claim 1, characterized in that, At least one parameter of the adjusted voltage drive signal includes: The voltage amplitude of the voltage drive signal is adjusted, wherein the adjustment amount is determined based on the deviation between the real-time monitored imaging quality index and the target imaging state. Adjust the phase difference of the voltage drive signal to minimize astigmatism error while maintaining imaging stability; Adjust the driving frequency of the voltage drive signal to appropriately extend the system optimization time and reduce overall optical loss while meeting the imaging response speed requirements.

8. The astigmatism correction and imaging optimization method for an electrowetting cylindrical lens optical system as described in claim 1, characterized in that, The implementation of astigmatism correction also includes: After the image quality reaches a stable state, switch to the compensation voltage mode; The compensation voltage is lower than the initial adjustment voltage set in the astigmatism correction model.

9. The astigmatism correction and imaging optimization method for an electrowetting cylindrical lens optical system as described in claim 1, characterized in that, Also includes: The aging degree of the system is assessed based on the cumulative imaging time and the number of adjustments; And when the aging degree exceeds the preset threshold, the safe correction range in the astigmatism correction model is adjusted accordingly.

10. The astigmatism correction and imaging optimization method for an electrowetting cylindrical lens optical system as described in claim 1, characterized in that, The astigmatism correction model is a neural network model trained based on historical imaging quality data and actual adjustment response data. The neural network model can adaptively optimize the combination of various parameters of the voltage driving signal based on the deviation between the real-time monitored imaging state and the target state.

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